Characterisation of new in vitro models and identification of potentially active drugs in angiosarcoma.

Angiosarcoma is a rare soft tissue sarcoma originating from endothelial cells. Given that current treatments for advanced disease have shown limited efficacy, alternative therapies need to be identified. In rare diseases, patient-derived cell models are crucial for screening anti-tumour activity. In this study, cell line models were characterised in 2D and 3D cultures. The cell lines' growth, migration and invasion capabilities were explored, confirming them as useful tools for preclinical angiosarcoma studies. By screening a drug library, we identified potentially effective compounds: 8-amino adenosine impacted cell growth and inhibited migration and invasion at considerably low concentrations as a single agent. No synergistic effect was detected when combining with paclitaxel, gemcitabine or doxorubicin. These results suggest that this compound could be a potentially useful drug in the treatment of AGS.

Designing patient-oriented combination therapies for acute myeloid leukemia based on efficacy/toxicity integration and bipartite network modeling.

Acute myeloid leukemia (AML), a heterogeneous and aggressive blood cancer, does not respond well to single-drug therapy. A combination of drugs is required to effectively treat this disease. Computational models are critical for combination therapy discovery due to the tens of thousands of two-drug combinations, even with approved drugs. While predicting synergistic drugs is the focus of current methods, few consider drug efficacy and potential toxicity, which are crucial for treatment success. To find effective new drug candidates, we constructed a bipartite network using patient-derived tumor samples and drugs. The network is based on drug-response screening and summarizes all treatment response heterogeneity as drug response weights. This bipartite network is then projected onto the drug part, resulting in the drug similarity network. Distinct drug clusters were identified using community detection methods, each targeting different biological processes and pathways as revealed by enrichment and pathway analysis of the drugs' protein targets. Four drugs with the highest efficacy and lowest toxicity from each cluster were selected and tested for drug sensitivity using cell viability assays on various samples. Results show that ruxolitinib-ulixertinib and sapanisertib-LY3009120 are the most effective combinations with the least toxicity and the best synergistic effect on blast cells. These findings lay the foundation for personalized and successful AML therapies, ultimately leading to the development of drug combinations that can be used alongside standard first-line AML treatment.

Drug repurposing platform for deciphering the druggable SARS-CoV-2 interactome.

The coronavirus disease 2019 (COVID-19) pandemic has heavily challenged the global healthcare system. Despite the vaccination programs, the new virus variants are circulating. Further research is required for understanding of the biology of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and for discovery of therapeutic agents against the virus. Here, we took advantage of drug repurposing to identify if existing drugs could inhibit SARS-CoV-2 infection. We established an open high throughput platform for in vitro screening of drugs against SARS-CoV-2 infection. We screened ∼1000 drugs for their ability to inhibit SARS-CoV-2-induced cell death in the African green monkey kidney cell line (Vero-E6), analyzed how the hit compounds affect the viral N (nucleocapsid) protein expression in human cell lines using high-content microscopic imaging and analysis, determined the hit drug targets in silico, and assessed their ability to cause phospholipidosis, which can interfere with the viral replication. Duvelisib was found by in silico interaction assay as a potential drug targeting virus-host protein interactions. The predicted interaction between PARP1 and S protein, affected by Duvelisib, was further validated by immunoprecipitation. Our results represent a rapidly applicable platform for drug repurposing and evaluation of the new emerging viruses' responses to the drugs. Further in silico studies help us to discover the druggable host pathways involved in the infectious cycle of SARS-CoV-2.

Robust scoring of selective drug responses for patient-tailored therapy selection.

Most patients with advanced malignancies are treated with severely toxic, first-line chemotherapies. Personalized treatment strategies have led to improved patient outcomes and could replace one-size-fits-all therapies, yet they need to be tailored by testing of a range of targeted drugs in primary patient cells. Most functional precision medicine studies use simple drug-response metrics, which cannot quantify the selective effects of drugs (i.e., the differential responses of cancer cells and normal cells). We developed a computational method for selective drug-sensitivity scoring (DSS), which enables normalization of the individual patient's responses against normal cell responses. The selective response scoring uses the inhibition of noncancerous cells as a proxy for potential drug toxicity, which can in turn be used to identify effective and safer treatment options. Here, we explain how to apply the selective DSS calculation for guiding precision medicine in patients with leukemia treated across three cancer centers in Europe and the USA; the generic methods are also widely applicable to other malignancies that are amenable to drug testing. The open-source and extendable R-codes provide a robust means to tailor personalized treatment strategies on the basis of increasingly available ex vivo drug-testing data from patients in real-world and clinical trial settings. We also make available drug-response profiles to 527 anticancer compounds tested in 10 healthy bone marrow samples as reference data for selective scoring and de-prioritization of drugs that show broadly toxic effects. The procedure takes <60 min and requires basic skills in R.

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.

Drug sensitivity profiling of 3D tumor tissue cultures in the pediatric precision oncology program INFORM.

The international precision oncology program INFORM enrolls relapsed/refractory pediatric cancer patients for comprehensive molecular analysis. We report a two-year pilot study implementing ex vivo drug sensitivity profiling (DSP) using a library of 75-78 clinically relevant drugs. We included 132 viable tumor samples from 35 pediatric oncology centers in seven countries. DSP was conducted on multicellular fresh tumor tissue spheroid cultures in 384-well plates with an overall mean processing time of three weeks. In 89 cases (67%), sufficient viable tissue was received; 69 (78%) passed internal quality controls. The DSP results matched the identified molecular targets, including BRAF, ALK, MET, and TP53 status. Drug vulnerabilities were identified in 80% of cases lacking actionable (very) high-evidence molecular events, adding value to the molecular data. Striking parallels between clinical courses and the DSP results were observed in selected patients. Overall, DSP in clinical real-time is feasible in international multicenter precision oncology programs.

Genome-wide siRNA screening reveals several host receptors for the binding of human gut commensal Bifidobacterium bifidum.

Bifidobacterium spp. are abundant gut commensals, especially in breast-fed infants. Bifidobacteria are associated with many health-promoting effects including maintenance of epithelial barrier and integrity as well as immunomodulation. However, the protective mechanisms of bifidobacteria on intestinal epithelium at molecular level are poorly understood. In this study, we developed a high-throughput in vitro screening assay to explore binding receptors of intestinal epithelial cells for Bifidobacterium bifidum. Short interfering RNAs (siRNA) were used to silence expression of each gene in the Caco-2 cell line one by one. The screen yielded four cell surface proteins, SERPINB3, LGICZ1, PKD1 and PAQR6, which were identified as potential receptors as the siRNA knock-down of their expression decreased adhesion of B. bifidum to the cell line repeatedly during the three rounds of siRNA screening. Furthermore, blocking of these host cell proteins by specific antibodies decreased the binding of B. bifidum significantly to Caco-2 and HT29 cell lines. All these molecules are located on the surface of epithelial cells and three out of four, SERPINB3, PKD1 and PAQR6, are involved in the regulation of cellular processes related to proliferation, differentiation and apoptosis as well as inflammation and immunity. Our results provide leads to the first steps in the mechanistic cascade of B. bifidum-host interactions leading to regulatory effects in the epithelium and may partly explain how this commensal bacterium is able to promote intestinal homeostasis.

Minimal information for chemosensitivity assays (MICHA): a next-generation pipeline to enable the FAIRification of drug screening experiments.

Chemosensitivity assays are commonly used for preclinical drug discovery and clinical trial optimization. However, data from independent assays are often discordant, largely attributed to uncharacterized variation in the experimental materials and protocols. We report here the launching of Minimal Information for Chemosensitivity Assays (MICHA), accessed via https://micha-protocol.org. Distinguished from existing efforts that are often lacking support from data integration tools, MICHA can automatically extract publicly available information to facilitate the assay annotation including: 1) compounds, 2) samples, 3) reagents and 4) data processing methods. For example, MICHA provides an integrative web server and database to obtain compound annotation including chemical structures, targets and disease indications. In addition, the annotation of cell line samples, assay protocols and literature references can be greatly eased by retrieving manually curated catalogues. Once the annotation is complete, MICHA can export a report that conforms to the FAIR principle (Findable, Accessible, Interoperable and Reusable) of drug screening studies. To consolidate the utility of MICHA, we provide FAIRified protocols from five major cancer drug screening studies as well as six recently conducted COVID-19 studies. With the MICHA web server and database, we envisage a wider adoption of a community-driven effort to improve the open access of drug sensitivity assays.

Implementing a Functional Precision Medicine Tumor Board for Acute Myeloid Leukemia.

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 compound screening identifies navitoclax combined with irradiation as a candidate therapy for HPV-negative head and neck squamous cell carcinoma.

Conventional chemotherapeutic agents are nonselective, often resulting in severe side effects and the development of resistance. Therefore, new molecular-targeted therapies are urgently needed to be integrated into existing treatment regimens. Here, we performed a high-throughput compound screen to identify a synergistic interaction between ionizing radiation and 396 anticancer compounds. The assay was run using five human papillomavirus (HPV)-negative head and neck squamous cell carcinoma (HNSCC) cell lines cultured on the human tumor-derived matrix Myogel. Our screen identified several compounds with strong synergistic and antagonistic effects, which we further investigated using multiple irradiation doses. Navitoclax, which emerged as the most promising radiosensitizer, exhibited synergy with irradiation regardless of the p53 mutation status in all 13 HNSCC cell lines. We performed a live cell apoptosis assay for two representative HNSCC cell lines to examine the effects of navitoclax and irradiation. As a single agent, navitoclax reduced proliferation and induced apoptosis in a dose-dependent manner, whereas the navitoclax-irradiation combination arrested cell cycle progression and resulted in substantially elevated apoptosis. Overall, we demonstrated that combining navitoclax with irradiation resulted in synergistic in vitro antitumor effects in HNSCC cell lines, possibly indicating the therapeutic potential for HNSCC patients.

High miR-30 Expression Associates with Improved Breast Cancer Patient Survival and Treatment Outcome.

Deregulated miRNA expression has been suggested in several stages of breast cancer pathogenesis. We have studied the miR-30 family, in particular miR-30d, in relation to breast cancer patient survival and treatment outcomes. With tumor specimens from 1238 breast cancer patients, we analyzed the association of miR-30d expression with tumor characteristics with the 5-year occurrence of breast cancer-specific death or distant metastasis (BDDM), and with 10-year breast cancer survival (BCS). We conducted a two-stage drug-screen to investigate the impact of miR-30 family members (miR-30a-30e) on sensitivity to doxorubicin and lapatinib in six breast cancer cell lines HCC1937, HCC1954, MDA-MB-361, MCF7, MDA-MB-436 and CAL-120, using drug sensitivity scores (DSS) to compare the miR-30 family mimics to their specific inhibitors. The study was complemented with Ingenuity Pathway Analysis (IPA) with the METABRIC data. We found that while high miR-30d expression is typical for aggressive tumors, it predicts better metastasis-free (pBDDM = 0.035, HR = 0.63, 95% CI = 0.4-0.9) and breast cancer-specific survival (pBCS = 0.018, HR = 0.61, 95% CI = 0.4-0.9), especially in HER2-positive (pBDDM = 0.0009), ER-negative (pBDDM = 0.003), p53-positive (pBDDM = 0.011), and highly proliferating (pBDDM = 0.0004) subgroups, and after adjuvant chemotherapy (pBDDM = 0.035). MiR-30d predicted survival independently of standard prognostic markers (pBDDM = 0.0004). In the drug-screening test, the miR-30 family sensitized the HER2-positive HCC1954 cell line to lapatinib (p < 10-2) and HCC1937, MDA-MB-361, MDA-MB-436 and CAL120 to doxorubicin (p < 10-4) with an opposite impact on MCF7. According to the pathway analysis, the miR-30 family has a suppressive effect on cell motility and metastasis in breast cancer. Our results suggest prognostic and predictive potential for the miR-30 family, which warrants further investigation.

In Vitro Identification and In Vivo Confirmation of Inhibitors for Sweet Potato Chlorotic Stunt Virus RNA Silencing Suppressor, a Viral RNase III.

Sweet potato virus disease (SPVD), caused by synergistic infection of Sweet potato chlorotic stunt virus (SPCSV) and Sweet potato feathery mottle virus (SPFMV), is responsible for substantial yield losses all over the world. However, there are currently no approved treatments for this severe disease. The crucial role played by RNase III of SPCSV (CSR3) as an RNA silencing suppressor during the viruses' synergistic interaction in sweetpotato makes it an ideal drug target for developing antiviral treatment. In this study, high-throughput screening (HTS) of small molecular libraries targeting CSR3 was initiated by a virtual screen using Glide docking, allowing the selection of 6,400 compounds out of 136,353. We subsequently developed and carried out kinetic-based HTS using fluorescence resonance energy transfer technology, which isolated 112 compounds. These compounds were validated with dose-response assays including kinetic-based HTS and binding affinity assays using surface plasmon resonance and microscale thermophoresis. Finally, the interference of the selected compounds with viral accumulation was verified in planta In summary, we identified five compounds belonging to two structural classes that inhibited CSR3 activity and reduced viral accumulation in plants. These results provide the foundation for developing antiviral agents targeting CSR3 to provide new strategies for controlling sweetpotato virus diseases.IMPORTANCE We report here a high-throughput inhibitor identification method that targets a severe sweetpotato virus disease caused by coinfection with two viruses (SPCSV and SPFMV). The disease is responsible for up to 90% yield losses. Specifically, we targeted the RNase III enzyme encoded by SPCSV, which plays an important role in suppressing the RNA silencing defense system of sweetpotato plants. Based on virtual screening, laboratory assays, and confirmation in planta, we identified five compounds that could be used to develop antiviral drugs to combat the most severe sweetpotato virus disease.

Minimal information for Chemosensitivity assays (MICHA): A next-generation pipeline to enable the FAIRification of drug screening experiments.

Chemosensitivity assays are commonly used for preclinical drug discovery and clinical trial optimization. However, data from independent assays are often discordant, largely attributed to uncharacterized variation in the experimental materials and protocols. We report here the launching of MICHA (Minimal Information for Chemosensitivity Assays), accessed via https://micha-protocol.org. Distinguished from existing efforts that are often lacking support from data integration tools, MICHA can automatically extract publicly available information to facilitate the assay annotation including: 1) compounds, 2) samples, 3) reagents, and 4) data processing methods. For example, MICHA provides an integrative web server and database to obtain compound annotation including chemical structures, targets, and disease indications. In addition, the annotation of cell line samples, assay protocols and literature references can be greatly eased by retrieving manually curated catalogues. Once the annotation is complete, MICHA can export a report that conforms to the FAIR principle (Findable, Accessible, Interoperable and Reusable) of drug screening studies. To consolidate the utility of MICHA, we provide FAIRified protocols from five major cancer drug screening studies, as well as six recently conducted COVID-19 studies. With the MICHA webserver and database, we envisage a wider adoption of a community-driven effort to improve the open access of drug sensitivity assays.

High NRF2 Levels Correlate with Poor Prognosis in Colorectal Cancer Patients and with Sensitivity to the Kinase Inhibitor AT9283 In Vitro.

Aberrant hyperactivation of nuclear factor erythroid 2 (NF-E2) p45-related factor 2 (NRF2) is a common event in many tumour types and associates with resistance to therapy and poor patient prognosis; however, its relevance in colorectal tumours is not well-established. Measuring the expression of surrogate genes for NRF2 activity in silico, in combination with validation in patients' samples, we show that the NRF2 pathway is upregulated in colorectal tumours and that high levels of nuclear NRF2 correlate with a poor patient prognosis. These results highlight the need to overcome the protection provided by NRF2 and present an opportunity to selectively kill cancer cells with hyperactive NRF2. Exploiting the CRISPR/Cas9 technology, we generated colorectal cancer cell lines with hyperactive NRF2 and used them to perform a drug screen. We identified AT9283, an Aurora kinase inhibitor, for its selectivity towards killing cancer cells with hyperactive NRF2 as a consequence to either genetic or pharmacological activation. Our results show that hyperactivation of NRF2 in colorectal cancer cells might present a vulnerability that could potentially be therapeutically exploited by using the Aurora kinase inhibitor AT9283.

Therapeutic targeting of KSP in preclinical models of high-risk neuroblastoma.

Neuroblastoma is a childhood malignancy with often dismal prognosis; relapse is common despite intense treatment. Here, we used human tumor organoids representing multiple MYCN-amplified high-risk neuroblastomas to perform a high-throughput drug screen with approved or emerging oncology drugs. Tumor-selective effects were calculated using drug sensitivity scores. Several drugs with previously unreported anti-neuroblastoma effects were identified by stringent selection criteria. ARRY-520, an inhibitor of kinesin spindle protein (KSP), was among those causing reduced viability. High expression of the KSP-encoding gene KIF11 was associated with poor outcome in neuroblastoma. Genome-scale loss-of-function screens in hundreds of human cancer cell lines across 22 tumor types revealed that KIF11 is particularly important for neuroblastoma cell viability. KSP inhibition in neuroblastoma patient-derived xenograft (PDX) cells resulted in the formation of abnormal monoastral spindles, mitotic arrest, up-regulation of mitosis-associated genes, and apoptosis. In vivo, KSP inhibition caused regression of MYCN-amplified neuroblastoma PDX tumors. Furthermore, treatment of mice harboring orthotopic neuroblastoma PDX tumors resulted in increased survival. Our results suggested that KSP inhibition could be a promising treatment strategy in children with high-risk neuroblastoma.

Development of FRET-based high-throughput screening for viral RNase III inhibitors.

The class 1 ribonuclease III (RNase III) encoded by Sweet potato chlorotic stunt virus (CSR3) suppresses RNA silencing in plant cells and thereby counters the host antiviral response by cleaving host small interfering RNAs, which are indispensable components of the plant RNA interference (RNAi) pathway. The synergy between sweet potato chlorotic stunt virus and sweet potato feathery mottle virus can reduce crop yields by 90%. Inhibitors of CSR3 might prove efficacious to counter this viral threat, yet no screen has been carried out to identify such inhibitors. Here, we report a novel high-throughput screening (HTS) assay based on fluorescence resonance energy transfer (FRET) for identifying inhibitors of CSR3. For monitoring CSR3 activity via HTS, we used a small interfering RNA substrate that was labelled with a FRET-compatible dye. The optimized HTS assay yielded 109 potential inhibitors of CSR3 out of 6,620 compounds tested from different small-molecule libraries. The three best inhibitor candidates were validated with a dose-response assay. In addition, a parallel screen of the selected candidates was carried out for a similar class 1 RNase III enzyme from Escherichia coli (EcR3), and this screen yielded a different set of inhibitors. Thus, our results show that the CSR3 and EcR3 enzymes were inhibited by distinct types of molecules, indicating that this HTS assay could be widely applied in drug discovery of class 1 RNase III enzymes.

Patient-Derived Organoids from Multiple Colorectal Cancer Liver Metastases Reveal Moderate Intra-patient Pharmacotranscriptomic Heterogeneity.

PURPOSE: Molecular tumor heterogeneity may have important implications for the efficacy of targeted therapies in metastatic cancers. Inter-metastatic heterogeneity of sensitivity to anticancer agents has not been well explored in colorectal cancer. EXPERIMENTAL DESIGN: We established a platform for ex vivo pharmacogenomic profiling of patient-derived organoids (PDO) from resected colorectal cancer liver metastases. Drug sensitivity testing (n = 40 clinically relevant agents) and gene expression profiling were performed on 39 metastases from 22 patients. RESULTS: Three drug-response clusters were identified among the colorectal cancer metastases, based primarily on sensitivities to EGFR and/or MDM2 inhibition, and corresponding with RAS mutations and TP53 activity. Potentially effective therapies, including off-label use of drugs approved for other cancer types, could be nominated for eighteen patients (82%). Antimetabolites and targeted agents lacking a decisive genomic marker had stronger differential activity than most approved chemotherapies. We found limited intra-patient drug sensitivity heterogeneity between PDOs from multiple (2-5) liver metastases from each of ten patients. This was recapitulated at the gene expression level, with a highly proportional degree of transcriptomic and pharmacological variation. One PDO with a multi-drug resistance profile, including resistance to EGFR inhibition in a RAS-mutant background, showed sensitivity to MEK plus mTOR/AKT inhibition, corresponding with low-level PTEN expression. CONCLUSIONS: Intra-patient inter-metastatic pharmacological heterogeneity was not pronounced and ex vivo drug screening may identify novel treatment options for metastatic colorectal cancer. Variation in drug sensitivities was reflected at the transcriptomic level, suggesting potential to develop gene expression-based predictive signatures to guide experimental therapies.

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.

Combined gene essentiality scoring improves the prediction of cancer dependency maps.

BACKGROUND: Probing genetic dependencies of cancer cells can improve our understanding of tumour development and progression, as well as identify potential drug targets. CRISPR-Cas9-based and shRNA-based genetic screening are commonly utilized to identify essential genes that affect cancer growth. However, systematic methods leveraging these genetic screening techniques to derive consensus cancer dependency maps for individual cancer cell lines are lacking. FINDING: In this work, we first explored the CRISPR-Cas9 and shRNA gene essentiality profiles in 42 cancer cell lines representing 10 cancer types. We observed limited consistency between the essentiality profiles of these two screens at the genome scale. To improve consensus on the cancer dependence map, we developed a computational model called combined essentiality score (CES) to integrate the genetic essentiality profiles from CRISPR-Cas9 and shRNA screens, while accounting for the molecular features of the genes. We found that the CES method outperformed the existing gene essentiality scoring approaches in terms of ability to detect cancer essential genes. We further demonstrated the power of the CES method in adjusting for screen-specific biases and predicting genetic dependencies in individual cancer cell lines. INTERPRETATION: Systematic comparison of the CRISPR-Cas9 and shRNA gene essentiality profiles showed the limitation of relying on a single technique to identify cancer essential genes. The CES method provides an integrated framework to leverage both genetic screening techniques as well as molecular feature data to determine gene essentiality more accurately for cancer cells.