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Novel fibrosis therapeutics: walking down the TGF-beta pathway

Recent weeks brought startling news of clinical trial successes in the treatment of Idiopathic Pulmonary Fibrosis (IPF). The clinical and commercial consequences have been heavily reviewed elsewhere (eg. GLPG and FGEN).

This short commentary will focus on the underpinning science, with particular reference to the TGF-beta (TGFb) pathway and the role of that pathway in fibrotic disease.

First a quick primer on the recent advances in IPF drug development. Fibrogen pulled off a successful and surprising 48 week Phase 2 trial of pamrevlumab, an old antibody targeting CTGF, while Galapogos followed with very compelling early Phase 2a data of GLPG1690, an autotaxin inhibitor, including the apparent reversal in decline of lung function that is the hallmark of IPF (nb. small sample size, short analysis period (12 weeks). These two new drugs are poised to contribute to the next wave of IPF therapeutics, joining pirfenidone (Esbriettm, Roche) and nintedanib (Ofevtm, Boehringer Ingelheim), approved for the treatment of IPF in 2014. Of note, pirfenidone and nintedanib are considered moderately efficacious, slowing but not reversing the rate of decline in lung function, and only modestly improving life expectancy, if at all. Therefore, if pamrevlumab or GLPG1690 can differentiate by either reversing lung damage or increasing life expectancy, they would be expected to overtake the earlier drugs.

What’s interesting is that the mechanism of each of these drugs intersects with the activity of TGFb, a dominant cytokine that normally controls wound healing and other tissue repair activities. When dysregulated, TGFb becomes pathogenic, supporting disease processes spanning oncology and fibrosis. We can visualize the initiation and progression of fibrosis as a series of steps controlled, at least in part, by the continuous activity of TGFb signaling through the TGFb-receptors. Indeed, pirfenidone’s mechanism of action includes inhibition of TGFb-receptor signaling, among other activities (both pirfenidone and nintedanib are tyrosine kinase receptor inhibitors, and neither is particularly selective, hitting multiple receptors simultaneously).

Here is a cartoon with different stages of fibrosis induction and progression, simplified:

 cascade 1

Without dwelling overly on the various pathologies at work, we can point to several critical steps underpinning the cascade:

1) influx of inflammatory cells upon injury

2) increase in autotaxin and therefore LPA (autotaxin cleaves the abundant lipid moiety LPC, to release LPA)

3) triggering of the LPA receptors, that, among other activities, are responsible for the activation of beta integrins, leading to the release of TGFb from sequestration

4) activation of TGFb receptors, that, among other activities, induce the secretion of growth factors including CTGF

5) induced production of TGFb by the action of CTGF

6) myofibroblast activation and ECM deposition leading to fibrosis

Now we can overlay some of the therapeutics developed for IPF on the cascade (simplified even further here):

 cascade 2

Note that Fibrogen’s anti-CTGF antibody pamrevlumab sits along side the approved drugs pirfenidone and nintedanib, a bit downstream of the initiation of the fibrotic cascade. The clinical data to date suggest that pamrevlumab has activity similar to pirfenidone and nintedanib. It is critical to stress here that CTGF secretion is tightly controlled by, among other things, TGFb. Further, in the fibrotic setting, TGFb appears to be a master regulator of CTGF expression, and, as noted above, the regulation is feed forward, since CTGF signaling through it’s receptor induces expression of more TGFb. It makes sense then that an anti-CTGF antibody could derail this chronic signaling loop, and thereby provide therapeutic benefit by reducing TGFb levels as well as tempering the pathologic activity of CTGF.

Further downstream is the anti-LOXL2 antibody, simtuzumab, formally under development at Gilead for IPF and NASH (a form of liver fibrosis). LOXL2 enzyme is responsible for crosslinking collagen, thus contributing to ECM deposition. Perhaps being too far down the fibrotic cascade is ineffective, as simtuzumab was dropped based on poor efficacy in Phase 2 trials.

Turning upstream we find autotaxin inhibition, integrin inhibition and LPA inhibition. The mechanism of action of all three targets is related, since, as noted above, autotaxin cleaves LPC to yield LPA; LPA in turns binds the LPA receptors. LPA receptors are G-protein coupled receptors with diverse functions, prominent among them being the activation of integrin beta strands. Among the integrins then, integrin avb6 has been tied directly to TGFb release in the setting of lung fibrosis. This complex system is tightly regulated, with the relationship of autotaxin/LPA/LPA receptor/integrin/TGFb preserved in different organ systems. The relative contribution of the different LPA receptors and different integrins does vary however.

The three upstream therapeutics illustrated have presented a mixed clinical picture. The anti-LPA antibody Lpathomab was a clinical flop, and the company developing it, Lpath Inc., reversed merged into oblivion several years ago. Anti-LPA might have worked if the antibody were optimal, however the suspicion at the time was that the antibody bound to only some of the myriad isoforms of LPA, and was therefore ineffective. Biogen’s anti-alpha-v beta-6 antibody STX-100 has finished a Phase 2 trial in IPF but has not reported data. This may be due to issues of efficacy and/or safety or may be due to Biogen’s frank disinterest in fibrosis (or anything outside of neurology). However, we simply don’t know. And finally, we have the new data on the Galapogos autotaxin inhibitor, GLPG1690.

The results obtained in the small Phase 2 trial of GLPG1690 in IPF were very good, and if they hold up the drug could certainly be best-in-class for IPF. Further, as a therapeutic sitting at the top of the fibrotic cascade, there is every reason to believe that this drug will show promising efficacy in other fibrotic conditions.

We expect updates from these programs and others at the fall academic meetings, starting with the European Respiratory Society Congress on 9-13 September. Importantly, Fibrogen will report data from combination studies of pamrevlumab plus pirfenidone or nintedanib in IPF.

 stay tuned.

A quick look at the axitinib data and IO/VEGF inhibitor combos

Yes I know, there has been a lot of talk of immunotherapy combination trials (and tribulations). But in reviewing #ASCO17 slides I stumbled on some interesting results. (These are screen grabs actually and I don’t have a source for all the photos – my apologies to the folks that posted these pics!)

What looked interesting at ASCO? Lots of cool stories emerged, but I think this one was overlooked:

This slide grabbed my attention- this is a summary of data on PD-(L)-1 inhibitors combined with targeted inhibitors in advanced renal cell carcinoma (RCC). I’ve tagged some of the drugs just below the slide:

Screen Shot 2017-06-09 at 10.02.56 AM

The studies shown in the graph can be a little misleading, as the monotherapy studies are in the second-line or later setting, while the combination data are in the first line (treatment-naive) setting. For example the nivolumab result is from the CheckMate 025 trial vs. everolimus (an mTOR inhibitor from Novartis) in patients with advanced RCC for who had relapsed previous treatment with one or two regimens of antiangiogenic (i.e. anti-VEGF) therapy (Motzer et al 2013: http://www.nejm.org/doi/pdf/10.1056/NEJMoa1510665). Just to note in passing, nivolumab therapy in this setting triggered an overall response rate (ORR) of 25% of patients, and had a modest but significant impact on progression free survival (PFS) and median overall survival (mOS). Indeed, 30% of the nivolumab patients that responded were alive 5 years later, as reported at ASCO last year (http://meetinglibrary.asco.org/record/122769/abstract).

The atezolizumab plus bevacizumab data come from the IMmotion 150 trial was presented at the ASCO GU meeting in February 2017 (http://meetinglibrary.asco.org/record/140798/abstract). In that study the combination was compared to atezolizumab alone or sunitinib alone, in treatment-naive (frontline) patients. The combination produced an ORR = 32%, just a bit better that atezolizumab alone (26%) or sunitinib alone (29%) but there was a significant impact on PFS at 12 months particularly in patients having PD-L1+ tumors. Overall, patients achieved a median PFS of 11.7 months with the combination, 8.4 months with sunitinib, and 6.1 months with single-agent atezolizumab. PD-L1-positive tumors yielded a median PFS of 14.7 months with the combination, 7.8 months with sunitinib, and 5.5 months with atezolizumab monotherapy.  There was no improvement in the hazard ratio (a statistic that measures the chance of patient death in different cohorts in the trial, as compared to each other – arm vs arm).

Against that promising, if early, backdrop, the very interesting results here are shown wide right on the graph above – the combination of axitinib, a pan-VEGF-receptor inhibitor, with avelumab (anti-PD-L1, EMD Serona/Pfizer) and pembrolizumab (anti-PD-1, Merck). These studies are ongoing in the front-line setting. The overall response rate of axitinib with pembrolizumab of 67% is startlingly high, although these data are from a small study (I can’t find pembrolizumab or avelumab monotherapy data as a comparison, but the atezolizumab  monotherapy responses at 26% in the IMmotion 150 trial gives us a baseline for anti-PD-(L)-1 monotherapy in the frontline setting).

Axitinib is a second generation VEGF-R inhibitor with improved selectivity over earlier compounds, and also over some competing compounds (e.g. pazopanib from Novartis). Axitinib monotherapy in second line RCC produces an ORR around 40%, perhaps a little higher, with a modest impact on PFS when compared to sorafenib (~ 5 months v 3 months ). In the first line setting the monotherapy data are a bit better, with ORR reported at 50%+ and PFS of about a year, with a median overall survival (mOS) of about 2 years (similar to other VEGF inhibitors).

In that context an ORR of 55% (axitinib plus avelumab) or 67% (axitinib plus penbrolizumab) might be just additive (approximately 50% (axitinib) + 25% (anti-PD-(L)-1)). But then I came across this screen grab from the avelumab combo study:

Screen Shot 2017-06-09 at 12.16.02 PM

Notice here that the X-axis is in weeks, and so we have ongoing responses of greater than 1 year (52 weeks) in 14/32 patients (44%) and, as noted on the slide, a total of 24/32 patients (75%) with an ongoing response with a minimum of 24 weeks. Of interest, partial responses (PR, red triangles) evolved into complete responses (CR, blue triangles) over time, suggesting an ongoing immune response. Durability of response is critical here, but it certainly looks like this cohort will handsomely beat the 2 year mOS mark, which will best axitinib alone. The avelumab plus axitinib study (http://meetinglibrary.asco.org/record/144685/abstract) and the pembrolizumab plus axitinib study (https://jitc.biomedcentral.com/articles/10.1186/2051-1426-3-S2-P353) certainly make the case for this particular combination (with axitinib); in contrast the combination of pembrolizumab with another kinase inhibitor pazopanib (that blocks VEGFR-2, KIT and PDGFR-β)  resulted in intolerable liver toxicity (http://meetinglibrary.asco.org/record/152938/abstract).

Ok so why is this important? I think there are a few interesting themes here. 1st, the combination of anti-PD-(L)-1 antibodies with standard of care (SOC) treatment, in this case, axitinib, has produce a result that dwarves what we see with epacadostat, the IDO inhibitor that would be expected to aid in blocking tumor immunosuppression. That is not to say that the epacadostat combination will not bring real benefit (again, it’s the durability plus the response that counts). It may very well do so, and it may do so with less toxicity (we’ve not discussed adverse events, which can be a differentiating feature).  However, the concept that pairing immune checkpoint inhibitors would unleash the anti-tumor immune response to bring synergistic activity is not robustly supported in this indication (not yet anyway).

That brings up the second interesting point, which is: against an ORR of 55% (using the avelumab plus axitinib data), how should we judge novel agents? If this is ‘noise’ arising from combination with SOC, how will novel agents overcome this and show a positive signal? The answer, simply, is in randomized, controlled trials (RCTs). Dr James Mule made this point at our IO combinations panel at the Sach’s pre-ASCO conference, and he used this slide from Lerrink to show how few immunotherapy combination studies are randomized (h/t to Dr Mule and to Lerrink):

Screen Shot 2017-06-11 at 11.20.31 AM

 

 

 

 

 

 

 

So despite the need for RCTs, we don’t see many yet. Another tactic maybe to drive a biomarker forward alongside a novel agent, in order to be able to select patients and differentiate in that manner. Easier said then done, but a possibility. The magnitude of potential noise is illustrated in this graph from EvaluatePharma’s nice IO combo report:

Screen Shot 2017-06-11 at 12.20.41 PMTheir report uncovered 765 immunotherapy combo trials across every conceivable indication. That is a lot of data to sift through.

Ok, the 3rd point. What I love about this example (renal cell cancer) is the focus on biology. Note here that we’ve not tried to be comprehensive about the RCC field, the treatment landscape, other novel targeted agents in development, the oncogenic drivers, the mutation burden, the tumor microenvironment, or the microbiome (all of these rooks will come home to roost in time).  Instead we focused on two classes of therapeutics that are playing well together – anti-angiogenic drugs (anti-VEGF, VEGFR inhibitors) and anti-PD-(L)-1 antibodies. That’s all. But this gives us a new way to think about the treatment and competitive landscape in immunotherapy – specifically, how are companies building on this early data.

To look at this I used an IO combo database that I’m beta-testing for Beacon-Intelligence, and an interesting theme emerged. So, a quick share (this is a quick look and again, not a comprehensive one by me: one can quickly find more studies in the database or online)

Screen Shot 2017-06-11 at 11.38.21 AM

So, not suprisingly, Roche/Genentech has a slew a trials designed to pair it’s anti-VEGF antibody bevacizumab with atezolizumab plus SOC in a range of indications including RCC.  What is a bit surprising is the appearance of Eli Lilly, bringing along it’s own anti-PD-L1 (LY3300054). While Lilly is not really considered an immunotherapy player, it does have some keen assets to deploy: anti-VEGFR2 antibody (ramacirumab) and, although not shown here, the multi-kinase inhibitor galunisertib that potently inhibits TGFBR1. The VEGF and TGFbeta pathways are intricately intertwined and both have profound impact on tumor biology, stromal biology, and immune biology. From a biology perspective, Lilly suddenly looks like an immunotherapy player in the making. Here I think is an interesting lesson, that is, follow the biology, not the molecule (btw, a search on TGFbeta combination therapy leads down another rabbit hole, best saved for later).

stay tuned

 

Angst in the IO Combo field – part 2 (lessons from #AACR17)

I posed this question regarding IO combinations in the last post, leading up to AACR:

“Why the perception of angst then? The sentiment has been summed up as “everything will work a little, so what do we research/fund/advance? How do we choose? How will we differentiate”?

I was mulling over these questions as I prepared remarks for Jefferies Immuno-oncology conference – the slides below are taken from the deck I presented.

Even the comment “everything will work a little” now seems to be an overreach. We could instead say: “most combinations won’t work at all”, meaning they won’t work better than anti-PD-1/PD-L1 monotherapy or anti-CTLA4 monotherapy, or, that they won’t work better than those therapies used in combination with standard of care.

Remember two years ago? We were going to take an anti-PD-1 to “release the brake” and add anti-4-1BB or anti-OX40 to “step on the gas”. While it is still early, this seems to be an empty paradigm. Why? Certainly the 4-1BB and OX40 pathways are intensely potent when used to drive T cells directly (e.g. anti-CD3 + anti-4-1BB in vitro or as used in a CAR-T cell). Is it too early to tell? Have the wrong patients been enrolled in trials? Are the antibodies no good? Is it the Fc? IS THE TUMOR COLD?

So here we go, onto the next paradigm, summed up in the phrase “make cold tumors hot”. What happened to stepping on the gas?

At AACR, Dan Chen (from Genentech, a Roche company) laid out the case for using not 1, not 2, not 3, not 4, not 5 … but up to 11 different therapeutics to successfully treat a given tumor – he exaggerated to make the point that none of the current immune checkpoint inhibitors (ICIs) should be expected to work in synergy with anti-PD-1 therapy, a priori. Why not? Because the ICIs are the really big levers, and the rest are smaller levers, where smaller simply means a pathway or biology that is less fundamental to immune anti-tumor responses than the ICIs. In order to see robust activity with these smaller levers, you need to apply them to carefully selected patients. An example was given by Jennifer Michaelson (from Jounce, and before that, Biogen) who stressed the need for biomarkers to guide the clinical application of an agonist anti-ICOS antibody (another gas pedal). The “cold tumor hot” gang that includes oncolytic virus approaches, onco-vaccine approaches, TLRs, STING and so on have not yet really articulated a strategy to identify patients likely to respond except in those tumor types associated with viral infection.

All of these accessory immuno-modulatory therapies, including the agonist antibodies (anti-4-1BB, anti-ICOS), the myeloid cell modulators (anti-CSF1R), the soluble mediator inhibitors (A2AR, IDO), the innate triggers (STING) we can lump as immune-oncology (IO) drugs, to distinguish these from ICIs.

The apparent strength of some ICI-standard of care combinations, and the apparent weakness of the early ICI-IO combinations has some startling implications. Let’s look at the current landscape:

Screen Shot 2017-04-07 at 7.36.33 AM

and here are the approved ICIs:

Screen Shot 2017-04-07 at 7.58.47 AM

Note the concentration of indications – melanoma, NSCLC, H&N, bladder, Hodgkin lymphoma, with single approvals in Merkel cell and RCC. Certainly the list will expand but if we concentrate on melanoma and NSCLC for a moment, we can outline the key challenges:

1) get the response rates up, and 2) prevent relapses. What do we mean by this? In advanced and/or metastatic melanoma the best overall response rate (ORR) using ICI monotherapy is about 30% in previously treated patients and up to 40% in patients naive to therapy (not previously treated with anything).  Within the responders there are two subsets of interest – durable responders (those that will survive for 3 years or more: about 20% of the responders) and relapses (those who initially responded, but then relapsed on ICI therapy: about 30% of the responders).  So if we just call out the durable responders we have between 6% and 8% of the original patient population in the trial receiving durable benefit. The idea of course is to get this number up.

Before turning to relapses, lets look at NSCLC.

Screen Shot 2017-04-07 at 8.19.53 AM

 

Again the lessons here are pretty clear – get the ORR up, improve durability of response, move ICI to early line therapy. The median overall survival data (OS) for advanced NSCLC patients treated with ICI looks very modest (12 months v 8-10 months with chemo) but this obscures the fact that the OS curve is pulled to the right by a relatively small number of durable responders: again, a small percentage of patients do very well.

So what next? Pharma is taking diverse approaches to improving ICI responses across many indications, To keep it simple I pulled out just two portfolios – those of Bristol Myers Squibb and Roche/Genentech.  We can note in passing that the Merck (US) portfolio is  relatively similar to the BMS portfolio, and the Astra Zeneca portfolio is  relatively similar to Roche, as is the portfolio from the Merck (Germany)/Pfizer collaboration.

Screen Shot 2017-04-07 at 8.34.43 AM

 

A few comments on these portfolios from the pharma level and as relates to small company programs and those who invest in those programs/companies:

Screen Shot 2017-04-07 at 8.39.35 AM

The latter point is critically important for biotech and investors to the consider and revisit often: how will my program generate compelling data? How is the indication landscape shifting as I spend 2-3 years moving a program forward? Is there a milestone of sufficient signal to rise above the noise of a thousand other ICI-ICI, ICI-IO, or ICI-SOC trials? I had the uneasy experience of walking through the poster sessions at AACR17 last week, past lovely bits of work that no one was paying any attention to. That’s a lousy feeling for the person presenting the poster and a very lousy place to be if you are a small biotech company.

A final slide:

Screen Shot 2017-04-07 at 8.39.46 AM

 

It’s an incomplete list of course.

More on resistance and relapses next time.

stay tuned.

The conference season grind

With the International Immunotherapy meeting just behind us and #SITC2016 (or #SITC16, we’ll see which wins) looming, we are just at the beginning of the long grind of medical and bio-partnering conferences that characterizes the academic year. I’ll be attending a bunch of these (see below) and telling the Aleta Biotherapeutics story as we develop novel methods of targeting cellular therapeutics for the treatment of cancer. It’s a compelling story we think and we are happy to discuss it, just give a shout to Paul or Roy: paul.rennert@aletabio.com and roy.lobb@aletabio.com.

Here’s a partial conference list:

-  Society for the Immunotherapy of Cancer Annual Meeting (SITC); Bethesda, Nov 10-13

-  American Society for Hematology Annual Meeting (ASH), SanDiego, Dec 3-6

-  JP Morgan Healthcare (JPM), San Francisco, Jan 9-12, 2017

-  World Adoptive T-Cell Therapy Summit, Lisbon : Feb 6-7, 2017 —> Invited talk featuring Aleta cell therapy technology 

-  Sach’s IO Biopartnering, NYC, March 28, 2017 —> IO combinations session chair

-  American Society for Cancer Research Annual Meeting (AACR), Washington: April 1-5, 2017 —-> Aleta cell therapy abstract submitted

As always I’ll be live tweeting every conference @PDRennert.

To discuss  Aleta and other projects of interest just get in touch. We are looking forward to a highly productive conference cycle.  Cheers – Paul

part 2: Second CRI-CIMT-EATI-AACR International Cancer Immunotherapy Conference: Translating Science into Survival

Highlights from Day 2

 The Analysis of Tumor Microenvironments:  gaining depth and granularity.

Dr. Wolf Fridman (Cordeliers Research Centre, Paris), abstract 1A10, presented tools for the analysis of cell populations in the TME of colorectal cancer (CRC) and clear cell renal cell cancers (RCC). The CRC analysis produced 4 distinct subtypes, with wildly variable cell types and pathogenic pathways supported by the dominant cell populations. The subtypes aligned with standard CRC immunohistochemcial analyses, with molecular classifications, and with prognoses. Several subtypes were readily apparent in RCC. The results provide a compelling framework for cancer classification across indications and may allow the more precise pairing of immuno- and other therapeutics in a wide variety of cancer indications.

Dr. Shimon Sakaguchi (Osaka University), abstract 1A12, elucidated distinct T regulatory subsets in tumors and applied a Treg classification scheme to CRC, an indication in which the role of Tregs has been controversial. By carefully delineating suppressive and (paradoxically) inflammatory Treg populations, diverse roles in different CRC subtypes were proposed. Notably, the inflammatory subset appeared in the context of tumor invasion by bacteria that can access the tumor interstitial space as the mucosa is breached. Importantly, anti-CCR4 mAb treatment could selectively deplete the suppressive Treg subset and restore anti-tumor immunity in their models.

Dr David Denardo (Wash U School of Medicine, St Louis), abstract 1A14, introduced the hyper-fibrotic TME that characterizes pancreatic ductal adenocarcinoma (PDAC). The TME is composed of a collagen-I rich desmoplastic stroma that houses large numbers of immunosuppressive cells, creating both physical and biological barriers to T cell entry into the tumor. Fibrosis is induced and sustained by the TGFbeta pathway, leading to hyper-activation of focal adhesion kinase (FAK). A FAK inhibitor had monotherapeutic activity in a PDAC mouse model, leading to collapse of the fibrotic architecture and loss of the immunosuppressive myeloid cell compartment. In combination, FAK inhibition was synergistic with anti-PD-1 and anti-CTLA4 in PDAC mouse model that does not respond to either therapeutic given as monotherapy.

In related TGFbeta therapeutic development, Dr Maureen O’Connor-McCourt (Formation Biologics, Montreal), abstract B058, hosted a poster on a new and novel TGFbeta TRAP protein that is selective for TGFbeta isoforms 1 and 3 (but not 2).  Merck Germany has an anti-PD-L1/TGFbeta TRAP bispecific (presented a few weeks ago in Boston). This remains a very hot area.

Given the positive CAR T news from KITE yesterday, their poster on TCR technology is worth a quick mention. Lorenzo Fanchi, abstract B044, hung a poster detailing the derivation and subsequent creation of patient-specific TCRs targeting the antigens mel526, mel624, and mel888. The TCRs were challenged in vitro and in vivo (in mouse) with PDX-matched tumors and HLA-matched tumor cell lines (the HLA-typing was not disclosed). Although mouse, the TCR therapy (20 x 1oe6 cells) sustained long term survival in 2/6 animals, which was an encouraging result.

KITEs CAR T cells

Screen Shot 2016-03-29 at 4.54.24 PM

Love that cover, if you like the band they live here: http://thecars.org/)

I don’t think we’ll see the very best of the CARs at AACR16; that may have to wait until ASCO, but an abstract jumped out at me today as it addresses one of the issues I discussed in the last post (http://www.sugarconebiotech.com/?page_id=37).  As a brief refresh the last post was concerned primarily with 2 issues:

1)  The cell population used to make the CAR T cell prep, with emphasis on a mixture of naive CD4+ T cells plus central memory CD8+ T cells, based on work coming out of labs with close ties to Juno.

2)  The ability of CARs once injected to expand appropriately if antigen were not abundant (or as abundant as CD19 is on leukemia and lymphoma cells). This is a common observation not tied to any particular lab or company.

An interesting paper to be presented at AACR is by Timothy Langer et al. from the lab of Adrian Bot at Kite Pharma, in collaboration with the NCI (Steven Rosenberg, Steven Feldman, James N. Kochenderfer). The abstract is #2305: link.

What Langer et al. describe is the phenotype of Non-Hodgkin Lymphoma patient T cells prior to their transduction/expansion and the relationship of this phenotype to outcome.

In summary they took peripheral blood containing circulating T cells from the patients, then isolated the T cell fraction.  Using textbook CAR T techniques they activated the T cells then transduced them with a retroviral vector that encodes their CAR19 construct. Then they expanded the T cells further in culture with IL-2 until they reached the target “dose” of the T cells (which serve as the “drug”).

Importantly they froze down the starting peripheral blood prep and the CAR T cells then went back, thawed them both out (patient by patient matched samples) and analyzed by flow cytometry. Basically they set out to compare the starting point, the “drug” and then the result (i.e. the outcome for the patient). Now some caveats: sample size is small (n = 14), and it’s not clear to me which trial this is (so what the outcome data are). Regardless, it’s a starting point for a dataset that will surely grow over time.

OK, what did they find?

- all 14 samples yielded useful CAR T cells (i.e. were transduced and expanded successfully)

- all patients samples were different, in particular the ratio of CD4+ T cells to CD8+ T cells varied from patient to patient.

- since the abstract doesn’t show the data we don’t know those ratios, we might guess that there were more CD8s than CD4s in most patients, but we don’t know at this point

- however, the CD4/CD8 ratio in the starting prep for each patient was positively correlated with the CD4/CD8 ratio in the “drug”

- the starting T cell population generally had similar percentage of effector T cells (by definition these include activated T cells, effector memory T cells, and central memory T cells of both the CD4 and CD8 lineage) and naive T cells (not activated at all).

- the CAR T cell preps showed a shift to a greater percentage of naive T cells, that is, either they preferentially expanded (likely) or the effector T cells died out (less likely).

Now it gets interesting:

All of the CAR T cell preps were active in vitro and were delivered to their respective patients. Clinical responses occurred regardless of the CAR T cell prep phenotype including CD4/CD8 ratio or effector T cell/naive T cell ratio. So whatever was determining successful responses in the patients or unsuccessful responses – this wasn’t it. They are now further characterizing the cell phenotypes to look at other parameters, e.g. PD-1 expression. They reference these trials as using essentially this exact protocol: NCT02348216, NCT02601313.

This is a markedly different story than told by the Riddell lab recently, as we discussed last time (http://www.sugarconebiotech.com/?page_id=37). So, as Jules Winnfield (the immortal SLJ) said in Pulp Fiction, “well then allow me to retort…”

I’ll be fascinated to watch how this plays out among different groups, each of which is doing its’ very best to get the manufacturing process highly optimized to produce the best possible CAR Ts for patients.

stay tuned

thoughts on attending the Immuno-oncology Biopartnering conference in NYC

Thought bubbles on the way back from the Sach’s IO Biopartnering meeting, a very interesting event:

(http://www.sachsforum.com/4th-annual-cancer-biopartnering–investment-forum-24th-february-2016-new-york-academy-of-sciences.html)

-  The Sach’s Conferences are small, focused and diverse. Sach’s is much better known in Europe than in the US, and the NYC location brings European firms and companies into the fold. Therefore you meet interesting people you might otherwise never come across.

Investors:

-  When in provincial Cambridge Massachusetts, i.e. the village of Kendall Square and environs, we visualize investors as the VCs that are in the square, across the river in Boston, or out in Waltham. With more experience one might add investment and hedge funds and the associated bankers of greater Boston. What you find in NY is an entirely new investment ecosystem, one that is very active and a good crowd to know. fyi, you won’t find these guys at Area 4 or Voltage.

-  It follows that similar ecosystems exist on the west coast, in Europe (France stands out), England’s golden triangle (London >>> Cambridge >>> Oxford), and in diverse far flung locales (Tel Aviv, Singapore, Hong Kong to name just a few). While many firms have preferred investment geographies, all of them are aggressively spreading their money further afield. It is well worth getting to know as many investors as possible, worldwide.

Science and deals:

-  The conference cliche was “good science wins”. This sounds trite but contains an essential kernel of truth applicable to the suddenly challenging financial landscape. We should remember that, since 2014, VCs and other investors raised massive funding rounds. This money will need to be invested during each fund’s life cycle (these average about 5 years). Equally large hordes of cash are sitting inside of large biopharma and on the balance sheets of newly IPO’d companies. The upshot of all this is that “good science” has a good chance of attracting financing.

-  A second theme, less of a cliche and more of a hope, was that M&A activity will surge in 2016 due to the suddenly more affordable valuations of small and midsize biotech.  In this scenario the biggest biopharmas will get bigger by buying up a bunch of smaller companies – but do we believe they will all buy “good science”? Investors may harbor a doubt about this, but we’ll see.

Good science:

-  So what is “good science”: I’d argue that in the IO space good science in small biotechs has one or more of the following qualities:

  • Novel IO assets, IO combinations and bispecific antibodies that will synergize with existing checkpoint inhibitors.
  • Next generation vaccines, oncolytics, small molecule inhibitors that have tumor cell cytotoxic activity and/or immune activating activity. This is a broad class with assets of diverse MOA and quality, but winners will emerge here.
  • CAR-T and TCR 5.0 (or 6.0) – whatever that will be.
  • Personalized medicine solutions that guide treatment choices.

What can we observe now? Can we recognize “good science”?

Lets dial back a week or so to the response to three talks given in the cellular therapy session at the recent AAAS meeting:                           (https://aaas.confex.com/aaas/2016/webprogram/Session12231.html).

The three talks were widely (wildly in some cases) misrepresented, misinterpreted or both as documented in detail on social media  - see for example                 http://www.healthnewsreview.org/2016/02/aaas-stories-hype-immunotherapy-cancer-saliva-test/ …

and  http://scienceblog.cancerresearchuk.org/2016/02/16/immunotherapy-cancer-cure-headlines-distract-from-fascinating-science/ …

and  https://acspressroom.wordpress.com/2016/02/16/car-t/ …

Fortunately two of the three presentations are now represented by publications. The papers are in advanced publication at Leukemia (paper-1) and Nature Biotechnology (paper-2). I’ll review these papers and comment on how these data reflect back on the public perceptions of the AAAS presentations, and how they fit into other recent findings in the field, in the next post.

stay tuned

comments are welcome

Why we should all buy immunotherapy company stocks

Someone recently commented that Bristol Myer Squibb’s (BMS) stock price was getting dangerously high on a forward price/earnings basis (this was before the Dec/Jan selloff). Technically this may have been true, but the science says this company (and Merck; MRK) and a few others are here for the long run, with therapeutics that will fundamentally transform cancer care. So I ask: who cares about PE? My view is to buy the key companies and watch them thrive for a decade or more, regardless of competition and possible price controls.

How can I be so bold (or stupid) you might ask.

I recently gave a talk for a local non-profit, the Boston Pharmaceutical & Bioscience Society (PBSS), which provides mentorship for students and job seekers in the biopharmaceutical industry. I decided to use cancer indications to advance the story of immunotherapy, and as I did I realized that we are sitting right at the bottom of a boon time for cancer treatment – for patients and biopharma of course, and ultimately for the payers and insurers. Why? because cured patients will be relatively cheap to maintain.

Cured.

A magic word, much thrown about. In some hematologic malignancies and solid tumor indications (breast for example), cures can be anticipated if you catch the right patient at the right time. This has been the exception however, while the rule has been – to be blunt -let’s try to slow down you’re time to death.

So what is the evidence that immunotherapy will produce these extraordinary cures? We can start with advanced, metastatic melanoma. 10 years ago this diagnosis was a death sentence, with more than half of patients dead in a year, nearly all dead in 5 years. What’s changed? We can walk though some clinical trial data to sort this out.

here is where we start sometime pre-2010:

Figure 1

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The key points being that chemo was a lousy solution, with 2/3rds of patients dead the first year, and that the available immunotherapy (high-dose IL-2) was not much better, except for a very few “super-responders” who experienced long term survival. This was a hint that immune-based therapy could work – the key was to get better response with less toxicity.

Most of those following immunotherapy know the “long-tail” story – that when a cohort of patients is treated with an anti-CTLA4 mAb or an anti-PD-1 mAb – a subset (10% or more depending on the indication) do very well and have durable responses and long term survival, creating a long-tail on progression free survival (PFS) and median overall survival (OS) curves, which you can see at the bottom of this figure showing response to ipilimumab (anti-CTLA4; BMS):

Figure 2

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The OS curve on the right shows the long tail of survival out to nearly 5 years, and recent meta-analyses have highlighted patients living 10 years or longer, which is, as far as we can tell, a cure. We’ll note here the dosing regiment used for nearly all of these patients: treated once a month for 4 months. Period. We can conclude from this that when immune therapy works it does so by resetting the immune system response to the cancer – in some cases, permanently. Ipilimumab has a challenging toxicity profile, but in the face of such an improvement in response rate and in survival at 1 year – which jumped 15% over the chemo example in figure 1 – patients and their physicians will try to manage such toxicity.

If we look at results from anti-PD-1 treatment, the picture brightens again, as seen in this data from nivolumab clinical trials:

Figure 3

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Note that a few things have changed here. First, chemo alone (dacarbazine) is doing better here than the data we saw in figure 1: 42% are alive at year 1 although nearly all of them have progressive disease. But the objective response rate (ORR) for treatment with anti-PD-1 (nivolumab) is a whopping 40%, and nearly 2/3rds of patients are alive at 1 year. This is an amazing improvement over chemotherapy.

Now to fast-forward a bit. Ipilimumab and nivolumab are antibody therapeutics developed by BMS. Pembrolizumab is an anti-PD-1 antibody therapeutic developed by Merck (MRK). MRK cleverly designed a advanced metastatic melanoma trial in patients who had tried (and relapsed after) ipilimumab treatment. The results were outstanding:

Figure 4

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Two doses were tested here, but to simplify let’s just focus on the range of survival at one year: 58-72%. And look at chemo, right where we expect it at 30% alive at 1 year. Twice as many pembrolizumab-treated patients are alive at 12 months in this trial.

Rhe obvious thing to do next was to combine antagonists of CTLA4 and PD-1, which BMS got right to work doing. In the resulting trials the ORR jumped to 60%, although toxicity jumped as well, as seen here:

Figure 5

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Grade 3-4 AEs are very nasty and not to be taken lightly. However with further dose modification of ipilimumab, and careful treatment, these adverse events (AEs) can be managed, especially in the context of an otherwise lethal cancer condition. Nonetheless, many patients experienceing AEs dropped out of the trial, i.e. discontinued treatment, and now things get really interesting:

Figure 6

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So, 67% of patients comtinued to respond after treatment discontinuation! Now, consider that these patients only got four doses of ipilimumab – but they are kept on nivolumab for 2 years. Do they have to be on anti-PD-1 (nivolumab) for 2 years? Hell no, and this data proves it. So in a cost-constrained healthcare environment one might very well consider shorter courses of treatment with the potential to go back on to the drug (in this case, nivolumab) as needed. This is of course an abstraction at this point, but I’m certain we will see such trials performed in due course.

So where are we now:

Figure 7

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This approval signals that a fundamental change in the treatment of advance metastatic melanoma occurred in about 7 years, from standard-of-care chemotherapy to immunotherapy. It would be unfair to ignore the contribution of other therapeutic modalities, notably of BRAF and MEK inhibitors, but those do not produce cures, while immunotherapy is clearly able to do so in a small percent of patients (that percentage to be determined).

So just to recap, from where we started to where we ended up:

Figure 8

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The change in numbers from the dacarbazine line to the ipi.nivo combo line is staggering. The larger point in that the immunotherapy companies – BMS, MRK, AZN, Roche and others – will prosecute many other indications in exactly the same manner, with CTLA4, PD-1, PD-L1 and other, novel, therapeutics in a dizzying number of combinations. The value of such a class of medicines cannot be calculated in any meaningful way – each company is paying for hundreds of immunotherapy clinical trials – but it will take only a fraction of these trials to be successful to expand immunotherapy across the universe of oncology indications. The value of that success will be enormous – for patients, companies, shareholders and even, as mentioned, insurers and payers as we move patients from chronic and intensive treatment, to potentially curative treatments.

How does one guess the biopharma winners? Personally I like the bucket approach, as the ultimate winners will more than pay for the losers (and there will be some losers). As we ease into 2016 with certainly bearish sentiment about the biotech sector, I’m buying bluechip immunotherapy companies, and I won’t even look at them again for a year or two.

stay tuned.

The Tumor Ecosystem: some thoughts stirred up at the NY International Immunotherapy meeting

Ecosystems in tumor immunity

The buzzword ‘ecosystem’ has popped like a spring dandelion, and it is now used everywhere in biotech. I’m as guilty as anyone of rapid adoption: the term does capture essential elements of modern biomedical science. Complex and interlaced, with key control nodes at work at all levels – scientific, financial, clinical, commercial – and also dynamic, constantly driving adaptation, and, we hope, innovation. Scientifically the ecosystem connections are easily spotted. CRISPR technology appears in cellular therapies including TCRs and CAR-Ts as we simultaneously learn that the mechanisms of immune checkpoint suppression deployed by tumor cells can derail genetically engineered CAR T cells as readily as normal T cells. Further, those genetically engineered CAR T cells and TCRs owe their existence in large measure to our newly developed ability to sequence tumors at the individual level, with great sensitivity, to identify novel targets. The whole enterprise in turn requires ever faster, cheaper, smaller and more reliable equipment (RNA spin columns and PCR cyclers and cloning kits and desktop sequencers and on and on) and software to handle the data. Enterprises like these in turn drive discovery and innovation.

Within the tumor is another ecosystem – the tumor microenvironment or TME. While TME is a fine term it does blur the notion that this microenvironment is in nearly all cases part of a larger environment and not a walled-off terrarium (perhaps primary pancreatic cancer is an exception, within its fibrous fortress). The tumor ecosystem is a more encompassing term, allowing for the ebb and flow of vastly different elements: waves of immune cells attempting attack, dead zones of necrotic tissue being remodeled, tendrils of newly forming blood vessels, a fog of lactate, a drizzle of adenosine, energy, builders, destroyers, progenitors, phagocytes, parasites, predators. When viewed this way we might wonder how any single drug could treat a tumor, since it is not a singular thing that we attack with a drug, but an ever-changing world we are seeking to destroy.

So it’s hard to do.

Our understanding of the tumor as a complex entity was first informed by pathology, then microscopy, then histology and immunohistochemistry, myriad other techniques and of course genetics, the latter leading to the identification of tumor oncogenes, tumor epigenetics, tumor mutations (referred to above) etc, etc. This ecosystem – that of the cell and it’s mutational hardware and software (genome and exome, or genotype and phenotype) we can hardly claim to understand at all, not matter how many arrows we might draw on a figure for a paper or a review. A few recent examples: we think that tumor cells adapt to immune infiltration in part by engaging CTLA4 expressed on T cells, and when that fails they secrete IDO1, or express PD-L1 on their cell surface, or the tumor cells direct tumor associated cells to do the work for them – maybe monocytes, or macrophages, perhaps fibroblasts, perhaps the endothelium, i.e. the ecosystem. As we know from studying patterns of response to PD-1 and PD-L1 therapeutics, it is hardly so simple, as patients who don’t express the therapeutic target will respond to therapy and patients who express the therapeutic target sometimes, in fact often, will not respond. Which just says we don’t know what we don’t know, but we’ll learn, the hard way, in clinical trials.

The abundance of therapeutic targets and our lack of knowledge is best displayed, with some irony, when we try to show what we do know, as in this figure from our recent paper on immune therapeutic targets:

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from http://www.nature.com/nrd/journal/v14/n8/full/nrd4591.html 

The picture is static, and fails to represent or visualize complexity (spatial, temporal, random, quantum), and we therefore cannot formulate meaningful hypotheses from the representation. Without meaningful hypotheses we just have observations. With observations we can only flail away hopefully, and be happy when we are right 15 or 20% of the time, as is the case with most PD-1 and PD-L1-directed immune therapeutics in most tumor indications, at least as monotherapies. Why focus so on the PD-1 pathway? Because at least for now, it is the singular benchmark immune therapeutic, stunning really in inducing anti-tumor immunity in subsets of cancer patients.

The success of the “PD-1″ franchise has created another ecosystem, clinical and commercial. The key approved drugs, and the 3 or 4 moving quickly toward approval, are held by some of the world’s largest drug companies (BMS, Merck, Astra Zeneca, Sanofi, Pfizer, Roche). Playing in that sandbox has proven very lucrative for some small companies, and very difficult for many others. There is competition for resources, for patients, for assets and ideas. This has created new niches in the commercial ecosystem, as companies try to differentiate from each other and carve out their own turf – Eli Lilly for example has focused on TME targets, distinguishing itself from other oncology pharmaceutical companies in choice of targets, followed closely of course by smaller contenders – Jounce, with a T cell program directed at ICOS but perhaps more buzz around their macrophage targeting programs, and Surface, whose targets are kept subterranean for now. Tesaro and others are betting on anti-PD-1 antibodies paired rationally with antibodies to second targets in bispecific format. Enumeral is focused on building rationale for specific combinations of immune therapeutics in specific indications, perhaps even for the right subset of patients within that indication. And so on.

It’s complicated.

Lets imagine you are right now pondering an interesting idea, have a small stake, and want to engage this landscape of shifting ecosystems. What might you do?

Lets start with a novel target. You’ve read some papers, woven together some interesting ideas, formulated some useful hypotheses. The protein has been around, maybe there are patents, but not in the immune oncology space, so you think you might have some freedom to operate. Good, best of both worlds. You dig around, find you can buy your target as purified protein, or find a cell line that expresses the target. Now what? Maybe you would hire an Adimab or Morphosys or X-Body to perform an antibody screen. Different companies, varied technologies, but all directed at antibody discovery. My favorite of this group was X-Body, who had an extraordinary platform to screen human antibody sequences and produce antibodies with really stunning activity and diversity. Juno bought them in early 2015, seeking the antibody platform and a TCR screening platform built with the same technology. I hadn’t seen anything quite so powerful until recently, with the introduction of a novel screening technology from Vaccinex. This platform is about as diverse as the X-Body platform (i.e. ~108 Vh sequences and up to 106 Vl sequences; that’s a lot of possible Vh-Vl pairs). What sets them apart is that the entire selection process happens as full length IgG in mammalian cells rather than surrogates like bacteria or yeast.  The net result is a reduction in risk associated with manufacturing.  They’ve used it to power their own clinical programs and have selection deals with some big names including Five Prime Therapeutics. Remarkably (I think) you can access their platform to screen targets for your own, i.e. external, use. Their website explains the platform further (http://www.vaccinex.com/activmab/) but here is one nice sample of their work on FZD4 (a nice target by the way):

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So now via Vaccinex or someone else you’ve acquired a panel of antibodies that you are ready to test for immune modulatory activity in models that are relevant to immune oncology. You can build out a lab (expensive, time-consuming), find a collaborator with a lab, or find a skilled CRO. The immune checkpoint space was until recently devoid of really focused CRO activity, that is, having diverse modelling capability and careful benchmarking. However, Aquila BioMedical in Scotland, UK placed a solid bet on developing these capabilities around a year ago, and that effort is yielding a terrific suite of assays in both mouse and human cell systems, with multiple readouts, solid benchmarking (e.g. to nivolumab) and careful controls. I like this very much, rich in functional data in a way that a binding assay simply can’t reproduce. Aquila BioMedical seeks to become a driving force in this area, and I like their chances very much: see http://www.aquila-bm.com/research-development/immuno-oncology/ for more information on assays like this IFNgamma secretion assay:

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Those are clean and robust data.

Now you come to the point of translation to actual use, that is, targeting an indication. How does one proceed? We can probe the TCGA and other databanks for clues, stare at the IHC data online (not recommended), try to cobble together enough samples to do our own analyses (highly recommended but difficult). The goal is to make some educated guesses about two distinct features of the tumor ecosystem: First, is your target expressed on a relevant cell within the ecosystem (tumor, TME, vasculature, draining lymph nodes, etc) in a specific indication or indications, and second, is that ecosystem likely to respond in a clinically meaningful way to manipulation of your target with your antibody?

That second question is a troubling one. What we are really asking is that we deconstruct the ecosystem and look for clues as to how the therapeutic might impact that ecosystem. What are we looking for during deconstruction? Several things, and they are assessed using diverse techniques, adding to the challenge. First, a highly mutated tumor is more likely to respond to immune therapy, and there are several aspects to these phenomena. One is to understand the underlying genomic changes underpinning the oncogenetics of the tumor: what is driving its ability to outcompete the natural surroundings – in our ecosystem analogy perhaps the tumor can be considered starting out life as an invasive species. Genomic sequencing can accurately identify the mutations that support the tumor, but also a potentially vast array of “passenger” mutations that accumulate when tumors turn off the usual mutation repair machinery. Various algorithms exists that can predict which mutated proteins may be immunogenic, that is, capable of stimulating an anti-tumor immune response. Another method designed to determine if an immune response has in fact be stimulated (and has stalled) is to sequence the mRNA expressed in the tumor: exome sequencing. This will reveal, among other things, what the TCR usage is within the tumor, and that in turn will inform you if there is a very narrow anti-tumor response and a broad one, based on the breadth of TCR clonality. That sounds complex, but really isn’t – suffice to say that a broader TCR response in suggestive of immune potential, leashed T cells awaiting clear orders to attack.

More complex is the nature of those orders, and counter-orders. Various methods are being developed to measure the “quality” of the immune response that confronts the tumor. Are key costimulatory molecules present on T cells that would allow stimulation? Are the T cells instead coated with immunosuppressive receptors? Are the tumor cells masked with inhibitory proteins, are they secreting immunosuppressive factors, have they hidden themselves from immune view by downregulating the proteins that T cells “see” (i.e. the MHC complex). What are the cells within the TME doing? Are they monocytes, macrophages, fibroblasts? Where are the T cells? Within the tumor, or shunted off to the side, at the margin between the tumor and normal tissue? Are NK cells present? And on and on it goes. It seems impossible to answer all these diverse questions.

You might try IHC, as mentioned, or targeted PCR for select genes, and Flow Cytometry to look at the distribution of proteins on various cells, or try deep sequencing. All of this is achievable with equipment, labs and people, or by assembling various collaborators, but all in all, quite a challenge. Very recently an interesting company called MedGenome came to my attention, offering a diverse range of services, starting with neo-epitope prioritization and immune response analyses. These offerings, plus some routine IHC, should give most researchers a comprehensive look into tumor ecosystems, informing indication selection, mechanism of action studies and patient profiling. They explain the technology at http://medgenome.com/oncomd/. This is a schematic they sent me showing their neoepitope prioritization scheme that enriches for peptides that trigger anti-tumor immunity, e.g. in a vaccine setting or perhaps in a cellular therapeutic format.

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It’s a good start on democratizing a suite of assays typically available only to specialty academic labs and well-funded biotechs and pharma companies.

So now you’ve gotten your antibodies (Vaccinex), performed critical in vitro (and soon, in vivo) assays (Aquila Biomedical), and analyzed the tumor immune ecosystem for indication mapping (Medgenome).

You’ll have spent some money but moved quickly and confidently forward with your preclinical development program. Your seed stake is diminished though, and it’s time to raise real money. Now what? … now you face the financial/clinical/commercial ecosystem.

stay tuned.

SugarCone Biotech will be at the International Cancer Immunotherapy Conference: Translating Science into Survival 2015

Paul Rennert, Founder & Principal of SugarCone Biotech LLC, will be attending the joint CRI/AACR event: International Cancer Immunotherapy Conference: Translating Science into Survival 2015. SugarCone Biotech LLC is a consulting firm specializing in biotech strategy and investment.

The conference is in NYC Sept 16-19 and promises to be the key immunotherapy meeting of the fall conference season. Please reach out to connect with Paul at rennertp@sugarconebiotech.com.

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