Making drugs from T cells: The quantitative pharmacology of engineered T cell therapeutics.

Engineered T cells have emerged as highly effective treatments for hematological cancers. Hundreds of clinical programs are underway in efforts to expand the efficacy, safety, and applications of this immuno-therapeutic modality. A primary challenge in developing these "living drugs" is the complexity of their pharmacology, as the drug product proliferates, differentiates, traffics between tissues, and evolves through interactions with patient immune systems. Using publicly available clinical data from Chimeric Antigen Receptor (CAR) T cells, we demonstrate how mathematical models can be used to quantify the relationships between product characteristics, patient physiology, pharmacokinetics and clinical outcomes. As scientists work to develop next-generation cell therapy products, mathematical models will be integral for contextualizing data and facilitating the translation of product designs to clinical strategy.

Deconvolution of clinical variance in CAR-T cell pharmacology and response.

Chimeric antigen receptor T cell (CAR-T) expansion and persistence vary widely among patients and predict both efficacy and toxicity. However, the mechanisms underlying clinical outcomes and patient variability are poorly defined. In this study, we developed a mathematical description of T cell responses wherein transitions among memory, effector and exhausted T cell states are coordinately regulated by tumor antigen engagement. The model is trained using clinical data from CAR-T products in different hematological malignancies and identifies cell-intrinsic differences in the turnover rate of memory cells and cytotoxic potency of effectors as the primary determinants of clinical response. Using a machine learning workflow, we demonstrate that product-intrinsic differences can accurately predict patient outcomes based on pre-infusion transcriptomes, and additional pharmacological variance arises from cellular interactions with patient tumors. We found that transcriptional signatures outperform T cell immunophenotyping as predictive of clinical response for two CD19-targeted CAR-T products in three indications, enabling a new phase of predictive CAR-T product development.

Virtual clinical trial simulations for a novel KRASG12C inhibitor (ASP2453) in non-small cell lung cancer.

KRAS is a small GTPase family protein that relays extracellular growth signals to cell nucleus. KRASG12C mutations lead to constitutive proliferation signaling and are prevalent across human cancers. ASP2453 is a novel, highly potent, and selective inhibitor of KRASG12C . Although preclinical data suggested impressive efficacy, it remains unclear whether ASP2453 will show more favorable clinical response compared to more advanced competitors, such as AMG 510. Here, we developed a quantitative systems pharmacology (QSP) model linking KRAS signaling to tumor growth in patients with non-small cell lung cancer. The model was parameterized using in vitro ERK1/2 phosphorylation and in vivo xenograft data for ASP2453. Publicly disclosed clinical data for AMG 510 were used to generate a virtual population, and tumor size changes in response to ASP2453 and AMG 510 were simulated. The QSP model predicted ASP2453 exhibits greater clinical response than AMG 510, supporting potential differentiation and critical thinking for clinical trials.

Reproducibility of Quantitative Systems Pharmacology Models: Current Challenges and Future Opportunities.

The provision of model code is required for publication in CPT: Pharmacometrics & Systems Pharmacology, enabling quantitative systems pharmacology (QSP) model availability. A searchable repository of published QSP models would enhance model accessibility. We assess the feasibility of establishing such a resource based on 18 QSP models published in this journal. However, because of the diversity of software platforms (nine), file formats, and functionality, such a resource is premature. We evaluated 12 of the models (those coded in R, PK-Sim/MoBi, and MATLAB) for functionality. Of the 12, only 4 were executable in that figures from the associated manuscript could be generated via a "run" script. Many researchers are aware of the challenges involved in repurposing published models. We offer some ideas to enable model sharing going forward, including annotation guidelines, standardized formats, and the inclusion of "run" scripts. If practitioners can agree to some minimum standards for the provision of model code, model reuse and extension would be accelerated.

Erratum: Clinical responses to ERK inhibition in BRAFV600E-mutant colorectal cancer predicted using a computational model.

[This corrects the article DOI: 10.1038/s41540-017-0016-1.].

Clinical responses to ERK inhibition in BRAFV600E-mutant colorectal cancer predicted using a computational model.

Approximately 10% of colorectal cancers harbor BRAFV600E mutations, which constitutively activate the MAPK signaling pathway. We sought to determine whether ERK inhibitor (GDC-0994)-containing regimens may be of clinical benefit to these patients based on data from in vitro (cell line) and in vivo (cell- and patient-derived xenograft) studies of cetuximab (EGFR), vemurafenib (BRAF), cobimetinib (MEK), and GDC-0994 (ERK) combinations. Preclinical data was used to develop a mechanism-based computational model linking cell surface receptor (EGFR) activation, the MAPK signaling pathway, and tumor growth. Clinical predictions of anti-tumor activity were enabled by the use of tumor response data from three Phase 1 clinical trials testing combinations of EGFR, BRAF, and MEK inhibitors. Simulated responses to GDC-0994 monotherapy (overall response rate = 17%) accurately predicted results from a Phase 1 clinical trial regarding the number of responding patients (2/18) and the distribution of tumor size changes ("waterfall plot"). Prospective simulations were then used to evaluate potential drug combinations and predictive biomarkers for increasing responsiveness to MEK/ERK inhibitors in these patients.

HER2+ Cancer Cell Dependence on PI3K vs. MAPK Signaling Axes Is Determined by Expression of EGFR, ERBB3 and CDKN1B.

Understanding the molecular pathways by which oncogenes drive cancerous cell growth, and how dependence on such pathways varies between tumors could be highly valuable for the design of anti-cancer treatment strategies. In this work we study how dependence upon the canonical PI3K and MAPK cascades varies across HER2+ cancers, and define biomarkers predictive of pathway dependencies. A panel of 18 HER2+ (ERBB2-amplified) cell lines representing a variety of indications was used to characterize the functional and molecular diversity within this oncogene-defined cancer. PI3K and MAPK-pathway dependencies were quantified by measuring in vitro cell growth responses to combinations of AKT (MK2206) and MEK (GSK1120212; trametinib) inhibitors, in the presence and absence of the ERBB3 ligand heregulin (NRG1). A combination of three protein measurements comprising the receptors EGFR, ERBB3 (HER3), and the cyclin-dependent kinase inhibitor p27 (CDKN1B) was found to accurately predict dependence on PI3K/AKT vs. MAPK/ERK signaling axes. Notably, this multivariate classifier outperformed the more intuitive and clinically employed metrics, such as expression of phospho-AKT and phospho-ERK, and PI3K pathway mutations (PIK3CA, PTEN, and PIK3R1). In both cell lines and primary patient samples, we observed consistent expression patterns of these biomarkers varies by cancer indication, such that ERBB3 and CDKN1B expression are relatively high in breast tumors while EGFR expression is relatively high in other indications. The predictability of the three protein biomarkers for differentiating PI3K/AKT vs. MAPK dependence in HER2+ cancers was confirmed using external datasets (Project Achilles and GDSC), again out-performing clinically used genetic markers. Measurement of this minimal set of three protein biomarkers could thus inform treatment, and predict mechanisms of drug resistance in HER2+ cancers. More generally, our results show a single oncogenic transformation can have differing effects on cell signaling and growth, contingent upon the molecular and cellular context.

Computational modeling of ERBB2-amplified breast cancer identifies combined ErbB2/3 blockade as superior to the combination of MEK and AKT inhibitors.

Crosstalk and compensatory circuits within cancer signaling networks limit the activity of most targeted therapies. For example, altered signaling in the networks activated by the ErbB family of receptors, particularly in ERBB2-amplified cancers, contributes to drug resistance. We developed a multiscale systems model of signaling networks in ERBB2-amplified breast cancer to quantitatively investigate relationships between biomarkers (markers of network activity) and combination drug efficacy. This model linked ErbB receptor family signaling to breast tumor growth through two kinase cascades: the PI3K/AKT survival pathway and the Ras/MEK/ERK growth and proliferation pathway. The model predicted molecular mechanisms of resistance to individual therapeutics. In particular, ERBB2-amplified breast cancer cells stimulated with the ErbB3 ligand heregulin were resistant to growth arrest induced by inhibitors of AKT and MEK or coapplication of two inhibitors of the receptor ErbB2 [Herceptin (trastuzumab) and Tykerb (lapatinib)]. We used model simulations to predict the response of ErbB2-positive breast cancer xenografts to combination therapies and verified these predictions in mice. Treatment with trastuzumab, lapatinib, and the ErbB3 inhibitor MM-111 was more effective in inhibiting tumor growth than the combination of AKT and MEK inhibitors and even induced tumor regression, indicating that targeting both ErbB3 and ErbB2 may be an improved therapeutic approach for ErbB2-positive breast cancer patients.

Creating and analyzing pathway and protein interaction compendia for modelling signal transduction networks.

BACKGROUND: Understanding the information-processing capabilities of signal transduction networks, how those networks are disrupted in disease, and rationally designing therapies to manipulate diseased states require systematic and accurate reconstruction of network topology. Data on networks central to human physiology, such as the inflammatory signalling networks analyzed here, are found in a multiplicity of on-line resources of pathway and interactome databases (Cancer CellMap, GeneGo, KEGG, NCI-Pathway Interactome Database (NCI-PID), PANTHER, Reactome, I2D, and STRING). We sought to determine whether these databases contain overlapping information and whether they can be used to construct high reliability prior knowledge networks for subsequent modeling of experimental data. RESULTS: We have assembled an ensemble network from multiple on-line sources representing a significant portion of all machine-readable and reconcilable human knowledge on proteins and protein interactions involved in inflammation. This ensemble network has many features expected of complex signalling networks assembled from high-throughput data: a power law distribution of both node degree and edge annotations, and topological features of a "bow tie" architecture in which diverse pathways converge on a highly conserved set of enzymatic cascades focused around PI3K/AKT, MAPK/ERK, JAK/STAT, NFκB, and apoptotic signaling. Individual pathways exhibit "fuzzy" modularity that is statistically significant but still involving a majority of "cross-talk" interactions. However, we find that the most widely used pathway databases are highly inconsistent with respect to the actual constituents and interactions in this network. Using a set of growth factor signalling networks as examples (epidermal growth factor, transforming growth factor-beta, tumor necrosis factor, and wingless), we find a multiplicity of network topologies in which receptors couple to downstream components through myriad alternate paths. Many of these paths are inconsistent with well-established mechanistic features of signalling networks, such as a requirement for a transmembrane receptor in sensing extracellular ligands. CONCLUSIONS: Wide inconsistencies among interaction databases, pathway annotations, and the numbers and identities of nodes associated with a given pathway pose a major challenge for deriving causal and mechanistic insight from network graphs. We speculate that these inconsistencies are at least partially attributable to cell, and context-specificity of cellular signal transduction, which is largely unaccounted for in available databases, but the absence of standardized vocabularies is an additional confounding factor. As a result of discrepant annotations, it is very difficult to identify biologically meaningful pathways from interactome networks a priori. However, by incorporating prior knowledge, it is possible to successively build out network complexity with high confidence from a simple linear signal transduction scaffold. Such reduced complexity networks appear suitable for use in mechanistic models while being richer and better justified than the simple linear pathways usually depicted in diagrams of signal transduction.

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.

Dynamic interaction networks in a hierarchically organized tissue.

Intercellular (between cell) communication networks maintain homeostasis and coordinate regenerative and developmental cues in multicellular organisms. Despite the importance of intercellular networks in stem cell biology, their rules, structure and molecular components are poorly understood. Herein, we describe the structure and dynamics of intercellular and intracellular networks in a stem cell derived, hierarchically organized tissue using experimental and theoretical analyses of cultured human umbilical cord blood progenitors. By integrating high-throughput molecular profiling, database and literature mining, mechanistic modeling, and cell culture experiments, we show that secreted factor-mediated intercellular communication networks regulate blood stem cell fate decisions. In particular, self-renewal is modulated by a coupled positive-negative intercellular feedback circuit composed of megakaryocyte-derived stimulatory growth factors (VEGF, PDGF, EGF, and serotonin) versus monocyte-derived inhibitory factors (CCL3, CCL4, CXCL10, TGFB2, and TNFSF9). We reconstruct a stem cell intracellular network, and identify PI3K, Raf, Akt, and PLC as functionally distinct signal integration nodes, linking extracellular, and intracellular signaling. This represents the first systematic characterization of how stem cell fate decisions are regulated non-autonomously through lineage-specific interactions with differentiated progeny.

The AC133+CD38-, but not the rhodamine-low, phenotype tracks LTC-IC and SRC function in human cord blood ex vivo expansion cultures.

Phenotypic markers associated with human hematopoietic stem cells (HSCs) were developed and validated using uncultured cells. Because phenotype and function can be dissociated during culture, better markers to prospectively track and isolate HSCs in ex vivo cultures could be instrumental in advancing HSC-based therapies. Using an expansion system previously shown to increase hematopoietic progenitors and SCID-repopulating cells (SRCs), we demonstrated that the rhodamine-low phenotype was lost, whereas AC133 expression was retained throughout culture. Furthermore, the AC133(+)CD38(-) subpopulation was significantly enriched in long-term culture-initiating cells (LTC-IC) and SRCs after culture. Preculture and postculture analysis of total nucleated cell and LTC-IC number, and limiting dilution analysis in NOD/SCID mice, showed a 43-fold expansion of the AC133(+)CD38(-) subpopulation that corresponded to a 7.3-fold and 4.4-fold expansion of LTC-ICs and SRCs in this subpopulation, respectively. Thus, AC133(+)CD38(-) is an improved marker that tracks and enriches for LTC-IC and SRC in ex vivo cultures.

Cell-cell interaction networks regulate blood stem and progenitor cell fate.

Communication networks between cells and tissues are necessary for homeostasis in multicellular organisms. Intercellular (between cell) communication networks are particularly relevant in stem cell biology, as stem cell fate decisions (self-renewal, proliferation, lineage specification) are tightly regulated based on physiological demand. We have developed a novel mathematical model of blood stem cell development incorporating cell-level kinetic parameters as functions of secreted molecule-mediated intercellular networks. By relation to quantitative cellular assays, our model is capable of predictively simulating many disparate features of both normal and malignant hematopoiesis, relating internal parameters and microenvironmental variables to measurable cell fate outcomes. Through integrated in silico and experimental analyses, we show that blood stem and progenitor cell fate is regulated by cell-cell feedback, and can be controlled non-cell autonomously by dynamically perturbing intercellular signalling. We extend this concept by demonstrating that variability in the secretion rates of the intercellular regulators is sufficient to explain heterogeneity in culture outputs, and that loss of responsiveness to cell-cell feedback signalling is both necessary and sufficient to induce leukemic transformation in silico.

The systematic production of cells for cell therapies.

Stem cells have emerged as the starting material of choice for bioprocesses to produce cells and tissues to treat degenerative, genetic, and immunological disease. Translating the biological properties and potential of stem cells into therapies will require overcoming significant cell-manufacturing and regulatory challenges. Bioprocess engineering fundamentals, including bioreactor design and process control, need to be combined with cellular systems biology principles to guide the development of next-generation technologies capable of producing cell-based products in a safe, robust, and cost-effective manner. The step-wise implementation of these bioengineering strategies will enhance cell therapy product quality and safety, expediting clinical development.

Understanding cellular networks to improve hematopoietic stem cell expansion cultures.

Efforts to develop culture technologies capable of eliciting robust human blood stem cell growth have met with limited success. Considering that adult stem cell cultures are complex systems, comprising multiple cell types with dynamically changing intracellular signalling environments and cellular compositions, this is not surprising. Typically treated as single-input single-output systems, adult stem cell cultures are better described as complex, non-linear, multiple-input multiple-output systems wherein the proliferation of subpopulations of cells leads to the formation of intercellular endogenously secreted protein interaction networks. Genomic and proteomic tools need to be applied to generate high-throughput (and ideally high-content) biological measurements of stem cell culture evolution. Datasets describing cellular interaction networks need to be integrated into predictive models of in vitro stem cell development. Ultimately, such models will serve as a starting point for the rational design of blood stem cell expansion bioprocesses utilizing dynamic system perturbations to achieve the preferential expansion of target cell populations.

Dynamic changes in cellular and microenvironmental composition can be controlled to elicit in vitro human hematopoietic stem cell expansion.

OBJECTIVE: The absence of effective strategies for the ex vivo expansion of human hematopoietic stem cells (HSCs) limits the development of many cell-based therapies. Prior attempts to stimulate HSC expansion have focused on media supplementation using cytokines and growth factors. In these cultures, cellular and microenvironmental compositions change with time. In this study, the impact of controlling these dynamic changes on HSC output is determined. MATERIALS AND METHODS: Cord blood-derived lin(-) cells were cultured for 8 days in serum-free medium supplemented with stem cell factor, Flt3 ligand, and thrombopoietin. Functional, phenotypic, and molecular (gene and protein) analyses were used to characterize dynamic changes in cellular and microenvironmental composition. The effects of these changes and the mechanism behind their effects on HSC expansion were assessed using a selection/media exchange-based global culture manipulation (GCM) technique. RESULTS: We show that the direct secretion of negative regulators by culture-generated lin(+) cells, and the indirect stimulation of cells to secrete negative regulators by culture-conditioned media, limits in vitro HSC generation. The GCM strategy was able to abrogate these effects to produce elevated numbers of LTC-ICs (14.6-fold relative to input), migrating rapid NOD/SCID repopulating cells (12.1-fold), and long-term NOD/SCID repopulating cells (5.2-fold). CONCLUSIONS: Cellular and microenvironmental changes that occur during all in vitro HSC cultures can significantly affect HSC output through the direct or indirect secretion of negative regulators. This study provides insight into the mechanisms regulating HSC fate in vitro and describes a novel methodology to regulate overall in vitro microenvironmental dynamics to enable the generation of clinically relevant numbers of HSCs.