Combined chemosensitivity and chromatin profiling prioritizes drug combinations in CLL.

The Bruton tyrosine kinase (BTK) inhibitor ibrutinib has substantially improved therapeutic options for chronic lymphocytic leukemia (CLL). Although ibrutinib is not curative, it has a profound effect on CLL cells and may create new pharmacologically exploitable vulnerabilities. To identify such vulnerabilities, we developed a systematic approach that combines epigenome profiling (charting the gene-regulatory basis of cell state) with single-cell chemosensitivity profiling (quantifying cell-type-specific drug response) and bioinformatic data integration. By applying our method to a cohort of matched patient samples collected before and during ibrutinib therapy, we identified characteristic ibrutinib-induced changes that provide a starting point for the rational design of ibrutinib combination therapies. Specifically, we observed and validated preferential sensitivity to proteasome, PLK1, and mTOR inhibitors during ibrutinib treatment. More generally, our study establishes a broadly applicable method for investigating treatment-specific vulnerabilities by integrating the complementary perspectives of epigenetic cell states and phenotypic drug responses in primary patient samples.

Axitinib effectively inhibits BCR-ABL1(T315I) with a distinct binding conformation.

The BCR-ABL1 fusion gene is a driver oncogene in chronic myeloid leukaemia and 30-50% of cases of adult acute lymphoblastic leukaemia. Introduction of ABL1 kinase inhibitors (for example, imatinib) has markedly improved patient survival, but acquired drug resistance remains a challenge. Point mutations in the ABL1 kinase domain weaken inhibitor binding and represent the most common clinical resistance mechanism. The BCR-ABL1 kinase domain gatekeeper mutation Thr315Ile (T315I) confers resistance to all approved ABL1 inhibitors except ponatinib, which has toxicity limitations. Here we combine comprehensive drug sensitivity and resistance profiling of patient cells ex vivo with structural analysis to establish the VEGFR tyrosine kinase inhibitor axitinib as a selective and effective inhibitor for T315I-mutant BCR-ABL1-driven leukaemia. Axitinib potently inhibited BCR-ABL1(T315I), at both biochemical and cellular levels, by binding to the active form of ABL1(T315I) in a mutation-selective binding mode. These findings suggest that the T315I mutation shifts the conformational equilibrium of the kinase in favour of an active (DFG-in) A-loop conformation, which has more optimal binding interactions with axitinib. Treatment of a T315I chronic myeloid leukaemia patient with axitinib resulted in a rapid reduction of T315I-positive cells from bone marrow. Taken together, our findings demonstrate an unexpected opportunity to repurpose axitinib, an anti-angiogenic drug approved for renal cancer, as an inhibitor for ABL1 gatekeeper mutant drug-resistant leukaemia patients. This study shows that wild-type proteins do not always sample the conformations available to disease-relevant mutant proteins and that comprehensive drug testing of patient-derived cells can identify unpredictable, clinically significant drug-repositioning opportunities.

8-chloro-adenosine activity in FLT3-ITD acute myeloid leukemia.

Nucleoside analogs represent the backbone of several distinct chemotherapy regimens for acute myeloid leukemia (AML) and combination with tyrosine kinase inhibitors has improved survival of AML patients, including those harboring the poor-risk FLT3-ITD mutation. Although these compounds are effective in killing proliferating blasts, they lack activity against quiescent leukemia stem cells (LSCs), which contributes to initial treatment refractoriness or subsequent disease relapse. The reagent 8-chloro-adenosine (8-Cl-Ado) is a ribose-containing, RNA-directed nucleoside analog that is incorporated into newly transcribed RNA rather than in DNA, causing inhibition of RNA transcription. In this report, we demonstrate antileukemic activities of 8-Cl-Ado in vitro and in vivo and provide mechanistic insight into the mode of action of 8-Cl-Ado in AML. 8-Cl-Ado markedly induced apoptosis in LSC, with negligible effects on normal stem cells. 8-Cl-Ado was particularly effective against AML cell lines and primary AML blast cells harboring the FLT3-ITD mutation. FLT3-ITD is associated with high expression of miR-155. Furthermore, we demonstrate that 8-Cl-Ado inhibits miR-155 expression levels accompanied by induction of DNA-damage and suppression of cell proliferation, through regulation of miR-155/ErbB3 binding protein 1(Ebp1)/p53/PCNA signaling. Finally, we determined that combined treatment of NSG mice engrafted with FLT3-ITD + MV4-11 AML cells with 8-Cl-Ado and the FLT3 inhibitor AC220 (quizartinib) synergistically enhanced survival, compared with that of mice treated with the individual drugs, suggesting a potentially effective approach for FLT3-ITD AML patients.

JAK1/2 and BCL2 inhibitors synergize to counteract bone marrow stromal cell-induced protection of AML.

The bone marrow (BM) provides a protective microenvironment to support the survival of leukemic cells and influence their response to therapeutic agents. In acute myeloid leukemia (AML), the high rate of relapse may in part be a result of the inability of current treatment to effectively overcome the protective influence of the BM niche. To better understand the effect of the BM microenvironment on drug responses in AML, we conducted a comprehensive evaluation of 304 inhibitors, including approved and investigational agents, comparing ex vivo responses of primary AML cells in BM stroma-derived and standard culture conditions. In the stroma-based conditions, the AML patient cells exhibited significantly reduced sensitivity to 12% of the tested compounds, including topoisomerase II, B-cell chronic lymphocytic leukemia/lymphoma 2 (BCL2), and many tyrosine kinase inhibitors (TKIs). The loss of TKI sensitivity was most pronounced in patient samples harboring FLT3 or PDGFRB alterations. In contrast, the stroma-derived conditions enhanced sensitivity to Janus kinase (JAK) inhibitors. Increased cell viability and resistance to specific drug classes in the BM stroma-derived conditions was a result of activation of alternative signaling pathways mediated by factors secreted by BM stromal cells and involved a switch from BCL2 to BCLXL-dependent cell survival. Moreover, the JAK1/2 inhibitor ruxolitinib restored sensitivity to the BCL2 inhibitor venetoclax in AML patient cells ex vivo in different model systems and in vivo in an AML xenograft mouse model. These findings highlight the potential of JAK inhibitors to counteract stroma-induced resistance to BCL2 inhibitors in AML.

Proposed diagnostic criteria for classical chronic myelomonocytic leukemia (CMML), CMML variants and pre-CMML conditions.

Chronic myelomonocytic leukemia (CMML) is a myeloid neoplasm characterized by dysplasia, abnormal production and accumulation of monocytic cells and an elevated risk of transforming into acute leukemia. Over the past two decades, our knowledge about the pathogenesis and molecular mechanisms in CMML has increased substantially. In parallel, better diagnostic criteria and therapeutic strategies have been developed. However, many questions remain regarding prognostication and optimal therapy. In addition, there is a need to define potential pre-phases of CMML and special CMML variants, and to separate these entities from each other and from conditions mimicking CMML. To address these unmet needs, an international consensus group met in a Working Conference in August 2018 and discussed open questions and issues around CMML, its variants, and pre-CMML conditions. The outcomes of this meeting are summarized herein and include diag nostic criteria and a proposed classification of pre-CMML conditions as well as refined minimal diagnostic criteria for classical CMML and special CMML variants, including oligomonocytic CMML and CMML associated with systemic mastocytosis. Moreover, we propose diagnostic standards and tools to distinguish between 'normal', pre-CMML and CMML entities. These criteria and standards should facilitate diagnostic and prognostic evaluations in daily practice and clinical studies in applied hematology.

Hyperactive STAT5 hijacks T cell receptor signaling and drives immature T cell acute lymphoblastic leukemia.

T cell acute lymphoblastic leukemia (T-ALL) is an aggressive immature T cell cancer. Mutations in IL7R have been analyzed genetically, but downstream effector functions such as STAT5A and STAT5B hyperactivation are poorly understood. Here, we studied the most frequent and clinically challenging STAT5BN642H driver in T cell development and immature T cell cancer onset and compared it with STAT5A hyperactive variants in transgenic mice. Enhanced STAT5 activity caused disrupted T cell development and promoted an early T cell progenitor-ALL phenotype, with upregulation of genes involved in T cell receptor (TCR) signaling, even in absence of surface TCR. Importantly, TCR pathway genes were overexpressed in human T-ALL and mature T cell cancers and activation of TCR pathway kinases was STAT5 dependent. We confirmed STAT5 binding to these genes using ChIP-Seq analysis in human T-ALL cells, which were sensitive to pharmacologic inhibition by dual STAT3/5 degraders or ZAP70 tyrosine kinase blockers in vitro and in vivo. We provide genetic and biochemical proof that STAT5A and STAT5B hyperactivation can initiate T-ALL through TCR pathway hijacking and suggest similar mechanisms for other T cell cancers. Thus, STAT5 or TCR component blockade are targeted therapy options, particularly in patients with chemoresistant clones carrying STAT5BN642H.

Development of metastatic HER2(+) breast cancer is independent of the adaptive immune system.

The tumour-modulating effects of the endogenous adaptive immune system are rather paradoxical. Whereas some clinical and experimental observations offer compelling evidence for the existence of immunosurveillance, other studies have revealed promoting effects of the adaptive immune system on primary cancer development and metastatic disease. We examined the functional significance of the adaptive immune system as a regulator of spontaneous HER2(+) breast tumourigenesis and pulmonary metastasis formation, using the MMTV-NeuT mouse model in which mammary carcinogenesis is induced by transgenic expression of the activated HER2/neu oncogene. Although T and B lymphocytes infiltrate human and experimental HER2(+) breast tumours, genetic elimination of the adaptive immune system does not affect development of premalignant hyperplasias or primary breast cancers. In addition, we demonstrate that pulmonary metastasis formation in MMTV-NeuT mice is not dependent on the adaptive immune system. Thus, our findings reveal that spontaneous HER2-driven mammary tumourigenesis and metastasis formation are neither suppressed, nor altered by immunosurveillance mechanisms, nor promoted by the adaptive immune system.

Deep Morphology Learning Enhances Ex Vivo Drug Profiling-Based Precision Medicine.

Drug testing in patient biopsy-derived cells can identify potent treatments for patients suffering from relapsed or refractory hematologic cancers. Here we investigate the use of weakly supervised deep learning on cell morphologies (DML) to complement diagnostic marker-based identification of malignant and nonmalignant cells in drug testing. Across 390 biopsies from 289 patients with diverse blood cancers, DML-based drug responses show improved reproducibility and clustering of drugs with the same mode of action. DML does so by adapting to batch effects and by autonomously recognizing disease-associated cell morphologies. In a post hoc analysis of 66 patients, DML-recommended treatments led to improved progression-free survival compared with marker-based recommendations and physician's choice-based treatments. Treatments recommended by both immunofluorescence and DML doubled the fraction of patients achieving exceptional clinical responses. Thus, DML-enhanced ex vivo drug screening is a promising tool in the identification of effective personalized treatments. SIGNIFICANCE: We have recently demonstrated that image-based drug screening in patient samples identifies effective treatment options for patients with advanced blood cancers. Here we show that using deep learning to identify malignant and nonmalignant cells by morphology improves such screens. The presented workflow is robust, automatable, and compatible with clinical routine. This article is highlighted in the In This Issue feature, p. 476.

Functional Precision Medicine Provides Clinical Benefit in Advanced Aggressive Hematologic Cancers and Identifies Exceptional Responders.

Personalized medicine aims to match the right drug with the right patient by using specific features of the individual patient's tumor. However, current strategies of personalized therapy matching provide treatment opportunities for less than 10% of patients with cancer. A promising method may be drug profiling of patient biopsy specimens with single-cell resolution to directly quantify drug effects. We prospectively tested an image-based single-cell functional precision medicine (scFPM) approach to guide treatments in 143 patients with advanced aggressive hematologic cancers. Fifty-six patients (39%) were treated according to scFPM results. At a median follow-up of 23.9 months, 30 patients (54%) demonstrated a clinical benefit of more than 1.3-fold enhanced progression-free survival compared with their previous therapy. Twelve patients (40% of responders) experienced exceptional responses lasting three times longer than expected for their respective disease. We conclude that therapy matching by scFPM is clinically feasible and effective in advanced aggressive hematologic cancers. SIGNIFICANCE: This is the first precision medicine trial using a functional assay to instruct n-of-one therapies in oncology. It illustrates that for patients lacking standard therapies, high-content assay-based scFPM can have a significant value in clinical therapy guidance based on functional dependencies of each patient's cancer.See related commentary by Letai, p. 290.This article is highlighted in the In This Issue feature, p. 275.

Metabolic drug survey highlights cancer cell dependencies and vulnerabilities.

Interrogation of cellular metabolism with high-throughput screening approaches can unravel contextual biology and identify cancer-specific metabolic vulnerabilities. To systematically study the consequences of distinct metabolic perturbations, we assemble a comprehensive metabolic drug library (CeMM Library of Metabolic Drugs; CLIMET) covering 243 compounds. We, next, characterize it phenotypically in a diverse panel of myeloid leukemia cell lines and primary patient cells. Analysis of the drug response profiles reveals that 77 drugs affect cell viability, with the top effective compounds targeting nucleic acid synthesis, oxidative stress, and the PI3K/mTOR pathway. Clustering of individual drug response profiles stratifies the cell lines into five functional groups, which link to specific molecular and metabolic features. Mechanistic characterization of selective responses to the PI3K inhibitor pictilisib, the fatty acid synthase inhibitor GSK2194069, and the SLC16A1 inhibitor AZD3965, bring forth biomarkers of drug response. Phenotypic screening using CLIMET represents a valuable tool to probe cellular metabolism and identify metabolic dependencies at large.

Cell-surface SLC nucleoside transporters and purine levels modulate BRD4-dependent chromatin states.

Metabolism negotiates cell-endogenous requirements of energy, nutrients and building blocks with the immediate environment to enable various processes, including growth and differentiation. While there is an increasing number of examples of crosstalk between metabolism and chromatin, few involve uptake of exogenous metabolites. Solute carriers (SLCs) represent the largest group of transporters in the human genome and are responsible for the transport of a wide variety of substrates, including nutrients and metabolites. We aimed to investigate the possible involvement of SLC-mediated solutes uptake and cellular metabolism in regulating cellular epigenetic states. Here, we perform a CRISPR-Cas9 transporter-focused genetic screen and a metabolic compound library screen for the regulation of BRD4-dependent chromatin states in human myeloid leukaemia cells. Intersection of the two orthogonal approaches reveal that loss of transporters involved with purine transport or inhibition of de novo purine synthesis lead to dysfunction of BRD4-dependent transcriptional regulation. Through mechanistic characterization of the metabolic circuitry, we elucidate the convergence of SLC-mediated purine uptake and de novo purine synthesis on BRD4-chromatin occupancy. Moreover, adenine-related metabolite supplementation effectively restores BRD4 functionality on purine impairment. Our study highlights the specific role of purine/adenine metabolism in modulating BRD4-dependent epigenetic states.

Recent advances in combinatorial drug screening and synergy scoring.

Treatment of complex diseases such as cancer, cardiovascular disease, diabetes or neurological disorders frequently warrants the utilization of drug combinations for therapeutic intervention. In fact, the most successful example is the current standard of care for HIV patients. However, identification of successful drug cocktails is not a simple task and is hampered by lack of standardization in terminology, experimental protocols and models as well as data analysis. Here we discuss the most recent developments in combinatorial drug screening by covering technological advancements in screening strategies, cellular model systems as well as novel drug classes. We believe the research progress being made provides promising basis to build on and identify, develop and optimize efficacious clinically relevant combinatorial drug treatments.

Differentiation status of primary chronic myeloid leukemia cells affects sensitivity to BCR-ABL1 inhibitors.

Tyrosine kinase inhibitors (TKI) are the mainstay treatment of BCR-ABL1-positive leukemia and virtually all patients with chronic myeloid leukemia in chronic phase (CP CML) respond to TKI therapy. However, there is limited information on the cellular mechanisms of response and particularly on the effect of cell differentiation state to TKI sensitivity in vivo and ex vivo/in vitro. We used multiple, independent high-throughput drug sensitivity and resistance testing platforms that collectively evaluated 295 oncology compounds to characterize ex vivo drug response profiles of primary cells freshly collected from newly-diagnosed patients with BCR-ABL1-positive leukemia (n = 40) and healthy controls (n = 12). In contrast to the highly TKI-sensitive cells from blast phase CML and Philadelphia chromosome-positive acute lymphoblastic leukemia, primary CP CML cells were insensitive to TKI therapy ex vivo. Despite maintaining potent BCR-ABL1 inhibitory activity, ex vivo viability of cells was unaffected by TKIs. These findings were validated in two independent patient cohorts and analysis platforms. All CP CML patients under study responded to TKI therapy in vivo. When CP CML cells were sorted based on CD34 expression, the CD34-positive progenitor cells showed good sensitivity to TKIs, whereas the more mature CD34-negative cells were markedly less sensitive. Thus in CP CML, TKIs predominantly target the progenitor cell population while the differentiated leukemic cells (mostly cells from granulocytic series) are insensitive to BCR-ABL1 inhibition. These findings have implications for drug discovery in CP CML and indicate a fundamental biological difference between CP CML and advanced forms of BCR-ABL1-positive leukemia.

From drug response profiling to target addiction scoring in cancer cell models.

Deconvoluting the molecular target signals behind observed drug response phenotypes is an important part of phenotype-based drug discovery and repurposing efforts. We demonstrate here how our network-based deconvolution approach, named target addiction score (TAS), provides insights into the functional importance of druggable protein targets in cell-based drug sensitivity testing experiments. Using cancer cell line profiling data sets, we constructed a functional classification across 107 cancer cell models, based on their common and unique target addiction signatures. The pan-cancer addiction correlations could not be explained by the tissue of origin, and only correlated in part with molecular and genomic signatures of the heterogeneous cancer cells. The TAS-based cancer cell classification was also shown to be robust to drug response data resampling, as well as predictive of the transcriptomic patterns in an independent set of cancer cells that shared similar addiction signatures with the 107 cancers. The critical protein targets identified by the integrated approach were also shown to have clinically relevant mutation frequencies in patients with various cancer subtypes, including not only well-established pan-cancer genes, such as PTEN tumor suppressor, but also a number of targets that are less frequently mutated in specific cancer types, including ABL1 oncoprotein in acute myeloid leukemia. An application to leukemia patient primary cell models demonstrated how the target deconvolution approach offers functional insights into patient-specific addiction patterns, such as those indicative of their receptor-type tyrosine-protein kinase FLT3 internal tandem duplication (FLT3-ITD) status and co-addiction partners, which may lead to clinically actionable, personalized drug treatment developments. To promote its application to the future drug testing studies, we have made available an open-source implementation of the TAS calculation in the form of a stand-alone R package.

Quantitative scoring of differential drug sensitivity for individually optimized anticancer therapies.

We developed a systematic algorithmic solution for quantitative drug sensitivity scoring (DSS), based on continuous modeling and integration of multiple dose-response relationships in high-throughput compound testing studies. Mathematical model estimation and continuous interpolation makes the scoring approach robust against sources of technical variability and widely applicable to various experimental settings, both in cancer cell line models and primary patient-derived cells. Here, we demonstrate its improved performance over other response parameters especially in a leukemia patient case study, where differential DSS between patient and control cells enabled identification of both cancer-selective drugs and drug-sensitive patient sub-groups, as well as dynamic monitoring of the response patterns and oncogenic driver signals during cancer progression and relapse in individual patient cells ex vivo. An open-source and easily extendable implementation of the DSS calculation is made freely available to support its tailored application to translating drug sensitivity testing results into clinically actionable treatment options.

Individualized systems medicine strategy to tailor treatments for patients with chemorefractory acute myeloid leukemia.

UNLABELLED: We present an individualized systems medicine (ISM) approach to optimize cancer drug therapies one patient at a time. ISM is based on (i) molecular profiling and ex vivo drug sensitivity and resistance testing (DSRT) of patients' cancer cells to 187 oncology drugs, (ii) clinical implementation of therapies predicted to be effective, and (iii) studying consecutive samples from the treated patients to understand the basis of resistance. Here, application of ISM to 28 samples from patients with acute myeloid leukemia (AML) uncovered five major taxonomic drug-response subtypes based on DSRT profiles, some with distinct genomic features (e.g., MLL gene fusions in subgroup IV and FLT3-ITD mutations in subgroup V). Therapy based on DSRT resulted in several clinical responses. After progression under DSRT-guided therapies, AML cells displayed significant clonal evolution and novel genomic changes potentially explaining resistance, whereas ex vivo DSRT data showed resistance to the clinically applied drugs and new vulnerabilities to previously ineffective drugs. SIGNIFICANCE: Here, we demonstrate an ISM strategy to optimize safe and effective personalized cancer therapies for individual patients as well as to understand and predict disease evolution and the next line of therapy. This approach could facilitate systematic drug repositioning of approved targeted drugs as well as help to prioritize and de-risk emerging drugs for clinical testing.