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Pan-RAF inhibitors have shown promise as antitumor agents in RAS and RAF mutated solid cancers. However, the efficacy of pan-RAF inhibitors in acute myeloid leukemia (AML) has not previously been explored. In AML, the RAS–RAF–MEK–ERK (MAPK) pathway is one of the most aberrantly activated oncogenic pathways, but previous targeting of this pathway by MEK inhibitors has not proven effective in clinical trials. Here we show that pan-RAF inhibition, but not MEK inhibition, induced cell death in 29% of AML samples while being nontoxic toward healthy bone marrow cells. Mechanistically, pan-RAF inhibition downregulated MCL1 protein synthesis and induced apoptosis in cells dependent on MCL1 for their survival. Furthermore, the combination of a pan-RAF and a BCL2 inhibitor overcame resistance to either compound alone in AML cell lines, as well as synergized and induced long-term responses ex vivo in AML patient samples relapsed or refractory to azacitidine + venetoclax treatment. Together, our results indicate that pan-RAF inhibition, alone or in combination with BCL2 inhibition, is a promising treatment strategy for AML.

Large-scale multiparameter screening has become increasingly feasible and straightforward to perform thanks to developments in technologies such as high-content microscopy and high-throughput flow cytometry. The automated toolkits for analyzing similarities and differences between large numbers of tested conditions have not kept pace with these technological developments. Thus, effective analysis of multiparameter screening datasets becomes a bottleneck and a limiting factor in unbiased interpretation of results. Here we introduce compaRe, a toolkit for large-scale multiparameter data analysis, which integrates quality control, data bias correction, and data visualization methods with a mass-aware gridding algorithm-based similarity analysis providing a much faster and more robust analyses than existing methods. Using mass and flow cytometry data from acute myeloid leukemia and myelodysplastic syndrome patients, we show that compaRe can reveal interpatient heterogeneity and recognizable phenotypic profiles. By applying compaRe to high-throughput flow cytometry drug response data in AML models, we robustly identified multiple types of both deep and subtle phenotypic response patterns, highlighting how this analysis could be used for therapeutic discoveries. In conclusion, compaRe is a toolkit that uniquely allows for automated, rapid, and precise comparisons of large-scale multiparameter datasets, including high-throughput screens.Biology has seen huge advances in technology in recent years. This has led to state-of-the-art techniques which can test hundreds of conditions simultaneously, such as how cancer cells respond to different drugs. In addition to this, each of the tens of thousands of cells studied can be screened for multiple variables, such as certain proteins or genes. This generates massive datasets with large numbers of parameters, which researchers can use to find similarities and differences between the tested conditions. Analyzing these ‘high-throughput’ experiments, however, is no easy task, as the data is often contaminated with meaningless information, or ‘background noise’, as well as sources of bias, such as non-biological variations between experiments. As a result, most analysis methods can only probe one parameter at a time, or are unautomated and require manual interpretation of the data. Here, Chalabi Hajkarim et al. have developed a new toolkit that can analyze multiparameter datasets faster and more robustly than current methods. The kit, which was named ‘compaRe’, combines a range of computational tools that automatically ‘clean’ the data of background noise or bias: the different conditions are then compared and any similarities are visually displayed using a graphical interface that is easy to explore. Chalabi Hajkarim et al. used their new method to study data from patients with acute myeloid leukemia (AML) and myelodysplastic syndrome, two forms of cancer that disrupt the production of functional immune cells. The toolkit was able to identify subtle differences between the patients and categorize them into groups based on the proteins present on immune cells. Chalabi Hajkarim et al. also applied compaRe to high-throughput data on cells from patients and mouse models with AML that had been treated with large numbers of specific drugs. This revealed that different cell types in the samples responded to the treatments in distinct ways. These findings suggest that the toolkit created by Chalabi Hajkarim et al. can automatically, rapidly and precisely compare large multiparameter datasets collected using high-throughput screens. In the future, compaRe could be used to identify drugs that illicit a specific response, or to predict how newly developed treatments impact different cell types in the body.

Multiparametric phenotypic screening of cells, for example assessing their responses to small molecules or knockdown/knockout of specific genes, is a powerful approach to understanding cellular systems and identifying potential new therapeutic strategies. However, automated tools for analyzing similarities and differences between a large number of tested conditions have not been readily available. Methods designed for clustering cells cannot identify differences between samples effectively. We introduce compaRe for ultra-fast and robust analysis of multiparametric high-throughput screening. Applying a mass-aware gridding algorithm using hypercubes, compaRe performs automatic and effective similarity comparison for hundreds to thousands of tests and provides information about the treatment effect. Particularly for screening data, compaRe is equipped with modules to remove various sources of bias.Benchmarking tests show that compaRe can circumvent batch effects and perform a similarity analysis substantially faster than conventional analysis tools. Applying compaRe to high-throughput flow cytometry screening data, we were able to distinguish subtle phenotypic drug responses in a human sample and a genetically engineered mouse model with acute myeloid leukemia (AML). compaRe revealed groups of drugs with similar responses even though their mechanisms are distinct from each other. In another screening, compaRe effectively circumvented batch effects and grouped samples from AML and myelodysplastic syndrome (MDS) patients using clinical flow cytometry data.

Pan-RAF inhibitors have shown promise as antitumor agents in RAS and RAF mutated solid cancers. However, the efficacy of pan-RAF inhibitors in acute myeloid leukemia (AML) has not previously been explored. In AML, the RAS-RAF-MEK-ERK (MAPK) pathway is one of the most aberrantly activated oncogenic pathways, but previous targeting of this pathway by MEK inhibitors has not proven effective in clinical trials. Here we show that pan-RAF inhibition, but not MEK inhibition, induced cell death in 29% of AML samples while being nontoxic toward healthy bone marrow cells. Mechanistically, pan-RAF inhibition downregulated MCL1 protein synthesis and induced apoptosis in cells dependent on MCL1 for their survival. Furthermore, the combination of a pan-RAF and a BCL2 inhibitor overcame resistance to either compound alone in AML cell lines, as well as synergized and induced long-term responses ex vivo in AML patient samples relapsed or refractory to azacitidine + venetoclax treatment. Together, our results indicate that pan-RAF inhibition, alone or in combination with BCL2 inhibition, is a promising treatment strategy for AML.

Summary Knowledge of the full target space of bioactive substances, approved and investigational drugs as well as chemical probes, provides important insights into therapeutic potential and possible adverse effects. The existing compound-target bioactivity data resources are often incomparable due to non-standardized and heterogeneous assay types and variability in endpoint measurements. To extract higher value from the existing and future compound target-profiling data, we implemented an open-data web platform, named Drug Target Commons (DTC), which features tools for crowd-sourced compound-target bioactivity data annotation, standardization, curation, and intra-resource integration. We demonstrate the unique value of DTC with several examples related to both drug discovery and drug repurposing applications and invite researchers to join this community effort to increase the reuse and extension of compound bioactivity data., Graphical Abstract, Highlights • DTC is a crowd-sourcing-based web platform to annotate drug-target bioactivity data • The open environment improves data harmonization for drug repurposing applications • DTC offers a comprehensive, reproducible, and sustainable bioactivity knowledge base, Tang et al. launches a novel crowd-sourcing effort to standardize the collection, management, curation, and annotation of the notoriously heterogeneous compound-target bioactivity measurements. The web-based community platform aims to provide the most comprehensive, reproducible, and sustainable bioactivity knowledge base for the end users.

Introduction Acute myeloid leukaemia (AML) is a malignancy characterised by impaired cell differentiation and uncontrollable accumulation of immature myeloid progenitor cells in the bone marrow. Although most patients respond to the first line chemotherapeutic treatment, the majority of patients eventually relapse and therefore the overall survival from AML remains very poor. A therapy based on inducing the immature leukemic cells to differentiate may turn AML into a curable disease, as has been shown in the case of Acute Promyelocytic Leukaemia (APL), the M3 subtype of AML. However, AML is a heterogeneous disease and we still lack knowledge of how different genetic alterations disturb normal hematopoiesis and how the cell differentiation blockade may be lifted in the various subtypes of AML. Thus, our aims are 1) to identify therapies reversing aberrant cell differentiation in AML by using flow cytometry-based high-throughput drug screening 2) to establish links between molecular subtypes of AML and responsiveness to identified cell differentiating therapies. Material and methods Our approach is to explore drug-induced cell differentiation in primary samples from AML patients and genetically engineered AML mouse models using high-throughput flow cytometry. Specifically, we characterise samples by their expressed cell differentiation-related cell surface markers with and without drug treatment to identify compounds capable of inducing cell differentiation ex vivo . The primary AML patient samples are also profiled by exome- and RNA-sequencing. Results and discussions Preliminary results with a Flt3/Npm1 –mutated mouse model suggest that several compounds elicit cell differentiation-state specific responses, some of which could be indicative of a dynamic shift towards cell differentiation. For example, treatment with mTOR-kinase inhibitors sapanisertib and vistusertib resulted in a concentration dependent increase of differentiating cells marked by CD11b expression and decrease of stem cells marked by CD34 expression. Conclusion We have established a high-throughput flow cytometric screening platform to identify drugs that are able to induce differentiation in primary AML samples and genetically engineered mouse models of AML. By integrating drug response data with genetic background information from samples we aim to reveal novel genotype-phenotype relationships in the level of cell differentiation that could eventually be translated into clinically relevant biomarkers and treatment options for AML patients.

Acute myeloid leukemia (AML) is a hematopoietic malignancy characterized by impaired differentiation and uncontrollable proliferation of myeloid progenitor cells. Due to high relapse rates, overall survival for this rapidly progressing disease is poor. The significant challenge in AML treatment is disease heterogeneity stemming from variability in maturation state of leukemic cells of origin, genetic aberrations among patients, and existence of multiple disease clones within a single patient. Disease heterogeneity and the lack of biomarkers for drug sensitivity lie at the root of treatment failure as well as selective efficacy of AML chemotherapies and the emergence of drug resistance. Furthermore, standard-of-care treatment is aggressive, presenting significant tolerability concerns to the commonly advanced-age AML patient. In this review, we examine the concept and potential of molecular stratification, particularly with biologically relevant drug responses, in identifying low-toxicity precision therapeutic combinations and clinically relevant biomarkers for AML patient care as a way to overcome these challenges in AML treatment.

The RAS-RAF-MEK-ERK (MAPK) pathway is deregulated in many cancers including acute myeloid leukemia (AML), where it facilitates cancer progression, chemo-resistance and cancer relapse. Therefore, the inhibition of this pathway has been explored for the treatment of AML, especially in the ~10% of AML cases where the leukemic cells carry activating mutations of the NRAS or KRAS oncogenes. However, MEK inhibitors, the best explored class of MAPK inhibitors, have not proven effective in the clinical setting. A novel clinically investigated drug class in this context are the paradox-breaker pan-RAF inhibitors that prevent the reactivation of the MAPK-pathway and have exhibited single agent efficacy in preclinical testing for solid cancers. Therefore, it is relevant to explore the utility of this class of drugs for AML treatment. We found that the paradox breaker pan-RAF inhibitors LY3009120 (currently in clinical testing), AZ-628 and TAK-632 as well as other MAPK inhibitors effectively downregulated MAPK signaling activity (ERK phosphorylation) in AML cells. However, when comparing cell viability (CellTiter Glo) and cell death (CellTox Green) responses in 101 AML patient sample cohort, only the pan-RAF inhibitors induced cytotoxicity while the other MAPK-pathway inhibitors generally induced a temporary cytostatic response. Specifically, LY3009120 induced cell death in 42 samples out of 101 (42 %) whereas only 9/101 (9 %) underwent cell death in response to trametinib (MEK-inhibitor) (Fig. panel A). Importantly, LY3009120 did not induce cell death in mononuclear bone marrow cells obtained from healthy individuals (5 samples) or in non-hematopoietic cell lines (Fig. panel A). To get insight into the mechanism regulating cell death in response to pan-RAF inhibition we first characterized that cells underwent rapid caspase-3/7 mediated apoptotic cell death in response to pan-RAF inhibitors (Fig. panel B). Therefore, we analyzed the proteins involved in apoptosis pathways and found that MCL-1 protein, but not mRNA levels, were downregulated upon treatment with pan-RAF inhibitors but not with MEK-inhibitors. This suggests that pan-RAF inhibition negatively regulated the translation of MCL-1, thus uncovering a new mode of MCL-1 regulation by RAF kinases. Importantly, pan-RAF inhibitors downregulated MCL-1 levels independently of caspase activity, indicating that MCL-1 downregulation precedes the apoptotic response. We explored the link between somatic mutation status and response to LY3009120 in our AML patient sample cohort. KRAS, KIT, FLT3-TKD, BCOR, JAK2 and PTPN11 mutations were found to correlate positively with pan-RAF inhibitor response, while SRSF2, RUNX1, GATA2 and STAG2 mutations negatively correlated with response and appeared to override positively correlating mutations. As pan-RAF inhibition downregulates MCL-1, an important survival protein whose increased expression is known to cause resistance to BCL-2 inhibitors, we hypothesized that inhibition of RAF kinases can overcome the resistance to BCL-2 inhibitors. In order to test the hypothesis, we generated venetoclax (BCL-2 inhibitor) resistant MOLM-13 cells and treated them with single and combined doses of venetoclax and LY3009120. As expected, combined inhibition of BCL-2 and RAF kinases significantly increased cytotoxicity as compared to either of the drugs alone (Fig. panel C). Conversely, to see if BCL-2 inhibition can overcome resistance to pan-RAF inhibitors, we treated pan-RAF inhibitor resistant HL-60 and THP-1 cells with single and combined doses of venetoclax and LY3009120. Interestingly, the drug combination exhibited synergistic cytotoxicity in HL-60 cells (where LY3009120 alone downregulates MCL-1) but not in THP-1 cells (where LY3009120 alone does not downregulate MCL-1). This indicates that MCL-1 downregulation upon pan-RAF inhibitor treatment is critical for the observed combination cytotoxicity. In summary, our results show that paradox breaker pan-RAF inhibitors are effective as single agents to induce cell death in AML blasts, likely through a combined MAPK pathway and MCL-1 dependent mechanism. Moreover, combined inhibition of RAF kinases and BCL-2 can overcome resistance of AML cells to either drug alone and lead to superior anti-leukemic activity. Figure. Figure. Disclosures Heckman: Celgene: Research Funding; Novartis: Research Funding; Orion Pharma: Research Funding. Porkka:Novartis: Honoraria, Research Funding; Celgene: Honoraria, Research Funding. Wennerberg:Novartis: Research Funding.