Time filter

Source Type

Popovic R.,Northwestern University | Teater M.,Institute for Computational Biomedicine | Jiang Y.,Institute for Computational Biomedicine | Ezponda T.,Northwestern University | And 25 more authors.
Cancer Cell | Year: 2013

The EZH2 histone methyltransferase is highly expressed in germinal center (GC) B cells and targeted by somatic mutations in B cell lymphomas. Here, we find that EZH2 deletion or pharmacologic inhibition suppresses GC formation and functions. EZH2 represses proliferation checkpoint genes and helps establish bivalent chromatin domains at key regulatory loci to transiently suppress GC B cell differentiation. Somatic mutations reinforce these physiological effects through enhanced silencing of EZH2 targets. Conditional expression of mutant EZH2 in mice induces GC hyperplasia and accelerated lymphomagenesis in cooperation with BCL2. GC B cell (GCB)-type diffuse large B cell lymphomas (DLBCLs) are mostly addicted to EZH2 but not the more differentiated activated B cell (ABC)-type DLBCLs, thus clarifying the therapeutic scope of EZH2 targeting. © 2013 Elsevier Inc.


Romanel A.,University of Trento | Tandefelt D.G.,Institute of Cancer Research | Conteduca V.,Institute of Cancer Research | Conteduca V.,Instituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori IRST | And 32 more authors.
Science Translational Medicine | Year: 2015

Androgen receptor (AR) gene aberrations are rare in prostate cancer before primary hormone treatment but emerge with castration resistance. To determine AR gene status using a minimally invasive assay that could have broad clinical utility, we developed a targeted next-generation sequencing approach amenable to plasma DNA, covering all AR coding bases and genomic regions that are highly informative in prostate cancer. We sequenced 274 plasma samples from 97 castration-resistant prostate cancer patients treated with abiraterone at two institutions. We controlled for normal DNA in patients' circulation and detected a sufficiently high tumor DNA fraction to quantify AR copy number state in 217 samples (80 patients). Detection of AR copy number gain and point mutations in plasma were inversely correlated, supported further by the enrichment of nonsynonymous versus synonymous mutations in AR copy number normal as opposed to AR gain samples. Whereas AR copy number was unchanged from before treatment to progression and no mutant AR alleles showed signal for acquired gain, we observed emergence of T878A or L702H AR amino acid changes in 13% of tumors at progression on abiraterone. Patients with AR gain or T878A or L702H before abiraterone (45%) were 4.9 and 7.8 times less likely to have a ≥50 or ≥90% decline in prostate-specific antigen (PSA), respectively, and had a significantly worse overall [hazard ratio (HR), 7.33; 95% confidence interval (CI), 3.51 to 15.34; P = 1.3 × 10-9) and progression-free (HR, 3.73; 95% CI, 2.17 to 6.41; P = 5.6 × 10-7) survival. Evaluation of plasma AR by next-generation sequencing could identify cancers with primary resistance to abiraterone.


Culjkovic-Kraljacic B.,University of Montréal | Fernando T.M.,Hematology and Oncology Division | Marullo R.,Hematology and Oncology Division | Calvo-Vidal N.,Hematology and Oncology Division | And 18 more authors.
Blood | Year: 2016

Aggressive double- And triple-hit (DH/TH) diffuse large B-cell lymphomas (DLBCLs) feature activation of Hsp90 stress pathways. Herein, we show that Hsp90 controls posttranscriptional dynamics of keymessengerRNA(mRNA) species including those encoding BCL6, MYC, and BCL2. Using a proteomics approach, we found that Hsp90 binds to andmaintains activity of eIF4E. eIF4E drives nuclear export and translation of BCL6, MYC, and BCL2 mRNA. eIF4E RNA-immunoprecipitation sequencing in DLBCL suggests that nuclear eIF4E controls an extended program that includes B-cell receptor signaling, cellular metabolism, and epigenetic regulation. Accordingly, eIF4E was required for survival of DLBCL including the most aggressive subtypes, DH/TH lymphomas. Indeed, eIF4E inhibition induces tumor regression in cell line and patient-derived tumorgrafts of TH-DLBCL, even in the presence of elevated Hsp90 activity. Targeting Hsp90 is typically limited by counterregulatory elevation of Hsp70B, which induces resistance to Hsp90 inhibitors. Surprisingly, we identify Hsp70mRNA as an eIF4E target. In this way, eIF4E inhibition can overcome drug resistance to Hsp90 inhibitors. Accordingly, rational combinatorial inhibition of eIF4E and Hsp90 inhibitors resulted in cooperative antilymphoma activity in DH/TH DLBCL in vitro and in vivo. © 2016 by The American Society of Hematology. © 2016 by The American Society of Hematology.


PubMed | Institute for Computational Biomedicine, Cornell College and Cornell University
Type: | Journal: Journal of visualized experiments : JoVE | Year: 2016

Understanding tumor clonality is critical to understanding the mechanisms involved in tumorigenesis and disease progression. In addition, understanding the clonal composition changes that occur within a tumor in response to certain micro-environment or treatments may lead to the design of more sophisticated and effective approaches to eradicate tumor cells. However, tracking tumor clonal sub-populations has been challenging due to the lack of distinguishable markers. To address this problem, a VDJ-seq protocol was created to trace the clonal evolution patterns of diffuse large B cell lymphoma (DLBCL) relapse by exploiting VDJ recombination and somatic hypermutation (SHM), two unique features of B cell lymphomas. In this protocol, Next-Generation sequencing (NGS) libraries with indexing potential were constructed from amplified rearranged immunoglobulin heavy chain (IgH) VDJ region from pairs of primary diagnosis and relapse DLBCL samples. On average more than half million VDJ sequences per sample were obtained after sequencing, which contain both VDJ rearrangement and SHM information. In addition, customized bioinformatics pipelines were developed to fully utilize sequence information for the characterization of IgH-VDJ repertoire within these samples. Furthermore, the pipeline allows the reconstruction and comparison of the clonal architecture of individual tumors, which enables the examination of the clonal heterogeneity within the diagnosis tumors and deduction of clonal evolution patterns between diagnosis and relapse tumor pairs. When applying this analysis to several diagnosis-relapse pairs, we uncovered key evidence that multiple distinctive tumor evolutionary patterns could lead to DLBCL relapse. Additionally, this approach can be expanded into other clinical aspects, such as identification of minimal residual disease, monitoring relapse progress and treatment response, and investigation of immune repertoires in non-lymphoma contexts.


News Article | February 3, 2016
Site: news.yahoo.com

FILE - In this March 30, 2011, file photo, a bedbug is displayed at the Smithsonian Museum in Washington. Researchers from Weill Cornell and scientists at the American Museum of Natural History have traced the nefarious pest through the New York City subway system and discovered a genetic diversity among the bloodsucking creatures. (AP Photo/Carolyn Kaster, File) More NEW YORK (AP) — Scientists have mapped the genome of bedbugs in New York City, then traced fragments of the nefarious pests' DNA through the subway system. In the grubby recesses of hundreds of stations, they discovered surprising genetic diversity among the bloodsucking creatures. The next step is to figure out how the information can be put to good use, such as to develop better insecticides or blood thinners. But these goals will take further medical research. For now, the focus is on two main players in New York life: the subway and bedbugs. Scientists already have found that genetic traces of bedbugs in northern Manhattan are more closely related to those in the island's southern part, while there are bigger variations between the Upper East Side and Upper West Side. Geneticist Christopher Mason, who worked on the project, says the reason for that can be found simply by looking at a subway map: In Manhattan, for instance, subway lines run the length of the island north to south, while there's no subway link through Central Park between the East Side and the West Side. Not that bedbugs are riding the subway, noted George Amato, an evolutionary biologist at the American Museum of Natural History who also worked on bedbug project. He says New York's bedbugs "move around with people, dogs, and people's items — and they probably move most easily the way people move most easily." Amato collaborated with Mason, who works at Weill Cornell Medicine's Institute for Computational Biomedicine. A bedbug colony at the famed museum was used for the genome map. A similar map was assembled by an international research team at 36 institutions, including the University of Cincinnati. The New York team's resulting scientific paper on the subject was published Tuesday in Nature Communications. A second paper on bedbug genetics, from the University of Cincinnati, also appeared Tuesday in the same publication. To learn how the bedbug has evolved and spread, the New York team took DNA sample swabs from 1,400 city locations including subway cars, turnstiles, ticket vending kiosks, and above ground places like parks. Amato said there are many ways small fragments of the critters' DNA, or DNA of a related species, could get into the subway — clinging to the clothes of some of the 6 million daily riders and their belongings, or washed down into the stations. Amato said the first rough bedbug genetic sequence emerged about a year ago, but it took months to refine the model into an accurate genome. "Before this, people were just feeling their way through in the dark; this genome turns the light on for various areas of other research," said Amato. "Our team is now moving on to the genetics of cockroaches and other living fossils." This story has been corrected to show that fragments of bedbugs' DNA or related species' DNA were collected at subways, not whole bedbugs.


News Article | February 3, 2016
Site: www.biosciencetechnology.com

Scientists have mapped the genome of bedbugs in New York City, then traced fragments of the nefarious pests' DNA through the subway system. In the grubby recesses of hundreds of stations, they discovered surprising genetic diversity among the bloodsucking creatures. The next step is to figure out how the information can be put to good use, such as to develop better insecticides or blood thinners. But these goals will take further medical research. For now, the focus is on two main players in New York life: the subway and bedbugs. Scientists already have found that genetic traces of bedbugs in northern Manhattan are more closely related to those in the island's southern part, while there are bigger variations between the Upper East Side and Upper West Side. Geneticist Christopher Mason, who worked on the project, says the reason for that can be found simply by looking at a subway map: In Manhattan, for instance, subway lines run the length of the island north to south, while there's no subway link through Central Park between the East Side and the West Side. Not that bedbugs are riding the subway, noted George Amato, an evolutionary biologist at the American Museum of Natural History who also worked on bedbug project. He says New York's bedbugs "move around with people, dogs, and people's items - and they probably move most easily the way people move most easily." Amato collaborated with Mason, who works at Weill Cornell Medicine's Institute for Computational Biomedicine. A bedbug colony at the famed museum was used for the genome map. A similar map was assembled by an international research team at 36 institutions, including the University of Cincinnati. The New York team's resulting scientific paper on the subject was published Tuesday in Nature Communications. A second paper on bedbug genetics, from the University of Cincinnati, also appeared Tuesday in the same publication. To learn how the bedbug has evolved and spread, the New York team took DNA sample swabs from 1,400 city locations including subway cars, turnstiles, ticket vending kiosks, and above ground places like parks. Amato said there are many ways small fragments of the critters' DNA, or DNA of a related species, could get into the subway - clinging to the clothes of some of the 6 million daily riders and their belongings, or washed down into the stations. Amato said the first rough bedbug genetic sequence emerged about a year ago, but it took months to refine the model into an accurate genome. "Before this, people were just feeling their way through in the dark; this genome turns the light on for various areas of other research," said Amato. "Our team is now moving on to the genetics of cockroaches and other living fossils."


Gayvert K.M.,Institute for Computational Biomedicine | Gayvert K.M.,Precision for Medicine | Gayvert K.M.,Sloan Kettering Cancer Center | Madhukar N.S.,Institute for Computational Biomedicine | And 4 more authors.
Cell Chemical Biology | Year: 2016

Over the past decade, the rate of drug attrition due to clinical trial failures has risen substantially. Unfortunately it is difficult to identify compounds that have unfavorable toxicity properties before conducting clinical trials. Inspired by the effective use of sabermetrics in predicting successful baseball players, we sought to use a similar “moneyball” approach that analyzes overlooked features to predict clinical toxicity. We introduce a new data-driven approach (PrOCTOR) that directly predicts the likelihood of toxicity in clinical trials. PrOCTOR integrates the properties of a compound's targets and its structure to provide a new measure, the PrOCTOR score. Drug target network connectivity and expression levels, along with molecular weight, were identified as important indicators of adverse clinical events. Our method provides a data-driven, broadly applicable strategy to identify drugs likely to possess manageable toxicity in clinical trials and will help drive the design of therapeutic agents with less toxicity. © 2016 Elsevier Ltd


Zhang C.,Institute for Computational Biomedicine | Zhang C.,New York Presbyterian Hospital Weill Cornell Medicine | Cleveland K.,New York Presbyterian Hospital Weill Cornell Medicine | Schnoll-Sussman F.,New York Presbyterian Hospital Weill Cornell Medicine | And 7 more authors.
Genome Biology | Year: 2015

Identifying the microbiome composition from primary tissues directly affords an opportunity to study the causative relationships between the host microbiome and disease. However, this is challenging due the low abundance of microbial DNA relative to the host. We present a systematic evaluation of microbiome profiling directly from endoscopic biopsies by whole genome sequencing. We compared our methods with other approaches on datasets with previously identified microbial composition. We applied this approach to identify the microbiome from 27 stomach biopsies, and validated the presence of Helicobacter pylori by quantitative PCR. Finally, we profiled the microbial composition in The Cancer Genome Atlas gastric adenocarcinoma cohort. © 2015 Zhang et al.


PubMed | Sloan Kettering Cancer Center, New York Presbyterian Hospital Weill Cornell Medicine and Institute for Computational Biomedicine
Type: | Journal: Genome biology | Year: 2015

Identifying the microbiome composition from primary tissues directly affords an opportunity to study the causative relationships between the host microbiome and disease. However, this is challenging due the low abundance of microbial DNA relative to the host. We present a systematic evaluation of microbiome profiling directly from endoscopic biopsies by whole genome sequencing. We compared our methods with other approaches on datasets with previously identified microbial composition. We applied this approach to identify the microbiome from 27 stomach biopsies, and validated the presence of Helicobacter pylori by quantitative PCR. Finally, we profiled the microbial composition in The Cancer Genome Atlas gastric adenocarcinoma cohort.

Loading Institute for Computational Biomedicine collaborators
Loading Institute for Computational Biomedicine collaborators