The Fund for Innovation in Cancer Informatics – as of April 22, 2022
- ICI Fund Established in 2017.
- Awarded 20 Major Grants and 11 Discovery Grants. Over $5 million.
ICI Outcomes Presented and Published:
- Annals of Oncology
- Biomed Central
- Blood Advances
- Cancerres (AACR Journal) – 2
- Cancer Direct
- Cancer Discovery (AACR) – 2
- Genome Biology
- JCO Precision Oncology
- JCO Clinical Cancer Inform
- NAR Cancer
- Nature – 9
- Nucleic Acids Research
- PLOS Computational Biology
- Science Direct
- Science Magazine - 2
- Scientific Advances
- Genomeweb.com (DF)
- MSKCC blog
- Bioinformatics (MD Anderson)
Models and Data Deposited and Made Public Through:
- Matchminer.org Tool open-source computational tool for matching patients to clinical trials
- Genomenexus.org - Tool for exploring annotation and functional impact of genetic variants
- Crc Microbiome Explorer - https://crc-microbiome.stanford.edu Resource for exploring the microbiome in colorectal cancer data sets
Additional Resources Developed (or in development):
- SIGNAL: Population-scale resource of Germline Variants and Accompanying Somatic Alterations in Cancer
- Novel DL prognostic model for mCRPC progressing on next-generation hormone therapies.
- SPaRTAN, a computational framework for linking cell-surface receptors to transcriptional regulators
- Atlas of clinically-distinct cell states and cellular ecosystems across human solid tumors
- REFLECT – precision combination therapy portal
- G2S – web-service for annotating genomic variants in 3D protein structures
Additional grants applied for and awarded b/c Projects initially funded by ICI:
- NIH R21 –(2)
- NIH KO8
- Prostate Cancer Research Program Idea Development Award,
- Prostate Cancer Foundation YIA
- Susan B. Koman
- Hiring postdocs
- Leslie/Toska: Showed that PI3K signaling is mediated by widespread alternatively spliced isoforms that are linked to proliferation, metabolism, and splicing in PIK3CA mutant cells, and established an atlas of PI3K-mediated splicing programs that could identify potential therapeutic targets in breast cancer.
- Gao/Berger/Solit: Using a large data set of over 17,000 cancer patients with sequencing data, to better understand the role of germline pathogenicity in tumorigenesis as well as its potential for improving clinical management.
- Chiu/Chen: Developed a deep learning model that predicts cancer dependencies - essentially, genes that specific cancer requires for proliferation - by integrative genomic profiles to explore cancer pathways.
- Somasundaram/Osmanbeyoglu: Developed SPaRTAN, a computational method to link single-cell proteomics and transcriptomics to correlate cell-surface receptors with downstream transcritional networks.
- Ji/Xia: Developed a pipeline to identify microbiome dysbiosis in colorectal cancer patients, and developed a web-based tool for data exploration.
- Pritchard/Ha: Created Griffin, a tool to improve liquid biopsies by analyzing cell-free DNA for nucleosome occupy, which increased the potential for both sensitive cancer detection and disease-subtyping.
- Vasan/Reznick: Showed that tandem mutations in PIK3CA led to both increased oncogenicity as well as a heightened susceptibility to PIK3CA inhibitors.
- Beroukhim/Shapira: Published a series of widely cited papers analyzing and describing the functional impact of genomic rearrangements that can result in the deletion, amplification, or reordering of whole genomic segments, ranging in size from kilobases to whole chromosomes, and identifying structural variants in non-protein-coding regions.