Research ArticleCancer

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

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Science Translational Medicine  01 Apr 2020:
Vol. 12, Issue 537, eaaw0262
DOI: 10.1126/scitranslmed.aaw0262
  • Fig. 1 Study approach and overview.

    (A) Activity-based nanosensors were administered to mice by intratracheal instillation. (B) At the tumor periphery, disease-associated proteases cleave protease substrates, liberating mass spectrometry (MS)–encoded reporters from the PEG scaffold. (C and D) These reporters are small enough to diffuse into the bloodstream and passively filter into the urine (C) for detection by LC-MS/MS (D). (E) Classification was performed on a training cohort of mice and subsequently applied to an independent test cohort to provide a positive or negative readout of malignancy.

  • Fig. 2 Proteases are up-regulated in lung cancer and enable classification of human disease.

    (A to C) Existing mouse (A and B) and human (C) gene expression datasets were analyzed to identify extracellular endoproteases overexpressed in lung cancer. Protease genes in red were selected for the LUAD protease panel. (D) GSEA was performed in the TCGA (human) dataset using orthologs of the top 20 overexpressed protease genes in KP tumors (P = 0.0002). Red bars are genes included in the LUAD protease panel. (E) A set of 15 proteases was selected as the LUAD protease panel. Red, FoldDisease > 1; gray, FoldDisease < 1, where FoldDisease is gene expression in disease relative to control. Black, not included in the dataset. (F) GLM classification on the TCGA dataset using the 15 protease genes in the LUAD protease panel as features. AUC for the test cohort is shown as a function of the number of proteases included in the classifier (n = 50 combinations of protease genes for each point). Points are means ± SD.

  • Fig. 3 LUAD substrate panel cleavage patterns are driven by protease class.

    (A) All 15 proteases in the LUAD protease panel were screened against a panel of 14 Förster resonance energy transfer (FRET)–paired protease substrates, and fluorescence activation was monitored over 45 min. (B) Kinetic fluorescence curves are shown for 14 FRET-paired substrates with (top) and without (bottom) addition of MMP3. (C) Fluorescence fold changes at 45 min (average of two replicates) were log2 transformed, and hierarchical clustering was performed to cluster proteases (vertical) by their substrate specificities and substrates (horizontal) by their protease specificities. Proteases labeled in green, orange, or blue represent metalloproteases, serine proteases, or aspartic proteases, respectively.

  • Fig. 4 Intrapulmonary-administered nanoparticle scaffolds penetrate deep within the lung and reach the periphery of KP tumors.

    (A) Wild-type mice were treated intratracheally or intravenously with VT750-labeled PEG-840kDa, and biodistribution was assessed. (B) Fluorescence imaging of organs was performed 60 min after intratracheal delivery. Clockwise from top-left: lung, spleen, heart, liver, and kidneys. (C) Organ-specific biodistribution was quantified (n = 4 each condition). Error bars represent SDs. (D) Healthy mice were either untreated (above; n = 1) or treated with intratracheal administration of biotin-labeled PEG scaffold (below; n = 2), followed by excision of lungs and immunohistochemical staining for biotin (brown). (E) Advanced-stage (16.5 weeks) KP mice were either untreated (top; n = 3) or treated with intratracheal administration of biotin-labeled PEG scaffold (bottom; n = 3), followed by excision of lungs and immunohistochemical staining as in (D).

  • Fig. 5 Activity-based nanosensors distinguish between diseased and healthy mice.

    (A) Tumor development was monitored by microCT in healthy (left; n = 11) and KP5wk (n = 12), KP7.5wk (n = 12), and KP10.5wk (n = 11) mice. The right three panels represent time series of a single mouse, with arrow indicating development of a single nodule over time. The quantification of tumor volume is shown to the right of each image, and the percentage of mice with detectable tumors at each time point (% detected) is shown above each panel. (B) Urine output of activity-based nanosensors administered to KP and control animals at 5 weeks (KP, n = 11; control, n = 9), 7.5 weeks (KP, n = 11; control, n = 12), and 10.5 weeks (KP, n = 12; control, n = 12) after tumor induction. For clarity, PP06 is presented on a larger-scale y axis. *Padj < 0.05, **Padj < 0.01, and ***Padj < 0.001 by two-tailed t test with Holm-Sidak correction; #Padj < 0.05 and ##Padj < 0.01 by Mann-Whitney test with Bonferroni correction. Error bars represent SEMs. (C to E) PCA of mean-normalized urinary reporters for KP mice and controls at 5 weeks (KP, n = 11; control, n = 9) (C), 7.5 weeks (KP, n = 11; control, n = 12) (D), and 10.5 weeks (KP, n = 12; control, n = 12) (E).

  • Fig. 6 Machine learning enables sensitive and specific classification of two genetic subtypes of LUAD.

    (A to C) ROC curves showing performance of a single random forest classifier trained on urinary reporters from a subset of KP7.5wk, EA7.5wk, and healthy controls in discriminating an independent test cohort of KP (A), EA (B), or a combination of KP and EA (C) mice from healthy controls at all three time points. (D) ROC curve showing performance of a random forest classifier trained on urinary reporters from KP7.5wk and EA7.5wk mice versus LPS and healthy control mice in discriminating an independent test cohort of KP7.5wk, EA7.5wk, and a combination of the two (termed “LUAD”) from healthy and LPS-treated mice. All ROC curves are averages over 10 independent train-test trials and show the results in the test cohort. n = 5 to 31; details of cohort sample sizes are shown in table S3.

  • Table 1 Reporter and substrate sequences for in vivo urinary diagnostics.

    ANP, 3-amino-3-(2-nitro-phenyl)propionic acid; Cha, 3-cyclohexylalanine; Cys(Me), methyl-cysteine; lowercase letters, d-amino acids.

    NameReporterPhotolabile groupSubstrateNanocarrier
    PP01e(+2G)(+6V)ndneeGFFsArANPGGPQGIWGQCPEG-840kDa
    PP02eG(+6V)ndneeGF(+1F)s(+1A)rANPGGPVGLIGCPEG-840kDa
    PP03e(+3G)(+1V)ndneeGFFs(+4A)rANPGGPVPLSLVMCPEG-840kDa
    PP04e(+2G)Vndnee(+2G)FFs(+4A)rANPGGPLGLRSWCPEG-840kDa
    PP05eGVndnee(+3G)(+1F)Fs(+4A)rANPGGPLGVRGKCPEG-840kDa
    PP06e(+2G)(+6V)ndnee(+3G)(+1F)(+1F)s(+1A)rANPGGfPRSGGGCPEG-840kDa
    PP07eG(+6V)ndnee(+3G)(+1F)Fs(+4A)rANPGGLGPKGQTGCPEG-840kDa
    PP08e(+3G)(+1V)ndneeG(+10F)FsArANPGGGSGRSANAKGCPEG-840kDa
    PP09eGVndneeGF(+10F)s(+4A)rANPGGKPISLISSGCPEG-840kDa
    PP10e(+2G)(+6V)ndneeG(+10F)(+1F)s(+1A)rANPGGILSRIVGGGCPEG-840kDa
    PP11e(+3G)(+1V)ndnee(+2G)(+10F)Fs(+4A)rANPGGSGSKIIGGGCPEG-840kDa
    PP12eGVndneeG(+10F)(+10F)sArANPGGPLGMRGGCPEG-840kDa
    PP13e(+2G)(+6V)ndnee(+3G)(+10F)(+1F)s(+4A)rANPGGP-(Cha)-G-Cys(Me)-HAGCPEG-840kDa
    PP14e(+3G)(+1V)ndnee(+2G)(+10F)(+10F)sArANPGGAPFEMSAGCPEG-840kDa

Supplementary Materials

  • stm.sciencemag.org/cgi/content/full/12/537/eaaw0262/DC1

    Methods

    Fig. S1. KP model genetically and histologically recapitulates human lung adenocarcinoma.

    Fig. S2. Human LUAD-associated proteases are not overexpressed in benign lung diseases.

    Fig. S3. LUAD protease panel genes are enriched across genetic and histological lung cancer subtypes.

    Fig. S4. Peptide substrates are cleaved by one or a combination of metallo, serine, and aspartic proteases.

    Fig. S5. Clearance of PEG-840kDa nanoparticles from lungs follows single phase exponential decay kinetics.

    Fig. S6. No toxicity is observed in mice treated with intrapulmonary activity-based nanosensors.

    Fig. S7. Activity-based nanosensors are stable to aerosolization.

    Fig. S8. Aerosolized nanoparticles penetrate deep within the lung and avoid distribution to off-target organs.

    Fig. S9. Free reporters enter the bloodstream after pulmonary delivery and are detectable in the urine by mass spectrometry.

    Fig. S10. Multiple reporters are differentially enriched in the urine of healthy mice and KP mice at 7.5 and 10.5 weeks.

    Fig. S11. Extrapulmonary disease is undetectable by intrapulmonary activity-based nanosensors.

    Fig. S12. Intrapulmonary activity-based nanosensors differentiate mice bearing Alk-driven lung cancer from healthy controls.

    Fig. S13. Pulmonary activity-based nanosensor cleavage profile is distinct in lung cancer and benign lung inflammation.

    Table S1. Reporter and substrate sequences for in vitro recombinant protease screen.

    Table S2. Quantification of tumor burden in KP mice by microCT.

    Table S3. Composition of training and test cohorts for random forest classification.

    Data file S1. Raw data from figures.

  • The PDF file includes:

    • Methods
    • Fig. S1. KP model genetically and histologically recapitulates human lung adenocarcinoma.
    • Fig. S2. Human LUAD-associated proteases are not overexpressed in benign lung diseases.
    • Fig. S3. LUAD protease panel genes are enriched across genetic and histological lung cancer subtypes.
    • Fig. S4. Peptide substrates are cleaved by one or a combination of metallo, serine, and aspartic proteases.
    • Fig. S5. Clearance of PEG-840kDa nanoparticles from lungs follows single phase exponential decay kinetics.
    • Fig. S6. No toxicity is observed in mice treated with intrapulmonary activity-based nanosensors.
    • Fig. S7. Activity-based nanosensors are stable to aerosolization.
    • Fig. S8. Aerosolized nanoparticles penetrate deep within the lung and avoid distribution to off-target organs.
    • Fig. S9. Free reporters enter the bloodstream after pulmonary delivery and are detectable in the urine by mass spectrometry.
    • Fig. S10. Multiple reporters are differentially enriched in the urine of healthy mice and KP mice at 7.5 and 10.5 weeks.
    • Fig. S11. Extrapulmonary disease is undetectable by intrapulmonary activity-based nanosensors.
    • Fig. S12. Intrapulmonary activity-based nanosensors differentiate mice bearing Alk-driven lung cancer from healthy controls.
    • Fig. S13. Pulmonary activity-based nanosensor cleavage profile is distinct in lung cancer and benign lung inflammation.
    • Table S1. Reporter and substrate sequences for in vitro recombinant protease screen.
    • Table S2. Quantification of tumor burden in KP mice by microCT.
    • Table S3. Composition of training and test cohorts for random forest classification.
    • Legend for data file S1

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    Other Supplementary Material for this manuscript includes the following:

    • Data file S1 (Microsoft Excel format). Raw data from figures.

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