Evaluation of tumor-immune interaction.
Immunotherapy with immune checkpoint inhibitors (ICIs) has completely changed the therapeutic landscape for many types of solid tumors. Several combinations of immunotherapy with chemotherapy, molecular targeted drugs, or combinations of different ICIs are now approved for the first-line treatment of various cancers. However, not all types of cancer respond equally well to ICIs, and even in responsive cancers, only a subset of patients experiences durable responses and favorable long-term outcomes. This is because primary and acquired resistance occurs in a considerable proportion of patients across different cancer types.
Therefore, it is crucial to establish reliable predictive biomarkers to distinguish ICI responders from non-responders, who may suffer unnecessary costs and toxicities, and to identify candidates for rational combination therapies. Currently, tumor mutational burden and PD-L1 expression are the two major variables used as biomarkers that have been validated in phase III clinical trials. Additionally, several other factors associated with response or resistance to ICIs across cancer types have been proposed as biomarkers, based on molecular profiling of cancers treated with different immunotherapies. These include an immune-inflamed phenotype, expression of T cell signaling pathway genes such as IFNγ, microsatellite instability, somatic copy-number alterations, HLA class I diversity, T cell repertoire clonality change, WNT-β-catenin signaling, TGFβ expression and even commensal microbiota.
However, as single biomarkers, none of the above is sufficient to identify individual patients who will likely benefit from immunotherapy. Unlike conventional cancer therapies, immunotherapies, including ICIs, do not directly target tumor cells; instead, they affect tumor cells through the patient’s immune system or the tumor microenvironment (TME). Therefore, the different components that affect tumor-immune interactions need to be taken into account when developing predictive biomarkers for immunotherapy. Comprehensive analysis of multiple different functional pathways and molecular networks that reveal integrated mechanisms of tumor-immune interactions are crucial for this purpose. General and local cancer immunity status in each patient needs to be taken into consideration.
To this end, Blank et al. proposed the concept of the cancer immunogram that integrates multiparameter biomarkers to visualize the immunological status of an individual patient. We have applied this concept to lung cancer patients and developed an immunogram reflecting the cancer-immunity cycle. Since then, van Dijk et al. have reported an immunogram informative specifically for urothelial cancer patients. Although immunograms may be useful for visualizing the landscape of the tumor microenvironment and the compromised steps of anti-tumor immunity in each patient, both Blank et al. and van Dijk et al. had only theorized that they could be useful to patients but had not tested the concept in clinical practice. In contrast, we analyzed real-world lung cancer patient data to generate immunograms with potential applications for personalized immunotherapy. We utilized RNA-Seq data from the TCGA cohort as a standard and set up a scoring scale to quantify parameters incorporated in the immunogram. We propose a novel versatile scoring method for constructing such individual immunograms.
To develop combinatorial and personalized immunotherapy.
Immune profiling, including bulk transcriptome assays with microarrays or bulk RNA-Seq as well as fluorescence-based flow cytometry, contributes valuable insights into the mechanisms behind tumor-immune cell interactions and can uncover mechanisms and biomarkers for prediction of therapeutic responses. Recently, high-dimensional technologies such as scRNA-Seq and cytometry by time of flight (CyTOF) are being increasingly employed for immune profiling in cancer in order to detect rare immune subsets and dissect phenotypic and functional heterogeneity. Using these new technologies, deep immunophenotyping at the single-cell level is possible in the individual patient. Deep phenotyping of the TME of each patient will be a useful guide to potential individual target molecules or cells and to determining the therapeutic strategies to be adapted for each patient.
The future of immunotherapy will be combinatorial and personalized treatments adapted to each patient’s cancer-immune interactions in the TME. Clearly, instigating this type of personalized therapy will be challenging for many reasons, not the least of which will be the high cost and labor-intensive nature. However, it is to be expected that technical advances will overcome some of these difficulties. Fortunately, the cost of NGS is decreasing every year, and the development of a cost-effective targeted scRNA-Seq platform using our original VFAC will hopefully contribute to this.
To demonstrate the feasibility of an immunological data-guided personalized adaptive approach to immunotherapy, whereby immunomodulatory strategies are tailored to the patient’s specific TME, we utilized tumor-bearing mice model. The TME of growing tumors was immunologically assessed and the animals were treated based on those results. Using scRNA-Seq, but not bulk RNA-Seq, it was possible to determine the molecules in the tumors that were involved in generating an immunosuppressive microenvironment. Tumors currently considered non-responsive to immune checkpoint therapy might be convertible to responders by elucidating and regulating the complicated network of cancer cells and immune cells in the individual patient TME.