5711 results for "canc*"

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• Molecular Biomarkers Predicting Lung Cancer Response Phenotypes

  We have used a "chemistry first" approach to discover druggable acquired vulnerabilities that arised in the pathogenesis of non-small cell lung cancer (NSCLC). We screened chemical libraries (~200,000 compounds) for chemical toxins that killed subsets of NSCLC but not normal human lung epithelial cells (HBECs). We first screened a panel of 12 NSCLC lines that represented a variety of known oncogenotypes and identified chemicals with large Z scores and appropriate properties including re-supply, chemistry, and reproducible drug response phenotypes. This was then narrowed down to a list of 202 chemicals and 18 drugs with known targeting (henceforth called "Precision Oncology Probe Set", or POPS). These, and a panel of 30 clinically available drugs, targeted therapies, and drug combinations, already in use or in trials for NSCLC treatment, were then tested on a panel of 96 NSCLC lines for their drug response phenotypes in 12-point dose response curves. This information was analyzed using scanning ranked KS (Kolmogorov-Smirnov) and elastic net biostatistics approaches to identify molecular biomarkers (mutations, mRNA expression, copy number variation, protein expression, and metabolomics) which could predict for sensitivity or resistance to a particular chemical toxin or treatment regimen. From this we have discovered that: our approach identifies already known molecular biomarker of drug sensitivities (e.g. EGFR mutations and EGFR TK inhibitors); many clinically available chemotherapy agents have molecular biomarkers predicting preclinical model drug responses; the POP set of chemical toxins provides novel drug response phenotype patterns in the large NSCLC panel different from those found with clinically available agents including a therapeutic window; many of the POP toxins only hit a small percentage (~5%) of the NSCLC panel but the POP set as a whole provides "coverage" of the entire NSCLC panel; there are simple, one or 2 component molecular biomarkers (mutations, mRNA expression) that predict responses to the different chemical toxins in the NSCLC panel; and that the molecular biomarkers provide some information on the targets and pathways involved in response to the chemical toxins. Thus, we have identified a group of chemical toxins with selectivity for subsets of NSCLC and associated tumor molecular biomarkers to facilitate their development for precision medicine, and also, in some cases, information on the targets and pathways interdicted by these chemical compounds. In addition, we have discovered NSCLC predictive biomarkers for clinically available agents.

  Study phs001823

• RNASeq of Plasmacytoid Dendritic Cells in Head and Neck Squamous Cell Cancer Patients

  Plasmacytoid dendritic cells (pDC) are a subset of dendritic cells with unique immunophenotypic properties and functions. While their role in antiviral immunity through production of type I interferons is well-established, their contributions to anti-tumor immunity are less clear. While some evidence demonstrates that pDC in the tumor microenvironment (TME) may drive CD4+ T cell to become Foxp3+ T regulatory cells, little is understood about the relationship of pDC with cytotoxic CD8+ T cell, the key player in antitumor immune responses. In this study, we perform comprehensive immunophenotyping and functional analysis of pDC from the TME and draining lymph nodes of patients with head and neck squamous cell carcinoma (HNSCC) and identify a novel pDC subset characterized by expression of the TNF receptor superfamily member CD134 (OX40). We show that OX40 expression is expressed on intratumoral pDC in both humans and mice in a tumor-model specific fashion and that this subset of pDC enhances tumor associated-antigen (TAA)-specific CD8+ T cell responses. Through transcriptomic profiling of OX40-expressing pDC from the TME, we further characterize gene signatures unique to this pDC subset that support its role as an important immunostimulatory immune population in the TME.

  Study phs001824

• Study of 5'UTR Mutations in Prostate Cancer

  The 5' untranslated region (5' UTR) of mRNA controls the translation of specific transcripts through encoded cis-regulatory elements. Yet exceedingly little is known about the prevalence and function of 5' UTR mutations in cancer. We have uncovered many 5' UTR mutations in our analysis of localized and castration resistant prostate cancer patients, and hypothesize that systematic mutations in critical 5' UTR regulatory regions perturb gene expression to drive prostate cancer progression. To elucidate the functional 5' UTR regulatory landscape of prostate cancer, we have taken a multi-faceted approach including ribosome profiling, in vitro reporter assays and the development of a novel high throughput method to functionally access the impact of recurrent 5' UTR mutations on both transcription and translation.

  Study phs001825

• Anonymized germline variants of prospectively characterized clinical cancer specimens

  These clinical specimens represent solid tumors and matched blood controls that were collected as part of patients' routine care at Memorial Sloan Kettering Cancer Center. They were sequenced on the targeted platform MSK-IMPACT. Here, we performed germline variant calling on the normal blood specimens and assessed their zygosity in the concomitant tumor specimens for comprehensive exploration of the landscape of pathogenic germline variants in patients with advanced cancer.

  Study phs001858

• Elucidating Transcription Regulation by Epigenetics in Neuroblastoma

  PURPOSE Whole-genome profiles of the epigenetic modification 5-hydroxymethylcytosine (5-hmC) are robust diagnostic biomarkers in adult patients with cancer. We investigated if 5-hmC profiles would serve as novel prognostic markers in neuroblastoma, a clinically heterogeneous pediatric cancer. Because this DNA modification facilitates active gene expression, we hypothesized that 5-hmC profiles would identify transcriptomic networks driving the clinical behavior of neuroblastoma. PATIENTS AND METHODS Nano-hmC-Seal sequencing was performed on DNA from Discovery (n = 51), Validation (n = 38), and Children's Oncology Group (n = 20) cohorts of neuroblastoma tumors. RNA was isolated from 48 tumors for RNA sequencing. Genes with differential 5-hmC or expression between clusters were identified using DESeq2. A 5-hmC model predicting outcome in high-risk patients was established using linear discriminant analysis. RESULTS Comparison of low- versus high-risk tumors in the Discovery cohort revealed 577 genes with differential 5-hmC. Hierarchical clustering of tumors from the Discovery and Validation cohorts using these genes identified two main clusters highly associated with established prognostic markers, clinical risk group, and outcome. Genes with increased 5-hmC and expression in the favorable cluster were enriched for pathways of neuronal differentiation and KRAS activation, whereas genes involved in inflammation and the PRC2 complex were identified in the unfavorable cluster. The linear discriminant analysis model trained on high-risk Discovery cohort tumors was prognostic of outcome when applied to high-risk tumors from the Validation and Children's Oncology Group cohorts (hazard ratio, 3.8). CONCLUSION 5-hmC profiles may be optimal DNA-based biomarkers in neuroblastoma. Analysis of transcriptional networks regulated by these epigenomic modifications may lead to a deeper understanding of drivers of neuroblastoma phenotype. "Reprinted from Applebaum et. JCO Precision Oncology, 2019, with permission from creative commons license."

  Study phs001831

• Whole-Exome Sequencing and Targeted DNA Sequencing of Matched Ocular Melanocytosis and Uveal Melanoma

  Ocular melanocytosis is the most important predisposing condition for the eye cancer uveal melanoma (UM). Here we used whole-exome and deep targeted sequencing to identify for the first time a clonal Gq pathway mutation in ocular melanocytosis, which gave rise to UM. Additionally, we elucidated the order in which canonical genetic aberrations were acquired during tumor evolution. These findings provide a mechanistic explanation for the well-known clinical association of ocular melanocytosis with UM, and they provide new insights into UM tumor evolution. Reprinted from: Durante MA, Field MG, Sanchez MI, Covington KR, Decatur CL, Dubovy SR, Harbour JW. Genomic evolution of uveal melanoma arising in ocular melanocytosis, with permission from Cold Spring Harbor Molecular Case Studies.

  Study phs001835

• Deep Sequencing of 3 Cancer Cell Lines on 2 Sequencing Platforms (Illumina HiSeqX and NovaSeq)

  To test the performance of a new sequencing platform, develop an updated somatic calling pipeline and establish a reference for future benchmarking experiments, we performed whole-genome sequencing of 3 common cancer cell lines (COLO-829, HCC-1143 and HCC-1187) along with their matched normal cell lines to great sequencing depths (up to 278X coverage) on both Illumina HiSeqX and NovaSeq sequencing instruments. This dataset can be used for benchmarking purposes by the genomic community and as a reference call set for scientists using these 3 cell lines.

  Study phs001839

• Cross-Species Single-Cell Analysis of Pancreatic Ductal Adenocarcinoma Reveals Antigen-Presenting Cancer-Associated Fibroblasts

  Cancer-associated fibroblasts (CAFs) are major players in the progression and drug resistance of pancreatic ductal adenocarcinoma (PDAC). CAFs constitute a diverse cell population consisting of several recently described subtypes, although the extent of CAF heterogeneity has remained undefined. Here we employ single-cell RNA-sequencing to thoroughly characterize the neoplastic and tumor microenvironment content of human PDAC tumors. Six human PDAC tumor specimens from six patients were collected, and processed for single-cell RNA-sequencing analysis. Adjacent-normal pancreas tissue was also collected from two of the patients. Tumor samples were digested, and fluorescence-activated cell sorting was used to isolate viable cells. For one tumor sample, viable, CD45-negative, CD31-negative, and EpCAM-negative cells were also isolated to enrich for CAFs. The 10X Chromium platform was then used to isolate single cells for RNA-sequencing analysis. This work has demonstrated the differences in immune cell populations between adjacent-normal and tumor tissues, and identified subpopulations of epithelial cells and CAFs present in PDAC tumors. This high-throughput analysis is a resource to better understand the cell populations present in PDAC, and may ultimately aid in the development of more effective therapies for this deadly malignancy.

  Study phs001840

• Gabriella Miller Kids First Pediatric Research Program in Genetics at the Intersection of Childhood Cancer and Birth Defects

  The Gabriella Miller Kids First Pediatric Research Program (Kids First) is a trans-NIH effort initiated in response to the 2014 Gabriella Miller Kids First Research Act and supported by the NIH Common Fund. This program focuses on gene discovery in pediatric cancers and structural birth defects and the development of the Gabriella Miller Kids First Pediatric Data Resource (Kids First Data Resource). All of the WGS and phenotypic data from this study are accessible through dbGaP and kidsfirstdrc.org, where other Kids First datasets can also be accessed. Birth defects and childhood cancer share biological pathways that are important for cell growth and division. We propose that sequencing pediatric patients suffering both conditions will allow us to discover the underlying genes and in turn advance our understanding of the causes of these devastating diseases.

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• Characterization of Structural Variants in Acute Myeloid Leukemia Patients

  Acute myeloid leukemia (AML) is the most common form of acute leukemia in adults and is expected to increase in frequency as the population ages. Most subtypes of AML are difficult to treat, in part due to a lack of understanding of possible molecular targets. Furthermore, the peak incidence of AML is in the sixth decade, and patients over the age of 65 do not tolerate chemotherapeutic regimens well. Moreover, AML has been highly studied as a model for other cancers. An RNA Biology Program is established aiming to develop comprehensive datasets through the profiling of all different classes of RNA expression, RNA editing, alternative splicing, and non-coding RNAs in CD34+ normal hematopoietic stem cells (HSC), their differentiated progeny, and AML clinical samples that could be utilized to understand leukemia. These datasets will allow us to computationally identify and characterize various RNA species and processes to enable the development of effective therapeutics to AML, leukemia and other types of cancers. Epigenetic factors such as methylation and histone modifications will also be characterized from these clinical samples allowing for integrated dataset analysis to look at the cross-talk between RNA and DNA/epigenomics in leukemia. The following is a subsidiary study of the above study description where whole genome sequencing was performed on two collected AML patients' samples using Illumina and Oxford Nanopore sequencing technologies. The aim is to characterize the roles of structural variants (SV) in RNA biology which are potentially important for AML development using our own established long-read SV characterization tool, NanoVar. The advent of Oxford Nanopore sequencing technologies has opened a new avenue for better SV characterization by having longer sequencing reads. NanoVar is an accurate and rapid SV characterization tool that utilizes low-depth Nanopore sequencing data. NanoVar makes use of split-reads and hard-clipped reads for SV discovery and employs a simulation-trained neural network classifier for true positive enrichment. NanoVar exhibited higher accuracy and faster computational speed amongst other long-read SV detection tools in simulated data. In AML patient data, NanoVar displayed excellent accuracy in SV characterization (16/16 SVs validated in two patients) by using only 4x depth sequencing data. In summary, NanoVar improves the accuracy and speed of SV characterization at a lower sequencing cost, an approach compatible with clinical studies.

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