Framing technology challenges associated with improving cancer immunotherapies

The rapid development of cancer immunotherapies over the past 5–10 years is not only revolutionizing clinical cancer care,¹ but it is also making the immunotherapy field a proving ground for many new measurement and computational technologies. An understanding of the technological needs of this field can be gleaned by placing those needs within the context of state-of-the-art treatments. Modern cancer immunotherapies fall into three broad categories. The first are checkpoint inhibitors, such as anti-PD1 (ref. 2) or anti-CTLA4,³ both of which were highlighted by the 2018 Nobel Prize for Medicine. The second class are cell based therapies, which range from the use of tumor-infiltrating lymphocytes (TILs),⁴ to various generations of engineered T cell therapies,^5,6 including chimeric antigen receptor (CAR)-T cells,⁷ and tumor-antigen-specific T cell receptor-engineered therapies.^8,9 Cancer vaccines constitute the third class of immunotherapies that have existed for many years with some notable successes,¹⁰ but have also recently experienced a renaissance¹¹ as researchers have developed deeper insights into immune system/tumor interactions. These therapy classes are each advancing hand-in-hand with new analytic tools and other technologies.

Cancer immunotherapies are most often designed to generate and/or activate tumor-killing populations of T cells. This means that those T cells must recognize antigens¹² that are uniquely and uniformly presented within the tumor but not in healthy tissues, and the T cells themselves must remain in an activated, effector state for the duration of tumor removal, as well as provide a memory reservoir for combatting future tumor recurrence. These criteria frame several technology and measurement challenges, including some which are being addressed through the use of microchip-based approaches. Lab-on-chip examples include on-chip tissue models,¹⁵ which have the unique potential to provide continuous views of tumor/immune interactions within a precisely controlled environment. Analytic platforms include new generations of drop-seq tools for single cell multi-omics^13,14 and highly multiplex methods for probing the function of selected T cell phenotypes,^16,17 or for identifying very rare tumor-antigen-specific T cell populations¹⁸ and for pairing the antigen-specificity with the T cell receptor α/β genes.¹⁹ General themes behind these platforms include methods to extract ever deeper information across multiple analyte classes, and from very rare cell populations. The near-term needs of the field dictate that this trend will likely continue, as emerging technologies will be designed to further interrogate those cells for additional markers of exhaustion, antigen experience, checkpoint exposure, etc. Importantly, these tools will also be used to populate databases from which new therapy concepts can emerge. Within the rapidly-advancing field of cancer immunotherapy, new tools, whether they are microchip platforms, new bioengineered cells or biomolecular reagents, or computational algorithms, are often most effectively developed within the context of larger, clinically driven programs.

Two additional characteristics of immunotherapy are driving new technology development. The first is that cancer immunotherapy is a treatment class in which the most extreme concepts of personalized medicine are being realized. Virtually all cell based therapies, as well as many cancer vaccines, are custom-designed for individual patients, with, in many cases, design parameters extracted from tumor genomic and transcriptional profiles. In the spirit of full disclosure, my own company, PACT Pharma, is seeking to develop personalized cell based therapies. The level of personalization that is now being tested in the clinic was unthinkable just a decade ago. The newness of personalized cancer immunotherapies means that, as a rule, they are still extremely expensive. An urgent and unmet need is to develop technologies that can assist in the democratization of such treatments. A second characteristic involves the efficacy of the therapy itself. When immunotherapy works, it can be curative, but it still only works on a subset of cancers, and not all patients who do respond exhibit durable responses. A unique characteristic of the biology of immuno-oncology is that it can invariably be mined to generate new hypotheses for how to improve treatments. Such hypotheses might include approaches for improved bioengineering of T cells, or the potential identification of new immune checkpoints, etc. While this characteristic gives cancer immunotherapy a very bright future, it also means that finding technologies that can rapidly and inexpensively validate or negate such hypotheses is an urgent and rapidly expanding need.

A note from the Lab on a Chip editors

We invite authors to submit manuscripts addressing these and related challenges for inclusion in a thematic collection in Lab on a Chip focused on immunoengineering and immunotherapy.

References

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