Accurate estimation of homologue-specific DNA concentration-ratios in cancer samples allows long-range haplotyping

Interpretation of allelic copy measurements at polymorphic markers in cancer samples presents distinctive challenges and opportunities. Due to frequent gross chromosomal alterations occurring in cancer (aneuploidy), many genomic regions are present at homologous-allele imbalance. Within such regions, the unequal contribution of alleles at heterozygous markers allows for direct phasing of the haplotype derived from each individual parent. In addition, genome-wide estimates of homologue specific copy- ratios (HSCRs) are important for interpretation of the cancer genome in terms of fixed integral copy-numbers. We describe HAPSEG, a probabilistic method to interpret bi- allelic marker data in cancer samples. HAPSEG operates by partitioning the genome into segments of distinct copy number and modeling the four distinct genotypes in each segment. We describe general methods for fitting these models to data which are suit- able for both SNP microarrays and massively parallel sequencing data. In addition, we demonstrate a specially tailored error-model for interpretation of systematic variations arising in microarray platforms. The ability to directly determine haplotypes from cancer samples represents an opportunity to expand reference panels of phased chromosomes, which may have general interest in various population genetic applications. In addition, this property may be exploited to interrogate the relationship between germline risk and cancer phenotype with greater sensitivity than is possible using unphased genotype. Finally, we exploit the statistical dependency of phased genotypes to enable the fitting of more elaborate sample-level error-model parameters, allowing more accurate estimation of HSCRs in cancer samples

Subtype-specific genomic alterations define new targets for soft tissue sarcoma therapy

2011 February 1Soft-tissue sarcomas, which result in approximately 10,700 diagnoses and 3,800 deaths per year in the United States1, show remarkable histologic diversity, with more than 50 recognized subtypes2. However, knowledge of their genomic alterations is limited. We describe an integrative analysis of DNA sequence, copy number and mRNA expression in 207 samples encompassing seven major subtypes. Frequently mutated genes included TP53 (17% of pleomorphic liposarcomas), NF1 (10.5% of myxofibrosarcomas and 8% of pleomorphic liposarcomas) and PIK3CA (18% of myxoid/round-cell liposarcomas, or MRCs). PIK3CA mutations in MRCs were associated with Akt activation and poor clinical outcomes. In myxofibrosarcomas and pleomorphic liposarcomas, we found both point mutations and genomic deletions affecting the tumor suppressor NF1. Finally, we found that short hairpin RNA (shRNA)-based knockdown of several genes amplified in dedifferentiated liposarcoma, including CDK4 and YEATS4, decreased cell proliferation. Our study yields a detailed map of molecular alterations across diverse sarcoma subtypes and suggests potential subtype-specific targets for therapy.Memorial Sloan-Kettering Cancer Center (Soft Tissue Sarcoma Program Project P01 CA047179

Somatic rearrangements across cancer reveal classes of samples with distinct patterns of DNA breakage and rearrangement-induced hypermutability

Whole-genome sequencing using massively parallel sequencing technologies enables accurate detection of somatic rearrangements in cancer. Pinpointing large numbers of rearrangement breakpoints to base-pair resolution allows analysis of rearrangement microhomology and genomic location for every sample. Here we analyze 95 tumor genome sequences from breast, head and neck, colorectal, and prostate carcinomas, and from melanoma, multiple myeloma, and chronic lymphocytic leukemia. We discover three genomic factors that are significantly correlated with the distribution of rearrangements: replication time, transcription rate, and GC content. The correlation is complex, and different patterns are observed between tumor types, within tumor types, and even between different types of rearrangements. Mutations in the APC gene correlate with and, hence, potentially contribute to DNA breakage in late-replicating, low %GC, untranscribed regions of the genome. We show that somatic rearrangements display less microhomology than germline rearrangements, and that breakpoint loci are correlated with local hypermutability with a particular enrichment for C ↔ G transversions

Calibrating genomic and allelic coverage bias in single-cell sequencing

Artifacts introduced in whole-genome amplification (WGA) make it difficult to derive accurate genomic information from single-cell genomes and require different analytical strategies from bulk genome analysis. Here, we describe statistical methods to quantitatively assess the amplification bias resulting from whole-genome amplification of single-cell genomic DNA. Analysis of single-cell DNA libraries generated by different technologies revealed universal features of the genome coverage bias predominantly generated at the amplicon level (1–10 kb). The magnitude of coverage bias can be accurately calibrated from low-pass sequencing (∼0.1 × ) to predict the depth-of-coverage yield of single-cell DNA libraries sequenced at arbitrary depths. We further provide a benchmark comparison of single-cell libraries generated by multi-strand displacement amplification (MDA) and multiple annealing and looping-based amplification cycles (MALBAC). Finally, we develop statistical models to calibrate allelic bias in single-cell whole-genome amplification and demonstrate a census-based strategy for efficient and accurate variant detection from low-input biopsy samples.National Cancer Institute (U.S.) (Grant P30-CA14051

Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples

Detection of somatic point substitutions is a key step in characterizing the cancer genome. Mutations in cancer are rare (0.1–100/Mb) and often occur only in a subset of the sequenced cells, either due to contamination by normal cells or due to tumor heterogeneity. Consequently, mutation calling methods need to be both specific, avoiding false positives, and sensitive to detect clonal and sub-clonal mutations. The decreased sensitivity of existing methods for low allelic fraction mutations highlights the pressing need for improved and systematically evaluated mutation detection methods. Here we present MuTect, a method based on a Bayesian classifier designed to detect somatic mutations with very low allele-fractions, requiring only a few supporting reads, followed by a set of carefully tuned filters that ensure high specificity. We also describe novel benchmarking approaches, which use real sequencing data to evaluate the sensitivity and specificity as a function of sequencing depth, base quality and allelic fraction. Compared with other methods, MuTect has higher sensitivity with similar specificity, especially for mutations with allelic fractions as low as 0.1 and below, making MuTect particularly useful for studying cancer subclones and their evolution in standard exome and genome sequencing data

Nuclear Factor I/B is an Oncogene in Small Cell Lung Cancer

Small cell lung cancer (SCLC) is an aggressive cancer often diagnosed after it has metastasized. Despite the need to better understand this disease, SCLC remains poorly characterized at the molecular and genomic levels. Using a genetically engineered mouse model of SCLC driven by conditional deletion of Trp53 and Rb1 in the lung, we identified several frequent, high-magnitude focal DNA copy number alterations in SCLC. We uncovered amplification of a novel, oncogenic transcription factor, Nuclear factor I/B (Nfib), in the mouse SCLC model and in human SCLC. Functional studies indicate that NFIB regulates cell viability and proliferation during transformation.National Cancer Institute (U.S.) (grant P30-CA14051)David H. Koch Institute for Integrative Cancer Research at MIT (Ludwig Center for Molecular Oncology)Howard Hughes Medical InstituteAlfred P. Sloan Foundation (Research Fellowship)International Association for the Study of Lung Cance

Human Mycobacterium bovis Infection and Bovine Tuberculosis Outbreak, Michigan, 1994–2007

Mycobacterium bovis is endemic in Michigan’s white-tailed deer and has been circulating since 1994. The strain circulating in deer has remained genotypically consistent and was recently detected in 2 humans. We summarize the investigation of these cases and confirm that recreational exposure to deer is a risk for infection in humans

Whole-genome and multisector exome sequencing of primary and post-treatment glioblastoma reveals patterns of tumor evolution

Glioblastoma (GBM) is a prototypical heterogeneous brain tumor refractory to conventional therapy. A small residual population of cells escapes surgery and chemoradiation, resulting in a typically fatal tumor recurrence ~7 mo after diagnosis. Understanding the molecular architecture of this residual population is critical for the development of successful therapies. We used whole-genome sequencing and whole-exome sequencing of multiple sectors from primary and paired recurrent GBM tumors to reconstruct the genomic profile of residual, therapy resistant tumor initiating cells. We found that genetic alteration of the p53 pathway is a primary molecular event predictive of a high number of subclonal mutations in glioblastoma. The genomic road leading to recurrence is highly idiosyncratic but can be broadly classified into linear recurrences that share extensive genetic similarity with the primary tumor and can be directly traced to one of its specific sectors, and divergent recurrences that share few genetic alterations with the primary tumor and originate from cells that branched off early during tumorigenesis. Our study provides mechanistic insights into how genetic alterations in primary tumors impact the ensuing evolution of tumor cells and the emergence of subclonal heterogeneity