Course Description

This advanced module offers a comprehensive journey through the molecular, genomic, and computational foundations of cancer research. Designed for students, researchers, and healthcare professionals, it integrates biological principles with hands-on training in cutting-edge bioinformatics and machine learning tools, preparing participants to contribute to precision oncology and personalized medicine.

• Foundations of Cancer Biology

  • Oncogenes, tumor suppressor genes, and the hallmarks of cancer.

  • Mechanisms of tumor initiation, progression, and metastasis.

• Cancer Genomics

  • Somatic mutations, copy number alterations, and structural variants.

  • Databases and portals: cBioPortal, TCGA, COSMIC.

  • Genome analysis workflows: variant calling, annotation, CNV detection.

  • Tools: GATK, VarScan, CNVkit.

• Transcriptomics in Cancer

  • RNA-Seq for differential gene expression.

  • Fusion gene detection and alternative splicing analysis.

  • Tools: STAR, HISAT2, FusionCatcher.

• Functional Genomics & Pathway Analysis

  • Gene set enrichment and pathway mapping.

  • Network-based approaches to cancer biology.

  • Tools: DAVID, Enrichr, STRING.

• Pharmacogenomics & Drug Sensitivity

  • Identification of actionable mutations.

  • Predicting drug response using genomic data.

  • Databases: GDSC, CCLE, PharmGKB.

• Radiomics & Imaging Analysis

  • Introduction to radiomics in oncology imaging.

  • Image preprocessing, feature extraction, and radiomic feature analysis.

  • Tools: PyRadiomics, 3D Slicer.

• Machine Learning in Cancer Diagnosis & Prognosis

  • Basics of ML and deep learning in oncology.

  • Feature selection, model building, and predictive modeling.

  • Tools: scikit-learn, TensorFlow, Keras.

• Clinical Decision Support Systems (CDSS)

  • Integrating genomic and clinical data for decision-making.

  • Case studies and applications in oncology.

  • Tools: Watson for Oncology, Oncoshot.

• Ethical, Legal & Social Implications (ELSI)

  • Patient privacy, data sharing, and regulatory frameworks.

  • Ethical considerations in cancer genomics research.

• Future Directions & Emerging Technologies

  • Advances in genomics, radiomics, and AI-driven oncology.

  • Opportunities and challenges in precision medicine