Cancer Genomic Workshop-2
Decoding the Driver Mutations Master the integration of AI-driven variant prioritization and clinical interpretation to transform cancer treatment strategies.
Course Description
As we navigate the complexities of the 2026 oncological landscape, the transition from broad chemotherapy to Precision Oncology is driven by high-resolution genomic data. This workshop provides an immersive deep dive into the Cancer Genomics workflow, focusing on the identification of Somatic Mutations, Copy Number Alterations (CNAs), and Structural Variants. Participants will leverage Machine Learning algorithms to differentiate between "driver" and "passenger" mutations, utilizing AI-powered clinical decision support systems (CDSS) to map genetic profiles to FDA-approved therapies. We explore the latest in Liquid Biopsy (ctDNA) analysis and Single-Cell Sequencing, providing the technical expertise to interpret the tumor microenvironment. By the end of this workshop, you will be proficient in using Genomic Foundation Models to predict drug resistance and patient outcomes with clinical-grade accuracy.
What You'll Learn
Tumor-Normal Pairing: Master the bioinformatic techniques for subtracting germline noise to isolate somatic signals.
Driver Mutation Identification: Use AI-driven tools like CHASMplus to predict the functional impact of missense mutations.
Structural Variation in Cancer: Detect complex rearrangements and gene fusions using high-throughput sequencing data.
Liquid Biopsy Analysis: Understand the computational challenges of detecting Circulating Tumor DNA (ctDNA) at ultra-low frequencies.
Immunotherapy Biomarkers: Calculate Tumor Mutational Burden (TMB) and Microsatellite Instability (MSI) for checkpoint inhibitor response.
AI Drug Matching: Navigate databases like OncoKB and COSMIC using automated scripts to identify targeted therapy opportunities.
Curriculum
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Tumor-Normal Pairing: Master the bioinformatic techniques for subtracting germline noise to isolate somatic signals.
Lesson -
Driver Mutation Identification: Use AI-driven tools like CHASMplus to predict the functional impact of missense mutations.
Lesson -
Structural Variation in Cancer: Detect complex rearrangements and gene fusions using high-throughput sequencing data.
Lesson -
Liquid Biopsy Analysis: Understand the computational challenges of detecting Circulating Tumor DNA (ctDNA) at ultra-low frequencies.
Lesson -
Immunotherapy Biomarkers: Calculate Tumor Mutational Burden (TMB) and Microsatellite Instability (MSI) for checkpoint inhibitor response.
Lesson -
AI Drug Matching: Navigate databases like OncoKB and COSMIC using automated scripts to identify targeted therapy opportunities.
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