Applied Statistics for Human Genetics Using R

High Diagnostic Accuracy of Epigenetic Imprinting Biomarkers in Thyroid Nodules

PURPOSE

To explore the novel diagnostic value of epigenetic imprinting biomarkers in thyroid nodules.

PATIENTS AND METHODS

A total of 550 patients with fine-needle aspiration (FNA)-evaluated and histopathologically confirmed thyroid nodules were consecutively recruited from eight medical centers. Quantitative chromogenic imprinted gene in situ hybridization (QCIGISH) was used to assess the allelic expression of imprinted genes SNRPN and HM13, on the basis of which a diagnostic grading model for thyroid nodules was developed. The model was retrospectively trained on 124 postsurgical thyroid samples, optimized on 32 presurgical FNA samples, and prospectively validated on 394 presurgical FNA samples. Blinded central review-based cytopathologic and histopathologic diagnoses were used as the reference standard.

RESULTS

For thyroid malignancy, the QCIGISH test achieved an overall diagnostic sensitivity of 100% (277/277), a specificity of 91.5% (107/117; 95% CI, 86.4 to 96.5), a positive predictive value (PPV) of 96.5% (95% CI, 94.4 to 98.6), and a negative predictive value (NPV) of 100% in the prospective validation, with a diagnostic accuracy of 97.5% (384/394; 95% CI, 95.9 to 99.0). QCIGISH demonstrated a PPV of 97.8% (95% CI, 94.7 to 100) and NPV of 100%, with a diagnostic accuracy of 98.2% (111/113; 95% CI, 95.8 to 100), for indeterminate Bethesda III-V thyroid nodules. QCIGISH demonstrated a PPV of 96.6% (95% CI, 91.9 to 100) and a NPV of 100%, with a diagnostic accuracy of 97.5% (79/81; 95% CI, 94.2 to 100), for Bethesda III-IV. For Bethesda VI, QCIGISH showed a 100% (184/184) accuracy.

CONCLUSION

This imprinting biomarker-based test can effectively distinguish malignant from benign thyroid nodules. The high PPV and NPV make the test both an excellent rule-in and rule-out diagnostic tool. With such a diagnostic performance and its technical simplicity, this novel thyroid molecular test is clinically widely applicable.

Epigenetic imprinting alterations as effective diagnostic biomarkers for early-stage lung cancer and small pulmonary nodules

Background

Early 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.

Results

Using 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.

Conclusions

Our findings demonstrated the diagnostic value of epigenetic imprinting alterations as highly accurate translational biomarkers for a more definitive diagnosis of suspicious lung lesions.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13148-021-01203-5.

Novel visualized quantitative epigenetic imprinted gene biomarkers diagnose the malignancy of ten cancer types

Background

Epigenetic alterations are involved in most cancers, but its application in cancer diagnosis is still limited. More practical and intuitive methods to detect the aberrant expressions from clinical samples using highly sensitive biomarkers are needed. In this study, we developed a novel approach in identifying, visualizing, and quantifying the biallelic and multiallelic expressions of an imprinted gene panel associated with cancer status. We evaluated the normal and aberrant expressions measured using the imprinted gene panel to formulate diagnostic models which could accurately distinguish the imprinting differences of normal and benign cases from cancerous tissues for each of the ten cancer types.

Results

The Quantitative Chromogenic Imprinted Gene In Situ Hybridization (QCIGISH) method developed from a 1013-case study which provides a visual and quantitative analysis of non-coding RNA allelic expressions identified the guanine nucleotide-binding protein, alpha-stimulating complex locus (GNAS), growth factor receptor-bound protein (GRB10), and small nuclear ribonucleoprotein polypeptide N (SNRPN) out of five tested imprinted genes as efficient epigenetic biomarkers for the early-stage detection of ten cancer types. A binary algorithm developed for cancer diagnosis showed that elevated biallelic expression (BAE), multiallelic expression (MAE), and total expression (TE) measurements for the imprinted gene panel were associated with cell carcinogenesis, with the formulated diagnostic models achieving consistently high sensitivities (91–98%) and specificities (86–98%) across the different cancer types.

Conclusions

The QCIGISH method provides an innovative way to visually assess and quantitatively analyze individual cells for cancer potential extending from hyperplasia and dysplasia until carcinoma in situ and invasion, which effectively supplements standard clinical cytologic and histopathologic diagnosis for early cancer detection. In addition, the diagnostic models developed from the BAE, MAE, and TE measurements of the imprinted gene panel GNAS, GRB10, and SNRPN could provide important predictive information which are useful in early-stage cancer detection and personalized cancer management.