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Advances in Cancer Drug Discovery

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For many years, the only available options for treating cancer were to surgically remove it or to kill the rapidly dividing cells with cytotoxic chemicals or radiotherapy. These were blunt tools compared to today’s therapeutic strategies. Although all three modalities still play a vital role, their use is now often in combination with a more sophisticated generation of targeted drugs and immunotherapies. Indeed, there is now said to be five pillars of cancer treatment: surgery, radiotherapy, cytotoxic chemotherapy, precision therapy and immunotherapy.1 Yet therapeutic strategies must keep evolving, much like the tumors they are designed to treat. In this article, we look at some of the latest approaches in cancer drug discovery.

New approaches in immunotherapy

Although undoubtedly a breakthrough in cancer treatment, immunotherapy still only works well in a minority of patients and for
certain cancer types. Attention is now directed towards improving response rates and finding ways to harness the power of immune cells for all types of cancer.

At the Vall d’Hebron Institute of Oncology in Spain,
Alena Gros is developing T-cell therapies to treat cancer. However, she’s using a unique approach – by identifying tumor-reactive T cells from a patient’s peripheral blood.

“One of the challenges with T-cell therapy is to try to identify T cells capable of recognizing mutations in the patient's tumors without having to surgically remove the tumor,” explained Gros. “We have previously identified T cells from the blood of patients with metastatic melanoma that expressed PD-1, and found that the T cells expressing this marker were enriched with neoantigen specificity – meaning they can recognize the mutated antigens in melanoma.”

The team decided to take this approach and apply it to gastrointestinal (GI) cancers which are less immunogenic and where there is a clear unmet clinical need for new treatment options.1 “When we started looking at the circulating PD-1 positive cells in GI cancer, we were able to identify CD8+ killer or CD4+ positive helper T cells to tumor neoantigens in six of the seven patients studied.”

This suggests that there is potential to develop personalized T cell therapies for these patients. However, they also found that the T cells from the GI patients were less efficient at killing tumor cells than those from the metastatic melanoma patients. “We're trying to understand why there is a biological difference between these tumors that makes one less susceptible to T-cell mediated killing. If we can understand this, we can perhaps enhance the anti-tumor efficacy of these T cells and generate a T-cell product that's highly capable of recognizing the tumor and mediating tumor regression.”

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Understanding immunotherapy response

Researchers are also shedding light on is the factors that influence a patient’s response to immunotherapy, uncovering ways to enhance effectiveness of treatment.2 Most immunotherapy strategies focus on
killer T cells – immune cells that home in on unique tumor antigens and destroy tumor cells. These cells are activated by a complex formed from the family of major histocompatibility complex I (MHC I) genes, that are fairly well understood. However, this research explored the role of the MHC II gene family – which code for cell surface MHC II complexes that activate helper T cells.

“For killer T cells we’re relatively good at looking at a patient’s tumor, seeing what mutations are present and figuring out which mutations are most likely to trigger T cells to respond,” said Elise Alspach, PhD, at Washington University School of Medicine in a recent
press release. “But the ability to do this for helper T cells has lagged far behind.”

By studying mice with tumor xenografts, they found that checkpoint inhibitor therapy was more effective when both killer and helper T cells were activated. The same was true of vaccines, which were more effective when targets activating both helper and killer T cells are present.

“Just because a killer T cell is present doesn’t mean it is actively killing tumor cells,” said Alspach. “We found that not only do you need helper T cells to recruit the killer T cells, the helper T cells need to be there to coax the killer T cells to mature into an active state in which they are capable of killing cells.”

Researchers are also now discovering and harnessing more advanced cell-analysis techniques that enable them to assess the biology and function of different immune subtypes. For example, a recent study harnessed the power of mathematical algorithms to produce a fluorescent cell imaging technology that provides a user-friendly way for researchers to visualize molecular pathways within cells when evaluating new drugs.3

Such tools are essential for assessing the mechanistic basis behind individual patient outcomes to novel treatments such as immunotherapy, which in turn can help identify immune profiles that predict an individual patient’s response – the so-called ‘immune set point’.4 This knowledge will help drive the development of “better”, more effective immunotherapies, expanding their potential for a broader range of patients and tumor types.

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Targeting drug resistance

One of the greatest barriers to successful cancer treatment is the emergence of resistance as tumors evolve, acquiring new advantageous mutations that allow them to survive. Now, researchers at The Institute of Cancer Research in London plan to use mathematical models to predict the evolutionary path of tumors as part of the world’s first Darwinian drug discovery program. The program recognizes that the traditional “shock and awe” approach of chemotherapy has failed because it helps to fuel the survival of the fittest, most aggressive cancer cells. By contrast, using an approach called “evolutionary herding” the researchers will forecast how tumors will respond when treated with a particular drug, and use that drug to force cells down a vulnerable evolutionary path. Moreover, a new class of drugs being developed at the center have the potential to target the very mechanisms tumors use to adapt. The new drugs, APOBEC inhibitors, are designed to reduce the rate of mutation in cancer cells, which in turn can slow down evolution and delay resistance.

“Artificial intelligence and mathematical predictive methods have huge potential to get inside cancer’s head and predict what it is going to do next and how it will respond to new treatments,”
said Andrea Sottoriva, Deputy Director of Cancer Evolution at the new center. “By encouraging cancer to evolve resistance to a treatment of our choice, we can cause it to develop weaknesses against other drugs – and hopefully send it down dead ends to its own destruction.”

An innovative era in cancer drug discovery

Despite the tremendous advances of the past decade with new treatment modalities such as targeted agents and immunotherapy coming to the fore, there is still a continued unmet need for new treatments for intractable cancers and to improve response rates. From optimizing immunotherapy to second-guessing cancer’s next evolutionary step – there has never been a more innovative era in cancer drug discovery.


1. Gros A, Tran E, Parkhurst MR, et al. Recognition of human gastrointestinal cancer neoantigens by circulating PD-1+ lymphocytes. J Clin Invest. 2019 Nov 1;129(11):4992-5004. doi: 10.1172/JCI127967.

2. Alspach E, Lussier DM, Miceli AP, et al. MHC-II neoantigens shape tumour immunity and response to immunotherapy. Nature. 2019 Oct;574(7780):696-701. doi: 10.1038/s41586-019-1671-8. Epub 2019 Oct 23.

3. Smith JT, Yao R, Sinsuebphon N, et al. Fast fit-free analysis of fluorescence lifetime imaging via deep learning. Proc Natl Acad Sci USA. 2019 Nov 12. pii: 201912707. doi: 10.1073/pnas.1912707116

4. Chen DS and Mellman I. Elements of cancer immunity and the cancer-immune set point. Nature. 2017 Jan 18; 541(7637):321-330. doi: 10.1038/nature21349.