We generated ex vivo drug-response and multiomics profiling data for a prospective series of 252 samples from 186 patients with acute myeloid leukemia (AML). A functional precision medicine tumor board (FPMTB) integrated clinical, molecular, and functional data for application in clinical treatment decisions. Actionable drugs were found for 97% of patients with AML, and the recommendations were clinically implemented in 37 relapsed or refractory patients. We report a 59% objective response rate for the individually tailored therapies, including 13 complete responses, as well as bridging five patients with AML to allogeneic hematopoietic stem cell transplantation. Data integration across all cases enabled the identification of drug response biomarkers, such as the association of IL15 overexpression with resistance to FLT3 inhibitors. Integration of molecular profiling and large-scale drug response data across many patients will enable continuous improvement of the FPMTB recommendations, providing a paradigm for individualized implementation of functional precision cancer medicine. Significance: Oncogenomics data can guide clinical treatment decisions, but often such data are neither actionable nor predictive. Functional ex vivo drug testing contributes significant additional, clinically actionable therapeutic insights for individual patients with AML. Such data can be generated in four days, enabling rapid translation through FPMTB. See related commentary by Letai, p. 290. This article is highlighted in the In This Issue feature, p. 275

High-throughput drug combination screening provides a systematic strategy to discover unexpected combinatorial synergies in pre-clinical cell models. However, phenotypic combinatorial screening with multi-dose matrix assays is experimentally expensive, especially when the aim is to identify selective combination synergies across a large panel of cell lines or patient samples. Here we implemented DECREASE, an efficient machine learning model that requires only a limited set of pairwise dose-response measurements for accurate prediction of drug combination synergy and antagonism. Using a compendium of 23,595 drug combination matrices tested in various cancer cell lines, and malaria and Ebola infection models, we demonstrate how cost-effective experimental designs with DECREASE capture almost the same degree of information for synergy and antagonism detection as the fully-measured dose-response matrices. Measuring only the diagonal of the matrix provides an accurate and practical option for combinatorial screening. The open-source web-implementation enables applications of DECREASE to both pre-clinical and translational studies.

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.

Background A major barrier to effective treatment of glioblastoma (GBM) is the large intertumoral heterogeneity at the genetic and cellular level. In early phase clinical trials, patient heterogeneity in response to therapy is commonly observed; however, how tumor heterogeneity is reflected in individual drug sensitivities in the treatment-naïve glioblastoma stem cells (GSC) is unclear. Methods We cultured 12 patient-derived primary GBMs as tumorspheres and validated tumor stem cell properties by functional assays. Using automated high-throughput screening (HTS), we evaluated sensitivity to 461 anticancer drugs in a collection covering most FDA-approved anticancer drugs and investigational compounds with a broad range of molecular targets. Statistical analyses were performed using one-way ANOVA and Spearman correlation. Results Although tumor stem cell properties were confirmed in GSC cultures, their in vitro and in vivo morphology and behavior displayed considerable tumor-to-tumor variability. Drug screening revealed significant differences in the sensitivity to anticancer drugs ( p < 0.0001). The patient-specific vulnerabilities to anticancer drugs displayed a heterogeneous pattern. They represented a variety of mechanistic drug classes, including apoptotic modulators, conventional chemotherapies, and inhibitors of histone deacetylases, heat shock proteins, proteasomes and different kinases. However, the individual GSC cultures displayed high biological consistency in drug sensitivity patterns within a class of drugs. An independent laboratory confirmed individual drug responses. Conclusions This study demonstrates that patient-derived and treatment-naïve GSC cultures maintain patient-specific traits and display intertumoral heterogeneity in drug sensitivity to anticancer drugs. The heterogeneity in patient-specific drug responses highlights the difficulty in applying targeted treatment strategies at the population level to GBM patients. However, HTS can be applied to uncover patient-specific drug sensitivities for functional precision medicine. Electronic supplementary material The online version of this article (10.1186/s12885-019-5861-4) contains supplementary material, which is available to authorized users.

Poor chemotherapy response remains a major treatment challenge for high-grade serous ovarian cancer. 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-naïve 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 ALDH1A1) as compared to adherent growth-condition cells, and increased resistance to platinum and taxane. A set of eight stemness markers separated treatment-naïve 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-naïve tumours correlated with chemoresistance and reduced survival. In DSRT, 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.

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.fiAvailability and implementation, Supplementary information: R code and Supplementary data are available at Bioinformatics online.

Most non-small cell lung cancers (NSCLCs) contain non-targetable mutations, including KRAS, TP53 or STK11/LKB1 alterations. By coupling ex vivo drug sensitivity profiling with in vivo drug response studies, we aimed to identify drug vulnerabilities for these NSCLC subtypes. Primary adenosquamous carcinoma (ASC) or adenocarcinoma (AC) cultures were established from Kras G12D ;Lkb1 fl/fl (KL) tumors or AC cultures from Kras G12D ;p53 fl/fl (KP) tumors. While p53 null cells readily propagated as conventional cultures, Lkb1 null cells required conditional reprograming for establishment. Drug response profiling revealed short-term response to MEK inhibition, yet, long-term clonogenic assays demonstrated resistance, associated with sustained or adaptive activation of receptor tyrosine kinases (RTKs): activation of ERBBs in KL cultures, or FGFR in AC cultures. Furthermore, pan-ERBB inhibition reduced the clonogenicity of KL cultures, which was exacerbated by combinatorial MEK inhibition, while combinatorial MEK and FGFR inhibition suppressed clonogenicity of AC cultures. Importantly, in vivo studies confirmed KL-selective sensitivity to pan-ERBB inhibition, which correlated with high ERBB ligand expression and activation of ERBB receptors, implying that ERBB network activity may serve as a predictive biomarker of drug response. Interestingly, in human NSCLCs, phosphorylation of EGFR or ERBB3 was frequently detected in ASCs and squamous cell carcinomas. We conclude that analysis of in situ ERBB signaling networks in conjunction with ex vivo drug response profiling and biochemical dissection of adaptive RTK activities may serve as valid diagnostic approach to identify tumors sensitive to ERBB network inhibition.

In vitro cancer drug testing carries a low predictive value. We developed the human leiomyoma–derived matrix “Myogel” to better mimic the human tumor microenvironment (TME). We hypothesized that Myogel could provide an appropriate microenvironment for cancer cells, thereby allowing more in vivo–relevant drug testing. We screened 19 anticancer compounds, targeting the epidermal growth factor receptor (EGFR), MEK, and PI3K/mTOR on 12 head and neck squamous cell carcinoma (HNSCC) cell lines cultured on plastic, mouse sarcoma–derived Matrigel (MSDM), and Myogel. We applied a high-throughput drug screening assay under five different culturing conditions: cells in two-dimensional (2D) plastic wells and on top or embedded in Matrigel or Myogel. We then compared the efficacy of the anticancer compounds to the response rates of 19 HNSCC monotherapy clinical trials. Cancer cells on top of Myogel responded less to EGFR and MEK inhibitors compared to cells cultured on plastic or Matrigel. However, we found a similar response to the PI3K/mTOR inhibitors under all culturing conditions. Cells grown on Myogel more closely resembled the response rates reported in EGFR-inhibitor monotherapy clinical trials. Our findings suggest that a human tumor matrix improves the predictability of in vitro anticancer drug testing compared to current 2D and MSDM methods.