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

NCT ID: NCT06160596

Last Updated: 2023-12-07

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

RECRUITING

Total Enrollment

1020 participants

Study Classification

OBSERVATIONAL

Study Start Date

2023-11-01

Study Completion Date

2028-05-01

Brief Summary

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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

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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

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

Study Design

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Observational Model Type

CASE_CONTROL

Study Time Perspective

RETROSPECTIVE

Study Groups

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PDAC STAGE IV SURVIVORS & CONTROLS

Metastatic pancreatic ductal adenocarcinoma (PDAC) (Other histologies such as adenosquamous carcinoma, hepatoid carcinoma, anaplastic undifferentiated carcinoma and medullary carcinoma, acinar cell carcinoma, neuroendocrine tumors, Solid pseudopapillary neoplasm, Pancreatoblastoma, Serous cystadenocarcinoma are excluded)

Long term survival multimodal analysis

Intervention Type GENETIC

* 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)

SMALL CELL LUNG CANCER EXTENSIVE STAGE SURVIVORS & CONTROLS

Extensive small cell lung cancer (SCLC) (Other histologies excluded: combined SCLC with some areas of non-small cell lung cancer (NSCLC), carcinoid tumors, typical and atypical, large cell neuroendocrine carcinoma of the lung).

Long term survival multimodal analysis

Intervention Type GENETIC

* 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)

GLIOBLASTOMA SURVIVORS & CONTROLS

Glioblastoma (GBM) (IDH mutated excluded)

Long term survival multimodal analysis

Intervention Type GENETIC

* 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)

Interventions

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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)

Intervention Type GENETIC

Eligibility Criteria

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Inclusion Criteria

FOR SURVIVORS


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 :


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).


1. \<18 years old at diagnosis.
3. Tumor sample not available or not reaching the required quality for multi-omic analyses.
Minimum Eligible Age

18 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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Gustave Roussy, Cancer Campus, Grand Paris

OTHER

Sponsor Role collaborator

Centre Leon Berard

OTHER

Sponsor Role collaborator

Vall d'Hebron Institute of Oncology

OTHER

Sponsor Role collaborator

Istituto Europeo di Oncologia

OTHER

Sponsor Role collaborator

Charite University, Berlin, Germany

OTHER

Sponsor Role collaborator

Cure 51

OTHER

Sponsor Role lead

Responsible Party

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Responsibility Role SPONSOR

Principal Investigators

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Julieta Rodriguez, MD

Role: PRINCIPAL_INVESTIGATOR

Gustave Roussy, Cancer Campus, Grand Paris

Locations

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Gustave Roussy Cancer Campus, Grand Paris

Villejuif, , France

Site Status RECRUITING

Countries

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France

Central Contacts

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Wolikow Nicolas, Master

Role: CONTACT

Phone: 0033772042022

Email: [email protected]

Simon Istolainen, Master

Role: CONTACT

Phone: 0033626955716

Email: [email protected]

Facility Contacts

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Julieta Rodriguez, MD

Role: primary

Christophe Javaud

Role: backup

Related Links

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http://www.cure51.com

corporate website of sponsor

Other Identifiers

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2022-A02541-42

Identifier Type: -

Identifier Source: org_study_id