What is Cancer?

Under scientific review

This introductory page is under scientific review. Use it for orientation, not clinical decision-making.

Cancer is fundamentally a disease of the genome.At its core, cancer occurs when normal cells acquire genetic alterations that allow them to grow and divide uncontrollably. Sources: [1]

Note: Information reflects 2025 standards. Numbers and code examples are illustrative; actual values vary by cancer type and study.

The Simple Definition

Cancer is a group of diseases characterized by uncontrolled cell growth and the ability to invade and spread to other parts of the body. Sources: [2]

But for developers and data scientists, we need a more precise, technical understanding.

The Technical Definition

As a teaching mnemonic for developers, cancer can be sketched as:

Normal cell + somatic/genetic alterations + selection over time + tissue environment -> malignant clone

This is not a formal mathematical model. It is a simplified pointer toward clonal evolution and multistep carcinogenesis: cells acquire heritable changes, variants with growth/survival advantages expand, and the tissue microenvironment shapes which clones persist. Sources: [3]

Key Characteristics

1. Genomic Instability

Cancer cells have unstable genomes, meaning they accumulate mutations faster than normal cells. While a healthy cell has an absurdly low mutation rate thanks to DNA repair mechanisms, a tumor cell turns off these brakes (repair deficiency) and accumulates errors exponentially. Sources: [4]

2. Clonal Evolution

Cancer develops through a process similar to Darwinian evolution:

1. Mutation occurs in a single cell
2. Selection favors cells with growth advantages
3. Expansion of the fittest clone
4. Repeat the process

3. Heterogeneity

Cancer is not a single disease but hundreds of different diseases, each with unique:

• Genetic profiles
• Behavior patterns
• Treatment responses

Types of Cancer

By Tissue of Origin

Type	Origin	Examples
Carcinomas	Epithelial tissues	Breast, lung, colon cancer
Sarcomas	Connective tissues	Bone, muscle cancers
Leukemias	Blood cells	ALL, AML, CLL
Lymphomas	Lymphatic system	Hodgkin's, non-Hodgkin's

By Genetic Profile

Modern cancer classification increasingly relies on molecular characteristics: Sources: [6]

Traditional: Lung Cancer
Modern: EGFR-mutant Lung Adenocarcinoma

The Data Perspective

For data scientists, cancer presents several interesting characteristics:

High-Dimensional Data

• Genomic: about 20,000 protein-coding genes, plus many non-coding RNAs, pseudogenes, regulatory elements, and structural features Sources: [7]
• Transcriptomic: Expression levels of all genes
• Proteomic: Protein abundance
• Clinical: Demographics, treatments, outcomes

Sparse and Noisy

• Most genes are not mutated in any given cancer
• Measurement noise is significant
• Missing data is common

Heterogeneous

• Different cancer types have different patterns
• Even within types, significant variation exists
• Batch effects are common in genomic data

Why This Matters for Your Code

Understanding cancer biology helps you:

1. Choose appropriate features for machine learning models
2. Interpret results in biological context
3. Validate findings against known biology
4. Communicate effectively with domain experts

Example: Tumor Mutational Burden (TMB)

For data scientists, mutation burden is not a binary value, but a continuous spectrum that varies dramatically depending on the tumor's etiology (origin). Sources: [8]

• Pediatric Cancers: Generally have a low mutational burden (< 1 mutation per megabase). They are often driven by a single devastating gene fusion, occurring in tissues that haven't had time to suffer environmental damage.
• Standard Adult Cancers (e.g., Breast, Prostate): Intermediate burden (1 to 10 mut/Mb). Result from decades of natural cell aging.
• Carcinogen-Induced Cancers (e.g., Lung, Melanoma): Extremely high burden (10 to 100+ mut/Mb). Cigarette smoke (lung) and UV radiation (melanoma) relentlessly shred the DNA over time.

The Clinical Application (Immunotherapy): If a melanoma tumor has 30 mutations per megabase, it will produce highly abnormal proteins. The immune system recognizes these proteins as "Foreign" (Neoantigens) and attacks the tumor. This is why algorithms that predict neoantigens from sequencing data form the basis for developing Cancer Vaccines (mRNA) and Cell Therapies based on high TMB. Sources: [9]

Next Steps

Now that you understand what cancer is, let's explore:

• The Genetic Basis of Cancer - How DNA changes drive cancer
• Cancer Hallmarks - The key characteristics of cancer cells
• Tumor Progression - How cancer develops over time

Key Takeaways

• Cancer is a genetic disease caused by accumulated mutations Sources: [1]
• It's not one disease but hundreds of different diseases
• Understanding the biology is crucial for effective data analysis
• The genomic perspective provides a framework for computational approaches

References

1. Hanahan, D., & Weinberg, R.A. (2011). Hallmarks of cancer: The next generation. Cell, 144(5), 646–674. doi:10.1016/j.cell.2011.02.013.
2. National Cancer Institute / WHO patient materials definitions of cancer.
3. Nowell PC. The clonal evolution of tumor cell populations. Science. 1976;194(4260):23-28. doi:10.1126/science.959840.
4. Negrini, S., Gorgoulis, V.G., & Halazonetis, T.D. (2010). Genomic instability—an evolving hallmark of cancer. Nature Reviews Molecular Cell Biology, 11, 220–228. https://www.nature.com/articles/nrm2858
5. Martincorena I, Raine KM, Gerstung M, et al. Universal patterns of selection in cancer and somatic tissues. Cell. 2017;171(5):1029-1041.e21. doi:10.1016/j.cell.2017.09.042; Lynch M. Rate, molecular spectrum, and consequences of human mutation. PNAS. 2010;107(3):961-968. https://pmc.ncbi.nlm.nih.gov/articles/PMC2914198/
6. EGFR‑mutant lung adenocarcinoma as a precision oncology subtype (TLCR 2022): https://tlcr.amegroups.com/article/view/83462/html.
7. Harrow J, Frankish A, Gonzalez JM, et al. GENCODE: the reference human genome annotation for The ENCODE Project. Genome Research. 2012;22(9):1760-1774. doi:10.1101/gr.135350.111.
8. Frontiers in Immunology (2023) high‑TMB distributions across TCGA: https://pmc.ncbi.nlm.nih.gov/articles/PMC9998480/; Lawrence, M.S., et al. (2013/2016) TCGA mutation burden patterns: https://pmc.ncbi.nlm.nih.gov/articles/PMC4930685/
9. Journal for ImmunoTherapy of Cancer (2025) 13:e010311 (8440 pts) https://jitc.bmj.com/content/13/2/e010311; Nature Medicine (2023) https://www.nature.com/articles/s41591-022-02163-w; Meta‑analysis (2022) https://pmc.ncbi.nlm.nih.gov/articles/PMC8956924/