BIOMARCADORES PRONÓSTICOS

Ivana Sullivan, MD, PhDHospital de la Santa Creu i Sant Pau

Barcelona

DISCLOSURES

• Advisory Board: Roche, Novartis, Boehringer Ingelheim• Speaker bureau: Roche, Merck Sharp & Dohme, Pfizer, Bristol-Myers Squibb,

AstraZeneca• Travel grants: Roche, Bristol-Myers Squibb, Novartis, Pfizer, Boehringer Ingelheim

OUTLINE

• Distinguishing prognostic and predictive biomarkers

• Role of TMB in NSCLC: tissue and plasma (t/pTMB)

• Emerging role of biomarkers in NSCLC (STK11, ARID1A, KEAP1)

• Targeted therapy: beyond EGFR, ALK, ROS1

• Conclusions

Distinguishing prognostic and predictive biomarkers

Definition

Clinical or biologicalcharacteristic thatprovides information onthe likely patient healthoutcome (e.g. diseaserecurrence) irrespectiveof the treatment.

It indicates the likelybenefit to the patient fromthe treatment, compared to their condition at baseline.

PRO

GN

OST

ICPR

EDIC

TIVE

Ruberg and Shen, 2015

Role of t/bTMB in NSCLC

Background

• Tumor mutation burden (TMB), the number of somatic mutations per

megabase (mut/Mb) is an emerging biomarker.

• Both, tissue(t)/blood(b) tumor mutation burden (t/bTMB) have been

explored as biomarkers in response to:

– Anti-PD-(L)1 antibody alone

– Combination therapy with PD-L1/ CTLA-4 blockade

– Combination therapy with chemo-IO

TMB: Alternative biomarker emerging

Mutational burden associated with improved clinical outcomes in mNSCLC treated with

pembrolizumab

Neoantigen heterogeneity impacts anti-tumor immunity

Rizvi, et al. Science 2015 McGranahan, et al. Science 2016

Preliminary analyses of tTMB in NSCLC treated with nivolumab

Nivolumab Arm (Checkmate 026)

Carbone et al. N Engl J Med 2017

Preliminary analyses of tTMB in NSCLC treated with IO-(IO) + CT

Hellmann MD, et al. Cancer Cell 2018; Hellman, et al. N Engl J Med 2018

Whole exome sequencing in tumor tissue samples from patients with NSCLC treated with nivolumab + ipilimumab demonstrates the potential of TMB as an independent biomarker of efficacy, distinct from PD-L1

NSCLC (Checkmate 227; TMB ≥ 10 mut/Mb and <1% PD-L1)Nivolumab/Ipilimumab

tTMB: non-squamous mNSCLC

Langer OA04.05 (KEYNOTE 021 C/G)

Garrassino OA04.06(KEYNOTE 189)

Total n 24 + 123 616

Biomarker evaluable Cohort C (CT-IO): 12/24 (50%)Cohort G (CT-IO): 32/60 (53.3%)

Cohort G (CT only): 26/63 (41.3%)

293 (48%)

Analysis Performed WES of tumor tissue and matched normal DNA

Cut Points explored 175 mut/exome (13 mut/MB) 150 mut/exome (10 mut/MB)175 mut/exome (13 mut/MB)

# pts ≥ cut point 48% 52% (>150)46% (>175)

A Priori Hypothesis tTMB is positively associated with improved outcomes

Statistical analysis OS and PFS: Cox proportional hazards modelsORR: logistic regression and ROC curve analysis

OA04.05 (Langer et al.)

No association between tTMB and Tumor Response

OA04.06 (Garassino et al.)

0 10 20 30 40 50 60 70 80 90 100

10

100

1000

PD-L1 TPS, %

tTM

B, m

ut/e

xom

e

30

300

ResponderNonresponder

Pembro + Pem + PlatinumNo association between tTMB and PD-L1

(r = -0.08, two-sided P = 0.27)

0 4 8 12 16 20 24 28 320

20406080

100

Time, months

OS,

%

No. at Risk100 83 74 67 434 30 18 13 0

449

00

7821

195

0 4 8 12 16 20 24 28 320

20406080

100

Time, months

OS,

%

No. at Risk111 93 78 70 441 36 21 15 1

4711

00

8425

196

0 4 8 12 16 20 24 28 320

20406080

100

Time, months

OS,

%

tTMB-Evaluable Population (N = 293)HR 0.64 (95% CI, 0.46-0.88)

TMB ≥175 mut/exome (N = 134)HR 0.64 (95% CI 0.38-1.07)

TMB ≥150 Mut/exome (N = 159)HR 0.68 (95% CI 0.43-1.09)

tTMB does not predict for clinical outcomes after CT-IO

CheckMate 227: Peters et al.

N = 1189

PD-L1expression

< 1%N = 550

NIVO + (low-dose) IPIbn = 396

Chemoc

n = 397

NIVOd

n = 396

NIVO + (low-dose) IPIbn = 187

Chemoc

n = 186

NIVOe + chemoc

n = 177

R1:1:1Key Eligibility Criteria

• Stage IV or recurrent NSCLC• No prior systemic therapy• No sensitizing EGFR mutations

or known ALK alterations• No untreated CNS metastases • ECOG PS 0–1

Stratified by SQ vs NSQ R1:1:1

Database lock: July 2, 2019; minimum follow-up for primary endpoint: 29.3 monthsaNCT02477826; bNIVO (3 mg/kg Q2W) + IPI (1 mg/kg Q6W); cNSQ: pemetrexed + cisplatin or carboplatin, Q3W for ≤ 4 cycles, with optional pemetrexed maintenance following chemo or NIVO + pemetrexed maintenance following NIVO + chemo; SQ: gemcitabine + cisplatin, or gemcitabine + carboplatin, Q3W for ≤ 4 cycles; dNIVO (240 mg Q2W); eNIVO (360 mg Q3W); fTMB primary endpoint analysis conducted at January 24, 2018 database lock in subset of patients randomized to NIVO + IPI or chemo; alpha allocated was 0.025; gAlpha allocated was 0.025 overall (0.023 for final analysis)

PD-L1expression

≥ 1%

Part 1b

Part 1a

Treatment until disease progression, unacceptable toxicity, or for 2 years for

immunotherapy

Secondary endpoints (PD-L1 hierarchy):• PFS: NIVO + chemo vs chemo in PD-L1 < 1%• OS: NIVO + chemo vs chemo in PD-L1 < 1%• OS: NIVO vs chemo in PD-L1 ≥ 50%

Independent co-primary endpoints: NIVO + IPI vs chemo• PFS in high TMB (≥10 mut/Mb) populationf

• OS in PD-L1 ≥ 1% populationg

Part 1 Study Designa

Median OS, monthsNIVO + IPI Chemo HR HR (95% CI)

n = 583 n = 583Randomized groups Stratified Stratified

PD-L1All randomized (N = 1166) 17.1 13.9 0.73PD-L1 < 1% (n = 373) 17.2 12.2 0.62PD-L1 ≥ 1% (n = 793) 17.1 14.9 0.79a

Additional exploratory subgroups analysesb,c Unstratified Unstratified

PD-L11–49% (n = 396) 15.1 15.1 0.94≥ 50% (n = 397) 21.2 14.0 0.70

TMBd

(mut/Mb)low, < 10 (n = 380) 16.2 12.6 0.75high, ≥ 10 (n = 299) 23.0 16.4 0.68

0,25 0,5 1 2

NIVO + IPI Chemo

OS for NIVO + IPI vs Chemo by tumor PD-L1 expression,TMB status, and combined subgroups in all randomized patients

• No consistent correlation was observed between survival outcomes with NIVO + IPI vs chemo and PD-L1 or TMB alone or in combination

aStratified HR (97.72% CI); bPatients were not stratified by TMB or PD-L1 ≥ or < 50% – subgroup analyses therefore may be impacted by imbalances and should be interpreted with caution; cNot controlled by randomization; dUnstratified HR for NIVO + IPI vs chemo in TMB-evaluable (n = 679) and non-evaluable (n = 487) patients was 0.74 (95% CI, 0.61–0.88) and 0.74 (95% CI, 0.60–0.92), respectively.

bTMB – current evidence

Peters et al. AACR 2019

500 gene assay~2.1 Mb coverage

MYSTIC

Gandara DR, et al. Nature Med 2018

Key Eligibility:• Stage IV NSCLC• Treatment Naïve• Starting 1L Pembro-

Based therapy

Exclusions:• EGFR, ALK, ROS1,

BRAF • Prior chemo• Prior IO

Plasma TMB500 gene panel

~ 2.1 Mb Coverage

Statistics:• Comparisons of pTMB between 9-week RECIST and 6-month

DCB

• Using PFS, a cutoff of high pTMB (≥16 mut/Mb) and low pTMB (<16 mut/Mb) was defined

• Kaplan-Meier curves for PFS and OS were generated for those cut-offs

Patients started SOC therapy and followed for response and survival

MA25.04 (Aggarwal et al.)

Survival Outcomes – PFS and OS by pTMB using a cutoff 16 mut/Mb

MA25.04 (Aggarwal et al.)

Emerging role of biomarkers in NSCLC (STK11, KEAP, ARID1A)

Worse PFS/OS in STK11mut with Chemo-Pembro

Abstr.102 (Skoulidis et al.)

N=497

17 institutions from US and EU

*STK11 = LKB1

MYSTIC TRIAL

Evaluable: 943 (84%)

OA04.07 (Rizvi et al.): MYSTIC TRIAL

Mutation prevalence according to bTMB and PD-L1

*Subset of total bTMB evaluable patients (n=809)

OA04.07 (Rizvi et al.): MYSTIC TRIALObjective response rates according to mutation status

ORR , objective response rates

OA04.07 (Rizvi et al.): MYSTIC TRIAL

No clear predictive role for STK11m

*Mutation-evaluable patients

MA11.11 (Heymach et al.)

- Retrospective multi-center international study

- Three cohorts:1. Metastatic non-squamous NSCLC treated with 1st line

PCP with available genomic profiling INCLUDING STK11 (Cohort 1)

2. Metastatic STK11 and/or KEAP1 mutant non-squamousNSCLC treated with 1st line PC prior to regulatoryapproval of PCP (Cohort 2)

3. Metastatic STK11 and KEAP1 wild-type non-squamousNSCLC treated with 1st line PC chemotherapy (Cohort 3)

- All non-synonymous STK11 and KEAP1 mutations andbi-allelic deletions included

- Patients with brain metastases eligible (treated oruntreated)

Targeted therapy: beyond EGFR, ALK, ROS1

RET: BLU-667 (Gainor et al.)

(Praseltinib)

BLU-667 Demonstrates Substantial Antitumor Activity <br />in RET Fusion+ Advanced NSCLC

• Active across RET fusion genotypes• Active Regardless of Prior Checkpoint Treatment• Active Against Intracranial Metastases RR 78% (7/9)

RET: LOXO-292 (Drilon et al.)

ORR: 68%CNS RR: 91%

ORR: 85%

PFS: 18.4 m PFS: NR

Entrectinib Integrated analysis (phase 1 ALKA-372-001 & STARTRK-1 and phase 2 STARTRK-2)

NTRK: Entrectinib (Doebele et al.)• RD: 600 mg QD VO• CNS-active,

selective TRK/ROS1/ALK TKI

CNS efficacy(phase 1 ALKA-372-001 & STARTRK-1 and phase 2 STARTRK-2)

NTRK: Entrectinib (Paz-Ares et al.)

- Limited activity of other kinase- Activity in CNS disease

NTRK: Larotrectinib (Farago et al.)

NTRK: Larotrectinib (Farago et al.)

KRASG12C: AMG 510 (Godivan et al.)

*960 mg oral daily dose was identified as the expansion dose and recommended phase 2 dose

KRASG12C: AMG 510 (Godivan et al.)

KRASG12C: MRTX849 (Jänne et al.)

CONCLUSIONS

• No correlation between TMB and outcomes, including OS

• PD-L1 and TMB were not correlated

• Plasma approach may provide a different perspective of measurement of TMB: what cut-point?• Its role should be validated in larger prospective studies

• STK11/LKB1 and KEAP1 mutations define a subset of patients resistant to PD-1 blockade• Adverse prognostic biomarkers?

• ARID1A: good predictive/prognostic biomarker?

• Mutations beyond EGFR-ALK-ROS1 represent a small piece of pie…

• ...But this small piece of pie really matters given the huge benefit that patients can obtain when receiving targeted therapies (Dra. Majem, GECP 2019)

BIOMARCADORES PRONÓSTICOS

Ivana Sullivan, MD, PhDHospital de la Santa Creu i Sant Pau