Citations

Abstract Gallbladder cancer (GBC) is one of the deadliest cancers, with a 5-year-survival-rate of less than 5 percent for late-stage disease. The response rate to chemotherapy among GBC patients is generally poor. Recent research has attempted to identify diagnostic, prognostic, and predictive biomarkers, however, currently, no biomarkers can accurately diagnose GBC and predict patients’ prognosis. Integrative analysis of molecular and clinical characterization has not been fully established, and minimal improvement has been made to the survival of these patients, in part due to the heterogeneity of GBC. Machine learning techniques have been proven to empower analysis of big data in oncology, allowing for improvement in the generation of biomarkers to predict patient outcomes. Using machine learning, we can utilize high-throughput RNA sequencing with clinicopathologic data to develop a predictive tool for GBC prognosis. Current predictive models for GBC outcomes often utilize clinical data only, with the highest C-statistic reported being 0.71. C-statistic values over 0.7 generally indicate good models, however 0.8 is the threshold for strong predictive models. We aim to build a superior algorithm to predict overall survival in GBC patients with advanced disease, using machine learning approaches to prioritize biomarkers for GBC prognosis. We have identified over 80 fresh frozen GBC tissue samples from Mayo Clinic Rochester, Dongsan Medical Center in Daegu, Korea, University of the Witwatersrand, in Johannesburg, South Africa, Lithuanian University of Health Science in Vilnius, Lithuania, and University of Calgary in Calgary, Canada, from patients enrolled between 2012 and 2021. We will perform next-generation RNA sequencing on these tissue samples. The patients’ clinical, pathologic and survival data will be abstracted from the medical record uniformly across sites. Feature engineering and dimensionality reduction will be performed. Then random forests, support vector machines, and gradient boosting machines will be applied to train the data. Variable importance will prioritize multi-omic markers. Standard 5-fold cross validation will be used to assess performance of each ML algorithm. If overall survival can be better predicted with the addition patients’ transcriptional sequencing data compared to using clinical profiles alone, we can gain a greater understanding of key biomarkers driving the tumor phenotype. Citation Format: Linsey Jackson, Loretta Allotey, Valles Kenneth, Gavin Oliver, Asha Nair, Daniel O'Brien, Rondell Graham, Mitesh Borad, Arjun Athreya, Lewis Roberts. Prognostic biomarkers for gallbladder cancer: A machine learning approach [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1944.

Abstract Background Infection by hepatitis B and C viruses causes inflammation of the liver and can lead to cirrhosis, liver failure, and hepatocellular carcinoma. The WHO’s ambition to eliminate viral hepatitis by 2030 requires strategies specific to the dynamic disease profiles each nation faces. Large-scale human movement from high-prevalence nations to the United States and Canada have altered the disease landscape, likely warranting adjustments to present elimination approaches. However, the nature and magnitude of the new disease burden remains unknown. This study aims to generate a modeled estimate of recent HBV and HCV prevalence changes to the United States and Canada due to migration. Methods Total migrant populations from 2010-2019 were obtained from United Nations Migrant Stock database. Country-of-origin HBV and HCV prevalences were obtained for the select 40 country-of-origin nations from the Polaris Observatory and systematic reviews. A standard pivot table was used to evaluate the disease contribution from and to each nation. Disease progression estimates were generated using the American Association for the Study of the Liver guidelines and outcome data. Results Between 2010 and 2019, 7,676,937 documented migrants arrived in US and Canada from the selected high-volume nations. Primary migrant source regions were East Asia and Latin America. Combined, an estimated 878,995 migrants were HBV positive, and 226,428 HCV positive. The majority of both migrants (6,477,506) and new viral hepatitis cases (HBV=840,315 and HCV=215,359) were found in the United States. The largest source of HBV cases stemmed from the Philippines, and HCV cases from El Salvador. Conclusion Massive human movement has significantly changed HBV and HCV disease burdens in both the US and Canada over the past decade and the long-term outcomes of cirrhosis and HCC are also expected to increase. These increases are likely to disproportionally impact individuals of the migrant and refugee communities and screening and treatment programs must be strategically adjusted in order to reduce morbidity, mortality, and healthcare expenses. Disclosures All Authors: No reported disclosures