NCT06160596

Analyzing and Solving Exceptional Long-term Survivors in Solid Tumors With Poor Prognosis

Analyzing and Solving Exceptional Long-term Survivors in Solid Tumors With Poor Prognosis: A 3 Cohorts Case Control Matched Study

• Status: Recruiting
• Phase: —
• Study type: Observational
• Enrollment: 1,020 (estimated)
• Sponsor: Cure 51 · Academic / Other
• Sex: All
• Age: 18 Years
• Healthy volunteers: Not accepted

Summary

This is a retrospective, exploratory, multi-center, translational, 3 cohorts case control matched study conducted in patients harboring a solid tumor with poor prognosis who presented a long-term (case) and standard (standard) survival. Patients with: * Cohort A: metastatic pancreatic ductal adenocarcinoma * Cohort B: glioblastoma IDHwt * Cohort C: extensive small cell lung cancer This research aims to integrate data generated from clinical records, imaging, multi-omics and bioinformatics approaches to discriminate case and control and then to identify new therapeutic targets. Analyses will be performed depending on the tumor samples available with at least 3 omics levels and according to scientific advances; genomic, epigenomic, proteomics, metabolomics, transcriptomic, microbiomic.

Detailed description

We propose for the first time to build a large collection of samples from unexpected survivors and controls with standard survival to identify biomarkers of resistance and/or survival which would help developing new cancer therapeutics. Biological samples and clinical records will be collected and then centralised to extract the data of any patients who have survived more than 5 years for the cohorts of PDAC and SCLC and more than 3 years for the cohort of GMB-IDHwt from the day of diagnosis. In addition to the clinical record of the patient describing his/her history (including multiscale imaging, pathology, biological sample analysis), we will collect every point of data possible with current technologies, such as multi-omics including genome, proteome, transcriptome, epigenomic, metabolome and microbiome. The data set of these multi-omic groups are combined and are complementary to identify a certain biological function and its cellular source. Such complementary effects and synergistic interactions between omic layers in the life course can only be captured by integrative study of multiple molecular layers. Artificial intelligence (AI), specifically machine learning algorithms, will also help to understand these multi-omics data. AI can also bring a new layer of biomarker discovery enabling the analysis of whole slide images of biopsies with computer vision and linking those biomarkers to the multi omics genomic features. After interpreting the comprehensive data with our set-up bioinformatics team in coordination with the various centres, we expect to find molecular signatures and consequently therapeutic approaches to address patients and physicians unmet needs.

Conditions

• Pancreas Adenocarcinoma
• Small-cell Lung Cancer
• Glioblastoma, IDH-wildtype

Interventions

Type	Name	Description
GENETIC	Long term survival multimodal analysis	* To describe global signatures (Digital histology, Radiomic, Genomic, Transcriptomic, Proteomic, (Epigenomic) and clinical signature) that are associated with a patient's unexpected survival compared to standard patients across three cohorts of solid tumors with unmet medical needs. * To describe global signatures in the overall population (pan-cohort). * To describe clinical, digital pathology, radiomic, genomic, transcriptomic, proteomic and epigenomic signatures associated with patients' unexpected survival compared to standard patients for each cohort and in all cohorts (pan-cohort)

Timeline

• Start date: 2023-11-01
• Primary completion: 2025-11-01
• Completion: 2028-05-01
• First posted: 2023-12-07
• Last updated: 2023-12-07

Locations

1 site across 1 country: France