Abstract
BackgroundEarly lung cancer detection remains a clinical challenge for standard diagnostic biopsies due to insufficient tumor morphological evidence. As epigenetic alterations precede morphological changes, expression alterations of certain imprinted genes could serve as actionable diagnostic biomarkers for malignant lung lesions.ResultsUsing the previously established quantitative chromogenic imprinted gene in situ hybridization (QCIGISH) method, elevated aberrant allelic expression of imprinted genes GNAS, GRB10, SNRPN and HM13 was observed in lung cancers over benign lesions and normal controls, which were pathologically confirmed among histologically stained normal, paracancerous and malignant tissue sections. Based on the differential imprinting signatures, a diagnostic grading model was built on 246 formalin-fixed and paraffin-embedded (FFPE) surgically resected lung tissue specimens, tested against 30 lung cytology and small biopsy specimens, and blindly validated in an independent cohort of 155 patients. The QCIGISH diagnostic model demonstrated 99.1% sensitivity (95% CI 97.5–100.0%) and 92.1% specificity (95% CI 83.5–100.0%) in the blinded validation set. Of particular importance, QCIGISH achieved 97.1% sensitivity (95% CI 91.6–100.0%) for carcinoma in situ to stage IB cancers with 100% sensitivity and 91.7% specificity (95% CI 76.0–100.0%) noted for pulmonary nodules with diameters ≤ 2 cm.ConclusionsOur findings demonstrated the diagnostic value of epigenetic imprinting alterations as highly accurate translational biomarkers for a more definitive diagnosis of suspicious lung lesions.
Highlights
Lung cancer detection remains a clinical challenge for standard diagnostic biopsies due to insufficient tumor morphological evidence
Despite the preliminary model achieving 92% sensitivity and 88% specificity for lung cancer diagnosis, opportunities to further advance the diagnostic performance of quantitative chromogenic imprinted gene in situ hybridization (QCIGISH) in clinical applications need to be explored
QCIGISH lung cancer diagnostic grading model validation in lung cytology and small biopsy specimens We blindly validated the final QCIGISH diagnostic grading model in an independent cohort of 155 patients achieving an overall sensitivity of 99.1% (116/117, 95% CI 97.5–100.0%) and specificity of 92.1% (35/38, 95% CI 83.5–100.0%) with receiver operating characteristic (ROC) areas under the curve (AUC) of 0.99 (Fig. 4A, B)
Summary
Lung cancer detection remains a clinical challenge for standard diagnostic biopsies due to insufficient tumor morphological evidence. Compared to late-stage lung cancer, early-stage lung cancer showed better prognosis and longer survival [2]. Low-dose computed tomography (LDCT) screening has made great contribution to the early discovery of lung cancer and reducing lung cancer mortality [3]. The presurgical diagnosis of early-stage lung cancer from standard diagnostic biopsies is still challenging because. Zhou et al Clinical Epigenetics (2021) 13:220 of insufficient tumor morphological evidence to make a definitive pathological diagnosis [4]. Several genetic [5,6,7] and epigenetic biomarkers [8,9,10] have been developed for early cancer detection. The reliability and efficiency of these biomarkers have yet to be optimized for clinical applications [11]
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