Variant Allele Frequency Estimation Research Articles

Modeling clonal structure over narrow time frames via circulating tumor DNA in metastatic breast cancer

BackgroundCirculating tumor DNA (ctDNA) offers minimally invasive means to repeatedly interrogate tumor genomes, providing opportunities to monitor clonal dynamics induced by metastasis and therapeutic selective pressures. In metastatic cancers, ctDNA profiling allows for simultaneous analysis of both local and distant sites of recurrence. Despite the promise of ctDNA sampling, its utility in real-time genetic monitoring remains largely unexplored.MethodsIn this exploratory analysis, we characterize high-frequency ctDNA sample series collected over narrow time frames from seven patients with metastatic triple-negative breast cancer, each undergoing treatment with Cabozantinib, a multi-tyrosine kinase inhibitor (NCT01738438, https://clinicaltrials.gov/ct2/show/NCT01738438). Applying orthogonal whole exome sequencing, ultra-low pass whole genome sequencing, and 396-gene targeted panel sequencing, we analyzed 42 plasma-derived ctDNA libraries, representing 4–8 samples per patient with 6–42 days between samples. Integrating tumor fraction, copy number, and somatic variant information, we model tumor clonal dynamics, predict neoantigens, and evaluate consistency of genomic information from orthogonal assays.ResultsWe measured considerable variation in ctDNA tumor faction in each patient, often conflicting with RECIST imaging response metrics. In orthogonal sequencing, we found high concordance between targeted panel and whole exome sequencing in both variant detection and variant allele frequency estimation (specificity = 95.5%, VAF correlation, r = 0.949), Copy number remained generally stable, despite resolution limitations posed by low tumor fraction. Through modeling, we inferred and tracked distinct clonal populations specific to each patient and built phylogenetic trees revealing alterations in hallmark breast cancer drivers, including TP53, PIK3CA, CDK4, and PTEN. Our modeling revealed varied responses to therapy, with some individuals displaying stable clonal profiles, while others showed signs of substantial expansion or reduction in prevalence, with characteristic alterations of varied literature annotation in relation to the study drug. Finally, we predicted and tracked neoantigen-producing alterations across time, exposing translationally relevant detection patterns.ConclusionsDespite technical challenges arising from low tumor content, metastatic ctDNA monitoring can aid our understanding of response and progression, while minimizing patient risk and discomfort. In this study, we demonstrate the potential for high-frequency monitoring of evolving genomic features, providing an important step toward scalable, translational genomics for clinical decision making.

Abstract 3436: Early mutated genes and subsequent mutational trajectory in breast cancer subtypes

Abstract Current analyses on somatic mutations mainly involve counting samples as either carriers or non-carriers. Such a binary approach can identify the frequently mutated genes, but may miss the early mutated genes that are critically involved in tumorigenesis, but only in a small specific group of patients. This work is an attempt to identify such early mutated genes that drive the etiology of each breast cancer subtype by analyzing the variant allele frequency (VAF) of somatic mutations, which is a good proxy for mutational timeline. We further studied their pathway associations and subsequent trajectory of mutational progression. In this study, we analyzed the 72,084 non-synonymous somatic mutations in 16,164 genes of 817 TCGA breast cancer samples (HER2+: 65, Luminal A: 415, Luminal B: 176, Basal: 136 and Normal-like: 25) using VAF adjusted by sample purity derived from multiple methods. Gene allele frequency (GAF) is obtained by choosing the maximum VAF per gene per sample. For each subtype, we shortlisted 100 genes with highest number of mutation carriers and ranked them by decreasing average GAF across the carriers. We assume genes with higher average GAF are more likely to harbor clonal mutations that occur during early tumor progression. We then performed Inflo Network Analysis on RNA-Seq expression data to identify the dysregulated pathway interactions associated with the early mutated genes. The top two genes with the highest presence of mutations within each subtype are as follows: (a) HER2+: ERBB2 (VAF = 0.39 ± 0.21, n = 4), KAT6A (0.31 ± 0.09, 4), (b) Luminal A: CTCF (0.41 ± 0.19, 13), MAP2K4 (0.37 ± 0.14, 24), (c) Luminal B: MAP2K4 (0.46 ± 0.11, 5), TP53 (0.46 ± 0.17, 64), and (d) Basal: SCN10A (0.46 ± 0.29, 5), MYH9 (0.43 ± 0.16, 5). Among them, the genes with the strongest support are MAP2K4 (n = 24) for Luminal A and TP53 (n = 64) for Luminal B. Network analysis with Inflo on the same samples reveals that MAP2K4 mutation carriers are most strongly associated with the down regulations of “Regulation of Androgen receptor activity (target: JUN)” and “regulation of p38-alpha and p38-beta (target: DUSP8)” in Luminal A, with a difference in average Inflo score of -2.24 and -2.22 respectively; whereas TP53 mutation carriers are most strongly associated with the down regulations of “direct p53 effectors (target: RPS27L)” and “RB tumor suppressor/ checkpoint signaling in response to dna damage (target: CDK2)” in Luminal B, with a difference of -2.98 and -2.67 respectively. We found further that both PIK3CA and TTN were frequently co-mutated with TP53 in all subtypes. However, TTN mutations in general have lower VAF, indicating their late emergence in tumor progression. Our work shows the potential of VAF analysis for identifying driver genes, understanding tumor progression and evaluating the impact of a mutation on a patient. As future work, we may improve the VAF estimates by adjusting for CNVs and weight each variant by pathogenicity. Citation Format: Yee Him Cheung, Jie Wu, Vinay Varadan, Nevenka Dimitrova. Early mutated genes and subsequent mutational trajectory in breast cancer subtypes [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3436.

QuantumClone: clonal assessment of functional mutations in cancer based on a genotype-aware method for clonal reconstruction

MotivationIn cancer, clonal evolution is assessed based on information coming from single nucleotide variants and copy number alterations. Nonetheless, existing methods often fail to accurately combine information from both sources to truthfully reconstruct clonal populations in a given tumor sample or in a set of tumor samples coming from the same patient. Moreover, previously published methods detect clones from a single set of variants. As a result, compromises have to be done between stringent variant filtering [reducing dispersion in variant allele frequency estimates (VAFs)] and using all biologically relevant variants.ResultsWe present a framework for defining cancer clones using most reliable variants of high depth of coverage and assigning functional mutations to the detected clones. The key element of our framework is QuantumClone, a method for variant clustering into clones based on VAFs, genotypes of corresponding regions and information about tumor purity. We validated QuantumClone and our framework on simulated data. We then applied our framework to whole genome sequencing data for 19 neuroblastoma trios each including constitutional, diagnosis and relapse samples. We confirmed an enrichment of damaging variants within such pathways as MAPK (mitogen-activated protein kinases), neuritogenesis, epithelial-mesenchymal transition, cell survival and DNA repair. Most pathways had more damaging variants in the expanding clones compared to shrinking ones, which can be explained by the increased total number of variants between these two populations. Functional mutational rate varied for ancestral clones and clones shrinking or expanding upon treatment, suggesting changes in clone selection mechanisms at different time points of tumor evolution.Availability and implementationSource code and binaries of the QuantumClone R package are freely available for download at https://CRAN.R-project.org/package=QuantumClone.Supplementary information Supplementary data are available at Bioinformatics online.

Identification of genes with clonal mutations for the tumorigenesis of breast cancer subtypes.

e13006 Background: Current analyses on somatic mutations mainly involve counting samples as either carriers or non-carriers. Such a binary approach misses to take into account the fraction of tumor cells that carry a mutation, which is a significant measure of the influence of a mutation on the overall phenotype, such as gene expressions, subtype and pathway activity. Methods: In this study, we re-analyzed the 72,084 non-synonymous somatic mutation in 16,164 genes of 817 TCGA breast cancer samples (HER2+: 65, Luminal A: 415, Luminal B: 176, Basal: 136 and Normal-like: 25) using variant allele frequency (VAF) adjusted by sample purity derived from multiple methods. We obtained a gene-based VAF by choosing the maximum VAF among all mutations found in each gene for each sample. For each breast cancer subtype, we filtered for top 100 genes with highest number of mutation carriers and then ranked them by decreasing average VAF across all samples. We assume genes with higher average VAF are more likely to harbor clonal mutations in early tumor progression. We further evaluated the Pearson correlation between VAF of genes having at least ten mutation carriers and the expression levels of ESR1, PGR and ERBB2 in Luminal A subtype. Results: The top two genes with highest VAF for each of the four subtypes are as follows: (a) HER2+: ERBB2 (VAF = 0.39 ± 0.21, n = 4), KAT6A (0.31 ± 0.09, 4), (b) Luminal A: CTCF (0.41 ± 0.19, 13), MAP2K4 (0.37 ± 0.14, 24), (c) Luminal B: MAP2K4 (0.46 ± 0.11, 5), TP53 (0.46 ± 0.17, 64), and (d) Basal: SCN10A (0.46 ± 0.29, 5), MYH9 (0.43 ± 0.16, 5). The strongest correlations for Luminal A are: (i) SPEN (corr. = 0.85, n= 14), KMT2C (0.73, 24) and DMD (0.73, 11) with ESR1, (ii) DMD (0.69, 11) and NBL1 (0.67, 10) with PGR, and (iii) TBX3 (-0.51, 12) and MUC12 (0.47, 14) with ERBB2. Conclusions: While carrier count is effective for identifying genes prone to mutations, average VAF opens another perspective for uncovering genes that tend to harbor clonal mutations. Our work shows the potential of VAF analysis for identifying driver genes, understanding tumor progression and evaluating the impact of a mutation on a patient. As future work, we may improve the VAF estimates by adjusting for copy number variations and weight each variant by pathogenicity.

ABRA: improved coding indel detection via assembly-based realignment.

Motivation: Variant detection from next-generation sequencing (NGS) data is an increasingly vital aspect of disease diagnosis, treatment and research. Commonly used NGS-variant analysis tools generally rely on accurately mapped short reads to identify somatic variants and germ-line genotypes. Existing NGS read mappers have difficulty accurately mapping short reads containing complex variation (i.e. more than a single base change), thus making identification of such variants difficult or impossible. Insertions and deletions (indels) in particular have been an area of great difficulty. Indels are frequent and can have substantial impact on function, which makes their detection all the more imperative.Results: We present ABRA, an assembly-based realigner, which uses an efficient and flexible localized de novo assembly followed by global realignment to more accurately remap reads. This results in enhanced performance for indel detection as well as improved accuracy in variant allele frequency estimation.Availability and implementation: ABRA is implemented in a combination of Java and C/C++ and is freely available for download at https://github.com/mozack/abra.Contact: lmose@unc.edu; parkerjs@email.unc.eduSupplementary information: Supplementary data are available at Bioinformatics online.

Rare variant discovery and calling by sequencing pooled samples with overlaps

For many complex traits/diseases, it is believed that rare variants account for some of the missing heritability that cannot be explained by common variants. Sequencing a large number of samples through DNA pooling is a cost-effective strategy to discover rare variants and to investigate their associations with phenotypes. Overlapping pool designs provide further benefit because such approaches can potentially identify variant carriers, which is important for downstream applications of association analysis of rare variants. However, existing algorithms for analysing sequence data from overlapping pools are limited. We propose a complete data analysis framework for overlapping pool designs, with novelties in all three major steps: variant pool and variant locus identification, variant allele frequency estimation and variant sample decoding. The framework can be used in combination with any design matrix. We have investigated its performance based on two different overlapping designs and have compared it with three state-of-the-art methods, by simulating targeted sequencing and by pooling real sequence data. Results on both datasets show that our algorithm has made significant improvements over existing ones. In conclusion, successful discovery of rare variants and identification of variant carriers using overlapping pool strategies critically depend on many steps, from generation of design matrixes to decoding algorithms. The proposed framework in combination with the design matrixes generated based on the Chinese remainder theorem achieves best overall results. Source code of the program, termed VIP for Variant Identification by Pooling, is available at http://cbc.case.edu/VIP.