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Emerging biomarkers for immunotherapy in lung cancer

Prof Keith M KerrDepartment of Pathology

Aberdeen University Medical School, Aberdeen Royal InfirmaryAberdeen, UK

DisclosuresConsultancy• AbbVie, AstraZeneca, Boehringer Ingelheim, Bristol-Myers Squibb,

Eli Lilly, Merck Serono, Merck Sharp & Dohme, Novartis, Pfizer, Roche, Ventana

Honoraria (speaker)• AstraZeneca, Boehringer Ingelheim, Bristol-Myers Squibb,

Eli Lilly, Merck Serono, Merck Sharp & Dohme, Novartis, Pfizer, Roche, Ventana

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Why Do We Need Biomarkers for Immunotherapy?

3I-O, immuno-oncology.

Precision medicine is a reality for many tumour types

Not all patients respond to and benefit from these treatments

• Enrich the treatment population for benefit

• How many to treat, to get one response?

• Benefit is relative to standard of careAvoidance of harm?

• There are toxicities from these drugs• Is there a subgroup of patients who

fair worse on I-O treatment?• Alternative treatment would be better

Financial burden of expensive therapy

Therapeutic Aims & Assumptions Potential Biomarkers

• Inhibit the interaction of PD-1 and PD-L1• In order to ‘take the brakes off’ an existing tumour-

specific immune response

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Numbers of mutations in diagnostic panels

PD-1~PD-L1Interaction is present

THIS is the drug target!

Whole tumour mutation burden: Whole Exome Sequencing

• A surrogate for probable neoantigen load is tumour mutational burden (TMB)

Specific immunity is available – the tumour is ‘inflamed’

• We hope this tumour-specific immune response actually exists– This requires immunogenicity– Which requires antigenicity (neoantigens) to

be ‘visible’ to the immune system

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Other possible factors impacting therapy response

PD1~PD-L1 inhibition is active

There is an anti-tumour immune response to

re-activate

The tumour is immunogenic

Soluble immune inhibitors

Tumour Micro-environment

Inhibitory cell populations

Inhibitory tumour metabolism

Host factors

General immune status

Gut microbiome

Other checkpoints

Tumour ‘invisible’ or impervious to immune killing

‘The Lottery Model’The Cancer ImmunogramBlank CU et al. Science 2016

In Advanced NSCLC, PD-L1 Expression by Immunohistochemistry Can Enrich for Response

• Response rates in the ITT population are 14% to 20%1-6

• Response rates in ‘selected’ populations are 30% to 45%2,4,5

• Cohort in the ‘PD-L1-negative’ group do less well on second-line immune checkpoint inhibitors3,6

• The required ‘power’ of the biomarker to improve outcome depends upon the activity of the standard of care comparator. This differs between 1st and 2nd line therapy.

6ITT, intent-to-treat.1. Brahmer J et al. N Engl J Med. 2015;373:123-135. 2. Spira AI et al. ASCO 2015; Abstract 8010.3. Borghaei H et al. N Engl J Med. 2015;373:1627-1639. 4. Herbst RS et al. Lancet.2015;387:1540-1550. 5. Reck M et al. N Engl J Med. 2016;375:1823-1833.6. Rittmeyer A et al. Lancet 2017;389:255-265

For NSCLC, PD-L1 IHC Has the Ability to Enrich for Response and Treatment Benefit, however…….

PD-L1 IHC is not a perfect biomarker

• Not all ‘positive’ patients respond

• Occasional responders in ‘biomarker negative’ populations

• But no biomarker is perfect

• Expectations in the real, clinical world

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Possible Confounders of Tumour Cell PD-L1 IHC Prediction• Heterogeneity of expression—potential for sampling error

• Cutoffs in a biological continuum

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Binary Output Versus Biological Continuum

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Biological continuum of biomarker expression

Biomarker is ABSENT or at a LOW LEVELYou are unlikely to

benefit from therapy

Biomarker is PRESENT at a HIGH levelYou are likely to

benefit from therapy

THRESHOLD CUTOFF

Biomarker is PRESENT at an INTERMEDIATE LEVEL

You may benefit from therapy

50% 80%1%

Biomarker is ABSENT You are unlikely to

benefit from therapy

Biomarker is PRESENTYou are likely to

benefit from therapy

Binary Output Versus Biological Continuum

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Biological continuum of biomarker expression

Biomarker is ABSENT or at a LOW LEVELYou are unlikely to

benefit from therapy

Biomarker is PRESENT at a HIGH levelYou are likely to

benefit from therapy

THRESHOLD CUTOFF

Biomarker is PRESENT at an INTERMEDIATE LEVEL

You may benefit from therapy

50% 80%1%

Biomarker is ABSENT You are unlikely to

benefit from therapy

Biomarker is PRESENTYou are likely to

benefit from therapySame principle may apply to TumourMutation Burden,

Immune Gene expression signatures and other IO biomarkers with a

quantitative determination

Possible Confounders of Tumour Cell PD-L1 IHC Prediction• Heterogeneity of expression— potential for sampling error• Cutoffs in a biological continuum

• Expression on tumour cells (TC) and/or immune cells (IC)• Intrinsic induction of PD-L1: oncogenic pathway driven• Other immune regulatory mechanisms are also active• Technical issues

• Does the assay correctly identify patients?• Did the assay work this time?

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Intrinsic induction of PD-L1 expression• Upregulation related to oncogenic pathway activation in the tumour

• JAK-STAT• PI3K, MET• Probably others due to crosstalk

Implies PD-L1 expression that may not be immunologically active

We have little idea, so far, how to identify these cases

Teng MWL et al. Cancer Res 2013

But life in complex…………….Five most advanced PD1 axis inhibitors and their biomarker assays

Drug Company PD-L1 Diagnostic Ab clone

Staining Platform

Clinically relevant cut offs *

Biomarker status

Nivolumab Bristol-Myers Squibb

28-8 (Dako) Dako Link 48 TC ≥1% , 5%, 10% Complementary

Pembrolizumab Merck/MSD 22C3 (Dako) Dako Link 48 TC > 1, 50% Companion

Atezolizumab Genentech/Roche SP142 (Ventana) VentanaBenchMarkULTRA

TC > 1, 5, 50%

IC > 1, 5, 10%Complementary

Durvalumab Astra-Zeneca SP263 (Ventana) Ventana

Benchmark

TC ≥ 25% Not sure

Avelumab Pfizer/ Merck Serono

73-10 (Dako) Dako Link 48 TC ≥ 1, 50, 80% Unknown

Five drug- PD-L1 IHC assay combinations?• Trial validated assay for each drug?

• It is unlikely that labs will provide multiple tests• Staining platform availability?

• One Trail validated assay for all drugs?• Lab Developed Test?

• Laboratory developed tests (LDTs)• ‘Modification’ of the companion/complementary assay• LDT built around a ‘trial’ clone• LDT built around another clone

Outcome of an IHC test is a function of the primary antibody AND the detection system used

Drug Assay based on clone……….

Nivolumab 28-8

Pembrolizumab 22C3Atezolizumab SP142

Durvalumab SP263Avelumab 73-10

26/38 cases(60.5%)

26/38 cases (60.5%)

20/38 cases (52.6%)

Number of cases (%) with PD-L1 expression above the assay specific selected cut-off

30/38 cases(78.9%)

Treatment populations defined by different assays and related cut-points are different

• Stating the obvious but………..

• The cut-point stays with the DRUG, and not the assay

Hirsch FR et al. JTO 2016

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Several ‘technical comparability’ studies have shown broadly similar results…………..

• Across the spectrum of PD-L1 tumour cell expression, three assays show similar performance

• Dako 28-8, Dako 22C3, Ventana SP263• The Ventana SP142 assay stains fewer tumour cells

Scheel A et al Mod Pathol 2016; Hirsch FR et al. JTO 2016; Rimm DL et al. JAMA Oncol 2017; Ratcliffe M et al Clin Can Res 2017; Adam J et al. Ann Oncol 2018 (Feb), Hendry S et al JTO 2018; Scheel AH et al, Histopathol 2018.

Tsao MS, Kerr KM, Hirsch FR et al, Blueprint 2

Blueprint 2: Cases above cut offs by different assays – TC staining

Tsao MS et al. WCLC 2017

Small number so only broad conclusionsØ 22C3, 28-8 and SP263 more or less the sameØ SP263 a little more sensitive?

Ø SP142 less sensitiveØ 73-10 more sensitive

Actual clinical data?

PD-L1 assays: same, different?

• SP263 may be a little more sensitive

Hendry S et al. JTO March 2018

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Are their clinical data to support the technical comparability of some assays?

Different scoring algorithms – same ‘class’ effectSame patients?

Alternative PD-L1 IHC biomarkers adequately predict outcome for 2nd line Nivolumab in NSCLC

Fujimoto D et al. JTO March 2018

Test Reliability?

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Can 25 pathologists agree with each other about PD-L1 scoring?

Fleiss Kappa: >0.90: excellent0.75-0.9: good

0.40-0.59: weak0.20-0.39: minimal<0.01-0.20: slight/none

Koo TK & Li MY, J Chiropr Med 2016;15:155-63

YES!

For TC score(TPS%)

NO!

For IC score

Tsao MS, Kerr KM, Hirsch FR et al, Blueprint 2 WCLC 2017

Trial validated assay or go ‘off piste’?

Performance of LDTs

Frequently ‘sub-standard’

• 13/27 (48%) of LDTs failed to make the grade (k > 0.75) 1

• Nordic EQA• 22C3, 28-8, SP263-based Assay

(77-92% pass rate)• 22C3 or E1L3N clone LDTs

(20% pass rate)

• UKNEQAS• Trial validated assays - 100% pass• LDTs - 35% Borderline; 65% Failed

Although a good LDT can be developed

• An E1L3N-based LDT matched Dako assays after intensive validation

• German QUIP programme• Adequate performance of LDT

Rimm DL et al. Lancet Oncol 2017

Scheel A et al. Histopathol 2018

1. Adam J et al. Ann Oncol 2017

Therapeutic Aims & Assumptions Potential Biomarkers

• Inhibit the interaction of PD-1 and PD-L1• In order to ‘take the brakes off’ an existing tumour-

specific immune response

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Numbers of mutations in diagnostic panels

PD-1~PD-L1Interaction is present

THIS is the drug target!

Whole tumour mutation burden: Whole Exome Sequencing

• A surrogate for probable neoantigen load is tumour mutational burden (TMB)

Specific immunity is available – the tumour is ‘inflamed’

• We hope this tumour-specific immune response actually exists– This requires immunogenicity– Which requires antigenicity (neoantigens) to

be ‘visible’ to the immune system

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Cancer – immune system interaction

Teng MWL et al. Cancer Res 2013

Tumour inflammation and Immune inhibitory mechanisms

Is the Tumour ‘Inflamed’?

• No actual evidence that the ‘inflammation’ is tumour specific, but this is inferred and assumed

• Approaches in NSCLC have, so far, been rather complicated…………

• A molecular rather than a morphological approach to ‘inflammation’

• All of these factors are potentially confounded if ‘inflammation’ is prognostic

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Immune Gene Signatures and Atezolizumab

31CI, confidence interval; HR, hazard ratio; Teff, T-effector; IFN-γ, interferon-gamma.Adapted from Fehrenbacher L et al. Lancet. 2016;387:1837-1846.

Poplar Trial: 2L NSCLC

8-gene mRNA signature:CD8A, GZMA, GZMB, IFNγ, EOMES, CXCL9, CXCL10 and TBX21

6.8 mo(95% CI: 5.9, 7.4 )

11.3 mo(95% CI: 9.1, 13.0)

HR, 0.505 (95% CI: 0.377, 0.675)P < 0.0001

Minimum follow-up: 9.5 mo

Landmark PFS, % Arm B: atezo + bev + CP

Arm C: bev + CP

6-month 72% 57%

12-month 46% 18%

IMPower 150 Trial: 1L NSCLC

Morphological or Molecular Inflammation?

• Immune infiltrate or Immune desert?

• Where are the immune cells?

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‘Immune desert’ ‘Inflammed stroma’ ‘Inflammed infiltrated’

‘Immune excluded’

Morphology or Molecular Inflammation?

• Immune infiltrate or Immune desert?

• Where are the immune cells?

• Which immune cells are present?• Which immune cells matter?

• Immune cell presence or proximity?• Multiplex IHC?

CD8 CD4CD1a CD68CD163FoxP3 etc

CD8IHC

Conde E et al, Histopathol 2018

Image courtesy of Dr L Sholl, Boston, USA

Therapeutic Aims & Assumptions Potential Biomarkers

• Inhibit the interaction of PD-1 and PD-L1• In order to ‘take the brakes off’ an existing tumour-

specific immune response

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Numbers of mutations in diagnostic panels

PD-1~PD-L1Interaction is present

THIS is the drug target!

Whole tumour mutation burden: Whole Exome Sequencing

• A surrogate for probable neoantigen load is tumour mutational burden (TMB)

Specific immunity is available – the tumour is ‘inflamed’

• We hope this tumour-specific immune response actually exists– This requires immunogenicity– Which requires antigenicity (neoantigens) to

be ‘visible’ to the immune system

a probabilistic ‘neo-antigen lottery’ model

Analysis of the Association Between TMB and PD-L1 Expressiona

CheckMate 026 TMB Analysis: Nivolumab in First-line NSCLC

aAll patients had ≥1% PD-L1 tumor expression

• There was no association between TMB and PD-L1 expression in patients with ≥1% PD-L1 tumor expression

PD-L1 (% tumor expression)a

High TMB

7550

1000

316

100

32

10

0 25 100

TMB

(no

. of m

isse

nse

mut

atio

ns)

Low/medium TMB

243

Slide courtesy ofJohn Haanen, NKI

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Whole Exome Sequencing Versus Surrogate Panels for TMB

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1. Frampton GM, et al. Nat Biotechnol. 2013;31:1023–1031. 2. Peters S et al. Oral presentation at AACR 2017. CT082. 3. Kowanetz M et al. Oral presentation at WCLC 2016. OA20.01.

100

50

1

FoundationOne panel* (mutations/MB)

Tota

l exo

me

mut

atio

ns(m

utat

ions

/MB

)

10

501 10 100

Kowanetz et al., 2016, WCLC.3

POPLAR: Genes in FoundationOnePanel vs Whole Exome3

Checkmate 026: Total Exome Mutations vs Genes in FoundationOne Panel1,2

*Based on in sil ico analysis fi ltering on 315 genes in FoundationOne comprehensive genomic profile (Foundation Medicine, Inc, Cambridge, MA, USA)1FM1=FoundationOne; MB=megabase; TMB=tumor mutation burden; WES=whole-exome sequencing.

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0FM1

(mut

atio

ns/M

B)

20

0

10

30

25 50 75WES (mutations/MB)

Carbone DP et al. N Engl J Med. 2017;376(25):2415-2426.

Tumour Mutation Burden (TMB)

• How to measure it?• Whole exome sequencing, Large multigene diagnostic panels

• What is a HIGH TMB?• Numerous definitions, often ‘above median’ or ‘upper tertile’• ‘>10 mutations per megabase’ is becoming established

• There is no consensus on how this should be done

Surrogates of Surrogates of Surrogates of……………………………….of tumour mutational burden

In several tumour types…

• Polymerase E (POLE) mutations1,2

• Mismatch repair genes (MMR)1

• Microsatellite instability (MSI)1,2

• KRAS, STK11, TP53 and EGFR mutation in NSCLC2,3

• ...and by smoking history1

391. Chabanon RM et al. Clin Cancer Res. 2016;22:4309-4321. 2. Tanvetyanon T. Transl Can Res. 2017;6:S424-S426. 3. Ross JS et al. Annal Oncol.2017;28: https://doi.org/10.1093/annonc/mdx376.004.

All of these have been associated with better (enriched) responses to IO

Combinations of Biomarkers for Immunotherapy?

PD-L1 IHC

Surrogates of immune response

Immune cellsGene signature

TumourMutational burden

Surrogates of TMB

1. Adapted from Rizvi NA et al. Science. 2015;348:124-128. 2. Adapted from Fehrenbacher L et al. Lancet. 2016;387:1837-1846. 3. Adapted from McGranahan N et al. Science. 2016;351:1463-1469.

T-effector and IFN-ᵧ gene signature subgroups2

All tumours1

Neonatal clonal architecture and clinical benefit of immune checkpoint blockade3

Microbiome

Metabolism Other stuff

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So far we have very little data on biomarker combinations or direct comparisons of different biomarkers in trials

• TMB plus High PD-L1 IHC better than IHC alone?• Checkmate 026 trial

• Immune gene expression signature (Teff) no better than PD-L1 IHC• IMPower 150 trial

What is coming?

• Combination therapies• IO combinations• IO plus chemotherapy• IO in stage 3 disease• IO as adjuvant or neo-adjuvant therapy

• Relative benefit versus toxicity (cost) for combination therapy• >50% vs 1-49% vs <1% PD-L1 expressors

Conclusions

• PD-L1 assessment will remain• Inflammation and the Tumour Microenvironment will be important

but assessment needs to be clarified• Generic or specific inflammation• Cell types• Cytokines

• Predictions of ‘foreignness’• Actual TMB• Surrogates of TMB• Neoantigens