Ming Teh

RUNX3, A Novel Tumor Suppressor, Is Frequently Inactivated in Gastric Cancer by Protein Mislocalization

Loss of RUNX3 expression is suggested to be causally related to gastric cancer as 45% to 60% of gastric cancers do not express RUNX3 mainly due to hypermethylation of the RUNX3 promoter. Here, we examined for other defects in the properties of RUNX3 in gastric cancers that express RUNX3. Ninety-seven gastric cancer tumor specimens and 21 gastric cancer cell lines were examined by immunohistochemistry using novel anti-RUNX3 monoclonal antibodies. In normal gastric mucosa, RUNX3 was expressed most strongly in the nuclei of chief cells as well as in surface epithelial cells. In chief cells, a significant portion of the protein was also found in the cytoplasm. RUNX3 was not detectable in 43 of 97 (44%) cases of gastric cancers tested and a further 38% showed exclusive cytoplasmic localization, whereas only 18% showed nuclear localization. Evidence is presented suggesting that transforming growth factor-β is an inducer of nuclear translocation of RUNX3, and RUNX3 in the cytoplasm of cancer cells is inactive as a tumor suppressor. RUNX3 was found to be inactive in 82% of gastric cancers through either gene silencing or protein mislocalization to the cytoplasm. In addition to the deregulation of mechanisms controlling gene expression, there would also seem to be at least one other mechanism controlling nuclear translocation of RUNX3 that is impaired frequently in gastric cancer.

Diagnostic potential of near-infrared Raman spectroscopy in the stomach: differentiating dysplasia from normal tissue

Raman spectroscopy is a molecular vibrational spectroscopic technique that is capable of optically probing the biomolecular changes associated with diseased transformation. The purpose of this study was to explore near-infrared (NIR) Raman spectroscopy for identifying dysplasia from normal gastric mucosa tissue. A rapid-acquisition dispersive-type NIR Raman system was utilised for tissue Raman spectroscopic measurements at 785 nm laser excitation. A total of 76 gastric tissue samples obtained from 44 patients who underwent endoscopy investigation or gastrectomy operation were used in this study. The histopathological examinations showed that 55 tissue specimens were normal and 21 were dysplasia. Both the empirical approach and multivariate statistical techniques, including principal components analysis (PCA), and linear discriminant analysis (LDA), together with the leave-one-sample-out cross-validation method, were employed to develop effective diagnostic algorithms for classification of Raman spectra between normal and dysplastic gastric tissues. High-quality Raman spectra in the range of 800–1800 cm−1 can be acquired from gastric tissue within 5 s. There are specific spectral differences in Raman spectra between normal and dysplasia tissue, particularly in the spectral ranges of 1200–1500 cm−1 and 1600–1800 cm−1, which contained signals related to amide III and amide I of proteins, CH3CH2 twisting of proteins/nucleic acids, and the C=C stretching mode of phospholipids, respectively. The empirical diagnostic algorithm based on the ratio of the Raman peak intensity at 875 cm−1 to the peak intensity at 1450 cm−1 gave the diagnostic sensitivity of 85.7% and specificity of 80.0%, whereas the diagnostic algorithms based on PCA-LDA yielded the diagnostic sensitivity of 95.2% and specificity 90.9% for separating dysplasia from normal gastric tissue. Receiver operating characteristic (ROC) curves further confirmed that the most effective diagnostic algorithm can be derived from the PCA-LDA technique. Therefore, NIR Raman spectroscopy in conjunction with multivariate statistical technique has potential for rapid diagnosis of dysplasia in the stomach based on the optical evaluation of spectral features of biomolecules.

In vivo diagnosis of esophageal cancer using image-guided Raman endoscopy and biomolecular modeling.

The aim of this work was to evaluate the biochemical foundation and clinical merit of multimodal image-guided Raman endoscopy technique for real-time in vivo diagnosis of cancer in the esophagus during clinical endoscopic examinations. A novel fiber-optic Raman endoscopy system was utilized for in vivo esophageal Raman measurements at 785 nm laser excitation within 0.5 second under the multimodal wide-field endoscopic imaging (white light reflectance (WLR) imaging, narrow-band imaging (NBI) and autofluorescence imaging (AFI) guidance. A total of 75 esophageal tissue sites from 27 patients were measured, in which 42 in vivo Raman spectra were from normal tissues and 33 in vivo Raman spectra were from malignant tumors as confirmed by histopathology. The biomolecular modeling (non-negativity-constrained least-squares minimization (NNCLSM) utilizing six basis reference spectra from the representative biochemicals (i.e., actin, collagen, DNA, histones, triolein and glycogen) were employed to estimate the biochemical compositions of esophageal tissue. The resulting diagnostically significant fit coefficients were further utilized through linear discriminant analysis (LDA) and leave-one tissue site-out, cross validation method to develop diagnostic algorithms for esophageal cancer diagnosis. High-quality in vivo Raman spectra in the range of 800–1800 cm−1 can be acquired from normal and cancerous esophageal mucosa in real-time under multimodal endoscopic imaging guidance. Esophageal cancer tissue showed distinct Raman signals mainly associated with cell proliferation, lipid reduction, abnormal nuclear activity and neovasculation. The fit coefficients for actin, DNA, histones, triolein, and glycogen were found to be most significant for construction of the LDA diagnostic model, giving rise to an accuracy of 96.0% (i.e., sensitivity of 97.0% and specificity of 95.2%) for in vivo diagnosis of esophageal cancer. This study demonstrates that multimodal image-guided Raman endoscopy technique in conjunction with biomolecular modeling has promising potential for the real-time, in vivo diagnosis and detection of esophageal cancer during clinical endoscopic examination.

Integrated Raman spectroscopy and trimodal wide-field imaging techniques for real-time in vivo tissue Raman measurements at endoscopy

We report an integrated Raman spectroscopy and trimodal (white-light reflectance, autofluorescence, and narrow-band) imaging techniques for real-time in vivo tissue Raman measurements at endoscopy. A special 1.8 mm endoscopic Raman probe with filtering modules is developed, permitting effective elimination of interference of fluorescence background and silica Raman in fibers while maximizing tissue Raman collections. We demonstrate that high-quality in vivo Raman spectra of upper gastrointestinal tract can be acquired within 1 s or subseconds under the guidance of wide-field endoscopic imaging modalities, greatly facilitating the adoption of Raman spectroscopy into clinical research and practice during routine endoscopic inspections.

Diet, reproductive factors and lung cancer risk among Chinese women in Singapore: evidence for a protective effect of soy in nonsmokers.

The factors associated with risk of lung cancer among nonsmokers have not been fully elucidated, but dietary factors have consistently been shown to play a role. Chinese women are unique in having a high incidence of lung cancer despite a low smoking prevalence. This population is also known to have a high intake of soy, a dietary source of phytoestrogens. We conducted a hospital‐based case‐control study among Singapore Chinese women, comprising 303 cases and 765 age‐matched controls, of whom 176 cases and 663 controls were lifetime nonsmokers. Data on demographic background, reproductive factors and dietary intake of fruit, vegetables and soy foods were obtained by in‐person interview. We observed an inverse association between intake of total, cruciferous and non‐cruciferous vegetables and risk of lung cancer among smokers. Although smokers in the highest tertile of fruit intake also had a lower risk, this was not statistically significant. Higher intake of soy foods significantly reduced risk of lung cancer among lifetime nonsmokers, but not among smokers. When soy isoflavonoid intake in mg/week was computed based on frequency and portion size of intake of eight common local soy foods, the adjusted OR among nonsmokers for the highest tertile compared to the lowest was 0.56, 95% CI 0.37–0.85 (p for trend <0.01). Fruit intake was also significantly associated with reduced lung cancer risk among nonsmokers, but the effect was not significant after adjustment for soy intake. On the other hand, soy intake remained an independent predictor of risk after controlling for fruit intake. Reproductive effects were also primarily confined to lifetime nonsmokers, among whom having 3 or more livebirths (adjusted OR 0.65, 0.44–0.96) and a menstrual cycle length of more than 30 days (OR 0.46, 0.25–0.84) accorded a significantly reduced risk of lung cancer. Place of birth was significantly associated with risk among nonsmokers (OR 2.6, 1.7–3.9 for China‐born vs. local born) but not among smokers. When analysis was restricted to nonsmokers with adenocarcinomas, the dietary effects were consistent or enhanced. On stepwise regression, soy intake and cycle length emerged as the independent dietary and reproductive predictors of lung cancer risk in nonsmokers. These findings are consistent with other evidence suggesting an involvement of estrogen‐related pathways in lung cancer among non‐smoking women.

Whole-genome reconstruction and mutational signatures in gastric cancer

BackgroundGastric cancer is the second highest cause of global cancer mortality. To explore the complete repertoire of somatic alterations in gastric cancer, we combined massively parallel short read and DNA paired-end tag sequencing to present the first whole-genome analysis of two gastric adenocarcinomas, one with chromosomal instability and the other with microsatellite instability.ResultsIntegrative analysis and de novo assemblies revealed the architecture of a wild-type KRAS amplification, a common driver event in gastric cancer. We discovered three distinct mutational signatures in gastric cancer - against a genome-wide backdrop of oxidative and microsatellite instability-related mutational signatures, we identified the first exome-specific mutational signature. Further characterization of the impact of these signatures by combining sequencing data from 40 complete gastric cancer exomes and targeted screening of an additional 94 independent gastric tumors uncovered ACVR2A, RPL22 and LMAN1 as recurrently mutated genes in microsatellite instability-positive gastric cancer and PAPPA as a recurrently mutated gene in TP53 wild-type gastric cancer.ConclusionsThese results highlight how whole-genome cancer sequencing can uncover information relevant to tissue-specific carcinogenesis that would otherwise be missed from exome-sequencing data.

Characterizing variability in in vivo Raman spectra of different anatomical locations in the upper gastrointestinal tract toward cancer detection

Raman spectroscopy is an optical vibrational technology capable of probing biomolecular changes of tissue associated with cancer transformation. This study aimed to characterize in vivo Raman spectroscopic properties of tissues belonging to different anatomical regions in the upper gastrointestinal (GI) tract and explore the implications for early detection of neoplastic lesions during clinical gastroscopy. A novel fiber-optic Raman endoscopy technique was utilized for real-time in vivo tissue Raman measurements of normal esophageal (distal, middle, and proximal), gastric (antrum, body, and cardia) as well as cancerous esophagous and gastric tissues from 107 patients who underwent endoscopic examinations. The non-negativity-constrained least squares minimization coupled with a reference database of Raman active biochemicals (i.e., actin, histones, collagen, DNA, and triolein) was employed for semiquantitative biomolecular modeling of tissue constituents in the upper GI. A total of 1189 in vivo Raman spectra were acquired from different locations in the upper GI. The Raman spectra among the distal, middle, and proximal sites of the esophagus showed no significant interanatomical variability. The interanatomical variability of Raman spectra among normal gastric tissue (antrum, body, and cardia) was subtle compared to cancerous tissue transformation, whereas biomolecular modeling revealed significant differences between the two organs, particularly in the gastroesophageal junction associated with proteins, DNA, and lipids. Cancerous tissues can be identified across interanatomical regions with accuracies of 89.3% [sensitivity of 92.6% (162∕175); specificity of 88.6% (665∕751)], and of 94.7% [sensitivity of 90.9% (30∕33); specificity of 93.9% (216∕230)] in the gastric and esophagus, respectively, using partial least squares-discriminant analysis together with the leave-one tissue site-out, cross validation. This work demonstrates that Raman endoscopy technique has promising clinical potential for real-time, in vivo diagnosis and detection of malignancies in the upper GI at the molecular level.

In vivo diagnosis of gastric cancer using Raman endoscopy and ant colony optimization techniques

This study aims to evaluate the clinical utility of image‐guided Raman endoscopy for in vivo diagnosis of neoplastic lesions in the stomach at gastroscopy. A rapid‐acquisition image‐guided Raman endoscopy system with 785‐nm excitation has been developed to acquire in vivo gastric tissue Raman spectra within 0.5 sec during clinical gastroscopic examinations. A total of 1,063 in vivo Raman spectra were acquired from 238 tissue sites of 67 gastric patients, in which 934 Raman spectra were from normal tissue whereas 129 Raman spectra were from neoplastic gastric tissue. The swarm intelligence‐based algorithm (i.e., ant colony optimization (ACO) integrated with linear discriminant analysis (LDA)) was developed for spectral variables selection to identify the biochemical important Raman bands for differentiation between normal and neoplastic gastric tissue. The ACO‐LDA algorithms together with the leave‐one tissue site‐out, cross validation method identified seven diagnostically important Raman bands in the regions of 850–875, 1,090–1,110, 1,120–1,130, 1,170–1,190, 1,320–1,340, 1,655–1,665 and 1,730–1,745 cm−1 related to proteins, nucleic acids and lipids of tissue and provided a diagnostic sensitivity of 94.6% and specificity of 94.6% for distinction of gastric neoplasia. The predictive sensitivity of 89.3% and specificity of 97.8% were also achieved for an independent test validation dataset (20% of total dataset). This work demonstrates for the first time that the real‐time image‐guided Raman endoscopy associated with ACO‐LDA diagnostic algorithms has potential for the noninvasive, in vivo diagnosis and detection of gastric neoplasia during clinical gastroscopy.

Real-time Raman spectroscopy for in vivo, online gastric cancer diagnosis during clinical endoscopic examination.

Optical spectroscopic techniques including reflectance, fluorescence and Raman spectroscopy have shown promising potential for in vivo precancer and cancer diagnostics in a variety of organs. However, data-analysis has mostly been limited to post-processing and off-line algorithm development. In this work, we develop a fully automated on-line Raman spectral diagnostics framework integrated with a multimodal image-guided Raman technique for real-time in vivo cancer detection at endoscopy. A total of 2748 in vivo gastric tissue spectra (2465 normal and 283 cancer) were acquired from 305 patients recruited to construct a spectral database for diagnostic algorithms development. The novel diagnostic scheme developed implements on-line preprocessing, outlier detection based on principal component analysis statistics (i.e., Hotellings T2 and Q-residuals) for tissue Raman spectra verification as well as for organ specific probabilistic diagnostics using different diagnostic algorithms. Free-running optical diagnosis and processing time of < 0.5 s can be achieved, which is critical to realizing real-time in vivo tissue diagnostics during clinical endoscopic examination. The optimized partial least squares-discriminant analysis (PLS-DA) models based on the randomly resampled training database (80% for learning and 20% for testing) provide the diagnostic accuracy of 85.6% [95% confidence interval (CI): 82.9% to 88.2%] [sensitivity of 80.5% (95% CI: 71.4% to 89.6%) and specificity of 86.2% (95% CI: 83.6% to 88.7%)] for the detection of gastric cancer. The PLS-DA algorithms are further applied prospectively on 10 gastric patients at gastroscopy, achieving the predictive accuracy of 80.0% (60/75) [sensitivity of 90.0% (27/30) and specificity of 73.3% (33/45)] for in vivo diagnosis of gastric cancer. The receiver operating characteristics curves further confirmed the efficacy of Raman endoscopy together with PLS-DA algorithms for in vivo prospective diagnosis of gastric cancer. This work successfully moves biomedical Raman spectroscopic technique into real-time, on-line clinical cancer diagnosis, especially in routine endoscopic diagnostic applications.

Digital pathology: exploring its applications in diagnostic surgical pathology practice

&NA; There has been a recent upsurge in worldwide attention on digital pathology, which has transformed from static snapshots from camera‐equipped microscopes to its modern form that encompasses scanning of whole glass slides with evaluation of histological images on a computer screen, along with management of its accompanying information. Although it has been widely accepted in education and research, its implementation in diagnostic surgical pathology practice is not without challenges in workflow integration, technological infrastructure, pathologist acclimatisation, global standardisation for clinical practice, and cost issues, among others. Nonetheless, early adopters have harnessed its benefits in specific niches, like frozen section services and remote second opinion consultations. Its tremendous potential is worthy of further validation to compare with conventional glass slide evaluation, even while it is already paving the way for advancement into virtual three‐dimensional imaging technology, with a glimpse into a possible future digital diagnostic pathology practice.