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Predictive Multimodal Signatures Associated With Response to Treatment and Prognosis of Patients With Stage IV Non-small Cell Lung Cancer (DEEP-Lung-IV)

The safety and scientific validity of this study is the responsibility of the study sponsor and investigators. Listing a study does not mean it has been evaluated by the U.S. Federal Government. Know the risks and potential benefits of clinical studies and talk to your health care provider before participating. Read our disclaimer for details.
 
ClinicalTrials.gov Identifier: NCT04994795
Recruitment Status : Recruiting
First Posted : August 6, 2021
Last Update Posted : April 5, 2022
Sponsor:
Information provided by (Responsible Party):
Sophia Genetics SAS

Brief Summary:
Predicting response to therapy and disease progression in stage IV NSCLC patients treated with pembrolizumab monotherapy, chemotherapy-pembrolizumab combination therapy or chemotherapy alone in the first-line setting.

Condition or disease Intervention/treatment
Non-small Cell Lung Cancer Metastatic Other: Predictive models (data collection)

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Study Type : Observational
Estimated Enrollment : 4000 participants
Observational Model: Cohort
Time Perspective: Other
Official Title: Deep Learning-Enabled Exploration of Predictive Signatures in a Multicenter Retrospective and Prospective Observational Study Allowing the Analysis of the Aggregation of Multimodal Clinical, Biological, Genomic and Radiomics Data Associated With the Response to Treatment and Prognosis of Patients With Stage IV Non-small Cell Lung Cancer
Actual Study Start Date : July 16, 2021
Estimated Primary Completion Date : August 2023
Estimated Study Completion Date : February 2024

Resource links provided by the National Library of Medicine

MedlinePlus related topics: Lung Cancer

Group/Cohort Intervention/treatment
Pembrolizumab monotherapy Other: Predictive models (data collection)
Machine learning predictive models

Chemotherapy and pembrolizumab combination therapy Other: Predictive models (data collection)
Machine learning predictive models

Chemotherapy doublet Other: Predictive models (data collection)
Machine learning predictive models




Primary Outcome Measures :
  1. Treatment response at first evaluation [ Time Frame: 6-12 weeks after treatment start ]
    Predict treatment response at first evaluation using baseline data


Secondary Outcome Measures :
  1. Progression-Free Survival [ Time Frame: Through study completion, expected 6-14 months contingent on cohort ]
    Predict Progression-Free Survival (PFS) using data at baseline and first evaluation

  2. Overall Survival [ Time Frame: Through study completion, expected 8-20 months contingent on cohort ]
    Predict Overall Survival (OS) using data at baseline and first evaluation

  3. Duration of Response [ Time Frame: Through study completion, expected 6-14 months contingent on cohort ]
    Predict Duration of Response (DoR) using data at baseline and first evaluation

  4. Time-To-Progression [ Time Frame: Through study completion, expected 6-14 months contingent on cohort ]
    Predict Time-To-Progression (TTP) using data at baseline and first evaluation



Information from the National Library of Medicine

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Ages Eligible for Study:   18 Years and older   (Adult, Older Adult)
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   No
Sampling Method:   Probability Sample
Study Population
Adult patients diagnosed with stage IV NSCLC (de novo or earlier stage progression to stage IV), without oncogene-activating mutations eligible for targeted therapy, that are treated in the first-line setting with either pembrolizumab monotherapy, chemotherapy and pembrolizumab combination therapy, or chemotherapy.
Criteria

Inclusion Criteria:

  • Adult ≥18 years old
  • Patient diagnosed with Stage IV NSCLC (de novo or earlier stage progression to stage IV)
  • Absence of oncogene activating mutations eligible patients to targeted therapy (EGFR, ALK)
  • Cohort A: Received first line treatment with pembrolizumab monotherapy
  • Cohort B: Received first line treatment with chemotherapy and pembrolizumab combination therapy
  • Cohort C: Received first line treatment with chemotherapy doublet

Exclusion Criteria:

  • Prior anti-cancer therapy for actual stage IV NSCLC
  • Critical data missing (e.g., PD-L1 status, baseline millimetric imaging, first evaluation millimetric imaging)
  • Patients participating in other clinical trials that modify the standard of care

Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT04994795


Contacts
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Contact: Philippe Menu, MD-PhD, MBA +41216941060 DeepLungIV@sophiagenetics.com

Locations
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United States, Iowa
Holden Comprehensive Cancer Center at University of Iowa Health Care Recruiting
Iowa City, Iowa, United States, 52242
Contact: Muhammad Furqan, MD       muhammad-furqan@uiowa.edu   
United States, Massachusetts
UMASS Memorial Health Recruiting
Worcester, Massachusetts, United States, 01655
Contact: Alexander Bankier, MD       alexander.bankier@umassmemorial.org   
United States, Wisconsin
Carbone Comprehensive Cancer Center at University of Wisconsin Recruiting
Madison, Wisconsin, United States, 53705
Contact: Mark Schiebler, MD       mschiebler@uwhealth.org   
Canada
Sunnybrook Health Sciences Centre Toronto Recruiting
Toronto, Canada
Contact: Anastasia Oikonomou, MD       anastasia.oikonomou@sunnybrook.ca   
France
Avicenne Hospital Recruiting
Bobigny, France, 93000
Contact: Boris Duchemann, MD       boris.duchemann@aphp.fr   
CHU Bordeaux Recruiting
Bordeaux, France
Contact: Rémi Veillon, Dr       remi.veillon@chu-bordeaux.fr   
Ambroise Paré Hospital Recruiting
Boulogne-Billancourt, France, 92100
Contact: Etienne Giroux-Leprieur, Pr       etienne.giroux-leprieur@aphp.fr   
Hospices Civils de Lyon Recruiting
Lyon, France, 69002
Contact: Sébastien Couraud, Pr       sebastien.couraud@chu-lyon.fr   
CHU de Nantes Recruiting
Nantes, France, 44093
Contact: Elvire Pons-Tostivint, MD       elvire.pons@chu-nantes.fr   
Tenon Hospital Recruiting
Paris, France, 75020
Contact: Jacques Cadranel, Pr       jacques.cadranel@aphp.fr   
Foch Hospital Recruiting
Suresnes, France, 92150
Contact: Perrine Créquit, MD       p.crequit@hopital-foch.com   
Centre Hospitalier de Toulon Recruiting
Toulon, France, 83100
Contact: Clarisse Audigier-Valette, MD       clarisse.audigier-valette@ch-toulon.fr   
Germany
University Hospital Leipzig Recruiting
Leipzig, Germany, 04103
Contact: Armin Frille, MD       armin.frille@medizin.uni-leipzig.de   
Israel
Shaare Zadek Medical Center Recruiting
Jerusalem, Israel
Contact: Nir Peled, Pr       nirp@szmc.org.il   
Sponsors and Collaborators
Sophia Genetics SAS
Investigators
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Study Director: Philippe Menu, MD-PhD, MBA SOPHiA GENETICS
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Responsible Party: Sophia Genetics SAS
ClinicalTrials.gov Identifier: NCT04994795    
Other Study ID Numbers: SGDLIV
First Posted: August 6, 2021    Key Record Dates
Last Update Posted: April 5, 2022
Last Verified: March 2022

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Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No
Keywords provided by Sophia Genetics SAS:
NSCLC
Immunotherapy
Chemotherapy
Predictive models
Radiomics
Multimodal
Genomics
Machine learning
Additional relevant MeSH terms:
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Lung Neoplasms
Carcinoma, Non-Small-Cell Lung
Respiratory Tract Neoplasms
Thoracic Neoplasms
Neoplasms by Site
Neoplasms
Lung Diseases
Respiratory Tract Diseases
Carcinoma, Bronchogenic
Bronchial Neoplasms