Abstract 6402: Treatment resistance and survival prediction using a machine learning gene expression signature in pancreatic adenocarcinoma
Authors: Jianfang Liu, Xiaoying Lin, Craig D. Shriver, Hai Hu
Journal: Cancer Research (2024)

Abstract Background: Pancreatic adenocarcinoma (PAAD) is highly aggressive and difficult to treat. The 5-year survival rate is only 12.5%. The Cancer Genome Atlas (TCGA) data has shown about 51% of PAAD patients didn’t achieve complete response after first course of treatment (CRAFCT); this study is to use TCGA data to develop survival-treatment associated gene signature to screen the patients, and provide novel therapeutic strategies to customize patient treatment. Methods: RNAseq data of 177 PAAD patients and their clinical data were downloaded from TCGA data portal and processed. The dataset was randomly split into training and testing data. RNAseq data analysis and survival analyses were performed on the training data to identify the survival-treatment associated genes, and a survival-treatment gene signature was derived and independently tested on the test data using signature score by cutoff, logistic regression, and random forest. Furthermore, the signature was validated by an independent dataset of 96 cases. Results: Among the 177 patients, 137 patients had the information of CRAFCT, and 122 patients had specific treatment information. Among those 122 patients, 99% received chemotherapy, 36% received radiation therapy. About half of the PAAD patients (51%) didn’t achieve CRAFCT. RNAseq data analysis identified 304 significantly expressed genes associated with the status of complete response Yes vs No, and survival analyses further selected 55 genes associated with both treatment and survival as a signature. This signature, using z-scaled scores and the cut-off of 0.3, was applied to the 99 patients of training data; the accuracy is 0.66, and the patients predicted to be Yes had a significantly better (p=0.005, HR=0.42) overall survival than those predicted No. To further test the signature, we applied it to the test data of 78 patients; the random forest model showed patients screened to be Yes had a significantly better (p=0.016, HR=0.43) overall survival than those screened No. The signature (using 40 of the 55 genes) was applied to an independent dataset of 96 cases to significantly screen the patients to be Yes vs No (resistance to treatment) with p=0.04 and HR=0.57, showing the 55-gene signature is valid and robust. Conclusion: A 55 survival-treatment associated gene signature was developed and validated robustly, and can potentially be applied as a novel strategy to screen patients for treatment response/resistance. These 55 genes could also be targeted to develop new drugs for those difficult to treat PAAD patients. Disclaimer: The contents of this publication are the sole responsibility of the author(s) and do not necessarily reflect the views, opinions or policies of USUHS, HJF, the DoD or the Departments of the Army, Navy or Air Force. Mention of trade names, commercial products, or organizations does not imply endorsement by the U.S. Government. Citation Format: Jianfang Liu, Xiaoying Lin, Craig D. Shriver, Hai Hu. Treatment resistance and survival prediction using a machine learning gene expression signature in pancreatic adenocarcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6402.