Non-small Cell Lung Cancer (NSCLC)Onc AINCT05711914
Predicting Response to PD-1 Checkpoint Blockade Using Deep Learning Analysis of Imaging and Clinical Data
An observational study of Non-small Cell Lung Cancer (NSCLC), run by Centre Hospitalier Universitaire de Nīmes at 1 site in France.
Trial summary
Immunotherapy has transformed cancer treatment with the PD-1 class of checkpoint inhibitors - pembrolizumab and nivolumab -- demonstrating durable responses in Stage IV metastatic tumors such as non-small cell lung cancer and melanoma. Read more about this study
- Who is it for?
- Ages 18 to 100 years
- Conditions the study is for
- Non-small Cell Lung Cancer (NSCLC)
- How many people
- 300 participants
- Where
- 1 siteFrance
- Study dates
- 31 Jan 2021to 31 Dec 2022
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 100 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: patients with non-small cell lung cancer (NSCLC), having undergone treatment with immunotherapy
Who may be able to join
None listed.
Who cannot join
- -
Trial locations and contacts
1 site in 1 country.
About this study
Immunotherapy has transformed cancer treatment with the PD-1 class of checkpoint inhibitors - pembrolizumab and nivolumab -- demonstrating durable responses in Stage IV metastatic tumors such as non-small cell lung cancer and melanoma. Despite these numerous successes, PD-1/PD-L1 checkpoint blockade therapies do have a number of shortcomings.
Many approaches to predict response to PD-1/PD-L1 checkpoint therapy have been investigated with limited success. Recent efforts exploring the utility of quantitative imaging biomarkers to predict response to PD-[L]1 immunotherapy have shown promise. The purpose of this retrospective multicenter study is to develop a multi-omic classifier to predict response to PD-1/PD-L1 checkpoint blockade for mutation negative (EGFR, ALK and ROS1) NSCLC
Read the detailed description
Recent Phase III studies have demonstrated the effectiveness of atezolizumab (PD-L1) in metastatic triple-negative breast cancer [3] and small cell lung cancer, while the standard of care for Stage III non-small cell lung cancer has changed with positive results of the PACIFIC Phase III study, where durvalumab (PD-L1) administered after chemoradiation showed a significant increase in overall survival.
Low response rates, generally in the 15% to 20% range in most diseases when used as a single agent, high therapy cost globally ($150,000 or more per year in the U.S) and serious immune-mediated adverse events, particularly when PD-1/PD-L1 inhibitors are combined with the CTLA-4 inhibitors (ipilimumab). Unpredictable and low patient response rates coupled with high drugs costs and serious toxicities can significantly burden healthcare systems, third-party payers and patients. Clearly, diagnostic tools to stratify patients according to response likelihood are necessary as PD-[L]1 checkpoint inhibitors continue to gain adoption.
The standard-of-care biomarker is an immunohistochemistry (IHC) test that measures levels of the PD-L1 protein expressed in tumor samples. Tumor mutational burden, presence of Tumor-Infiltrating Lymphocytes and inflammatory cytokines are being explored in multiple clinical trials involving PD-(L)1 often in combination with additional immuno-oncology (IO) therapies In such an approach, a non-invasive imaging scan can provide insight and information on the patient's entire tumor burden rather than a sample of a subset of lesions (as provided by biopsy or serum-based assays). When diagnostic images that depict all treatable lesions are further analyzed with computational techniques such as machine-learning and artificial intelligence, resulting in the identification of relevant imaging biomarkers, an accurate overall assessment of patient response to PD-[L]1 therapy may be attainable.
- Conditions
- Non-small Cell Lung Cancer (NSCLC)
- Medical subject headings
- Carcinoma, Non-Small-Cell Lung
What is being measured
The primary outcome is the main question the study is designed to answer.
developing a multi-omic classifier for predicting PD-1 response
Once sufficient patient data are accumulated, imaging data (both baseline and follow-up scans) will be annotated (segmented) to delineate lesions, lymph nodes, surrounding organs, etc…
Measured: during one month
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted3 Feb 2023
- Study start31 Jan 2021
- Primary completion31 Mar 2022
- Study completion31 Dec 2022
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Non-small Cell Lung Cancer (NSCLC)Studies of the same condition, open and under way.
- More trials run by Centre Hospitalier Universitaire de NīmesThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Other
- Time perspective
- Retrospective
- Enrolment
- 300 participants (actual)
Sponsor and investigators
- Lead sponsor
- Centre Hospitalier Universitaire de Nīmes
- Sponsor type
- University, hospital or other
- Collaborators
- MEDEXPRIM, GRATICULE
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05711914
- Sponsor's protocol ID
- Local2021/JPB-01
- Acronym
- Onc AI
- Status last verified
- Jan 2023
- First submitted
- 25 Jan 2023
- Last updated
- 3 Feb 2023
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 3 Feb 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Predicting Response to PD-1 Checkpoint Blockade Using Deep Learning Analysis of Imaging and Clinical Data
- ClinicalTrials.gov ID
- NCT05711914
- Acronym / sponsor ID
- Onc AI / Local2021/JPB-01
- Conditions
- Non-small Cell Lung Cancer (NSCLC)
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 300 participants (actual)
- Lead sponsor
- Centre Hospitalier Universitaire de NīmesUniversity, hospital or other
- Collaborators
- MEDEXPRIM, GRATICULE
- Study start
- 31 Jan 2021 (actual)
- Primary completion
- 31 Mar 2022 (actual)
- Study completion
- 31 Dec 2022 (actual)
- First posted
- 3 Feb 2023 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
Immunotherapy has transformed cancer treatment with the PD-1 class of checkpoint inhibitors - pembrolizumab and nivolumab -- demonstrating durable responses in Stage IV metastatic tumors such as non-small cell lung cancer and melanoma. Despite these numerous successes, PD-1/PD-L1 checkpoint blockade therapies do have a number of shortcomings.
Many approaches to predict response to PD-1/PD-L1 checkpoint therapy have been investigated with limited success. Recent efforts exploring the utility of quantitative imaging biomarkers to predict response to PD-[L]1 immunotherapy have shown promise. The purpose of this retrospective multicenter study is to develop a multi-omic classifier to predict response to PD-1/PD-L1 checkpoint blockade for mutation negative (EGFR, ALK and ROS1) NSCLC
Detailed description
Recent Phase III studies have demonstrated the effectiveness of atezolizumab (PD-L1) in metastatic triple-negative breast cancer [3] and small cell lung cancer, while the standard of care for Stage III non-small cell lung cancer has changed with positive results of the PACIFIC Phase III study, where durvalumab (PD-L1) administered after chemoradiation showed a significant increase in overall survival.
Low response rates, generally in the 15% to 20% range in most diseases when used as a single agent, high therapy cost globally ($150,000 or more per year in the U.S) and serious immune-mediated adverse events, particularly when PD-1/PD-L1 inhibitors are combined with the CTLA-4 inhibitors (ipilimumab). Unpredictable and low patient response rates coupled with high drugs costs and serious toxicities can significantly burden healthcare systems, third-party payers and patients. Clearly, diagnostic tools to stratify patients according to response likelihood are necessary as PD-[L]1 checkpoint inhibitors continue to gain adoption.
The standard-of-care biomarker is an immunohistochemistry (IHC) test that measures levels of the PD-L1 protein expressed in tumor samples. Tumor mutational burden, presence of Tumor-Infiltrating Lymphocytes and inflammatory cytokines are being explored in multiple clinical trials involving PD-(L)1 often in combination with additional immuno-oncology (IO) therapies In such an approach, a non-invasive imaging scan can provide insight and information on the patient's entire tumor burden rather than a sample of a subset of lesions (as provided by biopsy or serum-based assays). When diagnostic images that depict all treatable lesions are further analyzed with computational techniques such as machine-learning and artificial intelligence, resulting in the identification of relevant imaging biomarkers, an accurate overall assessment of patient response to PD-[L]1 therapy may be attainable.
Study design
- Observational model
- Other
- Time perspective
- Retrospective
Who may qualify
- Ages
- 18 to 100 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- patients with non-small cell lung cancer (NSCLC), having undergone treatment with immunotherapy
Key inclusion criteria (0 listed)
Key exclusion criteria (1 listed)
- -
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
developing a multi-omic classifier for predicting PD-1 response
Time frame: during one month
Once sufficient patient data are accumulated, imaging data (both baseline and follow-up scans) will be annotated (segmented) to delineate lesions, lymph nodes, surrounding organs, etc…
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 3 Feb 2023.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Non-small Cell Lung Cancer (NSCLC), run by Centre Hospitalier Universitaire de Nīmes at 1 site in France.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 0 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 3 Feb 2023. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in France. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
developing a multi-omic classifier for predicting PD-1 response (time frame: during one month). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 3 Feb 2023.