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

Targeted genomic rearrangements using CRISPR/Cas technology

Genomic rearrangements are frequently observed in cancer cells but have been difficult to generate in a highly specific manner for functional analysis. Here we report the application of CRISPR/Cas technology to successfully generate several types of chromosomal rearrangements implicated as driver events in lung cancer, including the CD74-ROS1 translocation event and the EML4-ALK and KIF5B-RET inversion events. Our results demonstrate that Cas9-induced DNA breaks promote efficient rearrangement between pairs of targeted loci, providing a highly tractable approach for the study of genomic rearrangements

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 retrotransposition in human cancer revealed by whole-genome and exome sequencing

Retrotransposons constitute a major source of genetic variation, and somatic retrotransposon insertions have been reported in cancer. Here, we applied TranspoSeq, a computational framework that identifies retrotransposon insertions from sequencing data, to whole genomes from 200 tumor/normal pairs across 11 tumor types as part of The Cancer Genome Atlas (TCGA) Pan-Cancer Project. In addition to novel germline polymorphisms, we find 810 somatic retrotransposon insertions primarily in lung squamous, head and neck, colorectal, and endometrial carcinomas. Many somatic retrotransposon insertions occur in known cancer genes. We find that high somatic retrotransposition rates in tumors are associated with high rates of genomic rearrangement and somatic mutation. Finally, we developed TranspoSeq-Exome to interrogate an additional 767 tumor samples with hybrid-capture exome data and discovered 35 novel somatic retrotransposon insertions into exonic regions, including an insertion into an exon of the PTEN tumor suppressor gene. The results of this large-scale, comprehensive analysis of retrotransposon movement across tumor types suggest that somatic retrotransposon insertions may represent an important class of structural variation in cancer.National Cancer Institute (U.S.) (grant U24CA143867)National Cancer Institute (U.S.) (grant U24CA126546

The Menin Tumor Suppressor Protein Is an Essential Oncogenic Cofactor for MLL-Associated Leukemogenesis

SummaryThe Mixed-Lineage Leukemia (MLL) protein is a histone methyltransferase that is mutated in clinically and biologically distinctive subsets of acute leukemia. MLL normally associates with a cohort of highly conserved cofactors to form a macromolecular complex that includes menin, a product of the MEN1 tumor suppressor gene, which is mutated in heritable and sporadic endocrine tumors. We demonstrate here that oncogenic MLL fusion proteins retain an ability to stably associate with menin through a high-affinity, amino-terminal, conserved binding motif and that this interaction is required for the initiation of MLL-mediated leukemogenesis. Furthermore, menin is essential for maintenance of MLL-associated but not other oncogene induced myeloid transformation. Acute genetic ablation of menin reverses aberrant Hox gene expression mediated by MLL-menin promoter-associated complexes, and specifically abrogates the differentiation arrest and oncogenic properties of MLL-transformed leukemic blasts. These results demonstrate that a human oncoprotein is critically dependent on direct physical interaction with a tumor suppressor protein for its oncogenic activity, validate a potential target for molecular therapy, and suggest central roles for menin in altered epigenetic functions underlying the pathogenesis of hematopoietic cancers

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

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

Effects of Pubertal Growth Variation on Knee Mechanics During Walking in Female and Male Adolescents

Introduction: Puberty substantially alters the body\u27s mechanical properties, neuromuscular control, and sex differences therein, likely contributing to increased, sex-biased knee injury risk during adolescence. Female adolescents have higher risk for knee injuries than male adolescents of similar age engaging in similar physical activities, and much research has investigated sex differences in mechanical risk factors. However, few studies address the considerable variation in pubertal growth (timing, pace), knee mechanics, and injury susceptibility within sexes, or the impact of such growth variation on mechanical injury risk. Objectives: The present study tested for effects of variation in pubertal growth on established mechanical knee injury risk factors, examining relationships between and within sexes. Methods: Pubertal growth indices describing variation in the timing and rate of pubertal growth were developed using principal component analysis and auxological data from serial stature measurements. Linear mixed models were applied to evaluate relationships between these indices and knee mechanics during walking in a sample of adolescents. Results: Later developing female adolescents with slower pubertal growth had higher extension moments throughout stance, whereas earlier developers had higher valgus knee angles and moments. In male adolescents, faster and later growth were related to higher extension moments throughout gait. In both sexes, faster growers had higher internal rotation moments at foot-strike. Conclusions: Pubertal growth variation has important effects on mechanical knee injury risk in adolescence, affecting females and males differently. Earlier developing females exhibit greater injury risk via frontal plane factors, whereas later/faster developing males have elevated risk via sagittal plane mechanisms

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