Nuclear morphometry as a biomarker for bronchial intraepithelial neoplasia: correlation with genetic damage and cancer development

Quantitative nuclear phenotype signatures predict nodal disease in oral squamous cell carcinoma

BACKGROUND

Early-stage oral squamous cell carcinoma (OSCC) patients have a one-in-four risk of regional metastasis (LN+), which is also the most significant prognostic factor for survival. As there are no validated biomarkers for predicting LN+ in early-stage OSCC, elective neck dissection often leads to over-treatment and under-treatment. We present a machine-learning-based model using the quantitative nuclear phenotype of cancer cells from the primary tumor to predict the risk of nodal disease.

METHODS AND FINDINGS

Tumor specimens were obtained from 35 patients diagnosed with primary OSCC and received surgery with curative intent. Of the 35 patients, 29 had well (G1) or moderately (G2) differentiated tumors, and six had poorly differentiated tumors. From each, two consecutive sections were stained for hematoxylin & eosin and Feulgen-thionin staining. The slides were scanned, and images were processed to curate nuclear morphometric features for each nucleus, measuring nuclear morphology, DNA amount, and chromatin texture/organization. The nuclei (n = 384,041) from 15 G1 and 14 G2 tumors were randomly split into 80% training and 20% test set to build the predictive model by using Random Forest (RF) analysis which give each tumor cell a score, NRS. The area under ROC curve (AUC) was 99.6% and 90.7% for the training and test sets, respectively. At the cutoff score of 0.5 as the median NRS of each region of interest (n = 481), the AUC was 95.1%. We then developed a patient-level model based on the percentage of cells with an NRS ≥ 0.5. The prediction performance showed AUC of 97.7% among the 80% (n = 23 patient) training set and with the cutoff of 61% positive cells achieved 100% sensitivity and 91.7% specificity. When applying the 61% cutoff to the 20% test set patients, the model achieved 100% accuracy.

CONCLUSIONS

Our findings may have a clinical impact with an easy, accurate, and objective biomarker from routine pathology tissue, providing an unprecedented opportunity to improve neck management decisions in early-stage OSCC patients.

Large-scale DNA organization is a prognostic marker of breast cancer survival

Breast cancer is the leading cause of cancer-related deaths among women worldwide. We investigated whether changes in large-scale DNA organization (LDO) of tumor epithelial nuclei are an indicator of the aggressiveness of the tumor. We tested our algorithm on a set of 172 duplicates TMA cores samples coming from 95 breast cancer patients. Thirty-five patients died of breast cancer, and 60 were still alive 10 years after surgery. Duplicates cores were used to create training and test set. The TMA slides were stained with Feulgen-thionin and imaged using our in-house high-resolution Imaging system. Automated segmentation of cell nuclei followed by manual selection of intact, in-focus nuclei resulted in an average of 50 cell nuclei per sample available for analysis. Using forward stepwise linear discriminant analysis, a combination of six features that combined linearly gave the best discrimination between the two groups of cells: cells collected from 'deceased' patients TMA specimens and cells collected from "survivors" patients TMA specimens. Five of these features measure the spatial organization of DNA chromatin. The resulting canonical score is named cell LDO score. A patient LDO score, percentage of cell nuclei with a cell LDO score higher than a predefined cutoff value, was processed for the specimens in the test set, and a cutoff value was defined to classify patients with a low or a high LDO score. Using this binary test, 82.1% of patients were correctly classified are "deceased" or "survivors," with a specificity of 79% and a sensitivity of 88%. The relative risk of death of an individual with a high LDO score was nine times higher than for a patient with a low LDO score. When testing the combination of LDO score, node status, histological grade, and tumor grade to predict breast cancer survival, LDO was the most significant predictor. LDO classification was also highly associated with survival for only grade 1 and 2 patients as well as for only grade 3 patients. Our result confirms the potential of LDO to measure phenotypic changes associated with more aggressive disease and could be evaluated to identify patients more likely to benefit from adjuvant therapies.

Quantification of large scale DNA organization for predicting prostate cancer recurrence

This study investigates whether Genomic Organization at Large Scales (which we propose to call GOALS) as quantified via nuclear phenotype characteristics and cell sociology features (describing cell organization within tissue) collected from prostate tissue microarrays (TMAs) can separate biochemical failure from biochemical nonevidence of disease (BNED) after radical prostatectomy (RP). Of the 78 prostate cancer tissue cores collected from patients treated with RP, 16 who developed biochemical relapse (failure group) and 16 who were BNED patients (nonfailure group) were included in the analyses (36 cores from 32 patients). A section from this TMA was stained stoichiometrically for DNA using the Feulgen-Thionin methodology, and scanned with a Pannoramic MIDI scanner. Approximately 110 nuclear phenotypic features, predominately quantifying large scale DNA organization (GOALS), were extracted from each segmented nuclei. In addition, the centers of these segmented nuclei defined a Voronoi tessellation and subsequent architectural analysis. Prostate TMA core classification as biochemical failure or BNED after RP using GOALS features was conducted (a) based on cell type and cell position within the epithelium (all cells, all epithelial cells, epithelial >2 cell layers away from basement membrane) from all cores, and (b) based on epithelial cells more than two cell layers from the basement membrane using a Classifier trained on Gleason 6, 8, 9 (16 cores) only and applied to a Test set consisting of the Gleason 7 cores (20 cores). Successful core classification as biochemical failure or BNED after RP by a linear classifier was 75% using all cells, 83% using all epithelial cells, and 86% using epithelial >2 layers. Overall success of predicted classification by the linear Classifier of (b) was 87.5% using the Training Set and 80% using the Test Set. Overall success of predicted progression using Gleason score alone was 75% for Gleason >7 as failures and 69% for Gleason >6 as failures. © 2017 International Society for Advancement of Cytometry.

Evaluation of Quantitative Digital Pathology in the Assessment of Barrett Esophagus-Associated Dysplasia

OBJECTIVES

Barrett esophagus (BE) is a precursor lesion that confers an increased risk of esophageal adenocarcinoma. Two issues confront the diagnosis of patients with BE: (1) sampling error at the time of endoscopy and (2) variability among pathologists in grading dysplasia. The purpose of our study was to evaluate quantitative digital pathology (QDP) as a marker of dysplasia and stratification from low-grade to high-grade dysplasia to intramucosal adenocarcinoma in BE.

METHODS

Sixty-one esophageal biopsy specimens with BE were selected and divided into six groups according to the dysplasia grade. QDP image analysis was carried out by an in-house automated quantitative system on sections. The values of 110 nuclear features that analyze the morphology and chromatin texture were generated for each nucleus.

RESULTS

A progressive correlation was found between nuclear morphometric features and chromatin features with BE dysplasia. The chromatin texture was the best discriminator of the class diagnosis. There was a significant difference between the chromatin features of isolated low-grade dysplasia vs low-grade dysplasia that was associated with higher grade lesions in other biopsy tissue fragments.

CONCLUSIONS

QDP is a promising tool in the new era of digital pathology. Pending clinical validation studies, analysis of chromatin texture could contribute to the differential diagnosis of BE class and the detection of concomitant high-grade lesions if not sampled.

Automated classification of oral premalignant lesions using image cytometry and Random Forests-based algorithms

PURPOSE

A major challenge for the early diagnosis of oral cancer is the ability to differentiate oral premalignant lesions (OPL) at high risk of progressing into invasive squamous cell carcinoma (SCC) from those at low risk. Our group has previously used high-resolution image analysis algorithms to quantify the nuclear phenotypic changes occurring in OPLs. This approach, however, requires a manual selection of nuclei images. Here, we investigated a new, semi-automated algorithm to identify OPLs at high risk of progressing into invasive SCC from those at low risk using Random Forests, a tree-based ensemble classifier.

METHODS

We trained a sequence of classifiers using morphometric data calculated on nuclei from 29 normal, 5 carcinoma in situ (CIS) and 28 SCC specimens. After automated discrimination of nuclei from other objects (i.e., debris, clusters, etc.), a nuclei classifier was trained to discriminate abnormal nuclei (8,841) from normal nuclei (5,762). We extracted voting scores from this trained classifier and created an automated nuclear phenotypic score (aNPS) to identify OPLs at high risk of progression.

RESULTS

The new algorithm showed a correct classification rate of 80% (80.6% sensitivity, 79.3% specificity) at the cellular level for the test set, and a correct classification rate of 75% (77.8% sensitivity, 71.4% specificity) at the tissue level with a negative predictive value of 76% and a positive predictive value of 74% for predicting progression among 71 OPLs, performed on par with the manual method in our previous study.

CONCLUSIONS

We conclude that the newly developed aNPS algorithm serves as a crucial asset in the implementation of high-resolution image analysis in routine clinical pathology practice to identify lesions that require molecular evaluation or more frequent follow-up.

Cooperation and competition in the dynamics of tissue architecture during homeostasis and tumorigenesis

The construction of a network of cell-to-cell contacts makes it possible to characterize the patterns and spatial organization of tissues. Such networks are highly dynamic, depending on the changes of the tissue architecture caused by cell division, death and migration. Local competitive and cooperative cell-to-cell interactions influence the choices cells make. We review the literature on quantitative data of epithelial tissue topology and present a dynamical network model that can be used to explore the evolutionary dynamics of a two dimensional tissue architecture with arbitrary cell-to-cell interactions. In particular, we show that various forms of experimentally observed types of interactions can be modelled using game theory. We discuss a model of cooperative and non-cooperative cell-to-cell communication that can capture the interplay between cellular competition and tissue dynamics. We conclude with an outlook on the possible uses of this approach in modelling tumorigenesis and tissue homeostasis.

Epigenetically induced changes in nuclear textural patterns and gelatinase expression in human fibrosarcoma cells

OBJECTIVE

Chromatin texture patterns of tumour cell nuclei can serve as cancer biomarkers, either to define diagnostic classifications or to obtain relevant prognostic information, in a large number of human tumours. Epigenetic mechanisms, mainly DNA methylation and histone post-translational modification, have been shown to influence chromatin packing states, and therefore nuclear texture. The aim of this study was to analyse effects of these two mechanisms on chromatin texture, and also on correlation with gelatinase expression, in human fibrosarcoma tumour cells.

MATERIALS AND METHODS

We investigated effects of DNA hypomethylating agent 5-aza-2'-deoxycytidine (5-azadC) and histone deacetylase inhibitor trichostatin A (TSA) on nuclear textural characteristics of human HT1080 fibrosarcoma cells, evaluated by image cytometry, and expression of gelatinases MMP-2 and MMP-9, two metalloproteinases implicated in cancer progression and metastasis.

RESULTS

5-azadC induced significant variation in chromatin higher order organization, particularly chromatin decondensation, associated with reduction in global DNA methylation, concomitantly with increase in MMP-9, and to a lesser extent, MMP-2 expression. TSA alone did not have any effect on HT1080 cells, but exhibited differential activity when added to cells treated with 5-azadC. When treated with both drugs, nuclei had higher texture abnormalities. In this setting, reduction in MMP-9 expression was observed, whereas MMP-2 expression remained unaffected.

CONCLUSIONS

These data show that hypomethylating drug 5-azadC and histone deacetylase inhibitor TSA were able to induce modulation of higher order chromatin organization and gelatinase expression in human HT1080 fibrosarcoma cells.

Technologies supporting analytical cytology: clinical, research and drug discovery applications

The tools and techniques developed for analytical cytology have become invaluable in expanding the development of cancer screening programs and biomarker discovery for personalized medicine. Detecting cellular, molecular, and functional changes of diseased tissue as defined by quantitative analytical methodologies has enhanced the field of medical diagnostics and prognostics. The focus of this review is to outline applications and recent technical advances in flow cytometry, laser scanning cytometry, image cytometry, and quantitative image analysis, as they pertain to clinical, research, and drug discovery applications.

Diagnosis of lung cancer in individuals with solitary pulmonary nodules by plasma microRNA biomarkers

Background

Making a definitive preoperative diagnosis of solitary pulmonary nodules (SPNs) found by CT has been a clinical challenge. We previously demonstrated that microRNAs (miRNAs) could be used as biomarkers for lung cancer diagnosis. Here we investigate whether plasma microRNAs are useful in identifying lung cancer among individuals with CT-detected SPNs.

Methods

By using quantitative reverse transcriptase PCR analysis, we first determine plasma expressions of five miRNAs in a training set of 32 patients with malignant SPNs, 33 subjects with benign SPNs, and 29 healthy smokers to define a panel of miRNAs that has high diagnostic efficiency for lung cancer. We then validate the miRNA panel in a testing set of 76 patients with malignant SPNs and 80 patients with benign SPNs.

Results

In the training set, miR-21 and miR-210 display higher plasma expression levels, whereas miR-486-5p has lower expression level in patients with malignant SPNs, as compared to subjects with benign SPNs and healthy controls (all P ≤ 0.001). A logistic regression model with the best prediction was built on the basis of miR-21, miR-210, and miR-486-5p. The three miRNAs used in combination produced the area under receiver operating characteristic curve at 0.86 in distinguishing lung tumors from benign SPNs with 75.00% sensitivity and 84.95% specificity. Validation of the miRNA panel in the testing set confirms their diagnostic value that yields significant improvement over any single one.

Conclusions

The plasma miRNAs provide potential circulating biomarkers for noninvasively diagnosing lung cancer among individuals with SPNs, and could be further evaluated in clinical trials.

Natural history of bronchial preinvasive lesions

Preinvasive bronchial lesions defined as dysplasia and carcinoma in situ (CIS) have been considered as precursors of squamous cell carcinoma of the lung. The risk and rate of progression of preinvasive lesions to invasive squamous cell carcinoma as well as the mechanism of progression or regression are incompletely understood. While the evidence for the multistage, stepwise progression model is weak with relatively few documented lesions that progress through various grades of dysplasia to CIS and then to invasive carcinoma, the concept of field carcinogenesis is strongly supported. The presence of high-grade dysplasia or CIS is a risk marker for lung cancer both in the central airways and peripheral lung. Genetic alterations such as loss of heterozygosity in chromosome 3p or chromosomal aneusomy as well as host factors such as the inflammatory load and levels of anti-inflammatory proteins in the lung influence the progression or regression of preinvasive lesions. CIS is different than severe dysplasia at the molecular level and has different clinical outcome. Molecular analysis of dysplastic lesions that progress to CIS or invasive cancer and rare lesions that progress rapidly from hyperplasia or metaplasia to CIS or invasive cancer will shed light on the key molecular determinants driving development to an invasive phenotype versus those associated with tobacco smoke damage.

In vivo optical coherence tomography imaging of preinvasive bronchial lesions

PURPOSE

Optical coherence tomography (OCT) is an optical imaging method that can visualize cellular and extracellular structures at and below tissue surface. The objective of the study was to determine if OCT could characterize preneoplastic changes in the bronchial epithelium identified by autofluorescence bronchoscopy.

EXPERIMENTAL DESIGN

A 1.5-mm fiberoptic probe was inserted via a bronchoscope into the airways of 138 volunteer heavy smokers participating in a chemoprevention trial and 10 patients with lung cancer to evaluate areas that were found to be normal or abnormal on autofluorescence bronchoscopy. Radial scanning of the airways was done to generate OCT images in real time. Following OCT imaging, the same sites were biopsied for pathologic correlation.

RESULTS

A total of 281 OCT images and the corresponding bronchial biopsies were obtained. The histopathology of these areas includes 145 normal/hyperplasia, 61 metaplasia, 39 mild dysplasia, 10 moderate dysplasia, 6 severe dysplasia, 7 carcinoma in situ, and 13 invasive carcinomas. Quantitative measurement of the epithelial thickness showed that invasive carcinoma was significantly different than carcinoma in situ (P=0.004) and dysplasia was significantly different than metaplasia or hyperplasia (P=0.002). In addition, nuclei of the cells corresponding to histologic results became more discernible in lesions that were moderate dysplasia or worse compared with lower-grade lesions.

CONCLUSION

Preliminary data suggest that autofluorescence bronchoscopy-guided OCT imaging of bronchial lesions is technically feasible. OCT may be a promising nonbiopsy tool for in vivo imaging of preneoplastic bronchial lesions to study their natural history and the effect of chemopreventive intervention.

Potential use of quantitative tissue phenotype to predict malignant risk for oral premalignant lesions

The importance of early diagnosis in improving mortality and morbidity rates of oral squamous cell carcinoma (SCC) has long been recognized. However, a major challenge for early diagnosis is our limited ability to differentiate oral premalignant lesions (OPL) at high risk of progressing into invasive SCC from those at low risk. We investigated the potential of quantitative tissue phenotype (QTP), measured by high-resolution image analysis, to identify severe dysplasia/carcinoma in situ (CIS; known to have an increased risk of progression) and to predict progression to cancer within hyperplasia or mild/moderate dysplasia. We generated a nuclear phenotype score (NPS), a combination of five nuclear morphometric features that best discriminate 4,027 "normal" nuclei (selected from 29 normal oral biopsies) from 4,298 "abnormal" nuclei (selected from 30 SCC biopsies). This NPS was then determined for a set of 69 OPLs. Severe dysplasia/CIS showed a significant increase in NPS compared with hyperplasia or mild/moderate dysplasia. However, within the latter group, elevated NPS was strongly associated with the presence of high-risk loss of heterozygosity (LOH) patterns. There was a statistical difference between NPS of hyperplasia or mild/moderate dysplasia that progressed to cancer and those that did not. Individuals with a high NPS had a 10-fold increase in relative risk of progression. In the multivariate Cox model, LOH and NPS together were the strongest predictors for cancer development. These data suggest that QTP could be used to identify lesions that require molecular evaluation and should be integrated with such approaches to facilitate the identification of hyperplasia or mild/moderate dysplasia OPLs at high risk of progression.

Visualization and other emerging technologies as change makers for oral cancer prevention

The genomic era has fueled a rapid emergence of new information at the molecular level with a great potential for developing innovative approaches to detection, risk assessment, and management of oral cancers and premalignant disease. As yet, however, little research has been done on complementary approaches that would use different technology in conjunction with molecular approaches to create a rapid and cost-effective strategy for patient assessment and management. In our ongoing 8-year longitudinal study, a set of innovative technologies is being validated alone and in combination to best correlate with patient outcome. The plan is to use these devices in a step-by-step sequence to guide key clinicopathological decisions on patient risk and treatment. The devices include a hand-held visualization device that makes use of tissue autofluorescence to detect and delineate abnormal lesions and fields requiring follow-up, to be used in conjunction with optical contrast agents such as toluidine blue. In addition, two semi-automated high-resolution computer microscopy systems will be used to quantitate the protein expression phenotype of cell nuclei in tissue sections and exfoliated cell brushings. Previously identified risk-associated molecular changes are being used to validate these systems as well as to establish their place in a population-based triage program that will filter out high-risk cases in the community and funnel them to dysplasia clinics where higher-cost molecular tools will guide intervention. A critical development for the translation of this technology into community settings is the establishment of an effective methodology for education and training of health practitioners on the front lines.

In vivo assessment and evaluation of lung tissue morphologic and physiological changes from non-contact endoscopic reflectance spectroscopy for improving lung cancer detection

We present a method for lung cancer detection exploiting reflectance spectra measured in vivo during endoscopic imaging of the lung. The measured reflectance spectra were analyzed using a specially developed light-transport model to obtain quantitative information about cancer-related, physiological, and morphologic changes in the superficial bronchial mucosa layers. The light-transport model allowed us to obtain the absorption coefficient (mua) and further to derive the micro-vascular blood volume fraction in tissue and the tissue blood oxygen saturation. The model also allowed us to obtain the scattering coefficient (mus) and the anisotropy coefficient (g) and further to derive the tissue scattering micro-particle volume fraction and size distribution. The specular component of the reflectance signal and the instrument response were accounted for during the analysis. The method was validated using 100 reflectance spectra measured in vivo in a noncontact fashion from 22 lung patients (50 normal tissue/benign lesion sites and 50 malignant lesion sites). The classification between normal tissue/benign lesions and malignant lesions was further investigated using the derived quantitative parameters and discriminant function analysis. The results demonstrated significant differences between the normal tissue/benign lesions and the malignant lesions in terms of tissue blood volume fraction, blood oxygen saturation, tissue scatterer volume fractions, and size distribution. The results also showed that the malignant lung lesions can be differentiated from normal tissue/benign lesions with both diagnostic sensitivity and specificity of better than 80%.