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Genomics of Cancer Drug Resistance

The London College, Cranford

Dr. Anum Munir is a PhD computational biologist specialising in multiomics integration and acquired drug resistance. She is the creator of the Resistant Cancer Cell Line (RCCL) database and has authored over 67 peer-reviewed publications in high-impact peer-reviewed journals.

Currently a computational biology/bioinformatics researcher (honorary) at the University of Kent and a lecturer at The London College UCK, Dr Anum has extensive expertise in building bioinformatics pipelines using Python, R, and Bash. Her mentorship focuses on helping clinicians and scientists apply machine learning to solve complex problems in oncology and drug discovery.

Main Areas of Interest

• Genomic and transcriptomic mechanisms of cancer drug resistance
• Bioinformatics pipelines for WES, WGS, and RNA-Seq data analysis
• Multi-omics integration for biomarker discovery in cancer
• Machine learning approaches for predicting drug resistance and therapeutic response
• Cancer cell line models and resistance evolution

Specialties

Oncology

Genetics & Genomics

Techniques You Will Learn

• End-to-end bioinformatics pipelines for WES/WGS (alignment, variant calling, filtering, annotation)
• RNA-Seq analysis for differential gene expression and pathway enrichment
• Integration of multi-omics datasets (genomics, transcriptomics, drug response data)
• Identification of resistance-associated variants and biomarkers
• Use of programming tools (Python, R, Bash) for data analysis and workflow automation
• Application of machine learning methods for predictive modeling and biomarker discovery
• Interpretation of drug sensitivity, dose–response data, and resistance phenotypes in cancer cell lines

Modules

1. Study Design & Bioinformatics Pipelines for Drug Resistance
Introduction to experimental design using cancer cell lines and clinical datasets. Overview of WES/WGS/RNA-Seq workflows, including data acquisition, preprocessing, and pipeline structuring.

2. Variant Analysis & Genomic Drivers of Resistance
Identification and interpretation of somatic mutations and resistance-associated variants. Hands-on concepts in variant calling, filtering, annotation, and linking genomic alterations to drug resistance.

3. Transcriptomics & Multi-Omics Integration
RNA-Seq analysis for differential expression and pathway analysis. Integration of genomic and transcriptomic data to uncover resistance mechanisms and biological pathways.

4. Biomarker Discovery & Predictive Modeling
Application of machine learning and statistical modeling to identify and validate biomarkers. Translating multi-omics findings into predictive models for drug response and resistance.

Program Fee


Total Number of Modules	4
Time duration of Each module	45 minutes
Total Program Cost	$502.5

Who Can Apply

Postgraduate students (Master’s/PhD) in Bioinformatics, Computational Biology, Biotechnology, Genomics, or related life sciences
Early-career researchers, postdoctoral fellows, and research scientists interested in cancer genomics and drug resistance
Medical professionals or clinician-scientists with an interest in translational oncology and precision medicine
Individuals with a basic understanding of molecular biology, genetics, and cancer biology
Prior exposure to programming (e.g., Python, R, or Bash) and omics data analysis is desirable but not mandatory

Host Details

Host Name: Anum Munir

Affiliation: The London College, Cranford

Address: The London College, Cranford

Website URL:

Disclaimer:It is mandatory that all applicants carry workplace liability insurance, e.g., https://www.protrip-world-liability.com (Erasmus students use this package and typically costs around 5 € per month - please check) in addition to health insurance when you join any of the onsite Trialect partnered fellowships.

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