ACTA2 Expression Predicts Survival and Is Associated with Response to Immune Checkpoint Inhibitors in Gastric Cancer.

PURPOSE: We sought to identify biomarkers that predict overall survival (OS) and response to immune checkpoint inhibitors (ICI) for patients with gastric cancer. EXPERIMENTAL DESIGN: This was a retrospective study of multiple independent cohorts of patients with gastric cancer. The association between tumor ACTA2 expression and OS and ICI response were determined in patients whose tumors were analyzed with bulk mRNA sequencing. Single-cell RNA sequencing (scRNA-seq) and digital spatial profiling data were used to compare tumors from patients with gastric cancer who did and did not respond to ICI. RESULTS: Increasing tumor ACTA2 expression was independently associated with worse OS in a 567-patient discovery cohort [HR, 1.28 per unit increase; 95% confidence interval (CI), 1.02-1.62]. This finding was validated in three independent cohorts (n = 974; HR, 1.52 per unit increase; 95% CI, 1.34-1.73). Of the 108 patients treated with ICI, 56% of patients with low ACTA2 expression responded to ICI versus 25% of patients with high ACTA2 expression (P = 0.004). In an analysis of a publicly available scRNA-seq dataset of 5 microsatellite instability-high patients treated with ICI, the patient who responded to ICI had lower tumor stromal ACTA2 expression than the 4 nonresponders. Digital spatial profiling of tumor samples from 4 ICI responders and 5 ICI nonresponders revealed that responders may have lower ACTA2 expression in α-SMA-positive cancer-associated fibroblasts (CAF) than nonresponders (median: 5.00 vs. 5.50). CONCLUSIONS: ACTA2 expression is associated with survival and ICI response in patients with gastric cancer. ACTA2 expression in CAFs, but not in other cellular compartments, appears to be associated with ICI response.

Spatial analysis of tumor-infiltrating lymphocytes in histological sections using deep learning techniques predicts survival in colorectal carcinoma.

This study aimed to explore the prognostic impact of spatial distribution of tumor-infiltrating lymphocytes (TILs) quantified by deep learning (DL) approaches based on digitalized whole-slide images stained with hematoxylin and eosin in patients with colorectal cancer (CRC). The prognostic impact of spatial distributions of TILs in patients with CRC was explored in the Yonsei cohort (n = 180) and validated in The Cancer Genome Atlas (TCGA) cohort (n = 268). Two experienced pathologists manually measured TILs at the most invasive margin (IM) as 0-3 by the Klintrup-Mäkinen (KM) grading method and this was compared to DL approaches. Inter-rater agreement for TILs was measured using Cohen's kappa coefficient. On multivariate analysis of spatial TIL features derived by DL approaches and clinicopathological variables including tumor stage, microsatellite instability, and KRAS mutation, TIL densities within 200 µm of the IM (f_im200) remained the most significant prognostic factor for progression-free survival (PFS) (hazard ratio [HR] 0.004 [95% confidence interval, CI, 0.0001-0.15], p = 0.0028) in the Yonsei cohort. On multivariate analysis using the TCGA dataset, f_im200 retained prognostic significance for PFS (HR 0.031 [95% CI 0.001-0.645], p = 0.024). Inter-rater agreement of manual KM grading was insignificant in the Yonsei (κ = 0.109) and the TCGA (κ = 0.121) cohorts. The survival analysis based on KM grading showed statistically significant different PFS in the TCGA cohort, but not the Yonsei cohort. Automatic quantification of TILs at the IM based on DL approaches shows prognostic utility to predict PFS, and could provide robust and reproducible TIL density measurement in patients with CRC.

A spatiotemporal analysis of opioid poisoning mortality in Ohio from 2010 to 2016.

Opioid-related deaths have severely increased since 2000 in the United States. This crisis has been declared a public health emergency, and among the most affected states is Ohio. We used statewide vital statistic data from the Ohio Department of Health (ODH) and demographics data from the U.S. Census Bureau to analyze opioid-related mortality from 2010 to 2016. We focused on the characterization of the demographics from the population of opioid-related fatalities, spatiotemporal pattern analysis using Moran's statistics at the census-tract level, and comorbidity analysis using frequent itemset mining and association rule mining. We found higher rates of opioid-related deaths in white males aged 25-54 compared to the rest of Ohioans. Deaths tended to increasingly cluster around Cleveland, Columbus and Cincinnati and away from rural regions as time progressed. We also found relatively high co-occurrence of cardiovascular disease, anxiety or drug abuse history, with opioid-related mortality. Our results demonstrate that state-wide spatiotemporal and comorbidity analysis of the opioid epidemic could provide novel insights into how the demographic characteristics, spatiotemporal factors, and/or health conditions may be associated with opioid-related deaths in the state of Ohio.

Radiomics Features of 18F-Fluorodeoxyglucose Positron-Emission Tomography as a Novel Prognostic Signature in Colorectal Cancer.

The aim of this study was to investigate the prognostic value of radiomics signatures derived from 18F-fluorodeoxyglucose (18F-FDG) positron-emission tomography (PET) in patients with colorectal cancer (CRC). From April 2008 to Jan 2014, we identified CRC patients who underwent 18F-FDG-PET before starting any neoadjuvant treatments and surgery. Radiomics features were extracted from the primary lesions identified on 18F-FDG-PET. Patients were divided into a training and validation set by random sampling. A least absolute shrinkage and selection operator Cox regression model was applied for prognostic signature building with progression-free survival (PFS) using the training set. Using the calculated radiomics score, a nomogram was developed, and its clinical utility was assessed in the validation set. A total of 381 patients with surgically resected CRC patients (training set: 228 vs. validation set: 153) were included. In the training set, a radiomics signature labeled as a rad_score was generated using two PET-derived features, such as gray-level run length matrix long-run emphasis (GLRLM_LRE) and gray-level zone length matrix short-zone low-gray-level emphasis (GLZLM_SZLGE). Patients with a high rad_score in the training and validation set had a shorter PFS. Multivariable analysis revealed that the rad_score was an independent prognostic factor in both training and validation sets. A radiomics nomogram, developed using rad_score, nodal stage, and lymphovascular invasion, showed good performance in the calibration curve and comparable predictive power with the staging system in the validation set. Textural features derived from 18F-FDG-PET images may enable detailed stratification of prognosis in patients with CRC.

Hispanic/Latino Patients with Gastric Adenocarcinoma Have Distinct Molecular Profiles Including a High Rate of Germline CDH1 Variants.

Hispanic/Latino patients have a higher incidence of gastric cancer and worse cancer-related outcomes compared with patients of other backgrounds. Whether there is a molecular basis for these disparities is unknown, as very few Hispanic/Latino patients have been included in previous studies. To determine the genomic landscape of gastric cancer in Hispanic/Latino patients, we performed whole-exome sequencing (WES) and RNA sequencing on tumor samples from 57 patients; germline analysis was conducted on 83 patients. The results were compared with data from Asian and White patients published by The Cancer Genome Atlas. Hispanic/Latino patients had a significantly larger proportion of genomically stable subtype tumors compared with Asian and White patients (65% vs. 21% vs. 20%, P < 0.001). Transcriptomic analysis identified molecular signatures that were prognostic. Of the 43 Hispanic/Latino patients with diffuse-type cancer, 7 (16%) had germline variants in CDH1. Variant carriers were significantly younger than noncarriers (41 vs. 50 years, P < 0.05). In silico algorithms predicted five variants to be deleterious. For two variants that were predicted to be benign, in vitro modeling demonstrated that these mutations conferred increased migratory capability, suggesting pathogenicity. Hispanic/Latino patients with gastric cancer possess unique genomic landscapes, including a high rate of CDH1 germline variants that may partially explain their aggressive clinical phenotypes. Individualized screening, genetic counseling, and treatment protocols based on patient ethnicity and race may be necessary. SIGNIFICANCE: Gastric cancer in Hispanic/Latino patients has unique genomic profiles that may contribute to the aggressive clinical phenotypes seen in these patients.

A Genome-wide Functional Signature Ontology Map and Applications to Natural Product Mechanism of Action Discovery.

Gene expression signature-based inference of functional connectivity within and between genetic perturbations, chemical perturbations, and disease status can lead to the development of actionable hypotheses for gene function, chemical modes of action, and disease treatment strategies. Here, we report a FuSiOn-based genome-wide integration of hypomorphic cellular phenotypes that enables functional annotation of gene network topology, assignment of mechanistic hypotheses to genes of unknown function, and detection of cooperativity among cell regulatory systems. Dovetailing genetic perturbation data with chemical perturbation phenotypes allowed simultaneous generation of mechanism of action hypotheses for thousands of uncharacterized natural products fractions (NPFs). The predicted mechanism of actions span a broad spectrum of cellular mechanisms, many of which are not currently recognized as "druggable." To enable use of FuSiOn as a hypothesis generation resource, all associations and analyses are available within an open source web-based GUI (http://fusion.yuhs.ac).

Chemistry-First Approach for Nomination of Personalized Treatment in Lung Cancer.

Diversity in the genetic lesions that cause cancer is extreme. In consequence, a pressing challenge is the development of drugs that target patient-specific disease mechanisms. To address this challenge, we employed a chemistry-first discovery paradigm for de novo identification of druggable targets linked to robust patient selection hypotheses. In particular, a 200,000 compound diversity-oriented chemical library was profiled across a heavily annotated test-bed of >100 cellular models representative of the diverse and characteristic somatic lesions for lung cancer. This approach led to the delineation of 171 chemical-genetic associations, shedding light on the targetability of mechanistic vulnerabilities corresponding to a range of oncogenotypes present in patient populations lacking effective therapy. Chemically addressable addictions to ciliogenesis in TTC21B mutants and GLUT8-dependent serine biosynthesis in KRAS/KEAP1 double mutants are prominent examples. These observations indicate a wealth of actionable opportunities within the complex molecular etiology of cancer.