L’importanza dei biomarker nella strategia...

L’importanza dei biomarker nella strategia terapeutica

Aldo Scarpa

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

University of Verona

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

Schalper KA et al, JNCI 2015

Prognostic Effect of CD8+, TILs

Gentles AJ et al, Nat Med 2015

Prognostic Effect of genes and infiltrating immune cells

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 )

Rossi G et al, IJSP 2009

Diagnostic algorithm in NSCLC

What your pathologist is doing for you

H&E TTF-1 p63Squamous

Adenocarcinoma

Diagnostic algorithm in NSCLC

What your pathologist is doing for you

‘Suspect’ NSCLC Morphology

Non-Squamous Squamous

IHC [TTF-1, p63]

Diagnostic algorithm in NSCLC

‘Evidence-Based’ Algorythm

Pathology Report

Zhou C et al, ASCO 2012 – Ann Oncol 2015

Diagnostic algorithm in NSCLC

EGFR mutant: TKIs

MOLECULAR PATHOLOGY allows to globally improve survival ………..over 3 yrs!

Diagnostic algorithm in NSCLC

ALK-rearranged

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

………by FISH …….by IHC

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’

‘Suspect’ NSCLC Morphology

Non-Squamous Squamous

EGFR wt

ALK/ROS1 non-rearr.

Clinical Indication #1

Chemotherapy

IHC [TTF-1, p63]

EGFR mut

ALK/ROS1 rearr.

Diagnostic algorithm in NSCLC

‘Evidence-Based’ Algorythm

TKIs

Pathology Report

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

Brahmer J et al, WCLC 2017

Overa

ll surv

ival, %

Time, months

100

80

60

40

20

0

0

154151

3

136123

6

121107

9

11288

12

10680

33

00

15

9670

18

8961

21

8355

24

5231

27

2216

30

55

No. at riskPembroChemo

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%

IHC [PD-L1 Assay] Clinical

Indication #2

Diagnostic algorithm in NSCLC

‘Evidence-Based’ Algorythm ‘Suspect’

NSCLC Morphology

Non-Squamous Squamous

EGFR wt

ALK/ROS1 non-rearr.

Clinical Indication #1

IHC [TTF-1, p63]

EGFR mut

ALK/ROS1 rearr.

Pathology Report

TKIs

Diagnostic algorithm in NSCLC

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?

Clin

ical

Indi

catio

n

‘Suspect’ NSCLC Morphology

Non-Squamous Squamous

IHC [TTF-1, p63] IHC [ab-ALK D5F3] Ventana IHC [ab-PD-L1 22C3] Dako

Pathology Report

Diagnostic algorithm in NSCLC

What if……………….

ALK+

EGFR wt

ROS1 non-rearr.

EGFR mut

ROS1 rearr.

TKIs

TPS>50% TPS<50%

PEMBRO Chemo

Diagnostic algorithm in NSCLC 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

Barlesi F et al, ESMO 2016

Diagnostic algorithm in NSCLC 2° line Atezolizumab: no restrictions according to

histology or PD-L1 …………EVEN IF……… OAK [Ph. III]

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).

Baas P et al, ASCO 2016

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

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%

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)

Diagnostic algorithm in NSCLC

Are PD-L1 IHC-assays similar?

Hirsch F et al, JTO 2016

Diagnostic algorithm in NSCLC

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.

•  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

Diagnostic algorithm in NSCLC

Conclusions

Non-LTSa(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

Docetaxel75 mg/m2 IV 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 IC1/2/3b

N = 753

Teff Gene Signature

PDL1PD-L1 expression on TC and IC

IFNG Pre-existing immunityCXCL9

ventana

0,250.25 1.0 2.0

PFS HRFavors atezolizumab Favors docetaxel

0.94

1.110.91

1.300.73

1.100.66

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%

Teff

exp

ressio

n

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

Atezolizumab, ≥ medianAtezolizumab, < medianDocetaxel, ≥ median Docetaxel, < median

Pro

gre

ss

ion

-Fre

e S

urv

iva

l (%

)

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

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

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=0.000735Fisher’s exact test

7.4%

35.7%28.6%

KL

KP

K-only

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

P=0.0018, log-rank test

mPFS 1.8mmPFS 3.0mmPFS 2.7m

mPFS 1.8mmPFS 2.7m

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

mOS 6.4m

mOS 16.0mmOS 16.1m

P=0.0045, log-rank test

mOS 6.4mmOS 16.0m

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

PFS

OS

Skoulidis F et al, WCLC 2017

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

Skoulidis F et al, WCLC 2017

•  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 TMBI/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.

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

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.

Peng W et al, Cancer Discovery 2017

PTEN Loss promotes resistance to Immunotherapy  

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

Peng W et al, Cancer Discovery 2017

PTEN Loss and anti-PD1 therapy: 39 melanoma pts  

Targeting the immunosuppressive microenvironment  

Manegold C et al, J Thor Oncol 2016

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

Peng W et al, Cancer Discovery 2017

VEGF is critical in PTEN-loss immune resistance  

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

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).

Spigel D et al, ASCO 2016

Total Mutational Burden (TMB) & I-O Efficacy

McGranahan et al, Science 2016 Sensitivity to PD-1 blockade enhanced in tumors enriched for clonal neoantigens.

Neoantigen Intratumor Heterogeneity (ITH) & Clonal Neoantigens

Gandara D et al, ESMO 2017

TMB as a predictor of benefit of Atezolizumab

•  Training Set: POPLAR, Validation Set: OAK

TMB and Microsatellite Instability

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

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

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

BAT25 BAT26 NR21 NR22 NR24

PMS2 MLH1

MSH2 MSH6

N

T

N

N

N

N

T

T T

T

T

T T

T

BAT25 BAT26 NR21 NR22 NR24

PMS2 MLH1

MSH2 MSH6

N

T

T T

T T

MSS

MSI

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

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

E.U. FP7 grant no 602783

5X1000 grant n. 12182

Ministry of Health FIMP, J33G13000210001

Ministry of University and Research

(FIRB RBAP10AHJB);

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