Oncogenes and Tumor Suppressors
Under scientific review
This introductory page is under scientific review. Use it for orientation, not clinical decision-making.
Cancer is fundamentally a disease of gene regulation. Understanding oncogenes and tumor suppressors—the yin and yang of cancer biology—is essential for understanding how normal cells become cancerous.
Skeptic's corner: Not all mutations are created equal. Driver mutations cause cancer; passenger mutations are just along for the ride. The challenge is distinguishing between them and understanding their context.
The Two-Hit Hypothesis
Knudson's Model (1971)
- Tumor suppressors: Require both alleles to be inactivated
- Oncogenes: Require only one allele to be activated
- Implications: Different mutation patterns and inheritance
Modern Understanding
- Multiple hits: Cancer requires 4-6 driver mutations
- Temporal order: Some mutations must occur before others
- Context matters: Same mutation can have different effects
Oncogenes: The Accelerators
Oncogenes are mutated versions of normal genes (proto-oncogenes) that promote cancer when activated.
Mechanisms of Activation
Point Mutations
- Example: RAS family (KRAS, NRAS, HRAS)
- Effect: Constitutive activation
- Frequency: ~30% of all cancers
Gene Amplification
- Example: MYC, HER2, CCND1
- Effect: Overexpression
- Frequency: ~15% of all cancers
Chromosomal Translocations
- Example: BCR-ABL, MYC-IGH
- Effect: Fusion proteins or overexpression
- Frequency: ~10% of all cancers
Epigenetic Changes
- Example: Promoter hypomethylation
- Effect: Overexpression
- Frequency: Variable
Tumor Suppressors: The Brakes
Tumor suppressors are genes that normally prevent cancer by controlling cell growth, DNA repair, and apoptosis.
Mechanisms of Inactivation
Point Mutations
- Example: p53, RB, APC
- Effect: Loss of function
- Frequency: ~50% of all cancers
Deletions
- Example: CDKN2A, PTEN
- Effect: Complete loss
- Frequency: ~20% of all cancers
Epigenetic Silencing
- Example: Promoter hypermethylation
- Effect: Transcriptional silencing
- Frequency: ~15% of all cancers
Key Oncogenes
RAS Family
- Genes: KRAS, NRAS, HRAS
- Function: GTPase signaling
- Mutations: G12V, G13D, Q61H
- Cancers: Pancreatic, colorectal, lung
- Therapeutic status: Difficult to target
MYC
- Function: Transcription factor
- Activation: Amplification, translocation
- Cancers: Burkitt lymphoma, neuroblastoma
- Therapeutic status: Experimental
HER2 (ERBB2)
- Function: Receptor tyrosine kinase
- Activation: Amplification, overexpression
- Cancers: Breast, gastric
- Therapeutic status: Targeted (Trastuzumab)
PIK3CA
- Function: PI3K catalytic subunit
- Mutations: H1047R, E545K
- Cancers: Breast, colorectal, endometrial
- Therapeutic status: Targeted (Alpelisib)
Key Tumor Suppressors
p53 (TP53)
- Function: Master regulator, apoptosis
- Mutations: R175H, R248Q, R273H
- Cancers: Most cancer types
- Therapeutic status: Experimental
RB (RB1)
- Function: Cell cycle control
- Mutations: Deletions, point mutations
- Cancers: Retinoblastoma, osteosarcoma
- Therapeutic status: CDK4/6 inhibitors
APC
- Function: Wnt pathway regulation
- Mutations: Truncating mutations
- Cancers: Colorectal, desmoid tumors
- Therapeutic status: Wnt inhibitors
PTEN
- Function: PI3K pathway regulation
- Mutations: Deletions, point mutations
- Cancers: Prostate, endometrial, glioblastoma
- Therapeutic status: PI3K inhibitors
Laboratory Techniques
Mutation Detection
Detecting these mutations requires sequencing tumor DNA and comparing it to a matched normal sample (germline).
- Variant Calling: Tools like GATK Mutect2 or Strelka2 identify somatic variants from BAM files and output a VCF (Variant Call Format).
- Annotation: Raw VCFs only contain genomic coordinates (e.g.,
chr17:7577120 G>A). Tools like VEP (Variant Effect Predictor), SnpEff, or Funcotator map these coordinates to genes (e.g., TP53) and predict the protein-level effect (e.g., R273H). - Filtering: Variants are filtered against databases like COSMIC, ClinVar, and gnomAD to distinguish true cancer driver mutations from passenger mutations or rare germline polymorphisms.
Expression Analysis
- qPCR: Gene expression levels
- Western blot: Protein expression
- Immunohistochemistry: Tissue expression
- RNA-seq: Genome-wide expression
Clinical Relevance
Diagnostic Markers
- HER2 amplification: Breast cancer treatment
- KRAS mutations: Colorectal cancer treatment
- EGFR mutations: Lung cancer treatment
- BRCA1/2 mutations: Hereditary cancer risk
Therapeutic Targets
Oncogene Targeting
- HER2: Trastuzumab, Pertuzumab
- EGFR: Erlotinib, Gefitinib
- BRAF: Vemurafenib, Dabrafenib
- ALK: Crizotinib, Ceritinib
Targeting Downstream of Tumor Suppressors
- p53 loss: MDM2 inhibitors (Experimental), synthetic lethality approaches
- RB loss: Cells lose reliance on CDK4/6. Note that CDK4/6 inhibitors actually require intact RB to function; they do not restore it.
- PTEN loss: PI3K/AKT/mTOR inhibitors (these target the pathway that becomes hyperactive due to PTEN loss, they do not "restore" PTEN)
Research Applications
Drug Discovery
- Target identification: Oncogenes, tumor suppressors
- Biomarker development: Mutation panels
- Resistance mechanisms: Secondary mutations
Precision Medicine
- Molecular profiling: Tumor sequencing
- Targeted therapy: Mutation-guided treatment
- Clinical trials: Biomarker-driven studies
Practical Considerations
Sample Requirements
- Tumor tissue: Fresh or FFPE
- Normal tissue: Germline comparison
- Blood: Germline DNA
Data Analysis
- Variant calling: GATK, Mutect2
- Annotation: VEP, ANNOVAR
- Pathogenicity: SIFT, PolyPhen, CADD
FAQ
Q: Why are tumor suppressors harder to target than oncogenes? A: Oncogenes undergo gain-of-function mutations, creating hyperactive proteins that can often be inhibited by small molecules or antibodies. Tumor suppressors undergo loss-of-function mutations. It is pharmacologically extremely difficult to "restore" or replace a missing or truncated protein, making them notoriously hard to target directly.
Q: Can we restore tumor suppressor function? A: This is challenging because it requires replacing the entire gene, but we can target the pathways they control.
Q: How do we know if a mutation is a driver? A: Driver mutations are recurrent, functionally significant, and associated with cancer development.
References (APA Style)
Vogelstein, B., Papadopoulos, N., Velculescu, V. E., Zhou, S., Diaz, L. A., & Kinzler, K. W. (2013). Cancer genome landscapes. Science, 339(6127), 1546-1558.
Hanahan, D., & Weinberg, R. A. (2011). Hallmarks of cancer: The next generation. Cell, 144(5), 646-674.
Kandoth, C., McLellan, M. D., Vandin, F., Ye, K., Niu, B., Lu, C., ... & Ding, L. (2013). Mutational landscape and significance across 12 major cancer types. Nature, 502(7471), 333-339.
Contributing
- Review existing content for accuracy
- Add missing oncogenes or tumor suppressors
- Create practical examples and code snippets
- Cite recent research and clinical trials
This article provides the foundation for understanding how oncogenes and tumor suppressors drive cancer development. Master these concepts to understand cancer biology and targeted therapy.