Spatial characterization and quantification of CD40 expression across cancer types.

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.

Quantitative assessment of Siglec-15 expression in lung, breast, head, and neck squamous cell carcinoma and bladder cancer.

Immune checkpoint blockade with programmed cell death (PD-1)/programmed death-ligand 1 (PD-L1) inhibitors has resulted in significant progress in the treatment of various cancer types. However, not all patients respond to PD-1/PD-L1 blockade, underscoring the importance of identifying new potential targets for immunotherapy. One promising target is the immune system modulator Siglec-15. In this study, we assess Siglec-15 expression in solid tumors, with a focus on lung, breast, head and neck squamous and bladder cancers. Using quantitative immunofluorescence (QIF) with a previously validated antibody, we found increased Siglec-15 expression in both tumor and immune cells in all the four cancer types. Siglec-15 was seen to be predominantly expressed by the stromal immune cells (83% in lung, 70.1% in breast, 95.2% in head and neck squamous cell and 89% in bladder cancers). Considerable intra-tumoral heterogeneity was noted across cancer types. As previously described for non-small cell lung cancer (NSCLC), Siglec-15 expression was seen to be mutually exclusive to PD-L1 in all the four cancer types, although this differential expression was maintained but somewhat diminished in head and neck squamous cell carcinoma (HNSCC). Siglec-15 was not prognostic either for overall survival (OS) or progression-free survival (PFS). In summary, we show broad expression of this potential immune modulatory target in a wide range of cancer types. These data suggest potential future clinical trials in these tumor types.

Development of an immunohistochemical assay for Siglec-15.

Siglec-15, a member of sialic-acid binding immunoglobulin type lectins, is normally expressed by myeloid cells and upregulated in some human cancers and represents a promising new target for immunotherapy. While PD-L1 blockade is an important strategy for immunotherapy, its effectiveness is limited. The expression of Siglec-15 has been demonstrated to be predominantly mutually exclusive to PD-L1 in certain cancer histologies. Thus, there is significant opportunity for Siglec-15 as an immunotherapeutic target for patients that do not respond to PD-1/PD-L1 inhibition. The aim of this study was to prospectively develop an immunohistochemical (IHC) assay for Siglec-15 to be used as a companion diagnostic for future clinical trials. Here, we create and validate an IHC assay with a novel recombinant antibody to the cytoplasmic domain of Siglec-15. To find an enriched target, this antibody was first used in a quantitative fluorescence (QIF) assay to screen a broad range of tumor histologies to determine tumor types where Siglec-15 demonstrated high expression. Based on this and previous data, we focused on development of a chromogenic IHC assay for lung cancer. Then we developed a scoring system for this assay that has high concordance amongst pathologist readers. We then use this chromogenic IHC assay to test the expression of Siglec-15 in two cohorts of NSCLC. We found that this assay shows a higher level of staining in both tumor and immune cells compared to previous QIF assays utilizing a polyclonal antibody. However, similar to that study, only a small percentage of positive Siglec-15 cases showed high expression for PD-L1. This validated assay for Siglec-15 expression may support development of a companion diagnostic assay to enrich for patients expressing the Siglec-15 target for therapy.

Quantitative assessment of the immune microenvironment in African American Triple Negative Breast Cancer: a case-control study.

PURPOSE: Triple negative breast cancer (TNBC) is more common in African American (AA) than Non-AA (NAA) population. We hypothesize that tumor microenvironment (TME) contributes to this disparity. Here, we use multiplex quantitative immunofluorescence to characterize the expression of immunologic biomarkers in the TME in both populations. PATIENTS AND METHODS: TNBC tumor resection specimen tissues from a 100-patient case: control cohort including 49 AA and 51 NAA were collected. TME markers including CD45, CD14, CD68, CD206, CD4, CD8, CD20, CD3, Ki67, GzB, Thy1, FAP, aSMA, CD34, Col4, VWF and PD-L1 we quantitatively assessed in every field of view. Mean expression levels were compared between cases and controls. RESULTS: Although no significant differences were detected in individual lymphoid and myeloid markers, we found that infiltration with CD45+ immune cells (p = 0.0102) was higher in TNBC in AA population. AA TNBC tumors also had significantly higher level of lymphocytic infiltration defined as CD45+ CD14- cells (p = 0.0081). CD3+ T-cells in AA tumors expressed significantly higher levels of Ki67 (0.0066) compared to NAAs, indicating that a higher percentage of AA tumors contained activated T-cells. All other biomarkers showed no significant differences between the AA and NAA group. CONCLUSIONS: While the TME in TNBC is rich in immune cells in both racial groups, there is a numerical increase in lymphoid infiltration in AA compared to NAA TNBC. Significantly, higher activated T cells seen in AA patients raises the possibility that there may be a subset of AA patients with improved response to immunotherapy.

A new tool for technical standardization of the Ki67 immunohistochemical assay.

Ki67, a nuclear proliferation-related protein, is heavily used in anatomic pathology but has not become a companion diagnostic or a standard-of-care biomarker due to analytic variability in both assay protocols and interpretation. The International Ki67 Working Group in breast cancer has published and has ongoing efforts in the standardization of the interpretation of Ki67, but they have not yet assessed technical issues of assay production representing multiple sources of variation, including antibody clones, antibody formats, staining platforms, and operators. The goal of this work is to address these issues with a new standardization tool. We have developed a cell line microarray system in which mixes of human Karpas 299 or Jurkat cells (Ki67+) with Sf9 (Spodoptera frugiperda) (Ki67-) cells are present in incremental standardized ratios. To validate the tool, six different antibodies, including both ready-to-use and concentrate formats from six vendors, were used to measure Ki67 proliferation indices using IHC protocols for manual (bench-top) and automated platforms. The assays were performed by three different laboratories at Yale and analyzed using two image analysis software packages, including QuPath and Visiopharm. Results showed statistically significant differences in Ki67 reactivity between each antibody clone. However, subsets of Ki67 assays using three clones performed in three different labs show no significant differences. This work shows the need for analytic standardization of the Ki67 assay and provides a new tool to do so. We show here how a cell line standardization system can be used to normalize the staining variability in proliferation indices between different antibody clones in a triple negative breast cancer cohort. We believe that this cell line standardization array has the potential to improve reproducibility among Ki67 assays and laboratories, which is critical for establishing Ki67 as a standard-of-care assay.

Yiqi Chutan Tang Reduces Gefitinib-Induced Drug Resistance in Non-Small-Cell Lung Cancer by Targeting Apoptosis and Autophagy.

High incidence and mortality rates for non-small-cell lung cancer (NSCLC) lead to low survival rates. Epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKI) are commonly first prescribed for NSCLC patients with EGFR mutations. However, most patients with sensitizing EGFR mutations become resistant to EGFR-TKI after 9-13 months treatment. Yiqi Chutan Tang (YQCT) has been prescribed as a treatment to this issue for over 20 years. In this report, high-performance liquid chromatography (HPLC) analysis, flow cytometry, western blot analysis, and functional annotation analysis were applied to uncover the molecular mechanisms of YQCT. Our results show the application of YQCT reduces gefitinib-induced drug resistance, induces slight cell cycle arrest, enhances gefitinib-induced apoptosis, and activates the autophagy. These results indicate that at the molecular level YQCT can reduce drug resistance and improve anti-cancer effects when associated with gefitinib, which could be a result of enhancement of apoptosis and autophagy in the EGFR-TKI resistant cells of NSCLC. This research provides a new treatment strategy for patients with EGFR-TKI resistance in NSCLC. © 2019 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.

Understanding the Effectiveness of Natural Compound Mixtures in Cancer through Their Molecular Mode of Action.

Many approaches to cancer management are often ineffective due to adverse reactions, drug resistance, or inadequate target specificity of single anti-cancer agents. In contrast, a combinatorial approach with the application of two or more anti-cancer agents at their respective effective dosages can achieve a synergistic effect that boosts cytotoxicity to cancer cells. In cancer, aberrant apoptotic pathways allow cells that should be killed to survive with genetic abnormalities, leading to cancer progression. Mutations in apoptotic mechanism arising during the treatment of cancer through cancer progression can consequently lead to chemoresistance. Natural compound mixtures that are believed to have multiple specific targets with minimal acceptable side-effects are now of interest to many researchers due to their cytotoxic and chemosensitizing activities. Synergistic interactions within a drug mixture enhance the search for potential molecular targets in cancer cells. Nonetheless, biased/flawed scientific evidence from natural products can suggest false positive therapeutic benefits during drug screening. In this review, we have taken these factors into consideration when discussing the evidence for these compounds and their synergistic therapeutic benefits in cancer. While there is limited evidence for clinical efficacy for these mixtures, in vitro data suggest that these preparations merit further investigation, both in vitro and in vivo.

High-throughput transcriptome profiling indicates ribosomal RNAs to be associated with resistance to immunotherapy in non-small cell lung cancer (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.

New Therapies in Melanoma: Current Trends, Evolving Paradigms, and Future Perspectives.

Melanoma is an aggressive skin cancer with increasing incidence and mortality worldwide. For many years the therapeutic strategies were limited to surgery, radiotherapy, and chemotherapy. Recent advances in immunology and cancer biology have led to the discovery and development of novel therapeutics, such as immune checkpoint inhibitors (ICIs) and targeted therapies, which have revolutionized the clinical care of patients with metastatic melanoma. Despite recent successes with ICIs, many melanoma patients do not experience long-term benefits from ICI therapies, highlighting the need for alternative treatments with novel targets such as lymphocyte-activated gene 3 (LAG-3). In this review, we explore new therapeutic agents and novel combinations that are being tested in early-phase clinical trials. We discuss newer promising tools such as nanotechnology to develop nanosystems that act as drug carriers and/or light absorbents to potentially improve therapy outcomes. Finally, we also highlight challenges such as management after resistance and intervention with novel immunotherapies and the lack of predictive biomarkers to stratify patients to targeted treatments after primary treatment failure.

Integrative deep learning analysis improves colon adenocarcinoma patient stratification at risk for mortality.

BACKGROUND: Colorectal cancers are the fourth most diagnosed cancer and the second leading cancer in number of deaths. Many clinical variables, pathological features, and genomic signatures are associated with patient risk, but reliable patient stratification in the clinic remains a challenging task. Here we assess how image, clinical, and genomic features can be combined to predict risk. METHODS: We developed and evaluated integrative deep learning models combining formalin-fixed, paraffin-embedded (FFPE) whole slide images (WSIs), clinical variables, and mutation signatures to stratify colon adenocarcinoma (COAD) patients based on their risk of mortality. Our models were trained using a dataset of 108 patients from The Cancer Genome Atlas (TCGA), and were externally validated on newly generated dataset from Wayne State University (WSU) of 123 COAD patients and rectal adenocarcinoma (READ) patients in TCGA (N = 52). FINDINGS: We first observe that deep learning models trained on FFPE WSIs of TCGA-COAD separate high-risk (OS < 3 years, N = 38) and low-risk (OS > 5 years, N = 25) patients (AUC = 0.81 ± 0.08, 5 year survival p < 0.0001, 5 year relative risk = 1.83 ± 0.04) though such models are less effective at predicting overall survival (OS) for moderate-risk (3 years < OS < 5 years, N = 45) patients (5 year survival p-value = 0.5, 5 year relative risk = 1.05 ± 0.09). We find that our integrative models combining WSIs, clinical variables, and mutation signatures can improve patient stratification for moderate-risk patients (5 year survival p < 0.0001, 5 year relative risk = 1.87 ± 0.07). Our integrative model combining image and clinical variables is also effective on an independent pathology dataset (WSU-COAD, N = 123) generated by our team (5 year survival p < 0.0001, 5 year relative risk = 1.52 ± 0.08), and the TCGA-READ data (5 year survival p < 0.0001, 5 year relative risk = 1.18 ± 0.17). Our multicenter integrative image and clinical model trained on combined TCGA-COAD and WSU-COAD is effective in predicting risk on TCGA-READ (5 year survival p < 0.0001, 5 year relative risk = 1.82 ± 0.13) data. Pathologist review of image-based heatmaps suggests that nuclear size pleomorphism, intense cellularity, and abnormal structures are associated with high-risk, while low-risk regions have more regular and small cells. Quantitative analysis shows high cellularity, high ratios of tumor cells, large tumor nuclei, and low immune infiltration are indicators of high-risk tiles. INTERPRETATION: The improved stratification of colorectal cancer patients from our computational methods can be beneficial for treatment plans and enrollment of patients in clinical trials. FUNDING: This study was supported by the National Cancer Institutes (Grant No. R01CA230031 and P30CA034196). The funders had no roles in study design, data collection and analysis or preparation of the manuscript.

Subsets of IFN Signaling Predict Response to Immune Checkpoint Blockade in Patients with Melanoma.

PURPOSE: IFN signaling in the tumor microenvironment is a critical determinant of both response and resistance of cancer to immune checkpoint inhibitors (ICI). We hypothesized that distinct patterns of IFN signaling in melanoma are associated with clinical response or resistance to ICIs. EXPERIMENTAL DESIGN: Two tissue microarrays containing samples from 97 patients with metastatic melanoma who received nivolumab, pembrolizumab, or a combination of ipilimumab and nivolumab at Yale New Haven Hospital between 2011 and 2017 were randomized into discovery and validation cohorts. Samples were stained and visualized using multiplexed immunofluorescence microscopy for STAT1, STAT1 phosphorylated at Y701 (pSTAT1Y701), and PD-L1, and signals were quantified using the automated quantitative analysis method of quantitative immunofluorescence. Treatment response was assessed using RECIST, and overall survival was analyzed. For in vitro studies, human melanoma cell lines were stimulated with IFNγ and IFNß, and Western blotting was performed. RESULTS: Pretreatment STAT1 levels were higher in responders to ICIs [complete response/partial response/stable disease (SD) for > 6 months] than in nonresponders (SD < 6 months/progressive disease). Higher pretreatment STAT1 levels were associated with improved survival after ICIs in both the discovery and validation cohorts. Western blot analysis of human melanoma cell lines stimulated with IFN demonstrated distinct patterns of upregulation of STAT1 compared with pSTAT1Y701 and PD-L1. When combining STAT1 and PD-L1 markers, patients with STAT1highPD-L1low tumors had improved survival compared with those with STAT1lowPD-L1high tumors. CONCLUSIONS: STAT1 may better predict melanoma response to ICIs than current strategies, and combined STAT1 and PD-L1 biomarkers may provide insight into IFN-responsive versus IFN-resistant states.

Digital Spatial Profiling Links Beta-2-microglobulin Expression with Immune Checkpoint Blockade Outcomes in Head and Neck Squamous Cell Carcinoma.

Programmed cell death protein-1 (PD-1)-targeted immunotherapy is approved for recurrent or metastatic head and neck squamous cell carcinoma (R/M HNSCC) treatment. Although its efficacy correlates with PD-L1 expression, response is limited even among positive cases. We employed digital spatial profiling (DSP) to discover potential biomarkers of immunotherapy outcomes in HNSCC. Fifty prospectively collected, pretreatment biopsy samples from patients with anti-PD-1-treated R/M HNSCC, were assessed using DSP, for 71 proteins in four molecularly defined compartments (tumor, leukocyte, macrophage, and stroma). Markers were evaluated for associations with progression-free (PFS) and overall survival (OS). High beta-2 microglobulin (B2M), LAG-3, CD25, and 4-1BB in tumor; high B2M, CD45, CD4 in stroma, and low fibronectin in the macrophage compartment, correlated with prolonged PFS. Improved PFS and OS were observed for cases with high B2M by quantitative and mRNA. Findings were validated in an independent cohort for PFS (HR, 0.41; 95% confidence interval, 0.19-0.93; P = 0.034). B2M-high tumors showed enrichment with immune cell and immune checkpoint markers. Our study illustrates B2M expression is associated with improved survival for immune checkpoint inhibitor (ICI)-treated HNSCC. Significance: In the current study, DSP revealed the positive association of B2M expression in the tumor compartment with immunotherapy outcomes in R/M HNSCC.

Phase II Window Study of Olaparib Alone or with Cisplatin or Durvalumab in Operable Head and Neck Cancer.

Purpose: We conducted a phase II randomized noncomparative window of opportunity (WOO) trial to evaluate the inhibition of cellular proliferation and the modulation of immune microenvironment after treatment with olaparib alone or in combination with cisplatin or durvalumab in patients with operable head and neck squamous cell carcinoma (HNSCC). Experimental Design: Forty-one patients with HNSCC were randomized to cisplatin plus olaparib (arm A), olaparib alone (arm B), no treatment (arm C) or durvalumab plus olaparib (arm D). The primary endpoint was to evaluate the percentage of patients in each arm that achieved a reduction of at least 25% in Ki67. Secondary endpoints included objective response rate (ORR), safety, and pathologic complete response (pCR) rate. Paired baseline and resection tumor biopsies and blood samples were evaluated for prespecified biomarkers. Results: A decrease in Ki67 of at least 25% was observed in 44.8% of treated patients, as measured by quantitative immunofluorescence. The ORR among treated patients was 12.1%. pCR was observed in 2 patients. Two serious adverse events occurred in 2 patients.Programmed death ligand 1 (PD-L1) levels [combined positive score (CPS)] were significantly higher after treatment in arms A and D. Expression of CD163 and colony-stimulating factor 1 receptor (CSF1R) genes, markers of M2 macrophages, increased significantly posttreatment whereas the expression of CD80, a marker of M1 macrophages, decreased. Conclusion: Preoperative olaparib with cisplatin or alone or with durvalumab was safe in the preoperative setting and led to decrease in Ki67 of at least 25% in 44.8% of treated patients. Olaparib-based treatment modulates the tumor microenvironment leading to upregulation of PD-L1 and induction of protumor features of macrophages. Significance: HNSCC is characterized by defective DNA repair pathways and immunosuppressive tumor microenvironment. PARP inhibitors, which promote DNA damage and "reset" the inflammatory tumor microenvironment, can establish an effective antitumor response. This phase II WOO trial in HNSCC demonstrated the immunomodulatory effects of PARP inhibitor-induced DNA damage. In this chemo-naïve population, PARP inhibitor-based treatment, reduced tumor cell proliferation and modulated tumor microenvironment. After olaparib upregulation of PD-L1 and macrophages, suggests that combinatorial treatment might be beneficial. Synopsis: Our WOO study demonstrates that preoperative olaparib results in a reduction in Ki67, upregulation of PD-L1 CPS, and induction of protumor features of macrophages in HNSCC.

Association of PD-1/PD-L1 Co-location with Immunotherapy Outcomes in Non-Small Cell 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.

Discovery of Biomarkers of Resistance to Immune Checkpoint Blockade in NSCLC Using High-Plex Digital Spatial Profiling.

INTRODUCTION: Despite the clinical efficacy of immune checkpoint inhibitors (ICIs) in NSCLC, only approximately 20% of patients remain disease-free at 5 years. Here, we use digital spatial profiling to find candidate biomarker proteins associated with ICI resistance. METHODS: Pretreatment samples from 56 patients with NSCLC treated with ICI were analyzed using the NanoString GeoMx digital spatial profiling method. A panel of 71 photocleavable oligonucleotide-labeled primary antibodies was used for protein detection in four molecular compartments (tumor, leukocytes, macrophages, and immune stroma). Promising candidates were orthogonally validated with quantitative immunofluorescence. Available pretreatment samples from 39 additional patients with NSCLC who received ICI and 236 non-ICI-treated patients with operable NSCLC were analyzed to provide independent cohort validation. RESULTS: Biomarker discovery using the protein-based molecular compartmentalization strategy allows 284 protein variables to be assessed for association with ICI resistance by univariate analysis using continuous log-scaled data. Of the 71 candidate protein biomarkers, CD66b in the CD45+CD68 molecular compartment (immune stroma) predicted significantly shorter overall survival (OS) (hazard ratio [HR] 1.31, p = 0.016) and was chosen for validation. Orthogonal validation by quantitative immunofluorescence illustrated that CD66b was associated with resistance to ICI therapy but not prognostic for poor outcomes in untreated NSCLC (discovery cohort [OS HR 2.49, p = 0.026], validation cohort [OS HR 2.05, p = 0.046], non-ICI-treated cohort [OS HR 1.67, p = 0.06]). CONCLUSIONS: Using the technique, we have discovered that CD66b expression is indicative of resistance to ICI therapy in NSCLC. Given that CD66b identifies neutrophils, further studies are warranted to characterize the role of neutrophils in ICI resistance.

Baseline gene expression profiling determines long-term benefit to programmed cell death protein 1 axis blockade.

Treatment with immune checkpoint inhibitors has altered the course of malignant melanoma, with approximately half of the patients with advanced disease surviving for more than 5 years after diagnosis. Currently, there are no biomarker methods for predicting outcome from immunotherapy. Here, we obtained transcriptomic information from a total of 105 baseline tumor samples comprising two cohorts of patients with advanced melanoma treated with programmed cell death protein 1 (PD-1)-based immunotherapies. Gene expression profiles were correlated with progression-free survival (PFS) within consecutive clinical benefit intervals (i.e., 6, 12, 18, and 24 months). Elastic net binomial regression models with cross validation were utilized to compare the predictive value of distinct genes across time. Lasso regression was used to generate a signature predicting long-term benefit (LTB), defined as patients who remain alive and free of disease progression at 24 months post treatment initiation. We show that baseline gene expression profiles were consistently able to predict long-term immunotherapy outcomes with high accuracy. The predictive value of different genes fluctuated across consecutive clinical benefit intervals, with a distinct set of genes defining benefit at 24 months compared to earlier outcomes. A 12-gene signature was able to predict LTB following anti-PD-1 therapy with an area under the curve (AUC) equal to 0.92 and 0.74 in the training and validation set, respectively. Evaluation of LTB, via a unique signature may complement objective response classification and characterize the logistics of sustained antitumor immune responses.

Programmed Death-Ligand 1 and Programmed Death-Ligand 2 mRNAs Measured Using Closed-System Quantitative Real-Time Polymerase Chain Reaction Are Associated With Outcome and High Negative Predictive Value in Immunotherapy-Treated NSCLC.

INTRODUCTION: Immune checkpoint inhibitors (ICIs) have become standard of care in lung cancer management, but only a relatively small percentage of patients treated respond. Current predictive biomarkers, including immunohistochemical detection of programmed death-ligand 1 (PD-L1), are insufficient for determining who will respond or, more importantly in the adjuvant setting, who will not respond to ICI therapy. Here, we investigate an alternative method of assessment of PD-L1 to predict nonresponse. METHODS: This study uses a research use only quantitative real-time reverse transcription polymerase chain reaction assay on the GeneXpert system, to test for the association between four target immune genes, CD274 (PD-L1), PDCD1LG2 (programmed death-ligand 2 [PD-L2]), CD8A, and IRF1, and response to ICI therapy. Tissues were collected from 122 patients with advanced NSCLC before ICI therapy in a retrospective cohort, macrodissected, and analyzed using the GeneXpert. RESULTS: Both high PD-L1 and PD-L2 mRNA expression levels were associated with improved long-term benefit at 24 months (p = 0.047 for both PD-L1 and PD-L2) and overall survival (PD-L1, p = 0.048; PD-L2, p = 0.049). Both PD-L1 and PD-L2 mRNA levels were higher in patients with KRAS mutations. Most importantly, low PD-L1 mRNA level had a high negative predictive value of 0.92 for absence of long-term benefit. CONCLUSIONS: With further validation of this assay in low-stage patients, an assessment of PD-L1 mRNA rather than protein, could be a method to determine which low-stage patients that should not be treated with ICIs in the adjuvant setting. This approach may also be a useful objective method for selecting patients for treatment in the advanced setting.

Objective assessment of tumor infiltrating lymphocytes as a prognostic marker in melanoma using machine learning algorithms.

BACKGROUND: The prognostic value of tumor-infiltrating lymphocytes (TILs) assessed by machine learning algorithms in melanoma patients has been previously demonstrated but has not been widely adopted in the clinic. We evaluated the prognostic value of objective automated electronic TILs (eTILs) quantification to define a subset of melanoma patients with a low risk of relapse after surgical treatment. METHODS: We analyzed data for 785 patients from 5 independent cohorts from multiple institutions to validate our previous finding that automated TIL score is prognostic in clinically-localized primary melanoma patients. Using serial tissue sections of the Yale TMA-76 melanoma cohort, both immunofluorescence and Hematoxylin-and-Eosin (H&E) staining were performed to understand the molecular characteristics of each TIL phenotype and their associations with survival outcomes. FINDINGS: Five previously-described TIL variables were each significantly associated with overall survival (p<0.0001). Assessing the receiver operating characteristic (ROC) curves by comparing the clinical impact of two models suggests that etTILs (electronic total TILs) (AUC: 0.793, specificity: 0.627, sensitivity: 0.938) outperformed eTILs (AUC: 0.77, specificity: 0.51, sensitivity: 0.938). We also found that the specific molecular subtype of cells representing TILs includes predominantly cells that are CD3+ and CD8+ or CD4+ T cells. INTERPRETATION: eTIL% and etTILs scores are robust prognostic markers in patients with primary melanoma and may identify a subgroup of stage II patients at high risk of recurrence who may benefit from adjuvant therapy. We also show the molecular correlates behind these scores. Our data support the need for prospective testing of this algorithm in a clinical trial. FUNDING: This work was also supported by a sponsored research agreements from Navigate Biopharma and NextCure and by grants from the NIH including the Yale SPORE in in Skin Cancer, P50 CA121974, the Yale SPORE in Lung Cancer, P50 CA196530, NYU SPORE in Skin Cancer P50CA225450 and the Yale Cancer Center Support Grant, P30CA016359.

Multiplex Quantitative Analysis of Tumor-Infiltrating Lymphocytes, Cancer-Associated Fibroblasts, and CD200 in Pancreatic Cancer.

Pancreatic cancer is marked by a desmoplastic tumor microenvironment and low tumor immunogenicity, making it difficult for immunotherapy drugs to improve outcomes for patients. Tumor-infiltrating lymphocytes (TILs) and cancer-associated fibroblasts (CAFs) are seen in the tumor microenvironment of patients with pancreatic ductal adenocarcinoma (PDAC). In this work, we sought to characterize the expression levels and potential prognostic value of TILs (CD4, CD8, and CD20) and CAFs (Thy-1, FAP, and SMA) in a large retrospective cohort of PDAC patients. Additionally, we investigated the expression levels and prognostic significance of CD200, an immunoinhibitory protein that has shown interest as a potential target for immune checkpoint blockade. We measured the expression levels of these seven proteins with multiplexed immunofluorescence staining and quantitative immunofluorescence (QIF). We found CD8 and FAP to be independent predictors of progression-free survival and overall survival. CD200 was found to be heterogeneously expressed in both the tumor and stromal compartments of PDAC, with the majority of patients having positive stromal expression and negative tumor expression. This work demonstrates the potential clinical utility of CD8 and FAP in PDAC patients, and it sheds light on the expression patterns of CD200 in pancreatic cancer as the protein is being tested as a target for immune checkpoint blockade.

Biomarker Discovery in Patients with Immunotherapy-Treated Melanoma with Imaging Mass Cytometry.

PURPOSE: Imaging mass cytometry (IMC) is among the first tools with the capacity for multiplex analysis of more than 40 targets, which provides a novel approach to biomarker discovery. Here, we used IMC to characterize the tumor microenvironment of patients with metastatic melanoma who received immunotherapy in efforts to find indicative factors of treatment response. In spite of the new power of IMC, the image analysis aspects are still limited by the challenges of cell segmentation. EXPERIMENTAL DESIGN: Here, rather than segment, we performed image analysis using a newly designed version of the AQUA software to measure marker intensity in molecularly defined compartments: tumor cells, stroma, T cells, B cells, and macrophages. IMC data were compared with quantitative immunofluorescence (QIF) and digital spatial profiling. RESULTS: Validation of IMC results for immune markers was confirmed by regression with additional multiplexing methods and outcome assessment. Multivariable analyses by each compartment revealed significant associations of 12 markers for progression-free survival and seven markers for overall survival (OS). The most compelling indicative biomarker, beta2-microglobulin (B2M), was confirmed by correlation with OS by QIF in the discovery cohort and validated in an independent published cohort profiled by mRNA expression. CONCLUSIONS: Using digital image analysis based on pixel colocalization to assess IMC data allowed us to quantitively measure 25 markers simultaneously on formalin-fixed, paraffin-embedded tissue microarray samples. In addition to showing high concordance with other multiplexing technologies, we identified a series of potentially indicative biomarkers for immunotherapy in metastatic melanoma, including B2M.