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Screening drugs on patient biopsies from solid tumours has immense potential, but is challenging due to the small amount of available material. To address this, we present here a plug-based microfluidics platform for functional screening of drug combinations. Integrated Braille valves allow changing the plug composition on demand and enable collecting >1200 data points (56 different conditions with at least 20 replicates each) per biopsy. After deriving and validating efficient and specific drug combinations for two genetically different pancreatic cancer cell lines and xenograft mouse models, we additionally screen live cells from human solid tumours with no need for ex vivo culturing steps, and obtain highly specific sensitivity profiles. The entire workflow can be completed within 48 h at assay costs of less than US$ 150 per patient. We believe this can pave the way for rapid determination of optimal personalized cancer therapies. Cancer patients exhibit specific sensitivities toward drug combinations that cannot be easily predicted. Here the authors setup a microfluidic platform that allows testing of multiple drug combinations correctly predicting sensitivity in vivo and they use it on patients biopsies to define effective drugs.

Three-dimensional (3D) cell culture technologies, such as organoids, are physiologically relevant models for basic and clinical applications. Automated microfluidics offers advantages in high-throughput and precision analysis of cells but is not yet compatible with organoids. Here, we present an automated, high-throughput, microfluidic 3D organoid culture and analysis system to facilitate preclinical research and personalized therapies. Our system provides combinatorial and dynamic drug treatments to hundreds of cultures and enables real-time analysis of organoids. We validate our system by performing individual, combinatorial, and sequential drug screens on human-derived pancreatic tumor organoids. We observe significant differences in the response of individual patient-based organoids to drug treatments and find that temporally-modified drug treatments can be more effective than constant-dose monotherapy or combination therapy in vitro. This integrated platform advances organoids models to screen and mirror real patient treatment courses with potential to facilitate treatment decisions for personalized therapy. The use of organoids in personalized medicine is promising but high throughput platforms are needed. Here the authors develop an automated, high-throughput, microfluidic 3D organoid culture system that allows combinatorial and dynamic drug treatments and real-time analysis of organoids.

While the potential of patient-derived organoids (PDOs) to predict patients’ responses to anti-cancer treatments has been well recognized, the lengthy time and the low efficiency in establishing PDOs hamper the implementation of PDO-based drug sensitivity tests in clinics. We first adapt a mechanical sample processing method to generate lung cancer organoids (LCOs) from surgically resected and biopsy tumor tissues. The LCOs recapitulate the histological and genetic features of the parental tumors and have the potential to expand indefinitely. By employing an integrated superhydrophobic microwell array chip (InSMAR-chip), we demonstrate hundreds of LCOs, a number that can be generated from most of the samples at passage 0, are sufficient to produce clinically meaningful drug responses within a week. The results prove our one-week drug tests are in good agreement with patient-derived xenografts, genetic mutations of tumors, and clinical outcomes. The LCO model coupled with the microwell device provides a technically feasible means for predicting patient-specific drug responses in clinical settings. The lengthy time in establishing patient-derived organoids(PDOs) hampers the implementation of PDO-based drug sensitivity tests in clinics. Here, the authors show a microwell array-based method to predict patient’s responses to anti-cancer therapies in a week using lung cancer organoids.

Key Points Drug discovery approaches for cancer, as for other therapeutic areas, have typically been divided into two classes: target-based drug discovery (TDD) and phenotypic drug discovery (PDD). Cancer drug discovery poses substantial challenges for both targeted and 'classical' phenotypic drug discovery owing to the number, diversity and plasticity of molecular mechanisms and phenotypes underlying tumour initiation and growth. Discovery origins for all 48 small-molecule cancer drugs approved by the US Food and Drug Administration between 1999 and 2013, and for agents in Phase II and II clinical trials at the end of 2013, were analysed and classified. Although a significant number of approved and investigational cancer drugs could be easily classified as targeted, the majority of which (21 out of 29) are kinase inhibitors, we concluded that very few drugs (four out of 48) were discovered entirely by 'classical' PDD. The remainder were discovered by, or developed from chemical lead matter discovered by a combination of phenotypic and target-based assays. Drug discovery using cytoxicity assays and cancer cell lines, although yielding many of the current standard-of-care chemotherapies, is unlikely to result in further drugs with novel mechanisms of action. Knowledge of the molecular pathways and targets required for specific disease-associated phenotypes, along with the ability to use more disease-relevant cell models, improves the probability of discovering drugs with novel mechanisms of action and clinical efficacy in molecularly defined patient populations. We introduce the concept of 'mechanism-informed phenotypic drug discovery' (MIPDD) to include phenotypic assays for specific molecular pathways and targets. Determining the causal relationships between target inhibition and phenotypic effects may well open up new and unexpected avenues of cancer biology. Such an approach presents the best means of discovering drugs that have both an optimal molecular mechanism of action and a diagnostic hypothesis to enable patient selection leading to clinical responses. There has been a resurgence of interest in the use of phenotypic screens in drug discovery as an alternative to target-focused approaches. Moffat and colleagues investigated the contribution of phenotypic assays in oncology by analysing the origins of the new small-molecule cancer drugs approved by the US Food and Drug Administration over the past 15 years. They also discuss technical and biological advances that could empower phenotypic drug discovery in oncology by enabling the development of mechanistically informed phenotypic screens. There has been a resurgence of interest in the use of phenotypic screens in drug discovery as an alternative to target-focused approaches. Given that oncology is currently the most active therapeutic area, and also one in which target-focused approaches have been particularly prominent in the past two decades, we investigated the contribution of phenotypic assays to oncology drug discovery by analysing the origins of all new small-molecule cancer drugs approved by the US Food and Drug Administration (FDA) over the past 15 years and those currently in clinical development. Although the majority of these drugs originated from target-based discovery, we identified a significant number whose discovery depended on phenotypic screening approaches. We postulate that the contribution of phenotypic screening to cancer drug discovery has been hampered by a reliance on 'classical' nonspecific drug effects such as cytotoxicity and mitotic arrest, exacerbated by a paucity of mechanistically defined cellular models for therapeutically translatable cancer phenotypes. However, technical and biological advances that enable such mechanistically informed phenotypic models have the potential to empower phenotypic drug discovery in oncology.

Tumor organoids derived from the most common subtypes of primary liver cancer recapitulate the histologic and molecular features of the tissues of origin, even after long-term culture. These in vitro models, as well as those for colorectal cancer reported in Crespo et al. in a previous issue, are amenable for drug screening and allow the identification of therapeutic approaches with potential for cancer treatment. Human liver cancer research currently lacks in vitro models that can faithfully recapitulate the pathophysiology of the original tumor. We recently described a novel, near-physiological organoid culture system, wherein primary human healthy liver cells form long-term expanding organoids that retain liver tissue function and genetic stability. Here we extend this culture system to the propagation of primary liver cancer (PLC) organoids from three of the most common PLC subtypes: hepatocellular carcinoma (HCC), cholangiocarcinoma (CC) and combined HCC/CC (CHC) tumors. PLC-derived organoid cultures preserve the histological architecture, gene expression and genomic landscape of the original tumor, allowing for discrimination between different tumor tissues and subtypes, even after long-term expansion in culture in the same medium conditions. Xenograft studies demonstrate that the tumorogenic potential, histological features and metastatic properties of PLC-derived organoids are preserved in vivo . PLC-derived organoids are amenable for biomarker identification and drug-screening testing and led to the identification of the ERK inhibitor SCH772984 as a potential therapeutic agent for primary liver cancer. We thus demonstrate the wide-ranging biomedical utilities of PLC-derived organoid models in furthering the understanding of liver cancer biology and in developing personalized-medicine approaches for the disease.

In silico methods of phenotypic screening are necessary to reduce the time and cost of the experimental in vivo screening of anticancer agents through dozens of millions of natural and synthetic chemical compounds. We used the previously developed PASS (Prediction of Activity Spectra for Substances) algorithm to create and validate the classification SAR models for predicting the cytotoxicity of chemicals against different types of human cell lines using ChEMBL experimental data. A training set from 59,882 structures of compounds was created based on the experimental data (IG50, IC50, and % inhibition values) from ChEMBL. The average accuracy of prediction (AUC) calculated by leave-one-out and a 20-fold cross-validation procedure during the training was 0.930 and 0.927 for 278 cancer cell lines, respectively, and 0.948 and 0.947 for cytotoxicity prediction for 27 normal cell lines, respectively. Using the given SAR models, we developed a freely available web-service for cell-line cytotoxicity profile prediction (CLC-Pred: Cell-Line Cytotoxicity Predictor) based on the following structural formula: http://way2drug.com/Cell-line/.

In the wake of the success of modern immunotherapy, oncolytic viruses (OVs) are currently seen as a potential therapeutic option for patients with cancer who do not respond or fail to achieve durable responses following treatment with immune checkpoint inhibitors. OVs offer a multifaceted therapeutic platform because they preferentially replicate in tumour cells, can be engineered to express transgenes that augment their cytotoxic and immunostimulatory activities, and modulate the tumour microenvironment to optimize immune-mediated tumour eradication, both at locoregional and systemic sites of disease. Lysis of tumour cells releases tumour-specific antigens that trigger both the innate and adaptive immune systems. OVs also represent attractive combination partners with other systemically delivered agents by virtue of their highly favourable safety profiles. Rational combinations of OVs with different immune modifiers and/or antitumour agents, based on mechanisms of tumour resistance to immune-mediated attack, may benefit the large, currently underserved, population of patients who respond poorly to immune checkpoint inhibition.

Cancer drug development has been riddled with high attrition rates, in part, due to poor reproducibility of preclinical models for drug discovery. Poor experimental design and lack of scientific transparency may cause experimental biases that in turn affect data quality, robustness and reproducibility. Here, we pinpoint sources of experimental variability in conventional 2D cell-based cancer drug screens to determine the effect of confounders on cell viability for MCF7 and HCC38 breast cancer cell lines treated with platinum agents (cisplatin and carboplatin) and a proteasome inhibitor (bortezomib). Variance component analysis demonstrated that variations in cell viability were primarily associated with the choice of pharmaceutical drug and cell line, and less likely to be due to the type of growth medium or assay incubation time. Furthermore, careful consideration should be given to different methods of storing diluted pharmaceutical drugs and use of DMSO controls due to the potential risk of evaporation and the subsequent effect on dose-response curves. Optimization of experimental parameters not only improved data quality substantially but also resulted in reproducible results for bortezomib- and cisplatin-treated HCC38, MCF7, MCF-10A, and MDA-MB-436 cells. Taken together, these findings indicate that replicability (the same analyst re-performs the same experiment multiple times) and reproducibility (different analysts perform the same experiment using different experimental conditions) for cell-based drug screens can be improved by identifying potential confounders and subsequent optimization of experimental parameters for each cell line.

Drug screening based on in-vitro primary tumor cell culture has demonstrated potential in personalized cancer diagnosis. However, the limited number of tumor cells, especially from patients with early stage cancer, has hindered the widespread application of this technique. Hence, we developed a digital microfluidic system for drug screening using primary tumor cells and established a working protocol for precision medicine. Smart control logic was developed to increase the throughput of the system and decrease its footprint to parallelly screen three drugs on a 4 × 4 cm 2 chip in a device measuring 23 × 16 × 3.5 cm 3 . We validated this method in an MDA-MB-231 breast cancer xenograft mouse model and liver cancer specimens from patients, demonstrating tumor suppression in mice/patients treated with drugs that were screened to be effective on individual primary tumor cells. Mice treated with drugs screened on-chip as ineffective exhibited similar results to those in the control groups. The effective drug identified through on-chip screening demonstrated consistency with the absence of mutations in their related genes determined via exome sequencing of individual tumors, further validating this protocol. Therefore, this technique and system may promote advances in precision medicine for cancer treatment and, eventually, for any disease. In-vitro platforms for personalized cancer diagnosis is required high sensitivity. Here, the authors developed a digital microfluidic system for drug screening using primary tumor cells and established a working protocol for precision medicine.

An improved strategy for large-scale combinatorial CRISPR screening enables the identification of synergistic drug targets for cancer. Identification of effective combination therapies is critical to address the emergence of drug-resistant cancers, but direct screening of all possible drug combinations is infeasible. Here we introduce a CRISPR-based double knockout (CDKO) system that improves the efficiency of combinatorial genetic screening using an effective strategy for cloning and sequencing paired single guide RNA (sgRNA) libraries and a robust statistical scoring method for calculating genetic interactions (GIs) from CRISPR-deleted gene pairs. We applied CDKO to generate a large-scale human GI map, comprising 490,000 double-sgRNAs directed against 21,321 pairs of drug targets in K562 leukemia cells and identified synthetic lethal drug target pairs for which corresponding drugs exhibit synergistic killing. These included the BCL2L1 and MCL1 combination, which was also effective in imatinib-resistant cells. We further validated this system by identifying known and previously unidentified GIs between modifiers of ricin toxicity. This work provides an effective strategy to screen synergistic drug combinations in high-throughput and a CRISPR-based tool to dissect functional GI networks.