Mathematical framework for activity-based cancer biomarkers

Sucrose-Powered Liposome Nanosensors for Urinary Glucometer-Based Monitoring of Cancer

Timely detection of early-stage cancer holds immense potential in enhancing prognostic outcomes. There is an increasing desire for versatile tools to enable simple, sensitive, and cost-effective cancer detection. By exploiting the extraintestinal metabolic inertness and efficiency renal clearance of sucrose, we designed a liposome nanosensor using sucrose as a messenger to convert tumor-specific esterase activity into glucose meter readout, enabling economical and sensitive urinalysis for cancer detection in point-of-care testing (POCT). Our results demonstrate that the nanosensors exhibited significant signal differences between tumor-bearing and healthy mice in both orthotopic and metastatic tumor models. Additionally, efficient elimination of the nanosensors through the hepatobiliary pathway was observed with no significant toxicity. Such a non-invasive diagnostic modality significantly assists in personalized pharmacological treatment and follow-up efficacy assessment. We envision that this modular liposome nanosensor platform might be applied for economically detecting diverse diseases via a simple urinary test.

Mathematical modeling of the evolution of resistance and aggressiveness of high-grade serous ovarian cancer from patient CA-125 time series

A time-series analysis of serum Cancer Antigen 125 (CA-125) levels was performed in 791 patients with high-grade serous ovarian cancer (HGSOC) from the Australian Ovarian Cancer Study to evaluate the development of chemoresistance and response to therapy. To investigate chemoresistance and better predict the treatment effectiveness, we examined two traits: resistance (defined as the rate of CA-125 change when patients were treated with therapy) and aggressiveness (defined as the rate of CA-125 change when patients were not treated). We found that as the number of treatment lines increases, the data-based resistance increases (a decreased rate of CA-125 decay). We use mathematical models of two distinct cancer cell types, treatment-sensitive cells and treatment-resistant cells, to estimate the values and evolution of the two traits in individual patients. By fitting to individual patient HGSOC data, our models successfully capture the dynamics of the CA-125 level. The parameters estimated from the mathematical models show that patients with inferred low growth rates of treatment-sensitive cells and treatment-resistant cells (low model-estimated aggressiveness) and a high death rate of treatment-resistant cells (low model-estimated resistance) have longer survival time after completing their second-line of therapy. These findings show that mathematical models can characterize the degree of resistance and aggressiveness in individual patients, which improves our understanding of chemoresistance development and could predict treatment effectiveness in HGSOC patients.

Targeting and monitoring ovarian cancer invasion with an RNAi and peptide delivery system

RNA interference (RNAi) therapeutics are an emerging class of medicines that selectively target mRNA transcripts to silence protein production and combat disease. Despite the recent progress, a generalizable approach for monitoring the efficacy of RNAi therapeutics without invasive biopsy remains a challenge. Here, we describe the development of a self-reporting, theranostic nanoparticle that delivers siRNA to silence a protein that drives cancer progression while also monitoring the functional activity of its downstream targets. Our therapeutic target is the transcription factor SMARCE1, which was previously identified as a key driver of invasion in early-stage breast cancer. Using a doxycycline-inducible shRNA knockdown in OVCAR8 ovarian cancer cells both in vitro and in vivo, we demonstrate that SMARCE1 is a master regulator of genes encoding proinvasive proteases in a model of human ovarian cancer. We additionally map the peptide cleavage profiles of SMARCE1-regulated proteases so as to design a readout for downstream enzymatic activity. To demonstrate the therapeutic and diagnostic potential of our approach, we engineered self-assembled layer-by-layer nanoparticles that can encapsulate nucleic acid cargo and be decorated with peptide substrates that release a urinary reporter upon exposure to SMARCE1-related proteases. In an orthotopic ovarian cancer xenograft model, theranostic nanoparticles were able to knockdown SMARCE1 which was in turn reported through a reduction in protease-activated urinary reporters. These LBL nanoparticles both silence gene products by delivering siRNA and noninvasively report on downstream target activity by delivering synthetic biomarkers to sites of disease, enabling dose-finding studies as well as longitudinal assessments of efficacy.

Dynamic modelling and tristability analysis of misfolded α-synuclein degraded via autophagy in Parkinson's disease

The widely-accepted hallmark pathology of Parkinson's disease (PD) is the presence of Lewy bodies with characteristic abnormal aggregated α-synuclein (αSyn). Growing physiological evidence suggests that there is a pivotal role for the autophagy-lysosome pathway (ALP) in the clearance of misfolded αSyn (αSyn∗). This work establishes a mathematical model for αSyn∗ degradation through the ALP. Qualitative simulations are used to uncover the tristable behavior of αSyn∗, i.e., the lower, medium, and upper steady states, which correspond to the healthy, critical, and disease stages of PD, respectively. Time series and codimension-1 bifurcation analysis suggest that the system shows tristability dynamics. Furthermore, variations in the key parameters influence the tristable dynamic behavior, and the distribution of tristable regions is exhibited more comprehensively in codimension-2 bifurcation diagrams. In addition, robustness analysis demonstrates that tristability is a robust property of the system. These results may be valuable in therapeutic strategies for the prevention and treatment of PD.

Caged Luciferase Inhibitor-Based Bioluminescence Switching Strategy Enables Efficient Detection of Serum APN Activity and the Identification of Its Roles in Metastasis of Non-Small Cell Lung Cancer

Bioluminogenic probes emerged as powerful tools for imaging and analysis of various bioanalyses, but traditional approaches would be limited to the low sensitivity during determine the low activity of protease in clinical specimens. Herein, we proposed a caged luciferase inhibitor-based bioluminescence-switching strategy (CLIBS) by using a cleavable luciferase inhibitor to modulate the activity of luciferase reporter to amplify the detective signals, which led to the enhancement of detection sensitivity, and enabled the determination of circulating Aminopeptidase N (APN) activity in thousands of times diluted serum. By applying the CLIBS to serum samples in non-small cell lung cancer (NSCLC) patients from two clinical cohorts, we revealed that, for the first time, higher circulating APN activities but not its concentration, were associated with more NSCLC metastasis or higher metastasis stages by subsequent clinical analysis, and can serve as an independent factor for forecasting NSCLC patients' risk of metastasis.

An Activity-Based Nanosensor for Minimally-Invasive Measurement of Protease Activity in Traumatic Brain Injury

Current screening and diagnostic tools for traumatic brain injury (TBI) have limitations in sensitivity and prognostication. Aberrant protease activity is a central process that drives disease progression in TBI and is associated with worsened prognosis; thus direct measurements of protease activity could provide more diagnostic information. In this study, a nanosensor is engineered to release a measurable signal into the blood and urine in response to activity from the TBI-associated protease calpain. Readouts from the nanosensor were designed to be compatible with ELISA and lateral flow assays, clinically-relevant assay modalities. In a mouse model of TBI, the nanosensor sensitivity is enhanced when ligands that target hyaluronic acid are added. In evaluation of mice with mild or severe injuries, the nanosensor identifies mild TBI with a higher sensitivity than the biomarker GFAP. This nanosensor technology allows for measurement of TBI-associated proteases without the need to directly access brain tissue, and has the potential to complement existing TBI diagnostic tools.

CRISPR-Cas-amplified urinary biomarkers for multiplexed and portable cancer diagnostics

Synthetic biomarkers, bioengineered sensors that generate molecular reporters in diseased microenvironments, represent an emerging paradigm in precision diagnostics. Despite the utility of DNA barcodes as a multiplexing tool, their susceptibility to nucleases in vivo has limited their utility. Here we exploit chemically stabilized nucleic acids to multiplex synthetic biomarkers and produce diagnostic signals in biofluids that can be 'read out' via CRISPR nucleases. The strategy relies on microenvironmental endopeptidase to trigger the release of nucleic acid barcodes and polymerase-amplification-free, CRISPR-Cas-mediated barcode detection in unprocessed urine. Our data suggest that DNA-encoded nanosensors can non-invasively detect and differentiate disease states in transplanted and autochthonous murine cancer models. We also demonstrate that CRISPR-Cas amplification can be harnessed to convert the readout to a point-of-care paper diagnostic tool. Finally, we employ a microfluidic platform for densely multiplexed, CRISPR-mediated DNA barcode readout that can potentially evaluate complex human diseases rapidly and guide therapeutic decisions.

Liquid biopsies for early diagnosis of brain tumours: in silico mathematical biomarker modelling

Brain tumours are the biggest cancer killer in those under 40 and reduce life expectancy more than any other cancer. Blood-based liquid biopsies may aid early diagnosis, prediction and prognosis for brain tumours. It remains unclear whether known blood-based biomarkers, such as glial fibrillary acidic protein (GFAP), have the required sensitivity and selectivity. We have developed a novel in silico model which can be used to assess and compare blood-based liquid biopsies. We focused on GFAP, a putative biomarker for astrocytic tumours and glioblastoma multi-formes (GBMs). In silico modelling was paired with experimental measurement of cell GFAP concentrations and used to predict the tumour volumes and identify key parameters which limit detection. The average GBM volumes of 449 patients at Leeds Teaching Hospitals NHS Trust were also measured and used as a benchmark. Our model predicts that the currently proposed GFAP threshold of 0.12 ng ml-1 may not be suitable for early detection of GBMs, but that lower thresholds may be used. We found that the levels of GFAP in the blood are related to tumour characteristics, such as vasculature damage and rate of necrosis, which are biological markers of tumour aggressiveness. We also demonstrate how these models could be used to provide clinical insight.

Urinary detection of early responses to checkpoint blockade and of resistance to it via protease-cleaved antibody-conjugated sensors

Immune checkpoint blockade (ICB) therapy does not benefit the majority of treated patients, and those who respond to the therapy can become resistant to it. Here we report the design and performance of systemically administered protease activity sensors conjugated to anti-programmed cell death protein 1 (αPD1) antibodies for the monitoring of antitumour responses to ICB therapy. The sensors consist of a library of mass-barcoded protease substrates that, when cleaved by tumour proteases and immune proteases, are released into urine, where they can be detected by mass spectrometry. By using syngeneic mouse models of colorectal cancer, we show that random forest classifiers trained on mass spectrometry signatures from a library of αPD1-conjugated mass-barcoded activity sensors for differentially expressed tumour proteases and immune proteases can be used to detect early antitumour responses and discriminate resistance to ICB therapy driven by loss-of-function mutations in either the B2m or Jak1 genes. Biomarkers of protease activity may facilitate the assessment of early responses to ICB therapy and the classification of refractory tumours based on resistance mechanisms.

Peptide-based urinary monitoring of fibrotic nonalcoholic steatohepatitis by mass-barcoded activity-based sensors

[Figure: see text].

Synthetic biomarkers: a twenty-first century path to early cancer detection

Detection of cancer at an early stage when it is still localized improves patient response to medical interventions for most cancer types. The success of screening tools such as cervical cytology to reduce mortality has spurred significant interest in new methods for early detection (for example, using non-invasive blood-based or biofluid-based biomarkers). Yet biomarkers shed from early lesions are limited by fundamental biological and mass transport barriers - such as short circulation times and blood dilution - that limit early detection. To address this issue, synthetic biomarkers are being developed. These represent an emerging class of diagnostics that deploy bioengineered sensors inside the body to query early-stage tumours and amplify disease signals to levels that could potentially exceed those of shed biomarkers. These strategies leverage design principles and advances from chemistry, synthetic biology and cell engineering. In this Review, we discuss the rationale for development of biofluid-based synthetic biomarkers. We examine how these strategies harness dysregulated features of tumours to amplify detection signals, use tumour-selective activation to increase specificity and leverage natural processing of bodily fluids (for example, blood, urine and proximal fluids) for easy detection. Finally, we highlight the challenges that exist for preclinical development and clinical translation of synthetic biomarker diagnostics.

Interfacing Biomaterials with Synthetic T Cell Immunity

The clinical success of cancer immunotherapy is providing exciting opportunities for the development of new methods to detect and treat cancer more effectively. A new generation of biomaterials is being developed to interface with molecular and cellular features of immunity and ultimately shape or control anti-tumor responses. Recent advances that are supporting the advancement of engineered T cells are focused here. This class of cancer therapy has the potential to cure disease in subsets of patients, yet there remain challenges such as the need to improve response rates and safety while lowering costs to expand their use. To provide a focused overview, recent strategies in three areas of biomaterials research are highlighted: low-cost cell manufacturing to broaden patient access, noninvasive diagnostics for predictive monitoring of immune responses, and strategies for in vivo control that enhance anti-tumor immunity. These research efforts shed light on some of the challenges associated with T cell immunotherapy and how engineered biomaterials that interface with synthetic immunity are gaining traction to solve these challenges.

Urinary detection of lung cancer in mice via noninvasive pulmonary protease profiling

Lung cancer is the leading cause of cancer-related death, and patients most commonly present with incurable advanced-stage disease. U.S. national guidelines recommend screening for high-risk patients with low-dose computed tomography, but this approach has limitations including high false-positive rates. Activity-based nanosensors can detect dysregulated proteases in vivo and release a reporter to provide a urinary readout of disease activity. Here, we demonstrate the translational potential of activity-based nanosensors for lung cancer by coupling nanosensor multiplexing with intrapulmonary delivery and machine learning to detect localized disease in two immunocompetent genetically engineered mouse models. The design of our multiplexed panel of sensors was informed by comparative transcriptomic analysis of human and mouse lung adenocarcinoma datasets and in vitro cleavage assays with recombinant candidate proteases. Intrapulmonary administration of the nanosensors to a Kras- and Trp53-mutant lung adenocarcinoma mouse model confirmed the role of metalloproteases in lung cancer and enabled accurate detection of localized disease, with 100% specificity and 81% sensitivity. Furthermore, this approach generalized to an alternative autochthonous model of lung adenocarcinoma, where it detected cancer with 100% specificity and 95% sensitivity and was not confounded by lipopolysaccharide-driven lung inflammation. These results encourage the clinical development of activity-based nanosensors for the detection of lung cancer.

The Tumor Proteolytic Landscape: A Challenging Frontier in Cancer Diagnosis and Therapy

In recent decades, dysregulation of proteases and atypical proteolysis have become increasingly recognized as important hallmarks of cancer, driving community-wide efforts to explore the proteolytic landscape of oncologic disease. With more than 100 proteases currently associated with different aspects of cancer development and progression, there is a clear impetus to harness their potential in the context of oncology. Advances in the protease field have yielded technologies enabling sensitive protease detection in various settings, paving the way towards diagnostic profiling of disease-related protease activity patterns. Methods including activity-based probes and substrates, antibodies, and various nanosystems that generate reporter signals, i.e., for PET or MRI, after interaction with the target protease have shown potential for clinical translation. Nevertheless, these technologies are costly, not easily multiplexed, and require advanced imaging technologies. While the current clinical applications of protease-responsive technologies in oncologic settings are still limited, emerging technologies and protease sensors are poised to enable comprehensive exploration of the tumor proteolytic landscape as a diagnostic and therapeutic frontier. This review aims to give an overview of the most relevant classes of proteases as indicators for tumor diagnosis, current approaches to detect and monitor their activity in vivo, and associated therapeutic applications.

A survivin-driven, tumor-activatable minicircle system for prostate cancer theranostics

Gene vectors regulated by tumor-specific promoters to express transgenes specifically in cancer cells are an emerging approach for cancer diagnosis and treatment. Minicircles are shortened plasmids stripped of prokaryotic sequences that have potency and safety characteristics beneficial for clinical translation. Previously, we developed minicircles driven by the tumor-specific survivin promoter, which exhibits elevated transcriptional activity in aggressive cancers, to express a secreted reporter for blood-based cancer detection. Here we present the first activatable, cancer theranostic minicircle system featuring a pair of diagnostic and therapeutic minicircles expressing Gaussia luciferase for urine-based cancer detection or cytosine deaminase:uracil phosphoribosyltransferase for gene-directed enzyme prodrug therapy. Diagnostic minicircles revealed urinary reporter output related to cellular survivin levels. Notably, mice with aggressive prostate tumors exhibited significantly higher urine reporter activity than mice with non-aggressive tumors and healthy mice after intratumoral minicircle administration. Therapeutic minicircles displayed specific cytotoxicity in survivin-rich cancer cells and significantly attenuated growth of aggressive orthotopic prostate tumors in mice. Use of these minicircles together creates a theranostic system that can first identify individuals carrying aggressive prostate cancer via a urinary test, followed by stringent control of tumor progression in stratified individuals who carry high-risk prostate lesions.

Oncological Ligand-Target Binding Systems and Developmental Approaches for Cancer Theranostics

Targeted treatment of cancer hinges on the identification of specific intracellular molecular receptors on cancer cells to stimulate apoptosis for eventually inhibiting growth; the development of novel ligands to target biomarkers expressed by the cancer cells; and the creation of novel multifunctional carrier systems for targeted delivery of anticancer drugs to specific malignant sites. There are numerous receptors, antigens, and biomarkers that have been discovered as oncological targets (oncotargets) for cancer diagnosis and treatment applications. Oncotargets are critically important to navigate active anticancer drug ingredients to specific disease sites with no/minimal effect on surrounding normal cells. In silico techniques relating to genomics, proteomics, and bioinformatics have catalyzed the discovery of oncotargets for various cancer types. Effective oncotargeting requires high-affinity probes engineered for specific binding of receptors associated with the malignancy. Computational methods such as structural modeling and molecular dynamic (MD) simulations offer opportunities to structurally design novel ligands and optimize binding affinity for specific oncotargets. This article proposes a streamlined approach for the development of ligand-oncotarget bioaffinity systems via integrated structural modeling and MD simulations, making use of proteomics, genomic, and X-ray crystallographic resources, to support targeted diagnosis and treatment of cancers and tumors.

Multicompartment modeling of protein shedding kinetics during vascularized tumor growth

Identification of protein biomarkers for cancer diagnosis and prognosis remains a critical unmet clinical need. A major reason is that the dynamic relationship between proliferating and necrotic cell populations during vascularized tumor growth, and the associated extra- and intra-cellular protein outflux from these populations into blood circulation remains poorly understood. Complementary to experimental efforts, mathematical approaches have been employed to effectively simulate the kinetics of detectable surface proteins (e.g., CA-125) shed into the bloodstream. However, existing models can be difficult to tune and may be unable to capture the dynamics of non-extracellular proteins, such as those shed from necrotic and apoptosing cells. The models may also fail to account for intra-tumoral spatial and microenvironmental heterogeneity. We present a new multi-compartment model to simulate heterogeneously vascularized growing tumors and the corresponding protein outflux. Model parameters can be tuned from histology data, including relative vascular volume, mean vessel diameter, and distance from vasculature to necrotic tissue. The model enables evaluating the difference in shedding rates between extra- and non-extracellular proteins from viable and necrosing cells as a function of heterogeneous vascularization. Simulation results indicate that under certain conditions it is possible for non-extracellular proteins to have superior outflux relative to extracellular proteins. This work contributes towards the goal of cancer biomarker identification by enabling simulation of protein shedding kinetics based on tumor tissue-specific characteristics. Ultimately, we anticipate that models like the one introduced herein will enable examining origins and circulating dynamics of candidate biomarkers, thus facilitating marker selection for validation studies.

Engineering synthetic breath biomarkers for respiratory disease

Human breath contains many volatile metabolites. However, few breath tests are currently used in the clinic to monitor disease due to bottlenecks in biomarker identification. Here we engineered breath biomarkers for respiratory disease by local delivery of protease-sensing nanoparticles to the lungs. The nanosensors shed volatile reporters upon cleavage by neutrophil elastase, an inflammation-associated protease with elevated activity in lung diseases such as bacterial infection and alpha-1 antitrypsin deficiency. After intrapulmonary delivery into mouse models with acute lung inflammation, the volatile reporters are released and expelled in breath at levels detectable by mass spectrometry. These breath signals can identify diseased mice with high sensitivity as early as 10 min after nanosensor administration. Using these nanosensors, we performed serial breath tests to monitor dynamic changes in neutrophil elastase activity during lung infection and to assess the efficacy of a protease inhibitor therapy targeting neutrophil elastase for the treatment of alpha-1 antitrypsin deficiency.

MMP-14 (MT1-MMP) Is a Biomarker of Surgical Outcome and a Potential Mediator of Hearing Loss in Patients With Vestibular Schwannomas

Improved biomarkers are needed for vestibular schwannoma (VS), the most common tumor of the cerebellopontine angle, as existing clinical biomarkers have poor predictive value. Factors such as tumor size or growth rate do not shed light on the pathophysiology of associated sensorineural hearing loss (SNHL) and suffer from low specificity and sensitivity, whereas histological markers only sample a fraction of the tumor and are difficult to ascertain before tumor treatment or surgical intervention. Proteases play diverse and critical roles in tumorigenesis and could be leveraged as a new class of VS biomarkers. Using a combination of in silico, in vitro, and ex vivo approaches, we identified matrixmetalloprotease 14 (MMP-14; also known as MT1-MMP), from a panel of candidate proteases that were differentially expressed through the largest meta-analysis of human VS transcriptomes. The abundance and proteolytic activity of MMP-14 in the plasma and tumor secretions from VS patients correlated with clinical parameters and the degree of SNHL. Further, MMP-14 plasma levels correlated with surgical outcomes such as the extent of resection. Finally, the application of MMP-14 at physiologic concentrations to cochlear explant cultures led to damage to spiral ganglion neuronal fibers and synapses, thereby providing mechanistic insight into VS-associated SNHL. Taken together, MMP-14 represents a novel molecular biomarker that merits further validation in both diagnostic and prognostic applications for VS.

Activity-Based Diagnostics: An Emerging Paradigm for Disease Detection and Monitoring

Diagnostics to accurately detect disease and monitor therapeutic response are essential for effective clinical management. Bioengineering, chemical biology, molecular biology, and computer science tools are converging to guide the design of diagnostics that leverage enzymatic activity to measure or produce biomarkers of disease. We review recent advances in the development of these 'activity-based diagnostics' (ABDx) and their application in infectious and noncommunicable diseases. We highlight efforts towards both molecular probes that respond to disease-specific catalytic activity to produce a diagnostic readout, as well as diagnostics that use enzymes as an engineered component of their sense-and-respond cascade. These technologies exemplify how integrating techniques from multiple disciplines with preclinical validation has enabled ABDx that may realize the goals of precision medicine.

An Activity-Based Nanosensor for Traumatic Brain Injury

Currently, traumatic brain injury (TBI) is detected by medical imaging; however, medical imaging requires expensive capital equipment, is time- and resource-intensive, and is poor at predicting patient prognosis. To date, direct measurement of elevated protease activity has yet to be utilized to detect TBI. In this work, we engineered an activity-based nanosensor for TBI (TBI-ABN) that responds to increased protease activity initiated after brain injury. We establish that a calcium-sensitive protease, calpain-1, is active in the injured brain hours within injury. We then optimize the molecular weight of a nanoscale polymeric carrier to infiltrate into the injured brain tissue with minimal renal filtration. A calpain-1 substrate that generates a fluorescent signal upon cleavage was attached to this nanoscale polymeric carrier to generate an engineered TBI-ABN. When applied intravenously to a mouse model of TBI, our engineered sensor is observed to locally activate in the injured brain tissue. This TBI-ABN is the first demonstration of a sensor that responds to protease activity to detect TBI.

Synthetic immunity by remote control

Cell-based immunotherapies, such as T cells engineered with chimeric antigen receptors (CARs), have the potential to cure patients of disease otherwise refractory to conventional treatments. Early-on-treatment and long-term durability of patient responses depend critically on the ability to control the potency of adoptively transferred T cells, as overactivation can lead to complications like cytokine release syndrome, and immunosuppression can result in ineffective responses to therapy. Drugs or biologics (e.g., cytokines) that modulate immune activity are limited by mass transport barriers that reduce the local effective drug concentration, and lack site or target cell specificity that results in toxicity. Emerging technologies that enable site-targeted, remote control of key T cell functions - including proliferation, antigen-sensing, and target-cell killing - have the potential to increase treatment precision and safety profile. These technologies are broadly applicable to other immune cells to expand immune cell therapies across many cancers and diseases. In this review, we highlight the opportunities, challenges and the current state-of-the-art for remote control of synthetic immunity.

Designing combination therapies with modeling chaperoned machine learning

Chemotherapy resistance is a major challenge to the effective treatment of cancer. Thus, a systematic pipeline for the efficient identification of effective combination treatments could bring huge biomedical benefit. In order to facilitate rational design of combination therapies, we developed a comprehensive computational model that incorporates the available biological knowledge and relevant experimental data on the life-and-death response of individual cancer cells to cisplatin or cisplatin combined with the TNF-related apoptosis-inducing ligand (TRAIL). The model's predictions, that a combination treatment of cisplatin and TRAIL would enhance cancer cell death and exhibit a "two-wave killing" temporal pattern, was validated by measuring the dynamics of p53 accumulation, cell fate, and cell death in single cells. The validated model was then subjected to a systematic analysis with an ensemble of diverse machine learning methods. Though each method is characterized by a different algorithm, they collectively identified several molecular players that can sensitize tumor cells to cisplatin-induced apoptosis (sensitizers). The identified sensitizers are consistent with previous experimental observations. Overall, we have illustrated that machine learning analysis of an experimentally validated mechanistic model can convert our available knowledge into the identity of biologically meaningful sensitizers. This knowledge can then be leveraged to design treatment strategies that could improve the efficacy of chemotherapy.

Deconvolving multiplexed protease signatures with substrate reduction and activity clustering

Proteases are multifunctional, promiscuous enzymes that degrade proteins as well as peptides and drive important processes in health and disease. Current technology has enabled the construction of libraries of peptide substrates that detect protease activity, which provides valuable biological information. An ideal library would be orthogonal, such that each protease only hydrolyzes one unique substrate, however this is impractical due to off-target promiscuity (i.e., one protease targets multiple different substrates). Therefore, when a library of probes is exposed to a cocktail of proteases, each protease activates multiple probes, producing a convoluted signature. Computational methods for parsing these signatures to estimate individual protease activities primarily use an extensive collection of all possible protease-substrate combinations, which require impractical amounts of training data when expanding to search for more candidate substrates. Here we provide a computational method for estimating protease activities efficiently by reducing the number of substrates and clustering proteases with similar cleavage activities into families. We envision that this method will be used to extract meaningful diagnostic information from biological samples.

The activity of enzymatic proteins, which are called proteases, drives numerous important processes in health and disease: including cancer, immunity, and infectious disease. Many labs have developed useful diagnostics by designing sensors that measure the activity of these proteases. However, if we want to detect multiple proteases at the same time, it becomes impractical to design sensors that only detect one protease. This is due to a phenomenon called protease promiscuity, which means that proteases will activate multiple different sensors. Computational methods have been created to solve this problem, but the challenge is that these often require large amounts of training data. Further, completely different proteases may be detected by the same subset of sensors. In this work, we design a computational method to overcome this problem by clustering similar proteases into "subfamilies", which increases estimation accuracy. Further, our method tests multiple combinations of sensors to maintain accuracy while minimizing the number of sensors used. Together, we envision that this work will increase the amount of useful information we can extract from biological samples, which may lead to better clinical diagnostics.

Renal clearable catalytic gold nanoclusters for in vivo disease monitoring

Ultrasmall gold nanoclusters (AuNCs) have emerged as agile probes for in vivo imaging, as they exhibit exceptional tumour accumulation and efficient renal clearance properties. However, their intrinsic catalytic activity, which can enable an increased detection sensitivity, has yet to be explored for in vivo sensing. By exploiting the peroxidase-mimicking activity of AuNCs and the precise nanometre-size filtration of the kidney, we designed multifunctional protease nanosensors that respond to disease microenvironments to produce a direct colorimetric urinary readout of the disease state in less than one hour. We monitored the catalytic activity of AuNCs in the collected urine of a mouse model of colorectal cancer in which tumour-bearing mice showed a 13-fold increase in colorimetric signal compared to healthy mice. The nanosensors were eliminated completely through hepatic and renal excretion within four weeks of injection with no evidence of toxicity. We envision that this modular approach will enable the rapid detection of a diverse range of diseases by exploiting their specific enzymatic signatures.

Non-invasive early detection of acute transplant rejection via nanosensors of granzyme B activity

The early detection of the onset of transplant rejection is critical for the long-term survival of patients. The diagnostic gold standard for detecting transplant rejection involves a core biopsy, which is invasive, has limited predictive power and carries a morbidity risk. Here, we show that nanoparticles conjugated with a peptide substrate specific for the serine protease granzyme B, which is produced by recipient T cells during the onset of acute cellular rejection, can serve as a non-invasive biomarker of early rejection. When administered systemically in mouse models of skin graft rejection, these nanosensors preferentially accumulate in allograft tissue, where they are cleaved by granzyme B, releasing a fluorescent reporter that filters into the recipient's urine. Urinalysis then discriminates the onset of rejection with high sensitivity and specificity before features of rejection are apparent in grafted tissues. Moreover, in mice treated with subtherapeutic levels of immunosuppressive drugs, the reporter signals in urine can be detected before graft failure. This method may enable routine monitoring of allograft status without the need for biopsies.

Protease activity sensors noninvasively classify bacterial infections and antibiotic responses

BACKGROUND

Respiratory tract infections represent a significant public health risk, and timely and accurate detection of bacterial infections facilitates rapid therapeutic intervention. Furthermore, monitoring the progression of infections after intervention enables 'course correction' in cases where initial treatments are ineffective, avoiding unnecessary drug dosing that can contribute to antibiotic resistance. However, current diagnostic and monitoring techniques rely on non-specific or slow readouts, such as radiographic imaging and sputum cultures, which fail to specifically identify bacterial infections and take several days to identify optimal antibiotic treatments.

METHODS

Here we describe a nanoparticle system that detects P. aeruginosa lung infections by sensing host and bacterial protease activity in vivo, and that delivers a urinary detection readout. One protease sensor is comprised of a peptide substrate for the P. aeruginosa protease LasA. A second sensor designed to detect elastases is responsive to recombinant neutrophil elastase and secreted proteases from bacterial strains.

FINDINGS

In mice infected with P. aeruginosa, nanoparticle formulations of these protease sensors-termed activity-based nanosensors (ABNs)-detect infections and monitor bacterial clearance from the lungs over time. Additionally, ABNs differentiate between appropriate and ineffective antibiotic treatments acutely, within hours after the initiation of therapy.

INTERPRETATION

These findings demonstrate how activity measurements of disease-associated proteases can provide a noninvasive window into the dynamic process of bacterial infection and resolution, offering an opportunity for detecting, monitoring, and characterizing lung infections. FUND: National Cancer Institute, National Institute of Environmental Health Sciences, National Institutes of Health, National Science Foundation Graduate Research Fellowship Program, and Howard Hughes Medical Institute.

Classification of prostate cancer using a protease activity nanosensor library

Improved biomarkers are needed for prostate cancer, as the current gold standards have poor predictive value. Tests for circulating prostate-specific antigen (PSA) levels are susceptible to various noncancer comorbidities in the prostate and do not provide prognostic information, whereas physical biopsies are invasive, must be performed repeatedly, and only sample a fraction of the prostate. Injectable biosensors may provide a new paradigm for prostate cancer biomarkers by querying the status of the prostate via a noninvasive readout. Proteases are an important class of enzymes that play a role in every hallmark of cancer; their activities could be leveraged as biomarkers. We identified a panel of prostate cancer proteases through transcriptomic and proteomic analysis. Using this panel, we developed a nanosensor library that measures protease activity in vitro using fluorescence and in vivo using urinary readouts. In xenograft mouse models, we applied this nanosensor library to classify aggressive prostate cancer and to select predictive substrates. Last, we coformulated a subset of nanosensors with integrin-targeting ligands to increase sensitivity. These targeted nanosensors robustly classified prostate cancer aggressiveness and outperformed PSA. This activity-based nanosensor library could be useful throughout clinical management of prostate cancer, with both diagnostic and prognostic utility.

Nanosensors to Detect Protease Activity In Vivo for Noninvasive Diagnostics

Proteases are multi-functional enzymes that specialize in the hydrolysis of peptide-bonds and control broad biological processes including homeostasis and allostasis. Moreover, dysregulated protease activity drives pathogenesis and is a functional biomarker of diseases such as cancer; therefore, the ability to detect protease activity in vivo may provide clinically relevant information for biomedical diagnostics. The goal of this protocol is to create nanosensors that probe for protease activity in vivo by producing a quantifiable signal in urine. These protease nanosensors consist of two components: a nanoparticle and substrate. The nanoparticle functions to increase circulation half-life and substrate delivery to target disease sites. The substrate is a short peptide sequence (6-8 AA), which is designed to be specific to a target protease or group of proteases. The substrate is conjugated to the surface of the nanoparticle and is terminated by a reporter, such as a fluorescent marker, for detection. As dysregulated proteases cleave the peptide substrate, the reporter is filtered into urine for quantification as a biomarker of protease activity. Herein we describe construction of a nanosensor for matrix metalloproteinase 9 (MMP9), which is associated with tumor progression and metastasis, for detection of colorectal cancer in a mouse model.

Improving cancer detection through combinations of cancer and immune biomarkers: a modelling approach

Background

Early cancer diagnosis is one of the most important challenges of cancer research, since in many cancers it can lead to cure for patients with early stage diseases. For epithelial ovarian cancer (which is the leading cause of death among gynaecologic malignancies) the classical detection approach is based on measurements of CA-125 biomarker. However, the poor sensitivity and specificity of this biomarker impacts the detection of early-stage cancers.

Methods

Here we use a computational approach to investigate the effect of combining multiple biomarkers for ovarian cancer (e.g., CA-125 and IL-7), to improve early cancer detection.

Results

We show that this combined biomarkers approach could lead indeed to earlier cancer detection. However, the immune response (which influences the level of secreted IL-7 biomarker) plays an important role in improving and/or delaying cancer detection. Moreover, the detection level of IL-7 immune biomarker could be in a range that would not allow to distinguish between a healthy state and a cancerous state. In this case, the construction of solution diagrams in the space generated by the IL-7 and CA-125 biomarkers could allow us predict the long-term evolution of cancer biomarkers, thus allowing us to make predictions on cancer detection times.

Conclusions

Combining cancer and immune biomarkers could improve cancer detection times, and any predictions that could be made (at least through the use of CA-125/IL-7 biomarkers) are patient specific.

Beyond the Michaelis-Menten equation: Accurate and efficient estimation of enzyme kinetic parameters

Examining enzyme kinetics is critical for understanding cellular systems and for using enzymes in industry. The Michaelis-Menten equation has been widely used for over a century to estimate the enzyme kinetic parameters from reaction progress curves of substrates, which is known as the progress curve assay. However, this canonical approach works in limited conditions, such as when there is a large excess of substrate over enzyme. Even when this condition is satisfied, the identifiability of parameters is not always guaranteed, and often not verifiable in practice. To overcome such limitations of the canonical approach for the progress curve assay, here we propose a Bayesian approach based on an equation derived with the total quasi-steady-state approximation. In contrast to the canonical approach, estimates obtained with this proposed approach exhibit little bias for any combination of enzyme and substrate concentrations. Importantly, unlike the canonical approach, an optimal experiment to identify parameters with certainty can be easily designed without any prior information. Indeed, with this proposed design, the kinetic parameters of diverse enzymes with disparate catalytic efficiencies, such as chymotrypsin, fumarase, and urease, can be accurately and precisely estimated from a minimal amount of timecourse data. A publicly accessible computational package performing such accurate and efficient Bayesian inference for enzyme kinetics is provided.

An experimental and computational framework for engineering multifunctional nanoparticles: designing selective anticancer therapies

A key challenge in the treatment of cancer with nanomedicine is to engineer and select nanoparticle formulations that lead to the desired selectivity between tumorigenic and non-tumorigenic cells. To this aim, novel designed nanomaterials, deep biochemical understanding of the mechanisms of interaction between nanomaterials and cells, and computational models are emerging as very useful tools to guide the design of efficient and selective nanotherapies. This works shows, using a combination of detailed experimental approaches and simulations, that the specific targeting of cancer cells in comparison to non-tumorigenic cells can be achieved through the custom design of multivalent nanoparticles. A theoretical model that provides simple yet quantitative predictions to tune the nanoparticles targeting and cytotoxic properties by their degree of functionalization is developed. As a case study, a system that included a targeting agent and a drug and is amenable to controlled experimental manipulation and theoretical analysis is used. This study shows how at defined functionalization levels multivalent nanoparticles can selectively kill tumor cells, while barely affecting non-tumorigenic cells. This work opens a way to the rational design of multifunctionalized nanoparticles with defined targeting and cytotoxic properties for practical applications.

Ultrasensitive tumour-penetrating nanosensors of protease activity

The ability to identify cancer lesions with endogenous biomarkers is currently limited to tumours ~1 cm in diameter. We recently reported an exogenously administered tumour-penetrating nanosensor that sheds, in response to tumour-specific proteases, peptide fragments that can then be detected in the urine. Here, we report the optimization, informed by a pharmacokinetic mathematical model, of the surface presentation of the peptide substrates to both enhance on-target protease cleavage and minimize off-target cleavage, and of the functionalization of the nanosensors with tumour-penetrating ligands that engage active trafficking pathways to increase activation in the tumour microenvironment. The resulting nanosensor discriminated sub-5 mm lesions in human epithelial tumours and detected nodules with median diameters smaller than 2 mm in an orthotopic model of ovarian cancer. We also demonstrate enhanced receptor-dependent specificity of signal generation in the urine in an immunocompetent model of colorectal liver metastases, and in situ activation of the nanosensors in human tumour microarrays when re-engineered as fluorogenic zymography probes.

Magnetically Actuated Protease Sensors for in Vivo Tumor Profiling

Targeted cancer therapies require a precise determination of the underlying biological processes driving tumorigenesis within the complex tumor microenvironment. Therefore, new diagnostic tools that capture the molecular activity at the disease site in vivo are needed to better understand tumor behavior and ultimately maximize therapeutic responses. Matrix metalloproteinases (MMPs) drive multiple aspects of tumorigenesis, and their activity can be monitored using engineered peptide substrates as protease-specific probes. To identify tumor specific activity profiles, local sampling of the tumor microenvironment is necessary, such as through remote control of probes, which are only activated at the tumor site. Alternating magnetic fields (AMFs) provide an attractive option to remotely apply local triggering signals because they penetrate deep into the body and are not likely to interfere with biological processes due to the weak magnetic properties of tissue. Here, we report the design and evaluation of a protease-activity nanosensor that can be remotely activated at the site of disease via an AMF at 515 kHz and 15 kA/m. Our nanosensor was composed of thermosensitive liposomes containing functionalized protease substrates that were unveiled at the target site by remotely triggered heat dissipation of coencapsulated magnetic nanoparticles (MNPs). This nanosensor was combined with a unique detection assay to quantify the amount of cleaved substrates in the urine. We applied this spatiotemporally controlled system to determine tumor protease activity in vivo and identified differences in substrate cleavage profiles between two mouse models of human colorectal cancer.

Sustained-release synthetic biomarkers for monitoring thrombosis and inflammation using point-of-care compatible readouts

Postoperative infection and thromboembolism represent significant sources of morbidity and mortality but cannot be easily tracked after hospital discharge. Therefore, a molecular test that could be performed at home would significantly impact disease management. Our lab has previously developed intravenously delivered 'synthetic biomarkers' that respond to dysregulated proteases to produce a urinary signal. These assays, however, have been limited to chronic diseases or acute diseases initiated at the time of diagnostic administration. Here, we formulate a subcutaneously administered sustained release system by using small PEG scaffolds (<10 nm) to promote diffusion into the bloodstream over a day. We demonstrate the utility of a thrombin sensor to identify thrombosis and an MMP sensor to measure inflammation. Finally, we developed a companion paper ELISA using printed wax barriers with nanomolar sensitivity for urinary reporters for point-of-care detection. Our approach for subcutaneous delivery of nanosensors combined with urinary paper analysis may enable facile monitoring of at-risk patients.

Photoactivated Spatiotemporally-Responsive Nanosensors of in Vivo Protease Activity

Proteases play diverse and important roles in physiology and disease, including influencing critical processes in development, immune responses, and malignancies. Both the abundance and activity of these enzymes are tightly regulated and highly contextual; thus, in order to elucidate their specific impact on disease progression, better tools are needed to precisely monitor in situ protease activity. Current strategies for detecting protease activity are focused on functionalizing synthetic peptide substrates with reporters that emit detection signals following peptide cleavage. However, these activity-based probes lack the capacity to be turned on at sites of interest and, therefore, are subject to off-target activation. Here we report a strategy that uses light to precisely control both the location and time of activity-based sensing. We develop photocaged activity-based sensors by conjugating photolabile molecules directly onto peptide substrates, thereby blocking protease cleavage by steric hindrance. At sites of disease, exposure to ultraviolet light unveils the nanosensors to allow proteases to cleave and release a reporter fragment that can be detected remotely. We apply this spatiotemporally controlled system to probe secreted protease activity in vitro and tumor protease activity in vivo. In vitro, we demonstrate the ability to dynamically and spatially measure metalloproteinase activity in a 3D model of colorectal cancer. In vivo, veiled nanosensors are selectively activated at the primary tumor site in colorectal cancer xenografts to capture the tumor microenvironment-enriched protease activity. The ability to remotely control activity-based sensors may offer a valuable complement to existing tools for measuring biological activity.