Multidiagnostic approaches for patient selection biomarkers in immuno-oncology

Within oncology, precision medicine — which is an approach for disease treatment and prevention that takes into account the variability in genes, environment and lifestyle for each individual — aims to target tumour antigens, in addition to activating host immune cells, to better recognise tumour cells as threats.

The precision medicine movement began with the identification of genetic alterations in cancer cells and driver mutations to develop molecularly targeted therapies. Advances in polymerase chain reaction (PCR) and highly multiplex platforms have led to the discovery of different genetic profiles that exist across tumour types. Further, innovations in diagnostic testing technologies and data analysis — from next generation sequencing (NGS) and multiplex immunohistochemistry (IHC) to in situ hybridisation (ISH) and multi omics approaches — have allowed researchers to identify more complex tumour biomarkers and critical biomarkers of the TME.

These scientific and technological advances allowed assays to be translated from research to clinical practice, becoming companion and complementary diagnostic (CDx) assays, often developed alongside immunotherapies. CDx tests play an important role not only in matching patients to new precision medicines, but also in guiding the dosing or sequence of how these therapies are delivered. At the same time, advances in clinical trial designs — such as adaptive, basket and umbrella — along with new regulatory policies, have accelerated approvals for precision medicines, especially for rare indications.

Yet, despite these advances, there remains a need for the development of more multi-diagnostic approaches for immunotherapy treatment decisions, as the complexity of matching a therapy to a tumour and its microenvironment is far greater than solely matching a drug to a genomic mutation. That’s because unlike targeted therapies, where diagnostic assays are simpler and more focused on specific genes, immunotherapy requires more complex testing with the assessment of multiple biomarkers of the tumor and TME.

Here we delve into the need for more multi-diagnostic approaches to matching patients to the right immunotherapies in oncology, and the current state of CDx strategies.

The case for multi-diagnostic approaches in immuno-oncology

The ideal biomarker test accurately matches therapies with patients who will benefit from them. Biomarkers for immunotherapies are as unique as the immunotherapies and indications they treat. In certain therapies — such as T cell receptor, CAR-T and antibody-based therapies — using one biomarker may be all that is required. However, biomarkers for therapies that target checkpoint pathways are more complicated, such as those that target CTLA-4 and PD-1/L1.

Using programmed death-ligand 1 (PD-L1) or programmed cell death protein 1 (PD-1) as an example, patients with an elevated expression of PD-L1 have exhibited improved clinical responses to immunotherapy-based immune checkpoint inhibitors. Due to this success, checkpoint inhibitors targeted to PD-L1/PD-1 have been approved — including pembrolizumab (Keytruda) and nivolumab (Opdivo) for varied lymphomas and solid tumours; atezolizumab (Tecentriq) for bladder cancer; avelumab (Bavencio) for metastatic Merkel cell carcinoma; and durvalumab (Imfinzi) for bladder and non-small cell lung cancer (NSCLC).

Although many patients benefit from immunotherapy using PD-1/PD-L1 checkpoint inhibitors, the objective response rate for the PD-1/PD-L1 blockade is only 20 to 30 percent, with many unknown factors affecting the therapeutic effectiveness of PD-1/PD-L1 blockade. Moreover, most of the clinical evidence and corresponding diagnostic testing is optimised for a single treatment modality. There have only been a few biomarkers approved for patient selection, such as the first predictive biomarker for checkpoint inhibitors, PD-L1 IHC. While these biomarkers identify certain patients and may work with certain drugs, the challenge is identifying more patients who are likely to respond. Response rates will be limited with a one-biomarker approach, especially for new immunotherapies in development.

As these immunotherapies continue to enter the market, more clinical research programmes will incorporate different immuno-oncology therapies, as well as standard treatments in combination trials, requiring a more complex testing algorithm, or other diagnostics, to evaluate the tumour and its microenvironment. As a result, predicting patient response and identifying the right patients for these studies require precise and accurate biomarker selection.

Therefore, taking a multi-diagnostic approach could better identify eligible patients who may benefit most from either approved immunotherapies or those in clinical trials.

Developing a multi-diagnostic strategy

When considering the co-development of a diagnostic assay (ultimately, for approval as a complementary or companion diagnostic) and a targeted therapy, multiple technical, regulatory and operational factors need to be evaluated for successful implementation in a clinical trial. Different genomic and immunologic screening tools can be used to develop a multi-diagnostic approach for immuno-oncology clinical trial assay development. A comprehensive, multi-biomarker assay can be established to assess the tumour and its microenvironment including gene expression (GEX) profiling, tumour mutational burden (TMB) and microsatellite instability (MSI).

• MSI - An important factor in the occurrence and development of tumours, MSI can be used to describe a phenotype of mismatch repair genes, where a lack of these genes in tumour cells, or defects in the process of replication repair, or the possibility of gene mutations is increased. Tumours can be classified as MSI high, low or stable. An MSI high tumour may indicate a better response to checkpoint inhibitors. Approaches can range from PCR, NGS and IHC assays.
• TMB - Another predictive biomarker based on the hypothesis that higher level mutations will result in higher levels of immunogenic tumour antigens is TMB, defined as the total number of somatic mutations per coding area of a tumour genome. To determine the value of TMB as a predictive biomarker, a standardised panel, workflow and data analysis pipeline for TMB assessment is needed. The methods used to assess tumour mutation burden in clinical studies have included whole exome sequencing and targeted NGS panels. It is important to note that when comparing TMB and MSI, TMB does not necessarily correlate with MSI. For example, a tumour may be high TMB status and MSI stable. When considering TMB, challenges remain including a lack of standardisation between test methods and diagnostic cut-off values of different tumour types. In addition, turnaround time can be weeks, which can be impractical for treatment decisions.
• GEX - This approach can be used as a potential CDx as an inflammation signature of the TME. The signature can range from four to 50 different genes, and is designed to provide an inflammation or immune cell score. The assay uses RNA extracted from tumour tissue. For example, the oncomine immune response research assay was used to detect the correlation between two different signatures in NSCLC. Both signatures (M1 and Peripheral T cell) were better at discriminating between durable and nondurable clinical benefit, than tumour infiltrating lymphocytes (TIL) and TMB.

Using the right tools for the right job

The development of cancer immunotherapies has provided an expanded arsenal in the war on cancer. Predictive biomarkers and diagnostics will help clinical use of this arsenal in the most effective way possible. Beyond multi-diagnostic approaches, other factors need to be considered during clinical trial design and CDx development, including cost, turnaround time, sample requirements and access to high complexity testing. Innovations, as well as coordination between clinical, translation, regulatory and commercial teams, are needed to identify, validate and launch diagnostic assays that will advance the precision medicine revolution.