<p>For individuals in the EAGLE study, all consisting of Italians, a pathway-level susceptibility effect was computed for each pathway in MSigDB as the sum of relevant SNP variant alleles weighted by their log odds ratios from the TRICL lung adenocarcinoma GWAS meta-analysis. Pathway values were also multiplied by cumulative smoking pack-years to produce pathway-smoking interactions. The pathways and pathway-smoking interactions that differ most between lung adenocarcinoma cases and healthy controls (t-test P < 0.05) in the EAGLE study are presented. Pearson correlation coefficients between these pathways and pathway-smoking interactions, and each of age, sex, and smoking pack-years are presented as well.</p>
165 Background: Academic institutions are adopting automated tools to help develop and maintain large databases. Leveraging advances in clinical data warehouses and artificial intelligence can potentially decrease resource utilization and the time required to conduct research by reducing intensive manual human curation of data. Methods: This research was performed as part of the institutional Data-Driven Determinants for COVID-19 Oncology Discovery Effort (D3CODE), IRB-approved protocol 2020-0348. Data were obtained from both structured and unstructured data sources within the MD Anderson electronic health record to validate a retrospective analysis for COVID-19 positive patients undergoing radiation treatments. Multiple data sources were identified, integrated and analyzed using the Syntropy Foundry platform, part of the Context Engine Data Management System at M.D. Anderson Cancer Center (MDACC) to include patient demographics, clinical notes, laboratory values, radiology reports, and oncology treatments. The platform allows real-time updating of these data-sources with integrated statistical packages to streamline rapid statistical analyses. We conducted a retrospective review of patients treated within 3 weeks of COVID infection between March 2020 and November 2022. Patient data was obtained using Foundry. Patients who tested positive for COVID-19 and had their treatment postponed were categorized as having a treatment delay. Patients who started radiation therapy and subsequently had an interruption in treatment due to contracting COVID-19 were categorized as having a treatment break. The Kaplan-Meier method was used to compare survival outcomes and was conducted within Foundry. Results: Syntropy Foundry helped develop a database comprising of 380 COVID-19 positive patients treated with radiation therapy between March 2020 through November 2022. It validated patient demographic information and research variables including patient age, histology, cancer stage, treatment plans, radiographic scans, recurrences, and follow up time that were separately performed manually. It reduced the time and human resources required to retrospectively collect and review these patient charts. Conclusions: Syntropy Foundry and ongoing efforts to leverage machine learning models to facilitate the interpretation of the large amount of accessible clinical data can potentially improve quality and reduce resources needed to generate large research databases. Future applications using artificial intelligence with Foundry to identify clinical recurrence and report patient outcome are under development.
<div>Abstract<p>Tumor hypoxia is a negative prognostic factor that is implicated in oncogenic signal activation, immune escape, and resistance to treatment. Identifying the mechanistic role of hypoxia in immune escape and resistance to immune-checkpoint inhibitors may aid the identification of therapeutic targets. We and others have shown that V-domain Ig suppressor of T-cell activation (VISTA), a negative checkpoint regulator in the B7 family, is highly expressed in the tumor microenvironment in tumor models and primary human cancers. In this study, we show that <i>VISTA</i> and HIF1α activity are correlated in a cohort of colorectal cancer patients. High <i>VISTA</i> expression was associated with worse overall survival. We used the CT26 colon cancer model to investigate the regulation of VISTA by hypoxia. Compared with less hypoxic tumor regions or draining lymph nodes, regions of profound hypoxia in the tumor microenvironment were associated with increased VISTA expression on tumor-infiltrating myeloid-derived suppressor cells (MDSC). Using chromatin immunoprecipitation and genetic silencing, we show that hypoxia-inducible factor (HIF)-1α binding to a conserved hypoxia response element in the <i>VISTA</i> promoter upregulated VISTA on myeloid cells. Further, antibody targeting or genetic ablation of VISTA under hypoxia relieved MDSC-mediated T-cell suppression, revealing VISTA as a mediator of MDSC function. Collectively, these data suggest that targeting VISTA may mitigate the deleterious effects of hypoxia on antitumor immunity.</p></div>
The COVID-19 pandemic altered the workplace for medical education. As restrictions ease, the opportunities provided by virtual rotations remain. Radiation oncology rotations based on virtual participation with patients (consultations, follow-ups, and brachytherapy), contouring and reviewing external beam plans, didactics, and unstructured office hours have been well received at multiple institutions. Virtual rotations decrease barriers to access including lack of a radiation oncology department at one's home institute and the high cost of travel and housing. Furthermore, rotations can be adapted to preclinical students and those with prior radiation oncology rotation experience. However, the virtual format creates and exacerbates several challenges including technical difficulties with electronic medical record or treatment planning software, lack of the spontaneous interactions common to in-person rotations, and unexpected delays in the clinic. We recommend early scheduled time with information technology services to troubleshoot any potential issues, scheduled office hours with faculty and videoconferencing with nonphysician team members to mitigate omission of in-person introductions, and provision of complete contact information for all staff scheduled to meet with students to facilitate communication when unexpected clinic issues arise. Although we are all excited for quarantine restrictions to safely be lifted, we support the continued development of virtual away rotations as a flexible, more affordable option to increase exposure to the field.
Background: Standard of care for locally advanced (stage II-III) esophageal adenocarcinoma is neoadjuvant chemoradiation followed by esophagectomy. Pathologic complete response (pCR) to neoadjuvant therapy occurs in 25-30% of patients. Three-year overall and progression-free survival of patients with pCR are nearly double those of incomplete responders (86% and 80% versus 48% and 39%, respectively). Therefore accurate prediction of pCR prior to esophagectomy can potentially spare patients the intraoperative risks and postoperative changes in quality of life associated with surgery. However, no well-established biomarkers of pCR currently exist.Methods: Paired pre-treatment biopsies and post-chemoradiation resections from patients with stage II-III esophageal adenocarcinoma between 2003 and 2017 were retrieved at a single institution. pCR was defined as absence of cancer cells in the treated specimen, while pathologic poor response (pPR) was defined as greater than 10% of tumor cells remaining in the specimen. In total, 27 pCR and 25 pPR resections were identified along with 20 and 12 of their corresponding DNA samples from pre-treatment biopsies that passed quality control on the OncoScan platform to call copy number alteration events. Within every cytogenetic band in the human genome, the quantity of bases gained by each sample was computed as the sum of gained genomic segment lengths weighted by the surplus copy number of each segment. These cytogenetic band gains were compared between pCR and pPR samples. An independent validation dataset of 47 patients with stage II-III esophageal adenocarcinoma was obtained from The Cancer Genome Atlas (TCGA).Results: In 3-fold cross-validation, genomic gains in chromosome 14q11 and chromosome 19p13 displayed the most significant difference between pCR and pPR samples in all 3 training sets. Across the test sets, average AUC for correctly predicting pCR/pPR status was 0.829. Selecting 1.1 megabases as the cutoff that optimizes tradeoff between sensitivity and specificity in the discovery dataset also stratified survival in TCGA. Among TCGA patients, those possessing genomic gains in chr14q11 and chr19p13 that exceed 1.1 megabases had superior 5-year overall survival (44% versus 0%, HR = 0.324, P = 0.0249) with median survival of 46 months versus 20 months.Conclusion: Genomic gains in 14q11 and 19p13 may be a valuable biomarker for inference of pCR/pPR among patients with esophageal adenocarcinoma. To our knowledge, it is the first such biomarker with demonstrated utility in an independent set of patients. Further prospective validation is warranted.Citation Format: David C. Qian, Joel A. Lefferts, Bassem I. Zaki, Pavlo Mishyn, Elizabeth B. Brickley, Yue Xue, Mikhail Lisovsky. Prediction of pathologic complete response to neoadjuvant therapy for esophageal adenocarcinoma using copy number alterations [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1657.
<p>Response prediction scores for 22 rounds of leave-one-out cross-validation are presented. The test set individual in each round is denoted by a black asterisk, while training set responders and non-responders are denoted by blue dots and red dots, respectively. Test set individuals with scores above/below the dashed line were predicted to be vaccine responders/non-responders. Of the variants found to be associated with vaccine response among training set individuals (P < 0.005), up-weighting in the prediction model was applied to those within genes that participate in the following pathways: (A) no pathway, (B) KEGG: Extracellular matrix receptor interactions, (C) KEGG: Natural killer cell mediated cytotoxicity, (D) PID: αVβ3 integrin pathway, (E) PID: Caspase pathway, (F) PID: SHP2 pathway, (G) REACTOME: Cell surface interactions at the vascular wall, (H) REACTOME: PPARα activates gene expression, (I) PID: αMβ2 integrin pathway, (J) PID: Integrin 1 pathway, and (K) REACTOME: Metabolism of amino acids and derivatives.</p>
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