Hyperspectral cell sociology reveals spatial tumor-immune cell interactions associated with lung cancer recurrence

Background

The tumor microenvironment (TME) is a complex mixture of tumor epithelium, stroma and immune cells, and the immune component of the TME is highly prognostic for tumor progression and patient outcome. In lung cancer, anti-PD-1 therapy significantly improves patient survival through activation of T cell cytotoxicity against tumor cells. Direct contact between CD8+ T cells and target cells is necessary for CD8+ T cell activity, indicating that spatial organization of immune cells within the TME reflects a critical process in anti-tumor immunity. Current immunohistochemistry (IHC) imaging techniques identify immune cell numbers and densities, but lack assessment of cell–cell spatial relationships (or “cell sociology”). Immune functionality, however, is often dictated by cell-to-cell contact and cannot be resolved by simple metrics of cell density (for example, number of cells per mm²). To address this issue, we developed a Hyperspectral Cell Sociology technology platform for the analysis of cell–cell interactions in multi-channel IHC-stained tissue.

Methods

Tissue sections of primary tumors from lung adenocarcinoma patients with known clinical outcome were stained using multiplex IHC for CD3, CD8, and CD79a, and hyperspectral image analysis determined the phenotype of all cells. A Voronoi diagram for each cell was used to approximate cell boundaries, and the cell type of all neighboring cells was identified and quantified. Monte Carlo analysis was used to assess whether cell sociology patterns were likely due to random distributions of the cells.

Results

High density of intra-tumoral CD8+ T cells was significantly associated with non-recurrence of tumors. A cell sociology pattern of CD8+ T cells surrounded by tumor cells was more significantly associated with non-recurrence compared to CD8+ T cell density alone. CD3+ CD8- T cells surrounded by tumor cells was also associated with non-recurrence, but at a similar significance as cell density alone. Cell sociology metrics improved recurrence classifications of 12 patients. Monte Carlo re-sampling analysis determined that these cell sociology patterns were non-random.

Conclusion

Hyperspectral Cell Sociology expands our understanding of the complex interplay between tumor cells and immune infiltrate. This technology could improve predictions of responses to immunotherapy and lead to a deeper understanding of anti-tumor immunity.

Electronic supplementary material

The online version of this article (10.1186/s40425-018-0488-6) contains supplementary material, which is available to authorized users.

Abstract PR11: Hyperspectral imaging tools capture the spatial organization of cell subsets within the tumor microenvironment

Immune cells are a major component of the tumor microenvironment (TME). The spatial organization of immune cell subpopulations within the TME is recognized to have biologic significance and clinical relevance. For example, spatial organization of immune cell subsets within the TME is critical for the inhibition of cytotoxic T-cell activity through direct interaction of ligand (PD-L1) with receptor (PD-1)). However, precise spatial deconvolution is limited by the lack of imaging algorithms for in situ multiplex single cell analyses as flow cytometry does not preserve data in the spatial dimension. To this end, we have developed a hyperspectral imaging platform designed for analyzing multichannel immunohistochemical-stained tissue sections for generating cell density data and reconstructing spatial architecture for tumor biology as well as clinical association studies.

Whole-tissue sections from 20 lung adenocarcinomas with at least 5 years’ follow-up were stained for CD3 (pan-T cell), CD8 (cytotoxic T cell), and CD79a (B cell and plasma cell) and counterstained with hematoxylin. Multispectral images were acquired for five fields of view and analyzed to quantify cell types. Regions of Interest (ROIs) were then identified and analyzed in order to quantify cell-cell spatial relationships. Nonrandom patterns of immune cell distributions were identified using the Monte Carlo resampling method (500 iterations). Cell counts, densities, spatial relationships, and significant immune cell distributions were associated with clinical features (Kruskal-Wallis p<0.001).

Our analysis generated 234 image files for analysis, with an average of 16,400 cells per image. The densities of intratumoral CD8+ cytotoxic T cells were significantly higher in nonrecurrent cases, agreeing with literature reports. Similarly, cell sociology deductions identified relationships associated with metastasis: tumor cells in nonmetastatic cases had increased numbers of CD8+ cytotoxic T-cell neighbors. Following Monte Carlo analysis, nonrandom cell~cell spatial proximities emerged that were not identified at a cell density level.

We have developed a hyperspectral imaging platform capable of quantifying cell-cell spatial relationships within tissue sections. This technology can be applied to larger clinical cohorts for the study of therapeutically targetable immune cell subsets with the goal of identifying patterns that correlate with clinical response and patient outcome.

This abstract is also being presented as Poster B16.

Citation Format: Katey S.S. Enfield, Spencer D. Martin, Sonia H.Y. Kung, Paul Gallagher, Calum E. MacAulay, Martial Guillaud, Wan L. Lam. Hyperspectral imaging tools capture the spatial organization of cell subsets within the tumor microenvironment [abstract]. In: Proceedings of the Fifth AACR-IASLC International Joint Conference: Lung Cancer Translational Science from the Bench to the Clinic; Jan 8-11, 2018; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2018;24(17_Suppl):Abstract nr PR11.

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.

Dual-mode endomicroscopy for detection of epithelial dysplasia in the mouth: a descriptive pilot study

Dual-mode endomicroscopy is a diagnostic tool for early cancer detection. It combines the high-resolution nuclear tissue contrast of fluorescence endomicroscopy with quantified depth-dependent epithelial backscattering as obtained by diffuse optical microscopy. In an in vivo pilot imaging study of 27 oral lesions from 21 patients, we demonstrate the complementary diagnostic value of both modalities and show correlations between grade of epithelial dysplasia and relative depth-dependent shifts in light backscattering. When combined, the two modalities provide diagnostic sensitivity to both moderate and severe epithelial dysplasia in vivo.

Fluorescence confocal endomicroscopy of the cervix: pilot study on the potential and limitations for clinical implementation

Current diagnostic capabilities and limitations of fluorescence endomicroscopy in the cervix are assessed by qualitative and quantitative image analysis. Four cervical tissue types are investigated: normal columnar epithelium, normal and precancerous squamous epithelium, and stromal tissue. This study focuses on the perceived variability within and the subtle differences between the four tissue groups in the context of endomicroscopic in vivo pathology. Conclusions are drawn on the general ability to distinguish and diagnose tissue types, on the need for imaging depth control to enhance differentiation, and on the possible risks for diagnostic misinterpretations.

Fluorescence confocal endomicroscopy of the cervix: pilot study on the potential and limitations for clinical implementation

Current diagnostic capabilities and limitations of fluorescence endomicroscopy in the cervix are assessed by qualitative and quantitative image analysis. Four cervical tissue types are investigated: normal columnar epithelium, normal and precancerous squamous epithelium, and stromal tissue. This study focuses on the perceived variability within and the subtle differences between the four tissue groups in the context of endomicroscopic in vivo pathology. Conclusions are drawn on the general ability to distinguish and diagnose tissue types, on the need for imaging depth control to enhance differentiation, and on the possible risks for diagnostic misinterpretations.

Automated Region-based Prostate Cancer Cell Nuclei Localization. Part of a Prognostic Modality Tool for Prostate Cancer Patients

BACKGROUND

Prostate cancer is a disease of disrupted cell genomes. Quantification of DNA from cytology preparations can yield prognostic information about tissue biological behaviors; however, this process is very labor-intensive to perform. Quantitative digital pathology can measure the structural chromatin changes associated with neoplasia and can enable prognostic and predictive assays based on imaging of sectioned prostate tissue.

OBJECTIVE

To design an automated system to recognize and localize cell nuclei in images of stained sectioned tissue (first step in enabling quantitative digital pathology).

STUDY DESIGN

Images of Feulgen-thionin-stained prostate cancer tissue microarray constructed from the surgical specimens of 33 prostate cancer patients were acquired for this study. We implemented a new image segmentation technique to overcome tissue complexity, cell clusters, background noise, image and tissue inhomogeneities, and other imaging issues that introduce uncertainties into the segmentation method and developed a fully automated system to localized prostate cell nuclei.

RESULTS

We applied our algorithm on our dataset and obtained a 96.6% true-positive rate and a 12% false-positive rate.

CONCLUSION

In this paper we present a new method to automatically localize thionin-stained prostate cancer cells, enabling the extraction of various nuclear and cell sociology features with high precision.

Abstract PR06: Racial/ethnic variation in the prevalence of vaccine-preventable human papillomavirus genotypes

Introduction: The bi- and quadrivalent human papillomavirus (HPV) vaccines that have been on the market for ≥ 9 years protect against high-risk HPV types 16 and 18, the two HPV genotypes causally associated with ≥ 70% of cervical cancers. The recent nonavalent vaccine, which became commercially available in 2014, additionally protects against high-risk types 31, 33, 45, 52, and 58. The quadri- and nonavalent vaccines also protect against low-risk HPV types 6 and 11, which cause genital warts. The objective of this analysis is to compare the prevalence of vaccine-preventable HPV genotypes among non-Hispanic white, non-Hispanic black, Hispanic, and Asian women and determine the risk of cervical dysplasia (CIN2+) associated with the genotypes covered by each of the vaccines.

Methods: Patients in this analysis represent a subset of those participating in a clinical trial to evaluate new optical technologies for the diagnosis of cervical dysplasia. Trial participants were non-pregnant women, age ≥18 years, with a recent abnormal Pap test and were recruited at participating colposcopy clinics in Houston, El Paso, and Vancouver. As part of the parent trial, a subset of participants provided a biopsy and a cervical swab. Histologic diagnosis was based on biopsies and evaluated by a certified pathologist to represent the histology of the most severe biopsy. HPV genotyping was conducted using the Roche Linear Array. Prevalent HPV types were grouped according to their inclusion in each of the vaccines: bivalent (16, 18), quadrivalent (16, 18, 6, 11), and nonavalent (16, 18, 31, 33, 45, 52, 58, 6, 11). Women were considered to be potentially protected by a particular vaccine if all or some (in the case of multiple infections) type-specific infections were covered by the vaccine. Results for the bi- and quadrivalent vaccines were grouped given the small number of women additionally protected by the quadri- versus bivalent vaccine.

Results: The sample for this analysis (n= 419) is comprised of 206 non-Hispanic white, 39 non-Hispanic Black, 142 Hispanic, and 32 Asian women. The prevalence of HPV genotypes covered by the bi- and quadrivalent vaccines was significantly greater among non-Hispanic white (38.3%) and Asian women (37.5%) compared to non-Hispanic black (15.4%) and Hispanic women (19.7%). The increase in the proportion of women potentially protected by the nonavalent vaccine versus the bi-/quadrivalent vaccine was highest among non-Hispanic blacks (33.3% increase) followed by Hispanics (18.3% increase). However, the prevalence of HPV genotypes not covered by any vaccine was also highest among these racial/ethnic groups. Specifically, 42.3% and 35.9% of Hispanic and non-Hispanic black women, respectively, had ≥ 1 genotype not covered by any vaccine, compared to 24.3% and 15.6% among non-Hispanic white and Asian women, respectively. Non-vaccine HPV types were primarily high-risk (68%). Among women with a CIN2+ diagnosis, 37.3% had genotypes included in the bi/quadrivalent vaccine and an additional 46.7% had genotypes included in the nonavalent (but not the bi/quadrivalent) vaccine. However, 16.0% of CIN2+ diagnoses occurred among women with HPV genotypes not covered by any vaccine.

Conclusions: Our data suggest racial/ethnic differences in the proportion of women protected by the new nonavalent HPV vaccine. In particular, non-Hispanic black and Hispanic women had the greatest prevalence of HPV types covered by the nonavalent but not the bi-/quadrivalent vaccines. Nonetheless, a large proportion of women from these racial/ethnic groups were infected with genotypes not covered by any vaccine. While HPV 16 and 18 are known to cause most cervical cancers, non-vaccine genotypes were associated with 16% of CIN2+ histology, a diagnosis often requiring significant therapeutic intervention.

This abstract is also presented as Poster C38.

Citation Format: Indu Varier, Jane R. Montealegre, Christina M. Gutierrez, Laura M. Dillon, Martial Guillaud, Karen A. Storthz, Michele Follen, Andrew G. Sikora, Michael E. Scheurer. Racial/ethnic variation in the prevalence of vaccine-preventable human papillomavirus genotypes. [abstract]. In: Proceedings of the Eighth AACR Conference on The Science of Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; Nov 13-16, 2015; Atlanta, GA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2016;25(3 Suppl):Abstract nr PR06.

Abstract A13: Quantitative pathology toolbox: Improvement in prediction of progression risk for oral premalignant lesions using both interactive and automated image analysis

For the promise of Quantitative Pathology (QP) to be clinically accepted it must provide new information that is clinically usable (and hence actionable). This must be accomplished in a fashion that is not onerous (expensive/time-consuming/require specialized training) to the pathologist relative to the value of the information provided. As an example a major barrier to oral cancer prevention is the inability to predict progression risk for oral premalignant lesions by conventional pathology alone that can be addressed by QP. We present two approaches for the quantitative analysis of FFPE sectioned oral tissue. In one approach the user selects the area of epithelium to be analyzed and visually filters the cells to be analyzed, the other is an automated approach in which the user only circles the epithelium to be analyzed. In both approaches the cell nuclei are automatically segmented, 110 features per nuclei calculated and used to determine how normal or cancer like the nucleus is (and in the fully automated approach if the objects are single intact nucleus or not), then the distribution of nuclei values within the area of interest is used to generate a Quantitative Pathology Scores (QPS) for the tissue. These tissue measures were used alone or in combination with other markers to perform risk assessment in patients from a very large oral cancer prediction longitudinal study. Also the scores can be combined with other risk markers such as Loss of Heterozygosity (LOH) analysis to improve risk stratification. A combination of LOH based predictors and QPS thresholds were trained to refine three previously validated LOH defined- risk groups. The combined model defined a low, a medium and a high risk of progression to cancer categories. For the 104 low risk cases so classified, 98.1% do not progress to cancer (used to define a relative risk [RR] of 1). In contrast, 15% of the 106 classified medium risk cases (RR= 7.85) and 65% of the 26 high-risk cases (RR = 34) progress. This is a substantial improvement over just the LOH based classification and significantly better than dysplasia grade for risk prediction. In a validation set of 43 mild to moderate dysplasia cases with long term follow-up, 100% of the 23 cases classified as low risk by the combined algorithm did not progress, 43% of the 7 cases classified as medium risk by the combined algorithm progressed and 92.3% (12 out 13) of the cases classified as high risk by the combined algorithm progressed. These validation results strongly support the combination of these approaches for facilitating risk prediction and improving patient management. This combined risk model is also a suitable intermediate endpoint biomarker of transformation risk for oral tissue and is being used in multicenter (8) Canadian Optically guides Oral Cancer Surgical Trial as part of the quantitative evaluation of surgical margin tissue. This work was funded by the NIDCR, NIH and by the TFRI.

Note: This abstract was withdrawn after the Proceedings were printed and, therefore, was not presented at the conference.

Citation Format: Calum MacAulay, Miriam Rosin, Lewei Zhang, Catherine Poh, Michele Williams, Martial Guillaud. Quantitative pathology toolbox: Improvement in prediction of progression risk for oral premalignant lesions using both interactive and automated image analysis. [abstract]. In: Proceedings of the Thirteenth Annual AACR International Conference on Frontiers in Cancer Prevention Research; 2014 Sep 27-Oct 1; New Orleans, LA. Philadelphia (PA): AACR; Can Prev Res 2015;8(10 Suppl): Abstract nr A13.

Geometrical and topological analysis of in vivo confocal microscopy images reveals dynamic maturation of epidermal structures during the first years of life

Reflectance confocal microscopy is successfully used in infant skin research. Infant skin structure, function, and composition are undergoing a maturation process. We aimed to uncover how the epidermal architecture and cellular topology change with time. Images were collected from three age groups of healthy infants between one and four years of age and adults. Cell centers were manually identified on the images at the stratum granulosum (SG) and stratum spinosum (SS) levels. Voronoi diagrams were used to calculate geometrical and topological parameters. Infant cell density is higher than that of adults and decreases with age. Projected cell area, cell perimeter, and average distance to the nearest neighbors increase with age but do so distinctly between the two layers. Structural entropy is different between the two strata, but remains constant with time. For all ages and layers, the distribution of the number of nearest neighbors is typical of a cooperator network architecture. The topological analysis provides evidence of the maturation process in infant skin. The differences between infant and adult are more pronounced in the SG than SS, while cell cooperation is evident in all cases of healthy skin examined.

Abstract 847: Human papillomavirus (HPV) type distribution in multi-ethnic cohort of women: Implications for vaccination programs

Introduction: HPV is linked to many genital and oropharyngeal cancers, and current HPV vaccines target 2 oncogenic HPV types (16 and 18), which are estimated to account for 70% of cervical cancer cases. We sought to determine type distributions among a multi-ethnic cohort of women to understand what proportion of infections are covered by current and proposed vaccines.

Methods: We analyzed cervical specimens from a cohort of 536 non-pregnant women from four clinical centers in the United States and Canada. HPV genotyping was performed using the Linear Array® HPV Genotyping Test (Roche), which detects 37 HPV types. We calculated prevalence of HPV types (individually and grouped by those types included in the currently approved bivalent and quadrivalent vaccines and the nonavalent vaccine currently being developed) by age, race/ethnicity, and histology.

Results: Overall the prevalence of any HPV type in the entire cohort was 57%. More than a quarter of all specimens showed infection with multiple HPV infections with 6 types being the most detected in a single specimen. The prevalence of oncogenic types on the array ranged from 36% among women with normal histology to 88% and 92% among those with low-grade and high-grade lesions, respectively. Among women with high-grade lesions the prevalence of types 16/18 was only 45% while the prevalence of types in the nonavalent vaccine was 85%. Prevalence of oncogenic types decreased by age group with women less than 30 having a prevalence of 63%, while in those over age 50 it was 34%. HPV16 was the most prevalent type among non-Hispanic white women (19%), but not among African-American (0%) or Hispanic (2%) women. HPV58 and HPV58/59 were the most common types among African-American and Hispanic women, respectively. Among non-Hispanic whites, 50% of prevalent oncogenic types were covered by current vaccines, while 81% would be covered by the nonavalent vaccine. In comparison, only 32% of infections among African-American and 29% among Hispanic women were covered by current vaccines, compared to 86% and 75%, respectively, for the nonavalent vaccine. In fact, African American women had 2.5-fold higher prevalence of HPV 58 compared to non-Hispanic white women. Of note, Asian women were more than four-times as likely to be infected with multiple HPV genotypes compared to non-Hispanic white women.

Conclusions: Our findings suggest that a nonavalent vaccine would cover more of the prevalent HPV genotypes present across racial/ethnic groups when compared to current vaccines. These results also suggest that even though more infections occur among younger women (<30), a significant proportion of older women (>50) are also infected. Further, 14-25% of currently prevalent HPV types would still not be covered by next generation vaccines.

Citation Format: Michael E. Scheurer, Hung N. Luu, Martial Guillaud, Jane Montealegre, Laura M. Dillon, Michele Follen, Karen Adler-Storthz. Human papillomavirus (HPV) type distribution in multi-ethnic cohort of women: Implications for vaccination programs. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 847. doi:10.1158/1538-7445.AM2015-847

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.

Economic evaluation of DNA ploidy analysis vs liquid-based cytology for cervical screening

Background:

DNA ploidy analysis involves automated quantification of chromosomal aneuploidy, a potential marker of progression toward cervical carcinoma. We evaluated the cost-effectiveness of this method for cervical screening, comparing five ploidy strategies (using different numbers of aneuploid cells as cut points) with liquid-based Papanicolaou smear and no screening.

Methods:

A state-transition Markov model simulated the natural history of HPV infection and possible progression into cervical neoplasia in a cohort of 12-year-old females. The analysis evaluated cost in 2012 US$ and effectiveness in quality-adjusted life-years (QALYs) from a health-system perspective throughout a lifetime horizon in the US setting. We calculated incremental cost-effectiveness ratios (ICERs) to determine the best strategy. The robustness of optimal choices was examined in deterministic and probabilistic sensitivity analyses.

Results:

In the base-case analysis, the ploidy 4 cell strategy was cost-effective, yielding an increase of 0.032 QALY and an ICER of $18 264/QALY compared to no screening. For most scenarios in the deterministic sensitivity analysis, the ploidy 4 cell strategy was the only cost-effective strategy. Cost-effectiveness acceptability curves showed that this strategy was more likely to be cost-effective than the Papanicolaou smear.

Conclusion:

Compared to the liquid-based Papanicolaou smear, screening with a DNA ploidy strategy appeared less costly and comparably effective.

Prediction using hierarchical data: Applications for automated detection of cervical cancer

Although the Papanicolaou smear has been successful in decreasing cervical cancer incidence in the developed world, there exist many challenges for implementation in the developing world. Quantitative cytology, a semi-automated method that quantifies cellular image features, is a promising screening test candidate. The nested structure of its data (measurements of multiple cells within a patient) provides challenges to the usual classification problem. Here we perform a comparative study of three main approaches for problems with this general data structure: a) extract patient-level features from the cell-level data; b) use a statistical model that accounts for the hierarchical data structure; and c) classify at the cellular level and use an ad hoc approach to classify at the patient level. We apply these methods to a dataset of 1,728 patients, with an average of 2,600 cells collected per patient and 133 features measured per cell, predicting whether a patient had a positive biopsy result. The best approach we found was to classify at the cellular level and count the number of cells that had a posterior probability greater than a threshold value, with estimated 61% sensitivity and 89% specificity on independent data. Recent statistical learning developments allowed us to achieve high accuracy.

Targeting of chemoprevention to high-risk potentially malignant oral lesions: challenges and opportunities

Worldwide, oral cancer is responsible for 170,000 deaths per year. Intervention to prevent this disease is a long sought after goal. Chemoprevention studies have focused on clinicopathological features of potentially malignant lesions (PML) in an effort to prevent their progression to cancer. However, prediction of future behavior for such lesions is difficult and remains a major challenge to such intervention. Different approaches to this problem have been tested in the past 20years. Early genetic progression models identified critical regions of allelic imbalance at 3p and 9p, and provided the basis for molecular markers to identify progressing PMLs. Subsequently, technological advances, such as genome-wide high-throughput array platforms, computer imaging, visualization technology and next generation sequencing, have broadened the scope for marker development and have the potential of further improving our ability to identify high-risk lesions in the near future either alone or in combination. In this article, we examine the milestones in the development of markers for PML progression. We emphasize the critical importance of networks among scientists, health professionals and community to facilitate the validation and application of putative markers into clinical practice. With a growing number of new agents to validate, it is necessary to coordinate the design and implementation of strategies for patient recruitment, integration of marker assessment, and the final translation of such approaches into clinical use.

Evaluation of HPV infection and smoking status impacts on cell proliferation in epithelial layers of cervical neoplasia

Accurate cervical intra-epithelial neoplasia (CIN) lesion grading is needed for effective patient management. We applied computer-assisted scanning and analytic approaches to immuno-stained CIN lesion sections to more accurately delineate disease states and decipher cell proliferation impacts from HPV and smoking within individual epithelial layers. A patient cohort undergoing cervical screening was identified (n = 196) and biopsies of varying disease grades and with intact basement membranes and epithelial layers were obtained (n = 261). Specimens were sectioned, stained (Mib1), and scanned using a high-resolution imaging system. We achieved semi-automated delineation of proliferation status and epithelial cell layers using Otsu segmentation, manual image review, Voronoi tessellation, and immuno-staining. Data were interrogated against known status for HPV infection, smoking, and disease grade. We observed increased cell proliferation and decreased epithelial thickness with increased disease grade (when analyzing the epithelium at full thickness). Analysis within individual cell layers showed a ≥50% increase in cell proliferation for CIN2 vs. CIN1 lesions in higher epithelial layers (with minimal differences seen in basal/parabasal layers). Higher rates of proliferation for HPV-positive vs. -negative cases were seen in epithelial layers beyond the basal/parabasal layers in normal and CIN1 tissues. Comparing smokers vs. non-smokers, we observed increased cell proliferation in parabasal (low and high grade lesions) and basal layers (high grade only). In sum, we report CIN grade-specific differences in cell proliferation within individual epithelial layers. We also show HPV and smoking impacts on cell layer-specific proliferation. Our findings yield insight into CIN progression biology and demonstrate that rigorous, semi-automated imaging of histopathological specimens may be applied to improve disease grading accuracy.

Oral mucosa optical biopsy by a novel handheld fluorescent confocal microscope specifically developed: technologic improvements and future prospects

OBJECTIVE

This pilot study evaluated the baseline effectiveness of a novel handheld fluorescent confocal microscope (FCM) specifically developed for oral mucosa imaging and compared the results with the literature.

STUDY DESIGN

Four different oral sites (covering the mucosa of the lip and of the ventral tongue, the masticatory mucosa of the gingiva, and the specialized mucosa of the dorsal tongue) in 6 healthy nonsmokers were imaged by an FCM made up of a confocal fiberoptic probe ergonomically designed for in vivo oral examination, using light at the wavelength of 457 nm able to excite the fluorophore acriflavine hydrochloride, topically administered. In total, 24 mucosal areas were examined.

RESULTS

The FCM was able to distinctly define epithelial cells, bacterial plaque, and inflammatory cells and to image submucosal structures by detecting their intrinsic fluorescence.

CONCLUSIONS

When compared with other devices, this FCM allowed the user to image each oral site at higher magnification, thus resulting in a clearer view.

Numerical investigation of two-dimensional light scattering patterns of cervical cell nuclei to map dysplastic changes at different epithelial depths

We use an extensive set of quantitative histopathology data to construct realistic three-dimensional models of normal and dysplastic cervical cell nuclei at different epithelial depths. We then employ the finite-difference time-domain method to numerically simulate the light scattering response of these representative models as a function of the polar and azimuthal scattering angles. The results indicate that intensity and shape metrics computed from two-dimensional scattering patterns can be used to distinguish between different diagnostic categories. Our numerical study also suggests that different epithelial layers and angular ranges need to be considered separately to fully exploit the diagnostic potential of two-dimensional light scattering measurements.