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Coding Station

Research Project

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.

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.

RPF project CanceNanoMED

Development of a software product for tumor perfusion analysis and for derivation of a perfusion biomarker. The proposed software quantifies tumor perfusion using data from ultrasound imaging. The software is tested and the perfusion marker is validated using a large number of in vivo data from murine tumors.  /Post-doctoral Fellow  for the RPF project CanceNanoMED

Post-doctoral Research Fellow for the project “ CancerNanoMED ": My research objectives in this project were the development of a software product for tumor perfusion analysis and for derivation of a perfusion biomarker. The proposed software quantifies tumor perfusion using data from ultrasound imaging. Dynamic contrast enhanced ultrasound (DCEUS) is a technique that provides information about the micro- and macro-vasculature of tumors and other organs. Ultrasound imaging of the tumor site and application of ultrasound wave pressures that are not destructive for the microbubbles can measure the intensity of the bubbles through time and construct a time-intensity curve (TIC) that corresponds to the entire tumor. Several parameters derived from the TIC could be employed for quantification of tumor perfusion and potentially be used as biomarkers for prediction of tumor response to cancer therapies. Such parameters include the rise time, the mean transmit time, the peak intensity and the area under the curve. TICs many times are, however, noisy and affected by recirculation of contrast microbubbles. The software product communicates in real time with the ultrasound system and once the TIC is derived it proceeds with the calculation of the perfusion parameters described above. The software will store in its memory the values of these parameters for the entire duration of the therapy along with measures of the response to treatment, such as the volume and size of the tumor, measured with conventional ultrasound imaging. A user-friendly interface allows the user to plot the values of the perfusion parameters through time and as a function of the different measures of tumor response to therapy so that conclusions for the most suitable perfusion index can be derived.

The software is tested and the perfusion marker is validated using a large number of in vivo data from murine breast and pancreatic tumors. For these tumor types and in accordance with the clinical practice, conventional chemotherapy is employed: doxorubicin for the fibrosarcoma and gemcitabine for the pancreatic tumors. Following cancer cell implantation, mice are monitored on a daily basis and tumor volume is recorded every second day with the use of whole-body bioluminescence imaging. When tumors reach a size of ~5 mm in diameter, perfusion quantification with DCEUS is carried out along with shear wave elastography and tumor volume measurements. It is expected that the analysis performed will identify the proper measure of tumor perfusion that could be most successfully related to tumor response to chemo- and nano-therapy.

ERC project “CancerFingerPrints"

Development of an AFM-based software product that will communicate with AFM systems to be used as a commercial tool for the measurement of a NanoMechanical biomarker which will quantify the mechanical FingerPrints of Cancer. This biomarker aims to: (i) predict the patient’s response to chemotherapy (response prediction) and (ii) monitor treatment outcomes, in the case of novel approaches that target tumor mechanical properties. /Post-doctoral Fellow for the ERC project “CancerFingerPrints"

Post-doctoral Research Fellow for the project “ CancerFigerPrints ": My research objectives in this project are the development of an AFM-based software product that will communicate with AFM systems to be used as a commercial tool for the measurement of a NanoMechanical biomarker which will quantify the mechanical FingerPrints of Cancer.  AFM mechanical properties characterizations with AFM force spectroscopy and force volume will be performed with AFM systems in contact mode under PBS and force curves will be collected and analyzed by the software. This biomarker aims to: (i) predict the patient’s response to chemotherapy (response prediction) and (ii) monitor treatment outcomes, in the case of novel approaches that target tumor mechanical properties. It is expected that the analysis performed will identify the proper measure of tumor mechanical properties that could be most successfully related to tumor response to chemo- and nano-therapy. 

ERC project “ReEngineeringCancer "

Development of strategies that modulate the mechanical Tumor Microenvironment to improve tumor perfusion, and mathematical model simulations to identify optimal treatment strategies based on the combined use of nanomedicines and immunotherapeutic drugs. /Post-doctoral Fellow, for the ERC project “ReEngineeringCancer ",

Post-doctoral Research Fellow for the “ReEngineeringCancer" project: My research objectives in this project were  to develop mathematical models for the study of the mechanisms of angiogenesis in brain tumors, incorporating the effect of co-option of the host vasculature by cancer cells, which is a strategy with which some cancer cells can sustain tumor progression (articles in PNAS 2019, 2020). It is well accepted that some solid tumors can grow in a non-angiogenic fashion by exploiting the pre-existing, host vasculature in a process that is termed vessel co-option. Non-angiogenic tumors have been reported in brain, lung, liver, and skin in both mouse tumor models and humans . In this case, cancer cells co-opt existing host vessels to maintain oxygen supply. It is also possible, however, that the co-opted vessels become dysfunctional, creating hypoxia and leading to robust hypoxia-induced angiogenesis. Cancer cells can also co-opt blood vessels when they extravasate to the metastatic site to form a metastatic lesion. Interestingly, vessel co-option has been related to resistance to anti-angiogenic treatment and anti-vascular endothelial growth factor (VEGF) therapies using monoclonal antibodies or multitargeted tyrosine kinase inhibitors (TKIs) have shown modest efficacy in recurrent glioblastoma multiforme (rGBM). To support that, it is now accepted that glioblastoma invasion to the brain can be achieved not only by cell migration along the brain's fiber tracks but also by co-opting brain blood vessels. Despite the intense research in the field, a mechanistic understanding of these processes is still incomplete.In this study, I developed a new mathematical model to study the mechanisms of angiogenic and non-angiogenic tumor growth in brain tumors. The group of Professor Rakesh K. Jain at Harvard Medical School and Massachusetts General Hospital provided me with the experimental data to validate the model. Specifically, in the model, cancer cells migrate towards better oxygenated intratumoral regions, co-opting and eventually compressing blood vessels. Blood vessel compression results in reduced vessel functionality and hypoxia, which in turn triggers the production of angiopoietins 1 and 2 (Ang1 and Ang2) by tumor endothelial cells as well as production of VEGF by cancer cells and stroma cell-derived factor 1 alpha (SDF1α) by both cancer and endothelial cells. Model formulation was guided by multiple sets of experimental data, and its predictions agreed qualitatively—and in many cases quantitatively—with data on the spatiotemporal evolution of vessel cooption and compression, VEGF and Ang2 levels, vascular density distribution, and tumor oxygenation. Interestingly, according to the model, inhibition of VEGF-dependent angiogenesis will not completely eliminate tumor vasculature, and, hence, tumor growth can continue through this therapy. I found that anti-VEGF inhibition needs to be applied judiciously and that dual blocking of cooption and VEGF can decrease tumor vasculature and growth, but only under certain conditions of cancer cell proliferation and migration. 

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