KDM5B promotes immune evasion by recruiting SETDB1 to silence retroelements.

Tumours use various strategies to evade immune surveillance1,2. Immunotherapies targeting tumour immune evasion such as immune checkpoint blockade have shown considerable efficacy on multiple cancers3,4 but are ineffective for most patients due to primary or acquired resistance5-7. Recent studies showed that some epigenetic regulators suppress anti-tumour immunity2,8-12, suggesting that epigenetic therapies could boost anti-tumour immune responses and overcome resistance to current immunotherapies. Here we show that, in mouse melanoma models, depletion of KDM5B-an H3K4 demethylase that is critical for melanoma maintenance and drug resistance13-15-induces robust adaptive immune responses and enhances responses to immune checkpoint blockade. Mechanistically, KDM5B recruits the H3K9 methyltransferase SETDB1 to repress endogenous retroelements such as MMVL30 in a demethylase-independent manner. Derepression of these retroelements activates cytosolic RNA-sensing and DNA-sensing pathways and the subsequent type-I interferon response, leading to tumour rejection and induction of immune memory. Our results demonstrate that KDM5B suppresses anti-tumour immunity by epigenetic silencing of retroelements. We therefore reveal roles of KDM5B in heterochromatin regulation and immune evasion in melanoma, opening new paths for the development of KDM5B-targeting and SETDB1-targeting therapies to enhance tumour immunogenicity and overcome immunotherapy resistance.

Reproducible Reporting of the Collection and Evaluation of Annotations for Artificial Intelligence Models.

This work puts forth and demonstrates the utility of a reporting framework for collecting and evaluating annotations of medical images used for training and testing artificial intelligence (AI) models in assisting detection and diagnosis. AI has unique reporting requirements, as shown by the AI extensions to the Consolidated Standards of Reporting Trials (CONSORT) and Standard Protocol Items: Recommendations for Interventional Trials (SPIRIT) checklists and the proposed AI extensions to the Standards for Reporting Diagnostic Accuracy (STARD) and Transparent Reporting of a Multivariable Prediction model for Individual Prognosis or Diagnosis (TRIPOD) checklists. AI for detection and/or diagnostic image analysis requires complete, reproducible, and transparent reporting of the annotations and metadata used in training and testing data sets. In an earlier work by other researchers, an annotation workflow and quality checklist for computational pathology annotations were proposed. In this manuscript, we operationalize this workflow into an evaluable quality checklist that applies to any reader-interpreted medical images, and we demonstrate its use for an annotation effort in digital pathology. We refer to this quality framework as the Collection and Evaluation of Annotations for Reproducible Reporting of Artificial Intelligence (CLEARR-AI).

Training pathologists to assess stromal tumour-infiltrating lymphocytes in breast cancer synergises efforts in clinical care and scientific research.

A growing body of research supports stromal tumour-infiltrating lymphocyte (TIL) density in breast cancer to be a robust prognostic and predicive biomarker. The gold standard for stromal TIL density quantitation in breast cancer is pathologist visual assessment using haematoxylin and eosin-stained slides. Artificial intelligence/machine-learning algorithms are in development to automate the stromal TIL scoring process, and must be validated against a reference standard such as pathologist visual assessment. Visual TIL assessment may suffer from significant interobserver variability. To improve interobserver agreement, regulatory science experts at the US Food and Drug Administration partnered with academic pathologists internationally to create a freely available online continuing medical education (CME) course to train pathologists in assessing breast cancer stromal TILs using an interactive format with expert commentary. Here we describe and provide a user guide to this CME course, whose content was designed to improve pathologist accuracy in scoring breast cancer TILs. We also suggest subsequent steps to translate knowledge into clinical practice with proficiency testing.

Analysis of the genomic landscapes of Barbadian and Nigerian women with triple negative breast cancer.

PURPOSE: Triple negative breast cancer (TNBC) is an aggressive breast cancer subtype that disproportionately affects women of African ancestry (WAA) and is often associated with poor survival. Although there is a high prevalence of TNBC across West Africa and in women of the African diaspora, there has been no comprehensive genomics study to investigate the mutational profile of ancestrally related women across the Caribbean and West Africa. METHODS: This multisite cross-sectional study used 31 formalin-fixed paraffin-embedded (FFPE) samples from Barbadian and Nigerian TNBC participants. High-resolution whole exome sequencing (WES) was performed on the Barbadian and Nigerian TNBC samples to identify their mutational profiles and comparisons were made to African American, European American and Asian American sequencing data obtained from The Cancer Genome Atlas (TCGA). Whole exome sequencing was conducted on tumors with an average of 382 × coverage and 4335 × coverage for pooled germline non-tumor samples. RESULTS: Variants detected at high frequency in our WAA cohorts were found in the following genes NBPF12, PLIN4, TP53 and BRCA1. In the TCGA TNBC cases, these genes had a lower mutation rate, except for TP53 (32% in our cohort; 63% in TCGA-African American; 67% in TCGA-European American; 63% in TCGA-Asian). For all altered genes, there were no differences in frequency of mutations between WAA TNBC groups including the TCGA-African American cohort. For copy number variants, high frequency alterations were observed in PIK3CA, TP53, FGFR2 and HIF1AN genes. CONCLUSION: This study provides novel insights into the underlying genomic alterations in WAA TNBC samples and shines light on the importance of inclusion of under-represented populations in cancer genomics and biomarker studies.

ISAC Probe Tag Dictionary: Standardized Nomenclature for Detection and Visualization Labels Used in Cytometry and Microscopy Imaging.

Since the advent of microscopy imaging and flow cytometry, there has been an explosion in the number of probes, consisting of a component binding to an analyte and a detectable tag, to mark areas of interest in or on cells and tissue. Probe tags have been created to detect and/or visualize probes. Over time, these probe tags have increased in number. The expansion has resulted in arbitrarily created synonyms of probe tags used in publications and software. The synonyms are problematic for readability of publications, accuracy of text/data mining, and bridging data from multiple platforms, protocols, and databases for Big Data analysis. Development and implementation of a universal language for probe tags will ensure equivalent quality and level of data being reported or extracted for clinical/scientific evaluation as well as help connect data from many platforms. The International Society for Advancement of Cytometry Data Standards Task Force composed of academic scientists and industry hardware/software/reagent manufactures have developed recommendations for a standardized nomenclature for probe tags used in cytometry and microscopy imaging. These recommendations are shared in this technical note in the form of a Probe Tag Dictionary. © 2020 International Society for Advancement of Cytometry.

Immune Cell and Cell Cluster Phenotyping, Quantitation, and Visualization Using In Silico Multiplexed Images and Tissue Cytometry.

Phenotyping immune cells and cell clusters in situ, including their activation state and function, can aid in interpretation of spatial relationships within the tissue microenvironment. Immune cell phenotypes require multiple biomarkers. However, conventional microscopy setups can only image up to four biomarkers at one time. In this report, we describe and give an example of a workflow to phenotype, quantitate, and visualize greater than four biomarkers in silico utilizing multiplexed fluorescence histology and the TissueFAXS quantitative imaging system with a conventional microscopy setup. Biomarkers were conjugated to Cy3 or Cy5. Multiplexed staining was performed on formalin-fixed paraffin-embedded tissue sections. We imaged the slides, inactivated the dyes, and repeated the process until all biomarkers were stained. Phenotype profiles were built based on in silico combinations of the biomarkers. We used algorithms that aligned all images to create a composite image, isolated each cell in the image, and identified biomarker positive cells in the image. The in silico phenotypes were quantitated and displayed through flow cytometry-like histograms and dot scatterplots in addition to backgating into the tissue images. The advantage of our workflow is that it provides visual verification of cell isolation and identification as well as highlight characteristics of cells and cell clusters. © 2018 International Society for Advancement of Cytometry.

Immune correlates of talactoferrin alfa in biopsied tumor of relapsed/refractory metastatic non-small cell lung cancer patients.

CONTEXT: Talactoferrin alfa (TLF) is a unique recombinant form of human lactoferrin. The hypothesized mechanism of action involves TLF binding to the intestinal endothelium inducing dendritic cell maturation and cytokine release leading to infiltration of tumor with monocytes and T-lymphocytes and inhibition of tumor growth. OBJECTIVE: Based on promising phase II trial results, this correlative study was undertaken to examine immune mechanism of action of TLF in metastatic non-small cell lung cancer (NSCLC) patients. METHODS: Talactoferrin was administered orally at 1.5 g bid weeks 1-12 with 2 weeks off on a 14-week cycle. Enrolled patients had a pathologic diagnosis of NSCLC previously treated with at least two lines of systemic treatment. Patients had core biopsy of tumor before initiation of talactoferrin and at week 7 on TLF. Flow cytometry and quantitative immunohistochemistry for immune correlates were performed on the biopsied specimens. RESULTS: Four patients with metastatic NSCLC were enrolled. The trial was halted pre-maturely in light of negative phase III trial results. For the two patients who had repeat on-treatment tumor biopsies, a consistent increase in monocytes as a percentage of total immune cells was observed. Otherwise, no clear trend of increase or decrease was observed in any other immune cell parameters compared to matched patient pre-treatment biopsies. CONCLUSION: Repeat biopsies for immune correlates by flow cytometry and quantitative immunohistochemistry in NSCLC patients are feasible. In the few patients sampled before trial closure, increased monocytes as a total percentage of the immune cell population within tumor was observed in response to TLF.

Tumor-Associated Macrophages Expand Chemoresistant, Ovarian Cancer Stem-Like Cells.

The persistence of ovarian cancer stem-like cells (OvCSCs) after chemotherapy resistance has been implicated in relapse. However, the ability of these relatively quiescent cells to produce the robust tumor regrowth necessary for relapse remains an enigma. Since normal stem cells exist in a niche, and tumor-associated macrophages (TAMs) are the highest abundance immune cell within ovarian tumors, we hypothesized that TAMs may influence OvCSC proliferation. To test this, we optimized OvCSC enrichment by sphere culture and in vitro polarization of monocytes to a TAM-like M2 phenotype. Using cocultures that permitted the exchange of only soluble factors, we found that M2 macrophages increased the proliferation of sphere cells. Longer-term exposure (5-7 days) to soluble TAM factors led to retention of some stem cell features by OvCSCs but loss of others, suggesting that TAMs may support an intermediate stemness phenotype in OvCSCs. Although TAM coculture decreased the percentage of OvCSCs surviving chemotherapy, it increased the overall number. We therefore sought to determine the influence of this interaction on chemotherapy efficacy in vivo and found that inhibiting macrophages improved chemotherapy response. Comparing the gene expression changes in OvCSCs cocultured with TAMs to publicly available patient data identified 34 genes upregulated in OvCSCs by exposure to soluble TAM factors whose expression correlates with outcome. Overall, these data suggest that TAMs may influence OvCSC proliferation and impact therapeutic response.

A review of the impact of energy balance on triple-negative breast cancer.

Cancer cells cannot proliferate without sufficient energy to generate biomass for rapid cell division, as well as to fuel their functions at baseline. For this reason, many recent observational and interventional studies have focused on increasing energy expenditure and/or reducing energy intake during and after cancer treatment. The impact of variance in diet composition and in exercise on cancer outcomes has been detailed extensively elsewhere and is not the primary focus of this review. Instead, in this translational, narrative review we examine studies of how energy balance impacts anticancer immune activation and outcomes in triple-negative breast cancer (TNBC). We discuss preclinical, clinical observational, and the few clinical interventional studies on energy balance in TNBC. We advocate for the implementation of clinical studies to examine how optimizing energy balance-through changes in diet and/or exercise-may optimize the response to immunotherapy in people with TNBC. It is our conviction that by taking a holistic approach that includes energy balance as a key factor to be considered during and after treatment, cancer care may be optimized, and the detrimental effects of cancer treatment and recovery on overall health may be minimized.

Image analysis-based tumor infiltrating lymphocytes measurement predicts breast cancer pathologic complete response in SWOG S0800 neoadjuvant chemotherapy trial.

We assessed the predictive value of an image analysis-based tumor-infiltrating lymphocytes (TILs) score for pathologic complete response (pCR) and event-free survival in breast cancer (BC). About 113 pretreatment samples were analyzed from patients with stage IIB-IIIC HER-2-negative BC randomized to neoadjuvant chemotherapy ± bevacizumab. TILs quantification was performed on full sections using QuPath open-source software with a convolutional neural network cell classifier (CNN11). We used easTILs% as a digital metric of TILs score defined as [sum of lymphocytes area (mm2)/stromal area(mm2)] × 100. Pathologist-read stromal TILs score (sTILs%) was determined following published guidelines. Mean pretreatment easTILs% was significantly higher in cases with pCR compared to residual disease (median 36.1 vs.14.8%, p < 0.001). We observed a strong positive correlation (r = 0.606, p < 0.0001) between easTILs% and sTILs%. The area under the prediction curve (AUC) was higher for easTILs% than sTILs%, 0.709 and 0.627, respectively. Image analysis-based TILs quantification is predictive of pCR in BC and had better response discrimination than pathologist-read sTILs%.

Strategies to mitigate the toxicity of cancer therapeutics.

Cancer therapeutics are dynamically evolving, and include traditional chemotherapy and hormone therapy, as well as more recently developed treatment modalities, such as tyrosine kinase inhibitors, monoclonal antibodies and the revolutionary approach based on immune checkpoint inhibition. These regimens are unfortunately not free of adverse events, and patients with cancer are a susceptible population experiencing a myriad of disease and treatment toxicities combined. In this review, we present the latest overview of the management of the most common systemic cancer treatment symptoms and the science of symptom management supporting these strategies. We discuss cancer-related cognitive impairment, ocular toxicity, ototoxicity, oral mucosal toxicities, gastrointestinal toxicities, renal toxicity, aromatase inhibitor-induced musculoskeletal symptoms, chemotherapy-induced peripheral neuropathy, and immunotherapy-induced autoimmunity derived from systemic therapies for cancer. In summary, we review the future directions and ideal goals of symptom science research in order to benefit patients utilizing a comprehensive individualized approach.

Leveraging the Dynamic Immune Environment Triad in Patients with Breast Cancer: Tumour, Lymph Node, and Peripheral Blood.

During the anti-tumour response to breast cancer, the primary tumour, the peripheral blood, and the lymph nodes each play unique roles. Immunological features at each site reveal evidence of continuous immune cross-talk between them before, during and after treatment. As such, immune responses to breast cancer are found to be highly dynamic and truly systemic, integrating three distinct immune sites, complex cell-migration highways, as well as the temporal dimension of disease progression and treatment. In this review, we provide a connective summary of the dynamic immune environment triad of breast cancer. It is critical that future studies seek to establish dynamic immune profiles, constituting multiple sites, that capture the systemic immune response to breast cancer and define patient-selection parameters resulting in more significant overall responses and survival rates for breast cancer patients.

Development of Training Materials for Pathologists to Provide Machine Learning Validation Data of Tumor-Infiltrating Lymphocytes in Breast Cancer.

The High Throughput Truthing project aims to develop a dataset for validating artificial intelligence and machine learning models (AI/ML) fit for regulatory purposes. The context of this AI/ML validation dataset is the reporting of stromal tumor-infiltrating lymphocytes (sTILs) density evaluations in hematoxylin and eosin-stained invasive breast cancer biopsy specimens. After completing the pilot study, we found notable variability in the sTILs estimates as well as inconsistencies and gaps in the provided training to pathologists. Using the pilot study data and an expert panel, we created custom training materials to improve pathologist annotation quality for the pivotal study. We categorized regions of interest (ROIs) based on their mean sTILs density and selected ROIs with the highest and lowest sTILs variability. In a series of eight one-hour sessions, the expert panel reviewed each ROI and provided verbal density estimates and comments on features that confounded the sTILs evaluation. We aggregated and shaped the comments to identify pitfalls and instructions to improve our training materials. From these selected ROIs, we created a training set and proficiency test set to improve pathologist training with the goal to improve data collection for the pivotal study. We are not exploring AI/ML performance in this paper. Instead, we are creating materials that will train crowd-sourced pathologists to be the reference standard in a pivotal study to create an AI/ML model validation dataset. The issues discussed here are also important for clinicians to understand about the evaluation of sTILs in clinical practice and can provide insight to developers of AI/ML models.

Comprehensive Analysis of Metabolic Isozyme Targets in Cancer.

Metabolic reprogramming is a hallmark of malignant transformation, and loss of isozyme diversity (LID) contributes to this process. Isozymes are distinct proteins that catalyze the same enzymatic reaction but can have different kinetic characteristics, subcellular localization, and tissue specificity. Cancer-dominant isozymes that catalyze rate-limiting reactions in critical metabolic processes represent potential therapeutic targets. Here, we examined the isozyme expression patterns of 1,319 enzymatic reactions in 14 cancer types and their matching normal tissues using The Cancer Genome Atlas mRNA expression data to identify isozymes that become cancer-dominant. Of the reactions analyzed, 357 demonstrated LID in at least one cancer type. Assessment of the expression patterns in over 600 cell lines in the Cancer Cell Line Encyclopedia showed that these reactions reflect cellular changes instead of differences in tissue composition; 50% of the LID-affected isozymes showed cancer-dominant expression in the corresponding cell lines. The functional importance of the cancer-dominant isozymes was assessed in genome-wide CRISPR and RNAi loss-of-function screens: 17% were critical for cell proliferation, indicating their potential as therapeutic targets. Lists of prioritized novel metabolic targets were developed for 14 cancer types; the most broadly shared and functionally validated target was acetyl-CoA carboxylase 1 (ACC1). Small molecule inhibition of ACC reduced breast cancer viability in vitro and suppressed tumor growth in cell line- and patient-derived xenografts in vivo. Evaluation of the effects of drug treatment revealed significant metabolic and transcriptional perturbations. Overall, this systematic analysis of isozyme expression patterns elucidates an important aspect of cancer metabolic plasticity and reveals putative metabolic vulnerabilities. SIGNIFICANCE: This study exploits the loss of metabolic isozyme diversity common in cancer and reveals a rich pool of potential therapeutic targets that will allow the repurposing of existing inhibitors for anticancer therapy. See related commentary by Kehinde and Parker, p. 1695.

Tumor immune microenvironment of self-identified African American and non-African American triple negative breast cancer.

Differences in the tumor immune microenvironment may result in differences in prognosis and response to treatment in cancer patients. We hypothesized that differences in the tumor immune microenvironment may exist between African American (AA) and NonAA patients, due to ancestry-related or socioeconomic factors, that may partially explain differences in clinical outcomes. We analyzed clinically matched triple-negative breast cancer (TNBC) tissues from self-identified AA and NonAA patients and found that stromal TILs, PD-L1 IHC-positivity, mRNA expression of immune-related pathways, and immunotherapy response predictive signatures were significantly higher in AA samples (p < 0.05; Fisher's Exact Test, Mann-Whitney Test, Permutation Test). Cancer biology and metabolism pathways, TAM-M2, and Immune Exclusion were significantly higher in NonAA samples (p < 0.05; Permutation Test, Mann-Whitney Test). There were no differences in somatic tumor mutation burden. Overall, there is greater immune infiltration and inflammation in AA TNBC and these differences may impact response to immune checkpoint inhibitors and other therapeutic agents that modulate the immune microenvironment.

Predictive Markers of Response to Neoadjuvant Durvalumab with Nab-Paclitaxel and Dose-Dense Doxorubicin/Cyclophosphamide in Basal-Like Triple-Negative Breast Cancer.

PURPOSE: We examined gene expression, germline variant, and somatic mutation features associated with pathologic response to neoadjuvant durvalumab plus chemotherapy in basal-like triple-negative breast cancer (bTNBC). EXPERIMENTAL DESIGN: Germline and somatic whole-exome DNA and RNA sequencing, programmed death ligand 1 (PD-L1) IHC, and stromal tumor-infiltrating lymphocyte scoring were performed on 57 patients. We validated our results using 162 patients from the GeparNuevo randomized trial. RESULTS: Gene set enrichment analysis showed that pathways involved in immunity (adaptive, humoral, innate), JAK-STAT signaling, cancer drivers, cell cycle, apoptosis, and DNA repair were enriched in cases with pathologic complete response (pCR), whereas epithelial-mesenchymal transition, extracellular matrix, and TGFß pathways were enriched in cases with residual disease (RD). Immune-rich bTNBC with RD was enriched in CCL-3, -4, -5, -8, -23, CXCL-1, -3, -6, -10, and IL1, -23, -27, -34, and had higher expression of macrophage markers compared with immune-rich cancers with pCR that were enriched in IFNγ, IL2, -12, -21, chemokines CXCL-9, -13, CXCR5, and activated T- and B-cell markers (GZMB, CD79A). In the validation cohort, an immune-rich five-gene signature showed higher expression in pCR cases in the durvalumab arm (P = 0.040) but not in the placebo arm (P = 0.923) or in immune-poor cancers. Independent of immune markers, tumor mutation burden was higher, and PI3K, DNA damage repair, MAPK, and WNT/ß-catenin signaling pathways were enriched in germline and somatic mutations in cases with pCR. CONCLUSIONS: The TGFß pathway is associated with immune-poor phenotype and RD in bTNBC. Among immune-rich bTNBC RD, macrophage/neutrophil chemoattractants dominate the cytokine milieu, and IFNγ and activated B cells and T cells dominate immune-rich cancers with pCR.

A Novel Immunomodulatory 27-Gene Signature to Predict Response to Neoadjuvant Immunochemotherapy for Primary Triple-Negative Breast Cancer.

A precise predictive biomarker for TNBC response to immunochemotherapy is urgently needed. We previously established a 27-gene IO signature for TNBC derived from a previously established 101-gene model for classifying TNBC. Here we report a pilot study to assess the performance of a 27-gene IO signature in predicting the pCR of TNBC to preoperative immunochemotherapy. We obtained RNA sequencing data from the primary tumors of 55 patients with TNBC, who received neoadjuvant immunochemotherapy with the PD-L1 blocker durvalumab. We determined the power and accuracy in predicting pCR for the immunomodulatory (IM) subtype identified by the 101-gene model, the 27-gene IO signature, and PD-L1 expression by immunohistochemistry (IHC). The pCR rate was 45% (25/55). The odds ratios for pCR were as follows: IM subtype by 101-gene model, 3.14 (p = 0.054); 27-gene IO signature, 4.13 (p = 0.012); PD-L1 expression by IHC, 2.63 (p = 0.106); 27-gene IO signature in combination with PD-L1 expression by IHC, 6.53 (p = 0.003). The 27-gene IO signature has the potential to predict the pCR of primary TNBC to neoadjuvant immunochemotherapy. Further analysis in a large cohort is needed.

Tumor-Specific Major Histocompatibility-II Expression Predicts Benefit to Anti-PD-1/L1 Therapy in Patients With HER2-Negative Primary Breast Cancer.

PURPOSE: Immunotherapies targeting PD-1/L1 enhance pathologic complete response (pCR) rates when added to standard neoadjuvant chemotherapy (NAC) regimens in early-stage triple-negative, and possibly high-risk estrogen receptor-positive breast cancer. However, immunotherapy has been associated with significant toxicity, and most patients treated with NAC do not require immunotherapy to achieve pCR. Biomarkers discerning patients benefitting from the addition of immunotherapy from those who would achieve pCR to NAC alone are clearly needed. In this study, we tested the ability of MHC-II expression on tumor cells, to predict immunotherapy-specific benefit in the neoadjuvant breast cancer setting. PATIENTS AND METHODS: This was a retrospective tissue-based analysis of 3 cohorts of patients with breast cancer: (i) primary nonimmunotherapy-treated breast cancers (n = 381), (ii) triple-negative breast cancers (TNBC) treated with durvalumab and standard NAC (n = 48), and (iii) HER2-negative patients treated with standard NAC (n = 87) or NAC and pembrolizumab (n = 66). RESULTS: HLA-DR positivity on ≥5% of tumor cells, defined a priori, was observed in 10% and 15% of primary non-immunotherapy-treated hormone receptor-positive and triple-negative breast cancers, respectively. Quantitative assessment of MHC-II on tumor cells was predictive of durvalumab + NAC and pembrolizumab + NAC (ROC AUC, 0.71; P = 0.01 and AUC, 0.73; P = 0.001, respectively), but not NAC alone (AUC, 0.5; P = 0.99). CONCLUSIONS: Tumor-specific MHC-II has a strong candidacy as a specific biomarker of anti-PD-1/L1 immunotherapy benefit when added to standard NAC in HER2-negative breast cancer. Combined with previous studies in melanoma, MHC-II has the potential to be a pan-cancer biomarker. Validation is warranted in existing and future phase II/III clinical trials in this setting.

Pathology of spontaneous and immunotherapy-induced tumor regression in a murine model of melanoma.

We evaluated the spontaneous and immunotherapy-induced histological changes in the tumor microenvironment of a mouse melanoma regression model consisting of immunocompetent C57BL/6J mice implanted with syngeneic YUMMER1.7 melanoma cells. We focused on tumor regression phenotypes and spatial relationships of melanoma cells with B cells and neutrophils since this was not previously described. We found common themes to the host response to cancer irrespective of the mode of tumor regression. In nonregression tumors, melanoma cells were epithelioid shaped and tightly packed. In regression tumors, melanoma cells were spindle shaped and discohesive. B cells including plasmablasts and plasma cells were numerous and were increased with immunotherapy. Neutrophils were in direct contact with dead or dying melanoma cells. Immunotherapy increased neutrophil counts and induced neutrophil extracellular traps (NETs)-like formations and geographic necrosis. Beyond tumor regression, the increase in the B cell and neutrophil response could play a role in immunotherapy-induced adverse reactions.

Multiplex Quantitative Analysis of Tumor-Infiltrating Lymphocytes and Immunotherapy Outcome in Metastatic Melanoma.

PURPOSE: Because durable response to programmed cell death 1 (PD-1) inhibition is limited to a subset of melanoma patients, new predictive biomarkers could have clinical utility. We hypothesize that pretreatment tumor-infiltrating lymphocyte (TIL) profiles could be associated with response. EXPERIMENTAL DESIGN: Pretreatment whole tissue sections from 94 melanoma patients treated with anti-PD-1 therapy were profiled by multiplex immunofluorescence to perform TIL quantification (CD4, CD8, CD20) and assess TIL activation (CD3, GZMB, Ki67). Two independent image analysis technologies were used: inForm (PerkinElmer) to determine cell counts, and AQUA to measure protein by quantitative immunofluorescence (QIF). TIL parameters by both methodologies were correlated with objective response or disease control rate (ORR/DCR) by RECIST 1.1 and survival outcome. RESULTS: Pretreatment lymphocytic infiltration, by cell counts or QIF, was significantly higher in complete or partial response than in stable or progressive disease, particularly for CD8 (P < 0.0001). Neither TIL activation nor dormancy was associated with outcome. CD8 associations with progression-free survival (HR > 3) were independently significant in multivariable analyses and accounted for similar CD3 associations in anti-PD-1-treated patients. CD8 was not associated with melanoma prognosis in the absence of immunotherapy. Predictive performance of CD8 cell count (and QIF) had an area under the ROC curve above 0.75 (ORR/DCR), which reached 0.83 for ipilimumab plus nivolumab. CONCLUSIONS: Pretreatment lymphocytic infiltration is associated with anti-PD-1 response in metastatic melanoma. Quantitative TIL analysis has potential for application in digital precision immuno-oncology as an "indicative" companion diagnostic.