Cancer Treatment Development is Going Combinatorial with Science Solutions

Digital Solutions

A schematic of antibody dependent cell mediated cytotoxicity, a mechanism which many combinatorial cancer treatments will make use of. Source: Simon Caulton.

The combinatorial future of cancer treatment approaches the clinic with incredible momentum and promises to provide excellent outcomes (and a higher price tag) for patients everywhere. The new landscape of cancer treatment will include standard chemotherapy agents, immunostimulatory drugs, genetically engineered immunotherapy solutions, biologics, nanotherapeutics, various types of vaccines, and traditional radiotherapy.1 2 It’s an incredible time to be in the field, but there’s still a lot of work to be done. Combinatorial testing of new cancer treatments will require an effective information technology platform that can promote collaboration between the many topic experts that will be needed to bring combination treatments to the clinic.

Influential researchers have looked forward to combination treatment strategies for quite a while, and many of the first strategies to test are the ones they theorized.3 Much of the combination oncology experimentation will rest on their prior research, which touched on multi-domain phenomena—like immunostimulation in the context of cancer—but didn’t explicitly examine downstream patient health impacts.4 The exact indication will vary depending on the application, but the picture is clear: oncologists now have a wider and more effective toolkit than ever before and are waiting on researchers to test different combinations so they can be used in the clinic. Still, there are quite a few steps between the theory, the preliminary experiments, an effective oncology treatment, and an effective combination oncology treatment.

The Complex Nature of Combinatorial Treatment Development

The workflow from theory to clinical trial has been relatively straightforward—as ridiculous as that sounds—up until now:

  • Prior research into disease pathology suggests a new treatment hypothesis
  • New hypothesis tested in vitro
  • New hypothesis tested in vivo
  • Treatment based on new hypothesis developed
  • Treatment based on new hypothesis tested in vitro and then in vivo
  • Treatment based on new hypothesis begins clinical trials
  • Treatment based on new hypothesis enters the clinical practice
  • New treatment alternative indications researched
  • New treatment alternative indications tested in further clinical trials, if necessary

While this list is extremely simplified, the process from theory to clinic can proceed without taking into account the pipelines of completely different hypotheses or hypothetical treatments. There’s also little chance of research within one type of cancer overlapping with the research interests of those studying a different type. All researchers keep up with the hottest topics in their field and will definitely alter their experimentation based on it, but at the end of the day, a treatment that is proven to be effective can stand on its own. But this is not the case with the combinatorial treatments of the future. New treatments will be developed and tested in different disease contexts based on the combinatorial successes or failures of many other treatment pipelines.

Simply put, if one of your collaborators has success with a certain combination of treatments, your research group may add another treatment to the combination and start with your own experiments—either in the same disease context of your collaborator or a different one. If a few pieces have been proven to work well together, there’s nothing stopping you from layering on even more elements or testing those pieces somewhere completely different. The research process is no longer about separate groups working on individual projects in parallel; it’s the summation of all groups working on many partially overlapping projects and layering new projects on top of each other’s successes. 

Using the Same Pieces to Solve Multiple Puzzles

A large part of the challenge is moving generalized techniques like immunotherapy, gene therapy, cell therapy, and antibody therapy into a new oncological disease context before using them in conjunction with the established mainline treatments. For instance, can radiotherapy work in conjunction with an immunotherapeutic vaccine? Or would the radiation’s immunosuppressive effect cancel out the vaccine’s immunostimulatory properties when challenged with a tumor antigen of a certain type of cancer, but not another?5

There’s practically an unlimited number of combinations and disease contexts to test, and there’s enough knowledge and manpower to find problems that fit the many new solutions that are hungry to be used—once a technique is proven in one disease context, it’s only a matter of time until someone tries it in another.6 7 Researchers have been chipping away at these kinds of questions for combinations of individual new treatments and individual main line treatments, but the real clinical power will come from combining three or more new treatments with the main line treatments.

The sheer amount of knowledge and data needed for combinatorial treatment development is quite daunting, and researchers won’t be able to do it on their own. Developing combinatorial treatments will be a collaborative effort between multiple research groups, and they’ll need to efficiently share all their knowledge and data in order to make each experiment shine.

Designing a combinatorial cancer treatment requires knowledge of the following topics:

  • Multiple targetable aspects or sites relevant to tumor pathology or development
  • Main line treatment’s on-target and off-target effects relative to the new treatment’s intended mechanism of action
  • New treatment’s interaction with efficacy of main line treatment
  • Main line treatment’s interaction with efficacy of new treatments
  • New treatment’s off-target effects
  • New treatment’s “overkill” on-target effects, which may lead to side effects
  • Applicability of new treatment to a different oncological target than the main line treatment
  • Effect of new treatment on immune cells involved in tumor control
  • Effect of main line treatment on immune cells involved in tumor control
  • Effect of new and main line treatments on tumor cells themselves8

Bringing Knowledge to Bear

Traditional collaboration methods won’t be enough to experimentally determine which combinatorial treatments are effective and which aren’t. Low information bandwidth communiques and slow responses to collaborator requests for data are tolerable when the research context is extremely narrow, but combinatorial cancer treatment is virtually limitless.

hat’s more, research groups won’t be collaborating with just one or two other groups; they’ll be collaborating with dozens, many of which may have an unfamiliar speciality. The chances are good that every group will want access to all their collaborator’s data, pronto—anything less would slow the pace of research to a crawl.

BIOVIA’s Collaborative Science Solutions is the collaboration, data sharing, and experimental planning software that today’s researchers will use to develop the combinatorial cancer treatments of tomorrow. Using Collaborative Science Solutions, you can quickly access your collaborator’s in vitro treatment efficacy data and plan your next experiment with all the background you need—and vice versa. Contact us today to find out how you can use BIOVIA solutions to start testing combinations of cancer treatments.


  1.  “Leukocyte-mediated Delivery of Nanotherapeutics in Inflammatory and Tumor Sites.” 2017,
  2. “Combinatorial treatments including vaccines, chemotherapy and monoclonal antibodies for cancer therapy.” August 2008,
  3. “Combining immunotherapy and targeted therapies in cancer treatment.” April 2012,
  4. “Impact of gefitinib in early stage treatment on circulating cytokines and lymphocytes for patients with advanced non-small cell lung cancer.” May 2016,–peer-reviewed-article-OTT
  5. “Radiotherapy and MVA-MUC1-IL-2 vaccine act synergistically for inducing specific immunity to MUC-1 tumor antigen.” January 2017
  6. “Update on immune checkpoint inhibitors in gynecological cancers.” December 2016,
  7. “Long-term Response to Nivolumab and Acute Renal Failure in a Patient with Metastatic Papillary Renal Cell Carcinoma and a PD-L1 Tumor Expression Increased with Sunitinib Therapy: A Case Report.” November 2016,
  8. “Decitabine treatment sensitizes tumor cells to T-cell-mediated cytotoxicity in patients with myelodysplastic syndromes.” February 2017,