The 3rd annual Immuno-Oncology 360° meeting, #IO360nyc, took a 360-degree view of the IO space with the first day dedicated to science, the second to clinical data, and the third to the “business” landscape.
With over 800 clinical trials in process for combination IO therapeutics and almost no holds for toxic events, it is estimated that approximately 80 FDA approvals will be issued over the next two to four years. Thus, there is a significant need for efficient and effective patient selection methodologies. The following #IO360nyc presentations highlighted hot topics in the developing patient selection space:
- Priti Hegde, Biomarker Lead Oncology and Daniel Chen VP & Global Head of Cancer Immunotherapy Development for Genentech, @Genentech, proposed that the future of IO treatment selection would be similar to the infectious disease panels that are currently being deployed. Patient populations are segmented into three groups: Inflamed – which have CD8 cells in the TME, Immune Excluded – which have CD8 cells in the TME periphery but not inside, and Immune Desert – where there are no CD8 cells in the TME region.
- David Kaufman, Executive Director Translation IO Lead and Roy Baynes, CMO of Merck, @Merck, presented a similar approach but segmented patient populations into only two categories, T-Cell Inflamed and Non-Inflamed, with the need for biomarkers to do patient selection in combination therapies.
- Axel Hoos, SVP and Head of IO at Glaxo SmithKline, @GSK, gave a thought provoking talk on GSK’s plan for the development of IO drugs. They see cells as “the medicine of the future,” and are developing a modular approach where each type of technology, such as enhancing T-cells, blocking pathways and bedside manufacturing, can be combined and tested.
I found it interesting that there was little discussion regarding the use of contextual tissue biomarkers to drive decisions such as patient selection. Upon reflection, I propose that the application of digital tools, is sometimes overlooked due to some common misperceptions including the perception that IHC might lack precision, sensitivity or accuracy. While this may have been the case with traditional pathology techniques, Flagship’s cTA™ platform has leapfrogged the capabilities of its predecessors as well as other patient selection methodologies that are available today.
Flagship’s cTA™ platform overcomes the limitations of conventional pathology by providing an accurate, reproducible, high complexity data profile of the tissue. It accomplishes this by analyzing and providing multiple measurements for every cell and groups of cells across the entire tissue. The process relies on our patented “Cell Based Tissue Analysis” approach, which creates over 300 data points for every cell in the tissue. These data points describe morphometric characteristics, variations and organization of the cells; which are combined to create feature evaluations of the tissue architecture and organization. These features are used to classify cells into their phenotypes, such as tumor vs TME vs immune infiltrate, and to categorize cells for data interpretation and scoring. The appropriate fit-for-purpose data approach is used to create a summary score which statistically represents the tissue, and describes the relevant aspects of biomarker content and very importantly, context. The data that cTA™ produces provides a more sensitive and accurate approach which reflects the true biological measurement, with high reproducibility. By identifying patient’ phenotypes with Precision, Sensitivity and Accuracy, the likelihood of selecting the most and best patients for response in clinical trials is greatly increased.
I propose that digital tools, such as Flagship’s cTA™ platform must be among the lexicon of approaches that developers consider for precise, sensitive, and accurate patient selection. I welcome your feedback at @flagshiptrevor or addressed to me at email@example.com.