Deconvolving multiplexed protease signatures with substrate reduction and activity clustering

AND-gated protease-activated nanosensors for programmable detection of anti-tumour immunity

The forward design of biosensors that implement Boolean logic to improve detection precision primarily relies on programming genetic components to control transcriptional responses. However, cell- and gene-free nanomaterials programmed with logical functions may present lower barriers for clinical translation. Here we report the design of activity-based nanosensors that implement AND-gate logic without genetic parts via bi-labile cyclic peptides. These actuate by releasing a reporter if and only if cleaved by a specific pair of proteases. AND-gated nanosensors that detect the concomitant activity of the granzyme B protease secreted by CD8 T cells and matrix metalloproteinases overexpressed by cancer cells identify the unique condition of cytotoxic T cell killing of tumour cells. In preclinical mouse models, AND-gated nanosensors discriminate tumours that are responsive to immune checkpoint blockade therapy from B2m-/- tumours that are resistant to it, minimize signals from tissues without co-localized protease expression including the lungs during acute influenza infection, and release a reporter locally in tissue or distally in the urine for facile detection.

Early detection of pancreatic cancer by a high-throughput protease-activated nanosensor assay

Pancreatic ductal adenocarcinoma (PDAC) is among the top causes of cancer-related death. Patients are frequently diagnosed in the more advanced stages when effective treatment options are limited; however, earlier detection of PDAC by liquid biopsy may expand treatment options and improve survival outcomes. Here, we developed a noninvasive detection assay for PDAC based on serum protease activity to leverage the increase in cancer-associated protease activity in the peripheral blood of patients with PDAC. We screened a series of protease-cleavable peptide probes for the discrimination of PDAC samples versus healthy controls and noncancerous pancreatic disease. We identified a single MMP-sensitive probe, which could distinguish PDAC from controls with 79 ± 6% accuracy. We further developed this probe into a rapid magnetic nanosensor assay, termed PAC-MANN, that measures serum protease cleavage of a target-probe nanosensor with a simple fluorescent readout. In a longitudinal cohort of patients undergoing surgical removal of the primary tumor, the probe cleavage signal was reduced by 16 ± 24% after surgery. In a separate blinded retrospective study, the PAC-MANN assay identified PDAC samples with 98% specificity and 73% sensitivity across all stages and distinguished 100% of patients with noncancer pancreatic disease relative to patients with PDAC. The PAC-MANN assay combined with the clinical biomarker CA 19-9 was 85% sensitive for detection of stage I PDAC with 96% specificity. Therefore, the PAC-MANN assay is a rapid, high-throughput method that uses small blood volumes with the potential to enhance early PDAC detection, specifically among individuals at high risk of developing PDAC.

Embracing enzyme promiscuity with activity-based compressed biosensing

The development of protease-activatable drugs and diagnostics requires identifying substrates specific to individual proteases. However, this process becomes increasingly difficult as the number of target proteases increases because most substrates are promiscuously cleaved by multiple proteases. We introduce a method-substrate libraries for compressed sensing of enzymes (SLICE)-for selecting libraries of promiscuous substrates that classify protease mixtures (1) without deconvolution of compressed signals and (2) without highly specific substrates. SLICE ranks substrate libraries using a compression score (C), which quantifies substrate orthogonality and protease coverage. This metric is predictive of classification accuracy across 140 in silico (Pearson r = 0.71) and 55 in vitro libraries (r = 0.55). Using SLICE, we select a two-substrate library to classify 28 samples containing 11 enzymes in plasma (area under the receiver operating characteristic curve [AUROC] = 0.93). We envision that SLICE will enable the selection of libraries that capture information from hundreds of enzymes using fewer substrates for applications like activity-based sensors for imaging and diagnostics.

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.

Dimensionless parameter predicts bacterial prodrug success

Understanding mechanisms of antibiotic failure is foundational to combating the growing threat of multidrug-resistant bacteria. Prodrugs-which are converted into a pharmacologically active compound after administration-represent a growing class of therapeutics for treating bacterial infections but are understudied in the context of antibiotic failure. We hypothesize that strategies that rely on pathogen-specific pathways for prodrug conversion are susceptible to competing rates of prodrug activation and bacterial replication, which could lead to treatment escape and failure. Here, we construct a mathematical model of prodrug kinetics to predict rate-dependent conditions under which bacteria escape prodrug treatment. From this model, we derive a dimensionless parameter we call the Bacterial Advantage Heuristic (BAH) that predicts the transition between prodrug escape and successful treatment across a range of time scales (1-104  h), bacterial carrying capacities (5 × 104 -105  CFU/µl), and Michaelis constants (KM  = 0.747-7.47 mM). To verify these predictions in vitro, we use two models of bacteria-prodrug competition: (i) an antimicrobial peptide hairpin that is enzymatically activated by bacterial surface proteases and (ii) a thiomaltose-conjugated trimethoprim that is internalized by bacterial maltodextrin transporters and hydrolyzed by free thiols. We observe that prodrug failure occurs at BAH values above the same critical threshold predicted by the model. Furthermore, we demonstrate two examples of how failing prodrugs can be rescued by decreasing the BAH below the critical threshold via (i) substrate design and (ii) nutrient control. We envision such dimensionless parameters serving as supportive pharmacokinetic quantities that guide the design and administration of prodrug therapeutics.

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.