Natural disease course modeling of achondroplasia to evaluate the efficacy of recifercept in the absence of a placebo control arm in phase II study.

While randomized, placebo-controlled, double-blinded clinical studies are the gold standard for evaluating the efficacy of investigational drugs, the use of placebo in children with achondroplasia should be limited because it provides no clinical benefit while exhausting study participants' treatment window. Recifercept is an investigational drug for treating children with achondroplasia aged 2-10 years. An alternative efficacy evaluation method, instead of a placebo control arm, was employed in the phase II study. Prior to participating in the phase II study, participants completed a natural history (NH) study. Based on the NH data, a multi-variate linear mixed effects natural disease course model of three anthropometric end points (standing height, sitting height, and arm span) was developed. The model was validated using published growth charts of children with achondroplasia. Subsequently, the model was used to simulate the natural growth trajectories (without any treatment) of the three anthropometric end points for the individuals enrolled in the phase II study. To quantify the efficacy of recifercept, the simulations were compared with the observations post-recifercept treatment in the phase II study. For all the tested doses of recifercept, at the individual level, the observations were comparable to the simulations at 6 and 12 months post-recifercept treatment, suggesting no treatment effect. The results contributed to the decision of terminating recifercept clinical development. This work delivers a framework that could eliminate the need for placebo in clinical trials yet provide sufficient evidence for evaluating the efficacy of an investigational drug.

Sources of inter-individual variability leading to significant changes in anti-PD-1 and anti-PD-L1 efficacy identified in mouse tumor models using a QSP framework.

While anti-PD-1 and anti-PD-L1 [anti-PD-(L)1] monotherapies are effective treatments for many types of cancer, high variability in patient responses is observed in clinical trials. Understanding the sources of response variability can help prospectively identify potential responsive patient populations. Preclinical data may offer insights to this point and, in combination with modeling, may be predictive of sources of variability and their impact on efficacy. Herein, a quantitative systems pharmacology (QSP) model of anti-PD-(L)1 was developed to account for the known pharmacokinetic properties of anti-PD-(L)1 antibodies, their impact on CD8+ T cell activation and influx into the tumor microenvironment, and subsequent anti-tumor effects in CT26 tumor syngeneic mouse model. The QSP model was sufficient to describe the variability inherent in the anti-tumor responses post anti-PD-(L)1 treatments. Local sensitivity analysis identified tumor cell proliferation rate, PD-1 expression on CD8+ T cells, PD-L1 expression on tumor cells, and the binding affinity of PD-1:PD-L1 as strong influencers of tumor growth. It also suggested that treatment-mediated tumor growth inhibition is sensitive to T cell properties including the CD8+ T cell proliferation half-life, CD8+ T cell half-life, cytotoxic T-lymphocyte (CTL)-mediated tumor cell killing rate, and maximum rate of CD8+ T cell influx into the tumor microenvironment. Each of these parameters alone could not predict anti-PD-(L)1 treatment response but they could shift an individual mouse's treatment response when perturbed. The presented preclinical QSP modeling framework provides a path to incorporate potential sources of response variability in human translation modeling of anti-PD-(L)1.

Quantitative systems pharmacology modeling provides insight into inter-mouse variability of Anti-CTLA4 response.

Clinical responses of immuno-oncology therapies are highly variable among patients. Similar response variability has been observed in syngeneic mouse models. Understanding of the variability in the mouse models may shed light on patient variability. Using a murine anti-CTLA4 antibody as a case study, we developed a quantitative systems pharmacology model to capture the molecular interactions of the antibody and relevant cellular interactions that lead to tumor cell killing. Nonlinear mixed effect modeling was incorporated to capture the inter-animal variability of tumor growth profiles in response to anti-CTLA4 treatment. The results suggested that intratumoral CD8+ T cell kinetics and tumor proliferation rate were the main drivers of the variability. In addition, simulations indicated that nonresponsive mice to anti-CTLA4 treatment could be converted to responders by increasing the number of intratumoral CD8+ T cells. The model provides a mechanistic starting point for translation of CTLA4 inhibitors from syngeneic mice to the clinic.

Antibody-mediated targeting of TNFR2 activates CD8+ T cells in mice and promotes antitumor immunity.

Tumor necrosis factor receptor 2 (TNFR2) is the alternate receptor for TNF and can mediate both pro- and anti-inflammatory activities of T cells. Although TNFR2 has been linked to enhanced suppressive activity of regulatory T cells (Tregs) in autoimmune diseases, the viability of TNFR2 as a target for cancer immunotherapy has been underappreciated. Here, we show that new murine monoclonal anti-TNFR2 antibodies yield robust antitumor activity and durable protective memory in multiple mouse cancer cell line models. The antibodies mediate potent Fc-dependent T cell costimulation and do not result in significant depletion of Tregs Corresponding human agonistic monoclonal anti-TNFR2 antibodies were identified and also had antitumor effects in humanized mouse models. Anti-TNFR2 antibodies could be developed as a novel treatment option for patients with cancer.

Distinct signaling programs control human hematopoietic stem cell survival and proliferation.

Several growth factors (GFs) that together promote quiescent human hematopoietic stem cell (HSC) expansion ex vivo have been identified; however, the molecular mechanisms by which these GFs regulate the survival, proliferation. and differentiation of human HSCs remain poorly understood. We now describe experiments in which we used mass cytometry to simultaneously measure multiple surface markers, transcription factors, active signaling intermediates, viability, and cell-cycle indicators in single CD34+ cord blood cells before and up to 2 hours after their stimulation with stem cell factor, Fms-like tyrosine kinase 3 ligand, interleukin-3, interleukin-6, and granulocyte colony-stimulating factor (5 GFs) either alone or combined. Cells with a CD34+CD38-CD45RA-CD90+CD49f+ (CD49f+) phenotype (∼10% HSCs with >6-month repopulating activity in immunodeficient mice) displayed rapid increases in activated STAT1/3/5, extracellular signal-regulated kinase 1/2, AKT, CREB, and S6 by 1 or more of these GFs, and ß-catenin only when the 5 GFs were combined. Certain minority subsets within the CD49f+ compartment were poorly GF-responsive and, among the more GF-responsive subsets of CD49f+ cells, different signaling intermediates correlated with the levels of the myeloid- and lymphoid-associated transcription factors measured. Phenotypically similar, but CD90-CD49f- cells (MPPs) contained lower baseline levels of multiple signaling intermediates than the CD90+CD49f+ cells, but showed similar response amplitudes to the same GFs. Importantly, we found activation or inhibition of AKT and ß-catenin directly altered immediate CD49f+ cell survival and proliferation. These findings identify rapid signaling events that 5 GFs elicit directly in the most primitive human hematopoietic cell types to promote their survival and proliferation.

Distinguishing autocrine and paracrine signals in hematopoietic stem cell culture using a biofunctional microcavity platform.

Homeostasis of hematopoietic stem cells (HSC) in the mammalian bone marrow stem cell niche is regulated by signals of the local microenvironment. Besides juxtacrine, endocrine and metabolic cues, paracrine and autocrine signals are involved in controlling quiescence, proliferation and differentiation of HSC with strong implications on expansion and differentiation ex vivo as well as in vivo transplantation. Towards this aim, a cell culture analysis on a polymer microcavity carrier platform was combined with a partial least square analysis of a mechanistic model of cell proliferation. We could demonstrate the discrimination of specific autocrine and paracrine signals from soluble factors as stimulating and inhibitory effectors in hematopoietic stem and progenitor cell culture. From that we hypothesize autocrine signals to be predominantly involved in maintaining the quiescent state of HSC in single-cell niches and advocate our analysis platform as an unprecedented option for untangling convoluted signaling mechanisms in complex cell systems being it of juxtacrine, paracrine or autocrine origin.

Signaling Networks among Stem Cell Precursors, Transit-Amplifying Progenitors, and their Niche in Developing Hair Follicles.

The hair follicle (HF) is a complex miniorgan that serves as an ideal model system to study stem cell (SC) interactions with the niche during growth and regeneration. Dermal papilla (DP) cells are required for SC activation during the adult hair cycle, but signal exchange between niche and SC precursors/transit-amplifying cell (TAC) progenitors that regulates HF morphogenetic growth is largely unknown. Here we use six transgenic reporters to isolate 14 major skin and HF cell populations. With next-generation RNA sequencing, we characterize their transcriptomes and define unique molecular signatures. SC precursors, TACs, and the DP niche express a plethora of ligands and receptors. Signaling interaction network analysis reveals a bird's-eye view of pathways implicated in epithelial-mesenchymal interactions. Using a systematic tissue-wide approach, this work provides a comprehensive platform, linked to an interactive online database, to identify and further explore the SC/TAC/niche crosstalk regulating HF growth.

Bringing Blood Stem Cell Phenotype, Genotype, and Function Closer Together.

Imperfect purity, subtypes, and retrospective functional assays compromise efforts to define the molecular identity of hematopoietic stem cells (HSCs). In this issue of Cell Stem Cell, Wilson et al. (2015) use a single-cell-based bioinformatics-experimental strategy to extract a consensus molecular signature from heterogeneous HSC pools. Their data and strategy provide a powerful resource for stem cell characterization.

Intercellular network structure and regulatory motifs in the human hematopoietic system.

The hematopoietic system is a distributed tissue that consists of functionally distinct cell types continuously produced through hematopoietic stem cell (HSC) differentiation. Combining genomic and phenotypic data with high-content experiments, we have built a directional cell-cell communication network between 12 cell types isolated from human umbilical cord blood. Network structure analysis revealed that ligand production is cell type dependent, whereas ligand binding is promiscuous. Consequently, additional control strategies such as cell frequency modulation and compartmentalization were needed to achieve specificity in HSC fate regulation. Incorporating the in vitro effects (quiescence, self-renewal, proliferation, or differentiation) of 27 HSC binding ligands into the topology of the cell-cell communication network allowed coding of cell type-dependent feedback regulation of HSC fate. Pathway enrichment analysis identified intracellular regulatory motifs enriched in these cell type- and ligand-coupled responses. This study uncovers cellular mechanisms of hematopoietic cell feedback in HSC fate regulation, provides insight into the design principles of the human hematopoietic system, and serves as a foundation for the analysis of intercellular regulation in multicellular systems.

Blood stem cell fate regulation by Delta-1-mediated rewiring of IL-6 paracrine signaling.

Increasing evidence supports the importance of cell extrinsic regulation in stem cell fate control. Hematopoietic stem cells (HSC) are responsive to local signals from their niche and to systemic feedback from progenitors and mature cells. The Notch ligand Delta-1 (DL1), a key component of the stem cell niche, regulates human hematopoietic lineage development in a dose-dependent manner and has been used clinically for primitive progenitor expansion. How DL1 acts to regulate HSC fate and whether these actions are related to its lineage skewing effects are poorly understood. Here we demonstrate that, although DL1 activates signal transducer and activator of transcription 3 signaling similarly to the gp130-activating cytokine interleukin-6 (IL-6), it has opposite effects on myeloid cell production. Mechanistically, these different outcomes are attributable to a DL1-mediated reduction in membrane (m)-bound IL-6 receptor (R) expression, converting progenitor cells from being directly IL-6 responsive to requiring both IL-6 and soluble (s) IL-6R for activation. Concomitant reduction of both mIL-6R (by DL1 supplementation) and sIL-6R (using dynamically fed cultures) reduced myeloid cell production and led to enhanced outputs of human HSCs. This work describes a new mode of cytokine action in which DL1 changes cytokine receptor distributions on hematopoietic cells, altering feedback networks and their impact on stem cell fate.

Rapid expansion of human hematopoietic stem cells by automated control of inhibitory feedback signaling.

Clinical hematopoietic transplantation outcomes are strongly correlated with the numbers of cells infused. Anticipated novel therapeutic implementations of hematopoietic stem cells (HSCs) and their derivatives further increase interest in strategies to expand HSCs ex vivo. A fundamental limitation in all HSC-driven culture systems is the rapid generation of differentiating cells and their secreted inhibitory feedback signals. Herein we describe an integrated computational and experimental strategy that enables a tunable reduction in the global levels and impact of paracrine signaling factors in an automated closed-system process by employing a controlled fed-batch media dilution approach. Application of this system to human cord blood cells yielded a rapid (12-day) 11-fold increase of HSCs with self-renewing, multilineage repopulating ability. These results highlight the marked improvements that control of feedback signaling can offer primary stem cell culture and demonstrate a clinically relevant rapid and relatively low culture volume strategy for ex vivo HSC expansion.

PERT: a method for expression deconvolution of human blood samples from varied microenvironmental and developmental conditions.

The cellular composition of heterogeneous samples can be predicted using an expression deconvolution algorithm to decompose their gene expression profiles based on pre-defined, reference gene expression profiles of the constituent populations in these samples. However, the expression profiles of the actual constituent populations are often perturbed from those of the reference profiles due to gene expression changes in cells associated with microenvironmental or developmental effects. Existing deconvolution algorithms do not account for these changes and give incorrect results when benchmarked against those measured by well-established flow cytometry, even after batch correction was applied. We introduce PERT, a new probabilistic expression deconvolution method that detects and accounts for a shared, multiplicative perturbation in the reference profiles when performing expression deconvolution. We applied PERT and three other state-of-the-art expression deconvolution methods to predict cell frequencies within heterogeneous human blood samples that were collected under several conditions (uncultured mono-nucleated and lineage-depleted cells, and culture-derived lineage-depleted cells). Only PERT's predicted proportions of the constituent populations matched those assigned by flow cytometry. Genes associated with cell cycle processes were highly enriched among those with the largest predicted expression changes between the cultured and uncultured conditions. We anticipate that PERT will be widely applicable to expression deconvolution strategies that use profiles from reference populations that vary from the corresponding constituent populations in cellular state but not cellular phenotypic identity.

Effects of volume conductor and source configuration on simulated magnetogastrograms.

Recordings of the magnetic fields (MFs) arising from gastric electrical activity (GEA) have been shown to be able to distinguish between normal and certain abnormal GEA. Mathematical models provide a powerful tool for revealing the relationship between the underlying GEA and the resultant magnetogastrograms (MGGs). However, it remains uncertain the relative contributions that different volume conductor and dipole source models have on the resultant MFs. In this study, four volume conductor models (free space, sphere, half space and an anatomically realistic torso) and two dipole source configurations (containing 320 moving dipole sources and a single equivalent moving dipole source) were used to simulate the external MFs. The effects of different volume conductor models and dipole source configurations on the MF simulations were examined. The half space model provided the best approximation of the MFs produced by the torso model in the direction normal to the coronal plane. This was despite the fact that the half space model does not produce secondary sources, which have been shown to contribute up to 50% of the total MFs when an anatomically realistic torso model was used. We conclude that a realistic representation of the volume conductor and a detailed dipole source model are likely to be necessary when using a model-based approach for interpreting MGGs.

Falling-edge, variable threshold (FEVT) method for the automated detection of gastric slow wave events in high-resolution serosal electrode recordings.

High resolution (HR) multi-electrode mapping is increasingly being used to evaluate gastrointestinal slow wave behaviors. To create the HR activation time (AT) maps from gastric serosal electrode recordings that quantify slow wave propagation, it is first necessary to identify the AT of each individual slow wave event. Identifying these ATs has been a time consuming task, because there has previously been no reliable automated detection method. We have developed an automated AT detection method termed falling-edge, variable threshold (FEVT) detection. It computes a detection signal transform to accentuate the high 'energy' content of the falling edges in the serosal recording, and uses a running median estimator of the noise to set the time-varying detection threshold. The FEVT method was optimized, validated, and compared to other potential algorithms using in vivo HR recordings from a porcine model. FEVT properly detects ATs in a wide range of waveforms, making its performance substantially superior to the other methods, especially for low signal-to-noise ratio (SNR) recordings. The algorithm offered a substantial time savings (>100 times) over manual-marking whilst achieving a highly satisfactory sensitivity (0.92) and positive-prediction value (0.89).

Automated detection of gastric slow wave events and estimation of propagation velocity vector fields from serosal high-resolution mapping.

High-resolution (HR; multi-electrode) recordings have led to detailed spatiotemporal descriptions of gastric slow wave activity. The large amount of data conveyed by the HR recordings demands an automated way of extracting the key measures such as activation times. In this study, a derivative-based method of identifying slow wave events was proposed. The raw signal was filtered using a second order Butterworth filter (low-pass; 10 Hz). The signal in each channel was differentiated and a threshold was taken as the 4.5x of the average of the negative first derivatives. An active event was defined where the first derivatives of the signal were more negative than the threshold. The accuracy of the method was validated against manually marked times, with a positive predictive value of 0.71. The detected activation times were interpolated using a second-order polynomial, the coefficients of which were evaluated using a previously developed least-square fitting method. The velocity fields were calculated, showing detailed spatiotemporal profile of slow wave propagation. The average of slow wave propagation velocity was 5.86 +/- 0.07 mms(-1).

Volume conductor effects on simulated magnetogastrograms.

We simulated the magnetic field due to gastric electrical activity (GEA) using a temporally and spatially moving dipole source. The contributions of the volume conductor to the total magnetic field were examined. The volume conductor was represented using three simplified models (free-space, spherical and half-space) and an anatomically realistic torso model. We compared the patterns and the directions of the resultant magnetic fields generated using these volume conductor models. We concluded that all the simplified models produced significantly different magnetic fields when compared to the anatomically realistic model. Therefore, an anatomically realistic model is necessary for any modeling studies to accurately calculate the magnetic fields from GEA.