Noninvasive nanoparticles for lung cancer

Previously developed nanoparticle technology has been shown to detect the hallmark protease activity of many cancers, amplifying it into a urinary readout. Now, Kirkpatrick et al. optimize protease activity–based nanosensors for the detection of lung cancer. Intratracheal instillation of nanosensors enabled detection of localized lung adenocarcinoma in two immunocompetent, autochthonous mouse models. The sensors distinguished between lung cancer and lung inflammation, and did not detect protease activity in a colorectal cancer xenograft model. Further work will need to confirm the approach for human lung cancer and other lung cancer subtypes and to formulate the nanosensors for intrapulmonary delivery in patients.

Abstract

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.

INTRODUCTION

Lung cancer is the most common cause of cancer-related death (25.3% of cancer deaths in the United States), with dismal 18.6% 5-year survival rates (1). Underlying this high mortality is the fact that 57% of patients with lung cancer have distant spread of disease at the time of diagnosis (1). Because patients with regional or localized disease have 6- to 13-fold higher 5-year survival rates than patients with distant metastases (1), substantial effort has been dedicated to early detection of lung cancer. In the United States, screening with low-dose computed tomography (LDCT) is recommended in high-risk patients [adults aged 55 to 80 years with a 30 pack-year smoking history (2)] and enabled a relative reduction in mortality of 20% when compared to chest radiography in the National Lung Screening Trial (NLST) (3). However, in addition to expense (4) and risks associated with radiation exposure (5), LDCT suffers from high false-positive rates (3), leading to a considerable burden of complications incurred during unnecessary follow-up procedures. Transthoracic needle biopsy, for example, is associated with a 15% rate of pneumothorax and a 6.6% rate of pneumothorax requiring chest drainage (6). Overall, the risk of dying or suffering a major complication in an LDCT-screened patient with a benign nodule is 4.1 and 4.5 per 10,000, respectively (5). As a result of these limitations, screening by LDCT has not been widely adopted outside of the United States (7), and there is an urgent need to develop diagnostic tests that increase the effectiveness of lung cancer screening.

Great strides in molecular diagnostics have yielded promising approaches that may be used in conjunction with or as an alternative to LDCT for lung cancer screening. Circulating tumor DNA (ctDNA) has emerged as a promising tool for noninvasive molecular profiling of lung cancer (8, 9). However, the presence of ctDNA scales with tumor burden, and there are thus fundamental sensitivity limits for early-stage disease (8, 10). In patients with a suspicious nodule identified by LDCT, transcriptional profiling of bronchial brushings can enhance the diagnostic sensitivity of bronchoscopy alone (11), leveraging the “field of injury” that results from smoking and other environmental exposures. However, as with any invasive procedure, bronchoscopy carries the risk of attendant complications such as pneumothorax (3, 5).

Rather than relying on imaging or the detection of endogenous biomarkers in circulation, we have developed a class of “activity-based nanosensors” that monitor for a disease state by detecting and amplifying the activity of aberrant proteases and that function as urinary reporters (12–19). Protease activity is dysregulated in cancer, and proteases across all catalytic classes play a direct role in tumorigenesis (20, 21). Activity-based nanosensors leverage dysregulated protease activity to overcome the insensitivity of previous biomarker assays, amplifying disease-associated signals generated in the tumor microenvironment and providing a concentrated urine-based readout. We have previously explored the sensitivity of this approach using mathematical modeling (22) and cell transplant models (16). However, to drive accurate diagnosis in a heterogeneous disease, a diagnostic must also be highly specific. Here, we explored the potential to attain both sensitive and specific lung cancer detection by multiplexing 14 activity-based nanosensors in two immunocompetent, autochthonous mouse models driven by either Kras/Trp53 (KP) mutations or Eml4-Alk (EA) fusion. Clinically, activity-based nanosensors may have utility as an alternative to invasive follow-up procedures in patients with positive LDCT findings.

DISCUSSION

In this work, we present an advance toward clinical translation of a new class of biomarkers, activity-based nanosensors. We found that such multiplexed nanosensors, when delivered by intratracheal instillation, performed with specificity of 100% and sensitivity up to 95% for detection of localized disease in two autochthonous LUAD models representing Kras/Trp53 and Alk-mutant disease. Furthermore, we found that LPS-induced lung inflammation did not result in false positives. Our approach overcomes the intrinsic sensitivity limitation of blood-based diagnostic assays for localized disease by profiling disease activity directly within the tumor microenvironment and providing multiple steps of signal amplification (22). Using intrapulmonary delivery, we further ensured that virtually all nanosensors reached the lung and bypassed nonspecific activation in off-target organs.

This study represents a step toward clinical implementation of activity-based nanosensors for lung cancer testing, validating the efficacy of the tool in two autochthonous, immunocompetent models of localized LUAD. The use of genetically engineered mouse models offered several advantages over cell transplant models, including the ability to explore stage-specific differences, as well as proteolytic contributions from immune cells. Activity-based nanosensors detected disease as early as 7.5 weeks after initiating the KP model, when only grade 1 AAH and grade 2 adenomas are present (24). Furthermore, although metalloprotease-sensitive nanosensors were, as expected, preferentially cleaved in KP mice at both 7.5 and 10.5 weeks, the activation of PP11 (a serine protease-sensitive substrate) in KP_10.5wk mice could point to an unexpected role of serine protease activity in tumor progression at this disease stage. One hypothesis is that tumor-infiltrating immune cells, which secrete a multitude of serine proteases (37), may contribute to nanosensor cleavage in KP_10.5wk mice. Neutrophils are known to infiltrate KP tumors around 10 weeks after tumor induction (38). The potential capacity of activity-based nanosensors to measure immune-mediated protease activity (18) raises the prospect of rapid, noninvasive, and longitudinal immunotherapy response monitoring.

Here, we report improved sensitivity of activity-based nanosensors relative to previous work by our group, as well as existing and emerging blood-based diagnostics for cancer. We found that our nanosensors could detect tumors in KP_7.5wk mice, whose total tumor volumes were, on average, only 2.78 mm³, more than an order of magnitude smaller than our most sensitive method to date (36 mm³ in an ovarian cancer model) (16). By comparison, in the LS174T colorectal cancer xenograft model, ctDNA is detectable when tumor volumes reach 1000 mm³ (39), carcinoembryonic antigen is detectable around 135 to 330 mm³ (12, 39), and intravenously administered activity-based nanosensors have previously been shown to detect disease in this model around 130 mm³ (12). Last, in the autochthonous Kras^G12D-mutant “K” lung cancer model, ctDNA bearing the Kras^G12D mutation was only detectable when average tumor volumes were 7.1 mm³ (40), even with collection of 2.5% of the total mouse blood volume, scaling to 125 ml in humans.

In the NLST, 96.4% of positive LDCT findings were false positives (3, 5), and many of these patients went on to suffer major complications during invasive follow-up procedures (4, 5). Therefore, there is a need to develop noninvasive diagnostic methods that can distinguish between lung cancer and benign lung disease. Here, we demonstrated the specificity of activity-based nanosensors for lung cancer, rather than benign lung inflammation, through multiplexing and machine learning. Although fewer than half of the 14 reporters were differentially enriched in the urine of KP mice and healthy controls, several more had diagnostic power in EA mice, and others were informative in the classification of malignant versus inflammatory disease. As a result, we found that a pretrained random forest classifier could distinguish between lung cancer-bearing mice (regardless of subtype) and benign disease controls. Although a clinical study would be necessary to directly assess the effectiveness of activity-based nanosensors in the setting of LDCT lung cancer screening, our results suggest that activity-based nanosensors may complement LDCT for discrimination of malignant lesions from benign disease.

Although this work represents a step toward translation of activity-based nanosensors for lung cancer detection, there are limitations that must be addressed before clinical implementation. In this work, we demonstrated the sensitivity and specificity of intrapulmonary activity-based nanosensors for localized lung cancer in two genetically engineered mouse models of LUAD. Although the advantages of such models over xenograft models in recapitulating human disease are numerous (41), mouse models cannot fully capture the native oncogenic properties or heterogeneity found in human lung cancer, and further in vivo validation is needed to confirm the generalizability of activity-based nanosensors to other lung cancer subtypes. Similarly, although activity-based nanosensors can discriminate between lung cancer and LPS-driven lung inflammation, it is possible that clinical lung cancer testing may be confounded by other benign lung disease etiologies or chronic exposure to tobacco smoke. Because of the inherent limitations of mouse models, clinical trials will be necessary to fully validate the robustness of activity-based nanosensors in detecting lung cancer and distinguishing malignant from benign and extrapulmonary disease in humans. Last, the intrapulmonary delivery methods presented here must be optimized before clinical translation. Here, we delivered activity-based nanosensors by intratracheal instillation and demonstrated their stability after aerosolization. However, a clinically relevant intrapulmonary delivery method such as dry powder inhalation or nebulization will be required for clinical implementation.

In summary, intrapulmonary activity-based nanosensors perform with high sensitivity and specificity for detection of localized lung cancer in autochthonous mouse models via a noninvasive urine test. To engineer these nanosensors, we leveraged analysis of LUAD gene expression datasets to nominate candidate proteases, screened these proteases in vitro against a panel of peptide substrates, and directly delivered nanosensors carrying these substrates into the lungs of mice. Activity-based nanosensors may have clinical utility as a rapid, safe, and cost-effective follow-up to LDCT, reducing the number of patients referred for invasive testing. With further optimization and validation studies, activity-based nanosensors may one day provide an accurate, noninvasive, and radiation-free strategy for lung cancer testing.