Dataset Information

Prognostic impact of a novel gene expression profile classifier for the discrimination between metastatic and non-metastatic primary colorectal cancer tumors.

ABSTRACT: Despite significant advances have been achieved in the genetic characterization of sporadic colorectal cancer (sCRC), the precise genetic events leading to the development of distant metastasis remain poorly understood. Thus, accurate prediction of metastatic disease in newly-diagnosed sCRC patients remains a challenge. Here, we evaluated the specific genes and molecular pathways associated with the invasive potential of colorectal tumor cells, through the assessment of the gene expression profile (GEP) of coding and non-coding genes in metastatic (MTX) vs. non-metastatic (non-MTX) primary sCRC tumors followed for >5 years. Overall, MTX tumors showed up-regulation of genes associated with tumor progression and metastatic potential while non-MTX cases displayed GEP associated with higher cell proliferation, activation of DNA repair and anti-tumoral immune/inflammatory responses. Based on only 19 genes a specific GEP that classifies sCRC tumors into two MTX-like and non-MTX-like molecular subgroups was defined which shows an independent prognostic impact on patient overall survival, particularly when it is combined with the lymph node status at diagnosis. In summary, we show an association between the global GEP of primary sCRC cells and their metastatic potential and defined a GEP-based classifier that provides the basis for further prognostic stratification of sCRC patients who are at risk of distant metastases.

SUBMITTER: Gutierrez ML

PROVIDER: S-EPMC5746100 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature

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Prognostic impact of a novel gene expression profile classifier for the discrimination between metastatic and non-metastatic primary colorectal cancer tumors.

Gutiérrez María Laura ML Corchete Luis Antonio LA Sarasquete María Eugenia ME Del Mar Abad María M Bengoechea Oscar O Fermiñán Encarna E Anduaga María Fernanda MF Del Carmen Sofía S Iglesias Manuel M Esteban Carmen C Angoso María M Alcazar Jose Antonio JA García Jacinto J Orfao Alberto A Muñoz-Bellvís Luis L Sayagués José María JM

Oncotarget 20171121 64

Despite significant advances have been achieved in the genetic characterization of sporadic colorectal cancer (sCRC), the precise genetic events leading to the development of distant metastasis remain poorly understood. Thus, accurate prediction of metastatic disease in newly-diagnosed sCRC patients remains a challenge. Here, we evaluated the specific genes and molecular pathways associated with the invasive potential of colorectal tumor cells, through the assessment of the gene expression profi ...[more]

PMID: 29296198

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Project description:ObjectivesTo assess imaging features of primary renal sarcomas in order to better discriminate them from non-sarcoma renal tumors.MethodsAdult patients diagnosed with renal sarcomas from 1995 to 2018 were included from 11 European tertiary referral centers (Germany, Belgium, Turkey). Renal sarcomas were 1:4 compared to patients with non-sarcoma renal tumors. CT/MRI findings were assessed using 21 predefined imaging features. A random forest model was trained to predict "renal sarcoma vs. non-sarcoma renal tumors" based on demographics and imaging features.Resultsn = 34 renal sarcomas were included and compared to n = 136 non-sarcoma renal tumors. Renal sarcomas manifested in younger patients (median 55 vs. 67 years, p < 0.01) and were more complex (high RENAL score complexity 79.4% vs. 25.7%, p < 0.01). Renal sarcomas were larger (median diameter 108 vs. 43 mm, p < 0.01) with irregular shape and ill-defined margins, and more frequently demonstrated invasion of the renal vein or inferior vena cava, tumor necrosis, direct invasion of adjacent organs, and contact to renal artery or vein, compared to non-sarcoma renal tumors (p < 0.05, each). The random forest algorithm yielded a median AUC = 93.8% to predict renal sarcoma histology, with sensitivity, specificity, and positive predictive value of 90.4%, 76.5%, and 93.9%, respectively. Tumor diameter and RENAL score were the most relevant imaging features for renal sarcoma identification.ConclusionRenal sarcomas are rare tumors commonly manifesting as large masses in young patients. A random forest model using demographics and imaging features shows good diagnostic accuracy for discrimination of renal sarcomas from non-sarcoma renal tumors, which might aid in clinical decision-making.Key points• Renal sarcomas commonly manifest in younger patients as large, complex renal masses. • Compared to non-sarcoma renal tumors, renal sarcomas more frequently demonstrated invasion of the renal vein or inferior vena cava, tumor necrosis, direct invasion of adjacent organs, and contact to renal artery or vein. • Using demographics and standardized imaging features, a random forest showed excellent diagnostic performance for discrimination of sarcoma vs. non-sarcoma renal tumors (AUC = 93.8%, sensitivity = 90.4%, specificity = 76.5%, and PPV = 93.9%).

Project description:BackgroundColorectal cancer (CRC) ranks as the second-leading cause of cancer-related death worldwide with metastases being the main cause of cancer-related death. Here, we investigated the genomic and transcriptomic alterations in matching adjacent normal tissues, primary tumors, and metastatic tumors of CRC patients.MethodsWe performed whole genome sequencing (WGS), multi-region whole exome sequencing (WES), simultaneous single-cell RNA-Seq, and single-cell targeted cDNA Sanger sequencing on matching adjacent normal tissues, primary tumors, and metastatic tumors from 12 metastatic colorectal cancer patients (n=84 for genomes, n=81 for exomes, n=9120 for single cells). Patient-derived tumor organoids were used to estimate the anti-tumor effects of a PPAR inhibitor, and self-renewal and differentiation ability of stem cell-like tumor cells.ResultsWe found that the PPAR signaling pathway was prevalently and aberrantly activated in CRC tumors. Blocking of PPAR pathway both suppressed the growth and promoted the apoptosis of CRC organoids in vitro, indicating that aberrant activation of the PPAR signaling pathway plays a critical role in CRC tumorigenesis. Using matched samples from the same patient, distinct origins of the metastasized tumors between lymph node and liver were revealed, which was further verified by both copy number variation and mitochondrial mutation profiles at single-cell resolution. By combining single-cell RNA-Seq and single-cell point mutation identification by targeted cDNA Sanger sequencing, we revealed important phenotypic differences between cancer cells with and without critical point mutations (KRAS and TP53) in the same patient in vivo at single-cell resolution.ConclusionsOur data provides deep insights into how driver mutations interfere with the transcriptomic state of cancer cells in vivo at a single-cell resolution. Our findings offer novel knowledge on metastatic mechanisms as well as potential markers and therapeutic targets for CRC diagnosis and therapy. The high-precision single-cell RNA-seq dataset of matched adjacent normal tissues, primary tumors, and metastases from CRCs may serve as a rich resource for further studies.

Dataset Information

Prognostic impact of a novel gene expression profile classifier for the discrimination between metastatic and non-metastatic primary colorectal cancer tumors.

Publications

Prognostic impact of a novel gene expression profile classifier for the discrimination between metastatic and non-metastatic primary colorectal cancer tumors.

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