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.

Background: Despite the impressive outcomes with immune checkpoint inhibitor (ICI) in non-small cell lung cancer (NSCLC), only a minority of the patients show long-term benefits from ICI. In this study, we used retrospective cohorts of ICI treated patients with NSCLC to discover and validate spatially resolved protein markers associated with resistance to programmed cell death protein-1 (PD-1) axis inhibition. Methods: Pretreatment samples from 56 patients with NSCLC treated with ICI were collected and analyzed in a tissue microarray (TMA) format in including four different tumor regions per patient using the GeoMx platform for spatially informed transcriptomics. 34 patients had assessable tissue with tumor compartment in all 4 TMA spots, 22 with leukocyte compartment and 12 with CD68 compartment. The patients’ tissue that was not assessable in fourfold redundancy in each compartment was designated as the validation cohort; cytokeratin (CK) (N=22), leukocytes CD45 (N=31), macrophages, CD68 (N=43). The human whole transcriptome, represented by~18,000 individual genes assessed by oligonucleotide-tagged in situ hybridization, was sequenced on the NovaSeq platform to quantify the RNAs present in each region of interest. Results: 54,000 gene variables were generated per case, from them 25,740 were analyzed after removing targets with expression lower than a prespecified frequency. Cox proportional-hazards model analysis was performed for overall and progression-free survival (OS, PFS, respectively). After identifying genes significantly associated with limited survival benefit (HR>1)/progression per spot per patient, we used the intersection of them across the four TMA spots per patient. This resulted in a list of 12 genes in the tumor-cell compartment (RPL13A, GNL3, FAM83A, CYBA, ACSL4, SLC25A6, EPAS1, RPL5, APOL1, HSPD1, RPS4Y1, ADI1). RPL13A, GNL3 in tumor-cell compartment were also significantly associated with OS and PFS, respectively, in the validation cohort (CK: HR, 2.48; p=0.02 and HR, 5.33; p=0.04). In CD45 compartment, secreted frizzled-related protein 2, was associated with OS in the discovery cohort but not in the validation cohort. Similarly, in the CD68 compartment ARHGAP and PNN interacting serine and arginine rich protein were significantly associated with PFS and OS, respectively, in the majority but not all four spots per patient. Conclusion: This work highlights RPL13A and GNL3 as potential indicative biomarkers of resistance to PD-1 axis blockade that might help to improve precision immunotherapy strategies for lung cancer.

Purpose: Programmed cell death protein 1 (PD-1)/programmed death-ligand 1 (PD-L1) interaction suppresses local T-cell responses and promotes peripheral tolerance. In the current study, we focus on PD-1/PD-L1 co-location as a surrogate for this interaction and assess its association with immunotherapy outcomes in patients with non–small cell lung cancer (NSCLC). Experimental Design: Pretreatment biopsies from a retrospective cohort of 154 immunotherapy-treated patients with advanced NSCLC were analyzed. Expression of PD-1 and PD-L1 was assessed by multiplexed quantitative immunofluorescence (QIF) and PD-1 expression in the same pixels as PD-L1 (called a co-location score) was measured using an algorithm to define overlapping expression areas. Co-location scores were correlated with immunotherapy outcomes and PD-L1 tumor proportion score. Results: PD-1/PD-L1 co-location score was associated with best overall response (P = 0.0012), progression-free survival (P = 0.0341), and overall survival after immunotherapy (P = 0.0249). The association was driven by patients receiving immune checkpoint inhibitors in the second or subsequent line of treatment. PD-L1 tumor proportion score by IHC was also correlated with best overall response and progression-free survival. PD-L1 measured within the tumor compartment by QIF did not show any significant association with either best overall response or overall survival. Finally, co-location score was not associated with PD-L1 expression by either method. Conclusions: On the basis of our findings, co-location score shows promise as a biomarker associated with outcome after immunotherapy. With further validation, it could have value as a predictive biomarker for the selection of patients with NSCLC receiving treatment with immune checkpoint inhibitors.

Sarcomas are tumors that originate from mesenchymal cells. The variety of sarcomas' response to chemotherapy and the wide range of prognosis reflect their heterogeneity. In order to improve the rates of response, the research has been orientated toward other forms of therapy, such as targeted therapies and immunotherapy or toward combinations of them. Immune checkpoint inhibitors (ICIs) have been the highlight of immunotherapy in the last decade. Although ICIs are already included in the guidelines of different malignancies, their clinical benefit in sarcomas is still under study. Alveolar soft part sarcomas, undifferentiated pleomorphic sarcomas and other subtypes of sarcoma with high presence of tertiary lymphoid structures tend to respond to ICIs, but further investigation is still needed. Furthermore, the search of predictive biomarkers to determine the type of sarcomas that are sensitive to ICIs is still very challenging. This review will focus on the results of clinical trials, which examine the effect of ICIs and their combination with chemotherapy, targeted therapies and other forms of immunotherapy in sarcomas.

Purpose: We aim to improve the prediction of response or resistance to immunotherapies in patients with melanoma. This goal is based on the hypothesis that current gene signatures predicting immunotherapy outcomes show only modest accuracy due to the lack of spatial information about cellular functions and molecular processes within tumors and their microenvironment. Experimental Design: We collected gene expression data spatially from three cellular compartments defined by CD68+ macrophages, CD45+ leukocytes, and S100B+ tumor cells in 55 immunotherapy-treated melanoma specimens using Digital Spatial Profiling–Whole Transcriptome Atlas. We developed a computational pipeline to discover compartment-specific gene signatures and determine if adding spatial information can improve patient stratification. Results: We achieved robust performance of compartment-specific signatures in predicting the outcome of immune checkpoint inhibitors in the discovery cohort. Of the three signatures, the S100B signature showed the best performance in the validation cohort (N = 45). We also compared our compartment-specific signatures with published bulk signatures and found the S100B tumor spatial signature outperformed previous signatures. Within the eight-gene S100B signature, five genes (PSMB8, TAX1BP3, NOTCH3, LCP2, and NQO1) with positive coefficients predict the response, and three genes (KMT2C, OVCA2, and MGRN1) with negative coefficients predict the resistance to treatment. Conclusions: We conclude that the spatially defined compartment signatures utilize tumor and tumor microenvironment–specific information, leading to more accurate prediction of treatment outcome, and thus merit prospective clinical assessment.

Background: CD40, a TNF receptor family member, is expressed by a variety of immune cells and is involved in the activation of both adaptive and innate immune responses. Here, we used quantitative immunofluorescence (QIF) to evaluate CD40 expression on the tumor epithelium of solid tumors in large patient cohorts of lung, ovarian, and pancreatic cancers. Methods: Tissue samples from nine different solid tumors (bladder, breast, colon, gastric, head and neck, non-small cell lung cancer (NSCLC), ovarian, pancreatic and renal cell carcinoma), constructed in tissue microarray format, were initially assessed for CD40 expression by QIF. CD40 expression was then evaluated on the large available patient cohorts for three of the tumor types demonstrating high CD40 positivity rate; NSCLC, ovarian and pancreatic cancer. The prognostic impact of CD40 expression on tumor cells was also investigated. Results: CD40 expression on tumor cells was found to be common, with 80% of the NSCLC population, 40% of the ovarian cancer population, and 68% of the pancreatic adenocarcinoma population displaying some degree of CD40 expression on cancer cells. All of three of these cancer types displayed considerable intra-tumoral heterogeneity of CD40 expression, as well as partial correlation between expression of CD40 on tumor cells and on surrounding stromal cells. CD40 was not found to be prognostic for overall survival in NSCLC, ovarian cancer, or pancreatic adenocarcinoma. Conclusions: The high percentage of tumor cells expressing CD40 in each of these solid tumors should be considered in the development of therapeutic agents designed to target CD40.