UCSF Computational Cancer Community (C3) Wiki
This a recurring monthly meeting focused on cancer genomics and computational cancer biology and oncology. It provides a forum for UCSF labs to share largely unpublished work and to get feedback and input.
Computational Biology Resources at UCSF
Past seminars
Mapping the landscape of cancer vulnerabilities in human cancer
Mapping the landscape of cancer vulnerabilities in human cancer
Tumor Subtype and Phenotype Prediction from cell-free DNA
Identification of a compressed CRISPR guide library for genome-wide screening
Transcriptional fidelity enhances cancer cell line selection in pediatric cancers
3D spatial characterization of epigenomic intratumoral heterogeneity in glioblastoma
GENEVA: A scalable in vivo drug discovery platform
The Computational Biology and Informatics Shared Resource
Affiliation: Computational Biology and Informatics Core, Helen Diller Family Comprehensive Cancer Center, UCSF
Summary: N/A
When: November 8, 2021 9:15 AM
The 5-hydroxymethylcytosine landscape of prostate cancer
Global views of development and cancer
Harmonizing Transcriptomic Data To Discover Clinically Relevant Overexpressed Genes In Pediatric Cancer Patients
Affiliation: Treehouse Childhood Cancer Initiative
Summary: Geoff Lyle is a Data Analyst with the Treehouse Childhood Cancer Initiative at the University of California, Santa Cruz. His work as a Behavioral Therapist with children with autism combined with his scientific interest and programmatic skills led him to work in pediatric cancer genomic research with Dr. Olena Vaske. Utilizing transcriptomic data from TCGA, TARGET and several other sources he and Dr. Vaske analyzed relative gene expression outliers derived from RNA-seq data from clinical partners throughout the United States and Canada. This technique has assisted physicians in finding potential therapies in recurrent/refractory pediatric cancer patients.
When: September 13, 2021 9:00 AM
Discovering the Anticancer Potential of Existing Drugs by Systematic Viability Profiling
Single-Molecule Epigenomic Views of Chromatin Architecture
Affiliation: Principal Investigator and Sandler Faculty Fellow, Department of Biochemistry & Biophysics, UCSF
Summary: N/A
When: June 14, 2021 9:00 AM
Origins of Cancer Genome Complexity Revealed by Haplotype-Resolved Genomic Analysis of Barrett’s Esophagus to Esophageal Adenocarcinoma Progression
Affiliation: Assistant Professor, Department of Medicine, UCSF
Summary: N/A
When: May 10, 2021 9:00 AM
Studying Resistance in Cancer
Affiliation: Director of Cancer Genome Computational Analysis Group, Broad Institute of MIT and Harvard
Summary: Cancer progresses via an evolutionary process in which subclones with increased fitness can expand and take over less fit clones. Therapy exerts additional pressure on the cancer cells, often resulting in the selection of subclones with unique resistance mechanisms that eventually drive recurrence of the disease. Preventing or delaying the emergence of resistance remains a major medical need and is a highly active topic of research.
In this talk, I will describe different approaches to studying resistance, including studying pre- and post-resistance samples; modeling the dynamics of subclones and estimating their fitness; and comparing frequencies of events in unmatched cohorts of treated and untreated cases. I will describe recent analytical tools (PhylogicNDT) that we have developed for studying tumor heterogeneity, dynamics, and timing of events, which we have applied to discover mechanisms or resistance. In addition, I will demonstrate the power of using cell-free DNA collected before and after treatment, as well as the power of using autopsy samples to uncover the emergence of resistance. Finally, I will discuss the implications of leveraging the heterogeneity of resistance mechanisms to enhance our ability to map these changes, and potentially develop strategies to delay or overcome their emergence.
In this talk, I will describe different approaches to studying resistance, including studying pre- and post-resistance samples; modeling the dynamics of subclones and estimating their fitness; and comparing frequencies of events in unmatched cohorts of treated and untreated cases. I will describe recent analytical tools (PhylogicNDT) that we have developed for studying tumor heterogeneity, dynamics, and timing of events, which we have applied to discover mechanisms or resistance. In addition, I will demonstrate the power of using cell-free DNA collected before and after treatment, as well as the power of using autopsy samples to uncover the emergence of resistance. Finally, I will discuss the implications of leveraging the heterogeneity of resistance mechanisms to enhance our ability to map these changes, and potentially develop strategies to delay or overcome their emergence.
When: April 5, 2021 9:00 AM
Deconvolving the Epigenome by Learning from the Transcriptome
Affiliation: Goodarzi Lab, UCSF
Summary: Identifying upstream regulators of complex biological processes such as metastasis from bulk chromatin accessibility data is challenging. We developed a deep learning framework that allows for deconvolution of heterogeneous chromatin accessibility data. Our method does so by integrating genomic sequence, chromatin accessibility, and single-cell transcriptome data. This approach allows for investigating the epigenome of clusters of single cells which only have minor differences in their transcriptome. It also allows for better integration of scRNA-seq and scATAC-seq data.
We developed a deep learning framework that allows for deconvolution of heterogeneous chromatin accessibility data. Our method does so by integrating genomic sequence, chromatin accessibility, and single-cell transcriptome data.
We developed a deep learning framework that allows for deconvolution of heterogeneous chromatin accessibility data. Our method does so by integrating genomic sequence, chromatin accessibility, and single-cell transcriptome data.
When: March 8, 2021 9:00 AM
Leveraging Multi-Omics Data To Better Understand Cancer
‘Real World’ and Discovery-Based Genomic Analysis in Pediatric Cancer