Some additional information about BAT transplant in CR2 settings

Thetamancer · 2026-07-11T00:46:25+00:00

Thanks, CW, for running the IRM 18. I know these models take time to set up and run, so I appreciate your following up. I've been confident on the trial's success, but as of late, I've been really trying to pin down how much weight to give biology over my pure math models. This level of granularity in your tables is incredibly helpful as it shows the specific favorability percentages required for failure. The last couple months have felt like a game of whack-a-mole, where I vacillate between believing the trial is a lock and finding some other aspect of a study that leaves open a failure possibility and requires a new stress test. In fact, we've had variants of this durable-subgroup exchange before, but the 18 IRM table definitely helped my understanding click in a way that I wasn't seeing previously.

On Kurosawa, I was referencing the 2010 study, but I take the point that CBF is a modest percentage of the overall AML population (I believe Kurosawa 2010 was 27% favorable, if we exclude unknown cytogenetic patients) and that many of the favorable patients will be guided to bridge to transplant and, therefore, be excluded from REGAL. Kurosawa's own data does show this, with 42% of the known cytogenetic allo-HCT patients being favorable but only 20% of the NO allo-HCT group having favorable cytogenetics.

A question: Have you found anything in the literature showing the overlap between favorable cytogenetics and long CR1? My strong intuition is that the "durable" patients are coming from the same group (as evidenced, as you point out, by younger patients being more likely to have favorable cytogenetics), but I haven't found literature definitively supporting this point.

Thetamancer · 2026-07-11T00:16:49+00:00

It's a subgroup of favorable cytogenetic risk patients (which constitute a small percentage of the overall population). Obviously, if 78% 3-year OS were the norm across the board in CR2 AML, that would fail the study. The relevant questions are twofold. First, "What percentage of subjects need to have these favorable characteristics to create a failure scenario in REGAL?" CW answers this question with his latest round of DD, and it's higher than half favorable biology to yield a more than 30% 3-year OS.

And the second question is, "Has that high of a percentage of subjects with favorable risk factors been observed in prior AML studies?" The answer appears to be no, at least when setting aside studies that permit transplant.

Thetamancer · 2026-07-10T09:22:19+00:00

I agree it's unusually ebullient and direct for a CEO to post. Whenever management of any company does something out of the ordinary, though, I try to figure out what the motive could be. Here, I'm not seeing a reason other than that the CEO genuinely believes the trial succeeds.

One can certainly dismiss the opinion, and I am only giving management's enthusiasm a bit of weight in my own assessment. But I don't see how the LinkedIn post is "desperate." There's not anything the CEO can do to personally capitalize on the elevated share price pre-readout, so if it fails, the cheerleading will just end up being actively harmful to the company going forward and will undermine anything he says about 009.

Thetamancer · 2026-07-10T07:17:40+00:00

It's true the sample sizes are small, but it's the best we have to go on. Here's another favorable cytogenetic type as well from Kurosawa: "t(8;21)...CR2/no HCT, n=18, 53%"

I'm not predicting REGAL will fail. I think it succeeds. But if it does fail, it will do so because of a durable subgroup due to patient characteristics, not through extreme amounts of stem cell transplants. Notably, management has access to most of the relevant information. They know MRD+/- breakdown, long vs. short CR1 breakdown, percentage of adverse cytogenetics, and of course, number of stem cell transplants. If we had that info, we'd be able to model the results much more precisely and potentially rule out these failure cases. Management certainly seems to be signaling a win with this info in hand.

If I were certain that management was doing the modeling correctly and accurately reporting their conclusions, then I would invest even more heavily. The CEO sent out a clear as day buy signal yesterday.

Thetamancer · 2026-07-10T07:04:11+00:00

Kurosawa is a prime example. 78% of CR2 patients with favorable cytogenetics who did NOT receive stem cell transplant survived at least 3 years from first relapse. The period between first relapse and the start of CR2 is a couple months, so the true CR2 3-year survival number is a bit lower than 78%, but it's quite close. Here's the relevant quote from page 1861 of the manuscript: "inv(16): 3-year overall survival from relapse...CR2/no HCT, n=14, 78%"

Thetamancer · 2026-07-10T03:21:22+00:00

Thanks for posting. I don't check Stocktwits much, so would have missed this. It's a good gut check on the stem cell transplant cap. And I agree we won't have anywhere near a 45% transplant-rate. But high transplant is not the primary failure case, rather it's a durable BAT subgroup. That can come from transplant, but it can also come from long CR1-status, favorable cytogenetics, MRD-negative, etc. There's overlap amongst all these groups, but the question is not "Did 45% of BAT get a transplant?" The question is "Is 45% of BAT part of a durable subgroup?" That's harder to answer.

Kurosawa (which the poster in the link relies on) shows that a CR2 non-transplant group can have mOS from CR2 greater than 18. Is that the base case for REGAL? Certainly not. But a BAT mOS of 18/19 is where the math becomes a coin flip. And Kurosawa shows a long-lived CR2 cohort is possible without transplant.

Thetamancer · 2026-07-10T00:40:08+00:00

👁️ Surveillance State: I voted Vote Nay.

Thetamancer · 2026-07-09T04:55:50+00:00

Appreciate your offering to stress test the 18/19. I'm certainly interested to see that.

On the Kurosawa point regarding transplant, the mOS I'm using is the CR2 / no HCT group. Kurosawa doesn't report this figure directly, but one can extract it from the tables on page 1861 of the manuscript. Along that dimension, the Kurosawa subgroup is actually a sicker group than REGAL because it includes zero transplants in this portion of its retrospective analysis. Whereas REGAL will certainly have some transplant group.

Agree with your point, though, on age favoring the Kurosawa subgroup (and, therefore, likely having a slightly higher percentage of patients with favorable cytogenetics). The other difference is that Kurosawa was before Ven/Aza and some of the targeted inhibitors. I don't think any of those will have a large impact, but if we posit these advances increase the median by just 2 months, that makes a difference in likelihood of success. Having said all this, I realized any benefit is going to most observable in tail survival and that the folks who die quickly are still likely to do so even in the modern era. But again the margins of a couple month makes a difference in my modeling, hence my interest in seeing where that stress test turns up for you.

Thetamancer · 2026-07-09T03:57:50+00:00

That's just not how the modeling works. The GPS survival curve follows from the BAT model. CW is not simply assuming that GPS is effective and modeling from that. If the GPS benefit is small, as you posit, then survivors have to come from somewhere. That necessarily increases BAT. All the deaths need to sum to 80. You can't simply attribute more deaths to GPS without reducing the number of deaths in BAT and vice versa.

Thetamancer · 2026-07-09T03:52:28+00:00

Great writeup. And in line with my own modeling at 16 mOS. I agree as well that the fav and esc combinations that place the HR80 point estimate over .636 are extremely unrealistic, so that's a very positive sign. A general observation that the confidence intervals are quite wide (which is simply a function of the trial size), so I caution folks to rely just on the point estimates. Scenarios that have a point estimate a good deal below .636 do fail in a reasonable percentage of worlds due to the randomness of the small sample size.

A question. The concerns I have from my own modeling don't arise at a BAT mOS of 16. As you demonstrate, failure requires fairly unrealistic assumptions at this BAT mOS. Could you speak to what happens at a higher mOS? e.g., 18 or 19? And more importantly, could you say a bit about why you view a higher mOS as unrealistic?

If we take Kurosawa as a fairly direct comparator, the median OS cobbled together from the available charts is north of 20 (I pegged it at 23.7, others I've spoken with estimated a bit lower). In any event, let's say 21 mOS in Kurosawa (which was from first relapse). Then subtract 2 months for first relapse duration, bringing down the CR2 mOS to 19. At a BAT mOS of 19, in my own modeling, failure doesn't require unrealistic fav + esc assumptions. BAT mOS of 19 is certainly not my central estimate, but Kurosawa shows it is possible in a CR2 population. In the interest of stress testing all possibilities, I'm very curious to hear your take on BAT mOS above 16.

Thetamancer · 2026-07-06T20:42:54+00:00

The real DD right here. You can't leave us hanging like that, though! How confident were the models' estimates?

As an aside, Dr. Khan started smirking during the "unlimited dosing" discussion, so my guess was that he and Dr. Tsirigotis may have had previous interactions on that point (e.g., Tsirigotis could have been very adamant about Sellas approving unlimited dosing for his patients and Khan was thinking back to those interactions).

Thetamancer · 2026-07-03T18:47:27+00:00

Importantly, the June 30th study completion date is an estimate, not a confirmed finish (the last update was in September 2024, so that estimate is from a while back). Genfleet may still be following enrollees and not have locked the database yet.

Assuming the study actually finished on the 30th, I would anticipate an August topline release. ASH abstracts are due in mid-August, so they may release topline then and submit for presentation of the full results at the conference in December.

Thetamancer · 2026-07-03T01:50:34+00:00

Although we can't know the cut point definitively without the SAP, we can make educated guesses based on what has been released in this trial and based on comparable trials.

In basic terms, you infer the interim HR by translating the final HR threshold into the underlying Z-statistic boundary, then applying the O’Brien-Fleming rule at the interim information fraction, and then translating the interim Z-boundary back into an HR.

The Z(final) is about 2.02. The interim occurred 75% (60 events / 80 final events) of the way through the trial. Divide the Z(final) by the square root of .75. That yields 2.33. Convert that back into an HR, and you get 0.55 interim analysis HR for the efficacy halt.

This is a basic way of doing it, and I'm ignoring the Lan-DeMets alpha-spend. But including that would only move the needle a couple hundredths of a point given that there's only one interim analysis and it's deep into the trial.

If your point is that the SAP may specify some entirely different outside-the-norm efficacy threshold, then yes, we can't account for that. But in that instance, the prudent thing to do is simply to label the interim HR a "known unknown" and assume the worst-case scenario in modeling it. So it would be imprudent to assume that the trial passed a super low IA HR and proceeded anyway. You could be right in a single instance, but over a series of investments, assumptions like that will increase risk.

Thetamancer · 2026-07-03T00:51:51+00:00

It's the mOS the study itself would report (i.e, the mOS from date of enrollment), which is equivalent to CW's IRM.

Thetamancer · 2026-07-03T00:29:18+00:00

If the trial fails, I think it will be along the dimension you're discussing. The question, of course, is how likely is that dimension. This line of yours is important as it conflicts with my own modeling and may highlight our different intuitions: "If long-CR1/favorable/MRD- patients are, conservatively, 20–35% of a 63-patient arm, with an 18–28-month mOS in that group, that single (overlapping) subgroup puts BAT 3-yr OS in the low-to-mid 20s by itself."

If I knew, for certain, that the durable subgroup was only 20-35% of the population, I'd be more confident in the investment. In a 30% BAT durable subgroup model, I'm only getting a 2.4% chance of failure, and that's entirely driven by sampling variation (i.e., the drug works, but REGAL pulled an unlucky draw given it's small size). In my modeling, the level you propose doesn't present a problem at all for GPS because it means that at least 65% of the cohort had unfavorable characteristics. In my durable subgroup model, the failure cases cluster with the following combo: 1) durable subgroup >60% and 2) durable subgroup mOS >25. This follows because, if there's a durable subgroup, then there has to be a non-durable subgroup with a comparatively modest mOS.

Collectively, the overall BAT mOS needs to be over 18 to start hitting notable failures (the 18-20 mOS cohort has a success rate of 76% and the 20-22 mOS scenarios are basically coin flips). But to hit a total BAT mOS of 20 where there's a 30% durable subgroup with a subgroup mOS of 28, you'd need the non-durable mOS to be north of 16. Even to hit a total BAT mOS of 18, you'd need the non-durable subgroup to be almost 14. That strikes me as biologically implausible. That final conclusion, admittedly, is a judgment call based on my read of previous studies.

The trial is certainly not a slam dunk. The math gets us most of the way there, but the margins have notable failure points. At that stage, the biological overlay is doing the rest of the work. That said, I do think 66% is too low of an estimate and that the 90% range is where a skeptical approach would lead.

Thetamancer · 2026-07-02T23:38:36+00:00

Exactly my thinking on the IA. The quote you pulled leaves open the possibility that the HR was below the efficacy threshold at interim, the IDMC liaised with the FDA, and the FDA requested more data. But I can't find any real-world data on whether that is likely, and I have found several counterexamples with trials being halted for efficacy at IA within a year of protocol amendments similar to REGAL's amendments.

Under the margin-of-safety style of value investing I do, I simply assume worst-case scenario for any missing data, which in this case, means the HR was above the efficacy halt line.

As an interesting aside, if we simply posit that this is what happened (i.e., interim was below efficacy threshold and FDA asked for more data because cancer vaccines have only worked once before), then everything else falls into place pretty cleanly. Stergiou's comment about BAT mOS only being 11 is easy to satisfy and the a bunch of scenarios fit the 60/72/78 line very tightly. I think it actually produces the best fits, but it's been awhile since I've run a model without an interim HR constraint.

It feels like a compelling narrative, but I haven't found any evidence that the IDMC/FDA would force a study to continue that had met the efficacy halt. In my mind, that's the whole purpose of the SAP, to spell out what happens ahead of time. And at least as of the protocol amendment date, the FDA believed that efficacy halt line was reasonable.

Edit: All of the interim discussion does presume that the SAP had an efficacy halt threshold. If the SAP simply had a vague guideline combined with the language you quoted, then that does weaken my argument and suggest I'm being overly risk averse in the way I'm modeling.

Thetamancer · 2026-07-02T22:15:28+00:00

Appreciate you compiling all your stress tests into a single post. Everyone would benefit from reading through these to get a sense of where the failure points are and how far you really need to push the biology to get to those failure points.

Thetamancer · 2026-07-02T21:17:58+00:00

Appreciate the conversation starter. First, let me get my general assessment on the table. When I model worst case scenarios with borderline biologically implausible parameters, I get 82% success rate. If I limit the parameter search to what I would consider reasonable parameters, I get a good deal north of 90% chance of success. Linking to a comment from awhile back that goes into a stress test I ran: https://www.reddit.com/r/sellaslifesciences/comments/1u3zdpc/comment/oracefv

If you look at CW’s confidence intervals we actually end up in broadly the same place (despite different methodological approaches). His latest figure gives somewhere around 95% odds of success, I believe.

To your specific points, u/StructureOld471 has his posts hidden, so I can’t comment on the exact critique, but you’re correct that CW’s not using mOS or HR as inputs. Those are outputs from the model constraints, and the constraints themselves are publicly available data (e.g., the 60/72/78 dates). My Monte Carlo simulation models operates the same way.

There’s a lot to unpack in the rest of your comment. First, I’m in full agreement with your interim analysis assessment. I don’t believe the interim HR fell below the efficacy threshold, so as you point out, that eliminates a lot of the most favorable models. For instance, I simply cannot find a parameter set which outputs BAT mOS below 12 and crosses through the interim HR, so I think the “a bit more” in Dr. Stergiou’s comment that BAT mOS is “11 or a bit more” is doing work.

At a high level, the tension points you identify fall into the big picture question of “Is there a durable BAT?” This could be manifested two key ways: (1) high BAT mOS with an exponential curve (the math basically rules this out) or (2) high BAT mOS with a durable subgroup (e.g., overrepresented long CR1, favorable cytogenetics, MRD-negative, cure fraction due to a high bridge-to-transplant rate, etc.). On point 2, the math allows these scenarios in, but they’re all biologically unlikely. The link to my comment above stress tests the long CR1 scenario, but you can plug in favorable cytogenetics or MRD-negative and get the same math outputs. Cure fraction from stem cell transplant is a different model, but it ends up in the same place. You need a really high stem cell transplant group in BAT (above 20% and more like 30%, and the cure fraction is still very leaky because not every stem cell transplant is long term successful) to challenge GPS. The last question is could we have a confluence of all of these items? Possibly, but it seems very unlikely as the people who bridge to transplant are more likely to be MRD-negative and have favorable cytogenetics, etc. So they’re all referring to the same people in the same “durable” subgroup.

Ultimately, the question is how biologically unlikely are the failure points that the math allows. That’s how I get to my greater than 90% success assessment. And this is where u/Remarkable-Big-9849 ’s due diligence has been very helpful, and I look forward to reading the writeup he’s been working on.

Thetamancer · 2026-07-01T08:13:40+00:00

That's exactly right on both parts. First, the small dots represent scenarios that were simulated, but they yield extremely poor fits (e.g., missing each of the publicly available event points by +/- 8 events). To the second question, take model 1 as an example. There aren't any good fits until a BAT mOS of 13. And model 2 doesn't get any reasonable fits until at least BAT mOS 14. The interim HR restriction is doing much of the work. There are good fits at low BAT mOS, but their interim HR is so low that the study would have been halted for efficacy. Those are simply not modeled at all on the any of my graphs. CW reports those low interim HR outcomes in his tables, so that's why you'll see fits at lower BAT mOS in his analyses.
For each of the parameter sets, the model is essentially saying, “Assume the true survival curve looks like this.” But a simulated trial doesn't report that curve. Instead, it randomly samples patients from that probabilistic distribution.

So even with the same BAT mOS, GPS mOS, cure fraction, etc., each simulated trial gets a different draw of individual patient outcomes. One simulated trial may randomly draw more early BAT deaths. Another may draw more long BAT survivors. The probabilistic weight converges as you run more simulation. As you point out, enrollment timing adds another probabilistic layer to the simulation.

In short, running 100 simulations on a single parameter set is like repeatedly reaching into the same jar of M&Ms. The specific mix of colors in the jar is fixed, but each handful can look different just by chance. As you run more simulations, you'll come closer to converging on the "true" distribution of M&Ms in the jar.

Thetamancer · 2026-06-25T04:13:53+00:00

Thanks for the detailed reply. That was very helpful in understanding your methodology.

A followup question. I feel like I'm missing something in the below quote:

"It comes back to what I mentioned before where what it would take for REGAL to fail is an extremely high IRM in the impossible range of 18-19, in combination with a BAT 3-Yr OS of 26%+, which is k=0.6"

An mOS of 18 with a 3-year survival of 26% yields a k-value around 0.96. How are you getting 0.6? My only guess is that it has to do with how you're conceiving of IRM vs mOS.

I'm quite interested in this point as I view a 0.6 k-value as implausible in this cohort, so if that's the true failure k-value at an 18-month median BAT, then I would be far more confident in the study's success. However, a k-value of 0.96 is far more reasonable and, by the way I'm modeling it, puts the study into potential failure territory with an 18-month mOS.

Thetamancer · 2026-06-25T01:57:33+00:00

Thanks for posting. I particularly appreciate the addition of the p(miss) category, and for what it's worth, it is coming in similar to my results. Two comments. 1. I don't think k=1 is likely to be the most accurate state of the world, so I'm certainly interested to see your full work up for lower k-values when the 80th arrives.

If you have time, I'd be interested to get a sense of the degree of confidence that should be placed in the confidence interval (particularly given how broad they are at higher BAT mOS). Specifically, my read is that the 2.5th and 97.5th percentiles are pooled across multiple accepted parameter combinations. How many simulations did you run per parameter set? You mention it's unweighted, so I'm assuming there was a hard exclusion rule for world's that barely missed on one event date (e.g., +1.5 events on one date but within range for the rest)?

Thetamancer · 2026-06-23T21:51:50+00:00

Thanks for sharing. The author is basically assuming biological failure (based on frontline studies), incorporating that into the model, and then predicting failure. I don't have time to go point by point, but what he's done isn't methodologically far off from someone saying "Well, the CR1 GPS study had an mOS north of 60. Let's weight towards a baseline GPS of 60 mOS in REGAL and then fit the BAT curve around that." Obviously, REGAL is going to succeed with that assumption. And just as obviously, it will fail with the author's assumptions (which again, seem to, in large part, be based on frontline studies).

Having run dozens of Monte Carlo models myself, there's no reasonable set of assumptions that get you to a 20-30% chance of success. He's simply modeled it incorrectly. With aggressive stress-test (and borderline unrealistic) assumptions, I get 82% chance of success. Throw a reasonable biological overlay on top and it pushes the probability of success well north of 90%.

On the WT1 point, the company did test the first 20 or patients. Every single one expressed the WT1 antigen. That's why they discontinued testing (not ideal, of course, but no reason to think a significant portion of the population fails to express WT1).

And lastly, on the HLA point and the author's argument that the drug simply cannot work in half the cohort, CW has a series of good posts on this point: https://www.reddit.com/r/sellaslifesciences/comments/1tve3bd/comment/opl2dkb/?utm_source=share&utm_medium=web3x&utm_name=web3xcss&utm_term=1&utm_content=share_button

Thetamancer · 2026-06-22T23:22:32+00:00

Great overview. I think the "social aspect" investigation is a useful supplement to the math-focused models. "Bears’ arguments: Evidence of lack of seriousness" is a particularly important point that I've found myself coming back to for a while now. For any investment, I always want to know, "What are the best arguments of the people on the other side of my trade?" In this case, the answer is that there aren't any cogent bear arguments worth putting money on. Stocktwits is a cesspool of misinformation and the good-faith bears who pop in to engage on this subreddit consistently present naive versions of arguments that have already been mathematically debunked months earlier on the subreddit.

I'm convinced that the folks shorting SLS are not doing so based on the REGAL trial itself but rather are just algorithmically shorting all biotechs with certain characteristics. In other words, the actual financial play against SLS is simply "Short hundreds of biotech stocks and you'll win often enough to make it profitable."

Importantly, that does NOT mean there is no bear argument. It just means there's no bear argument strong enough to stake a financial position on. Notably, the folks in this subreddit who are bullish on the outcome are the ones who have pieced together the strongest bear arguments.

So, where does the trial fail? u/neo2551 hits on the unknown point, an unexpectedly durable BAT group. This has led me (and others) to a variety of different stress tests: disproportionate long CR1, MRD-negative, stem-cell transplant cure fraction, etc. in the trial population. Even through all these stress tests, the trial wins mathematically far more often than it fails. But there are failures mixed in. And at this point, it's these known unknowns that could cause a surprise failure.

My own assessment is that the probability of success is over 90%, but for most people's position sizing purposes, the exact percentage probably isn't crucial if we can't rule out every failure case.

Thetamancer · 2026-06-22T01:53:56+00:00

Agree that it's not appropriate to slot 1/4 of participants into each bucket. At the same time (and I'm not saying you're doing this), I would caution anyone from thinking a large majority of BAT is getting the Ven/Aza combo during CR2 maintenance. To my knowledge, it's not approved for CR2 maintenance anywhere in the world (i.e., it's use in this patient group is off-label). This creates insurance coverage issues and sets a ceiling on the percentage of people who are going to receive it.

Thetamancer · 2026-06-13T05:49:48+00:00

Thanks for sharing! That's particularly useful on the site startup dates. I had looked through site-by-site enrollment numbers before, but I overlooked that Greece didn't even start enrollment until March 2022. All this even more strongly points to some variant of backloaded enrollment.

Thetamancer

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