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  • 1
    In: Cell Systems, Elsevier BV, Vol. 5, No. 5 ( 2017-11), p. 485-497.e3
    Type of Medium: Online Resource
    ISSN: 2405-4712
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2017
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  • 2
    In: Cell, Elsevier BV, Vol. 163, No. 4 ( 2015-11), p. 1011-1025
    Type of Medium: Online Resource
    ISSN: 0092-8674
    RVK:
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    Language: English
    Publisher: Elsevier BV
    Publication Date: 2015
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  • 3
    In: Clinical Cancer Research, American Association for Cancer Research (AACR), Vol. 21, No. 4_Supplement ( 2015-02-15), p. A12-A12
    Abstract: Background: The efficacy of androgen signaling inhibitors such as Abiraterone (Abi) or Enzalutamide (Enz) has changed the standard of care in mCRPC. However, adaptive resistance to these agents is a consistent outcome with this therapy that undermines their benefit. The mechanisms underlying acquired resistance to Abi or Enz are poorly understood. The goals of the WCDT project are to identify the molecular pathways underlying the adaptive response to these targeted therapies through expression and mutational analysis of metastatic biopsies. Methods: Following central radiologic review, eligible mCRPC pts underwent biopsy at one of 5 WCDT clinical sites, using a uniform biopsy protocol. Tissue was both frozen, and formalin fixed/paraffin embedded (FFPE). Frozen samples were subject to laser capture microdissection for isolation of RNA and DNA enriched for mCRPC. FFPE tissue underwent a CLIA-certified assessment of a mutational panel, IHC for PTEN, and fluorescence in situ hybridization (FISH) for AR+. Pathway assessment is performed using RNA-seq and mutation data from mCRPC biopsies mapped onto a comprehensive pathway database connecting a tumor sample with genetic regulatory logic. Results: 70 of 300 planned mCRPC pts have undergone a metastasis biopsy. To date, biopsies have been obtained prior to treatment and following progression from one patient receiving Abi and one receiving Enz. Data collection from biopsies has been possible in 52 of 72 samples (72% success rate), and clinically actionable results have been returned to the care providers for 50 samples. The most commonly mutated gene assessed by the mutational panel was p53. Importantly, acquired mutation did not appear to be a mechanism for drug resistance in mCRPC, as the prevalence of tumors positive for mutations in genes contained in the panel was lower in patients who had progressed on Abi or Enz (9 of 16, 56%) than it was in treatment naïve patients (14 of 17, 82%). Gene expression-based signatures uncovered several pathways enriched in Abiraterone naïve compared to resistant samples. Conclusions: Genomic sequencing and expression analysis can be accomplished in small bone and soft tissue mCRPC biopsies. Pathway-based gene expression analysis appears to be a promising strategy to identify adaptive processes and targeting opportunities in Abi resistant mCRPC. Citation Format: Jack F. Youngren, Adam Foye, George Thomas, Joshua M. Stuart, Ted Goldstein, Baertsch Robert, Adrian Bivol, Artem Sokolov, Charles J. Ryan, Nader Pourmand, Tomasz M. Beer, Christopher P. Evans, Christopher P. Evans, Primo Lara, Jr., Martin E. Gleave, Kim N. Chi, Robert E. Reiter, Matthew Rettig, Owen Witte, Eric J. Small. Identification of pathways associated with abiraterone resistance in metastatic castration resistant prostate cancer: Preliminary results from the SU2C/AACR West Coast Prostate Cancer Dream Team. [abstract] . In: Proceedings of the AACR Precision Medicine Series: Drug Sensitivity and Resistance: Improving Cancer Therapy; Jun 18-21, 2014; Orlando, FL. Philadelphia (PA): AACR; Clin Cancer Res 2015;21(4 Suppl): Abstract nr A12.
    Type of Medium: Online Resource
    ISSN: 1078-0432 , 1557-3265
    RVK:
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2015
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  • 4
    In: Journal of Clinical Oncology, American Society of Clinical Oncology (ASCO), Vol. 32, No. 15_suppl ( 2014-05-20), p. 11078-11078
    Type of Medium: Online Resource
    ISSN: 0732-183X , 1527-7755
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    Language: English
    Publisher: American Society of Clinical Oncology (ASCO)
    Publication Date: 2014
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  • 5
    Online Resource
    Online Resource
    American Society of Clinical Oncology (ASCO) ; 2013
    In:  Journal of Clinical Oncology Vol. 31, No. 15_suppl ( 2013-05-20), p. 5006-5006
    In: Journal of Clinical Oncology, American Society of Clinical Oncology (ASCO), Vol. 31, No. 15_suppl ( 2013-05-20), p. 5006-5006
    Abstract: 5006 Background: Integrative analysis that combines expression data, copy number variation, sequence, and epigenetic data with thousands of known biological interactions can help identify strong biological associations and novel “genotype to phenotype” associations. Methods: We performed multi-dataset differential expression analysis with Statistical Analysis of Microarrays (SAM) and gene set enrichment analysis (GSEA) across 10 publicly-available PCa microarray datasets to identify genes and pathways differentially expressed between local (n=471) and metastatic (n=220) PCa. We used PARADIGM, an integrated pathway analysis method, on PCa tumor samples with mRNA expression, copy number variation, and TMPRSS2:ERG fusion status data to infer differential fusion gene-related “activities” for pathway features (genes, protein complexes, etc) within a “Superimposed Pathway” representing a comprehensive collection of genetic interactions currently containing 20,314 known interactions among 16,362 concepts representing 6916 proteins, 7345 complexes, 1449 families, 55 RNAs, 15 miRNAs and 582 processes. Results: Out of the 7571 genes tested (those genes having data in two or more studies), the meta-analysis on gene expression revealed 210 (1.8% FDR) positively associated with PCa metastasis and 403 (0.94% FDR) negatively associated genes (threshold was p 〈 0.001, 2-tailed Student’s t-test). GSEA highlighted cell proliferation, cell cycle control, and DNA damage repair pathways with metastatic tumors. The PARADIGM analysis identified a network containing 914 features connected by 1137 edges. PLK1 was identified as both highly expressed in metastatic PCa and as one of the fourteen hubs in the largest (596-feature) sub-network identified by PARADIGM. PLK1 was also associated with high Gleason Sum and recurrent disease in independent local PCa datasets. Conclusions: Using an approach pioneered by members of our SU2C/PCF supported PCa Dream Team, integrated analysis across multiple PCa datasets associates PLK1 activity with aggressive PCa and suggests it may provide a novel treatment target for at least a genetic sub-set of advanced PCa.
    Type of Medium: Online Resource
    ISSN: 0732-183X , 1527-7755
    RVK:
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    Language: English
    Publisher: American Society of Clinical Oncology (ASCO)
    Publication Date: 2013
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  • 6
    In: BMJ Open, BMJ, Vol. 12, No. 2 ( 2022-02), p. e052032-
    Abstract: Parkinson’s disease (PD) is a neurodegenerative disorder associated with progressive disability. While the precise aetiology is unknown, there is evidence of significant genetic and environmental influences on individual risk. The Australian Parkinson’s Genetics Study seeks to study genetic and patient-reported data from a large cohort of individuals with PD in Australia to understand the sociodemographic, genetic and environmental basis of PD susceptibility, symptoms and progression. Participants In the pilot phase reported here, 1819 participants were recruited through assisted mailouts facilitated by Services Australia based on having three or more prescriptions for anti-PD medications in their Pharmaceutical Benefits Scheme records. The average age at the time of the questionnaire was 64±6 years. We collected patient-reported information and sociodemographic variables via an online (93% of the cohort) or paper-based (7%) questionnaire. One thousand five hundred and thirty-two participants (84.2%) met all inclusion criteria, and 1499 provided a DNA sample via traditional post. Findings to date 65% of participants were men, and 92% identified as being of European descent. A previous traumatic brain injury was reported by 16% of participants and was correlated with a younger age of symptom onset. At the time of the questionnaire, constipation (36% of participants), depression (34%), anxiety (17%), melanoma (16%) and diabetes (10%) were the most reported comorbid conditions. Future plans We plan to recruit sex-matched and age-matched unaffected controls, genotype all participants and collect non-motor symptoms and cognitive function data. Future work will explore the role of genetic and environmental factors in the aetiology of PD susceptibility, onset, symptoms, and progression, including as part of international PD research consortia.
    Type of Medium: Online Resource
    ISSN: 2044-6055 , 2044-6055
    Language: English
    Publisher: BMJ
    Publication Date: 2022
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  • 7
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 75, No. 22_Supplement_1 ( 2015-11-15), p. PR10-PR10
    Abstract: We applied biologically-motivated feature transformations coupled with established machine learning methods to predict gene essentiality in CCLE cell line models. By leveraging additional large datasets, such as The Cancer Genome Atlas PanCancer12 data and MSigDB pathway definitions, we improved the robustness and biological interpretability of our models. We developed a multi-pathway learning (MPL) approach that associates a genetic pathway from MSigDB with a distinct kernel for use in a multiple kernel learning setting. We evaluated the performance of MPL compared to several other regression methods including random forests, kernel ridge regression, and elastic net linear models, We combined multiple approaches using an ensemble technique on the diverse set of predictors. We found that the best performing method was an ensemble combining MPL and random forest predictions. Both models utilized features derived from both gene expression and copy number data, the latter of which were filtered to those predicted as driver events in prior pan-cancer studies. The ensemble method was a joint winner in the recent DREAM 9 gene essentiality prediction challenge. MPL also demonstrated merit as a feature selector when used with other downstream methods. The ensemble performed best at predicting the essentiality of genes involved in cell cycle control (cyclins and cyclin-dependent kinases), protein degradation (proteasome complex), cell proliferation signaling (sonic hedgehog, Aurora-B, RAC1), apoptosis (RB1,TP53) and hypoxia response (VEGF, VHL). Many of the key genes in those pathways are known to be drivers of cancer progression, suggesting our method's utility as a biomarker for detecting key tumorigenic events. The advantage of MPL is that mechanistically coherent gene sets are automatically selected as high scoring pathway kernels (HSPKs). We investigated whether the HSPKs identify cellular processes relevant to the loss of key genes. To do this, we inspected the HSPKs for a few of the most abundantly mutated genes in cancer. The MPL predictor for TP53 included the targets of this transcription factor as well as HSPKs involved in apoptosis, a cellular process regulated by TP53. The retinoblastoma gene (RB1) MPL predictor included RB1 targets as well as HSPKs involved in the regulation of histone deacetylase (HDAC) that interacts with RB1 to suppress DNA synthesis. These findings suggest trends in the MPL results could reveal a pathway-level view of the synthetic lethal architecture of cells. Such a map, that links patterns of pathway expression to potential genetic vulnerabilities, could provide an invaluable tool for exploring new avenues to target cancer cells. Citation Format: Vladislav Uzunangelov, Evan Paull, Sahil Chopra, Daniel Carlin, Adrian Bivol, Kyle Ellrott, Kiley Graim, Yulia Newton, Sam Ng, Artem Sokolov, Joshua Stuart. Multiple Pathway Learning accurately predicts gene essentiality in the Cancer Cell Line Encyclopedia. [abstract]. In: Proceedings of the AACR Special Conference on Translation of the Cancer Genome; Feb 7-9, 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 1):Abstract nr PR10.
    Type of Medium: Online Resource
    ISSN: 0008-5472 , 1538-7445
    RVK:
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    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2015
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  • 8
    Online Resource
    Online Resource
    American Association for Cancer Research (AACR) ; 2015
    In:  Cancer Research Vol. 75, No. 22_Supplement_1 ( 2015-11-15), p. A2-64-A2-64
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 75, No. 22_Supplement_1 ( 2015-11-15), p. A2-64-A2-64
    Abstract: Background: Molecular-based subtypes most certainly play a role in cancer progression and treatment. The recent results from the The Cancer Genome Atlas (TCGA) Pan-Cancer-12 analyses revealed connections between the cell of origin and patient outcomes. For example, bladder cancers were found to relate to three major Pan-Cancer integrative subtypes, with adeno-like and squamous-like bladder cancers associated with poorer prognosis than tumors with bladder-distinct profiles. Furthermore, both adeno-like lung and squamous-like bladder cancers were found to be associated with the most aggressive form of the disease. Methods: We are collecting a catalog of molecular signatures for each subtype found from Pan-Cancer analyses in TCGA and from relevant external datasets. Our goal is to map every tumor sample to one or more signatures in this collection using machine-learning methods. This mapping will allow us to predict the subclonal composition of primary tumor biopsies and to compare them to inferences from the variant allele frequency analysis, shedding light on the gene expression changes associated with key events in tumor evolution. As a pilot study, we compared signatures derived from metastatic prostate samples to subtypes of primary prostate tumors. Our goal is to test whether a molecular profile of metastatic disease can be recognized early on in primary tumors. To do so, we used unsupervised classification of mRNA expression profiles to define clusters of metastatic disease from external datasets as well as separately for primary tumors including the TCGA prostate adenocarcinoma dataset. We then performed an all-against-all comparison of signatures derived from metastatic subtypes to signatures derived from primary tumor subtypes. Results: The majority of metastatic tumors are most closely associated with one out of four primary subtypes, suggesting we have identified a possible primary signature associated with more aggressive disease. The finding is supported by enrichment analysis of clinical variables in the primary subtypes. Specifically, the primary subtype most often associated with the metastatic tumors have higher Gleason scores and higher tumor grade. In addition, several molecular pathways (e.g. BioCarta Vitamin D Receptor and KEGG Integrins in Angiogenesis pathways) and genes (e.g. MMP9, FGA, and LYZ) were found to be associated with the location of metastasis. Conclusions: Training molecular subtype recognizers may hold promise for detecting minor populations of subclones in primary and metastatic tumors. The subclone decomposition can be used to detect the presence of more aggressive disease that may resist standard treatment regimens. We are now expanding our signature catalog to include a more comprehensive collection and applying to additional subtypes of interest. We will make all datasets and signatures available through a mature version of the UCSC TumorMap portal. Citation Format: Kiley Graim, Yulia Newton, Adrian Bivol, Artem Sokolov, Kyle Ellrott, Robert Baertsch, Joshua Stuart. A signature catalog to classify tumor mixtures: Application to recognition of metastatic disease in prostate cancer. [abstract]. In: Proceedings of the AACR Special Conference on Translation of the Cancer Genome; Feb 7-9, 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 1):Abstract nr A2-64.
    Type of Medium: Online Resource
    ISSN: 0008-5472 , 1538-7445
    RVK:
    RVK:
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2015
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  • 9
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 75, No. 22_Supplement_2 ( 2015-11-15), p. PR02-PR02
    Abstract: We applied biologically-motivated feature transformations coupled with established machine learning methods to predict gene essentiality in CCLE cell line models. By leveraging additional large datasets, such as The Cancer Genome Atlas PanCancer12 data and MSigDB pathway definitions, we improved the robustness and biological interpretability of our models. We developed a multi-pathway learning (MPL) approach that associates a genetic pathway from MSigDB with a distinct kernel for use in a multiple kernel learning setting. We evaluated the performance of MPL compared to several other regression methods including random forests, kernel ridge regression, and elastic net linear models, We combined multiple approaches using an ensemble technique on the diverse set of predictors. We found that the best performing method was an ensemble combining MPL and random forest predictions. Both models utilized features derived from both gene expression and copy number data, the latter of which were filtered to those predicted as driver events in prior pan-cancer studies. The ensemble method was a joint winner in the recent DREAM 9 gene essentiality prediction challenge. MPL also demonstrated merit as a feature selector when used with other downstream methods. The ensemble performed best at predicting the essentiality of genes involved in cell cycle control (cyclins and cyclin-dependent kinases), protein degradation (proteasome complex), cell proliferation signaling (sonic hedgehog, Aurora-B, RAC1), apoptosis (RB1,TP53) and hypoxia response (VEGF, VHL). Many of the key genes in those pathways are known to be drivers of cancer progression, suggesting our method's utility as a biomarker for detecting key tumorigenic events. The advantage of MPL is that mechanistically coherent gene sets are automatically selected as high scoring pathway kernels (HSPKs). We investigated whether the HSPKs identify cellular processes relevant to the loss of key genes. To do this, we inspected the HSPKs for a few of the most abundantly mutated genes in cancer. The MPL predictor for TP53 included the targets of this transcription factor as well as HSPKs involved in apoptosis, a cellular process regulated by TP53. The retinoblastoma gene (RB1) MPL predictor included RB1 targets as well as HSPKs involved in the regulation of histone deacetylase (HDAC) that interacts with RB1 to suppress DNA synthesis. These findings suggest trends in the MPL results could reveal a pathway-level view of the synthetic lethal architecture of cells. Such a map, that links patterns of pathway expression to potential genetic vulnerabilities, could provide an invaluable tool for exploring new avenues to target cancer cells. Citation Format: Vladislav Uzunangelov, Evan Paull, Sahil Chopra, Daniel Carlin, Adrian Bivol, Kyle Ellrott, Kiley Graim, Yulia Newton, Sam Ng, Artem Sokolov, Joshua Stuart. Multiple Pathway Learning accurately predicts gene essentiality in the Cancer Cell Line Encyclopedia. [abstract]. In: Proceedings of the AACR Special Conference on Computational and Systems Biology of Cancer; Feb 8-11 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 2):Abstract nr PR02.
    Type of Medium: Online Resource
    ISSN: 0008-5472 , 1538-7445
    RVK:
    RVK:
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2015
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    detail.hit.zdb_id: 1432-1
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  • 10
    In: PLOS ONE, Public Library of Science (PLoS), Vol. 17, No. 11 ( 2022-11-17), p. e0277680-
    Abstract: The UK Biobank genotyped about 500k participants using Applied Biosystems Axiom microarrays. Participants were subsequently sequenced by the UK Biobank Exome Sequencing Consortium. Axiom genotyping was highly accurate in comparison to sequencing results, for almost 100,000 variants both directly genotyped on the UK Biobank Axiom array and via whole exome sequencing. However, in a study using the exome sequencing results of the first 50k individuals as reference (truth), it was observed that the positive predictive value (PPV) decreased along with the number of heterozygous array calls per variant. We developed a novel addition to the genotyping algorithm, Rare Heterozygous Adjusted (RHA), to significantly improve PPV in variants with minor allele frequency below 0.01%. The improvement in PPV was roughly equal when comparing to the exome sequencing of 50k individuals, or to the more recent ~200k individuals. Sensitivity was higher in the 200k data. The improved calling algorithm, along with enhanced quality control of array probesets, significantly improved the positive predictive value and the sensitivity of array data, making it suitable for the detection of ultra-rare variants.
    Type of Medium: Online Resource
    ISSN: 1932-6203
    Language: English
    Publisher: Public Library of Science (PLoS)
    Publication Date: 2022
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