Lead Investigator: Benjamin Haibe-Kains, University Health Network
Title of Proposal Research: Biomarker discovery for immunotherapy response via pan-cancer and cancer-specific analyses
Vivli Data Request: 9377
Funding Source: Ongoing Supervisor funding
Potential Conflicts of Interest: None
Summary of the Proposed Research:
In recent years, there have been significant advancements in cancer treatment through the use of immune checkpoint blockade (ICB). ICB works by unleashing the body’s own immune system to attack and destroy cancer cells. This approach has led to the development of new drugs that are currently being studied in clinical trials. However, despite the growing amount of data from these trials, it is still challenging to determine which factors can predict how well a patient will respond to immunotherapy. Biomarkers are measurable indicators that can be objectively evaluated and used to assess various biological processes, conditions, or responses within the body. Many potential biomarkers have been identified, but the complexity and variability of tumors make it difficult to apply these findings in clinical settings and 60-80% of treated patients do not derive any clinical benefit.
To address this issue, we recently conducted a meta-analysis of public clinical datasets from various cancer patients (melanoma, lung, bladder etc.) who received immunotherapy. Meta-analysis is a statistical technique used in data analysis to combine and analyze the results of multiple independent studies for particular research questions. While this analysis showed promise, it had limitations such as a lack of detailed molecular data and small sample sizes for certain types of cancer. To overcome these limitations, we aim to gather and integrate data from a wide range of ICB clinical trials, including both molecular and clinical information.
If successful, this project will result in a powerful informatics tool capable of analyzing genomic data from multiple sources. These analyses will help researchers develop reliable genomic predictors for how patients with specific types of cancer will respond to immunotherapy. These predictors can then be used to categorize patients based on their expected response, which will, in turn, improve the design of future clinical trials. Ultimately, this approach aims to increase the effectiveness of immunotherapy in treating cancer patients, offering them better chances of successful treatment.
Requested Studies:
Study of Nivolumab Given Sequentially With Ipilimumab in Subjects With Advanced or Metastatic Melanoma (CheckMate 064)
Data Contributor: Bristol Myers Squibb
Study ID: NCT01783938
Sponsor ID: NCT01783938
Study of Nivolumab (BMS-936558) Plus Ipilimumab Compared With Ipilimumab Alone in the Treatment of Previously Untreated, Unresectable, or Metastatic Melanoma (CheckMate 069)
Data Contributor: Bristol Myers Squibb
Study ID: NCT01927419
Sponsor ID: NCT01927419