Cancer Fundamentals
Welcome to the fundamentals section! Here you'll find the essential concepts to understand cancer from a technical and scientific perspective — with a pragmatic lens for developers, data scientists and engineers.
Skeptic's corner: Correlation ≠ causation; in biology, effect sizes, confounders and experimental design matter. When in doubt, demand controls and reproducibility.
What you'll learn
- Cell & Molecular Biology: Cell cycle, checkpoints, DNA repair, apoptosis, senescence
- Genetics & Genomics: Oncogenes vs tumor suppressors, mutational processes, structural variants
- Epigenetics: DNA methylation, histone marks, chromatin remodeling, non‑coding RNAs
- Biochemistry & Metabolism: Warburg effect, one‑carbon metabolism, ROS, metabolic reprogramming
- Tumor Microenvironment (TME): Stroma, hypoxia, angiogenesis, immune infiltration
- Immunology: Immune surveillance/escape, checkpoints (PD‑1/PD‑L1, CTLA‑4), CAR‑T basics
- Clinical Translation: Diagnostics, staging (TNM), biomarker types, treatment classes & resistance
- Bioinformatics: From FASTQ to VCF, expression, single‑cell & spatial, radiomics
- Data Ethics: Bias, leakage, privacy (LGPD), FAIR data, reproducibility
Available Articles
What is cancer?Definitions, benign vs malignant, clonal evolution.Cell cycleHow normal checkpoints fail during tumorigenesis.DNA damage and repairMMR, BER, NER, HR, NHEJ, and genomic instability.Oncogenes and suppressorsDrivers, brakes, and the two-hit model.HallmarksThe 2000, 2011, and 2022 framework in one place.Tumor microenvironmentStroma, immune contexture, hypoxia, angiogenesis.Cancer metabolismWarburg effect, dependencies, metabolic targeting.Immuno-oncologyImmune surveillance, checkpoints, escape, CAR-T basics.FASTQ to variantsSequencing workflow from raw reads to annotated calls.Single-cell and spatialModern tumor atlases and cellular neighborhoods.BiomarkersAssays, validation, companion diagnostics.Clinical trials 101Trial phases, endpoints, bias, and translation.Veterinary oncologyCompanion animal cancer and comparative oncology.ML pitfallsLeakage, bias, confounding, and small-n large-p traps.Data governancePrivacy, consent, FAIR data, LGPD, reproducibility.
Who is this for?
- Developers/Engineers building tools for health & science
- Data scientists working with omics/clinical datasets
- Students from technical fields curious about biology
- Product people translating science into usable systems
Learning Path
Level 1 — Basic Concepts
- What is cancer? Neoplasia vs hyperplasia; benign vs malignant; clonal evolution
- Cell basics: organelles, cell cycle (G1/S/G2/M), checkpoints (p53, RB)
- DNA structure & flow of information (Central Dogma); transcription vs translation
Level 2 — Molecular Mechanisms
- Gene regulation: enhancers, TFs, epigenetic switches
- DNA damage & repair: MMR, BER, NER, HR vs NHEJ; genomic instability
- Oncogenic signaling: RTK–RAS–MAPK, PI3K–AKT–mTOR, WNT, TGF‑β, p53 axis
- Cell death & senescence: apoptosis, necroptosis, ferroptosis, cellular senescence
- Metabolism: glycolysis rewiring, glutamine addiction, lactate shuttle
Level 3 — Systems & Clinical
- Hallmarks of Cancer (2000) + 2011 expansion + New Dimensions (2022)
- Tumor Microenvironment: CAFs, ECM, angiogenesis (VEGF), hypoxia (HIF‑1α)
- Immuno‑oncology: antigen presentation, checkpoints, immune escape
- Diagnostics: IHC, FISH, NGS panels, liquid biopsy (ctDNA/CTCs)
- Therapeutics: chemo, targeted (kinase inhibitors), immunotherapy (CPI, CAR‑T, bispecifics)
- Resistance mechanisms: on‑target/off‑target, lineage plasticity, bypass tracks
Key Concepts
Hallmarks of Cancer (core)
- Sustaining proliferative signaling
- Evading growth suppressors
- Resisting cell death
- Enabling replicative immortality
- Inducing angiogenesis
- Activating invasion & metastasis
Enabling characteristics: Genomic instability & mutation; Tumor‑promoting inflammation. Added/expanded after 2000: 2011 added deregulating cellular energetics and avoiding immune destruction as emerging hallmarks, plus genomic instability and tumor-promoting inflammation as enabling characteristics; 2022 proposed unlocking phenotypic plasticity, non-mutational epigenetic reprogramming, polymorphic microbiomes, and senescent cells.
Cancer Types (by origin)
- Carcinomas (epithelial), Sarcomas (mesenchymal), Leukemias/Lymphomas (hematologic), CNS tumors.
Computational Foundations
From sample to insight (short pipeline)
- Wet lab → Data: DNA/RNA extraction → library prep → sequencing
- QC: FastQC/MultiQC; remove adapters, assess coverage/contamination
- Alignment/Quantification: BWA‑MEM/STAR; or pseudo‑alignment (Salmon)
- Variant Calling: GATK/Mutect2; germline vs somatic; CNV/SV callers
- Annotation: VEP/ANNOVAR; ClinVar, COSMIC, gnomAD
- Expression analysis: normalization, DE, GSEA; batch correction
- Single‑cell/spatial: cell calling, clustering, trajectory, neighborhood analysis
- Integration: omics + clinical; feature engineering; survival models
ML Tasks
- Subtype classification, survival (Cox, RSF), response prediction, radiomics
- Pitfalls: leakage, confounders (batch/site), class imbalance, optimistic CV, small‑n large‑p
Data & Governance
- LGPD: lawful bases, minimization, pseudonymization
- FAIR: findable, accessible, interoperable, reusable
- Reproducibility: containers, pinned versions, pipelines (Snakemake/Nextflow)
Practical Labs (hands‑on)
- Lab 1: QC and alignment of WES data → variant calling & annotation (toy dataset)
- Lab 2: RNA‑seq differential expression + pathway enrichment
- Lab 3: Build a basic survival model (Kaplan–Meier, Cox) with clinical data
- Lab 4: Single‑cell clustering and marker discovery
- Lab 5: Simple radiomics pipeline from DICOM to features to classifier
Provide datasets via internal mirrors or public repositories; include ready‑to‑run notebooks and Dockerfiles.
Glossary (quick)
- Oncogene: gene whose activated form drives tumorigenesis
- Tumor suppressor: gene whose loss of function enables cancer
- Driver vs passenger: causal vs incidental alterations
- TMB: tumor mutational burden (mut/Mb)
- MSI: microsatellite instability (defective MMR)
- ctDNA: circulating tumor DNA in plasma
FAQ
- Is every mutation important? No. Prioritize drivers (functional evidence, recurrence, pathway impact).
- Why do mouse results fail in humans? Model limitations (species, microenvironment, dosage, endpoints).
- Do AI models replace clinicians? No. They augment decisions; regulation and validation are mandatory.
References (APA Style)
- Hanahan, D., & Weinberg, R. A. (2011). Hallmarks of cancer: The next generation. Cell, 144(5), 646-674.
- Hanahan, D. (2022). Hallmarks of cancer: New dimensions. Cancer Discovery, 12(1), 31-46.
- Alberts, B., Johnson, A., Lewis, J., Morgan, D., Raff, M., Roberts, K., & Walter, P. (2015). Molecular biology of the cell (6th ed.). Garland Science.
- Weinberg, R. A. (2013). The biology of cancer (2nd ed.). Garland Science.
Style: Use APA format across the site (international standard). For journal articles: Author, A. A. (Year). Title of article. Journal Name, Volume(Issue), pages. For books: Author, A. A. (Year). Title of book (Edition). Publisher.
Contributing
- Review existing content for accuracy & clarity
- Add missing concepts, figures, glossaries
- Create notebooks/labs with deterministic pipelines
- Cite sources (ABNT) and avoid overclaiming
This section is the technical foundation for understanding cancer — built for builders. Keep it sharp, reproducible and clinically grounded.