Algorithmically Tailored Immunotherapy: The Amazon Method of Curing Cancer?

“Customers who bought this item also bought…” Well, in the case of therapies for life-threatening diseases, Amazon’s familiar trope does not sound too helpful since it will frequently, and quite predictably, list “all of the above,” especially before insurance benefits have run out. And yet, it is a highly valuable if nascent computational concept, even though a long road still lay ahead before it may start to limit wasteful and disheartening trial-and-error in clinical reality.

The first effective oncological immunotherapy was approved just a few years ago. Today, patients with advanced skin cancer receive this therapy where medication supports the immune system in its struggle against cancer. Data analysis of several thousand skin cancer patients who were originally predicted to have only a few months to live shows that in about one-fifth of them, even two to three years after immunotherapy, tumors show no signs of return. This turned into a medical sensation that was all the more impressive as these drugs showed results not just with skin cancers but also with other types of malignancies, and now immunotherapeutics are approved also for lung, bladder and kidney cancers, adding more variants almost by the day.

The downside is that immunotherapy is effective only in about 20 percent of patients with most cancers. There are varieties, such as colon cancer, that are largely resistant to immunotherapy. Again, bioinformatics holds considerable promise to change this picture, since colorectal cancer is one of the most common types, with 1.4 million new cases per year world-wide. 

To be able to predict which combination of immunotherapy and standard therapy would be effective in a given patient, researchers breed in the lab mini organs, so-called organoids, which have been surgically removed from cancerous tissue. These organ-like structures are small tumors in their own right with the patient’s own genetic footprint. They are therefore ideal for testing the effect of various medications on the immune system of a specific individual. Data from these extensive tests will then be fed into a computer model that should ultimately predict which combination therapy would be most effective for each patient. A first review of a comprehensive study of over 600 colorectal cancer patients in Cancer Genome Atlas showed that infiltrating antibody cells of a tumor are very different depending on its origin, patient’s genetic predisposition, and tumor environment.

The number of mutations also varies greatly from tumor to tumor – from several hundred to several thousand per patient. This heterogeneity of one and the same tumor could signal a turning point in the search of cancer immunotherapy that fits all patients. It appears that hardly two colon cancer patients have identical mutation profiles. Therefore, breeding individual organoids that react very differently to treatment is essential.

Manipulating organoids by genetic and pharmacological means yields a lot of knowledge about drug treatment of tumors that is required to make them respond to immunotherapy. Technology for breeding organoids exists only since a few years and can be combined with high-throughput screening technology, which allows accurate characterization of organoids and determination of molecular profiles.

In order to derive targeted information for immunotherapy of cancers from bioinformatic analyses, an additional step is required: creation of tailored mathematical simulation models to process data feeds. This permits meaningful simulation of a wide variety of combination therapies. The number of possible combinatory permutations is enormous: in recent years, at least 70 new cancer drugs were approved, in addition to conventional chemotherapy still in use, and at least six new immunotherapeutic drugs. Algorithmic models can, in principle and depending on their quality, distill individualized combination therapies for each patient, creating a form of targeted precision medicine based on detailed knowledge of the specific mutation profiles of each tumor.

Key to that approach is the supply of the greatest possible amount of data from different clinical trials. Individual patient profiles need to be combined with data of similar patient groups and information about the medication; after evaluation, specific therapy recommendations could be expected to result. This process of advanced combinatorics is ultimately similar to the way Amazon evaluates customer interest from past purchases of individuals and larger groups; in oncology, scientists are aiming at specific recommendations for customized cancer therapy. Human oncologists would be overwhelmed with distilling and interpreting such data floods with any degree of accuracy, but combinations of AI and BigData methods can be expected to show the pathway toward the most effective therapy – not only for each type of cancer but also for each individual’s situation. With immunotherapy cost currently around $150,000-240,000 per year (and rising rapidly), accurate prediction as to what therapy combinations will be effective for which patient is not only key to survival for the individual but also for an already heavily strained health care system. The choices involved in the process inescapably demand answers to that most inconvenient of political questions: how much is temporary human life worth?

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