Kamalakar Gulukota

Germline Mutations in ATM and BRCA1/2 Distinguish Risk for Lethal and Indolent Prostate Cancer and are Associated with Early Age at Death

BACKGROUND Germline mutations in BRCA1/2 and ATM have been associated with prostate cancer (PCa) risk. OBJECTIVE To directly assess whether germline mutations in these three genes distinguish lethal from indolent PCa and whether they confer any effect on age at death. DESIGN, SETTING, AND PARTICIPANTS A retrospective case-case study of 313 patients who died of PCa and 486 patients with low-risk localized PCa of European, African, and Chinese descent. Germline DNA of each of the 799 patients was sequenced for these three genes. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS Mutation carrier rates and their effect on lethal PCa were analyzed using the Fishers exact test and Cox regression analysis, respectively. RESULTS AND LIMITATIONS The combined BRCA1/2 and ATM mutation carrier rate was significantly higher in lethal PCa patients (6.07%) than localized PCa patients (1.44%), p=0.0007. The rate also differed significantly among lethal PCa patients as a function of age at death (10.00%, 9.08%, 8.33%, 4.94%, and 2.97% in patients who died ≤ 60 yr, 61-65 yr, 66-70 yr, 71-75 yr, and over 75 yr, respectively, p=0.046) and time to death after diagnosis (12.26%, 4.76%, and 0.98% in patients who died ≤ 5 yr, 6-10 yr, and>10 yr after a PCa diagnosis, respectively, p=0.0006). Survival analysis in the entire cohort revealed mutation carriers remained an independent predictor of lethal PCa after adjusting for race and age, prostate-specific antigen, and Gleason score at the time of diagnosis (hazard ratio=2.13, 95% confidence interval: 1.24-3.66, p=0.004). A limitation of this study is that other DNA repair genes were not analyzed. CONCLUSIONS Mutation status of BRCA1/2 and ATM distinguishes risk for lethal and indolent PCa and is associated with earlier age at death and shorter survival time. PATIENT SUMMARY Prostate cancer patients with inherited mutations in BRCA1/2 and ATM are more likely to die of prostate cancer and do so at an earlier age.

A Bayesian feature allocation model for tumor heterogeneity

We develop a feature allocation model for inference on genetic tumor variation using next-generation sequencing data. Specifically, we record single nucleotide variants (SNVs) based on short reads mapped to human reference genome and characterize tumor heterogeneity by latent haplotypes defined as a scaffold of SNVs on the same homologous genome. For multiple samples from a single tumor, assuming that each sample is composed of some sample-specific proportions of these haplotypes, we then fit the observed variant allele fractions of SNVs for each sample and estimate the proportions of haplotypes. Varying proportions of haplotypes across samples is evidence of tumor heterogeneity since it implies varying composition of cell subpopulations. Taking a Bayesian perspective, we proceed with a prior probability model for all relevant unknown quantities, including, in particular, a prior probability model on the binary indicators that characterize the latent haplotypes. Such prior models are known as feature allocation models. Specifically, we define a simplified version of the Indian buffet process, one of the most traditional feature allocation models. The proposed model allows overlapping clustering of SNVs in defining latent haplotypes, which reflects the evolutionary process of subclonal expansion in tumor samples.

MAD Bayes for Tumor Heterogeneity--Feature Allocation With Exponential Family Sampling

We propose small-variance asymptotic approximations for inference on tumor heterogeneity (TH) using next-generation sequencing data. Understanding TH is an important and open research problem in biology. The lack of appropriate statistical inference is a critical gap in existing methods that the proposed approach aims to fill. We build on a hierarchical model with an exponential family likelihood and a feature allocation prior. The proposed implementation of posterior inference generalizes similar small-variance approximations proposed by Kulis and Jordan and Broderick, Kulis, and Jordan for inference with Dirichlet process mixture and Indian buffet process prior models under normal sampling. We show that the new algorithm can successfully recover latent structures of different haplotypes and subclones and is magnitudes faster than available Markov chain Monte Carlo samplers. The latter are practically infeasible for high-dimensional genomics data. The proposed approach is scalable, easy to implement, and benefits from the flexibility of Bayesian nonparametric models. More importantly, it provides a useful tool for applied scientists to estimate cell subtypes in tumor samples. R code is available on http://www.ma.utexas.edu/users/yxu/. Supplementary materials for this article are available online.

Bayclone: Bayesian nonparametric inference of tumor subclones using NGS data.

In this paper, we present a novel feature allocation model to describe tumor heterogeneity (TH) using next-generation sequencing (NGS) data. Taking a Bayesian approach, we extend the Indian buffet process (IBP) to define a class of nonparametric models, the categorical IBP (cIBP). A cIBP takes categorical values to denote homozygous or heterozygous genotypes at each SNV. We define a subclone as a vector of these categorical values, each corresponding to an SNV. Instead of partitioning somatic mutations into non-overlapping clusters with similar cellular prevalences, we took a different approach using feature allocation. Importantly, we do not assume somatic mutations with similar cellular prevalence must be from the same subclone and allow overlapping mutations shared across subclones. We argue that this is closer to the underlying theory of phylogenetic clonal expansion, as somatic mutations occurred in parent subclones should be shared across the parent and child subclones. Bayesian inference yields posterior probabilities of the number, genotypes, and proportions of subclones in a tumor sample, thereby providing point estimates as well as variabilities of the estimates for each subclone. We report results on both simulated and real data. BayClone is available at http://health.bsd.uchicago.edu/yji/soft.html.

Bayesian inference for intratumour heterogeneity in mutations and copy number variation

Tumor samples are heterogeneous. They consist of different subclones that are characterized by differences in DNA nucleotide sequences and copy numbers on multiple loci. Heterogeneity can be measured through the identification of the subclonal copy number and sequence at a selected set of loci. Understanding that the accurate identification of variant allele fractions greatly depends on a precise determination of copy numbers, we develop a Bayesian feature allocation model for jointly calling subclonal copy numbers and the corresponding allele sequences for the same loci. The proposed method utilizes three random matrices, L , Z and w to represent subclonal copy numbers ( L ), numbers of subclonal variant alleles ( Z ) and cellular fractions of subclones in samples ( w ), respectively. The unknown number of subclones implies a random number of columns for these matrices. We use next-generation sequencing data to estimate the subclonal structures through inference on these three matrices. Using simulation studies and a real data analysis, we demonstrate how posterior inference on the subclonal structure is enhanced with the joint modeling of both structure and sequencing variants on subclonal genomes. Software is available at http://compgenome.org/BayClone2.

Ultra-fast local-haplotype variant calling using paired-end DNA-sequencing data reveals somatic mosaicism in tumor and normal blood samples

Somatic mosaicism refers to the existence of somatic mutations in a fraction of somatic cells in a single biological sample. Its importance has mainly been discussed in theory although experimental work has started to emerge linking somatic mosaicism to disease diagnosis. Through novel statistical modeling of paired-end DNA-sequencing data using blood-derived DNA from healthy donors as well as DNA from tumor samples, we present an ultra-fast computational pipeline, LocHap that searches for multiple single nucleotide variants (SNVs) that are scaffolded by the same reads. We refer to scaffolded SNVs as local haplotypes (LH). When an LH exhibits more than two genotypes, we call it a local haplotype variant (LHV). The presence of LHVs is considered evidence of somatic mosaicism because a genetically homogeneous cell population will not harbor LHVs. Applying LocHap to whole-genome and whole-exome sequence data in DNA from normal blood and tumor samples, we find wide-spread LHVs across the genome. Importantly, we find more LHVs in tumor samples than in normal samples, and more in older adults than in younger ones. We confirm the existence of LHVs and somatic mosaicism by validation studies in normal blood samples. LocHap is publicly available at http://www.compgenome.org/lochap.

Germline mutations in ATM and BRCA1/2 distinguish risk for lethal and indolent prostate cancer and are associated with early age at death

Rong Na a,b,y, S. Lilly Zheng b,c,y, Misop Han d,y, Hongjie Yu , Deke Jiang , Sameep Shah , Charles M. Ewing , Liti Zhang , Kristian Novakovic b [5_TD

Bayesian Feature Allocation Models for Tumor Heterogeneity

DIFF], Jacqueline Petkewicz [5_TD

Estimating Latent Cell Subpopulations with Bayesian Feature Allocation Models

DIFF], Kamalakar Gulukota , Donald L. Helseth Jr , Margo Quinn , Elizabeth Humphries , Kathleen E. Wiley [6_TD

MP57-01 GERMLINE MUTATIONS IN DNA REPAIR GENES ARE SIGNIFICANTLY ENRICHED IN LETHAL PROSTATE CANCER AND ARE ASSOCIATED WITH DISEASE SURVIVAL

DIFF], Sarah[7_TD