Abstract B040: Streamlining survival analysis with an automated machine learning pipeline: A case study in kidney renal clear cell carcinoma
Authors: Harpreet Kaur, Pijush Das, Kevin Camphausen, Uma Shankavaram
Journal: Cancer Research (2025)

Abstract Background: Accurate survival prediction in cancer patients is crucial for personalized oncology, directly influencing treatment decisions and patient care. Integrating multi-omics data with clinical information can significantly enhance prognostic models. However, existing methods often necessitate extensive manual preprocessing and specialized bioinformatics expertise, limiting their practical use in research and clinical settings. This study presents the Cancer Patient Survival Model (CPSM), an R package providing an automated, streamlined pipeline for generating reliable, interpretable survival predictions from complex, high-dimensional data. Objective: The objective of this case study is to evaluate the utility of CPSM in analyzing survival probabilities and identifying prognostic markers for Kidney Renal Clear Cell Carcinoma (KIRC), using comprehensive data from The Cancer Genome Atlas (TCGA). Methods: We analyzed RNA sequencing data comprising 38,712 transcripts along with clinical variables from 534 KIRC patients. Using the preprocessing modules of the CPSM, 526 samples were retained and randomly split into training (90%) and independent test (10%) sets. CPSM's feature selection modules identified three distinct feature sets: (1) three key clinical variables (age, tumor subtype, histological grade), (2) a Prognostic Index (PI) score from nine genes selected via LASSO Cox regression, and (3) a set of univariate significant genes. Using CPSM’s model development module, we constructed four survival models: (1) Model 1 - clinical features only, (2) Model 2 - the PI score alone, (3) Model 3 - a combination of the PI score and key clinical features, and (4) Model 4 - an integration of significant univariate clinical and gene expression features. The performance of the models was evaluated using the concordance index (C-Index) and Integrated Brier Score (IBS). Results: Model 3, which integrated clinical features and the PI score, showed the best performance, achieving a C-Index of 0.79 in the training set and 0.76 in the test set, with IBS values of 0.16 and 0.23, respectively. A specific patient case (TCGA-CJ-6030-01) demonstrated that Model 3 predicted a median survival time of 73.81 months, within 5% of the actual survival time, underscoring its clinical relevance. Moreover, CPSM produced an intuitive nomogram with a C-Index of 0.76, allowing predictions of 1-year, 3-year, and 5-year survival probabilities, serving as a valuable tool for clinical decision-making. Conclusions: CPSM signifies a notable advancement in automated survival analysis by effectively integrating molecular and clinical data. The superior performance of Model 3 highlights the advantages of combining PI scores with clinical features to enhance prognostic accuracy in KIRC. This study emphasizes CPSM's potential utility in personalized oncology and establishes a foundation for future research on its application across various cancer types, including the integration of additional multi-omic data to further improve predictive performance. Citation Format: Harpreet Kaur, Pijush Das, Kevin Camphausen, Uma Shankavaram. Streamlining survival analysis with an automated machine learning pipeline: A case study in kidney renal clear cell carcinoma [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Functional and Genomic Precision Medicine in Cancer: Different Perspectives, Common Goals; 2025 Mar 11-13; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2025;85(5 Suppl):Abstract nr B040.