Early Detection of Lung Cancer Using Artificial Intelligence-Enhanced Optical Nanosensing of Chromatin Alterations in Field Carcinogenesis

Authors

Ali Daneshkhah
Sravya Prabhala
Parvathi Viswanathan
Hariharan Subramanian
Jianan Lin
Andrew S Chang
Ankit Bharat
Hemant Kumar Roy
Vadim Backman

Publication Date

8-22-2023

Journal

Scientific Reports

DOI

10.1038/s41598-023-40550-6

PMID

37608214

PMCID

PMC10444865

PubMedCentral® Posted Date

8-22-2023

PubMedCentral® Full Text Version

Post-print

Published Open-Access

yes

Keywords

Humans, Artificial Intelligence, Early Detection of Cancer, Lung Neoplasms, Chromatin, Carcinogenesis/, Biophysics, Biotechnology, Cancer, Structural biology, Biomarkers, Diseases, Health care, Medical research, Risk factors, Optics and photonics

Abstract

Supranucleosomal chromatin structure, including chromatin domain conformation, is involved in the regulation of gene expression and its dysregulation has been associated with carcinogenesis. Prior studies have shown that cells in the buccal mucosa carry a molecular signature of lung cancer among the cigarette-smoking population, the phenomenon known as field carcinogenesis or field of injury. Thus, we hypothesized that chromatin structural changes in buccal mucosa can be predictive of lung cancer. However, the small size of the chromatin chain (approximately 20 nm) folded into chromatin packing domains, themselves typically below 300 nm in diameter, preclude the detection of alterations in intradomain chromatin conformation using diffraction-limited optical microscopy. In this study, we developed an optical spectroscopic statistical nanosensing technique to detect chromatin packing domain changes in buccal mucosa as a lung cancer biomarker: chromatin-sensitive partial wave spectroscopic microscopy (csPWS). Artificial intelligence (AI) was applied to csPWS measurements of chromatin alterations to enhance diagnostic performance. Our AI-enhanced buccal csPWS nanocytology of 179 patients at two clinical sites distinguished Stage-I lung cancer versus cancer-free controls with an area under the ROC curve (AUC) of 0.92 ± 0.06 for Site 1 (in-state location) and 0.82 ± 0.11 for Site 2 (out-of-state location).

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