Drug-perturbation-based stratification of blood cancer

Challenges with Approved Targeted Therapies against Recurrent Mutations in CLL: A Place for New Actionable Targets

Chronic lymphocytic leukemia (CLL) is characterized by a high degree of genetic variability and interpatient heterogeneity. In the last decade, novel alterations have been described. Some of them impact on the prognosis and evolution of patients. The approval of BTK inhibitors, PI3K inhibitors and Bcl-2 inhibitors has drastically changed the treatment of patients with CLL. The effect of these new targeted therapies has been widely analyzed in TP53-mutated cases, but few data exist about the response of patients carrying other recurrent mutations. In this review, we describe the biological pathways recurrently altered in CLL that might have an impact on the response to these new therapies together with the possibility to use new actionable targets to optimize treatment responses.

Targeted PI3K/AKT-hyperactivation induces cell death in chronic lymphocytic leukemia

Current therapeutic approaches for chronic lymphocytic leukemia (CLL) focus on the suppression of oncogenic kinase signaling. Here, we test the hypothesis that targeted hyperactivation of the phosphatidylinositol-3-phosphate/AKT (PI3K/AKT)-signaling pathway may be leveraged to trigger CLL cell death. Though counterintuitive, our data show that genetic hyperactivation of PI3K/AKT-signaling or blocking the activity of the inhibitory phosphatase SH2-containing-inositol-5'-phosphatase-1 (SHIP1) induces acute cell death in CLL cells. Our mechanistic studies reveal that increased AKT activity upon inhibition of SHIP1 leads to increased mitochondrial respiration and causes excessive accumulation of reactive oxygen species (ROS), resulting in cell death in CLL with immunogenic features. Our results demonstrate that CLL cells critically depend on mechanisms to fine-tune PI3K/AKT activity, allowing sustained proliferation and survival but avoid ROS-induced cell death and suggest transient SHIP1-inhibition as an unexpectedly promising concept for CLL therapy.

Proteomics, Personalized Medicine and Cancer

As of 2020 the human genome and proteome are both at >90% completion based on high stringency analyses. This has been largely achieved by major technological advances over the last 20 years and has enlarged our understanding of human health and disease, including cancer, and is supporting the current trend towards personalized/precision medicine. This is due to improved screening, novel therapeutic approaches and an increased understanding of underlying cancer biology. However, cancer is a complex, heterogeneous disease modulated by genetic, molecular, cellular, tissue, population, environmental and socioeconomic factors, which evolve with time. In spite of recent advances in treatment that have resulted in improved patient outcomes, prognosis is still poor for many patients with certain cancers (e.g., mesothelioma, pancreatic and brain cancer) with a high death rate associated with late diagnosis. In this review we overview key hallmarks of cancer (e.g., autophagy, the role of redox signaling), current unmet clinical needs, the requirement for sensitive and specific biomarkers for early detection, surveillance, prognosis and drug monitoring, the role of the microbiome and the goals of personalized/precision medicine, discussing how emerging omics technologies can further inform on these areas. Exemplars from recent onco-proteogenomic-related publications will be given. Finally, we will address future perspectives, not only from the standpoint of perceived advances in treatment, but also from the hurdles that have to be overcome.

Ex vivo drug screening defines novel drug sensitivity patterns for informing personalized therapy in myeloid neoplasms

Precision medicine approaches such as ex vivo drug sensitivity screening (DSS) are appealing to inform rational drug selection in myelodysplastic syndromes (MDSs) and acute myeloid leukemia, given their marked biologic heterogeneity. We evaluated a novel, fully automated ex vivo DSS platform that uses high-throughput flow cytometry in 54 patients with newly diagnosed or treatment-refractory myeloid neoplasms to evaluate sensitivity (blast cytotoxicity and differentiation) to 74 US Food and Drug Administration-approved or investigational drugs and 36 drug combinations. After piloting the platform in 33 patients, we conducted a prospective feasibility study enrolling 21 patients refractory to hypomethylating agents (HMAs) to determine whether this assay could be performed within a clinically actionable time frame and could accurately predict clinical responses in vivo. When assayed for cytotoxicity, ex vivo drug sensitivity patterns were heterogeneous, but they defined distinct patient clusters with differential sensitivity to HMAs, anthracyclines, histone deacetylase inhibitors, and kinase inhibitors (P < .001 among clusters) and demonstrated synergy between HMAs and venetoclax (P < .01 for combinations vs single agents). In our feasibility study, ex vivo DSS results were available at a median of 15 days after bone marrow biopsy, and they informed personalized therapy, which frequently included venetoclax combinations, kinase inhibitors, differentiative agents, and androgens. In 21 patients with available ex vivo and in vivo clinical response data, the DSS platform had a positive predictive value of 0.92, negative predictive value of 0.82, and overall accuracy of 0.85. These data demonstrate the utility of this approach for identifying potentially useful and often novel therapeutic drugs for patients with myeloid neoplasms refractory to standard therapies.

Mutational landscape of chronic myeloid leukemia: more than a single oncogene leukemia

The BCR-ABL1 fusion gene, which causes aberrant kinase activity and uncontrolled cell proliferation, is the hallmark of chronic myeloid leukemia (CML). The development of tyrosine kinase inhibitors (TKI) that target the BCR-ABL oncoprotein has led to dramatic improvement in CML management. However, some challenges remain to be addressed in the TKI era, including patient stratification and the selection of frontline TKIs and CML progression. Additionally, with the emerging goal of treatment-free remission (TFR) in CML management, biomarkers that predict the outcomes of stopping TKI remain to be identified. Notably, recent reports have revealed the power of genome screening in understanding the role of genome aberrations other than BCR-ABL1 in CML pathogenesis. These studies have discovered the presence of disease-phase specific mutations and linked certain mutations to inferior responses to TKI treatment and CML progression. A personalized approach that incorporates genetic data in tailoring treatment strategies has been successfully implemented in acute leukemia, and it represents a promising approach for the management of high-risk CML patients. In this article, we will review current knowledge about the mutational profile in different phases of CML as well as patterns of mutational dynamics in patients having different outcomes. We highlight the effects of somatic mutations involving certain genes (e.g. epigenetic modifiers) on the outcomes of TKI treatment. We also discuss the potential value of incorporating genetic data in treatment decisions and the routine care of CML patients as a future direction for optimizing CML management.

A Detailed Catalogue of Multi-Omics Methodologies for Identification of Putative Biomarkers and Causal Molecular Networks in Translational Cancer Research

Recent advances in sequencing and biotechnological methodologies have led to the generation of large volumes of molecular data of different omics layers, such as genomics, transcriptomics, proteomics and metabolomics. Integration of these data with clinical information provides new opportunities to discover how perturbations in biological processes lead to disease. Using data-driven approaches for the integration and interpretation of multi-omics data could stably identify links between structural and functional information and propose causal molecular networks with potential impact on cancer pathophysiology. This knowledge can then be used to improve disease diagnosis, prognosis, prevention, and therapy. This review will summarize and categorize the most current computational methodologies and tools for integration of distinct molecular layers in the context of translational cancer research and personalized therapy. Additionally, the bioinformatics tools Multi-Omics Factor Analysis (MOFA) and netDX will be tested using omics data from public cancer resources, to assess their overall robustness, provide reproducible workflows for gaining biological knowledge from multi-omics data, and to comprehensively understand the significantly perturbed biological entities in distinct cancer types. We show that the performed supervised and unsupervised analyses result in meaningful and novel findings.

HELQ and EGR3 expression correlate with IGHV mutation status and prognosis in chronic lymphocytic leukemia

Background

IGHV mutation status is a crucial prognostic biomarker for CLL. In the present study, we investigated the transcriptomic signatures associating with IGHV mutation status and CLL prognosis.

Methods

The co-expression modules and hub genes correlating with IGHV status, were identified using the GSE28654, by ‘WGCNA’ package and R software (version 4.0.2). The over-representation analysis was performed to reveal enriched cell pathways for genes of correlating modules. Then 9 external cohorts were used to validate the correlation of hub genes expression with IGHV status or clinical features (treatment response, transformation to Richter syndrome, etc.). Moreover, to elucidate the significance of hub genes on disease course and prognosis of CLL patients, the Kaplan–Meier analysis for the OS and TTFT of were performed between subgroups dichotomized by the median expression value of individual hub genes.

Results

2 co-expression modules and 9 hub genes ((FCRL1/FCRL2/HELQ/EGR3LPL/LDOC1/ZNF667/SOWAHC/SEPTIN10) correlating with IGHV status were identified by WGCNA, and validated by external datasets. The modules were found to be enriched in NF-kappaB, HIF-1 and other important pathways, involving cell proliferation and apoptosis. The expression of hub genes was revealed to be significantly different, not only between CLL and normal B cell, but also between various types of lymphoid neoplasms. HELQ expression was found to be related with response of immunochemotherapy treatment significantly (p = 0.0413), while HELQ and ZNF667 were expressed differently between stable CLL and Richter syndrome patients (p < 0.0001 and p = 0.0278, respectively). By survival analysis of subgroups, EGR3 expression was indicated to be significantly associated with TTFT by 2 independent cohorts (GSE39671, p = 0.0311; GSE22762, p = 0.0135). While the expression of HELQ and EGR3 was found to be associated with OS (p = 0.0291 and 0.0114 respectively).The Kras, Hedgehog and IL6-JAK-STAT3 pathways were found to be associating with the expression of hub genes, resulting from GSEA.

Conclusions

The expression of HELQ and EGR3 were correlated with IGHV mutation status in CLL patients. Additionally, the expression of HELQ/EGR3 were prognostic markers for CLL associating with targetable cell signaling pathways.

The balance between the intronic miR-342 and its host gene Evl determines hematopoietic cell fate decision

Protein-coding and non-coding genes like miRNAs tightly control hematopoietic differentiation programs. Although miRNAs are frequently located within introns of protein-coding genes, the molecular interplay between intronic miRNAs and their host genes is unclear. By genomic integration site mapping of gamma-retroviral vectors in genetically corrected peripheral blood from gene therapy patients, we identified the EVL/MIR342 gene locus as a hotspot for therapeutic vector insertions indicating its accessibility and expression in human hematopoietic stem and progenitor cells. We therefore asked if and how EVL and its intronic miRNA-342 regulate hematopoiesis. Here we demonstrate that overexpression (OE) of Evl in murine primary Lin^- Sca1⁺ cKit⁺ cells drives lymphopoiesis whereas miR-342 OE increases myeloid colony formation in vitro and in vivo, going along with a profound upregulation of canonical pathways essential for B-cell development or myelopoietic functions upon Evl or miR-342 OE, respectively. Strikingly, miR-342 counteracts its host gene by targeting lymphoid signaling pathways, resulting in reduced pre-B-cell output. Moreover, EVL overexpression is associated with lymphoid leukemia in patients. In summary, our data show that one common gene locus regulates distinct hematopoietic differentiation programs depending on the gene product expressed, and that the balance between both may determine hematopoietic cell fate decision.

Advances and Perspectives in the Treatment of T-PLL

PURPOSE OF REVIEW

T cell prolymphocytic leukemia (T-PLL) is a rare mature T cell tumor. Available treatment options in this aggressive disease are largely inefficient and patient outcomes are highly dissatisfactory. Current therapeutic strategies mainly employ the CD52-antibody alemtuzumab as the most active single agent. However, sustained remissions after sole alemtuzumab-based induction are exceptions. Responses after available second-line strategies are even less durable. More profound disease control or rare curative outcomes can currently only be expected after a consolidating allogeneic hematopoietic stem cell transplantation (allo-HSCT) in best first response. However, only 30-50% of patients are eligible for this procedure. Major advances in the molecular characterization of T-PLL during recent years have stimulated translational studies on potential vulnerabilities of the T-PLL cell. We summarize here the current state of "classical" treatments and critically appraise novel (pre)clinical strategies.

RECENT FINDINGS

Alemtuzumab-induced first remissions, accomplished in ≈ 90% of patients, last at median ≈ 12 months. Series on allo-HSCT in T-PLL, although of very heterogeneous character, suggest a slight improvement in outcomes among transplanted patients within the past decade. Dual-action nucleosides such as bendamustine or cladribine show moderate clinical activity as single agents in the setting of relapsed or refractory disease. Induction of apoptosis via reactivation of p53 (e.g., by inhibitors of HDAC or MDM2) and targeting of its downstream pathways (i.e., BCL2 family antagonists, CDK inhibitors) are promising new approaches. Novel strategies also focus on inhibition of the JAK/STAT pathway with the first clinical data. Implementations of immune-checkpoint blockades or CAR-T cell therapy are at the stage of pre-clinical assessments of activity and feasibility. The recommended treatment strategy in T-PLL remains a successful induction by infusional alemtuzumab followed by a consolidating allo-HSCT in eligible patients. Nevertheless, long-term survivors after this "standard" comprise only 10-20%. The increasingly revealed molecular make-up of T-PLL and the tremendous expansion of approved targeted compounds in oncology represent a "never-before" opportunity to successfully tackle the voids in T-PLL. Approaches, e.g., those reinstating deficient cell death execution, show encouraging pre-clinical and first-in-human results in T-PLL, and urgently have to be transferred to systematic clinical testing.

Ex Vivo Analysis of Primary Tumor Specimens for Evaluation of Cancer Therapeutics

The use of ex vivo drug sensitivity testing to predict drug activity in individual patients has been actively explored for almost 50 years without delivering a generally useful predictive capability. However, extended failure should not be an indicator of futility. This is especially true in cancer research where ultimate success is often preceded by less successful attempts. For example, both immune- and genetic-based targeted therapies for cancer underwent numerous failed attempts before biological understanding, improved targets, and optimized drug development matured to facilitate an arsenal of transformational drugs. Similarly, the concept of directly assessing drug sensitivity of primary tumor biopsies-and the use of this information to help direct therapeutic approaches-has a long history with a definitive learning curve. In this review, we will survey the history of ex vivo testing as well as the current state of the art for this field. We will present an update on methodologies and approaches, describe the use of these technologies to test cutting-edge drug classes, and describe an increasingly nuanced understanding of tumor types and models for which this strategy is most likely to succeed. We will consider the relative strengths and weaknesses of predicting drug activity across the broad biological context of cancer patients and tumor types. This will include an analysis of the potential for ex vivo drug sensitivity testing to accurately predict drug activity within each of the biological hallmarks of cancer pathogenesis.

IGLV3-21R110 identifies an aggressive biological subtype of chronic lymphocytic leukemia with intermediate epigenetics

B-cell receptor (BCR) signaling is crucial for chronic lymphocytic leukemia (CLL) biology. IGLV3-21-expressing B cells may acquire a single point mutation (R110) that triggers autonomous BCR signaling, conferring aggressive behavior. Epigenetic studies have defined 3 CLL subtypes based on methylation signatures reminiscent of naïve-like (n-CLL), intermediate (i-CLL), and memory-like (m-CLL) B cells with different biological features. i-CLL carries a borderline IGHV mutational load and significantly higher use of IGHV3-21/IGLV3-21. To determine the clinical and biological features of IGLV3-21R110 CLL and its relationship to these epigenetic subtypes, we characterized the immunoglobulin gene of 584 CLL cases using whole-genome/exome and RNA sequencing. IGLV3-21R110 was detected in 6.5% of cases: 30 (38%) of 79 i-CLLs, 5 (1.7%) of 291 m-CLLs, and 1 (0.5%) of 189 n-CLLs. All stereotype subset 2 cases carried IGLV3-21R110, whereas 62% of IGLV3-21R110 i-CLL cases had nonstereotyped BCR immunoglobulins. IGLV3-21R110 i-CLL had a significantly higher number of SF3B1 and ATM mutations and total number of driver alterations. However, the R110 mutation was the sole alteration in 1 i-CLL and was accompanied only by del(13q) in 3. Although IGHV mutational status varied, IGLV3-21R110 i-CLL transcriptomically resembled n-CLL/unmutated IGHV CLL with a specific signature including WNT5A/B overexpression. In contrast, i-CLL lacking IGLV3-21R110 mirrored m-CLL/mutated IGHV. Patients with IGLV3-21R110 i-CLL had a short time to first treatment and overall survival similar to those of n-CLL/unmutated IGHV patients, whereas patients with non-IGLV3-21R110 i-CLL had a good prognosis similar to that of patients with m-CLL/mutated IGHV. IGLV3-21R110 defines a CLL subgroup with specific biological features and an unfavorable prognosis independent of IGHV mutational status and epigenetic subtype.

IL-15 superagonist N-803 improves IFNγ production and killing of leukemia and ovarian cancer cells by CD34+ progenitor-derived NK cells

Allogeneic natural killer (NK) cell transfer is a potential immunotherapy to eliminate and control cancer. A promising source are CD34 + hematopoietic progenitor cells (HPCs), since large numbers of cytotoxic NK cells can be generated. Effective boosting of NK cell function can be achieved by interleukin (IL)-15. However, its in vivo half-life is short and potent trans-presentation by IL-15 receptor α (IL-15Rα) is absent. Therefore, ImmunityBio developed IL-15 superagonist N-803, which combines IL-15 with an activating mutation, an IL-15Rα sushi domain for trans-presentation, and IgG1-Fc for increased half-life. Here, we investigated whether and how N-803 improves HPC-NK cell functionality in leukemia and ovarian cancer (OC) models in vitro and in vivo in OC-bearing immunodeficient mice. We used flow cytometry-based assays, enzyme-linked immunosorbent assay, microscopy-based serial killing assays, and bioluminescence imaging, for in vitro and in vivo experiments. N-803 increased HPC-NK cell proliferation and interferon (IFN)γ production. On leukemia cells, co-culture with HPC-NK cells and N-803 increased ICAM-1 expression. Furthermore, N-803 improved HPC-NK cell-mediated (serial) leukemia killing. Treating OC spheroids with HPC-NK cells and N-803 increased IFNγ-induced CXCL10 secretion, and target killing after prolonged exposure. In immunodeficient mice bearing human OC, N-803 supported HPC-NK cell persistence in combination with total human immunoglobulins to prevent Fc-mediated HPC-NK cell depletion. Moreover, this combination treatment decreased tumor growth. In conclusion,  N-803 is a promising IL-15-based compound that boosts HPC-NK cell expansion and functionality in vitro and in vivo. Adding N-803 to HPC-NK cell therapy could improve cancer immunotherapy.

An Autochthonous Mouse Model of Myd88- and BCL2-Driven Diffuse Large B-cell Lymphoma Reveals Actionable Molecular Vulnerabilities

Based on gene expression profiles, diffuse large B cell lymphoma (DLBCL) is sub-divided into germinal center B cell-like (GCB) and activated B cell-like (ABC) DLBCL. Two of the most common genomic aberrations in ABC-DLBCL are mutations in MYD88, as well as BCL2 copy number gains. Here, we employ immune phenotyping, RNA-Seq and whole exome sequencing to characterize a Myd88 and Bcl2-driven mouse model of ABC-DLBCL. We show that this model resembles features of human ABC-DLBCL. We further demonstrate an actionable dependence of our murine ABC-DLBCL model on BCL2. This BCL2 dependence was also detectable in human ABC-DLBCL cell lines. Moreover, human ABC-DLBCLs displayed increased PD-L1 expression, compared to GCB-DLBCL. In vivo experiments in our ABC-DLBCL model showed that combined venetoclax and RMP1-14 significantly increased the overall survival of lymphoma bearing animals, indicating that this combination may be a viable option for selected human ABC-DLBCL cases harboring MYD88 and BCL2 aberrations.

The proliferative history shapes the DNA methylome of B-cell tumors and predicts clinical outcome

We report a systematic analysis of the DNA methylation variability in 1,595 samples of normal cell subpopulations and 14 tumor subtypes spanning the entire human B-cell lineage. Differential methylation among tumor entities relates to differences in cellular origin and to de novo epigenetic alterations, which allowed us to build an accurate machine learning-based diagnostic algorithm. We identify extensive patient-specific methylation variability in silenced chromatin associated with the proliferative history of normal and neoplastic B cells. Mitotic activity generally leaves both hyper- and hypomethylation imprints, but some B-cell neoplasms preferentially gain or lose DNA methylation. Subsequently, we construct a DNA methylation-based mitotic clock called epiCMIT, whose lapse magnitude represents a strong independent prognostic variable in B-cell tumors and is associated with particular driver genetic alterations. Our findings reveal DNA methylation as a holistic tracer of B-cell tumor developmental history, with implications in the differential diagnosis and prediction of clinical outcome.

Predicting likelihood of in vivo chemotherapy response in canine lymphoma using ex vivo drug sensitivity and immunophenotyping data in a machine learning model

We report a precision medicine platform that evaluates the probability of chemotherapy drug efficacy for canine lymphoma by combining ex vivo chemosensitivity and immunophenotyping assays with computational modelling. We isolated live cancer cells from fresh fine needle aspirates of affected lymph nodes and collected post‐treatment clinical responses in 261 canine lymphoma patients scheduled to receive at least 1 of 5 common chemotherapy agents (doxorubicin, vincristine, cyclophosphamide, lomustine and rabacfosadine). We used flow cytometry analysis for immunophenotyping and ex vivo chemosensitivity testing. For each drug, 70% of treated patients were randomly selected to train a random forest model to predict the probability of positive Veterinary Cooperative Oncology Group (VCOG) clinical response based on input variables including antigen expression profiles and treatment sensitivity readouts for each patient's cancer cells. The remaining 30% of patients were used to test model performance. Most models showed a test set ROC‐AUC > 0.65, and all models had overall ROC‐AUC > 0.95. Predicted response scores significantly distinguished (P < .001) positive responses from negative responses in B‐cell and T‐cell disease and newly diagnosed and relapsed patients. Patient groups with predicted response scores >50% showed a statistically significant reduction (log‐rank P < .05) in time to complete response when compared to the groups with scores <50%. The computational models developed in this study enabled the conversion of ex vivo cell‐based chemosensitivity assay results into a predicted probability of in vivo therapeutic efficacy, which may help improve treatment outcomes of individual canine lymphoma patients by providing predictive estimates of positive treatment response.

Anti-leukemic effect of CDK9 inhibition in T-cell prolymphocytic leukemia

T-cell prolymphocytic leukemia (T-PLL) is an aggressive malignancy characterized by chemotherapy resistance and a median survival of less than 2 years. Here, we investigated the pharmacological effects of the novel highly specific cyclin-dependent kinase 9 (CDK9) inhibitor LDC526 and its clinically used derivate atuveciclib employing primary T-PLL cells in an ex vivo drug sensitivity testing platform. Importantly, all T-PLL samples were sensitive to CDK9 inhibition at submicromolar concentrations, while conventional cytotoxic drugs were found to be largely ineffective. At the cellular level LDC526 inhibited the phosphorylation at serine 2 of the RNA polymerase II C-terminal domain resulting in decreased de novo RNA transcription. LDC526 induced apoptotic leukemic cell death through down-regulating MYC and MCL1 both at the mRNA and protein level. Microarray-based transcriptomic profiling revealed that genes down-modulated in response to CDK9 inhibition were enriched for MYC and JAK-STAT targets. By contrast, CDK9 inhibition increased the expression of the tumor suppressor FBXW7, which may contribute to decreased MYC and MCL1 protein levels. Finally, the combination of atuvecliclib and the BCL2 inhibitor venetoclax exhibited synergistic anti-leukemic activity, providing the rationale for a novel targeted-agent-based treatment of T-PLL.

B cell signalling pathways-New targets for precision medicine in chronic lymphocytic leukaemia

The B cell receptor (BCR) is a master regulator of B cells, controlling cellular processes such as proliferation, migration and survival. Cell signalling downstream of the BCR is aberrantly activated in the B cell malignancy chronic lymphocytic leukaemia (CLL), supporting the pathophysiology of the disease. This insight has led to development and approval of small molecule inhibitors that target components of the BCR pathway. These advances have greatly improved the management of CLL, but the disease remains incurable. This may partly be explained by the inter-patient heterogeneity of the disease, also when it comes to treatment responses. Precision medicine is therefore required to optimize treatment and move towards a cure. Here, we discuss how the introduction of BCR signalling inhibitors has facilitated the development of functional in vitro assays to guide clinical treatment decisions on use of the same therapeutic agents in individual patients. The cellular responses to these agents can be analysed in high-throughput assays such as dynamic BH3 profiling, phospho flow experiments and drug sensitivity screens to identify predictive biomarkers. This progress exemplifies the positive synergy between basal and translational research needed to optimize patient care.

Multi-kernel linear mixed model with adaptive lasso for prediction analysis on high-dimensional multi-omics data

MOTIVATION

The use of human genome discoveries and other established factors to build an accurate risk prediction model is an essential step toward precision medicine. While multi-layer high-dimensional omics data provide unprecedented data resources for prediction studies, their corresponding analytical methods are much less developed.

RESULTS

We present a multi-kernel penalized linear mixed model with adaptive lasso (MKpLMM), a predictive modeling framework that extends the standard linear mixed models widely used in genomic risk prediction, for multi-omics data analysis. MKpLMM can capture not only the predictive effects from each layer of omics data but also their interactions via using multiple kernel functions. It adopts a data-driven approach to select predictive regions as well as predictive layers of omics data, and achieves robust selection performance. Through extensive simulation studies, the analyses of PET-imaging outcomes from the Alzheimer's Disease Neuroimaging Initiative study, and the analyses of 64 drug responses, we demonstrate that MKpLMM consistently outperforms competing methods in phenotype prediction.

AVAILABILITY AND IMPLEMENTATION

The R-package is available at https://github.com/YaluWen/OmicPred.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Phenotypic profiling with a living biobank of primary rhabdomyosarcoma unravels disease heterogeneity and AKT sensitivity

Cancer therapy is currently shifting from broadly used cytotoxic drugs to patient-specific precision therapies. Druggable driver oncogenes, identified by molecular analyses, are present in only a subset of patients. Functional profiling of primary tumor cells could circumvent these limitations, but suitable platforms are unavailable for most cancer entities. Here, we describe an in vitro drug profiling platform for rhabdomyosarcoma (RMS), using a living biobank composed of twenty RMS patient-derived xenografts (PDX) for high-throughput drug testing. Optimized in vitro conditions preserve phenotypic and molecular characteristics of primary PDX cells and are compatible with propagation of cells directly isolated from patient tumors. Besides a heterogeneous spectrum of responses of largely patient-specific vulnerabilities, profiling with a large drug library reveals a strong sensitivity towards AKT inhibitors in a subgroup of RMS. Overall, our study highlights the feasibility of in vitro drug profiling of primary RMS for patient-specific treatment selection in a co-clinical setting.

Patient-specific precision medicine approaches are important for future cancer therapies. Here, the authors show that functional drug profiling with Rhabdomyosarcoma cells isolated from PDX and primary patient tumors uncovers patient-specific vulnerabilities.

COCOA: coordinate covariation analysis of epigenetic heterogeneity

A key challenge in epigenetics is to determine the biological significance of epigenetic variation among individuals. We present Coordinate Covariation Analysis (COCOA), a computational framework that uses covariation of epigenetic signals across individuals and a database of region sets to annotate epigenetic heterogeneity. COCOA is the first such tool for DNA methylation data and can also analyze any epigenetic signal with genomic coordinates. We demonstrate COCOA’s utility by analyzing DNA methylation, ATAC-seq, and multi-omic data in supervised and unsupervised analyses, showing that COCOA provides new understanding of inter-sample epigenetic variation. COCOA is available on Bioconductor (http://bioconductor.org/packages/COCOA).

Targeting chronic lymphocytic leukemia with N-methylated thrombospondin-1-derived peptides overcomes drug resistance

Chronic lymphocytic leukemia (CLL), the most common adulthood leukemia in Western countries, is a very heterogeneous disease characterized by a peripheral accumulation of abnormal CD5+ B lymphocytes in the immune system. Despite new therapeutic developments, there remains an unmet medical need for CLL. Here, we demonstrate that the use of N-methylated thrombospondin-1 (TSP-1)-derived peptides is an efficient way to kill the malignant CLL cells, including those from high-risk individuals with poor clinical prognosis, del11q, del17p, 2p gain, or complex karyotype. PKT16, our hit N-methylated peptide, triggers the elimination of the leukemic cells, sparing the nontumor cells, including the hematopoietic precursors, and reduces the in vivo tumor burden of a CLL-xenograft mice model. A complementary analysis underscores the improved cytotoxic efficiency of PKT16 compared with the previously described TSP-1-derived probes, such as PKHB1. PKT16 elicits an original caspase-independent programmed necrotic mode of cell death, different from necroptosis or ferroptosis, implicating an intracellular Ca2+ deregulation that provokes mitochondrial damage, cell cycle arrest, and the specific death of the malignant CLL cells. The activation of the Gαi proteins and the subsequent drop of cyclic adenosine monophosphate levels and protein kinase A activity regulate this cytotoxic cascade. Remarkably, PKT16 induces the molecular hallmarks of immunogenic cell death, as defined by the calreticulin plasma membrane exposure and the release of adenosine triphosphate and high-mobility group box 1 protein from the dying CLL cells. Thus, PKT16 appears to be able to stimulate an anticancer in vivo immune response. Collectively, our results pave the way toward the development of an efficient strategy against CLL.

Miniaturized Drug Sensitivity and Resistance Test on Patient-Derived Cells Using Droplet-Microarray

Testing the sensitivity of patient-derived tumor cells ex vivo can potentially help determining the appropriate treatment for each patient and spot the development of resistance to a given therapy. The number of cells obtainable from a biopsy is, however, often insufficient for performing ex vivo tests in conventional microtiter plates. Here, we introduce a novel Droplet-Microarray platform based on a hydrophilic-superhydrophobic patterned surface that enables screenings using only 100 cells and 30 picomoles of a drug per individual nanoliter-sized droplet. We demonstrate that the dose-response of as few as 100 primary patient-derived chronic lymphocytic leukemia (CLL) cells to anticancer compounds on the Droplet-Microarray platform resembles the dose-response obtained in 384-well plates requiring 20,000 tumor cells per experiment. The extremely miniaturized Droplet-Microarray platform thus carries great potential for ex vivo drug sensitivity and resistance tests on patient-derived tumor cells and potentially for implementing such tests in medical practice of precision medicine.

SurvivalMeth: a web server to investigate the effect of DNA methylation-related functional elements on prognosis

Aberrant DNA methylation is a fundamental characterization of epigenetics for carcinogenesis. Abnormality of DNA methylation-related functional elements (DMFEs) may lead to dysfunction of regulatory genes in the progression of cancers, contributing to prognosis of many cancers. There is an urgent need to construct a tool to comprehensively assess the impact of DMFEs on prognosis. Therefore, we developed SurvivalMeth (http://bio-bigdata.hrbmu.edu.cn/survivalmeth) to explore the prognosis-related DMFEs, which documented many kinds of DMFEs, including 309,465 CpG island-related elements, 104,748 transcript-related elements, 77,634 repeat elements, as well as cell-type specific 1,689,653 super enhancers (SE) and 1,304,902 CTCF binding regions for analysis. SurvivalMeth is a convenient tool which collected DNA methylation profiles of 36 cancers and allowed users to query their genes of interest in different datasets for prognosis. Furthermore, SurvivalMeth not only integrated different combinations, including single DMFE, multiple DMFEs, SEs and clinical data, to perform survival analysis on preupload data but also allowed for uploading customized DNA methylation profile of DMFEs from various diseases to analyze. SurvivalMeth provided a comprehensive resource and automated analysis for prognostic DMFEs, including DMFE methylation level, correlation analysis, clinical analysis, differential analysis, DMFE annotation, survival-related detailed result and visualization of survival analysis. In summary, we believe that SurvivalMeth will facilitate prognostic research of DMFEs in diverse cancers.

High-throughput dynamic BH3 profiling may quickly and accurately predict effective therapies in solid tumors

Despite decades of effort, the sensitivity of patient tumors to individual drugs is often not predictable on the basis of molecular markers alone. Therefore, unbiased, high-throughput approaches to match patient tumors to effective drugs, without requiring a priori molecular hypotheses, are critically needed. Here, we improved upon a method that we previously reported and developed called high-throughput dynamic BH3 profiling (HT-DBP). HT-DBP is a microscopy-based, single-cell resolution assay that enables chemical screens of hundreds to thousands of candidate drugs on freshly isolated tumor cells. The method identifies chemical inducers of mitochondrial apoptotic signaling, a mechanism of cell death. HT-DBP requires only 24 hours of ex vivo culture, which enables a more immediate study of fresh primary tumor cells and minimizes adaptive changes that occur with prolonged ex vivo culture. Effective compounds identified by HT-DBP induced tumor regression in genetically engineered and patient-derived xenograft (PDX) models of breast cancer. We additionally found that chemical vulnerabilities changed as cancer cells expanded ex vivo. Furthermore, using PDX models of colon cancer and resected tumors from colon cancer patients, our data demonstrated that HT-DBP could be used to generate personalized pharmacotypes. Thus, HT-DBP appears to be an ex vivo functional method with sufficient scale to simultaneously function as a companion diagnostic, therapeutic personalization, and discovery tool.

Dissecting intratumour heterogeneity of nodal B-cell lymphomas at the transcriptional, genetic and drug-response levels

Tumour heterogeneity encompasses both the malignant cells and their microenvironment. While heterogeneity between individual patients is known to affect the efficacy of cancer therapy, most personalized treatment approaches do not account for intratumour heterogeneity. We addressed this issue by studying the heterogeneity of nodal B-cell lymphomas by single-cell RNA-sequencing and transcriptome-informed flow cytometry. We identified transcriptionally distinct malignant subpopulations and compared their drug-response and genomic profiles. Malignant subpopulations from the same patient responded strikingly differently to anti-cancer drugs ex vivo, which recapitulated subpopulation-specific drug sensitivity during in vivo treatment. Infiltrating T cells represented the majority of non-malignant cells, whose gene-expression signatures were similar across all donors, whereas the frequencies of T-cell subsets varied significantly between the donors. Our data provide insights into the heterogeneity of nodal B-cell lymphomas and highlight the relevance of intratumour heterogeneity for personalized cancer therapy.

Chromosome Abnormalities: New Insights into Their Clinical Significance in Cancer

Chromosomal abnormalities, consisting of numerical and structural chromosome abnormalities, are a common characteristic of cancer. Numerical chromosome abnormalities, mainly including aneuploidy and chromosome instability, are caused by chromosome segregation errors in mitosis, whereas structural chromosome abnormalities are a consequence of DNA damage and comprise focal/arm-level chromosome gain or loss. Recent advances have started to unveil the mechanisms by which chromosomal abnormalities can facilitate tumorigenesis and change the cellular fitness and the expression or function of RNAs and proteins. Accumulating evidence suggests that chromosome abnormalities represent a genomic signature that is linked to cancer prognosis and reaction to chemotherapy and immunotherapy. In this review, we discuss the most recent findings on the role of chromosome abnormalities in tumorigenesis and cancer progression, with a particular emphasis on how aneuploidy and chromosome instability influence cancer therapy and prognosis. We also highlight the distribution and clinical application of the structural chromosome abnormalities in various cancer types. A better understanding of the role of chromosome abnormalities will be beneficial to the development of precision oncology and suggest future directions for the field.

Exploring dimension-reduced embeddings with Sleepwalk

Dimension-reduction methods, such as t-SNE or UMAP, are widely used when exploring high-dimensional data describing many entities, for example, RNA-seq data for many single cells. However, dimension reduction is commonly prone to introducing artifacts, and we hence need means to see where a dimension-reduced embedding is a faithful representation of the local neighborhood and where it is not. We present Sleepwalk, a simple but powerful tool that allows the user to interactively explore an embedding, using color to depict original or any other distances from all points to the cell under the mouse cursor. We show how this approach not only highlights distortions but also reveals otherwise hidden characteristics of the data, and how Sleepwalk's comparative modes help integrate multisample data and understand differences between embedding and preprocessing methods. Sleepwalk is a versatile and intuitive tool that unlocks the full power of dimension reduction and will be of value not only in single-cell RNA-seq but also in any other area with matrix-shaped big data.

Survey of ex vivo drug combination effects in chronic lymphocytic leukemia reveals synergistic drug effects and genetic dependencies

Drug combinations that target critical pathways are a mainstay of cancer care. To improve current approaches to combination treatment of chronic lymphocytic leukemia (CLL) and gain insights into the underlying biology, we studied the effect of 352 drug combination pairs in multiple concentrations by analysing ex vivo drug response of 52 primary CLL samples, which were characterized by "omics" profiling. Known synergistic interactions were confirmed for B-cell receptor (BCR) inhibitors with Bcl-2 inhibitors and with chemotherapeutic drugs, suggesting that this approach can identify clinically useful combinations. Moreover, we uncovered synergistic interactions between BCR inhibitors and afatinib, which we attribute to BCR activation by afatinib through BLK upstream of BTK and PI3K. Combinations of multiple inhibitors of BCR components (e.g., BTK, PI3K, SYK) had effects similar to the single agents. While PI3K and BTK inhibitors produced overall similar effects in combinations with other drugs, we uncovered a larger response heterogeneity of combinations including PI3K inhibitors, predominantly in CLL with mutated IGHV, which we attribute to the target's position within the BCR-signaling pathway. Taken together, our study shows that drug combination effects can be effectively queried in primary cancer cells, which could aid discovery, triage and clinical development of drug combinations.

Methylome-based cell-of-origin modeling (Methyl-COOM) identifies aberrant expression of immune regulatory molecules in CLL

Background

In cancer, normal epigenetic patterns are disturbed and contribute to gene expression changes, disease onset, and progression. The cancer epigenome is composed of the epigenetic patterns present in the tumor-initiating cell at the time of transformation, and the tumor-specific epigenetic alterations that are acquired during tumor initiation and progression. The precise dissection of these two components of the tumor epigenome will facilitate a better understanding of the biological mechanisms underlying malignant transformation. Chronic lymphocytic leukemia (CLL) originates from differentiating B cells, which undergo extensive epigenetic programming. This poses the challenge to precisely determine the epigenomic ground state of the cell-of-origin in order to identify CLL-specific epigenetic aberrations.

Methods

We developed a linear regression model, methylome-based cell-of-origin modeling (Methyl-COOM), to map the cell-of-origin for individual CLL patients based on the continuum of epigenomic changes during normal B cell differentiation.

Results

Methyl-COOM accurately maps the cell-of-origin of CLL and identifies CLL-specific aberrant DNA methylation events that are not confounded by physiologic epigenetic B cell programming. Furthermore, Methyl-COOM unmasks abnormal action of transcription factors, altered super-enhancer activities, and aberrant transcript expression in CLL. Among the aberrantly regulated transcripts were many genes that have previously been implicated in T cell biology. Flow cytometry analysis of these markers confirmed their aberrant expression on malignant B cells at the protein level.

Conclusions

Methyl-COOM analysis of CLL identified disease-specific aberrant gene regulation. The aberrantly expressed genes identified in this study might play a role in immune-evasion in CLL and might serve as novel targets for immunotherapy approaches. In summary, we propose a novel framework for in silico modeling of reference DNA methylomes and for the identification of cancer-specific epigenetic changes, a concept that can be broadly applied to other human malignancies.

Electronic supplementary material

Supplementary information accompanies this paper at 10.1186/s13073-020-00724-7.

Microfluidics as an Enabling Technology for Personalized Cancer Therapy

Tailoring patient-specific treatments for cancer is necessary in order to achieve optimal results but requires new diagnostic approaches at affordable prices. Microfluidics has immense potential to provide solutions for this, as it enables the processing of samples that are not available in large quantities (e.g., cells from patient biopsies), is cost efficient, provides a high level of automation, and allows the set-up of complex models for cancer studies. In this review, individual solutions in the fields of genetics, circulating tumor cell monitoring, biomarker analysis, phenotypic drug sensitivity tests, and systems providing controlled environments for disease modeling are discussed. An overview on how these early stage achievements can be combined or developed further is showcased, and the required translational steps before microfluidics becomes a routine tool for clinical applications are critically discussed.

Multi-omics Data Integration, Interpretation, and Its Application

To study complex biological processes holistically, it is imperative to take an integrative approach that combines multi-omics data to highlight the interrelationships of the involved biomolecules and their functions. With the advent of high-throughput techniques and availability of multi-omics data generated from a large set of samples, several promising tools and methods have been developed for data integration and interpretation. In this review, we collected the tools and methods that adopt integrative approach to analyze multiple omics data and summarized their ability to address applications such as disease subtyping, biomarker prediction, and deriving insights into the data. We provide the methodology, use-cases, and limitations of these tools; brief account of multi-omics data repositories and visualization portals; and challenges associated with multi-omics data integration.

Multiset sparse partial least squares path modeling for high dimensional omics data analysis

BACKGROUND

Recent technological developments have enabled the measurement of a plethora of biomolecular data from various omics domains, and research is ongoing on statistical methods to leverage these omics data to better model and understand biological pathways and genetic architectures of complex phenotypes. Current reviews report that the simultaneous analysis of multiple (i.e. three or more) high dimensional omics data sources is still challenging and suitable statistical methods are unavailable. Often mentioned challenges are the lack of accounting for the hierarchical structure between omics domains and the difficulty of interpretation of genomewide results. This study is motivated to address these challenges. We propose multiset sparse Partial Least Squares path modeling (msPLS), a generalized penalized form of Partial Least Squares path modeling, for the simultaneous modeling of biological pathways across multiple omics domains. msPLS simultaneously models the effect of multiple molecular markers, from multiple omics domains, on the variation of multiple phenotypic variables, while accounting for the relationships between data sources, and provides sparse results. The sparsity in the model helps to provide interpretable results from analyses of hundreds of thousands of biomolecular variables.

RESULTS

With simulation studies, we quantified the ability of msPLS to discover associated variables among high dimensional data sources. Furthermore, we analysed high dimensional omics datasets to explore biological pathways associated with Marfan syndrome and with Chronic Lymphocytic Leukaemia. Additionally, we compared the results of msPLS to the results of Multi-Omics Factor Analysis (MOFA), which is an alternative method to analyse this type of data.

CONCLUSIONS

msPLS is an multiset multivariate method for the integrative analysis of multiple high dimensional omics data sources. It accounts for the relationship between multiple high dimensional data sources while it provides interpretable results through its sparse solutions. The biomarkers found by msPLS in the omics datasets can be interpreted in terms of biological pathways associated with the pathophysiology of Marfan syndrome and of Chronic Lymphocytic Leukaemia. Additionally, msPLS outperforms MOFA in terms of variation explained in the chronic lymphocytic leukaemia dataset while it identifies the two most important clinical markers for Chronic Lymphocytic Leukaemia AVAILABILITY: http://uva.csala.me/mspls.https://github.com/acsala/2018_msPLS.

Mechanistic insights into transcription factor cooperativity and its impact on protein-phenotype interactions

Recent high-throughput transcription factor (TF) binding assays revealed that TF cooperativity is a widespread phenomenon. However, a global mechanistic and functional understanding of TF cooperativity is still lacking. To address this, here we introduce a statistical learning framework that provides structural insight into TF cooperativity and its functional consequences based on next generation sequencing data. We identify DNA shape as driver for cooperativity, with a particularly strong effect for Forkhead-Ets pairs. Follow-up experiments reveal a local shape preference at the Ets-DNA-Forkhead interface and decreased cooperativity upon loss of the interaction. Additionally, we discover many functional associations for cooperatively bound TFs. Examination of the link between FOXO1:ETV6 and lymphomas reveals that their joint expression levels improve patient clinical outcome stratification. Altogether, our results demonstrate that inter-family cooperative TF binding is driven by position-specific DNA readout mechanisms, which provides an additional regulatory layer for downstream biological functions.

Although transcription factor (TF) cooperativity is widespread, a global mechanistic understanding of the role of TF cooperativity is still lacking. Here the authors introduce a statistical learning framework that provides structural insight into TF cooperativity and its functional consequences based on next generation sequencing data and provide mechanistic insights into TF cooperativity and its impact on protein-phenotype interactions.

Optimized Xenograft Protocol for Chronic Lymphocytic Leukemia Results in High Engraftment Efficiency for All CLL Subgroups

Preclinical drug development for human chronic lymphocytic leukemia (CLL) requires robust xenograft models recapitulating the entire spectrum of the disease, including all prognostic subgroups. Current CLL xenograft models are hampered by inefficient engraftment of good prognostic CLLs, overgrowth with co-transplanted T cells, and the need for allogeneic humanization or irradiation. Therefore, we aimed to establish an effective and reproducible xenograft protocol which allows engraftment of all CLL subtypes without the need of humanization or irradiation. Unmanipulated NOD.Cg-Prkdc^scidIl2rg^tm1Sug/JicTac (NOG) mice in contrast to C.Cg-Rag2^tm1Fwa-/-Il2rg^tm1Sug/JicTac (BRG) mice allowed engraftment of all tested CLL subgroups with 100% success rate, if CLL cells were fresh, injected simultaneously intra-peritoneally and intravenously, and co-transferred with low fractions of autologous T cells (2%–4%). CLL transplanted NOG mice (24 different patients) developed CLL pseudofollicles in the spleen, which increased over 4–6 weeks, and were then limited by the expanding autologous T cells. Ibrutinib treatment studies were performed to validate our model, and recapitulated treatment responses seen in patients. In conclusion, we developed an easy-to-use CLL xenograft protocol which allows reliable engraftment for all CLL subgroups without humanization or irradiation of mice. This protocol can be widely used to study CLL biology and to explore novel drug candidates.

JAK/STAT-Activating Genomic Alterations Are a Hallmark of T-PLL

T-cell prolymphocytic leukemia (T-PLL) is a rare and poor-prognostic mature T-cell leukemia. Recent studies detected genomic aberrations affecting JAK and STAT genes in T-PLL. Due to the limited number of primary patient samples available, genomic analyses of the JAK/STAT pathway have been performed in rather small cohorts. Therefore, we conducted—via a primary-data based pipeline—a meta-analysis that re-evaluated the genomic landscape of T-PLL. It included all available data sets with sequence information on JAK or STAT gene loci in 275 T-PLL. We eliminated overlapping cases and determined a cumulative rate of 62.1% of cases with mutated JAK or STAT genes. Most frequently, JAK1 (6.3%), JAK3 (36.4%), and STAT5B (18.8%) carried somatic single-nucleotide variants (SNVs), with missense mutations in the SH2 or pseudokinase domains as most prevalent. Importantly, these lesions were predominantly subclonal. We did not detect any strong association between mutations of a JAK or STAT gene with clinical characteristics. Irrespective of the presence of gain-of-function (GOF) SNVs, basal phosphorylation of STAT5B was elevated in all analyzed T-PLL. Fittingly, a significant proportion of genes encoding for potential negative regulators of STAT5B showed genomic losses (in 71.4% of T-PLL in total, in 68.4% of T-PLL without any JAK or STAT mutations). They included DUSP4, CD45, TCPTP, SHP1, SOCS1, SOCS3, and HDAC9. Overall, considering such losses of negative regulators and the GOF mutations in JAK and STAT genes, a total of 89.8% of T-PLL revealed a genomic aberration potentially explaining enhanced STAT5B activity. In essence, we present a comprehensive meta-analysis on the highly prevalent genomic lesions that affect genes encoding JAK/STAT signaling components. This provides an overview of possible modes of activation of this pathway in a large cohort of T-PLL. In light of new advances in JAK/STAT inhibitor development, we also outline translational contexts for harnessing active JAK/STAT signaling, which has emerged as a ‘secondary’ hallmark of T-PLL.

Adaptive penalization in high-dimensional regression and classification with external covariates using variational Bayes

Penalization schemes like Lasso or ridge regression are routinely used to regress a response of interest on a high-dimensional set of potential predictors. Despite being decisive, the question of the relative strength of penalization is often glossed over and only implicitly determined by the scale of individual predictors. At the same time, additional information on the predictors is available in many applications but left unused. Here, we propose to make use of such external covariates to adapt the penalization in a data-driven manner. We present a method that differentially penalizes feature groups defined by the covariates and adapts the relative strength of penalization to the information content of each group. Using techniques from the Bayesian tool-set our procedure combines shrinkage with feature selection and provides a scalable optimization scheme. We demonstrate in simulations that the method accurately recovers the true effect sizes and sparsity patterns per feature group. Furthermore, it leads to an improved prediction performance in situations where the groups have strong differences in dynamic range. In applications to data from high-throughput biology, the method enables re-weighting the importance of feature groups from different assays. Overall, using available covariates extends the range of applications of penalized regression, improves model interpretability and can improve prediction performance.

Developmental subtypes assessed by DNA methylation-iPLEX forecast the natural history of chronic lymphocytic leukemia

Alterations in global DNA methylation patterns are a major hallmark of cancer and represent attractive biomarkers for personalized risk stratification. Chronic lymphocytic leukemia (CLL) risk stratification studies typically focus on time to first treatment (TTFT), time to progression (TTP) after treatment, and overall survival (OS). Whereas TTFT risk stratification remains similar over time, TTP and OS have changed dramatically with the introduction of targeted therapies, such as the Bruton tyrosine kinase inhibitor ibrutinib. We have shown that genome-wide DNA methylation patterns in CLL are strongly associated with phenotypic differentiation and patient outcomes. Here, we developed a novel assay, termed methylation-iPLEX (Me-iPLEX), for high-throughput quantification of targeted panels of single cytosine guanine dinucleotides from multiple independent loci. Me-iPLEX was used to classify CLL samples into 1 of 3 known epigenetic subtypes (epitypes). We examined the impact of epitype in 1286 CLL patients from 4 independent cohorts representing a comprehensive view of CLL disease course and therapies. We found that epitype significantly predicted TTFT and OS among newly diagnosed CLL patients. Additionally, epitype predicted TTP and OS with 2 common CLL therapies: chemoimmunotherapy and ibrutinib. Epitype retained significance after stratifying by biologically related biomarkers, immunoglobulin heavy chain mutational status, and ZAP70 expression, as well as other common prognostic markers. Furthermore, among several biological traits enriched between epitypes, we found highly biased immunogenetic features, including IGLV3-21 usage in the poorly characterized intermediate-programmed CLL epitype. In summary, Me-iPLEX is an elegant method to assess epigenetic signatures, including robust classification of CLL epitypes that independently stratify patient risk at diagnosis and time of treatment.

HIF-1α is over-expressed in leukemic cells from TP53-disrupted patients and is a promising therapeutic target in chronic lymphocytic leukemia

In chronic lymphocytic leukemia (CLL), the hypoxia-inducible factor 1 (HIF-1) regulates the response of tumor cells to hypoxia and their protective interactions with the leukemic microenvironment. In this study, we demonstrate that CLL cells from TP53-disrupted (TP53^dis) patients have constitutively higher expression levels of the α-subunit of HIF-1 (HIF-1α) and increased HIF-1 transcriptional activity compared to the wild-type counterpart. In the TP53^dis subset, HIF-1α upregulation is due to reduced expression of the HIF-1α ubiquitin ligase von Hippel-Lindau protein (pVHL). Hypoxia and stromal cells further enhance HIF-1α accumulation, independently of TP53 status. Hypoxia acts through the downmodulation of pVHL and the activation of the PI3K/AKT and RAS/ERK1-2 pathways, whereas stromal cells induce an increased activity of the RAS/ERK1-2, RHOA/RHOA kinase and PI3K/AKT pathways, without affecting pVHL expression. Interestingly, we observed that higher levels of HIF-1A mRNA correlate with a lower susceptibility of leukemic cells to spontaneous apoptosis, and associate with the fludarabine resistance that mainly characterizes TP53^dis tumor cells. The HIF-1α inhibitor BAY87-2243 exerts cytotoxic effects toward leukemic cells, regardless of the TP53 status, and has anti-tumor activity in Em-TCL1 mice. BAY87-2243 also overcomes the constitutive fludarabine resistance of TP53^dis leukemic cells and elicits a strongly synergistic cytotoxic effect in combination with ibrutinib, thus providing preclinical evidence to stimulate further investigation into use as a potential new drug in CLL.

Novel CHK1 inhibitor MU380 exhibits significant single-agent activity in TP53-mutated chronic lymphocytic leukemia cells

Introduction of small-molecule inhibitors of B-cell receptor signaling and BCL2 protein significantly improves therapeutic options in chronic lymphocytic leukemia. However, some patients suffer from adverse effects mandating treatment discontinuation, and cases with TP53 defects more frequently experience early progression of the disease. Development of alternative therapeutic approaches is, therefore, of critical importance. Here we report details of the anti-chronic lymphocytic leukemia single-agent activity of MU380, our recently identified potent, selective, and metabolically robust inhibitor of checkpoint kinase 1. We also describe a newly developed enantioselective synthesis of MU380, which allows preparation of gram quantities of the substance. Checkpoint kinase 1 is a master regulator of replication operating primarily in intra-S and G₂/M cell cycle checkpoints. Initially tested in leukemia and lymphoma cell lines, MU380 significantly potentiated efficacy of gemcitabine, a clinically used inducer of replication stress. Moreover, MU380 manifested substantial single-agent activity in both TP53-wild type and TP53-mutated leukemia and lymphoma cell lines. In chronic lymphocytic leukemia-derived cell lines MEC-1, MEC-2 (both TP53-mut), and OSU-CLL (TP53-wt) the inhibitor impaired cell cycle progression and induced apoptosis. In primary clinical samples, MU380 used as a single-agent noticeably reduced the viability of unstimulated chronic lymphocytic leukemia cells as well as those induced to proliferate by anti-CD40/IL-4 stimuli. In both cases, effects were comparable in samples harboring p53 pathway dysfunction (TP53 mutations or ATM mutations) and TP53-wt/ATM-wt cells. Lastly, MU380 also exhibited significant in vivo activity in a xenotransplant mouse model (immunodeficient strain NOD-scid IL2Rγ^null) where it efficiently suppressed growth of subcutaneous tumors generated from MEC-1 cells.

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.

AKT/mTORC2 Inhibition Activates FOXO1 Function in CLL Cells Reducing B-Cell Receptor-Mediated Survival

PURPOSE

To determine whether inhibition of mTOR kinase-mediated signaling represents a valid therapeutic approach for chronic lymphocytic leukemia (CLL).

EXPERIMENTAL DESIGN

Stratification of mTOR activity was carried out in patients with primary CLL samples and an aggressive CLL-like mouse model. The potency of dual mTOR inhibitor AZD8055 to induce apoptosis in primary CLL cells was assessed in the presence/absence of B-cell receptor (BCR) ligation. Furthermore, we addressed the molecular and functional impact of dual mTOR inhibition in combination with BTK inhibitor ibrutinib.

RESULTS

Differential regulation of basal mTORC1 activity was observed in poor prognostic CLL samples, with elevated p4EBP1T37/46 and decreased p70S6 kinase activity, suggesting that dual mTORC1/2 inhibitors may exhibit improved response in poor prognostic CLL compared with rapalogs. AZD8055 treatment of primary CLL cells significantly reduced CLL survival in vitro compared with rapamycin, preferentially targeting poor prognostic subsets and overcoming BCR-mediated survival advantages. Furthermore, AZD8055, and clinical analog AZD2014, significantly reduced CLL tumor load in mice. AKT substrate FOXO1, while overexpressed in CLL cells of poor prognostic patients in LN biopsies, peripheral CLL cells, and mouse-derived CLL-like cells, appeared to be inactive. AZD8055 treatment partially reversed FOXO1 inactivation downstream of BCR crosslinking, significantly inhibiting FOXO1T24 phosphorylation in an mTORC2-AKT-dependent manner, to promote FOXO1 nuclear localization, activity, and FOXO1-mediated gene regulation. FOXO1 activity was further significantly enhanced on combining AZD8055 with ibrutinib.

CONCLUSIONS

Our studies demonstrate that dual mTOR inhibitors show promise as future CLL therapies, particularly in combination with ibrutinib.

Mutations in the RAS-BRAF-MAPK-ERK pathway define a specific subgroup of patients with adverse clinical features and provide new therapeutic options in chronic lymphocytic leukemia

Mutations in genes of the RAS-BRAF-MAPK-ERK pathway have not been fully explored in patients with chronic lymphocytic leukemia. We, therefore, analyzed the clinical and biological characteristics of chronic lymphocytic leukemia patients with mutations in this pathway and investigated the in vitro response of primary cells to BRAF and ERK inhibitors. Putative damaging mutations were found in 25 of 452 patients (5.5%). Among these, BRAF was mutated in nine patients (2.0%), genes upstream of BRAF (KITLG, KIT, PTPN11, GNB1, KRAS and NRAS) were mutated in 12 patients (2.6%), and genes downstream of BRAF (MAPK2K1, MAPK2K2, and MAPK1) were mutated in five patients (1.1%). The most frequent mutations were missense, subclonal and mutually exclusive. Patients with these mutations more frequently had increased lactate dehydrogenase levels, high expression of ZAP-70, CD49d, CD38, trisomy 12 and unmutated immunoglobulin heavy-chain variable region genes and had a worse 5-year time to first treatment (hazard ratio 1.8, P=0.025). Gene expression analysis showed upregulation of genes of the MAPK pathway in the group carrying RAS-BRAF-MAPK-ERK pathway mutations. The BRAF inhibitors vemurafenib and dabrafenib were not able to inhibit phosphorylation of ERK, the downstream effector of the pathway, in primary cells. In contrast, ulixertinib, a pan-ERK inhibitor, decreased phospho-ERK levels. In conclusion, although larger series of patients are needed to corroborate these findings, our results suggest that the RAS-BRAF-MAPK-ERK pathway is one of the core cellular processes affected by novel mutations in chronic lymphocytic leukemia, is associated with adverse clinical features and could be pharmacologically inhibited.