Cancer immunotherapy: it's time to better predict patients' response

Abstract

In less than a decade, half a dozen immune checkpoint inhibitors have been approved and are currently revolutionising the treatment of many cancer (sub)types. With the clinical evaluation of novel delivery approaches (e.g. oncolytic viruses, cancer vaccines, natural killer cell-mediated cytotoxicity) and combination therapies (e.g. chemo/radio-immunotherapy) as well as the emergence of novel promising targets (e.g. TIGIT, LAG-3, TIM-3), the 'immunotherapy tsunami' is not about to end anytime soon. However, this enthusiasm in the field is somewhat tempered by both the relatively low percentage (<15%) of patients who display an effective anti-cancer immune response and the inability to accurately identify them. Recently, several existing or acquired features/parameters have been shown to impact the efficacy of immune checkpoint inhibitors. In the present review, we critically discuss current knowledge regarding predictive biomarkers for checkpoint inhibitor-based immunotherapy, highlight the missing/unclear links and emphasise the importance of characterising each neoplasm and its microenvironment in order to better guide the course of treatment.

Fig. 1. Descriptive overview of current and emerging immune checkpoint receptor-ligand complexes.

Inhibitory (red dot) and stimulatory (green dot) proteins which are currently being targeted by FDA-approved monoclonal antibodies or next-generation immunotherapeutic drugs (ongoing clinical trials) are illustrated (non-exhaustive list). A dotted line connects the currently approved drugs with their respective targets. These monoclonal antibodies are approved alone (regular), in combination (italics) or both (italics and underlined) depending on the cancer (sub)type. CD cluster of differentiation, GITR glucocorticoid-induced TNFR-related protein, HCC hepatocellular carcinoma, HNSCC head and neck squamous cell carcinoma, ICOS inducible T-cell costimulator, LAG-3 lymphocyte-activation gene 3, NSCLC non-small cell lung carcinoma, PMBCL primary mediastinal large B-cell lymphoma, RCC renal cell carcinoma, SCLC small cell lung carcinoma, SIRPα signal-regulatory protein α, TIGIT T-cell immunoglobulin and ITIM domain, TIM-3 T-cell immunoglobulin and mucin-domain containing-3, VISTA V-domain Ig suppressor of T-cell activation. Data have been retrieved from https://www.cancerresearch.org/scientists/immuno-oncology-landscape/pd-1-pd-l1-landscape (March 2021).

Fig. 2. Overview of the different parameters (potential biomarkers) that might influence the patient’s response to immune checkpoint inhibitors, and their potential associations.

A high tumour mutational burden (TMB) results in the appearance of neoantigens. These latter are detected by antigen-presenting cells (e.g. dendritic cells, macrophages), which are responsible for the rise of tumour-neoantigen-specific CD8⁺ T-cell clones. In turn, tumour-infiltrating lymphocytes (TILs) indirectly increase the inflammation-driven expression of PD-L1 on both cancer and immune cells through the secretion of interferon (IFN)-γ. In parallel, cancer-associated fibroblasts might produce transforming growth factor (TGF)-β, which promotes a tolerogenic anti-tumour immune response through various mechanisms. Epigenetic modifications have been shown to influence several factors including the infiltration/phenotype of immune cells encountered within the tumour microenvironment, the TMB and PD-L1 expression. HLA and the intestinal microbiota are host-dependent features that are being increasingly studied in the context of cancer immunotherapy. Finally, the predictive value of many peripheral blood-based biomarkers has been assessed over the past few years.

Fig. 3. Representation of individual bacterial species (composing gut flora) that might influence checkpoint inhibitor-based immunotherapy (non-exhaustive list).

Bifidobacterium longum, Collinsella aerofaciens, Alistipes putredinis and Prevotella copri have been shown to be associated with a more favourable response to anti-PD-L1 therapy. Patients enriched for Faecalibacterium prausnitzii responded better to anti-CTLA-4 therapy. Although the reasons are still unknown, some bacterial species (e.g. Roseburia intestinalis, Ruminococcus obeum) or families (e.g. Bacteroides) are associated with a poor response to anti-CTLA-4 or anti-PD-L1 therapy.

Fig. 4. Overview of the main peripheral blood-based predictive biomarkers for immune checkpoint inhibitor-based cancer immunotherapy.

AMC absolute monocyte count, CD cluster of differentiation, CRP C-reactive protein, ctDNA circulating tumour DNA, LDH lactate dehydrogenase, MDSC myeloid-derived suppressor cells, NLR neutrophil/lymphocyte ratio, REC relative eosinophil count, RLC relative lymphocyte count, Tex exhausted T cells.

Fig. 5. Proposed workflow for deciding an optimal treatment course.

The collaboration between oncologists ①, pathologists (2a, evaluations of both PD-L1 expression and intratumoral immune cell infiltration) and geneticists [2b, assessment of tumour mutational burden (TMB)] would lead to the establishment of a score that would guide the course of treatment (3a). Patients with a favourable/high score would be treated with immune checkpoint inhibitors, while patients considered as having a low probability of an efficient/durable response would undergo an alternative treatment (e.g. radiotherapy, chemotherapy) alone or prior to immunotherapy (after re-evaluation).