TY - JOUR
T1 - PhySigs
T2 - 25th Pacific Symposium on Biocomputing, PSB 2020
AU - Christensen, Sarah
AU - Leiserson, Mark D.M.
AU - El-Kebir, Mohammed
N1 - Funding Information:
Acknowledgments. S.C. was supported by the National Science Foundation (grant: IIS 15-13629). M.E-K. was supported by the National Science Foundation (grant: CCF 18-50502).
PY - 2020
Y1 - 2020
N2 - Distinct mutational processes shape the genomes of the clones comprising a tumor. These processes result in distinct mutational patterns, summarized by a small number of mutational signatures. Current analyses of clone-specific exposures to mutational signatures do not fully incorporate a tumor's evolutionary context, either inferring identical exposures for all tumor clones, or inferring exposures for each clone independently. Here, we introduce the Tree-constrained Exposure problem to infer a small number of exposure shifts along the edges of a given tumor phylogeny. Our algorithm, PhySigs, solves this problem and includes model selection to identify the number of exposure shifts that best explain the data. We validate our approach on simulated data and identify exposure shifts in lung cancer data, including at least one shift with a matching subclonal driver mutation in the mismatch repair pathway. Moreover, we show that our approach enables the prioritization of alternative phylogenies inferred from the same sequencing data. PhySigs is publicly available at https://github.com/elkebir-group/PhySigs.
AB - Distinct mutational processes shape the genomes of the clones comprising a tumor. These processes result in distinct mutational patterns, summarized by a small number of mutational signatures. Current analyses of clone-specific exposures to mutational signatures do not fully incorporate a tumor's evolutionary context, either inferring identical exposures for all tumor clones, or inferring exposures for each clone independently. Here, we introduce the Tree-constrained Exposure problem to infer a small number of exposure shifts along the edges of a given tumor phylogeny. Our algorithm, PhySigs, solves this problem and includes model selection to identify the number of exposure shifts that best explain the data. We validate our approach on simulated data and identify exposure shifts in lung cancer data, including at least one shift with a matching subclonal driver mutation in the mismatch repair pathway. Moreover, we show that our approach enables the prioritization of alternative phylogenies inferred from the same sequencing data. PhySigs is publicly available at https://github.com/elkebir-group/PhySigs.
KW - Cancer.
KW - Convex optimization
KW - Intra-Tumor heterogeneity
KW - Phylogeny
UR - http://www.scopus.com/inward/record.url?scp=85076026020&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85076026020&partnerID=8YFLogxK
M3 - Conference article
C2 - 31797599
AN - SCOPUS:85076026020
VL - 25
SP - 226
EP - 237
JO - Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
JF - Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
SN - 2335-6936
IS - 2020
Y2 - 3 January 2020 through 7 January 2020
ER -