On statistical modeling of sequencing noise in high depth data to assess tumor evolution

Rabadán, Raúl; Bhanot, Gyan; Marsilio, Sonia; Chiorazzi, Nicholas; Pasqualucci, Laura; Khiabanian, Hossein

doi:10.1101/128587

Cited by 3 publications

(3 citation statements)

References 49 publications

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“…The TME often comprises only a small fraction of the tumor and bulk RNA-seq reads; however, the precise identification of small TME cellular subsets, such as natural killer (NK) cells, is essential because they significantly impact therapeutic response (legend continued on next page) ll Article and clinical outcome across diverse diseases. Addressing technical noise is essential during cellular deconvolution to accurately identify cell subsets from bulk RNA-seq (Ding et al, 2015;Rabadan et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Precise reconstruction of the TME using bulk RNA-seq and a machine learning algorithm trained on artificial transcriptomes

et al. 2022

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Section: Introductionmentioning

confidence: 99%

Precise reconstruction of the TME using bulk RNA-seq and a machine learning algorithm trained on artificial transcriptomes

et al. 2022

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“…We note that previous work has shown that DNA sequencing count data is well-represented by a negative binomial distribution (Rabadan et al, 2018).…”

Section: Latent Subcountsmentioning

confidence: 70%

A Bayesian Nonparametric Model for Inferring Subclonal Populations from Structured DNA Sequencing Data

Schein

Sarsani

et al. 2020

Preprint

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There are distinguishing features or “hallmarks” of cancer that are found across tumors, individuals, and types of cancer, and these hallmarks can be driven by specific genetic mutations. Yet, within a single tumor there is often extensive genetic heterogeneity as evidenced by single-cell and bulk DNA sequencing data. The goal of this work is to jointly infer the underlying genotypes of tumor subpopulations and the distribution of those subpopulations in individual tumors by integrating single-cell and bulk sequencing data. Understanding the genetic composition of the tumor at the time of treatment is important in the personalized design of targeted therapeutic combinations and monitoring for possible recurrence after treatment.We propose a hierarchical Dirichlet process mixture model that incorporates the correlation structure induced by a structured sampling arrangement and we show that this model improves the quality of inference. We develop a representation of the hierarchical Dirichlet process prior as a Gamma-Poisson hierarchy and we use this representation to derive a fast Gibbs sampling inference algorithm using the augment-and-marginalize method. Experiments with simulation data show that our model outperforms standard numerical and statistical methods for decomposing admixed count data. Analyses of real acute lymphoblastic leukemia cancer sequencing dataset shows that our model improves upon state-of-the-art bioinformatic methods. An interpretation of the results of our model on this real dataset reveals co-mutated loci across samples.

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“…For each residue, all codon changes included in the library design were identified in the mapped reads and counted. To distinguish a true amino acid change from sequencing errors, background noise was modeled by aggregating negative binomial distributions 40 . Briefly, if the total number of reads for a locus in sample I is N i among which n i reads harbor the amino acid change of interest, the distribution of n i follows a binomial distribution given N i and a priori probability of occurrence as error.…”

Section: Saturation Mutagenesis Analysismentioning

confidence: 99%

Computational structure prediction methods enable the systematic identification of oncogenic mutations

Reglero

Swamy

et al. 2022

Preprint

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Oncogenic mutations are associated with the activation of key pathways necessary for the initiation, progression and treatment-evasion of tumors. While large genomic studies provide the opportunity of identifying these mutations, the vast majority of variants have unclear functional roles presenting a challenge for the use of genomic studies in the clinical/therapeutic setting. Recent developments in predicting protein structures enable the systematic large-scale characterization of structures providing a link from genomic data to functional impact. Here, we observed that most oncogenic mutations tend to occur in protein regions that undergo conformation changes in the presence of the activating mutation or when interacting with a protein partner. By combining evolutionary information and protein structure prediction, we introduce the Evolutionary and Structure (ES) score, a computational approach that enables the systematic identification of hotspot somatic mutations in cancer. The predicted sites tend to occur in Short Linear Motifs and protein-protein interfaces. We test the use of ES-scores in genomic studies in pediatric leukemias that easily recapitulates the main mechanisms of resistance to targeted and chemotherapy drugs. To experimentally test the functional role of the predictions, we performed saturated mutagenesis in NT5C2, a protein commonly mutated in relapsed pediatric lymphocytic leukemias. The approach was able to capture both commonly mutated sites and identify previously uncharacterized functionally relevant regions that are not frequently mutated in these cancers. This work shows that the characterization of protein structures provides a link between large genomic studies, with mostly variants of unknown significance, to functional systematic characterization, prioritizing variants of interest in the therapeutic setting and informing on their possible mechanisms of action.

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On statistical modeling of sequencing noise in high depth data to assess tumor evolution

Cited by 3 publications

References 49 publications

Precise reconstruction of the TME using bulk RNA-seq and a machine learning algorithm trained on artificial transcriptomes

Precise reconstruction of the TME using bulk RNA-seq and a machine learning algorithm trained on artificial transcriptomes

A Bayesian Nonparametric Model for Inferring Subclonal Populations from Structured DNA Sequencing Data

Computational structure prediction methods enable the systematic identification of oncogenic mutations

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