Cancer immunotherapy is the deliberate and specific enhancement of the immune system to fight cancer. Both major categories of cancer immunotherapy, active and passive, have proven efficacious against multiple cancer types. The goal of active immunotherapy is to generate a tumor-specific immune response by the patient's own immune system. By inducing immunological memory, successful active immunotherapy produces an enduring response. Chimeric antigen receptor therapy, in which T cells are harvested, genetically altered, and re-introduced to the body, is a form of active immunotherapy aimed at educating the patient's immune system to recognize and attack cancer cells.
Passive immunotherapy, by contrast, relies on repeated administration of immune system modifying factors. Unlike active immunotherapies, the effects of passive immunotherapies are immediate, as there is no need to wait for the patient to mount an immune response. However, continued dosing is required for continued effect. Monoclonal antibody therapies, directed against a variety of tumor-associated antigens, are passive immunotherapies, as are checkpoint inhibitors, which are also monoclonal antibodies, but ones that are directed specifically at proteins such as PD-1, PD-L1, and CTLA-4. These are "checkpoint" proteins, which are involved in the attenuation of T cell activity. By inhibiting them with checkpoint inhibitors, cancer-fighting T cells can be kept active.
The problem of heterogeneity
The development and therapeutic use of passive immunotherapies, in particular, relies on an accurate understanding of the cellular composition of the tumor microenvironment and the complex interactions between cells that occur there. However, any attempt to analyze tumor composition is initially beset by the problem of heterogeneity. Tumors are composed of malignant cells, normal host cells, stroma cells recruited by the tumor for scaffolding, vasculature, and other requirements of growth and maintenance, a wide variety of immune cells, and other cells. This heterogeneity impedes detailed characterization of tumor composition and cellular activity and makes the analysis of rare cell populations from bulk sample data especially difficult.
Single-cell genomics and other single-cell analysis technologies have emerged as a solution to the analytical problem posed by tumor heterogeneity. Next-generation sequencing systems now have the ability to sequence the DNA or RNA of a single cell. Various nucleic acid tagging and bar-coding strategies have emerged to enable accurate assignment of data produced by sequencing instrumentation back to its cell of origin. The use of oil droplets and other microscale structures as reaction chambers for cell lysis, nucleic acid barcoding, and other manipulations is allowing researchers to step away from comparatively slow, tedious, microplate-based workflows.
Single-cell immunotherapy research successes
Characterizing heterogeneity
Sufficient scRNA-Seq (single-cell RNA sequencing) data already exists in publicly accessible databases to enable entirely "dry" studies that can reveal substantial new insights. A research article published in February 2019 details one such study in which investigators from the United States and China collaborated to explore the genetic heterogeneity of lung carcinoma.
Prompted by the knowledge that established immunotherapies are effective in only a fraction of adenocarcinoma patients, the authors analyzed scRNA-Seq data to characterize the intratumor heterogeneity of immune response genes. Data for the study were acquired from the NCBI, the Broad Institute, and the DNA Data Bank of Japan. The study revealed that IFN-γ signaling pathway genes are heterogeneously expressed and co-regulated with certain other genes, including MHC class II genes, in individual cancer cells. Moreover, the downregulation of genes in IFN-γ signaling pathways corresponds to acquired resistance to therapy. Heterogenous expression of neoantigens and CTA (cancer testis antigen) was also found.
These findings have important implications for cancer immunotherapy research. The characterized heterogeneity in IFN-γ expression could be a factor limiting the efficacy of therapy directed toward its signaling pathways. The findings may also help explain why neoantigen and CTA vaccine clinical trials have failed, even though, for some subjects, outcomes were good. The authors suggest that single-cell gene expression analysis could have prognostic value, and that their findings provide a rationale for combinatorial immunotherapies.
Finding predictive associations
Transcriptome profiling of patients treated with checkpoint inhibitors can identify new prognostic indicators, molecular mechanisms, and targets for checkpoint immunotherapy. In an article published November 2018, researchers from institutions in Massachusetts and Texas describe a study in which they profiled the transcriptomes of 16,291 individual immune cells from 48 tumor samples from melanoma patients treated with checkpoint inhibitors. Noting that their ability to interpret the cellular basis of response to checkpoint inhibitors from previous studies had been limited by the problem of bulk tumor heterogeneity, the researchers used scRNA-Seq to analyze the immune cells, and they then associated cell transcriptomes with patient treatment histories and clinical statuses.
The study identified and characterized several CD8+ T cell states associated with melanoma lesion growth. The presence of the TCF7 protein in CD8+ cells was found to be predictive of response to checkpoint therapy and was associated with better outcomes. This information is of potential value for prioritization of therapeutic approaches prior to checkpoint blockade therapy and as a subject stratification factor in clinical trials.
Profiling the tumor microenvironment
With the practical value of single-cell analysis well established, researchers are now bringing multiple analytical strategies to bear on individual cells. These multifactorial interrogations can combine single-cell genomics with single-cell proteomics, interactomics, epigenomics, or metabolomics to yield ever-more comprehensive views of complex interactions taking place within the tumor microenvironment.
In one such study, described in an article published November 2018, researchers from the United States, Russia, Japan, and Singapore used scRNA-Seq and CyTOF mass spectrometry to investigate the microenvironments of mouse tumors during successful immune checkpoint cancer therapy. While many previous studies focused on intratumoral T cells and their immune checkpoint therapy protein targets, these investigators looked at changes to both lymphoid and myeloid cells during immune checkpoint therapy.
This prolific multi-omics study produced a substantial set of scientific findings regarding the dynamic effects of immune checkpoint therapy on intratumoral cell subpopulations and the origins of those cells. The study also serves as a proof-of-concept for single-cell, multidimensional analysis of drug and drug combination effects.
The fast, collaborative future
As evident in the preceding examples, cancer immunotherapy research is strongly enabled by single-cell genomics, publicly accessible databases, and global scientific collaboration. Research collaborations are now, in addition, supported by international consortia such as The Cancer Genome Atlas, which contains clinical, transcriptomic, genomic, and methylation data of multiple human tumor types. These repositories will become extraordinarily valuable as the proportion of single-cell data they hold increases. With rich collaboration resources, viable multi-omics approaches, and practical single-cell analysis solutions to the problem of tumor heterogeneity, cancer immunotherapy research is poised for rapid advance.