Toward a systems-level probing of tumor clonality

Grody, Emanuelle I.; Abraham, Ajay; Shukla, Vipul; Goyal, Yogesh

doi:10.1016/j.isci.2023.106574

Cited by 4 publications

References 176 publications

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Optimizing design of genomics studies for clonal evolution analysis

Srivatsa,

Schwartz

2024

Bioinformatics Advances

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Motivation Genomic biotechnology has rapidly advanced, allowing for the inference and modification of genetic and epigenetic information at the single-cell level. While these tools hold enormous potential for basic and clinical research, they also raise difficult issues of how to design studies to deploy them most effectively. In designing a genomic study, a modern researcher might combine many sequencing modalities and sampling protocols, each with different utility, costs, and other tradeoffs. This is especially relevant for studies of somatic variation, which may involve highly heterogeneous cell populations whose differences can be probed via an extensive set of biotechnological tools. Efficiently deploying genomic technologies in this space will require principled ways to create study designs that recover desired genomic information while minimizing various measures of cost. Results The central problem this paper attempts to address is how one might create an optimal study design for a genomic analysis, with particular focus on studies involving somatic variation that occur most often with application to cancer genomics. We pose the study design problem as a stochastic constrained nonlinear optimization problem. We introduce a Bayesian optimization framework that iteratively optimizes for an objective function using surrogate modeling combined with pattern and gradient search. We demonstrate our procedure on several test cases to derive resource and study design allocations optimized for various goals and criteria, demonstrating its ability to optimize study designs efficiently across diverse scenarios. Availability and implementation https://github.com/CMUSchwartzLab/StudyDesignOptimization

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Optimizing design of genomics studies for clonal evolution analysis

Srivatsa,

Schwartz

2024

Bioinformatics Advances

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Optimizing Design of Genomics Studies for Clonal Evolution Analysis

Srivatsa,

Schwartz

2024

Preprint

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Genomic biotechnologies have seen rapid development over the past two decades, allowing for both the inference and modification of genetic and epigenetic information at the single cell level. While these tools present enormous potential for basic research, diagnostics, and treatment, they also raise difficult issues of how to design research studies to deploy these tools most effectively. In designing a study at the population or individual level, a researcher might combine several different sequencing modalities and sampling protocols, each with different utility, costs, and other tradeoffs. The central problem this paper attempts to address is then how one might create an optimal study design for a genomic analysis, with particular focus on studies involving somatic variation, typically for applications in cancer genomics. We pose the study design problem as a stochastic constrained nonlinear optimization problem and introduce a simulation-centered optimization procedure that iteratively optimizes the objective function using surrogate modeling combined with pattern and gradient search. Finally, we demonstrate the use of our procedure on diverse test cases to derive resource and study design allocations optimized for various objectives for the study of somatic cell populations.

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Prevalence of and gene regulatory constraints on transcriptional adaptation in single cells

Mellis,

Melzer,

Bodkin

et al. 2024

Genome Biol

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Background Cells and tissues have a remarkable ability to adapt to genetic perturbations via a variety of molecular mechanisms. Nonsense-induced transcriptional compensation, a form of transcriptional adaptation, has recently emerged as one such mechanism, in which nonsense mutations in a gene trigger upregulation of related genes, possibly conferring robustness at cellular and organismal levels. However, beyond a handful of developmental contexts and curated sets of genes, no comprehensive genome-wide investigation of this behavior has been undertaken for mammalian cell types and conditions. How the regulatory-level effects of inherently stochastic compensatory gene networks contribute to phenotypic penetrance in single cells remains unclear. Results We analyze existing bulk and single-cell transcriptomic datasets to uncover the prevalence of transcriptional adaptation in mammalian systems across diverse contexts and cell types. We perform regulon gene expression analyses of transcription factor target sets in both bulk and pooled single-cell genetic perturbation datasets. Our results reveal greater robustness in expression of regulons of transcription factors exhibiting transcriptional adaptation compared to those of transcription factors that do not. Stochastic mathematical modeling of minimal compensatory gene networks qualitatively recapitulates several aspects of transcriptional adaptation, including paralog upregulation and robustness to mutation. Combined with machine learning analysis of network features of interest, our framework offers potential explanations for which regulatory steps are most important for transcriptional adaptation. Conclusions Our integrative approach identifies several putative hits—genes demonstrating possible transcriptional adaptation—to follow-up on experimentally and provides a formal quantitative framework to test and refine models of transcriptional adaptation. Supplementary Information The online version contains supplementary material available at 10.1186/s13059-024-03351-2.

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Toward a systems-level probing of tumor clonality

Cited by 4 publications

References 176 publications

Optimizing design of genomics studies for clonal evolution analysis

Optimizing design of genomics studies for clonal evolution analysis

Optimizing Design of Genomics Studies for Clonal Evolution Analysis

Prevalence of and gene regulatory constraints on transcriptional adaptation in single cells

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