Immunotherapy, specifically the use of immune checkpoint inhibitors (ICIs), has revolutionized the treatment of advanced-stage solid tumors. ICIs work by targeting specific proteins that help keep immune responses in check, allowing the immune system to recognize and attack cancer cells more effectively. When these checkpoints are inhibited, the natural immune response is enhanced, often leading to significant tumor regression. Prominent ICIs, such as PD-1, PD-L1, and CTLA-4 inhibitors, have shown remarkable efficacy in various cancers, including melanoma, lung, bladder, and kidney cancers. This therapeutic approach not only offers potential for durable responses but also provides hope for patients who previously had limited treatment options. As research continues, the scope and utility of ICIs are expected to expand, paving the way for improved outcomes in advanced cancer care.
The discovery of novel predictive biomarkers is of most importance, especially considering the limitations and challenges associated with currently used routine biomarkers like PD-L1 and TMB. While these existing biomarkers have provided valuable insights into patient responses to treatments, they are not without flaws. The inherent heterogeneity of tumors means that a single biomarker might not provide a comprehensive view of the cancer landscape in an individual patient. Furthermore, variations in assay methodologies can lead to inconsistent results, which may affect treatment decisions. Moreover, no biomarker is 100% predictive; both PD-L1 and TMB have instances where they do not accurately forecast therapeutic responses. By identifying and validating new predictive biomarkers, we can pave the way for more precise and individualized therapeutic approaches, optimizing treatment outcomes and minimizing unnecessary interventions.
For this project, we are partnering with prestigious institutions such as Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Imperial College London, Amsterdam University Medical Center, and Oslo University Hospital to develop a range of AI-based imaging predictive biomarkers, aiming to enhance the precision of immunotherapy treatments.
The project is ongoing.
Key Publication:
- Rakaee M, Adib E, Ricciuti B, Sholl LM, Shi W, Alessi J V., Cortellini A, Fulgenzi CAM, Viola P, Pinato DJ, Hashemi S, Bahce I, Houda I, Ulas EB, Radonic T, Väyrynen JP, Richardsen E, Jamaly S, Andersen S, Donnem T, Awad MM, Kwiatkowski DJ. Association of Machine Learning-Based Assessment of Tumor-Infiltrating Lymphocytes on Standard Histologic Images With Outcomes of Immunotherapy in Patients With NSCLC. JAMA Oncology. 2022 DOI