Svitlana Tyekucheva

Cancer-Specific High-Throughput Annotation of Somatic Mutations: Computational Prediction of Driver Missense Mutations

Large-scale sequencing of cancer genomes has uncovered thousands of DNA alterations, but the functional relevance of the majority of these mutations to tumorigenesis is unknown. We have developed a computational method, called Cancer-specific High-throughput Annotation of Somatic Mutations (CHASM), to identify and prioritize those missense mutations most likely to generate functional changes that enhance tumor cell proliferation. The method has high sensitivity and specificity when discriminating between known driver missense mutations and randomly generated missense mutations (area under receiver operating characteristic curve, >0.91; area under Precision-Recall curve, >0.79). CHASM substantially outperformed previously described missense mutation function prediction methods at discriminating known oncogenic mutations in P53 and the tyrosine kinase epidermal growth factor receptor. We applied the method to 607 missense mutations found in a recent glioblastoma multiforme sequencing study. Based on a model that assumed the glioblastoma multiforme mutations are a mixture of drivers and passengers, we estimate that 8% of these mutations are drivers, causally contributing to tumorigenesis.

A novel direct activator of AMPK inhibits prostate cancer growth by blocking lipogenesis

5′AMP‐activated kinase (AMPK) constitutes a hub for cellular metabolic and growth control, thus representing an ideal therapeutic target for prostate cancers (PCas) characterized by increased lipogenesis and activation of mTORC1 pathway. However, whether AMPK activation itself is sufficient to block cancer cell growth remains to be determined. A small molecule screening was performed and identified MT 63–78, a specific and potent direct AMPK activator. Here, we show that direct activation of AMPK inhibits PCa cell growth in androgen sensitive and castration resistant PCa (CRPC) models, induces mitotic arrest, and apoptosis. In vivo, AMPK activation is sufficient to reduce PCa growth, whereas the allelic loss of its catalytic subunits fosters PCa development. Importantly, despite mTORC1 blockade, the suppression of de novo lipogenesis is the underpinning mechanism responsible for AMPK‐mediated PCa growth inhibition, suggesting AMPK as a therapeutic target especially for lipogenesis‐driven PCas. Finally, we demonstrate that MT 63–78 enhances the growth inhibitory effect of AR signaling inhibitors MDV3100 and abiraterone. This study thus provides a rationale for their combined use in CRPC treatment.

curatedOvarianData: clinically annotated data for the ovarian cancer transcriptome

This article introduces a manually curated data collection for gene expression meta-analysis of patients with ovarian cancer and software for reproducible preparation of similar databases. This resource provides uniformly prepared microarray data for 2970 patients from 23 studies with curated and documented clinical metadata. It allows users to efficiently identify studies and patient subgroups of interest for analysis and to perform meta-analysis immediately without the challenges posed by harmonizing heterogeneous microarray technologies, study designs, expression data processing methods and clinical data formats. We confirm that the recently proposed biomarker CXCL12 is associated with patient survival, independently of stage and optimal surgical debulking, which was possible only through meta-analysis owing to insufficient sample sizes of the individual studies. The database is implemented as the curatedOvarianData Bioconductor package for the R statistical computing language, providing a comprehensive and flexible resource for clinically oriented investigation of the ovarian cancer transcriptome. The package and pipeline for producing it are available from http://bcb.dfci.harvard.edu/ovariancancer. Database URL: http://bcb.dfci.harvard.edu/ovariancancer

Comparative Meta-analysis of Prognostic Gene Signatures for Late-Stage Ovarian Cancer

BACKGROUND Ovarian cancer is the fifth most common cause of cancer deaths in women in the United States. Numerous gene signatures of patient prognosis have been proposed, but diverse data and methods make these difficult to compare or use in a clinically meaningful way. We sought to identify successful published prognostic gene signatures through systematic validation using public data. METHODS A systematic review identified 14 prognostic models for late-stage ovarian cancer. For each, we evaluated its 1) reimplementation as described by the original study, 2) performance for prognosis of overall survival in independent data, and 3) performance compared with random gene signatures. We compared and ranked models by validation in 10 published datasets comprising 1251 primarily high-grade, late-stage serous ovarian cancer patients. All tests of statistical significance were two-sided. RESULTS Twelve published models had 95% confidence intervals of the C-index that did not include the null value of 0.5; eight outperformed 97.5% of signatures including the same number of randomly selected genes and trained on the same data. The four top-ranked models achieved overall validation C-indices of 0.56 to 0.60 and shared anticorrelation with expression of immune response pathways. Most models demonstrated lower accuracy in new datasets than in validation sets presented in their publication. CONCLUSIONS This analysis provides definitive support for a handful of prognostic models but also confirms that these require improvement to be of clinical value. This work addresses outstanding controversies in the ovarian cancer literature and provides a reproducible framework for meta-analytic evaluation of gene signatures.

A network of epigenetic regulators guides developmental haematopoiesis in vivo

The initiation of cellular programs is orchestrated by key transcription factors and chromatin regulators that activate or inhibit target gene expression. To generate a compendium of chromatin factors that establish the epigenetic code during developmental haematopoiesis, a large-scale reverse genetic screen was conducted targeting orthologues of 425 human chromatin factors in zebrafish. A set of chromatin regulators was identified that target different stages of primitive and definitive blood formation, including factors not previously implicated in haematopoiesis. We identified 15 factors that regulate development of primitive erythroid progenitors and 29 factors that regulate development of definitive haematopoietic stem and progenitor cells. These chromatin factors are associated with SWI/SNF and ISWI chromatin remodelling, SET1 methyltransferase, CBP–p300–HBO1–NuA4 acetyltransferase, HDAC–NuRD deacetylase, and Polycomb repressive complexes. Our work provides a comprehensive view of how specific chromatin factors and their associated complexes play a major role in the establishment of haematopoietic cells in vivo.

Human-macaque comparisons illuminate variation in neutral substitution rates

BackgroundThe evolutionary distance between human and macaque is particularly attractive for investigating local variation in neutral substitution rates, because substitutions can be inferred more reliably than in comparisons with rodents and are less influenced by the effects of current and ancient diversity than in comparisons with closer primates. Here we investigate the human-macaque neutral substitution rate as a function of a number of genomic parameters.ResultsUsing regression analyses we find that male mutation bias, male (but not female) recombination rate, distance to telomeres and substitution rates computed from orthologous regions in mouse-rat and dog-cow comparisons are prominent predictors of the neutral rate. Additionally, we demonstrate that the previously observed biphasic relationship between neutral rate and GC content can be accounted for by properly combining rates at CpG and non-CpG sites. Finally, we find the neutral rate to be negatively correlated with the densities of several classes of computationally predicted functional elements, and less so with the densities of certain classes of experimentally verified functional elements.ConclusionOur results suggest that while female recombination may be mainly responsible for driving evolution in GC content, male recombination may be mutagenic, and that other mutagenic mechanisms acting near telomeres, and mechanisms whose effects are shared across mammalian genomes, play significant roles. We also have evidence that the nonlinear increase in rates at high GC levels may be largely due to hyper-mutability of CpG dinucleotides. Finally, our results suggest that the performance of conservation-based prediction methods can be improved by accounting for neutral rates.

Integrating diverse genomic data using gene sets

We introduce and evaluate data analysis methods to interpret simultaneous measurement of multiple genomic features made on the same biological samples. Our tools use gene sets to provide an interpretable common scale for diverse genomic information. We show we can detect genetic effects, although they may act through different mechanisms in different samples, and show we can discover and validate important disease-related gene sets that would not be discovered by analyzing each data type individually.

De novo fatty acid synthesis at the mitotic exit is required to complete cellular division

Although the regulation of the cell cycle has been extensively studied, much less is known about its coordination with the cellular metabolism. Using mass spectrometry we found that lysophospholipid levels decreased drastically from G2/M to G1 phase, while de novo phosphatidylcholine synthesis, the main phospholipid in mammalian cells, increased, suggesting that enhanced membrane production was concomitant to a decrease in its turnover. In addition, fatty acid synthesis and incorporation into membranes was increased upon cell division. The rate-limiting reaction for de novo fatty acid synthesis is catalyzed by acetyl-CoA carboxylase. As expected, its inhibiting phosphorylation decreased prior to cytokinesis initiation. Importantly, the inhibition of fatty acid synthesis arrested the cells at G2/M despite the presence of abundant fatty acids in the media. Our results suggest that de novo lipogenesis is essential for cell cycle completion. This “lipogenic checkpoint” at G2/M may be therapeutically exploited for hyperproliferative diseases such as cancer.

Establishing the baseline level of repetitive element expression in the human cortex

BackgroundAlthough nearly half of the human genome is comprised of repetitive sequences, the expression profile of these elements remains largely uncharacterized. Recently developed high throughput sequencing technologies provide us with a powerful new set of tools to study repeat elements. Hence, we performed whole transcriptome sequencing to investigate the expression of repetitive elements in human frontal cortex using postmortem tissue obtained from the Stanley Medical Research Institute.ResultsWe found a significant amount of reads from the human frontal cortex originate from repeat elements. We also noticed that Alu elements were expressed at levels higher than expected by random or background transcription. In contrast, L1 elements were expressed at lower than expected amounts.ConclusionsRepetitive elements are expressed abundantly in the human brain. This expression pattern appears to be element specific and can not be explained by random or background transcription. These results demonstrate that our knowledge about repetitive elements is far from complete. Further characterization is required to determine the mechanism, the control, and the effects of repeat element expression.

Comparing Platforms for Messenger RNA Expression Profiling of Archival Formalin-Fixed, Paraffin-Embedded Tissues

Archival formalin-fixed, paraffin-embedded (FFPE) tissue specimens represent a readily available but largely untapped resource for gene expression profiling-based biomarker discovery. Several technologies have been proposed to cope with the bias from RNA cross-linking and degradation associated with archival specimens to generate data comparable with RNA from fresh-frozen materials. Direct comparison studies of these RNA expression platforms remain rare. We compared two commercially available platforms for RNA expression profiling of archival FFPE specimens from clinical studies of prostate and ovarian cancer: the Affymetrix Human Gene 1.0ST Array following whole-transcriptome amplification using the NuGen WT-Ovation FFPE System V2, and the NanoString nCounter without amplification. For each assay, we profiled 7 prostate and 11 ovarian cancer specimens, with a block age of 4 to 21 years. Both platforms produced gene expression profiles with high sensitivity and reproducibility through technical repeats from FFPE materials. Sensitivity and reproducibility remained high across block age within each cohort. A strong concordance was shown for the transcript expression values for genes detected by both platforms. We showed the biological validity of specific gene signatures generated by both platforms for both cohorts. Our study supports the feasibility of gene expression profiling and large-scale signature validation on archival prostate and ovarian tumor specimens using commercial platforms. These approaches have the potential to aid precision medicine with biomarker discovery and validation.