Tumour evolvability metrics predict recurrence in advanced localised prostate cancer (normal data)
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PROVIDER: EGAS00001006098 | EGA |
REPOSITORIES: EGA
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Project description:There is a need for quantitative measurements of evolutionary metrics in controlled clinical trials with long term follow-up information. This is particularly true in advanced localised prostate cancer, which can recur more than a decade after diagnosis. Here we mapped genomic intra-tumour heterogeneity in 642 tumour samples from 114 patients who took part in the IMRT and DELINEATE clinical trials, for which full clinical information and 12y median follow-up was available. We concomitantly assessed phenotypic (morphological) heterogeneity using Deep Learning in 1,923 histological sections from 250 IMRT patients (fully overlapping with the genetic set). This study shows that combining genomics with AI-aided histopathology in clinical trials leads to novel clinical biomarkers.
This EGA repository contains data produced from tumour samples using low coverage whole genome sequencing and a prostate cancer specific gene panel data following compression of unique molecular identifiers.
| EGAD00001010274 | EGA
Project description:There is a need for quantitative measurements of evolutionary metrics in controlled clinical trials with long term follow-up information. This is particularly true in advanced localised prostate cancer, which can recur more than a decade after diagnosis. Here we mapped genomic intra-tumour heterogeneity in 642 tumour samples from 114 patients who took part in the IMRT and DELINEATE clinical trials, for which full clinical information and 12y median follow-up was available. We concomitantly assessed phenotypic (morphological) heterogeneity using Deep Learning in 1,923 histological sections from 250 IMRT patients (fully overlapping with the genetic set). This study shows that combining genomics with AI-aided histopathology in clinical trials leads to novel clinical biomarkers.
This EGA repository contains data produced from tumour samples using low coverage whole genome sequencing and a prostate cancer specific gene panel data following compression of unique molecular identifiers.
| EGAD00001010295 | EGA
Project description:There is a need for quantitative measurements of evolutionary metrics in controlled clinical trials with long term follow-up information. This is particularly true in advanced localised prostate cancer, which can recur more than a decade after diagnosis. Here we mapped genomic intra-tumour heterogeneity in 642 tumour samples from 114 patients who took part in the IMRT and DELINEATE clinical trials, for which full clinical information and 12y median follow-up was available. We concomitantly assessed phenotypic (morphological) heterogeneity using Deep Learning in 1,923 histological sections from 250 IMRT patients (fully overlapping with the genetic set). This study shows that combining genomics with AI-aided histopathology in clinical trials leads to novel clinical biomarkers.
This EGA repository contains data produced from tumour samples using low coverage whole genome sequencing and a prostate cancer specific gene panel data following compression of unique molecular identifiers.
| EGAS00001006096 | EGA
Project description:Standard RNA analyses using microarrays and low-coverage polyadenylation enriched RNA-Sequencing (RNA-Seq) cannot fully characterize the complexity of the cancer transcriptome. To fully elucidate the transcriptome of prostate tumours, we performed ultra-deep total RNA-Seq on 144 localized prostate tumours with long-term clinical follow up. Analysis of linear RNAs identified a transcriptomic subtype associated with the aggressive intraductal carcinoma subhistology, and a fusion gene profile that differentiates localized from metastatic prostate cancers. Analysis of back-splicing events identified widespread RNA circularization, with the average tumour expressing 7,140 distinct circular RNAs. The degree of aberrant circRNA production is correlated to disease progression in multiple clinical cohorts. Loss of function screens identified 11.3% of the screened circRNAs as essential to prostate cancer proliferation, and for 93.6% of these, their parental linear genes are not required for proliferation. Follow-up studies on circCSNK1G3 revealed its role in regulating cell cycle progression. Ultra-deep transcriptome sequencing thus provides a more comprehensive view of the linear and circular transcriptional and functional landscapes of localized prostate cancer.
2019-02-07 | GSE113121 | GEO
Project description:Standard RNA analyses using microarrays and low-coverage polyadenylation enriched RNA-Sequencing (RNA-Seq) cannot fully characterize the complexity of the cancer transcriptome. To fully elucidate the transcriptome of prostate tumours, we performed ultra-deep total RNA-Seq on 144 localized prostate tumours with long-term clinical follow up. Analysis of linear RNAs identified a transcriptomic subtype associated with the aggressive intraductal carcinoma subhistology, and a fusion gene profile that differentiates localized from metastatic prostate cancers. Analysis of back-splicing events identified widespread RNA circularization, with the average tumour expressing 7,140 distinct circular RNAs. The degree of aberrant circRNA production is correlated to disease progression in multiple clinical cohorts. Loss of function screens identified 11.3% of the screened circRNAs as essential to prostate cancer proliferation, and for 93.6% of these, their parental linear genes are not required for proliferation. Follow-up studies on circCSNK1G3 revealed its role in regulating cell cycle progression. Ultra-deep transcriptome sequencing thus provides a more comprehensive view of the linear and circular transcriptional and functional landscapes of localized prostate cancer.
2019-02-07 | GSE113120 | GEO
Project description:Standard RNA analyses using microarrays and low-coverage polyadenylation enriched RNA-Sequencing (RNA-Seq) cannot fully characterize the complexity of the cancer transcriptome. To fully elucidate the transcriptome of prostate tumours, we performed ultra-deep total RNA-Seq on 144 localized prostate tumours with long-term clinical follow up. Analysis of linear RNAs identified a transcriptomic subtype associated with the aggressive intraductal carcinoma subhistology, and a fusion gene profile that differentiates localized from metastatic prostate cancers. Analysis of back-splicing events identified widespread RNA circularization, with the average tumour expressing 7,140 distinct circular RNAs. The degree of aberrant circRNA production is correlated to disease progression in multiple clinical cohorts. Loss of function screens identified 11.3% of the screened circRNAs as essential to prostate cancer proliferation, and for 93.6% of these, their parental linear genes are not required for proliferation. Follow-up studies on circCSNK1G3 revealed its role in regulating cell cycle progression. Ultra-deep transcriptome sequencing thus provides a more comprehensive view of the linear and circular transcriptional and functional landscapes of localized prostate cancer.
2019-02-07 | GSE113119 | GEO
Project description:Current prostate cancer prognostic models are based on pre-treatment prostate-specific antigen (PSA) levels, biopsy Gleason score, and clinical staging but in practice are inadequate to accurately predict clinical disease progression. Hence, we sought to develop a molecular panel for prostate cancer progression by reasoning that molecular profiles might further improve current clinical models. We analyzed a Swedish Watchful Waiting cohort (1977â1999) with up to 30 years of clinical follow up using a novel method for gene expression profiling. This cDNA-mediated annealing, selection, ligation, and extension (DASL) method enabled the use of formalin-fixed paraffin-embedded transurethral resection of prostate (TURP) samples taken at the time of the initial diagnosis. We determined the expression profiles of 6100 genes for 281 men divided in two extreme groups: men who died of prostate cancer or developed metastases and men who survived more than 10 years without metastases (lethals and indolents, respectively). Several models using clinical and molecular features were evaluated for their ability to distinguish lethal from indolent cases. Surprisingly, none of the predictive models using molecular profiles significantly improved over models using clinical variables only. We reasoned that tumor sampling might preclude the identification of the dominant metastatic nodule. Additional computational analysis confirmed that molecular heterogeneity within both the lethal and indolent classes is widespread in prostate cancer as compared to other types of tumors. Thus the determination of the molecularly dominant tumor nodule may be limited by sampling at time of initial diagnosis, may not be present at time of initial diagnosis, or may occur as the disease progresses preventing the development of molecular biomarkers for prostate cancer progression. 281 cases from the population-based Swedish-Watchful Waiting cohort. The cohort consists of men with localized prostate cancer (clinical stage T1-T2, Mx, N0); Training set: first 186 samples; Validation cohort: remaining 95 cases from the same population.
2010-05-07 | E-GEOD-16560 | biostudies-arrayexpress
Project description:Background Understanding the heterogeneous genotypes and phenotypes of prostate cancer is fundamental to improving the way we treat this disease. As yet, there are no validated descriptions of prostate cancer subgroups derived from integrated genomics linked with clinical outcome. Methods In a study of 482 tumour, benign and germline samples from 259 men with primary prostate cancer, we used integrative analysis of copy number alterations (CNA) and array transcriptomics to identify genomic loci that affect expression levels of mRNA in an expression quantitative trait loci (eQTL) approach, to stratify patients into subgroups that we then associated with future clinical behavior, and compared with either CNA or transcriptomics alone. Findings We identified five separate patient subgroups with distinct genomic alterations and expression profiles based on 100 discriminating genes in our separate discovery and validation sets of 125 and 99 men. These subgroups were able to consistently predict biochemical relapse (p=0.0017 and p=0.016 respectively) and were further validated in a third cohort with long-term follow-up (p=0.027). We show the relative contributions of gene expression and copy number data on phenotype, and demonstrate the improved power gained from integrative analyses. We confirm alterations in six genes previously associated with prostate cancer (MAP3K7, MELK, RCBTB2, ELAC2, TPD52, ZBTB4) in prostate cancer, and also identify 94 genes not previously linked to prostate cancer progression that would not have been detected using either transcript or copy number data alone. We confirm a number of previously published molecular changes associated with high risk disease, including MYC amplification, and NKX3-1, RB1 and PTEN deletions, as well as over-expression of PCA3 and AMACR, and loss of MSMB in tumour tissue. A subset of the 100 genes outperforms established clinical predictors of poor prognosis (PSA, Gleason score), as well as previously published gene signatures (p=0•0001). We further show how our molecular profiles can be used for the early detection of aggressive cases in a clinical setting, and inform treatment decisions. Interpretation For the first time this study demonstrates the importance of integrated genomic analyses incorporating both benign and tumour tissue data in identifying molecular alterations leading to generation of robust gene sets that are predictive of clinical outcome in independent patient cohorts. A total of 482 samples from 289 men with prostate cancer from two cohorts were included in this study. The discovery cohort comprised 125 tumour samples from radical prostatectomy (RP) with 118 matched benign samples, and 85 matched blood samples. An additional 4 benign samples from men undergoing Holmium laser enucleation of the prostate (HoLEP) and 16 radical prostatectomy samples from men with castrate-resistant prostate cancer, with 13 matched blood samples were also included. These were assayed on several platforms, including Illumina HT12v4 gene expression arrays, Illumina OMNI2.5M genotyping arrays and Affymetrix SNP6 genotyping arrays. The validation cohort comprised 103 tumour tissue samples from men with prostate cancer, with 99 matched benign tissue samples and 103 matched blood samples. This datasheet describes samples in the DISCOVERY COHORT only, with complete, QCd Illumina HT12v4 data for 13 CRPC samples, 113 tumour samples and 73 matched benign samples. Extensive clinical metadata is available in the associated publication Ross-Adams et al. (2015, Suppl. Table 2)
2015-07-30 | E-GEOD-70768 | biostudies-arrayexpress