Kedar Hastak, MS, PhD
Manager, Field Applications Scientist

Prevailing Cancer Immunotherapy Challenges

In recent years, we have made great strides in our understanding of the complex interaction between the immune system and cancer which have led to significant advances in cancer immunotherapies (CI). Despite the successful application of CI across a wide range of indications, only a minority of patients experience significant improvement in survival from these therapies. There are several obstacles that still exist for the field including:

  • The inability of current biomarkers to accurately predict treatment efficacy and patient response and the need for additional biomarkers
  • Need for identification of targetable antigens (neoantigens)
  • Determining the dominant drivers of cancer immunity and understanding the molecular and cellular drivers of primary vs secondary immune escape
  • Understanding the development of resistance to cancer immunotherapies
  • More effective assessment of cancer immunotherapy combinations (1,2)

Below, we expand upon a few of these areas and discuss particular considerations for resolving the multifaceted issues facing the field of immuno-oncology.

One of the major impediments of widely implementing immunotherapies is the lack of cancer-specific, targetable antigens, also known as “neoantigens”. Targeting tumor-associated antigens which are expressed by both tumor and normal tissues may be an option, however, it can lead to off-target toxicities and low efficacy. Therefore, it is important to develop an approach that can both effectively target these neoantigens and enhance T cell reactivity. Thus, the challenge to building an efficacious neoantigen-based cancer vaccine is to not only accurately identify, but also select, and prioritize the putative immunogenic neoantigen candidates. This may be achieved partly through whole exome and transcriptome sequencing of the tumor to detect various source mutations including single nucleotide variants, insertions, deletions, and fusions. It also requires accurate human leukocyte antigen (HLA) typing from whole exome sequencing of the germline sample to derive the patient’s HLA genotype for neoantigen predictions. This is followed by complex computational methods to characterize the neoantigens and synthesize the top candidates for vaccine development. At Personalis®, we have been actively refining a machine learning model to improve neoantigen identification and predictions. The model is well aligned with the major findings from the recent TESLA Consortium study (3) with the integration of similar features into the design including the presentation features as well as foreignness metric. Our prediction algorithm, Systematic HLA Epitope Ranking Pan Algorithm (SHERPA™), is developed and trained on a proprietary machine learning model using high-quality immunopeptidomics data generated from genetically engineered monoallelic K562 cell lines for predicting putative patient-specific neoantigens.

Another challenge is to effectively determine the driver of cancer immunity.  Initially tumors were identified as inflamed based on high expression of PD-L1 by tumor cells or tumor-infiltrating immune cells and/or the prevalence of tumor-infiltrating lymphocytes (TILs) (2,4).

While inflamed tumors tend to respond to checkpoint inhibitors (CPIs), more recent data has shown that additional biomarkers beyond PD-L1 play a very critical role in inflamed tumors (Figure 1). These are IFNγ signatures, B cells, and genomic instability as defined by MSI or high tumor mutational burden (TMB).

Tumor immunity continuum
Figure 1:  Tumor Immunity and response to checkpoint inhibitors. Image from Top 10 Challenges in Cancer Immunotherapy, Immunity Perspective, Priti S. Hegde and Daniel S. Chen, Immunity 52, January 14, 2020.

A study from Griss, et al. (5) has shown that tumor-associated B cells are vital in melanoma response and that the depletion of B cells decreases tumor-related inflammation and CD8+ T cell numbers. Moreover, tumors containing an abundance of plasmablast-like B cells are associated with an enhanced survival benefit to CPI therapies (6).

Studies have shown that tumors such as bladder, colon, and non-small cell lung cancer (NSCLC) with high TMB and MSI respond well to CPI. However, inflamed tumors can undergo loss of heterozygosity (LOH) in the HLA locus (5). Thus, there is a need to identify these complex biomarkers when determining cancer drivers as this can impair the ability of T cells to recognize neoantigens. To this regard, Personalis has developed a multi-dimensional approach modeling both tumor and immune-related molecular mechanisms that resulted in a composite neoantigen presentation score (NEOPS™) which takes into account antigen presentation impairments, such as HLA allele-specific LOH while calculating neoantigen burden (7). However, there is also still an existing need for highly accurate HLA LOH algorithms to allow clinical utility, but the field lacks robust, allele-specific validation approaches. Moreover, algorithms of unknown sensitivity and specificity have led to significant discrepancies in the estimated occurrence of HLA LOH as an immune escape mechanism across tumor types (8,9). To address this obstacle, Personalis has also developed a machine learning algorithm to detect HLA LOH (DASH™ – Deletion of Allele-Specific HLAs) and established the accuracy of the algorithm with an allele-specific PCR validation strategy. Further, investigation of the frequencies of HLA LOH across 23 tumor types in a cohort of approximately 800 patients (Figure 2) demonstrated the prevalence of HLA LOH in many cancer types using this optimized DASH algorithm (9).

Figure 2:  HLA LOH prevalence across a large pan-cancer cohort profiled by ImmunoID NeXT.

Even though high TMB has shown some promise for being predictive of response to CPIs, studies have also shown that PD-L1 expression is a better biomarker of survival benefit compared to TMB in Phase III trials in NSCLC (10,11). Moreover, a number of cancers are characterized by the presence of inflammation without high TMB. These include renal cell carcinoma (RCC), hepatocellular carcinoma (HCC), triple-negative breast cancer, gastric cancer, and, to an extent, head and neck cancers. Conversely, diseases such as small cell lung cancer (SCLC) represent tumors characterized by high TMB in the absence of other markers associated with inflamed tumors. Thus, utilizing the tumor mutational landscape as a predictive biomarker is complex and seems to have very unique indication-specific considerations (Figure 3).

TMB and association with Immune Phenotype
Figure 3:  TMB and association with Immune Phenotype Image from Top 10 Challenges in Cancer Immunotherapy, Immunity Perspective, Priti S. Hegde and Daniel S. Chen, Immunity 52, January 14, 2020.

It is now well understood that genomic instability is not the only source of cancer antigens in tumors and recognized that viral genomes like hepatitis B and C, human papillomavirus, Merkel cell polyomavirus and Epstein-Barr can integrate into the host genome leading to genomic instability as well.  This provides a strong source of immunogenic antigens in cancers such as head and neck squamous cell carcinoma, HCC, cervical cancer, Merkel cell carcinoma, and some subsets of gastric cancer (12). The ImmunoID NeXT Platform® enables accurate detection of oncoviruses and their genotypes from both the comprehensive DNA and RNA data.

Given the complexity of the interaction of the tumor with the immune microenvironment, it is critical to understand why some patients respond to CPIs and a large subset do not; “primary immune escape”. On the other hand, patients who respond initially may become resistant to therapy, a phenomenon termed, ‘‘secondary immune escape’’. These mechanisms of escape/resistance may be similar or diverse and studying the key drivers may help the design of effective combination therapies. For example, the KEYNOTE-162/TOPACIO trial evaluating pembrolizumab and niraparib in patients with metastatic triple-negative breast cancer (TNBC) showed durable responses irrespective of BRCA1/2 or PD-L1 status or prior platinum exposure with the highest overall response rate in BRCA mutant patients (13). However, studying the mechanisms of primary and secondary resistance is challenging due to difficulty in obtaining pre and post-treatment tumors. Yet, as cancer is an evolving disease that can develop and manifest with a very heterogenous nature throughout clinical progression, this a challenge that desperately needs to be addressed. Further, identifying robust predictive biomarkers poses a major challenge as these mutations are unique and diversely distributed across cancer indications. Identification of these mutations will require a comprehensive sequencing technique that can simultaneously detect point mutations or small insertions or deletions, copy number changes and even gene expression changes and fusions. Another prevailing challenge in clinical settings is the limited biopsy tissues embedded in paraffin which can hamper the quality of the sequencing.

Fortunately, immuno-oncology research has been revitalized in the past few years by key findings that have advanced our knowledge of the complex cancer ecosystem. The field is now thriving to expand and seek innovative ways to take on the plethora of challenges mentioned above.  These complexities require high throughput, comprehensive approaches that are tailored for precision oncology development.  Thus, the ImmunoID NeXT Platform® represents an end-to-end solution for immuno-oncology and all precision oncology applications. It combines the pioneering NeXT™ assay, which employs enhanced WES and WTS, with sophisticated analytical engines to provide researchers with comprehensive genomic/transcriptomic data to accelerate their biomarker discovery programs. As shown in Figure 4, the unique design of the assay and analytical algorithms deliver critical tumor- and immune-related information including:

  • T-cell receptor (TCR) alpha & beta repertoire
  • Neoantigen detection and neoantigen load and NEOPS
  • Tumor mutational burden (TMB)
  • Microsatellite instability (MSI) characterization
  • Human leukocyte antigens (HLA) typing, HLA and beta-2 microglobulin (B2M) somatic mutations, and HLA loss of heterozygosity (LOH)
  • Tumor escape and resistance mechanisms
  • Oncoviral detection
NeXT Assay Design
Figure 4:  ImmunoID NeXT Platform® represents an end-to-end solution for immuno-oncology and all precision oncology applications.

References

  1. Cancer Immunotherapy, Part 3: Challenges and Future Trends, C. Lee Ventola, P&T,  August 2017 • Vol. 42 No. 8, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5521300/
  2. Top 10 Challenges in Cancer Immunotherapy, Immunity Perspective, Priti S. Hegde and Daniel S. Chen, Immunity 52, January 14, 2020
  3. Wells DK, Buuren MM, Dang KK, Hubbard-Lucey VM, Sheehan KCF, Campbell KM et. al. (2020) Key Parameters of Tumor Epitope Immunogenicity Revealed Through a Consortium Approach Improve Neoantigen Prediction. Cell 183:818-834.
  4. Ayers, M., Nebozhyn, M., Cristescu, R., McClanahan, T.K., Perini, R., Rubin, E., Cheng, J.D., Kaufman, D.R., and Loboda, A. (2019). Molecular Profiling of Cohorts of Tumor Samples to Guide Clinical Development of Pembrolizumab as Monotherapy. Clin. Cancer Res. 25, 1564–1573.
  5. McGranahan, N., Rosenthal, R., Hiley, C.T., Rowan, A.J., Watkins, T.B.K., Wilson, G.A., Birkbak, N.J., Veeriah, S., Van Loo, P., Herrero, J., and Swanton, C.; TRACERx Consortium (2017). Allele-Specific HLA Loss and Immune Escape in Lung Cancer Evolution. Cell 171, 1259–1271.e11.
  6. Griss, J., Bauer, W., Wagner, C., Simon, M., Chen, M., Grabmeier-Pfisters-hammer, K., Maurer-Granofszky, M., Roka, F., Penz, T., Bock, C., et al. (2019). B cells sustain inflammation and predict response to immune check-point blockade in human melanoma. Nat. Commun. 10, 4186.
  7. Charles W. Abbott, Sean M. Boyle, Rachel Marty Pyke, Lee D. McDaniel, Eric Levy, Fábio C.P. Navarro, Dattatreya Mellacheruvu, Simo V. Zhang, Mengyao Tan, Rose Santiago, Zeid M. Rusan, Pamela Milani, Gabor Bartha, Jason Harris, Rena McClory, Michael P. Snyder, Sekwon Jang and Richard Chen. Clinical Cancer Research, Volume 27, Issue 15.
  8. Havel JJ, Diego Chowell D and Chan TA. The evolving landscape of biomarkers for checkpoint inhibitor immunotherapy. Nature Reviews Cancer volume 19, pages 133–150 (2019)
  9. Pyke RM, Mellacheruvu D, Abbott C,  Dea S, Levy E, Zhang SV, et. al Cancer Research, Proceedings: AACR Annual Meeting 2021; April 10-15, 2021 and May 17-21, 2021.
  10. Hellmann, M.D., Ciuleanu, T.E., Pluzanski, A., Lee, J.S., Otterson, G.A., Audigier-Valette, C., Minenza, E., Linardou, H., Burgers, S., Salman, P., et al. (2018). Nivolumab plus Ipilimumab in Lung Cancer with a High Tumor Mutational Burden. N. Engl. J. Med. 378, 2093–2104.
  11. Herbst, R.S., Soria, J.C., Kowanetz, M., Fine, G.D., Hamid, O., Gordon, M.S., Sosman, J.A., McDermott, D.F., Powderly, J.D., Gettinger, S.N., et al. (2014). Predictive correlates of response to the anti-PD-L1 antibody MPDL3280A in cancer patients. Nature 515, 563–567.
  12. Simoni, Y., Becht, E., Fehlings, M., Loh, C.Y., Koo, S.L., Teng, K.W.W., Yeong, J.P.S., Nahar, R., Zhang, T., Kared, H., et al. (2018). Bystander CD8+ T cells are abundant and phenotypically distinct in human tumour infiltrates. Nature 557, 575–579.
  13. Shaveta Vinayak, Sara M. Tolaney, Lee Steven Schwartzberg, Monica M. Mita, Georgia Anne-Lee McCann, Antoinette R. Tan, Andrea Elisabeth Wahner Hendrickson, Andres Forero-Torres, Carey K. Anders, Gerburg M. Wulf et. al. Journal of Clinical Oncology, 2018 by American Society of Clinical Oncology, Volume 36, Issue 15_suppl

To learn more on how ImmunoID NeXT Platform® can help accelerate your translational research efforts, please visit our website at www.personalis.com/immunoid-next-platform/.