AMED3001 Cancer
Cancer Evolution & Molecular Technologies
Week 8 frames cancer as an evolving system and introduces the technologies used to study it — the basis for the exam's data-interpretation questions. It covers multistep carcinogenesis and the colon adenoma-to-carcinoma sequence, clonal (Darwinian) evolution versus the cancer-stem-cell theory of heterogeneity, and the sequencing and -omics toolkit (whole-genome, whole-exome and targeted panels, RNA-seq, single-cell and spatial methods, liquid biopsy). Comparing technologies and reading their outputs is examinable in the final (50%, confirm on Canvas).
What this chapter covers
- 01Multistep carcinogenesis; transformation needs cooperating mutations (myc + ras together, not alone)
- 02Colon adenoma-to-carcinoma sequence: APC loss → KRAS activation → SMAD4/other TSG loss → p53 loss
- 03Tumour heterogeneity theories: Darwinian clonal expansion vs cancer-stem-cell; drug-resistant clones evolve under therapy
- 04Sequencing: Sanger vs next-generation; whole-genome (>30×) vs whole-exome (>50-100×) vs targeted panel (>500×)
- 05RNA-seq for differential expression → heatmaps (Row Z-score, red up/blue down) and volcano plots
- 06Single-cell RNA-seq and spatial profiling; clustering with t-SNE/UMAP
- 07Liquid biopsy: cfDNA (all sources) vs ctDNA (tumour-derived); ctDNA absent post-treatment = good response
Order the genetic events in the colon adenoma-to-carcinoma sequence
- +1Normal colonic epithelium → early adenoma: loss of APC (a tumour-suppressor gene), releasing β-catenin and initiating clonal expansion.
- +1Early → intermediate adenoma: activation of KRAS (an oncogene, gain-of-function), driving proliferation.
- +1Intermediate → late adenoma: loss of further tumour-suppressor genes such as SMAD4 (in the TGF-β pathway).
- +1Late adenoma → carcinoma: loss of p53 (a tumour-suppressor gene), removing the DNA-damage/apoptosis safeguard, followed by invasion and metastasis.
Key terms
- Clonal (Darwinian) evolution
- The theory that tumours evolve through successive clonal expansions as new mutations confer selective advantages, generating intratumour heterogeneity and drug-resistant clones.
- Intratumour heterogeneity
- Genetic and phenotypic diversity among cells within a single tumour; it lets a drug kill one clone while resistant clones survive and regrow.
- Next-generation sequencing
- Massively parallel DNA sequencing; scopes trade breadth for depth — whole-genome (>30×), whole-exome (>50-100×) and targeted panels (>500×, highest accuracy, cheapest, fastest).
- RNA-seq
- Sequencing of cDNA from cellular RNA to quantify differential gene expression; outputs include clustered heatmaps (Row Z-score) and volcano plots.
- ctDNA
- Circulating tumour DNA — the tumour-derived fraction of cell-free DNA in blood; its absence after treatment signals good response, its presence predicts recurrence.
- Adenoma-carcinoma sequence
- The stepwise colorectal progression model: APC loss → KRAS activation → SMAD4/other TSG loss → p53 loss, illustrating multistep carcinogenesis.
Cancer Evolution & Molecular Technologies FAQ
Why can one mutation rarely cause cancer on its own?
Transformation usually needs cooperating mutations: in classic experiments myc or ras alone did not make a tumour, but myc plus ras did. Cancer is multistep, accumulating oncogene activations and tumour-suppressor losses — as the colon adenoma-to-carcinoma sequence shows.
What is the difference between whole-genome, whole-exome and targeted-panel sequencing?
They trade breadth for depth and cost. Whole-genome reads everything at lower depth (>30×) and highest cost; whole-exome reads all coding exons (>50-100×) more cheaply; a targeted panel reads a few genes very deeply (>500×) with the highest accuracy, fastest turnaround and lowest cost. Choosing the right one is a common short answer.
What does ctDNA tell you after treatment?
Circulating tumour DNA is the tumour-derived portion of cell-free DNA. If ctDNA is undetectable after treatment it signals a good response; if it is detected it predicts recurrence, often earlier than clinical symptoms — the basis of minimal-residual-disease monitoring.
Can AI help me with the sequencing technologies in AMED3001?
Yes — Sia can compare the sequencing scopes, explain how RNA-seq produces a heatmap, and quiz you on the adenoma-carcinoma sequence; it explains the reasoning and checks yours rather than doing the graded task.
Exam move
Learn the colon adenoma-to-carcinoma sequence as an ordered chain with each gene labelled TSG or oncogene, and build a compact table comparing whole-genome, whole-exome and targeted-panel sequencing on breadth, depth, cost and turnaround. Because this week grounds the exam's data-interpretation questions, connect each technology to the output you must read (RNA-seq → heatmap/volcano; scRNA-seq → UMAP). Ask Sia to quiz the sequence and the technology comparison; keep it warm for the final and confirm details on Canvas.
Working through Cancer Evolution & Molecular Technologies in AMED3001? Sia is AskSia’s AI Biology tutor — ask any AMED3001 Cancer Evolution & Molecular Technologies question and get a clear, step-by-step explanation grounded in how AMED3001 is taught and assessed. Read this chapter free, then take your hardest questions to Sia.
