Center for Global Research Data

Machine learning model to predict cancer immunotherapy response

Lead Investigator: Diego Chowell, Icahn School of Medicine at Mount Sinai
Title of Proposal Research: Machine learning model to predict cancer immunotherapy response
Vivli Data Request: 7810
Funding Source: None
Potential Conflicts of Interest: None

Summary of the Proposed Research:

Recently, as a treatment of cancer, immune system based therapy, also known as immunotherapy, is widely used. Immune checkpoints are part of the immune system and they exist to prohibit an immune response from being too strong that it kills normal cells in the body.

Immune checkpoints start working when proteins on the surface of T-cells, a type of immune cells that are developed from stem cells in the bone marrow and have an essential role in the adaptive immune response, recognize and bind to partner proteins on other cells such as tumor cells. When the binding is solid, an “off” signal is sent to the T-cells which can prevent the immune system from attacking the tumor cells.

Immunotherapy treatments called immune checkpoint blockade “blocks” the checkpoint proteins from binding with their partners, hence, prevents the “off” signal from being sent. This allows the T-cells to kill tumor cells.

Immune checkpoint blockade (ICB) has saved the lives of many patients with metastatic cancers. Metastatic cancer is a cancer that has spread from the part of the body where it started (the primary site) to other parts of the body. When cancer cells break away from a tumor, they can travel to other parts of the body through the bloodstream or the lymph system. In the United States, 6% of women have metastatic breast cancer when they are first diagnosed.

Melanoma is a type of cancer that ICB therapy is widely used for the first line treatment these days. The 3-year overall survival for advanced melanoma has increased from 12% before 2010, when standard of care was chemotherapy, to approximately 60% using ICB therapy such as PD-1/PD-L inhibitors. Although the effect of this treatment is significantly evident, the cost of treatment is still quite high and only less than 20% of cancer patients show response to this type of immunotherapy. Therefore, it is crucial that we build a guideline that can aid a patient to have the most beneficial treatment with positive outcomes. An accurate method for pre-therapy identification of patients whose tumors will respond to ICB would be of significant benefit.

 Requested Studies:

A Phase III, Open-Label, Multicenter, Randomized Study to Investigate the Efficacy and Safety of Atezolizumab (Anti-PD-L1 Antibody) Compared With Docetaxel in Patients With Non-Small Cell Lung Cancer After Failure With Platinum Containing Chemotherapy
Data Contributor: Roche
Study ID: NCT02008227
Sponsor ID: GO28915

A Phase III, Open-Label, Randomized Study of Atezolizumab in Combination With Bevacizumab Compared With Sorafenib in Patients With Untreated Locally Advanced or Metastatic Hepatocellular Carcinoma
Data Contributor: Roche
Study ID: NCT03434379
Sponsor ID: YO40245

A Phase I/III, Randomized, Double-Blind, Placebo-Controlled Study of Carboplatin Plus Etoposide With or Without Atezolizumab (Anti-PD-L1 Antibody) in Patients With Untreated Extensive-Stage Small Cell Lung Cancer
Data Contributor: Roche
Study ID: NCT02763579
Sponsor ID: GO30081

A Phase III, Double-Blinded, Randomized, Placebo-Controlled Study of Atezolizumab Plus Cobimetinib and Vemurafenib Versus Placebo Plus Cobimetinib and Vemurafenib in Previously Untreated BRAFV600 Mutation-Positive Patients With Unresectable Locally Advanced or Metastatic Melanoma
Data Contributor: Roche
Study ID: NCT02908672
Sponsor ID: CO39262

A Phase III, Open-Label, Randomized Study of Atezolizumab (MPDL3280A, Anti-Pd-L1 Antibody) in Combination With Carboplatin or Cisplatin + Pemetrexed Compared With Carboplatin or Cisplatin + Pemetrexed in Patients Who Are Chemotherapy-Naive and Have Stage IV Non-Squamous Non-Small Cell Lung Cancer
Data Contributor: Roche
Study ID: NCT02657434
Sponsor ID: GO29438

A Phase III, Open-Label, Multicenter, Randomized Study Evaluating the Efficacy and Safety of Atezolizumab (MPDL3280A, Anti-PD-L1 Antibody) in Combination With Carboplatin+Paclitaxel or Atezolizumab in Combination With Carboplatin+Nab-Paclitaxel Versus Carboplatin+Nab-Paclitaxel in Chemotherapy-Naive Patients With Stage IV Squamous Non-Small Cell Lung Cancer
Data Contributor: Roche
Study ID: NCT02367794
Sponsor ID: GO29437

A Phase III Multicenter, Randomized, Open-Label Study Evaluating the Efficacy and Safety of Atezolizumab (MPDL3280A, Anti-PD-L1 Antibody) in Combination With Carboplatin+Nab-Paclitaxel for Chemotherapy-Naive Patients With Stage IV Non-Squamous Non-Small Cell Lung Cancer
Data Contributor: Roche
Study ID: NCT02367781
Sponsor ID: GO29537

A Phase III, Open-Label, Randomized Study of Atezolizumab (MPDL3280A, Anti-PD-L1 Antibody) in Combination With Carboplatin+Paclitaxel With or Without Bevacizumab Compared With Carboplatin + Paclitaxel + Bevacizumab in Chemotherapy-Naïve Patients With Stage IV Non-Squamous Non-Small Cell Lung Cancer
Data Contributor: Roche
Study ID: NCT02366143
Sponsor ID: GO29436

A Phase III, Open-Label, Multicenter, Randomized Study to Investigate the Efficacy and Safety of Atezolizumab (Anti-PD-L1 Antibody) Compared With Chemotherapy in Patients With Locally Advanced or Metastatic Urothelial Bladder Cancer After Failure With Platinum-Containing Chemotherapy
Data Contributor: Roche
Study ID: NCT02302807
Sponsor ID: GO29294

A Phase III, Open-Label, Randomized Study of Atezolizumab (Anti-PD-L1 Antibody) in Combination With Bevacizumab Versus Sunitinib in Patients With Untreated Advanced Renal Cell Carcinoma
Data Contributor: Roche
Study ID: NCT02420821
Sponsor ID: WO29637