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Establishing a New Set of Biological Goals
for Checkpoint Inhibitors
and IO Combinations
Paul Rennert, President & CSO
Aleta Biotherapeutics
Outline
• Restate the goal
• IO combo reality check
• Generating new hypotheses
• TME disruption
• IO lessons and CAR T
Restate
the goal
• Establishing a New Set of Biological
Goals for Checkpoint Inhibitors and
IO Combinations .....
• New goal:
✧ Propose several hypotheses to
explain why we have sometimes
misread pathways of anti-tumor
immunity
✧ Use these hypotheses to think
about how we can best move
forward
Why pursue novel immune checkpoint
pathways and IO combinations?
• Most patients do not respond to immune checkpoint
therapy
4
...reading anti-tumor
immunity
• We and many
others took a
reductionist view
which yielded a
rich list of
potential targets
5Mahoney et al. 2015. NRDD
Where we've been...
• The guiding therapeutic principal has been "remove the
brakes and step on the gas" (ie. on T cells)
✧ a T cell centric view
• We've been protein superfamily centric (IgSF, TNFRSF), a
hangover from the human genome sequencing era
• We proposed that the right combinations of checkpoint
inhibition and/or T cell activation for each indication
would boost response rates – more patients would respond
6
What we got right
• We focused on standard of care combinations: chemotherapy,
irradiation, targeted therapeutics (eg. BRAF & VEGF targeting)
• We focused on NK cells, the myeloid lineage and the tumor
microenvironment
7
• and on tumor
cell/stromal
interactions
Mahoney et al. 2015. NRDD
regardless, clinical progress has been slow
• novel checkpoints have less clinical tumor than anticipated– 1st
generation IDO inhibitors, CSF1R antagonists, antibodies to
TIM-3, Lag3, VISTA etc
• agonist antibodies to stimulate T cells have underperformed to
date: 41BB, OX40 etc.
• utility may be uncovered for these targets, but...
• the preclinical data sets were compelling for many of
these pathways– why has clinical translation been
difficult?
8
IO combo
clinical
translation
• Q: why has clinical translation been
difficult despite compelling
preclinical data?
• A: compelling preclinical and
translational stories can be built on
problematic evidence
Sources of problematic evidence – one
• the mouse (Mus musculus)
• as we use them: inbred, genetically and immunologically
customized
• are these animals actually useful for modeling anti-tumor
immunity?
10
Mouse immunity
• Evolved to mature quickly &
reproduce: 2 months lifespan
in the wild. Cause of death:
predation, infection,
parasitism
• The mouse immune system
has evolved to fight off
pathogens and parasites
• We are fortunate that PD-1
produced a clear signal in
chronic viral infection
models, otherwise we might
have missed it
11
Sources of problematic evidence – two
• Retrospective analysis of target association with disease
outcome (prognosis) data
12
survivor's analyte x
not survivor's analyte x
Observation vs hypothesis testing
13
• Observation: expression of X correlates with
patient outcome in this indication
• Typical hypothesis: X is an important
component of disease pathogenesis
• However, the formal hypothesis is the null
hypothesis
• The null hypothesis is that X has nothing to
do with patient outcome and disease
pathogenesis, ie. that the observed
correlation is spurious
How do we normally go about testing our
hypotheses?
14
• Hypothesis: X is an important component of
disease pathogenesis
How do we usually go about testing our
hypotheses?
15
• we buy more mice
Sources of problematic evidence – three
16
• lack of randomized control trials in Phase 2
• difficult to address given the state of the IO clinical space:
✧ massive numbers of trials
✧ many patients are refractory to SOC and refractory to
PDx therapy
✧ designing an appropriate control arm
• this means that for many targets we are moving into late
clinical development without having formally tested the
null hypothesis
analysis of
PDx
treatment
response
data
• Building new hypotheses
• Analyses of response, resistance &
relapse characteristics (pre vs post-
therapy) have been very informative
Analyses of response, resistance & relapse
in tumors
• An immune signature is a good sign (CD3, CD8)
• Resistance and relapse mechanisms are becoming clearer:
• T cell intrinsic: phosphatases that shut down signaling
• tumor cell intrinsic: mutations that block response to IFNg
• signatures that reflect TME status: fibroblasts, myeloid cells,
TGF-b, b-catenin, VEGF and others
• TME status reflects, in part, immune exclusion and immune
deserts, ie. cold tumor microenvironments
18
TME disruption
• IO/chemotherapy combos
• IO/targeted therapy combos
• Emerging novel IO combos (eg. PDx
with TLR9, PD-L1 x TGbR-trap)
• Several novel IO pathways (eg.
targeting CD47)
A few examples of IO combo success
20
indication PDx combination type control cohort
RCC pembrolizumab axitinib VEGFRi sunitinib
TNBC atezolizumab abraxane nab-paclitaxel
nab-paclitaxel
alone
CRPC durvalumab olaparib PARPi -
NSCLC pembrolizumab dual chemo
carboplatin and
nab-paclitaxel
carboplatin and
nab-paclitaxel
• chemo, targeted therapy, synthetic lethality – there are
many other examples
what do these strategies have in common?
• The combo therapy induces tumor cell death, with the
potential for de novo immune responses
• The combo therapy perturbs the TME, providing a window
in which T cells can proliferate and mount a cytotoxic attack
• The combo therapy disrupts immunosuppressive pathways
(eg. VEGFRi)
21
Example: VEGF
targeting
22
© Beacon Intelligence/Hanson Wade 2018
"New biological goals" for novel IO agents
and proposed IO combos
• Articulate a molecular hypothesis - what is the immune
mechanism of action
• Pursue a therapeutic hypothesis - how is the immuno-therapy
relevant in a specific indication or indications in the context of
current standard of care (including anti-PDx)
23

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Biological Goals for Novel Immune Checkpoints

  • 1. 1 Establishing a New Set of Biological Goals for Checkpoint Inhibitors and IO Combinations Paul Rennert, President & CSO Aleta Biotherapeutics
  • 2. Outline • Restate the goal • IO combo reality check • Generating new hypotheses • TME disruption • IO lessons and CAR T
  • 3. Restate the goal • Establishing a New Set of Biological Goals for Checkpoint Inhibitors and IO Combinations ..... • New goal: ✧ Propose several hypotheses to explain why we have sometimes misread pathways of anti-tumor immunity ✧ Use these hypotheses to think about how we can best move forward
  • 4. Why pursue novel immune checkpoint pathways and IO combinations? • Most patients do not respond to immune checkpoint therapy 4
  • 5. ...reading anti-tumor immunity • We and many others took a reductionist view which yielded a rich list of potential targets 5Mahoney et al. 2015. NRDD
  • 6. Where we've been... • The guiding therapeutic principal has been "remove the brakes and step on the gas" (ie. on T cells) ✧ a T cell centric view • We've been protein superfamily centric (IgSF, TNFRSF), a hangover from the human genome sequencing era • We proposed that the right combinations of checkpoint inhibition and/or T cell activation for each indication would boost response rates – more patients would respond 6
  • 7. What we got right • We focused on standard of care combinations: chemotherapy, irradiation, targeted therapeutics (eg. BRAF & VEGF targeting) • We focused on NK cells, the myeloid lineage and the tumor microenvironment 7 • and on tumor cell/stromal interactions Mahoney et al. 2015. NRDD
  • 8. regardless, clinical progress has been slow • novel checkpoints have less clinical tumor than anticipated– 1st generation IDO inhibitors, CSF1R antagonists, antibodies to TIM-3, Lag3, VISTA etc • agonist antibodies to stimulate T cells have underperformed to date: 41BB, OX40 etc. • utility may be uncovered for these targets, but... • the preclinical data sets were compelling for many of these pathways– why has clinical translation been difficult? 8
  • 9. IO combo clinical translation • Q: why has clinical translation been difficult despite compelling preclinical data? • A: compelling preclinical and translational stories can be built on problematic evidence
  • 10. Sources of problematic evidence – one • the mouse (Mus musculus) • as we use them: inbred, genetically and immunologically customized • are these animals actually useful for modeling anti-tumor immunity? 10
  • 11. Mouse immunity • Evolved to mature quickly & reproduce: 2 months lifespan in the wild. Cause of death: predation, infection, parasitism • The mouse immune system has evolved to fight off pathogens and parasites • We are fortunate that PD-1 produced a clear signal in chronic viral infection models, otherwise we might have missed it 11
  • 12. Sources of problematic evidence – two • Retrospective analysis of target association with disease outcome (prognosis) data 12 survivor's analyte x not survivor's analyte x
  • 13. Observation vs hypothesis testing 13 • Observation: expression of X correlates with patient outcome in this indication • Typical hypothesis: X is an important component of disease pathogenesis • However, the formal hypothesis is the null hypothesis • The null hypothesis is that X has nothing to do with patient outcome and disease pathogenesis, ie. that the observed correlation is spurious
  • 14. How do we normally go about testing our hypotheses? 14 • Hypothesis: X is an important component of disease pathogenesis
  • 15. How do we usually go about testing our hypotheses? 15 • we buy more mice
  • 16. Sources of problematic evidence – three 16 • lack of randomized control trials in Phase 2 • difficult to address given the state of the IO clinical space: ✧ massive numbers of trials ✧ many patients are refractory to SOC and refractory to PDx therapy ✧ designing an appropriate control arm • this means that for many targets we are moving into late clinical development without having formally tested the null hypothesis
  • 17. analysis of PDx treatment response data • Building new hypotheses • Analyses of response, resistance & relapse characteristics (pre vs post- therapy) have been very informative
  • 18. Analyses of response, resistance & relapse in tumors • An immune signature is a good sign (CD3, CD8) • Resistance and relapse mechanisms are becoming clearer: • T cell intrinsic: phosphatases that shut down signaling • tumor cell intrinsic: mutations that block response to IFNg • signatures that reflect TME status: fibroblasts, myeloid cells, TGF-b, b-catenin, VEGF and others • TME status reflects, in part, immune exclusion and immune deserts, ie. cold tumor microenvironments 18
  • 19. TME disruption • IO/chemotherapy combos • IO/targeted therapy combos • Emerging novel IO combos (eg. PDx with TLR9, PD-L1 x TGbR-trap) • Several novel IO pathways (eg. targeting CD47)
  • 20. A few examples of IO combo success 20 indication PDx combination type control cohort RCC pembrolizumab axitinib VEGFRi sunitinib TNBC atezolizumab abraxane nab-paclitaxel nab-paclitaxel alone CRPC durvalumab olaparib PARPi - NSCLC pembrolizumab dual chemo carboplatin and nab-paclitaxel carboplatin and nab-paclitaxel • chemo, targeted therapy, synthetic lethality – there are many other examples
  • 21. what do these strategies have in common? • The combo therapy induces tumor cell death, with the potential for de novo immune responses • The combo therapy perturbs the TME, providing a window in which T cells can proliferate and mount a cytotoxic attack • The combo therapy disrupts immunosuppressive pathways (eg. VEGFRi) 21
  • 22. Example: VEGF targeting 22 © Beacon Intelligence/Hanson Wade 2018
  • 23. "New biological goals" for novel IO agents and proposed IO combos • Articulate a molecular hypothesis - what is the immune mechanism of action • Pursue a therapeutic hypothesis - how is the immuno-therapy relevant in a specific indication or indications in the context of current standard of care (including anti-PDx) 23