ONCSC 7700 - 013 CANCER DATA SCIENCE JOURNAL CLUB WEBSITE

Course Director: Aik Choon Tan, Ph.D.

Course Teaching Assistant:
Min Hu

Class: Thursday 9:30am - 10:30am
Venue: HCI Research South Conference Room 4C

COURSE TOPICS

1. TOPIC 1 - GENE SET ANALYSIS

   Classic & Concept: (AC Tan - 9/4/2025)

   • [SLIDES BY AC]
   • GSEA Paper: Subramanian et al. (2005). Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. PNAS. [PDF].
   • Mootha et al. (2003). PGC-1-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat. Genetics. [PDF]
   • Liberzon et al. (2015). The Molecular Signature Database (MSigDB) hallmark gene set collection. Cell Systems. 1(6): 417-425. [PDF]

     Variations & Expansions:

   • Irizarry et al. (2009). Gene set enrichment analysis made simple. Stat Methods Med Res. 18(6): 565-575. [PDF]
   • Hanzelmann et al. (2013). GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics. 14: Article number: 7. [PDF] [SLIDES BY HUI 10162025]
   • Powers et al. (2018). GSEA-InContext: identifying novel and common patterns in expression experiments. Bioinformatics. 34(13): i555-i564. [PDF]
   • Cousins et al. (2023). Gene set proximity analysis: expanding gene set enrichment analysis through learned geometric embeddings, with drug-repurposing applications in COVID-19. Bioinformatics. 39(1): btac735. [PDF]
     

     Applications:

   • Ma et al. (2020). Integrative differential expression and gene set enrichment analysis using summary statistics for scRNA-seq studies. Nature Communications. 11: Article number: 1585. [PDF]
   • Franchini et al. (2023). Single-cell gene set enrichment analysis and transfer learning for functional annotation of scRNA-seq data. NAR Genomics and Bioinformatics, 5(1): lqad024. [PDF]
   • Fan et al. (2024). irGSEA: the integration of single-cell rank-based gene set enrichment analysis. Briefings in Bioinformatics. 25)4_: bbae243. [PDF] [SLIDES BY MIN 10022025]
     

     AI:

   • Joachimiak et al. (2024). Gene Set Summarization using Large Language Models. ArXiv:2305.13338v3. [PDF] [SLIDES BY JENNY 09182025]
   • Wang et al. (2025). GeneAgent: self-verification language agent for gene-set analysis using domain databases. Nature Methods. 22:1677-1685. [PDF]
   
   
   

2. TOPIC 2 - COMPUTATIONAL DRUG REPURPOSING

   Classic & Concept: (AC Tan - 10/30/2025)

   • [SLIDES BY AC]
   • Lamb et al. (2006). The Connectivity Map: Using Gene-Expression Signatures to Connect Small Molecules, Genes, and Disease. Science. 313(5795):1929-1935. [PDF]
   • Subramaniam et al. (2017). A Next Generation Connectivity Map: L1000 Platform and the First 1,000,000 Profiles. Cell. 171(6):1437-1452.e17. [PDF]
     

     Variations & Expansions:

   • Sirota et al. (2011). Discovery and preclinical validation of drug indications using compendia of public gene expression data. Science Translational Medicine. 3(96):96ra77. [PDF]
   • Kim et al. (2014). Bioinformatics-driven discovery of rational combination for overcoming EGFR-mutant lung cancer resistance to EGFR therapy. Bioinformatics. 30(17):2393-2398. [PDF]
   • Abelin et al. (2016). Reduced-representation Phosphosignatures Measured by Quantitative Targeted MS Capture Cellular States and Enable Large-scale Comparison of Drug-induced Phenotypes. Molecular Cellular Proteomics. 15(5): 1622-1641. [PDF]
   • De Abrew et al. (2019). Use of connectivity mapping to support read across: A deeper dive using data from 186 chemicals, 19 cell lines and 2 case studies. Toxicology. 423:84-94. .[PDF]
   • He et al. (2023). ASGARD is A Single-cell Guided Pipeline to Aid Repurposing of Drugs. Nature Communications. Article Number 993. [PDF]
     

     Applications:

   • Jahchan et al. (2013). A Drug Repositioning Approach Identifies Tricyclic Antidepressants as Inhibitors of Small Cell Lung Cancer and Other Neuroendocrine Tumors. Cancer Discovery. 3(12):1364-1377. [PDF]
   • Dudley et al. (2011). Computational repositioning of the anticonvulsant topiramate for inflammatory bowel disease. Science Translational Medicine. 3(96):96ra76. [PDF]
   • Wang et al. (2021). Tumor immunological phenotype signature-based high-throughput screening for the discovery of combination immunotherapy compounds. Science Advances. 7(4). DOI: 10.1126/sciadv.abd7851. [PDF]
     

     AI:

   • Kraus et al. (2025). RxRx3-core: Benchmarking drug-target interactions in High-Content Microscopy. arXiv:2503.20158v2. [PDF]
   • Chandrasekaran et al. (2025). Morphological map of under- and overexpression of genes in human cells. Nature Methods. 22:1742-1752. [PDF]
   • Ren et al. (2024). A small-molecule TNIK inhibitor targets fibrosis in preclinical and clinical models. Nature Biotechnology. 43:63-75. [PDF]
   • Xu et al. (2025). A generative AI-discovered TNIK inhibitor for idiopathic pulmonary fibrosis: a randomized phase 2a trial. Nature Medicine. 31:2602-2610. [PDF]
   • Insilico Medicine
   
   
   

3. TOPIC 3 - COMPUTATIONAL TUMOR MICROBIOME

   Classic & Concept: (AC Tan - 1/22/2026)

   • Nejman, et al. (2020). The human tumor microbiome is composed of tumor type-specific intracellular bacteria. Science, 368(6494):973-980. [PDF]
   • Li, Ma, et al. (2025). The new microbiome on the block: challenges and opportunities of using human tumor sequencing data to study microbes. Nature Methods, 22(9):1788-1799.
     

     Technologies:

   • Nino et al (2022). Effect of the intratumoral microbiota on spatial and cellular heterogeneity in cancer. Nature, 611(7937):810-817. [PDF] <
   • Sepich-Poore et al (2024). Robustness of cancer microbiome signals over a broad range of methodological variation. Oncogene, 43(15):1127-1148. [PDF]
     

     Applications:

   • Baruch et al (2021).Fecal microbiota transplant promotes response in immunotherapy-refractory melanoma patients. Science, 371(6529):602-609. [PDF]
   • AI/ML:
   • Wang et al. (2024). A Bioinformatics Tool for Identifying Intratumoral Microbes from the ORIEN Dataset. Cancer Research Communications. 4(2):293-302. [PDF]
   • Dravillas, Coleman, et al (2024). The Tumor Microbiome as a Predictor of Outcomes in Patients with Metastatic Melanoma Treated with Immune Checkpoint Inhibitors.Cancer Research Communications. 4(8):1978-1990. [PDF]