Cancer Genomics
Decoding tumours driver by driver — somatic mutations, copy-number, mutational signatures and signalling rewiring — to inform precision oncology.
What this area is.
Cancer genomics maps what has gone wrong in a tumour and why it matters. We call somatic variants, separate drivers from passengers, quantify mutational burden and infer the mutational processes that shaped the genome.
Layering expression subtyping (PAM50, CMS), copy-number and pathway analysis on top, we connect molecular profiles to prognosis and therapy — across breast, colorectal, pancreatic, thyroid, renal and ovarian cancers.
Tools & technologies
What we do.
Core methods we apply in cancer genomics.
Somatic variant calling
Mutect2, Strelka and consensus calling with rigorous filtering.
Driver & signature analysis
Driver detection and decomposition of mutational signatures.
TMB & MSI profiling
Immunotherapy-relevant biomarkers from sequencing data.
Subtyping
PAM50 (breast) and CMS (colorectal) molecular classification.
Copy-number & structural
CNV, amplifications, deletions and fusions across the genome.
Survival modelling
Linking molecular features to outcome with Kaplan–Meier and Cox models.
From data to insight.
How a cancer genomics project flows end to end.
Tumour/normal
paired sequencing
Somatic calling
Mutect2 · Strelka
Drivers
signatures · hotspots
Subtype
PAM50 · CMS
Associate
TMB · MSI · CNV
Outcome
survival & therapy
Publication-grade figures.
Interactive, live-rendered visualisations used in cancer genomics.
Where we go deep.
Pan-cancer cohorts
Large multi-study analyses spanning TCGA, MSK-IMPACT, AACR GENIE and CPTAC.
Tumour heterogeneity
Subclonal structure and how it drives resistance.
Therapy & resistance
Molecular correlates of response and relapse.
Questions we answer.
A few of the things people ask about cancer genomics — and our short answers. Ask CGB-AI for more.
What is a driver mutation?
A driver confers a selective growth advantage; most mutations are passengers. We use recurrence, functional impact and signatures to tell them apart.
Why does TMB matter?
High tumour mutational burden often predicts better immunotherapy response — a clinically actionable, sequencing-derived biomarker.
Publications in Cancer Genomics.
Drawn from our full record of 173 papers, filtered to this area.
Start a cancer genomics project.
Tell us the biological question and the data you have — we will map out an approach.