Transcript

Biomarcadores e Imunoterapia:

além do PD-L1

Luiz Henrique Araujo, MD, PhD

Instituto COI de Educação e Pesquisa

Instituto Nacional do Câncer (INCA)

Sumário

• Introdução

• Expressão tumoral de PD-L1

• Carga mutacional

• Perfil mutacional

• Outros biomarcadores

• Conclusões

Sumário

• Introdução

• Expressão tumoral de PD-L1

• Carga mutacional

• Perfil mutacional

• Outros biomarcadores

• Conclusões

Primary sensitivity = biomarkers

Atividade clínica dos inibidores de PD-1/PD-L1

Supervisão imunológica contra o câncer

Imagem adaptada de Mellman I et al.¹

APC = células apresentadoras de antígenos; MHC = complexo principal de histocompatibilidade; TCR = receptor dos linfócitos T.

1. Mellman I et al. Nature. 2011;480(7378):480-489. 2. Dunn GP et al. Nat Immunol. 2002;3(11):991-998.

MHC

APC

TCR

Células T

Célula tumoral

Antígeno específico ao tumor

Microambiente tumoralTecido linfóide

Ativação e expansão clonal

Uso de PD-L1 como biomarcador clínico

Topalian SL, et al. Nature Rev Cancer. 2016, 16:275-287

Expressão de PD-L1 é heterogênea e varia de acordo com a plataforma

E1L3N SP142

Negative

Positive

1 mm

H&E

JMcLaughlin,K Schalper and D Rimm (Yale Pathology)

DrugPD-L1 IHC

AssayPD-L1 scoring

Cut-offs reported in

clinical trials

FDA Diagnostic

Status

Nivolumab 28-8 Tumor cells 1%, 5%, 10% Complementary

Pembrolizumab 22C3 Tumor cells (TPS) 1%, 50% Companion

Atezolizumab SP142Tumor cells (TC) 1%, 5%, 50%

ComplementaryImmune cells (IC) 1%, 5%, 10%

Durvalumab SP263 Tumor cells 25% Unknown

Avelumab 73-10 Tumor cells 1%, 50%, 80% Unknown

TPS: tumor proportional score; TC: staining on tumor cell; IC: staining on immune cells

Diferentes anticorpos e plataformas

BLUEPRINT-2: Comparação entre 5 ensaios

Each circle represents the mean of all scores (glass slide & digital combined)

SP142

28-8H & E

73-10

22C3

SP263

Regulação da expressão de PD-L1

Pardoll D. Nat Rev Cancer 2012; 12: 252-64

SG com pembrolizumabe em CPNPC avançado

Fase 2/3 (KEYNOTE-010)

Roy S. Herbst, presented at ESMO ASIA 2015; Lancet 2015

Median OS Pembro PDL1 ≥ 50% = 14.9 and 17.3 mo Median OS Pembro PDL ≥ 1% = 10.4 and 12.7 mo

2 vs 10 mg/kg: HR 1.122 vs 10 mg/kg: HR 1.17

Pembro 10 mg/kg

Docetaxel

Pembro 2 mg/kg

Pembro 10 mg/kg

Docetaxel

Pembro 2 mg/kg

1-yr OS rate = 34.6%

1-yr OS rate = 52.3%

1-yr OS rate = 43.2%

Inclusion criteria: PD-L1 TPS ≥1%

1. Reck M et al. N Engl J Med. 2016; DOI: 10.1056/NEJMoa160677.

2. Reck M et al. Ann Oncol. 2016;27(suppl 6):LBA8 PR.

3. Keytruda [package insert]. Kenilworth, NJ, USA: Merck Sharp & Dohme Corp.; 2016.

Data cutoff date: May 9, 2016.

From the N Engl J Med. Reck,M. et al. Pembrolizumab versus chemotherapy for PD-L1–positive non– small-

cell lung cancer. 2016;375:1823-1833. 2016 Massachusetts Medical Society. Adapted with permission from

Massachusetts Medical Society.

• KEYNOTE-024 (NCT02142738) demonstrated that pembrolizumab had superior efficacy over platinum-based chemotherapy as first-line therapy for

patients with advanced NSCLC with PD-L1 TPS ≥50%1,2

JR Brahmer, WCLC 2017

KEYNOTE-024 (NCT02142738): Efficacy Outcomes

Sumário

• Introdução

• Expressão tumoral de PD-L1

• Carga mutacional

• Perfil mutacional

• Outros biomarcadores

• Conclusões

NSCLC is among the most deranged neoplasias (average ~ 10

somatic mutations/Mb)

Taxa de mutações não-sinônimas como biomarcador para

imunoterapia

CANCER IMMUNOLOGY

Mutational landscapedeterminessensitivity toPD-1blockadeinnon–small cell lungcancerNaiyer A. Rizvi ,1,2*† M atthew D. Hellmann,1,2* Alexandra Snyder,1,2,3* Pia Kvistbor g,4

Vladimir M akarov,3 Jonathan J. Havel,3 W ill iam Lee,5 Jianda Yuan,6 Phi ll ip W ong,6

Teresa S. Ho,6 M artin L. M i ller,7 Natasha Rekhtman,8 Andre L. M oreira,8

Fawzia I brahim,1 Cameron Bruggeman,9 Bi llel Gasmi,10 Rober ta Zappasodi ,10

Yuka M aeda,10 Chr is Sander,7 Edward B. Garon,11 Taha M erghoub,1,10

Jedd D. W olchok,1,2,10 Ton N. Schumacher,4 Timothy A. Chan2,3,5‡

Immune checkpoint inhibitors, which unleash a patient ’s own T cells to kill tumors, are

revolut ionizing cancer treatment. To unravel the genomic determinants of response

to this therapy, we used whole-exome sequencing of non–small cell lung cancers treated

with pembrolizumab, an antibody targeting programmed cell death-1 (PD-1). In two

independent cohorts, higher nonsynonymous mutation burden in tumors was associated

with improved objective response, durable clinical benefit , and progression-free survival.

Efficacy also correlated with the molecular smoking signature, higher neoantigen

burden, and DNA repair pathway mutations; each factor was also associated with mutation

burden. In one responder, neoant igen-specific CD8+ T cell responses paralleled tumor

regression, suggest ing that ant i–PD-1 therapy enhances neoantigen-specif ic T cell

reactivity. Our results suggest that the genomic landscape of lung cancers shapes

response to ant i–PD-1 therapy.

Today, more than a century since the initial

observation that the immune system can re-

ject human cancers (1), immune checkpoint

inhibitorsaredemonstrating that adaptive

immunity can be harnessed for the treat-

ment of cancer (2–7). In advanced non–small cell

lung cancer (NSCLC), therapies with an antibody

targetingprogrammed cell death-1(anti–PD-1) dem-

onstrated response ratesof 17to 21%, with some

responses being remarkably durable (3, 8).

Understanding the molecular determinants of

response to immunotherapies such as anti–PD-1

therapy is one of the critical challenges in oncol-

ogy. Among the best responses have been in

melanomas and NSCLCs, cancers largely caused

by chronic exposure to mutagens [ultraviolet light

(9) and carcinogens in cigarette smoke (10), re-

spectively]. However, there is a large variability

in mutation burden within tumor types, ranging

from 10sto 1000sof mutations(11–13). Thisrange

is particularly broad in NSCLCs because tumors

in never-smokers generally have few somatic mu-

tations compared with tumors in smokers (14).

We hypothesized that the mutational landscape

of NSCLCs may influence response to anti–PD-1

therapy. To examinethishypothesis, wesequenced

the exomes of NSCLCs from two independent

cohorts of patients treated with pembrolizumab,

a humanized immunoglobulin G (IgG) 4-kappa

isotype antibody to PD-1(n = 16 and n = 18, re-

spectively), and their matched normal DNA (fig.

S1and table S1) (15).

Overall, tumor DNAsequencinggenerated mean

target coverage of 164x, and a mean of 94.5%of

the target sequence wascovered to a depth of at

least 10x; coverage and depth were similar be-

tween cohorts, as well as between those with or

without clinical benefit (fig. S2). We identified a

median of 200 nonsynonymous mutations per

sample(range11to 1192). Themedian number of

exonic mutations per sample was 327 (range 45

to 1732).Thequantity and rangeof mutationswere

similar to published series of NSCLCs (16, 17)

(fig. S3). Thetransition/transversion ratio (Ti/Tv)

was 0.74 (fig. S4), also similar to previously de-

scribed NSCLCs (16–18). To ensureaccuracy of our

sequencing data, targeted resequencing with an

orthogonal method (Ampliseq) was performed

using 376 randomly selected variants, and muta-

tionswereconfirmed in 357of thosevariants (95%).

Higher somatic nonsynonymous mutation

burden was associated with clinical efficacy of

pembrolizumab. In the discovery cohort (n = 16),

the median number of nonsynonymous muta-

tions was 302 in patients with durable clinical

benefit (DCB) (partial or stable response lasting

>6 months) versus 148 with no durable benefit

(NDB) (Mann-Whitney P= 0.02) (Fig. 1A). Seventy-

three percent of patients with high nonsynon-

ymous burden (defined as above the median

burden of thecohort, 209) experienced DCB, com-

pared with 13% of those with low mutation bur-

den (below median) (Fisher’sexact P= 0.04). Both

confirmed objective response rate (ORR) and

progression-free survival (PFS) were higher in

patients with high nonsynonymousburden [ORR

63% versus 0%, Fisher’s exact P = 0.03; median

PFS14.5 versus 3.7 months, log-rank P = 0.01;

hazard ratio (HR) 0.19, 95%confidence interval

(CI) 0.05 to 0.70] (Fig. 1B and table S2).

Thevalidation cohort included an independent

set of 18 NSCLC samples from patients treated

with pembrolizumab. The clinical characteristics

were similar in both cohorts. The median non-

synonymous mutation burden was 244 in tu-

mors from patients with DCB compared to 125

in those with NDB (Mann-Whitney P = 0.04)

(Fig. 1C). The ratesof DCB and PFSwere again sig-

nificantly greater in patients with a nonsynon-

ymous mutation burden above 200, the median

of the validation cohort (DCB 83% versus 22%,

Fisher’sexact P= 0.04; median PFSnot reached

versus 3.4 months, log-rank P = 0.006; HR 0.15,

95% CI 0.04 to 0.59) (Fig. 1D and table S2).

In the discovery cohort, there was high con-

cordancebetween nonsynonymousmutation bur-

den and DCB, with an area under the receiver

operator characteristic (ROC) curve(AUC) of 87%

(Fig. 1E). Patients with nonsynonymous muta-

tion burden ≥178, the cut point that combined

maximal sensitivity with best specificity, had a

likelihood ratio for DCB of 3.0; the sensitivity

and specificity of DCB using this cut point was

100%(95%CI 59 to 100%) and 67%(29 to 93%),

respectively. Applying this cut point to the

validation cohort, the rate of DCB in patients

with tumorsharboring≥178 mutationswas75%

compared to 14% in those with <178, corre-

sponding to a sensitivity of 86% and a specific-

ity of 75%.

There were few but important exceptions. Five

of 18 tumors with ≥178 nonsynonymous muta-

tions had NDB, and one tumor with a very low

burden (56 nonsynonymous mutations) responded

to pembrolizumab. However, this response was

transient, lasting 8 months. Acrossboth cohorts,

thiswas theonly patient with a tumor mutation

burden <178 and confirmed objective response.

Notably, although higher nonsynonymous mu-

tation burden correlated with improved ORR,

DCB, and PFS (Fig. 1, F and G), this correlation

was less evident when examining total exonic

mutation burden (table S2).

Wenext examined all 34 exomescollectively to

determine how patterns of mutational changes

were associated with clinical benefit to pembro-

lizumab (tables S4 and S5). C-to-A transversions

were more frequent, and C-to-T transitions were

less frequent, in patients with DCB compared to

124 3 APRI L 2015 • VOL 348 I SSUE 6230 sciencemag.org SCI EN CE

1Department of Medicine, Memorial Sloan Kettering Cancer

Center, New York, NY10065, USA. 2Weill Cornell Medical

College, New York, NY, 10065, USA. 3Human Oncology and

Pathogenesis Program, Memorial Sloan Kettering Cancer

Center, New York, NY10065, USA. 4Division of Immunology,

Netherlands Cancer Institute, 1066 CX Amsterdam,

Netherlands. 5Department of Radiation Oncology, Memorial

Sloan Kettering Cancer Center, New York, NY10065, USA.6Immune Monitoring Core, Ludwig Center for Cancer

Immunotherapy, Memorial Sloan Kettering Cancer Center,

New York, NY10065, USA. 7Computation Biology Program,

Memorial Sloan Kettering Cancer Center, New York, NY

10065, USA. 8Department of Pathology, Memorial Sloan

Kettering Cancer Center, New York, NY10065, USA.9Department of Mathematics, Columbia University, New

York, NY, 10027, USA. 10Ludwig Collaborative Laboratory,

Memorial Sloan Kettering Cancer Center, New York, NY

10065, USA. 11David Geffen School of Medicine at UCLA,

2825 Santa Monica Boulevard, Suite 200, Santa Monica, CA

90404, USA.

*These authors contributed equally to this work. †Present address:

Division of Hematology/ Oncology, New York-Presbyterian/ Columbia

University, New York, NY, USA. ‡Corresponding author. E-mail:

[email protected]

RESEARCH | REPORTS

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CANCER IMMUNOLOGY

Mutational landscapedeterminessensitivity to PD-1blockade innon–small cell lungcancerNaiyer A. Rizvi ,1,2*† M atthew D. Hellmann,1,2* Alexandra Snyder,1,2,3* Pia Kvistbor g,4

Vladimir M akarov,3 Jonathan J. Havel,3 W ill iam Lee,5 Jianda Yuan,6 Phi l l ip W ong,6

Teresa S. Ho,6 M artin L. M i l ler,7 Natasha Rekhtman,8 Andre L. M oreira,8

Fawzia I brahim,1 Cameron Bruggeman,9 Bi l lel Gasmi,10 Rober ta Zappasodi ,10

Yuka M aeda,10 Chr is Sander,7 Edward B. Garon,11 Taha M erghoub,1,10

Jedd D. W olchok,1,2,10 Ton N. Schumacher,4 Timothy A. Chan2,3,5‡

Immune checkpoint inhibitors, which unleash a patient ’s own T cells to kill tumors, are

revolut ionizing cancer treatment. To unravel the genomic determinants of response

to this therapy, we used whole-exome sequencing of non–small cell lung cancers treated

with pembrolizumab, an antibody targeting programmed cell death-1 (PD-1). In two

independent cohorts, higher nonsynonymous mutat ion burden in tumors was associated

with improved object ive response, durable clinical benefit , and progression-free survival.

Efficacy also correlated with the molecular smoking signature, higher neoantigen

burden, and DNA repair pathway mutat ions; each factor was also associated with mutat ion

burden. In one responder, neoant igen-specific CD8+ T cell responses paralleled tumor

regression, suggest ing that ant i–PD-1 therapy enhances neoantigen-specif ic T cell

reactivity. Our results suggest that the genomic landscape of lung cancers shapes

response to ant i–PD-1 therapy.

Today, more than a century since the initial

observation that the immune system can re-

ject human cancers (1), immune checkpoint

inhibitorsaredemonstrating that adaptive

immunity can be harnessed for the treat-

ment of cancer (2–7). In advanced non–small cell

lung cancer (NSCLC), therapies with an antibody

targetingprogrammed cell death-1(anti–PD-1) dem-

onstrated response rates of 17to 21%, with some

responses being remarkably durable (3, 8).

Understanding the molecular determinants of

response to immunotherapies such as anti–PD-1

therapy is one of the critical challenges in oncol-

ogy. Among the best responses have been in

melanomas and NSCLCs, cancers largely caused

by chronic exposure to mutagens [ultraviolet light

(9) and carcinogens in cigarette smoke (10), re-

spectively]. However, there is a large variability

in mutation burden within tumor types, ranging

from 10sto 1000sof mutations(11–13). Thisrange

is particularly broad in NSCLCs because tumors

in never-smokers generally have few somatic mu-

tations compared with tumors in smokers (14).

We hypothesized that the mutational landscape

of NSCLCs may influence response to anti–PD-1

therapy. To examinethishypothesis, wesequenced

the exomes of NSCLCs from two independent

cohorts of patients treated with pembrolizumab,

a humanized immunoglobulin G (IgG) 4-kappa

isotype antibody to PD-1 (n = 16 and n = 18, re-

spectively), and their matched normal DNA (fig.

S1and table S1) (15).

Overall, tumor DNA sequencinggenerated mean

target coverage of 164x, and a mean of 94.5% of

the target sequence wascovered to a depth of at

least 10x; coverage and depth were similar be-

tween cohorts, as well as between those with or

without clinical benefit (fig. S2). We identified a

median of 200 nonsynonymous mutations per

sample(range11to 1192). Themedian number of

exonic mutations per sample was 327 (range 45

to 1732). Thequantity and rangeof mutationswere

similar to published series of NSCLCs (16, 17)

(fig. S3). Thetransition/transversion ratio (Ti/Tv)

was 0.74 (fig. S4), also similar to previously de-

scribed NSCLCs (16–18). To ensure accuracy of our

sequencing data, targeted resequencing with an

orthogonal method (Ampliseq) was performed

using 376 randomly selected variants, and muta-

tionswereconfirmed in 357of thosevariants(95%).

Higher somatic nonsynonymous mutation

burden was associated with clinical efficacy of

pembrolizumab. In the discovery cohort (n = 16),

the median number of nonsynonymous muta-

tions was 302 in patients with durable clinical

benefit (DCB) (partial or stable response lasting

>6 months) versus 148 with no durable benefit

(NDB) (Mann-Whitney P= 0.02) (Fig. 1A). Seventy-

three percent of patients with high nonsynon-

ymous burden (defined as above the median

burden of thecohort, 209) experienced DCB, com-

pared with 13% of those with low mutation bur-

den (below median) (Fisher’sexact P= 0.04). Both

confirmed objective response rate (ORR) and

progression-free survival (PFS) were higher in

patients with high nonsynonymous burden [ORR

63% versus 0%, Fisher’s exact P = 0.03; median

PFS14.5 versus 3.7 months, log-rank P = 0.01;

hazard ratio (HR) 0.19, 95% confidence interval

(CI) 0.05 to 0.70] (Fig. 1B and table S2).

Thevalidation cohort included an independent

set of 18 NSCLC samples from patients treated

with pembrolizumab. The clinical characteristics

were similar in both cohorts. The median non-

synonymous mutation burden was 244 in tu-

mors from patients with DCB compared to 125

in those with NDB (Mann-Whitney P = 0.04)

(Fig. 1C). The ratesof DCB and PFSwere again sig-

nificantly greater in patients with a nonsynon-

ymous mutation burden above 200, the median

of the validation cohort (DCB 83% versus 22%,

Fisher’sexact P= 0.04; median PFSnot reached

versus 3.4 months, log-rank P = 0.006; HR 0.15,

95% CI 0.04 to 0.59) (Fig. 1D and table S2).

In the discovery cohort, there was high con-

cordancebetween nonsynonymousmutation bur-

den and DCB, with an area under the receiver

operator characteristic (ROC) curve (AUC) of 87%

(Fig. 1E). Patients with nonsynonymous muta-

tion burden ≥178, the cut point that combined

maximal sensitivity with best specificity, had a

likelihood ratio for DCB of 3.0; the sensitivity

and specificity of DCB using this cut point was

100%(95%CI 59 to 100%) and 67%(29 to 93%),

respectively. Applying this cut point to the

validation cohort, the rate of DCB in patients

with tumorsharboring ≥178 mutationswas75%

compared to 14% in those with <178, corre-

sponding to a sensitivity of 86% and a specific-

ity of 75%.

There were few but important exceptions. Five

of 18 tumors with ≥178 nonsynonymous muta-

tions had NDB, and one tumor with a very low

burden (56 nonsynonymous mutations) responded

to pembrolizumab. However, this response was

transient, lasting 8 months. Acrossboth cohorts,

thiswas theonly patient with a tumor mutation

burden <178 and confirmed objective response.

Notably, although higher nonsynonymous mu-

tation burden correlated with improved ORR,

DCB, and PFS (Fig. 1, F and G), this correlation

was less evident when examining total exonic

mutation burden (table S2).

Wenext examined all 34 exomescollectively to

determine how patterns of mutational changes

were associated with clinical benefit to pembro-

lizumab (tables S4 and S5). C-to-A transversions

were more frequent, and C-to-T transitions were

less frequent, in patients with DCB compared to

124 3 APRI L 2015 • VOL 348 I SSUE 6230 sciencemag.org SCI EN CE

1Department of Medicine, Memorial Sloan Kettering Cancer

Center, New York, NY10065, USA. 2Weill Cornell Medical

College, New York, NY, 10065, USA. 3Human Oncology and

Pathogenesis Program, Memorial Sloan Kettering Cancer

Center, New York, NY10065, USA. 4Division of Immunology,

Netherlands Cancer Institute, 1066 CX Amsterdam,

Netherlands. 5Department of Radiation Oncology, Memorial

Sloan Kettering Cancer Center, New York, NY10065, USA.6Immune Monitoring Core, Ludwig Center for Cancer

Immunotherapy, Memorial Sloan Kettering Cancer Center,

New York, NY10065, USA. 7Computation Biology Program,

Memorial Sloan Kettering Cancer Center, New York, NY

10065, USA. 8Department of Pathology, Memorial Sloan

Kettering Cancer Center, New York, NY10065, USA.9Department of Mathematics, Columbia University, New

York, NY, 10027, USA. 10Ludwig Collaborative Laboratory,

Memorial Sloan Kettering Cancer Center, New York, NY

10065, USA. 11David Geffen School of Medicine at UCLA,

2825 Santa Monica Boulevard, Suite 200, Santa Monica, CA

90404, USA.

*These authors contributed equally to this work. †Present address:

Division of Hematology/ Oncology, New York-Presbyterian/ Columbia

University, New York, NY, USA. ‡Corresponding author. E-mail:

[email protected]

RESEARCH | REPORTS

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NDB (Mann-Whitney P = 0.01 for both) (fig. S5).

A previously validated binary classifier to identi-

fy the molecular signature of smoking (17) was

applied to differentiate transversion-high (TH,

smoking signature) from transversion-low (TL,

never-smoking signature) tumors. Efficacy was

greatest in patients with tumors harboring the

smoking signature. The ORR in TH tumors was

56% versus 17% in TL tumors (Fisher ’s exact P =

0.03); therateof DCB was77%versus22%(Fisher’s

exact P = 0.004); the PFS was also significantly

longer in TH tumors (median not reached versus

3.5 months, log-rank P = 0.0001) (Fig. 2A). Self-

reported smoking history did not significantly

discriminate those most likely to benefit from

pembrolizumab. The rates of neither DCB nor

PFS were significantly different in ever-smokers

versus never-smokers (Fisher’s exact P = 0.66 and

log-rank P = 0.29, respectively) or heavy smokers

(median pack-years>25) versus light/never smokers

(pack-years≤25) (Fisher’s exact P = 0.08 and log-

rank P= 0.15, respectively). Themolecular smoking

signature correlated more significantly with non-

synonymous mutation burden than smoking his-

tory (fig. S6, A and B).

Although carcinogens in tobacco smoke are

largely responsible for the mutagenesis in lung

cancers (19), the wide range of mutation burden

within both smokers and never-smokers impli-

cates additional pathways contributing to the

accumulation of somatic mutations. We found

deleterious mutations in a number of genes that

are important in DNA repair and replication. For

example, in three responders with the highest

mutation burden, we identified deleterious mu-

tations in POLD1, POLE, and MSH2 (Fig. 3). Of

particular interest, a POLD1E374K mutation was

identified in a never-smoker with DCB whose tu-

mor harbored the greatest nonsynonymous muta-

tion burden (n = 507) of all never-smokers in our

series. POLD1Glu374 lies in the exonuclease proof-

reading domain of Pol d (20), and mutation of

this residue may contribute to low-fidelity repli-

cation of the lagging DNA strand. Consistent with

this hypothesis, this tumor exome had a relatively

low proportion of C-to-A transversions (20%) and

predominance of C-to-T transitions (51%), similar

to other POLD1 mutant, hypermutated tumors

(21) and distinct from smoking-related lung can-

cers. Another responder, with the greatest muta-

tion burden in our series, had a C284Y mutation

in POLD1, which is also located in the exonu-

clease proofreading domain. We observed non-

sense mutations in PRKDC, the catalytic subunit

of DNA-dependent protein kinase (DNA-PK),

and RAD17. Both genes are required for proper

DNA repair and maintenance of genomic integ-

rity (22, 23).

Genes harboring deleterious mutations com-

mon to four or moreDCB patientsand not present

in NDB patients included POLR2A, KEAP1, PAPPA2,

PXDNL, RYR1, SCN8A, and SLIT3. Mutations in

KRAS were found in 7of 14 tumors from patients

with DCB compared to 1of 17 in the NDB group,

a finding that may be explained by the asso-

ciation between smoking and the presence of

KRAS mutations in NSCLC (24). There were no

mutations or copy-number alterations in antigen-

presentation pathway–associated genes or CD274

SCI EN CE sciencemag.org 3 APRI L 2015 • VOL 348 I SSUE 6230 125

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0

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1 0 0

0

2 0 0

4 0 0

6 0 0

8 0 0

0

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8 0 0

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0

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0

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4 0 0

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Discovery Cohort Validation Cohort

Months

All Tumors

DCB NDB DCB NDB DCB NDB

High nonsynonymous burden

Low nonsynonymous burden

High nonsynonymous burden

Low nonsynonymous burden

High nonsynonymous burden

Low nonsynonymous burden

800

600

400

200

0

1200

800

400

200

0

100

50

0

% S

ensi

tivity

1 - % Specificity 50 100

1200

800

400

200

0 # N

onsy

nony

mou

s m

utat

ions

/tum

or

100

50

0 4 8 12 16 20 24

Months

100

50

0 4 8 12 16 20 24

Per

cent

pro

gres

sion

-free

Months

100

50

0 4 8 12 16 20 24

Per

cent

pro

gres

sion

-free

Per

cent

pro

gres

sion

-free

Fig. 1. Nonsynonymous mutat ion burden associated wit h clinical bene-

f it of ant i–PD-1 therapy. (A) Nonsynonymous mutation burden in tumors

from patients with DCB (n = 7) or with NDB (n = 9) (median 302 versus

148, Mann-Whitney P = 0.02). (B) PFS in tumors with higher nonsynony-

mous mutation burden (n = 8) compared to tumors with lower nonsynony-

mous mutation burden (n = 8) in patients in the discovery cohort (HR 0.19,

95% CI 0.05 to 0.70, log-rank P = 0.01). (C) Nonsynonymous mutation

burden in tumors with DCB (n = 7) compared to those with NDB (n = 8) in

patients in the validat ion cohort (median 244 versus 125, Mann-Whitney

P = 0.04). (D) PFS in tumors with higher nonsynonymous mutation burden

(n = 9) compared to those with lower nonsynonymous mutat ion burden

(n = 9) in pat ients in the validat ion cohort (HR 0.15, 95% CI 0.04 to 0.59,

log-rank P = 0.006). (E) ROC curve for the correlat ion of nonsynonymous

mutat ion burden with DCB in discovery cohort. AUC is 0.86 (95% CI 0.66

to 1.05, null hypothesis test P = 0.02). Cut-off of ≥178 nonsynonymous mu-

tations is designated by triangle. (F) Nonsynonymous mutat ion burden in

patients with DCB (n = 14) compared to those with NDB (n = 17) for the

ent ire set of sequenced tumors (median 299 versus 127, Mann-Whitney P =

0.0008). (G) PFS in those with higher nonsynonymous mutation burden

(n = 17) compared to those with lower nonsynonymous mutat ion burden

(n = 17) in the entire set of sequenced tumors (HR 0.19, 95% CI 0.08-0.47,

log-rank P = 0.0004). In (A), (C), and (F), median and interquartile ranges of

total nonsynonymous mutations are shown, with individual values for each

tumor shown with dots.

RESEARCH | REPORTS

DCB 83% vs 22%, P = 0.04

PFS NR vs 3.4 ms, HR 0.15, 95% CI 0.04 to 0.59, P = 0.006

CANCER IMMUNOLOGY

Mutational landscapedeterminessensitivity toPD-1blockadeinnon–small cell lungcancerNaiyer A. Rizvi ,1,2*† M atthew D. Hellmann,1,2* Alexandra Snyder,1,2,3* Pia Kvistbor g,4

Vladimir M akarov,3 Jonathan J. Havel,3 W ill iam Lee,5 Jianda Yuan,6 Phi ll ip W ong,6

Teresa S. Ho,6 M artin L. M i ller,7 Natasha Rekhtman,8 Andre L. M oreira,8

Fawzia I brahim,1 Cameron Bruggeman,9 Bi llel Gasmi,10 Rober ta Zappasodi ,10

Yuka M aeda,10 Chr is Sander,7 Edward B. Garon,11 Taha M erghoub,1,10

Jedd D. W olchok,1,2,10 Ton N. Schumacher,4 Timothy A. Chan2,3,5‡

Immune checkpoint inhibitors, which unleash a patient ’s own T cells to kill tumors, are

revolut ionizing cancer treatment. To unravel the genomic determinants of response

to this therapy, we used whole-exome sequencing of non–small cell lung cancers treated

with pembrolizumab, an antibody targeting programmed cell death-1 (PD-1). In two

independent cohorts, higher nonsynonymous mutation burden in tumors was associated

with improved objective response, durable clinical benefit , and progression-free survival.

Efficacy also correlated with the molecular smoking signature, higher neoantigen

burden, and DNA repair pathway mutations; each factor was also associated with mutation

burden. In one responder, neoant igen-specific CD8+ T cell responses paralleled tumor

regression, suggest ing that ant i–PD-1 therapy enhances neoantigen-specif ic T cell

reactivity. Our results suggest that the genomic landscape of lung cancers shapes

response to ant i–PD-1 therapy.

Today, more than a century since the initial

observation that the immune system can re-

ject human cancers (1), immune checkpoint

inhibitorsaredemonstrating that adaptive

immunity can be harnessed for the treat-

ment of cancer (2–7). In advanced non–small cell

lung cancer (NSCLC), therapies with an antibody

targetingprogrammed cell death-1(anti–PD-1) dem-

onstrated response ratesof 17to 21%, with some

responses being remarkably durable (3, 8).

Understanding the molecular determinants of

response to immunotherapies such as anti–PD-1

therapy is one of the critical challenges in oncol-

ogy. Among the best responses have been in

melanomas and NSCLCs, cancers largely caused

by chronic exposure to mutagens [ultraviolet light

(9) and carcinogens in cigarette smoke (10), re-

spectively]. However, there is a large variability

in mutation burden within tumor types, ranging

from 10sto 1000sof mutations(11–13). Thisrange

is particularly broad in NSCLCs because tumors

in never-smokers generally have few somatic mu-

tations compared with tumors in smokers (14).

We hypothesized that the mutational landscape

of NSCLCs may influence response to anti–PD-1

therapy. To examinethishypothesis, wesequenced

the exomes of NSCLCs from two independent

cohorts of patients treated with pembrolizumab,

a humanized immunoglobulin G (IgG) 4-kappa

isotype antibody to PD-1(n = 16 and n = 18, re-

spectively), and their matched normal DNA (fig.

S1and table S1) (15).

Overall, tumor DNAsequencinggenerated mean

target coverage of 164x, and a mean of 94.5%of

the target sequence wascovered to a depth of at

least 10x; coverage and depth were similar be-

tween cohorts, as well as between those with or

without clinical benefit (fig. S2). We identified a

median of 200 nonsynonymous mutations per

sample(range11to 1192). Themedian number of

exonic mutations per sample was 327 (range 45

to 1732).Thequantity and rangeof mutationswere

similar to published series of NSCLCs (16, 17)

(fig. S3). Thetransition/transversion ratio (Ti/Tv)

was 0.74 (fig. S4), also similar to previously de-

scribed NSCLCs (16–18). To ensureaccuracy of our

sequencing data, targeted resequencing with an

orthogonal method (Ampliseq) was performed

using 376 randomly selected variants, and muta-

tionswereconfirmed in 357of thosevariants (95%).

Higher somatic nonsynonymous mutation

burden was associated with clinical efficacy of

pembrolizumab. In the discovery cohort (n = 16),

the median number of nonsynonymous muta-

tions was 302 in patients with durable clinical

benefit (DCB) (partial or stable response lasting

>6 months) versus 148 with no durable benefit

(NDB) (Mann-Whitney P= 0.02) (Fig. 1A). Seventy-

three percent of patients with high nonsynon-

ymous burden (defined as above the median

burden of thecohort, 209) experienced DCB, com-

pared with 13% of those with low mutation bur-

den (below median) (Fisher’sexact P= 0.04). Both

confirmed objective response rate (ORR) and

progression-free survival (PFS) were higher in

patients with high nonsynonymousburden [ORR

63% versus 0%, Fisher’s exact P = 0.03; median

PFS14.5 versus 3.7 months, log-rank P = 0.01;

hazard ratio (HR) 0.19, 95%confidence interval

(CI) 0.05 to 0.70] (Fig. 1B and table S2).

Thevalidation cohort included an independent

set of 18 NSCLC samples from patients treated

with pembrolizumab. The clinical characteristics

were similar in both cohorts. The median non-

synonymous mutation burden was 244 in tu-

mors from patients with DCB compared to 125

in those with NDB (Mann-Whitney P = 0.04)

(Fig. 1C). The ratesof DCB and PFSwere again sig-

nificantly greater in patients with a nonsynon-

ymous mutation burden above 200, the median

of the validation cohort (DCB 83% versus 22%,

Fisher’sexact P= 0.04; median PFSnot reached

versus 3.4 months, log-rank P = 0.006; HR 0.15,

95% CI 0.04 to 0.59) (Fig. 1D and table S2).

In the discovery cohort, there was high con-

cordancebetween nonsynonymousmutation bur-

den and DCB, with an area under the receiver

operator characteristic (ROC) curve(AUC) of 87%

(Fig. 1E). Patients with nonsynonymous muta-

tion burden ≥178, the cut point that combined

maximal sensitivity with best specificity, had a

likelihood ratio for DCB of 3.0; the sensitivity

and specificity of DCB using this cut point was

100%(95%CI 59 to 100%) and 67%(29 to 93%),

respectively. Applying this cut point to the

validation cohort, the rate of DCB in patients

with tumorsharboring≥178 mutationswas75%

compared to 14% in those with <178, corre-

sponding to a sensitivity of 86% and a specific-

ity of 75%.

There were few but important exceptions. Five

of 18 tumors with ≥178 nonsynonymous muta-

tions had NDB, and one tumor with a very low

burden (56 nonsynonymous mutations) responded

to pembrolizumab. However, this response was

transient, lasting 8 months. Acrossboth cohorts,

thiswas theonly patient with a tumor mutation

burden <178 and confirmed objective response.

Notably, although higher nonsynonymous mu-

tation burden correlated with improved ORR,

DCB, and PFS (Fig. 1, F and G), this correlation

was less evident when examining total exonic

mutation burden (table S2).

Wenext examined all 34 exomescollectively to

determine how patterns of mutational changes

were associated with clinical benefit to pembro-

lizumab (tables S4 and S5). C-to-A transversions

were more frequent, and C-to-T transitions were

less frequent, in patients with DCB compared to

124 3 APRI L 2015 • VOL 348 I SSUE 6230 sciencemag.org SCI EN CE

1Department of Medicine, Memorial Sloan Kettering Cancer

Center, New York, NY10065, USA. 2Weill Cornell Medical

College, New York, NY, 10065, USA. 3Human Oncology and

Pathogenesis Program, Memorial Sloan Kettering Cancer

Center, New York, NY10065, USA. 4Division of Immunology,

Netherlands Cancer Institute, 1066 CX Amsterdam,

Netherlands. 5Department of Radiation Oncology, Memorial

Sloan Kettering Cancer Center, New York, NY10065, USA.6Immune Monitoring Core, Ludwig Center for Cancer

Immunotherapy, Memorial Sloan Kettering Cancer Center,

New York, NY10065, USA. 7Computation Biology Program,

Memorial Sloan Kettering Cancer Center, New York, NY

10065, USA. 8Department of Pathology, Memorial Sloan

Kettering Cancer Center, New York, NY10065, USA.9Department of Mathematics, Columbia University, New

York, NY, 10027, USA. 10Ludwig Collaborative Laboratory,

Memorial Sloan Kettering Cancer Center, New York, NY

10065, USA. 11David Geffen School of Medicine at UCLA,

2825 Santa Monica Boulevard, Suite 200, Santa Monica, CA

90404, USA.

*These authors contributed equally to this work. †Present address:

Division of Hematology/ Oncology, New York-Presbyterian/ Columbia

University, New York, NY, USA. ‡Corresponding author. E-mail:

[email protected]

RESEARCH | REPORTS

on

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on

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5, 2

015

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ag.o

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on

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5, 2

015

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CANCER IMMUNOLOGY

Mutational landscapedeterminessensitivity to PD-1blockade innon–small cell lungcancerNaiyer A. Rizvi ,1,2*† M atthew D. Hellmann,1,2* Alexandra Snyder,1,2,3* Pia Kvistbor g,4

Vladimir M akarov,3 Jonathan J. Havel,3 W ill iam Lee,5 Jianda Yuan,6 Phi l l ip W ong,6

Teresa S. Ho,6 M artin L. M i l ler,7 Natasha Rekhtman,8 Andre L. M oreira,8

Fawzia I brahim,1 Cameron Bruggeman,9 Bi l lel Gasmi,10 Rober ta Zappasodi ,10

Yuka M aeda,10 Chr is Sander,7 Edward B. Garon,11 Taha M erghoub,1,10

Jedd D. W olchok,1,2,10 Ton N. Schumacher,4 Timothy A. Chan2,3,5‡

Immune checkpoint inhibitors, which unleash a patient ’s own T cells to kill tumors, are

revolut ionizing cancer treatment. To unravel the genomic determinants of response

to this therapy, we used whole-exome sequencing of non–small cell lung cancers treated

with pembrolizumab, an antibody targeting programmed cell death-1 (PD-1). In two

independent cohorts, higher nonsynonymous mutat ion burden in tumors was associated

with improved object ive response, durable clinical benefit , and progression-free survival.

Efficacy also correlated with the molecular smoking signature, higher neoantigen

burden, and DNA repair pathway mutat ions; each factor was also associated with mutat ion

burden. In one responder, neoant igen-specific CD8+ T cell responses paralleled tumor

regression, suggest ing that ant i–PD-1 therapy enhances neoantigen-specif ic T cell

reactivity. Our results suggest that the genomic landscape of lung cancers shapes

response to ant i–PD-1 therapy.

Today, more than a century since the initial

observation that the immune system can re-

ject human cancers (1), immune checkpoint

inhibitorsaredemonstrating that adaptive

immunity can be harnessed for the treat-

ment of cancer (2–7). In advanced non–small cell

lung cancer (NSCLC), therapies with an antibody

targetingprogrammed cell death-1(anti–PD-1) dem-

onstrated response rates of 17to 21%, with some

responses being remarkably durable (3, 8).

Understanding the molecular determinants of

response to immunotherapies such as anti–PD-1

therapy is one of the critical challenges in oncol-

ogy. Among the best responses have been in

melanomas and NSCLCs, cancers largely caused

by chronic exposure to mutagens [ultraviolet light

(9) and carcinogens in cigarette smoke (10), re-

spectively]. However, there is a large variability

in mutation burden within tumor types, ranging

from 10sto 1000sof mutations(11–13). Thisrange

is particularly broad in NSCLCs because tumors

in never-smokers generally have few somatic mu-

tations compared with tumors in smokers (14).

We hypothesized that the mutational landscape

of NSCLCs may influence response to anti–PD-1

therapy. To examinethishypothesis, wesequenced

the exomes of NSCLCs from two independent

cohorts of patients treated with pembrolizumab,

a humanized immunoglobulin G (IgG) 4-kappa

isotype antibody to PD-1 (n = 16 and n = 18, re-

spectively), and their matched normal DNA (fig.

S1and table S1) (15).

Overall, tumor DNA sequencinggenerated mean

target coverage of 164x, and a mean of 94.5% of

the target sequence wascovered to a depth of at

least 10x; coverage and depth were similar be-

tween cohorts, as well as between those with or

without clinical benefit (fig. S2). We identified a

median of 200 nonsynonymous mutations per

sample(range11to 1192). Themedian number of

exonic mutations per sample was 327 (range 45

to 1732). Thequantity and rangeof mutationswere

similar to published series of NSCLCs (16, 17)

(fig. S3). Thetransition/transversion ratio (Ti/Tv)

was 0.74 (fig. S4), also similar to previously de-

scribed NSCLCs (16–18). To ensure accuracy of our

sequencing data, targeted resequencing with an

orthogonal method (Ampliseq) was performed

using 376 randomly selected variants, and muta-

tionswereconfirmed in 357of thosevariants(95%).

Higher somatic nonsynonymous mutation

burden was associated with clinical efficacy of

pembrolizumab. In the discovery cohort (n = 16),

the median number of nonsynonymous muta-

tions was 302 in patients with durable clinical

benefit (DCB) (partial or stable response lasting

>6 months) versus 148 with no durable benefit

(NDB) (Mann-Whitney P= 0.02) (Fig. 1A). Seventy-

three percent of patients with high nonsynon-

ymous burden (defined as above the median

burden of thecohort, 209) experienced DCB, com-

pared with 13% of those with low mutation bur-

den (below median) (Fisher’sexact P= 0.04). Both

confirmed objective response rate (ORR) and

progression-free survival (PFS) were higher in

patients with high nonsynonymous burden [ORR

63% versus 0%, Fisher’s exact P = 0.03; median

PFS14.5 versus 3.7 months, log-rank P = 0.01;

hazard ratio (HR) 0.19, 95% confidence interval

(CI) 0.05 to 0.70] (Fig. 1B and table S2).

Thevalidation cohort included an independent

set of 18 NSCLC samples from patients treated

with pembrolizumab. The clinical characteristics

were similar in both cohorts. The median non-

synonymous mutation burden was 244 in tu-

mors from patients with DCB compared to 125

in those with NDB (Mann-Whitney P = 0.04)

(Fig. 1C). The ratesof DCB and PFSwere again sig-

nificantly greater in patients with a nonsynon-

ymous mutation burden above 200, the median

of the validation cohort (DCB 83% versus 22%,

Fisher’sexact P= 0.04; median PFSnot reached

versus 3.4 months, log-rank P = 0.006; HR 0.15,

95% CI 0.04 to 0.59) (Fig. 1D and table S2).

In the discovery cohort, there was high con-

cordancebetween nonsynonymousmutation bur-

den and DCB, with an area under the receiver

operator characteristic (ROC) curve (AUC) of 87%

(Fig. 1E). Patients with nonsynonymous muta-

tion burden ≥178, the cut point that combined

maximal sensitivity with best specificity, had a

likelihood ratio for DCB of 3.0; the sensitivity

and specificity of DCB using this cut point was

100%(95%CI 59 to 100%) and 67%(29 to 93%),

respectively. Applying this cut point to the

validation cohort, the rate of DCB in patients

with tumorsharboring ≥178 mutationswas75%

compared to 14% in those with <178, corre-

sponding to a sensitivity of 86% and a specific-

ity of 75%.

There were few but important exceptions. Five

of 18 tumors with ≥178 nonsynonymous muta-

tions had NDB, and one tumor with a very low

burden (56 nonsynonymous mutations) responded

to pembrolizumab. However, this response was

transient, lasting 8 months. Acrossboth cohorts,

thiswas theonly patient with a tumor mutation

burden <178 and confirmed objective response.

Notably, although higher nonsynonymous mu-

tation burden correlated with improved ORR,

DCB, and PFS (Fig. 1, F and G), this correlation

was less evident when examining total exonic

mutation burden (table S2).

Wenext examined all 34 exomescollectively to

determine how patterns of mutational changes

were associated with clinical benefit to pembro-

lizumab (tables S4 and S5). C-to-A transversions

were more frequent, and C-to-T transitions were

less frequent, in patients with DCB compared to

124 3 APRI L 2015 • VOL 348 I SSUE 6230 sciencemag.org SCI EN CE

1Department of Medicine, Memorial Sloan Kettering Cancer

Center, New York, NY10065, USA. 2Weill Cornell Medical

College, New York, NY, 10065, USA. 3Human Oncology and

Pathogenesis Program, Memorial Sloan Kettering Cancer

Center, New York, NY10065, USA. 4Division of Immunology,

Netherlands Cancer Institute, 1066 CX Amsterdam,

Netherlands. 5Department of Radiation Oncology, Memorial

Sloan Kettering Cancer Center, New York, NY10065, USA.6Immune Monitoring Core, Ludwig Center for Cancer

Immunotherapy, Memorial Sloan Kettering Cancer Center,

New York, NY10065, USA. 7Computation Biology Program,

Memorial Sloan Kettering Cancer Center, New York, NY

10065, USA. 8Department of Pathology, Memorial Sloan

Kettering Cancer Center, New York, NY10065, USA.9Department of Mathematics, Columbia University, New

York, NY, 10027, USA. 10Ludwig Collaborative Laboratory,

Memorial Sloan Kettering Cancer Center, New York, NY

10065, USA. 11David Geffen School of Medicine at UCLA,

2825 Santa Monica Boulevard, Suite 200, Santa Monica, CA

90404, USA.

*These authors contributed equally to this work. †Present address:

Division of Hematology/ Oncology, New York-Presbyterian/ Columbia

University, New York, NY, USA. ‡Corresponding author. E-mail:

[email protected]

RESEARCH | REPORTS

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Mutações deletérias em genes de reparo do DNA e de replicação

Neo-antígenos e clonalidade

McGrahanan N, et al. Science. 2016

First release: 3 March 2016 www.sciencemag.org (Page numbers not final at time of first release) 10

First release: 3 March 2016 www.sciencemag.org (Page numbers not final at time of first release) 10

Total Exome Mutations vs Genes in FoundationOne Panela

CheckMate 026 TMB Analysis: Nivolumab in First-line NSCLC

20

aBased on in silico analysis filtering on 315 genes in FoundationOne comprehensive genomic profile (Foundation Medicine, Inc, Cambridge, MA, USA)1

1.Frampton GM, et al. Nat Biotechnol 2013;31:1023–1031

100

50

1

FoundationOne panela (mutations/MB)

To

tal

exo

me m

uta

tio

ns

(mu

tati

on

s/M

B)

10

501 10 100

Biology of TMB and PD-L1 Expression

21

High TMB

High PD-L1

PD-L1 (% tumor expression)

High TMB

7550

1000

316

100

32

10

0 25 100

TM

B

(no

. o

f m

iss

en

se

mu

tati

on

s)

Low/medium TMB

243

• There was no association between TMB and PD-L1 expression

in patients with ≥1% PD-L1 tumor expression

High TMB

Low PD-L1

Low/medium TMB

High PD-L1

Tumors may have T cells excluded,

preventing inflammation with lack

of PD-L1 upregulation

PD-L1 may be a surrogate

marker of high TMB-induced

inflammation (T cell infiltration and

interferon-γ–associated activation)

TMB may not be the only driver of

inflammation, and alternate

pathways (eg, quality neoantigens

and strong immunogenicity) may

lead to PD-L1 upregulation

22

PFS by Tumor Mutation Burden SubgroupCheckMate 026 TMB Analysis: Nivolumab in First-line NSCLC

Nivolumab

Chemotherapy

47 30 26 21 16 12 4 1

60 42 22 15 9 7 4 1

111 54 30 15 9 7 2 1 1

94 65 37 23 15 12 5 0 0

Nivolumab

n = 47 n = 60

9.7

(5.1, NR)

5.8

(4.2, 8.5)

Chemotherapy

Median PFS, months

(95% CI)

High TMB

PF

S (

%)

3 6 9 12 15 18 21

No. at RiskMonths

100

90

80

70

60

50

40

30

20

10

0

0

Nivolumab

Chemotherapy

0 3 6 9 12

Months

15 18 21 24

Nivolumab

Chemotherapy

100

90

80

70

60

50

40

30

20

10

0

n = 111 n = 94

4.1

(2.8, 5.4)

6.9

(5.5, 8.6)

HR = 1.82 (95% CI: 1.30, 2.55)

Nivolumab Chemotherapy

(95% CI)

Median PFS, months

Low/medium TMB

HR = 0.62 (95% CI: 0.38, 1.00)

23

ORR by TMB Subgroup and PD-L1 Expressiona

CheckMate 026 TMB Analysis: Nivolumab in First-line NSCLC

75

2532 32

34

46

1623

0

10

20

30

40

50

60

70

80

90

100

OR

R (

%)

16 31 41 70 32 28 41 53

Nivolumab Arm Chemotherapy Arm

PD-L1 ≥50% PD-L1 1–49% PD-L1 ≥50% PD-L1 1–49% PD-L1 ≥50% PD-L1 1–49% PD-L1 ≥50% PD-L1 1–49%

n =

High TMB Low/medium TMB High TMB Low/medium TMB

aORR was 45.6% in patients with ≥50% PD-L1 expression in the nivolumab arm of the TMB-evaluable population

PFS by TMB Subgroup and PD-L1 Expression CheckMate 026 TMB Analysis: Nivolumab in First-line NSCLC

32 24 13 12 7 5 2 1

28 18 9 3 2 2 2 0

53 35 23 13 10 8 3 0

41 30 14 10 5 4 2 0

No. at Risk

High TMB, PD-L1 ≥50%

High TMB, PD-L1 1–49%

Low/medium TMB, PD-L1 1–49%

Low/medium TMB, PD-L1 ≥50%

16 13 10 8 8 6 2 0 0

31 17 16 13 8 6 2 1 0

70 33 18 9 7 5 1 1 1

41 21 12 6 2 2 1 0 0

24

Months

100

75

50

25

0

6 18930 12 15 21

Months

100

75

50

25

0

6 1893

PF

S (

%)

0 12 15 2421

High TMB, PD-L1 ≥50%

High TMB, PD-L1 1–49%

Low/medium TMB, PD-L1 1–49%

Low/medium TMB, PD-L1 ≥50%

Low/medium TMB, PD-L1 ≥50%

High TMB, PD-L1 1–49%

Low/medium TMB, PD-L1 1–49%

High TMB, PD-L1 ≥50%

Nivolumab Arm Chemotherapy Arm

ORR by Tumor Mutation Burden SubgroupCheckMate 032 Exploratory TMB Analysis Nivo ± Ipi in Previously Treated SCLC

0

10

20

30

40

50

TMB-evaluable Low TMB Medium TMB High TMB

OR

R, %

Nivolumab Nivolumab + ipilimumab

42 27 44 25 47 26n 133 78

11.3

28.2

4.8

22.2

6.8

16.0

21.3

46.2

Sumário

• Introdução

• Expressão tumoral de PD-L1

• Carga mutacional

• Perfil mutacional (driver)

• Outros biomarcadores

• Conclusões

Adapted from Spigel ASCO 2016

Perfil mutacional vs TMB

Patients with KRAS/STK11 co-mutated LUAC exhibit poor clinical response to PD-1 axis inhibitors

ORR (RECIST 1.1) P=0.000735

Fisher’s exact test

7.4%

35.7%28.6%

KL

KP

K-only

Skoulidis et al. IASLC 18th World Conference on Lung Cancer

Sumário

• Introdução

• Expressão tumoral de PD-L1

• Carga mutacional

• Perfil mutacional

• Outros biomarcadores

• Conclusões

Teff signature was defined by mRNA expression of 3 genes (PDL1, CXCL9, IFNG) and derived from

a broader 9-gene signature from POPLAR1

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 patients identified as PD-L1 positive by IHC and also identifies

a unique subset of patients within the PD-L1–negative population

Teff Gene Signature and Overlap With PD-L1 IHC in tumor specimens from study OAK

Kowanetz et al. OAK Teff biomarker. WCLC 2017.

Teff Gene

Signature

PDL1

IFNG

CXCL9

Pre-existing

immunity

PD-L1 expression

on TC and IC

Teff Gene Signature vs PD-L1 IHC (SP142)a

36%14% 20%

Teff

≥ median

TC1/2/3

or IC1/2/3b

N = 753

Increasing atezolizumab PFS benefit was observed with higher Teff gene expression

Patients with Teff expression ≥ median experienced a significant PFS benefit

Association Between Teff Gene Signature and PFS in OAK

0,250.25 1.0 2.0

PFS HRFavors atezolizumab Favors docetaxel

0.94

1.11

0.91

1.30

0.73

1.10

0.66PFS 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%

Te

ffe

xp

res

sio

n

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

Pro

gre

ssio

n-F

ree S

urv

ival

(%)

Months

n (%)

189 (25%)

564 (75%)

382 (51%)

371 (49%)

566 (75%)

187 (25%)

753 (100%)

Kowanetz et al. OAK Teff biomarker. WCLC 2017.

Teff gene signature is a more sensitive biomarker of PFS than PD-L1 IHC

At a similar prevalence, Teff gene expression identified a larger number of patients who experienced a

significant PFS benefit with atezolizumab therapy

Atezolizumab Clinical Benefit in Subgroups Defined by

Teff Gene Signature and PD-L1 IHC (SP142) in OAK

PFS OS

PD-L1 IHC (SP142)

TC1/2/3 or IC1/2/3

Teff Signature

≥ Median

PD-L1 IHC (SP142)

TC1/2/3 or IC1/2/3

Teff Signature

≥ Median

Prevalence 55% 51% 55% 51%

HR

(95% CI)

0.93

(0.76, 1.15)

0.73

(0.58, 0.91)

0.74

(0.58, 0.95)

0.59

(0.46, 0.76)

HR (95% CI)

BEP (N = 753)

0.94

(0.81, 1.10)

0.71

(0.59, 0.85)

Kowanetz et al. OAK Teff biomarker. WCLC 2017.

Reck M, et al. IMpower150 PFS

analysis.35The T-effector (Teff) gene signature is defined by expression of PD-L1, CXCL9 and IFNγ and is a surrogate of both PD-L1 IHC expression and pre-existing immunity (Kowanetz M, et al. WCLC, 2017).

IMpower150 study populations and objectives

ITTAll randomised

patients

Teff-high WTa

High T-effector gene signature expression

Teff-low WTa

Low T-effector gene signature expression

Co-primary objectives• Investigator-assessed PFS in ITT-WT

• Investigator-assessed PFS in Teff-high

WT

• OS in ITT-WT

1

EGFR/ALK +(13% of patients)

ITT-WTa

(87% of patients)

a WT refers to patients without EGFR

or ALK genetic alterations.

Key secondary objectives• Investigator-assessed PFS and OS in ITT

• Investigator-assessed PFS in PD-L1 IHC

subgroups

• Independent review facility (IRF)-assessed PFS

• ORR and DOR per RECIST v1.1

• Safety in ITT

2

Reck M, et al. IMpower150 PFS

analysis.36INV, investigator.Data cutoff: September 15, 2017

INV-assessed PFS in Teff-high WT (Arm B vs Arm C)

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 < 0.0001Minimum follow-up: 9.5 mo

Landmark PFS, %Arm B:

atezo + bev + CP

Arm C:

bev + CP

6-month 72% 57%

12-month 46% 18%

Reck M, et al. IMpower150 PFS

analysis.37

a ITT, EGFR/ALK mutants, and ITT-WT % prevalence out of ITT (n = 800); Teff % prevalence out those tested in ITT-WT (n = 658); PD-L1 IHC % prevalence out of ITT-WT (n = 692). b Patients with a sensitising EGFR mutation or ALK translocation must have disease progression or intolerance of treatment with one or more approved targeted therapies.c Stratified HRs for ITT, ITT-WT and Teff-high WT populations; unstratified HRs for all other subgroups.Data cutoff: September 15, 2017

PFS in key biomarker populations

0,25

Population n (%)a

ITT (including EGFR/ALK mutant +) 800 (100%)

EGFR/ALK mutant + onlyb 108 (14%)

ITT-WT 692 (87%)

Teff-high (WT) 284 (43%)

Teff-low (WT) 374 (57%)

PD-L1 IHC TC2/3 or IC2/3 (WT) 244 (35%)

PD-L1 IHC TC1/2/3 or IC1/2/3 (WT) 354 (51%)

PD-L1 IHC TC0 and IC0 (WT) 338 (49%)

PD-L1 IHC TC3 or IC3 (WT) 135 (20%)

PD-L1 IHC TC0/1/2 or IC0/1/2 (WT) 557 (80%)

Median PFS, mo

1.0

In favour of Arm C:

bev + CP

Hazard Ratioc

In favour of Arm B:

atezo + bev + CP

0.61

0.59

0.76

0.48

0.50

0.77

0.51

0.62

1.25

0.39

0.68

Arm B Arm C

8.3 6.8

9.7 6.1

8.3 6.8

11.3 6.8

7.3 7.0

11.1 6.8

11.0 6.8

7.1 6.9

12.6 6.8

8.0 6.8

Reck M, et al. IMpower150 PFS

analysis.

0

10

20

30

40

50

60

70

80

Arm B:atezo + bev + CP

Arm C:bev + CP

Re

sp

on

se

(%

)

38

a Investigator-assessed ORR.b Censored value.Data cutoff: September 15, 2017

ORRa and DOR in ITT-WT and Teff-high WT

0

10

20

30

40

50

60

70

80

Arm B:atezo + bev + CP

Arm C:bev + CP

Re

sp

on

se

(%

)

CR/PR:

1%/47%

CR/PR:

4%/60% CR/PR:

2%/51%

CR/PR:

4%/65%

CR

PR

ORR: 48%

ORR: 64%

ORR: 54%

ORR: 69%

ITT-WT Teff-high WT

CR

PR

Median DOR

(range), mo9.0 (0.4-24.9b) 5.7 (0.0b-22.1)

Median DOR

(range), mo11.2 (0.5-24.9b) 5.7 (0.0b-22.1)

Arm B:

atezo + bev +

CP

Arm B:

atezo + bev +

CP

Arm C:

bev + CP

Arm C:

bev + CP

Tumor and Immune Biomarkers Being Evaluated to Predict Better

Outcomes to Immuno-Oncology Therapy

• Biomarkers indicative of

hypermutation & neo-antigens may

predict response to IO treatment

Examples:

‒ TMB, MSI-High, Neo-Antigens

Tumor Antigens

• Biomarkers that identify tumor

immune system evasion beyond

PD-1/CTLA-4 to inform new IO

targets and rational combinations

Examples:

‒ Tregs, MDSCs, IDO, LAG-3

Tumor Immune

Suppression

• Biomarkers (intra- or peri-tumoral) indicative of an inflamed phenotype may predict response to IO treatment

Examples:

‒ PD-L1, Inflammatory Signatures

Inflamed Tumor

Microenvironment

• Biomarkers which characterize the

host environment, beyond tumor

microenvironment, may predict

response to IO treatment

Examples:

‒ Microbiome, Germline Genetics

Host Environment

Tumor

Antigens

Tumor Immune

Suppression

Inflamed

Tumor

IDO = indoleamine-2,3 dioxygenase; LAG-3 = lymphocyte activation gene-3; MDSCs = myeloid-derived suppressor cells; MSI-High = microsatellite instability high; TMB = tumor mutational burden. Adapted from Blank C.U. et al., Science 2016;352:658–660. 39

Sumário

• Introdução

• Expressão tumoral de PD-L1

• Carga mutacional

• Perfil mutacional

• Outros biomarcadores

• Conclusões

Conclusões

• A expressão de PD-L1 nas células tumorais está associada com maiorresposta e maior sobrevida em pacientes tratados com terapia anti-PD1/PD-L1.

• Carga mutacional tem pouca sobreposição com PD-L1 e tem se mostradoum consistente preditor de resposta.

• Perfil mutacional pode predizer, pelo menos parcialmente, a carga mutacionale benefício ao tratamento.

• Provavelmente, será necessário a combinação de vários biomarcadores parase identificar a melhor estratégia do uso da imunoterapia.


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