Cell-of-origin-specific proteomics of extracellular vesicles.

The ability to assign cellular origin to low-abundance secreted factors in extracellular vesicles (EVs) would greatly facilitate the analysis of paracrine-mediated signaling. Here, we report a method, named selective isolation of extracellular vesicles (SIEVE), which uses cell type-specific proteome labeling via stochastic orthogonal recoding of translation (SORT) to install bioorthogonal reactive groups into the proteins derived from the cells targeted for labeling. We establish the native purification of intact EVs from a target cell, via a bioorthogonal tetrazine ligation, leading to copurification of the largely unlabeled EV proteome from the same cell. SIEVE enables capture of EV proteins at levels comparable with those obtained by antibody-based methods, which capture all EVs regardless of cellular origin, and at levels 20× higher than direct capture of SORT-labeled proteins. Using proteomic analysis, we analyze nonlabeled cargo proteins of EVs and show that the enhanced sensitivity of SIEVE allows for unbiased and comprehensive analysis of EV proteins from subpopulations of cells as well as for cell-specific EV proteomics in complex coculture systems. SIEVE can be applied with high efficiency in a diverse range of existing model systems for cell-cell communication and has direct applications for cell-of-origin EV analysis and for protein biomarker discovery.

Multi-site clonality analysis uncovers pervasive heterogeneity across melanoma metastases.

Metastatic melanoma carries a poor prognosis despite modern systemic therapies. Understanding the evolution of the disease could help inform patient management. Through whole-genome sequencing of 13 melanoma metastases sampled at autopsy from a treatment naïve patient and by leveraging the analytical power of multi-sample analyses, we reveal evidence of diversification among metastatic lineages. UV-induced mutations dominate the trunk, whereas APOBEC-associated mutations are found in the branches of the evolutionary tree. Multi-sample analyses from a further seven patients confirmed that lineage diversification was pervasive, representing an important mode of melanoma dissemination. Our analyses demonstrate that joint analysis of cancer cell fraction estimates across multiple metastases can uncover previously unrecognised levels of tumour heterogeneity and highlight the limitations of inferring heterogeneity from a single biopsy.

Unraveling tumor-immune heterogeneity in advanced ovarian cancer uncovers immunogenic effect of chemotherapy.

In metastatic cancer, the degree of heterogeneity of the tumor microenvironment (TME) and its molecular underpinnings remain largely unstudied. To characterize the tumor-immune interface at baseline and during neoadjuvant chemotherapy (NACT) in high-grade serous ovarian cancer (HGSOC), we performed immunogenomic analysis of treatment-naive and paired samples from before and after treatment with chemotherapy. In treatment-naive HGSOC, we found that immune-cell-excluded and inflammatory microenvironments coexist within the same individuals and within the same tumor sites, indicating ubiquitous variability in immune cell infiltration. Analysis of TME cell composition, DNA copy number, mutations and gene expression showed that immune cell exclusion was associated with amplification of Myc target genes and increased expression of canonical Wnt signaling in treatment-naive HGSOC. Following NACT, increased natural killer (NK) cell infiltration and oligoclonal expansion of T cells were detected. We demonstrate that the tumor-immune microenvironment of advanced HGSOC is intrinsically heterogeneous and that chemotherapy induces local immune activation, suggesting that chemotherapy can potentiate the immunogenicity of immune-excluded HGSOC tumors.

Comprehensive Benchmarking and Integration of Tumor Microenvironment Cell Estimation Methods.

Various computational approaches have been developed for estimating the relative abundance of different cell types in the tumor microenvironment (TME) using bulk tumor RNA data. However, a comprehensive comparison across diverse datasets that objectively evaluates the performance of these approaches has not been conducted. Here, we benchmarked seven widely used tools and gene sets and introduced ConsensusTME, a method that integrates gene sets from all the other methods for relative TME cell estimation of 18 cell types. We collected a comprehensive benchmark dataset consisting of pan-cancer data (DNA-derived purity, leukocyte methylation, and hematoxylin and eosin-derived lymphocyte counts) and cell-specific benchmark datasets (peripheral blood cells and tumor tissues). Although none of the methods outperformed others in every benchmark, ConsensusTME ranked top three in all cancer-related benchmarks and was the best performing tool overall. We provide a Web resource to interactively explore the benchmark results and an objective evaluation to help researchers select the most robust and accurate method to further investigate the role of the TME in cancer (www.consensusTME.org). SIGNIFICANCE: This work shows an independent and comprehensive benchmarking of recently developed and widely used tumor microenvironment cell estimation methods based on bulk expression data and integrates the tools into a consensus approach.