Hallmark Analytics For Cancer and Disease

John Catanzaro
3 min readJan 26, 2019


2019 John A. Catanzaro

Hallmark Cancer Analytics is not just including tumor microenviroment analytics, it also includes cancer cell analytics. The cancer cell mutations / molecular expression can be dramatically different than the tumor cell matrix expression found in the tumor microenvironment. There can be loss of expression in both as well. DNA / RNA encoding for expressed cancer cell and tumor matrix antigens with continual periodic reanalysis is essential. Evaluation of immunogenicity and interrogation immune interruptions are mapped. All cancer related proteins (molecular products) are mapped. The entire schema engineering is for precision personalized immunocentric development which will keep in real time movement and advanced of any changes with mutation, migration, proliferation, evasion, escape, surveillance, defense and regulation in order to update the patient-specific vaccination.

In this schema, we do not settle for resistance, we anticipate it and re-engineer the regime to overcome the resistance. This vastly differs from the current chemotherapeutic and biologics model, where if there is resistance another regimen or biologic is administered if it follows research study guidelines and if all fails, there are no more options. Immunocentrics is offering evolving and revision options.

In addition, the magnitude of immune-centered analytics for disease and autoimmunity is astounding. “New data published in Nature Biotechnology, represents the largest ever analysis of immune cell signaling research, mapping more than 3,000 previously unlisted cellular interactions, and yielding the first ever immunocentric modular classification of diseases. These data serve to rewrite the reference book on immune-focused inter-cellular communications and disease relationships.

The immune system is highly complex and dynamic, and with a new immunology paper published every 30 minutes, there is no practical way for a human to grapple with the sheer size and diversity of the field. As this body of data grows, machine learning methods will be the only practical way of fully leveraging all the efforts being made to advance immunology and science in general.

Standardizing and contextualizing the full body of cell-cytokine relationships is vital in our ability to broaden immune system understanding. Based on this curated knowledge base, 355 hypotheses for entirely novel cell-cytokine interactions were generated through the application of validated prediction technologies.

These alone, represent discoveries born out of a better contextual understanding of existing immune system knowledge. This potential becomes even more powerful when such knowledge can be integrated with other rich data sources and AI technologies to generate significant new clues in the fight against disease.”

Read more at: https://phys.org/news/2018-06-unique-immune-focused-ai-largest-library.html#jCp