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Vol 21, No. 12, December 2017   |   Issue PDF view/purchase
Cancer Immunotherapy: where we stand and move forward
Over the past few decades, immunotherapy has been studied intensively as a promising treatment for cancer. Newer types of immune treatments are currently being studied, and they will impact how we treat cancer in the future. Dr Chen Shu-Jen and Dr Poon Song Ling share more with us.

The current advance in cancer immunotherapy is now at the forefront of personalized cancer management. Using immune-checkpoint inhibitors, immunotherapy has been upheld as a game changer to potentially eradicate this deadly disease, thanks to its promising clinical outcomes in a variety of advanced-stage cancers, in particular, melanoma. In this article, the authors will introduce the essence of cancer immunotherapy.

What is cancer immunotherapy?

Simply put, cancer immunotherapy is administered to restore patients’ immune response to attack, and eventually eliminate tumour cells. Immunological therapeutics, such as checkpoint inhibitors, target the core of carcinogenesis process – the mutation that drives the development of cancer. How cancer immunotherapy works as the promise to eradicate cancer is based on three advantages: memory, adaptability, and specificity1.

  • Memory: the human immune system has proved to be more capable of controlling malignant tumours. Such long-lasting anti-tumour capacity of memory T cells plays a crucial role in the control of malignant tumours.
  • Adaptability: the immune system can be finely tuned to address the consequences of immune-incompetence, such as in cancer.
  • Specificity: immunotherapy treatment activates T cells and B cells that target specific pathogenic mechanisms in subgroups of cancer patients.

To evade the immune surveillance, many tumors secret check-point molecules to suppress the function of T cells. As such, the anti-tumour activity of checkpoint inhibitors targeting the immune check-point proteins works by releasing the “brakes” on T cell so that the immune system can differentiate cancer cells from normal cells and start an attack against it.

What immune checkpoint inhibitors are available?

As of November 2017, the US FDA approved six immune checkpoint inhibitors and their therapeutic indication(s). These include ipilimumab (CTLA-4 blockade) for melanoma, nivolumab (PD-1 inhibitor) for melanoma, lung cancer, kidney cancer, bladder cancer, head and neck cancer, and Hodgkin's lymphoma, pembrolizumab (PD-1 inhibitor) for melanoma and lung cancer, atezolizumab (PD-L1 inhibitor) for bladder cancer, durvalumab (PD-L1 inhibitor) for locally advanced or metastatic urothelial carcinoma, and avelumab (PD-L1 inhibitor) for Merkel-cell carcinoma.

Programme cell death 1 (PD-1) is one of the checkpoint proteins that expresses on T cells. Programme cell death ligand 1 (PD-L1) is another checkpoint protein that can be found on the normal cells2. When PD-1 binds to the PD-L1 expressing on the tumour cells, it prohibits the T cells from recognizing the tumour cells as “foreign cells” and stops T cells from killing them2. Cancer cells may express abundant PD-L1 on their surface as a shield to avoid being identified by patient’s immune system. Both PD-1 and PD-L1 play the role of checkpoint or guard to notify the immune system whether they should start an attack against the “foreign” cells (i.e., cancer cells).

Substantial efforts have been made to develop biomarkers that can stratify patients likely to experience a response or clinically benefit from immunotherapeutic agents to those that showed lack of benefits either initially (primary resistance), or secondary (acquired resistance). Among all, using immunohistochemistry staining to assess PD-L1 expression in tumour cells has recently been approved by US FDA as biomarkers for the use of pembrolizumab in non-small cell lung cancer3.

Why checkpoint inhibitors fail to deliver expected clinical outcomes?

Despite these exciting advances for checkpoint inhibitor-based immunotherapy, the objective response rate of either PD-1 or PD-L1 inhibitors across different cancer types, unfortunately, ranges between 10 to 20 per cent. This indicates the disappointment that majority of patients demonstrate innate resistance. One may wonder: why do many PD-L1-positive cancers not respond to checkpoint inhibitor and how do certain cancers that initially respond to checkpoint inhibitors later become resistant? The answers can be addressed in two fold.

For one, PD-L1 staining fails to be a drastic success across cancer types. One of the main reasons for the failure is due to the lack of standardization method4. That is, there is a substantial descripancy in using different PD-L1 antibodies for different checkpoint inhibitors with various cut-off values and scoring methods to define positivity of PD-L15. As a result, it is increasingly difficult for cancer specialists to reach an overall consensus on the use of this biomarker.

Secondly, we need to move beyond PD-1 and PD-L1 centric antitumour activities, and look at a broader mutational landscape – neoantigen. Neoantigen is a short peptide derived from specific mutations of a cancer cell that presents to T cells for recognition. A recent study demonstrated that HLA and B2M genes are among the top 100 necessary genes required in cancer cells for immunotherapy to work6. This is because these two genes form a complex which is required to present neoantigen so that T cells can detect and attack the cancer cells.

By analysing the tumour samples collected before the checkpoint inhibitor therapy starts and after the resistance develops, researchers have discovered the emerging of loss-of-function B2M mutations in the patient group who develop resistance to ongoing immunotherapy7,8.

Tumour Mutational Burden: promising biomarkers for immune checkpoint inhibitors

Up-to-date, remarkable clinical efficacy of immune checkpoint blockade has been noted in patients with melanoma or non-small cell lung cancer that harbours a higher number of somatic mutations9,10, for instance, high tumour mutational burden (TMB). These mutations in the tumour are the source of neoantigen that allows the cancer cells to be recognized by the immune system and therefore enhances T cells reactivity towards the tumour.

Therefore, it is predictable and proven that patients with defective DNA repair machinery (i.e., deficient in DNA mismatch repair machinery, dMMR or high in microsatellite instability, high MSI) demonstrated higher count in TMB (i.e., higher neoantigen load) since the mutations generated during replication or caused by carcinogen exposure will not get corrected properly11. Thus, the significant improvement in objective response rate observed in pembrolizumab-treated patients with dMMR leads to the FDA approval of using MSI status as a biomarker for pembrolizumab and nivolumab across different cancer types12.

The correlation between high TMB with clinical benefits from checkpoint inhibitor blockade was initially generated by using whole-exome sequencing to quantify the somatic mutations in the responsive versus non-responsive checkpoint inhibitor-treated tumours9,10. However, conducting whole exome sequencing in every tumour is expensive and impractical. An alternative way is to use a next-generation sequencing based targeted panel to extrapolate and estimate TMB.

Currently, the flagship panel (i.e., ACTOnco) provided by ACT genomics, comprises 440 cancer-related genes, is capable of providing TMB data. TMB estimated with the ACTOnco panel is highly correlated with the TMB measured with whole exome sequencing. The threshold for TMB was defined by retrospectively using the cut off value at 17.1 mutations per coding megabase to examine the recovery rate for the patients that responses to PD-1 blockade in non-small cell lung cancer10. This analysis showed that the definition of high TMB (i.e., 17.1 mutations/megabase) greatly enhance the sensitivity in detecting the responder to PD-1 blockade (Table 1).

On top of TMB data, MSI data as well as mutation data were also incorporated in immune-related genes such as antigen-presenting and presentation genes to provide a contextual prediction to the response to immunotherapy. Importantly, this panel does not just interrogate the susceptibility of a tumour towards checkpoint inhibitor, but it can also provide information towards targeted therapy, hormonal therapy, and chemotherapy.

In addition to using comprehensive genomic profiling for the response prediction to checkpoint inhibitors, there is also the need to characterize the dynamic interactions between tumour cells with the immunomodulators within the tumour microenvironment (TME)13. This involves not only measuring the expression of immunoinhibitory and immunostimulatory molecules but also the cytokines, and chemokines that govern T cells’ priming, activation, and trafficking.

The way forward

Not all patients are likely to benefit from immunotherapy. To ensure the ability of checkpoint inhibitors in illicit dramatic and durable responses, well-validated biomarkers, including positive predictor (TMB) and negative predictor (loss of B2M), are required to further progress this field towards precision medicine.

Undoubtedly, both neoantigens and an intact antigen machinery (presenting and processing) play an important role for checkpoint inhibitors to work. The studies mentioned in this article have identified the mechanisms of immunotherapy resistance. More importantly, one is able to explain why a cancer would never respond in the first place (i.e., primary resistance), and when a patient stops responding to checkpoint inhibitors (i.e. acquired resistance).


  1. Sharma P. and Allison JP. The future of immune checkpoint therapy. Science. 348, 56-61 (2015). doi: 10.1126/science.aaa8172
  2. Sharpe A.H. and Pauken K.E. The diverse functions of the PD1 inhibitory pathway. Nat Rev Immunol. (2017). doi: 10.1038/nri.2017.108.
  3. 3. Sul J. et al., FDA approval summary: Pembrolizumab for the treatment of patients with metastatic non-small cell lung cancer whose tumors express programmed death-ligand 1. Oncologist 5, 643-650 (2016). doi:10.1634/theoncologist.2015-0498.
  4. Hirsch. F.R. et al., PD-L1 immunohistochemistry assays for lung cancer: results from phase 1 of the blueprint PD-L1 IHC assay comparison project. J. Thorac. Oncol. 12, 208-222 (2017). doi:10.1016/j.jtho.2016.11.2228.
  5. McLaughlin J. et al., Quantitative assessment of the heterogeneity of PD-L1 expression in non-small-cell lung cancer. JAMA Oncol. 2, 46-54 (2016). doi:10.1038/modpathol.2016.186.
  6. Patel S.J. et al., Identification of essential genes for cancer immunotherapy. Nature 548, 537-542 (2017). doi:10.1038/nature23477.
  7. Zaretsky J.M. et al., Mutations associated with acquired resistance to PD-1 blockade in melanoma. N Engl J Med. 375, 819-829 (2016). doi:10.1056/NEJMoa1604958.
  8. Sade-Feldman M. et al., Resistance to checkpoint blockade therapy through inactivation of antigen presentation. Nat Commun. 8, 1136 (2017). doi:10.1038/s41467-017-01062-w.
  9. Chan T.A. et al., Genetic basis for clinical response to CTLA-4 blockade in melanoma. N. Engl. J. Med. 371, 2189-2199 (2015). doi:10.1056/NEJMc1508163.
  10. Rizvi N.A. et al., Cancer immunology. Mutational landscape determines sensitivity to PD-1 blockade in non-small cell lung cancer. Science. 348, 124-128 (2015). doi:10.1126/science.aaa1348.
  11. KEYNOTE-164 and KEYNOTE-158
  12. FDA approves first cancer treatment for any solid tumor with a specific genetic feature. (2017) https://www.fda.gov/newsevents/newsroom/pressannouncements/ucm560167.htm
  13. Chen D.S. and Mellman I. Elements of cancer immunity and the cancer-immune set point. Nature. 541, 321-330 (2017). doi: 10.1038/nature21349.

About the Authors

Dr Chen Shu-Jen received her Ph.D. in Biochemistry from Virginia Commonwealth University and did her postdoctoral training at Baylor College of Medicine. She joined SUNY Buffalo as Research Assistant Professor and subsequently came back to Taiwan to join the National Health Research Institute in 1998 as Assistant Investigator where she established the high throughput screening program (anti-cancer & anti-viral drug discovery) for the institute. In 2001, Dr Chen served as the Head of In Vitro Pharmacology at TaiGen Biotechnology for five years, before moving on to Chang Gung University as an Associate Professor at the Dept. of Biomedical Sciences, to build the microarray and next-generation sequencing platforms for the Molecular Medicine Research Center, and focused on developing biomarkers for cancer diagnosis and for treatment prediction. As the co-founder and Chief Scientific Officer for ACT Genomics, Dr Chen is devoted in making “Precision Cancer Medicine” a reality using the latest cutting edge sequencing technology.

Dr Poon Song Ling received her Ph.D. degree from the University of British Columbia in Canada. Subsequently, she joined National Cancer Centre Singapore to conduct her postdoctoral training in translational research. Her principal research interests circumscribe the topic of genetic instability in cancer cells. She seeks to utilize next generation sequencing in conjunction with high throughput screening to elucidate the roles of aberrant signaling pathways in cancer cell biology for the development of cancer therapeutics. She has published widely in leading journals including Nature Genetics, Science Translational Medicine, and Cancer Discovery. Currently, Dr. Poon is the medical scientific liaison of ACT Genomics.

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