u/Confident-Web-7118

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.

Curve	Traced Median	Published	Delta
LIT+Ven	16.9	16.6	0.3
LIT no-Ven	12.8	12.5	0.3
LIT favorable	25.3	25.1	0.2
LIT intermediate	17.4	17.5	0.1
LIT adverse	10.7	10.6	0.1
IT+Ven	30.8	30.3	0.5
IT adverse	18.7	19	0.3
IT favorable	NR	NR	N/A since Not Reached

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

Cohort	n	3-Yr OS (traced)	At risk at 36 mo
Overall	362	35.4%	-
LIT + Ven	198	32.50%	34/198
Whole LIT	257	29.5%	-
LIT without Ven	59	18.50%	7/59
IT (all)	105	49-51%	-

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

ELN 2024 (LIT)	n	Traced 3-yr OS
Favorable	123	43.60%
Intermediate	71	28.90%
Adverse	63	4.10%

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.

Bucket set	Implied component medians (fav/int/adv)	Mixture median @ 38/27/35	vs IRM 16	Mixture 3-Yr OS	Solved k
Kugler whole-LIT	25.0 / 17.5 / 10.6	16.5	0.5	26.30%	0.81
whole-LIT x 0.9	22.2 / 16.0 / 10.0	15.2	-0.8	23.70%	0.903
whole-LIT x 0.8	19.7 / 14.7 / 9.4	13.9	-2.1	21.00%	1
Kugler LIT+Ven	27.3 / 19.1 / 11.6	18.1	2.1	30.40%	0.668
LIT+Ven x 0.9	23.7 / 17.2 / 10.8	16.3	0.3	27.30%	0.773
LIT+Ven x 0.8838	23.2 / 16.9 / 10.6	16	0	26.80%	0.79
LIT+Ven x 0.8	20.7 / 15.4 / 10.0	14.7	-1.3	24.30%	0.88
Traced empirical curves	25.2 / 17.3 / 10.7	14.2	-1.8	25.80%	-

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

esc	disc	buckets (fav/int/adv)	pre-esc med	post-esc med	fav	BAT2y	BAT3y	BAT4y	BAT5y	k	cureC	psi	HR@IA	HR@80	Pana	simHR	Psim	Psnh	GPSd@IA	BATd@IA	GPSa@IA	BATa@IA	GPSa@80	BATa@80	RESID	GPSmOS	GPS3y	GPS4y	GPS5y	uncRAW	uncMOS	BAT3yS	HR80S	PsimS	PsimSd	Pnostop	PnsCons	PsimIW
10%	x0.827	34.64 / 25.79 / 14.22	15.1	16	38%	39%	27%	20%	14%	0.78	0.37	1.11	0.556	0.483	82%	0.463	82%	80%	23.6	35.2	39.4	27.8	31.6	15.4	1.14	40	52%	47%	44%	11.2	14	23%	0.369	97%	62%	50%	77%	63
14%	x0.800	33.53 / 24.97 / 13.77	14.6	16	38%	39%	27%	20%	14%	0.78	0.36	1.09	0.556	0.486	82%	0.467	81%	80%	23.7	35.2	39.3	27.8	31.5	15.4	1.15	40	52%	47%	44%	11.4	14.4	23%	0.371	97%	62%	50%	77%	62
18%	x0.772	32.33 / 24.07 / 13.27	14.2	16	38%	39%	27%	20%	15%	0.77	0.35	1.06	0.557	0.49	81%	0.471	81%	79%	23.7	35.2	39.3	27.8	31.3	15.6	1.15	39	51%	47%	43%	11.5	14.8	23%	0.373	97%	61%	50%	77%	62

https://preview.redd.it/cmi0gupa02jh1.png?width=1893&format=png&auto=webp&s=db30bd30ea96cd1fe490dcfb7e66263454fe2bb6

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, 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):

https://preview.redd.it/gks7ic0xz9gh1.png?width=1543&format=png&auto=webp&s=1fb3de962c5e20ba08fe2adb0e73d987ff9f4ab4

"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:

https://preview.redd.it/ik9qb1ywy9gh1.png?width=1641&format=png&auto=webp&s=2e6d0c3c994f323274c56ddd75d76620672f7c72

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

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)

Feature	Hashii 2026 (MCI)	GPS	Biological Significance
Number of peptides	1-2 (killer +/- helper)	4 (3 long + 1 short)	More epitopes = broader immune coverage, harder for tumor to escape via antigen loss
Killer peptide target	WT1-235 region (HLA-A*24:02)	WT1-126 region (HLA-A*02:01) + cross-presented epitopes from long peptides	Different WT1 regions targeted, both use heteroclitic modifications
Heteroclitic modification	Yes, M to Y at position 2 of WT1-235	Yes, RtoY at position 1 of WT1-126	Both enhance MHC binding, same design principle, different epitopes
Helper (CD4) component	Limited or absent in original formulation, later versions added WT1-332 helper	3 dedicated long peptides with class II epitopes covering broad HLA-DR types	GPS has a much stronger, broader CD4 component, Fujiki et al. 2021 showed this raises CD8 induction from 9% to 64%
HLA restriction	HLA-A*24:02 only (60% of Japanese, 20% of Caucasians)	Non-HLA-restricted by design, long peptides generate responses across multiple HLA types	GPS can treat all patients regardless of HLA type, Hashii excludes 40% of Japanese and 80% of Caucasians
GM-CSF adjuvant	No	Yes, sargramostim at injection site	GM-CSF recruits DCs to the injection site, enhancing antigen uptake and presentation, a well-established immunological amplifier
Montanide ISA51	Yes	Yes	Same depot adjuvant, creates slow-release antigen reservoir
Peptide length	Short (9-mer)	Mix of short (9-mer) and long (19-22-mer)	Long peptides require DC uptake and cross-presentation, generating more durable and diverse CD8 responses than short peptides that load directly onto MHC-I
Cross-presentation potential	Minimal, short peptides load exogenously onto MHC-I	High, long peptides are processed by DCs and cross-presented on multiple class I alleles	This is the mechanism by which GPS generates CD8 responses beyond HLA-A*02

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.

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:

subgroup	% of BAT arm	3-yr OS (the model input)	illustrative mOS (not a model input)
CBF [t(8,21)/inv(16)]	11.20%	44%	28 mo
NPM1 (no FLT3)	28.80%	21%	17 mo
Intermediate	42.00%	13%	15 mo
Adverse	18.00%	1.50%	9 mo
Blended arm	100%	16.70%	14 mo

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)

fav	esc	chemo 3y	BAT 3y	BAT 4y	BAT 5y	k	BAT mOS	GPS mOS	GPS 3y	GPS 4y	GPS 5y	pool mOS	flag
20%	14%	13%	18%	10%	5%	1.11	16m	67m	60%	56%	52%	23.3m	REALISTIC
20%	25%	13%	22%	14%	9%	0.95	16m	51m	57%	51%	46%	23.5m	esc > cap
20%	35%	13%	26%	18%	13%	0.82	16m	42m	53%	46%	40%	23.7m	elevated
20%	40%	13%	28%	20%	15%	0.75	16m	38m	52%	44%	38%	23.7m	elevated
20%	45%	13%	30%	23%	18%	0.69	16m	36m	50%	42%	35%	23.7m	elevated
20%	50%	13%	32%	25%	20%	0.63	16m	34m	48%	39%	32%	23.8m	elevated
20%	65%	13%	37%	32%	29%	0.44	16m	29m	42%	32%	24%	23.7m	impossible
30%	14%	15%	20%	11%	6%	1.05	16m	60m	59%	54%	50%	23.4m	REALISTIC
30%	25%	15%	24%	15%	10%	0.9	16m	47m	56%	49%	44%	23.6m	esc > cap
30%	35%	15%	27%	20%	14%	0.78	16m	40m	52%	45%	39%	23.7m	elevated
30%	40%	15%	29%	22%	17%	0.72	16m	37m	51%	43%	36%	23.7m	elevated
30%	45%	15%	31%	24%	19%	0.66	16m	35m	49%	40%	33%	23.8m	elevated
30%	50%	15%	32%	26%	22%	0.6	16m	33m	47%	38%	31%	23.8m	impossible
30%	65%	15%	38%	33%	30%	0.42	16m	29m	42%	30%	20%	24.1m	impossible
40%	14%	17%	21%	13%	8%	0.99	16m	54m	58%	52%	48%	23.5m	hi-fav, capped esc
40%	25%	17%	25%	17%	12%	0.85	16m	44m	54%	48%	42%	23.6m	esc > cap
40%	35%	17%	28%	21%	16%	0.74	16m	38m	51%	43%	37%	23.7m	elevated
40%	40%	17%	30%	23%	18%	0.68	16m	35m	50%	41%	34%	23.8m	elevated
40%	45%	17%	32%	25%	21%	0.62	16m	33m	48%	39%	32%	23.8m	elevated
40%	50%	17%	33%	27%	23%	0.57	16m	32m	46%	37%	30%	23.8m	impossible
40%	65%	17%	38%	34%	31%	0.4	16m	29m	42%	28%	18%	24.1m	impossible
60%	14%	20%	24%	16%	11%	0.87	16m	45m	55%	48%	43%	23.6m	hi-fav, capped esc
60%	25%	20%	28%	20%	15%	0.76	16m	39m	52%	44%	38%	23.7m	esc > cap
60%	35%	20%	31%	24%	19%	0.66	16m	35m	49%	40%	33%	23.8m	elevated
60%	40%	20%	32%	26%	21%	0.61	16m	33m	47%	39%	31%	23.8m	impossible
60%	45%	20%	34%	28%	24%	0.56	16m	31m	46%	37%	29%	23.8m	impossible
60%	50%	20%	35%	30%	26%	0.51	16m	30m	44%	35%	27%	23.7m	impossible
60%	65%	20%	40%	36%	33%	0.36	16m	28m	41%	26%	14%	24.2m	impossible
100%	14%	27%	31%	24%	19%	0.66	16m	35m	49%	41%	34%	23.8m	hi-fav, capped esc
100%	25%	27%	33%	27%	23%	0.58	16m	32m	46%	37%	30%	23.8m	esc > cap
100%	35%	27%	35%	30%	26%	0.5	16m	30m	44%	34%	27%	23.7m	impossible
100%	40%	27%	36%	32%	28%	0.46	16m	29m	43%	33%	25%	23.7m	impossible
100%	45%	27%	38%	33%	30%	0.43	16m	28m	42%	32%	24%	23.7m	impossible
100%	50%	27%	39%	35%	31%	0.39	16m	29m	42%	27%	17%	24.1m	impossible
100%	65%	27%	42%	39%	37%	0.27	16m	27m	40%	20%	8%	24.3m	impossible

Trial Outcome (fav x esc, IRM 16)

fav	esc	BAT 3y	HR@IA	HR@80	HRobs	95% CI	Ba@IA	Ga@IA	Ba@80	Ga@80	RMST g	RMST b	R diff	P(win)
20%	14%	18%	0.481	0.327	0.35	[0.23,0.54]	25.2	41.3	10	37	32.2	19.7	12.5	100%
20%	25%	22%	0.509	0.393	0.421	[0.27,0.65]	26.1	40.4	12.5	34.4	31.3	20.4	10.9	97%
20%	35%	26%	0.532	0.464	0.496	[0.32,0.77]	26.8	39.6	14.8	32	30.5	21	9.5	87%
20%	40%	28%	0.542	0.503	0.539	[0.35,0.83]	27.2	39.2	16.1	30.8	30.1	21.3	8.8	77%
20%	45%	30%	0.552	0.546	0.584	[0.38,0.91]	27.6	38.8	17.3	29.6	29.8	21.6	8.1	65%
20%	50%	32%	0.561	0.592	0.633	[0.41,0.98]	27.9	38.5	18.5	28.3	29.4	21.9	7.5	51%
20%	65%	37%	0.582	0.751	0.804	[0.52,1.25]	29	37.4	22.4	24.5	28.3	22.7	5.6	15%
30%	14%	20%	0.492	0.351	0.375	[0.24,0.58]	25.6	41	10.9	36	31.8	20	11.9	99%
30%	25%	24%	0.517	0.418	0.447	[0.29,0.69]	26.4	40.1	13.3	33.6	31	20.7	10.4	94%
30%	35%	27%	0.538	0.488	0.523	[0.34,0.81]	27.1	39.4	15.6	31.3	30.3	21.2	9	81%
30%	40%	29%	0.548	0.528	0.565	[0.36,0.88]	27.4	39	16.8	30.1	29.9	21.5	8.4	70%
30%	45%	31%	0.557	0.57	0.61	[0.39,0.95]	27.8	38.6	18	28.9	29.6	21.8	7.8	57%
30%	50%	32%	0.565	0.616	0.659	[0.42,1.02]	28.1	38.3	19.2	27.7	29.2	22.1	7.1	44%
30%	65%	38%	0.564	0.769	0.823	[0.53,1.28]	29.2	37.8	22.8	23.7	28.4	22.8	5.6	12%
40%	14%	21%	0.502	0.376	0.402	[0.26,0.62]	25.9	40.6	11.8	35.1	31.5	20.3	11.3	98%
40%	25%	25%	0.526	0.444	0.475	[0.31,0.74]	26.6	39.8	14.2	32.7	30.7	20.9	9.8	90%
40%	35%	28%	0.545	0.514	0.55	[0.36,0.85]	27.3	39.1	16.4	30.5	30	21.4	8.6	74%
40%	40%	30%	0.554	0.553	0.592	[0.38,0.92]	27.6	38.8	17.5	29.3	29.7	21.7	8	63%
40%	45%	32%	0.562	0.595	0.637	[0.41,0.99]	28	38.4	18.6	28.2	29.4	21.9	7.4	50%
40%	50%	33%	0.569	0.64	0.685	[0.44,1.06]	28.3	38.1	19.8	27.1	29	22.2	6.8	37%
40%	65%	38%	0.568	0.792	0.847	[0.55,1.31]	29.3	37.6	23.3	23.3	28.3	22.9	5.4	10%
60%	14%	24%	0.522	0.432	0.462	[0.30,0.72]	26.5	40	13.8	33.1	30.9	20.8	10.1	92%
60%	25%	28%	0.541	0.5	0.535	[0.35,0.83]	27.2	39.3	15.9	30.9	30.2	21.3	8.9	78%
60%	35%	31%	0.557	0.57	0.61	[0.39,0.94]	27.8	38.6	17.9	28.9	29.6	21.8	7.8	58%
60%	40%	32%	0.564	0.608	0.65	[0.42,1.01]	28.1	38.3	19	27.9	29.3	22	7.2	46%
60%	45%	34%	0.57	0.648	0.694	[0.45,1.08]	28.4	38	20	26.9	29	22.2	6.7	35%
60%	50%	35%	0.576	0.691	0.739	[0.48,1.15]	28.6	37.7	21	25.8	28.7	22.5	6.2	25%
60%	65%	40%	0.577	0.839	0.897	[0.58,1.39]	29.5	37.3	24.1	22.4	28	23	4.9	6%
100%	14%	31%	0.557	0.568	0.607	[0.39,0.94]	27.8	38.7	17.9	29	29.6	21.8	7.8	58%
100%	25%	33%	0.568	0.632	0.677	[0.44,1.05]	28.2	38.2	19.6	27.3	29.1	22.2	6.9	39%
100%	35%	35%	0.577	0.697	0.745	[0.48,1.16]	28.7	37.7	21.2	25.7	28.6	22.5	6.1	24%
100%	40%	36%	0.58	0.731	0.782	[0.50,1.21]	28.9	37.5	21.9	24.9	28.4	22.6	5.8	18%
100%	45%	38%	0.584	0.767	0.821	[0.53,1.27]	29.1	37.3	22.7	24.2	28.2	22.8	5.4	13%
100%	50%	39%	0.571	0.805	0.862	[0.56,1.34]	29.3	37.5	23.5	23	28.2	22.9	5.3	9%
100%	65%	42%	0.592	0.94	1.006	[0.65,1.56]	30	36.7	25.9	20.5	27.3	23.3	4	2%

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

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.

Feature	REGAL	QUAZAR (AML‑001)	Aza/Ven R / R across studies	Kurosawa 2010	VIALE‑M
Design	Ph3 RCT, open‑label	Ph3 RCT, double‑blind	Retrospective (Mayo)	Retrospective (Japan)	Ph3 RCT, double‑blind
N	126	472	N/A	CR2/no‑HCT subgroups (n=14-82)	112 (of 360, terminated)
Disease state	CR2 (remission)	CR1 (remission)	active failure (R/R)	CR2 (remission)	CR1/CRi (remission)
Remission line	2nd	1st	none (failed frontline)	2nd	1st
Refractory	0% (all CR2)	0% (all CR1)	N/A	0% (all CR2)	0% (all CR1)
Median age	67 (57% greater than or equal to 65)	68 (greater than or equal to 55)	75	53 (16-70), youngest	CR1 maint (65)
Cytogenetic mix	depleted of adverse (CR2 selects)	mixed	complex	reported by risk group	mixed
TP53	5-10%	low-moderate	about 29%	cytogenetic‑era (pre‑TP53)	low-moderate
Setting	maintenance	maintenance	salvage (active disease)	observational (no‑HCT)	maintenance
Transplant	ineligible (0%)	non‑candidates (0%)	about 3.7% (Gangat 2023 Haematologica is a great resource)	HCT vs no‑HCT subgroups	not to SCT (0%)
Primary endpoint	OS	OS	(observational)	(prognostic factors)	RFS
Reference arm	BAT (inv. choice)	placebo	(salvage regimens)	no‑HCT, by cytogenetics	oral‑aza (Onureg)
Reference mOS	BAT 8-13 True Onset mOS	placebo 14.8, Onureg 24.7	4 mo (failure), SCT high (only near-cure/cure for AML)	by cytogenetics	oral‑aza 26.7 design
Reference 3‑yr OS	13-19% approximation	placebo 25%, Onureg 40%	5% (SCT subset may be 33%, given 2-Yr OS is about 61%)	CBF 64%, intermediate 19%,adverse 35%	42% design (no readout)
Status	pending (78/80)	positive (approved)	real‑world	retrospective (2010)	failed/terminated

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.

group	3‑yr OS	n
inv(16)	78%	14
t(8,21)	53%	18
intermediate	19%	82
unfavorable	(35%, n=18 - small‑N outlier)	18

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

https://preview.redd.it/sai70tdeqgbh1.png?width=973&format=png&auto=webp&s=f2b285f513ba65d2ea3d5aeffa5fdbc45a1987e5

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:

control arm	setting	3‑yr OS
VIALE‑M oral‑aza (design)	CR1, maintenance	42% (no readout)
QUAZAR Onureg	CR1, maintenance	40%
QUAZAR placebo	CR1, no active drug, older	25%, upper bound for a CR2 arm
Kurosawa CR2 intermediate	CR2, younger (53), no‑HCT	19%
REGAL BAT Approximation	CR2, older (67), best‑available	Approximately 13-19%
Kurosawa CR2 CBF favorable	CR2, younger, no‑HCT	19% non-CBF, 64% CBF patients (which make up 15% of AML, and 7% of patients over 65)

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

k	BAT 3y	GPS 3y	GPS mOS	pool mOS	HR@IA	HR@80	HRobs	95% CI	BATa@IA	GPSa@IA	BATa@80	GPSa@80	P(win)
0.4	33%	49%	35	24.3	0.393	0.558	0.598	[0.39,0.93]	25.9	40.9	19.8	26.6	61%
0.5	29%	51%	37	23.7	0.358	0.447	0.479	[0.31,0.74]	24.5	41.9	16.9	29.9	90%
0.6	24%	55%	42	23.7	0.318	0.362	0.387	[0.25,0.60]	23.1	43.3	14.2	32.7	99%
0.7	20%	59%	49	23.6	0.282	0.295	0.315	[0.20,0.49]	21.7	44.7	11.7	35.2	100%
0.8	17%	62%	56	23.4	0.248	0.242	0.259	[0.17,0.40]	20.4	46.1	9.4	37.6	100%
0.9	13%	66%	64	23.2	0.218	0.201	0.215	[0.14,0.33]	19.1	47.4	7.3	39.6	100%
1	10%	68%	72	23	0.192	0.17	0.182	[0.12,0.28]	17.9	48.6	5.6	41.4	100%
1.1	8%	71%	79	22.7	0.169	0.146	0.156	[0.10,0.24]	16.8	49.8	4.2	42.9	100%
1.2	6%	72%	84	22.4	0.149	0.128	0.137	[0.09,0.21]	15.8	50.8	3.1	44.1	100%
1.3	4%	74%	88	22.1	0.132	0.115	0.123	[0.08,0.19]	14.9	51.8	2.2	45.1	100%

BAT IRM 12

k	BAT 3y	GPS 3y	GPS mOS	pool mOS	HR@IA	HR@80	HRobs	95% CI	BATa@IA	GPSa@IA	BATa@80	GPSa@80	P(win)
0.4	34%	47%	33	24.3	0.429	0.606	0.648	[0.42,1.01]	26.7	40.1	20.6	25.9	47%
0.5	30%	49%	35	23.7	0.402	0.496	0.531	[0.34,0.82]	25.5	40.9	17.9	29	79%
0.6	26%	53%	40	23.7	0.366	0.41	0.438	[0.28,0.68]	24.3	42.2	15.3	31.5	95%
0.7	22%	57%	46	23.6	0.331	0.339	0.363	[0.23,0.56]	23.1	43.4	12.9	34	99%
0.8	19%	60%	53	23.5	0.299	0.283	0.302	[0.20,0.47]	21.9	44.6	10.6	36.3	100%
0.9	16%	63%	61	23.3	0.27	0.238	0.254	[0.16,0.39]	20.8	45.7	8.6	38.4	100%
1	13%	66%	70	23.1	0.243	0.202	0.216	[0.14,0.34]	19.7	46.8	6.8	40.2	100%
1.1	10%	69%	79	22.8	0.219	0.175	0.187	[0.12,0.29]	18.7	47.9	5.3	41.8	100%
1.2	7%	71%	87	22.6	0.197	0.153	0.164	[0.11,0.25]	17.8	48.9	4.1	43.1	100%
1.3	6%	72%	93	22.3	0.178	0.137	0.146	[0.09,0.23]	16.9	49.8	3.1	44.2	100%

BAT IRM 13

k	BAT 3y	GPS 3y	GPS mOS	pool mOS	HR@IA	HR@80	HRobs	95% CI	BATa@IA	GPSa@IA	BATa@80	GPSa@80	P(win)
0.4	35%	46%	32	24.2	0.465	0.653	0.699	[0.45,1.08]	27.4	39.4	21.3	25.2	34%
0.5	32%	48%	34	23.7	0.445	0.546	0.584	[0.38,0.91]	26.4	40	18.8	28.1	65%
0.6	28%	52%	38	23.7	0.414	0.459	0.491	[0.32,0.76]	25.3	41.1	16.3	30.5	88%
0.7	24%	55%	43	23.7	0.384	0.386	0.413	[0.27,0.64]	24.3	42.1	14	32.9	97%
0.8	21%	58%	50	23.5	0.354	0.326	0.349	[0.23,0.54]	23.3	43.2	11.8	35.1	100%
0.9	18%	61%	58	23.4	0.326	0.278	0.297	[0.19,0.46]	22.3	44.2	9.8	37.1	100%
1	15%	64%	67	23.2	0.3	0.238	0.255	[0.16,0.40]	21.4	45.1	8	39	100%
1.1	12%	66%	77	23	0.275	0.207	0.221	[0.14,0.34]	20.5	46.1	6.5	40.6	100%
1.2	10%	69%	88	22.7	0.253	0.182	0.195	[0.13,0.30]	19.7	47	5.1	42	100%
1.3	7%	70%	98	22.5	0.233	0.162	0.173	[0.11,0.27]	18.9	47.8	4	43.2	100%

BAT IRM 14

k	BAT 3y	GPS 3y	GPS mOS	pool mOS	HR@IA	HR@80	HRobs	95% CI	BATa@IA	GPSa@IA	BATa@80	GPSa@80	P(win)
0.4	36%	44%	31	24.2	0.5	0.7	0.749	[0.48,1.16]	28.1	38.8	22	24.5	23%
0.5	33%	47%	32	23.7	0.489	0.596	0.638	[0.41,0.99]	27.2	39.2	19.6	27.2	49%
0.6	29%	50%	36	23.7	0.464	0.509	0.545	[0.35,0.84]	26.3	40.1	17.3	29.5	76%
0.7	26%	53%	41	23.7	0.437	0.435	0.466	[0.30,0.72]	25.5	41	15.1	31.8	92%
0.8	23%	56%	47	23.6	0.412	0.373	0.399	[0.26,0.62]	24.6	41.9	13	33.9	98%
0.9	20%	59%	54	23.5	0.386	0.321	0.344	[0.22,0.53]	23.8	42.7	11	35.9	100%
1	17%	62%	63	23.3	0.361	0.278	0.298	[0.19,0.46]	23	43.6	9.2	37.7	100%
1.1	14%	64%	75	23.1	0.338	0.243	0.26	[0.17,0.40]	22.2	44.4	7.6	39.4	100%
1.2	12%	66%	88	22.9	0.316	0.215	0.23	[0.15,0.36]	21.5	45.2	6.3	40.9	100%
1.3	9%	68%	103	22.7	0.296	0.192	0.205	[0.13,0.32]	20.8	45.9	5.1	42.1	100%

BAT IRM 15

k	BAT 3y	GPS 3y	GPS mOS	pool mOS	HR@IA	HR@80	HRobs	95% CI	BATa@IA	GPSa@IA	BATa@80	GPSa@80	P(win)
0.4	37%	43%	30	24.2	0.534	0.746	0.798	[0.51,1.24]	28.7	38.2	22.7	23.9	16%
0.5	34%	45%	31	23.7	0.533	0.647	0.692	[0.45,1.07]	28	38.4	20.4	26.4	35%
0.6	31%	49%	34	23.8	0.514	0.561	0.6	[0.39,0.93]	27.2	39.2	18.2	28.6	60%
0.7	28%	52%	38	23.7	0.493	0.486	0.52	[0.34,0.81]	26.5	39.9	16.1	30.7	82%
0.8	25%	55%	44	23.6	0.472	0.422	0.452	[0.29,0.70]	25.8	40.6	14.1	32.8	94%
0.9	22%	57%	50	23.5	0.45	0.368	0.394	[0.25,0.61]	25.1	41.4	12.2	34.7	98%
1	19%	60%	59	23.4	0.428	0.322	0.345	[0.22,0.53]	24.4	42.1	10.4	36.5	100%
1.1	16%	62%	71	23.2	0.407	0.284	0.304	[0.20,0.47]	23.8	42.8	8.8	38.2	100%
1.2	14%	64%	87	23	0.386	0.252	0.27	[0.17,0.42]	23.1	43.5	7.4	39.7	100%
1.3	11%	66%	106	22.8	0.367	0.226	0.242	[0.16,0.37]	22.5	44.1	6.2	41	100%

BAT IRM 16

k	BAT 3y	GPS 3y	GPS mOS	pool mOS	HR@IA	HR@80	HRobs	95% CI	BATa@IA	GPSa@IA	BATa@80	GPSa@80	P(win)
0.4	38%	42%	29	24.1	0.568	0.791	0.847	[0.55,1.31]	29.3	37.6	23.3	23.3	10%
0.5	35%	44%	30	23.7	0.577	0.697	0.746	[0.48,1.16]	28.7	37.7	21.2	25.7	24%
0.6	32%	47%	33	23.8	0.565	0.613	0.656	[0.42,1.02]	28.1	38.3	19.1	27.7	44%
0.7	29%	50%	36	23.7	0.551	0.539	0.577	[0.37,0.89]	27.5	38.9	17.1	29.7	67%
0.8	27%	53%	41	23.7	0.535	0.475	0.508	[0.33,0.79]	26.9	39.5	15.2	31.7	84%
0.9	24%	55%	47	23.6	0.518	0.419	0.448	[0.29,0.69]	26.4	40.1	13.3	33.5	94%
1	21%	58%	55	23.5	0.5	0.37	0.396	[0.26,0.61]	25.8	40.7	11.6	35.3	98%
1.1	18%	60%	67	23.3	0.482	0.329	0.352	[0.23,0.55]	25.3	41.3	10.1	36.9	100%
1.2	16%	62%	83	23.2	0.464	0.294	0.315	[0.20,0.49]	24.7	41.9	8.6	38.4	100%
1.3	14%	64%	108	23	0.446	0.265	0.283	[0.18,0.44]	24.2	42.4	7.3	39.8	100%