Breeze 2.0: an interactive web-tool for visual analysis and comparison of drug response data.

Functional precision medicine (fPM) offers an exciting, simplified approach to finding the right applications for existing molecules and enhancing therapeutic potential. Integrative and robust tools ensuring high accuracy and reliability of the results are critical. In response to this need, we previously developed Breeze, a drug screening data analysis pipeline, designed to facilitate quality control, dose-response curve fitting, and data visualization in a user-friendly manner. Here, we describe the latest version of Breeze (release 2.0), which implements an array of advanced data exploration capabilities, providing users with comprehensive post-analysis and interactive visualization options that are essential for minimizing false positive/negative outcomes and ensuring accurate interpretation of drug sensitivity and resistance data. The Breeze 2.0 web-tool also enables integrative analysis and cross-comparison of user-uploaded data with publicly available drug response datasets. The updated version incorporates new drug quantification metrics, supports analysis of both multi-dose and single-dose drug screening data and introduces a redesigned, intuitive user interface. With these enhancements, Breeze 2.0 is anticipated to substantially broaden its potential applications in diverse domains of fPM.

Novel precision medicine approaches and treatment strategies in hematological malignancies.

Genetic testing has been applied for decades in clinical routine diagnostics of hematological malignancies to improve disease (sub)classification, prognostication, patient management, and survival. In recent classifications of hematological malignancies, disease subtypes are defined by key recurrent genetic alterations detected by conventional methods (i.e., cytogenetics, fluorescence in situ hybridization, and targeted sequencing). Hematological malignancies were also one of the first disease areas in which targeted therapies were introduced, the prime example being BCR::ABL1 inhibitors, followed by an increasing number of targeted inhibitors hitting the Achilles' heel of each disease, resulting in a clear patient benefit. Owing to the technical advances in high-throughput sequencing, we can now apply broad genomic tests, including comprehensive gene panels or whole-genome and whole-transcriptome sequencing, to identify clinically important diagnostic, prognostic, and predictive markers. In this review, we give examples of how precision diagnostics has been implemented to guide treatment selection and improve survival in myeloid (myelodysplastic syndromes and acute myeloid leukemia) and lymphoid malignancies (acute lymphoblastic leukemia, diffuse large B-cell lymphoma, and chronic lymphocytic leukemia). We discuss the relevance and potential of monitoring measurable residual disease using ultra-sensitive techniques to assess therapy response and detect early relapses. Finally, we bring up the promising avenue of functional precision medicine, combining ex vivo drug screening with various omics technologies, to provide novel treatment options for patients with advanced disease. Although we are only in the beginning of the field of precision hematology, we foresee rapid development with new types of diagnostics and treatment strategies becoming available to the benefit of our patients.

Application of precision medicine in clinical routine in haematology-Challenges and opportunities.

Precision medicine is revolutionising patient care in cancer. As more knowledge is gained about the impact of specific genetic lesions on diagnosis, prognosis and treatment response, diagnostic precision and the possibility for optimal individual treatment choice have improved. Identification of hallmark genetic aberrations such as the BCR::ABL1 gene fusion in chronic myeloid leukaemia (CML) led to the rapid development of efficient targeted therapy and molecular follow-up, vastly improving survival for patients with CML during recent decades. The assessment of translocations, copy number changes and point mutations are crucial for the diagnosis and risk stratification of acute myeloid leukaemia and myelodysplastic syndromes. Still, the often heterogeneous and complex genetic landscape of haematological malignancies presents several challenges for the implementation of precision medicine to guide diagnosis, prognosis and treatment choice. This review provides an introduction and overview of the important molecular characteristics and methods currently applied in clinical practice to guide clinical decision making in haematological malignancies of myeloid and lymphoid origin. Further, experimental ways to guide the choice of targeted therapy for refractory patients are reviewed, such as functional precision medicine using drug profiling. An example of the use of pipeline studies where the treatment is chosen according to the molecular characteristics in rare solid malignancies is also provided. Finally, the future opportunities and remaining challenges of precision medicine in the real world are discussed.

Targeting SAMHD1 with hydroxyurea in first-line cytarabine-based therapy of newly diagnosed acute myeloid leukaemia: Results from the HEAT-AML trial.

BACKGROUND: Treatment of newly diagnosed acute myeloid leukaemia (AML) is based on combination chemotherapy with cytarabine (ara-C) and anthracyclines. Five-year overall survival is below 30%, which has partly been attributed to cytarabine resistance. Preclinical data suggest that the addition of hydroxyurea potentiates cytarabine efficacy by increasing ara-C triphosphate (ara-CTP) levels through targeted inhibition of SAMHD1. OBJECTIVES: In this phase 1 trial, we evaluated the feasibility, safety and efficacy of the addition of hydroxyurea to standard chemotherapy with cytarabine/daunorubicin in newly diagnosed AML patients. METHODS: Nine patients were enrolled and received at least two courses of ara-C (1 g/m2 /2 h b.i.d. d1-5, i.e., a total of 10 g/m2 per course), hydroxyurea (1-2 g d1-5) and daunorubicin (60 mg/m2 d1-3). The primary endpoint was safety; secondary endpoints were complete remission rate and measurable residual disease (MRD). Additionally, pharmacokinetic studies of ara-CTP and ex vivo drug sensitivity assays were performed. RESULTS: The most common grade 3-4 toxicity was febrile neutropenia (100%). No unexpected toxicities were observed. Pharmacokinetic analyses showed a significant increase in median ara-CTP levels (1.5-fold; p = 0.04) in patients receiving doses of 1 g hydroxyurea. Ex vivo, diagnostic leukaemic bone marrow blasts from study patients were significantly sensitised to ara-C by a median factor of 2.1 (p = 0.0047). All nine patients (100%) achieved complete remission, and all eight (100%) with validated MRD measurements (flow cytometry or real-time quantitative polymerase chain reaction [RT-qPCR]) had an MRD level <0.1% after two cycles of chemotherapy. Treatment was well-tolerated, and median time to neutrophil recovery >1.0 × 109 /L and to platelet recovery >50 × 109 /L after the start of cycle 1 was 19 days and 22 days, respectively. Six of nine patients underwent allogeneic haematopoietic stem-cell transplantation (allo-HSCT). With a median follow-up of 18.0 (range 14.9-20.5) months, one patient with adverse risk not fit for HSCT experienced a relapse after 11.9 months but is now in second complete remission. CONCLUSION: Targeted inhibition of SAMHD1 by the addition of hydroxyurea to conventional AML therapy is safe and appears efficacious within the limitations of the small phase 1 patient cohort. These results need to be corroborated in a larger study.

Breeze: an integrated quality control and data analysis application for high-throughput drug screening.

SUMMARY: High-throughput screening (HTS) enables systematic testing of thousands of chemical compounds for potential use as investigational and therapeutic agents. HTS experiments are often conducted in multi-well plates that inherently bear technical and experimental sources of error. Thus, HTS data processing requires the use of robust quality control procedures before analysis and interpretation. Here, we have implemented an open-source analysis application, Breeze, an integrated quality control and data analysis application for HTS data. Furthermore, Breeze enables a reliable way to identify individual drug sensitivity and resistance patterns in cell lines or patient-derived samples for functional precision medicine applications. The Breeze application provides a complete solution for data quality assessment, dose-response curve fitting and quantification of the drug responses along with interactive visualization of the results. AVAILABILITY AND IMPLEMENTATION: The Breeze application with video tutorial and technical documentation is accessible at https://breeze.fimm.fi; the R source code is publicly available at https://github.com/potdarswapnil/Breeze under GNU General Public License v3.0. CONTACT: swapnil.potdar@helsinki.fi. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The transcriptome-wide landscape of molecular subtype-specific mRNA expression profiles in acute myeloid leukemia.

Molecular classification of acute myeloid leukemia (AML) aids prognostic stratification and clinical management. Our aim in this study is to identify transcriptome-wide mRNAs that are specific to each of the molecular subtypes of AML. We analyzed RNA-sequencing data of 955 AML samples from three cohorts, including the BeatAML project, the Cancer Genome Atlas, and a cohort of Swedish patients to provide a comprehensive transcriptome-wide view of subtype-specific mRNA expression. We identified 729 subtype-specific mRNAs, discovered in the BeatAML project and validated in the other two cohorts. Using unique proteomics data, we also validated the presence of subtype-specific mRNAs at the protein level, yielding a rich collection of potential protein-based biomarkers for the AML community. To enable the exploration of subtype-specific mRNA expression by the broader scientific community, we provide an interactive resource to the public.

ALDH1A1-related stemness in high-grade serous ovarian cancer is a negative prognostic indicator but potentially targetable by EGFR/mTOR-PI3K/aurora kinase inhibitors.

Poor chemotherapy response remains a major treatment challenge for high-grade serous ovarian cancer (HGSC). Cancer stem cells are the major contributors to relapse and treatment failure as they can survive conventional therapy. Our objectives were to characterise stemness features in primary patient-derived cell lines, correlate stemness markers with clinical outcome and test the response of our cells to both conventional and exploratory drugs. Tissue and ascites samples, treatment-naive and/or after neoadjuvant chemotherapy, were prospectively collected. Primary cancer cells, cultured under conditions favouring either adherent or spheroid growth, were tested for stemness markers; the same markers were analysed in tissue and correlated with chemotherapy response and survival. Drug sensitivity and resistance testing was performed with 306 oncology compounds. Spheroid growth condition HGSC cells showed increased stemness marker expression (including aldehyde dehydrogenase isoform I; ALDH1A1) as compared with adherent growth condition cells, and increased resistance to platinum and taxane. A set of eight stemness markers separated treatment-naive tumours into two clusters and identified a distinct subgroup of HGSC with enriched stemness features. Expression of ALDH1A1, but not most other stemness markers, was increased after neoadjuvant chemotherapy and its expression in treatment-naive tumours correlated with chemoresistance and reduced survival. In drug sensitivity and resistance testing, five compounds, including two PI3K-mTOR inhibitors, demonstrated significant activity in both cell culture conditions. Thirteen compounds, including EGFR, PI3K-mTOR and aurora kinase inhibitors, were more toxic to spheroid cells than adherent cells. Our results identify stemness markers in HGSC that are associated with a decreased response to conventional chemotherapy and reduced survival if expressed by treatment-naive tumours. EGFR, mTOR-PI3K and aurora kinase inhibitors are candidates for targeting this cell population. © 2019 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Clonal heterogeneity influences drug responsiveness in renal cancer assessed by ex vivo drug testing of multiple patient-derived cancer cells.

Renal cell cancer (RCC) has become a prototype example of the extensive intratumor heterogeneity and clonal evolution of human cancers. However, there is little direct evidence on how the genetic heterogeneity impacts on drug response profiles of the cancer cells. Our goal was to determine how genomic clonal evolution impacts drug responses. Finding from our study could help to define the challenge that clonal evolution poses on cancer therapy. We established multiple patient-derived cells (PDCs) from different tumor regions of four RCC patients, verified their clonal relationship to each other and to the uncultured tumor tissue by genome sequencing. Furthermore, comprehensive drug-sensitivity testing with 460 oncological drugs was performed on all PDC clones. The PDCs retained many cancer-specific copy number alterations and mutations in driver genes such as VHL, PBRM1, PIK3C2A, KMD5C and TSC2 genes. The drug testing highlighted vulnerability in the PDCs toward approved RCC drugs, such as the mTOR-inhibitor temsirolimus, but also novel sensitivities were uncovered. The individual PDC clones from different tumor regions in a patient showed distinct drug-response profiles, suggesting that genomic heterogeneity contributes to the variability in drug responses. Studies of multiple PDCs from a patient with cancer are informative for elucidating cancer heterogeneity and for the determination on how the genomic evolution is manifested in cancer drug responsiveness. This approach could facilitate tailoring of drugs and drug combinations to individual patients.

Drug sensitivity testing on patient-derived sarcoma cells predicts patient response to treatment and identifies c-Sarc inhibitors as active drugs for translocation sarcomas.

BACKGROUND: Heterogeneity and low incidence comprise the biggest challenge in sarcoma diagnosis and treatment. Chemotherapy, although efficient for some sarcoma subtypes, generally results in poor clinical responses and is mostly recommended for advanced disease. Specific genomic aberrations have been identified in some sarcoma subtypes but few of them can be targeted with approved drugs. METHODS: We cultured and characterised patient-derived sarcoma cells and evaluated their sensitivity to 525 anti-cancer agents including both approved and non-approved drugs. In total, 14 sarcomas and 5 healthy mesenchymal primary cell cultures were studied. The sarcoma biopsies and derived cells were characterised by gene panel sequencing, cancer driver gene expression and by detecting specific fusion oncoproteins in situ in sarcomas with translocations. RESULTS: Soft tissue sarcoma cultures were established from patient biopsies with a success rate of 58%. The genomic profile and drug sensitivity testing on these samples helped to identify targeted inhibitors active on sarcomas. The cSrc inhibitor Dasatinib was identified as an active drug in sarcomas carrying chromosomal translocations. The drug sensitivity of the patient sarcoma cells ex vivo correlated with the response to the former treatment of the patient. CONCLUSIONS: Our results show that patient-derived sarcoma cells cultured in vitro are relevant and practical models for genotypic and phenotypic screens aiming to identify efficient drugs to treat sarcoma patients with poor treatment options.

Impact of normalization methods on high-throughput screening data with high hit rates and drug testing with dose-response data.

MOTIVATION: Most data analysis tools for high-throughput screening (HTS) seek to uncover interesting hits for further analysis. They typically assume a low hit rate per plate. Hit rates can be dramatically higher in secondary screening, RNAi screening and in drug sensitivity testing using biologically active drugs. In particular, drug sensitivity testing on primary cells is often based on dose-response experiments, which pose a more stringent requirement for data quality and for intra- and inter-plate variation. Here, we compared common plate normalization and noise-reduction methods, including the B-score and the Loess a local polynomial fit method under high hit-rate scenarios of drug sensitivity testing. We generated simulated 384-well plate HTS datasets, each with 71 plates having a range of 20 (5%) to 160 (42%) hits per plate, with controls placed either at the edge of the plates or in a scattered configuration. RESULTS: We identified 20% (77/384) as the critical hit-rate after which the normalizations started to perform poorly. Results from real drug testing experiments supported this estimation. In particular, the B-score resulted in incorrect normalization of high hit-rate plates, leading to poor data quality, which could be attributed to its dependency on the median polish algorithm. We conclude that a combination of a scattered layout of controls per plate and normalization using a polynomial least squares fit method, such as Loess helps to reduce column, row and edge effects in HTS experiments with high hit-rates and is optimal for generating accurate dose-response curves. CONTACT: john.mpindi@helsinki.fi. AVAILABILITY AND IMPLEMENTATION: Supplementary information: R code and Supplementary data are available at Bioinformatics online.

Androgen receptor-interacting protein HSPBAP1 facilitates growth of prostate cancer cells in androgen-deficient conditions.

Hormonal therapies targeting androgen receptor (AR) are effective in prostate cancer (PCa), but often the cancers progress to fatal castrate-resistant disease. Improved understanding of the cellular events during androgen deprivation would help to identify survival and stress pathways whose inhibition could synergize with androgen deprivation. Toward this aim, we performed an RNAi screen on 2,068 genes, including kinases, phosphatases, epigenetic enzymes and other druggable gene targets. High-content cell spot microarray (CSMA) screen was performed in VCaP cells in the presence and absence of androgens with detection of Ki67 and cleaved ADP-ribose polymerase (cPARP) as assays for cell proliferation and apoptosis. Thirty-nine candidate genes were identified, whose silencing inhibited proliferation or induced apoptosis of VCaP cells exclusively under androgen-deprived conditions. One of the candidates, HSPB (heat shock 27 kDa)-associated protein 1 (HSPBAP1), was confirmed to be highly expressed in tumor samples and its mRNA expression levels increased with the Gleason grade. We found that strong HSPBAP1 immunohistochemical staining (IHC) was associated with shorter disease-specific survival of PCa patients compared with negative to moderate staining. Furthermore, we demonstrate that HSPBAP1 interacts with AR in the nucleus of PCa cells specifically during androgen-deprived conditions, occupies chromatin at PSA/klk3 and TMPRSS2/tmprss2 enhancers and regulates their expression. In conclusion, we suggest that HSPBAP1 aids in sustaining cell viability by maintaining AR signaling during androgen-deprived conditions.

High-content screening of drug combinations of an mPGES-1 inhibitor in multicellular tumor spheroids leads to mechanistic insights into neuroblastoma chemoresistance.

High-throughput drug screening enables the discovery of new anticancer drugs. Although monolayer cell cultures are commonly used for screening, their limited complexity and translational efficiency require alternative models. Three-dimensional cell cultures, such as multicellular tumor spheroids (MCTS), mimic tumor architecture and offer promising opportunities for drug discovery. In this study, we developed a neuroblastoma MCTS model for high-content drug screening. We also aimed to decipher the mechanisms underlying synergistic drug combinations in this disease model. Several agents from different therapeutic categories and with different mechanisms of action were tested alone or in combination with selective inhibition of prostaglandin E2 by pharmacological inhibition of microsomal prostaglandin E synthase-1 (mPGES-1). After a systematic investigation of the sensitivity of individual agents and the effects of pairwise combinations, GFP-transfected MCTS were used in a confirmatory screen to validate the hits. Finally, inhibitory effects on multidrug resistance proteins were examined. In summary, we demonstrate how MCTS-based high-throughput drug screening has the potential to uncover effective drug combinations and provide insights into the mechanism of synergy between an mPGES-1 inhibitor and chemotherapeutic agents.

A phenotypic screening approach to target p60AmotL2-expressing invasive cancer cells.

BACKGROUND: Tumor cells have the ability to invade and form small clusters that protrude into adjacent tissues, a phenomenon that is frequently observed at the periphery of a tumor as it expands into healthy tissues. The presence of these clusters is linked to poor prognosis and has proven challenging to treat using conventional therapies. We previously reported that p60AmotL2 expression is localized to invasive colon and breast cancer cells. In vitro, p60AmotL2 promotes epithelial cell invasion by negatively impacting E-cadherin/AmotL2-related mechanotransduction. METHODS: Using epithelial cells transfected with inducible p60AmotL2, we employed a phenotypic drug screening approach to find compounds that specifically target invasive cells. The phenotypic screen was performed by treating cells for 72 h with a library of compounds with known antitumor activities in a dose-dependent manner. After assessing cell viability using CellTiter-Glo, drug sensitivity scores for each compound were calculated. Candidate hit compounds with a higher drug sensitivity score for p60AmotL2-expressing cells were then validated on lung and colon cell models, both in 2D and in 3D, and on colon cancer patient-derived organoids. Nascent RNA sequencing was performed after BET inhibition to analyse BET-dependent pathways in p60AmotL2-expressing cells. RESULTS: We identified 60 compounds that selectively targeted p60AmotL2-expressing cells. Intriguingly, these compounds were classified into two major categories: Epidermal Growth Factor Receptor (EGFR) inhibitors and Bromodomain and Extra-Terminal motif (BET) inhibitors. The latter consistently demonstrated antitumor activity in human cancer cell models, as well as in organoids derived from colon cancer patients. BET inhibition led to a shift towards the upregulation of pro-apoptotic pathways specifically in p60AmotL2-expressing cells. CONCLUSIONS: BET inhibitors specifically target p60AmotL2-expressing invasive cancer cells, likely by exploiting differences in chromatin accessibility, leading to cell death. Additionally, our findings support the use of this phenotypic strategy to discover novel compounds that can exploit vulnerabilities and specifically target invasive cancer cells.

Delineating functional and molecular impact of ex vivo sample handling in precision medicine.

Consistent handling of samples is crucial for achieving reproducible molecular and functional testing results in translational research. Here, we used 229 acute myeloid leukemia (AML) patient samples to assess the impact of sample handling on high-throughput functional drug testing, mass spectrometry-based proteomics, and flow cytometry. Our data revealed novel and previously described changes in cell phenotype and drug response dependent on sample biobanking. Specifically, myeloid cells with a CD117 (c-KIT) positive phenotype decreased after biobanking, potentially distorting cell population representations and affecting drugs targeting these cells. Additionally, highly granular AML cell numbers decreased after freezing. Secondly, protein expression levels, as well as sensitivity to drugs targeting cell proliferation, metabolism, tyrosine kinases (e.g., JAK, KIT, FLT3), and BH3 mimetics were notably affected by biobanking. Moreover, drug response profiles of paired fresh and frozen samples showed that freezing samples can lead to systematic errors in drug sensitivity scores. While a high correlation between fresh and frozen for the entire drug library was observed, freezing cells had a considerable impact at an individual level, which could influence outcomes in translational studies. Our study highlights conditions where standardization is needed to improve reproducibility, and where validation of data generated from biobanked cohorts may be particularly important.

Identification of miR-193b targets in breast cancer cells and systems biological analysis of their functional impact.

Identification of protein targets for microRNAs (miRNAs) is a significant challenge due to the complexity of miRNA-mediated regulation. We have previously demonstrated that miR-193b targets estrogen receptor-α (ERα) and inhibits estrogen-induced growth of breast cancer cells. Here, we applied a high-throughput strategy using quantitative iTRAQ (isobaric tag for relative and absolute quantitation) reagents to identify other target proteins regulated by miR-193b in breast cancer cells. iTRAQ analysis of pre-miR-193b transfected MCF-7 cells resulted in identification of 743 unique proteins, of which 39 were down-regulated and 44 up-regulated as compared with negative control transfected cells. Computationally predicted targets of miR-193b were highly enriched (sevenfold) among the proteins whose level of expression decreased after miR-193b transfection. Only a minority of these (13%) showed similar effect at the mRNA level illustrating the importance of post-transcriptional regulation. The most significantly repressed proteins were selected for validation experiments. These data confirmed 14-3-3ζ (YWHAZ), serine hydroxyl transferase (SHMT2), and aldo-keto reductase family 1, member C2 (AKR1C2) as direct, previously uncharacterized, targets of miR-193b. Functional RNAi assays demonstrated that specific combinations of knockdowns of these target genes by siRNAs inhibited growth of MCF-7 cells, mimicking the effects of the miR-193b overexpression. Interestingly, the data imply that besides targeting ERα, the miR-193b effects include suppression of the local production of estrogens and other steroid hormones mediated by the AKR1C2 gene, thus provoking two separate molecular mechanisms inhibiting steroid-dependent growth of breast cancer cells. In conclusion, we present here a proteomic screen to identify targets of miR-193b, and a systems biological approach to mimic its effects at the level of cellular phenotypes. This led to the identification of multiple genes whose combinatorial knock-down likely mediates the strong anti-cancer effects observed for miR-193b in breast cancer cells.

The drug efficacy testing in 3D cultures platform identifies effective drugs for ovarian cancer patients.

Most patients with advanced ovarian cancer (OC) relapse and progress despite systemic therapy, pointing to the need for improved and tailored therapy options. Functional precision medicine can help to identify effective therapies for individual patients in a clinically relevant timeframe. Here, we present a scalable functional precision medicine platform: DET3Ct (Drug Efficacy Testing in 3D Cultures), where the response of patient cells to drugs and drug combinations are quantified with live-cell imaging. We demonstrate the delivery of individual drug sensitivity profiles in 20 samples from 16 patients with ovarian cancer in both 2D and 3D culture formats, achieving over 90% success rate in providing results six days after operation. In this cohort all patients received carboplatin. The carboplatin sensitivity scores were significantly different for patients with a progression free interval (PFI) less than or equal to 12 months and those with more than 12 months (p < 0.05). We find that the 3D culture format better retains proliferation and characteristics of the in vivo setting. Using the DET3Ct platform we evaluate 27 tailored combinations with results available 10 days after operation. Notably, carboplatin and A-1331852 (Bcl-xL inhibitor) showed an additive effect in four of eight OC samples tested, while afatinib and A-1331852 led to synergy in five of seven OC models. In conclusion, our 3D DET3Ct platform can rapidly define potential, clinically relevant data on efficacy of existing drugs in OC for precision medicine purposes, as well as provide insights on emerging drugs and drug combinations that warrant testing in clinical trials.

Prediction model for drug response of acute myeloid leukemia patients.

Despite some encouraging successes, predicting the therapy response of acute myeloid leukemia (AML) patients remains highly challenging due to tumor heterogeneity. Here we aim to develop and validate MDREAM, a robust ensemble-based prediction model for drug response in AML based on an integration of omics data, including mutations and gene expression, and large-scale drug testing. Briefly, MDREAM is first trained in the BeatAML cohort (n = 278), and then validated in the BeatAML (n = 183) and two external cohorts, including a Swedish AML cohort (n = 45) and a relapsed/refractory acute leukemia cohort (n = 12). The final prediction is based on 122 ensemble models, each corresponding to a drug. A confidence score metric is used to convey the uncertainty of predictions; among predictions with a confidence score >0.75, the validated proportion of good responders is 77%. The Spearman correlations between the predicted and the observed drug response are 0.68 (95% CI: [0.64, 0.68]) in the BeatAML validation set, -0.49 (95% CI: [-0.53, -0.44]) in the Swedish cohort and 0.59 (95% CI: [0.51, 0.67]) in the relapsed/refractory cohort. A web-based implementation of MDREAM is publicly available at https://www.meb.ki.se/shiny/truvu/MDREAM/ .

Miniaturized and multiplexed high-content screening of drug and immune sensitivity in a multichambered microwell chip.

Here, we present a methodology based on multiplexed fluorescence screening of two- or three-dimensional cell cultures in a newly designed multichambered microwell chip, allowing direct assessment of drug or immune cell cytotoxic efficacy. We establish a framework for cell culture, formation of tumor spheroids, fluorescence labeling, and imaging of fixed or live cells at various magnifications directly in the chip together with data analysis and interpretation. The methodology is demonstrated by drug cytotoxicity screening using ovarian and non-small cell lung cancer cells and by cellular cytotoxicity screening targeting tumor spheroids of renal carcinoma and ovarian carcinoma with natural killer cells from healthy donors. The miniaturized format allowing long-term cell culture, efficient screening, and high-quality imaging of small sample volumes makes this methodology promising for individualized cytotoxicity tests for precision medicine.

Integrative multi-omics and drug response profiling of childhood acute lymphoblastic leukemia cell lines.

Acute lymphoblastic leukemia (ALL) is the most common childhood cancer. Although standard-of-care chemotherapeutics are sufficient for most ALL cases, there are subsets of patients with poor response who relapse in disease. The biology underlying differences between subtypes and their response to therapy has only partially been explained by genetic and transcriptomic profiling. Here, we perform comprehensive multi-omic analyses of 49 readily available childhood ALL cell lines, using proteomics, transcriptomics, and pharmacoproteomic characterization. We connect the molecular phenotypes with drug responses to 528 oncology drugs, identifying drug correlations as well as lineage-dependent correlations. We also identify the diacylglycerol-analog bryostatin-1 as a therapeutic candidate in the MEF2D-HNRNPUL1 fusion high-risk subtype, for which this drug activates pro-apoptotic ERK signaling associated with molecular mediators of pre-B cell negative selection. Our data is the foundation for the interactive online Functional Omics Resource of ALL (FORALL) with navigable proteomics, transcriptomics, and drug sensitivity profiles at https://proteomics.se/forall .