SciClone: Inferring Clonal Architecture and Tracking the Spatial and Temporal Patterns of Tumor Evolution
By Christopher A. Miller, B. S. White, N. Dees, Malachi Griffith, J. Welch, Obi L. Griffith, R. Vij, M. Tomasson, T. Graubert, M. Walter and 7 more. PLoS Comput. Biol., 2014
443 citations recorded by the source corpus.
The sensitivity of massively-parallel sequencing has confirmed that most cancers are oligoclonal, with subpopulations of neoplastic cells harboring distinct mutations. A fine resolution view of this clonal architecture provides insight into tumor heterogeneity, evolution, and treatment response, all of which may have clinical implications. Single tumor analysis already contributes to understanding these phenomena. However, cryptic subclones are frequently revealed by additional patient samples (e.g., collected at relapse or following treatment), indicating that accurately characterizing a tumor requires analyzing multiple samples from the same patient. To address this need, we present SciClone, a computational method that identifies the number and genetic composition of subclones by analyzing the variant allele frequencies of somatic mutations. We use it to detect subclones in acute myeloid leukemia and breast cancer samples that, though present at disease onset, are not evident from a single primary tumor sample. By doing so, we can track tumor evolution and identify the spatial origins of cells resisting therapy.
Subfields: Cancer Research, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/e771e0491f677cd89359226840b612195a5ca77e
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In Cancer Research
In Molecular Biology
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Citations in the corpus
- Cancer Evolution: Mathematical Models and Computational Inference (2014, methodology): “An alternative is the work of Miller et al. (2014) which uses a variational Bayesian mixture model for subclone identification.”
- Clonal Heterogeneity and Tumor Evolution: Past, Present, and the Future. (2017, unknown): “…majority of tools focus on somatic point mutations, and either restrict the analysis to copy neutral regions of the genome or make the assumption that a single mutation will be present at the same copy number state in every cancer cell (Carter et al., 2012; Miller et al., 2014; Roth et al., 2014).”
- Tumor immune microenvironment characterization in clear cell renal cell carcinoma identifies prognostic and immunotherapeutically relevant messenger RNA signatures (2016, result): “We performed clonality assessment on the TCGA ccRCC cohort using SciClone [60] (see “Clonality assessment” in “Methods”); and consistent with the NSCLC study, found that more clonal tumors (i.”