All posts by Paul Rennert

What aren’t we better as weighing results and making sound conclusions?

Yesterday on Biotech Clubhouse the conversation veered from the anti-vaccine movement to recent FDA actions. It was an interesting discussion, and got me wondering:

What aren’t we better as weighing results and making sound conclusions?

Whether its vaccines preventing SARS-Cov2 infection, or greenhouse gases causing Climate Change, we find it hard to convince people that our hypotheses are supported by the available evidence.

First, it’s not new. We had the “HIV doesn’t cause AIDS” debacle, aka HIV Denialism, spearheaded most famously by the now-disgraced molecular biologist Peter Duesberg and most damagingly by the former South African President Thabo Mbeki.  Mbeki in particular railed against testing, and delayed the deployment of retroviral therapies in South Africa for years, policies that led to the unnecessary death of hundreds of thousands of citizens. President Ronald Reagan remained stone-cold silent on the subject of HIV and AIDs for more than 4 years while the epidemic grew ever larger in the US.

You think we’d learn.

Scientific denialism is the extreme result of disputing scientific findings are disputed on irrational grounds. As we see in the anti-vaxxer community, the arguments are not scientifically grounded but rely instead on a bevy of illogical arguments, including rhetorical fallacies, eg. appeals to false authority, appeals to “fairness”, and the right to disagree.  But at heart, ignoring and discounting observations that clearly support critical scientific hypotheses is a unifying theme of denialism.

You might wonder: why focus on these hypotheses?  Why not simply state the facts. The answer is that we can find gradations of scientific denialism that extend from crazy anti-vaxxers to the recent aducanumab approval, with a lot of gray area in between that involves hypothesis testing.

This is a hypothesis that is also considered to be a true statement:


This is a true statement as far as we have tested it – no spiders have been found that have more or fewer than 8 legs, and those with less have accidently lost a leg that was originally there. This exercise, of viewing (collectively) many spiders and deriving the statement is an example of inductive reasoning, meaning we have taken individual observations that THIS SPIDER HAS 8 LEGS, which are singular statements, and have formulated the hypothesis that ALL SPIDERS HAVE 8 LEGS, and having (collectively) failed to disprove the hypothesis it has become a universal statement, considered true.

Formally, this is an exercise in evidence-based epistemology. We can ignore the philosophy though, and focus on what “evidence-based” means here:

1) we started with observations: THESE SPIDERS HAVE 8 LEGS.

2) we (perhaps subconsciously) formulated a hypothesis – NO SPIDERS EXIST THAT DO NOT HAVE 8 LEGS (unless they lose 1 or 2 by accident)

3) we collectively have tested the hypotheses and drawn the appropriate evidence-based conclusion that, indeed, ALL SPIDERS HAVE 8 LEGS.

So far, we’re good. But there is an asymmetric element to our hypotheses, first articulated by Karl Popper. Note that the universal statement ALL SPIDERS HAVE 8 LEGS can never be verified by any number of singular statements because there is in theory always another spider to examine. However, they can be refuted by a singular statement, eg.


(Popper used ALL SWANS ARE WHITE as his example of a universal statement, readily disproved by the appearance of a BLACK SWAN, an image repurposed to describe a rare and unexpected “black swan event”. To be fair, black swans are rather common which is why Popper used them in his example.)

Popper used the asymmetry inherent in the hypothesis (impossible to prove, easy to disprove) to formulate new criteria for statements about the world, specifically, that scientific statements are fundamentally different from other classes of statements such as metaphysical statements (eg. “man is good”). In Popper’s view, scientific statements can provide evidence-based truth because they can be falsified. His “Criteria of Falsifiability” states that “only those hypotheses which can potentially be contradicted by singular statements qualify as scientific”.[1]

It’s never so cut and dry as spider’s legs, and the acceptance or rejection of a hypothesis can be less straightforward than the weight of any single observation (thus we use statistics to bracket the uncertainty of our measurements). Regardless we can make the statement that the “empirical content” of a hypothesis is a useful measure of its inherent value.

Simply put, there is more empirical content in the statement ALL SPIDERS HAVE 8 LEGS than in the statement ALL SPIDERS HAVE LEGS.

Empirical content reflects the testability and falsifiability of a hypothesis, and an empirically rich hypothesis yields many predictions and therefore many potentially falsifying statements. We can see the richness of complex hypotheses in their ability to generate testable predictions, and the most powerful scientific hypotheses can survive making incorrect predictions. Hypotheses having truly robust empirical power become Theories, as in the Theory of Evolution or the Theory of Relativity.

So, another hypothesis is:


This hypothesis has a lot of empirical content and predicts, at a minimum, that vaccinated individuals should get sick less often than unvaccinated individuals, and that vaccinated individuals should spread the virus to other people less frequently than non-vaccinated individuals. We can make these predictions based by our experience with other vaccines: flu vaccines for example.

When we review the data we find our predictions are robustly supported: in every instance in which it has been studied BNT162b2 prevents SARS-COV2 virus infection and reduces viral spreading. Note that the foundational hypothesis is eminently falsifiable – just ask GSK or Merck or Sanofi or Curevac, all of whose vaccines failed – but with respect to BNT162b2, and Janssen’s JNJ-78436735 vaccine, and Moderna’s mRNA-1273 vaccine – the hypotheses have withstood rigorous testing. So that’s good!

In contrast, a weak hypothesis generates predictions that fail more often than not.  

One example is the hypothesis that hydroxychloroquine can be used to treat SARS-COV2 patients. A study, published in 2005, suggested that chloroquine could prevent SARS (the older virus) from infecting cells kept in culture.[2] This was a simple study published in an obscure journal, but it did make predictions: that chloroquine acted by interfering with the “terminal glycosylation of the cellular receptor, angiotensin-converting enzyme 2” thereby blocking virus/cell interaction, and that chloroquine might be useful in treating SARS patients. This study, accessed over 1M times online and widely shared on social media, was offered as evidence that a form of chloroquine called hydroxychloroquine could treat SARS-Cov2 patients. Thus, the observation (chloroquine blocked SARS infection of cells in a cell culture dish) generated a hypothesis:


This hypothesis was tested clinically, many times over, and failed each time. Note that the empirical power of the hypothesis is not zero: angiotensin-converting enzyme 2 (ACE-2) plays a critical role in mediating SARS-COV2 infectivity, but hydroxychloroquine doesn’t block this pathway in patients. Therefore, the hypothesis failed a critical prediction and must be rejected.

Anti-vaccine sentiment offers an extreme example of Science Denialism, and its adherents have many and complicated ways of rationalizing their views built for example on religious, political and cultural mores. Regardless, the active disregard or denial of results that contradict their viewpoint is a central component of their stance.

In science we expect a more clear-headed evaluation of results in the context of hypothesis testing. And yet, much of the published scientific research literature cannot be reproduced. Venture capital firms and pharmaceutic companies that perform ‘wet diligence’ have consistently concluded that 2/3rds of the results in the literature are not reproducible (wet diligence refers to the hiring of an independent lab to repeat published experiments). Note here that we are not referring to fraud, nor are we saying that 2/3rds of published results are wrong. Science is hard!  Indeed, the reproduction of results is a core element of the scientific literature; failure to reproduce results is a part of the process that drives science forward, because this process allows us to reject a hypothesis and move on.

Except when we don’t: the counter-weight to the process of rejecting hypotheses in the face of data is the use of ad hoc explanations for results that run counter to the predictions made. This practice is remarkably common in academic labs, in biotech and pharma labs, and in investor communities. “Oh the samples must be mixed up, let’s run the analysis again” is one simple example. But the slope is slippery: tossing out inconvenient results as “outliers”, declaring a specific assay or model irrelevant because it didn’t show you what you wanted to see, the mining of statistics in search of significance, aka ‘p-hacking’, selective display of data and so on. Nonetheless, most ad hoc responses to inconvenient results are pretty harmless, since they fail to survive scrutiny. Of course, a lot of people’s time and energy is wasted trying to reproduce bad experiments.

A rule of thumb, articulated by James Farris[3] among many others, is that each use of an ad hoc explanation to dismiss a result predicted by a hypothesis weakens that hypothesis. Farris made this argument in defense of the use of parsimony in evolutionary systematics, where the minimum number of explanations is used mathematically to derive relationships from a data set. This works as well with fossils as it does with genomic sequences, so we see parsimony can be a powerful tool because it specifically reduces the number of ad hoc explanations.

That ad hoc justifications for inconvenient results should be avoided may seem obvious, but again, the slope is slippery, and made more so when pressure is brought to bear on a hypothesis that is not scientific. This is true of our anti-vaxxers as discussed. It appears to also be true of the approval of aducanumab for the treatment of Alzheimer’s Disease (AD).  This issue has been covered in detail, see for example the in-depth analyses by Derek Lowe and by STAT News.[4],[5]

The drug was approved despite inconsistent Phase 3 clinical trial results, and despite the use of post-hoc analysis to identify potentially responding patients. The use of post hoc analysis to generate an ad hoc explanation for inconvenient results sounds confusing, but basically you could say the after the fact (post hoc) the company looked at just some patients to conclude the clinical trial worked, for this population (an ad hoc explanation). As stressed earlier, ad hoc explanations weaken the original hypothesis, in this case that aducanumab can treat AD. If we applied the parsimony principle to the available clinical results, we would reject this hypothesis.

So, what should have happened, of we were to apply scientific rigor here? The hypothesis being tested had changed (which is fine, happens all the time, and should). Specifically, the hypothesis:


was altered to:


That hypothesis could, and should have been, the foundation for a new clinical trial. The approval means the hypothesis will be tested in the public domain, without benefit of the clinical trial design that could tell us if its working, or not. This is an excellent example of ad hoc justification undermining the principle of evidence-based truth.

Back in July the Biotech Clubhouse panel worried aloud that the FDA approval of aducanumab and the resulting outcry and controversy would further confuse, and embolden, a public already resistant to the scientific advice for mask and vaccine use to prevent SARS-Cov2. And here we find the real danger in the slippery slope: if we won’t hold the line on scientific integrity, how can we reason with a skeptical general public?

Next time, counterpoint: how successful hypothesis testing led to novel and effective treatments for a nasty autoimmune disease.

Stay tuned.

[1]  –  from Conjectures and Refutations by K. Popper (1963)

[2] – Virology Journal

[3] – Farris on explanation and parsimony

[4] – Derek Lowe’s blog

[5] – STAT

Advantage Albumin, Immunovant in Limbo

Pathway covered: FcRN

Companies mentioned: Immunovant, Momenta/JNJ, Alexion, Argenix

Immunovant stock (NASDAQ: IMVT) got hammered earlier this month on news of a clinical hold in its phase 2b trial for IMVT-1401, a treatment for thyroid eye disease (Graves Disease). The company said it voluntarily decided to pause dosing “out of an abundance of caution,” due to elevated total cholesterol.  Apparently, cholesterol levels were not measured in the earlier Phase 1/2 trials. The company was hammered again 2 days ago when they released earnings, noting that a second trial in Warm AutoImmune Hemolytic Anemia (WAIHA) was also stopped. This is a bundle of bad news at several levels.

Level 1 – The drug.

 IMVT-1401 is an anti-FcRN antibody, designed to prevent IgG from recycling through FcRN-mediated cell internalization and re-release. This is a heavily prosecuted target in disease indications, like Graves, that are thought to be driven by pathogenic autoantibodies. The thinking is pretty straightforward – preventing recycling of IgG will cause a drop in the entire antibody pool, including the pathogenic ones.

Here is some of the activity ongoing in this space:

Screen Shot 2021-02-18 at 5.53.25 PM

One can appreciate pretty quickly that these programs are tightly focused on just a handful of rare diseases.  IMVT-1401 is Immunovant’s only drug, and so a delay here could become a critical issue in the face of robust competition.

Level 2 – The target.

As noted, FcRN recycles IgG by binding and shunting the bound IgG through the endosomal pathway, thereby avoiding the lysosomal pathway that would break down the IgG rather than release it intact.  A second protein uses the same trick – albumin. Notably, the IgG and albumin binding sites on FcRN are distinct, and anti-FcRN antibodies that block IgG uptake do not necessarily block albumin binding.

But IMVT-1401 from Immunovant appears to block both IgG and albumin, as does Nipocalimab from Momenta/JNJ.  In their 2020 10-K, Immunovant revealed drops in serum albumin to the lower range of normal in their Phase 1 studies: “Dose-dependent and reversible albumin reductions were observed in the single-ascending and multiple-ascending dose cohorts … Mean reduction in albumin levels at day 28 were 20% in the 340 mg multiple-dose cohort, and 31% in the 680 mg multiple-dose cohort. For subjects in the 340 mg and 680 mg cohorts, the mean albumin levels at day 28 were 37.5 g/L and 32.4 g/L, respectively (normal range 36-51 g/L). These reductions were not associated with any AEs or clinical symptoms and did not lead to any study discontinuations.”

Why is this important?

Albumin is a globular protein with many functions – one function is to bind (non-specifically) to lipids in blood, including cholesterol and its fractions (HDL and LDL). Therefore, reduction in the total albumin concentration in blood can result in a change in the amount of cholesterol held within cells versus released into circulation (cholesterol efflux). This causes circulating cholesterol to rise. Indeed, low serum albumin is itself considered a risk factor for cardiovascular disease and stroke. Notably, albumin reduction to even lower levels (below 25 g/L) were reported by Momenta in their Nipocalimab clinical trials (corporate deck dated November 2019, Appendix) but we have not had reports of an impact on total cholesterol with this drug.

One thing to consider in passing is that Momenta claims their anti-FcRN specifically binds the IgG binding site and does not bind the albumin. This suggests that specificity may not be enough here to prevent an impact on albumin levels and, potentially, increase cholesterol. That would be a worst-case scenario- a drug class effect. As Jacob Pleith wrote a few weeks ago, “Immunovant’s decision yesterday to pause clinical trials of IMVT-1401 … sent ripples through the anti-FcRn space..”. In response to the Immunovant hold, Argenix quickly noted that they measured total cholesterol, HDL and LDL in two separate trials of Efgartigimod and saw no impact. So yes, the news caused ripples.

Level 3 – The indication.

A reasonable conclusion is that the problem is indication-specific, and indeed this was the assumption made until the announcement this week that the WAIHA trial was also paused along with the Graves Disease trial.

Graves is a thyroid condition and is associated with elevated cholesterol levels itself. Therefore, the combination of a drug effect on top of the disease effect causes concern. The measured increases were modest – LDL-C increased by ~ 65% in the 680mg dose, ~40% at the 340mg dose, and ~25% in the 255mg dose – but this was after only 12 weeks of treatment, in an indication that would likely require life-long chronic dosing.  One might just shrug and say “pravastatin is free” so just give them statins and get on with it. That’s not a bad proposition for these patients.

All of this would make sense: the drug decreased circulating albumin, causing cholesterol to rise, in patients already at cardiovascular risk … but why stop the WAIHA trial?  We have to await more data from the competing programs in other diseases to fully understand what is happening,

In the meantime, a prediction: the hold is in place while the company gets its regulatory ducks in a row, which is a smart de-risking move, then that hold will be lifted.

Stay tuned.

Fidgeting with TIGIT – Part 2 – pathway complexity

Part 2 of 2

Pathways and targets covered: TIGIT, PVR, PVRL2, PVRIG, DNAM-1

Companies mentioned: Compugen, Surface Oncology, Eli Lilly, Merck, Roche

In Part 1 ( I did a drive-by on TIGIT targeting, more or less in isolation.  But TIGIT exists in a complex web of ligands and receptors, expressed on diverse cell types.  Here is a simplified view:


In the context of anti-tumor immunity we want to know how ligands are expressed on myeloid cells, dendritic cells and the tumor cells in the tumor microenvironment (TME) and how the receptors are expressed on infiltrating T and NK cells.  Expression of both ligands, PVRL2 and PVR, is upregulated in many cancers.  Also, shed PVR is thought to prevent productive signaling through DNAM-1.  TIGIT and PVRIG expression is bright on activated T cells, including PD-1-positive T cells. DNAM-1 expression is variable, in part due to internalization and degradation induced by PVR binding.  DNAM-1 activity is also negatively regulated in cis by TIGIT and is reportedly downregulated in the TME of many cancers.

So, it’s complicated.

As noted above we can break this into two main pathways. Compugen has argued that these pathways represent parallel means of regulating DNAM-1 interactions by coopting ligand binding, ie. that the negative signaling receptors TIGIT and PVRIG both compete with DNAM-1 for ligand engagement. These data below are from a Compugen paper (DOI: 10.1158/2326-6066.CIR-18-0442).  The cytokine data are from a 2-week antigen-dependent activation assay that yields CD8+ T cell responses that can be measured in the presence of blocking antibodies.


The take home message is that one only sees synergistic activation of IFNy when the parallel pathways are both blocked, thus blocking both TIGIT/PVRL2 or TIGIT/PVRIG or PVR/PVRl2 or PVR/PVRIG, but not both sides of the same pathway, eg. TIGIT/PVR.  Here again are the two pathways:


The effect can be duplicated by blocking either side plus blocking PD-1.  Compugen also showed that blocking PVRIG induced upregulation of TIGIT, suggesting a compensatory effect that may be overcome by blocking both sides..

These data are the basis for Compugen’s “triple” combination study in collaboration with BMS (NCT04570839).  The trial will evaluate the simultaneous blockade of three immune checkpoint pathways, PVRIG (COM701), TIGIT (BMS-986207) and PD-1 (nivolumab), in patients with advanced solid tumors including those that have failed anti-PD-1 or anti-PD-L1 as prior therapy.  A quick note here: Compugen’s anti-PVRIG is an IgG4 isotype antibody (like nivolumab) and the BMS anti-TIGIT is a FcγR-null IgG1. The idea here (as with anti-PD-1) is that you don’t want to deplete the T cells, just block the pathways. The role of FcR-engagement in this space is controversial as was discussed in Part 1 (

Compared to the wealth of programs targeting TIGIT only a few efforts to tackle the other pathways have been disclosed.  Surface Oncology presented anti-CD112R (PVRIG) data in an AACR 2020 poster.  This included in vivo data using syngeneic mouse models, including in the rechallenge setting.


Note the focus on comparing antibody isotypes (mouse IgG2a > mouse IgG1), which is reminiscent of the findings surrounding anti-TIGIT antibody isotypes, where Fc-effector function may be important for efficacy (note that mouse IgG2a is equivalent to human IgG1).  We’ve not touched on the complexity of target expression on both T cells and NK cells, but here Surface shows a role for each, as depletion of either cell type prevented activity in their in vivo model:


Similar to Merck’s publication, Surface identified a key role for FcgR-engagement in mediating activity.  Finally, they demonstrated synergy with anti-PD-1 treatment:


Compugen’s anti-PVRIG data were generated with an IgG4, showing additive activity with anti-PD-L1.  These data are compared to gene KO results across three different models (from SITC 2019 poster):


The poster also showed robust biophysical characterization of the anti-PVRIG antibodies where the isotype perhaps mattered less.  The in vivo modeling data are somewhat modest – note the short duration of modeling. Regardless, the combination KO data suggest that blocking both arms (TIGIT and PVRIG) is more efficacious than blocking either single arm, which is in line with their in vitro analyses.

A proposed mechanism of action for blocking two pathways

An interesting model is that blocking either pathway (PVRL2/PVRIG or PVR/TIGIT) will “free” DNAM-1 to engage ligands to transduce a productive immune signal.  Of note TIGIT blockade can also release DNAM-1 (from disruptive interaction in cis) as shown by the Roche group:


Therefore, anti-TIGIT and anti-PVRIG (CD112R) blockade may both increase surface DNAM-1 expression or availability. This is important since several papers have directly examined the role of DNAM-1 expression in immune responses. DNAM-1 is degraded upon activation, in a phosphorylation followed by ubiquitination-dependent manner. Mark Smythe’s lab has examined DNAM-1 degradation in the context of anti-tumor immunity. In vivo modeling data showed an improvement in tumor control when DNAM-1 degradation is blocked, and synergy with immune checkpoint blockade is also demonstrated –  nb. this paper is full of very untraditional gating of flow cytometry data (  A follow-on paper from the same lab uses DNAM-1 expression to examine the functional state of TIL, suggesting that down-regulation of DNAM-1 in the tumor microenvironment contributes to T cell dysfunction – this echoes Compugen’s analyses.  There is little work directed to DNAM-1 itself although apparently Eli Lilly has a agonist antibody in the clinic (LY3435151).

TIGIT-related pathways in IO resistance

TIGIT is reproducibly identified on PD-1-positive T cells and appears as a signal of resistance to anti-PD-1 therapy. DNAM-1 downregulation is consistently seen in TIL subsets linked to exhaustion.   Of course, whether these two observations are linked is not known.  In contrast, PVR upregulation has not been identified in any of the many unbiased profiling studies on mechanisms of resistance to or relapse from anti-PD-1 or anti-PD-L1 therapy.  We should recognize that these relationships are complicated – recall that CGEN showed that PVRIG blockade increased TIGIT expression and Roche showed that TIGIT binding in cis can disrupt DNAM-1 activity.   Some of these features are likely to be seen in the context of anti-PVR blockade – we do not have enough data to know.

Of note we do have enough (clinical) data that shows that single agent anti-TIGIT antibody treatment is ineffective, and co-administration of anti-PD-L1 or anti-PD-1 is needed.  It is reasonable to further hypothesize that antagonism of both sides of this complex inhibitory network – eg. anti-TIGIT plus anti-PVRIG – may produce optimal synergy with anti-PD-1 or anti-PD-L1 therapies.

Compugen has presented anti-PVRIG monotherapy clinical data that suggests some activity:


Several partial responses were reported.  With respect to their conclusions we will want to assess the biomarker data alluded to below:


Compugen, in collaboration with BMS, is running a “triple” study: anti-TIGIT, anti-PVRIG, anti-PD-1 (nivolumab).  This sounds promising and will yield useful information one way or the other, since, as noted earlier, the anti-TIGIT antibody being used is an IgG4, as is the anti-PVRIG antibody – activity with this combination would further complicate our understanding of the mechanisms of action.

Updates will post as we get more clinical data from these interesting targets.

Stay tuned.



Fidgeting about TIGIT

Part 1 of 2

Pathways and targets covered: TGF-beta, PD-L1, PD-1, TIGIT

Companies mentioned: Merck KgaA, GSK, Roche, Merck, Mereo, iTeos, BMS, Arcus/Gilead, Compugen, Seagen, Beigene, Innovent, Agenus

Last week we had the bad news that Merck KGaA and GSK had thrown in the towel on bintrafusp alfa therapy for first-line advanced NSCLC.  Bintrafusp alfa is an anti-PD-L1/TGFbR2 TRAP therapeutic designed to selectively antagonize TGF-beta isoforms 1 and 3 while also blocking PD-L1, thereby delivering two-for-one anti-immunosuppression.  Bintrafusp alfa was being tested in a head-to-head trial vs. pembrolizumab and showed no added benefit in a patient population selected for PD-L1-high tumor expression (50%+ of cells in the tumor biopsy sample positive for expression).

This stirred up a fair amount of discussion, as TGF-beta blocking therapies are in vogue for immuno-oncology (IO), with small molecules, biologics, RNA-antagonists and genetic knockouts (in CAR T cells) all in the pipeline. I have high hopes for this space, despite the news out of Darmstadt. And to be fair, the press release stressed the ongoing bintrafusp alfa trials in bladder cancer, cervical cancer, and NSCLC using various drug combinations, and noted new trials in urothelial cancer and TNBC (  Still, the failure stung, due mainly to the promise of the early (open label) Phase 1 expansion cohort data that had suggested significant benefit from the therapy.

This got me thinking about TIGIT, another hot IO target.  The last time I wrote about TIGIT I ended with this question: “How to select patients who should respond to anti-TIGIT co-therapy (or anti-TIM-3 or anti-LAG-3)…?” ( This is a question we should ask about any pathway – including TGF-beta of course – particularly as we are now in the post-immune-checkpoint era, that is, in a setting where many patients in the most IO-responsive indications like melanoma and NSCLC will have already been treated with an anti-PD-1 or anti-PD-L1.  So, is there anything known about TIGIT expression that can guide us in patient (or indication) selection?

Roche leads the field with tiragolumab an anti-TIGIT Fc-competent IgG1 that has shown activity in combination with the anti-PD-L1 antibody atezolizumab in first-line NSCLC, and only in patients with PD-L1- expressing tumors (> 1% of cells in the tumor biopsy sample positive for expression).  We can pause here to recall that this is about where we started the discussion above regarding the TGF-beta TRAP/anti-PD-L1 asset from Merck KGaA, being trialed in the PD-L1-high (>50%) setting in NSCLC.

In front-line NSCLC (EGFR and ALK wildtype), Roche reported responses higher than with atezolizumab alone. Data were shown at AACR and then updated at ASCO.  Here are some of the ASCO data:


The response rate with dual therapy looks rather better than atezo alone, especially in the PD-L1 high cohort (middle panel).  Atezo alone appears to have underperformed, with an ORR = 21% (left panel, all patient data (ITT)).  In the comparable phase 3 trial of atezo vs chemotherapy in front-line NSCLC (also EGFR and ALK wildtype) the ORR = 38.3% in the atezo arm (n=285) and 28.6% in the chemotherapy arm (n=287), see Regardless the 66% response rate in the PD-L1-high cohort (middle panel) attracted attention.

The PFS data were also striking when compared to the prior trial.  This is tiragolumab plus atezolizumab / PD-L1 high cohort:


We can go back and compare this to the atezo alone Phase 3 interim data shown at ESMO in 2019 (I was stuck in the overflow “room” which was a curtained space on the floor of the Barcelona convention center).  This is the PD-L1-high cohort:


Here the median PFS is 8 months, certainly shorter than what is shown for tiragolumab plus atezolizumab, but again, note the disparity with the atezo alone arm of the study (medPFS for = 4 months).

Just to be clear, here are the PD-L1-high patient data compared:


We’re left with the always troubling question of variability between trials and the possibility that the tiragolumab plus atezolizumab results are a fluke.  Unfortunately, we will have to wait and see.

There are two features here worth noting.  One is that TIGIT, the target, is expressed on T cells, along with PD-1.  So far this makes sense – they might very well synergize, particularly given the function of DNAM-1 in the context of T cell signaling (see part 2).  But the anti-TIGIT antibody is an IgG1 isotype, thought to trigger ADCC and CDC-mediated target cell (ie. the T cell) death.  But we want the T cells, that’s the whole point of blocking PD-L1 with atezo.  So what the heck is going on here?

Merck seems to have an answer, but first, some more data.  Merck’s anti-TIGIT antibody, vibostolimab, like Roche’s tiragolumab, is a wildtype IgG1.  Early data on the combination of vibostolimab and pembrolizumab (anti-PD-1), presented at ESMO2020, looked promising in immune checkpoint naïve patients (75% had prior chemotherapy, the rest were treatment naïve):



We can benchmark these results to monotherapy, just as we did with the Roche data, focusing on the PD-L1-positive subset (here we can see data using a cutoff of >1% or >50% of cells positive in the tumor biopsy):



The results compare favorably with pembro-alone using the >1% PD-L1 cutoff and are similar to pembro-alone using the >50% PD-L1 cutoff.  As usual it is difficult to compare between trials, but the signal is encouraging.

Preclinically, Merck has addressed the MOA, stressing the requirement for the intact Fc functionality imparted by the IgG1 antibody isotype.  As mentioned earlier, the mechanistic puzzle is that canonical IgG1 activity includes the triggering of target cell killing via ADCC and CDC mediated cytotoxicity.  Of course, TIGIT is expressed on the very T cells we want to preserve and activate, not kill.  Given this reality we need alternate hypotheses for the action of the IgG1 antibodies.  The predominant hypothesis is that anti-TIGIT antibodies are selectively depleting T-regulatory cells that are TIGIT-bright and immunosuppressive.  This is reminiscent of the now-T-regulatory cells that are TIGIT-bright and immunosuppressive.  It’s an easy hypothesis to advance, similar to the now-debunked arguments made on behalf of anti-CTLA4 and anti-GITR antibodies, and very likely incorrect.

Merck has demonstrated in preclinical models that antagonistic anti-TIGIT antibodies having a  FcgR-engaging isotype induce strong anti-tumor efficacy whereas anti-tumor activity is drastically reduced when using the same anti-TIGIT antibodies that are null for FcgR-engagement (doi: 10.3389/fimmu.2020.573405). These results are consistent with data presented by multiple groups, eg. Mereo and iTeos.  The Merck team further showed shown that FcgR engagement persistently activated myeloid lineage antigen-representing cells APCs, including the induction of proinflammatory cytokines and chemokines while TIGIT blockade simultaneously enhanced T cell activation including elevated secretion of granzyme B and perforin, which synergizes with anti-PD-1 antagonism.  I favor this hypothesis.  Nb. This suggests we’ve a lot to learn still about the best way to engage Fcg receptors, a theme I introduced in the last post (link).

Where does this hypothesis leave everyone else in the TIGIT space?  Let’s line them up:


A few quick notes: EMD Serono/Merck KGaA and Innovent have anti-TIGIT programs without disclosed isotype information; Arcus has disclosed a second, Fc-competent, anti-TIGIT program (AB308); Agenus is developing both IgG1 and IgG4 anti-TIGIT antibodies.

A question: is Seagen’s hyper-killing IgG1 a step too far?

In summary, we have preliminary data in NSCLC that suggest that anti-TIGIT may synergize with anti-PD-1 or anti-PD-L1 therapies, consistent with the expression of TIGIT on PD-1 positive (ie. activated) T cells.  We have several hypotheses addressing the Fc-end of the therapeutics, and some information on why blocking TIGIT may enhance T cell responses.

Other than selecting patients with PD-L1-positive tumors, can we gate on TIGIT expression?  Apparently not, at least not in NSCLC, as just reported at the World Conference on Lung Cancer (abstract P77.02 – Efficacy of Tiragolumab + Atezolizumab in PD-L1 IHC and TIGIT Subgroups in the Phase II CITYSCAPE Study in First-Line NSCLC).

Here’s their text:

“Among the 135 enrolled patients with PD-L1-positive NSCLC (intent-to-treat [ITT] population), 113 had results from the SP263 assay and 105 had results from the TIGIT assay. The biomarker-evaluable populations (BEP) for both of these assays were similar to the ITT population. Comparable PFS improvement with tira + atezo relative to atezo monotherapy was seen in PD-L1–high (≥50% TC) subgroups defined by SP263 (PFS HR 0.23, 95% CI: 0.10–0.53) when compared with PD-L1-high subgroups defined by 22C3. However, for patients whose tumors were defined as TIGIT-high (≥5% IC), no strong association with PFS improvement was observed.

Biomarker subgroup Subgroup, n (BEP, N) PFS HR (CI) relative to atezo monotherapy arm
ITT (PD-L1 IHC 22C3 >1% TPS) 135 (135) 0.58 (0.39–0.88)
PD-L1 IHC 22C3 (≥50% TPS) 58 (135) 0.30* (0.15–0.61)
PD-L1 IHC SP263 (≥50% TC) 45 (113) 0.23* (0.10–0.53)
TIGIT IHC (≥5% IC) 49 (105) 0.62* (0.30–1.32)
*Unstratified HR

Prevalence of PD-L1 subgroups in the BEP was comparable with previous reports for both IHC assays. The PFS benefit observed with tira + atezo in patients with tumors defined as PD-L1-high by 22C3 was also observed using the SP263 IHC assay, but not in tumors classified as TIGIT-high using an exploratory TIGIT IHC assay. Our results suggest that PD-L1 expression, assessed by 22C3 or SP263, may be a biomarker for tira + atezo combination therapy in metastatic PD-L1-positive untreated NSCLC.”

So that the answer to the question we started with, can we pick patients, is ‘no’ for TIGIT expression, at least in this indication.

Regardless, to actually understand what blocking TIGIT does, we need to better understand the pathway.

That will be discussed in Part 2, coming soon.

Stay tuned.

A few things I Iearned in 2020: an immune-oncology perspective

Teaching immune cells how to kill, and other things I learned in 2020

Therapeutics and targets mentioned: 4-1BB, Bispecific-engagers, CAR-T, CD39/CD73/A2AR, CD47, FcαRI, FcγRIIa, Flt3L, GM-CSF, IL-2, Immune Checkpoints, LILBR2/ILT-4, OX40, PD-1, Siglec10/CD24, STING, TIGIT/DNAM-1, TIL, TLR7/8  & 9.

Companies mentioned: Agenus, Aleta Biotherapeutics, Alkermes, Alligator, Apexigen, AstraZeneca, Celldex, GSK, IgM Biosciences, I-Mab, Immune-Onc, Iovance, Jounce, Merck, Nektar, Seagen, Roche.

Two talks given at SITC 2019 session set me thinking about the quality of immune cell interactions, the outcomes for the interacting cells and the implications for cancer immunotherapy. These talks, by Ron Germain and Michael Dustin, presented the lives of immune cells in a series of diverse locations with a complex cast of characters.  Learnings regarding immune geography and cell:cell contact are increasingly important as we consider how best to advance cell therapies for diverse hematologic malignancies and solid tumors (

These investigators work to understand the cell biology that supports a productive immune encounter, and this depends in part on location as much as it does on cell type. The bio-pharma field has focused on T cells as the major target cell type for cancer immunotherapy, but it is clear that B cells, myeloid cells, dendritic cells, NK cells and neutrophils can play unique and critical roles.  Immunology insights gained in 2020 will influence how we think about immune-checkpoint therapeutics, cell therapeutics and tumor resistance to therapy.  Historically, we can link these lessons back to two of the very earliest “applied” immune-therapeutics, the cytokines IL-2 and GM-CSF, that trigger distinct subsets of immune cells.

Part 1: Location, location, location.

In January 2020 four papers were published that described the correlation between the presence of tertiary lymphoid organs and B cells with successful immune checkpoint therapy in diverse cancer indications (see here).  This was an interesting finding and one that I think remains under-appreciated by the immuno-oncology drug development field.

These papers raised an interesting question – why are tertiary lymphoid structures (TLS) and by extension, secondary lymphoid organs such as lymph nodes, spleen, and Peyers patches, important for successful immune checkpoint blockade therapy (ICB)?  Aren’t we just waking up exhausted T cells, or moving T cells from the tumor margin into the tumor bed?  Isn’t that how anti-PD-1/anti-PD-L1 antibodies work?  Why should you need a TLS or lymph node?

These questions compel us to once again deconstruct the tumor and its surroundings.  One might start with the immediate tumor microenvironment (TME) under direct control by tumor cells, stroma and stroma-embedded fibroblasts and myeloid cells.  A second view might consider the vascularized tumor bed, with access to blood vessels and lymphatics.  A third view: the invasive tumor margin, where tumor cells are invading normal tissue.  A fourth: sites within the tumor where immune cells are present, either active or immobilized.  Fifth: associated lymphoid tissues and organs.  And so on, although it won’t help to make things too complicated.  Not by coincidence the list overlaps with the phases of the tumor-immunity cycle (Chen & Mellman, 2013).

 As to why you need a TLS or lymph node, the answer probably lies in the quality of the T cell pool.  As we learned from the work of many labs (reviewed here: T cell exhaustion is a complex state, with subsets of cells having distinct functionality and fates.  Indeed, ‘exhaustion’ may be too broad a term.  For example, we know from Stephen Rosenberg’s work that TILs can be isolated from bulk tumor tissue, expanded using IL-2, and thereby “re-animated” ex vivo. Therefore, TILs are not always terminally exhausted.  Iovance has successfully exploited these findings and shown efficacy in late-stage clinical trials using patient-derived TILs to treat melanoma and cervical cancer.

These efforts can be traced back to the approval of high dose IL-2 for the treatment of renal cell carcinoma in 1992 and metastatic melanoma in 1998.  That 1992 date is notable, as IL-2 was discovered only 16 years earlier in Dr Robert Gallo’s lab (link).  Those approvals also are the basis of extensive efforts to produce less toxic variants of IL-2 by engineering selective IL-2 receptor engagement, as exemplified by the drug development work of Nektar, Alkermes, Roche and many others.  IL-2 is also used in the expansion of NK cells, indicating the pleiotropic activities of this cytokine.

Of note, TILs expanded in the presence of IL-2 can exhibit a differentiated phenotype that can shorten their long-term persistence and survival in vivo.  Recent analyses of successful TIL therapy have stressed the importance of a “stem-like” T cell population that has both proliferative and self-renewal capacity and fosters the development of long-lived memory T cells (Rosenberg lab: here).  I note in passing that their analyses suggest that strategies aimed at the CD39/CD73/A2AR pathway may have limited clinical impact.  A similar population of T cells has been associated with successful ICB therapy (discussed: link) and may play a role in productive CAR-T cell expansion.

A specific type of dendritic cell (DC) has been identified as a critical component of ICB therapy and this brings us back to lymph nodes and to TLS.  The cDC1 dendritic cell subset is implicated in the support of T cell mediated anti-tumor immunity (discussed by Gajewski & Cron here).  These are interesting cells that can be found in lymphoid organs, in inflamed tissues and within tumors.  Tumor antigens can make their way into lymphoid tissues by direct antigen drainage (review) with specific regions within lymph nodes supporting distinct DC populations and supporting distinct T cell responses (it turns out that B cells help with this spatial organization).  Tumor antigens can also be carried from the tumor into the lymph nodes by cDC1 themselves (link).  So now we have a narrative that accounts for the benefit of having lymphoid tissue in the context of anti-PD-1/PD-L1 therapy – this organized lymphoid tissue amplifies any existing anti-tumor response with a de novo response, sending additional T cell soldiers to the tumor front lines.

There are additional puzzles hidden within this narrative.  Possibly the one that bothers me the most is seeming failure of therapies that target T cell agonist pathways – notably 4-1BB and OX40 – to improve the response unleashed by ICB therapy.  Without burrowing deep into an immunology rabbit hole, I propose that anti-4-1BB and anti-OX40 agonist antibodies fail because they amplify signals in the wrong place or at the wrong time.  The immune system is tightly regulated and unkind to inappropriate signals.  Along these lines it is worth noting that completely blocking PD-1 will also backfire, as has been shown in disparate experimental systems (example).  This is translationally important, as PD-1-knockout CAR-T cells were eliminated in patients, either by active elimination or due to competitive disadvantage (paper, and presentation by Carl June, ASGCT 2020).  In contrast, signals that activate the DC compartment – GM-CSF, Flt3L and agonists that target CD40 (see Roche, Apexigen, Alligator, Seagen, Celldex and others) – do appear to augment anti-tumor immunity, and this may be the ideal way to think about boosting ICB therapies and perhaps CAR T cell therapies (hint).  A historical note: GM-CSF expression is a critical component of the T-VEC oncolytic viral therapy approved in 2015, just about 20 years after the first amino acid sequence data became available from the labs of Metcalf, Burgess, Dunn and colleagues during 1984-5 (here is a history by Glenn Dranoff).

Part 2: Knocking on other doors.

If location is critical, perhaps it’s time to move back to the TME.  I’ve thought for a long time that some TME-directed efforts are misguided.  I suspect several cell types commonly associated with the TME are epiphenomena that perhaps amplify, but do not create, the immunosuppressive microenvironment.  T-regulatory cells (T-regs) are one such cell type, and suppressive myeloid cells may be another.  The immuno-oncology drug development field has, to date, fallen short in attempts to deplete or alter these cell types for clinical benefit.

This should be surprising since T-regs and myeloid suppressor cells are abundant in TMEs across indications, but I would argue that tumor cells themselves and associated cell types in the tumor stroma, notably fibroblasts, are dominant.  ICB resistance signatures include VEGF, beta-catenin and TGF-beta – these factors appear to create the immunosuppressive milieu and subvert incoming immune cells.  Depleting T-regs or attempting to convert immunosuppressive myeloid cells (eg. ‘M2s’) to pro-inflammatory myeloid cells (eg. ‘M1s’) does not address the underlying immunosuppressive TME, which has arisen as a result of selective pressure on the tumor cell population.  I’ve discussed ICB resistance previously (see here and here).

However, the immunosuppressive TME and its attendant cell types can be upended, most notably by triggering evolutionarily ancient pathways that trump the immunosuppressive signals.  Many of these pathways are well known – the TLR7/8 and TLR9 agonists, the STING agonists, and the CD47 pathway inhibitors being prosecuted by many companies (see eg. AstraZeneca’s MEDI9197, a TLR7/8 agonist, Glaxo’s GSK3745417 STING agonist, I-Mab’s CD47 program, among many others).  Of note, localization of agonist signaling is critical in this space as well.  For example, TLR signaling is generally targeted at tumor cells directly, whereas it is debated whether STING agonists should target myeloid-lineage cells within the TME, tumor cells themselves, or both.

I particularly like the idea of engineering CD47 antagonism into other modalities, eg. T cell engagers.  Indeed, blocking CD47 to induce myeloid cell phagocytic activity is an active field, and this has encouraged a search for similar signals, for example, the Siglec10/CD24 pathway.  Moving even further afield we encounter quite novel myeloid cell signals and can consider pathways that are not as widely targeted.  One is the ILT (aka LILBR) system, where most activity is centered on antibodies to ILT2 and ILT4.  Here we begin to intersect with multiple cell types, as ILT2 is expressed by monocytes, macrophages, DC, B cells, and subsets of T cells and NK cells, and ILT4 is expressed by neutrophils, myeloid cells and DCs. These proteins have inhibitory signaling domains that are triggered by MHC binding, including to the HLA-G protein, normally expressed on myeloid lineage antigen-presenting cells (macrophages, DCs) where expression serves to immune-suppress interacting cells.  HLA-G is also overexpressed on many tumor cell types.  Thus, the ILT/HLA-G system appears to be another immune checkpoint, perhaps with a broader range of activity than the PD-1 system.  Merck has shown early positive clinical data using an antagonist anti-ILT4 antibody, MK-4830 (from Agenus), in combination with pembrolizumab (anti-PD-1) in heavily pretreated cancer patients (presented at ESMO 2020).  Jounce Therapeutics and Immune-Onc showed preclinical data at SITC 2020 on their anti-LILBR2 (ILT-4) programs, and there are additional efforts underway.  I suspect this field will grow quickly, and perhaps match the TIGIT/DNAM-1 space in interest and complexity.

Part 3. Fc-hacking immune responses.

As mentioned above, the immune system has strict rules and regulations, and can be resistant to having these over-ridden by therapeutics.  Hacks are possible of course, as shown by the success of CAR-T cells and the T-cell engager bispecifics.  Along these lines, decades of work on the Fc-domains of antibodies has allowed fine tuning of biologic therapies.  We are all familiar with optimization of ADCC and CDC activity (up or down), but more recent advances are less widely known.  I want to explore two examples – one will bring us back to LN and cDC1 activation, the other will advance the discussion on myeloid cell activation and will introduce the interaction of myeloid cells and neutrophils as a novel component of the anti-cancer immune response.

Jeffrey Ravitch’s lab recently published a method for Fc engineering of IgG antibodies for selective high-affinity binding to the activating Fcγ receptor FcγRIIa (paper).  In a viral respiratory model (in mice having human FcγRs) this Fc-hack resulted in an enhanced ability to prevent or treat lethal viral respiratory infection, with increased maturation of dendritic cells and the induction of anti-viral CD8+ T cell responses. Specifically, they noted up-regulation of CD40 expression in the cDC1 subset—the dendritic cell population specialized for cross-presentation and CD8 T cell stimulation in the lung virus model, and the very same DC subset we discussed earlier in the context of TLS and LN-mediated anti-tumor responses.  Just to close the circle, Fumito Ito and colleagues used irradiation, Flt3L, TLR and CD40 stimulation to demonstrate cDC1 induction of stem-cell line CD8+ T cells in a variety of murine tumor models (linked here).  It follows that engineering antibodies with the selectivity demonstrated in the Ravetch paper will find utility in the anti-tumor field.

I started off by referencing presentations from Ron Germain and Michael Dustin at SITC 2019, over a year ago.  Dr Germain presented a story that really struck a chord for me (see Uderhardt et al. 2019).  In tissue injury and pathogen infection models, neutrophils comprise the first line of defense, as innate immune signals cause them to swarm at the affected site. Early infiltrating neutrophils undergo activation induced cell death, which can drastically amplify the response and potentially cause tissue damage. In order to terminate this potent immune response tissue-resident macrophages rapidly sense neutrophil activity and cell death and extend membrane processes to limit the damage.  This ‘‘cloaking’’ mechanism thus limits neutrophil activation.  Of note, neutrophils can be abundant in tumors where they have been linked to diverse activities ranging from potent anti-tumor immunity to immune-suppression.  Neutrophils, like myeloid cells and NK cells, can be hacked using Fc-receptor engagement.  Neutrophils express FcγRIIA, just discussed in the context of cDC1 activation, and therefore it will be interesting to examine the activation of these (and other the FcγRIIA-expressing cells) in the context of IgG Fc-engineering.  Neutrophils and myeloid cells also express FcαRI, a very interesting receptor that when engaged by IgA-isotype antibodies triggers targeted cell killing.  Neutrophils will engage in phagocytosis, degranulation and reactive oxygen production to mediate killing after FcαRI engagement, while myeloid cells will be triggered to engulf targeted cells. The specific responses induced depend on the valency of IgA (monomeric, dimeric, aggregated) but it seems likely that the Fc-domain can be hacked in order to optimize productive engagement.  With a recent spotlight shown on IgM as an Fc-engaging platform (see IgM Biosciences) we can anticipate accelerated drug development across all of these diverse Ig-classes.

To wrap up – as we move forward in the related disciplines of immuno-oncology and cell therapy, we should consider these principles:  optimizing T cell/DC interactions, localizing immune checkpoint therapy to lymphoid tissues, and engaging additional cells to bring the full power of the immune system to the anti-tumor battle.

Stay tuned.

T cell fitness and genetic engineering

This is a subject we have been thinking about in great detail and this publication in Cell was a trigger for me to start organizing those thoughts. Here is the full reference to the paper discussed: In press, Roth et al., Pooled Knockin Targeting for Genome Engineering of Cellular Immunotherapies, Cell (2020).

My thanks to Mark Paris from Daiichi Sankyo for his tip to read this paper.

Screen Shot 2020-05-04 at 9.01.39 AM

This publication ( is by Theodore Roth and colleagues from Alexander Marson’s lab at UCSF.  They present a nice technological advance, the development of a process by which a pool of genes are knocked into a locus, allowing one to examine the consequence of altering the responsiveness of a cell, in this case, a T cell. This type of work springs from a long lineage of genetic manipulation strategies, from random mutagenesis, to random then targeted gene knockouts (in cells and animals) and gene knockins (what we once called transgenics) and elegant gene-editing technologies (gene therapy, CRISPR/Cas-9, cell therapy, gene-delivery) and so on.

The focus in this paper is on optimizing T cell activity in the setting of solid tumors, something we think about every waking hour at Aleta Biotherapeutics ( So, let’s see what we’ve got here.

The pooled knockin strategy relies on two key elements – DNA barcoding, a well-developed technology that has its roots in high throughput library screening technologies, and locus targeting via HDR, which can be achieved using CRISPR/Cas9 and guide templates. Put these two things together and you now have the ability to mix and match genes of interest (following these via their specific barcodes) and place then into the desired locus – here that locus is the TRAC (the TCR locus). They also knocked in a defined TCR (for NY-ESO-1). So, this is a nice system with a known TCR and various immune modifications. There are some limitations. Only 2000-3000 base pairs will fit into the targeting vector (here using a non-viral method). It appears that only a fraction of the targeted T cells are functionally transfected (around 15% per Figure C and note that not every knocked-in cell has both the TCR and the extra gene). The expression level in primary human T cells is high, but I’m guessing expression is of limited duration (although at least 10 days, Figure S5). This is used here as a screening tool, where the goal is to identify critical pathways that reduce or enhance T cell activities (proliferation, effector function, release from immunosuppression).

The authors used a pooling approach to introduce one or two coding sequences from a short list of proteins implicated in T cell biology. Some sequences were modified to be dominant-negative or to be “switch receptors”, where the extracellular domain of the receptor is coupled to a T cell-relevant signaling component (eg. FAS-CD28, TGFβRII-4-1BB). Here are the components they used for their library:

Screen Shot 2020-05-04 at 9.05.55 AM

As we can see from the list there are interesting immune checkpoints, death receptors, cytokine receptors and signaling components that can be mixed and matched. The pool is made and transfected into primary T cells that are then put under selective pressure. The T cells that are enriched under that selective pressure are then analyzed by barcode sequencing to see who the “winners” are, as shown in this schematic from Figure 1A:

Screen Shot 2020-05-04 at 9.30.06 AM

The first screen was simple TCR stimulation (anti-CD3/anti-CD28) which rather robustly showed that a FAS truncation allowed for better cell proliferation (Figure 3B in the paper). This is an expected result – activated T cells undergo FAS-mediated cell death (activation-induced cell death, AICD) that is triggered by FAS-ligand expression, ie. activated T cells kill each other using this pathway. Since there are only T cells in this TCR stimulation culture a lot of other pathways are rendered irrelevant and therefore don’t appear (PD-1 for example):

Screen Shot 2020-05-04 at 9.32.16 AM

The key data are on the far right, showing a 2-4 fold increase in T cell number relative to input. The knockins in light blue showed a statistically meaningful increase vs. input number, across 4 different donor T cells (each circle is a different donor).

The second selective pressure was to stimulate the T cells in the presence of soluble TGFβ (see Figure 3D). As one might guess, the TGFβRII dominant-negative (dn) and switch receptors now come into play: TGFβRII-MyD88, TGFβRII-4-1BB, TGFβRII-dn. The FAS-dn and switch receptors are also represented as are two T cell proliferative components: the IL2RA and TCF-7 (aka TCF-1). These latter hits suggest that amping up T cell proliferation can allow the pool to outrun TGFβ-mediated immunosuppression, at least in vitro. Again, refer to Figure 3D in the paper for the results.

Several other selective pressures were applied in vitro, including tumor cytotoxicity using the NY-ESO-expressing melanoma cell line A375. Of more interest, the A375 cell line was used to establish a xenograft tumor in immunodeficient NSG mice, and the knockin pools of transfected T cells were injected into the mice after the tumor had established. A technical note here – 10 million T cells were injected, of which approximately 1 million were transfected – and 5 days later the tumors were removed and the TIL (tumor-infiltrating lymphocytes) were isolated by screening for the TCR. Bar-code analysis of the TCR-positive TIL allowed the team to identify which transfected T cells got in and expanded. This is tricky, because you’ve allowed time for extensive proliferation (so T cells that are dividing quickly will dominate) and you don’t know what you lost when the T cell pool encountered NY-ESO-positive tumor cells (did some die or did some traffic out of the tumor?). We should expect these data to be noisy and they are, but clear “winners” emerge, namely the TCF-7 transfectants, the TGFβRII-dn, and TGFβRII switch receptors with 4-1BB and also with the TLR signaling component MyD88. Since A375 melanoma cells do not make TGFβ (as far as I know) we have to assume that the T cells themselves are making this, and this is the TGFβ that is triggering these potent (NF-κB triggering) signaling components.

The TGFβRII-dn and switch receptors supported increased IL-2 and IFNγ production – note that IFNγ should have induced PD-L1 on the melanoma cells, but none of the PD-1 based cassettes had any notable effect (from Figure 6B):

Screen Shot 2020-05-04 at 9.32.16 AM

As with the PD-1 pathway, neither the FAS switch receptors nor the FAS-dn construct seemed to play a role in this setting. It’s not clear if FAS-L was upregulated in the tumor model, so that might explain the result.

There was a stark difference in T cell phenotype induced by TCF-7 versus the TGFβRII synthetic constructs. They are in fact polar opposites in some ways (CCR7 expression, Granzyme B expression, IFNγ expression – see Figure 6 E in the paper). Finally, the authors made a bona fide, polycistronic, TCR construct expressing the TGFβRII-4-1BB cassette or the TCF-7 sequence, used this to transduce donor T cells and then tested these for anti-tumor efficacy in vivo (Figure 7). T cells expressing the NY-ESO TCR and the TGFβRII-4-1BB cassette were able to clear the tumor completely. So that’s a very nice result.

Let’s put this into broader context. The table below is a small representation of the literature on genes associated with T cell anti-tumor responses, presented in no particular order. In the left column is the technology used to do the work, then the target, the result, the DOI if you want to read more and then some notes where applicable. I left off a lot of papers, my apologies to those labs.

Technology Target Result notes Reference
dominant-negative transgene FAS increased T cell persistence  and anti-tumor activity 10.1172/JCI121491
transgene overexpression c-Jun reversed tonic-signal induced exhaustion in T cells AP-1 driven 10.1038/s41586-019-1805-z
knockout  Reginase-1 increased T cell persistence, fitness, and anti-tumor activity > Batf and < PTPN2, SOCS1 10.1038/s41586-019-1821-z
knockout PTPN2 increased Lck, STAT5 signaling, and anti-tumor responses multiple papers 10.15252/embj.2019103637
disruption by random integration TET-2 improved CAR-CD19 clinical outcome   10.1172/JCI130144
CRISPR screen (CD8) Dhx37 increased tumor infiltration and effector function multiple papers 10.1016/j.cell.2019.07.044
dominant-negative transgene TGFβRII increased T cell proliferation, effector function, persistence, and anti-tumor activity multiple papers 10.1016/j.ymthe.2018.05.003
integration site association TGFβRII associated with positive clinical outcomes many other sites also identified 10.1172/JCI130144
pooled shRNA screen PP2r2d increased TCR activation, cytokine secretion, T cell trafficking into tumor   10.1038/nature12988
knockout NR4a complex increased CD8 effector T cell function and solid tumor control linked to Nf-kB, AP-1 activity, multiple papers 10.1038/s41586-019-0985-x
T cell profiling Tcf1/TCF-7 increased T cell stemness and anti-tumor activity (with anti-PD-1) multiple papers 10.1016/j.immuni.2018.11.014

I won’t go through all these but there are a few things to note here. One is the appearance of the three pathways we just discussed in the context of the pooled KI paper: FAS, TGFβRII and TCF-7. As mentioned earlier the FAS/FAS-L connection to AICD has been known for a long time, and that information has already been exploited in the context of CAR T cell engineering. Elaboration of the roles of TGFβ in mediating tumor resistance to immune therapy is a more recent advance, but now well established. As noted above I think one interesting question raised by this paper is the source of the TGFβ in the in vitro and in vivo tumor models. I’ve assumed this is T cell derived and understanding the trigger for TGFβ activation in these settings would be very interesting. The role of Tcf1 (aka TCF-7) in anti-tumor immunity has recently been explored in detail in the context of T cell “stemness” leading to the hypothesis that anti-PD-(L)-1 therapeutics work by releasing these T cell with stem-like properties, and allowing their maturity into effector T cell populations (see 10.1016/j.immuni.2018.12.021 and 10.1016/j.immuni.2018.11.014 for examples). It seems that in this knockin, enforcing TCF-7 (Tcf1) expression locked the T cells into a sort of limbo, proliferating, homing into the tumor, but failing to mature into effector cells with anti-tumor functions. A very interesting result. Development of a model in which canonical PD-1/PD-L1 immunosuppressive biology could be examined in order to probe for synergies would be a welcome next step.

Finally, word or two about some of the other targets. As shown in the paper, and as recently shown in the clinical setting (10.1126/science.aba7365), knockins are, at this time, an imperfect tool. Some of the targets listed in the table are associated with autoimmunity (eg. PTPN2) or T cell leukemia (eg. c-Jun, NR4a) and so care is needed when exploiting these targets. Safely engineering specific targets for improved cellular therapeutics will be an important advance on the road to durable and curative solid tumor therapy.

Stay tuned.

The next great drug hunt, part 2: WHAT RECENT PAPERS TELLS US ABOUT TGF-beta-MEDIATED IMMUNOSUPPRESSION (and what they don’t)

Back in 2017 I put together a presentation I informally called “The Big Stick Talk” for a series of local immuno-oncology and investor conferences, including at one of my favorite venues, the Jefferies IO conference hosted by Birin Amin which is always a great event. You can find the slides here (IO combos). The premise of the presentation was that resistance to immunotherapeutics (anti-PD-1, anti-PD-L1, anti-CTLA4) was driven by pathways that controlled complex biologies – TGF-β, beta-catenin and VEGF – and that other targets (eg. TIM-3, LAG-3, IDO, etc) were secondary features. It followed that drug development targeting the secondary phenomena were likely doomed to failure – and here we are now, 4 years later, with our focus back on those big biological pathways.

Today: the TGF-β story revisited.

As noted in the prior post (link), there are three distinct isoforms of TGF-β, and all three signal through the TGF receptor complex. All isoforms are expressed in an inactive form, bound as prodomains to the latency-associated peptide (LAP). As discussed last time, one way that active TGF-β isoforms 1 and 3 can be released or exposed to the receptor complex is via the action of specific alpha-v integrins that bind to an amino acid consensus sequence (RGD) on LAP and induce a conformational change when the integrin is activated.

The latent TGF-β complex (TGF-β/LAP) becomes more complicated with the addition of proteins that can covalently bind to the latent complex. This even larger latent complex (LLC) comes in a number of different forms depending on the identity of the covalently bound protein. Two proteins (LTBP1 and LTBP3) are used when the LLC is bound to the extracellular matrix (ECM): imagine a cell secreting and placing a protein complex, just so, onto ECM (collagen, fibrinogen, GAGs, elastin et al). Of course the ECM is also created by cells. Two different proteins, GARP (LRRC32) and LRRC33, hold the LLC on the surface of specific immune cells: regulatory T cells and differentiated myeloid cells.

That’s just by way of introducing this complex biology.

Of the three isoforms, isoform 2 is most often cited as the cause of the toxicity seen when pan-TGF-β inhibitors were used clinically, and drug development efforts avoid this isoform. The new paper from the team at Scholar Rock (link to paper) focuses on a specific isoform, TGF-β1, thereby avoiding β-2 altogether but also not targeting β-3. The rationale is that β-1 is the critical isoform in most cancers and is the specific cause of TGF-β-mediated immunosuppression in those cancers. In an analysis of mRNA expression data from the TCGA database, the authors found that TGF-β1 was the predominant isoform in most human cancers and further that β1 expression correlated with a gene signature of resistance to checkpoint inhibition (called IPRES) in 7 different cancer types for which immune checkpoint inhibitors have been approved.

The authors present an antibody, SRK-181, that binds to the TGF-β/LAP complex and prevents TGF-β from becoming activated. Remarkably, the antibody was screened so that it would block alpha-v integrin mediated activation of the latent complex in all 4 LLC contexts (ie. complexed with LTBP1, LTBP3, GARP or LLRC33). To be clear, the antibody only binds the complex, not to free TGF-β1. The binding potency is very good, in the low pM range. The activity of the antibody was assessed in cellular assays. The cell-based assay is very clever and deserves some explanation. Using a LN229 glioblastoma cell line that endogenously express LTBP1 and LTBP3 they transfected in TGF-β1 or TGF-β3-encoding plasmids. This allows these cells to now make LLCs that can be deposited on ECM. In order to also make LLCs that can stay on the cell surface they then co-transfected in either GARP or LLRC33 expression plasmids. Now the cells could be used to make all 4 of the LLCs.

Now, here it gets fun. As detailed in the last post, αvβ8 is an integrin that can regulate the release of TGF-β from the latent state. In the prior paper (found here), it was demonstrated that when “sprung” from a GARP-containing LLC, the TGF-β was not released from the cell surface but remained tethered to the complex in such a way as to be able to activate TGF-β-receptors on a second cell (remember, that was a two-cell assay). So, LN229 cells express the αvβ8 integrin and can activate latent TGF-β1 complexes. Therefore once the LLCs are expressed, either into the extracellular matrix or on the cell surface, they can be activated by endogenous αvβ8 integrins expressed in cis (ie. on the same cell).

To measure the activity of released TGF-β1 the transfected LN229 cells were co-cultured with a reporter cell that expresses luciferase then the TGF-β-receptor complex is activated. In this assay the antibody inhibited TGF-β1 release from all four LLCs and blocked activation of the reporter cells that express TGF-β-receptors. An interesting question to ask here is whether the TGF-β1 was actually released and was in solution or remained tethered, and whether this was different for each of the LLCs made. A simple bilayer culture system in which the reporter cells are physically separated from the LN229 cells would answer this question.

Ok but that’s enough on the assay – the biology is also very interesting. I want to focus just on the tumor microenvironment findings, although the in vitro assays and the in vivo tumor growth data are also of interest. The key finding is that the combination of an anti-PD-1 antibody and the anti-TGF-β/LAP complex antibody improved tumor control by a mechanism that includes an increase of the tumor by CD8-positive T cells. In contrast to the data reported by the Genentech group ( this was not obviously due to redistribution of the T cells from an “immune exclusion” zone, set up by cancer-associated fibroblasts and attendant collagen matrix. Rather the effect was associated with a decrease in myeloid lineage cells and an influx of T cells from the vasculature. So, not surprisingly, there may be two different TGF-β-dependent biologies at work in the two tumor models used (MBT-2 tumors in this study and EMT-6 tumors in the Genentech study). One reasonable explanation is the difference in TGF-β isoform expression between the two models. MBT-2 tumors only express isoform β-1 whereas EMT-6 tumor express both isoforms 1 and 3. Somewhat confusing though is the finding reported here that in their hands, the Scholar Rock group found a similar result (decrease of myeloid suppression and influx of T cell across the vasculature) using EMT-6 tumors as they did using MBT-2 tumors (shown in the supplemental data). In the Genentech paper an impact on a myeloid cell signature was not detected while fibroblast genes associated with matrix remodeling were significantly reduced. Importantly, in the Genentech study a pan-anti-TGF-β antibody was used, that would be able to block both isoforms. Whether there is differential contribution of isoforms 1 and 3 to the immunosuppressive biology seen in these syngeneic models is not known. The authors do note that the MBT-2 and EMT-6 tumors used expressed specific LLCs, with LRRC33 and LTBP1 being the most highly expressed. However, whether these different LLCs contain different TGF-β isoforms in various cells types within EMT-6 tumors (eg. fibroblasts, myeloid cells and endothelium) is not described. Since GARP does not appear to be present in these tumor types, a role for TGF-β-1 expressed by Tregs is ruled out. Clearly there is more to learn from these models and their translatability to the human tumor setting.

There is emerging clinical evidence of the relevance of the TGF-β pathway to cancer therapy. Preliminary clinical data from studies using M7824, an anti-PD-1-TGF-βR2-TRAP protein, reported responses across several indications including a complete response in cervical cancer and partial responses in pancreatic and anal cancers (link) An expansion cohort study of patients with advanced NSCLC (n = 80) treated with M7824 in the second-line setting showed an objective response rate of 86% in the subgroup with high PD-L1 tumor expression (link 2). In an expansion cohort of 30 patients with pretreated advanced biliary tract carcinomas, M7824 monotherapy demonstrated a 23.3% response rate, with some durable responses (link 3). This TRAP protein is stated to be selective for the blockade of TGF-β1 and TGF-β3 isoforms and of course also blocks the PD-1 pathway. In the cell therapy space, encoded inhibition of TGF-β (eg. by using a dominant-negative TGF-β-R on the CAR T cell) is one of a variety of methods being explored to increase the efficacy of CAR T cells in solid tumor therapy. Our publication (here) provides much more discussion regarding strategies for successful solid tumor cell therapy.

In summary the regulation of TGF-β activity remains a fascinating area for drug development. If we see benefits in line with those achieved in some cancers by combining anti-PD-1 therapy with anti-VEGF therapy, we will indeed have picked up another big stick, and learned how to use it.

Stay tuned.

THE NEXT GREAT DRUG HUNT: Integrins, TGF-beta and Drug Development in Oncology and Fibrosis

PART 1: Integrin αvβ8

Advances in our understanding of the regulation and function of TGF-β is driving novel drug development for the treatment of diverse diseases. This is a field I’ve followed for a long time and of course in the development of cell therapeutics we ( always have an eye on immunosuppressive pathways – indeed, the immunotherapy and cell therapy fields cross-fertilize often and productively (see

Several new papers in this space have caught my eye and I’m keen to share some key findings. This will be a multi-part post and today I want to talk about an integrin.

Long time readers will appreciate the importance of alpha v-integrin-mediated regulation of TGF-β release from the latent complex ( The model that first emerged around 2010 was elegant: various signaling pathways triggered GPCRs that could activate an integrin beta strand (paired with an alpha v integrin) and coordinate the release of TGF-β from the cell surface. Soluble TGF-β, free from restraint, could diffuse across nearby cells and trigger TGF-β-receptor activation. Three integrins have been linked to the regulation of TGF-β release: αvβ6, αvβ8 and αvβ3. The mechanism for releasing TGF-β from the latent complex on the cell surface requires a conformation change in the integrin structure. From this insight emerged diverse drug development efforts targeting specific integrins, targeting the ligands for specific GPCRs and so on. Notable examples include the anti-αvβ6 antibody STX-100 (Biogen), the autotaxin inhibitor GLPG1690 (Galapagos), small molecule inhibitors designed to block integrin conformational change, and isoform-specific anti-TGF-β biologics, among many others. The mechanism of action of these drugs includes reduction of free, active TGF-β and therefore reduced TGF-β-receptor signaling. STX-100 was withdrawn from clinical development due to toxicity – more on this another time. GLPG1690 is now in a Phase III trial (in IPF) having shown anti-fibrotic activity in earlier clinical trials – this drug has had an interesting life, originally partnered by Galapagos with Johnson & Johnson, later returned, and now part of the mega-partnership with Gilead. I’ve previously discussed these and many other drugs in development in the context of fibrosis pathogenesis ( We’ll look at novel TGF-β-directed antagonists and their role in immune-oncology in part 2, as part of a long-running thread (

So back to integrins. The dogma that emerged based on work from disparate labs was that an activated integrin was required to release TGF-β from the latent (inactive) complex on cell surfaces, allowing for precise regulation of TGF-β activity. More specifically, this model refers to the release of two of the three isoforms of TGF-β – isoforms 1 and 3. Isoform 2 regulation is different and relies on physical force acting directly on cells to trigger release. Of note, isoform 2 antagonism contributes to the toxicity associated with pan-TGF-β blockade but does not appear to contribute significantly to disease pathology either in fibrosis or in oncology. Therefore, specifically antagonizing TGF-β-1/3 without antagonizing TGF-β-2 is ideal – and the model we’ve just outlined allows for this specificity by targeting specific integrins.

The model that alpha v integrins mediated release of free, active TGF-β has held firm for nearly a decade. Now however there is a fascinating update to this model that involves the αvβ8 integrin. Work from the labs of Yifan Cheng and Steve Nishimura at UCSF has revealed a novel mechanism of TGF-β regulation that has interesting implications for drug development ( Uniquely, integrin αvβ8 lacks critical intracellular binding domains that allow an integrin to anchor to actin fibers within the cell. As a result, binding to αvβ8 does not cause the release of TGF-β from the latent complex on the cell surface but rather presents an active form of TGF-β on that cell surface, without release from the latent complex. Importantly the complex formed between αvβ8 and TGF-β is conformationally stable and relies (in their experimental system) on trans-interaction between one cell expressing αvβ8 and a second cell expressing TGF-β as displayed on a latent protein complex (here, containing the GARP protein), and expressing the TGF-β receptors. In this system TGF-β remains anchored to the GARP-complex, but the conformational rotation caused by αvβ8 binding allows anchored TGF-βto interact with TGF-β-RII, thereby recruiting TGF-β-RI and inducing signaling.

The focus on GARP (aka LRRC32) relates to this groups long-standing interest with T-regulatory cells, which uniquely express GARP. Biotech investors will recall the Abbvie/Argenx deal on this target, which is in clinical development ( A related protein called LRRC33 has been discovered on myeloid lineage cells.

More important, in my view, is that αvβ8 is expressed widely on tumor cells and has been variably reported to correlate with metastases (depending on the indication). This suggests that one means that tumor cells have of inducing TGF-β activation on interacting cells (eg. lymphocytes, myeloid cells and perhaps stromal cells) is via αvβ8 activity. The dependent hypothesis would be that such activation is immunosuppressive for those tumor-interacting cells. This is consistent with the known effects of TGF-β on immune cells in particular, but also stromal cells like fibroblasts. As an aside I like this model as one way of accounting for the appearance of T-regulatory cells and myeloid lineage suppressor cells in the tumor microenvironment as result of, rather than the cause of, immunosuppression, that is, these cells may be epi-phenomena of broad TGF-β-mediated immunosuppression. This may in turn explain why targeting such cells as T-regs and MDSCs has been largely unsuccessful to date as a therapeutic strategy for cancer.

There are some other implications. As the authors point put, the integrin/TGF-β complex is stable, and the binding domain that mediates the interaction is buried with the protein complex. It is unclear whether anti-TGF-β antagonists that target the canonical integrin binding cleft would be able to access this site within the complex. It’s possible that some of these drugs (whether antibodies or small molecules) can’t work in this setting. On the other hand, antibodies to αvβ8 clearly prevent the complex from forming and should block TGF-β-mediated immunosuppressive signaling in settings where αvβ8 expression is dominant. An anti-αvβ8 antibody strategy is being pursued by Venn Therapeutics (disclosure: I sit on Venn’s SAB). Further, the structural features identified in the paper include well-defined pockets that might be suitable for small molecule drugs. Indeed, one of the structural features in the b8 protein, consisting of hydrophobic residues, appears to account for the differential binding of various integrins (β6, β1, β2, β4, β7) to TGF-β, a remarkable finding. Analyses of the differences between the structure of β8 and other β integrins has been extensive across laboratories (see for another important paper). Small molecule drug discovery is well underway in this field (see for example Pliant Therapeutics and Morphic Therapeutics) and one might imagine that these novel results found an interested audience in many bio-pharma labs.

Next: what has Scholar Rock been up to, and what can we learn from their work?

Stay tuned.

Updates from #CowenHealthCare 2020 – CAR T and it’s competitors

If you work in cell therapy you have follow all kinds of therapeutic developments in indications of interest which for us at Aleta Biotherapeutics ( includes specific solid tumor indications, and several hematologic malignancies.

Over the last few days we’ve gotten interesting updates regarding diverse hematologic malignancies, including news about therapeutics for front line (newly treated) or relapsed or refractory (r/r) Non-Hodgkin Lymphoma (NHL), multiple myeloma (MM) and acute myeloid leukemia (AML) patients and the myelodysplastic syndromes (MDS). Note here that the reference to different lines of therapy – front line or early line vs r/r, because the treatment paradigms change as patients fail earlier lines of therapy, ie. as they become refractory to or relapse from their current therapy. This can become a long and arduous battle for patients who repeatedly fail treatment. Unfortunately, this is often the case in r/r MM, r/r AML and MDS and in some subtypes of r/r NHL.

On Monday (2 March 2020) I attended the “Cell Therapy & Myeloma” panel at the 40th Annual Cowen Heath Care Conference. This panel covered much more than the title implies, and I really liked the format which is built on the back of questions posed to the audience, to an (unnamed) group of specialists in the field who were polled in advance, and to the seated key opinion leaders (KOLs), in this case Dr Deepu Madduri (Mt Sinai) and Dr Jacob Soumerai (MGH).

They covered a lot of ground.

The first series of questions sought to pin down trends in r/r Follicular lymphoma (FL) a subtype of NHL that can become difficult to treat if patients fail successive lines of treatment. The Leukemia Lymphoma Society has a primer here –  There has been brisk drug development in r/r NHL including FL. Novel drug classes include CAR-CD19 T cells, bispecific T cell engagers, small molecule drugs (targeting PI3K, Bcl2, BTK, EZH2) and new antibodies. The Cowen panel worked through a series of questions regarding this landscape and there were several key takeaways.

One was the clear preference, by the anonymously polled specialists and by the seated panelists, for CAR-CD19 therapy as the most exciting new drug for r/r FL. The driver here is the durability of response (DOR) in really late line patients and the sense that both overall response rate (ORR) and DOR will only improve as these cell therapeutics move to earlier lines of therapy. It was striking that several classes of bispecific antibodies (the CD3 x CD20 and CD3 x CD19 bispecifics) elicited strong enthusiasm from the audience (mostly analysts and investors) but only muted enthusiasm from the KOLs. This lack of enthusiasm had 2 distinct bases: 1) limited data to date, and 2) “I can give a bispecific after I give a CAR T, but not the other way around”, which was a very interesting thought (and given despite of the few case reports of CD3 x CD20 bispecific therapy working in several relapsed CAR-T patients). I think that in later line patients these clinicians want to keep their options open as long as possible.

Among the other classes of therapeutics, Epizyme’s EZH2 inhibitor tazemetostat received significant support based on the ability to select EZH2-mutated patients, and on good DOR and on good tolerability, the latter thought to be better than the PI3Kdelta class inhibitors, BTK inhibitors or BCl2 inhibition. The consensus was that tazemetostat could see up to 20% market penetration in third line FL after the expected launch in June 2020.

Among the PI3Kdelta inhibitors, Bayer’s copanlisib was singled out as best-in-class with little differentiation among the others (from Gilead, Verastem, MEI, Incyte, or TG Therapeutics). Finally, in this setting of r/r FL, both venetoclax (a Bcl2-inhibitor) and polatuzumab vedotin (a CD79b antibody-drug conjugate), were relegated to minor use by the specialists and panelists.

The uptake of CAR-CD19 therapies has been brisk, and the panelists highlighted quicker payor approvals and the accelerating pace of referrals to cell therapy centers. The consensus is for 30% increase in patient number treated in 2020 (so roughly 1350 patients in the US, vs 1050 treated last year).

The discussion stayed on CAR-CD19 therapeutics to touch on some of the newer trials and entrants. Kite/Gilead is running a Phase III trial of axi-cel (axicabtagene ciloleucel, brand name Yescarta) in second line DLBCL patients vs a standard of care regimen of high dose chemotherapy followed by an autologous stem cell transplant. Data are anticipated in the second half of 2020. The Cowen moderators passed the question: will this trial show a progression free survival (PSF) benefit?  Mind you, this is a low bar since overall survival – the shining triumph of cell therapy – is not part of the question. The audience (again, mainly investors and analysts) was overwhelming positive, giving about 70% odds of a positive impact on PFS. Here the panelists agreed, citing the fact that this trial was enrolling high-risk patients and therefore the comparator arm of the trial (chemo + ASCT) should do very poorly. Success with this trial would move axi-cel up a line of therapy (from 3rd or later to 2nd or later) and bolster the health care value argument that patients may avoid ASCT altogether.  We are apparently already seeing this effect, as a talk at #TCMT20 highlighted the steep decline in transplants being done in DLBCL.

Sticking with axi-cel, this CAR-CD19 cell therapy was highlighted as the one most likely to be the market leader by 2023, based on the (currently) much shorter manufacturing and turnaround time as compared to tisa-cel (tisagenlecleucel from Novartis, brand name Kymriah). The panelists agreed with the specialist poll, despite the fact that they also felt that tisa-cel may be better tolerated by patients overall. Further, the panelists did note that the difference in manufacturing turnaround was likely to diminish as Novartis improves its product workflow. So we’ll have to wait and see.

Competition may also play a role.  The long-awaited Juno > Celgene > BristolMyers Squibb CAR-CD19 liso-cel (lisocabtagene maraleucel) should see its first approval soon, and several allogeneic and non-T cell based programs are advancing. Cowen’s moderators highlighted a number of these for discussion. Allogene CAR-CD19, called ALLO-501 is currently in a Phase 1 trial enrolling r/r diffuse large B cell lymphoma (DLBCL) and r/r FL patients, with initial data expected later this year. The moderators put forward the question: what percent of (responding) patients have to show a durable response for this to be an exciting option to the autologous CAR-CD19 products. It’s a complex question since the current approved CAR-CD19s show about a 50% durable response rate within the responders, where a goodly proportion of the patients that do not have a durable response are relapsing after a response, sometimes with CD19-negative lymphoma or leukemia (ie. the cancer has undergone natural selection and loses target antigen expression). The polled specialists and the panelists wanted to see a pretty high durable response rate, 35-40% (specialists) up to 50% (the panelists). If the field were to see responses as good as axi-cel, tisa-cel and liso-cel, this would be “a huge advance”, according to Dr Soumerai of MGH.

Of note, Allogene itself was a bit more cautious at their public company presentation later in the day. Dr David Chang, Allogene’s CEO, provided some guidance and set expectations. He noted that the company would report early data form the ALLO-501 program at #ASCO20 and/or #EHA20 but stressed the readouts of safety and degree of lymphodepletion from up to 3 dose cohorts, and with several different doses of their lymphodepletion agent ALLO-647, and anti-CD52 antibody. In the ALLO-501 trial this is given along with the lymphodepeleting chemotherapy combination of cyclophosphamide and fludarabine (Cy-Flu). Among the other allogeneic and off-the-shelf CAR-CD19 programs several were highlighted either by the audience (Fate Therapeutics induced CAR-NKs) or the panelists (the Takeda/MD Anderson NK program). Other programs from Atara, CRISPR, and Precision all would have to show some or more data in order to get the specialists or the panelists to take notice.

Notably, there was consensus among the audience, polled specialists and panelists that CD3 x CD20 bispecifics would be less efficacious than CAR T cells, regardless of the specific therapeutics (eg. from Roche or Regeneron or Genmab). Further, Dr Madduri expressed concern at the need to keep dosing patients both because of inconvenience and possible safety over time. Her view is that patients prefer a single dose CAR.

Finally in the r/r DLBCL space, both polled specialists and the panelists saw minimal roles for the anti-CD79b-drug conjugate polatuzumab vedotin (brand name Polivy, from Roche) or the anti-CD19-ADCC competent antibody tafasitamab (from Morphosys, which now has a 30 August PDUFA date with FDA).  Both of these biologics need to be given in combination with other therapeutics and there did not appear to be a benefit over standard combinations. More specifically, polatuzumab vedotin is given with rituximab and bendamustine and was considered “tolerable” but perhaps best used in a bridge to transplantation setting or a bridge to CAR-CD19 cell therapy. Tafasitamab was recently written up by Jabob Plieth here:

Turning to r/r MM there were a series of questions about lines of therapy and which were preferred. For newly diagnosed patients and for second-line patients the clearly favored standard of care was an ‘ImID’ (immunomodulatory agent, eg. revlimid) plus the anti-CD38 antibody daratumumab (brand name Darzalex, from Johnson & Johnson’s Janssen division) plus dexamethasone (aka triple therapy) with perhaps a proteasome inhibitor added (thus, a quad). The use of daratumumab in early line therapy will continue to grow as it is payor-approved for early-line use.

For later line therapy, the moderators first brought up selinexor (brand name Xpovio, from Karyopharm Therapeutics), a first-in-class, oral Selective Inhibitor of Nuclear Export (SINE), which was granted accelerated approval last year for use in in combination with dexamethasone for adult r/r MM patients who received at least four prior therapies and whose disease is refractory to at least two proteasome inhibitors, at least two ImIDs, and an anti-CD38 monoclonal antibody. There was a consensus view that this drug will see flat to diminishing use due to poor tolerability. Dr Madduri noted that she gives this drug once week rather than twice a day (as labeled) in an effort to improve patient tolerance and only used it as a bridge to clinical trial enrollment (ie. on something else, for example, CAR-BCMA cellular therapy. Curiously there were no questions about isatuximab-irfc (brand name Sarclisa, from Sanofi-Aventis), newly approved in combination with pomalidomide and dexamethasone for adult patients with r/r/ MM and at least two prior therapies (see this SITC writeup:

As for CAR T cells for multiple myeloma, the panelists were hesitant to pick a winner between the two advanced CAR-BCMA programs: bb2121 (Bluebird) and JNJ-4528 (from J&J, formally called LCAR-B38M) until J&J updated PFS data. At their public company presentation Nick Leschly, Bluebird’s CEO, noted that they will file the BLA for bb2121 (now called idecabtagene vicleucel or ide-cel) in the first half of this year, and would release longer-term follow-up data from the ide-cel clinical trials KarMMa and CRB-401 in the second half of the year. The BLA will be filed despite the “slow-down” from FDA necessitated by the agency’s request for additional lentivirus production characterization information from their chosen cell suspension manufacturing method (no details given). What the FDA has asked for apparently is both different from and more than the EU agency (EMA) wanted.

On the allogeneic CAR T cell front, Dr Chang at Allogene noted that they would have early data on ALLO-715 (their version of a CAR-BCMA therapy) at #ASH20. Here he noted they are considering dropping the Cy-Flu lymphodepletion and just using their anti-CD52 antibody to lymphodeplete, we’ll see (this doesn’t strike me as realistic).

In general both the polled specialists and the panelists were more enthusiastic about CAR-BCMA therapy than several other modalities, including belantamab mafodotin (from GSK), an antibody-drug conjugate, composed of an anti-BCMA monoclonal antibody bound to auristatin F. This drug was thought to be not quite good enough given the unmet need, there remain concerns about the ocular toxicity (the bane of ADC technology) and keen disappointment that the response rate dropped below 30% ORR in daratumumab-refractory patients. Clearly this therapeutic will see some use in late line therapy, and further clinical development has yielded results in earlier line as reported on 2 March (see A similar wait-and-see approach is being taken by these specialists and panelists to the CD3 x BCMA bispecifics, which are currently viewed as best for community hospital settings without CAR T cell capacity or for patients who cannot wait for the cell therapy production.

One theme in r/r MM is the concern that patients are still not being cured, even with cell therapies. The gradual relapse from CAR-BCMA treatment that one sees in all the clinical studies has been linked either to CAR T persistence being limited or to diminished BCMA antigen expression on the cancer cells. Of course, these two things may be related. One desire expressed by Dr Madduri was for a CAR-BCMA therapy with better persistence properties.

Two short notes while we’re here. Gilead stated at their public company presentation during Cowen Health Care that the value driver for the Forty Seven acquisition was the MDS data ( And hematologic drug heavyweight venetoclax (the Bcl-2 inhibitor from Abbvie) scored a miss in an AML confirmatory trial (https// In summary, a busy couple of days.

As many readers know, Aleta Biotherapeutics builds cellular therapeutics with exemplary persistence and fitness properties. We have two cell therapy programs heading for the clinic now. One will treat r/r AML patients both in the pediatric and adult patient populations. Our solid tumor program is designed to treat patients relapsing from breast or lung cancer with brain metastases. We also have a biologics program specifically created to ‘rescue’ CAR-CD19 T cells in patients relapsing from therapy. You can find out more at or email me at or just call me at 1-508-282-6370 and of course follow me on Twitter @PDRennert and @BioAleta.

That’s it for now.  Stay tuned.

New Horizons Across the Immunotherapy Landscape – Lymphoid Structures Drive Immune Checkpoint Therapy and the Efficacy of Cellular Therapeutics

We’d been hearing the rumors for months. But the simultaneous publication in Nature of three papers describing a critical role for lymphoid structures and B cells in supporting T cell anti-tumor immunity was a remarkable milestone in our evolving understanding of immuno-oncology. Really stunning work. Importantly, these papers fit into a new contextual framework and cap a series of studies that have come out over the last year or so that have enriched our understanding of how the immune system and tumor cell populations interact. This broader and still evolving contextual framework will impact immunotherapy drug development across the immune checkpoint field, the tumor vaccine space, innate immune approaches, the T-cell-directed biologics, and cellular therapies.

But first, these new papers are gorgeous:

The study presented by Petitprez et al. is focused on the response of sarcomas to immunotherapy ( The soft tissue sarcomas (STS) have mixed clinical responses to immune checkpoint blockade (ICB) treatment, and it is not clear what drives the variable response. In general, STS have been classified as having a low tumor mutational burden (TMB) and are considered non-immunogenic, or ‘cold’, and have little expression of PD-L1. A few STS subtypes are characterized by more complex genetic abnormalities and could potentially have more actionable mutations for the immune system to recognize. Regardless, two of most widely used biomarkers of ICB response (TMB-high or PD-L1-positive) are not generally relevant in STS. In this study, gene expression profiling was used to examine patterns of ICB response in patients across a wide variety of STS subtypes and pathologies. Three distinct genetic classes were identified that match known tumor microenvironments (TME) – immune desert (A), highly vascular (C), and inflamed (E) with two intermediates: B and D. These are well understood classifications and mirror many prior studies of the TME and ICB response and resistance. However, several details that emerged are critical – 1) the inflamed TME (E) and the intermediate form (D) were not associated with any particular STS classification, but were distributed across STS histologies, and 2) the E/D inflamed signature was characterized by a pronounced B cell signature, and by expression of the chemokine CXCL13. These results suggest secondary lymphoid organ development and organization.

A sidebar here: secondary lymphoid organs include spleen, lymph nodes, and Peyers patches and are characterized by critical structural features that include a T cell zone and adjacent B cell follicles that orchestrate coordinated immune responses, for example, to pathogens. Localized lymphoid organs can form in chronically inflamed tissues – these are the tertiary lymphoid structures (TLS) and are classically associated with prolonged inflammation and autoimmunity. The organization of cell types in these structures is controlled in part by chemokines, including CXCL13 and CXCR5.

Using immunohistochemical staining, the authors went searching for TLS in tumor sections, and, as suggested by the gene transcript data, TLS were found in groups E and a bit in D. The presence of TLS in tumors has been noted before, but here the presence of TLS, and of B cells, was associated with patient overall survival. Further in a small cohort of patients (n=47), classes E and D, those most likely to have TLS, responded best to ICB therapy. These observations suggest that B cells and TLS are important for successful ICB therapy.

OK, that’s one study, in an indication in which ICB therapy in general has not worked well. So, are these observations generalizable?

The next paper looked at these features in an indication that is very different from STS. The paper by Cabrita et al. focused on ICB response in melanoma ( Melanoma is notable for several features, having a very high TMB and being among the most ICB responsive indications. Indeed, in terms of immune-responsive tumor types, melanoma and STS are on the opposite ends of the spectrum. Nonetheless, the analysis of melanoma responsiveness to ICB yielded results strikingly similar to the results of the STS analysis.

Immunofluorescent staining was used to identify T and B cell clusters, and these were associated with the chemokines CXCL13 and CXCR5. The co-occurrence of T and B cells and the identification of a TLS gene signature predicted clinical response to ICB. Mechanistically, tumors that featured TLS and were rich in B cells also had an increased population of naive and/or memory T cells while those tumors without these features had an increased population of exhausted T cells. Notably, the T cell population enriched in the presence of TLS was CD8-positive – the subset associated with cytotoxic anti-tumor immunity. Whether the B cells themselves were also contributing directly to the anti-tumor response via production of anti-tumor antibodies appears less clear. As in the STS study, TMB was not correlated with TLS formation in melanoma.

The final study by Helmink et al. analyzed patients who had enrolled in a phase 2 clinical trial of neoadjuvant ICB therapy for high-risk resectable melanoma ( In the neoadjuvant setting, ICB therapy is given prior to the surgery that is performed to remove tumors. Gene expression and immune-staining analyses were used to assess TLS and B cell presence in tumors, similar to the prior studies. Of note the results were compared to an analysis of gene signatures in a neoadjuvant trial of ICB treatment of metastatic renal cell carcinoma (RCC). As in the other two studies the presence of B cells and TLS in the tumor prior to therapy was predictive of response to ICB. This was found in patients with metastatic melanoma and in patients with metastatic RCC. In addition, the authors showed that the B cell pool was differentiated, into memory B cells and plasma cells, suggesting that the TLS was productively driving both T and B cell differentiation and perhaps indicating a role for B cell adaptive memory in supporting the anti-tumor immune response.

In these three papers covering three indications (STS, melanoma, RCC), a signature of TLS enriched in B cells was associated with ICB responses and T cell activity, notably of CD8-positive T cells. A useful model for these findings is that organized lymphoid structures like TLS provide an environment in which tumor antigen can be productively displayed to the adaptive immune system, ie. T and B cell mediated immunity. In this setting B cells may have such a strong predictive signature for several reasons: B cells are antigen-presenting cells capable of supporting a persistent T cell response by restimulating memory T cells and by triggering de novo naive T cell responses to tumor antigens. Further, B cells engage in the productive costimulation of T cells via B7/CD28, CD40L/CD40 and adhesion molecule interactions and also produce cytokines that provide activation, proliferation and survival signals to T cells (eg. TNF, IL-2, IL-6, IFNy).

Additional productive areas of investigation would include analyses of dendritic cell populations and, of critical importance I think, the status of tumor-draining lymph nodes                       (see

These studies raise some interesting questions. For example, in the context of anti-PD-(L)-1 therapeutics, what is the pattern of PD-L1 expression that accounts for the anti-tumor response? In the current paradigm, PD-L1 expression on the tumor itself is considered the critical target. This has recently been complicated by the finding that PD-L1 expression on myeloid cells in the TME may also be relevant (eg. And we must recall that PD-1/PD-L1 interaction in lymphoid organ germinal centers regulates T cell / B cell interactions (
It may be that ICB treatment is influencing the anti-tumor immune response in multiple ways.

We’ve seen many novel immunotherapy agents falter and some of those results may reflect this more complex immune-tumor landscape. This leads one to wonder where and how novel agents might function and if they are actually beneficial to anti-tumor immunity. As one example, many PD-L1-based bispecific antibodies are under development. We might hypothesize that an anti-PD-L1/anti-4-1BB bispecific antibody would actually have multiple sites of action, not only in the tumor TME, but also in the TLS and draining lymph nodes. As another example, T cell engagers (BiTEs, DARTs et al), a design that works well in the hematologic cancers, may not work as well in solid tumors unless those tumors have TLS already. Finally, it’s a bit baffling that TMB does not correlate with TLS formation, and we might wonder how this result reflects upon efforts in the neoantigen space. And so on, as we think about tumor vaccines, and innate immune stimulation, and novel checkpoints, etc.

This brings us also to cell therapy and potential lessons for that field, specifically in the solid tumor setting. We have long recognized that CAR T cells that target CD19 (CD19-CARs, eg. Kymriah from Novartis and Yescarta from Kite/Gilead) have dramatically better persistence properties than CARs that target solid tumor antigens. We have hypothesized that the self-renewing nature of the antigen itself is an important aspect of persistence – CD19 is expressed on B cells that are continuously produced by the bone marrow.

In light of these new findings on ICB responses I think we can consider a second feature – the immunological relevance of antigen presentation to the CAR T cell. We have recently seen a rash of efforts to provide artificial and immunologically favorable antigen presentation to CAR T cells. The goal here is to improve CAR T cell fitness by providing the proper immunological niche (eg. a lymph node) and driving persistence. In one example an artificial CAR T ligand was developed that could be injected into a patient who is receiving a CAR T therapy ( This artificial CAR-T ligand binds to serum albumin, traffics into lymph nodes and is taken up and displayed by antigen-presenting cells. CAR-T cells trafficking through the lymph node are stimulated both by the displayed antigen and by costimulatory receptors and cytokines, much like the system envisioned in the ICB response/TLS papers described above. Similarly, a nanoparticulate RNA vaccine was used to deliver and express an artificial CAR antigen into a tumor-draining lymph node ( Again, this is designed to promote immunologically productive presentation of the target antigen to activate and expand the injected CAR-T cells. Notably, engagement of relevant stimulatory, chemokine and adhesion signals are known to favor the development of T cell memory, a critical element in long term immune protection.

Our in-house technology ( uses CD19-targeting by CAR-T cells to leverage persistence and also to take advantage of immunologically relevant antigen presentation. We do this by building CAR T cells to CD19 that simultaneously can target any antigen of interest. We enabled this ‘repurposing’ of CD19-CAR T cells by creating small, highly potent proteins that bridge the CD19-CAR T cell to the tumor antigens we choose, triggering T cell cytotoxicity and killing the tumor cell ( Every bridging protein contains the CD19 protein, so is the target of any CD19-CAR T cell. This bridging protein strategy enables facile multi-antigen targeting because the design is highly modular. We have built CD19-CAR T cells that secrete bridging proteins that recognize CD20, Clec12a, Her2, EGFR and many other different antigens and in some instances multiple antigens simultaneously. Because the CAR T cell is a CAR to CD19, this is a simple, pragmatic and universal solution. And because the bridging protein is secreted by the CD19-CAR T cells themselves, this becomes a simple matter of encoding everything into a lentiviral vector.

CD19-CAR T cell interaction with normal B cells will support production of immunologically relevant stimulatory signals including adhesion interactions, chemokine and cytokine signals, and costimulatory signals, even if the consequence for the B cell is cytotoxic. This organic presentation of immunologically relevant antigen does not require administration of additional agents or exogenous antigen, since B cells are themselves antigen presenting cells, are present in lymphoid organs and in circulation, and represent a self-renewing source of CD19 due to production by the bone marrow.

In the context of solid tumor treatment creating an expanded and persisting CAR T cell pool using CD19-positive B cells as a non-tumor dependent source of antigen becomes very attractive. Our lead solid tumor program uses a CD19-anti-Her2 bridging protein, where the CD19 portion is the CD19 extracellular domain, and the anti-Her2 portion is an anti-Her2 scFv. This bridging protein is encoded into a lentiviral vector downstream of a CD19-CAR sequence, separated by a P2A cleavage site. Thus we have made a ‘Her2-bridging CD19-CAR’ that can bind directly to CD19 on B cells via the CAR domain, and to CD19 painted onto a Her2-positive solid tumor cell, via the bridging protein.

Our lead indication is Her2-positive metastatic breast cancer and metastatic lung cancer, specifically in patients who have relapsed from standard of care therapy by developing CNS metastases. The reasoning is simple: the Her2-bridging CD19-CAR T cells can be injected systemically to become activated by CD19 expressed on normal B cells (and B cell aplasia is a manageable toxicity). The activated CARs will traffic systemically, all the while secreting the small (and short half-life) bridging proteins. Patients can remain on standard of care, including with anti-Her2 antibodies, because we will not need to ‘see’ Her2 in the periphery in order to trigger CAR activation and expansion. And of course, anti-Her2 antibodies like Herceptin can’t cross the blood brain barrier and get into the CNS at all. Once activated CD19-CAR T cells will enter the CNS, as activated T cells are known to do, and the secreted bridging proteins will coat Her2-positive tumor cells with CD19, allowing the CD19-CAR T cells to the attack and kill those cells. Those CAR T cells can also leave, recirculate, become stimulated again by encounter with B cells, and return to the CNS – a trick no direct CAR to Her2 can hope to duplicate. Not to oversell it, but I love this program, and it should work.

The idea that we can repurpose and send off a CD19-CAR T cell to attack any antigen and indication is compelling in its simplicity and modularity. We have already built programs for treating AML and B cell tumors. Our next wave of programs takes advantage of our ability to weave together bridging proteins with two and three antigen-binding domains – and this allows us to contemplate attacking very heterogeneous tumor types with a simple CAR – the CD19-CAR – that has exemplary persistence and fitness characteristics.

Stay tuned.