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. 2022 Aug;10(8):e004757.
doi: 10.1136/jitc-2022-004757.

Spatially resolved proteomic profiling identifies tumor cell CD44 as a biomarker associated with sensitivity to PD-1 axis blockade in advanced non-small-cell lung cancer

Myrto K Moutafi #  1 , Magdalena Molero #  2   3 , Sandra Martinez Morilla  1 , Javier Baena  2   4 , Ioannis A Vathiotis  1 , Niki Gavrielatou  1 , Laura Castro-Labrador  2 , Gorka Ruiz de Garibay  2   3 , Vera Adradas  2 , Daniel Orive  5   6 , Karmele Valencia  5   7   8 , Alfonso Calvo  5   6   7   8 , Luis M Montuenga  5   6   7   8 , S Ponce Aix  2   3   4   7 , Kurt A Schalper  1   9 , Roy S Herbst  9 , Luis Paz-Ares  2   3   4   7   10 , David L Rimm  1   9 , Jon Zugazagoitia  11   3   4   7
Affiliations

Spatially resolved proteomic profiling identifies tumor cell CD44 as a biomarker associated with sensitivity to PD-1 axis blockade in advanced non-small-cell lung cancer

Myrto K Moutafi et al. J Immunother Cancer. 2022 Aug.

Abstract

Background: Most patients with advanced non-small-cell lung cancer (NSCLC) fail to derive significant benefit from programmed cell death protein-1 (PD-1) axis blockade, and new biomarkers of response are needed. In this study, we aimed to discover and validate spatially resolved protein markers associated with sensitivity to PD-1 axis inhibition in NSCLC.

Methods: We initially assessed a discovery cohort of 56 patients with NSCLC treated with PD-1 axis inhibitors at Yale Cancer Center. Using the GeoMx Digital Spatial Profiling (DSP) system, 71 proteins were measured in spatial context on each spot in a tissue microarray. We used the AQUA method of quantitative immunofluorescence (QIF) to orthogonally validate candidate biomarkers. For external independent validation, we assessed whole tissue sections derived from 128 patients with NSCLC treated with single-agent PD-1 axis inhibitors at the 12 de Octubre Hospital (Madrid) using DSP. We further analyzed two immunotherapy untreated cohorts to address prognostic significance (n=252 from Yale Cancer Center; n=124 from University Clinic of Navarra) using QIF and DSP, respectively.

Results: Using continuous log-scaled data, we identified CD44 expression in the tumor compartment (pan-cytokeratin (CK)+) as a novel predictor of prolonged progression-free survival (PFS) (multivariate HR=0.68, p=0.043) in the discovery set. We validated by QIF that tumor CD44 levels assessed as continuous QIF scores were associated with longer PFS (multivariate HR=0.31, p=0.022) and overall survival (multivariate HR=0.29, p=0.038). Using DSP in an independent immunotherapy treated cohort, we validated that CD44 levels in the tumor compartment, but not in the immune compartment (panCK-/CD45+), were associated with clinical benefit (OR=1.22, p=0.018) and extended PFS under PD-1 axis inhibition using the highest tertile cutpoint (multivariate HR=0.62, p=0.03). The effect of tumor cell CD44 in predicting PFS remained significant after correcting for programmed death-ligand 1 (PD-L1) Tumor Proportion Score (TPS) in both cohorts. High tumor cell CD44 was not prognostic in the absence of immunotherapy. Using DSP data, intratumoral regions with elevated tumor cell CD44 expression showed prominent (fold change>1.5, adjusted p<0.05) upregulation of PD-L1, TIM-3, ICOS, and CD40 in two independent cohorts.

Conclusions: This work highlights CD44 as a novel indicative biomarker of sensitivity to PD-1 axis blockade that might help to improve immunotherapy strategies for NSCLC.

Keywords: biomarkers, tumor; immunotherapy; lung neoplasms; programmed cell death 1 receptor; tumor microenvironment.

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Conflict of interest statement

Competing interests: SMM is currently a Boehringer Ingelheim employee. LMM reports speakers honoraria from AstraZeneca and research grants from AstraZeneca and BMS. KAS reports consulting or advisory roles for Shattuck Labs, Pierre Fabre, EMD Serono, Clinica Alemana de Santiago, Genmab, Takeda, Merck Sharpe & Dohme, Bristol Myers Squibb, AstraZeneca, Agenus, Repertoire Therapeutics, OnCusp and Ariagen. Reports grants or research funding from Navigate Biopharma, Tesaro/GSK, Moderna, Takeda, Surface Oncology, Pierre Fabre Research Institute, Merck Sharpe & Dohme, Bristol Myers Squibb, AstraZeneca, Ribon Therapeutics, Akoya Biosciences, Boehringer Ingelheim and Eli Lilly. RSH has served as Non-Executive Director for Immunocore and is a member of the Board of Directors (non-executive/independent) for Junshi Biosciences; is a consultant for AbbVie, Armo Biosciences, AstraZeneca, Bristol Myers Squibb, Bayer HealthCare Pharmaceuticals, Bolt Biotherapeutics, Candel Therapeutics, Checkpoint Therapeutics, Cybrexa Therapeutics, DynamiCure Biotechnology, Eli Lilly and Company, eFFECTOR Therapeutics, EMD Serono, Foundation Medicine, Genentech/Roche, Genmab, Gilead, HiberCell, I‐Mab Biopharma, Immune‐Onc Therapeutics, Immunocore, Johnson & Johnson, Loxo Oncology, Merck and Company, Mirati Therapeutics, NextCure, Novartis, Ocean Biomedical, Oncocyte, Oncternal Therapeutics, Pfizer, Refactor Health, Ribbon Therapeutics, Sanofi, STCube Pharmaceuticals, Takeda, WindMIL Therapeutics, Xencor; has received research support from AstraZeneca, Eli Lilly and Company, Genentech/Roche, and Merck and Company; is a committee chair in American Association for Cancer Research, International Association for the Study of Lung Cancer, Society for Immunotherapy of Cancer, Southwest Oncology Group LP-A reports receiving honoraria from Amgen, AstraZeneca, Bayer, Blueprint Medicines, Bristol Myers Squibb, Celgene, Ipsen, Eli Lilly, Merck Serono, Mirati Therapeutics, Merck Sharp & Dohme, Novartis, Pfizer, PharmaMar, Roche/Genentech, Sanofi, Servier, and Takeda; leadership fees from Genomica and ALTUM Sequencing; research funding from AstraZeneca, Bristol Myers Squibb, Kura Oncology, PharmaMar, and Merck Sharp & Dohme; speaker fees from Bristol Myers Squibb, Eli Lilly, Merck Serono, Merck Sharp & Dohme Oncology, Pfizer, and Roche/Genentech; and travel, accommodation, and expenses from AstraZeneca, Bristol Myers Squibb, Merck Sharp & Dohme, Pfizer, Roche, and Takeda. DLR reports grants from Navigate Biopharma and Konica/Minolta/Invicro during the conduct of the study. Activities outside this work include honoraria and/or grants and/or instrument support from Akoya, Amgen, AstraZeneca, BMS, Cell Signaling Technology, Cepheid, Danaher, Konica/Minolta, Lilly, Merck, NanoString, NextCure, Odonate, Paige.AI, Roche, Sanofi, and Ventana. JZ has served as a consultant for AstraZeneca, BMS, Roche, Pfizer, Novartis, and Guardant Health. Reports speakers honoraria from BMS, Pfizer, Roche, AstraZeneca, NanoString and Guardant Health. Reports travel honoraria from BMS, Pfizer, Roche, AstraZeneca, and NanoString. Receives research support/funds from BMS, AstraZeneca, and Roche. The rest of the authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Identification of CD44 expression in the tumor compartment as a predictor of survival in YTMA471 discovery cohort. (A) Representative image of YTMA471 acquired using the GeoMx DSP system. (B–C) Representative TMA spot showing the fluorescence image (B) and the compartmentalized image created by fluorescence colocalization (C) using the GeoMx DSP system; panCK (green), CD45 (yellow), CD68 (red), SYTO13 (blue). (D–E) Representative TMA spot of low CD44 expression in panCK +tumor cells (D) and high CD44 expression in panCK +tumor cells (E) using QIF; panCK (green), CD44 (red), DAPI (blue). (F) Dynamic range of CD44 expression in the tumor compartment (panCK+) and in the stromal compartment (panCK–) using QIF. (G) Comparative analysis of CD44 levels measured by QIF in the tumor compartment and the stromal compartment. (H–I) Kaplan-Meier PFS curve (H) and OS curve (I) according to CD44 expression in the tumor compartment using QIF (optimal quartile cutpoint). CK, cytokeratin; DSP, digital spatial profiling; ns, not significant; OS, overall survival; PFS, progression-free survival; QIF, quantitative immunofluorescence; TMA, tissue microarray.
Figure 2
Figure 2
Validation of CD44 expression in the tumor compartment as an indicative biomarker of sensitivity to single-agent PD-1 axis blockade in NSCLC. (A) Representative image of a whole tissue section with 12 ROIs selected from immune-enriched (panCK+/CD45+) intratumoral areas using DSP. Fluorescence image is shown on the top, and the compartmentalized image at the bottom; panCK (green), CD45 (red), SYTO13 (blue). (B), Dynamic range of CD44 expression in the tumor compartment (panCK+) and in the immune compartment (panCK–/CD45+) using DSP; (C) Comparative analysis of CD44 levels measured by DSP in the tumor compartment and in the immune compartment. (D–E) Kaplan-Meier PFS curve (D) and OS curve (E) according to CD44 expression in the tumor compartment using DSP (tertile cutpoint). (F–G) Kaplan-Meier disease-free survival curves according to CD44 expression using DSP (F) or QIF (G) in immunotherapy untreated cohorts (CIMA-CUN cohort and YTMA423 cohort as F and G, respectively) (tertile cutpoint). CK, cytokeratin; DSP, digital spatial profiling; ns, not significant; NSCLC, non-small-cell lung cancer; OS, overall survival; PD-1, programmed cell death protein-1; PFS, progression-free survival; QIF, quantitative immunofluorescence; ROI, regions of interest.
Figure 3
Figure 3
CD44 levels in the tumor compartment and immune microenvironment features in human NSCLC. (A–B) Correlation between CD44 and CD80 levels in the tumor compartment in H12O_ITX1 cohort (A) and CIMA-CUN cohort (B). (C–D) Correlation between CD44 and PD-L1 levels in the tumor compartment in H12O_ITX1 cohort (C) and CIMA-CUN cohort (D). (E–F) Differentially expressed protein markers in ROIs with high CD44 expression in the tumor compartment (top tertile) relative to ROIs with low CD44 expression in the tumor compartment (rest) in H12O_ITX1 cohort (E) and CIMA-CUN cohort (F). The significance (FDR-adjusted p values) is represented relative to the FC in protein levels in CD44 high relative to CD44 low ROIs. Only statistically significant markers are highlighted. Markers with a FC >1.5 and FDR-adjusted p values<0.05 in the two cohorts are marked in red bold. DSP, digital spatial profiling; FC, fold change; FDR, false discovery rate; PD-L1, programmed death-ligand 1; NSCLC, non-small-cell lung cancer; ROI, regions of interest.
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