Molecular changes driving low-grade serous ovarian cancer and implications for treatment.

Low-grade serous ovarian cancer was previously thought to be a subtype of high-grade serous ovarian cancer, but it is now recognized as a distinct disease with unique clinical and molecular behaviors. The disease may arise de novo or develop from a serous borderline ovarian tumor. Although it is more indolent than high-grade serous ovarian cancer, most patients have advanced metastatic disease at diagnosis and recurrence is common. Recurrent low-grade serous ovarian cancer is often resistant to standard platinum-taxane chemotherapy, making it difficult to treat with the options currently available. New targeted therapies are needed, but their development is contingent on a deeper understanding of the specific biology of the disease. The known molecular drivers of low-grade tumors are strong hormone receptor expression, mutations in the mitogen-activated protein kinase (MAPK) pathway (KRAS, BRAF, and NRAS), and in genes related to the MAPK pathway (NF1/2, EIF1AX, and ERBB2). However, MAPK inhibitors have shown only modest clinical responses. Based on the discovery of CDKN2A mutations in low-grade serous ovarian cancer, cyclin-dependent kinases 4 and 6 (CDK4/6) inhibitors are now being tested in clinical trials in combination with hormone therapy. Additional mutations seen in a smaller population of low-grade tumors include USP9X, ARID1A, and PIK3CA, but no specific therapies targeting them have been tested clinically. This review summarizes the clinical, pathologic, and molecular features of low-grade serous ovarian cancer as they are now understood and introduces potential therapeutic targets and new avenues for research.

Proteomic Characterization of Undifferentiated Small Round Cell Sarcomas With EWSR1 and CIC::DUX4 Translocations Reveals Diverging Tumor Biology and Distinct Diagnostic Markers.

Undifferentiated small round cell sarcomas (USRS) of bone and soft tissue are a group of tumors with heterogenic genomic alterations sharing similar morphology. In the present study, we performed a comparative large-scale proteomic analysis of USRS (n = 42) with diverse genomic translocations including classic Ewing sarcomas with EWSR1::FLI1 fusions (n = 24) or EWSR1::ERG fusions (n = 4), sarcomas with an EWSR1 rearrangement (n = 2), CIC::DUX4 fusion (n = 8), as well as tumors classified as USRS with no genetic data available (n = 4). Proteins extracted from formalin-fixed, paraffin-embedded pretherapeutic biopsies were analyzed qualitatively and quantitatively using shotgun mass spectrometry (MS). More than 8000 protein groups could be quantified using data-independent acquisition. Unsupervised hierarchical cluster analysis based on proteomic data allowed stratification of the 42 cases into distinct groups reflecting the different molecular genotypes. Protein signatures that significantly correlated with the respective genomic translocations were identified and used to generate a heatmap of all 42 sarcomas with assignment of cases with unknown molecular genetic data to either the EWSR1- or CIC-rearranged groups. MS-based prediction of sarcoma subtypes was molecularly confirmed in 2 cases where next-generation sequencing was technically feasible. MS also detected proteins routinely used in the immunohistochemical approach for the differential diagnosis of USRS. BCL11B highly expressed in Ewing sarcomas, and BACH2 as well as ETS-1 highly expressed in CIC::DUX4-associated sarcomas, were among proteins identified by the present proteomic study, and were chosen for immunohistochemical confirmation of MS data in our study cohort. Differential expressions of these 3 markers in the 2 genetic groups were further validated in an independent cohort of n = 34 USRS. Finally, our proteomic results point toward diverging signaling pathways in the different USRS subgroups.

Spatial proteo-transcriptomic profiling reveals the molecular landscape of borderline ovarian tumors and their invasive progression.

Serous borderline tumors (SBT) are epithelial neoplastic lesions of the ovaries that commonly have a good prognosis. In 10-15% of cases, however, SBT will recur as low-grade serous cancer (LGSC), which is deeply invasive and responds poorly to current standard chemotherapy1,2,3. While genetic alterations suggest a common origin, the transition from SBT to LGSC remains poorly understood4. Here, we integrate spatial proteomics5 with spatial transcriptomics to elucidate the evolution from SBT to LGSC and its corresponding metastasis at the molecular level in both the stroma and the tumor. We show that the transition of SBT to LGSC occurs in the epithelial compartment through an intermediary stage with micropapillary features (SBT-MP), which involves a gradual increase in MAPK signaling. A distinct subset of proteins and transcripts was associated with the transition to invasive tumor growth, including the neuronal splicing factor NOVA2, which was limited to expression in LGSC and its corresponding metastasis. An integrative pathway analysis exposed aberrant molecular signaling of tumor cells supported by alterations in angiogenesis and inflammation in the tumor microenvironment. Integration of spatial transcriptomics and proteomics followed by knockdown of the most altered genes or pharmaceutical inhibition of the most relevant targets confirmed their functional significance in regulating key features of invasiveness. Combining cell-type resolved spatial proteomics and transcriptomics allowed us to elucidate the sequence of tumorigenesis from SBT to LGSC. The approach presented here is a blueprint to systematically elucidate mechanisms of tumorigenesis and find novel treatment strategies.

Robust dimethyl-based multiplex-DIA doubles single-cell proteome depth via a reference channel.

Single-cell proteomics aims to characterize biological function and heterogeneity at the level of proteins in an unbiased manner. It is currently limited in proteomic depth, throughput, and robustness, which we address here by a streamlined multiplexed workflow using data-independent acquisition (mDIA). We demonstrate automated and complete dimethyl labeling of bulk or single-cell samples, without losing proteomic depth. Lys-N digestion enables five-plex quantification at MS1 and MS2 level. Because the multiplexed channels are quantitatively isolated from each other, mDIA accommodates a reference channel that does not interfere with the target channels. Our algorithm RefQuant takes advantage of this and confidently quantifies twice as many proteins per single cell compared to our previous work (Brunner et al, PMID 35226415), while our workflow currently allows routine analysis of 80 single cells per day. Finally, we combined mDIA with spatial proteomics to increase the throughput of Deep Visual Proteomics seven-fold for microdissection and four-fold for MS analysis. Applying this to primary cutaneous melanoma, we discovered proteomic signatures of cells within distinct tumor microenvironments, showcasing its potential for precision oncology.

Deep Visual Proteomics defines single-cell identity and heterogeneity.

Despite the availabilty of imaging-based and mass-spectrometry-based methods for spatial proteomics, a key challenge remains connecting images with single-cell-resolution protein abundance measurements. Here, we introduce Deep Visual Proteomics (DVP), which combines artificial-intelligence-driven image analysis of cellular phenotypes with automated single-cell or single-nucleus laser microdissection and ultra-high-sensitivity mass spectrometry. DVP links protein abundance to complex cellular or subcellular phenotypes while preserving spatial context. By individually excising nuclei from cell culture, we classified distinct cell states with proteomic profiles defined by known and uncharacterized proteins. In an archived primary melanoma tissue, DVP identified spatially resolved proteome changes as normal melanocytes transition to fully invasive melanoma, revealing pathways that change in a spatial manner as cancer progresses, such as mRNA splicing dysregulation in metastatic vertical growth that coincides with reduced interferon signaling and antigen presentation. The ability of DVP to retain precise spatial proteomic information in the tissue context has implications for the molecular profiling of clinical samples.

Association of Complement and MAPK Activation With SARS-CoV-2-Associated Myocardial Inflammation.

IMPORTANCE: Myocardial injury is a common feature of patients with SARS-CoV-2 infection. However, the cardiac inflammatory processes associated with SARS-CoV-2 infection are not completely understood. OBJECTIVE: To investigate the inflammatory cardiac phenotype associated with SARS-CoV-2 infection compared with viral myocarditis, immune-mediated myocarditis, and noninflammatory cardiomyopathy by integrating histologic, transcriptomic, and proteomic profiling. DESIGN, SETTING, AND PARTICIPANTS: This case series was a cooperative study between the Ludwig Maximilian University Hospital Munich and the Cardiopathology Referral Center at the University of Tübingen in Germany. A cohort of 19 patients with suspected myocarditis was examined; of those, 5 patients were hospitalized with SARS-CoV-2 infection between March and May 2020. Cardiac tissue specimens from those 5 patients were compared with specimens from 5 patients with immune-mediated myocarditis, 4 patients with non-SARS-CoV-2 viral myocarditis, and 5 patients with noninflammatory cardiomyopathy, collected from January to August 2019. EXPOSURES: Endomyocardial biopsy. MAIN OUTCOMES AND MEASURES: The inflammatory cardiac phenotypes were measured by immunohistologic analysis, RNA exome capture sequencing, and mass spectrometry-based proteomic analysis of endomyocardial biopsy specimens. RESULTS: Among 19 participants, the median age was 58 years (range, 37-76 years), and 15 individuals (79%) were male. Data on race and ethnicity were not collected. The abundance of CD163+ macrophages was generally higher in the cardiac tissue of patients with myocarditis, whereas lymphocyte counts were lower in the tissue of patients with SARS-CoV-2 infection vs patients with non-SARS-CoV-2 virus-associated and immune-mediated myocarditis. Among those with SARS-CoV-2 infection, components of the complement cascade, including C1q subunits (transcriptomic analysis: 2.5-fold to 3.6-fold increase; proteomic analysis: 2.0-fold to 3.4-fold increase) and serine/cysteine proteinase inhibitor clade G member 1 (transcriptomic analysis: 1.7-fold increase; proteomic analysis: 2.6-fold increase), belonged to the most commonly upregulated transcripts and differentially abundant proteins. In cardiac macrophages, the abundance of C1q was highest in SARS-CoV-2 infection. Assessment of important signaling cascades identified an upregulation of the serine/threonine mitogen-activated protein kinase pathways. CONCLUSIONS AND RELEVANCE: This case series found that the cardiac immune signature varied in inflammatory conditions with different etiologic characteristics. Future studies are needed to examine the role of these immune pathways in myocardial inflammation.

A streamlined mass spectrometry-based proteomics workflow for large-scale FFPE tissue analysis.

Formalin fixation and paraffin-embedding (FFPE) is the most common method to preserve human tissue for clinical diagnosis, and FFPE archives represent an invaluable resource for biomedical research. Proteins in FFPE material are stable over decades but their efficient extraction and streamlined analysis by mass spectrometry (MS)-based proteomics has so far proven challenging. Herein we describe a MS-based proteomic workflow for quantitative profiling of large FFPE tissue cohorts directly from histopathology glass slides. We demonstrate broad applicability of the workflow to clinical pathology specimens and variable sample amounts, including low-input cancer tissue isolated by laser microdissection. Using state-of-the-art data dependent acquisition (DDA) and data independent acquisition (DIA) MS workflows, we consistently quantify a large part of the proteome in 100 min single-run analyses. In an adenoma cohort comprising more than 100 samples, total workup took less than a day. We observed a moderate trend towards lower protein identification in long-term stored samples (>15 years), but clustering into distinct proteomic subtypes was independent of archival time. Our results underscore the great promise of FFPE tissues for patient phenotyping using unbiased proteomics and they prove the feasibility of analyzing large tissue cohorts in a robust, timely, and streamlined manner. © 2020 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland.