Hiperprogresorese hiperrespondedores. Bases biológicasy ... · et al. N Engl J Med 2015 Champiat et al. Nat. Rev. Cancer 2018. ... >50% incremento en carga tumoral respecto al basal

Hiperprogresores e hiperrespondedores.

Bases biologicas y como detectarlos

Antonio CallesServicio de Oncología Médica

Unidad de Tumores Torácicos y Unidad de Fases 1

Hospital General Universitario Gregorio Marañón

Instituto Investigación Sanitaria HGUGM

Universidad Complutense de Madrid

Madrid, España

Conflicts of interest

• Honoraria: AstraZeneca, Boehringer-Ingelheim, Pfizer, Roche, Novartis, Merck

Sharp & Dohme, and Bristol-Myers Squibb.

• Consulting or advisory role: AstraZeneca, Boehringer-Ingelheim, Pfizer,

Roche/Genentech, Eli Lilly and Company, Novartis, Merck Sharp & Dohme,

and Bristol-Myers Squibb.

• Travel financial support: Roche, Boehringer-Ingelheim, Merck Sharp &

Dohme, and Bristol-Myers Squibb.

Hiperprogresión Hiperrespondedores

Borcoman et al. Ann Oncol 2019

Champiat et al. Nat. Rev. Cancer 2018

Patrones de respuesta a la inmunoterapia

Ferrara R. et al. JAMA Oncol. 2019

Concepto de “Hyperprogressive Disease” (HPD)

La definición de “Hyperprogressive Disease” (HPD) se basa en análisis retrospectivos

Champiat et al CCR 2017

Los pacientes que desarrollan HPD tienen peor supervivencia

NSCLC Mesothelioma


Zalcman et al. ESMO 2019

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

CR/P

R SD

PD

Pse

udoPD

HPD

19%

39%42%

5%

14%

0%

10%

20%

30%

40%

50%

60%

CR/PR SD PD

PseudoPD HP

D

10%

59%

31%

5%0%

Ferrara R. et al. WCLC 17; JAMA Oncol. 2018

Inmunoterapia Quimioterapia

La HPD también está descrita con la quimioterapia

Las muertes precoces en los ensayos clínicos podrían corresponder a HPD

Borghaei. et al. N Engl J Med 2015


Hipotesis biologicas de HPD en pacientes tratados con ICI


Hacia una definición de HPD: Intregración del tratamiento previo


Fórmulas para el cálculo de HPD

Tumor Growth Rate (TGR) (Champiat et al. 2016; Ferrara R. et al. 2018)

(assumes exponential tumor growth: Vpost= V0 (expTG x time)

Tumor Growth Kinetics (TGK) (Saada-Bouzid E. et al. 2017)

(assumes linear tumor growth)

Ferté C. et al. Clin. Cancer Res. 2014;

Champiat S. et al. Clin. Cancer Research 2016

Saasa-Bouzid E. et al. Ann. Oncol. 2017


Criterios habitualmente utilizados en la definición de HPD

• Progresión por RECIST en primera revaluación (8 semanas)

• >50% incremento en carga tumoral

• Incremento TGR > 2x entre el tratamiento PREVIO y el ACTUAL

• Incremento TGK ratio > 2

• Nuevas lesiones (en mismo órgano y/o nuevos órganos)

• Deterioro de PS a ECOG 2

• Muerte precoz (< 3 meses)

Ferté C. et al. Clin. Cancer Res. 2014;; Champiat S. et al. Clin. Cancer Res 2016; Saasa-Bouzid E. et al. Ann. Oncol. 2017; Ferrara R. et al.

JAMA Oncol. 2018; Kanjanapan et al. Cancer 2019; Kato et al. Clin Cancer Res 2017; Lo Russo et al. Clin Cancer Res 2019

Matos et al. Clin Cancer Res Published OnlineFirst November 22, 2019

Criterios de HPD utilizando RECIST 1.1

Garassino. AACR 2019

Estudios que evalúan HPD en tumores sólidos tratados con ICI

No hay características clínicas asociadas a HPD con ICI

Kanjanapan et al. Cancer 2019

Progresión por RECIST en primera revaluación (8 semanas)

+

Incremento TGR > 2x entre el tratamiento PREVIO y el ACTUAL HPD 7%

n=182 solid tumors

Alteraciones genéticas asociadas a HPD

Factors associated with HPDOdds ratio (95% CI)

multivariate

P value

multivariate

MDM2/4 amplification 10.8 (1-88-infinity) 0.001

EGFR alteration 8.36 (1.28-98.5) 0.004

Progresión antes de 2 meses

+

>50% incremento en carga tumoral respecto al basal

+

Incremento TGR > 2x entre el tratamiento PREVIO y el ACTUAL

Kato et al. Clin Cancer Res 2017

Análisis retrospectivo de 155 pacientes tratados con ICI

en los que se realizó NGS en MDACC

Rate of change in growth pattern in four cases with MDM2

amplification that progressed rapidly while on immunotherapy.

MDM2 pathway

La amplificacion de MDM2 es un posible marcador de HPD

Kato et al. Clin Cancer Res 2017 MDM2 amplification tumors included bladder (n=1), TNBC (n=1),

endometrial stromal sarcoma (n-=1), LUAC (n=2), HNSCC (n=1)

STK11/LKB1 mutations are a genomic determinant of resistance to anti-P1

blockade in NSCLC

Skoulidis et al. Cancer Discov 2018

La HPD podría ser más frecuente en presencia de mutaciones driver

Ferrara – JAMA Oncol 18

N = 406

Hyperprogression : 14%

Necesidad de encontrar un racional

Análisis de cfDNA para monitorización precoz de respuesta

Jensen et al. Mol Cancer Ther 2019

Borcoman et al. Ann Oncol 2019

Antibody-Fc/FcR Interaction on Macrophages as a Mechanism for Hyperprogressive

Disease in Non-small Cell Lung Cancer Subsequent to PD-1/PD-L1 Blockade

Lo Russo et al. Clin Cancer Res 2019

TAM reprogrammingM2-like CD163+CD33+PD-L1+

Radiomica para la identificacion de HPD

En el modelo final utiliza:• 4 variables radiómicas

• 4 covariantes clínicas : Presencia de metástasis hepáticas y

óseas, líneas de tratamientos previos y ratio neutrofilo-

linfocito (NLR)

• AUROC 0.804 (S=63.43%, E= 83.46%, P=73.41%)

Tunaly et al. Lung Cancer 2019

Hiper-respondedoresMás allá de la expresión de PD-L1

Estados de hiper-mutación

POLE-POL D1/APOBEC signatures

MSI-H/dMMR

HRD

TMB

Variabilidad individual

Clonalidad

HLA

TCR

Situación inflamatoria del tumor y del individuo

“Hot vs. cold tumors” (GEP, IFN-ɣ)

dLNR, LDH

Factores modificadores del individuo

Antibióticos, corticoides, IBPs

Microbioma

2

5

Landmark analysis of OS by response category status at 6 months*Pooled Checkmate 017/ 057 survival analysis

*In all randomized patients from Checkmate 017 and 057 studies alive at the 6-month landmark; 65.6% and 61.8% patients in the nivolumab and docetaxel treatment arms, respectively, were included in this analysis.

Antonia SJ et al. Lancet Oncol. 2019:doi: 10.1016/S1470-2045(19)30407-3..

CheckMate 017/057 4-Year Survival Outcomes

CR/PR

Nivo

93%

81%

63% 61%

100

0

40

60

80

20

0 126 18 24 30 4236 48 54 60 66

58%

Nivo

58%

80%

35%

24%

100

0

40

60

80

20

0 126 18 24 30 4236 48 54 60 66

19%

Nivo

60%

40%

13%

8%

100

0

40

60

80

20

0 126 18 24 30 4236 48 54 60 66

4%Docetaxel

88%

62%

38%

26%

12%18%

Docetaxel

62%

35%

7%2%

Docetaxel

41%

22%

12%8%

5%

70 5765 52 44 42 3739 24 7 0 0 66 3853 29 23 18 1315 10 2 0 0 144 5587 32 17 10 510 3 0 0 0

34 2130 15 13 10 79 4 0 0 0 102 3563 24 17 11 47 2 0 0 0 128 2852 18 15 13 810 5 1 0 0

Months from landmark analysis Months from landmark analysis Months from landmark analysis

OS

(%

)

OS

(%

)

OS

(%

)

Nivolumab

Docetaxel

No. at risk

Nivo(n=70)

Docetaxel(n=34)

Median OS(95% CI), mo

NR(25.6, NR)

17.1(11.1, 28.7)

HR (95% CI), vs PD

0.18(0.12, 0.27)

0.43(0.29, 0.65)

Nivo(n=66)

Docetaxel(n=102)


16.1(10.2, 23.5)

8.0(6.6, 10.4)

HR (95% CI), vs PD

0.52(0.37, 0.71)

0.80(0.61, 1.04)

Nivo(n=144)

Docetaxel(n=128)


9.1(6.2, 11.4)

4.8(3.4, 5.9)

HR (95% CI), vs PD

- -

SD PD

Beyond PD-L1 Expression: Tumour–Immune Classification

Teng et al. Cancer Res 2015

Cold Hot

Immune-ignored Immune-excluded

Multiplexed immunofluorescence

KeyPD-L1 PD-

1 CD8FOXP3CD68

Tumor DAPI

Post-treatment

Forde et al NEJM 2018

Calles et al JTO 2016

Conde et al. Hystopathology, 2017

PD-1

200μm

PD-L1 PD-L2

CD8

200μm 200μm

200μm

Bensch F et al. Nat Med 2018

89Zr-atezolizumab Immune-PET

The Tumor Immunogenicity – Inflammation Pathway

Mismatch

Repair

Defect

Micro-

Satellilte

Inestability

High

Mutation

Burden

POLE & D1

Mutations

Enviromental

Factors

Homologous

Recombination

Deficency

High

Tumor

Immuno-

genicity

Tumor

Inflammation

PD-L1

Positive

Clinically Validated Biomarkers

Adaptive

Response

Investigational Biomarkers

Microsatellite Instability as a Biomarker for PD-1 Blockade

Dudley J, et al. Clin Cancer Res 2016

Xiao and Freeman. Cancer Discov 2015

First tissue-agnostic FDA approval: Pembrolizumab for the treatment of

microsatellite instability-high solid tumors

Dung T. Le et al. Science 2017;357:409-413

+12 tumor types


Mismatch

Repair

Defect

Micro-

Satellilte

Inestability

High

Mutation

Burden

POLE & D1

Mutations

Enviromental

Factors

Homologous

Recombination

Deficency

High

Tumor

Immuno-

genicity

Tumor

Inflammation

PD-L1

Positive


Adaptive

Response


No Association Between TMB and PD-L1 Expression

Peters AACR 2017

Gandara ASCO 2018

PD-L1 (% Tumor Expression)a

High TMB

7550

1000

316

100

32

10

0 25 100

TM

B (N

o. o

f M

issen

se M

uta

tio

ns

)

Low/medium TMB

243

Tumor mutational load predicts survival after immunotherapy across

multiple cancer types

Samstein RM, et al. Nature Genetics 2019

MSK-IMPACT

N=1662 pts treated with ICI

Different TMB cutpoints by cancer type


Mismatch

Repair

Defect

Micro-

Satellilte

Inestability

High

Mutation

Burden

POLE & D1

Mutations

Enviromental

Factors

Homologous

Recombination

Deficency

High

Tumor

Immuno-

genicity

Tumor

Inflammation

PD-L1

Positive


Adaptive

Response


Neoantigen-based prediction: quantity, heterogeneity and quality

Havel, et al. Nat Rew Cancer 2019

DAI

Differential agretopicity index

Neoantigen fitness model:

- Tumour clonality

- DAI

- Microbial epitope homology

>> TMB to predict ICI benefit

Patient HLA class I genotype influences cancer response to checkpoint

blockade immunotherapy

Chowell et al. Science 2018;359:582-587

Individual Levels of Clonality of Immune Repertoire

Langerak et al. J Immunol 2017


Mismatch

Repair

Defect

Micro-

Satellilte

Inestability

High

Mutation

Burden

POLE & D1

Mutations

Enviromental

Factors

Homologous

Recombination

Deficency

High

Tumor

Immuno-

genicity

Tumor

Inflammation

PD-L1

Positive


Adaptive

Response


Immune signatures as biomarkers of response and mechanisms of

resistance to ICI

Ayers et al. Clin Cancer Res 2019

Ock et al. Nat Commun 2017

Chen et al. Cancer Discov 2016

IFN-gamma

Immune signatures as biomarkers of response and mechanisms of

resistance to ICI

Reck M et al. Impower 150 PFS analysis

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

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

0.6

10.5

9

0.7

6

0.4

80.5

0 0.7

7

0.5

1

0.6

2

1.25

0.3

9 0.6

8

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

Tumor and Immune Biomarkers Being Evaluated to Predict Better

Outcomes to Immuno-Oncology Therapy

• Biomarkers indicative of

hypermutation & neoantigens may

predict response to IO treatment

Examples:

‒ TMB, MSI-high, neoantigens

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.

Mezquita et al JAMA Oncol. 2018;4(3):351-357. doi:10.1001/jamaoncol.2017.4771

Lung Immune Prognostic Index (LIPI)

dNLR greater than 3 and LDH

Neutrophil to Lymphocyte Ratio

0 factors = good

1 factor = intermediate

2 factors = poor

Impact of Baseline Steroids on Efficacy of PD-1 and PD-L1 blockade

Arbour et al JCO 2018 Oct 1

Prednisone ≥ 10 mg or equivalent

Steroids are powerful immune

-suppresants

Gut microbes shape response to cancer immunotherapy

Proton pump inhibitors negatively impact survival of PD-1/PD-L1 inhibitors

Mukherjee S, et al. J Oncol Pharm Practice 2018

Homicsko K, et al. CheckMate 069 ESMO-IO 2018

Chalabi M, et al. OAK & POPLAR ESMO-IO 2018

J-C Soria AACR 2019

HiperrespondedoresHiperprogresión

❑ Efecto real vs. historia natural de la enfermedad

❑ Infrecuente: <10%

❑ No hay una definición globalmente aceptada

❑ Supervivencia muy limitada

❑ No hay claros biomarcadores

❑ Definición de nuevos largos supervivientes

❑ Infrecuentes: <20%

❑ Múltiples mecanismos potenciales identificados

❑ Muchos factores de confusión

❑ No es posible identificar pacientes a priori

Conclusiones

2 fenómenos extremos observados con el tratamiento con ICI que comparten similitudes

[email protected]

Gracias por vuestra

atención!

Documents

Hiperprogresorese hiperrespondedores. Bases biológicasy ... · et al. N Engl J Med 2015 Champiat et al. Nat. Rev. Cancer 2018. ... >50% incremento en carga tumoral respecto al basal