The E3 ubiquitin ligase Herc1 modulates the response to nucleoside analogs in acute myeloid leukemia.

For several decades, induction therapy with nucleoside analogs, in particular cytarabine (Ara-C) and, to a lesser extent, fludarabine, has been the standard of care for patients diagnosed with acute myeloid leukemia (AML). Still, the anti-tumor efficacy of nucleoside analogs is often limited by intrinsic and acquired drug resistance, thereby leading to poor therapeutic response and suboptimal clinical outcomes. Here, we used genome-wide CRISPR-based pharmacogenomic screening to map the genetic factors that modulate the response to nucleoside analogs in AML and identified the E3 ubiquitin ligase Herc1 as a key modulator of Ara-C response in the MLL/AF9 (MA) and the HOXA9/MEIS1 (HM) murine AML models both in vitro and in vivo. Loss of HERC1 enhanced nucleoside analog-induced cell death in both murine and human AML cell lines by compromising cell cycle progression. In-depth proteomic analysis and subsequent validation identified deoxycytidine kinase Dck as a novel target of Herc1 in MA and HM murine cells. We observed that HERC1 is overexpressed in AML compared to other cancer types and higher HERC1 expression is associated with shorter overall survival of patients with AML in the TCGA and BEAT-AML cohorts. Collectively, this study highlights the importance of HERC1 in the response of AML cells to nucleoside analogs, thereby establishing this E3 ubiquitin ligase as a novel predictive biomarker and potential therapeutic target for the treatment of AML.

Predicting Survival in Patients with Advanced NSCLC Treated with Atezolizumab Using Pre- and on-Treatment Prognostic Biomarkers.

Existing survival prediction models rely only on baseline or tumor kinetics data and lack machine learning integration. We introduce a novel kinetics-machine learning (kML) model that integrates baseline markers, tumor kinetics, and four on-treatment simple blood markers (albumin, C-reactive protein, lactate dehydrogenase, and neutrophils). Developed for immune-checkpoint inhibition (ICI) in non-small cell lung cancer on three phase II trials (533 patients), kML was validated on the two arms of a phase III trial (ICI and chemotherapy, 377 and 354 patients). It outperformed the current state-of-the-art for individual predictions with a test set C-index of 0.790, 12-months survival accuracy of 78.7% and hazard ratio of 25.2 (95% CI: 10.4-61.3, P < 0.0001) to identify long-term survivors. Critically, kML predicted the success of the phase III trial using only 25 weeks of on-study data (predicted HR = 0.814 (0.64-0.994) vs. final study HR = 0.778 (0.65-0.931)). Modeling on-treatment blood markers combined with predictive machine learning constitutes a valuable approach to support personalized medicine and drug development. The code is publicly available at https://gitlab.inria.fr/benzekry/nlml_onco.

Bridging the gap between two-stage and joint models: The case of tumor growth inhibition and overall survival models.

Many clinical trials generate both longitudinal biomarker and time-to-event data. We might be interested in their relationship, as in the case of tumor size and overall survival in oncology drug development. Many well-established methods exist for analyzing such data either sequentially (two-stage models) or simultaneously (joint models). Two-stage modeling (2stgM) has been challenged (i) for not acknowledging that biomarkers are endogenous covariable to the survival submodel and (ii) for not propagating the uncertainty of the longitudinal biomarker submodel to the survival submodel. On the other hand, joint modeling (JM), which properly circumvents both problems, has been criticized for being time-consuming, and difficult to use in practice. In this paper, we explore a third approach, referred to as a novel two-stage modeling (N2stgM). This strategy reduces the model complexity without compromising the parameter estimate accuracy. The three approaches (2stgM, JM, and N2stgM) are formulated, and a Bayesian framework is considered for their implementation. Both real and simulated data were used to analyze the performance of such approaches. In all scenarios, our proposal estimated the parameters approximately as JM but without being computationally expensive, while 2stgM produced biased results.

Uncertainty Computation at Finite Distance in Nonlinear Mixed Effects Models-a New Method Based on Metropolis-Hastings Algorithm.

The standard errors (SE) of the maximum likelihood estimates (MLE) of the population parameter vector in nonlinear mixed effect models (NLMEM) are usually estimated using the inverse of the Fisher information matrix (FIM). However, at a finite distance, i.e. far from the asymptotic, the FIM can underestimate the SE of NLMEM parameters. Alternatively, the standard deviation of the posterior distribution, obtained in Stan via the Hamiltonian Monte Carlo algorithm, has been shown to be a proxy for the SE, since, under some regularity conditions on the prior, the limiting distributions of the MLE and of the maximum a posterior estimator in a Bayesian framework are equivalent. In this work, we develop a similar method using the Metropolis-Hastings (MH) algorithm in parallel to the stochastic approximation expectation maximisation (SAEM) algorithm, implemented in the saemix R package. We assess this method on different simulation scenarios and data from a real case study, comparing it to other SE computation methods. The simulation study shows that our method improves the results obtained with frequentist methods at finite distance. However, it performed poorly in a scenario with the high variability and correlations observed in the real case study, stressing the need for calibration.

Tumor growth and overall survival modeling to support decision making in phase Ib/II trials: A comparison of the joint and two-stage approaches.

Model-based tumor growth inhibition (TGI) metrics are increasingly used to predict overall survival (OS) data in Phase III immunotherapy clinical trials. However, there is still a lack of understanding regarding the differences between two-stage or joint modeling methods to leverage Phase I/II trial data and help early decision-making. A recent study showed that TGI metrics such as the tumor growth rate constant KG may have good operating characteristics as early endpoints. This previous study used a two-stage approach that is easy to implement and intuitive but prone to bias as it does not account for the relationship between the longitudinal and time-to-event processes. A relevant alternative is to use a joint modeling approach. In the present article, we evaluated the operating characteristics of TGI metrics using joint modeling, assuming an OS model previously developed using historical data. To that end, we used TGI and OS data from IMpower150-a study investigating atezolizumab in over 750 patients suffering from non-small cell lung cancer-to mimic randomized Phase Ib/II trials varying in terms of number of patients included (40 to 15 patients per arm) and follow-up duration (24 to 6 weeks after the last patient included). In this context, joint modeling did not outperform the two-stage approach and provided similar operating characteristics in all the investigated scenarios. Our results suggest that KG geometric mean ratio could be used to support early decision-making provided that 30 or more patients per arm are included and followed for at least 12 weeks.

Leukemia aggressiveness is driven by chromatin remodeling and expression changes of core regulators.

Molecular mechanisms driving clonal aggressiveness in leukemia are not fully understood. We tracked and analyzed two mouse MLL-rearranged leukemic clones independently evolving towards higher aggressiveness. More aggressive subclones lost their growth differential ex vivo but restored it upon secondary transplantation, suggesting molecular memory of aggressiveness. Development of aggressiveness was associated with clone-specific gradual modulation of chromatin states and expression levels across the genome, with a surprising preferential trend of reversing the earlier changes between normal and leukemic progenitors. To focus on the core aggressiveness program, we identified genes with consistent changes of expression and chromatin marks that were maintained in vivo and ex vivo in both clones. Overexpressing selected core genes (Smad1 as aggressiveness driver, Irx5 and Plag1 as suppressors) affected leukemic progenitor growth in the predicted way and had convergent downstream effects on central transcription factors and repressive epigenetic modifiers, suggesting a broader regulatory network of leukemic aggressiveness.

Nucleic acid therapeutics as differentiation agents for myeloid leukemias.

Differentiation therapy has proven to be a success story for patients with acute promyelocytic leukemia. However, the remaining subtypes of acute myeloid leukemia (AML) are treated with cytotoxic chemotherapies that have limited efficacy and a high likelihood of resistance. As differentiation arrest is a hallmark of AML, there is increased interest in developing differentiation-inducing agents to enhance disease-free survival. Here, we provide a comprehensive review of current reports and future avenues of nucleic acid therapeutics for AML, focusing on the use of targeted nucleic acid drugs to promote differentiation. Specifically, we compare and discuss the precision of small interfering RNA, small activating RNA, antisense oligonucleotides, and aptamers to modulate gene expression patterns that drive leukemic cell differentiation. We delve into preclinical and clinical studies that demonstrate the efficacy of nucleic acid-based differentiation therapies to induce leukemic cell maturation and reduce disease burden. By directly influencing the expression of key genes involved in myeloid maturation, nucleic acid therapeutics hold the potential to induce the differentiation of leukemic cells towards a more mature and less aggressive phenotype. Furthermore, we discuss the most critical challenges associated with developing nucleic acid therapeutics for myeloid malignancies. By introducing the progress in the field and identifying future opportunities, we aim to highlight the power of nucleic acid therapeutics in reshaping the landscape of myeloid leukemia treatment.

Duration of and time to response in oncology clinical trials from the perspective of the estimand framework.

Duration of response (DOR) and time to response (TTR) are typically evaluated as secondary endpoints in early-stage clinical studies in oncology when efficacy is assessed by the best overall response and presented as the overall response rate. Despite common use of DOR and TTR in particular in single-arm studies, the definition of these endpoints and the questions they are intended to answer remain unclear. Motivated by the estimand framework, we present relevant scientific questions of interest for DOR and TTR and propose corresponding estimand definitions. We elaborate on how to deal with relevant intercurrent events which should follow the same considerations as implemented for the primary response estimand. A case study in mantle cell lymphoma illustrates the implementation of relevant estimands of DOR and TTR. We close the paper with practical recommendations to implement DOR and TTR in clinical study protocols.

Estimating drug concentration-response relationships by applying causal inference methods for continuous point exposures and time-to-event outcomes.

In clinical development, it is useful to characterize the causal relationship between individual drug concentrations and clinical outcomes in large phase III trials of new therapeutic agents because it can provide insights on whether increasing the currently administered drug dose may lead to better outcomes. However, estimating causal effects of drug concentration is complicated by the fact that drug concentration is a continuous measure and it is usually influenced by patient-level prognostic characteristics such as body weight and sex. In this article, we compare two approaches to estimate causal effects of continuous point exposures on time-to-event outcomes: (a) outcome regression (OR) and (b) weighting. In particular, we make the first direct comparison of the balancing weights, inverse probability weighting and OR methods for estimating the effects of continuous exposures on time-to-event outcomes in simulations and demonstrate that these methods can exhibit markedly different behaviours that subsequently lead to a change in the conclusions. To improve weighted exposure effect estimators, we also propose a new simple-to-apply diagnostic to detect when such estimators might be subject to severe bias, and demonstrate its effectiveness in simulations. Finally, we apply these methods to an example of multiple sclerosis drug development by providing causal effect estimates of average ocrelizumab concentrations on time-to-event disability progression outcomes.

A robust and reliable methodology to perform GECI-based multi-time point neuronal calcium imaging within mixed cultures of human iPSC-derived cortical neurons.

Introduction: Human induced pluripotent stem cells (iPSCs), with their ability to generate human neural cells (astrocytes and neurons) from patients, hold great promise for understanding the pathophysiology of major neuropsychiatric diseases such as schizophrenia and bipolar disorders, which includes alterations in cerebral development. Indeed, the in vitro neurodifferentiation of iPSCs, while recapitulating certain major stages of neurodevelopment in vivo, makes it possible to obtain networks of living human neurons. The culture model presented is particularly attractive within this framework since it involves iPSC-derived neural cells, which more specifically differentiate into cortical neurons of diverse types (in particular glutamatergic and GABAergic) and astrocytes. However, these in vitro neuronal networks, which may be heterogeneous in their degree of differentiation, remain challenging to bring to an appropriate level of maturation. It is therefore necessary to develop tools capable of analyzing a large number of cells to assess this maturation process. Calcium (Ca2+) imaging, which has been extensively developed, undoubtedly offers an incredibly good approach, particularly in its versions using genetically encoded calcium indicators. However, in the context of these iPSC-derived neural cell cultures, there is a lack of studies that propose Ca2+ imaging methods that can finely characterize the evolution of neuronal maturation during the neurodifferentiation process. Methods: In this study, we propose a robust and reliable method for specifically measuring neuronal activity at two different time points of the neurodifferentiation process in such human neural cultures. To this end, we have developed a specific Ca2+ signal analysis procedure and tested a series of different AAV serotypes to obtain expression levels of GCaMP6f under the control of the neuron-specific human synapsin1 (hSyn) promoter. Results: The retro serotype has been found to be the most efficient in driving the expression of the GCaMP6f and is compatible with multi-time point neuronal Ca2+ imaging in our human iPSC-derived neural cultures. An AAV2/retro carrying GCaMP6f under the hSyn promoter (AAV2/retro-hSyn-GCaMP6f) is an efficient vector that we have identified. To establish the method, calcium measurements were carried out at two time points in the neurodifferentiation process with both hSyn and CAG promoters, the latter being known to provide high transient gene expression across various cell types. Discussion: Our results stress that this methodology involving AAV2/retro-hSyn-GCaMP6f is suitable for specifically measuring neuronal calcium activities over multiple time points and is compatible with the neurodifferentiation process in our mixed human neural cultures.

PARP-1 improves leukemia outcomes by inducing parthanatos during chemotherapy.

Previous chemotherapy research has focused almost exclusively on apoptosis. Here, a standard frontline drug combination of cytarabine and idarubicin induces distinct features of caspase-independent, poly(ADP-ribose) polymerase 1 (PARP-1)-mediated programmed cell death "parthanatos" in acute myeloid leukemia (AML) cell lines (n = 3/10 tested), peripheral blood mononuclear cells from healthy human donors (n = 10/10 tested), and primary cell samples from patients with AML (n = 18/39 tested, French-American-British subtypes M4 and M5). A 3-fold improvement in survival rates is observed in the parthanatos-positive versus -negative patient groups (hazard ratio [HR] = 0.28-0.37, p = 0.002-0.046). Manipulation of PARP-1 activity in parthanatos-competent cells reveals higher drug sensitivity in cells that have basal PARP-1 levels as compared with those subjected to PARP-1 overexpression or suppression. The same trends are observed in RNA expression databases and support the conclusion that PARP-1 can have optimal levels for favorable chemotherapeutic responses.

CRISPR-Cas9 system: a novel and promising era of genotherapy for beta-hemoglobinopathies, hematological malignancy, and hemophilia.

Gene therapy represents a significant potential to revolutionize the field of hematology with applications in correcting genetic mutations, generating cell lines and animal models, and improving the feasibility and efficacy of cancer immunotherapy. Compared to different genetic engineering tools, clustered regularly interspaced short palindromic repeats (CRISPR) CRISPR-associated protein 9 (Cas9) emerged as an effective and versatile genetic editor with the ability to precisely modify the genome. The applications of genetic engineering in various hematological disorders have shown encouraging results. Monogenic hematological disorders can conceivably be corrected with single gene modification. Through the use of CRISPR-CAS9, restoration of functional red blood cells and hemostasis factors were successfully attained in sickle cell anemia, beta-thalassemia, and hemophilia disorders. Our understanding of hemato-oncology has been advanced via CRIPSR-CAS9 technology. CRISPR-CAS9 aided to build a platform of mutated genes responsible for cell survival and proliferation in leukemia. Therapeutic application of CRISPR-CAS9 when combined with chimeric antigen receptor (CAR) T cell therapy in multiple myeloma and acute lymphoblastic leukemia was feasible with attenuation of CAR T cell therapy pitfalls. Our review outlines the latest literature on the utilization of CRISPR-Cas9 in the treatment of beta-hemoglobinopathies and hemophilia disorders. We present the strategies that were employed and the findings of preclinical and clinical trials. Also, the review will discuss gene engineering in the field of hemato-oncology as a proper tool to facilitate and overcome the drawbacks of chimeric antigen receptor T cell therapy (CAR-T).

CBFA2T3-GLIS2-dependent pediatric acute megakaryoblastic leukemia is driven by GLIS2 and sensitive to navitoclax.

Pediatric acute megakaryoblastic leukemia (AMKL) is an aggressive blood cancer associated with poor therapeutic response and high mortality. Here we describe the development of CBFA2T3-GLIS2-driven mouse models of AMKL that recapitulate the phenotypic and transcriptional signatures of the human disease. We show that an activating Ras mutation that occurs in human AMKL increases the penetrance and decreases the latency of CBF2AT3-GLIS2-driven AMKL. CBFA2T3-GLIS2 and GLIS2 modulate similar transcriptional networks. We identify the dominant oncogenic properties of GLIS2 that trigger AMKL in cooperation with oncogenic Ras. We find that both CBFA2T3-GLIS2 and GLIS2 alter the expression of a number of BH3-only proteins, causing AMKL cell sensitivity to the BCL2 inhibitor navitoclax both in vitro and in vivo, suggesting a potential therapeutic option for pediatric patients suffering from CBFA2T3-GLIS2-driven AMKL.

The bone marrow stroma in human myelodysplastic syndrome reveals alterations that regulate disease progression.

Myelodysplastic syndromes (MDSs) are a heterogenous group of diseases affecting the hematopoietic stem cell that are curable only by stem cell transplantation. Both hematopoietic cell intrinsic changes and extrinsic signals from the bone marrow (BM) niche seem to ultimately lead to MDS. Animal models of MDS indicate that alterations in specific mesenchymal progenitor subsets in the BM microenvironment can induce or select for abnormal hematopoietic cells. Here, we identify a subset of human BM mesenchymal cells marked by the expression of CD271, CD146, and CD106. This subset of human mesenchymal cells is comparable with mouse mesenchymal cells that, when perturbed, result in an MDS-like syndrome. Its transcriptional analysis identified Osteopontin (SPP1) as the most overexpressed gene. Selective depletion of Spp1 in the microenvironment of the mouse MDS model, Vav-driven Nup98-HoxD13, resulted in an accelerated progression as demonstrated by increased chimerism, higher mutant myeloid cell burden, and a more pronounced anemia when compared with that in wild-type microenvironment controls. These data indicate that molecular perturbations can occur in specific BM mesenchymal subsets of patients with MDS. However, the niche adaptations to dysplastic clones include Spp1 overexpression that can constrain disease fitness and potentially progression. Therefore, niche changes with malignant disease can also serve to protect the host.

Nonlinear multilevel joint model for individual lesion kinetics and survival to characterize intra-individual heterogeneity in patients with advanced cancer.

In advanced cancer patients, tumor burden is calculated using the sum of the longest diameters (SLD) of the target lesions, a measure that lumps all lesions together and ignores intra-patient heterogeneity. Here, we used a rich dataset of 342 metastatic bladder cancer patients treated with a novel immunotherapy agent to develop a Bayesian multilevel joint model that can quantify heterogeneity in lesion dynamics and measure their impact on survival. Using a nonlinear model of tumor growth inhibition, we estimated that dynamics differed greatly among lesions, and inter-lesion variability accounted for 21% and 28% of the total variance in tumor shrinkage and treatment effect duration, respectively. Next, we investigated the impact of individual lesion dynamics on survival. Lesions located in the liver and in the bladder had twice as much impact on the instantaneous risk of death compared to those located in the lung or the lymph nodes. Finally, we evaluated the utility of individual lesion follow-up for dynamic predictions. Consistent with results at the population level, the individual lesion model outperformed a model relying only on SLD, especially at early landmark times and in patients with liver or bladder target lesions. Our results show that an individual lesion model can characterize the heterogeneity in tumor dynamics and its impact on survival in advanced cancer patients.

Defining estimands for efficacy assessment in single arm phase 1b or phase 2 clinical trials in oncology early development.

The addendum of the ICH E9 guideline on the statistical principles for clinical trials introduced the estimand framework. The framework is designed to strengthen the dialog between different stakeholders, to introduce greater clarity in the clinical trial objectives and to provide alignment between the estimand and statistical analysis. Estimand framework related publications thus far have mainly focused on randomized clinical trials. The intention of the Early Development Estimand Nexus (EDEN), a task force of the cross-industry Oncology Estimand Working Group (www.oncoestimand.org), is to apply it to single arms Phase 1b or Phase 2 trials designed to detect a treatment-related efficacy signal, typically measured by objective response rate. Key recommendations regarding the estimand attributes include that in a single arm early clinical trial, the treatment attribute should start when the first dose is received by the participant. Focusing on the estimation of an absolute effect, the population-level summary measure should reflect only the property used for the estimation. Another major component introduced in the ICH E9 addendum is the definition of intercurrent events and the associated possible ways to handle them. Different strategies reflect different clinical questions of interest that can be answered based on the journeys an individual subject can take during a trial. We provide detailed strategy recommendations for intercurrent events typically seen in early-stage oncology. We highlight where implicit assumptions should be made transparent as whenever follow-up is suspended, a while-on-treatment strategy is implied.

Entrectinib dose confirmation in pediatric oncology patients: pharmacokinetic considerations.

PURPOSE: Entrectinib is a central nervous system-active potent inhibitor of tropomyosin receptor kinase (TRK), with anti-tumor activity against neurotrophic NTRK gene fusion-positive tumors. This study investigates the pharmacokinetics of entrectinib and its active metabolite (M5) in pediatric patients and aims to understand whether the pediatric dose of 300 mg/m2 once daily (QD) provides an exposure that is consistent with the approved adult dose (600 mg QD). METHODS: Forty-three patients aged from birth to 22 years were administered entrectinib (250-750 mg/m2 QD) orally with food in 4-week cycles. Entrectinib formulations included capsules without acidulant (F1) and capsules with acidulant (F2B and F06). RESULTS: Although there was interpatient variability with F1, entrectinib and M5 exposures increased dose dependently. Lower systemic exposures were observed in pediatric patients receiving 400 mg/m2 QD entrectinib (F1) versus adults receiving either the same dose/formulation or the recommended flat dose of 600 mg QD (~ 300 mg/m2 for a 70 kg adult) due to suboptimal F1 performance in the pediatric study. The observed pediatric exposures following 300 mg/m2 QD entrectinib (F06) were comparable to those in adults receiving 600 mg QD. CONCLUSIONS: Overall, the F1 formulation of entrectinib was associated with lower systemic exposure in pediatric patients compared with the commercial acidulant formulation (F06). Systemic exposures achieved in pediatric patients with the F06 recommended dose (300 mg/m2) were within the known efficacious range in adults, confirming the adequacy of the recommended dose regimen with the commercial formulation.

Trained immunity and epigenetic memory in long-term self-renewing hematopoietic cells.

Immunologic memory is a feature typically ascribed to the adaptive arm of the immune system. However, recent studies have demonstrated that hematopoietic stem cells (HSCs) and innate immune cells such as monocytes and macrophages can gain epigenetic signatures to enhance their response in the context of reinfection. This suggests the presence of long-term memory, a phenomenon referred to as trained immunity. Trained immunity in HSCs can occur via changes in the epigenetic landscape and enhanced chromatin accessibility in lineage-specific genes, as well as through metabolic alterations. These changes can lead to a skewing in lineage bias, particularly enhanced myelopoiesis and the generation of epigenetically modified innate immune cells that provide better protection against pathogens on secondary infection. Here, we summarize recent advancements in trained immunity and epigenetic memory formation in HSCs and self-renewing alveolar macrophages, which was the focus of the Spring 2022 International Society for Experimental Hematology (ISEH) webinar.