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TRACERx 100: metastatic samples

ABSTRACT: Whole exome sequencing of 10 metastatic biopsies from four TRACERx100 patients (see EGA dataset EGAS00001002247), collected either after relapse or death. The data from these samples are initially published with Abbosh, C. et al. Phylogenetic ctDNA analysis depicts early stage lung cancer evolution. Nature http://dx.doi.org/10.1038/nature22364 (2017). Abstract: Earlier detection of relapse following primary surgery for non-small cell lung cancer and the characterization of emerging subclones seeding metastatic sites might offer new therapeutic approaches to limit tumor recurrence. The potential to non-invasively track tumor evolutionary dynamics in ctDNA of early-stage lung cancer is not established. Here we conduct a patient-specific approach to ctDNA profiling in the first 100 lung TRACERx (TRAcking Cancer Evolution through therapy (Rx)) study participants, including one patient co-recruited to the PEACE (Posthumous Evaluation of Advanced Cancer Environment) post-mortem study. We identify independent predictors of ctDNA release in early-stage non-small cell lung cancer and perform tumor volume limit of detection analyses. Through blinded profiling of post-operative plasma, we observe evidence of adjuvant chemotherapy resistance and identify patients destined to experience recurrence of their lung cancer. Finally, we show that phylogenetic ctDNA profiling tracks the subclonal nature of lung cancer relapse and metastases, providing a new approach for ctDNA driven therapeutic studies.

PROVIDER: EGAS00001002415 | EGA |

REPOSITORIES: EGA

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ena-DATASET-CRICK-25-04-2017-09:59:15:463-240 - samples

Project description:Whole exome sequencing of 10 metastatic biopsies from four TRACERx100 patients (see EGA dataset EGAS00001002247), collected either after relapse or death. The data from these samples are initially published with Abbosh, C. et al. Phylogenetic ctDNA analysis depicts early stage lung cancer evolution. Nature, http://dx.doi.org/10.1038/nature22364 (2017). Abstract: Earlier detection of relapse following primary surgery for non-small cell lung cancer and the characterization of emerging subclones seeding metastatic sites might offer new therapeutic approaches to limit tumor recurrence. The potential to non-invasively track tumor evolutionary dynamics in ctDNA of early-stage lung cancer is not established. Here we conduct a patient-specific approach to ctDNA profiling in the first 100 lung TRACERx (TRAcking Cancer Evolution through therapy (Rx)) study participants, including one patient co-recruited to the PEACE (Posthumous Evaluation of Advanced Cancer Environment) post-mortem study. We identify independent predictors of ctDNA release in early-stage non-small cell lung cancer and perform tumor volume limit of detection analyses. Through blinded profiling of post-operative plasma, we observe evidence of adjuvant chemotherapy resistance and identify patients destined to experience recurrence of their lung cancer. Finally, we show that phylogenetic ctDNA profiling tracks the subclonal nature of lung cancer relapse and metastases, providing a new approach for ctDNA driven therapeutic studies.

| EGAD00001003301 | EGA

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Project description:MicroRNA profiling and prediction of recurrence/relapse-free survival in stage I lung cancer

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miRNA expression profiling in early-stage epithelial ovarian cancer

Project description:Early-stage epithelial ovarian cancer (eEOC) patients have a generally favorable prognosis but heterogeneous behavior at recurrence. Accurate prediction of the risk of relapse is still a major concern, essentially to avoid overtreatment. We have identified a robust miRNA-based signature named MiROvaR able to predict early disease recurrence in case materials of mostly advanced-stage EOC patients. We challenged MiROvaR in the eEOC sub-setting (stage IA-IIB) and it proved to accurately classify eEOC patients according to their risk of relapse.

2022-12-21 | GSE169314 | GEO

Genomic approach to refine prognosis for adjuvant therapy in early stage non-small cell lung carcinoma

Project description:Abstract from paper - Potti A, et al Keywords: Outcome, recurrence, adjuvant therapy, Early stage Non-small cell Lung Cancer, Lung Metgene Predictor

2006-08-10 | GSE3593 | GEO

A circulating risk score, based on combination of exo-miR130a-3p and fibrinopeptide A, as predictive biomarker of relapse in early stage non-small cell lung cancer patients.

Project description:To date, the 5-year overall survival of early-stage non-small cell lung cancer (NSCLC) is still unsatisfactory at 59.8%. Therefore, reliable prognostic factors are needed. Growing evidence shows that cancer progression may depend on a double interconnection between cancer cells and the surrounding tumor microenvironment; hence, circulating molecules may represent promising markers of cancer recurrence. Here, we performed in-depth analysis of circolome-derived markers, including Exo-miRnome and peptidome in 67 radically resected NSCLCs, to identify a prognostic score. The miRnome profile selected exo-miR130a-3p as the most overexpressed in patients with relapse. Peptidome analysis identified 4 progressively more degraded forms of fibrinopeptide A (fpA) in progressing patients. Notably, the combination of exo-miR130a-3p and the greatest fpA (2-16) built a score where high-risk patients had 24 months of disease-free survival. Moreover, in vitro transfections showed that higher levels of exo-miR-130a-3p lead to a deregulation of some pathways involved in metastasis, in angiogenesis, such as in coagulation which could be linked to the FpA reduction. In conclusion, the identified risk score integrating circulating markers can help clinicians predict early-stage NSCLC patients who are more likely to relapse after initial surgery.

2022-03-16 | MSV000089072 | MassIVE

Development and validation of a secretome-specific prognostic and predictive gene expression signature for colorectal cancer

Project description:Relapse and metastatic progression is a frequent event in colorectal cancer patients detected at early stages. The risk of recurrence requires the development of new biomarkers to correctly predict biological behavior of early stage II and stage III patients and their response to adjuvant chemotherapy. Here, we combined the proteomic quantification of secreted proteins involved in metastasis with a transcriptional analysis to develop a risk score algorithm based on the expression of six genes (SEC6). The SEC6 signature was predictive of survival and recurrence for stage II and III patients in four different datasets including a total of 1534 patients and was also associated with deficient mismatch repair, CpG-island methylator positive status and BRAF mutation. SEC6 was also predictive of beneficial or detrimental effects from 5-Fluorouracil-containing regimes and the improved response to more aggressive chemotherapies based on FOLFOX and FOLFIRI. In summary, the SEC6 risk-score algorithm may constitute a new tool for decision-making in colorectal cancer management.

2022-12-27 | PXD032899 | Pride

Expression profiling defines a recurrence signature in lung adenocarcinoma

2007-05-01 | GSE5843 | GEO

Expression profiling defines a recurrence signature in lung squamous cell carcinoma

Project description:Lung cancer remains the leading cause of cancer death worldwide. Overall 5-year survival is about 10-15% and despite curative intent surgery, treatment failure is primarily due to recurrent disease. Conventional prognostic markers are unable to determine which patients with completely resected disease within each stage group are likely to relapse. To identify a gene signature associated with recurrent squamous cell carcinoma (SCC) of lung, we analyzed primary tumour gene expression for a total of fifty-one SCCs (stage I-III) on 22,323 element microarrays, comparing expression profiles for individuals who remained disease-free for a minimum of 36 months with those from individuals whose disease recurred within 18 months of complete resection. Cox proportional hazards modeling with leave-one-out cross-validation identified a 70-gene capable of predicting the likelihood of tumor recurrence and a 79-gene signature predictive for overall survival. These two signatures were pooled to generate a 111-gene classifier which achieved an overall predictive accuracy for disease recurrence of 72% (77% sensitivity, 67% specificity) in an independent set of fifty-eight stage I-III SCCs. This classifier also predicted differences in survival (log-rank P=0.0008, hazard ratio (HR), 3.8 [95% confidence interval, 1.6-8.7]), and was superior to conventional prognostic markers such as TNM stage or N stage in predicting patient outcome. Genome-wide profiling has revealed a distinct gene expression profile for recurrent lung SCC which may be clinically useful as a prognostic tool. Expression profiling using 22K element microarrays of 51 primary lung squamous cell carcinomas.

2010-07-01 | E-GEOD-5123 | biostudies-arrayexpress

Expression profiling defines a recurrence signature in lung squamous cell carcinoma

Project description:Lung cancer remains the leading cause of cancer death worldwide. Overall 5-year survival is about 10-15% and despite curative intent surgery, treatment failure is primarily due to recurrent disease. Conventional prognostic markers are unable to determine which patients with completely resected disease within each stage group are likely to relapse. To identify a gene signature associated with recurrent squamous cell carcinoma (SCC) of lung, we analyzed primary tumour gene expression for a total of fifty-one SCCs (stage I-III) on 22,323 element microarrays, comparing expression profiles for individuals who remained disease-free for a minimum of 36 months with those from individuals whose disease recurred within 18 months of complete resection. Cox proportional hazards modeling with leave-one-out cross-validation identified a 70-gene capable of predicting the likelihood of tumor recurrence and a 79-gene signature predictive for overall survival. These two signatures were pooled to generate a 111-gene classifier which achieved an overall predictive accuracy for disease recurrence of 72% (77% sensitivity, 67% specificity) in an independent set of fifty-eight stage I-III SCCs. This classifier also predicted differences in survival (log-rank P=0.0008, hazard ratio (HR), 3.8 [95% confidence interval, 1.6-8.7]), and was superior to conventional prognostic markers such as TNM stage or N stage in predicting patient outcome. Genome-wide profiling has revealed a distinct gene expression profile for recurrent lung SCC which may be clinically useful as a prognostic tool. Keywords: non-small cell lung carcinoma, squamous cell, tumor recurrence, expression profiling

2006-11-21 | GSE5123 | GEO

GNPS Systematic analyses of early-stage lung cancer by scRNA-seq and lipidomic reveals aberrant lipid metabolism as detection biomarkers

Project description:Lung cancer is the leading cause of cancer mortality and early detection is the key to improve survival. However, there are no reliable blood-based tests currently available for early-stage lung cancer diagnosis. Here, we performed single-cell RNA sequencing of early-stage lung cancer and found lipid metabolism was broadly dysregulated in different cell types and glycerophospholipid metabolism is the most significantly altered lipid metabolism-related pathway. Untargeted lipidomics were detected in an exploratory cohort of 311 participants. Through support vector machine algorithm-based and mass spectrum-based feature selection, we have identified nine lipids as the most important detection features and developed a LC-MS-based targeted assay utilizing multiple reaction monitoring. This target assay achieved 100.00% specificity on an independent validation cohort. In a hospital-based lung cancer screening cohort of 1036 participants examined by low dose CT and a prospective clinical cohort containing 109 participants, this assay reached over 90.00% sensitivity and 92.00% specificity. Accordingly, matrix-assisted laser desorption/ionization-mass spectrometry imaging assay confirmed the selected lipids were differentially expressed in early-stage lung cancer tissues in situ. Thus, this method, designated as Lung Cancer Artificial Intelligence Detector (LCAID), may be used for early detection of lung cancer or large-scale screening of high-risk populations in cancer prevention.

2021-11-05 | MSV000088324 | GNPS

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