Abstract 1061: Post-doctoral fellow
Authors: Kang-Lin Hsieh, Sankar Maity, Ganiraju Manya, Bradley Broom, Christopher Logothetis, Patrick Pilie
Journal: Cancer Research (2025)

Introduction: Prostate cancer research spans genetics, proteomics, metabolomics, cell signaling, and tumor microenvironment domains. While this data is valuable, integrating clinical and basic research findings presents challenges. The Prostate Cancer Cartography Initiative (PCCI) offers a unified data model for efficient research across clinical, co-clinical, and mechanistic studies. The data model divides cases into anatomic and biological groups, facilitating cross-domain knowledge translation and hypothesis generation through efficient data integration. Materials &amp; Methods: Our database incorporates data from 972 patients across three complementary sources: TCGA (primary prostate cancer), SU2C (metastatic patients), and MDA trials (intermediate-risk prostate cancer). For reproducibility and data consistency, we use NF core pipelines to harmonize data and derive copy loss, Single Nucleotide Variants/Insertions/Deletions (SNV &amp; Indel), and gene expression. After analyzing this information, we group patients based on clinical information such as Gleason scores or genomic information such as copy loss and SNV/Indel to evaluate how these conditions affect gene expression and pathways. Results: In our initial work, we focused on TCGA patients with Gleason scores, categorizing them into three groups (3+3, 3+4/4+3, and 8 or higher). We further subdivided each category based on genetic alterations: patients with no copy loss, patients with at least one copy loss, and patients with Single Nucleotide Variants or Insertions/Deletions (SNV &amp; Indel). We analyzed key genes including TP53 across these subgroups. Our initial results showed that medium-risk patients (Gleason scores 3+4/4+3) had significantly lower TP53 gene expression when they had both copy loss and SNV &amp; Indel. Patients with SNV &amp; Indel alone also showed lower TP53 gene expression, though this finding was not statistically significant (p = 0.06). High-risk patients (Gleason scores 8 or higher) demonstrated similar patterns. When analyzing how copy loss or SNV &amp; Indel affect genes near TP53, we found reduced expression in nearby genes—ALOX15B (logFC: -1.0153, p&amp;lt;0.001), TMEM220 (logFC: -0.719, p&amp;lt;0.001), and MYO1C (logFC: -0.348, p&amp;lt;0.001)—in patients with TP53 copy loss or SNV &amp; Indel. This pattern was confirmed in both medium-risk and high-risk groups. In our pathway analysis, including AD-target and homologous recombination pathways, patients without TP53 copy loss or SNV &amp; Indel showed higher Enrichment Scores. Discussion: The PCCI provides a platform to integrate these data and generate hypotheses. Our results show that genetic alterations—such as copy loss and SNV &amp; Indel—affect not only the TP53 but also nearby genes ALOX15B, TMEM220, and MYO1C. Notably, ALOX15B and MYO1C are tumor suppressor genes, which raises the question of how TP53 alteration leads to network changes in tumor suppressor genes. These findings further demonstrate the necessity of PCCI in facilitating hypothesis generation. Citation Format: Kang-Lin Hsieh, Sankar Maity, Ganiraju Manya, Bradley Broom, Christopher Logothetis, Patrick Pilie. Post-doctoral fellow [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 1061.