Multi-model analysis of gallbladder cancer reveals the role of OxLDL-absorbing neutrophils in promoting liver invasion.

BACKGROUND: Gallbladder cancer (GBC) is the most common and lethal malignancy of the biliary tract that lacks effective therapy. In many GBC cases, infiltration into adjacent organs or distant metastasis happened long before the diagnosis, especially the direct liver invasion, which is the most common and unfavorable way of spreading. METHODS: Single-cell RNA sequencing (scRNA-seq), spatial transcriptomics (ST), proteomics, and multiplexed immunohistochemistry (mIHC) were performed on GBC across multiple tumor stages to characterize the tumor microenvironment (TME), focusing specifically on the preferential enrichment of neutrophils in GBC liver invasion (GBC-LI). RESULTS: Multi-model Analysis reveals the immunosuppressive TME of GBC-LI that was characterized by the enrichment of neutrophils at the invasive front. We identified the context-dependent transcriptional states of neutrophils, with the Tumor-Modifying state being associated with oxidized low-density lipoprotein (oxLDL) metabolism. In vitro assays showed that the direct cell-cell contact between GBC cells and neutrophils led to the drastic increase in oxLDL uptake of neutrophils, which was primarily mediated by the elevated OLR1 on neutrophils. The oxLDL-absorbing neutrophils displayed a higher potential to promote tumor invasion while demonstrating lower cancer cytotoxicity. Finally, we identified a neutrophil-promoting niche at the invasive front of GBC-LI that constituted of KRT17+ GBC cells, neutrophils, and surrounding fibroblasts, which may help cultivate the oxLDL-absorbing neutrophils. CONCLUSIONS: Our study reveals the existence of a subset of pro-tumoral neutrophils with a unique ability to absorb oxLDL via OLR1, a phenomenon induced through cell-cell contact with KRT17+ GBC cells in GBC-LI.

High-resolution detection of copy number alterations in single cells with HiScanner.

Improvements in single-cell whole-genome sequencing (scWGS) assays have enabled detailed characterization of somatic copy number alterations (CNAs) at the single-cell level. Yet, current computational methods are mostly designed for detecting chromosome-scale changes in cancer samples with low sequencing coverage. Here, we introduce HiScanner (High-resolution Single-Cell Allelic copy Number callER), which combines read depth, B-allele frequency, and haplotype phasing to identify CNAs with high resolution. In simulated data, HiScanner consistently outperforms state-of-the-art methods across various CNA types and sizes. When applied to high-coverage scWGS data from human brain cells, HiScanner shows a superior ability to detect smaller CNAs, uncovering distinct CNA patterns between neurons and oligodendrocytes. For 179 cells we sequenced from longitudinal meningioma samples, integration of CNAs with point mutations revealed evolutionary trajectories of tumor cells. These findings show that HiScanner enables accurate characterization of frequency, clonality, and distribution of CNAs at the single-cell level in both non-neoplastic and neoplastic cells.

Identification of eight genes associated with recurrent patellar dislocation.

The inheritance of recurrent patellar dislocation (RPD) is known, but the susceptible gene remains unidentified. Here, we performed the first whole exome sequencing (WES) cohort study to identify the susceptible genes. The results showed eight genes were associated with this disease. Notably, the carboxypeptidase D (CPD) gene showed the highest relevance based on its gene function and tissue expression. Single-cell sequencing results indicate that the CPD gene is involved in the pathophysiological process of RPD through granulocytes. Implicated pathways include nuclear factor kappa B (NF-κB), mitogen-activated protein kinase (MAPK), and Wnt/ß-catenin signaling, potentially influencing CPD's role in RPD pathogenesis. This study identified the susceptible gene and investigates the potential pathogenesis of RPD, which provided a new prospect for the understanding of RPD. Besides, it would offer the theoretical basis for disease prevention and genetic counseling.

Patient-derived tumor-like cell clusters for personalized chemo- and immunotherapies in non-small cell lung cancer.

Many patient-derived tumor models have emerged recently. However, their potential to guide personalized drug selection remains unclear. Here, we report patient-derived tumor-like cell clusters (PTCs) for non-small cell lung cancer (NSCLC), capable of conducting 100-5,000 drug tests within 10 days. We have established 283 PTC models with an 81% success rate. PTCs contain primary tumor epithelium self-assembled with endogenous stromal and immune cells and show a high degree of similarity to the original tumors in phenotypic and genotypic features. Utilizing standardized culture and drug-response assessment protocols, PTC drug-testing assays reveal 89% overall consistency in prospectively predicting clinical outcomes, with 98.1% accuracy distinguishing complete/partial response from progressive disease. Notably, PTCs enable accurate prediction of clinical outcomes for patients undergoing anti-PD1 therapy by combining cell viability and IFN-γ value assessments. These findings suggest that PTCs could serve as a valuable preclinical model for personalized medicine and basic research in NSCLC.

De novo identification of expressed cancer somatic mutations from single-cell RNA sequencing data.

Identifying expressed somatic mutations from single-cell RNA sequencing data de novo is challenging but highly valuable. We propose RESA - Recurrently Expressed SNV Analysis, a computational framework to identify expressed somatic mutations from scRNA-seq data. RESA achieves an average precision of 0.77 on three in silico spike-in datasets. In extensive benchmarking against existing methods using 19 datasets, RESA consistently outperforms them. Furthermore, we applied RESA to analyze intratumor mutational heterogeneity in a melanoma drug resistance dataset. By enabling high precision detection of expressed somatic mutations, RESA substantially enhances the reliability of mutational analysis in scRNA-seq. RESA is available at https://github.com/ShenLab-Genomics/RESA .

TAGET: a toolkit for analyzing full-length transcripts from long-read sequencing.

Single-molecule Real-time Isoform Sequencing (Iso-seq) of transcriptomes by PacBio can generate very long and accurate reads, thus providing an ideal platform for full-length transcriptome analysis. We present an integrated computational toolkit named TAGET for Iso-seq full-length transcript data analyses, including transcript alignment, annotation, gene fusion detection, and quantification analyses such as differential expression gene analysis and differential isoform usage analysis. We evaluate the performance of TAGET using a public Iso-seq dataset and newly sequenced Iso-seq datasets from tumor patients. TAGET gives significantly more precise novel splice site prediction and enables more accurate novel isoform and gene fusion discoveries, as validated by experimental validations and comparisons with RNA-seq data. We identify and experimentally validate a differential isoform usage gene ECM1, and further show that its isoform ECM1b may be a tumor-suppressor in laryngocarcinoma. Our results demonstrate that TAGET provides a valuable computational toolkit and can be applied to many full-length transcriptome studies.

Comprehensive analysis of neoantigens derived from structural variation across whole genomes from 2528 tumors.

BACKGROUND: Neoantigens are critical for anti-tumor immunity and have been long-envisioned as promising therapeutic targets. However, current neoantigen analyses mostly focus on single nucleotide variations (SNVs) and indel mutations and seldom consider structural variations (SVs) that are also prevalent in cancer. RESULTS: Here, we develop a computational method termed NeoSV, which incorporates SV annotation, protein fragmentation, and MHC binding prediction together, to predict SV-derived neoantigens. Analysis of 2528 whole genomes reveals that SVs significantly contribute to the neoantigen repertoire in both quantity and quality. Whereas most neoantigens are patient-specific, shared neoantigens are identified with high occurrence rates in breast, ovarian, and gastrointestinal cancers. We observe extensive immunoediting on SV-derived neoantigens, especially on clonal events, which suggests their immunogenic potential. We also demonstrate that genomic alteration-related neoantigen burden, which integrates SV-derived neoantigens, depicts the tumor-immune interplay better than tumor neoantigen burden and may improve patient selection for immunotherapy. CONCLUSIONS: Our study fills the gap in the current neoantigen repertoire and provides a valuable resource for cancer vaccine development.

The dynamic cellular landscape of grafts with acute rejection after heart transplantation.

BACKGROUND: Acute cellular rejection (ACR) is a major barrier to the long-term survival of cardiac allografts. Although immune cells are well known to play critical roles in ACR, the dynamic cellular landscape of allografts with ACR remains obscure. METHODS: Single-cell RNA sequencing (scRNA-seq) was carried out for mouse cardiac allografts with ACR. Bioinformatic analysis was performed, and subsequent transplant experiments were conducted to validate the findings. RESULTS: Despite an overall large depletion of cardiac fibroblasts (CFBs), highly expanded cytotoxic T lymphocytes and a CXCL10+Gbp2+ subcluster of CFBs were enriched within grafts at the late stage. CXCL10+Gbp2+ CFBs featured strong interferon responsiveness and high expression of chemokines and major histocompatibility complex molecules, implying their involvement in the recruitment and activation of immune cells. Cell‒cell communication analysis revealed that CXCL9/CXCL10-CXCR3 might contribute to regulating CXCL10+Gbp2+ CFB-induced chemotaxis and immune cell recruitment. In vivo transplant studies revealed the therapeutic potential of CXCR3 antagonism in transplant rejection. CONCLUSIONS: The findings of our study unveiled a novel CFB subcluster that might mediate acute cardiac rejection. Targeting CXCR3 could prolong allograft survival.

The shaping of cancer genomes with the regional impact of mutation processes.

Mutation signature analysis has been used to infer the contributions of various DNA mutagenic-repair events in individual cancer genomes. Here, we build a statistical framework using a multinomial distribution to assign individual mutations to their cognate mutation signatures. We applied it to 47 million somatic mutations in 1925 publicly available cancer genomes to obtain a mutation signature map at the resolution of individual somatic mutations. Based on mutation signature-level genetic-epigenetic correlative analyses, mutations with transcriptional and replicative strand asymmetries show different enrichment patterns across genomes, and "transcribed" chromatin states and gene boundaries are particularly vulnerable to transcription-coupled repair activities. While causative processes of cancer-driving mutations can be diverse, as shown for converging effects of multiple mutational processes on TP53 mutations, the substantial fraction of recurrently mutated amino acids points to specific mutational processes, e.g., age-related C-to-T transition for KRAS p.G12 mutations. Our investigation of evolutionary trajectories with respect to mutation signatures further revealed that candidate pairs of early- vs. late-operative mutation processes in cancer genomes represent evolutionary dynamics of multiple mutational processes in the shaping of cancer genomes. We also observed that the local mutation clusters of kataegis often include mutations arising from multiple mutational processes, suggestive of a locally synchronous impact of multiple mutational processes on cancer genomes. Taken together, our examination of the genome-wide landscape of mutation signatures at the resolution of individual somatic mutations shows the spatially and temporally distinct mutagenesis-repair-replication histories of various mutational processes and their effects on shaping cancer genomes.

Single-cell gene fusion detection by scFusion.

Gene fusions can play important roles in tumor initiation and progression. While fusion detection so far has been from bulk samples, full-length single-cell RNA sequencing (scRNA-seq) offers the possibility of detecting gene fusions at the single-cell level. However, scRNA-seq data have a high noise level and contain various technical artifacts that can lead to spurious fusion discoveries. Here, we present a computational tool, scFusion, for gene fusion detection based on scRNA-seq. We evaluate the performance of scFusion using simulated and five real scRNA-seq datasets and find that scFusion can efficiently and sensitively detect fusions with a low false discovery rate. In a T cell dataset, scFusion detects the invariant TCR gene recombinations in mucosal-associated invariant T cells that many methods developed for bulk data fail to detect; in a multiple myeloma dataset, scFusion detects the known recurrent fusion IgH-WHSC1, which is associated with overexpression of the WHSC1 oncogene. Our results demonstrate that scFusion can be used to investigate cellular heterogeneity of gene fusions and their transcriptional impact at the single-cell level.

Single-cell transcriptomic analysis suggests two molecularly subtypes of intrahepatic cholangiocarcinoma.

Intrahepatic cholangiocarcinoma (iCCA) is a highly heterogeneous cancer with limited understanding of its classification and tumor microenvironment. Here, by performing single-cell RNA sequencing on 144,878 cells from 14 pairs of iCCA tumors and non-tumor liver tissues, we find that S100P and SPP1 are two markers for iCCA perihilar large duct type (iCCAphl) and peripheral small duct type (iCCApps). S100P + SPP1- iCCAphl has significantly reduced levels of infiltrating CD4+ T cells, CD56+ NK cells, and increased CCL18+ macrophages and PD1+CD8+ T cells compared to S100P-SPP1 + iCCApps. The transcription factor CREB3L1 is identified to regulate the S100P expression and promote tumor cell invasion. S100P-SPP1 + iCCApps has significantly more SPP1+ macrophage infiltration, less aggressiveness and better survival than S100P + SPP1- iCCAphl. Moreover, S100P-SPP1 + iCCApps harbors tumor cells at different status of differentiation, such as ALB + hepatocyte differentiation and ID3+ stemness. Our study extends the understanding of the diversity of tumor cells in iCCA.

Spatiotemporal Immune Landscape of Colorectal Cancer Liver Metastasis at Single-Cell Level.

Liver metastasis, the leading cause of colorectal cancer mortality, exhibits a highly heterogeneous and suppressive immune microenvironment. Here, we sequenced 97 matched samples by using single-cell RNA sequencing and spatial transcriptomics. Strikingly, the metastatic microenvironment underwent remarkable spatial reprogramming of immunosuppressive cells such as MRC1 + CCL18 + M2-like macrophages. We further developed scMetabolism, a computational pipeline for quantifying single-cell metabolism, and observed that those macrophages harbored enhanced metabolic activity. Interestingly, neoadjuvant chemotherapy could block this status and restore the antitumor immune balance in responsive patients, whereas the nonresponsive patients deteriorated into a more suppressive one. Our work described the immune evolution of metastasis and uncovered the black box of how tumors respond to neoadjuvant chemotherapy. SIGNIFICANCE: We present a single-cell and spatial atlas of colorectal liver metastasis and found the highly metabolically activated MRC1 + CCL18 + M2-like macrophages in metastatic sites. Efficient neoadjuvant chemotherapy can slow down such metabolic activation, raising the possibility to target metabolism pathways in metastasis.This article is highlighted in the In This Issue feature, p. 1.

Genomics landscape of 185 Streptococcus thermophilus and identification of fermentation biomarkers.

Streptococcus (S.) thermophilus, an indispensable dairy starter, has been used in autochthonous as well as industrial milk fermentation. However, the genetic architecture underlying S. thermophilus traits and phenotypes is largely unknown. Here, we sequenced 185 S. thermophilus strains, isolated from natural fermented dairy products of China and Mongolia and used comparative genomic and genome wide association study to provide novel point for genetic architecture underlying its traits and phenotypes. Genome analysis of S. thermophilus showed association of phylogeny with environmental and phenotypic features and revealed clades with high acid production potential or with substantial genome decay. A few S. thermophilus isolated from areas with high chloramphenicol emissions had a chloramphenicol-resistant gene CatB8. Most importantly, we defined a growth score and identified a missense mutation G1118698T located at the gene AcnA that were both predictive of acidification capability of S. thermophilus. Our findings provide novel insight in S. thermophilus genetic traits, antibiotic resistant and predictive of acidification capability which both may had huge help in culture starter screening.

Global immune characterization of HBV/HCV-related hepatocellular carcinoma identifies macrophage and T-cell subsets associated with disease progression.

Diverse immune cells in the tumor microenvironment form a complex ecosystem, but our knowledge of their heterogeneity and dynamics within hepatocellular carcinoma (HCC) still remains limited. To assess the plasticity and phenotypes of immune cells within HBV/HCV-related HCC microenvironment at single-cell level, we performed single-cell RNA sequencing on 41,698 immune cells from seven pairs of HBV/HCV-related HCC tumors and non-tumor liver tissues. We combined bio-informatic analyses, flow cytometry, and multiplex immunohistochemistry to assess the heterogeneity of different immune cell subsets in functional characteristics, transcriptional regulation, phenotypic switching, and interactions. We identified 29 immune cell subsets of myeloid cells, NK cells, and lymphocytes with unique transcriptomic profiles in HCC. A highly complex immunological network was shaped by diverse immune cell subsets that can transit among different states and mutually interact. Notably, we identified a subset of M2 macrophage with high expression of CCL18 and transcription factor CREM that was enriched in advanced HCC patients, and potentially participated in tumor progression. We also detected a new subset of activated CD8+ T cells highly expressing XCL1 that correlated with better patient survival rates. Meanwhile, distinct transcriptomic signatures, cytotoxic phenotypes, and evolution trajectory of effector CD8+ T cells from early-stage to advanced HCC were also identified. Our study provides insight into the immune microenvironment in HBV/HCV-related HCC and highlights novel macrophage and T-cell subsets that could be further exploited in future immunotherapy.

Patient-derived tumor-like cell clusters for drug testing in cancer therapy.

Several patient-derived tumor models emerged recently as robust preclinical drug-testing platforms. However, their potential to guide clinical therapy remained unclear. Here, we report a model called patient-derived tumor-like cell clusters (PTCs). PTCs result from the self-assembly and proliferation of primary epithelial, fibroblast, and immune cells, which structurally and functionally recapitulate original tumors. PTCs enabled us to accomplish personalized drug testing within 2 weeks after obtaining the tumor samples. The defined culture conditions and drug concentrations in the PTC model facilitate its clinical application in precision oncology. PTC tests of 59 patients with gastric, colorectal, or breast cancers revealed an overall accuracy of 93% in predicting their clinical outcomes. We implemented PTC to guide chemotherapy selection for a patient with mucinous rectal adenocarcinoma who experienced recurrence with metastases after conventional therapy. After three cycles of a nonconventional therapy identified by the PTC, the patient showed a positive response. These findings need to be validated in larger clinical trials, but they suggest that the PTC model could be prospectively implemented in clinical decision-making for therapy selection.