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It’s been a while since I’ve posted here. I’m not a person who knows Dr Bala, but I thought I’d chime in as someone who treats this disease (I have 3 on treatment right now).
I’m a radiation oncologist (MD MPH) and I was blown away by the results. Even though the study design was not perfect, with this kind of disease I cannot imagine it not getting approval due to its low toxicity and potential benefit.
I shared the results with other colleagues (including one an MD PhD) and the response was “wow! I wonder when this will be available to us!”
Any product that can give even a few months of survival is huge in glioblastoma. When word gets out to other physicians they will be asking for this.
Happy to be here!
I'm answering lots of science-type questions, but I will have a lot of questions on markets, etc. A lot of it is new to me. Seeing the posts from sojourner with graphs and terms like "flags, handles, etc" have little meaning to me, but I'm wanting to learn.
I won't be able to post everyday, but I'll do my best to answer any posts I see.
Thank you for the detailed history! It is much appreciated
In your experience, have you seen this many patients about 80+ alive out of 331 total this long for about 45+ months since surgery.
It's definitely odd (in a good way) and it's what drew me to this experimental treatment. If we look at OS in the Stupp protocol for the RT+TMZ group, at 3 years there was 16% OS and at 4 years 12%. Using the numbers you gave me, 80/331 would be 24% OS at 3.75 years (45 months). That's possibly double the OS of Stupp. There's a good chance that most of those patients received the intervention, which could be driving that number. Any way you slice it though, that number higher than Stupp.
Is there still a possibility L being as effective as SOC?
Yes, there's always that possibility. As I have said in other posts, any potential confounders measured or unmeasured can influence the outcome.
How comes Checkpoint Inhibitors are approved left and right. They employ the same dendritic cells to attack the tumor once their breaker is turned off, imo. In DcVax the very same dendritic cells are amplified and processed to be more potent with all the tumor proteins impregnated as a vaccine. Which one in your opinion will do a better job?
I can't speak to why they are routinely approved. Checkpoint inhibitors use monoclonal antibodies to block CTLA-4, PD-1, and PD-L1, which are molecules that inhibit immune cell activity. These are able to restore immune function in the tumor environment by various pathways, but usually T-Cell activation. Dendritic cells are antigen presenting cells. They are messengers that tell the T or B cells what to do. They "prime" them, in a sense, on what to target. They have been shown to assist with PD-1 checkpoint inhibitors. Checkpoint inhibitors will turn off the immune inhibition indiscriminately and can cause side effects (causing an upregulation of an immune response, basically like a drug induced autoimmune disorder). A dendritic cell that has been primed with a specific tumor antigen and then presents this to a T cell, the T cell now knows it must go kill that specific tumor. If dendritic cell therapy works, it should work way better. I think at this stage it may have applications in microscopic residual disease. I'm not convinced on gross tumor, yet. Tumors have many many ways of evading treatments, but seem to be more vulnerable when microscopic versus gross.
In a standard primary / secondary endpoint design the primary has an alpha target and only if that is reached then the secondary is evaluated against its alpha target. Often both .05 in a vanilla trial.
The secondary endpoint is still evaluated (see below)
The slang term for not evaluating the secondary after the primary failed is that the alpha was "spent" or "used up".
You are referring to the statistical power being "spent" in the primary analysis after it fails. Yes, this is a thing. It's not alpha being used up as you referred. Using the secondary endpoint after the primary failed is something that happens all the time. It's an ongoing issue in research/biostatistics, but drugs like carvedilol were approved by the FDA on a secondary endpoint. Here's an interesting article specifically on this subject written by a Professor of epidemiology and biostatistics:
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1120143/
From that article: "An individual patient faced with a serious condition may have only one opportunity to benefit from a potentially helpful treatment. Whatever the statistical results, the subgroup or secondary outcome results could provide the best available estimate of treatment effects for individual patients."
Like I said, it's problematic, but sometimes that's all you have.
You can not simply say all endpoints are evaluated against the trial alpha and if any hits it is stat sig. That is called multiplicity.
Multiplicity is something else. It's when you look at the same data set over and over until you find something significant. This mostly occurs in multivariate models where multiple combinations of variables are added and removed until a significant model appears. Looking at the secondary endpoint after failing the primary endpoint is not multiplicity.
Do not use sleep or worry about the fact you invested in a company that lives on a trial supposed to have results years ago and now rewriting the analysis plan despite clearly knowing significant results.
I'm done with our conversation. I'm wasting too much energy and time. So I will not longer bite. It's been a pleasure!
I am kind of stunned that somebody who understands trials would not know about these issues before buying in.
You, yourself, said "Second, the alpha allocation issue is fuzzy. Is it .02 for PFS with OS being only sequential at a subsequent .05 (which is how the protocol read to me). Have they changed it, or was this never the case? Nobody knows."
And I said I don't know as well. I imagine that they have set their alpha to 0.05. I'm not losing sleep over it. It's not as big of a deal as you are making it out to be.
On the trial alpha allocation issue, it is probably something you should try to check out. PFS is the primary endpoint, OS is the secondary. In a standard primary/secondary design (and this trial looks to have that, though some posters disagree) then if PFS fails there is no alpha left for OS. So P on OS would formally mean zip.
Once again, I have no idea what you mean. You are just saying words that make it seem like you know what you talking about to the casual reader.
"if PFS fails there is no alpha left for OS. So P on OS would formally mean zip"
This statement is nonsense and clearly shows you do not know what you are talking about. "No alpha left for OS"? Alpha is not something that gets used up and is gone. PFS and OS are two separate ends point that are independently measured and analyzed. Saying that the primary endpoint is PFS, means that the study was specifically powered to attempt to meet this endpoint, but not powered for OS. If that fails, it does not necessarily mean that OS will fail too.
I printed this paper and I'm going to take a look at it this weekend. I'll let you know what I think.
In general, there are some interesting ways to determine the composition of a tissue on an MRI with some of these techniques.
MRI Spectroscopy has some metabolites that can be used:
NAA - marker for neuronal viability, found in high concentrations in neurons, and reduced by any process that destroys neurons
Creatine - found in metabolically active tissue, reduced in gliomas
Choline - marker of cellular membrane turnover, elevated in neoplasms, demyelination, and gliosis. In Gliomas, elevation of choline beyond margins of contrast demonstrate cellular infiltration of tumor
Lactate - marker of anaerobic metabolism and is therefore elevated in necrotic areas, higher grade tumors
A recurrence on an MRI Spectroscopy would show: increased choline, decreased creatine, and the Choline/NAA ratio would be >2.
Radiation necrosis would have increased lactate and decreased choline, creatine, and NAA
Perfusion weighted MRIs can be helpful too. It can estimate the microvascular density (MVD) by measuring the cerebral blood volume. Radiation therapy leads to endothelial cell damage and small vessel injury, so there is reduced perfusion and reduced MVD. Tumor recurrence promotes angiogenesis (making new blood vessels) and thus increases MVD
First, the trials is an add-on to SOC. If it is "as effective" as SOC, then it is not effective at all. So could never be approved.
That's what I said
Second, the alpha allocation issue is fuzzy. Is it .02 for PFS with OS being only sequential at a subsequent .05 (which is how the protocol read to me). Have they changed it, or was this never the case? Nobody knows.
I have no idea what they have set alpha to in this study for PFS
And lastly, you also fall into the alpha trap. The P value does not state what you just asserted. It states that there would be a 97.5% chance that a placebo would not reproduce the same benefit (or larger) in the same trial. [Maybe your were just keeping it simple here. But this is one of my two pet peeves. Other being when people assert that a trial that failed to show a benefit showed no benefit]
What is an alpha trap? I don't think you know what a p-value is based off of your comment. I have no idea what you are talking about here with saying that a P-value states that there would be a "97.5% chance that a placebo would not reproduce the same benefit (or larger) in the same trial". What p-value? Where did you get that number? Are you referring to a specific trial or specific data? It's really not clear. I'm speaking about p-values and alpha (significance levels) in general.
Also, this sentence makes no sense: "Other being when people assert that a trial that failed to show a benefit showed no benefit".
I'm sorry if I hit on your pet peeves, but I think we are talking about completely difference things.
Let me explain what I was trying to say again:
When alpha is set to 0.05 (or 5%) this means there is a 5% probability that the null hypothesis is true (meaning that there is no association between two measured phenomena). When p is less than alpha, you can reject the null hypothesis and the alternate hypothesis is accepted. If alpha is switched to 0.1 this means there is a 10% probability that the null hypothesis is true.
Ronald Fisher initially set significance level at 0.05 and it stuck, but he never intended it to. There is a growing call for the end of statistical significance among statisticians. This was published in Nature a couple months ago: https://www.nature.com/articles/d41586-019-00857-9
I imagine this is based on their observations from their patient experiences, but I don't know
The Rube Goldberg machine has already been set in motion and all they can do is watch
Hi Alphapuppy, Photonic5 says that you have done a lot of work on this and have some compiled data to share. He's going to give me the work you've done. I'll take a look and let you know what I think!
It's great that it appears that DCVax is very safe. Safety is really important obviously, but if the experimental treatment does not work, what's the point? I think in the minds of most clinicians, and the FDA, efficacy and being better than standard if care is most important.
However, there are some cases where the toxicity may not outweigh any perceived marginal benefit. For example, there are some treatments that may have better efficacy and have more toxicity that are used. The chemotherapy regimens BEACOPP vs ABVD in Hodgkin's lymphoma are both very effective. BEACOPP might be a bit better, but it is more toxic. The US feels that it is not worth the toxicity, but in Europe they do and use that regimen.
There's a chance that it could be shown to be safe and as effective as standard of care, but then I doubt it would be put into use. In addition, there's a chance that it could trend towards being better, but not be significantly better. In this case, potentially an argument could be made, if there are potentially good reasons why it did not reach significance. We need to keep in mind that our determination of alpha < 0.05 for statistical significance is arbitrary. This means that there is a 5% risk of concluding that a difference exists when there is no actual difference. In other words 95% of the time what you perceive is not happening by chance alone. An alpha of 0.1 would mean that 90% of the time it's not by chance alone. That sounds pretty good still right? I think so, but many doctors get hung up on a P value <0.05.
If we were to have a close but no cigar statistical significance, but the percent difference appeared to be really trending to be better, the FDA could want to see another Phase III. But there's no way to really know
Gen X Physicists = Heisenberg (he wants to open a car wash and/or chicken joint and has features very similar to a Walter White in season 5) + Gollum (NWBO is his precious)
It's actually super helpful to read up on the latest studies, know some of the numbers, etc. for board preparations.
Thanks to everyone who said welcome! I can't respond to all, but I have seen (most) of the posts saying welcome.
What are some of your thoughts in regards to finalizing the SAP's?
I imagine they are already finalized, since usually you need to have a good idea of your statistical analysis before you start. Things can change, but hopefully they have a good team that can get this done in a timely manner.
Do you feel enough time has gone by that NWBO may have already submitted these to the 4 Regulatory Agencies?
Probably, but I don't have a crystal ball.
Do you think that NWBO may have already received revised SAP approval from any/all 4 RAs?
(see above)
What main revisions do you believe NWBO might be incorporating in these revised SAPs?
How to deal with pseudoprogression appropriately. (see below)
How to statistically interpret PFS events & pseudoprogression?
They would have to have a solid definition of pseudoprogression that can be confirmed as true pseudoprogression (which is hard to do and can be subjective). Up to 50% of patients may show signs of this after RT+TMZ. This is also influenced by MGMT methylation status. Tests like fMRI can be helpful if there is a question. Ultimately, there are physicians all across the study making independent judgements on if a patient truly progressed or not. This can be a form of bias in the study (and in reality cannot be controlled for unless they have a good definition to follow). Most likely those that were officially determined to have pseudoprogressed would have to be excluded from PFS as an "event", since it's not real progression.
Surgery with/without dyes (5 ALA)?
White blood cells counts?
Other factors?
I don't know much about surgery or anything about dyes, so I won't comment on that. I know TMZ can affect blood counts since it is an alkylating agent, but I'm not sure how this would confound results (unless it is somehow affecting DCVax's ability to mount an immune response). Other factors would be any potential confounder that was not accounted for. This could even be just better treatment delivery over the past 10 years (better surgeries, better radiation delivery, better TMZ management) that has just allowed standard of care to work better. Time is a huge issue in long cohort studies as medicine gets better. This could be controlled for in the statistical analysis by having something like "year of surgery " as a surrogate in multivariate analysis. They also need to make sure participants are stratified by something like modified RPA (recursive partitioning analysis, which is basically a performance status for gliomas and glioblastomas are III-VI). If a patient is already a poor performer, they need to be analyzed as a separate group.
What is your current expected timeframe for topline/data lock? From the PR it appears this will not be announced at ASCO ... are you thinking perhaps within weeks, or perhaps towards year-end?
I have no idea. I can only go by what little information they give. Anything else is just reading tea leaves.
Statistics: which of the trial results do you believe might have improved compared to their most recent blinded results disclosed?
I'm hoping to see survival improvement. This is one of those disease sites where we hang on improvements in survival by weeks. The first thing I thought when I saw Linda Liau's presentation was "how is this possible?" Even unblinded the survival is striking. The fact that it is a cross-over trial makes me suspect that it has to do with the interventional treatment (but if the big fat confounder is better standard of care delivery over the past 10 years or so, we have a problem).
Do you think that Primary Endpoint (PFS) might still come through OK? Or that the long tail of OS will need to "save the day/trial"?
It still could be fine, but good statistical analysis to control for any measurable confounders (pseudoprogression) would be really needed. "Statistical hand waving" as one of my attendings in residency once said. The long tail could be important, but I have no way of telling at this point. If we unblind and everyone did well no matter the intervention, the long tail does not matter.
Once unblinded, where do you figure the OS results might wind up for:
Treatment Group (early DCVax)? No idea, but I'm really excited to find out.
Placebo Group (no DCVax versus Late DCVax)? Where standard of care has been in the past (Class III 4 yr OS ~28% and MS ~21 months).
Or they could be nearly the same! And that's sad...
Assuming approval of DCVax-L, which country/RA do you believe might grant approval first?
I'm not an expert in international health policy or geopolitics, so you guess is as good as mine.
Research is a lot of blood, sweat, tears and hope. Many times it doesn't work out and it's heartbreaking. That's all a part of the scientific process of making us better understand the world we live in. Yes, making some money would be nice. But adding even a few months of survival to this disease would be life changing for anyone who is facing this diagnosis.
Photonic5, am I not giving you enough work to do since you are posting early in the morning when I know there are second checks, QAs, etc to be done?!
Hello everyone! I work with Photonic5 and he's the one who roped me into NWBO. I'm a radiation oncologist and I've drank the Kool-Aid.
I'm a "long" but I've only been one for a short time (i.e. since March).
I've reviewed what little data we have and I think it's potentially exciting. In addition to being a physician, I also have an MPH in epidemiology and have a lot of experience with clinical trials, study design, statistics, etc.
Feel free to ask me any questions.
Nice to meet everyone and Photonic5 needs to get back to work...