The tumor microenvironment (TME) plays a major role in the pathogenesis of multiple cancer types, including upper-gastrointestinal (GI) cancers that currently lack effective therapeutic options. Cancer-associated fibroblasts (CAF) are an essential component of the TME, contributing to tumorigenesis by secreting growth factors, modifying the extracellular matrix, supporting angiogenesis, and suppressing antitumor immune responses. Through an unbiased approach, we have established that IL-6 mediates cross-talk between tumor cells and CAF not only by supporting tumor cell growth, but also by promoting fibroblast activation. As a result, IL-6 receptor (IL6Rα) and downstream effectors offer opportunities for targeted therapy in upper-GI cancers. IL-6 loss suppressed tumorigenesis in physiologically relevant three-dimensional (3D) organotypic and 3D tumoroid models and murine models of esophageal cancer. Tocilizumab, an anti-IL6Rα antibody, suppressed tumor growth in part via inhibition of STAT3 and MEK/ERK signaling. Analysis of a pan-cancer TCGA dataset revealed an inverse correlation between IL-6 and IL6Rα overexpression and patient survival. Therefore, we expanded evaluation of tocilizumab to head and neck squamous cell carcinoma patient-derived xenografts and gastric adenocarcinoma xenografts, demonstrating suppression of tumor growth and altered STAT3 and ERK1/2 gene signatures. We used small-molecule inhibitors of STAT3 and MEK1/2 signaling to suppress tumorigenesis in the 3D organotypic model of esophageal cancer. We demonstrate that IL6 is a major contributor to the dynamic cross-talk between tumor cells and CAF in the TME. Our findings provide a translational rationale for inhibition of IL6Rα and downstream signaling pathways as a novel targeted therapy in oral-upper-GI cancers. These findings demonstrate the interaction of esophageal cancer and cancer-associated fibroblasts through IL-6 signaling, providing rationale for a novel therapeutic approach to target these cancers. .

PURPOSE Pancreatic cancer is an aggressive malignancy with patients often experiencing nonspecific symptoms before diagnosis. This study evaluates a machine learning approach to help identify patients with early-stage pancreatic cancer from clinical data within electronic health records (EHRs). MATERIALS AND METHODS From the Optum deidentified EHR data set, we identified early-stage (n = 3,322) and late-stage (n = 25,908) pancreatic cancer cases over 40 years of age diagnosed between 2009 and 2017. Patients with early-stage pancreatic cancer were matched to noncancer controls (1:16 match). We constructed a prediction model using eXtreme Gradient Boosting (XGBoost) to identify early-stage patients on the basis of 18,220 features within the EHR including diagnoses, procedures, information within clinical notes, and medications. Model accuracy was assessed with sensitivity, specificity, positive predictive value, and the area under the curve. RESULTS The final predictive model included 582 predictive features from the EHR, including 248 (42.5%) physician note elements, 146 (25.0%) procedure codes, 91 (15.6%) diagnosis codes, 89 (15.3%) medications, and 9 (1.5%) demographic features. The final model area under the curve was 0.84. Choosing a model cut point with a sensitivity of 60% and specificity of 90% would enable early detection of 58% late-stage patients with a median of 24 months before their actual diagnosis. CONCLUSION Prediction models using EHR data show promise in the early detection of pancreatic cancer. Although widespread use of this approach on an unselected population would produce high rates of false-positive tests, this technique may be rapidly impactful if deployed among high-risk patients or paired with other imaging or biomarker screening tools.

Objective To evaluate the predictive utility of the Hospital Frailty Risk Score (HFRS), a stratification tool based on the ICD-10 ( International Classification of Disease, Tenth Revision), and other risk factors for 30-day readmissions and mortality in a nationally representative cohort. Study Design Retrospective database review. Setting Nationwide Readmissions Database (2017). Methods Patients with head and neck cancer who underwent major surgical procedures were identified from the 2017 Nationwide Readmissions Database, representing 116 medical centers nationwide. Bivariate and multivariable logistic regression methods were used to identify factors associated with unplanned 30-day readmission, 30-day readmission mortality, and increased length of hospital stay. Results A total of 14,420 patients underwent major head and neck cancer surgery. Unplanned readmission occurred in 11% of patients. The most common reasons for unplanned readmission were procedural complications (26.5%), sepsis (7.3%), and respiratory failure (3.9%). Elevated frailty index (HFRS ≥5) was identified in 22% of patients. Frailty was associated with higher 30-day readmission rates (18.0% vs 9.5%, P < .01), which held on multivariate modeling (odds ratio [OR], 1.59 [95% CI, 1.37-1.85]). Frail patients spent more days in the hospital (8.2 vs 6.8, P = .02) and incurred more charges across hospital stays ($275,000 vs $188,000, P < .01). Patients >75 years old (OR, 1.26 [1.03-1.55]) and patients with electrolyte abnormalities (OR, 1.25 [1.07-1.46] were significantly more likely to be readmitted. Conclusion In this head and neck cancer surgical population, HFRS significantly predicted unplanned readmission. HFRS is a potential risk stratification tool and should be compared with other methods and explored in other cancer populations. Beyond the challenge of identifying at-risk patients, future work should explore potential interventions aimed at mitigating readmission.

Oncology research is increasingly incorporating molecular detection of circulating tumor DNA (ctDNA) as a tool for cancer surveillance and early detection. However, noninvasive monitoring of conditions with low tumor burden remains challenging, as the diagnostic sensitivity of most ctDNA assays is inversely correlated with total DNA concentration and ctDNA abundance. Here we present the Multiplex Enrichment using Droplet Pre-Amplification (MED-Amp) method, which combines single-molecule emulsification and short-round polymerase chain reaction (PCR) preamplification with digital droplet PCR detection of mutant DNA template. The MED-Amp assay increased mutant signal by over 50-fold with minimal distortion in allelic frequency. We demonstrate detection of as few as three mutant copies in wild-type DNA concentrations ranging from 5 to 50 ng. The MED-Amp assay successfully detected KRAS mutant ctDNA in 86% plasma samples obtained from patients with metastatic pancreatic ductal adenocarcinoma. This assay for high-sensitivity rare variant detection is appropriate for liquid biopsy samples or other limited clinical biospecimens.

Cancer patients frequently utilize the emergency department (ED) for a variety of diagnoses both related to and unrelated to their cancer, yet ED outcomes for cancer patients are not well documented. This study sought to define risks and identify predictors for inpatient admission and hospital mortality among cancer patients presenting to the ED. Patients and Methods:We utilized the National Emergency Department Sample to identify patients with and without a diagnosis of cancer presenting to the ED between January 2016 and December 2018. We used multivariable mixedeffects logistic regression models to assess the influence of cancer on outcomes of hospital admission after the ED visit and hospital mortality for the whole patient cohort and individual presenting diagnoses.Results: There were 340 million weighted ED visits, of which 8.3 million (2.3%) were associated with a cancer diagnosis. Compared to non-cancer patients, patients with cancer had an increased risk of inpatient admission (64.7% vs. 14.8%; p < 0.0001) and hospital mortality (4.6% vs. 0.5%; p < 0.0001). For each of the top 15 presenting diagnoses, cancer patients had increased risks of hospitalization (odds ratio [OR] range 2.0-13.2) or death (OR range 2.1-14.4). Although our dataset does not contain reliable estimation of stage, cancer site was the most robust individual predictor associated with the risk of hospitalization or death compared to other clinical or system-related factors.Conclusions: Cancer patients in the ED have high risks for hospital admission and death when compared to patients without cancer. Cancer patients represent a distinct population and may benefit from cancer-specific risk stratification or focused interventions to improve outcomes.