Whole-genome Mutational Analysis for Tumor-informed Detection of Circulating Tumor DNA in Patients with Urothelial Carcinoma.

BACKGROUND AND OBJECTIVE: Circulating tumor DNA (ctDNA) can be used for sensitive detection of minimal residual disease (MRD). However, the probability of detecting ctDNA in settings of low tumor burden is limited by the number of mutations analyzed and the plasma volume available. We used a whole-genome sequencing (WGS) approach for ctDNA detection in patients with urothelial carcinoma. METHODS: We used a tumor-informed WGS approach for ctDNA-based detection of MRD and evaluation of treatment responses. We analyzed 916 longitudinally collected plasma samples from 112 patients with localized muscle-invasive bladder cancer who received neoadjuvant chemotherapy (NAC) before radical cystectomy. Recurrence-free survival (primary endpoint), overall survival, and ctDNA dynamics during NAC were assessed. KEY FINDINGS AND LIMITATIONS: We found that WGS-based ctDNA detection is prognostic for patient outcomes with a median lead time of 131 d over radiographic imaging. WGS-based ctDNA assessment after radical cystectomy identified recurrence with sensitivity of 91% and specificity of 92%. In addition, genomic characterization of post-treatment plasma samples with a high ctDNA level revealed acquisition of platinum therapy-associated mutational signatures and copy number variations not present in the primary tumors. The sequencing depth is a limitation for studying tumor evolution. CONCLUSIONS AND CLINICAL IMPLICATIONS: Our results support the use of WGS for ultrasensitive ctDNA detection and highlight the possibility of plasma-based tracking of tumor evolution. WGS-based ctDNA detection represents a promising option for clinical use owing to the low volume of plasma needed and the ease of performing WGS, eliminating the need for personalized assay design. PATIENT SUMMARY: Detection of tumor DNA in blood samples from patients with cancer of the urinary tract is associated with poorer outcomes. Disease recurrence after surgery can be identified by the presence of tumor DNA in blood before it can be detected on radiography scans.

Decoding clinical biomarker space of COVID-19: Exploring matrix factorization-based feature selection methods.

One of the most critical challenges in managing complex diseases like COVID-19 is to establish an intelligent triage system that can optimize the clinical decision-making at the time of a global pandemic. The clinical presentation and patients' characteristics are usually utilized to identify those patients who need more critical care. However, the clinical evidence shows an unmet need to determine more accurate and optimal clinical biomarkers to triage patients under a condition like the COVID-19 crisis. Here we have presented a machine learning approach to find a group of clinical indicators from the blood tests of a set of COVID-19 patients that are predictive of poor prognosis and morbidity. Our approach consists of two interconnected schemes: Feature Selection and Prognosis Classification. The former is based on different Matrix Factorization (MF)-based methods, and the latter is performed using Random Forest algorithm. Our model reveals that Arterial Blood Gas (ABG) O2 Saturation and C-Reactive Protein (CRP) are the most important clinical biomarkers determining the poor prognosis in these patients. Our approach paves the path of building quantitative and optimized clinical management systems for COVID-19 and similar diseases.

Functional Effects of Cardiomyocyte Injury in COVID-19.

COVID-19 affects multiple organs. Clinical data from the Mount Sinai Health System show that substantial numbers of COVID-19 patients without prior heart disease develop cardiac dysfunction. How COVID-19 patients develop cardiac disease is not known. We integrated cell biological and physiological analyses of human cardiomyocytes differentiated from human induced pluripotent stem cells (hiPSCs) infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in the presence of interleukins (ILs) with clinical findings related to laboratory values in COVID-19 patients to identify plausible mechanisms of cardiac disease in COVID-19 patients. We infected hiPSC-derived cardiomyocytes from healthy human subjects with SARS-CoV-2 in the absence and presence of IL-6 and IL-1ß. Infection resulted in increased numbers of multinucleated cells. Interleukin treatment and infection resulted in disorganization of myofibrils, extracellular release of troponin I, and reduced and erratic beating. Infection resulted in decreased expression of mRNA encoding key proteins of the cardiomyocyte contractile apparatus. Although interleukins did not increase the extent of infection, they increased the contractile dysfunction associated with viral infection of cardiomyocytes, resulting in cessation of beating. Clinical data from hospitalized patients from the Mount Sinai Health System show that a significant portion of COVID-19 patients without history of heart disease have elevated troponin and interleukin levels. A substantial subset of these patients showed reduced left ventricular function by echocardiography. Our laboratory observations, combined with the clinical data, indicate that direct effects on cardiomyocytes by interleukins and SARS-CoV-2 infection might underlie heart disease in COVID-19 patients. IMPORTANCE SARS-CoV-2 infects multiple organs, including the heart. Analyses of hospitalized patients show that a substantial number without prior indication of heart disease or comorbidities show significant injury to heart tissue, assessed by increased levels of troponin in blood. We studied the cell biological and physiological effects of virus infection of healthy human iPSC-derived cardiomyocytes in culture. Virus infection with interleukins disorganizes myofibrils, increases cell size and the numbers of multinucleated cells, and suppresses the expression of proteins of the contractile apparatus. Viral infection of cardiomyocytes in culture triggers release of troponin similar to elevation in levels of COVID-19 patients with heart disease. Viral infection in the presence of interleukins slows down and desynchronizes the beating of cardiomyocytes in culture. The cell-level physiological changes are similar to decreases in left ventricular ejection seen in imaging of patients' hearts. These observations suggest that direct injury to heart tissue by virus can be one underlying cause of heart disease in COVID-19.

High dimensionality reduction by matrix factorization for systems pharmacology.

The extraction of predictive features from the complex high-dimensional multi-omic data is necessary for decoding and overcoming the therapeutic responses in systems pharmacology. Developing computational methods to reduce high-dimensional space of features in in vitro, in vivo and clinical data is essential to discover the evolution and mechanisms of the drug responses and drug resistance. In this paper, we have utilized the matrix factorization (MF) as a modality for high dimensionality reduction in systems pharmacology. In this respect, we have proposed three novel feature selection methods using the mathematical conception of a basis for features. We have applied these techniques as well as three other MF methods to analyze eight different gene expression datasets to investigate and compare their performance for feature selection. Our results show that these methods are capable of reducing the feature spaces and find predictive features in terms of phenotype determination. The three proposed techniques outperform the other methods used and can extract a 2-gene signature predictive of a tyrosine kinase inhibitor treatment response in the Cancer Cell Line Encyclopedia.

Pharmacological Functionalization of Protein-Based Nanorobots as a Novel Tool for Drug Delivery in Cancer.

The delivery of hydrophobic therapeutic agents to tumors is a challenge in the treatment of cancers. Here, we review recent advances in coiled-coil protein origami and discuss a proposed programmable protein origami structure, switchable by a protein kinase A/phosphatase switch, as an example of functionalization for designing future protein nanorobots.

Decoding Clinical Biomarker Space of COVID-19: Exploring Matrix Factorization-based Feature Selection Methods.

One of the most critical challenges in managing complex diseases like COVID-19 is to establish an intelligent triage system that can optimize the clinical decision-making at the time of a global pandemic. The clinical presentation and patients’ characteristics are usually utilized to identify those patients who need more critical care. However, the clinical evidence shows an unmet need to determine more accurate and optimal clinical biomarkers to triage patients under a condition like the COVID-19 crisis. Here we have presented a machine learning approach to find a group of clinical indicators from the blood tests of a set of COVID-19 patients that are predictive of poor prognosis and morbidity. Our approach consists of two interconnected schemes: Feature Selection and Prognosis Classification. The former is based on different Matrix Factorization (MF)-based methods, and the latter is performed using Random Forest algorithm. Our model reveals that Arterial Blood Gas (ABG) O 2 Saturation and C-Reactive Protein (CRP) are the most important clinical biomarkers determining the poor prognosis in these patients. Our approach paves the path of building quantitative and optimized clinical management systems for COVID-19 and similar diseases.

Physical bioenergetics: Energy fluxes, budgets, and constraints in cells.

Cells are the basic units of all living matter which harness the flow of energy to drive the processes of life. While the biochemical networks involved in energy transduction are well-characterized, the energetic costs and constraints for specific cellular processes remain largely unknown. In particular, what are the energy budgets of cells? What are the constraints and limits energy flows impose on cellular processes? Do cells operate near these limits, and if so how do energetic constraints impact cellular functions? Physics has provided many tools to study nonequilibrium systems and to define physical limits, but applying these tools to cell biology remains a challenge. Physical bioenergetics, which resides at the interface of nonequilibrium physics, energy metabolism, and cell biology, seeks to understand how much energy cells are using, how they partition this energy between different cellular processes, and the associated energetic constraints. Here we review recent advances and discuss open questions and challenges in physical bioenergetics.

EGFR Aggregation in the Brain.

Recent findings showed that preformed fibrils (PFFs) of misfolded proteins, including α-synuclein (α-syn) and amyloid-ß (Aß), activate EGFR in cell cultures and animal models of amyloid propagation. Comparing these supramolecular structures to normal EGFR ligands such as EGF and HB-EGF suggests that these PFFs might trigger the formation of high order clustering of EGFR that stimulates the aggregation of EGFR tyrosine kinase domain (EGFR-TKD) which is known to form fibrils. Subsequently, self-assembled fibril of EGFR-TKDs itself can serve as a seed to induce aggregation of monomeric forms of misfolded proteins in cytoplasm or endosomes. In this model, EGFR serves as an amyloidogenic receptor to facilitate (1) cellular uptake of exogenous PFFs and (2) seeding of endogenous misfolded proteins.

Physiology of cardiomyocyte injury in COVID-19.

COVID-19 affects multiple organs. Clinical data from the Mount Sinai Health System shows that substantial numbers of COVID-19 patients without prior heart disease develop cardiac dysfunction. How COVID-19 patients develop cardiac disease is not known. We integrate cell biological and physiological analyses of human cardiomyocytes differentiated from human induced pluripotent stem cells (hiPSCs) infected with SARS-CoV-2 in the presence of interleukins, with clinical findings, to investigate plausible mechanisms of cardiac disease in COVID-19 patients. We infected hiPSC-derived cardiomyocytes, from healthy human subjects, with SARS-CoV-2 in the absence and presence of interleukins. We find that interleukin treatment and infection results in disorganization of myofibrils, extracellular release of troponin-I, and reduced and erratic beating. Although interleukins do not increase the extent, they increase the severity of viral infection of cardiomyocytes resulting in cessation of beating. Clinical data from hospitalized patients from the Mount Sinai Health system show that a significant portion of COVID-19 patients without prior history of heart disease, have elevated troponin and interleukin levels. A substantial subset of these patients showed reduced left ventricular function by echocardiography. Our laboratory observations, combined with the clinical data, indicate that direct effects on cardiomyocytes by interleukins and SARS-CoV-2 infection can underlie the heart disease in COVID-19 patients.

Seeding Brain Protein Aggregation by SARS-CoV-2 as a Possible Long-Term Complication of COVID-19 Infection.

Postinfection complications of coronavirus disease 2019 (COVID-19) are still unknown, and one of the long-term concerns in infected people are brain pathologies. The question is that severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection may be an environmental factor in accelerating the sporadic neurodegeneration in the infected population. In this regard, induction of protein aggregation in the brain by SARS-CoV-2 intact structure or a peptide derived from spike protein subunits needs to be considered in futures studies. In this paper, we discuss these possibilities using pieces of evidence from other viruses.

Heparin-binding Peptides as Novel Therapies to Stop SARS-CoV-2 Cellular Entry and Infection.

Heparan sulfate proteoglycans (HSPGs) are cell surface receptors that are involved in the cellular uptake of pathologic amyloid proteins and viruses, including the novel coronavirus; severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Heparin and heparan sulfate antagonize the binding of these pathogens to HSPGs and stop their cellular internalization, but the anticoagulant effect of these agents has been limiting their use in the treatment of viral infections. Heparin-binding peptides (HBPs) are suitable nonanticoagulant agents that are capable of antagonizing binding of heparin-binding pathogens to HSPGs. Here, we review and discuss the use of HBPs as viral uptake inhibitors and will address their benefits and limitations to treat viral infections. Furthermore, we will discuss a variant of these peptides that is in the clinic and can be considered as a novel therapy in coronavirus disease 2019 (COVID-19) infection. SIGNIFICANCE STATEMENT: The need to discover treatment modalities for COVID-19 is a necessity, and therapeutic interventions such as heparin-binding peptides (HBPs), which are used for other cases, can be beneficial based on their mechanisms of actions. In this paper, we have discussed the application of HBPs as viral uptake inhibitors in COVID-19 and explained possible mechanisms of actions and the therapeutic effects.

Inhibition of Brain Epidermal Growth Factor Receptor Activation: A Novel Target in Neurodegenerative Diseases and Brain Injuries.

Several reports have been published recently demonstrating a beneficial effect of epidermal growth factor receptor (EGFR) inhibitors in improving pathologic and behavioral conditions in neurodegenerative diseases (NDDs) such as Alzheimer's disease and Amyotrophic Lateral Sclerosis (ALS) as well as the brain and spinal cord injuries (SCI). Despite successful therapeutic effects of EGFR inhibition in these pathologic conditions, there is still no report of proof-of-concept studies in well-characterized animal models using recently developed blood-brain barrier (BBB)-penetrating EGFR inhibitors, which is due to previous conflicting reports concerning the level of EGFR or activated EGFR in normal and pathologic conditions that caused target engagement to be a concern in any future EGFR inhibition therapy. In this review, the level of EGFR expression and activation in the developing central nervous system (CNS) compared with the adult CNS will be explained as well as how neuronal injury or pathologic conditions, especially inflammation and amyloid fibrils, induce reactive astrocytes leading to an increase in the expression and activation of EGFR and, finally, neurodegeneration. Furthermore, in this review, we will discuss two main molecular mechanisms that can be proposed as the neuroprotective effects of EGFR inhibition in these pathologic conditions. We will also review the recent advances in the development of BBB-penetrating EGFR inhibitors in cancer therapy, which may eventually be repositioned for NDDs and SCI therapy in the future. SIGNIFICANCE STATEMENT: Based on the lessons from the applications of EGFR inhibitors in oncology, it is concluded that EGFR inhibitors can be beneficial in treatment of neurodegenerative diseases and spinal cord injuries. They carry their therapeutic potentials through induction of autophagy and attenuation of reactive astrocytes.

A Systems Biology Roadmap to Decode mTOR Control System in Cancer.

Mechanistic target of rapamycin (mTOR) is a critical protein in the regulation of cell fate decision making, especially in cancer cells. mTOR acts as a signal integrator and is one of the main elements of interactions among the pivotal cellular processes such as cell death, autophagy, metabolic reprogramming, cell growth, and cell cycle. The temporal control of these processes is essential for the cellular homeostasis and dysregulation of mTOR signaling pathway results in different phenotypes, including aging, oncogenesis, cell survival, cell growth, senescence, quiescence, and cell death. In this paper, we have proposed a systems biology roadmap to study mTOR control system, which introduces the theoretical and experimental modalities to decode temporal and dynamical characteristics of mTOR signaling in cancer.

Diagnosis of Pancreatic Cystic Lesions by Virtual Slicing: Comparison of Diagnostic Potential of Needle-Based Confocal Laser Endomicroscopy versus Endoscopic Ultrasound-Guided Fine-Needle Aspiration.

BACKGROUND: Pancreatic cystic lesions are often challenging entities for diagnosis and management. EUS-FNA diagnostic accuracy is limited by paucicellularity of cytology specimens and sampling errors. Needle-based confocal laser endomicroscopy (nCLE) provides real-time imaging of the microscopic structure of the cystic lesion and could result in a more accurate diagnosis. AIMS AND OBJECTIVES: To determine the diagnostic utility of in vivo nCLE and EUS-FNA in the diagnosis and histologic characterization of pancreatic cystic lesions (PCL). MATERIALS AND METHODS: All patients diagnosed with PCL who had undergone nCLE and FNA over a 10-year period within a major urban teaching hospital were included in this study. All gastroenterology reports of the nCLE images and corresponding pathologist findings from the EUS-FNA were collected and compared with, a final diagnosis prospectively collected from clinicopathological and imaging data. RESULTS: A total of n=32 patients were included in this study, which consisted of n=13 serous cystadenoma (SCA), n=7 intraductal papillary mucinous neoplasms (IPMN), n=2 mucinous cystic neoplasms (MCN), n=3 well-differentiated neuroendocrine tumors, n=2 cysts, n=2 benign pancreatic lesions, n=1 adenocarcinoma, n=1 gastrointestinal stromal tumor (GIST) and n=1 lymphangioma. The overall diagnostic rate was higher in nCLE (87.5%) vs. EUS-FNA (71.9%) While the diagnostic accuracy of nCLE and EUS-FNA were comparable in characterization of benign vs. malignant lesions, the nCLE diagnosis demonstrated higher accuracy rate in identifying mucinous cystic neoplasms compared to EUS-FNA. CONCLUSION: nCLE is a useful companion diagnostic tool for pancreatic cystic lesions and could assist the cytopathologist to better triage the sample for required ancillary testing and treatment planning. The combination of nCLE and EUS-FNA may be especially helpful in reducing the proportion of cases categorized as non-diagnostic.

Dynamic modeling of signal transduction by mTOR complexes in cancer.

Signal integration has a crucial role in the cell fate decision and dysregulation of the cellular signaling pathways is a primary characteristic of cancer. As a signal integrator, mTOR shows a complex dynamical behavior which determines the cell fate at different cellular processes levels, including cell cycle progression, cell survival, cell death, metabolic reprogramming, and aging. The dynamics of the complex responses to rapamycin in cancer cells have been attributed to its differential time-dependent inhibitory effects on mTORC1 and mTORC2, the two main complexes of mTOR. Two explanations were previously provided for this phenomenon: 1-Rapamycin does not inhibit mTORC2 directly, whereas it prevents mTORC2 formation by sequestering free mTOR protein (Le Chatelier's principle). 2-Components like Phosphatidic Acid (PA) further stabilize mTORC2 compared with mTORC1. To understand the mechanism by which rapamycin differentially inhibits the mTOR complexes in the cancer cells, we present a mathematical model of rapamycin mode of action based on the first explanation, i.e., Le Chatelier's principle. Translating the interactions among components of mTORC1 and mTORC2 into a mathematical model revealed the dynamics of rapamycin action in different doses and time-intervals of rapamycin treatment. This model shows that rapamycin has stronger effects on mTORC1 compared with mTORC2, simply due to its direct interaction with free mTOR and mTORC1, but not mTORC2, without the need to consider other components that might further stabilize mTORC2. Based on our results, even when mTORC2 is less stable compared with mTORC1, it can be less inhibited by rapamycin.

Genomic signatures defining responsiveness to allopurinol and combination therapy for lung cancer identified by systems therapeutics analyses.

The ability to predict responsiveness to drugs in individual patients is limited. We hypothesized that integrating molecular information from databases would yield predictions that could be experimentally tested to develop transcriptomic signatures for specific drugs. We analyzed lung adenocarcinoma patient data from The Cancer Genome Atlas and identified a subset of patients in which xanthine dehydrogenase (XDH) expression correlated with decreased survival. We tested allopurinol, an FDA-approved drug that inhibits XDH, on human non-small-cell lung cancer (NSCLC) cell lines obtained from the Broad Institute Cancer Cell Line Encyclopedia and identified sensitive and resistant cell lines. We utilized the transcriptomic profiles of these cell lines to identify six-gene signatures for allopurinol-sensitive and allopurinol-resistant cell lines. Transcriptomic networks identified JAK2 as an additional target in allopurinol-resistant lines. Treatment of resistant cell lines with allopurinol and CEP-33779 (a JAK2 inhibitor) resulted in cell death. The effectiveness of allopurinol alone or allopurinol and CEP-33779 was verified in vivo using tumor formation in NCR-nude mice. We utilized the six-gene signatures to predict five additional allopurinol-sensitive NSCLC cell lines and four allopurinol-resistant cell lines susceptible to combination therapy. We searched the transcriptomic data from a library of patient-derived NSCLC tumors from the Jackson Laboratory to identify tumors that would be predicted to be sensitive to allopurinol or allopurinol + CEP-33779 treatment. Patient-derived tumors showed the predicted drug sensitivity in vivo. These data indicate that we can use integrated molecular information from cancer databases to predict drug responsiveness in individual patients and thus enable precision medicine.

Systems biology primer: the basic methods and approaches.

Systems biology is an integrative discipline connecting the molecular components within a single biological scale and also among different scales (e.g. cells, tissues and organ systems) to physiological functions and organismal phenotypes through quantitative reasoning, computational models and high-throughput experimental technologies. Systems biology uses a wide range of quantitative experimental and computational methodologies to decode information flow from genes, proteins and other subcellular components of signaling, regulatory and functional pathways to control cell, tissue, organ and organismal level functions. The computational methods used in systems biology provide systems-level insights to understand interactions and dynamics at various scales, within cells, tissues, organs and organisms. In recent years, the systems biology framework has enabled research in quantitative and systems pharmacology and precision medicine for complex diseases. Here, we present a brief overview of current experimental and computational methods used in systems biology.

A biomimetic gelatin-based platform elicits a pro-differentiation effect on podocytes through mechanotransduction.

Using a gelatin microbial transglutaminase (gelatin-mTG) cell culture platform tuned to exhibit stiffness spanning that of healthy and diseased glomeruli, we demonstrate that kidney podocytes show marked stiffness sensitivity. Podocyte-specific markers that are critical in the formation of the renal filtration barrier are found to be regulated in association with stiffness-mediated cellular behaviors. While podocytes typically de-differentiate in culture and show diminished physiological function in nephropathies characterized by altered tissue stiffness, we show that gelatin-mTG substrates with Young's modulus near that of healthy glomeruli elicit a pro-differentiation and maturation response in podocytes better than substrates either softer or stiffer. The pro-differentiation phenotype is characterized by upregulation of gene and protein expression associated with podocyte function, which is observed for podocytes cultured on gelatin-mTG gels of physiological stiffness independent of extracellular matrix coating type and density. Signaling pathways involved in stiffness-mediated podocyte behaviors are identified, revealing the interdependence of podocyte mechanotransduction and maintenance of their physiological function. This study also highlights the utility of the gelatin-mTG platform as an in vitro system with tunable stiffness over a range relevant for recapitulating mechanical properties of soft tissues, suggesting its potential impact on a wide range of research in cellular biophysics.

Modelling the effect of GRP78 on anti-oestrogen sensitivity and resistance in breast cancer.

Understanding the origins of resistance to anti-oestrogen drugs is of critical importance to many breast cancer patients. Recent experiments show that knockdown of GRP78, a key gene in the unfolded protein response (UPR), can re-sensitize resistant cells to anti-oestrogens, and overexpression of GRP78 in sensitive cells can cause them to become resistant. These results appear to arise from the operation and interaction of three cellular systems: the UPR, autophagy and apoptosis. To determine whether our current mechanistic understanding of these systems is sufficient to explain the experimental results, we built a mathematical model of the three systems and their interactions. We show that the model is capable of reproducing previously published experimental results and some new data gathered specifically for this paper. The model provides us with a tool to better understand the interactions that bring about anti-oestrogen resistance and the effects of GRP78 on both sensitive and resistant breast cancer cells.

Endoplasmic reticulum stress, the unfolded protein response, autophagy, and the integrated regulation of breast cancer cell fate.

How breast cancer cells respond to the stress of endocrine therapies determines whether they will acquire a resistant phenotype or execute a cell-death pathway. After a survival signal is successfully executed, a cell must decide whether it should replicate. How these cell-fate decisions are regulated is unclear, but evidence suggests that the signals that determine these outcomes are highly integrated. Central to the final cell-fate decision is signaling from the unfolded protein response, which can be activated following the sensing of stress within the endoplasmic reticulum. The duration of the response to stress is partly mediated by the duration of inositol-requiring enzyme-1 activation following its release from heat shock protein A5. The resulting signals appear to use several B-cell lymphoma-2 family members to both suppress apoptosis and activate autophagy. Changes in metabolism induced by cellular stress are key components of this regulatory system, and further adaptation of the metabolome is affected in response to stress. Here we describe the unfolded protein response, autophagy, and apoptosis, and how the regulation of these processes is integrated. Central topologic features of the signaling network that integrate cell-fate regulation and decision execution are discussed.