What Genomic Sequencing Has Taught Us About Tumor Mutations
By Satvika Kodali
Cancer was once viewed as a black box, we knew cells grew uncontrollably, but not why. Over the last two decades, advances in genomic sequencing have opened that box, letting scientists read the complete DNA of tumors and compare it to normal cells. This has transformed our understanding of cancer’s origins and revealed just how diverse and complex it is. Two landmark 2013 papers: Cancer Genome Landscapes by Bert Vogelstein and colleagues in Science, and Lessons from the Cancer Genome by Levi Garraway and Eric Lander in Cell helped summarize what we’ve learned from sequencing thousands of tumors. Here’s what those studies, and the decade of research they inspired, have taught us.
Key Terms to Know
Before diving in, let’s define a few important terms that often come up in cancer genomics:
-
SNPs (Single Nucleotide Polymorphisms):
A SNP (pronounced “snip”) is a change in a single DNA base. For example, one person has an A while another has a G at the same spot. Most SNPs are harmless and simply make us genetically unique. In cancer research, we often look for somatic mutations, which are similar one-base changes but occur in tumor cells rather than being inherited. -
Driver Genes and Driver Mutations:
A driver gene is a gene that, when mutated, helps cause or “drive” cancer. These mutations give the cell a growth advantage allowing it to divide faster or avoid dying when it should. Not all mutations matter, though many are passenger mutations, just random changes that don’t affect cancer growth. -
Oncogenes:
Oncogenes are a class of driver genes that normally promote cell growth in a controlled way. When they’re mutated or overactive, they can push cells into constant division. Classic examples include KRAS and EGFR. -
Tumor-Suppressor Genes:
These genes do the opposite, they act like brakes that stop cells from dividing too fast. When tumor-suppressor genes like TP53 or BRCA1 are inactivated, cancer cells lose a key layer of control.
What Sequencing Has Revealed
1. Every Tumor Has a Unique “Mutation Landscape”
When researchers began sequencing tumors, they found that each cancer type and even each patient’s tumor carries a unique set of mutations. Still, certain genes appear over and over again.
Vogelstein and his team described this as a “landscape” with mountains (genes commonly mutated across many cancers) and hills (genes mutated less frequently).
Most tumors contain hundreds or thousands of mutations, but only a few, often 2 to 8, are true drivers. The rest are passengers, reflecting the random DNA damage that accumulates over time.
2. Driver Mutations Cluster in Core Pathways
Even though the specific genes that mutate differ between patients, they often affect the same biological pathways. These include:
- Cell fate: how a cell decides what type of cell to become
- Cell survival: signals that tell a cell when to live or die
- Genome maintenance: systems that repair DNA damage
This helps explain why different mutations can lead to similar cancer behaviors: they disrupt the same fundamental processes, just in different ways.
3. Tumors Are Incredibly Diverse and Evolve Over Time
Sequencing has shown that a single tumor isn’t one uniform mass, it’s a mix of cell populations, or subclones, each with slightly different mutations.
As the cancer grows (and especially during treatment), some subclones die off while others survive and expand. This ongoing evolution makes cancer treatment challenging, since resistant clones can re-emerge later.
4. Mutation Burden Varies Widely
Some cancers, like skin and lung cancers, have thousands of mutations because they’re exposed to mutagens like UV light or cigarette smoke. Others, like childhood cancers, may have only a few dozen.
Tumors with defects in DNA repair often have hypermutated genomes, which can create more targets for the immune system, a fact that’s now being used in immunotherapy.
5. New Types of Cancer Genes Keep Emerging
Garraway and Lander’s paper highlighted how sequencing has revealed genes we never thought were involved in cancer: genes related to chromatin remodeling, RNA splicing, and metabolism, for example.
This expanded the definition of a “cancer gene” beyond the classic oncogenes and tumor suppressors, showing that almost any cellular process can be hijacked during tumor development.
6. Toward Precision Oncology
The biggest practical impact of all this sequencing is precision medicine: matching treatments to a patient’s specific mutations.
For example:
- A tumor with a mutated EGFR gene might respond to EGFR inhibitors.
- A tumor with a BRCA1 defect might be sensitive to PARP inhibitors.
However, because tumors evolve and have multiple drivers, a single targeted therapy isn’t always enough. Researchers are now exploring combination treatments and adaptive strategies that account for tumor evolution.
Why This Matters
Understanding tumor genomics doesn’t just help design better treatments, it also shows how biology operates as a dynamic, data-driven system.
As sequencing becomes cheaper and faster, every new dataset brings us closer to:
- Identifying new drug targets
- Understanding resistance mechanisms
- And improving early detection by recognizing mutation patterns in blood or tissue samples
For students and researchers alike, this field is a perfect mix of biology, computer science, and engineering, turning raw data into life-saving insights.
Conclusion
From the first cancer genomes sequenced in the early 2000s to today’s massive international databases, one thing is clear: genomic sequencing changed the way we think about cancer.
We now know that:
- Most tumors have a handful of key driver mutations.
- These mutations disrupt a few major biological pathways.
- Tumors evolve continuously, leading to genetic diversity.
- And while precision medicine is promising, it’s still a work in progress.
The journey from mutation discovery to actual cures is far from over, but genomic sequencing has given us the map and every base pair brings us closer to understanding how to stop cancer at its source.
References
- Vogelstein, B. et al. (2013). Cancer Genome Landscapes. Science, 339(6127), 1546–1558.
- Garraway, L.A., & Lander, E.S. (2013). Lessons from the Cancer Genome. Cell, 153(1), 17–37.