Metastatic castration-resistant prostate cancer (mCRPC) includes a subset of patients with particularly unfavorable prognosis characterized by combined defects in at least two of three tumor suppressor genes: PTEN, RB1, and TP53 as aggressive variant prostate cancer molecular signature (AVPC-MS). We aimed to identify circulating tumor cells (CTC) signatures that could inform treatment decisions of patients with mCRPC with cabazitaxel–carboplatin combination therapy versus cabazitaxel alone. Liquid biopsy samples were collected prospectively from 79 patients for retrospective analysis. CTCs were detected, classified, enumerated through a computational pipeline followed by manual curation, and subjected to single-cell genome-wide copy-number profiling for AVPC-MS detection. On the basis of immunofluorescence intensities, detected rare cells were classified into 8 rare-cell groups. Further morphologic characterization categorized CTC subtypes from 4 cytokeratin-positive rare-cell groups, utilizing presence of mesenchymal features and platelet attachment. Of 79 cases, 77 (97.5%) had CTCs, 24 (30.4%) were positive for platelet-coated CTCs (pc.CTCs) and 25 (38.5%) of 65 sequenced patients exhibited AVPC-MS in CTCs. Survival analysis indicated that the presence of pc.CTCs identified the subset of patients who were AVPC-MS–positive with the worst prognosis and minimal benefit from combination therapy. In AVPC-MS–negative patients, its presence showed significant survival improvement from combination therapy. Our findings suggest the presence of pc.CTCs as a predictive biomarker to further stratify AVPC subsets with the worst prognosis and the most significant benefit of additional platinum therapy. Implications: HDSCA3.0 can be performed with rare cell detection, categorization, and genomic characterization for pc.CTC identification and AVPC-MS detection as a potential predictive biomarker of mCRPC.

Liquid biopsies hold potential as minimally invasive sources of tumor biomarkers for diagnosis, prognosis, therapy prediction or disease monitoring. We present an approach for parallel single-object identification of circulating tumor cells (CTCs) and tumor-derived large extracellular vesicles (LEVs) based on automated high-resolution immunofluorescence followed by downstream multiplexed protein profiling. Identification of LEVs >6 µm in size and CTC enumeration was highly correlated, with LEVs being 1.9 times as frequent as CTCs, and additional LEVs were identified in 73% of CTC-negative liquid biopsy samples from metastatic castrate resistant prostate cancer. Imaging mass cytometry (IMC) revealed that 49% of cytokeratin (CK)-positive LEVs and CTCs were EpCAM-negative, while frequently carrying prostate cancer tumor markers including AR, PSA, and PSMA. HSPD1 was shown to be a specific biomarker for tumor derived circulating cells and LEVs. CTCs and LEVs could be discriminated based on size, morphology, DNA load and protein score but not by protein signatures. Protein profiles were overall heterogeneous, and clusters could be identified across object classes. Parallel analysis of CTCs and LEVs confers increased sensitivity for liquid biopsies and expanded specificity with downstream characterization. Combined, it raises the possibility of a more comprehensive assessment of the disease state for precise diagnosis and monitoring.

Background: As global populations age and become susceptible to neurodegenerative illnesses, new therapies for Alzheimer disease (AD) are urgently needed. Existing data resources for drug discovery and repurposing fail to capture relationships central to the disease’s etiology and response to drugs. Objective: We designed the Alzheimer’s Knowledge Base (AlzKB) to alleviate this need by providing a comprehensive knowledge representation of AD etiology and candidate therapeutics. Methods: We designed the AlzKB as a large, heterogeneous graph knowledge base assembled using 22 diverse external data sources describing biological and pharmaceutical entities at different levels of organization (eg, chemicals, genes, anatomy, and diseases). AlzKB uses a Web Ontology Language 2 ontology to enforce semantic consistency and allow for ontological inference. We provide a public version of AlzKB and allow users to run and modify local versions of the knowledge base. Results: AlzKB is freely available on the web and currently contains 118,902 entities with 1,309,527 relationships between those entities. To demonstrate its value, we used graph data science and machine learning to (1) propose new therapeutic targets based on similarities of AD to Parkinson disease and (2) repurpose existing drugs that may treat AD. For each use case, AlzKB recovers known therapeutic associations while proposing biologically plausible new ones. Conclusions: AlzKB is a new, publicly available knowledge resource that enables researchers to discover complex translational associations for AD drug discovery. Through 2 use cases, we show that it is a valuable tool for proposing novel therapeutic hypotheses based on public biomedical knowledge.

Multiple myeloma is an incurable malignancy that initiates from a bone marrow resident clonal plasma cell and acquires successive mutational changes and genomic alterations, eventually resulting in tumor burden accumulation and end-organ damage. It has been recently recognized that myeloma secondary genomic events result in extensive sub-clonal heterogeneity both in localized bone marrow areas and circulating peripheral blood plasma cells. Rare genomic subclones, including myeloma initiating cells, could be the drivers of disease progression and recurrence. Additionally, evaluation of rare myeloma cells in blood for disease monitoring has numerous advantages over invasive bone marrow biopsies. To this end, an unbiased method for detecting rare cells and delineating their genomic makeup enables disease detection and monitoring in conditions with low abundant cancer cells. In this study, we applied an enrichment-free four-plex (CD138, CD56, CD45, DAPI) immunofluorescence assay and single-cell DNA sequencing for morphogenomic characterization of plasma cells to detect and delineate common and rare plasma cells and discriminate between normal and malignant plasma cells in paired blood and bone marrow aspirates from five patients with newly diagnosed myeloma (N = 4) and monoclonal gammopathy of undetermined significance (n = 1). Morphological analysis confirms CD138+CD56+ cells in the peripheral blood carry genomic alterations that are clonally identical to those in the bone marrow. A subset of altered CD138+CD56- cells are also found in the peripheral blood consistent with the known variability in CD56 expression as a marker of plasma cell malignancy. Bone marrow tumor clinical cytogenetics is highly correlated with the single-cell copy number alterations of the liquid biopsy rare cells. A subset of rare cells harbors genetic alterations not detected by standard clinical diagnostic methods of random localized bone marrow biopsies. This enrichment-free morphogenomic approach detects and characterizes rare cell populations derived from the liquid biopsies that are consistent with clinical diagnosis and have the potential to extend our understanding of subclonality at the single-cell level in this disease. Assay validation in larger patient cohorts has the potential to offer liquid biopsy for disease monitoring with similar or improved disease detection as traditional blind bone marrow biopsies.

B-cell maturation antigen (BCMA), a key regulator of B-cell proliferation and survival, is highly expressed in almost all cases of plasma cell neoplasms and B-lymphoproliferative malignancies. BCMA is a robust biomarker of plasma cells and a therapeutic target with substantial clinical significance. However, the expression of BCMA in circulating tumor cells of patients with hematological malignancies has not been validated for the detection of circulating plasma and B cells. The application of BCMA as a biomarker in single-cell detection and profiling of circulating tumor cells in patients’ blood could enable early disease profiling and therapy response monitoring. Here, we report the development and validation of a slide-based immunofluorescence assay (i.e., CD138, BCMA, CD45, DAPI) for enrichment-free detection, quantification, and morphogenomic characterization of BCMA-expressing cells in patients (N = 9) with plasma cell neoplasms. Varying morphological subtypes of circulating BCMA-expressing cells were detected across the CD138(+/−) and CD45(+/−) compartments, representing candidate clonotypic post-germinal center B cells, plasmablasts, and both normal and malignant plasma cells. Genomic analysis by single-cell sequencing and correlation to clinical FISH cytogenetics provides validation, with data showing that patients across the different neoplastic states carry both normal and altered BCMA-expressing cells. Furthermore, altered cells harbor cytogenetic events detected by clinical FISH. The reported enrichment-free liquid biopsy approach has potential applications as a single-cell methodology for the early detection of BCMA+ B-lymphoid malignancies and in monitoring therapy response for patients undergoing anti-BCMA treatments.

BACKGROUND As global populations age and become susceptible to neurodegenerative illnesses, new therapies for Alzheimer disease (AD) are urgently needed. Existing data resources for drug discovery and repurposing fail to capture relationships central to the disease’s etiology and response to drugs. OBJECTIVE We designed the Alzheimer’s Knowledge Base (AlzKB) to alleviate this need by providing a comprehensive knowledge representation of AD etiology and candidate therapeutics. METHODS We designed the AlzKB as a large, heterogeneous graph knowledge base assembled using 22 diverse external data sources describing biological and pharmaceutical entities at different levels of organization (eg, chemicals, genes, anatomy, and diseases). AlzKB uses a Web Ontology Language 2 ontology to enforce semantic consistency and allow for ontological inference. We provide a public version of AlzKB and allow users to run and modify local versions of the knowledge base. RESULTS AlzKB is freely available on the web and currently contains 118,902 entities with 1,309,527 relationships between those entities. To demonstrate its value, we used graph data science and machine learning to (1) propose new therapeutic targets based on similarities of AD to Parkinson disease and (2) repurpose existing drugs that may treat AD. For each use case, AlzKB recovers known therapeutic associations while proposing biologically plausible new ones. CONCLUSIONS AlzKB is a new, publicly available knowledge resource that enables researchers to discover complex translational associations for AD drug discovery. Through 2 use cases, we show that it is a valuable tool for proposing novel therapeutic hypotheses based on public biomedical knowledge.