The Tumor Microbiome as a Predictor of Outcomes in Patients with Metastatic Melanoma Treated with Immune Checkpoint Inhibitors.

Emerging evidence supports the important role of the tumor microbiome in oncogenesis, cancer immune phenotype, cancer progression, and treatment outcomes in many malignancies. In this study, we investigated the metastatic melanoma tumor microbiome and its potential roles in association with clinical outcomes, such as survival, in patients with metastatic disease treated with immune checkpoint inhibitors (ICI). Baseline tumor samples were collected from 71 patients with metastatic melanoma before treatment with ICIs. Bulk RNA sequencing (RNA-seq) was conducted on the formalin-fixed, paraffin-embedded and fresh frozen tumor samples. Durable clinical benefit (primary clinical endpoint) following ICIs was defined as overall survival >24 months and no change to the primary drug regimen (responders). We processed RNA-seq reads to carefully identify exogenous sequences using the {exotic} tool. The age of the 71 patients with metastatic melanoma ranged from 24 to 83 years, 59% were male, and 55% survived >24 months following the initiation of ICI treatment. Exogenous taxa were identified in the tumor RNA-seq, including bacteria, fungi, and viruses. We found differences in gene expression and microbe abundances in immunotherapy-responsive versus nonresponsive tumors. Responders showed significant enrichment of bacteriophages in the phylum Uroviricota, and nonresponders showed enrichment of several bacteria, including Campylobacter jejuni. These microbes correlated with immune-related gene expression signatures. Finally, we found that models for predicting prolonged survival with immunotherapy using both microbe abundances and gene expression outperformed models using either dataset alone. Our findings warrant further investigation and potentially support therapeutic strategies to modify the tumor microbiome in order to improve treatment outcomes with ICIs. SIGNIFICANCE: We analyzed the tumor microbiome and interactions with genes and pathways in metastatic melanoma treated with immunotherapy and identified several microbes associated with immunotherapy response and immune-related gene expression signatures. Machine learning models that combined microbe abundances and gene expression outperformed models using either dataset alone in predicting immunotherapy responses.

The classification of melanocytic gene signatures.

Gene expression profiling technologies have revolutionized cell biology, enabling researchers to identify gene signatures linked to various biological attributes of melanomas, such as pigmentation status, differentiation state, proliferative versus invasive capacity, and disease progression. Although the discovery of gene signatures has significantly enhanced our understanding of melanocytic phenotypes, reconciling the numerous signatures reported across independent studies and different profiling platforms remains a challenge. Current methods for classifying melanocytic gene signatures depend on exact gene overlap and comparison with unstandardized baseline transcriptomes. In this study, we aimed to categorize published gene signatures into clusters based on their similar patterns of expression across clinical cutaneous melanoma specimens. We analyzed nearly 800 melanoma samples from six gene expression repositories and developed a classification framework for gene signatures that is resilient against biases in gene identification across profiling platforms and inconsistencies in baseline standards. Using 39 frequently cited published gene signatures, our analysis revealed seven principal classes of gene signatures that correlate with previously identified phenotypes: Differentiated, Mitotic/MYC, AXL, Amelanotic, Neuro, Hypometabolic, and Invasive. Each class is consistent with the phenotypes that the constituent gene signatures represent, and our classification method does not rely on overlapping genes between signatures. To facilitate broader application, we created WIMMS (what is my melanocytic signature, available at https://wimms.tanlab.org/), a user-friendly web application. WIMMS allows users to categorize any gene signature, determining its relationship to predominantly cited signatures and its representation within the seven principal classes.

SASH1 S519N Variant Links Skin Hyperpigmentation and Premature Hair Graying to Dysfunction of Melanocyte Lineage.

A better understanding of human melanocyte (MC) and MC stem cell biology is essential for treating MC-related diseases. This study employed an inherited pigmentation disorder carrying the SASH1S519N variant in a Hispanic family to investigate SASH1 function in the MC lineage and the underlying mechanism for this disorder. We used a multidisciplinary approach, including clinical examinations, human cell assays, yeast 2-hybrid screening, and biochemical techniques. Results linked early hair graying to the SASH1S519N variant, a previously unrecognized clinical phenotype in hyperpigmentation disorders. In vitro, we identified SASH1 as a regulator in MC stem cell maintenance and discovered that TNKS2 is crucial for SASH1's role. In addition, the S519N variant is located in one of multiple tankyrase-binding motifs and alters the binding kinetics and affinity of the interaction. In summary, this disorder links both gain and loss of pigmentation in the same individual, hinting to accelerated aging in human MC stem cells. The findings offer insights into the roles of SASH1 and TNKS2 in MC stem cell maintenance and the molecular mechanisms of pigmentation disorders. We propose that a comprehensive clinical evaluation of patients with MC-related disorders should include an assessment and history of hair pigmentation loss.

An Epigenomic fingerprint of human cancers by landscape interrogation of super enhancers at the constituent level.

Super enhancers (SE), large genomic elements that activate transcription and drive cell identity, have been found with cancer-specific gene regulation in human cancers. Recent studies reported the importance of understanding the cooperation and function of SE internal components, i.e., the constituent enhancers (CE). However, there are no pan-cancer studies to identify cancer-specific SE signatures at the constituent level. Here, by revisiting pan-cancer SE activities with H3K27Ac ChIP-seq datasets, we report fingerprint SE signatures for 28 cancer types in the NCI-60 cell panel. We implement a mixture model to discriminate active CEs from inactive CEs by taking into consideration ChIP-seq variabilities between cancer samples and across CEs. We demonstrate that the model-based estimation of CE states provides improved functional interpretation of SE-associated regulation. We identify cancer-specific CEs by balancing their active prevalence with their capability of encoding cancer type identities. We further demonstrate that cancer-specific CEs have the strongest per-base enhancer activities in independent enhancer sequencing assays, suggesting their importance in understanding critical SE signatures. We summarize fingerprint SEs based on the cancer-specific statuses of their component CEs and build an easy-to-use R package to facilitate the query, exploration, and visualization of fingerprint SEs across cancers.

Association of Baseline Tumor-Specific Neoantigens and CD8+ T-Cell Infiltration With Immune-Related Adverse Events Secondary to Immune Checkpoint Inhibitors.

PURPOSE: Recent evidence has shown that higher tumor mutational burden strongly correlates with an increased risk of immune-related adverse events (irAEs). By using an integrated multiomics approach, we further studied the association between relevant tumor immune microenvironment (TIME) features and irAEs. METHODS: Leveraging the US Food and Drug Administration Adverse Event Reporting System, we extracted cases of suspected irAEs to calculate the reporting odds ratios (RORs) of irAEs for cancers treated with immune checkpoint inhibitors (ICIs). TIME features for 32 cancer types were calculated on the basis of the cancer genomic atlas cohorts and indirectly correlated with each cancer's ROR for irAEs. A separate ICI-treated cohort of non-small-cell lung cancer (NSCLC) was used to evaluate the correlation between tissue-based immune markers (CD8+, PD-1/L1+, FOXP3+, tumor-infiltrating lymphocytes [TILs]) and irAE occurrence. RESULTS: The analysis of 32 cancers and 33 TIME features demonstrated a significant association between irAE RORs and the median number of base insertions and deletions (INDEL), neoantigens (r = 0.72), single-nucleotide variant neoantigens (r = 0.67), and CD8+ T-cell fraction (r = 0.51). A bivariate model using the median number of INDEL neoantigens and CD8 T-cell fraction had the highest accuracy in predicting RORs (adjusted r2 = 0.52, P = .002). Immunoprofile assessment of 156 patients with NSCLC revealed a strong trend for higher baseline median CD8+ T cells within patients' tumors who experienced any grade irAEs. Using machine learning, an expanded ICI-treated NSCLC cohort (n = 378) further showed a treatment duration-independent association of an increased proportion of high TIL (>median) in patients with irAEs (59.7% v 44%, P = .005). This was confirmed by using the Fine-Gray competing risk approach, demonstrating higher baseline TIL density (>median) associated with a higher cumulative incidence of irAEs (P = .028). CONCLUSION: Our findings highlight a potential role for TIME features, specifically INDEL neoantigens and baseline-immune infiltration, in enabling optimal irAE risk stratification of patients.

Exploring the Landscape of Immune Checkpoint Inhibitor-Induced Adverse Events Through Big Data Mining of Pan-Cancer Clinical Trials.

PURPOSE: Immune checkpoint inhibitors (ICIs) have significantly improved the survival of patients with cancer and provided long-term durable benefit. However, ICI-treated patients develop a range of toxicities known as immune-related adverse events (irAEs), which could compromise clinical benefits from these treatments. As the incidence and spectrum of irAEs differs across cancer types and ICI agents, it is imperative to characterize the incidence and spectrum of irAEs in a pan-cancer cohort to aid clinical management. DESIGN: We queried >400 000 trials registered at ClinicalTrials.gov and retrieved a comprehensive pan-cancer database of 71 087 ICI-treated participants from 19 cancer types and 7 ICI agents. We performed data harmonization and cleaning of these trial results into 293 harmonized adverse event categories using Medical Dictionary for Regulatory Activities. RESULTS: We developed irAExplorer (https://irae.tanlab.org), an interactive database that focuses on adverse events in patients administered with ICIs from big data mining. irAExplorer encompasses 71 087 distinct clinical trial participants from 343 clinical trials across 19 cancer types with well-annotated ICI treatment regimens and harmonized adverse event categories. We demonstrated a few of the irAE analyses through irAExplorer and highlighted some associations between treatment- or cancer-specific irAEs. CONCLUSION: The irAExplorer is a user-friendly resource that offers exploration, validation, and discovery of treatment- or cancer-specific irAEs across pan-cancer cohorts. We envision that irAExplorer can serve as a valuable resource to cross-validate users' internal datasets to increase the robustness of their findings.

A Bioinformatics Tool for Identifying Intratumoral Microbes from the ORIEN Dataset.

Evidence supports significant interactions among microbes, immune cells, and tumor cells in at least 10%-20% of human cancers, emphasizing the importance of further investigating these complex relationships. However, the implications and significance of tumor-related microbes remain largely unknown. Studies have demonstrated the critical roles of host microbes in cancer prevention and treatment responses. Understanding interactions between host microbes and cancer can drive cancer diagnosis and microbial therapeutics (bugs as drugs). Computational identification of cancer-specific microbes and their associations is still challenging due to the high dimensionality and high sparsity of intratumoral microbiome data, which requires large datasets containing sufficient event observations to identify relationships, and the interactions within microbial communities, the heterogeneity in microbial composition, and other confounding effects that can lead to spurious associations. To solve these issues, we present a bioinformatics tool, microbial graph attention (MEGA), to identify the microbes most strongly associated with 12 cancer types. We demonstrate its utility on a dataset from a consortium of nine cancer centers in the Oncology Research Information Exchange Network. This package has three unique features: species-sample relations are represented in a heterogeneous graph and learned by a graph attention network; it incorporates metabolic and phylogenetic information to reflect intricate relationships within microbial communities; and it provides multiple functionalities for association interpretations and visualizations. We analyzed 2,704 tumor RNA sequencing samples and MEGA interpreted the tissue-resident microbial signatures of each of 12 cancer types. MEGA can effectively identify cancer-associated microbial signatures and refine their interactions with tumors. SIGNIFICANCE: Studying the tumor microbiome in high-throughput sequencing data is challenging because of the extremely sparse data matrices, heterogeneity, and high likelihood of contamination. We present a new deep learning tool, MEGA, to refine the organisms that interact with tumors.

Immune landscape in molecular subtypes of human papillomavirus-negative head and neck cancer.

Head and neck squamous cell carcinomas (HNSCC) remain a poorly understood disease clinically and immunologically. HPV is a known risk factor of HNSCC associated with better outcome, whereas HPV-negative HNSCC are more heterogeneous in outcome. Gene expression signatures have been developed to classify HNSCC into four molecular subtypes (classical, basal, mesenchymal, and atypical). However, the molecular underpinnings of treatment response and the immune landscape for these molecular subtypes are largely unknown. Herein, we described a comprehensive immune landscape analysis in three independent HNSCC cohorts (>700 patients) using transcriptomics data. We assigned the HPV- HNSCC patients into these four molecular subtypes and characterized the tumor microenvironment using deconvolution method. We determined that atypical and mesenchymal subtypes have greater immune enrichment and exhibit a T-cell exhaustion phenotype, compared to classical and basal subtypes. Further analyses revealed different B cell maturation and antibody isotypes enrichment patterns, and distinct immune microenvironment crosstalk in the atypical and mesenchymal subtypes. Taken together, our study suggests that treatments that enhances B cell activity may benefit patients with HNSCC of the atypical subtypes. The rationale can be utilized in the design of future precision immunotherapy trials based on the molecular subtypes of HPV- HNSCC.

Gene partners of the EWSR1 fusion may represent molecularly distinct entities.

EWSR1 fusions are highly promiscuous and are associated with unique malignancies, clinical phenotypes, and molecular subtypes. However, rare fusion partners (RFP) of EWSR1 has not been well described. Here, we conducted a cross-sectional, retrospective study of 1,140 unique tumors harboring EWSR1 fusions. We identified 64 unique fusion partners. RFPs were identified more often in adults than children. Alterations in cell cycle control and DNA damage response genes as driving the differences between fusion partners. Potentially clinically actionable genomic variants were more prevalent in tumors harboring RFP than common fusions. While the data presented here is limited, tumors harboring RFP of EWSR1 may represent molecularly distinct entities and may benefit from further molecular testing to identify targeted therapeutic options.

Chemotherapy and Immune Checkpoint Blockade for Gastric and Gastroesophageal Junction Adenocarcinoma.

Importance: Combining immune checkpoint blockade (ICB) with chemotherapy improves outcomes in patients with metastatic gastric and gastroesophageal junction (G/GEJ) adenocarcinoma; however, whether this combination has activity in the perioperative setting remains unknown. Objective: To evaluate the safety and preliminary activity of perioperative chemotherapy and ICB followed by maintenance ICB in resectable G/GEJ adenocarcinoma. Design, Setting, and Participants: This investigator-initiated, multicenter, open-label, single-stage, phase 2 nonrandomized controlled trial screened 49 patients and enrolled 36 patients with resectable G/GEJ adenocarcinoma from February 10, 2017, to June 17, 2021, with a median (range) follow-up of 35.2 (17.4-73.0) months. Thirty-four patients were deemed evaluable for efficacy analysis, with 28 (82.4%) undergoing curative resection. This study was performed at 4 referral institutions in the US. Interventions: Patients received 3 cycles of capecitabine, 625 mg/m2, orally twice daily for 21 days; oxaliplatin, 130 mg/m2, intravenously and pembrolizumab, 200 mg, intravenously with optional epirubicin, 50 mg/m2, every 3 weeks before and after surgery with an additional cycle of pembrolizumab before surgery. Patients received 14 additional doses of maintenance pembrolizumab. Main Outcomes and Measures: The primary end point was pathologic complete response (pCR) rate. Secondary end points included overall response rate, disease-free survival (DFS), overall survival (OS), and safety. Results: A total of 34 patients (median [range] age, 65.5 [25-90] years; 23 [67.6%] male) were evaluable for efficacy. Of these patients, 28 (82.4%) underwent curative resection, 7 (20.6%; 95% CI, 10.1%-100%) achieved pCR, and 6 (17.6%) achieved a pathologic near-complete response. Of the 28 patients who underwent resection, 4 (14.3%) experienced disease recurrence. The median DFS and OS were not reached. The 2-year DFS was 67.8% (95% CI, 0.53%-0.87%) and the OS was 80.6% (95% CI, 0.68%-0.96%). Treatment-related grade 3 or higher adverse events for evaluable patients occurred in 20 patients (57.1%), and 12 (34.3%) experienced immune-related grade 3 or higher adverse events. Conclusion and Relevance: In this trial of unselected patients with resectable G/GEJ adenocarcinoma, capecitabine, oxaliplatin, and pembrolizumab resulted in a pCR rate of 20.6% and was well tolerated. This trial met its primary end point and supports the development of checkpoint inhibition in combination with perioperative chemotherapy in locally advanced G/GEJ adenocarcinoma. Trial Registration: ClinicalTrials.gov Identifier: NCT02918162.

SASH1 interacts with TNKS2 and promotes human melanocyte stem cell maintenance.

Both aging spots (hyperpigmentation) and hair graying (lack of pigmentation) are associated with aging, two seemingly opposite pigmentation phenotypes. It is not clear how they are mechanistically connected. This study investigated the underlying mechanism in a family with an inherited pigmentation disorder. Clinical examinations identified accelerated hair graying and skin dyspigmentation (intermixed hyper and hypopigmentation) in the family members carrying the SASH1 S519N variant. Cell assays indicated that SASH1 promoted stem-like characteristics in human melanocytes, and SASH1 S519N was defective in this function. Multiple assays showed that SASH1 binds to tankyrase 2 (TNKS2), which is required for SASH1's promotion of stem-like function. Further, the SASH1 S519N variant is in a bona fide Tankyrase-binding motif, and SASH1 S519N alters the binding kinetics and affinity. Results here indicate SASH1 as a novel protein regulating the appropriate balance between melanocyte stem cells (McSC) and mature melanocytes (MCs), with S519N variant causing defects. We propose that dysfunction of McSC maintenance connects multiple aging-associated pigmentation phenotypes in the general population.

Thyroid hormone enhances estrogen-mediated proliferation and cell cycle regulatory pathways in steroid receptor-positive breast Cancer.

Estrogen receptor (ER) α expression and associated signaling is a major driver of over two-thirds of all breast cancers (BC). ER targeting strategies are typically used as a first-line therapy in patients with steroid receptor positive (SR+) disease. Secondary resistance to anti-estrogenic agents may occur with clonal expansion and disease progression. Mechanisms underlying hormone resistance are an expanding field of significant translational importance. Cross-talk with other nuclear hormones, receptors, and signaling pathways, including thyroid hormones (TH) and their receptors (THRs), have been shown to promote endocrine therapy resistance in some studies. We have shown that TH replacement therapy (THRT) was independently and significantly associated with higher rates of relapse and mortality in SR positive (+), node-negative (LN-) BC patients, whereas it showed no association with outcomes in SR negative (-) patients. LN-, SR+ patients receiving THRT and tamoxifen had the worst outcomes, suggesting a pro-carcinogenic interaction that significantly and independently shortened survival and increased mortality. Using in vivo and in vitro models, we previously showed hormonal cross-talk, altered gene signaling, target gene activation, and resistance to tamoxifen in the presence of TH. In this report, we show TH ± E2 ± tamoxifen inhibits cell cycle control signaling, reduces apoptosis, and enhances cell proliferation, tumor growth, tamoxifen resistance, and clonal expansion. Mechanistically these changes involve numerous genes and pathways, including critical cell cycle regulatory proteins and genes identified using various molecular methods. These studies facilitate a greater mechanistic understanding of the biological and molecular impact of TH on SR+ BC.

Real-world pan-cancer landscape of frameshift mutations and their role in predicting responses to immune checkpoint inhibitors in cancers with low tumor mutational burden.

BACKGROUND: Pembrolizumab is FDA approved for tumors with tumor mutational burden (TMB) of ≥10 mutations/megabase (mut/Mb). However, the response to immune checkpoint inhibitors (ICI) varies significantly among cancer histologies. We describe the landscape of frameshift mutations (FSs) and evaluated their role as a predictive biomarker to ICI in a clinical cohort of patients. METHODS: Comprehensive genomic profiling was performed on a cohort of solid tumor samples examining at least 324 genes. The clinical cohort included patients with metastatic solid malignancies who received ICI monotherapy and had tumor sequencing. Progression-free survival (PFS), overall survival, and objective response rates (ORR) were compared between the groups. RESULTS: We analyzed 246,252 microsatellite stable (MSS) and 4561 samples with microsatellite instability across solid tumors. Histologies were divided into groups according to TMB and FS. MSS distribution: TMB-L (<10 mut/Mb)/FS-A (absent FS) (N=111,065, 45%), TMB-H (≥10 mut/Mb)/FS-A (N=15,313, 6%), TMB-L/FS-P (present ≥1 FS) (N=98,389, 40%) and TMB-H/FS-P (N=21,485, 9%). FSs were predominantly identified in the p53 pathway. In the clinical cohort, 212 patients were included. Groups: TMB-L/FS-A (N=80, 38%), TMB-H/FS-A (N=36, 17%), TMB-L/FS-P (N=57, 27%), TMB-H/FS-P (N=39, 18%). FSs were associated with a higher ORR to ICI, 23.8% vs 12.8% (p=0.02). TMB-L/FS-P had superior median PFS (5.1 months) vs TMB-L/FS-A (3.6 months, p<0.01). The 12-month PFS probability was 34% for TMB-L/FS-P vs 17.1% for TMB-L/FS-A. CONCLUSIONS: FSs are found in 47% of patients with MSS/TMB-L solid tumors in a pan-cancer cohort. FS may complement TMB in predicting immunotherapy responses, particularly for tumors with low TMB.

Deciphering the Tumor-Immune-Microbe Interactions in HPV-Negative Head and Neck Cancer.

Patients with human papillomavirus-negative head and neck squamous cell carcinoma (HPV-negative HNSCC) have worse outcomes than HPV-positive HNSCC. In our study, we used a published dataset and investigated the microbes enriched in molecularly classified tumor groups. We showed that microbial signatures could distinguish Hypoxia/Immune phenotypes similar to the gene expression signatures. Furthermore, we identified three highly-correlated microbes with immune processes that are crucial for immunotherapy response. The survival of patients in a molecularly heterogenous group shows significant differences based on the co-abundance of the three microbes. Overall, we present evidence that tumor-associated microbiota are critical components of the tumor ecosystem that may impact tumor microenvironment and immunotherapy response. The results of our study warrant future investigation to experimentally validate the conclusions, which have significant impacts on clinical decision-making, such as treatment selection.

H3K4me3 remodeling induced acquired resistance through O-GlcNAc transferase.

AIMS: Drivers of the drug tolerant proliferative persister (DTPP) state have not been well investigated. Histone H3 lysine-4 trimethylation (H3K4me3), an active histone mark, might enable slow cycling drug tolerant persisters (DTP) to regain proliferative capacity. This study aimed to determine H3K4me3 transcriptionally active sites identifying a key regulator of DTPPs. METHODS: Deploying a model of adaptive cancer drug tolerance, H3K4me3 ChIP-Seq data of DTPPs guided identification of top transcription factor binding motifs. These suggested involvement of O-linked N-acetylglucosamine transferase (OGT), which was confirmed by metabolomics analysis and biochemical assays. OGT impact on DTPPs and adaptive resistance was explored in vitro and in vivo. RESULTS: H3K4me3 remodeling was widespread in CPG island regions and DNA binding motifs associated with O-GlcNAc marked chromatin. Accordingly, we observed an upregulation of OGT, O-GlcNAc and its binding partner TET1 in chronically treated cancer cells. Inhibition of OGT led to loss of H3K4me3 and downregulation of genes contributing to drug resistance. Genetic ablation of OGT prevented acquired drug resistance in in vivo models. Upstream of OGT, we identified AMPK as an actionable target. AMPK activation by acetyl salicylic acid downregulated OGT with similar effects on delaying acquired resistance. CONCLUSION: Our findings uncover a fundamental mechanism of adaptive drug resistance that governs cancer cell reprogramming towards acquired drug resistance, a process that can be exploited to improve response duration and patient outcomes.

Grading of lung adenocarcinomas with simultaneous segmentation by artificial intelligence (GLASS-AI).

Preclinical genetically engineered mouse models (GEMMs) of lung adenocarcinoma are invaluable for investigating molecular drivers of tumor formation, progression, and therapeutic resistance. However, histological analysis of these GEMMs requires significant time and training to ensure accuracy and consistency. To achieve a more objective and standardized analysis, we used machine learning to create GLASS-AI, a histological image analysis tool that the broader cancer research community can utilize to grade, segment, and analyze tumors in preclinical models of lung adenocarcinoma. GLASS-AI demonstrates strong agreement with expert human raters while uncovering a significant degree of unreported intratumor heterogeneity. Integrating immunohistochemical staining with high-resolution grade analysis by GLASS-AI identified dysregulation of Mapk/Erk signaling in high-grade lung adenocarcinomas and locally advanced tumor regions. Our work demonstrates the benefit of employing GLASS-AI in preclinical lung adenocarcinoma models and the power of integrating machine learning and molecular biology techniques for studying the molecular pathways that underlie cancer progression.

Response to high dose ipilimumab plus temozolomide after progression on standard or low dose ipilimumab in advanced melanoma: a retrospective analysis.

Background: Despite advancements in checkpoint inhibitor-based immunotherapy, patients with advanced melanoma who have progressed on standard dose ipilimumab (Ipi) + nivolumab continue to have poor prognosis. Several studies support a dose-response activity of Ipi, and one promising combination is Ipi 10mg/kg (Ipi10) + temozolomide (TMZ). Methods: We performed a retrospective cohort analysis of patients with advanced melanoma treated with Ipi10+TMZ in the immunotherapy refractory/resistant setting (n = 6), using similar patients treated with Ipi3+TMZ (n = 6) as comparison. Molecular profiling by whole exome sequencing (WES) and RNA-seq of tumors harvested through one responder's treatment was performed. Results: With a median follow up of 119 days, patients treated with Ipi10+TMZ had statistically significant longer median progression free survival of 144.5 days (range 27-219) vs 44 (26-75) in Ipi3+TMZ, p=0.04, and a trend for longer median overall survival of 154.5 days (27-537) vs 89.5 (26-548). All patients in the Ipi10 cohort had progressed on prior Ipi+Nivo. WES revealed only 12 shared somatic mutations including BRAF V600E. RNA-seq showed enrichment of inflammatory signatures, including interferon responses in metastatic lesions after standard dose Ipi + nivo and Ipi10 + TMZ compared to the primary tumor, and downregulated negative immune regulators including Wnt and TGFb signaling. Conclusion: Ipi10+TMZ demonstrated efficacy including dramatic responses in patients with advanced melanoma refractory to prior Ipi + anti-PD1, even with CNS metastases. Molecular data suggest a potential threshold of Ipi dose for activation of sufficient anti-tumor immune response, and higher dose Ipi is required for some patients.

EGFR Inhibition by Cetuximab Modulates Hypoxia and IFN Response Genes in Head and Neck Squamous Cell Carcinoma.

Head and neck squamous cell carcinoma (HNSCC) has one of the most hypoxic and immunosuppressive tumor microenvironments (TME) among solid tumors. However, there is no proven therapeutic strategy to remodel the TME to be less hypoxic and proinflammatory. In this study, we classified tumors according to a Hypoxia-Immune signature, characterized the immune cells in each subgroup, and analyzed the signaling pathways to identify a potential therapeutic target that can remodel the TME. We confirmed that hypoxic tumors had significantly higher numbers of immunosuppressive cells, as evidenced by a lower ratio of CD8+ T cells to FOXP3+ regulatory T cells, compared with nonhypoxic tumors. Patients with hypoxic tumors had worse outcomes after treatment with pembrolizumab or nivolumab, anti-programmed cell death-1 inhibitors. Our expression analysis also indicated that hypoxic tumors predominantly increased the expression of the EGFR and TGFß pathway genes. Cetuximab, an anti-EGFR inhibitor, decreased the expression of hypoxia signature genes, suggesting that it may alleviate the effects of hypoxia and remodel the TME to become more proinflammatory. Our study provides a rationale for treatment strategies combining EGFR-targeted agents and immunotherapy in the management of hypoxic HNSCC. Significance: While the hypoxic and immunosuppressive TME of HNSCC has been well described, comprehensive evaluation of the immune cell components and signaling pathways contributing to immunotherapy resistance has been poorly characterized. We further identified additional molecular determinants and potential therapeutic targets of the hypoxic TME to fully leverage currently available targeted therapies that can be administered with immunotherapy.

PATH-SURVEYOR: pathway level survival enquiry for immuno-oncology and drug repurposing.

Pathway-level survival analysis offers the opportunity to examine molecular pathways and immune signatures that influence patient outcomes. However, available survival analysis algorithms are limited in pathway-level function and lack a streamlined analytical process. Here we present a comprehensive pathway-level survival analysis suite, PATH-SURVEYOR, which includes a Shiny user interface with extensive features for systematic exploration of pathways and covariates in a Cox proportional-hazard model. Moreover, our framework offers an integrative strategy for performing Hazard Ratio ranked Gene Set Enrichment Analysis and pathway clustering. As an example, we applied our tool in a combined cohort of melanoma patients treated with checkpoint inhibition (ICI) and identified several immune populations and biomarkers predictive of ICI efficacy. We also analyzed gene expression data of pediatric acute myeloid leukemia (AML) and performed an inverse association of drug targets with the patient's clinical endpoint. Our analysis derived several drug targets in high-risk KMT2A-fusion-positive patients, which were then validated in AML cell lines in the Genomics of Drug Sensitivity database. Altogether, the tool offers a comprehensive suite for pathway-level survival analysis and a user interface for exploring drug targets, molecular features, and immune populations at different resolutions.