CancerCOtreat

Horizon 2020 - Marie Skłodowska-Curie Actions Individual Fellowship Global (MSCA-IF-GF-2020-101028945 CancerCOtreat): Optimizing treatment of cancer patients infected with COVID-19 and other preconditions using mathematical modelling. May 2021 – April 2024; €256,236.

 

Project details

Fellow: Chrysovalantis Voutouri

Principal Investigator: Triantafyllos Stylianopoulos

Project ID: 101028945

Total Funding: €256,236

Topic(s): H2020- MSCA-IF-GF-2020- Marie Skłodowska-Curie Individual Fellowships Global (IF-GF)

Call for proposal: H2020- MSCA-IF-GF-2020

Funding scheme: MSCA-IF-GF

Coordinated in: Cyprus (University of Cyprus)

From May 2021 – April 2024

Summary

COVID-19 has created unprecedented challenges for our healthcare system, and until an effective vaccine is developed and made widely available, treatment options are limited. In addition to local complications in the lung, the virus can cause systemic inflammation leading to cytokine storm and disseminated microthrombosis, which can cause stroke, heart attack or pulmonary emboli . Risk factors for poor COVID-19 outcome include advanced age, hypertension, diabetes and obesity as well as any medical condition that can induce immuno-suppression, especially cancer . Interestingly, there is an interplay among these health conditions: Obesity, which is a worldwide public health problem, associates with increased risk and worse prognosis of many cancers. Furthermore, diabetes is often associated with obesity and obese cancer patients are often hypertensive, particularly when they are of advanced age . In fact, both COVID-19 and obesity induce inflammation and fibrosis by upregulating pro-inflammatory cytokines, which in turn can enhance tumor progression and create a desmoplastic/fibrotic tumor microenvironment that inhibits the delivery and efficacy of cancer therapeutics . The worse outcomes in cancer patients with COVID-19 and the other preconditions suggest that there may be common molecular pathways that overlap in these disease states. Indeed, the renin-angiotensin system (RAS) plays a central role in hypertension and obesity-induced inflammation and it can activate myofibroblasts of cancers to produce extracellular fibers, such as collagen. The RAS plays also a critical role in the high infection capability and mortality rate of the SARS-CoV-2 virus that causes COVID-19 . In particular, two receptors of the RAS seem to modulate the virus infection and mortality, the angiotensin converting enzyme 2 (ACE2) and angiotensin II receptor 1 (AT1R)4. The ACE2 receptor is employed by the SARS-CoV-2 to enter the cells4, whereas upregulation of AT1R –induces inflammation and increased levels of cytokines that can cause fibrosis and acute severe respiratory syndromes. Importantly, the Massachusetts General Hospital (MGH) laboratory that hosts the outgoing phase of this proposal, has extensively studied the repurposing of common RAS inhibitors for the prevention of fibrosis in preclinical solid tumor models, where fibrosis is a major barrier to cancer therapies . Their results has led to a successful clinical trial at MGH for patients suffering from pancreatic cancer .

Even though the interplay among these diseases and health conditions (cancer, COVID-19, obesity, diabetes, hypertension, age) is well documented by retrospective studies, the underlying mechanisms are poorly understood. A better understanding of the complex mechanisms and interactions can lead not only to the better treatment of cancer patients but also to the urgent need for a better clinical management of COVID-19 patients suffering from any of the pre-conditions. The hypothesis of the current proposal is that because of the complexity of the underlying mechanisms and interactions among the various components involved in these diseases, the response to treatment for COVID-19 and cancer patients is not intuitive and a mathematical model of a high level of sophistication is required to provide insights into the mechanisms and identify optimal treatment strategies.  There is a lack of mathematical models to predict tumor progression and therapeutic outcomes in cancer patients suffering from preconditions. Also, even though epidemiological and statistical modeling has been used for COVID-19 providing powerful insights into transmission dynamics and control of the disease , these models do not provide insights into the dynamics of the diseases progression as well as time-course of response to various therapeutic interventions in COVID-19 and cancer patients.

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Fig1. RAS, coagulation and virus pathways to be included in the systems biology model of COVID-19.

Overview of the project

This proposal will build upon the applicant’s strong experience on mathematical modeling of cancer progression and response to treatment in order to develop a mathematical framework to incorporate the common and complementary mechanisms of COVID-19 and other preconditions to cancer progression and treatment. The applicant will extend published mathematical models to include the infection of cells by the SARS-CoV-2 virus, ACE2 modulation and activity, release of pro-inflammatory cytokines, inflammation, and lung damage/oxygenation in the context of the RAS system. Cytokines that are central to COVID-19 (e.g., IL-6) and obesity/diabetes (e.g., IL-1β) will be explicitly accounted for in the model, incorporating all known signaling pathways, such as the canonical and trans signaling pathway of IL-6 and its interaction with IL-6 receptor and soluble IL-6R (Figure 1). Additional innate and adaptive immune cells such as neutrophils, macrophages and CD4+ T cells will be added to our current model along with the interaction of immune cells by viral particles and infected cells. Furthermore, the model will account for the role of immune cells in modulating the expression of pro-inflammatory cytokines such as IL1β, which is important in obesity, diabetes and COVID-19 . Subsequent events, such as the impairment of the vascular network owing to the infection of endothelial cells by the virus, changes in blood oxygenation owing to compromised ventilation, the thickening of the alveolar wall and the induction of fibrosis will be explicitly incorporated in the model and related to cancer treatment efficacy. To facilitate comparison with clinical outcomes – which are often limited in detail – I will define surrogate outcomes that can be directly reported by the model. For example, I will measure the time for viral load to return to zero, and compare this time to clinically-reported data on disease progression. Similarly, elevation of microthrombi, inflammatory factors or viral load above their respective thresholds will indicate death, and be compared with time-to-death data from the clinic.  These determinations will be repeated for patients with various preconditions (e.g., obesity, diabetes, hypertension, advanced age) and therapies.

I expect that incorporation of these new mechanisms will dramatically affect tumor progression and response to treatment. Possible mechanisms of interaction among solid tumor and obesity, diabetes or COVID-19 are: i) induction of tumor-angiogenesis due to the increased expression of specific pro-inflammatory cytokines that act also as angiogenic growth factors (e.g., vascular endothelial growth factor-VEGF) and the decreased oxygenation due to damage to lung tissue and impaired blood perfusion, ii) compromised intratumoral delivery of cancer therapeutics due to damaged vasculature caused by COVID-19 infection, iii) dysregulation of the innate and adaptive immune system, iv) earlier onset of the cytokine storm due to production of IL-1β and other cytokines.

The model will be validated with available clinical data from the literature but also using the patients’ database of Harvard Medical School teaching hospitals (e.g., MGH, Brigham and Women’s Hospital) as many cancer patients treated in these hospitals have been contracted by COVID-19 or suffer from any other of the preconditions involved in the proposed research. An Institutional Review Board (IRB) approval has been obtained for access to the patients’ database of the Harvard Medical School affiliated Hospitals. After validating model predictions and calibrating the model parameters with available pre-clinical and clinical data, optimal treatment strategies for cancer patients infected by the virus and/or suffer from other preconditions will be derived using a sensitivity analysis of the most critical model parameters. The efficacy of combinatorial therapeutic protocols will be tested in silico by multiple simulations incorporating existing cancer therapeutics (e.g., chemotherapy, immunotherapy) and current treatments for COVID-19 (e.g., anti-viral, anti-inflammatory, anti-thrombotic drugs). The mathematical analysis will provide guidelines for the combinatorial treatment and dosage-schedule that can lead to optimal treatment outcome of cancer based on the stage of tumor progression, the stage of COVID-19 progression and/or the other preconditions. Our simulations will also identify the areas of future investigations to fill the gaps in our current knowledge on the interactions between cancer, infectious diseases and preconditions.    

The proposed project will be implemented through three research objectives (ROs). RO1-Development of the mathematical framework. RO2-Model validation and calibration of model parameters. RO3-Optimize combination treatments.

Research Objectives

Research Objective 1: RO1-Development of the mathematical framework for cancer patients who contract COVID-19 Develop a multiscale, systems biology mathematical model to simulate COVID-19 infection and progression in cancer patients (T1.1), with and without preconditions(T1.2).

Research Objective 2: RO2-Model validation and calibration of model parameters. The proposed detailed mathematical framework is estimated to consist of approximately 102 equations and twice as many model parameters. Determination of model parameters and validation of model predictions is critical for the predictive capabilities of the model and it will be performed at multiple steps (T2.1).

Research Objective 3: RO3-Optimize combination treatments. After validating and calibrating the model predictions, we will run multiple simulations using various treatment protocols and model parameter values. The aim of this research objective will be the identification of optimal therapeutic strategies for cancer patients contracted by COVID-19 (T3.1), or being obese, hypertensive, diabetic, of advanced age or a combination of these (T3.2).

 

Publications (in peer-reviewed journals)

1. Subudhi S., C. Voutouri, C.C. Hardin, M.R. Nikmaneshi, A.B. Patel, A. Verma, M. Khadekar, S. Dutta, T. Stylianopoulos, R. K. Jain and L.L. Munn. Strategies to Minimize Heterogeneity and Optimize Clinical Trials in Acute Respiratory Distress Syndrome (ARDS): Insights from Mathematical Modeling. eBioMedicine [DOI: 10.1016/j.ebiom.2021.103809]

 

 

 

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This project has received funding from the Horizon 2020 research and innovation programme under grant agreement 101028945