Abstract In women with high-grade serous ovarian cancer, chemotherapy remains the primary standard treatment, despite growing recognition of the disease as highly heterogeneous. Here, we examine the feasibility and clinical utility of comprehensive multimodal molecular profiling to inform treatment decisions. We analyze blood, single-cell and bulk tumor tissue, and malignant ascites using up to eleven technologies (DNA, RNA, protein, and functional assays) within a four-week turnaround time. Hypothetical treatment recommendations are altered for 76% of patients, and multi-omics-guided maintenance therapy is associated with prolonged overall survival in a subset of patients. Subsequent cohort analysis reveals distinct cellular and molecular profiles in ascites-derived single-cells compared to solid tumor tissue, unique per-patient ex vivo drug responses, and a marked increase in cancer cell heterogeneity following chemotherapy exposure. This coincides with genomic signature alterations in whole-genome-amplified patients. Our data suggest that molecularly guided treatments should be tested as adjuvant therapies prior to chemotherapy in the future.

PURPOSE: High-dimensional tumor profiling has the potential to refine therapeutic decisions in metastatic melanoma, especially in the beyond-standard-of-care setting, but its clinical utility remains uncertain. The Tumor Profiler Study prospectively integrated multi-platform molecular, single-cell, spatial, and functional analyses into a 4-week clinical workflow. We report the biological and clinically actionable findings from the melanoma cohort. METHODS: Tumor and blood samples from 116 patients (126 tumors) underwent comprehensive profiling including CyTOF, single-cell RNA and DNA sequencing, imaging mass cytometry, proteotyping, bulk RNA/DNA sequencing, and two ex vivo drug-response platforms. Cross-modal integration defined melanoma phenotypes and identified treatment-relevant features. RESULTS: Single-cell protein profiling of 1.7 million melanoma cells identified six recurrent melanoma phenotypes present across melanoma subtypes and oncogenic drivers. Resulting tumor phenotypes stratified patients into six CyTOF-derived groups with distinct molecular characteristics and significantly different ex vivo responses to FDA-approved therapies. TYRP1-high classical melanocytic tumors showed broad drug resistance but reproducible sensitivity to cisplatin plus vindesine, consistent with elevated p53-associated DNA damage response pathways. Conversely, melanomas with heightened unfolded-protein-response activity exhibited marked sensitivity to proteasome inhibitors, supported by transcriptomic, proteomic, and functional readouts. CONCLUSION: This study establishes a clinically annotated, single-cell–resolved multi-omic atlas of advanced melanoma that links tumor phenotypes to actionable drug vulnerabilities. These findings provide a biological rationale for biomarker-driven, phenotype-guided interventional trials aimed at improving therapeutic outcomes in metastatic melanoma.

High-throughput screening (HTS) is essential in preclinical research to identify new drug candidates for specific diseases. This process typically generates large amounts of data that require effective storage, management, and analysis. Traditional methods for handling HTS data involve several standalone solutions, which can present challenges regarding data accessibility and reproducibility. We introduce Lab Data Management (LDM), an open-source web application developed to automate the management and visualization of HTS data. LDM provides a highly customizable data management system with an intuitive user interface for handling output data from various laboratory instruments, such as plate readers, microscopes, liquid handlers, and barcode readers. The app allows for results visualization and calculation of quality control metrics. An integrated Jupyter notebook can be used to retrieve the stored data and proceed with a more detailed analysis.

There is limited evidence supporting the feasibility of using omics and functional technologies to inform treatment decisions. Here we present results from a cohort of 116 melanoma patients in the prospective, multicentric observational Tumor Profiler (TuPro) precision oncology project. Nine independent technologies, mostly at single-cell level, were used to analyze 126 patient samples, generating up to 500 Gb of data per sample (40,000 potential markers) within 4 weeks. Among established and experimental markers, the molecular tumor board selected 54 to inform its treatment recommendations. In 75% of cases, TuPro-based data were judged to be useful in informing recommendations. Patients received either standard of care (SOC) treatments or highly individualized, polybiomarker-driven treatments (beyond SOC). The objective response rate in difficult-to-treat palliative, beyond SOC patients ( n = 37) was 38%, with a disease control rate of 54%. Progression-free survival of patients with TuPro-informed therapy decisions was 6.04 months, (95% confidence interval, 3.75–12.06) and 5.35 months (95% confidence interval, 2.89–12.06) in ≥third therapy lines. The proof-of-concept TuPro project demonstrated the feasibility and relevance of omics-based tumor profiling to support data-guided clinical decision-making. ClinicalTrials.gov identifier: NCT06463509 .

Deep single-cell multi-omic profiling offers a promising approach to understand and overcome drug resistance in relapsed or refractory (rr) acute myeloid leukemia (AML). Here, we combine single-cell ex vivo drug profiling (pharmacoscopy) with single-cell and bulk DNA, RNA, and protein analyses, alongside clinical data from 21 rrAML patients. Unsupervised data integration reveals reduced ex vivo response to the Bcl-2 inhibitor venetoclax (VEN) in patients treated with both a hypomethylating agent (HMA) and VEN, compared to those pre-exposed to chemotherapy or HMA alone. Integrative analysis identifies both known and unreported mechanisms of innate and treatment-related VEN resistance and suggests alternative treatments, like targeting increased proliferation with the PLK inhibitor volasertib. Additionally, high CD36 expression in VEN-resistant blasts associates with sensitivity to CD36-targeted antibody treatment ex vivo. This study demonstrates how single-cell multi-omic profiling can uncover drug resistance mechanisms and treatment vulnerabilities, providing a valuable resource for future AML research. The molecular mechanisms underlying drug resistance in relapsed or refractory (rr) acute myeloid leukemia (AML) remain to be explored. Here, the use of bulk and single cell multi-omics and ex vivo drug profiling for 21 rrAML patients reveals mechanisms of resistance to the Bcl-2 inhibitor venetoclax and treatment vulnerabilities.

Identifying cell types based on expression profiles is a pillar of single cell analysis. Existing machine-learning methods identify predictive features from annotated training data, which are often not available in early-stage studies. This can lead to overfitting and inferior performance when applied to new data. To address these challenges we present scROSHI, which utilizes previously obtained cell type-specific gene lists and does not require training or the existence of annotated data. By respecting the hierarchical nature of cell type relationships and assigning cells consecutively to more specialized identities, excellent prediction performance is achieved. In a benchmark based on publicly available PBMC data sets, scROSHI outperforms competing methods when training data are limited or the diversity between experiments is large.

This study investigated the image quality and choice of ultra-high b-value of two DWI breast-MRI research applications. The study cohort comprised 40 patients (20 malignant lesions). In addition to s-DWI with two m-b-values (b50 and b800) and three e-b-values (e-b1500, e-b2000, and e-b2500), z-DWI and IR m-b1500 DWI were applied. z-DWI was acquired with the same measured b-values and e-b-values as the standard sequence. For IR m-b1500 DWI, b50 and b1500 were measured, and e-b2000 and e-b2500 were mathematically extrapolated. Three readers used Likert scales to independently analyze all ultra-high b-values (b1500–b2500) for each DWI with regards to scan preference and image quality. ADC values were measured in all 20 lesions. z-DWI was the most preferred (54%), followed by IR m-b1500 DWI (46%). b1500 was significantly preferred over b2000 for z-DWI and IR m-b1500 DWI (p = 0.001 and p = 0.002, respectively). Lesion detection was not significantly different among sequences or b-values (p = 0.174). There were no significant differences in measured ADC values within lesions between s-DWI (ADC: 0.97 [±0.09] × 10−3 mm2/s) and z-DWI (ADC: 0.99 [±0.11] × 10−3 mm2/s; p = 1.000). However, there was a trend toward lower values in IR m-b1500 DWI (ADC: 0.80 [±0.06] × 10−3 mm2/s) than in s-DWI (p = 0.090) and z-DWI (p = 0.110). Overall, image quality was superior and there were fewer image artifacts when using the advanced sequences (z-DWI + IR m-b1500 DWI) compared with s-DWI. Considering scan preferences, we found that the optimal combination was z-DWI with a calculated b1500, especially regarding examination time.

The American Association for the Surgery of Trauma (AAST) renal trauma grading leads to a variable management of patients with high-grade renal injuries. For a better prediction of the risk for bleeding interventions, Keihani et al. introduced the multi-institutional genito-urinary trauma study (MiGUTS) renal trauma nomogram in 2019. The aim of this study was to conduct an external validation and generalization for all kidney trauma cases of the nomogram with a European cohort of a Swiss level 1 trauma center. We collected data from the clinical information system of the Kantonsspital Graubünden, Chur, Switzerland. All patients ≥ 18 years of age from 01.01.2008 to 01.12.2020 with a renal trauma who underwent computed tomography imaging of the abdomen were included. The descriptive analysis was performed by a t-test/Wilcoxon signed-rank test and a Chi-square test. The predictions of the nomogram were analysed by the Pearson correlation coefficient. The threshold of prediction of a bleeding intervention was optimized by a ROC analysis. Overall, 166 patients were included. Most patients were male (80.7

RATIONALE AND OBJECTIVES:High-resolution T2-weighted magnetic resonance imaging (MRI) of the pelvis is the main technique used for diagnosing benign and malignant uterine diseases. However, the procedure may be time-consuming and requires training and experience. Therefore, this study was performed to compare the image quality of standard clinical BLADE (stBLADE) with a prototypical accelerated simultaneous multi-slice (SMS) BLADE procedure with either improved temporal resolution (tr) at the same slice thickness (SL) or improved spatial resolution (sr) with the same examination time and a prototypical isotropic 3D SPACE procedure with inner-volume excitation and iterative denoising. MATERIALS AND METHODS:Patients who underwent clinically indicated MRI of the uterus were included in this prospective study and underwent stBLADE (acquisition time, 2 min 59 s; SL, 4 mm) and SMS BLADE (tr) with the same SL (4 mm) but reduced examination time (1 min 20 s) as well as SMS BLADE (sr) with thinner slices (3 mm) and comparable examination time (3 min 16 s). In addition, 3D SPACE was acquired in a sagittal orientation (5 min 36 s). The short axis of the cervix and the long axis of the corpus uteri were reconstructed in 1-mm and 3-mm SLs, retrospectively. Subjective overall image impression, delineation of anatomy/organs, lesion demarcation, and motion artifacts were assessed using a 5-point Likert scale and compared among the different techniques. The preferred sequence was then selected by three independent assessors. RESULTS:The analysis was based on 38 women (mean age, 44 ± 15 years). The overall image impression was similar for stBLADE, SMS BLADE (sr), and SMS BLADE (tr) but was significantly lower for 3D SPACE than stBLADE (p = 0.01). SMS BLADE (sr) was considered the preferred sequence because of slightly better performance in terms of overall image impression, organ delineation, and lesion demarcation, but without statistical significance. Both SMS BLADE (tr) and (sr) produced significantly fewer motion artifacts than stBLADE (p < 0.01 and p = 0.01), with no significant difference between SMS BLADE (tr) and (sr), while 3D SPACE had a significantly lower rating than stBLADE (p < 0.01). Image quality was rated as the least diagnostic criterion in all sequences and all cases. CONCLUSION:SMS BLADE (sr) was the preferred sequence for MRI of the female pelvis, with higher sr than stBLADE. SMS BLADE (tr) may also be used to reduce the acquisition time without compromising image quality. Despite its lower image quality, 3D SPACE can also reduce the examination time and improve the workflow because of the possibility of retrospective multiplanar reconstructions.

Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful technique to decipher tissue composition at the single-cell level and to inform on disease mechanisms, tumor heterogeneity, and the state of the immune microenvironment. Although multiple methods for the computational analysis of scRNA-seq data exist, their application in a clinical setting demands standardized and reproducible workflows, targeted to extract, condense, and display the clinically relevant information. To this end, we designed scAmpi ( S ingle C ell A nalysis m RNA pi peline), a workflow that facilitates scRNA-seq analysis from raw read processing to informing on sample composition, clinically relevant gene and pathway alterations, and in silico identification of personalized candidate drug treatments. We demonstrate the value of this workflow for clinical decision making in a molecular tumor board as part of a clinical study.

The application and integration of molecular profiling technologies create novel opportunities for personalized medicine. Here, we introduce the Tumor Profiler Study, an observational trial combining a prospective diagnostic approach to assess the relevance of in-depth tumor profiling to support clinical decision-making with an exploratory approach to improve the biological understanding of the disease.

BACKGROUND/AIM:To determine whether a prototypical compressed-sensing volume-interpolated breath-hold (csVIBE) provides diagnostic value in detecting rectosigmoid infiltration in deep infiltrating endometriosis (DIE).PATIENTS AND METHODS:csVIBE was employed in 151 women undergoing pelvic magnetic resonance imaging, of whom 43 had undergone surgery for suspected endometriosis. The accuracy of T2-weighted BLADE and BLADE/csVIBE, additional diagnostic value of csVIBE, and diagnostic confidence were rated by two readers. Additionally, the presence of the "mushroom cap sign" was assessed on BLADE and csVIBE.RESULTS:The diagnostic accuracy, sensitivity, and specificity of BLADE and BLADE/csVIBE were not significantly different between Readers A and B. For both readers, the confidence in the diagnosis increased with csVIBE, but this increase in the odds ratio was not significant for both readers. Both readers preferred csVIBE over BLADE with regard to detection of the "mushroom cap sign."CONCLUSION:csVIBE may provide a diagnostic benefit for surgical strategy selection through better delineation of the "mushroom cap sign."

Post-transplant lymphoproliferative disorder (PTLD) is a potentially fatal complication after organ transplantation frequently associated with the Epstein-Barr virus (EBV). Immunosuppressive treatment is thought to allow the expansion of EBV-infected B cells, which often express all eight oncogenic EBV latent proteins. Here, we assessed whether HLA-A2 transgenic humanized NSG mice treated with the immunosuppressant FK506 could be used to model EBV-PTLD. We found that FK506 treatment of EBV-infected mice led to an elevated viral burden, more frequent tumor formation and diminished EBV-induced T cell responses, indicative of reduced EBV-specific immune control. EBV latency III and lymphoproliferation-associated cellular transcripts were up-regulated in B cells from immunosuppressed animals, akin to the viral and host gene expression pattern found in EBV-PTLD. Utilizing an unbiased gene expression profiling approach, we identified genes differentially expressed in B cells of EBV-infected animals with and without FK506 treatment. Upon investigating the most promising candidates, we validated sCD30 as a marker of uncontrolled EBV proliferation in both humanized mice and in pediatric patients with EBV-PTLD. High levels of sCD30 have been previously associated with EBV-PTLD in patients. As such, we believe that humanized mice can indeed model aspects of EBV-PTLD development and may prove useful for the safety assessment of immunomodulatory therapies.

Increasing evidence suggests that antibody-drug conjugates (ADCs) can enhance anti-tumor immunity and improve clinical outcome. Here, we elucidate the therapeutic efficacy and immune-mediated mechanisms of a novel HER2-targeting ADC bearing a potent anthracycline derivate as payload (T-PNU) in a human HER2-expressing syngeneic breast cancer model resistant to trastuzumab and ado-trastuzumab emtansine. Mechanistically, the anthracycline component of the novel ADC induced immunogenic cell death leading to exposure and secretion of danger-associated molecular signals. RNA sequencing derived immunogenomic signatures and TCRβ clonotype analysis of tumor-infiltrating lymphocytes revealed a prominent role of the adaptive immune system in the regulation of T-PNU mediated anti-cancer activity. Depletion of CD8 T cells severely reduced T-PNU efficacy, thus confirming the role of cytotoxic T cells as drivers of the T-PNU mediated anti-tumor immune response. Furthermore, T-PNU therapy promoted immunological memory formation in tumor-bearing animals protecting those from tumor rechallenge. Finally, the combination of T-PNU and checkpoint inhibition, such as α-PD1, significantly enhanced tumor eradication following the treatment. In summary, a novel PNU-armed, HER2-targeting ADC elicited long-lasting immune protection in a murine orthotopic breast cancer model resistant to other HER2-directed therapies. Our findings delineate the therapeutic potential of this novel ADC payload and support its clinical development for breast cancer patients and potentially other HER2 expressing malignancies.

Differential gene expression (DGE) studies often suffer from poor interpretability of their primary results, i.e., thousands of differentially expressed genes. This has led to the introduction of gene set analysis (GSA) methods that aim at identifying interpretable global effects by grouping genes into sets of common context, such as, molecular pathways, biological function or tissue localization. In practice, GSA often results in hundreds of differentially regulated gene sets. Similar to the genes they contain, gene sets are often regulated in a correlative fashion because they share many of their genes or they describe related processes. Using these kind of neighborhood information to construct networks of gene sets allows to identify highly connected sub-networks as well as poorly connected islands or singletons. We show here how topological information and other network features can be used to filter and prioritize gene sets in routine DGE studies. Community detection in combination with automatic labeling and the network representation of gene set clusters further constitute an appealing and intuitive visualization of GSA results. The RICHNET workflow described here does not require human intervention and can thus be conveniently incorporated in automated analysis pipelines.

Identification of novel antibiotics remains a major challenge for drug discovery. The present study explores use of phenotypic readouts beyond classical antibacterial growth inhibition adopting a combined multiparametric high content screening and genomic approach. Deployment of the semi-automated bacterial phenotypic fingerprint (BPF) profiling platform in conjunction with a machine learning-powered dataset analysis, effectively allowed us to narrow down, compare and predict compound mode of action (MoA). The method identifies weak antibacterial hits allowing full exploitation of low potency hits frequently discovered by routine antibacterial screening. We demonstrate that BPF classification tool can be successfully used to guide chemical structure activity relationship optimization, enabling antibiotic development and that this approach can be fruitfully applied across species. The BPF classification tool could be potentially applied in primary screening, effectively enabling identification of novel antibacterial compound hits and differentiating their MoA, hence widening the known antibacterial chemical space of existing pharmaceutical compound libraries. More generally, beyond the specific objective of the present work, the proposed approach could be profitably applied to a broader range of diseases amenable to phenotypic drug discovery.

Differential gene expression (DGE) studies comprise poor interpretability of long lists of differentially expressed genes. Realising that genes are organized in pathways, researchers long time ago hat the solution to this problem, i.e., geneset analysis (GSA) or pathway analysis. Then came the observation that GSA studies often comprise poor interpretability of long lists of differentially regulated genesets. We propose here to utilize the fact that genesets are also related, for instance, by the genes they share, and construct geneset networks. Then the powerful toolbox of network analysis can be used to filter genesets based on context, not on p-value, and to detect closely related communities of genesets.

Differential gene expression (DGE) studies often suffer from poor interpretability of their primary results, i.e., thousands of differentially expressed genes. This has led to the introduction of gene set analysis (GSA) methods that aim at identifying interpretable global effects by grouping genes into sets of common context, such as, molecular pathways, biological function or tissue localization. In practice, GSA often results in hundreds of differentially regulated gene sets. Similar to the genes they contain, gene sets are often regulated in a correlative fashion because they share many of their genes or they describe related processes. Using these kind of neighborhood information to construct networks of gene sets allows to identify highly connected sub-networks as well as poorly connected islands or singletons. We show here how topological information and other network features can be used to filter and prioritize gene sets in routine DGE studies. Community detection in combination with automatic labeling and the network representation of gene set clusters further constitute an appealing and intuitive visualization of GSA results. The RICHNET workflow described here does not require human intervention and can thus be conveniently incorporated in automated analysis pipelines.