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

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.

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.

Eligibility Criteria:
Inclusion Criteria:

FOR SURVIVORS

* To be eligible the exceptional survivor patients must fulfill the following inclusion criteria:

  1. Adult patient (≥18 years old at diagnosis).
  2. Three distinct cohorts, one of patients harbouring metastatic pancreatic ductal adenocarcinoma, glioblastoma IDHwt, extensive small cell lung cancer.
  3. Long-term survival is defined as an exceptionally long survival ≥ 5 years from stage IV diagnosis for PDAC, extensive SCLC, and ≥ 3 years for GBM-IDHwt.
  4. Availability of at least one block sample and associated clinical annotations with following characteristics:

     * One block sample must be of sufficient quality and in sufficient quantity to perform multi-omic analyses, according to requirements specified in Lab manual
     * Any treatment prior to sample acquisition must be reported - all treatments accepted (standard / targeted);
     * Samples should be at least 5 years old for PDAC and SCLC and 3 years old for GBM

For CONTROL GROUPS :

* To be eligible the control patients must fulfill the following inclusion criteria:

  1. ≥18 years old at diagnosis.
  2. Three distinct cohorts, one of patients suffering from metastatic pancreatic ductal adenocarcinoma, one for glioblastoma, one for extensive small cell lung cancer.
  3. Paired to long-term survivors as mentioned in the methodology section
  4. Death or median overall survival with a variation of 10% before of beyond as reported in pivotal clinical trials in the specific type disease
  5. Availability of at least one tumor sample and associated clinical annotations with following characteristics:

     * Sample must be of sufficient quality and in sufficient quantity to perform multi-omic analyses
     * Any treatment prior to sample acquisition must be reported (treatment-naive samples should be preferred) - all treatments accepted (standard / targeted).

     Exclusion Criteria for both groups :
* Patient must not be enrolled if he/she fulfils one of the following non-inclusion criteria:

  1. \<18 years old at diagnosis.
  2. Hematological malignancy or solid tumors, which are not in the scope of tumor types, described in the inclusion criteria.
  3. Tumor sample not available or not reaching the required quality for multi-omic analyses.