Expanding the landscape of oncogenic drivers and treatment options in acral and mucosal melanomas by targeted genomic profiling.

Despite advancements in treating cutaneous melanoma, patients with acral and mucosal (A/M) melanomas still have limited therapeutic options and poor prognoses. We analyzed 156 melanomas (101 cutaneous, 28 acral, and 27 mucosal) using the Foundation One cancer-gene specific clinical testing platform and identified new, potentially targetable genomic alterations (GAs) in specific anatomic sites of A/M melanomas. Using novel pre-clinical models of A/M melanoma, we demonstrate that several GAs and corresponding oncogenic pathways associated with cutaneous melanomas are similarly targetable in A/M melanomas. Other alterations, including MYC and CRKL amplifications, were unique to A/M melanomas and susceptible to indirect targeting using the BRD4 inhibitor JQ1 or Src/ABL inhibitor dasatinib, respectively. We further identified new, actionable A/M-specific alterations, including an inactivating NF2 fusion in a mucosal melanoma responsive to dasatinib in vivo. Our study highlights new molecular differences between cutaneous and A/M melanomas, and across different anatomic sites within A/M, which may change clinical testing and treatment paradigms for these rare melanomas.

The Effect of Laparoscopic Gastric Ischemic Preconditioning Prior to Esophagectomy on Anastomotic Stricture Rate and Comparison with Esophagectomy-Alone Controls.

BACKGROUND: Benign anastomotic stricture is a recognized complication following esophagectomy. Laparoscopic gastric ischemic preconditioning (LGIP) prior to esophagectomy has been associated with decreased anastomotic leak rates; however, its effect on stricture and the need for subsequent endoscopic intervention is not well studied. METHODS: This was a case-control study at an academic medical center using consecutive patients undergoing oncologic esophagectomies (July 2012-July 2022). Our institution initiated an LGIP protocol on 1 January 2021. The primary outcome was the occurrence of stricture within 1 year of esophagectomy, while secondary outcomes were stricture severity and frequency of interventions within the 6 months following stricture. Bivariable comparisons were performed using Chi-square, Fisher's exact, or Mann-Whitney U tests. Multivariable regression controlling for confounders was performed to generate risk-adjust odds ratios and to identify the independent effect of LGIP. RESULTS: Of 253 esophagectomies, 42 (16.6%) underwent LGIP prior to esophagectomy. There were 45 (17.7%) anastomotic strictures requiring endoscopic intervention, including three patients who underwent LGIP and 42 who did not. Median time to stricture was 144 days. Those who underwent LGIP were significantly less likely to develop anastomotic stricture (7.1% vs. 19.9%; p = 0.048). After controlling for confounders, this difference was no longer significant (odds ratio 0.46, 95% confidence interval 0.14-1.82; p = 0.29). Of those who developed stricture, there was a trend toward less severe strictures and decreased need for endoscopic dilation in the LGIP group (all p < 0.20). CONCLUSION: LGIP may reduce the rate and severity of symptomatic anastomotic stricture following esophagectomy. A multi-institutional trial evaluating the effect of LGIP on stricture and other anastomotic complications is warranted.

Dynamic Anthropometrics in Pancreatic Cancer: Associations Between Body Composition Changes During Neoadjuvant Therapy and Survival Outcomes After Resection.

BACKGROUND: Assessment of individual tumor biology and response to systemic therapy in pancreatic ductal adenocarcinoma (PDAC) remains a clinical challenge. The significance of anthropometric (body composition) changes during chemotherapy as a surrogate for tumor biology in the setting of localized PDAC is unknown. METHODS: A retrospective, single-institution analysis of patients with PDAC who received neoadjuvant therapy (NAT) and pancreatectomy from 2017 to 2021 was performed. Radiologic anthropometric analysis used artificial intelligence-driven software to segment and compute total and sub-compartment muscle area, adipose tissue area, and attenuation values at the level of the L3 vertebra. Kaplan-Meier survival estimates, log-rank tests, and multivariable Cox regression models were used in survival analyses. RESULTS: The inclusion criteria were met by 138 patients. Although decreases in muscle and adipose tissue areas during NAT were predominant, a subset of patients experienced an increase in these compartments. Increases in muscle greater than 5% (hazard ratio [HR], 0.352; 95% confidence interval [CI] 0.135-0.918; p = 0.033) and increases in adipose tissue greater than 15% (HR, 0.375; 95% CI 0.144-0.978; p = 0.045), were significantly associated with improved survival, whereas loss of visceral fat greater than 15% was detrimental (HR 1.853; CI 1.099-3.124; p = 0.021). No significant associations with single time-point anthropometrics were observed. Gains in total muscle and adipose mass were associated with improved pathologic response to systemic therapy and less advanced pathologic tumor stage. CONCLUSIONS: Dynamic anthropometric analysis during NAT for PDAC is a stronger prognostic indicator than measurements taken at a single point in time. Repeated anthropometric analysis during preoperative chemotherapy may serve as a biomarker for individual tumor biology and response to therapy.

Radiologic Occult Metastases in Pancreatic Cancer: Analysis of Risk Factors and Survival Outcomes in the Age of Contemporary Neoadjuvant Multi-agent Chemotherapy.

BACKGROUND: Radiologic occult metastatic disease (ROMD) in patients with pancreatic ductal adenocarcinoma (PDAC) who undergo contemporary neoadjuvant chemotherapy (NAC) has not been well studied. This study sought to analyze the incidence, risk factors, and oncologic outcomes for patients who underwent the NAC approach for PDAC. METHODS: A retrospective review analyzed a prospectively maintained database of patients who had potentially resectable PDAC treated with NAC and were offered pancreatectomy at our institution from 2011 to 2022. Multivariable regression analysis was performed to assess risk factors associated with ROMD. Kaplan-Meier curves with log-rank analyses were generated to estimate time-to-event end points. RESULTS: The study enrolled 366 patients. Upfront and borderline resectable anatomic staging comprised 80% of the cohort, whereas 20% had locally advanced disease. The most common NAC regimen was FOLFIRINOX (n = 274, 75%). For 55 patients (15%) who harbored ROMD, the most common site was liver-only metastases (n = 33, 60%). The independent risk factors for ROMD were increasing CA19-9 levels during NAC (odds ratio [OR], 7.01; confidence interval [CI], 1.97-24.96; p = 0.008), indeterminate liver lesions (OR, 2.19; CI, 1.09-4.39; p = 0.028), and enlarged para-aortic lymph nodes (OR, 6.87; CI, 2.07-22.74; p = 0.002) on preoperative cross-sectional imaging. Receipt of palliative chemotherapy (p < 0.001) and eventual formal pancreatectomy (p = 0.04) were associated with survival benefit in the log-rank analysis. The median overall survival (OS) of the patients with ROMD was nearly 15 months from the initial diagnosis, with radiologic evidence of metastases occurring after a median of 2 months. CONCLUSIONS: Radiologic occult metastatic disease remains a clinical challenge associated with poor outcomes for patients who have PDAC treated with multi-agent NAC.

Immune Landscape of Epstein-Barr Virus-Associated Gastric Cancer: Analysis From a Western Academic Institution.

INTRODUCTION: Epstein-Barr virus-associated gastric cancer (EBVaGC) may be a meaningful biomarker for potential benefit from immunotherapy. Further investigation is needed to characterize the immune landscape of EBVaGC. We assessed our institutional frequency of surgically treated EBVaGC and analyzed the immunologic biomarker profile and tumor-infiltrating lymphocyte (TIL) phenotypes of a series of EBVaGC compared to non-EBVaGC cases. METHODS: Available tissue samples from all patients with biopsy-confirmed gastric adenocarcinoma who underwent resection with curative intent from 2012 to 2020 at our institution were collected. In situ hybridization was used to assess EBV status; multiplex immunohistochemistry was performed to assess mismatch repair status, Programmed Death-Ligand 1 (PD-L1) expression, and phenotypic characterization of TILs. RESULTS: Sixty-eight samples were included in this study. EBVaGC was present in 3/68 (4%) patients. Among all patients, 27/68 (40%) had positive PD-L1 expression; two of three (67%) EBVaGC patients exhibited positive PD-L1 expression. Compared to non-EBVaGC, EBV-positive tumors showed 5-fold to 10-fold higher density of TILs in both tumor and stroma and substantially elevated CD8+ T cell to Tregulatory cell ratio. The memory subtypes of CD8+ and CD4+ T cells were upregulated in EBVaGC tumors and stromal tissue compared to non-EBVaGC. CONCLUSIONS: The incidence of surgically resected EBVaGC at our center was 4%. EBVaGC tumors harbor elevated levels of TILs, including memory subtypes, within both tumor and tumor-related stroma. Robust TIL presence and upregulated PD-L1 positivity in EBVaGC may portend promising responses to immunotherapy agents. Further investigation into routine EBV testing and TIL phenotype of patients with gastric cancer to predict response to immunotherapy may be warranted.

Effect of Neoadjuvant Chemotherapy on Tumor-Infiltrating Lymphocytes in Resectable Gastric Cancer: Analysis from a Western Academic Center.

Tumor-infiltrating lymphocytes (TILs) are an emerging biomarker predictive of response to immunotherapy across a spectrum of solid organ malignancies. The characterization of TILs in gastric cancer (GC) treated with contemporary, multiagent neoadjuvant chemotherapy (NAC) is understudied. In this retrospective investigation, we analyzed the degree of infiltration, phenotype, and spatial distribution of TILs via immunohistochemistry within resected GC specimens treated with or without NAC at a Western center. We hypothesized that NAC executes immunostimulatory roles, as evidenced by an increased number of anti-tumor TILs in the tumor microenvironment. We found significantly elevated levels of conventional and memory CD8+ T cells, as well as total TILs (CD4+, CD8+, Treg, B cells), within chemotherapy-treated tumors compared with chemotherapy-naïve specimens. We also revealed important associations between survival and pathologic responses with enhanced TIL infiltration. Taken together, our findings advocate for an immunostimulatory role of chemotherapy and underscore the potential synergistic effect of combining chemotherapy with immunotherapy in resectable gastric cancer.

MCL1 inhibition targets Myeloid Derived Suppressors Cells, promotes antitumor immunity and enhances the efficacy of immune checkpoint blockade.

Immune checkpoint inhibitors (ICIs) are now the first-line treatment for patients with advanced melanoma. Despite promising clinical results, many patients fail to respond to these therapies. BH3 mimetics, a novel class of small molecule inhibitors that bind and inhibit anti-apoptotic members of the BCL2 family proteins such as BCL2 or MCL1, have been very successful in treating hematologic malignancies. However, there are limited studies on the immunomodulatory role of the BH3 mimetics. Several factors contribute to ICI resistance including myeloid-derived suppressor cells (MDSCs) that exert immunosuppressive effects through direct and indirect inhibition of antitumor immunity. Thus, targeting MDSCs to enhance antitumor immunity has the potential to enhance the efficacy of ICIs. In this study, we show that the MCL1 inhibitor S64315 reduces melanoma tumor growth in an immune cell-dependent manner in mice. Specifically, S64315 enhances antitumor immunity by reducing MDSC frequency and by promoting the activity of CD8+T cells. Additionally, human MDSCs are 10 times more sensitive to S64315 than cutaneous melanoma lines. Further, we found that a higher expression of MCL1 is associated with poor survival for patients treated with anti-PD-1. Finally, combining S64315 and anti-PD-1 significantly slowed tumor growth compared to either agent alone. Together, this proof-of-concept study demonstrates the potential of combining an MCL1 inhibitor with anti-PD-1 in the treatment of melanoma. It justifies the further development of next generation MCL1 inhibitors to improve efficacy of ICIs in treating malignant melanoma.

Gaining a new angle on pancreas cancer: A pre-operative thrombelastographic parameter predicts recurrence and survival among patients with resected periampullary and pancreatic adenocarcinoma.

BACKGROUND: It has previously been demonstrated that Thrombelastography(TEG) angle may be associated with recurrence and survival in pancreas cancer in a cohort of patients operated on at the University of Colorado in 2016-2017. Now approaching 10 years of follow-up, we revisit these associations and strengthen these claims with multivariate analysis. METHODS: Retrospective chart review was performed. Statistical analysis was conducted using STATA. Receiver operating characteristic(ROC) curves identified the performance of angle for predicting recurrence&survival. Unadjusted and adjusted cox regression models were used to identify significant predictors of these outcomes. RESULTS: 47 patients were included with median follow-up of 29.6 months. ROC curves for angle predicting recurrence and survival identified a cutoff of 44.5°. KM curves demonstrated that patients above the cutoff were more likely to recur(90%vs46 â€‹%,p â€‹= â€‹0.001) and less likely to survive(16%vs56 â€‹%,p â€‹= â€‹0.001). Angle remained significant on multivariate analyses (HR recurrence:3.64[1.32-10.25],HR survival:3.80[1.38-10.46]). CONCLUSIONS: TEG angle is independently associated with disease recurrence and overall survival in pancreas cancer. This may be identifying virulent tumor biology, but further studies are required. A prospective study is underway.

Multicellular immune ecotypes within solid tumors predict real-world therapeutic benefits with immune checkpoint inhibitors.

Background: Cancer initiation, progression, and immune evasion depend on the tumor microenvironment (TME). Thus, understanding the TME immune architecture is essential for understanding tumor metastasis and therapy response. This study aimed to create an immune cell states (CSs) atlas using bulk RNA-seq data enriched by eco-type analyses to resolve the complex immune architectures in the TME. Methods: We employed EcoTyper, a machine-learning (ML) framework, to study the real-world prognostic significance of immune CSs and multicellular ecosystems, utilizing molecular data from 1,610 patients with multiple malignancies who underwent immune checkpoint inhibitor (ICI) therapy within the ORIEN Avatar cohort, a well-annotated real-world dataset. Results: Our analysis revealed consistent ICI-specific prognostic TME carcinoma ecotypes (CEs) (including CE1, CE9, CE10) across our pan-cancer dataset, where CE1 being more lymphocyte-deficient and CE10 being more proinflammatory. Also, the analysis of specific immune CSs across different cancers showed consistent CD8+ and CD4+ T cell CS distribution patterns. Furthermore, survival analysis of the ORIEN ICI cohort demonstrated that ecotype CE9 is associated with the most favorable survival outcomes, while CE2 is linked to the least favorable outcomes. Notably, the melanoma-specific prognostic EcoTyper model confirmed that lower predicted risk scores are associated with improved survival and better response to immunotherapy. Finally, de novo discovery of ecotypes in the ORIEN ICI dataset identified Ecotype E3 as significantly associated with poorer survival outcomes. Conclusion: Our findings offer important insights into refining the patient selection process for immunotherapy in real-world practice and guiding the creation of novel therapeutic strategies to target specific ecotypes within the TME.

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.

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.

The Tumor Microbiome Reacts to Hypoxia and Can Influence Response to Radiation Treatment in Colorectal Cancer.

Tumor hypoxia has been shown to predict poor patient outcomes in several cancer types, partially because it reduces radiation's ability to kill cells. We hypothesized that some of the clinical effects of hypoxia could also be due to its impact on the tumor microbiome. Therefore, we examined the RNA sequencing data from the Oncology Research Information Exchange Network database of patients with colorectal cancer treated with radiotherapy. We identified microbial RNAs for each tumor and related them to the hypoxic gene expression scores calculated from host mRNA. Our analysis showed that the hypoxia expression score predicted poor patient outcomes and identified tumors enriched with certain microbes such as Fusobacterium nucleatum. The presence of other microbes, such as Fusobacterium canifelinum, predicted poor patient outcomes, suggesting a potential interaction between hypoxia, the microbiome, and radiation response. To experimentally investigate this concept, we implanted CT26 colorectal cancer cells into immune-competent BALB/c and immune-deficient athymic nude mice. After growth, in which tumors passively acquired microbes from the gastrointestinal tract, we harvested tumors, extracted nucleic acids, and sequenced host and microbial RNAs. We stratified tumors based on their hypoxia score and performed a metatranscriptomic analysis of microbial gene expression. In addition to hypoxia-tropic and -phobic microbial populations, analysis of microbial gene expression at the strain level showed expression differences based on the hypoxia score. Thus, hypoxia gene expression scores seem to associate with different microbial populations and elicit an adaptive transcriptional response in intratumoral microbes, potentially influencing clinical outcomes. SIGNIFICANCE: Tumor hypoxia reduces radiotherapy efficacy. In this study, we explored whether some of the clinical effects of hypoxia could be due to interaction with the tumor microbiome. Hypoxic gene expression scores associated with certain microbes and elicited an adaptive transcriptional response in others that could contribute to poor clinical outcomes.