Cancer genomics and precision oncology course

Oct 26

2 min read

I recently completed course HMX – Harvard Medical School

Cancer Genomics and Precision Oncology: The Genetic Basis of Cancer and its Treatment

As a computational biologist, I spend a lot of time analyzing molecular data from next-generation sequencing. I took this course to better understand how these data inform clinical decisions: which genomic alterations contribute to tumor progression, what makes them clinically actionable, and why a therapy can produce a strong response in one patient but little benefit in another.

A few takeaways stayed with me:

• Tumor sequencing provides a spatially and temporally limited snapshot. Intratumoral heterogeneity and differences between metastatic sites mean that a single biopsy may not capture the full spectrum of clinically relevant alterations.

• Identifying a driver alteration does not automatically establish therapeutic actionability. Tumor lineage, co-occurring alterations, pathway dependencies, and prior treatment can all influence response.

• Treatment imposes selective pressure on tumor populations. Resistance can emerge through expansion of pre-existing resistant sub-clones or the acquisition of new alterations.

• Genomic information is one layer of the problem. Gene expression, protein activity, and interactions with the tumor microenvironment also shape therapeutic response.

The molecular tumor board discussions were particularly valuable. Seeing clinicians integrate sequencing quality, variant interpretation, germline findings, biological mechanisms, and clinical history made the connection between genomic analysis and treatment selection much more concrete.

These questions are closely related to my work in computational oncology and mRNA cancer vaccine research. Tumor heterogeneity and evolution also matter when prioritizing candidate antigens: their distribution across tumor cells, expression, and potential for immune recognition all influence their relevance.

At SandboxAQ, continuous learning is part of how we approach new scientific domains. This course helped me strengthen the clinical context behind the computational analyses and hypotheses we develop for anti-cancer therapy when working with iOncologi.