Personalized Immune Therapy: A Slippery Fish?

April 15, 2016

Advances in basic science and clinical medicine in the past few decades have increasingly elevated the profile of personalized medicine, ie, the identification of individual tumor or biologic features that offer targets for therapy.

Advances in basic science and clinical medicine in the past few decades have increasingly elevated the profile of personalized medicine, ie, the identification of individual tumor or biologic features that offer targets for therapy. In the case of classic genomics, driver mutations such as epidermal growth factor receptor (EGFR) or human epidermal growth factor receptor 2 (HER2) can be reliably detected in a tumor, indicating a pathway that can be interrupted with a targeted drug. The identification of such clear predictive biomarkers placed personalized therapies within reach for some cancer patients, inspiring the hope that individualized treatment decisions lay around the corner for all areas of cancer treatment, including immunotherapies. While strides in this direction have undoubtedly been made, developing personalized regimens to overcome immune tolerance to tumors may prove more challenging than parallels with other targeted therapies might initially suggest.

The goal of immunotherapy is to sensitize (or resensitize) the host immune system to the presence of a tumor antigen. Several methods have been tested, including:

1.Vaccines. A vaccine presents a specific tumor protein or proteins to the immune system, coupled with an adjuvant to activate and prime dendritic cells.[1] So far, vaccines have not been shown, at least in isolation, to routinely induce objective antitumor responses, although a survival benefit has been demonstrated for at least one such agent.[2]

2. Immune checkpoint inhibition. As Carneiro et al nicely discuss in their review,[3] checkpoint inhibitors downregulate a tumor’s ability to employ either intrinsic or extrinsic immune inhibitory pathways, reactivating T cells that have become exhausted. We are all familiar with the recent triumphs achieved with the programmed death 1 (PD-1)/programmed death ligand 1 (PD-L1) and cytotoxic T-lymphocyte–associated antigen 4 (CTLA-4) pathways, which have a proven survival benefit in several tumor types, including melanoma, renal cell carcinoma, and lung adenocarcinoma.[4]

3. Immune agonists. If immune checkpoint inhibition removes a braking mechanism, a third strategy presses the accelerator: immune agonist antibodies, such as α4-1BB, αOX40, and αCD40, activate either T cells (4-1BB, OX40) or dendritic cells (CD40) via cross-linking antibodies. While translational efforts are still in relatively early stages across the board, 4-1BB activation has shown some efficacy in melanoma, although it initially demonstrated liver toxicity as well. It is now being reintroduced more cautiously, in combination with PD-1 blockade.[5]

4. Combinatorial regimens. Numerous combinations of immune therapies appear promising or have already led to improved outcomes, eg, combined CTLA-4/PD-1 inhibition in melanoma and renal cell carcinoma.[6-8]

Despite several recent US Food and Drug Administration approvals, only a minority of patients demonstrate durable responses to immune therapy, leading to unnecessary toxicities and costs for patients who receive these drugs without benefit. Predictive biomarkers could help to identify which patients are likely to respond, and might also point to future therapies. Carneiro et al outline some promising efforts to uncover tumor characteristics with therapeutic value, eg, microsatellite instability (MSI) for pembrolizumab, mutational burden in lung cancer, and PD-L1 expression on tumor cells or associated myeloid cells. Other areas of inquiry include vaccine targets such as overexpressed self-antigens, cancer-testis antigens like MAGE-A3, and mutation-associated neoantigens.[9] A methodology developed by the Ribas group[10] demonstrates yet another encouraging line of investigation: evidence in melanoma patients shows that the density of CD8+ T cells at the tumor margin, along with density of PD-1– and PD-L1–positive cells, can fairly reliably predict tumor response. In contrast to a binary assay for the presence of, for example, PD-L1, this is a complex discriminatory model that will require standardization and automation before it can be used widely. Further study will also be needed to show generalizability across other tumor types.

With the notable exceptions discussed above, other efforts to discover predictive immune biomarkers have generated exciting ideas and some progress, but fewer solid results. This may merely reflect the early stage of the field, or it may turn out that personalized cancer immunology is truly a slippery fish, one we may or may not wrap our hands around soon. Perhaps the best illustration of this complexity comes from animal studies: multiple groups, including our own, have shown that even genetically identical laboratory mice, implanted with genetically identical tumors and living in a single cage, show disparate responses to PD-1/PD-L1 blockade.[11,12] How to explain this variability when host and tumor genetic factors have been controlled for? One possibility involves the T- and B-cell repertoire of the host: even genetically identical animals have distinct repertoires because T- and B-cell receptors are generated through random recombination events in the thymus and bone marrow, respectively. A second possibility could be that epigenetic factors emerging after implantation could affect the host response to tumors. Finally, recent clinical and preclinical data show that variability in the host microbiome can profoundly influence the host response to immunotherapy.[13,14]

In addition to the implications of random events in the development of the host immune system, a predictive biomarker will be challenged by the dynamic nature of an immune response, in which PD-L1 and similar markers vary in time and space over the tumor parenchyma. Indeed, depending on the placement of a core needle, PD-L1 staining could conceivably vary from 100% to undetectable (Herbst RS, unpublished data), pointing to a potential requirement for multiple sampling, or perhaps eventually to more global image-based assessment approaches.[15]

In summary, the recent successes in cancer immunotherapy have launched widespread efforts to identify biomarkers to personalize treatments, while the complexity of the immune tumor response presents an exciting-but daunting-landscape for this research. Some of the most successful approaches to date have parallels with more classic tumor genomics: they identify a genetic feature that carries direct therapeutic implications. For example, MSI testing can be termed a genetic biomarker for immunotherapy, and offers an easily measured and portable assay. Other research has focused on more phenotypic features-of tumors, or of the quality and density of immune presence/response. Unlike the binary biomarkers in use in personalized targeted therapy, these phenotypic biomarkers occur on continuums, or involve multiple factors requiring statistical modeling, and overall present a far greater challenge. Given the plasticity and complexity of the immune response, we may find genetic biomarkers rare, and phenotypic biomarkers necessary. These approaches will probably require deep dives into basic and clinical immunity; but deep diving will likely be required to catch those slippery fish.

Financial Disclosure:Dr. Drake has consulted for AZ MedImmune, Bristol-Myers Squibb, Compugen, F-star, NexImmune, Potenza Therapeutics, Roche/Genentech, Merck, Tizona Therapeutics, and ImmuneXcite; has done sponsored research for Aduro Biotech and Bristol-Myers Squibb; has had patents licensed for AZ MedImmune and Bristol-Myers Squibb; and owns stock options in Compugen, NexImmune, Potenza Therapeutics, and Tizona Therapeutics. Dr. Hunter has no significant financial interest in or other relationship with the manufacturer of any product or provider of any service mentioned in this commentary.

References:

1. Drake CG, Lipson EJ, Brahmer JR. Breathing new life into immunotherapy: review of melanoma, lung and kidney cancer. Nat Rev Clin Oncol. 2014;11:24-37.

2. Kantoff PW, Higano CS, Shore ND, et al. Sipuleucel-T immunotherapy for castration-resistant prostate cancer. N Engl J Med. 2010;363:411-22.

3. Carneiro BA, Costa R, Taxter T, et al. Is personalized medicine here? Oncology (Williston Park). 2016;30:293-303, 307.

4. Topalian SL, Drake CG, Pardoll DM. Immune checkpoint blockade: a common denominator approach to cancer therapy. Cancer Cell. 2015;27:450-61.

5. Yonezawa A, Dutt S, Chester C, et al. Boosting cancer immunotherapy with anti-CD137 antibody therapy. Clin Cancer Res. 2015;21:3113-20.

6. Larkin J, Chiarion-Sileni V, Gonzalez R, et al. Combined nivolumab and ipilimumab or monotherapy in untreated melanoma. N Engl J Med. 2015;373:23-34.

7. Wolchok JD, Kluger H, Callahan MK, et al. Nivolumab plus ipilimumab in advanced melanoma. N Engl J Med. 2013;369:122-33.

8. Hammers HJ, Plimack ER, Infante JR, et al. Phase I study of nivolumab in combination with ipilimumab in metastatic renal cell carcinoma (mRCC). J Clin Oncol. 2014;32(suppl 5s):abstr 4504.

9. Rizvi NA, Hellmann MD, Snyder A, et al. Cancer immunology. Mutational landscape determines sensitivity to PD-1 blockade in non-small cell lung cancer. Science. 2015;348:124-8.

10. Tumeh PC, Harview CL, Yearley JH, et al. PD-1 blockade induces responses by inhibiting adaptive immune resistance. Nature. 2014;515:568-71.

11. Woo SR, Turnis ME, Goldberg MV, et al. Immune inhibitory molecules LAG-3 and PD-1 synergistically regulate T-cell function to promote tumoral immune escape. Cancer Res. 2012;72:917-27.

12. Lesterhuis WJ, Rinaldi C, Jones A, et al. Network analysis of immunotherapy-induced regressing tumours identifies novel synergistic drug combinations. Sci Rep. 2015;5:12298.

13. Sivan A, Corrales L, Hubert N, et al. Commensal Bifidobacterium promotes antitumor immunity and facilitates anti-PD-L1 efficacy. Science. 2015;350:1084-9.

14. Vetizou M, Pitt JM, Daillere R, et al. Anticancer immunotherapy by CTLA-4 blockade relies on the gut microbiota. Science. 2015;350:1079-84.

15. Tavare R, McCracken MN, Zettlitz KA, et al. Engineered antibody fragments for immuno-PET imaging of endogenous CD8+ T cells in vivo. Proc Natl Acad Sci USA. 2014;111:1108-13.