BACKGROUND: Drawing genotype-to-phenotype maps in tumors is of paramount importance for understanding tumor heterogeneity. Assignment of single cells to their tumor clones of origin can be approached by matching the genotypes of the clones to the mutations found in RNA sequencing of the cells. The confidence of the cell-to-clone mapping can be increased by accounting for additional measurements. Follicular lymphoma, a malignancy of mature B cells that continuously acquire mutations in parallel in the exome and in B cell receptor loci, presents a unique opportunity to join exome-derived mutations with B cell receptor sequences as independent sources of evidence for clonal evolution. METHODS: Here, we propose CACTUS, a probabilistic model that leverages the information from an independent genomic clustering of cells and exploits the scarce single cell RNA sequencing data to map single cells to given imperfect genotypes of tumor clones. RESULTS: We apply CACTUS to two follicular lymphoma patient samples, integrating three measurements: whole exome, single-cell RNA, and B cell receptor sequencing. CACTUS outperforms a predecessor model by confidently assigning cells and B cell receptor-based clusters to the tumor clones. CONCLUSIONS: The integration of independent measurements increases model certainty and is the key to improving model performance in the challenging task of charting the genotype-to-phenotype maps in tumors. CACTUS opens the avenue to study the functional implications of tumor heterogeneity, and origins of resistance to targeted therapies. CACTUS is written in R and source code, along with all supporting files, are available on GitHub ( https://github.com/LUMC/CACTUS ).
BACKGROUND: Drawing genotype-to-phenotype maps in tumors is of paramount importance for understanding tumor heterogeneity. Assignment of single cells to their tumor clones of origin can be approached by matching the genotypes of the clones to the mutations found in RNA sequencing of the cells. The confidence of the cell-to-clone mapping can be increased by accounting for additional measurements. Follicular lymphoma, a malignancy of mature B cells that continuously acquire mutations in parallel in the exome and in B cell receptor loci, presents a unique opportunity to join exome-derived mutations with B cell receptor sequences as independent sources of evidence for clonal evolution. METHODS: Here, we propose CACTUS, a probabilistic model that leverages the information from an independent genomic clustering of cells and exploits the scarce single cell RNA sequencing data to map single cells to given imperfect genotypes of tumor clones. RESULTS: We apply CACTUS to two follicular lymphoma patient samples, integrating three measurements: whole exome, single-cell RNA, and B cell receptor sequencing. CACTUS outperforms a predecessor model by confidently assigning cells and B cell receptor-based clusters to the tumor clones. CONCLUSIONS: The integration of independent measurements increases model certainty and is the key to improving model performance in the challenging task of charting the genotype-to-phenotype maps in tumors. CACTUS opens the avenue to study the functional implications of tumor heterogeneity, and origins of resistance to targeted therapies. CACTUS is written in R and source code, along with all supporting files, are available on GitHub ( https://github.com/LUMC/CACTUS ).
Authors: Aaron McKenna; Matthew Hanna; Eric Banks; Andrey Sivachenko; Kristian Cibulskis; Andrew Kernytsky; Kiran Garimella; David Altshuler; Stacey Gabriel; Mark Daly; Mark A DePristo Journal: Genome Res Date: 2010-07-19 Impact factor: 9.043
Authors: Itay Tirosh; Andrew S Venteicher; Christine Hebert; Leah E Escalante; Anoop P Patel; Keren Yizhak; Jonathan M Fisher; Christopher Rodman; Christopher Mount; Mariella G Filbin; Cyril Neftel; Niyati Desai; Jackson Nyman; Benjamin Izar; Christina C Luo; Joshua M Francis; Aanand A Patel; Maristela L Onozato; Nicolo Riggi; Kenneth J Livak; Dave Gennert; Rahul Satija; Brian V Nahed; William T Curry; Robert L Martuza; Ravindra Mylvaganam; A John Iafrate; Matthew P Frosch; Todd R Golub; Miguel N Rivera; Gad Getz; Orit Rozenblatt-Rosen; Daniel P Cahill; Michelle Monje; Bradley E Bernstein; David N Louis; Aviv Regev; Mario L Suvà Journal: Nature Date: 2016-11-02 Impact factor: 69.504
Authors: Amit G Deshwar; Shankar Vembu; Christina K Yung; Gun Ho Jang; Lincoln Stein; Quaid Morris Journal: Genome Biol Date: 2015-02-13 Impact factor: 13.583
Authors: Michael A Ortega; Olivier Poirion; Xun Zhu; Sijia Huang; Thomas K Wolfgruber; Robert Sebra; Lana X Garmire Journal: Clin Transl Med Date: 2017-12-28