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Exploring the Immune System and Cancer

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In recent years, immunotherapy has become well-established as the fourth pillar of cancer treatment in addition to the conventional mainstays of chemotherapy, radiotherapy and surgery. Yet it has taken many years of studying the relationship between the immune system and cancer to reach this point.

In theory, it should be entirely possible for the immune system to eliminate cancer cells. The cells carry enough mutations to be flagged, recognized and eliminated by the immune system.  However, people still develop and die from cancer. Their tumors, and the area surrounding the tumors – known as the tumor microenvironment – may contain many immune cells, but these cells are not working as they should.

In the 1990s, two Nobel-prize winning discoveries were made that accelerated progress in understanding the immune response.1 These discoveries showed that taking the brakes off the immune system could be a more successful approach than trying to stimulate it. This led to the first of a new class of immunotherapy treatments – a checkpoint inhibitor antibody that transformed survival in metastatic melanoma.2 There has since been many more immunotherapies approved, and a wide range of treatment modalities are being explored, from cell-based therapies to small-molecule cytokine inhibitors. Despite this renewed excitement and focus, clinical experience with these treatments suggests there is still much to learn about the immune system and cancer. In this article we look at two different approaches to optimizing immunotherapies. 

Tumor Microenvironment Explained

Immuno-oncology research investigates how the immune system interacts with cancer cells within the tumor microenvironment, a process that is not fully understood. Download this whitepaper to learn about the stages of immunoediting: elimination, equilibrium and escape.

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Helping the immune system to function fully

Much of the recent progress in immunotherapy development has been focused on cytotoxic T cells. These are cells that can be primed against a tumor antigen, and once activated, directly kill the diseased cell. Yet we know that even when primed T cells reach a tumor, they don’t always manage to kill the tumor cells. One reason is that tumors have found a way to put the brakes on the immune response by sending immunosuppressive signals to T cells to dampen down their activity. Blocking these inhibitory signals with drugs called checkpoint inhibitors has been a major focus of immunotherapy development in the past 10 to 15 years. But for most patients, taking the brakes off the immune response is not enough.

“Immunotherapy research to date has really focused on one angle, on one cell type, and there's a huge amount we don't know yet about how the immune system works and all the other ways we could harness it to treat cancer,” says Dr. Sophie Acton, a Cancer Research UK fellow and junior group leader at University College London, UK. “In order to get good T-cell killing of cancer cells, you first need to prime those T cells, which means you need other immune cells such as antigen-presenting cells. If that hasn't happened properly and you don't have populations of cytotoxic T cells that are tumor specific, no amount of a checkpoint inhibitor drug is going to do anything.”

Acton’s team is focused on understanding the support system that enables T cells to function at their best, with a view to reproducing this in the tumor microenvironment. “Most of our research in the past five years has been about how the lymph nodes work as a system,” she explains. “We’re not thinking about individual cells activating individual cells, but how do millions of cells organize themselves into effective responses? And how do the tissue structures that house them facilitate this?”

Previous studies have shown that lymph nodes closest to a tumor and those draining fluid from it can be dysfunctional and the dendritic cells in those draining lymph nodes are not doing their job properly.3 Acton’s team wants to build on this by understanding how the lymph node should work normally and what changes are occurring in the lymph node and how these affect function in proximity to a tumor. “We want to understand why the lymph systems don't work even though all the same cells are present. Is it related to the chronic nature of a tumor compared with the acute nature of the infection, or could having a handful of cancer cells in your draining lymph nodes mess up the structure and function of that tissue?”

Using genetic tools to manipulate the stromal cells – the non-immune cells including fibroblasts and endothelial cells – they are working to determine which pathways are critically important in allowing the lymph nodes to adapt so quickly during an immune response. “It’s hard to imagine another tissue doing what lymph nodes do: going through massive tissue remodeling phases to support an immune response and then rapidly returning to their normal steady homeostatic state ready to do it all again during another infection,” says Acton. “We hope to take lessons from this normal tissue and translate it into the complicated, disorganized situation we see in the tumor microenvironment.”

There are, Acton says, many cases where patients probably have the right anti-tumor cytotoxic T cells in their blood, and yet they don't respond to immunotherapy, and through their work they aim to understand what else the T cells need to become active. “Our hope is that we might in future support that immune response. Checkpoint inhibitor immunotherapy would still be used to unleash any suppressive signal, but by using this knowledge of the tissue niche that normally supports immunity, we can make sure there's plenty of supporting cells happily residing and active in the tumor microenvironment ready to do the job.”

Protein Biomarkers Show Promise in Predicting Patient Response to Cancer Immunotherapies

With recent studies suggesting that systemic host immune factors can predict patient response to immunotherapy treatment, there is now a mounting interest in using blood-based biomarkers to study immunotherapy response non-invasively. Download this whitepaper to discover how blood-based protein biomarkers can reliably predict response to cancer immunotherapy regimens.

View Whitepaper

Predicting response to immunotherapy

Until research provides us with new ways to support the immune system in fighting cancer, it remains important to optimize the use of the immunotherapy treatments we already have. Immunotherapy drugs such as checkpoint inhibitors are transforming the outlook for a subset of patients, but not for everyone.

“There are several significant problems for the physician when it comes to deciding whether an individual patient might benefit from immunotherapy. First, it is hard to predict if an immunotherapy drug will have an impact, given that so many people don’t respond,” says Zhihui Wang, associate research professor of mathematics in medicine, at Houston Methodist Research Institute, Houston, Texas, US. “Second, it’s hard for physicians to determine how long to keep the patient on the drug, and third, it’s difficult to identify the drug combination that will benefit them the most.” One reason for this is the lack of reliable, validated biomarkers in clinical practice that can determine the patient-specific immunotherapeutic outcome.

Wang’s team has developed a mathematical model that can identify those who will benefit from immunotherapy at an early stage in their treatment.4 The latest model is the third iteration of previous algorithms,5,6 but represents a significant step forward because it combines readily available information from hospital scans such as magnetic resonance imaging and computed tomography with histology data from routine biopsies.

“Our previous model focused on using imaging data to make predictions, but imaging is not always routinely available in every hospital,” says Wang. “With this latest model we’ve demonstrated that some of the model parameters can be informed by pathology data which is available across all hospitals, as physicians take biopsies for most solid tumors.”

The model determines changes in relative tumor mass over time after patients start checkpoint inhibitor therapy. Although change in tumor mass is influenced by complex biological crosstalk between the immune system and cancer cells, they simplified this crosstalk by combining three measurements into a single equation: the ability of malignant tumor cells to grow, the ability of immune cells to kill cancer cells within the tumor environment and the potential effectiveness of checkpoint inhibitor-based immunotherapy treatment. When they validated the model with clinical trial data, it correctly predicted the treatment response in 81.4% of patients using only tumor volume measurements and within two months of treatment initiation.

“From the conversations with our clinician collaborators, the most significant value they see is that our models can be used to separate the patient responders from the non-responders,” says Wang. “We call it a mathematical biomarker, capable of dividing patients into two groups: for the responder you can keep using the drug, and for the other group the physician now knows they need to use an alternative treatment.”

“We are now working to incorporate this model as a tool in a clinical trial with our collaborators at MD Anderson, and developing a user-friendly, easy-to-understand software to help physicians use the model to make treatment predictions,” says Wang. “In the future, we hope to add in data from other types of scans such as positron emission tomography (PET) and single photon emission tomography (SPECT), and to include information we can obtain from liquid biopsies – the regular blood tests patients have during treatment. The more data we can add, the more accurate the predictions are for each individual. That’s the beauty of the model.”


1. Ledford H, Else H, Warren M. Cancer immunologists scoop medicine Nobel prize. Nature 2018;562:20-21 doi: 10.1038/d41586-018-06751-0

2. Hodi FS, O'Day SJ, McDermott DF, et al. Improved survival with ipilimumab in patients with metastatic melanoma [published correction appears in N Engl J Med. 2010;363(13):1290]. N Engl J Med. 2010;363(8):711-723. doi: 10.1056/NEJMoa1003466

3. van Pul KM, Vuylsteke RJCLM, van de Ven R, et al. Selectively hampered activation of lymph node-resident dendritic cells precedes profound T cell suppression and metastatic spread in the breast cancer sentinel lymph node. J Immunother Cancer. 2019;7(1):133. doi: 10.1186/s40425-019-0605-1

4. Butner JD, Martin GV, Wang Z, et al. Early prediction of clinical response to checkpoint inhibitor therapy in human solid tumors through mathematical modeling. Elife. 2021;10:e70130. doi: 10.7554/eLife.70130

5. Butner JD, Elganainy D, Wang CX, et al. Mathematical prediction of clinical outcomes in advanced cancer patients treated with checkpoint inhibitor immunotherapy. Sci Adv. 2020;6(18):eaay6298. doi: 10.1126/sciadv.aay6298