Recent trends in immuno-oncology research and therapy
Some of the challenges in understanding immuno-oncology (I-O) research and treating cancer using I-O therapies arise from the complex interactions between a patient’s immune system and the tumor’s biology. After all, tumors are populations of cells, and populations are subject to evolutionary forces.
The immune system responds to threats by changing the environment, making it more hostile to the invader or malignant non-invader, and by sending in troops to isolate, remove, and kill said threats. This change in environment forces the threat, for survival’s sake, to change its biology. Which forces the immune system to change tactic. And so on and on. Eventually, the immune system loses because it just can’t run fast enough to keep ahead of the threat. It’s the classic Red Queen dilemma.
Cancer has an advantage in the race here, of course, because its evolutionary brakes are off, whereas the immune system still has rules it must follow. The aim of immuno-oncology (I-O) therapy is to restore the evolutionary balance between the patient’s own immune system and their cancer. I-O is, therefore, one of the fastest-growing areas in cancer research.
Some of the recent trends in I-O research and therapy are attempts to increase our understanding of the interplay between a patient’s immune system and the tumor’s biology. For now, let’s focus on understanding the mechanisms of cancer resistance to I-O therapies, which are a result of interactions between the immune system and tumor cells.
Understanding and overcoming resistance
Some have called it the number one challenge in cancer research today: resistance to I-O therapies. Many patients may never even achieve a response to an I-O therapeutic strategy (primary resistance)--and some who do achieve a response that may relapse after a period of effectiveness (acquired resistance).
Let’s talk a little bit about how primary and acquired resistance happen, and how we can study the mechanisms.
Primary resistance can occur before a patient has ever been exposed to an I-O therapy, and this can be a very serious challenge in I-O therapeutic strategies. Reportedly, most patients have primary resistance to PDL-1 blockade, and only 30%-40% of those who don’t have primary resistance have an objective response.
According to their 2017 review, Sharma et al explain that primary resistance can happen because of a number of tumor intrinsic (tumor biology) and tumor extrinsic (immune system) factors.
Such tumor intrinsic factors include
- failure to produce antigen
- changes in the antigen-presenting machinery--so that, even if the tumor produces antigen, it won’t present on the surface of the cell
- mechanisms that either keep T-cells away or hide the tumor cells from T-cells
Tumor extrinsic factors include
- absence of T-cells that target tumor-specific antigens
- presence of immunosuppressive cells
- inhibitory checkpoints in immune system cells
This leaves a lot of ground to cover when trying to understand mechanisms for how primary resistance occurs in a particular cancer type, let alone an individual patient’s tumor.
Real-time live-cell analysis can be useful here. We performed experiments in our lab to understand how the dynamic changes in cell surface checkpoint proteins can be quantified in living cells in response to a stimulus.
For instance, to study how MDA-MB-231 breast cancer cells respond to IFN-γ through regulation of the checkpoint inhibitor PD-L1, we treated cells with IFN-γ, then observed the increase in PD-L1 labeling over 72 hours in the IncuCyte Live-cell Analysis System. PD-L1 labeling (shown in green) was 3-fold increased over vehicle control at the highest concentrations of IFN-γ (green-florescence at about 1.8 µm2/well for control vs. about 5 µm2/well for IFN-γ at 50 ng/mL), without effecting the growth rate of the breast cancer cells.
It’s just one small example, but you can see how live-cell analysis may prove a useful format for further studies on the regulation of immune-cell signaling pathways in tumors.
I-O therapy has shown promising responses in a good proportion of patients, but acquired resistance is a real challenge. For instance, acquired resistance to anti-PD-1 therapy occurs in about 25% of all initial responders.
Some of the mechanisms for acquired resistance are the same—or very similar—to those for primary resistance. In their review, Sharma et al. say that the potential mechanisms of relapse (acquired resistance) to I-O therapy include
- loss of T cell function
- downregulation of tumor antigen presentation so T cells no longer recognize the tumor cells,
- development of escape mutation variants in the tumor cells
Sharma et al. mention that one strategy used to understand mechanisms of resistance to immune checkpoint blockade is through longitudinal studies on tumor samples across the course of treatment. The power of this strategy comes from the attempt to assess cancer treatment dynamically, rather than as is traditional, statically.
Real-time live-cell analysis is perfect for longitudinal studies because it offers the ability to quantify cell biology over time, and experiments can be set up in such a way that samples from multiple time points under multiple conditions can be studied simultaneously.
The mechanisms of cancer resistance to I-O therapies, one of the main challenges in I-O research today, are a result of interactions between the immune system and tumor cells. Primary resistance can happen before a patient has even been treated and acquired resistance can happen in patients who have achieved a response but then relapse after that period of effectiveness.
Primary resistance occurs because of a number of tumor intrinsic factors (such as failure to produce antigen or changes in antigen presentation) and immune system factors (such as a lack of T cells that target a specific cancer antigen). The mechanisms for acquired resistance are very similar to those for primary resistance (such as loss of T cell function or downregulation of tumor antigen presentation).
Whether you’re trying to understand primary or acquired resistance mechanisms, you need to understand how the immune system and tumor cells interact. Real-time live-cell analysis is a good tool to use here because it allows for a high-content, nuanced understanding of the dynamic changes in the immune system in response to tumor cell stimulus—and vice versa, in real-time, over multiple time points, and under multiple conditions.
Andrews MC, and Wargo JA. (2017). Immunotherapy resistance: the answers lie ahead—not in front—of us. J ImmunoTherapy Can. 5:10.
Gonzalez S, Volkova N, Beer P, Gerstung M. (2017). Immuno-oncology from the perspective of somatic evolution. Sem. in Can. Bio. 52, 75-85.
Sharma P, Hu-Lieskovan, S, Wargo JA, Ribas, A. (2017). Primary, Adaptive, and Acquired Resistance to Cancer Immunotherapy. Cell 168, 707-723.