Introduction
There are several types of T-cell lymphocytes, commonly referred to as T-cells, with the most common and well-known being cytotoxic (CD8+), helper (CD4+), and regulatory (suppressor) cells. T-cells are an integral component of the immune system’s adaptive response. Cytotoxic CD8+ T-cells act by eliminating cells that are cancerous, infected by intracellular pathogens (such as viruses or bacteria), or cells that are damaged in other ways. Whereas helper and regulatory CD4+ T-cells coordinate immune responses by signaling other cells to mount a defense against threats and play a crucial role in maintaining immune balance by dampening excessive immunity, respectively. Together, these T-cell types orchestrate and establish an adaptive defense against various biological threats.
Cancer cells have developed several mechanisms to evade immune detection, including restricting antigen recognition and presentation, as well as inducing T cell exhaustion in tumor infiltrating leukocytes (TILs). When T cells encounter tumor microenvironment, they experience metabolic stress that can impair their function, often due to increased expression of inhibitory proteins such as the programmed cell death (PD‐1), T cell immunoglobulin and mucin domain-containing protein 3 (LAG3) and cytotoxic T-lymphocyte associated antigen 4 (CTLA‐4). Recent studies provide evidence that, in addition to immune evasion, cancer cells extend long nanotube (NT) protrusions into the T cells. These NT protrusions allow the cancer cells to acquire or “hijack” mitochondria (MT) from T cells to fuel their metabolic requirements while also disrupting T cell function. Understanding the molecular mechanisms behind this mitochondrial hijacking is crucial for developing therapies to block this process. However, studying this mechanism in human cancer samples poses challenges associated with the difficulty in labeling mitochondria in TILs.
To overcome the obstacle associated with the labeling of mitochondria in TILs under physiological conditions, a team of scientists from the Children’s Hospital of Philadelphia (CHOP) have developed a computational algorithm known as MERCI (Mitochondrial-Enabled Reconstruction of Cellular Interactions). Published in the journal Cancer Cell, MERCI utilizes single-cell RNA sequencing (scRNA-seq) and computational methodologies to identify the cells in patient tumor samples that are recipients of MT from T-cells.
Elucidating MT Hijacking via MERCI
The MERCI algorithm was designed to detect cancer cells receiving MT by quantifying the MT single nucleotide variants (SNVs) originating from the donor and estimating the relative quantity of foreign MT. Therefore, MERCI operates by considering the combined use of MT mutation and gene expression profiles derived from single-cell RNA sequencing (scRNA-seq) samples. For each cell, MERCI integrates the MT gene expression and MT SNV (mtSNV) profiles to predict whether it is a recipient and to infer the relative proportion of mitochondria originating from the donor. Specifically, MERCI initially identified T cell-enriched mtSNVs based on the reference populations (cancer and T cells) and computed an “effective count statistic” (Neff) for each cancer cell to assign a DNA rank score to each cell. It then employs the average MT gene expression profiles of cancer and T cell populations as a reference and applies support vector regression (SVR) to estimate the relative quantity of transferred MT in the target cancer cells. SVR is a supervised learning algorithm that aims to fit a regression line while minimizing errors within a specified margin of tolerance. Each cell under consideration is assigned an RNA rank score based on the ordering of the SVR coefficient, which represents the relative proportion of T cell-derived MT. A rank transformation is applied to the scores to make them robust to outliers and less sensitive to sequencing depth or cancer type. Cells with scores exceeding a predefined cutoff for DNA or RNA rank are considered potential MT recipients.
After the proof of concept, MERCI was applied across human pan-cancer samples and discovered a previously unknown cancer phenotype associated with MT transfer. Cancer cells involved in MT transfer exhibited increased activity in cytoskeleton remodeling and overexpression of the tumor necrosis factor a (TNFa) pathway. MERCI also helped identify 17 genes that play a critical role in nanotube formation and MT transfer. The expression profiles of these 17 genes were used to develop a gene set enrichment score, referred to as a tumor MT transfer (TMT) score. Interestingly, higher TMT scores were linked with worse patient outcomes, likely due to T cell exhaustion and the stimulation of cancer cell proliferation.
Implications for Research and Development
The application of the MERCI algorithm to human cancer specimens has unveiled many distinct phenotypic traits linked to MT transfer activities. Elevated tumor MT transfer (TMT) scores for example, indicative of heightened MT exchange dynamics, exhibit a correlation with unfavorable patient prognoses – all of which are attributable to factors such as T cell exhaustion and heightened cancer cell proliferation. The findings stemming from this investigation offer a profound comprehension of mitochondrial dynamics within cancer cells and lay a foundation for the development of tailored therapeutic modalities. As expected, we see implications for this algorithm to find pivotal genes and pathways implicated in mitochondrial transfer which then hold promise for personalized treatment strategies in oncology.
Conclusion
The exploration into MT transfer phenomena between T-cells and cancer cells presents novel avenues for investigating metabolic interplays within tumor microenvironments. MERCI has the potential for prognostic application in guiding precision medicine especially in cancer! And if you’re curious, Bridge Informatics, harnesses these research insights to support pharmaceutical enterprises in advancing similar state of the art algorithms like MERCI for comprehensive data analytics and interpretation of your omics data.
Outsourcing Bioinformatics Analysis: How Bridge Informatics (BI) Can Help
We are passionate about empowering life science companies with cutting-edge technologies. BI’s data scientists prioritize studying, understanding, and reporting on the latest developments so we can advise our clients confidently. Our bioinformaticians are trained bench biologists, so they understand the biological questions driving your computational analysis.
From pipeline development and software engineering to deploying your existing bioinformatic tools, BI can help you on every step of your research journey. As experts across data types from leading sequencing platforms, we can help you tackle the challenging computational tasks of storing, analyzing and interpreting genomic and transcriptomic data. Click here to schedule a free introductory call with a member of our team.
Dan Ryder, MPH, PhD
Dan is the founder and CEO of Bridge Informatics, a professional services firm helping pharmaceutical companies translate genomic data into medicine. Unlike any other data analytics firm, Bridge forges sustainable communication change between their client’s biological and computational scientists. Dan is particularly passionate about improving communication between people of different scientific backgrounds, enabling bioinformaticians and software engineers to collectively succeed.
Prior to forming Bridge Informatics, Dan served in a variety of roles helping pharmaceutical clients solve early-phase drug discovery and development challenges.
Dan received both a Ph.D. in Biochemistry and Molecular Biology and an MPH in Disease Control from the University of Texas Health Science Center at Houston (UTHealth Houston). He completed his postdoctoral studies in Molecular Pathways of Energy Metabolism at the University of Florida College of Medicine. Dan received his undergraduate degree in Microbiology from the University of Texas at Austin.
