PERSONALIZED MEDICINE – Personalizing Cancer Immunotherapy: Trends in Biomarker Discovery


Throughout the past few decades, research has provided significant breakthroughs that have enhanced our understanding of the complex mechanisms and pathways that regulate the immune system’s response to cancer. These advances have led to great leaps in the field of cancer immunotherapy, and in recent years, immune checkpoint inhibitors and chimeric antigen receptor (CAR) T-cell therapeutics have revolutionized cancer treatment. However, the response rates and side effects associated with existing immunotherapies limit the number of patients who may benefit from these therapies. There remains an unmet need to enable selection of patients who would most benefit from a specific treatment option and exclude those who are unlikely to respond, especially given the price tag associated with many immunotherapies. Every tumor is unique, and biomarkers have the potential to help both researchers and clinicians select the right treatment for each patient at every stage of cancer therapy. The identification of biomarkers will help to fill critical knowledge gaps by providing not only predictive and prognostic information, but also insight into the underlying mechanisms of patient response or resistance to immunotherapy.1 However, due to tumor heterogeneity, the plasticity and diversity of cancer cells, and a multitude of other factors, biomarker development is a challenge. In this article, we explore four trends in cancer biomarker discovery.

PERSONALIZED MEDICINE - Personalizing Cancer Immunotherapy


Programmed death 1 (PD-1) is a key immune checkpoint inhibitory receptor expressed on activated tumor-specific CD4+ helped and CD8+ killer T lymphocytes. The main PD-1 ligand, programmed death ligand 1 (PD-L1), is a transmembrane protein expressed on a variety of cell types, including the dendritic cells, which play a critical role in both innate and adaptive immunity. PD-L1 binding inhibits the function of activated T-cells. By co-opting the PD-1/PD-L1 regulatory mechanism via expression of PDL1, tumor cells are able to bind to PD-1, inhibit T-cell activation, and evade the immune system.

Therapeutic antibody-mediated blockage of PD-1 or PD-L1 removes the suppressive effects of PD-L1 on cytotoxic T-cells, restoring host immunity against the tumor. To date, two PD-1 inhibitors [nivolumab (marketed as Opdivo®) and pembrolizumab (marketed as Keytruda®)] and three PD-L1 inhibitors [atezolizumab (marketed as Tecentriq®), avelumab (marketed as Bavencio®) and durvalumab (marketed as Imfinzi®)] have been approved for cancer immunotherapy. Consequently, defining biomarkers that predict therapeutic response to PD-1/PD-L1 blockade is important for appropriate patient selection.

Detection of tumor cell PD-L1 protein expression using immunohistochemistry (IHC) has been evaluated in clinical studies for correlation with response to PD-1 and PD-L1 immune checkpoint inhibitors. Currently, PD-L1 IHC 22C3 pharmDx (manufactured by Dako A/S, now Agilent Technologies), which is used to select patients for treatment with pembrolizumab, is the only FDA-approved companion diagnostic. The other three FDA-approved PD-L1 IHC assays serve as complementary diagnostics that may provide additional data that can be used to inform physician-patient dialogue around treatment decisions.

Recently, a Blueprint Working Group established in cooperation with the pharmaceutical industry compared the different IHC tests and cell scoring methods for PD-L1 expression. The Group concluded that more data are needed before an alternative assay can be used to read different specific therapy-related PD-L1 cut-offs.2 Thus, for now, PD-L1 IHC positivity is an imperfect biomarker of response and is not suitable as a definitive biomarker for selection for therapy with PD-1/PD-L1 inhibitors.3

Moreover, studies have shown that PD-L1 negativity is unreliable, as results may differ depending on the antibody, assay, or tissue sample. Low expression, tumor heterogeneity, and inducible genes can also lead to sampling errors or false negatives. In addition, it has been found that the treatment benefits of PD-1/PD-L1 inhibitors are not limited to patients whose tumors express PD-L1. In two pivotal trials of nivolumab, 20% to 30% of PD-L1-negative patients responded to treatment, compared with 50% of PD-L1-positive patients, suggesting that biomarkers other than PD-L1 may be predictive of response to nivolumab.4,5 Given these finds, it is likely that a more complex, multi-component predictive biomarker system will be required to refine patient selection.


Tumor mutation burden (TMB) is a measurement of the mutations carried by tumor cells. Typically, DNA sequencing is used to determine the number of acquired mutations in the tumor and TMB is reported as the number of mutations in a specific area of genetic material, such as mutations in a single cell, mutations in an entire tumor, or mutations per megabase. Currently, numerous studies are evaluating the association of TMB with response to immuno-oncology therapy.

It is thought that tumor cells with high TMB may have more neoantigens, cell surface molecules that are expressed solely on cancer cells and not on normal cells. These neoantigens can be recognized by T-cells, activating an anti-tumor immune response both in the tumor microenvironment and beyond. As such, it is hypothesized that a high TMB may correlate with a higher likelihood of responding to immunotherapy. In fact, research has shown that patients with high TMB have better overall survival when treated with PD-1/PD-L1 inhibitors, compared with patients with low TMB.6

More recently, at the 2017 International Association for the Study of Lung Cancer (IASLC) 18th World Conference on Lung Cancer, researchers presented data from CheckMate-032, an ongoing Phase I/II open-label trial evaluating the safety and efficacy of nivolumab monotherapy and nivolumab plus ipilimumab (marketed as Yervoy®) combination therapy in patients with advanced small cell lung cancer patients. The data demonstrated that response rate and 1-year overall survival nearly doubled in patients with a high TMB who were treated with combination therapy versus monotherapy. In addition, a high TMB predicted better outcomes, regardless of the treatment arm.7 These new findings provide compelling evidence supporting the clinical utility of TMB as a biomarker for treatment with nivolumab, both alone and in combination with ipilimumab.


The GVK Biosciences Online Clinical Biomarker Database, developed in collaboration with the US Food and Drug Administration, has identified tens of thousands of biomarkers. However, only a very small fraction of these have been developed into validated genomic biomarkers for FDA-approved drugs. So far, none have become in vitro companion diagnostics.1 For a predictive biomarker to be applied in the clinic, it must have analytic and clinical validity, in addition to clinical utility. Many organizations have published guidelines for the validation of diagnostic tests, with guidance and recommendations regarding analytic sensitivity, specificity, reproducibility, and assay robustness.8,9

The process of translating biological data into a predictive biomarker is complicated by the myriad host- and cancer-related factors that influence the complex interactions between the tumor and the immune system. The emergence of powerful genomic and proteomic technologies, along with advanced bioinformatic tools, has made it possible to simultaneously analyze thousands of biological molecules. These techniques are paving the way to truly personalized cancer therapy by enabling the discovery of new tumor signatures, which are both sensitive and specific enough for early cancer detection, monitoring of disease progression, and appropriate treatment selection.

The availability of novel technologies and high throughput approaches, such as mass cytometry, whole exome sequencing, gene expression profiling, and sequencing technology for T-cell receptor clonality assessment, opens new doors for immune biomarker development. However, it also brings new challenges. With these techniques, a single sample can be used to address a multitude of questions, but the resulting quantity and complexity of data creates new analytical and cost considerations. The Society for Immunotherapy of Cancer (SITC) convened a working group which published a white paper that evaluated new technologies and emerging biomarkers relevant to cancer immunotherapy and provided recommendations on best practices.10


Metabolic considerations, the tumor microenvironment, the microbiome, and signaling pathway modulation all affect the immune system. The tumor microenvironment refers to the network of cells and structures surrounding a tumor, including stroma, connective tissue, and immune regulatory cells. A substantial body of research shows that cross-talk occurs between cancer cells and immune cells in the tumor microenvironment, and that this communication influences tumor progression and immune or drug resistance. Data has also shown that conditions such as hypoxia, nutrient stress, and tumor cell death can alter the phenotype of immune cells, inducing a tumor-promoting reprogramming.11

Profiling tumor microenvironment at a genetic, molecular, and even metabolic level may help elucidate the mechanisms associated with resistance and guide the design of cancer immunotherapeutics. Unlike predictive or prognostic biomarkers, immune targets are biomarkers that might not correlate strongly with response to treatment, but may help direct the development of cancer therapies.

For example, in one study, Ras mutations were used as immune target biomarkers. Patients with advanced solid tumors bearing Ras mutations were given a cancer vaccine comprised of autologous peptides along with interleukin (IL)-2, granulocyte-macrophage colony-stimulating factor (GM-CSF), or both. Although the majority of patients developed antigen-specific immune responses, only one patient out of 57 generated productive immunity that went on to eliminate the tumor cells.1,11

This disparity led to the discovery that there is significant expansion of regulatory T-cells (Treg) in patients with colon cancer with mutated Ras, compared to both healthy individuals and patients with colon cancer with wild-type Ras. Mutant Ras activates the MEK-ERK-AP1 pathway to induce secretion of high levels of IL-10 and transforming growth factor (TGF)-β1, which generate local induction of Treg in the tumor microenvironment.13 Induction of Treg serves to support tumor immune escape by creating a suppressive tumor microenvironment that inhibits the anti-tumor response. Thus, the efficacy of a cancer vaccine in patients with Ras mutations may be increased by adding an agent that targets Treg.

More recently, researchers at the Wistar Institute and the Medical University of Vienna described the role of tumor-associated B cells (TABs) in melanoma progression and therapy resistance. They showed that TABs, which represent up to one-third of all tumor-infiltrating immune cells that, can promote tumor heterogeneity and are prevalent in advanced, therapy-resistant tumor tissues, suggesting that B-cell depletion might have therapeutic potential. The therapy-resistant tissues also showed increased expression of insulin-like growth factor (IGF)-1 and fibroblast growth factor receptor (FGFR-2), which could represent new therapeutic targets.14


Biomarkers are the foundation for personalized cancer therapy, but very few biomarkers have been successfully translated into clinical diagnostics for patient care. The challenges associated with biomarker development are outweighed by the opportunities inherent in the potential of biomarkers to guide treatment selection and therapeutic development  presented by the use of biomarkers. Ideally, biomarkers will enable the development of truly personalized cancer treatment plans, which could help avoid selection of ineffective therapies, unnecessary toxicities, and the subsequent need to treat those toxicities. In addition, the rational design of combination therapies will only be possible with an even greater understanding of the mechanisms of action and resistance in different tumor cells.1


  1. Gulley JL, et al. Immunotherapy biomarkers 2016: overcoming the barriers. J Immunother Cancer 2017;5:29.
  2. Hirsch FR, et al. PD-LD Immunohistochemistry assays for lung cancer: Results from Phase 1 of the Blueprint PD-L1 IHC Assay Comparison Project. J Thorac Oncol 2017;12(2):208-222.
  3. Boussiotis VA. Molecular and biochemical aspects of the PD-1 checkpoint pathway. N Engl J Med 2016;375:1767-1778.
  4. Weber JS, et al. Nivolumab versus chemotherapy in patients with advanced melanoma who progressed after ati-CTLA-4 treatment (CheckMate 037): a randomised, controlled, openlabel, phase 3 trial. Lancet Oncol 2015;16:375-384.
  5. Robert C, et al. Nivolumab in previously untreated melanoma without BRAF mutation. N Engl J Med 2015;372:320-330.
  6. Zugazagoitia J, et al. Current challenges in cancer treatment Clin Ther 2016;38(7):1551-1566.
  7. Rizvi N, et al. Impact of tumor mutation burden on the efficacy of nivolumab or nivolumab plus ipilimumab in small cell lung cancer: An exploratory analysis of CheckMate 032. 2017 World Conference on Lung Cancer. Abstract OA 07.03a. Presented October 16, 2017.
  8. Chau CH, Rixe O, McLeod H, Figg WD. Validation of analytic methods for biomarkers used in drug development. Clin Cancer Res 2008;14(19);5967-5976.
  9. Lee JW, et al. Method validation and measurement of biomarkers in nonclinical and clinical samples in drug development: a conference report. Pharm Res 2005;22(4):499-511.
  10. Yuan J, et al. Novel technologies and emerging biomarkers for personalized cancer immunotherapy. J Immunother Cancer 2016;4:3.
  11. Dehne N, et al. Cancer cell and macrophage cross-talk in the tumor microenvironment. Curr Opin Pharmacol 2017;35:12-19.
  12. Rahma OE, et al. The immunological and clinical effects of mutated ras peptide vaccine in combination with IL-1, GM-CSF, or both in patients with solid tumors. J Trans Med 2014;12:55.
  13. Zdanov S, et al. Mutant KRAS conversion of conventional T cells into regulatory T cells. Cancer Immunol Res 2016;4(4):354-365.
  14. Somasundaram R, et al. Tumor-associated B-cells induce tumor heterogeneity and therapy resistance. Nature Commun 2017;8(1):607.

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Dr. Emile Youssef is the Executive Medical Director, Oncology, at Clinical Research Organization. He earned his MB BCh (MD) from Cairo University, Egypt, and his PhD and board certification in oncology from Osaka City University Medical School, Osaka, Japan. He has 5 years of postdoctoral experience in oncology at the University of Texas MD Anderson Cancer Center in Houston, TX. He brings more than 10 years of experience in academia as a principal investigator in hematology/oncology indications focused on gastrointestinal and breast cancers as well as hematologic malignancies. Dr. Youssef has been a pioneer in the implementation of combining novel agents for epigenetic and chromatin remodeling with standard-of-care therapies in clinical development trials for myelodysplasia and leukemia and has published more than 20 peer-reviewed papers in top-rated clinical cancer journals.