bims-tumhet 2022-08-14 papers

Front Immunol. 2022 ;13 923194

Single-Cell RNA-Sequencing Atlas Reveals the Tumor Microenvironment of Metastatic High-Grade Serous Ovarian Carcinoma.

Yingqing Deng, Yuan Tan, Dongmei Zhou, Youhuang Bai, Ting Cao, Caizhou Zhong, Weilai Huang, Yuhua Ou, Linlang Guo, Qianqian Liu, Deling Yin, Lipai Chen, Xiping Luo, Deqiang Sun, Xiujie Sheng.

Ovarian cancer is the most common and lethal gynecological tumor in women worldwide. High-grade serous ovarian carcinoma (HGSOC) is one of the histological subtypes of epithelial ovarian cancer, accounting for 70%. It often occurs at later stages associated with a more fatal prognosis than endometrioid carcinomas (EC), another subtype of epithelial ovarian cancer. However, the molecular mechanism and biology underlying the metastatic HGSOC (HG_M) immunophenotype remain poorly elusive. Here, we performed single-cell RNA sequencing analyses of primary HGSOC (HG_P) samples, metastatic HGSOC (HG_M) samples, and endometrioid carcinomas (EC) samples. We found that ERBB2 and HOXB-AS3 genes were more amplified in metastasis tumors than in primary tumors. Notably, high-grade serous ovarian cancer metastases are accompanied by dysregulation of multiple pathways. Malignant cells with features of epithelial-mesenchymal transition (EMT) affiliated with poor overall survival were identified. In addition, cancer-associated fibroblasts with EMT-program were enriched in HG_M, participating in angiogenesis and immune regulation, such as IL6/STAT3 pathway activity. Compared with ECs, HGSOCs exhibited higher T cell infiltration. PRDM1 regulators may be involved in T cell exhaustion in ovarian cancer. The CX3CR1_macro subpopulation may play a role in promoting tumor progression in ovarian cancer with high expression of BAG3, IL1B, and VEGFA. The new targets we discovered in this study will be useful in the future, providing guidance on the treatment of ovarian cancer.

Keywords: T cells; high-grade serous ovarian carcinoma; myeloid cells; scRNA-seq; tumor microenvironment

DOI: https://doi.org/10.3389/fimmu.2022.923194

Nature. 2022 08;608(7922): 360-367

Spatially resolved clonal copy number alterations in benign and malignant tissue.

Defining the transition from benign to malignant tissue is fundamental to improving early diagnosis of cancer1. Here we use a systematic approach to study spatial genome integrity in situ and describe previously unidentified clonal relationships. We used spatially resolved transcriptomics2 to infer spatial copy number variations in >120,000 regions across multiple organs, in benign and malignant tissues. We demonstrate that genome-wide copy number variation reveals distinct clonal patterns within tumours and in nearby benign tissue using an organ-wide approach focused on the prostate. Our results suggest a model for how genomic instability arises in histologically benign tissue that may represent early events in cancer evolution. We highlight the power of capturing the molecular and spatial continuums in a tissue context and challenge the rationale for treatment paradigms, including focal therapy.

DOI: https://doi.org/10.1038/s41586-022-05023-2

Nat Cell Biol. 2022 Aug;24(8): 1192-1201

Untangling the web of intratumour heterogeneity.

Zheqi Li, Marco Seehawer, Kornelia Polyak.

Intratumour heterogeneity (ITH) is a hallmark of cancer that drives tumour evolution and disease progression. Technological and computational advances have enabled us to assess ITH at unprecedented depths, yet this accumulating knowledge has not had a substantial clinical impact. This is in part due to a limited understanding of the functional relevance of ITH and the inadequacy of preclinical experimental models to reproduce it. Here, we discuss progress made in these areas and illuminate future directions.

DOI: https://doi.org/10.1038/s41556-022-00969-x

Gigascience. 2022 Aug 10. pii: giac075. [Epub ahead of print]11

Stardust: improving spatial transcriptomics data analysis through space-aware modularity optimization-based clustering.

Simone Avesani, Eva Viesi, Luca Alessandrì, Giovanni Motterle, Vincenzo Bonnici, Marco Beccuti, Raffaele Calogero, Rosalba Giugno.

BACKGROUND: Spatial transcriptomics (ST) combines stained tissue images with spatially resolved high-throughput RNA sequencing. The spatial transcriptomic analysis includes challenging tasks like clustering, where a partition among data points (spots) is defined by means of a similarity measure. Improving clustering results is a key factor as clustering affects subsequent downstream analysis. State-of-the-art approaches group data by taking into account transcriptional similarity and some by exploiting spatial information as well. However, it is not yet clear how much the spatial information combined with transcriptomics improves the clustering result.
RESULTS: We propose a new clustering method, Stardust, that easily exploits the combination of space and transcriptomic information in the clustering procedure through a manual or fully automatic tuning of algorithm parameters. Moreover, a parameter-free version of the method is also provided where the spatial contribution depends dynamically on the expression distances distribution in the space. We evaluated the proposed methods results by analyzing ST data sets available on the 10x Genomics website and comparing clustering performances with state-of-the-art approaches by measuring the spots' stability in the clusters and their biological coherence. Stability is defined by the tendency of each point to remain clustered with the same neighbors when perturbations are applied.
CONCLUSIONS: Stardust is an easy-to-use methodology allowing to define how much spatial information should influence clustering on different tissues and achieving more stable results than state-of-the-art approaches.

Keywords: clustering; spatial transcriptomics analysis; stability scores, parameters tuning, software comparison

DOI: https://doi.org/10.1093/gigascience/giac075

Nat Cancer. 2022 Aug 08.

cfDNA methylome profiling for detection and subtyping of small cell lung cancers.

Small cell lung cancer (SCLC) is characterized by morphologic, epigenetic and transcriptomic heterogeneity. Subtypes based upon predominant transcription factor expression have been defined that, in mouse models and cell lines, exhibit potential differential therapeutic vulnerabilities, with epigenetically distinct SCLC subtypes also described. The clinical relevance of these subtypes is unclear, due in part to challenges in obtaining tumor biopsies for reliable profiling. Here we describe a robust workflow for genome-wide DNA methylation profiling applied to both patient-derived models and to patients' circulating cell-free DNA (cfDNA). Tumor-specific methylation patterns were readily detected in cfDNA samples from patients with SCLC and were correlated with survival outcomes. cfDNA methylation also discriminated between the transcription factor SCLC subtypes, a precedent for a liquid biopsy cfDNA-methylation approach to molecularly subtype SCLC. Our data reveal the potential clinical utility of cfDNA methylation profiling as a universally applicable liquid biopsy approach for the sensitive detection, monitoring and molecular subtyping of patients with SCLC.

DOI: https://doi.org/10.1038/s43018-022-00415-9