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.

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Analyzing the activity of proteases and their substrates is critical to defining the biological functions of these enzymes and to designing new diagnostics and therapeutics that target protease dysregulation in disease. While a wide range of databases and algorithms have been created to better predict protease cleavage sites, there is a dearth of computational tools to automate analysis of in vitro and in vivo protease assays. This necessitates individual researchers to develop their own analytical pipelines, resulting in a lack of standardization across the field. To facilitate protease research, here we present Protease Activity Analysis (PAA), a toolkit for the preprocessing, visualization, machine learning analysis, and querying of protease activity data sets. PAA leverages a Python-based object-oriented implementation that provides a modular framework for streamlined analysis across three major components. First, PAA provides a facile framework to query data sets of synthetic peptide substrates and their cleavage susceptibilities across a diverse set of proteases. To complement the database functionality, PAA also includes tools for the automated analysis and visualization of user-input enzyme–substrate activity measurements generated through in vitro screens against synthetic peptide substrates. Finally, PAA supports a set of modular machine learning functions to analyze in vivo protease activity signatures that are generated by activity-based sensors. Overall, PAA offers the protease community a breadth of computational tools to streamline research, taking a step toward standardizing data analysis across the field and in chemical biology and biochemistry at large.

Analyzing the activity of proteases and their substrates is critical to defining the biological functions of these enzymes and to designing new diagnostics and therapeutics that target protease dysregulation in disease. While a wide range of databases and algorithms have been created to better predict protease cleavage sites, there is a dearth of computational tools to automate analysis of in vitro and in vivo protease assays. This necessitates individual researchers to develop their own analytical pipelines, resulting in a lack of standardization across the field. To facilitate protease research, here we present Protease Activity Analysis (PAA), a toolkit for the preprocessing, visualization, machine learning analysis, and querying of protease activity datasets. PAA leverages a Python-based object-oriented implementation that provides a modular framework for streamlined analysis across three major components. First, PAA provides a facile framework to query datasets of synthetic peptide substrates and their cleavage susceptibilities across a diverse set of proteases. To complement the database functionality, PAA also includes tools for the automated analysis and visualization of user-input enzyme-substrate activity measurements generated through in vitro screens against synthetic peptide substrates. Finally, PAA can supports a set of modular machine learning functions to analyze in vivo protease activity signatures that are generated by activity-based sensors. Overall, PAA offers the protease community a breadth of computational tools to streamline research, taking a step towards standardizing data analysis across the field and in chemical biology and biochemistry at large.

Liquid biopsies using cell-free DNA (cfDNA) enable non-invasive detection and characterization of disease. Advances in sequencing methods have significantly improved the performance of liquid biopsies. Yet, despite these advances, sensitivity remains a fundamental challenge. In oncology, circulating tumor DNA (ctDNA) screening tests only detect 20-40% of stage I tumors and tests for minimal residual disease have only 25-50% sensitivity after surgery. The major barrier to better sensitivity is the intrinsic low level of ctDNA in plasma. Physical absence of tumor DNA molecules in a blood draw from a patient with low disease burden will result in a negative test, no matter the sensitivity of the ex vivo detection platform. To overcome this barrier, here we report a first-in-class intravenous DNA-binding priming agent that is given 2 hours prior to a blood draw to recover more ctDNA, boosting the detection of tumor mutations in plasma by 19-fold and increasing sensitivity from 6% to 84%. Given the rapid clearance of cfDNA from circulation, we reasoned that a priming agent that could bind and protect cfDNA from clearance could increase the tumor DNA recovered from plasma. We selected monoclonal antibodies (mAbs) as the class of molecules to use as cfDNA protectors given their persistence in circulation and ease of engineering. We identify a mAb that binds double-stranded DNA (dsDNA) and find on electrophoretic mobility shift assays that it binds both free and histone-bound dsDNA, the constituent components of cfDNA. We then demonstrate that this mAb can delay the clearance of dsDNA from plasma in vivo through co-injection of the mAb with free- and histone-bound dsDNA in mice. We further identify interactions with Fc-gamma-receptors as a key mediator of early clearance of dsDNA bound to the priming mAb. To address this early clearance and limit potential immune interactions, we engineer the mAb to abrogate its Fc effector function. The engineered variant decreases clearance of injected dsDNA by over 150-fold at one hour post-injection compared to dsDNA alone. We next evaluate the effect of our priming mAb on cancer detection. We use a targeted panel against 1,822 mutations in the MC26 murine colon carcinoma cell line to detect tumor mutations in the plasma of tumor bearing mice. The priming mAb results in 19-fold higher recovery of tumor DNA molecules compared to a control mAb. This improved recovery leads to detection of 77% of targeted sites in plasma compared to only 15% in the control group. In sensitivity analyses, higher recovery of mutant molecules improves sensitivity for cancer detection from 6% to 84% at 0.001% tumor fraction. In summary, we demonstrate an approach to overcome a key barrier in liquid biopsies. We envision that similar to contrast agents in clinical imaging, priming agents could significantly boost the diagnostic sensitivity of liquid biopsies and enable further applications across biomedicine. Citation Format: Shervin Tabrizi, Carmen Martin-Alonso, Kan Xiong, Timothy Blewett, Sainetra Sridhar, Zhenyi An, Sahil Patel, Sergio Rodriguez-Aponte, Christopher Naranjo, Douglas Shea, Todd Golub, Sangeeta N. Bhatia, Viktor A. Adalsteinsson, J. Christopher Love. A DNA-binding priming agent protects cell-free DNA and improves the sensitivity of liquid biopsies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3371.