l’importanza dei biomarker nella strategia...
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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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