An essay titled precision immunotherapy has won the award Michelson Philanthropies & Science Prize for Immunology. His actor, Alexander Obradovica researcher at the Columbia University Irving Medical Center (USA), proposes an individualized approach that identifies the cell-to-cell interactions involved in resistance to cancer treatment with immunotherapy by means of algorithms.
As detailed by Obradovic in his article, published this week in Science, “In the last decade, cancer treatment has been revolutionized by the rise of inhibitory immunotherapy, since it activates the antitumor immune responses extensively and independently of the mechanisms of tumor growth. However, many patients do not respond to such therapy and there are no reliable indicators of therapeutic failure.
Treating cancer with algorithms
For this reason, it proposes a more precise cancer immunotherapy tailored to each patient. This treatment and the single cell RNA sequencing data would be combined with traditional cancer drugs, using two algorithms developed by his team.
One of them infers protein activity (VIPER) and another predicts drug sensitivity (ARACNe). The objective is to identify groups of cells associated with resistance to treatment in different types of tumors.
“With the large amount of missing data in single cell RNA sequencing experiments, this is like solving a crossword puzzle,” Obradovic compares. “ARACNe is the dictionary that tells us which letters go with which words, and VIPER is the one that solves the crossword, finding the right words even when most of the letters are missing.”
The researcher explains SYNC that the algorithms “help to discern the immune characteristics of patients who respond to immunotherapy in two ways: first, the inference of protein activity allows a greater resolution of the different cell subtypes present in tumors, as well as regulatory protein markers of those subtypes.
Probabilistic biomarkers
He then points out, “you can define a distinctive signature of each cell subtype and verify its enrichment in large cohorts of patients who responded or not to immunotherapy, determining for each cell type whether it was protective, harmful or neutral. The informative cell type markers can then be used as new biomarkers to determine who is most likely to benefit from treatment.”
The expert points out that “by identifying the regulatory pathways active in cells associated with resistance to immunotherapy, this approach suggests targets to eliminate those cells and, therefore, can lead to the discovery of combination therapies effective in patients who do not respond to treatment.
To this would be added “the high throughput drug screeningwhich would make it possible to quickly personalize and prioritize the optimal combination alternatives for the resistance mechanisms identified in a specific patient”, he emphasizes.
Obradovic says that the best hope for the future of his research “is to move anticipated discoveries about drug combinations to the design of new clinical trials and patient care. In addition, it plans to study and integrate into its approach the immunological effects of radiotherapy.
The benefits of combination therapy
The expert also points out the benefits of this combination therapy: “A full understanding of how each treatment option affects tumor cells and the immune cells that surround them can be used to identify resistance markers at an early stage. And thus intervene on these markers with an additional initial therapy, both immunological and targeted”.
Furthermore, “understanding the immune dynamics which comes into play with chemotherapy and the radiotherapy it will also serve to determine the moment of administration of the drugs, in order to take full advantage of any proinflammatory effect”, he concludes.
This article was first published on SYNC