Description

Cancer effect size is a metric that quantifies the intensity of natural selection on somatic mutations in cancer cell lineages. The output of CancereffectsizeR primarily includes quantified cancer effect sizes (selection intensities) of somatic variants, i.e., how much each mutation contributes to cancer cell proliferation and survival in a given tumor cohort (it produces site-specific mutation rates). Because of the variants being single-nucleotide variants (SNVs), users will see that the endpoint coordinate on the chromosome is one greater than the start position.

Each variant is shown with its cancer effect size (scaled selection coefficient, γ). For display and shading, γ is transformed as s = 10 · log₁₀(γ), so neutral selection (γ = 1) corresponds to s = 0 and larger s means stronger positive selection. The inverse is γ = 10^s/10.

You do not need to back-transform anything. Hovering over a variant shows its consequence, the cancer effect size γ, the transformed score s (labeled with an asterisk so it is not mistaken for γ), and the number of TCGA patients carrying the variant. Example from the breast cancer track: TP53_R175H (missense) | cancer effect size gamma = 10,000 | transformed score* = 40 | TCGA patients with variant: 19 | *transformed score s = 10*log10(gamma), not gamma.

Why scores are never negative. The bigBed format requires the standard BED score field to be a whole number from 0 to 1000. The transformed score is therefore rounded, and negative values (γ < 1, negative selection) would be stored as 0. Variants whose original estimate was recorded only as γ < 10 (8 variants across all tracks) are stored with a score of 0 and shown as “gamma = <10”. Because s is rounded, γ is approximate (within about ±12%).

Consistent with established cancereffectsizeR practice, this track hub includes only recurrently mutated single-nucleotide variants (i.e., variants observed in two or more tumors within a cohort). Reliable estimation of site-specific selection intensity requires sufficient observations at a site to distinguish selective signal from stochastic variation in the underlying neutral mutation process; non-recurrent (singleton) variants are therefore not represented in these tracks (Mandell et al., Cancer Research, 2023).

You can search for specific cancer effect size variants by typing the gene_variant names (e.g., KIF1B_R1320W, PRAMEF1_R434W) in the UCSC Genome Browser search box. These names correspond to the mutation identifiers indexed in this track, enabling quick navigation to relevant variants.

Silent (synonymous) and other variant classes

Each variant is labeled with its consequence: missense, silent (synonymous, e.g. CLCNKA_I436I), nonsense, stop_lost or noncoding. The label appears on hover and on the item details page. To hide silent mutations, click the track name (or right-click the track and choose “Configure”), uncheck “silent” under Variant consequence, and click Submit. Silent variants are kept because cancereffectsizeR estimates their effect sizes in the same way as other variants; a high γ for a silent variant may reflect selection acting through splicing or gene regulation, or an underestimated local mutation rate, and should be interpreted with that in mind.

Number of variants per track

Every track applies the same rule: only variants seen in at least two TCGA patients of the cohort are shown. The number of variants therefore differs between cohorts mainly because of cohort size and the number of mutations per tumor; for example, lung adenocarcinomas carry many more mutations per tumor than breast carcinomas, so more sites recur.

Quick tutorial: zooming with drag-and-select

1. Zoom into a region: click and drag across the Base Position ruler at the top of the image, then click Zoom In in the pop-up box. Anywhere else in the image, hold the Shift key while dragging.
2. Dragging without Shift scrolls the view left or right; it does not select. Dragging a track’s side label up or down reorders the cohort tracks.
3. If no pop-up appears: it may have been switched off with “Don’t show this again”. Re-enable it under View → Configure Browser (keyboard shortcut: c then f), and make sure “Enable advanced javascript features” is on.
4. Alternatives that always work: type a gene (TP53), a variant (TP53_R175H) or coordinates (chr17:7,668,402-7,687,550) in the search box, or use the zoom buttons above the image.

20 TCGA Cancer Cohorts Included

Display Conventions and Configuration

The track is presented in bigBed format. The file has the following fields:

• chrom – Reference sequence chromosome
• chromStart – Zero-based start position of the variant (SNV)
• chromEnd – End position (exclusive) of the variant (SNV)
• name – Variant identifier, GENE_AACHANGE (coding) or chr:pos_ref>alt (noncoding)
• score – Transformed score s = 10 · log₁₀(γ), rounded (negative values stored as 0)
• gamma – Cancer effect size γ = 10^s/10 (“<10” where only an upper bound is available)
• n_patients – Number of TCGA patients in the cohort carrying the variant
• consequence – missense, silent, nonsense, stop_lost or noncoding

Methods

The cancer effect size in CancereffectsizeR is calculated by estimating the neutral mutation rate at each variant site using tissue-specific gene mutation rates and mutational signatures, then comparing the observed mutation rate to this neutral rate. The resulting score, called the selection intensity or cancer effect size, reflects the relative proliferative advantage that a mutation confers to cancer cells.

Contact

Please feel free to contact Dr. Jeffrey P. Townsend (jeffrey.townsend@yale.edu) with any questions and/or concerns regarding this track.

References

Mandell JD, Cannataro VL, Townsend JP. Estimation of neutral mutation rates and quantification of somatic variant selection using cancereffectsizeR. Cancer Research. 2023 Feb 15; 83(4):500–505. doi:10.1158/0008-5472.CAN-22-1508