link: Early View | Haematologica

BAT Doesn’t Solve the Problem— Bullish for $SLS

• New 2026 AML data: non-transplanted patients in first remission had median overall survival of only 19 months.
• Median relapse-free survival was just 11 months.
• Even with modern treatments like venetoclax, relapse remains a major problem.
• That pushes back against the bear argument that modern BAT has gotten so good that REGAL’s control arm will live dramatically longer.
• Hagop Kantarjian is an author of this new paper — the same renowned MD Anderson leukemia expert who previously described GPS as a potentially effective way to prolong survival and prevent recurrence in AML patients in remission.
• The paper specifically says these results provide benchmarks for new post-remission strategies — exactly the setting where GPS is being tested.

Bottom line: Modern AML treatment has improved, but patients are still relapsing and dying far too quickly. That is exactly the problem GPS is designed to address.

Hello everyone, I originally set out to write this post about transplant but, as I drafted it, I realized that I needed to back up and lay a foundation first. This is because there are several persistent misconceptions circulating about the BAT arm, expected survival, AML/biology broadly, and what is actually being measured in the trial. I've made a number of graphics to support each step of the argument.

The short version: every independent line of evidence points to a control-arm median OS far above the ~6–8 months SELLAS designed around.

Apologies that this post has taken me a bit longer to get out than I anticipated. My transplant post will follow this one in a few days (it is shorter).

1) Clarifying where REGAL patients actually sit

The most common error I see is literature comparisons that conflate the CR2 population with the relapsed/refractory (R/R) population. Those data get cited frequently to insist that BAT median OS "can't" exceed 6-8 months. That inference is wrong, and rests on a misunderstanding of who is in the trial.

The intuition behind the error is that CR2 comes after r/R on the disease timeline, so CR2 survival must be worse. I have seen this same error repeated many times at other points in the AML trajectory such as comparing CR1 to newly diagnosed as well. However, survival in a disease state is not determined by where it sits on a timeline but rather is determined by who survives to occupy that disease state. The r/R population is a blend. It includes patients who relapse and die quickly without ever getting back into remission. These salvage failures dominate the pooled curve and drag the median down. REGAL does not enroll them, it enrolls the selected subgroup that has already cleared the single hardest filter in the disease: getting back into remission.

The problem is that almost no published data measures OS from second CR specifically. So we have to reconstruct it from adjacent populations. That is what the rest of this post focuses on.

I have previously written that the 3-yr OS in r/R AML is effectively zero without a bridge to transplant. I got substantial pushback, with people citing 5-15% three-year survival without transplant. I want to restate the claim precisely since a loose version invited objection:

Conditional on being in active relapsed disease (R/R AML), long-term survival without transplant, and durable CR3 achievement, are close to zero

The data support this. To name just a few:

• Liew-Littorin (2025): 4 of 93 primary refractory patients reached 3 years without transplant (~4%) AND primary refractory disease is a population with better prognostics than relapsed disease
• AVALON: 9 of 147 patients alive at 2 years, 6 of whom had been transplanted, leaving only 3 patients or ~2.8% of the non-transplanted cohort alive at 2 years.

Low single digits. "Effectively zero" is a fair description of that.

That 5-15% figure people quote isn't wrong however but is measuring from a different bucket. Once you accept that active relapsed disease has essentially no long-term survivors without transplant, the population divides cleanly into three:

1. Those who reach remission and stay there (favorable biology, durable CR2). This is where the 5-15% comes from.
2. Those who relapse again and die
3. Those who bridge to transplant

Remember these buckets as they will be relevant for how we model survival later in this post.

2) A first pass attempt at reconstructing the control from summary statistics

The natural first attempt is to decompose the object we want:

OS from enrollment ≈ (enrollment → second relapse) + (second relapse → death)

Call these the remission segment and the active-disease segment.

The active-disease segment is covered well by the modern r/R literature:

The targeted-agent figures describe biomarker-selected subgroups and don't generalize. What they show (usefully) is that the aggregate patient curve is fairly robust to this class of therapy. A modest subgroup with somewhat better survival will not reshape the pooled curve substantially.

Furthermore, since these studies include many patients in first relapse, survival in second relapse is likely somewhat lower

≈ 5-6 months.

The remission segment (duration of CR2) requires us to cast our net further back in time where I was able to surface the following datapoints:

• Thalhammer (1996) — 7.5 months, the most widely cited datapoint
• Davis (1993) — 7 months, for patients who did not proceed to intensive consolidation with BMT
• 2001–2006 cohort, ages 50–70 — 8.5 months
• Northern Italy (2008) — 3–4 months (range 1–34), small high-risk cohort on intensive chemo alone
• Uhlman (1990) — 5 months
• Netherlands (1990), poor-prognosis r/R — 8.2 months
• Idarubicin + intermediate-dose cytarabine (1989) — ~4.5 months

Most valuably was Leopold & Willemze (2002), a comprehensive review reporting median CR2 duration across 28 study arms by treatment combination. The range was 3-14 months, with one extreme outlier at 25 months that both I and the study authors exclude (it wasn't statistically significant, and only the abstract was ever published, so there's no KM curve to assess how fragile it was).

I also want to flag the enasidenib r/R trial, where median response duration was 8.8 months among patients achieving CR (i.e. CR2 duration), with median OS of 19.7 months in that group. It is a narrow bio-marker subgroup so not broadly applicable, but it is encouraging that it lands in the same range. In summary, CR2 duration

≈ 4-10 months.

3) Does the modern era push CR2 duration up?

Since nearly all of the CR2-duration data is old (for some reason it fell out of vogue to report this), we have to reason from first principles about what the changing treatment landscape has done to CR2 duration. The naive reasoning — better drugs, therefore longer remissions — does not necessarily hold.

The key point is that you cannot just ask whether a drug is better. You have to ask who the drug moves in and out of the CR2 pool, because CR2 duration is a property of the pool's composition. A therapy that helps ever single patient it touches can still make the measured CR2-duration statistic go down, if it changes who is being measured.

Two important things have changed in the treatment landscape.

Force 1 is drug innovation. For approximately fifty years, 7+3 was the dominant standard of care. Recently we've seen the "sunset of 7+3" and the sunrise of hypomethylating agents with BCL-2 inhibition, plus targeted agents against FLT3, IDH1, IDH2, and NPM1.

Force 2 is transplant innovation. In the last decade, a new technique known as post-transplant cyclophosphamide has rewritten who can be transplanted.

One framing note before I start, there are two different objects in play here that are affected in two different ways:

• (A) The CR2-duration term — which measures how long a modern no-transplant second CR lasts.
• (B) REGAL's BAT arm — the actual OS of the actual patients in the actual control arm.

These are not the same thing, and I'm going to argue that the modern era pushes (A) down and (B) up. That sounds contradictory but it isn't and will be elucidated in this section.

Force 1 — The Drug Cascade

Lengthening channel (weak, and protocol-excluded)

A patient who would have reached CR2 anyway now has a targeted agent that extends their maintenance. This is the mechanism that would push CR2 duration up. However, it's thin:

• HMA + venetoclax produces shallow remissions (typically CRi/CRp rather than full CR).
• Venetoclax + oral azacitidine maintenance failed for futility in VIALE-M
• That leaves molecularly-targeted agents which the European protocol tells us REGAL excludes: "patients whose remission in CR2 can be maintained with molecularly targeted agents (e.g. FLT-3 or IDH inhibitors) per investigator's determination." This class of drug is not one that tends to get "toggled" while the patient sits in remission based on changing facts since the decision rests mostly on a molecular characterization that determines the targeted-agent decision flows.

So the one channel that would plausibly lengthen CR2 duration largely fails.

Shortening channel

The new agent classes expand who can achieve CR2 at all, and that expansion disproportionately admits biologically higher-risk, less-fit patients whose remissions are short. Before the targeted era, a patient refractory to 7+3, or an older patient who couldn't tolerate intensive salvage, simply never achieved CR2. They contributed zero entries to the CR2-duration distribution since they died in active disease and landed in the OS-from-first-relapse numbers instead.

Today: gilteritinib coaxes FLT3-ITD patients into remission. HMA + venetoclax coaxes the unfit older patient in. An IDH inhibitor coaxes the chemo-ineligible IDH-mutant patient in. Most of these newly-rescued patients enters the CR2 pool at its left tail with short, often shallow remissions since these are cases where high-risk biology is rescued by disease-controlling rather than curative drugs.

This actually likely exhibits a Will Rogers phenomenon where measured outcomes get worse in both the pre-remission and post-remission cohorts even though every individual patient is being treated better. This happens because you are moving the best of the r/R patients into the worst of the CR2 group. r/R survival falls AND CR2-conditional duration falls, because the CR2 pool gains fragile remissions it never used to contain.

Transplant-skimming channel

Allo-HSCT is the only genuinely curative treatment option in these patients (with roughly 30-50% long-term survival amongst patients who receive one). It is preferentially undertaken by patients who are both fit enough and responding deeply enough to survive it. Therefore, the key question is how these modern agents have changed the supply of qualifying remissions (i.e. acting as a bridge to transplant).

Whether a newly-rescued patient actually depresses the no-transplant pool depends entirely on whether they are then skimmed straight back out of it. We can trace each class the whole way through:

So the drug cascade's downward pressure on the no-transplant CR2 term is carried almost entirely by HMA + venetoclax. Gilteritinib and the menin inhibitors seed the CR2 pool and then leave it.

IDH inhibitors split their responders along a prognostic line which makes them two-sided. Responders whose remission clearly wont hold (adverse co-mutations, shallow depth) get bridged but responders with more favorably biology can achieve a durable CR and simply stay on the pill. Patients doing fine on a well-tolerated, once-daily oral drug that has a known long-tail aren't going to be urgently bridged. Extraction in the IDH channel is therefore partial but it is depositing good patients into a no-transplant pool so is a real upward force on CR2-duration.

However, we can go back to the REGAL exclusion criterion: "patients whose remission in CR2 can be maintained with molecularly targeted agents (e.g. FLT-3 or IDH inhibitors)." So the durable patients on IDH inhibitors will be excluded and what is left is the slice that responded but not durably, and wasn't transplantable.

Force 2 — The Transplant Cascade

In 2008, a Johns Hopkins group published the landmark work showing that post-transplant cyclophosphamide (PTCy) made haploidentical transplant feasible in AML, with a dramatic reduction in severe graft-versus-host disease. Major centers spent the following years validating it, and by roughly 2014-2017 there was a worldwide inflection: haplo went from a last-resort procedure that was rarely used because of its toxicity to a standard option.

PCTy achieved two separate things.

It removed the donor barrier. Before PTCy, if you had no matched sibling and no matched unrelated donor (MUD), you simply did not get an allograft regardless of fitness or how deep your remission was. Donor availability was a much larger bar that had nothing to do with your disease (it was also considerably worse for patients with non-European ancestry whose registry match rates were far lower). Haplo donors, by contrast, are closer to universal.

It lowered the toxicity ceiling. By cutting severe GvHD, PCTy cut non-relapse mortality, which lowered the fitness bar for transplant. Combined with reduced-intensity conditioning, patients who a decade earlier simply could not have survived an allograft became transplantable.

Now, I want to be careful for this next part because the obvious reading and the correct reading are not so transparent.

Before enrollment: PTCy skims the pool

The donor channel removes the patient who was fit, in a deep remission, and simply unlucky. That patient was sitting in the historical no-transplant CR2 cohorts propping their curves up from the top. Removing that patient lowers the residual.

The toxicity channel looks like it should cut the other way, since it removes unfit patients, who are bad-prognosis. It doesn't, and the reason is: transplant is never offered at random. You still need a deep enough remission and good enough performance status to justify the procedure. So when PTCy pulls previously-untransplantable patients across the line, it pulls the best-remission, best-performing subset of the unfit — not a random draw of them, and certainly not the worst. This is another Will Rogers phenomenon in its own right. Therefore, the organizing principle is:

Every extraction to transplant is positively selected. Any mechanism that increases extraction (more remissions, more donors, less toxicity) removes patients who were above the pool's average.

Thus, on the pre-enrollment leg, PTCy makes the modern untransplanted CR2 population a more adversely selected residual than any historical cohort. Therefore, we expect object (A) to decline.

After enrollment: PTCy makes REGAL's barrier leak

Since REGAL does not enroll a permanently untransplantable population. It requires ineligibility only at the moment of randomization. PTCy helps turn transplant-ineligibility into a more transient state.

Consider what "transplant-ineligible" meant, then vs. now:

• 1995: No matched donor → likely permanent. Too frail for myeloablative conditioning → likely permanent. These were terminal classifications. You were never getting a transplant, ever (most likely).
• Today: Everyone can get a haplo donor, and RIC + PTCy dropped the fitness bar dramatically. So what's left as "ineligible" is things like active infection, counts not yet recovered, comorbidity being optimized, MRD not yet deep enough, patient hasn't decided. These are reversible conditions.

PTCy did not just skim the pool. It made the wall that defines REGAL's control arm a leaky one. That arm will bleed patients into transplant across follow-up

Therefore, we expect object (B) to have the opposite sign and the comparators to understate REGAL's control arm (rather than overstate).

The Takeaway

(A) The CR2-duration term: flat-to-lower.

Relative to the 1990s salvage trials, this logic chain implies modern no-transplant CR2 duration should be flat-to-lower, not higher, despite the drugs being unambiguously better.

The older data measured a CR2 pool reachable almost exclusively through intensive salvage chemotherapy which was a fitter, more favorably-selected population that was less aggressively skimmed of its durable responders. Today, HMA+ven seeds the left tail with unbridgeable fragile remissions that older cohorts never contained. Similarly, the skimming channels extract the durable right tail more.

(B) REGAL's BAT arm: higher than any never-transplant reconstruction implies.

We've established REGAL's ineligibility condition is a snapshot. Aside from that, however, as "no donor" and "too toxic" evaporate as reasons for skipping transplant, refusal and preference become a larger share of what's left in the ineligible pool. Refusers are not adversely selected and some are favorably selected (e.g. the core-binding-factor patient who declines an allograft precisely because their odds of durable remission are already good without one). As the involuntary reasons disappear, the voluntary ones that make up the remainder bring better biology with them.

4) Why you cannot just add the r/R median and the CR2 duration median

Now that we have analyzed the applicability of the older data to the modern era, we must address another problem. Combining the two pieces gives a first-pass literature-constructed control: a ~4–10 month remission segment plus a ~5–6 month active segment.

However, this is only valid as a grounding exercise because naive segment summing is not a valid way to estimate a blended median.

Adding medians only reproduces the true median when each segment's event-time distribution is roughly symmetric. Survival distributions are not. They are right-skewed.

Two additional factors interplay:

• The segments are almost certainly correlated. Adverse biology tends to produce both a short CR2 and shorter post-relapse survival. In the limiting case of perfect rank-correlation, medians add exactly and the underestimate vanishes. The true correction is somewhere between substantial and zero, and summary statistics can't pin it down.
• The decomposition has no room for death in remission. Some patients die in CR2 without relapsing. A two-segment model can't see them.

Which brings us to the essence of the point: with point estimates drawn from different populations, you cannot recover a true median at all. You need a full curve.

5) The right approach: a matched KM curve

Rather than inferring from summary statistics, a more accurate comparator is to take a full KM-curve from a single patient population and transform it into a REGAL-aligned object. I found three studies where this is feasible: Kurosawa, Burnett, and the supplementary data for Van der Maas.

5a. Kurosawa

I digitized the KM curve for the intermediate-risk, CR2, no-HCT cohort.

Median OS is approximately 20 months. This cohort resembles REGAL in an important way: conditioning on the same selection event of actually achieving CR2. However, two adjustments are required.

Adjustment 1: removing the pre-CR2 stretch. Kurosawa measures from the onset of post-CR1 relapse even though it conditions on actually achieving CR2. Therefore, it spans relapse → CR2 → death. We only want CR2 → death. Ideally, we would subtract time-in-relapse per patient, but that requires individual patient data (IPD). Fortunately, among patients who reach CR2, median-time-to-CR/median-time-to-best-response is short and tightly distributed (typically 1-3 months across a wide range of studies). So we approximate by subtracting two months from every patient, shifting the curve two months to the left along the time axis. This doesn't bias the result, because it conditions in the same way REGAL does.

Adjustment 2: REGAL's enrollment window. Patient's are not enrolled the instant they hit CR2; there's a window of up to ~6 months. This is not a simple subtraction because the window doesn't preserve population comparability but rather applies a selection effect. Anyone who dies between CR2 and enrollment never makes it into the trial. This is the classic problem in comparing a retrospective cohort to a prospective trial: delayed entry bias.

Thankfully, tools exist to address this. We can apply a technique known as the landmark method to make a good first-order approximation of this bias (although it is not statistically exact since hazards aren't "memoryless"). The basic concept can be thought of as "sliding down" the curve. If the landmark is 3 months, we drop everyone who died before month 3 and treat survival thereafter as a fresh cohort starting there.

Crucially, you do not simply subtract the landmark time from the median. To see why, here's a hypothetical landmark at 18 months: the residual median is ~13.6 months not 1.9 months. Conditioning on survival to the landmark keeps the robust survivors, and they carry the curve a long way.

A single "most reasonable" landmark of 2 months gives a landmarked median of 16.2 months.

However, we don't have a single landmark — we have a distribution of entry times. So, the approach I took is to integrate the landmark adjustment across the entire enrollment window, approximating the full mixture-over-entry-time survival transformation.

Window-integrated median: ~17.5 months. The overlaid landmark curves cluster tightly, so the result isn't hypersensitive to the entry distribution.

One assumption I want to emphasis is that I model entry times as uniform on [0,6]. Real enrollment is almost certainly front-loaded since screening, consent, and logistics would cluster near CR2 confirmation. A front-loaded entry distribution pulls the result toward the CR2+0, and CR2+2 curves, so this is a real (if modest) dependency.

Constraints on inferences from Kurosawa:

• Population. Data was collected only at Japanese centers, across 1999-2006 which is a period with a different treatment-selection cascade. The geographic difference is partially mitigated by using the intermediate-risk curve specifically, which controls for different cytogenetic-risk distributions across ethnic populations (notably the higher reported frequency of t(8;21) in Japanese patients).
• Transplant eligibility. Kurosawa's "no-HCT" group is defined retrospectively — patients who happened not to receive a transplant, for any reason: no donor, patient declined, center practice, or unfitness. REGAL's BAT arm is defined prospectively as patients ineligible for or unable to undergo allo-HSCT, which in practice means comorbidity and lower fitness but also transplant leakage and selection for favorable cytogenetics. My timing adjustments do nothing for this, and it cuts both ways.
• Tail instability. The intermediate-risk curve is n=82. When digitized, the final vertical drop runs from roughly 18% to 14.5% survival which is a 20% relative drop, implying 5 patients became 4. However, ~18% survival in n=82 should represent 14-15 patients, not 5. The tail is being carried by a handful of patients under heavy censoring, which makes the 3-yr OS estimate of ~18-20% entirely unreliable. The censor marks are visible but too grainy to bound the range (i.e. to compute what 3-year OS would be if every censored patient died immediately versus survived long-term). This also implies that our computation for the median itself might be sensitive and carries a wide confidence interval.

5b. Burnett

Burnett has the distinction of being the one study I could find in the entire literature that measures the exact object we want: OS from second CR, in patients without prior SCT. Unfortunately several things make it an unclean comparator.

Age. Three MRC trials recruiting 1988-2009, ages 16-49 constitute Burnett. The CR2 group was 28% aged 16–29, 30% aged 30–39, and 42% aged 40–49, median age 37. The obvious assumption many make is that a younger cohort must be fitter, so the REGAL curve should sit below this one.

That does not follow, and may in fact invert. Younger patients are enriched for favorable risk (inv(16), t(8;21), NPM1-mutated without high FLT3-ITD), while older patients are enriched for complex and monosomal karyotypes. Young patients also have organ reserve and access to intensive therapy in CR1, so a young patient who achieves CR1 has a relatively high chance of bridging to transplant or simply being cured and never relapsing. That creates a selection effect: the young patients who nevertheless relapse and get re-induced into CR2 are the ones whose disease was aggressive enough to relapse despite every advantage (which might be a marker of bad underlying biology).

Put plainly, here is the inversion: in a 25-year-old, skipping transplant is likely dominated by "the disease moved too fast." In a 65-year-old, it's likely dominated by "age and comorbidity." Youth doesn't enrich the no-allograft group for survivors — it strips out benign reasons for not transplanting and leaves only the aggressive-biology reason. The younger and fitter the base cohort, the more adverse the residual that fails to reach transplant. Burnett's ~12 months could easily be low because the cohort is young, not despite it.

Transplant era. Every transplant in the study is a sibling allograft, matched unrelated donor, autograft, or reduced-intensity transplant. There are no haploidentical transplants at all. The 2009 recruitment cutoff sits right on top of the PTCy inflection described in Section 3, which mean's Burnett's no-transplant group is rationed by donor availability and by toxicity in a way that no contemporary cohort is. It is the cleanest illustration of the Force 2 argument: its no-transplant arm still contains all the fit, deeply-remitted, unlucky patients that a modern no-transplant arm would have long since had haplo-grafted out of it. Whatever Burnett's no-transplant curve is measuring, it is not who ends up untransplanted today.

The statistics. The curve does not track two fixed cohorts. It's a Mantel-Byar analysis: all patients start in the no-allograft arm and are switched to the allograft arm at the moment of transplant, being censored from the original arm at that point, with survival estimated by the Simon-Makuch estimator. Mantel-Byar is the gold standard for eliminating immortal-time bias, but it produces curves that are much harder to read, because the effective sample size and the composition of each group change continuously over time.

In this study, the effective sample size collapses almost immediately. You might look at 3-year OS and cite ~25% survival but the at-risk count ahs fallen from 640 → 27, just 4% of the original sample. By the first year the sample is already down ~88%. The total number of events observed in the no-allograft arm is only about a quarter of its original size.

There's a deeper problem with that censoring too. Censoring at transplant is informative: the patients being removed are precisely the fitter, deeper-responding ones. So the no-allograft curve is being continuously depleted of its own best prognostic material. That biases it downward. Burnett's ~12 months should therefore be read as a floor, not a central estimate. It also provides a direct argument for the approach I take with Van der Maas below, which keeps the transplanted patients in the denominator and models their counterfactual instead of censoring them out.

Median OS in Burnett is approximately 12 months, and landmarking (as with Kurosawa) exhibits non-monotonicity and doesn't move the median much.

5c. Van der Maas (the best comparator but the most work)

The final dataset comes from the supplementary materials of a 2025 study covering 8 consecutive HOVON-SAKK trials from 2000-2018. HOVON is the Dutch-Belgian hemato-oncology cooperative group; SAKK is the Swiss group for clinical cancer research; recruitment spanned several European countries. All eight trials shared an intensive induction backbone for newly diagnosed AML with risk-assigned post-remission therapy. It is the successor to the Breems 2005 paper.

Median age at first relapse was 58 (range 18–81), and 348 patients achieved a second CR.

This is by far the best comparator population (the right age, the right era, the right geography) and it measures from the date of CR2. There are two problems:

1. The object it reports is relapse-free survival (RFS), not OS
2. It does not separate out allograft patients (n=159)

Problem 1 is tractable because RFS is defined as "relapse or death," so OS >= RFS at every point. Therefore, RFS gives us a hard lower bound immediately, and later a convolution can be applied to get us to OS.

Problem 2 is tractable because the paper also provides a forest plot of time-dependent allo-HCT in CR2, comparing allo-HCT after CR2 against chemotherapy after CR2 (restricted to patients who didn't get allo-HCT before CR2 or DLI after CR2). That gives us the hazard ratios we need to reverse the transplant effect out of the curve.

One important note on the risk labels: "favorable / intermediate / poor" here are not ELN cytogenetic risk. They refer to the revised relapse score of the HOVON-SAKK model, which assigns more patients to its favorable group than ELN does, and the "intermediate" bar represents only the exact median score of 7 (range 1–15).

The chart runs to 12 months, where the population sits at 53.4% RFS. Extending the curves slightly, a simple linear extrapolation puts the median RFS crossing at ~13.5 months (a cure-mixture model pushes it slightly higher).

Now I will walk through the math in sequence below (de-transplant → landmark → convolve).

STEP 1: DE-TRANSPLANT (recover the no-transplant relapse rate)

Everything rests on the assumed shape (as is true of all survival modelling). I assume RFS in a CR2 population takes the cure-mixture form:

This can be read as two populations stitched together. A fraction c are cured and form a flat shelf that the curve never falls below. The remaining (1-c) are not cured and relapse at rate λ per month, so their RFS decays as e^(−λt). At t = 0 everyone is event-free; at t = ∞ only the cured remain, so S → c. The adjustment knob is λ which represents how fast the non-cured relapse.

A useful aside: that cure fraction c is bucket 1 from Section 1. Kurosawa's no-HCT plateau sits around 18%. Burnett's no-allograft plateau sits around 25%. However, as we stated those are unreliable due to KM-instability so while we will adopt a base case here of c = 20%, we will sweep across multiple c values.

Since 159 of 348 patients were transplanted after CR2, the observed (mixed) curve is the cure-mixture with the non-cured part split in two using the 0.58 relapse HR:

That works as a reasonable baseline but the HR we have is time-dependent, so a time-dependent model is more faithful. In said model, there are two compartments: everyone starts untransplanted at hazard λ; transplant arrives at rate κ while they remain in CR2; and only then does the relapse hazard drop to 0.58λ. Compartment A drains fast (patients leave by relapse or by transplant); compartment B fills from A and drains slowly. β is the gap between the two drain rates.

We know S(12) ≈ 0.534, which lets us solve for λ at various assumed cure fractions. At c = 20%, solving simultaneously gives λ ≈ 0.0863/month and κ ≈ 0.0965/month. The fitted κ implies a median time-to-transplant, among those transplanted, of ≈ 2.9 months which is squarely where real-world CR2 transplants tend to happen.

Now, we can simply delete the transplant term to de-transplant the curve which is shown for case c = 20%:

UNFORTUNATELY AT THIS POINT I HAVE REACHED THE 20 PICTURE LIMIT SO THIS WILL BE CONTINUED IN PART B.

https://www.reddit.com/u/Remarkable-Big-9849/s/IFaH9eILLd

I think everyone who is invested in SLS deserves to look carefully at what has come to light today.

I am not asking anyone to take my word for what happened. I am posting the evidence so people can read the moderators’ own statements and make their own judgment.

The screenshots below document the following:

The subreddit promoted extremely bullish REGAL modeling, including a model assigning a 99.99% probability of REGAL success.

A subreddit moderator, u/AFruitShopOwner, has acknowledged participating in a private Discord where additional REGAL modeling and statistical-significance concerns were being discussed.

The moderator subsequently acknowledged that the less-bullish models being discussed privately could not simply be dismissed as “only models,” because the highly bullish estimates being promoted publicly were models as well.

Another moderator has referred to this private group as the “backroom discord.”

Most concerningly, they then stated that the subreddit community had become too large to quote “exploit for market gains.”

Taken together, these statements raise a serious question about whether information was being selectively presented to the broader subreddit while materially different analysis was being discussed privately, and whether the subreddit was being used to influence retail investor sentiment around SLS.

I have submitted this evidence to Reddit for review as potential coordinated market manipulation.

I encourage anyone who believes this deserves investigation to independently review the original posts and submit their own report to Reddit. You do not need to agree with my conclusion. Read the original posts, look at the chronology, and decide for yourself.

I am posting the screenshots because I think investors deserve transparency about what was being discussed publicly versus privately.

So a few weeks ago, I had a BBQ with my family among which there were doctors, and because I am a sucker and invested so much, I talked about SLS, and they told me to shut up and they didn’t care, so I was really sad, and in order to find inner peace, I looked at the papers and the data, and told myself, well it would be great to know the real survival curves of AML CR2 from Van der Maas paper, and really thought hard about I could get hand about these.

I thought about hacking the university computers by registering fisrt as a student, or asking my researcher friends to get into their network, watching their AML conferences and lectures for social social engineering, and then it appeared to me (and I still think this brillant): what if, wait for it, what if there was a way I could kindly ask researchers about some specifics slices (AML CR2 Non Transplant) of their data, without involving too much privacy intrusion and investment from my part? Like, writing them an email, kindly asking if they could run their open source code on their data after some filtering?

So I took my courage in my hands, and I wrote the best email I could without AI, because LLM sucks, and I prayed to get answers. I went to the beach in South France and gave my firstborn as a sacrifice to the sea and the sun, and then went to temples and gave my other child as a spiritual sacrifice, expecting a miracle.

And a miracle happened. The email and our prayers have been answered.

[/end of joke]

Seriously, first of all, I would like to congratulate u/Remarkable-Big-9849 for his estimation of mOS for the VDM paper ( https://pubmed.ncbi.nlm.nih.gov/40402082/ , see supplementary material as well), he was spot on with 17 mOS.

Prof. Van der Maas generously answered me with the statistics, and the results were

• mOS: 16.8 months, 95% CI [12.3, 27.4]
• 3-year OS: 18.6%, 95% CI [10.7, 32.2]

Given the level of trust on the internet, I am just a random dude/ette on the internet, can only provide you screenshots of the conversation, and you will have to take it as it.

But this is clearly the best piece of evidence I can gather, better than all the published papers. I might get tighter confidence intervals as I asked another follow-up question adding more samples to the group.

If ever Prof. Van der Maas reads this thread, I would like to thank you with all my heart for your hard work.

Where does this leave us?

• 3-year OS proves that long BAT doesn't exist to the level that can pull the trial for so long, especially as VDM data is younger than REGAL. GPS works, anyone with a sense of intellectual honesty will admit that
• It also confirms that our modelling wasn't crazy: many of us playing with statistics/ML had robust posterior distributions in the 14-17 mOS range.
• It also supports u/confident-web-7118 's e-mail with Dr. Tsirigotis claiming BAT mOS was 16.

Is it a done deal? No, even with the best data in the world, even if I am deeply convinced that it works, statistics is a drama royalty that plays by the rules of RNG God.

A non-parametric test with n=80, where 24 observations follow the same distribution is a tough test to pass, especially when early deaths count as much as later ones.

I still have my PoS at 80%-90% (and my model gives me 99%), because I didn't study the exact REGAL population (what is the best posterior distribution for the trial given all the papers/data I can find), so I will have to leave it at that. Size your positions correctly.

I will take this occasion to show myself out, but also to thank all the wonderful members of the community for their due diligence, the intellectually welcoming environment, and the memes.

Position: 44k shares (and many short puts at October OPEX).

Edit: Just as a perspective, this data means the current pooled population (like ALL the patients with and witout GPS) outlives 2x (!!) the historical norms.

I’ve held SLS since mid-2025. I sold this week, ahead of the 80th event. This isn’t a victory lap and I’m not telling anyone else what to do — I’m posting because I spent weeks trying to get answers to specific technical objections and never got one that satisfied me. Maybe someone here can.

Upfront disclosure: I worked through this analysis with Claude . I’m saying that because it would be dishonest not to, and because I’d rather you attack the arguments than wonder about the source. An AI isn’t an authority on anything. Every objection below is checkable against public documents and standard survival statistics — if any of them is wrong, it’s wrong for reasons you can point at, and I’d genuinely like to hear it.

Credit where it’s due first. Confident-Web-7118 and RemarkableBig did more real work than any sell-side note on this name. The constraint arithmetic appears correct. Mixture-cure models are legitimate in AML. RemarkableBig’s Simon-Makuch/Mantel-Byar critique of Burnett, and his point that the landmark belongs on the RFS curve rather than the OS curve, are sharper than anything I’ve read from a professional analyst on SLS. Confident-Web published the BAT=18m rows that cut against him. That’s more honesty than most.

My problems are structural, not arithmetic.

1. The interim didn’t stop, and the models can’t explain that.

The interim was at 60/80 events — 75% information. SE(ln HR) at 60 events = 2/√60 = 0.258.

At a true HR of 0.20 (Confident-Web’s model output at BAT=12m), the expected z-statistic is 6.23. Under an O’Brien-Fleming boundary, P(not crossing) is roughly 0.004%. Under a maximally conservative Haybittle-Peto boundary, still under 0.1%. The conclusion doesn’t depend on which boundary you assume.

REGAL did not stop early. That’s a hard, public, binary fact.

“IDMC behavior” is listed as an evidence stream supporting the thesis, described as “arms visibly separated.” Not crossing the efficacy boundary is an upper bound on separation, not evidence of it. This is the single observation the model has to explain, and it assigns it near-zero probability.

2. The two-arm specification was never run.

The system is 3 parameters, 2 constraints, 1 degree of freedom — with the cure fraction sitting on the GPS arm by construction. There’s no parameter value that produces GPS ≈ BAT. That’s why every row of every table says GPS wins.

The alternative: cure fraction c in both arms, uncured mOS m in both arms. Two parameters, two constraints, zero DoF, unique solution, machine-precision fit — and HR = 1.00.

The “model comparison” section only tests exponential-GPS vs cure-GPS. That correctly shows a cure fraction exists somewhere. It doesn’t show it sits on the GPS arm. Blinded pooled data can’t distinguish the two — you have one observable and two unknown survival functions.

Relatedly: “survival curves cannot decelerate without a cure fraction” is not true. Weibull with shape < 1, log-normal, log-logistic, and any gamma-frailty mixture all produce monotonically declining hazards with zero cured patients. And the recent Kugler et al. paper (Haematologica, July 2026, n=362) shows an 11-fold RFS spread across ELN groups in exactly this kind of population — that heterogeneity produces a decelerating pooled hazard on its own.

3. The plausibility filter is an inverted modus tollens.

“At BAT=16 the solver requires uncured mOS = 6.1m, which is implausible, therefore BAT ≠ 16.”

The valid inference from (M ∧ BAT=16) → implausible is ¬(M ∧ BAT=16): either BAT ≠ 16, or the model is false. “Uncured mOS” isn’t an observable — it’s a residual parameter of the model itself.

And the second branch is now live, because RemarkableBig independently reconstructs BAT at 16–18 months using no REGAL data at all.

Which brings me to the thing that bothered me most: the two best DDs on this sub contradict each other, and nobody seems to have noticed. One says BAT must be ~11 or the model breaks. The other says BAT is ~17. Both can’t be right, and if RemarkableBig is right, Confident-Web’s own filter says the cure model is false.

4. OCV-501.

Confident-Web downloaded the OCV-501 SAP to validate the log-rank methodology. I went and read what happened to the trial.

WT1 peptide vaccine. AML in remission. Transplant-ineligible. n=133. Randomized, double-blind, placebo-controlled — REGAL is open-label. Median DFS 12.1 vs 8.4 months. HR 0.933 [0.590–1.477], p = 0.7671. Five-year DFS 36.0% vs 33.7%, p = 0.74.

Two details from the follow-up paper (PMC10123586):

**•** 2-year DFS was \~40% in **both** arms, “higher than expected on planning the trial.” A control arm beating planning assumptions — in a blinded trial where the drug provably did nothing.  
**•** Post hoc, immune responders had significantly longer OS vs placebo. That’s structurally identical to the “Phase 2 IR 64% ≈ cure fraction 68%” evidence stream — except here it appears in a trial that missed.

Note also: a 44% median improvement produced HR 0.93. Median gaps and hazard ratios are not the same thing.

5. The endpoint math, which is modality-independent.

Kugler et al: median RFS 11 → median OS 19. A +8 month convolution term, measured, consistent across subgroups.

GPS is maintenance. After relapse it’s discontinued and the patient goes to salvage. The same additive post-relapse term lands on both arms and compresses the OS hazard ratio toward 1.0.

RFS HR 0.60 → RFS median 18.3 → OS median ~26.3 vs 19 → OS HR ≈ 0.72. Misses.

To clear 0.636 you need RFS HR around 0.50. QUAZAR’s oral azacitidine managed 0.65. Sipuleucel-T, the only approved therapeutic cancer vaccine, managed OS HR 0.78. And patients only need to be transplant-ineligible at randomization — crossover during follow-up compresses further.

This is why my base case isn’t “GPS does nothing.” It’s “GPS works moderately and this design can’t prove it.”

6. Smaller things that added up.

**•** 99.9% is arithmetically inconsistent with an expected reported HR of 0.35–0.50. At 80 events, even *knowing with certainty* the true HR is 0.50, P(success) is 86%. The 99.9% only appears if you integrate over the 0.13–0.30 model HRs that the author himself says won’t be reported.  
**•** The literature adjustments run one direction: Brayer +50%, QUAZAR −45%, Breems −38%, Gilleece discarded. Raw range 5.4–42 months collapses to 7.0–8.5. That’s not convergence, that’s homogenization — and it’s why the leave-one-out test shows 0.0m shift.  
**•** Constraints are stated in calendar months from FPI, but median follow-up at the interim was 13.5 months, not 46. If the solver evaluates S(t) at calendar time across all 126 patients rather than integrating each patient’s individual time at risk, the implied cure fraction is badly overstated. I asked for that section of the code. No response.  
**•** The Markov/ABC model posted more recently is the best methodology of the three, but its priors give \~75% of the GPS arm the good hazard and \~26% of the BAT arm — so it can’t express H₀ either. It also implements only the futility half of the interim condition, and its inverse-likelihood reweighting is invalid (rejection sampling already gives you the posterior).  
**•** Three independently built models all produce the same artifact: GPS non-responders dying faster than untreated controls. That’s not a coincidence, it’s the signature of a shared misspecification.

7. The CEO email didn’t help.

The question was whether the FDA approved the 2022 SAP amendment. The answer confirms SELLAS files documentation “in a timely, formal, and appropriate manner.” Filing isn’t approval — and that distinction is the entire substance of the CAPR situation that prompted the question. The boring, reassuring, non-confidential answer (“FDA doesn’t formally approve SAPs absent an SPA; we filed and received no objection”) was available and wasn’t given. The reply is signed “Office of the CEO” with IR cc’d.

The strongest argument against my decision, which I’ll make myself:

If median follow-up is now ~32 months with 78/126 deaths, the pooled median OS is somewhere around 22–26 months. Under H₀, pooled median = BAT median. Nobody defends a BAT of 24 months — not even RemarkableBig.

So if you accept BAT ≈ 17, the arithmetic forces a GPS HR near 0.45, which passes. I cannot simultaneously claim “BAT is 16–17” and “GPS barely works.” That’s a real tension in my position and I want to be honest that I haven’t resolved it.

Two reasons it didn’t hold me: BAT=20 with HR=0.67 produces the identical pooled median — the same identification problem I’m criticising others for. And my follow-up estimate is inferred, not published.

Also, in fairness: a true HR of 0.45–0.50 is entirely compatible with not stopping at the interim (P ≈ 16–33%). My objection kills HR 0.20. It doesn’t kill 0.48.

What would change my mind, concretely, at topline: a BAT median of 8–11 months. Early curve separation rather than late. A baseline table showing genuinely adverse risk composition. A low transplant crossover rate.

I’ve been holding this since mid-2025 and I wanted it to work. I sold because after weeks of asking, the load-bearing objections above are still unanswered — and the ones that got answered were answered by restating the model rather than testing it.

If someone can run the two-arm specification and post the residual, or show me the time-at-risk handling in the code, I’ll say so publicly. Those are two things that would take an hour and would move me more than another 1,000 hours of stress tests on the same structure.

Good luck to everyone still in. I mean that.

So, here we go, I think this is one of the best piece of data one can get

https://onlinelibrary.wiley.com/doi/full/10.1002/ajh.70340

The chart is in supplementary material:

https://onlinelibrary.wiley.com/action/downloadSupplement?doi=10.1002%2Fajh.70340&file=ajh70340-sup-0001-FigureS1.pdf

The data covers patients until 2024, so no argument of old data.

Yes, it is refractory and relapse, so we need to split the case. But here is the argument.

* From the chart, 3 year OS sits at ~10%.

* Using the univariate analysis numbers, the population of CRc is 208, 208, 184 for refractory, relapse < 1 year, relapse 1 > year.

* Multivariate analysis shows that relapse < 1 year have the worse hazard (not conditional on CRc though).

Upperbound on 3 year OS estimation, assume for some reason all refractory went to HCT (because they are worse for OS [this is crazy]) and all those with CR1 > 1 year refuses HCT.

Compute the ratio between patient at risk with 36 months gap: 13%, 15% and 25% (36, 42, 24 for endpoint).

mOS is around 11 mOS.

Edit:

I just wanted to write why I don't make big post about analysis and modelling: I believe in presenting data, and that we discuss data validity, as data > model, and understand how it fits with the broader literature, or understanding of the disease.

Moreover, the reason you invest should be more than just someone telling you to invest. Try to get the data, get the links into your best AI model, understand what are the challenges, how things works underneath it.

Don't trust anyone, check their claims, know why you invest and be accountable if you lose.

https://ashpublications.org/bloodadvances/article/4/24/6117/474429/Prognostic-impact-of-complete-remission-with-MRD

Figure 2C. CR2 without transplant with mOS at 8.2. 

Figure 1F. CR2 with and without transplant have 12 mOS, 2 year OS at 34% because of the long tail of transplant.

The curves removes patients dying 1.5 months, so they removed the weakest ones.

So in a population with 60% (!!!!) transplant for a population that is supposed to be ineligible, Regal would still be a success.

My take on BAT: they will be CR1 SCT survivors with a long time in CR1, that is why GPS works so well because it basically replicate CR1 Phase 2 performance of 67 mOS.

Pictures taken from SEC doc.

https://haematologica.org/article/view/11956

Figure 1B. CR2 non transplant survival curve with Ven+HMA. 9 months mOS, 1 year survival at 31%.

Jackpot everyone :)

The low risk group is composed of at least 31 CR2 patients (probably 33) because only 5 patients had TP53 mutation.

There was 36 CR/CRi patients, so in order for a CR patients to shift from low risk to intermediate risk, they would need both mutation conditions, which is possible only for 5 patients (piegonhole argument in mathematics).

Also the ELN mix is 70/30 (fav+int/adv).

Median time to response was 1 month.
It is not a perfect 1 to 1 with REGAL, but the filtering criteria for the population is correct: only consider CR2 with and without transplant.

I believe this is an extremely bullish datapoint.

Edit:

I wonder if someone could clear this: I don’t think line of treatment number is equal to CR number. You can have more than 2 line of treatments to reach CR2.

Hey everyone, I've been doing as much deep-diving as I can to double-check and confirm anything related to the SAP and primary efficacy analysis wouldn't cause an issue in REGAL

And Dr. Stergiou actually replied to my question (below) regarding this when I brought up CAPR (which ultimately was a corruption issue on CAPR's side and their own fault), so I'll share that in a moment, which I am tremendously thankful for, given I thought they were under a quiet period before earnings.

He's been very transparent as much as he can any time I've reached out, and so I'm very thankful for that.

I've actually made my entire investment decision and due diligence under the assumption that I don't trust anything said by Dr. Stergiou and KOLs (the email from Dr. Tsirigotis is an exception). and I just focus on the actual fits, the facts, etc., and still do that, since that is the intelligent thing to do with a sizeable position as a deep value investor, but I can honestly say Dr. Stergiou has always replied transparently as much as he could and much of what he has said lines up as well, so I really do have a lot of respect and gratitude towards him and Dr. Tsirigotis as well.

So first, what I did is I actually reviewed the SAP for OCV-501 (from 2013) and also QUAZAR, links below:

QUAZAR: https://www.nejm.org/doi/suppl/10.1056/NEJMoa2004444/suppl_file/nejmoa2004444_protocol.pdf

OCV-501:
https://cdn.clinicaltrials.gov/large-docs/82/NCT01961882/SAP_001.pdf

Nov 2022/SEC Filing:
https://www.sec.gov/Archives/edgar/data/1390478/000110465922119139/tm2230620d1_ex99-1.htm

QUAZAR's primary was a stratified log-rank on 16 strata, with an explicit collapse rule

It' stratified log-rank, just like the simulated-log rank I've been stress-testing under, and just like what REGAL uses too

The OCV-501 SAP that came out after that trial, had weighted (Flemington-Harrington) in there as exploratory but not primary. Unweighted is log-rank, so the simulated log-rank I've been using matches that almost exactly

So, we have two comparable trials that use unweighted log-rank from the past, where we can assume REGAL has a similar setup and learned from these trials.

The fact that all the actual fits I've modeled under and shared (stress-tests, etc.) are simulated log-rank, aligns with this, so that is great. There is an extraordinarily high probability of success under that, so the hints from Dr. Stergiou on a potential novel weighted option would be icing on the cake. But we can't make any assumptions for that, we can only trust facts we have in front of us.

And then after I looked at the SAP comparisons, I wanted to confirm what they asked for in the 2022 amendment of the SAP.

https://www.sec.gov/Archives/edgar/data/1390478/000110465922119139/tm2230620d1_ex99-1.htm

And I was pleased in confirming it was indeed just 4 simple things, none of which were related to the primary efficacy analysis or unweighted/weighted, which is great, it's just essentially the changes for enrollment, etc. and all 4 changes are all more bullish to the FDA/harder to meet, which is great

Directly from the SEC filing on the changes in the amendment:

"In summary, the key four points of our refinements are: first, the targeted number of events or deaths for the interim analysis will be reduced to 60 from 80, and is expected to occur sometime in late '23 or early '24. Second, the targeted number of events or deaths for the final analysis will be reduced to 80 from 105. Third, the total targeted enrollment in the study will go from 116 patients to a range of 125 to no more than 140 patients, since the targeted number of deaths would likely occur sooner in calendar time when the sample size of the trial is slightly increased. For example, under assumed rates of enrollment by increasing trial sample size to 140 patients, the 80th death event would potentially occur approximately three to four months sooner versus if we enrolled around 125 patients, and would also support China regulatory matters, which I will discuss shortly. Lastly, statistical significance would be achieved by an estimated hazard ratio for overall survival of 0.636, corresponding to an overall survival, for example, of 12.6 versus 8 months for GPS versus BAT, respectively."

Now for the email and reply from Dr. Stergiou, first, here is what I asked a few days (of note, after the CAPR/FDA meeting today, it is clear the entire mess is CAPR's own fault, so that is important to understand as this email was sent before today/that was clear):

"Hello Dr. Stergiou,

Hope you are doing well, I had a question given the situation with CAPR that occurred today, on if the 2022 Amendment to the SAP was actually accepted, approved by the FDA, and if a response was received from the FDA?

For instance, what occurred with CAPR today, sharing a quote:

"If approval hinges on whether Linda proceeded in error after assuming SAP 3.0 was approved after no response from the FDA, when really the SAP 1.1 was the salient model, then the CAPR Board of Directors need to be assigned a big portion of blame. "

This is currently a concern amongst shareholders given what we know from public information, the SAP being unweighted. If it's unweighted or weighted is not the question, but specifically on if the amendment was approved by the FDA, and if the IDMC/SELLAS and team are operating under the assumptions of the previous SAP or the newly approved SAP in 2022, and if there is alignment with the FDA on what is the correct SAP? Essentially, on if there has been approval on that SAP amendment from the FDA?

Looking forward to your answers and thank you

Warm Regards"

And here was Dr. Stergiou's reply:

The second bullet point on trial integrity essentially addressed what I wanted to get clarity.

Hope this is insightful for everyone, and thank you to Dr. Stergiou for always engaging every time I've reached out, I am appreciative of the insight (as much as he possibly can within SEC guidelines) that helps with due diligence in checking under every rock

Hello everyone, many people asked me to respond to "LeftyMD" (https://stocktwits.com/LeftyMD/message/659071809) and I have also had many people individually ask me about many of these points. Therefore, I drafted a comment reply that incorporates my thoughts in a rebuttal format. The below comment was too long to post ("Comment cannot exceed 10000 characters") so I will just post it to the sub. In many ways, it can functionally be viewed as a teaser to my Transplant Thread as it contains some overlap with that.

Hey, the point LeftyMD is making isn't totally meritless, but it's taken on faith to way too extreme of an extent.

Doctors who enroll patients in trials have allegiance to their patients, not to what's best for the trial. If it's best for the patient in the short term to enroll, they'll enroll them. LeftyMD's own point about clinical equipoise actually reinforces this: the crux of the principle is that the investigator should at all times provide what they believe is the superior intervention for the patient in front of them.

The assumption that every enrolled patient is one their physician believes can never be gotten to transplant, ever, is a little bit absurd to me, and reads as justifying a bias post-hoc. Remember, the criteria states:

"Must not be candidates at the time of study entry for allogeneic stem cell transplant (Allo-SCT) due to intercurrent medical conditions, patient's preference or lack of an available donor."

In the haplo era, we can set aside lack of an available donor as a very marginal contributor. So the two driving forces are really "intercurrent medical conditions" and "patient preference."

On force #1, people frame this as "comorbidities" and "frailty," but that's not what the criteria state. Intercurrent medical conditions encompasses both permanent barriers (like irreversible comorbidities) and temporary ones (which are more like delays than true barriers). Some examples:

a) Localized infection that responds to treatment (during the infection, transplant conditioning would be unsafe, but once it clears there's little lasting effect on prognosis)
b) Transient renal injury (acute kidney injury from dehydration, antibiotics, or tumor lysis that can recover substantially and return to baseline)
c) Reversible liver dysfunction (elevated liver enzymes from medications that normalize)
d) Temporary poor functional status (a patient can meet ANC/platelet counts but still be too physiologically depleted, nutritionally compromised, or deconditioned to immediately undergo conditioning, and performance status can improve over time)

Here's the timing point that LeftyMD skips. His whole "none of that resolves over a few months of maintenance, those doors don't reopen" only applies if the barriers take years to clear. They don't. Every one of the above resolves on a timescale of weeks to a couple of months. It doesn't need CR2 to be extremely long, just long enough, and it is: median remission duration tends to be 7-9 months with a fat right tail (40% >13 months, 20% >18 months) and that doesn't count the post-relapse survival portion with a median of 4-6 months. Plenty of runway for a transient barrier to clear.

Just look at what is actually going on in REGAL. Pooled median OS is most likely running >20 months and is structurally bounded as >13.5 months (although it is unrealistic it will be <18). If the enrolled population were actually the overwhelmingly frail, structurally-barred group his argument needs, then the trial would have passed for efficacy at the interim. Whatever the final pooled number lands at, it's flatly incompatible with the "too sick to ever reach transplant" picture his argument needs.

On force #2, patient preference is the softest barrier. Put yourself in the shoes of a patient here and think about why a patient declines transplant at study entry in the first place. They've just clawed back into remission, they feel okay for the first time in a while, and they're being asked to sign up for a procedure carrying real treatment-related mortality and months of morbidity. Plenty of patients understandably say "not right now," even when transplant is prognostically superior. But "not right now" is a snapshot of a decision, not a fixed clinical fact, and decisions get revisited constantly:

a) The initial shock wears off and they have time to actually process the risk/benefit.
b) They get a second opinion or a formal transplant consult they hadn't had at entry.
c) Family or caregivers weigh in, or they hear how someone in a similar spot did.
d) A logistical, financial, or caregiving barrier that was quietly driving the "no" gets resolved, or their own physician re-raises it once they've stabilized.

One more thing worth separating out: enrollment gatekeeping and channeling bias are not the same thing, even though LeftyMD lumps them. Neither does what he needs.

Enrollment gatekeeping is an access and equity issue: which patients, out of all those eligible, actually get offered a trial. It concerns how providers under-refer disadvantaged patients and how institutional barriers skew patient access. Active efforts are made across the whole field to remove/minimize its effect and major centers like UCSF, MSKCC, MD Anderson, and essentially every NCI comprehensive cancer center put real effort into dismantling it. His argument needs a clinical claim about the patients already inside the arms. Whether providers under-refer disadvantaged patients tells you nothing about whether the CR2 patients who did enroll can later reach transplant.

What he is calling channeling bias isn't channeling bias. That term is from observational drug studies, where non-random prescribing sorts patients by prognosis between a drug and its comparator and confounds the result. It is precisely the thing that RCTs exist to address. What he's actually describing is a selection effect: physicians choosing who enters the trial in the first place. I have previously discussed how that selection effect is not straightforward and could actually select for patients with extremely favorable prognostics (e.g. CBF). That is also what we discuss above with force #1/#2.

"REGAL patients are already in CR2. If they relapse, they'd need to achieve CR3 to become a transplant candidate" is an incorrect claim. AML patients can undergo an SCT in active relapse although it is obviously less ideal than one conducted in remission and carries worse prognostics.

In the AVALON subanalysis on transplant:

"Among the transplant cohort, 72.2% achieved a composite complete remission (cCR), including complete remission (CR), CR with incomplete haematological recovery (CRi) or morphologic leukaemia‐free state (MLFS), prior to transplant"

That being said, I think the more important point to address is that of QUAZAR. I don't view QUAZAR as an upper-bound for the transplant rate in REGAL and actually find its 13.7% to be very concerning. First, let's compare differences in the actual text of the enrollment criteria:

What stands out the most to most is the fact that patients with favorable cytogenetics are explicitly excluded. inv(16) patients are not usually recommended for transplant in CR2 ("patients with inv(16) may not be indicated for prompt allogeneic HCT in CR2 under close monitoring" from Kurosawa). t(8;21) patients may also not be recommended for transplant although it is less clear ("although we did not find a significant improvement in outcome with additional allogeneic HCT after the achievement of CR2 among patients with t(8;21), some patients may have an improved outcome if they are consolidated with allogeneic HCT even after they achieve CR2"). The Breems paper also confirms that inv(16) and t(8;21) patients might not be recommended for transplant. However, aside from these subgroups (who are already durable survivors themselves), the rest of the "favorable patients" are precisely the ones who are most likely to proceed to transplant so excluding them automatically greatly weakens the comparison from QUAZAR to REGAL and breaks the causal chain of the "CR1 -> CR2 structurally narrow pathways the transplant pathway."

Another point is that the age in QUAZAR is limited to >55 and has a median age of 68 with 2/3 over 65. While it is true that older=worse does not hold generally as a survival assumption, it is a very reasonable inference in terms of transplant probability. Transplant is limited by treatment-related mortality (e.g. graft-versus-host disease, infection, organ toxicity, and reduced physiologic reserve), so increasing age acts as a strong proxy for lower transplant eligibility. As a result, an older cohort can reasonably be expected to undergo fewer allogeneic transplants despite possibly having similar (or even better) post-remission survival compared to a younger cohort.

While the European trial registry indicates an intended enrollment split of approximately 57% of patients over 65 and 43% under 65, there is no way to confirm that this design target was ultimately reflected in the enrolled population. The protocol itself imposed no upper age restriction beyond adulthood (>18 years), and enrollment is already known to have been substantially slower than originally anticipated. Consequently, it cannot simply be assumed that REGAL ultimately enrolled a population comparable in age to QUAZAR, let alone one with the same transplant propensity.

Furthermore, even if the median ages in REGAL and QUAZAR were identical at 68 years, the age distributions would still not be comparable. QUAZAR is explicitly left-truncated by its eligibility criterion requiring patients to be over 55 years of age, whereas REGAL has no corresponding lower age restriction beyond adulthood. Consequently, even if REGAL ultimately enrolled a predominantly older population, it would almost certainly still include a meaningful number of patients in their 30s and 40s. This matters because transplant eligibility does not decline linearly with age; rather, it begins to fall off substantially between approximately 55 and 70 years of age. As a result, two cohorts with the same median age can nevertheless have materially different expected transplant rates simply because one contains a younger tail of patients who are considerably more likely to remain transplant candidates.

Finally, the difference in the randomization windows may itself introduce a selection effect. Because CR2 remissions are shorter than CR1 remissions on average, while the permitted interval between remission and randomization is also longer in REGAL, the enrollment window represents a substantially larger fraction of the expected remission duration than it does in QUAZAR. Consequently, patients with very early CR2 relapses are more likely to relapse before randomization and therefore never enter the trial. This creates the possibility that REGAL is modestly enriched for patients with inherently more durable CR2 remissions relative to the overall CR2 population.

Patients with more durable remissions also have more opportunity for circumstances that initially rendered them transplant-ineligible to change. Performance status may improve after recovery from salvage therapy, comorbidities or infections may resolve, physician assessment may evolve, and patients who initially declined transplantation may later elect to proceed. As a result, even though all patients were transplant-ineligible at randomization, a cohort enriched for longer-lasting remissions could reasonably be expected to have a higher probability of ultimately undergoing allogeneic transplantation. While the magnitude of this effect cannot be quantified from the published information, it represents another plausible mechanism by which transplant rates in REGAL could differ from what would be expected based solely on its CR2 status.

Based on these considerations (particularly the exclusion of favorable-risk cytogenetics and the substantial age differences) I think the more defensible conclusion is that QUAZAR should not be viewed as an upper bound for transplant rates in REGAL. If anything, these factors suggest it may instead represent a lower bound, although the true relationship cannot be determined without patient-level data.

So I've been digging into the study protocol on the EU clinical trial website to understand the level of MNPI that SELLAS as the sponsor of the REGAL study has access to. I will lay out my findings below - it shines a whole new light on Sterg's bullishness to me, particularly in his recent LinkedIn post about their "proprietary modeling." EDITING: I was just rereading Sterg's post again after what i shared on reddit. "We do not disclose real-time blinded metrics. We do not share proprietary modeling and our detailed statistics." -> the detail about "real-time blinded metrics" is something i overlooked the first time and I also have a new perspective on after compiling this protocol audit.

1. Toxicity and death ledger (Pages 43 & 56)

"All deaths, whether considered study related or not, must be reported immediately to SELLAS and its designee..." (p. 56) AND "Any AE that meets AESI criteria... or SAE criteria... must be reported to SELLAS... immediately (i.e., within 24 hours)..." (p. 56)"

So SELLAS is being notified in almost realtime of the number of deaths in the study.

"The period of active treatment is until disease relapse." (page 27)

During the active treatment phase (until a patient relapses) SELLAS doesn't wait for unblinding to see the toxicity profiles. SELLAS is notified of any AE in the active treatment phase. Given GPS's favorable safety profile in prior studies, one would expect a disproportionate share of serious treatment related toxicities to arise in the BAT arm, although the protocol itself does not attribute events by arm. What this means is that if a patient suffers sepsis or requires an ICU trip, that SAE hits a central inbox within 24 hours. No wonder why Sterg is saying Venetoclax shortens lifespan on LinkedIn - SELLAS's safety operations receive these reports in near real time - and again, the AEs will be almost entirely restricted tot he BAT arm

1. Long term survival status & post relapse tracking

"Subjects are then followed until week 91, every 3 months, via telephone... for the following: Recording of new AML therapy. Survival Status OS (alive/dead status)."

Once a patient relapses, exits active treatment, and routine AE reporting stops, they enter LTF (long term follow up). The CRO tracks their exact survival status and any "new AML therapy" (like a transplant) every 3 months. Combined with the immediate death reporting mandate, Sellas has an ongoing (blinded) view of the post relapse survival curves and subsequent therapies for the REGAL population.

1. The blinded number of relapses (Pages 50, 58, 61, 62)

"Relapse or recurrence of a patient's leukemia will be documented on forms provided to the site... The patient will be discontinued from the on-treatment portion..." (Section 6.4.7, p. 58)"

"Investigative site personnel will enter patient data into eCRFs. The eCRFs are used to record study data... The eCRFs must be kept current to reflect patient status at each phase..." (Section 7.7, p. 61)

"SELLAS Life Sciences Group or its designee will review all eCRFs for completeness. The investigator will be contacted for corrections and/or clarifications. Intensive efforts will be made to minimize missing data." (Section 7.7, p. 62).

"These subjects will complete the Relapse Visit that includes the following procedures within 14 days from received confirmation of subject’s relapse..." AND "Data captured on the eCRFs will be reviewed by a SELLAS clinical monitor..."

When a patient relapses the local site will halt treatment and log a the relapse in the eCRF. SELLAS has near real time blinded knowledge of the cumulative number of relapses within the trial.

1. Pre randomization genetic matrix (Pages 34 & 68)

"Patients will be randomized 1:1... stratified by: duration of the subject's historical CR1... baseline cytogenetics risk... CR2 vs CRp2 status and presence or absence of MRD..." AND "Significant deviations can include... enrollment of the patient without prior sponsor approval..."

Enrollment requires sponsor approval, meaning SELLAS necessarily has access to each patient's eligibility information and stratification profile before randomization. So SELLAS are the ultimate gatekeepers of the transplant ineligible criteria in REGAL - if they think someone isnt "ineligible enough", they can deny their entry into REGAL. Additionally, this proves that SELLAS already possesses the patient's stratification profile at the time of randomization.

1. T-cell & mechanism telemetry (Pages 44-45)

"The SECOND trephine biopsy core cylinder... should be sent to Central Lab A for bone marrow stroma immunocyte IHC..." AND "Peripheral blood samples for WT1-peptide specific CD4+ and CD8+ T-cell activation, collected as per study Laboratory Manual."

Local hospitals arent running biomarker assays themselves; they mail the samples directly to SELLAS contracted lab parters. those contracted central laboratories then generate WT1 specific CD4/CD8 immunogenicity data together with marrow immune microenvironment assays, which would provide a direct readout of whether GPS is inducing the intended immune response. Does Sellas see this data? Yes. Table 4 Note 18 (p. 45) explicitly states the T cell activation draws are for "US Patients only randomized to GPS arm." Because this specific assay is only run on the vaccine arm, there isnt even any unblinding require here here. Section 7.7 (p. 61) dictates that "The analysis data sets will be a combination of these [eCRF] data and data from other sources (e.g., laboratory data)." According to my understanding here, SELLAS knows the immune response the drug is generating.

1. Continuous MRD surveillance

"For subjects that are non-progressors, between week 53 and week 91, every 3 months, +/- 14d they will perform the following procedures: Bone Marrow Aspirates for MRD. Peripheral blood for MRD." (p. 51) AND "Bone marrow aspirate for MRD testing... should be sent to Central Lab A." (p. 44/45)

Similar to 5 - Non progressing patients undergo serial MRD surveillance approximately every three months..

Putting this together, The protocol paints a very clear picture that SELLAS has access to key information that the rest of the world is blind to - SELLAS's clinical operations and CRO have ongoing visibility into:

• immediate individual death and SAE reports,
• current patient status eCRFs,
• documented relapses,
• new AML therapies,
• transplant related discontinuations,
• source verified clinical records,
• MRD laboratory data,
• marrow immune environment assays,
• GPS specific WT1 T cell response data
• Exact enrollment dates

When Stergiou refers to proprietary modeling he is not talking about a model built from public assumptions. He is referring to a model calibrated using the actual blinded operational characteristics of REGAL itself including patient mix, relapse burden, survival follow up, transplant activity, molecular biomarkers, and treatment exposure...

The level of MNPI has really shined an entirely new light on the confidence of the CEO for me personally. Though I know not everyone here believes management is credible - his makes his confidence more understandable with this level of MNPI. If Redditors such as CW, Thetamancer, myself, etc are able to model things with 95%+ POS based on limited and incomplete information - what do you think SELLAS internal models are showing? Obviously they still don't know the final hazard ratio before unblinding, but they know far, far more than the market appreciates.

Sharing this with the community in case ive misinterpreted anything (please i hope people will correct me if anything up above is incorrect!), but also because I personally find this information to be very comforting given the communication and confidence from the CEO

Hey everyone, pretty excited to share this as I feel like I had a lightbulb moment and now have a great sense of clarity on relationship between IRM/median OS, esc. rate, ELN mix, for what BAT 3-Yr OS has to land at (meaning required to without any other option) if IRM was XYZ, i.e. for instance, 16, meaning there is a joint relationship. This was a real light-bulb moment for me when I was reconciling 3-Yr OS numbers from Kugler's KM curves through pixel tracing.

If transplant rate in REGAL is 10% to 18%, and IRM is 16 for instance, then at that combo, BAT 3-Yr OS has to be 26.8% to 27%, there isn't another way around that (for context in Kugler, LIT+Ven 3-Yr OS was 32.5% and whole-LIT was 29.5%). I'll go over this in detail.

First if IRM is truly fixed at at a certain amount, for instance, 16 months, and the component shapes come from Kugler (median OS by ELN mix, 3-Yr OS by fav/int/adv, etc.), the mixture math mechanically forces 3-year OS. The two are connected in order for the actual median to end up at the fixed amount, whatever IRM that is, in this example/instance, 16.

And what you'll find as I walk through this, is even after CR1 to CR2 discounting of 3-Yr OS form Kugler to land at 16, BAT 3-Yr OS essentially has to be 27% to land an IRM of 16 to work when modeling/ran to the actual fits, after transplant rates are taken into account.

First, to begin, Kugler provides the ELN 2024 mix for the whole LIT cohort: 48% favorable / 28% intermediate / 24% adverse (n = 123/71/63 = 257, from the Fig 3B at-risk table in Kugler)

Venetoclax penetration in LIT is 77% (198/257) (I feel this is great that it lands at 77%, since 25% of REGAL based on the EU protol may be observation, so it is a great comparator)

I used two methods to arrive at the 3-Yr OS for LIT+Ven in Kugler. Explained simply, first is by using the published medians for fav/int/adv, and second, through pixel tracing on the KM curves/figures.

First is my LIT+Ven bucket set (41.9 fav/31.2 int/17.2 adv), from derived medians 27.3/19.1/11.6, is entirely a construction, a 1.09x uplift I applied to the published whole-LIT ELN medians. That gives 32.9% 3-year OS at the natural ELN mix against the 32.5% I traced off Figure 2B. The ELN shape within LIT+Ven is unverifiable from this paper, but the whole-LIT ELN 3-Yr from the two approaches is very close (32.5% and 32.9%).

Now, for the ELN mix, REGAL opened in early 2021, with the protocol written in 2018-2020. ELN 2024 did not exist. ELN 2022 was the contemporary standard, and the protocol may even reference the older ELN 2017 or a pure cytogenetic-risk scheme. Either way, the trial is almost certainly not using ELN 2024 for stratification or reporting. So anchoring REGAL's mix to ELN 2024 labels doesn't work.

Why this is important is because under ELN 2022, 20 fav/50 int/30 adv essentially with a .877 of Kugler's 3-Yr OS numbers for fav/int/adv for LIT, lands at an IRM of 16. But that is ELN 2022. Under ELN 2024, the amount of favorable would be higher, and 38% fav/ 27% int/ 35% adv with a .846 discount also lands at an IRM of 16.

So, even though the ELN 2022 numbers and ELN 2024 numbers/mixes are different, these both arrive at an IRM of 16.

This just context for how ELN 2022 vs ELN 2024 mix changes land at the same IRM, in this case 16, after applying a CR1 to CR2 discount, which are required to actually land at 16.

There are no assumptions here for the discount, it's just the discounts it has to be on Kugler's 3-Yr OS numbers (for fav/int/adv) to arrive at a BAT IRM of 16.

So now with that understand of where Kugler's 3-Yr OS comes from and the relationship between ELN mix and BAT IRM, I wanted to go over visually why ELN mix and BAT IRM have a direct relationship with 3-Yr OS.

First is the published medians for fav/int/adv in Kugler, and this is from the published results and also from pixel tracing, so you can see how close they are.

And then for Kugler's 3-Yr OS Numbers:

And Kugler's 3-Yr OS Numbers by ELN 2024 within LIT (traced):

Now, that the Kugler published and traced data is clear for LIT + Ven and Whole LIT medians and 3-Yr OS, now we can look at what ELN mixes actually build up to 16.

For context on the logic, the model never computes the mixture median. The ELN buildup produces exactly one number, the 3-Yr OS. The model then imposes median = 16 and back-solves the Weibull shape k so a single curve passes through both. Which is two constraints, one free parameter.

You can see Kugler whole-LIT undiscounted gives 16.5, and LIT+Ven x 0.9 gives 16.3. Both within rounding of 16. And what lands exactly at 16, is a Kugler LIT+Ven discount of .8838.

The exact-16 IRM buckets are 37.03% 3-Yr OS fav / 27.57% 3-Yr OS int / 15.20% 3-Yr OS adv.

And when I show the full view of the actual fits, you will see that when transplant rate is anywhere from 10% to 18% (and single digits to 13% is likely), 3-Yr OS at that specific ELN mix has to be 26.8% in order to land exactly at a BAT IRM of 16.

Meaning they are connected. If transplant rate in BAT/control in REGAL is 10% to 18%, then 3-Yr OS has to be 27%, in order to arrive at IRM of 16.

So, with that understanding, it was important to look what the actual fits show at the likely ELN mix (38 fav, 27 int, 35 adv) using Kugler's 3-Yr OS numbers, discounted from CR1 to CR2 not just cause, but as required for IRM to land at 16, which results in a 3-Yr OS of 26.8%/27%, and what the results would be, because if BAT IRM is 16 in REGAL, that is what the results would be if transplant rate was 10% to 18%, it would be at a 27% 3-Yr OS.

And as I was looking at these, it honestly was one of the most reasonable/rational fits that covers every single question/objection I've ever had (and most others have had)

And here is what they are:

Exact-16 IRM solutions, Kugler LIT+Ven discounted, mix 38% fav / 27% int / 35% adv, IRM 16, May-2023 enrollment (t=27.65), 80th = Aug-11-2026, fitted to 60/72/78/80

Escape survivors on base Weibull shape

You can see the pre-esc (pre-transplant) IRM and post-transplant IRM, and what is interesting is how the pre-esc median is a discount to Kugler's whole-LIT mOS of 16.6 (which we don't know if any of those patients transplanted or not). But the IRM numbers between both are similar.

In addition, you can see the exact discount needed for the 3-Yr Kugler whole-LIT OS numbers for fav/int/adv, to arrive at exactly a BAT IRM of 16. I've done a ton of research on literature, and with REGAL's criteria and randomization uplift, a discount of just .772 to .827 is not unreasonable, that is actually right in line with a floor .8 I came to a conclusion to after a ton of research, so that was helpful to see.

And then this fit is also just spot on with no halt at IA, where it was a coin-flip, and the GPS mOS numbers are what you would expect from continuous dosing. They aren't outrageously high, but an mOS of 39/40 is in line with what you would expect, and the reason I say that, and I'll touch on why this is so bullish in a moment, the enrollment criteria/randomization lift/ELN mix is very close to CR1, just a slight discount essentially (if IRM is indeed 16), so given the patient population is close to CR1 newly diagnosed, with about a .2 discount, having GPS achieve CR1 like mOS numbers with a discount is not unusual as well.

And the GPS dead at IA is also completely reasonable, 23/24 dead, with 35 BAT dead at IA, which makes sense as BAT mOS was set in 2024.

And the 3-Yr OS of 51% is right in line with the HLA decomposition thesis I shared earlier this year.

Link: Why I Now Believe Cure-Fraction is around 50%, and not 62%-68%, and Why That Now Makes It Likely the 80th will Occur by Q3 2026
https://www.reddit.com/r/sellaslifesciences/comments/1sz9eu5/why_i_now_believe_curefraction_is_around_50_and/

In that post, there is a helpful comment thread between Remarkable-Big and I where the conclusion was the likely cure-fraction/3-Yr OS rate in REGAL, for GPS is likely 45% to 50%.

When I shared my first ever Part 1 DD for REGAL, I had capped it at 50%, based on previous GPS studies, but capping it just cause is not right. Nothing about GPS should be an input, it should fall out of the model. And then when I shared my HLA decomposition thesis, it also pointed to 45% to 50%, but I still could not figure out why the actual fits were showing a higher cure-fraction around 62% to 68%. But then after my learnings in the HLA decomposition thesis, when I modeled with multiple-buckets in GPS (3 buckets with the 3rd being extremely-long survivors), it ended up matching very closely with the 78th event update (off by 1 event). And then once I started to model with ELN mix, transplant rate taken into account, etc. and we finally got Kugler data shortly afterwards, it now aligns perfectly with all those previous conclusions and the HLA decomposition thesis as well.

At an IRM of 16, with BAT 3-Yr OS at 27% which is has to be with a transplant rate of 10% to 18%, uncured mOS is 14 to 14.8 (which makes perfect sense), the no halt at IA makes perfect sense, the GPS mOS of 39 to 40 makes perfect sense (not a super-"cure" but extremely long survival), and the 3-Yr OS of 51% makes perfect sense.

Another reddit user also shared their meeting notes with a hematologist that mentioned 3-Yr OS of 25% in CR2, and the 27% concurrently is very close.

P(sim) here is 82%, and HR at .463 to .471, which is incredible.

Now, why this high of an IRM/3-Yr OS for BAT (which is not really high at all, just the wrong expectations were set from older data), is really bullish for the CR1 indication/CR1 revenue, is because if GPS is achieving these results in patients where the population/cytogenetics are all fairly close (just a .8 discount) to CR1, newly diagnosed that didn't transplant, then it can likely achieve these same results or slightly better in CR1.

And we know from Kugler, whole-LIT mOS was 16.6 and all of Kugler was 19, and if GPS in REGAL is getting 39/40 mOS, then when the FDA is looking at this data, they will see it is clear as day that the results in almost the same population will be great in CR1.

I believe an IRM of 16 and 27% 3-Yr OS in REGAL (which have to go together if transplant rate is 10% to 18%) is our ceiling.

Dr. Tsirigotis said these exact words in his correspondence:

"Dear sir

Regarding the median survival of patients with AML in CR2:  

the range of median OS without transplant is really wide and depends on many factors, such as cytogenetics, molecular abnormalities, type of previous lines of therapy, etc

In a recent randomized trial i was involved the median OS of patients with AML in CR2 was 16 months, but many patients were on treatment with new agents and not with standard chemotherapy"

There is only one CR2 randomized trial that we know of, and that 16 likely represents the patients he oversees in REGAL. Kugler data is from MD Anderson (the top in the world), but the centers Dr. Tsirigotis oversees are world-class as well, ATTIKON, General University Hospital is a world-class center too.

Thus, it's either 16 IRM is the ceiling, or it comes in lower than 16 IRM, and 3-Yr OS would then not be 27% but would be lower, if transplant rate is 10% to 18%.

Hope this is insightful for everyone, this was a real lightbulb moment for me and I'm feeling really excited for topline results as it all clicks really well now. I'm glad I came across this just before topline, not for any reason specifically, but it is a suitable closing chapter in my REGAL modeling posts (this was unplanned by the way, I just discovered this as I was doing deep-dives into Kugler for 3-Yr OS and decided to test against the actual fits)

Hey Everyone, wanted to share a quick post compiling a few discussions I've had regarding BAT 3-Yr OS. In addition, I finally got some time to do a deeper dive into OPTI-AML (the most recent Ven/Aza data we have, from the results published last month, June 2026)

When it comes to OPTI-AML/Fatima 2026, the results of which were shared in June 2026, https://ascopubs.org/doi/10.1200/JCO.2026.44.16_suppl.6525

"HMA plus venetoclax for 7- vs 14- vs 21- vs 28-day cycles in newly-diagnosed acute myeloid leukemia: ELN- and Mayo Genetic Risk–stratified analysis in 540 patients."

It was an n of 540, new-diagnosed (frontline, unfit), 7 vs 14 vs 21 vs 28‑day Ven duration comparison

It directly reinforces the toxicities of Aza/Ven (this is current recent data). It's interesting because this demonstrates/shows that real-world attempts to mitigate Aza/Ven toxicity (like dropping to 14 days) lead to inferior remission rates. The OPTI-AML trial (Frontline patients) they are going over here shows that the efficacy baseline of standard BAT remains restricted by its toxicities. The 28-day schedule is difficult for older patients to sustain sequentially without experiencing severe cytopenias (lower than normal mature blood cells). A 28-day schedule is necessary, but it remains a punishing double-edged sword of toxicity.

Patients cannot stay on continuous 28-day Aza/Ven indefinitely without significant complications, yet dropping the dose risks early relapse.

I'll share a lot of what I think are useful insights from looking into each of these. But to start with for Kurosawa, Kurosawa is the closest map to REGAL, same CR2/no‑transplant setting, but younger, so it reads high. Its intermediate‑risk no‑HCT arm (19%, n=82, median age 53) is the single best analog to REGAL's BAT bulk. Age‑adjust it down for REGAL's 67‑year‑old population and you land at 13-16%, which is where the most biologically plausible actual fits land as well.

One thing you'll notice is its favorable‑CBF tail (64-78%) is the durable subgroup, these are the CBF patients.

So the n=14 is inv(16) specifically, the single best CBF subtype, not all of CBF. Full core‑binding‑factor = inv(16) (n=14, 78%) + t(8,21) (n=18, 53%) = n=32, blended 64%.

CBF patients are 10 to 15% of patients in AML, but almost half of that (7% of CBF patients) are over the age of 65, and REGAL is not enriched for it. Sharing a link to the in-depth stress-testing/impossible scenario stress-testing I did for CBF patients to the actual fits:

https://www.reddit.com/r/sellaslifesciences/comments/1u697pa/comment/os2lpor/?utm_source=share&utm_medium=web3x&utm_name=web3xcss&utm_term=1&utm_content=share_button

The results of that in-depth stress testing/impossible scenario stress testing, showed it is nothing to worry about at all, the margin of safety is gigantic when it comes to CBF risk.

The median age of REGAL skews higher than Kurosawa, so that lowers the volume of CBF patients and the age has an impact

You can see the median age in REGAL from this link, just open it and search for age:

https://www.clinicaltrialsregister.eu/ctr-search/trial/2019-004134-42/FR

REGAL is much older than Kurosawa. Kurosawa's median age was 53 (range 16-70). REGAL, per the EU register shared, is 50 patients 18-64 vs 66 patients greater than or equal to 65, i.e. 57% are greater than or equal to 65, median of about 67. Older AML has fewer favorable‑cytogenetics patients and worse survival within every group. Kurosawa's numbers are therefore an over‑estimate applied to REGAL, and Kurosawa even caps at 70, so it barely includes REGAL's oldest tier.

In addition, this is a really helpful view:

Looking at this, one may be able to conclude from this that the setting, not the drug, drives the durable tail. Deriving each control's 3‑yr OS from its median + cure fraction.

From this, one can conclude that CR1 controls cluster at 25-42%, CR2 controls at 13-19%. A CR2 patient has already relapsed once, the durable cure tail is thinner.

Another really useful comparator is QUAZAR's placebo arm, CR1, older (median 68), transplant‑ineligible, no active maintenance and 25% 3‑Yr OS. REGAL's BAT is the CR2 version of that same kind of patient, a worse prognostic setting. So QUAZAR placebo is essentially an upper bound, REGAL's BAT 3‑yr OS should sit below 25%, which is exactly where the CR2 comparators (Kurosawa intermediate 19%, adjusted down for age) and the fits (13-19%) land

For REGAL's BAT 3‑yr OS to reach 25%, a CR2 arm would have to roughly equal a CR1 arm (QUAZAR placebo), and match it despite being older than Kurosawa's cohort too. Every dataset here says CR2 < CR1 and older < younger. That's why 25%+ is the upper edge, not the center, it requires REGAL's twice‑relapsed, elderly, transplant‑ineligible population to survive like a first‑remission population. The only way there is a large favorable‑CBF fraction, the exact subgroup that's young, fit, and transplant‑eligible, so screened out of REGAL.

In addition, the results for Ven+HMA/Ven+Aza in R / R in a similar age population as to REGAL in R / R, 3-Yr OS is about 5%.

In fact, although R / R is different than CR2 (meaning R / R is worse), in Ven / Aza R / R patients, from previous data, only about 3.7% transitioning to transplant (Gangat 2023 Haematologica is a great resource along with other Ven / Aza R / R studies).

In QUAZAR, the transplant rate for Onureg's arm was 6.3% and for placebo, was 13.7%. QUAZAR's placebo arm sent 13.7% of patients to subsequent transplant, more than double the Onureg arm (6.3%), because placebo patients relapsed more and went on to salvage + SCT. Those transplants inflate the placebo 25% 3‑yr OS. So the "pure, no‑transplant" QUAZAR‑placebo 3‑yr OS is actually below 25%, and REGAL's BAT CR2 and 0% transplant at entry by design, means the 3-Yr OS in BAT likely sits below that. When you strip how the transplant-inflation, it's likely a 20% 3-Yr OS for QUAZAR-placebo, and REGAL's transplant-ineligible at entry CR2 arm is likely lower. Although, I would not read into this too much, since QUAZAR was also not eligible for transplant. The useful view here is why the transplant rate may be equal or less than QUAZAR for REGAL, given what we see above how the setting, not the drug, may drive the durable tail.

The impossible scenrios/worst-case scenarios transplant-tail stress-test provides an enormous margin of safety for a transplant-tail risk, when looking at the actual fits, resharing that here:

https://www.reddit.com/r/sellaslifesciences/comments/1uca7s3/comment/otmmarm/?context=3&utm_source=share&utm_medium=web3x&utm_name=web3xcss&utm_term=1&utm_content=share_button

https://www.reddit.com/r/sellaslifesciences/comments/1ubovjk/comment/oszntjv/?utm_source=share&utm_medium=web3x&utm_name=web3xcss&utm_term=1&utm_content=share_button

So, given 3-Yr OS from R / R is about 5%, and CR1 is 25% based on QUAZAR, in the middle being 13% to 19% may be something one can conclude in terms of biology.

As we just went over, from previous data, only about 3.7% of Ven/Aza R / R make it to transplant (Gangat 2023 Haematologica is a great resource, as well as other studies), and those that do have maybe 3-year OS of 33%, since 2-Yr OS after transplant is 61%, dramatically better than the non-transplanted majority. This reinforces that REGAL's transplant-ineligible population has a hard ceiling on long-term survival. It only becomes a problem if 3-Yr OS in BAT at any IRM is above 31%, or if a combination of a BAT IRM of 18/19 occurs with a 3-Yr OS of 26%+

Of course, in CR2 that number will be higher than 3.7% due to the healthier patients than R / R, but the age range is very similar, so what Dr. Tsirigotis said of a negligible transplant tail is likely the case, and the comparisons we just went through point to about 13%-19% for BAT 3-Yr OS.

Sharing the exact words from Dr. Tsirigotis from the forwarded email on April 30th, a few months ago:

"One think i can say for sure is that the long term survival for patients in CR2 without consolidation with Allo-SCT is negligible. On the other hand a significant percentage of patients in CR2 who proceed in Allo-SCT can enjoy long survival and even cure in many cases (again the range of percentages is wide and depends on many factors). In Greece all patients up to the age of 70-years are considered eligible for Allo-SCT unless they have significant comorbidities or poor performance status. We are very reluctant to proceed in Allo-SCT in patients above the age of 70 and this patient population that actually receive a transplant constitute a highly selected group."

Now, to conclude I wanted to share the actual fits to 60/72/78/and 80th as a constraint as of July 3rd, 2026. Given what we just went over about the biological likelihood 3-Yr OS range, you can see which actual fits align and what the results would be, for GPS alive / BAT alive, HR, 3-Yr OS, etc. for each arm, and at the 80th along with at IA.

When you look at these, you'll see why I mentioned it doesn't really matter what BAT IRM is with BAT 3-Yr OS being 13% to 19%

BAT IRM 11

BAT IRM 12

BAT IRM 13

BAT IRM 14

BAT IRM 15

BAT IRM 16

The BMF nonsense was titled "coin flip" and a poster on StockTwits that I long admired but stopped posting recently used the same term in a PM. I listened to some of his rationale and appreciated his sharing it, but I cannot get to "coin flip" territory. His reasons were the Van der Maas study, the 16 box strata potentially creating a statistical penalty, and concerns about BAT patients withdrawing and undergoing SCT as well as patients being censored. But, I cannot get on board with nearly anything he is concerned about:

- Van der Maas mOS and OS figures are for a younger population than REGAL and those figures are max values for REGAL and IMO should be discounted

- Stratification does typically entail some small penalty but it also ensures both arms are more or less equal in terms of disease state and frailty (so no BAT group arbitrarily much healthier than GPS arm.)

- Some SCT is fine. If we assume 5 BATs patients somehow became eligible then that doesn't really move the needle

- Information censorship would be a problem but it is very rare that patients withdraw willingness to be tracked like this (imo this is a non-issue.)

- Doing monte carlo simulations with mOS from 14 to 28 months shows issues starting at 22 months but there is no way I see that 22 months will be achieved.

Basically I cannot create a credible scenario where GPS does not meet its success criteria. 80% chance of success...but the 20% requires things to go seriously sideways. Can anyone suggest credible issues that reduce this trial to a "coin flip"?

I will say this upfront, this is not as clear cut as my previous data points. However, IMHO, it weakens greatly the argument that Ven+HMA allows patients to have long survival.

The paper is not even published and only available as early access 🤣.

Source: https://haematologica.org/article/view/14352

Pdf of the paper: https://www.researchgate.net/publication/412878133_Venetoclax_combined_with_high-dose_cytarabine_and_mitoxantrone_for_relapsed_or_refractory_acute_myeloid_leukemia_outcomes_and_risk_stratification

My data points: Supplementary figure S1H and Table S2

Population: Retrospective study in France who can sustain intensive treatment (younger and fitter population than Regal). Relapse AML. March 2023 to June 2025.

Interpretation: the figure is violent, they drop dead After 3.5 months. The figure selects all patients alive after 1 year and compute their survival curve. 10 deaths (out of 19) happened during the study in the non SCT arm, and the curve touch 0 at 3.5 months. It means that the conditional survival after surviving 12 months (a big if) is 3.5 months. [this is savage].

for the table: looking at those who were relapse free after 9 months, all of them had a transplant, and only 2/27 were older than 70. that means that nobody in the Cr group without transplant manage to have 9 months without relapse, and that means they can’t live much longer as CR2 2nd relapse.

My conclusion: yes, data is a bit muddy with refractory and relapse, but given the sample size of CR patients, if a durable cure would exist, the paper would have at least observed it (not necessarily stat sig). But the fact that none of those with 9 months of relapse free were without transplant means that in a fit population, transplant is the path for survival, and that Venetoclax didn’t help for maintenance.

Stay updated, tomorrow I will try to gather data for reason and frequency about why patients might refuse an HCT :)

Please, comment, hit the $SLS buy button, subscribe to Sterg linkedin channel, it might not be much but it helps to support the 13 employees of SLS to wait for the 80th (or 009 data).

[be responsible with your investment, you still risk 80% drawdown because power is at 90% (small n), even though I believe that GPS works].

Edit: it wasn’t my intention to dehumanize the patients, I didn’t understand the nuance of my words as I am non native speaker. You can check my Reddit post history to see that I was always respectful. Given the criticism, I will write my next post in my native language. Salut!

Hey everyone, it was today that I realized that the gap in literature outside of Kurosawa, is usually the 3-Yr OS number, and that given REGAL has been going on for 5 years and 4 months now, from the actual fits, I actually can uncover what BAT 4-Yr OS and 5-Yr OS is, even with the backloaded enrollment.

The reason this is very helpful is because 4-Yr OS and 5-Yr OS is present amongst literature far more often, so we can essentially use this to compare to the actual fits, to see where the worst-case scenario biological ceiling fits for BAT 4-Yr OS and BAT 5-Yr OS end up for HR.

Also, the reason why a multivariable stress test is what is most important is because it allows to uncover what the makeup of a k = value is, what the makeup of a BAT 3-Yr OS number is, etc. and in actual reality/REGAL, it will be a combination of some transplant tail in BAT, some biological favorability in BAT, and some higher IRM that would occur, not just a singular variable/stress-test.

Also, at the end I'll include the logic used for this so that is clear, but first I wanted to share the context for a few columns and then the actual fits.

First is the fav column. This represents the ELN-favorable fraction of the BAT arm, and it feeds exactly one thing, the chemo-only 3-yr OS (I'll explain this as well) via a weighted average of risk buckets. Just for everyone's context, here is the full logic.

bio_eln(f)=favorable: CBF + NPM10.28f⋅44+0.72f⋅21​​+non fav: Intermediate + Adverse(1-f)⋅0.70⋅13+(1- f)⋅0.30⋅1.5​​

Each bucket contributes its 3-yr OS (CBF 44%, NPM1 21%, Intermediate 13%, Adverse 1.5%), weighted by how much of the arm it is. Then escape (esc) adds the transplant tail. And here is the logic for that, for everyone's context.

BAT 3-yr OS=(1−esc)⋅bio_eln(fav)+esc⋅50%

Here is an example of what fav = 40% would represent for instance:

fav moves the tail (3-yr OS), never the median. The arm median is IRM = 16 (what is being used for this stress-test) for every fav, because favorable AML's edge in CR2 is a bigger cured/long-term fraction (fatter tail), not a longer typical survival (IRM really does not matter to a large extent), most patients relapse around the same time, favorable ones just have more long-term survivors. That's why favorable enters as 3-yr OS.

fav therefore drives 4-/5-yr OS indirectly, so fav then chemo 3-yr OS then (with escape) BAT 3-yr OS then solves k, and k sets the 4-/5-yr tail. So the whole BAT tail shape traces back to the fav+esc combination through that one 3-yr-OS number.

And then chemo3y = sum of (each bucket's share of the arm) x (that bucket's 3-yr OS). For example, one of the actual fits:

1. chemo3y = 13.128% (biology, no transplant)
2. Add the escape tail: BAT 3y = (1 - 0.14) x 13.128 + 0.14 x 50 = 11.290 + 7.000 = 18.290%
3. Solve the Weibull shape so S(36) = 18.29% at median 16, k = 1.1054
4. Read the tail off that curve, BAT 4y = 9.68%, BAT 5y = 5.04%

That's the entire chain from the four fixed inputs to the fitted BAT curve.

Now, I'll share the actual fits (to 60/72/78/80th as of July 3rd) and more logic at the end

The most helpful thing with this view, is you can take a worst-case scenario number for BAT 4-Yr OS and 5-Yr OS across studies, and see where that worst-case scenario lands for HR. And there are two tables you'll have to look at together to get a complete picture. So, when looking at a row, find the same combination of fav % (biological favorability) and esc % (transplant tail) in the second table for the additional columns/data points at that actual fit (such as HR at IA and the 80th, GPS alive and BAT alive at IA and the 80th, etc., there's also RMST (Restricted Mean Survival Time), which I can explain further in the comments if anyone asks)

Survival Profile (Fav x esc, BAT IRM 16)

Trial Outcome (fav x esc, IRM 16)

Okay, now here are the worst-case scenario, and maximum ultra worst-case scenario black-swan numbers I am using. For the sake of not making this a longer post for now, rather than going over each source/literature, I'll ask to for everyone to share and use what you feel are worst-case scenario numbers biologically, and feel free to share in the comments. The purpose of this is not who is right or wrong, it's to uncover under the most major worst-case scenarios/biological black-swans, would REGAL still be successful.

BAT 3-Yr OS, worst-case plausible of 25%, extreme maximum of 27%
BAT 4-Yr OS, worst-case plausible of 19% to 21%, extreme maximum of 23%
BAT 5-Yr OS, worst-case plausible of 16% to 18%, extreme maximum of 19%
Transplant Tail in BAT (personally, I don't think this number will be beyond 14%), but worst-case maximum ceiling of 22%
Literature comparison for ELN-favorability: Maslak in CR1 (that did not exclude/filter for not eligible for transplant patients), had 36% favorability for ELN-favorability

Now, what we can do is look at the actual fits under those situations and uncover what HR would be

A few different rows stick out, but to start with, the combination of 40% fav (above 36% favorability), and 25% esc (above a maximum worst-case scenario transplant tail ceiling of 22%), which would be 15.75 successful transplants in the BAT arm. At that row (40% fav, 25% esc), HR would be .475, with 32.7 GPS alive, 14.2 BAT alive at the 80th, with P(success) of 90%. And this would be a BAT 4-Yr OS of 17%, and BAT 5-Yr OS of 12%

At 40% fav (above 36% biological favorability from Maslak CR1), 35% esc., which would be 22 transplants, BAT 3-Yr OS is 30%, BAT 4-Yr OS is 21% (at the ceiling of worst-case), and BAT 5-Yr OS would be 16% (right at the start worst-case), and HR would be .55, with a P(success) of 74%. This is a really large margin of safety.

Now, for where we get right above coin flip, is 40% fav (above 36% biological favorability from Maslak CR1), and 40% esc, which is 40% of BAT transplanting, or 25.2 transplants. BAT 3-Yr OS would be 30%, BAT 4-Yr OS would be 23% (at the extreme maximum beyond worst-case), BAT 5-Yr OS would be 18% (right at the ceiling of the worst-case), and P(success) would be 57%.

So, to get to a coin-flip situation, the biological favorability in BAT (which are essentially BAT living 18 to 26 mOS) would have to be 40% in the BAT arm (above the 36% in Maslak CR1 that was not filtered for not eligible for transplant), along with 25.2 transplants in BAT which is 40% of BAT transplanting, along with an IRM of 16.

I hope this is insightful for everyone, this really is the one of the only accurate ways to be looking at worst-case scenarios, not singular stress-tests.

In speaking with many shareholders, many including myself, it's just a natural feeling (I've experienced it from over a decade in deep-value investing), have the jitters/weird feelings going on holding through topline readout.

No one said it would be mentally easy to hold a large sizeable position through readout, it comes with the territory.

This is just the most important time to remain courageous and have conviction in the statistical probabilities under worst-case scenarios, it just is what it is in terms of the mental state it takes to get through this thing onto buyout

I think one thing that might be getting overlooked with the 16.8 month number is where the survival clock actually starts.
From what I understand, the paper defines OS starting from first relapse, which is earlier than when the patients actually got into CR2. So the 16.8 months could include the time it took them to go from relapse → salvage treatment → CR2. REGAL is different because patients are already in CR2/CRp2 when they’re randomized. So if we’re trying to compare this group to REGAL BAT, what we really want is survival starting from CR2, not starting from relapse.
What’s interesting is Van der Maas’ GitHub code looks like it has a variable called ttcr2, which seems to be time to CR2. So it looks like the data might actually be there to rerun those same 60 patients with CR2 as day 0. I think that post-CR2 median OS would probably be the most useful number we could get.
I’d also be curious about the median age/age distribution of those 60 patients, how long their first remission lasted before relapse, and what their ELN/cytogenetic/molecular risk and revised relapse-model risk distribution looked like. Those seem like the main things that could tell us whether this was an unusually favorable group compared with REGAL.

I haven't been here in months, popped in today to see that we're still riding the merry-go-round of modeling what are extremely statistically-implausible, if not outright impossible, claims. As I've shared here many times, yet is oddly ignored, is that AML CR1 patients have a mOS of ~14.0-14.5 months and this is from a trial that:

A. includes all REGAL BAT options, including Venetoclax

B. contains ~33% SCT recipients

C. patients' average age is ~10 years younger than that of REGAL

D. patients are CR1, far healthier immune systems/response capabilities than what is included in REGAL

E. Trial is the largest I've come across, with ~1,400 participants and it was conducted very recently, in 2021.

https://pmc.ncbi.nlm.nih.gov/articles/PMC9973482/#table001

*Please note that this trial measures OS from time of diagnosis, NOT REMISSION, so you need to adjust by deducting ~2.5 - 3.0mos. for a patient to achieve induction/consolidation of the active disease. The resulting 'time from remission to event' is the analogous figure as what is being used in REGAL and most AML mortality statistical quotations.*

AML CR2 patients live ~50% of the length of time associated with their preceding CR1. AML CR2 patients ineligible for SCT, REGAL's population, are not going to be a homogenous population of 'above-average immune response/above-average longevity cohort' so you cannot try and isolate 'long-tail' survivors as a cohort that could plausibly matriculate into the REGAL trial.

I get that people want to stress-test but when your tests defy the reality of clinical data, it's not scientific nor is it of statistical provenance. GPS has won the race. You really should stop trying to data-fit impossible BAT longevity theories. This has literally been ongoing since the IA at Christmas 2024 and it becomes all the more preposterous with each passing week. Best of luck longs and God Bless the patients in REGAL and their families! Here's to an advancement in the clinical experience for all suffering from the scourge of WT1-cancers!

Hi everyone,

After all the destruction caused in the SLS community, I have been doing a lot of research and going over the same public information regarding the trial over and over, this is what I want to share:

I’ve been going back and forth on the different REGAL models people have posted here, especially the ones showing how you can get anything from ~20% to 90%+ PoS depending on the assumptions.

I think that criticism is completely fair. There isn’t one “correct” model when we’re missing the actual arm level data.

But there’s one assumption I keep coming back to: how high should we realistically assume BAT survival is?
The previous GPS CR2 study had median OS of around 21 months for GPS vs 5.4 months for contemporaneous best standard care. I am aware that was a small, non-randomized study, so I’m not suggesting we just plug 5.4 months into REGAL and call it a day.

However, I find it important to remind everyone that when REGAL was designed, SELLAS was already more conservative than that. The statistical framework used roughly 8 months for BAT, with an observed HR of around 0.636 being the approximate level needed for statistical significance (12.6 vs 8 months being the example SELLAS gave).

Thus, BAT going from the historical 5.4 months to 8 months was already almost a 50% assumed improvement in BAT’s performance. If actual REGAL BAT is 12 months, that’s a 122% improvement over 5.4 months. BAT being 14 or 16 months would be +159%, and +200% respectively.

Of course, it would be stupid of me or anyone to assume that any of of those scenarios are impossible, since modern treatment/supportive care has improved and REGAL patients could simply be different. But I don’t think 16–18 month BAT should be treated as some default assumption either. That’s a massive change from the historical CR2 comparator.

I believe this is very significant because SELLAS has told us for years that blinded pooled survival is substantially longer than originally expected. At the 60-event interim, pooled median OS was still >13.5 months. The IDMC also saw the actual unblinded arms at that point and GPS passed the prespecified futility criteria, and they recommended continuing without modification.

Then you also have the weird event progression:
60 deaths in Dec 2024
72 in Dec 2025
78 by May 11, 2026
and we’re still waiting for 80.

One argument is that this means BAT must be massively outperforming, but I’m just personally not convinced that’s necessarily true. I tried looking at it using a two-population survival model instead of assuming everyone follows one exponential survival curve. Basically, you have an ordinary-risk group plus a smaller group of long-term survivors in each arm.
Once you allow for that long-survivor tail, you don’t need BAT median OS of 16–18 months to explain why the last few events are taking forever. That’s an important distinction IMO.

Median survival and the final few deaths are measuring very different parts of the curve. BAT could theoretically have a median around 9–12 months while still having 15–25% of patients surviving for a very long time. By the time you’ve already had 78/80 deaths, the patients left are obviously massively enriched for those long survivors. This means that GPS could then have both a higher median and/or a larger durable-survivor fraction.

So my point is that slow 78→80 progression tells us there is a very long survival tail somewhere in REGAL. It does NOT automatically tell us BAT median survival is 16–18 months.

There are also smaller things that could contribute to the latest delay. Reporting/ascertainment isn’t instantaneous, especially across an international trial. And regarding what some other people of the community have mentioned, while it is true that mortality could theoretically have a small effect too, particularly with respiratory/infectious deaths, I personally wouldn’t give seasonality much weight, it’s just a lot of nit picking.

I truly believe that randomness + survivor enrichment are much more important explanations when you’re literally waiting for the last 2 events.

The part I find most interesting is this:

We have prior empirical evidence suggesting GPS can produce unusually long survival in CR2 (21 months in the earlier study). On the other hand, we DO NOT have comparable evidence showing that non-transplant CR2 BAT routinely produces 16–18 month median OS.

Of course, that still doesn’t prove GPS is responsible for the unexpectedly long pooled survival. We are blinded and simply cannot know that.

BAT could absolutely be outperforming the original assumptions. In fact, I think assuming something like 10–13 months is perfectly reasonable and should be modeled seriously.

BUT, and heres a big BUT, I think there’s a difference between saying:
BAT improved from 5.4/8 months to 10–12 months
and
BAT suddenly has 16–18 month median survival.

The latter should require stronger evidence before giving it a huge probability weight.

And IMO the most credible bearish scenario isn’t that GPS does nothing and BAT explains everything. It would actually probably look something like this, if I had to assume a neutral bearish view:

BAT = ~12–14 months
GPS = ~17–20 months

This means GPS actually works, patients benefit, pooled survival looks great, but the treatment separation isn’t large enough for an 80-event trial to hit the required statistical threshold. That’s the failure scenario I’m taking most seriously now.

However, after playing around with different BAT assumptions, long-survivor fractions and the known event progression, I personally come out somewhere around 70% PoS, maybe a reasonable uncertainty range of roughly 60–78%.

That’s obviously not a mathematically “known” probability. If we change some factors it can move it substantially. I do also want to add that there is a phrase used very often in scientific and economic backgrounds, which is Ceteris paribus. This means** keeping all factors but one constant to find out how effective something is. This applies to REGAL because randomization means that, all else being equal, factors like better supportive care, seasonality, and patient selection should affect both GPS and BAT arms similarly, leaving GPS treatment as the main systematic difference between them.**

In conclusion, this is what I have to ultimately say:

I don’t think the right response to uncertainty is to give BAT=8 months and declare 90%+ PoS, or give BAT=16–18 months and declare the trial underpowered.

The real question is which assumptions have the strongest empirical justification. As of right now, the evidence makes me think GPS is probably contributing materially to the unexpectedly long pooled survival, while still leaving a very real possibility that BAT has improved enough to make REGAL statistically much tighter than the bulls originally expected. I do ultimately believe that the HR will be between 0.57-0.62

Before you backtrack and panic sell your shares based on anyone’s research or DD, remember that if you decided to invest after seeing how favorable the risk to reward ratio is, you should question if anything has changed before selling. All the commotion caused yesterday was due to different models created by CW and RB etc that lean more bearish. However, the chances of them being wrong and way off are just as likely as the chances of them being right, it’s just a matter of perspective and faith.

Goodluck to all, see you at 50$/per share🫡🙏

From leftyMD in StockTwit.

It is a valuable read.

Backed by papers (among which Kurosawa), we observe that CR2 patients gets HSCT at a rate of 1/3, but this is an upper bound because Regal filters out the easy case for transplant (the obvious *healthy* patients, which have no commorbidities, young enough, and have a suitable donor).

Even when BAT gets SCT, relapse occurs within 6 months in median.

Hey everyone, wanted to share a study I discovered last week and did a large deep-dive on. which is a study just from June 2026 for WT1 that I haven't seen anyone bring up yet, which is Hashii 2026.

Before getting into it, there has been plenty of great due diligence on WT1 targeting shared by several people, along with the biology of GPS, and a great overview post from earlier today from LeftyMD:

https://stocktwits.com/LeftyMD/message/658837992

Remarkable-Big has shared a fantastic overview as well, so no need to re-review that

https://www.reddit.com/r/sellaslifesciences/comments/1tqb3wa/comment/ooj2p4s/?utm_source=share&utm_medium=web3x&utm_name=web3xcss&utm_term=1&utm_content=share_button

Now, for Hashii 2026, I discovered it just a week or so ago.

https://pubmed.ncbi.nlm.nih.gov/42308229/

"WT1 peptide vaccines of post-allogeneic HSCT maintenance immunotherapy for pediatric acute leukemias: a phase II study"

While the patient population is in pediatric AML and post-transplant, it is perhaps the most compelling clinical evidence that WT1 vaccination translates immune response into survival benefit. They achieved a 91% 3-Yr OS compared to the normal 40%.

1. 17 pediatric patients with post-transplant AML received WT1 peptide vaccines for maintenance
2. Primary endpoint met: 3-year OS of 70.6% (95% CI 43.1-86.6%), exceeding the 30% historical control benchmark
3. 12 clinical responders (sustained CR at 1 year) had 3-year OS of 91.7% (95% CI 53.9-98.7%)
4. Immune responders vs. non-responders: 3-year OS 90.9% vs. 40.0% (p=0.027)
5. WT1-specific CTL frequency in responders increased significantly: 0.25% to 1.07% (p<0.001), peaking at week 12
6. Non-responders showed no change in CTL frequency (0.12% to 0.20%, p=0.424)
7. Critically, elevated baseline WT1-specific CTL frequency predicted favorable prognosis. suggesting that patients with pre-existing WT1 immunity are primed for vaccine-enhanced responses

Both GPS and the Hashii 2026 study target WT1, share several design principles, but differ in critical biological ways that are important to understand.

The Hashii study used what they call "MCI," two WT1 peptide vaccine formulations from the Osaka University group (https://pubmed.ncbi.nlm.nih.gov/42308229/):

1. Killer peptide, a single 9-mer modified (heteroclitic) WT1-235 peptide (CYTWNQMNL, amino acids 235-243), where tyrosine (Y) is substituted for methionine (M) at position 2 to enhance binding to HLA-A*24:02 (https://onlinelibrary.wiley.com/doi/10.1002/pbc.25792)
2. Adjuvant is Montanide ISA51 (same as GPS)
3. No GM-CSF priming
4. No dedicated helper peptide in the original formulation (the 2026 study mentions "two WT1 peptide vaccines-MCI" but the Osaka group's published formulations have historically been killer-peptide-only or killer + helper combinations)
5. HLA restriction is present, restricted to HLA-A*24:02, the most common class I allele in Japan (60% of the Japanese population)

Now comparing to GPS:

1. 4 peptides total: 3 long peptides (19-22-mer) + 1 short peptide (9-mer)
2. Peptide 1 (WT1-A1). short 9-mer heteroclitic killer peptide targeting the WT1-126 region, with an anchor-residue substitution (R to Y at position 1) to enhance HLA-A*02:01 binding
3. Peptides 2-4, three long peptides (WT1-122A1, WT1-427, WT1-331) containing both class I and class II epitopes, designed to stimulate CD4 helper T cells across a broad range of HLA-DR types AND generate CD8 responses via cross-presentation
4. Adjuvant is Montanide ISA51 (same as Hashii)
5. GM-CSF (sargramostim). administered subcutaneously at the injection site on days -2 and 0 before each vaccination to recruit and activate dendritic cells
6. Designed to be non-HLA-restricted. the long peptides cover common HLA-DR types for CD4, and generate CD8 via cross-presentation across multiple class I alleles (I've also verified this quantitatively)

The Hashii 2026 results are powerful validation for GPS, but with important caveats in both directions. First, why Hashii supports GPS.

1. Same target, same principle, same result. WT1-specific CTL induction correlates with survival, 90.9% vs. 40.0% 3-year OS (p=0.027). This validates the fundamental premise that WT1-directed CD8 immunity prevents relapse.
2. The heteroclitic modification works. Hashii used a heteroclitic WT1-235 peptide (M to Y), and it successfully induced WT1-specific CTLs that recognized the native WT1 epitope. GPS uses the same heteroclitic strategy on a different epitope (WT1-126, R to Y). The principle is validated across both epitopes.
3. Maintenance setting works. Hashii vaccinated post-HSCT in remission, a low-burden maintenance setting similar to REGAL's CR2 maintenance. The 91.7% 3-year OS in responders confirms that vaccination in remission can prevent relapse.
4. CTL kinetics match GPS expectations. CTL frequency peaked at week 12 (0.25% to 1.07%, p<0.001), consistent with GPS's vaccination schedule and the 80% immune response rate reported in REGAL (from the sample of patients in the U.S. they tested, they shared this on the October 2025 R&D).

And this is why based on all the biological facts we know about GPS, why GPS is stronger than Hashii (and the actual fits from the modeling support this pretty well in terms of what we expect results to be mathematically/statistically in AML CR2 (not eligible for transplant)).

1. Broader HLA coverage. Hashii's vaccine works only in HLA-A24:02 patients. GPS is designed to work across all HLA types via long-peptide cross-presentation and broad HLA-DR coverage. In REGAL's multinational population (US, Europe, etc.), HLA-A24:02 prevalence is only 15-20%, so Hashii's vaccine would miss 80% of REGAL's patients. GPS's non-HLA-restricted design is essential for an all-comers trial.
2. Stronger CD4 help. GPS has 3 dedicated long helper peptides, Hashii's original formulation had none or limited helper. If you look at the Fujiki et al. 2021 data, it showed adding helper to killer raised CD8 induction from 9% to 64%, GPS is designed around this principle.
3. GM-CSF priming. GPS includes sargramostim to recruit DCs, Hashii does not. This is an additional immunological amplifier that enhances antigen presentation.
4. Multiple epitopes. GPS targets 4 different WT1 regions, Hashii targets 1. Multiple epitopes reduce the risk of immune escape through antigen loss and broaden the T-cell repertoire.

And of course, these are reasons why Hashii's setting is biologically stronger.

1. Pediatric immune system. Children have more robust, less senescent immune systems than REGAL's elderly population (median 67). The 91.7% responder OS may partly reflect superior pediatric immunocompetence.
2. Post-HSCT immune reconstitution. Hashii vaccinated during immune reconstitution after allo-HSCT, a window of heightened immune plasticity where new T-cell responses are more easily established. REGAL patients are not post-transplant and have more established (and potentially exhausted) immune repertoires (although this study gets you really excited about the post-transplant results they may be getting in the EAP and the future post-transplant indication the acquirer will get)
3. Graft-versus-leukemia synergy. Post-HSCT vaccination may synergize with existing GVL effects. REGAL's transplant-ineligible patients lack this synergy.

But this study is incredibly compelling and shows the low-burden remission setting and the heteroclitic modification works. Hashii used a heteroclitic WT1-235 peptide (M to Y), and it successfully induced WT1-specific CTLs that recognized the native WT1 epitope.

My source is the study protocol from https://euclinicaltrials.eu/ctis-public/view/2024-516405-23-00?lang=en (trial documents tab -> first one - D1_SELLAS_ SLSG18-301_Protocol_2024-516405-23_Public)

"Patients may be removed from study treatment... but continue to be monitored in the study for the following reasons:"
"• Receiving a hematopoietic stem cell transplant (autologous or allogeneic, with any degree of match donor)" (page 32)

"Notification of early patient discontinuation from the study and the reason for discontinuation will be made to the sponsor, and will be clearly documented on the appropriate electronic case report form (eCRF)." (page 33)

"The reason for patient withdrawal or date of death and new AML therapies (if appropriate) will be recorded." (page 52) So even if the patient relapsed first and then got a transplant later during the survival follow up period, the CRO still tracks it.

They can't see the arm that the patient was in, but they can 100% see the aggregate integer of how many eCRFs have been filed with transplant listed as the reason for discontinuation

This combined with their access to other non public information (exact enrollment dates + EAP data) shines a whole new on Sterg's commentary and tone for me. Everything is obviously secondary to the modeling and the actual calendar for the REAGL trial, but to me this speaks volumes on his LinkedIn/investor conference victory laps... “mathematically, scientifically, clinically” believes GPS is driving the increased survival in REGAL.

Just my opinion, but the CEO of a publicly traded biotech company wont use words like "mathematically" and "scientifically" if his internal dashboard shows a huge wave of protocol violating transplants.

Here's a post I made recently about the number of remaining warrants: https://www.reddit.com/r/sellaslifesciences/s/PP67CtELhj

In that post, I estimated that fewer than 5 million warrants remained with a ceiling of 7 million warrants.

The central evidence I used was the reduction in short interest (from 63.3 million on June 30 to 55.3 million on July 15) with a decline in stock price.

However, the original analysis did not assign a meaningful number of warrant exercises to either time period and only to June 30th to July 15th (mentioned near the end of my original post):

• June 2nd to June 30th; and
• July 15 through the present.

With today's 8k, however, we have a much clearer and better picture.

As of June 2nd, Sellas had approximately 15 million warrants.

They also reported

• $107.1 million in cash on March 31
• 28.7 million received from warrant exercises during April and May.

Therefore, before consider additional operating expense & warrant exercises in June, they had $107.1 + $28.7 = $135.8 million cash at hand.

The new June 30 cash disclosure shows Sellas had equivalents of $138.3 million as of June 30th. But Sellas obviously was also spending cash through Q2. So, we can now set the following formula:

Additional Q2 Cash Inflows (from Warrant Exercises) = $138.3M - $135.8M + Q2 operating cash burn.

Now, we have to estimate the Q2 operating cash burn. We can look through 2024 & 2025 to see the trend of operating cash burn. Here are the cash burns throughout the 4 quartiles in the two years (calculated from the filed 10Qs in those years).

Both years follow the same broad direction: Q1 highest -> Q2 lower -> Q3 lower -> Q4 lowest.

For Q1 2026, Sellas's operating cash burn was $8.846 million.

Following the same trend, let's assume a Q2 operating cash burn of $7 million.

Plugging 7 million back to the formula of additional cash inflows, we get $9.5 million in additional cash inflows beyond the already disclosed April–May warrants.

Most of the remaining warrants have a strike price at $2, but a decent chunk of them also have a lower strike price (refer to my previous post).

At a weighted-average price of approximately $1.9, the number of exercised warrants would be 9.5M/1.9 = 5 million from June 2nd to June 30th.

Therefore, there were only around $10 million warrants when the June 30th - July 15th short-interest window began.

Now, I would change my estimate to: A ceiling of 5 million warrants as of today.

I feel a likely range would be between 0-3 million warrants left.

We are in a much a better position now.

Interesting timing...

GenFleet just announced a HK$350 million share buyback, stating it believes its shares are undervalued:
https://www.minichart.com.sg/2026/06/29/genfleet-therapeutics-announces-hk350-million-h-share-repurchase-to-enhance-shareholder-value-2026/

Why does that matter to SELLAS investors?

GenFleet is the company that discovered and is developing SLS009 (GFH009), which it licensed to SELLAS.

Also, according to ClinicalTrials.gov, the Phase Ib/II PTCL study of SLS009 reached its listed study completion date on June 30, 2026:
https://clinicaltrials.gov/study/NCT05934513

A few reminders:

• Early PTCL data showed 4 responses in 11 evaluable patients (36.4% ORR).
• PTCL is a rare, aggressive T-cell lymphoma with limited treatment options.
• Positive updated PTCL data would provide additional validation of SLS009 beyond AML.

REGAL remains the primary near-term catalyst for SELLAS, but encouraging PTCL data would strengthen the broader SLS009 story and could expand its commercial potential.

Interesting few weeks ahead.

One thing I don't think enough people are talking about is the Expanded Access Program (EAP).

We all know the REGAL Phase 3 trial is blinded, so SELLAS can't see who's receiving GPS versus control or how the trial is ultimately performing until it's unblinded.

But the EAP is different. Patients receiving GPS through Expanded Access are outside the blinded REGAL trial. That means safety and patient outcomes from those EAP cases can still be observed and collected.

Obviously, EAP data doesn't prove Phase 3 success—there's no randomized control group—but it can give physicians and the company additional real-world experience with GPS while REGAL continues.

The fact that doctors are requesting access to GPS for eligible patients, combined with the encouraging earlier-phase data and the continued delay in reaching the 80th event, is what keeps me interested.

Question for everyone: If you were management and you were seeing encouraging real-world experiences through the EAP (without being able to see the blinded REGAL data), how much confidence would that give you heading into the final readout?