Understanding stoichiometric constraints on growth using resource use efficiency imbalances.

Growth is a function of the net accrual of resources by an organism. Energy and elemental contents of organisms are dynamically linked through their uptake and allocation to biomass production, yet we lack a full understanding of how these dynamics regulate growth rate. Here, we develop a multivariate imbalance framework, the growth efficiency hypothesis, linking organismal resource contents to growth and metabolic use efficiencies, and demonstrate its effectiveness in predicting consumer growth rates under elemental and food quantity limitation. The relative proportions of carbon (%C), nitrogen (%N), phosphorus (%P), and adenosine triphosphate (%ATP) in consumers differed markedly across resource limitation treatments. Differences in their resource composition were linked to systematic changes in stoichiometric use efficiencies, which served to maintain relatively consistent relationships between elemental and ATP content in consumer tissues and optimize biomass production. Overall, these adjustments were quantitatively linked to growth, enabling highly accurate predictions of consumer growth rates.

Polymerase θ inhibition activates the cGAS-STING pathway and cooperates with immune checkpoint blockade in models of BRCA-deficient cancer.

Recently developed inhibitors of polymerase theta (POLθ) have demonstrated synthetic lethality in BRCA-deficient tumor models. To examine the contribution of the immune microenvironment to antitumor efficacy, we characterized the effects of POLθ inhibition in immunocompetent models of BRCA1-deficient triple-negative breast cancer (TNBC) or BRCA2-deficient pancreatic ductal adenocarcinoma (PDAC). We demonstrate that genetic POLQ depletion or pharmacological POLθ inhibition induces both innate and adaptive immune responses in these models. POLθ inhibition resulted in increased micronuclei, cGAS/STING pathway activation, type I interferon gene expression, CD8+ T cell infiltration and activation, local paracrine activation of dendritic cells and upregulation of PD-L1 expression. Depletion of CD8+ T cells compromised the efficacy of POLθ inhibition, whereas antitumor effects were augmented in combination with anti-PD-1 immunotherapy. Collectively, our findings demonstrate that POLθ inhibition induces immune responses in a cGAS/STING-dependent manner and provide a rationale for combining POLθ inhibition with immune checkpoint blockade for the treatment of HR-deficient cancers.

Practical Understanding of Cancer Model Identifiability in Clinical Applications.

Mathematical models are a core component in the foundation of cancer theory and have been developed as clinical tools in precision medicine. Modeling studies for clinical applications often assume an individual's characteristics can be represented as parameters in a model and are used to explain, predict, and optimize treatment outcomes. However, this approach relies on the identifiability of the underlying mathematical models. In this study, we build on the framework of an observing-system simulation experiment to study the identifiability of several models of cancer growth, focusing on the prognostic parameters of each model. Our results demonstrate that the frequency of data collection, the types of data, such as cancer proxy, and the accuracy of measurements all play crucial roles in determining the identifiability of the model. We also found that highly accurate data can allow for reasonably accurate estimates of some parameters, which may be the key to achieving model identifiability in practice. As more complex models required more data for identification, our results support the idea of using models with a clear mechanism that tracks disease progression in clinical settings. For such a model, the subset of model parameters associated with disease progression naturally minimizes the required data for model identifiability.

High Accuracy Indicators of Androgen Suppression Therapy Failure for Prostate Cancer-A Modeling Study.

Prostate cancer is a serious public health concern in the United States. The primary obstacle to effective long-term management for prostate cancer patients is the eventual development of treatment resistance. Due to the uniquely chaotic nature of the neoplastic genome, it is difficult to determine the evolution of tumor composition over the course of treatment. Hence, a drug is often applied continuously past the point of effectiveness, thereby losing any potential treatment combination with that drug permanently to resistance. If a clinician is aware of the timing of resistance to a particular drug, then they may have a crucial opportunity to adjust the treatment to retain the drug's usefulness in a potential treatment combination or strategy. In this study, we investigate new methods of predicting treatment failure due to treatment resistance using a novel mechanistic model built on an evolutionary interpretation of Droop cell quota theory. We analyze our proposed methods using patient PSA and androgen data from a clinical trial of intermittent treatment with androgen deprivation therapy. Our results produce two indicators of treatment failure. The first indicator, proposed from the evolutionary nature of the cancer population, is calculated using our mathematical model with a predictive accuracy of 87.3% (sensitivity: 96.1%, specificity: 65%). The second indicator, conjectured from the implication of the first indicator, is calculated directly from serum androgen and PSA data with a predictive accuracy of 88.7% (sensitivity: 90.2%, specificity: 85%). Our results demonstrate the potential and feasibility of using an evolutionary tumor dynamics model in combination with the appropriate data to aid in the adaptive management of prostate cancer.

Modeling the synergistic properties of drugs in hormonal treatment for prostate cancer.

Prostate cancer is one of the most prevalent cancers in men, with increasing incidence worldwide. This public health concern has inspired considerable effort to study various aspects of prostate cancer treatment using dynamical models, especially in clinical settings. The standard of care for metastatic prostate cancer is hormonal therapy, which reduces the production of androgen that fuels the growth of prostate tumor cells prior to treatment resistance. Existing population models often use patients' prostate-specific antigen levels as a biomarker for model validation and for finding optimal treatment schedules; however, the synergistic effects of drugs used in hormonal therapy have not been well-examined. This paper describes the first mathematical model that explicitly incorporates the synergistic effects of two drugs used to inhibit androgen production in hormonal therapy. The drugs are cyproterone acetate, representing the drug family of anti-androgens that affect luteinizing hormones, and leuprolide acetate, representing the drug family of gonadotropin-releasing hormone analogs. By fitting the model to clinical data, we show that the proposed model can capture the dynamics of serum androgen levels during intermittent hormonal therapy better than previously published models. Our results highlight the importance of considering the synergistic effects of drugs in cancer treatment, thus suggesting that the dynamics of the drugs should be taken into account in optimal treatment studies, particularly for adaptive therapy. Otherwise, an unrealistic treatment schedule may be prescribed and render the treatment less effective. Furthermore, the drug dynamics allow our model to explain the delay in the relapse of androgen the moment a patient is taken off treatment, which supports that this delay is due to the residual effects of the drugs.

Optimized RNA-targeting CRISPR/Cas13d technology outperforms shRNA in identifying functional circRNAs.

Short hairpin RNAs (shRNAs) are used to deplete circRNAs by targeting back-splicing junction (BSJ) sites. However, frequent discrepancies exist between shRNA-mediated circRNA knockdown and the corresponding biological effect, querying their robustness. By leveraging CRISPR/Cas13d tool and optimizing the strategy for designing single-guide RNAs against circRNA BSJ sites, we markedly enhance specificity of circRNA silencing. This specificity is validated in parallel screenings by shRNA and CRISPR/Cas13d libraries. Using a CRISPR/Cas13d screening library targeting > 2500 human hepatocellular carcinoma-related circRNAs, we subsequently identify a subset of sorafenib-resistant circRNAs. Thus, CRISPR/Cas13d represents an effective approach for high-throughput study of functional circRNAs.

Inhibition of TGFß1 and TGFß3 promotes hematopoiesis in Fanconi anemia.

Fanconi anemia (FA) is a chromosome instability syndrome with congenital abnormalities, cancer predisposition and bone marrow failure (BMF). Although hematopoietic stem and progenitor cell (HSPC) transplantation is the recommended therapy, new therapies are needed for FA patients without suitable donors. BMF in FA is caused, at least in part, by a hyperactive growth-suppressive transforming growth factor ß (TGFß) pathway, regulated by the TGFß1, TGFß2, and TGFß3 ligands. Accordingly, the TGFß pathway is an attractive therapeutic target for FA. While inhibition of TGFß1 and TGFß3 promotes blood cell expansion, inhibition of TGFß2 is known to suppress hematopoiesis. Here, we report the effects of AVID200, a potent TGFß1- and TGFß3-specific inhibitor, on FA hematopoiesis. AVID200 promoted the survival of murine FA HSPCs in vitro. AVID200 also promoted in vitro the survival of human HSPCs from patients with FA, with the strongest effect in patients progressing to severe aplastic anemia or myelodysplastic syndrome (MDS). Previous studies have indicated that the toxic upregulation of the nonhomologous end-joining (NHEJ) pathway accounts, at least in part, for the poor growth of FA HSPCs. AVID200 downregulated the expression of NHEJ-related genes and reduced DNA damage in primary FA HSPC in vitro and in in vivo models. Collectively, AVID200 exhibits activity in FA mouse and human preclinical models. AVID200 may therefore provide a therapeutic approach to improving BMF in FA.

Differences in Transcriptional Dynamics Between T-cells and Macrophages as Determined by a Three-State Mathematical Model.

HIV-1 viral transcription persists in patients despite antiretroviral treatment, potentially due to intermittent HIV-1 LTR activation. While several mathematical models have been explored in the context of LTR-protein interactions, in this work for the first time HIV-1 LTR model featuring repressed, intermediate, and activated LTR states is integrated with generation of long (env) and short (TAR) RNAs and proteins (Tat, Pr55, and p24) in T-cells and macrophages using both cell lines and infected primary cells. This type of extended modeling framework allows us to compare and contrast behavior of these two cell types. We demonstrate that they exhibit unique LTR dynamics, which ultimately results in differences in the magnitude of viral products generated. One of the distinctive features of this work is that it relies on experimental data in reaction rate computations. Two RNA transcription rates from the activated promoter states are fit by comparison of experimental data to model predictions. Fitting to the data also provides estimates for the degradation/exit rates for long and short viral RNA. Our experimentally generated data is in reasonable agreement for the T-cell as well macrophage population and gives strong evidence in support of using the proposed integrated modeling paradigm. Sensitivity analysis performed using Latin hypercube sampling method confirms robustness of the model with respect to small parameter perturbations. Finally, incorporation of a transcription inhibitor (F07#13) into the governing equations demonstrates how the model can be used to assess drug efficacy. Collectively, our model indicates transcriptional differences between latently HIV-1 infected T-cells and macrophages and provides a novel platform to study various transcriptional dynamics leading to latency or activation in numerous cell types and physiological conditions.

Predictability and identifiability assessment of models for prostate cancer under androgen suppression therapy.

The past two decades have seen the development of numerous mathematical models to study various aspects of prostate cancer in clinical settings. These models often contain large sets of parameters and rely on limited data sets for validation. The quantitative analysis of the dynamics of prostate cancer under treatment may be hindered by the lack of identifiability of the parameters from the available data, which limits the predictive ability of the model. Using three ordinary differential equation models as case studies, we carry out a numerical investigation of the identifiability and uncer- tainty quantification of the model parameters. In most cases, the parameters are not identifiable from time series of prostate-specific antigen, which is used as a clinical proxy for tumor progression. It may not be possible to define a finite confidence bound on an unidentifiable parameter, and the relative uncertainties in even identifiable parameters may be large in some cases. The Fisher information ma- trix may be used to determine identifiable parameter subsets for a given model. The use of biological constraints and additional types of measurements, should they become available, may reduce these uncertainties. Ensemble Kalman filtering may provide clinically useful, short-term predictions of pa- tient outcomes from imperfect models, though care must be taken when estimating "patient-specific" parameters. Our results demonstrate the importance of parameter identifiability in the validation and predictive ability of mathematical models of prostate tumor treatment. Observing-system simulation experiments, widely used in meteorology, may prove useful in the development of biomathematical models intended for future clinical application.

Dynamics and implications of models for intermittent androgen suppression therapy.

In this paper, we formulate a three cell population model of intermittent androgen suppression therapy for cancer patients to study the treatment resistance development. We compare it with other models that have different underlying cell population structure using patient prostate specific antigen (PSA) and androgen data sets. Our results show that in the absence of extensive data, a two cell population structure performs slightly better in replicating and forecasting the dynamics observed in clinical PSA data. We also observe that at least one absorbing state should be present in the cell population structure of a plausible model for it to produce treatment resistance under continuous hormonal therapy. This suggests that the heterogeneity of prostate cancer cell population can be represented by two types of cells differentiated by their level of dependence on androgen, where the two types are linked via an irreversible transformation.