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Cancer Isn't One Broken Gene — It's a Network That Stopped Regulating Itself
A single mutation rarely causes cancer on its own. It's the combination of disruptions across interconnected pathways — growth signals, DNA repair, immune surveillance — that lets a tumor form and spread. This pathway trains you to find those disruptions using real cancer genomics data.
From Mutation Noise to Real Disease Drivers
Two patients with "the same" cancer can have completely different underlying network disruptions — which is why a treatment that works for one patient can fail for another. This area is about finding the specific disruption driving a given tumor.
— Analyze large public cancer genomics datasets to find genes disrupted far more often than chance would predict
— Build protein and pathway networks to find "hub" genes whose disruption ripples out to affect many processes
— Study how a tumor's cell population diversifies over time (clonal evolution) and why that drives treatment resistance
— Investigate how tumors interact with surrounding immune cells to escape detection
Matching Therapy to the Tumor, Not the Textbook
Cancer treatment is moving from "one drug for one cancer type" toward matching therapies to the specific network disruption in an individual tumor. Network-based approaches are directly responsible for identifying new drug targets, explaining why certain drug combinations work synergistically, and predicting which patients are likely to respond to a given targeted therapy.
What a Summer Project Looks Like
Cancer Systems Medicine
Finding the Hidden Hubs: Driver Gene Prioritization in a Public Cancer Cohort
A student selects one cancer type and downloads real, de-identified genomic data from a public resource like The Cancer Genome Atlas. Using network analysis tools, they build a gene interaction network for that cancer type and calculate which genes sit at the most "central" positions — the ones most likely to be true biological drivers rather than incidental noise. They compare their computationally prioritized gene list against genes already known to be clinically important, evaluating how well the approach recovers known biology and what new candidates it surfaces. The project concludes with a written report and a network visualization suitable for a research poster, mentored throughout by a cancer biology or computational oncology researcher.
Multi-omics data analysisNetwork centrality methodsTCGA / cBioPortalScientific interpretation
Cancer Systems Medicine
Finding the Hidden Hubs: Driver Gene Prioritization in a Public Cancer Cohort
A student selects one cancer type and downloads real, de-identified genomic data from a public resource like The Cancer Genome Atlas. Using network analysis tools, they build a gene interaction network for that cancer type and calculate which genes sit at the most "central" positions — the ones most likely to be true biological drivers rather than incidental noise. They compare their computationally prioritized gene list against genes already known to be clinically important, evaluating how well the approach recovers known biology and what new candidates it surfaces. The project concludes with a written report and a network visualization suitable for a research poster, mentored throughout by a cancer biology or computational oncology researcher.
Multi-omics data analysis
Network centrality methods
TCGA / cBioPortal
Scientific interpretation
How Each Level Gets There
Every track takes a shared Fall foundations course, then this area-specific Spring course, then a live mentored summer project.
High School
HS-S-CA
Undergraduate
UG-S-CA
Medical Student
MS-S-CA
Clinician
CL-S-CA
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