Early detection of non-small cell lung cancer in liquid biopsies by ultrasensitive protease activity analysis

Udukala, Dinusha Nishani; Wendel, Sebastian O.; Wang, Hongwang; Yapa, Asanka S.; Covarrubias‐Zambrano, Obdulia; Janik, Katharine; Gadbury, Gary L.; Troyer, Deryl L.; Bossmann, Stefan H.

doi:10.20517/2394-4722.2020.45

Cited by 5 publications

(4 citation statements)

References 20 publications

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“…The detail procedures used for synthesis of nanobiosensors and individual components (dopamine coated Fe/Fe 3 O 4 nanoparticles, peptides, and both fluorescent dyes, TCPP, and cyanine 5.5) can be found in previous publications from the Bossmann research group 4,[13][14][15][16][17][18] .…”

Section: Synthesis Of Nanobiosensorsmentioning

confidence: 99%

“…Since 2007, established panels of proteases and arginase activities in serum have been measured using this technology [13][14][15][16][17][18] . More specifically, a panel of seven proteases (caspases B and E, matrix metalloproteinases (MMPs) 1, 3, and 9, urokinase plasminogen activator (UpA), neutrophil elastase and arginase were established by the Bossmann group for early detection of PDAC in 2018 13 .…”

Section: Introductionmentioning

confidence: 99%

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A New Hope for Liquid Biopsies: Early Detection of Pancreatic Cancer By Means of Protease Activity Detection in Serum Applying a Hierarchical Decision Structure

Covarrubias‐Zambrano

Agarwal

Kalubowilage

et al. 2022

Preprint

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Over the last 6 years, five-year survival rate for pancreatic cancer patients has increased from 6 to 10% after the initial diagnosis, which makes it one of the deadliest cancer types. This disease is known as the “silent killer” because early detection is challenging due to the location of the pancreas in the body and the nonspecific clinical symptoms. The Bossmann group has developed ultrasensitive nanobiosensors for protease/arginase detection comprised of Fe/Fe3O4nanoparticles, cyanine 5.5, and designer peptide sequences linked to TCPP. Initial data obtained from both gene expression analysis and protease/arginase activity detection in serum indicated the feasibility of early pancreatic cancer detection. Several matrix metalloproteinases (MMPs, -1, -3, and -9), cathepsins (CTS) B and E, neutrophil elastase, and urokinase plaminogen activator (uPA) have been identified as candidates for proximal biomarkers. In this study, we have confirmed our initial results from 2018 performing serum sample analysis assays using a larger group sample size (n=159), which included localized (n=33) and metastatic pancreatic cancer (n=50), pancreatitis (n=26), and an age-matched healthy control group (n=50). The data obtained from the eight nanobiosensors capable of ultrasensitive protease and arginase activity measurements were analyzed by means of an optimized information fusion-based hierarchical decision structure. This permits the modeling of early-stage detection of pancreatic cancer as a multi-class classification problem. The most striking result is that this methodology permits the detection of localized pancreatic cancers from serum analyses with 96% accuracy.

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Section: Synthesis Of Nanobiosensorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A New Hope for Liquid Biopsies: Early Detection of Pancreatic Cancer By Means of Protease Activity Detection in Serum Applying a Hierarchical Decision Structure

Covarrubias‐Zambrano

Agarwal

Kalubowilage

et al. 2022

Preprint

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“…This selection was based on Gene expression analysis [17] using data from NCBI GEO, Entrez Gene ID, Unigene ID and Gene Symbol [17] . Protease and Arginase activities in serum were measured with Fe/Fe 3 O 4 core/shell nanobiosensors with an average particle size of 15 nm [5] , [18] – [20] , [21] , [22] . The function principle is shown in Figure 1 (A) .…”

Section: Introductionmentioning

confidence: 99%

Early Detection of Pancreatic Cancers Using Liquid Biopsies and Hierarchical Decision Structure

Agarwal

Covarrubias‐Zambrano

Bossmann

et al. 2022

IEEE J. Transl. Eng. Health Med.

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Objective: Pancreatic cancer (PC) is a silent killer, because its detection is difficult and to date no effective treatment has been developed. In the US, the current 5-year survival rate of 11%. Therefore, PC has to be detected as early as possible. Methods and procedures: In this work, we have combined the use of ultrasensitive nanobiosensors for protease/arginase detection with information fusion based hierarchical decision structure to detect PC at the localized stage by means of a simple Liquid Biopsy. The problem of early-stage detection of pancreatic cancer is modelled as a multi-class classification problem. We propose a Hard Hierarchical Decision Structure (HDS) along with appropriate feature engineering steps to improve the performance of conventional multi-class classification approaches. Further, a Soft Hierarchical Decision Structure (SDS) is developed to additionally provide confidences of predicted labels in the form of class probability values. These frameworks overcome the limitations of existing research studies that employ simple biostatistical tools and do not effectively exploit the information provided by ultrasensitive protease/arginase analyses. Results: The experimental results demonstrate that an overall mean classification accuracy of around 92% is obtained using the proposed approach, as opposed to 75% with conventional multi-class classification approaches. This illustrates that the proposed HDS framework outperforms traditional classification techniques for early-stage PC detection. Conclusion: Although this study is only based on 31 pancreatic cancer patients and a healthy control group of 48 human subjects, it has enabled combining Liquid Biopsies and Machine Learning methodologies to reach the goal of earliest PC detection. The provision of both decision labels (via HDS) as well as class probabilities (via SDS) helps clinicians identify instances where statistical model-based predictions lack confidence. This further aids in determining if more tests are required for better diagnosis. Such a strategy makes the output of our decision model more interpretable and can assist with the diagnostic procedure. Clinical impact: With further validation, the proposed framework can be employed as a decision support tool for the clinicians to help in detection of pancreatic cancer at early stages.

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“…Both of the original articles focused on lung cancer. Rath et al [6] investigated a new mechanism of drug resistance for small-cell lung cancer (SCLC) circulating tumor cells (CTCs). They measured cellular fragments in SCLC CTC cell lines and compared to permanent SCLC lines.…”

mentioning

confidence: 99%

Advances in cancer early diagnosis with liquid biopsy-based approaches

Zhang

2021

JCMT

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Early detection of non-small cell lung cancer in liquid biopsies by ultrasensitive protease activity analysis

Cited by 5 publications

References 20 publications

A New Hope for Liquid Biopsies: Early Detection of Pancreatic Cancer By Means of Protease Activity Detection in Serum Applying a Hierarchical Decision Structure

A New Hope for Liquid Biopsies: Early Detection of Pancreatic Cancer By Means of Protease Activity Detection in Serum Applying a Hierarchical Decision Structure

Early Detection of Pancreatic Cancers Using Liquid Biopsies and Hierarchical Decision Structure

Advances in cancer early diagnosis with liquid biopsy-based approaches

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