Biology subjects

Schwartz, R.

Publications and source records attributed to Schwartz, R..

22 records · Page 2Linked to original sources

bioRxiv2020

Tumor heterogeneity assessed by sequencing and fluorescence in situ hybridization (FISH) data

Computational reconstruction of clonal evolution in cancers has become a crucial tool for understanding how tumors initiate and progress and how this process varies across patients. The field still struggles, however, with special challenges of applying phylogenetic methods to cancers, such as the prevalence and importance of copy number alteration (CNA) and structural variation (SV) events in tumor evolution, which are difficult to profile accurately by prevailing sequencing methods in such a way that subsequent reconstruction by phylogenetic inference algorithms is accurate. In the present work, we develop computational methods to combine sequencing with multiplex interphase fluorescence in situ hybridization (miFISH) to exploit the complementary advantages of each technology in inferring accurate models of clonal CNA evolution accounting for both focal changes and aneuploidy at whole-genome scales. We demonstrate on simulated data that incorporation of FISH data substantially improves accurate inference of focal CNA and ploidy changes in clonal evolution from deconvolving bulk sequence data. Analysis of real glioblastoma data for which FISH, bulk sequence, and single cell sequence are all available confirms the power of FISH to enhance accurate reconstruction of clonal copy number evolution in conjunction with bulk and optionally single-cell sequence data. Availabilitygithub.com/CMUSchwartzLab/FISH_deconvolution Contactrussells@andrew.cmu.edu

bioinformatics↗

bioRxiv2019

Improving personalized prediction of cancer prognoses with clonal evolution models

Cancer occurs via an accumulation of somatic genomic alterations in a process of clonal evolution. There has been intensive study of potential causal mutations driving cancer development and progression. However, much recent evidence suggests that tumor evolution is normally driven by a variety of mechanisms of somatic hypermutability, known as mutator phenotypes, which act in different combinations or degrees in different cancers. Here we explore the question of how and to which degree different mutator phenotypes act in a cancer predict its future progression. We develop a computational paradigm using evolutionary tree inference (tumor phylogeny) algorithms to derive features quantifying single-tumor mutational preferences, followed by a machine learning frame-work to identify key features predictive of progression. We build phylogenies tracing the evolution of subclones of cells in tumor tissues using a variety of somatic genomic alterations, including single nucleotide variations, copy number alterations, and structural variations. We demonstrate that mutation preference features derived from the phylogenies are predictive of clinical outcomes of cancer progression - overall survival and disease-free survival - based on the analyses on breast invasive carcinoma, lung adenocarcinoma, and lung squamous cell carcinoma. We further show that mutational phenotypes have predictive power even after accounting for traditional clinical and driver-centric predictors of progression. These results confirm the power of mutational phenotypes as an independent class of predictive biomarkers and suggest a strategy for enhancing the predictive power of conventional clinical or driver-centric genomic features.

cancer biology↗

bioRxiv2019

Tumor Copy Number Deconvolution Integrating Bulk and Single-Cell Sequencing Data

Characterizing intratumor heterogeneity (ITH) is crucial to understanding cancer development, but it is hampered by limits of available data sources. Bulk DNA sequencing is the most common technology to assess ITH, but mixes many genetically distinct cells in each sample, which must then be computationally deconvolved. Single-cell sequencing (SCS) is a promising alternative, but its limitations -- e.g., high noise, difficulty scaling to large populations, technical artifacts, and large data sets -- have so far made it impractical for studying cohorts of sufficient size to identify statistically robust features of tumor evolution. We have developed strategies for deconvolution and tumor phylogenetics combining limited amounts of bulk and single-cell data to gain some advantages of single-cell resolution with much lower cost, with specific focus on deconvolving genomic copy number data. We developed a mixed membership model for clonal deconvolution via non-negative matrix factorization (NMF) balancing deconvolution quality with similarity to single-cell samples via an associated efficient coordinate descent algorithm. We then improve on that algorithm by integrating deconvolution with clonal phylogeny inference, using a mixed integer linear programming (MILP) model to incorporate a minimum evolution phylogenetic tree cost in the problem objective. We demonstrate the effectiveness of these methods on semi-simulated data of known ground truth, showing improved deconvolution accuracy relative to bulk data alone.

bioinformatics↗

bioRxiv2018

Deconvolution and phylogeny inference of structural variations in tumor genomic samples

Phylogenetic reconstruction of tumor evolution has emerged as a crucial tool for making sense of the complexity of emerging cancer genomic data sets. Despite the growing use of phylogenetics in cancer studies, though, the field has only slowly adapted to many ways that tumor evolution differs from classic species evolution. One crucial question in that regard is how to handle inference of structural variations (SVs), which are a major mechanism of evolution in cancers but have been largely neglected in tumor phylogenetics to date, in part due to the challenges of reliably detecting and typing SVs and interpreting them phylogenetically. We present a novel method for reconstructing evolutionary trajectories of SVs from bulk whole-genome sequence data via joint deconvolution and phylogenetics, to infer clonal subpopulations and reconstruct their ancestry. We establish a novel likelihood model for joint deconvolution and phylogenetic inference on bulk SV data and formulate an associated optimization algorithm. We demonstrate the approach to be efficient and accurate for realistic scenarios of SV mutation on simulated data. Application to breast cancer genomic data from The Cancer Genome Atlas (TCGA) shows it to be practical and effective at reconstructing features of SV-driven evolution in single tumors. All code can be found at https://github.com/jaebird123/tusv

cancer biology↗