Airway inflammation accelerates pulmonary exacerbations in cystic fibrosis.

Airway inflammation underlies cystic fibrosis (CF) pulmonary exacerbations. In a prospective multicenter study of randomly selected, clinically stable adolescents and adults, we assessed relationships between 24 inflammation-associated molecules and the future occurrence of CF pulmonary exacerbation using proportional hazards models. We explored relationships for potential confounding or mediation by clinical factors and assessed sensitivities to treatments including CF transmembrane regulator (CFTR) protein synthesis modulators. Results from 114 participants, including seven on ivacaftor or lumacaftor-ivacaftor, representative of the US CF population during the study period, identified 10 biomarkers associated with future exacerbations mediated by percent predicted forced expiratory volume in 1 s. The findings were not sensitive to anti-inflammatory, antibiotic, and CFTR modulator treatments. The analyses suggest that combination treatments addressing RAGE-axis inflammation, protease-mediated injury, and oxidative stress might prevent pulmonary exacerbations. Our work may apply to other airway inflammatory diseases such as bronchiectasis and the acute respiratory distress syndrome.

Mathematical Modeling of Field Cancerization through the Lens of Cancer Behavioral Ecology.

Field cancerization is a process in which a normal tissue is replaced with pre-cancerous but histologically normal tissue. This transformed field can give rise to malignancy and contribute to tumor relapse. In this paper, we create a mathematical model of field cancerization from the perspective of cancer behavioral ecology. In our model, field cancerization arises from a breakdown in signaling integrity and control, and investigate implications for acute wounding, chronic wounding, aging, and therapeutic interventions. We find that restoration of communication networks can lead to cancer regression in the context of acute injury. Conversely, long term loss of controls, such as through chronic wounding or aging, can promote oncogenesis. These results are paralleled in therapeutic interventions: those that simply target cells in cancerous states may be less effective than those that reestablish signaling integrity. Viewing cancer as a corruption of communication systems rather than as a corruption of individual cells may lead to novel approaches for understanding and treating this disease.

Biomarkers or biotargets? Using competition to lure cancer cells into evolutionary traps.

Background and Objectives: Cancer biomarkers provide information on the characteristics and extent of cancer progression and help inform clinical decision-making. However, they can also play functional roles in oncogenesis, from enabling metastases and inducing angiogenesis to promoting resistance to chemotherapy. The resulting evolution could bias estimates of cancer progression and lead to suboptimal treatment decisions. Methodology: We create an evolutionary game theoretic model of cell-cell competition among cancer cells with different levels of biomarker production. We design and simulate therapies on top of this pre-existing game and examine population and biomarker dynamics. Results: Using total biomarker as a proxy for population size generally underestimates chemotherapy efficacy and overestimates targeted therapy efficacy. If biomarker production promotes resistance and a targeted therapy against the biomarker exists, this dynamic can be used to set an evolutionary trap. After chemotherapy selects for a high biomarker-producing cancer cell population, targeted therapy could be highly effective for cancer extinction. Rather than using the most effective therapy given the cancer's current biomarker level and population size, it is more effective to 'overshoot' and utilize an evolutionary trap when the aim is extinction. Increasing cell-cell competition, as influenced by biomarker levels, can help prime and set these traps. Conclusion and Implications: Evolution of functional biomarkers amplify the limitations of using total biomarker levels as a measure of tumor size when designing therapeutic protocols. Evolutionarily enlightened therapeutic strategies may be highly effective, assuming a targeted therapy against the biomarker is available.

A minimal model of learning: quantifying the cost and benefit of learning in changing environments.

Many organisms have the ability to learn, but the costs and benefits of learning are difficult to quantify. We construct a minimal mathematical model of learning in which a forager attempts to maximize the amount of resources (food) it collects in a changing environment. Our model has two learning parameters: α, corresponding to the duration of the forager's memory, and [Formula: see text], corresponding to how much the forager explores the environment to learn more about it. We analyse the effect of different regimes of environmental change on the optimal memory and exploration parameters [Formula: see text]. By comparing the fitness outcomes from learning foragers to the outcomes from foragers following fixed strategies, we explicitly quantify the fitness benefit (or cost) of learning as a function of environmental change. We find that in many environments, the marginal benefit of learning is surprisingly small. In every environment, it is possible to implement learning in such a way that performance is as bad or worse than following a fixed strategy. In some environments, even the best implementations of our minimal model of learning perform worse than the best fixed strategy. Finally, we find that variance in resource values negatively biases foragers' estimates for those values, potentially explaining experimental results showing that animals prefer less variable resources.

Cell facilitation promotes growth and survival under drug pressure in breast cancer.

The interplay of positive and negative interactions between drug-sensitive and resistant cells influences the effectiveness of treatment in heterogeneous cancer cell populations. Here, we study interactions between estrogen receptor-positive breast cancer cell lineages that are sensitive and resistant to ribociclib-induced cyclin-dependent kinase 4 and 6 (CDK4/6) inhibition. In mono- and coculture, we find that sensitive cells grow and compete more effectively in the absence of treatment. During treatment with ribociclib, sensitive cells survive and proliferate better when grown together with resistant cells than when grown in monoculture, termed facilitation in ecology. Molecular, protein, and genomic analyses show that resistant cells increase metabolism and production of estradiol, a highly active estrogen metabolite, and increase estrogen signaling in sensitive cells to promote facilitation in coculture. Adding estradiol in monoculture provides sensitive cells with increased resistance to therapy and cancels facilitation in coculture. Under partial inhibition of estrogen signaling through low-dose endocrine therapy, estradiol supplied by resistant cells facilitates sensitive cell growth. However, a more complete blockade of estrogen signaling, through higher-dose endocrine therapy, diminished the facilitative growth of sensitive cells. Mathematical modeling quantifies the strength of competition and facilitation during CDK4/6 inhibition and predicts that blocking facilitation has the potential to control both resistant and sensitive cancer cell populations and inhibit the emergence of a refractory population during cell cycle therapy.

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.

Strategy maps: Generalised giving-up densities for optimal foraging.

Finding a common currency for benefits and hazards is a major challenge in optimal foraging theory, often requiring complex computational methods. We present a new analytic approach that builds on the Marginal Value Theorem and giving-up densities while incorporating the nonlinear effect of predation risk. We map the space of all possible environments into strategy regions, each corresponding to a discrete optimal strategy. This provides a generalised quantitative measure of the trade-off between foraging rewards and hazards. This extends a classic optimal diet choice rule-of-thumb to incorporate the hazard of waiting for better resources to appear. We compare the dynamics of optimal decision-making for three foraging life-history strategies: One in which fitness accrues instantly, and two with delays before fitness benefit is accrued. Foragers with delayed-benefit strategies are more sensitive to predation risk than resource quality, as they stand to lose more fitness from a predation event than instant-accrual foragers.

Are Staphylococcus aureus Carrier Types Evidence of Population Heterogeneity?

Asymptomatic colonization by Staphylococcus aureus is a precursor for infection, so identifying the mode and source of transmission which leads to colonization could help in targeting interventions. Longitudinal studies have shown that some people are persistently colonized for years, while others seem to carry S. aureus for weeks or less, and conventional wisdom attributes this disparity to an underlying risk factor in the persistently colonized. We analyze published data with mathematical models of acquisition and carriage to compare this hypothesis with alternatives. The null model assumed a homogeneous population and still produced highly variable colonization durations (mean = 101.7 weeks; 5th percentile, 5.2 weeks; 95th percentile, 304.7 weeks). Simulations showed that this inherent variability, combined with censoring in longitudinal cohort studies, is sufficient to produce the appearance of "persistent carriers," "intermittent carriers," and "noncarriers" in data. Our estimates for colonization duration exhibited sensitivity to the assumption that false-positive test results can occur despite being rare, but our model-based approach simultaneously estimates specificity and sensitivity along with epidemiologic parameters. Our results show it is plausible that S. aureus colonizes people indiscriminately, and improved understanding of the types of exposures which result in colonization is essential.

Uncertainty quantification for ecological models with random parameters.

There is often considerable uncertainty in parameters in ecological models. This uncertainty can be incorporated into models by treating parameters as random variables with distributions, rather than fixed quantities. Recent advances in uncertainty quantification methods, such as polynomial chaos approaches, allow for the analysis of models with random parameters. We introduce these methods with a motivating case study of sea ice algal blooms in heterogeneous environments. We compare Monte Carlo methods with polynomial chaos techniques to help understand the dynamics of an algal bloom model with random parameters. Modelling key parameters in the algal bloom model as random variables changes the timing, intensity and overall productivity of the modelled bloom. The computational efficiency of polynomial chaos methods provides a promising avenue for the broader inclusion of parametric uncertainty in ecological models, leading to improved model predictions and synthesis between models and data.

Serial single-cell genomics reveals convergent subclonal evolution of resistance as early-stage breast cancer patients progress on endocrine plus CDK4/6 therapy.

Combining cyclin-dependent kinase (CDK) inhibitors with endocrine therapy improves outcomes for metastatic estrogen receptor positive (ER+) breast cancer patients but its value in earlier stage patients is unclear. We examined evolutionary trajectories of early-stage breast cancer tumors, using single cell RNA sequencing (scRNAseq) of serial biopsies from the FELINE clinical trial (#NCT02712723) of endocrine therapy (letrozole) alone or combined with the CDK inhibitor ribociclib. Despite differences in subclonal diversity evolution across patients and treatments, common resistance phenotypes emerged. Resistant tumors treated with combination therapy showed accelerated loss of estrogen signaling with convergent up-regulation of JNK signaling through growth factor receptors. In contrast, cancer cells maintaining estrogen signaling during mono- or combination therapy showed potentiation of CDK4/6 activation and ERK upregulation through ERBB4 signaling. These results indicate that combination therapy in early-stage ER+ breast cancer leads to emergence of resistance through a shift from estrogen to alternative growth signal-mediated proliferation.

Do mechanisms matter? Comparing cancer treatment strategies across mathematical models and outcome objectives.

When eradication is impossible, cancer treatment aims to delay the emergence of resistance while minimizing cancer burden and treatment. Adaptive therapies may achieve these aims, with success based on three assumptions: resistance is costly, sensitive cells compete with resistant cells, and therapy reduces the population of sensitive cells. We use a range of mathematical models and treatment strategies to investigate the tradeoff between controlling cell populations and delaying the emergence of resistance. These models extend game theoretic and competition models with four additional components: 1) an Allee effect where cell populations grow more slowly at low population sizes, 2) healthy cells that compete with cancer cells, 3) immune cells that suppress cancer cells, and 4) resource competition for a growth factor like androgen. In comparing maximum tolerable dose, intermittent treatment, and adaptive therapy strategies, no therapeutic choice robustly breaks the three-way tradeoff among the three therapeutic aims. Almost all models show a tight tradeoff between time to emergence of resistant cells and cancer cell burden, with intermittent and adaptive therapies following identical curves. For most models, some adaptive therapies delay overall tumor growth more than intermittent therapies, but at the cost of higher cell populations. The Allee effect breaks these relationships, with some adaptive therapies performing poorly due to their failure to treat sufficiently to drive populations below the threshold. When eradication is impossible, no treatment can simultaneously delay emergence of resistance, limit total cancer cell numbers, and minimize treatment. Simple mathematical models can play a role in designing the next generation of therapies that balance these competing objectives.

Will SARS-CoV-2 Become Just Another Seasonal Coronavirus?

The future prevalence and virulence of SARS-CoV-2 is uncertain. Some emerging pathogens become avirulent as populations approach herd immunity. Although not all viruses follow this path, the fact that the seasonal coronaviruses are benign gives some hope. We develop a general mathematical model to predict when the interplay among three factors, correlation of severity in consecutive infections, population heterogeneity in susceptibility due to age, and reduced severity due to partial immunity, will promote avirulence as SARS-CoV-2 becomes endemic. Each of these components has the potential to limit severe, high-shedding cases over time under the right circumstances, but in combination they can rapidly reduce the frequency of more severe and infectious manifestation of disease over a wide range of conditions. As more reinfections are captured in data over the next several years, these models will help to test if COVID-19 severity is beginning to attenuate in the ways our model predicts, and to predict the disease.

ATM and ATR Activation Through Crosstalk Between DNA Damage Response Pathways.

Cells losing the ability to self-regulate in response to damage are a hallmark of cancer. When a cell encounters damage, regulatory pathways estimate the severity of damage and promote repair, cell cycle arrest, or apoptosis. This decision-making process would be remarkable if it were based on the total amount of damage in the cell, but because damage detection pathways vary in the rate and intensity with which they promote pro-apoptotic factors, the cell's real challenge is to reconcile dissimilar signals. Crosstalk between repair pathways, crosstalk between pro-apoptotic signaling kinases, and signals induced by damage by-products complicate the process further. The cell's response to [Formula: see text] and UV radiation neatly illustrates this concept. While these forms of radiation produce lesions associated with two different pro-apoptotic signaling kinases, ATM and ATR, recent experiments show that ATM and ATR react to both forms of radiation. To simulate the pro-apoptotic signal induced by [Formula: see text] and UV radiation, we construct a mathematical model that includes three modes of crosstalk between ATM and ATR signaling pathways: positive feedback between ATM/ATR and repair proteins, ATM and ATR mutual upregulation, and changes in lesion topology induced by replication stress or repair. We calibrate the model to agree with 21 experimental claims about ATM and ATR crosstalk. We alter the model by adding or removing specific processes and then examine the effects of each process on ATM/ATR crosstalk by recording which claims the altered model violates. Not only is this the first mathematical model of ATM/ATR crosstalk, it provides a strong argument for treating pro-apoptotic signaling as a holistic effort rather than attributing it to a single dominant kinase.

SARS-CoV-2 innate effector associations and viral load in early nasopharyngeal infection.

COVID-19 causes severe disease with poor outcomes. We tested the hypothesis that early SARS-CoV-2 viral infection disrupts innate immune responses. These changes may be important for understanding subsequent clinical outcomes. We obtained residual nasopharyngeal swab samples from individuals who requested COVID-19 testing for symptoms at drive-through COVID-19 clinical testing sites operated by the University of Utah. We applied multiplex immunoassays, real-time polymerase chain reaction assays and quantitative proteomics to 20 virus-positive and 20 virus-negative samples. ACE-2 transcripts increased with infection (OR =17.4, 95% CI [CI] =4.78-63.8) and increasing viral N1 protein transcript load (OR =1.16, CI =1.10-1.23). Transcripts for two interferons (IFN) were elevated, IFN-λ1 (OR =71, CI =7.07-713) and IFN-λ2 (OR =40.2, CI =3.86-419), and closely associated with viral N1 transcripts (OR =1.35, CI =1.23-1.49 and OR =1.33 CI =1.20-1.47, respectively). Only transcripts for IP-10 were increased among systemic inflammatory cytokines that we examined (OR =131, CI =1.01-2620). We found widespread discrepancies between transcription and translation. IFN proteins were unchanged or decreased in infected samples (IFN-γ OR =0.90 CI =0.33-0.79, IFN-λ2,3 OR =0.60 CI =0.48-0.74) suggesting viral-induced shut-off of host antiviral protein responses. However, proteins for IP-10 (OR =3.74 CI =2.07-6.77) and several interferon-stimulated genes (ISG) increased with viral load (BST-1 OR =25.1, CI =3.33-188; IFIT1 OR =19.5, CI =4.25-89.2; IFIT3 OR =245, CI =15-4020; MX-1 OR =3.33, CI =1.44-7.70). Older age was associated with substantial modifications of some effects. Ambulatory symptomatic patients had an innate immune response with SARS-CoV-2 infection characterized by elevated IFN, proinflammatory cytokine and ISG transcripts, but there is evidence of a viral-induced host shut-off of antiviral responses. Our findings may characterize the disrupted immune landscape common in patients with early disease.

SARS-CoV-2 Innate Effector Associations and Viral Load in Early Nasopharyngeal Infection.

To examine innate immune responses in early SARS-CoV-2 infection that may change clinical outcomes, we compared nasopharyngeal swab data from 20 virus-positive and 20 virus-negative individuals. Multiple innate immune-related and ACE-2 transcripts increased with infection and were strongly associated with increasing viral load. We found widespread discrepancies between transcription and translation. Interferon proteins were unchanged or decreased in infected samples suggesting virally-induced shut-off of host anti-viral protein responses. However, IP-10 and several interferon-stimulated gene proteins increased with viral load. Older age was associated with modifications of some effects. Our findings may characterize the disrupted immune landscape of early disease.

How Should Cancer Models Be Constructed?

Choosing and optimizing treatment strategies for cancer requires capturing its complex dynamics sufficiently well for understanding but without being overwhelmed. Mathematical models are essential to achieve this understanding, and we discuss the challenge of choosing the right level of complexity to address the full range of tumor complexity from growth, the generation of tumor heterogeneity, and interactions within tumors and with treatments and the tumor microenvironment. We discuss the differences between conceptual and descriptive models, and compare the use of predator-prey models, evolutionary game theory, and dynamic precision medicine approaches in the face of uncertainty about mechanisms and parameter values. Although there is of course no one-size-fits-all approach, we conclude that broad and flexible thinking about cancer, based on combined modeling approaches, will play a key role in finding creative and improved treatments.

Is mammography screening beneficial: An individual-based stochastic model for breast cancer incidence and mortality.

The benefits of mammography screening have been controversial, with conflicting findings from various studies. We hypothesize that unmeasured heterogeneity in tumor aggressiveness underlies these conflicting results. Based on published data from the Canadian National Breast Screening Study (CNBSS), we develop and parameterize an individual-based mechanistic model for breast cancer incidence and mortality that tracks five stages of breast cancer progression and incorporates the effects of age on breast cancer incidence and all-cause mortality. The model accurately reproduces the reported outcomes of the CNBSS. By varying parameters, we predict that the benefits of mammography depend on the effectiveness of cancer treatment and tumor aggressiveness. In particular, patients with the most rapidly growing or potentially largest tumors have the highest benefit and least harm from the screening, with only a relatively small effect of age. However, the model predicts that confining mammography to populations with a high risk of acquiring breast cancer increases the screening benefit only slightly compared with the full population.

Circulating immune cell phenotype dynamics reflect the strength of tumor-immune cell interactions in patients during immunotherapy.

The extent to which immune cell phenotypes in the peripheral blood reflect within-tumor immune activity prior to and early in cancer therapy is unclear. To address this question, we studied the population dynamics of tumor and immune cells, and immune phenotypic changes, using clinical tumor and immune cell measurements and single-cell genomic analyses. These samples were serially obtained from a cohort of advanced gastrointestinal cancer patients enrolled in a trial with chemotherapy and immunotherapy. Using an ecological population model, fitted to clinical tumor burden and immune cell abundance data from each patient, we find evidence of a strong tumor-circulating immune cell interaction in responder patients but not in those patients that progress on treatment. Upon initiation of therapy, immune cell abundance increased rapidly in responsive patients, and once the peak level is reached tumor burden decreases, similar to models of predator-prey interactions; these dynamic patterns were absent in nonresponder patients. To interrogate phenotype dynamics of circulating immune cells, we performed single-cell RNA sequencing at serial time points during treatment. These data show that peripheral immune cell phenotypes were linked to the increased strength of patients' tumor-immune cell interaction, including increased cytotoxic differentiation and strong activation of interferon signaling in peripheral T cells in responder patients. Joint modeling of clinical and genomic data highlights the interactions between tumor and immune cell populations and reveals how variation in patient responsiveness can be explained by differences in peripheral immune cell signaling and differentiation soon after the initiation of immunotherapy.

Evaluation of a five-year predicted survival model for cystic fibrosis in later time periods.

We evaluated a multivariable logistic regression model predicting 5-year survival derived from a 1993-1997 cohort from the United States Cystic Fibrosis (CF) Foundation Patient Registry to assess whether therapies introduced since 1993 have altered applicability in cohorts, non-overlapping in time, from 1993-1998, 1999-2004, 2005-2010 and 2011-2016. We applied Kaplan-Meier statistics to assess unadjusted survival. We tested logistic regression model discrimination using the C-index and calibration using Hosmer-Lemeshow tests to examine original model performance and guide updating as needed. Kaplan-Meier age-adjusted 5-year probability of death in the CF population decreased substantially during 1993-2016. Patients in successive cohorts were generally healthier at entry, with higher average age, weight and lung function and fewer pulmonary exacerbations annually. CF-related diabetes prevalence, however, steadily increased. Newly derived multivariable logistic regression models for 5-year survival in new cohorts had similar estimated coefficients to the originals. The original model exhibited excellent calibration and discrimination when applied to later cohorts despite improved survival and remains useful for predicting 5-year survival. All models may be used to stratify patients for new studies, and the original coefficients may be useful as a baseline to search for additional but rare events that affect survival in CF.