Quantitative Systems Pharmacology Modeling of Avadomide-Induced Neutropenia Enables Virtual Clinical Dose and Schedule Finding Studies.

Avadomide is a cereblon E3 ligase modulator and a potent antitumor and immunomodulatory agent. Avadomide trials are challenged by neutropenia as a major adverse event and a dose-limiting toxicity. Intermittent dosing schedules supported by preclinical data provide a strategy to reduce frequency and severity of neutropenia; however, the identification of optimal dosing schedules remains a clinical challenge. Quantitative systems pharmacology (QSP) modeling offers opportunities for virtual screening of efficacy and toxicity levels produced by alternative dose and schedule regimens, thereby supporting decision-making in translational drug development. We formulated a QSP model to capture the mechanism of avadomide-induced neutropenia, which involves cereblon-mediated degradation of transcription factor Ikaros, resulting in a maturation block of the neutrophil lineage. The neutropenia model was integrated with avadomide-specific pharmacokinetic and pharmacodynamic models to capture dose-dependent effects. Additionally, we generated a disease-specific virtual patient population to represent the variability in patient characteristics and response to treatment observed for a diffuse large B-cell lymphoma trial cohort. Model utility was demonstrated by simulating the avadomide effect in the virtual population for various dosing schedules and determining the incidence of high-grade neutropenia, its duration, and the probability of recovery to low-grade neutropenia.

Bone marrow microenvironments that contribute to patient outcomes in newly diagnosed multiple myeloma: A cohort study of patients in the Total Therapy clinical trials.

BACKGROUND: The tumor microenvironment (TME) is increasingly appreciated as an important determinant of cancer outcome, including in multiple myeloma (MM). However, most myeloma microenvironment studies have been based on bone marrow (BM) aspirates, which often do not fully reflect the cellular content of BM tissue itself. To address this limitation in myeloma research, we systematically characterized the whole bone marrow (WBM) microenvironment during premalignant, baseline, on treatment, and post-treatment phases. METHODS AND FINDINGS: Between 2004 and 2019, 998 BM samples were taken from 436 patients with newly diagnosed MM (NDMM) at the University of Arkansas for Medical Sciences in Little Rock, Arkansas, United States of America. These patients were 61% male and 39% female, 89% White, 8% Black, and 3% other/refused, with a mean age of 58 years. Using WBM and matched cluster of differentiation (CD)138-selected tumor gene expression to control for tumor burden, we identified a subgroup of patients with an adverse TME associated with 17 fewer months of progression-free survival (PFS) (95% confidence interval [CI] 5-29, 49-69 versus 70-82 months, χ2 p = 0.001) and 15 fewer months of overall survival (OS; 95% CI -1 to 31, 92-120 versus 113-129 months, χ2 p = 0.036). Using immunohistochemistry-validated computational tools that identify distinct cell types from bulk gene expression, we showed that the adverse outcome was correlated with elevated CD8+ T cell and reduced granulocytic cell proportions. This microenvironment develops during the progression of premalignant to malignant disease and becomes less prevalent after therapy, in which it is associated with improved outcomes. In patients with quantified International Staging System (ISS) stage and 70-gene Prognostic Risk Score (GEP-70) scores, taking the microenvironment into consideration would have identified an additional 40 out of 290 patients (14%, premutation p = 0.001) with significantly worse outcomes (PFS, 95% CI 6-36, 49-73 versus 74-90 months) who were not identified by existing clinical (ISS stage III) and tumor (GEP-70) criteria as high risk. The main limitations of this study are that it relies on computationally identified cell types and that patients were treated with thalidomide rather than current therapies. CONCLUSIONS: In this study, we observe that granulocyte signatures in the MM TME contribute to a more accurate prognosis. This implies that future researchers and clinicians treating patients should quantify TME components, in particular monocytes and granulocytes, which are often ignored in microenvironment studies.

A generalizable data-driven multicellular model of pancreatic ductal adenocarcinoma.

BACKGROUND: Mechanistic models, when combined with pertinent data, can improve our knowledge regarding important molecular and cellular mechanisms found in cancer. These models make the prediction of tissue-level response to drug treatment possible, which can lead to new therapies and improved patient outcomes. Here we present a data-driven multiscale modeling framework to study molecular interactions between cancer, stromal, and immune cells found in the tumor microenvironment. We also develop methods to use molecular data available in The Cancer Genome Atlas to generate sample-specific models of cancer. RESULTS: By combining published models of different cells relevant to pancreatic ductal adenocarcinoma (PDAC), we built an agent-based model of the multicellular pancreatic tumor microenvironment, formally describing cell type-specific molecular interactions and cytokine-mediated cell-cell communications. We used an ensemble-based modeling approach to systematically explore how variations in the tumor microenvironment affect the viability of cancer cells. The results suggest that the autocrine loop involving EGF signaling is a key interaction modulator between pancreatic cancer and stellate cells. EGF is also found to be associated with previously described subtypes of PDAC. Moreover, the model allows a systematic exploration of the effect of possible therapeutic perturbations; our simulations suggest that reducing bFGF secretion by stellate cells will have, on average, a positive impact on cancer apoptosis. CONCLUSIONS: The developed framework allows model-driven hypotheses to be generated regarding therapeutically relevant PDAC states with potential molecular and cellular drivers indicating specific intervention strategies.

ADAPTS: Automated deconvolution augmentation of profiles for tissue specific cells.

Immune cell infiltration of tumors and the tumor microenvironment can be an important component for determining patient outcomes. For example, immune and stromal cell presence inferred by deconvolving patient gene expression data may help identify high risk patients or suggest a course of treatment. One particularly powerful family of deconvolution techniques uses signature matrices of genes that uniquely identify each cell type as determined from single cell type purified gene expression data. Many methods from this family have been recently published, often including new signature matrices appropriate for a single purpose, such as investigating a specific type of tumor. The package ADAPTS helps users make the most of this expanding knowledge base by introducing a framework for cell type deconvolution. ADAPTS implements modular tools for customizing signature matrices for new tissue types by adding custom cell types or building new matrices de novo, including from single cell RNAseq data. It includes a common interface to several popular deconvolution algorithms that use a signature matrix to estimate the proportion of cell types present in heterogenous samples. ADAPTS also implements a novel method for clustering cell types into groups that are difficult to distinguish by deconvolution and then re-splitting those clusters using hierarchical deconvolution. We demonstrate that the techniques implemented in ADAPTS improve the ability to reconstruct the cell types present in a single cell RNAseq data set in a blind predictive analysis. ADAPTS is currently available for use in R on CRAN and GitHub.

FRET-based analysis of the cardiac troponin T linker region reveals the structural basis of the hypertrophic cardiomyopathy-causing Δ160E mutation.

Mutations in the cardiac thin filament (TF) have highly variable effects on the regulatory function of the cardiac sarcomere. Understanding the molecular-level dysfunction elicited by TF mutations is crucial to elucidate cardiac disease mechanisms. The hypertrophic cardiomyopathy-causing cardiac troponin T (cTnT) mutation Δ160Glu (Δ160E) is located in a putative "hinge" adjacent to an unstructured linker connecting domains TNT1 and TNT2. Currently, no high-resolution structure exists for this region, limiting significantly our ability to understand its role in myofilament activation and the molecular mechanism of mutation-induced dysfunction. Previous regulated in vitro motility data have indicated mutation-induced impairment of weak actomyosin interactions. We hypothesized that cTnT-Δ160E repositions the flexible linker, altering weak actomyosin electrostatic binding and acting as a biophysical trigger for impaired contractility and the observed remodeling. Using time-resolved FRET and an all-atom TF model, here we first defined the WT structure of the cTnT-linker region and then identified Δ160E mutation-induced positional changes. Our results suggest that the WT linker runs alongside the C terminus of tropomyosin. The Δ160E-induced structural changes moved the linker closer to the tropomyosin C terminus, an effect that was more pronounced in the presence of myosin subfragment (S1) heads, supporting previous findings. Our in silico model fully supported this result, indicating a mutation-induced decrease in linker flexibility. Our findings provide a framework for understanding basic pathogenic mechanisms that drive severe clinical hypertrophic cardiomyopathy phenotypes and for identifying structural targets for intervention that can be tested in silico and in vitro.

Clinically Divergent Mutation Effects on the Structure and Function of the Human Cardiac Tropomyosin Overlap.

The progression of genetically inherited cardiomyopathies from an altered protein structure to clinical presentation of disease is not well understood. One of the main roadblocks to mechanistic insight remains a lack of high-resolution structural information about multiprotein complexes within the cardiac sarcomere. One example is the tropomyosin (Tm) overlap region of the thin filament that is crucial for the function of the cardiac sarcomere. To address this central question, we devised coupled experimental and computational modalities to characterize the baseline function and structure of the Tm overlap, as well as the effects of mutations causing divergent patterns of ventricular remodeling on both structure and function. Because the Tm overlap contributes to the cooperativity of myofilament activation, we hypothesized that mutations that enhance the interactions between overlap proteins result in more cooperativity, and conversely, those that weaken interaction between these elements lower cooperativity. Our results suggest that the Tm overlap region is affected differentially by dilated cardiomyopathy-associated Tm D230N and hypertrophic cardiomyopathy-associated human cardiac troponin T (cTnT) R92L. The Tm D230N mutation compacts the Tm overlap region, increasing the cooperativity of the Tm filament, contributing to a dilated cardiomyopathy phenotype. The cTnT R92L mutation causes weakened interactions closer to the N-terminal end of the overlap, resulting in decreased cooperativity. These studies demonstrate that mutations with differential phenotypes exert opposite effects on the Tm-Tn overlap, and that these effects can be directly correlated to a molecular level understanding of the structure and dynamics of the component proteins.

Synergistic role of ADP and Ca(2+) in diastolic myocardial stiffness.

Diastolic dysfunction in heart failure patients is evident from stiffening of the passive properties of the ventricular wall. Increased actomyosin interactions may significantly limit diastolic capacity, however, direct evidence is absent. From experiments at the cellular and whole organ level, in humans and rats, we show that actomyosin-related force development contributes significantly to high diastolic stiffness in environments where high ADP and increased diastolic [Ca(2+) ] are present, such as the failing myocardium. Our basal study provides a mechanical mechanism which may partly underlie diastolic dysfunction. Heart failure (HF) with diastolic dysfunction has been attributed to increased myocardial stiffness that limits proper filling of the ventricle. Altered cross-bridge interaction may significantly contribute to high diastolic stiffness, but this has not been shown thus far. Cross-bridge interactions are dependent on cytosolic [Ca(2+) ] and the regeneration of ATP from ADP. Depletion of myocardial energy reserve is a hallmark of HF leading to ADP accumulation and disturbed Ca(2+) handling. Here, we investigated if ADP elevation in concert with increased diastolic [Ca(2+) ] promotes diastolic cross-bridge formation and force generation and thereby increases diastolic stiffness. ADP dose-dependently increased force production in the absence of Ca(2+) in membrane-permeabilized cardiomyocytes from human hearts. Moreover, physiological levels of ADP increased actomyosin force generation in the presence of Ca(2+) both in human and rat membrane-permeabilized cardiomyocytes. Diastolic stress measured at physiological lattice spacing and 37°C in the presence of pathological levels of ADP and diastolic [Ca(2+) ] revealed a 76 ± 1% contribution of cross-bridge interaction to total diastolic stress in rat membrane-permeabilized cardiomyocytes. Inhibition of creatine kinase (CK), which increases cytosolic ADP, in enzyme-isolated intact rat cardiomyocytes impaired diastolic re-lengthening associated with diastolic Ca(2+) overload. In isolated Langendorff-perfused rat hearts, CK inhibition increased ventricular stiffness only in the presence of diastolic [Ca(2+) ]. We propose that elevations of intracellular ADP in specific types of cardiac disease, including those where myocardial energy reserve is limited, contribute to diastolic dysfunction by recruiting cross-bridges, even at low Ca(2+) , and thereby increase myocardial stiffness.

Definition of performance metrics and methods for screening for sudden cardiac arrest risk at a tertiary care medical center.

PURPOSE: The purpose of this study is to describe the results of manual and automatic electronic medical record-based screening of patients at risk of sudden cardiac arrest (SCA) based on measurements of left ventricular ejection fraction (LVEF). METHODS: Counseling regarding SCA risk and implantable cardioverter defibrillator (ICD) therapy is underutilized in patients with reduced LVEF. We developed and implemented an electronic medical record (EMR)-based system for screening of such patients to improve care. In phase one, manual screening of electronic records and LVEF databases was initially performed by trained cardiac device nurses. In phase two, records were screened automatically by a customized program, and candidate patient records were sent to cardiac device nurses for final review and disposition. RESULTS: In phase one, 2,531 patients with LVEF ≤35% were identified over 398 days. Manual EMR review showed that 1,918 patients (76%) received appropriate counseling regarding SCA risk, received ICDs, or had disqualifying comorbidities. In phase two, 1,081 patients with LVEF ≤35% were identified after automatic screening of 44,672 echocardiograms and EMR over 251 days. Of these, 513 patients (58%) received appropriate counseling regarding SCA risk, received ICDs, or had disqualifying comorbidities. CONCLUSIONS: These data detail the utilization of consultation regarding SCA risk and ICDs in patients with reduced LVEF at a tertiary care center with ready access to arrhythmia specialists. Notification of primary providers of reduced LVEF with recommendation for consultation was not effective in improving patient care.