An AI-assisted integrated, scalable, single-cell phenomic-transcriptomic platform to elucidate intratumor heterogeneity against immune response.

We present a novel framework combining single-cell phenotypic data with single-cell transcriptomic analysis to identify factors underpinning heterogeneity in antitumor immune response. We developed a pairwise, tumor-immune discretized interaction assay between natural killer (NK-92MI) cells and patient-derived head and neck squamous cell carcinoma (HNSCC) cell lines on a microfluidic cell-trapping platform. Furthermore we generated a deep-learning computer vision algorithm that is capable of automating the acquisition and analysis of a large, live-cell imaging data set (>1 million) of paired tumor-immune interactions spanning a time course of 24 h across multiple HNSCC lines (n = 10). Finally, we combined the response data measured by Kaplan-Meier survival analysis against NK-mediated killing with downstream single-cell transcriptomic analysis to interrogate molecular signatures associated with NK-effector response. As proof-of-concept for the proposed framework, we efficiently identified MHC class I-driven cytotoxic resistance as a key mechanism for immune evasion in nonresponders, while enhanced expression of cell adhesion molecules was found to be correlated with sensitivity against NK-mediated cytotoxicity. We conclude that this integrated, data-driven phenotypic approach holds tremendous promise in advancing the rapid identification of new mechanisms and therapeutic targets related to immune evasion and response.

CD38 marks the exhausted CD8+ tissue-resident memory T cells in hepatocellular carcinoma.

Introduction: Despite recent advances in immunotherapy for hepatocellular carcinoma (HCC), the overall modest response rate underscores the need for a better understanding of the tumor microenvironment (TME) of HCC. We have previously shown that CD38 is widely expressed on tumor-infiltrating leukocytes (TILs), predominantly on CD3+ T cells and monocytes. However, its specific role in the HCC TME remains unclear. Methods: In this current study, we used cytometry time-of-flight (CyTOF), bulk RNA sequencing on sorted T cells, and single-cell RNA (scRNA) sequencing to interrogate expression of CD38 and its correlation with T cell exhaustion in HCC samples. We also employed multiplex immunohistochemistry (mIHC) for validating our findings. Results: From CyTOF analysis, we compared the immune composition of CD38-expressing leukocytes in TILs, non-tumor tissue-infiltrating leukocytes (NIL), and peripheral blood mononuclear cells (PBMC). We identified CD8+ T cells as the dominant CD38-expressing TILs and found that CD38 expression was significantly higher in CD8+ TRM in TILs than in NILs. Furthermore, through transcriptomic analysis on sorted CD8+ TRM from HCC tumors, we observed a higher expression of CD38 along with T cell exhaustion genes, including PDCD1 and CTLA4, compared to the circulating memory CD8 T cells from PBMC. This was validated by scRNA sequencing that revealed co-expression of CD38 with PDCD1, CTLA4, and ITGAE (CD103) in T cells from HCC tumors. The protein co-expression of CD38 and PD-1 on CD8+ T cells was further demonstrated by mIHC on HCC FFPE tissues, marking CD38 as a T cell co-exhaustion marker in HCC. Lastly, the higher proportions of CD38+PD-1+ CD8+ T cells and CD38+PD-1+ TRM were significantly associated with the higher histopathological grades of HCC, indicating its role in the aggressiveness of the disease. Conclusion: Taken together, the concurrent expression of CD38 with exhaustion markers on CD8+ TRM underpins its role as a key marker of T cell exhaustion and a potential therapeutic target for restoring cytotoxic T cell function in HCC.

Trajectory of immune evasion and cancer progression in hepatocellular carcinoma.

Immune evasion is key to cancer initiation and later at metastasis, but its dynamics at intermediate stages, where potential therapeutic interventions could be applied, is undefined. Here we show, using multi-dimensional analyses of resected tumours, their adjacent non-tumour tissues and peripheral blood, that extensive immune remodelling takes place in patients with stage I to III hepatocellular carcinoma (HCC). We demonstrate the depletion of anti-tumoural immune subsets and accumulation of immunosuppressive or exhausted subsets along with reduced tumour infiltration of CD8 T cells peaking at stage II tumours. Corresponding transcriptomic modification occur in the genes related to antigen presentation, immune responses, and chemotaxis. The progressive immune evasion is validated in a murine model of HCC. Our results show evidence of ongoing tumour-immune co-evolution during HCC progression and offer insights into potential interventions to reverse, prevent or limit the progression of the disease.

Single-Cell RNA-seq Reveals Angiotensin-Converting Enzyme 2 and Transmembrane Serine Protease 2 Expression in TROP2+ Liver Progenitor Cells: Implications in Coronavirus Disease 2019-Associated Liver Dysfunction.

The recent coronavirus disease 2019 (COVID-19) pandemic is caused by severe acute respiratory syndrome coronavirus 2. COVID-19 was first reported in China (December 2019) and is now prevalent across the globe. Entry of severe acute respiratory syndrome coronavirus 2 into mammalian cells requires the binding of viral Spike (S) proteins to the angiotensin-converting enzyme 2 receptor. Once entered, the S protein is primed by a specialized serine protease, transmembrane serine protease 2 in the host cell. Importantly, besides the respiratory symptoms that are consistent with other common respiratory virus infections when patients become viremic, a significant number of COVID-19 patients also develop liver comorbidities. We explored whether a specific target cell-type in the mammalian liver could be implicated in disease pathophysiology other than the general deleterious response to cytokine storms. Here, we used single-cell RNA-seq to survey the human liver and identified potentially implicated liver cell-type for viral ingress. We analyzed ~300,000 single cells across five different (i.e., human fetal, healthy, cirrhotic, tumor, and adjacent normal) liver tissue types. This study reports on the co-expression of angiotensin-converting enzyme 2 and transmembrane serine protease 2 in a TROP2+ liver progenitor population. Importantly, we detected enrichment of this cell population in the cirrhotic liver when compared with tumor tissue. These results indicated that in COVID-19-associated liver dysfunction and cell death, a viral infection of TROP2+ progenitors in the liver might significantly impair liver regeneration in patients with liver cirrhosis.

Onco-fetal Reprogramming of Endothelial Cells Drives Immunosuppressive Macrophages in Hepatocellular Carcinoma.

We employed scRNA sequencing to extensively characterize the cellular landscape of human liver from development to disease. Analysis of ∼212,000 cells representing human fetal, hepatocellular carcinoma (HCC), and mouse liver revealed remarkable fetal-like reprogramming of the tumor microenvironment. Specifically, the HCC ecosystem displayed features reminiscent of fetal development, including re-emergence of fetal-associated endothelial cells (PLVAP/VEGFR2) and fetal-like (FOLR2) tumor-associated macrophages. In a cross-species comparative analysis, we discovered remarkable similarity between mouse embryonic, fetal-liver, and tumor macrophages. Spatial transcriptomics further revealed a shared onco-fetal ecosystem between fetal liver and HCC. Furthermore, gene regulatory analysis, spatial transcriptomics, and in vitro functional assays implicated VEGF and NOTCH signaling in maintaining onco-fetal ecosystem. Taken together, we report a shared immunosuppressive onco-fetal ecosystem in fetal liver and HCC. Our results unravel a previously unexplored onco-fetal reprogramming of the tumor ecosystem, provide novel targets for therapeutic interventions in HCC, and open avenues for identifying similar paradigms in other cancers and disease.

Single-Cell RNA Sequencing for Precision Oncology: Current State-of-Art.

Tumors exhibit genetic and phenotypic diversity leading to intra-tumor heterogeneity (ITH). Further complex ecosystem (stromal and immune cells) of tumors contributes into the ITH. This ITH allows tumors to overcome various selection pressures such as anti-cancer therapies and metastasis at distant organs. Single-cell RNA-seq (scRNA-seq) has provided unprecedented insights into ITH and its implications in drug resistance and metastasis. As scRNA-seq technology grows and provides many new findings, new tools on different programming platforms are frequently generated. Here, we aim to provide a framework and guidelines for new entrants into the field of scRNA-seq. In this review, we discuss the current state-of-art of scRNA-seq analysis step-by-step including filtering, normalization and analysis. First, we discuss the brief history of experimental methods, followed by data processing and implications in precision oncology.

ChronQC: a quality control monitoring system for clinical next generation sequencing.

Summary: ChronQC is a quality control (QC) tracking system for clinical implementation of next-generation sequencing (NGS). ChronQC generates time series plots for various QC metrics to allow comparison of current runs to historical runs. ChronQC has multiple features for tracking QC data including Westgard rules for clinical validity, laboratory-defined thresholds and historical observations within a specified time period. Users can record their notes and corrective actions directly onto the plots for long-term recordkeeping. ChronQC facilitates regular monitoring of clinical NGS to enable adherence to high quality clinical standards. Availability and implementation: ChronQC is freely available on GitHub (https://github.com/nilesh-tawari/ChronQC), Docker (https://hub.docker.com/r/nileshtawari/chronqc/) and the Python Package Index. ChronQC is implemented in Python and runs on all common operating systems (Windows, Linux and Mac OS X). Contact: tawari.nilesh@gmail.com or pauline.c.ng@gmail.com. Supplementary information: Supplementary data are available at Bioinformatics online.