PPubMed26 Aug 2014Cited 72×

SubcloneSeeker: a computational framework for reconstructing tumor clone structure for cancer variant interpretation and prioritization

Yi Qiao, Aaron R Quinlan, Amir A Jazaeri, Roeland GW Verhaak, David A Wheeler, Gabor T Marth

Abstract

'David A Wheeler' 'Gabor T Marth'] Many tumors are composed of genetically divergent cell subpopulations. We report SubcloneSeeker, a package capable of exhaustive identification of subclone structures and evolutionary histories with bulk somatic variant allele frequency measurements from tumor biopsies. We present a statistical framework to elucidate whether specific sets of mutations are present within the same subclones, and the order in which they occur. We demonstrate how subclone reconstruction provides crucial information about tumorigenesis and relapse mechanisms; guides functional study by variant prioritization, and has the potential as a rational basis for informed therapeutic strategies for the patient. SubcloneSeeker is available at: [https://github.com/yiq/SubcloneSeeker](). Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0443-x) contains supplementary material, which is available to authorized users.

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SubcloneSeeker exhaustively enumerates tumor subclone structures from cell‑prevalence data, often narrowing to a unique solution and enabling mutation prioritization, exemplified by insights into ovarian‑cancer chemoresistance.

What’s new

i.Introduces a novel algorithm that uses cell‑prevalence values (derived from allele frequencies) to infer tumor subclonal structure without needing linked SNV data
ii.Exhaustively enumerates all viable subclone structures, eliminating bias from parameter choices or prior distributions
iii.Demonstrates that incorporating multiple samples (e.g., primary and relapse) dramatically reduces the solution space, often to a single unique structure
iv.Provides methods to extract mutation co‑localization and timing information for functional prioritization
v.Applies the framework to ovarian cancer, revealing clonally driven chemoresistance and the temporal order of mutations

How they did it

The method converts allele‑frequency measurements into cell‑prevalence values for each unlinked somatic mutation, then exhaustively enumerates every subclone configuration consistent with those prevalences. By comparing data from multiple tumor phases (e.g., primary and relapse), inconsistent structures are pruned, yielding a compact solution set without relying on arbitrary parameters.

What they found

Simulation and experimental validation show the number of viable solutions is far smaller than the theoretical maximum
When primary and relapse samples are combined, the solution space often collapses to a single, unique subclone structure
Co‑localization and timing predictions correlate well with functional data, aiding mutation prioritization
In serous ovarian cancer, the analysis confirms chemoresistance is driven by specific clones, with 80% of tumors chemosensitive and 20% progressing despite platinum therapy

Caveats

✕If only a single bulk sample is available, the solution space may remain large, limiting interpretability
✕Accurate conversion of allele frequencies to cell‑prevalence values is required; measurement noise can affect results
✕Current implementation focuses on point‑mutation data and may not fully capture structural variants or complex copy‑number events
✕Exhaustive enumeration can become computationally demanding for tumors with very high numbers of subclones

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