L’importanza dei biomarker nella strategia terapeutica

(1)

L’importanza dei biomarker nella strategia terapeutica

Aldo Scarpa

ARC-NET Centre for Applied Research on Cancer and Department of Pathology

University of Verona

(2)

Modified - Drake et al, Nat Rev 2013

Support a non-specific enhancement of innate immune response

AGENT TARGET

Ipilimumab CTLA-4 Tremelimumab CTLA-4 Nivolumab PD-1 Pembrolizumab PD-1 Atezolizumab PD-L1 Durvalumab PD-L1

Avelumab PD-L1

Checkpoint Inhibitors

under Clinical Development for NSCLC

(3)

Schalper KA et al, JNCI 2015

Prognostic Effect of CD8+, TILs

(4)

Gentles AJ et al, Nat Med 2015

Prognostic Effect of genes and

infiltrating immune cells

(5)

Thus, biomarkers that predict response, resistance, or toxicity are of paramount importance to effectively develop these agents

PD-L1

TILs pre-existing immune response hallmark

Mutational load and neoantigens

Immunosuppressive cell types : immature dendritic cells, MDSCs, tumor-associated macrophages

M1 : pro-inflammatory M2 : anti-inflammatory

M2 + MDSC resistance

Immuno stimulatory immunoinhibitory Cytokines (cytokine signatures )

(6)

Rossi G et al, IJSP 2009

Diagnostic algorithm in NSCLC

What your pathologist is doing for you

(7)

H&E TTF-1 p63

SquamousAdenocarcinoma

What your pathologist is doing for you

(8)

‘Suspect’

NSCLC Morphology

Non-Squamous Squamous

IHC [TTF-1, p63]

‘Evidence-Based’ Algorythm

Pathology

Report

(9)

Zhou C et al, ASCO 2012 – Ann Oncol 2015

EGFR mutant: TKIs

MOLECULAR PATHOLOGY allows to globally

improve survival ………..over 3 yrs!

(10)

ALK-rearranged

Solomon B et al, NEJM 2014 Soria JC et al, Lancet 2017

………by FISH …….by IHC

(11)

Molecular analysis – positive samples*

17,8%

31,9%

*AT ANY TIME, during the history of the disease

N° 1787 926 482 63 241 269 15 89

15,8%

3,7% 4%

20%

3,3%

Patients

Characteristics % (N)

Median age 68 years

(24 - 94)

< 45 years-old 3% (51)

Females 36% (642)

Males 64% (1145)

Current smokers 31% (551) Former smokers 47% (848) Never smokers 22% (388) Adenocarcinoma 75.3% (1345)

Squamous 15.7% (280)

NSCLC NOS 3.9% (69)

1787 patients included

Predictive feature EGFR test EGFR mut ALK test ALK trans

Predictive histology (n=1448) 88% (1273) 24% (310) 61% (885) 9% (80)

Younger than 45 years (n= 51) 80% (41) 31.7% (13) 65% (33) 24% (8)

Never smokers (n= 386) 89% (345) 48.6% (168) 57% (220) 16.8% (37)

Females (n= 642) 86.6% (556) 35.4% (197) 56% (362) 11.3% (41)

Gobbini E et al, AIOM 2016

ALK-positive NSCLC:

ALK testing in the ‘Real World’

(12)

‘Suspect’

EGFR wt

ALK/ROS1 non-rearr.

Clinical Indication #1

Chemotherapy IHC [TTF-1, p63]

EGFR mut

ALK/ROS1 rearr.

‘Evidence-Based’ Algorythm

TKIs

Pathology

Report

(13)

First line immunotherapy

Pembro vs. CHEMO: PFS & ORR in PD-L1 TPS ≥50%

Reck M et al, ESMO 2016 & NEJM 2016

61.5% Men

18.5% Squamous 90.5% C/F Smokers

1934 Screened Patients 500 (30%) PD-L1 TPS ≥50%

Target HR 0.55

(14)

Brahmer J et al, WCLC 2017

Overall survival, %

Time, months 100

80

60

40

20

0 0 154 151

3 136 123

6 121 107

9 112

88

12 106

80

33 0 0 15

96 70

18 89 61

21 83 55

24 52 31

27 22 16

30 5 5 No. at risk

Pembro Chemo

Pembrolizumab Chemotherapy

70.3%

54.8%

51.5%

34.5%

Median (95%CI) 30.0 mo (18.3, NR) 14.2 mo (9.8, 19.0)

Events, n HR (95%CI) Pembrolizumab 73 0.63 (0.47, 0.86)

Chemotherapy 96 p=0.002

Censoring rate (55% of pts with event)

Control Arm: 63%

of discontinued pts received IO

•  27% pts at risk a 2 years

Pembro vs. CHEMO: OS in PD-L1 TPS ≥50%

(15)

IHC [PD-L1 Assay]

Clinical Indication #2

‘Evidence-Based’ Algorythm

‘Suspect’

EGFR wt

ALK/ROS1 non-rearr.

Clinical Indication #1

IHC [TTF-1, p63]

EGFR mut

ALK/ROS1 rearr.

Pathology Report

TKIs

(16)

NSCLC: Molecular Portrait at baseline

mEGFR

15% re-ALK 5%

re-ROS1 1%

PD-L1

TPS>50%

20%

PD-L1 TPS 0-49%

59%

mEGFR re-ALK re-ROS1

PD-L1 TPS>50% PD-L1 TPS 0-49%

Time-to-report: 3-4 weeks?

(17)

Clinical Indication

‘Suspect’

IHC [TTF-1, p63]

IHC [ab-ALK D5F3] Ventana IHC [ab-PD-L1 22C3] Dako

Pathology Report

What if ……….

ALK+

EGFR wt

ROS1 non-rearr.

EGFR mut

ROS1 rearr.

TKIs TPS>50% TPS<50%

PEMBRO Chemo

(18)

2° line Nivolumab: no restrictions according to histology or PD-L1…………EVEN IF………

Borghaei H et al, NEJM 2015 Reckamp KL et al, WCLC 2015

Squamous Non-Squamous

Boundary p<0.03 Boundary <0.0408

(19)

Barlesi F et al, ESMO 2016

2° line Atezolizumab: no restrictions according to histology or PD-L1 …………EVEN IF………

OAK [Ph. III]

(20)

Herbst R et al, Lancet 2016

Validated cut-offs matter

2° line Pembrolizumab: PD-L1

Garon P et al, AACR 2015

TPS ≥1% TPS ≥50%

HR 0.54 (p=0.0002) HR 0.50 (p<0.0001)

HR 0.71 (p=0.0008) HR 0.61 (p<0.0001)

Target HR 0.60

HR 0.71 (p=0.0008) HR 0.61 (p<0.0001).

(21)

Baas P et al, ASCO 2016

Pembro vs. DOC: ORR (and OS) according to PD-L1

(22)

Nivolumab Plus Ipilimumab in First-line NSCLC:<br />Efficacy Across All Tumor PD-L1 Expression Levels

Hellmann M et al, ASCO 2016

‘Boosting’ Nivo 1st line activity by adding IPI

Activity of adding IPI to NIVO significantly increases for patients with PD-L1 ≥1%

(23)

Hirsch F et al, JTO 2016

’Blueprint’ PD-L1 IHC Assay Comparison Project:

Analytical Evaluation Results (case-based score, 3 readers)

3/4 assays similar More dispersion

Tumoral Staining (TC) Immune Staining (IC)

Are PD-L1 IHC-assays similar?

(24)

Hirsch F et al, JTO 2016

Are PD-L1 IHC-assays similar?

•  3 (22C3, 28-8, SP263) of the 4 assays were closely aligned on TC staining whereas the SP142 (Ventana) showed consistently fewer TC stained.

•  All of the assays demonstrated IC staining, but with greater variability than with TC staining.

•  Despite similar analytical performance of PD-L1

expression for 3 assays, interchanging assays and cutoffs would lead to “misclassification” of PD-L1 status for some patients.

•  More data are required to inform on use of alternative staining assays upon which to choose different specific therapy-related PD-L1 cutoffs.

(25)

•  PD-L1 assays identify a subset of patients for which

immune checkpoints inihibitors might represent a ‘game- changer’.

•  Two clinical consultations after the pathology report may delay appropriate therapy.

•  Pathologists must be supported (with resources and technologies) to find the more cost-effective strategy to integrate multiple IHC platforms for lung cancer diagnosis and subsequent treatment optimization

Conclusions

(26)

Non-LTS^a

(Non‒long-term survivors) Patients that died within

24 months of randomization

LTS

(Long-term survivors) Patients who lived ≥ 24

months since randomization

R 1:1 Locally advanced or

metastatic NSCLC

• 1–2 prior lines of chemotherapy including at least 1 platinum-based therapy

• Any PD-L1 status

Atezolizumab 1200 mg IV q3w

Docetaxel 75 mg/m²IV q3w

PD or loss of clinical benefit

PD

Survival follow-up No crossover to

atezolizumab allowed

Teff Signature as a predictor of benefit of Atezolizumab

Kowanetz M et al, WCLC 2017

•  Teff gene signature is a surrogate for PD-L1 expression and pre-existing immunity

§  Teff signature was defined by mRNA expression of 3 genes (PDL1, CXCL9, IFNG) and derived from a broader 9-gene signature from POPLAR

§  In the OAK study, the Teff signature was associated with PD-L1 expression assessed by IHC (P = 7.3 x 10^-45)

•  Teff signature partially overlaps with PD-L1 IHC positive and identifies a unique subset of patients within the PD-L1–negative population

Teff Gene Signature vs PD-L1 IHC (SP142)

36%

14% 20%

Teff

≥ median

TC1/2/3 or IC1/2/3^b

N = 753

Teff Gene Signature PDL1

PD-L1

expression on TC and IC IFNG Pre-existing

immunity CXCL9

ventana

(27)

0,250.25 1.0 2.0 PFS HR

Favors atezolizumab Favors docetaxel 0.94

1.11 0.91

1.30 0.73

1.10 0.66

PFS HR (95% CI)

0.91 (0.76, 1.09) 1.11 (0.82, 1.49) 0.94 (0.81, 1.10) Population

Teff ≥ 25%

Teff < 25%

BEP

0.73 (0.58, 0.91) 1.30 (1.05, 1.61) Teff ≥ 50%

Teff < 50%

0.66 (0.48, 0.91) 1.10 (0.92, 1.31) Teff ≥ 75%

Teff < 75%

Teffexpression

Teff ≥ median, HR = 0.73 (0.58, 0.91) Teff < median, HR = 1.30 (1.05, 1.61)

Atezolizumab, ≥ median Atezolizumab, < median Docetaxel, ≥ median Docetaxel, < median

Progression-Free Survival (%)

Months n (%)

189 (25%) 564 (75%) 382 (51%) 371 (49%) 566 (75%) 187 (25%) 753 (100%)

Kowanetz M et al, WCLC 2017

Progression-Free Survival (PFS)

•  Increasing atezolizumab PFS benefit was observed with higher Teff gene expression

•  Patients with Teff expression ≥ median experienced a significant PFS benefit

Teff Signature as a predictor of benefit of Atezolizumab

(28)

STK11/LKB1 and KRAS Co-mutation as a predictor of resistance to immune therapy

Skoulidis F et al, WCLC 2017

•  STK11/LKB1 inactivation is associated with a cold tumor immune microenvironment in LUAC and promotes primary resistance to PD-1 blockade in syngeneic mice (Skoulidis Cancer Disc 2015, ASCO 2015 and ASCO 2017)

Skoulidis F et al, Cancer Disc 2015 Skoulidis F et al, ASCO 2015 Skoulidis F et al, ASCO 2017

(29)

Skoulidis F et al, WCLC 2017 Retrospective review of KRAS-mutant LUAC patients treated with IO (Alive for > 14 days after C1D1 IO)

•  174 KRAS-mutant LUAC included in the analysis

•  146 Nivolumab, 19 pembrolizumab, 9 anti-PD-1/PD-L1 + anti-CTLA-4

ORR (RECIST 1.1) P=0.000735

Fisher’s exact test

7.4%

35.7%

28.6%

KL

KP

K-only

(30)

P=0.0018, log-rank test

mPFS 1.8m mPFS 3.0m mPFS 2.7m

mPFS 1.8m

mPFS 2.7m

P=0.00038, log-rank test HR 1.87 (95% CI,1.32-2.66)

mOS 6.4m mOS 16.0m mOS 16.1m

P=0.0045, log-rank test

mOS 6.4m

mOS 16.0m

P=0.0015, log-rank test HR 1.99 (95% CI 1.29-3.06)

PFS

OS

(31)

•  STK11 loss-of function represents a major driver of de novo resistance to PD-1axis blockade in KRAS-mutant NSCLC.

•  STK11 loss of function enriched in TMB^I/H/PD-L1-negative LUAC and are associated with a cold tumor immune microenvironment.

•  A single genetic event (and therefore potentially a single mechanism) may account for up to 42% of primary resistance to PD-1 blockade, supporting science-driven targeted combination strategies to re-invigorate anti-tumor immunity in KL LUAC.

(32)

George S et al, Immunity 2017

PTEN Loss is associated with lower response to I-O

•  Biallelic PTEN loss was associated with induction of an immunosuppressive microenvironment.

(33)

Peng W et al, Cancer Discovery 2017

PTEN Loss promotes resistance to Immunotherapy

•  Reduced T cell–mediated antitumor activity against PTEN-silenced melanoma cells

(34)

PTEN Loss and anti-PD1 therapy: 39 melanoma pts

(35)

Targeting the immunosuppressive microenvironment

Manegold C et al, J Thor Oncol 2016

Combined inhibition of tumor angiogenesis and the immune checkpoint, PD-1

(36)

VEGF is critical in PTEN-loss immune resistance

•  Targeting VEGF may potentially revert PTEN loss-dependent immune resistance.

(37)

Conclusions

•  Phase III trials continue to indicate persistency of benefit with IO, irrespective of MoAbs and setting.

•  In these trials, no clinico-pathological or bio-molecular signature can be easily considered validated for clinical practice in order to

significantly maximize the benefit of IO (other than PD-L1 high expression).

•  Although not addressed for survival benefit, long-term follow-up analyses of

Phase Ib, Phase II and Real-World EAP studies with IO confirm a similar long-term outcome and overall prognosis.

•  Translational and clinical research are moving forward together to:

•  Explore if (and why) patients (featured by unknown factors) experience disease worsening during IO (although this observation requires prospective validation) .

•  Identify with sophisticated technologies and modeling predictive factors of resistance and sensitivity, at the baseline and during treatment.

•  Intercept those PD-L1-negative patients who derive significant benefit from IO (ex.

TMB,Teff).

(38)

Spigel D et al, ASCO 2016

Total Mutational Burden (TMB) & I-O Efficacy

(39)

McGranahan et al, Science 2016

Sensitivity to PD-1 blockade enhanced in tumors enriched for clonal neoantigens.

Neoantigen Intratumor Heterogeneity (ITH) & Clonal Neoantigens

(40)

Gandara D et al, ESMO 2017

TMB as a predictor of benefit of Atezolizumab

•  Training Set: POPLAR, Validation Set: OAK

(41)

TMB and Microsatellite Instability

(42)

MSI is the marker of dMMR machinery:

•  A tumour with a defective DNA mismatch repair (dMMR) system has thousands of mutations.

•  PolyA DNA microsatellites, due to their monomorphic composition, are highly prone to misalignments during DNA replication.

1. Definition of dMMR/MSI tumour

(43)

There are two clinically useful tests to detect a dMMR cancer

i) identification of MSI by molecular testing of poly-A microsatellites

: direct proof of dMMR

ii)

lack of immunohistochemical expression of MMR proteins:

indirect suggestion of a dMMR system, which should be confirmed with MSI molecular testing.

2. Diagnosis of dMMR

(44)

Figure 1 Model of the proposed mechanism of mismatch repair proteins, illustrating patterns of clinically relevant heterodimerization

Vilar, E. & Gruber, S. B. (2010) Microsatellite instability in colorectal cancer—the stable evidence Nat. Rev. Clin. Oncol. doi:10.1038/nrclinonc.2009.237

(45)

BAT25 BAT26 NR21 NR22 NR24 PMS2

MLH1

MSH2 MSH6

N

T N

N N

N

T

T T

T

BAT25 BAT26 NR21 NR22 NR24 PMS2

MLH1

MSH2 MSH6

N

T

T T

MSS

MSI

(46)

MLH1 MSH2

neg

pos

BAT25/26

instable stable

25 4

5 168

30 172

29

173 202

30 of 202 cases are MSI+ (15%)

IHC data were confirmed on whole sections

(47)

4- MSI testing suggestions based on available data are reported in the Table below.

Cancer type Testing suggestions MSI Prevalence

Colorectal All cancers 15%

Gastric All cancers 15%

Duodenal and ampulla of Vater All cancers Up to 10%

Esophageal Barrett's associated cancers 5%

Endometrial All cancers Up to 33%

Ovarian All cancers 10%

Cervical Advanced stage cancers 5%

Breast None <1%

Hepatocellular None No evidence

Pancreatic and periampullary Medullary histotype, cancers of <1% in pancreas cancer, up to 10 o/o periampullary area in cancers of periampullary area

Sebaceous Skin Tumour All tumours 25%

Melanoma None Inconsistent data

Lung Cancer None <1%

Glioma Pediatric, young adulls Controversial data 0-33%

Prostate Cancer Advanced stage cancers Up to 12%

Thyroid Cancer None No evidence

Head and Neck Cancer None 1%

Renal Cell Carcinoma None No evidence

Sarcoma None No evidence

(48)

E.U. FP7 grant no 602783

5X1000 grant n. 12182

Ministry of Health FIMP, J33G13000210001

Ministry of University and Research (FIRB RBAP10AHJB);

L’importanza dei biomarker nella strategia terapeutica