Program Structure

Module 1: Molecular Foundations of Cancer

• Hallmarks of cancer: how tumors sustain growth and evade control.
• Oncogenes vs tumor suppressors: mutations, amplification, loss-of-function.
• Key pathways overview: p53, RAS/MAPK, PI3K/AKT, cell cycle control.
• From genotype to phenotype: why the same mutation behaves differently across contexts.

Module 2: Tumor Genomics, Heterogeneity & Evolution

• Somatic mutation types: SNVs, indels, CNVs, structural variants (overview).
• Tumor heterogeneity: clonal evolution and microenvironment influence.
• Driver vs passenger mutations: how researchers distinguish significance.
• Resistance mechanisms: why therapies fail and how tumors adapt.

Module 3: CRISPR Essentials for Cancer Research

• CRISPR-Cas systems basics: Cas9, guide RNAs, PAM, editing outcomes.
• Knockout vs knock-in vs CRISPRi/CRISPRa (conceptual comparison).
• Design thinking: selecting genes, designing guides, and minimizing off-targets.
• Experimental planning: controls, validation, and interpretation discipline.

Module 4: CRISPR Functional Genomics Screens (How Targets Are Found)

• Concept of pooled CRISPR screens: essential genes, synthetic lethality, drug response.
• Screen design basics: libraries, readouts, and selection pressures.
• Data interpretation: enriched/depleted guides and what they imply biologically.
• Common pitfalls: bottlenecks, off-target confounding, and batch effects.

Module 5: Cancer Bioinformatics — Data Types & Workflows

• Core data types: DNA-seq, RNA-seq, methylation, proteomics (overview).
• Public datasets (concept): TCGA/GEO/cBioPortal type resources and what they provide.
• Clinical metadata linkage: stage, survival, treatment response (how it’s used).
• Quality control mindset: data cleaning, normalization, and bias awareness.

Module 6: Transcriptomics & Pathway Interpretation

• Differential expression concepts: tumor vs normal, responder vs non-responder.
• Signature thinking: immune markers, proliferation signals, EMT, angiogenesis.
• Pathway enrichment: GO/KEGG/Reactome concepts and careful interpretation.
• How expression links back to CRISPR target validation hypotheses.

Module 7: Biomarkers, Target Prioritization & Translational Logic

• Biomarker types: diagnostic, prognostic, predictive (clear distinctions).
• Target prioritization: druggability, selectivity, safety, pathway position.
• Integrating evidence: genomics + expression + CRISPR functional signals.
• From discovery to validation: what “next steps” look like in real research.

Module 8: Ethics, Safety & Responsible Cancer Research

• CRISPR ethics: misuse risks, off-target consequences, and containment.
• Human data ethics: privacy, consent, and responsible reporting.
• Overclaiming risks: correlation vs causation and reproducibility.
• How to write a transparent methods + limitations section.

Final Project

• Pick a cancer type or pathway and define a clear biological question.
• Design a CRISPR-based strategy (gene targets + guide logic + validation plan).
• Run a small bioinformatics interpretation workflow on a provided dataset (or sample case).
• Deliverables: short report linking data evidence + CRISPR experiment plan + expected outcomes.
• Example projects: identifying targets in EGFR pathway resistance, immune checkpoint response markers, synthetic lethal partners of tumor suppressors.