Mathematical modeling of cardio-oncology: Modeling the systemic effects of cancer therapeutics on the cardiovascular system.

Cardiotoxicity is a common side-effect of many cancer therapeutics; however, to-date there has been very little push to understand the mechanisms underlying this group of pathologies. This has led to the emergence of cardio-oncology, a field of medicine focused on understanding the effects of cancer and its treatment on the human heart. Here, we describe how mechanistic modeling approaches have been applied to study open questions in the cardiovascular system and how these approaches are being increasingly applied to advance knowledge of the underlying effects of cancer treatments on the human heart. A variety of mechanistic, mathematical modeling techniques have been applied to explore the link between common cancer treatments, such as chemotherapy, radiation, targeted therapy, and immunotherapy, and cardiotoxicity, nevertheless there is limited coverage in the different types of cardiac dysfunction that may be associated with these treatments. Moreover, cardiac modeling has a rich heritage of mathematical modeling and is well suited for the further development of novel approaches for understanding the cardiotoxicities associated with cancer therapeutics. There are many opportunities to combine mechanistic, bottom-up approaches with data-driven, top-down approaches to improve personalized, precision oncology to better understand, and ultimately mitigate, cardiac dysfunction in cancer patients.

Modeling collective cell behavior in cancer: Perspectives from an interdisciplinary conversation.

Collective cell behavior contributes to all stages of cancer progression. Understanding how collective behavior emerges through cell-cell interactions and decision-making will advance our understanding of cancer biology and provide new therapeutic approaches. Here, we summarize an interdisciplinary discussion on multicellular behavior in cancer, draw lessons from other scientific disciplines, and identify future directions.

Progenitor Hierarchy of Chronic Myelomonocytic Leukemia Identifies Inflammatory Monocytic-Biased Trajectory Linked to Worse Outcomes.

Myeloblast expansion is a hallmark of disease progression and comprises CD34+ hematopoietic stem and progenitor cells (HSPC). How this compartment evolves during disease progression in chronic myeloid neoplasms is unknown. Using single-cell RNA sequencing and high-parameter flow cytometry, we show that chronic myelomonocytic leukemia (CMML) CD34+ HSPC can be classified into three differentiation trajectories: monocytic, megakaryocyte-erythroid progenitor (MEP), and normal-like. Hallmarks of monocytic-biased trajectory were enrichment of CD120b+ inflammatory granulocyte-macrophage progenitor (GMP)-like cells, activated cytokine receptor signaling, phenotypic hematopoietic stem cell (HSC) depletion, and adverse outcomes. Cytokine receptor diversity was generally an adverse feature and elevated in CD120b+ GMPs. Hypomethylating agents decreased monocytic-biased cells in CMML patients. Given the enrichment of RAS pathway mutations in monocytic-biased cells, NRAS-competitive transplants and LPS-treated xenograft models recapitulated monocytic-biased CMML, suggesting that hematopoietic stress precipitates the monocytic-biased state. Deconvolution of HSPC compartments in other myeloid neoplasms and identifying therapeutic strategies to mitigate the monocytic-biased differentiation trajectory should be explored. SIGNIFICANCE: Our findings establish that multiple differentiation states underlie CMML disease progression. These states are negatively augmented by inflammation and positively affected by hypomethylating agents. Furthermore, we identify HSC depletion and expansion of GMP-like cells with increased cytokine receptor diversity as a feature of myeloblast expansion in inflammatory chronic myeloid neoplasms. This article is highlighted in the In This Issue feature, p. 476.

Quantification of T- and B-cell Immune Receptor Distribution Diversity Characterizes Immune Cell Infiltration and Lymphocyte Heterogeneity in Clear Cell Renal Cell Carcinoma.

Immune-modulating systemic therapies are often used to treat advanced cancer such as metastatic clear cell renal cell carcinoma (ccRCC). Used alone, sequence-based biomarkers neither accurately capture patient dynamics nor the tumor immune microenvironment. To better understand the tumor ecology of this immune microenvironment, we quantified tumor infiltration across three distinct ccRCC patient tumor cohorts using complementarity determining region-3 (CDR3) sequence recovery counts in tumor-infiltrating lymphocytes and a generalized diversity index (GDI) for CDR3 sequence distributions. GDI can be understood as a curve over a continuum of diversity scales that allows sensitive characterization of distributions to capture sample richness, evenness, and subsampling uncertainty, along with other important metrics that characterize tumor heterogeneity. For example, richness quantified the total unique sequence count, while evenness quantified similarities across sequence frequencies. Significant differences in receptor sequence diversity across gender and race revealed that patients with larger and more clinically aggressive tumors had increased richness of recovered tumoral CDR3 sequences, specifically in those from T-cell receptor alpha and B-cell immunoglobulin lambda light chain. The GDI inflection point (IP) allowed for a novel and robust measure of distribution evenness. High IP values were associated with improved overall survival, suggesting that normal-like sequence distributions lead to better outcomes. These results propose a new quantitative tool that can be used to better characterize patient-specific differences related to immune cell infiltration, and to identify unique characteristics of tumor-infiltrating lymphocyte heterogeneity in ccRCC and other malignancies. SIGNIFICANCE: Assessment of tumor-infiltrating T-cell and B-cell diversity in renal cell carcinoma advances the understanding of tumor-immune system interactions, linking tumor immune ecology with tumor burden, aggressiveness, and patient survival. See related commentary by Krishna and Hakimi, p. 764.

Geospatial Cellular Distribution of Cancer-Associated Fibroblasts Significantly Impacts Clinical Outcomes in Metastatic Clear Cell Renal Cell Carcinoma.

Cancer-associated fibroblasts (CAF) are highly prevalent cells in the tumor microenvironment in clear cell renal cell carcinoma (ccRCC). CAFs exhibit a pro-tumor effect in vitro and have been implicated in tumor cell proliferation, metastasis, and treatment resistance. Our objective is to analyze the geospatial distribution of CAFs with proliferating and apoptotic tumor cells in the ccRCC tumor microenvironment and determine associations with survival and systemic treatment. Pre-treatment primary tumor samples were collected from 96 patients with metastatic ccRCC. Three adjacent slices were obtained from 2 tumor-core regions of interest (ROI) per patient, and immunohistochemistry (IHC) staining was performed for αSMA, Ki-67, and caspase-3 to detect CAFs, proliferating cells, and apoptotic cells, respectively. H-scores and cellular density were generated for each marker. ROIs were aligned, and spatial point patterns were generated, which were then used to perform spatial analyses using a normalized Ripley's K function at a radius of 25 µm (nK(25)). The survival analyses used an optimal cut-point method, maximizing the log-rank statistic, to stratify the IHC-derived metrics into high and low groups. Multivariable Cox regression analyses were performed accounting for age and International Metastatic RCC Database Consortium (IMDC) risk category. Survival outcomes included overall survival (OS) from the date of diagnosis, OS from the date of immunotherapy initiation (OS-IT), and OS from the date of targeted therapy initiation (OS-TT). Therapy resistance was defined as progression-free survival (PFS) <6 months, and therapy response was defined as PFS >9 months. CAFs exhibited higher cellular clustering with Ki-67+ cells than with caspase-3+ cells (nK(25): Ki-67 1.19; caspase-3 1.05; p = 0.04). The median nearest neighbor (NN) distance from CAFs to Ki-67+ cells was shorter compared to caspase-3+ cells (15 µm vs. 37 µm, respectively; p < 0.001). Multivariable Cox regression analyses demonstrated that both high Ki-67+ density and H-score were associated with worse OS, OS-IT, and OS-TT. Regarding αSMA+CAFs, only a high H-score was associated with worse OS, OS-IT, and OS-TT. For caspase-3+, high H-score and density were associated with worse OS and OS-TT. Patients whose tumors were resistant to targeted therapy (TT) had higher Ki-67 density and H-scores than those who had TT responses. Overall, this ex vivo geospatial analysis of CAF distribution suggests that close proximity clustering of tumor cells and CAFs potentiates tumor cell proliferation, resulting in worse OS and resistance to TT in metastatic ccRCC.

The harsh microenvironment in early breast cancer selects for a Warburg phenotype.

The harsh microenvironment of ductal carcinoma in situ (DCIS) exerts strong evolutionary selection pressures on cancer cells. We hypothesize that the poor metabolic conditions near the ductal center foment the emergence of a Warburg Effect (WE) phenotype, wherein cells rapidly ferment glucose to lactic acid, even in normoxia. To test this hypothesis, we subjected low-glycolytic breast cancer cells to different microenvironmental selection pressures using combinations of hypoxia, acidosis, low glucose, and starvation for many months and isolated single clones for metabolic and transcriptomic profiling. The two harshest conditions selected for constitutively expressed WE phenotypes. RNA sequencing analysis of WE clones identified the transcription factor KLF4 as potential inducer of the WE phenotype. In stained DCIS samples, KLF4 expression was enriched in the area with the harshest microenvironmental conditions. We simulated in vivo DCIS phenotypic evolution using a mathematical model calibrated from the in vitro results. The WE phenotype emerged in the poor metabolic conditions near the necrotic core. We propose that harsh microenvironments within DCIS select for a WE phenotype through constitutive transcriptional reprogramming, thus conferring a survival advantage and facilitating further growth and invasion.

Spontaneous cell fusions as a mechanism of parasexual recombination in tumour cell populations.

The initiation and progression of cancers reflect the underlying process of somatic evolution, in which the diversification of heritable phenotypes provides a substrate for natural selection, resulting in the outgrowth of the most fit subpopulations. Although somatic evolution can tap into multiple sources of diversification, it is assumed to lack access to (para)sexual recombination-a key diversification mechanism throughout all strata of life. On the basis of observations of spontaneous fusions involving cancer cells, the reported genetic instability of polypoid cells and the precedence of fusion-mediated parasexual recombination in fungi, we asked whether cell fusions between genetically distinct cancer cells could produce parasexual recombination. Using differentially labelled tumour cells, we found evidence of low-frequency, spontaneous cell fusions between carcinoma cells in multiple cell line models of breast cancer both in vitro and in vivo. While some hybrids remained polyploid, many displayed partial ploidy reduction, generating diverse progeny with heterogeneous inheritance of parental alleles, indicative of partial recombination. Hybrid cells also displayed elevated levels of phenotypic plasticity, which may further amplify the impact of cell fusions on the diversification of phenotypic traits. Using mathematical modelling, we demonstrated that the observed rates of spontaneous somatic cell fusions may enable populations of tumour cells to amplify clonal heterogeneity, thus facilitating the exploration of larger areas of the adaptive landscape (relative to strictly asexual populations), which may substantially accelerate a tumour's ability to adapt to new selective pressures.

Investigating Inter- and Intrasample Diversity of Single-Cell RNA Sequencing Datasets.

Tumor heterogeneity can arise from a variety of extrinsic and intrinsic sources and drives unfavorable outcomes. With recent technological advances, single-cell RNA sequencing has become a way for researchers to easily assay tumor heterogeneity at the transcriptomic level with high resolution. However, ongoing research focuses on different ways to analyze this big data and how to compare across multiple different samples. In this chapter, we provide a practical guide to calculate inter- and intrasample diversity metrics from single-cell RNA sequencing datasets. These measures of diversity are adapted from commonly used metrics in statistics and ecology to quantify and compare sample heterogeneity at single-cell resolution.

Reassessing enzyme kinetics: Considering protease-as-substrate interactions in proteolytic networks.

Enzymes are catalysts in biochemical reactions that, by definition, increase rates of reactions without being altered or destroyed. However, when that enzyme is a protease, a subclass of enzymes that hydrolyze other proteins, and that protease is in a multiprotease system, protease-as-substrate dynamics must be included, challenging assumptions of enzyme inertness, shifting kinetic predictions of that system. Protease-on-protease inactivating hydrolysis can alter predicted protease concentrations used to determine pharmaceutical dosing strategies. Cysteine cathepsins are proteases capable of cathepsin cannibalism, where one cathepsin hydrolyzes another with substrate present, and misunderstanding of these dynamics may cause miscalculations of multiple proteases working in one proteolytic network of interactions occurring in a defined compartment. Once rates for individual protease-on-protease binding and catalysis are determined, proteolytic network dynamics can be explored using computational models of cooperative/competitive degradation by multiple proteases in one system, while simultaneously incorporating substrate cleavage. During parameter optimization, it was revealed that additional distraction reactions, where inactivated proteases become competitive inhibitors to remaining, active proteases, occurred, introducing another network reaction node. Taken together, improved predictions of substrate degradation in a multiple protease network were achieved after including reaction terms of autodigestion, inactivation, cannibalism, and distraction, altering kinetic considerations from other enzymatic systems, since enzyme can be lost to proteolytic degradation. We compiled and encoded these dynamics into an online platform (https://plattlab.shinyapps.io/catKLS/) for individual users to test hypotheses of specific perturbations to multiple cathepsins, substrates, and inhibitors, and predict shifts in proteolytic network reactions and system dynamics.

Leveraging Single-Cell RNA Sequencing Experiments to Model Intratumor Heterogeneity.

PURPOSE: Many cancers can be treated with targeted therapy. Almost inevitably, tumors develop resistance to targeted therapy, either from pre-existence or by evolving new genotypes and traits. Intratumor heterogeneity serves as a reservoir for resistance, which often occurs as a result of the selection of minor cellular subclones. On the level of gene expression, clonal heterogeneity can only be revealed using high-dimensional single-cell methods. We propose using a general diversity index (GDI) to quantify heterogeneity on multiple scales and relate it to disease evolution. MATERIALS AND METHODS: We focused on individual patient samples that were probed with single-cell RNA (scRNA) sequencing to describe heterogeneity. We developed a pipeline to analyze single-cell data via sample normalization, clustering, and mathematical interpretation using a generalized diversity measure, as well as to exemplify the utility of this platform using single-cell data. RESULTS: We focused on three sources of patient scRNA sequencing data: two healthy bone marrow (BM) donors, two patients with acute myeloid leukemia-each sampled before and after BM transplantation, four samples of presorted lineages-and six patients with lung carcinoma with multiregion sampling. While healthy/normal samples scored low in diversity overall, GDI further quantified the ways in which these samples differed. Whereas a widely used Shannon diversity index sometimes reveals fewer differences, GDI exhibits differences in the number of potential key drivers or clonal richness. Comparison of pre- and post-BM transplantation acute myeloid leukemia samples did not reveal differences in heterogeneity, although biological differences can exist. CONCLUSION: GDI can quantify cellular heterogeneity changes across a wide spectrum, even when standard measures, such as the Shannon index, do not. Our approach can be widely applied to quantify heterogeneity across samples and conditions.

Long-Term Cryopreservation and Revival of Tissue-Engineered Skeletal Muscle.

IMPACT STATEMENT: The ability to freeze, revive, and prolong the lifetime of tissue-engineered skeletal muscle without incurring any loss of function represents a significant advancement in the field of tissue engineering. Cryopreservation enables the efficient fabrication, storage, and shipment of these tissues. This in turn facilitates multidisciplinary collaboration between research groups, enabling advances in skeletal muscle regenerative medicine, organ-on-a-chip models of disease, drug testing, and soft robotics. Furthermore, the observation that freezing undifferentiated skeletal muscle enhances functional performance may motivate future studies developing stronger and more clinically relevant engineered muscle.

Computational predictions of cysteine cathepsin-mediated fibrinogen proteolysis.

Fibrin clot formation is a proteolytic cascade of events with thrombin and plasmin identified as the main proteases cleaving fibrinogen precursor, and the fibrin polymer, respectively. Other proteases may be involved directly in fibrin(ogen) cleavage, clot formation, and resolution, or in the degradation of fibrin-based scaffolds emerging as useful tools for tissue engineered constructs. Here, cysteine cathepsins are investigated for their putative ability to hydrolyze fibrinogen, since they are potent proteases, first identified in lysosomal protein degradation and known to participate in extracellular proteolysis. To further explore this, we used two independent computational technqiues, molecular docking and bioinformatics sequence analysis (PACMANS), to predict potential binding interactions and sites of hydrolysis between cathepsins K, L, and S and fibrinogen. By comparing the results from these two objective, computational methods, it was determined that cathepsins K, L, and S do bind and cleave fibrinogen α, ß, and γ chains at similar and unique sites. These differences were visualized experimentally by the unique cleaved fibrinogen banding patterns after incubation with each of the cathepsins, separately. In conclusion, human cysteine cathepsins K, L, and S are a new class of proteases that should be considered during fibrin(ogen) degradation studies both for disease processes where coagulation is a concern, and also in the implementation and design of bioengineered systems.

Investigating the Life Expectancy and Proteolytic Degradation of Engineered Skeletal Muscle Biological Machines.

A combination of techniques from 3D printing, tissue engineering and biomaterials has yielded a new class of engineered biological robots that could be reliably controlled via applied signals. These machines are powered by a muscle strip composed of differentiated skeletal myofibers in a matrix of natural proteins, including fibrin, that provide physical support and cues to the cells as an engineered basement membrane. However, maintaining consistent results becomes challenging when sustaining a living system in vitro. Skeletal muscle must be preserved in a differentiated state and the system is subject to degradation by proteolytic enzymes that can break down its mechanical integrity. Here we examine the life expectancy, breakdown, and device failure of engineered skeletal muscle bio-bots as a result of degradation by three classes of proteases: plasmin, cathepsin L, and matrix metalloproteinases (MMP-2 and MMP-9). We also demonstrate the use of gelatin zymography to determine the effects of differentiation and inhibitor concentration on protease expression. With this knowledge, we are poised to design the next generation of complex biological machines with controllable function, specific life expectancy and greater consistency. These results could also prove useful for the study of disease-specific models, treatments of myopathies, and other tissue engineering applications.

PACMANS: A bioinformatically informed algorithm to predict, design, and disrupt protease-on-protease hydrolysis.

Multiple proteases in a system hydrolyze target substrates, but recent evidence indicates that some proteases will degrade other proteases as well. Cathepsin S hydrolysis of cathepsin K is one such example. These interactions may be uni- or bi-directional and change the expected kinetics. To explore potential protease-on-protease interactions in silico, a program was developed for users to input two proteases: (1) the protease-ase that hydrolyzes (2) the substrate, protease. This program identifies putative sites on the substrate protease highly susceptible to cleavage by the protease-ase, using a sliding-window approach that scores amino acid sequences by their preference in the protease-ase active site, culled from MEROPS database. We call this PACMANS, Protease-Ase Cleavage from MEROPS ANalyzed Specificities, and test and validate this algorithm with cathepsins S and K. PACMANS cumulative likelihood scoring identified L253 and V171 as sites on cathepsin K subject to cathepsin S hydrolysis. Mutations made at these locations were tested to block hydrolysis and validate PACMANS predictions. L253A and L253V cathepsin K mutants significantly reduced cathepsin S hydrolysis, validating PACMANS unbiased identification of these sites. Interfamilial protease interactions between cathepsin S and MMP-2 or MMP-9 were tested after predictions by PACMANS, confirming its utility for these systems as well. PACMANS is unique compared to other putative site cleavage programs by allowing users to define the proteases of interest and target, and can also be employed for non-protease substrate proteins, as well as short peptide sequences.