Well, if you look at the # at risk, we're down to *1* patient by 4y, so the confidence intervals by the end of this curve should be widening to include 0%. So let's say the 5y OS in a bigger cohort would end up 30-40%. Still has to be some kind of therapeutic activity, right?
Well, not so fast. This is a highly selected cohort!
These patients had resectable local disease (better 5y OS) and had to remain without evidence of disease for a while to get their vaccine (3/19 patients dropped, leaving ones with better odds of complete resection)
ALSO, they had to have unusually high TMB to meet the 5+ neoantigen threshold -- PDAC has an exome small variant TMB ~1-2/megabase, so total ~30-60 mutations, of which a small minority would predicted as generating a neoantigens
...and what does high TMB mean in PDAC (like in many other cancers)? That's right, better response to checkpoint blockade () -- see the dose of atezolizumab at week 6.ascopubs.org/doi/full/10.12…
Fine, maybe the 16 patients on the trial had baseline better chances than the general PDAC population *but* they stratify by response to the vaccine -- doesn't that mean it worked?
First of all -- let's be clear about something that's obvious to some and very non-obvious to others: these aren't randomly assigned arms of an RCT. We're looking at all 16 patients in a single arm and splitting their relapse KM curves by magnitude of immune response to vaccine
Got it? Not an experiment +/- vaccine but rather a post-hoc stratification that risks getting causality mixed up.
People *want* to believe that immunological responses to the vaccines are keeping cancer away but...
what if the cohort is so small that splitting it into two smaller groups risks pulling in very unbalanced prognostic factors, either by chance or by virtue of healthier patients responding to the neoantigen vaccine more easily?
So, how balanced *are* the prognostic factors between these two groups?
Well, looking at the previous paper on this trial () you'll notice a few thingsnature.com/articles/s4158…
Immunological responders *also* had:
smaller tumors
more favorable pathological staging
less lymph node involvement
favorable location of the tumor in the pancreas (head vs. body/tail).
This *alone* potentially explains all subsequent differences in RFS curves
An interesting residual question is why did patients with less aggressive or earlier-stage tumors also tend to have stronger T-cell responses to the neoantigen vaccine (and confusingly worse T-cell responses to SARS-CoV-2 vaccination).
It's an interesting scientific mystery...
...but also beside the point of whether this trial deserves all the attention it's getting as a breakthrough in the treatment of pancreatic cancer.
If you want to know whether this therapeutic concept has legs, there's a much better trial to look at...
Moderna's honest-to-goodness randomized controlled trial of aPD1 +/- their mRNA neoantigen vaccine in a cancer type: "Individualised neoantigen therapy mRNA-4157 (V940) plus pembrolizumab versus pembrolizumab monotherapy in resected melanoma (KEYNOTE-942)"
In this trial you see an actual albeit modest effect between randomly assigned arms with and without treatment.
It's honestly the only evidence I've seen that neoAg vaccination has any therapeutic activity and deserves all the attention until BioNTech does an RCT.
Lost some words in there before “cancer type”, meant to say we have a much higher a priori expectation that this might work in melanoma than PDAC both bc of the higher mutational load and experience with other immunotherapies working.
@breichholf This would also explain the small effect size: they’re putting immune pressure on some tumors through, on average, 0-2 targets — the ones with zero don’t benefit, ones with 1 can be escaped from, 2+ probably more durable
@denis_mongin I do, however, have to block *a lot* of people on here -- it's a nasty mess of hate and propaganda, so I understand people not wanting to let this place colonize their consciousness
@yummocatvibes Looked back at the patient characteristics table, it 6:2 yes vs no splenectomy in the responders and 3:5, so entirely stark but still >2x more frequent. A whole cloud of potent confounding!
@varma_ashwin97 @wgibson Of course, the numbers are very stochastic but if even a subset of patients had strong T-cell responses to ~1 presented neoantigen, then a subset of those could get effective immune surveillance, even if transient/evolutionarily unstable.
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Let's say you want to publish in a top-tier journal and need to have a high accuracy predictor of something of great medical importance, such as survival of cancer patients in response to immunotherapy. The easiest route is just to cheat: (X,y).predict(X) model.fit
Since you're just memorizing the test data (or maybe a superset of it), then the predictor works better as you add more features.
Conversely, if you don't cheat but use kinda uninformative features (eg extracted 4mer subsequences from predicted neoantigens), you get models of very low predictive value -- there might be a tiny bit of signal in there (maybe driven by TMB) but mostly survival curves overlap
Finishing up my Defense Against the Dark Arts syllabus and realized I don't have anything that's really specific to large deep learning models applied to -omics or other biomedical data.
What are some egregious missteps there which show up in high profile papers?
Tentative list of topics:
1) Just make it up: image manipulation and tables which don't add up 2) Try everything and report the small p-values: uncorrected multiple hypothesis testing
3) My predictor is perfect on the training set: lack of independent validation data over-estimates accuracy
4) My predictor is perfect if I tell it the labels: information leakage between training and test datasets
Preprint live: biorxiv.org/content/10.110…
tl;dr We did not disprove the role of T-cells in clearing SARS-CoV-2 (sorry to disappoint) but we did manage to make a vaccine which induces strong but useless T-cell responses against SARS-CoV-2. Details & guess at interpretation below 1/n
Many SARS-CoV-2 vaccines present the immune system with the spike protein or the parts of the spike most vulnerable to neutralization (S1 portion or even just the RBD). This antigenic content can be made directly (protein subunit), encoded as mRNA, DNA, DNA in a virus, &c
2/n
Versions of all of these approaches can all effectively induce neutralizing antibodies. They also often achieve T-cell responses whose significance has been hotly debated.
(some people feel strongly that T-cells save us from new variants, some claim they do nothing at all)
3/n
In Feb 2020 SARS-CoV-2 still felt far away, a twitter feed of China + isolated cases in other countries (remember #ncov2019?)
I was driving to NC to start my new job at @UNC_Lineberger and was thinking about making a peptide vaccine for SARS-CoV-2...
2/
Why a peptide vaccine? It was honestly the primary approach I had experience with from my work with @BhardwajLab / @OpenVax on the PGV trials at @IcahnMountSinai. I had seen that peptides+poly-ICLC could get strong T-cell responses *and*...
3/