DNA Ploidy as a Potential Adjunct Prognostic Marker of Low-Risk Prostate Cancer Progression after Radical Prostatectomy

PURPOSE

Post prostatectomy PSA kinetics and General Grade Groups (GGG) are the strongest prognostic markers of biochemical recurrence (BCR) and prostate cancer (PCa)-specific mortality after radical prostatectomy. Despite having low-risk PCa, some patients will experience BCR, for some, clinically significant BCR. There is a need for an objective prognostic marker at the time of prostatectomy to improve risk stratification within this population. In this study, we investigated the prognostic potential of DNA ploidy.

MATERIALS AND METHODS

Prostatectomy samples from 97 patients with GGG1 and GGG2 with a low-risk CAPRA-S score were included in this study. PCa tissue with the worst Gleason pattern underwent tissue disaggregation, cell isolation and staining with a DNA stoichiometric stain. Using image cytometry, DNA ploidy was measured and a Ploidy Score (PS) was generated.

RESULTS

Among the 97 patients, 79 had no BCR, 18 experienced BCR, of which 14 had a PSA doubling time (PSA-DT) >1 year (low-risk group) and 4 had a PSA-DT of <1 year (high-risk group). Using Logistic regression analysis, only pathological T stage (pT) and PS independently predicted BCR with PS being the most significant (p = 0.001). The number of aneuploid cells was significantly higher in the high-risk group compared to the other groups (p = 1.7x10-11). PS combined with GGG diagnosis further stratified risk groups of biochemical recurrence free survival within CAPRA-S low-risk cohort.

CONCLUSION

DNA ploidy is an independent prognostic marker of BCR in low-risk PCa after radical prostatectomy, which could early on identify potentially aggressive PCa recurrences and introduce a more personalized approach to salvage treatments.

Identification of a serum-based microRNA signature that detects recurrent oral squamous cell carcinoma before it is clinically evident

BACKGROUND

Survival rates for oral squamous cell carcinoma (OSCC) have remained poor for decades, a fact largely attributable to late-stage diagnoses and high recurrence rates. We report analysis of serum miRNA expression in samples from patients with high-risk oral lesions (HRL, including OSCC/carcinoma in situ lesions) and healthy non-cancer controls, with the aim of non-invasively detecting primary or recurrent disease before it is clinically evident.

METHODS

Discovery, test, and validation sets were defined from a total of 468 serum samples (305 HRL and 163 control samples). Samples were analysed using multiple qRT-PCR platforms.

RESULTS

A two-miRNA classifier comprised of miR-125b-5p and miR-342-3p was defined following discovery and test analyses. Analysis in an independent validation cohort reported sensitivity and specificity of ~74% for this classifier. Significantly, when this classifier was applied to serial serum samples taken from patients both before treatment and during post-treatment surveillance, it identified recurrence an average of 15 months prior to clinical presentation.

CONCLUSIONS

These results indicate this serum miRNA classifier is effective as a simple, non-invasive monitoring tool for earlier detection of recurrent disease when lesions are typically smaller and amenable to a wider array of treatment options to improve survival.

Delineating spatial cell-cell interactions in the solid tumour microenvironment through the lens of highly multiplexed imaging

The growth and metastasis of solid tumours is known to be facilitated by the tumour microenvironment (TME), which is composed of a highly diverse collection of cell types that interact and communicate with one another extensively. Many of these interactions involve the immune cell population within the TME, referred to as the tumour immune microenvironment (TIME). These non-cell autonomous interactions exert substantial influence over cell behaviour and contribute to the reprogramming of immune and stromal cells into numerous pro-tumourigenic phenotypes. The study of some of these interactions, such as the PD-1/PD-L1 axis that induces CD8+ T cell exhaustion, has led to the development of breakthrough therapeutic advances. Yet many common analyses of the TME either do not retain the spatial data necessary to assess cell-cell interactions, or interrogate few (<10) markers, limiting the capacity for cell phenotyping. Recently developed digital pathology technologies, together with sophisticated bioimage analysis programs, now enable the high-resolution, highly-multiplexed analysis of diverse immune and stromal cell markers within the TME of clinical specimens. In this article, we review the tumour-promoting non-cell autonomous interactions in the TME and their impact on tumour behaviour. We additionally survey commonly used image analysis programs and highly-multiplexed spatial imaging technologies, and we discuss their relative advantages and limitations. The spatial organization of the TME varies enormously between patients, and so leveraging these technologies in future studies to further characterize how non-cell autonomous interactions impact tumour behaviour may inform the personalization of cancer treatment.​.

Polymorphisms in the Nonhomologous End-joining DNA Repair Pathway are Associated with HPV Integration in Cervical Dysplasia

Previous evidence indicates that human papillomavirus (HPV) integration status may be associated with cervical cancer development and progression. However, host genetic variation within genes that may play important roles in the viral integration process is understudied. The aim of this study was to examine the association between HPV16 and HPV18 viral integration status and SNPs in nonhomologous-end-joining (NHEJ) DNA repair pathway genes on cervical dysplasia. Women enrolled in two large trials of optical technologies for cervical cancer detection and positive for HPV16 or HPV18 were selected for HPV integration analysis and genotyping. Associations between SNPs and cytology (normal, low-grade, or high-grade lesions) were evaluated. Among women with cervical dysplasia, polytomous logistic regression models were used to evaluate the effect of each SNP on viral integration status. Of the 710 women evaluated [149 high-grade squamous intraepithelial lesion (HSIL), 251; low-grade squamous intraepithelial lesion (LSIL, 310 normal)], 395 (55.6%) were positive for HPV16 and 192 (27%) were positive for HPV18. Tag-SNPs in 13 DNA repair genes, including RAD50, WRN, and XRCC4, were significantly associated with cervical dysplasia. HPV16 integration status was differential across cervical cytology, but overall, most participants had a mix of both episomal and integrated HPV16. Four tag-SNPs in the XRCC4 gene were found to be significantly associated with HPV16 integration status. Our findings indicate that host genetic variation in NHEJ DNA repair pathway genes, specifically XRCC4, are significantly associated with HPV integration, and that these genes may play an important role in determining cervical cancer development and progression.

PREVENTION RELEVANCE

HPV integration in premalignant lesions and is thought to be an important driver of carcinogenesis. However, it is unclear what factors promote integration. The use of targeted genotyping among women presenting with cervical dysplasia has the potential to be an effective tool in assessing the likelihood of progression to cancer.

Use of DNA image cytometry in conducting oral cancer screening in rural India

OBJECTIVES

Oral cancer screening can assist in the early detection of oral potentially malignant lesions (OPMLs) and prevention of oral cancers. It can be challenging for clinicians to differentiate OPMLs from benign conditions. Adjunct screening tools such as fluorescence visualization (FV) and DNA image cytometry (DNA-ICM) have shown success in identifying OPMLs in high-risk clinics. For the first time we aimed to assess these technologies into Indian rural settings and evaluate if these tools helped clinicians identify high-risk lesions during screening.

METHODS

Dental students and residents screened participants in five screening camps held in villages outside of Hyderabad, India, using extraoral, intraoral, and FV examinations. Lesion and normal tissue brushings were collected for DNA-ICM analysis and cytology.

RESULTS

Of the 1116 participants screened, 184 lesions were observed in 152 participants. Based on white light examination (WLE), 45 lesions were recommended for biopsy. Thirty-five were completed on site; 25(71%) were diagnosed with low-grade dysplasias (17 mild dysplasia, 8 moderate dysplasia) and the remaining 10 showed no signs of dysplasia. FV loss was noted in all but one dysplastic lesion and showed a sensitivity of 96% and specificity of 17%. Cytology combined with DNA-ICM had a 64% sensitivity and 86% specificity in detecting dysplasia.

CONCLUSION

DNA-ICM combined with cytology identified majority of dysplastic lesions and identified additional lesions, which were not considered high-risk during WLE to biopsy on site. Efforts to follow-up with these participants are ongoing. FV identified most high-risk lesions but added limited value over WLE.

Abstract 1719: Investigating intra-tumor heterogeneity using multiplexed immunohistochemistry & deep learning: A new approach to spatially map the tumor microenvironment

The tumor microenvironment (TME) is a highly complex mixture containing epithelium, stroma and a diverse network of immune cells & the spatial organization of these immune cells within the TME reflects a crucial process in anti-tumor immunity. At present, the usual standard of care for assessing if a patient has cancer, its stage and its likely future biological behaviour is visual examination of one or more stained sections. Although recent advances in multiple immunostaining have enabled characterization of several parameters on a single tissue section. For a higher dimensional chromogen based methodology, we have developed a multiplexed IHC (mIHC) procedure combining multiple labels per round with several sequential rounds, enabling analysis of complex immune cell populations on a single slide through consecutive cycles of staining, destaining & hyperspectral imaging. By integrating serial imaging, sequential labeling & image registration, we are able to spatially map the TME. The process presented is using absorption microscopy, enabling these images to be done in a reasonable time frame.

Robust, accurate, segmentation of cell nuclei for overlapping nuclei is one of the most significant unsolved issues in digital pathology. Using the counterstain concentration images from our mIHC methodology, we have trained a deep learning segmentation method to accurately segment individual cell nuclei within overlapping clusters of nuclei. By combining a multiplexed IHC technique which enables the detection of multiple markers on a single slide with deep learning segmentation methods to segment every individual cell nuclei in tissue sections with an accuracy comparable to human annotation. We can analyze the cell-cell interactions between immune and tumour cells, enhancing our ability to perform molecularly based single cell analysis of multiple cell types simultaneously within the tissue. These two techniques joined can be scaled up to the entire tissue section level, improving our understanding of the biological aggressiveness of specific cancers & enabling an accurate spatial cell level representation of the tissue.

Citation Format: Kouther Noureddine, Paul Gallagher, Martial Guillaud, Calum MacAulay. Investigating intra-tumor heterogeneity using multiplexed immunohistochemistry & deep learning: A new approach to spatially map the tumor microenvironment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1719.

Improvement in Work Productivity After Group Voice Therapy in Professional Voice Users

OBJECTIVE

A disordered voice may significantly impair the ability of workers to perform optimally on the job, especially those classified as professional voice users (PVU). Voice therapy is a common treatment option for voice disorders, but there are few studies demonstrating its effect on work productivity. The objective of this study was to evaluate the change in work productivity in PVU after group voice therapy.

STUDY DESIGN

Prospective cohort study SETTING: Academic Voice Center METHODS: PVUs whose primary treatment for their voice disorder was voice therapy were recruited. Participants completed a 7-week group voice therapy course from January 2018 to December 2020. Participants completed the validated Work Productivity and Activity Impairment questionnaire (WPAI) which measured presenteeism (on the job work productivity impairment) and absenteeism (time missed from work), general self-efficacy scale (GSES), and Voice handicap index -10 (VHI-10) before and after group voice therapy. Changes in scores before and after therapy were compared using a Wilcoxon Signed-Rank test.

RESULTS

Twenty-seven PVU were recruited; 25 had complete data (100% female, mean age 45.4 years, 68% teachers). Presenteeism (SD) decreased from 72.0% (23.3) to 36.8% (24.8), which represented a significant improvement of 35.2% (27.8) [95% CI 21.7-38.7; P < 0.001]. Activity impairment decreased from 48.4% (32.0) to 25.6% (23.8), which represented a significant improvement of 22.8% (26.5) [95% CI 20.7-37.0; P < 0.00]. There was no change in absenteeism (P = 0.27). Patients had high mean GSES of 34.4 (3.7) and abnormal mean VHI-10 of 18.2 (7.2). Changes in VHI-10 and GSES were not significant.

CONCLUSION

PVU had an improvement in work productivity that was largely represented by decreased presenteeism after completing group voice therapy.

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.

Predicting progression of low-grade oral dysplasia using brushing based DNA ploidy and Chromatin Organization analysis

Most oral cancers arise from oral potentially malignant lesions, which show varying grades of dysplasia. Risk of progression increases with increasing grade of dysplasia, however risk prediction among oral low-grade dysplasia (LGDs) i.e., mild and moderate dysplasia can be challenging as only 5-15% transform. Moreover, grading of dysplasia is subjective and varies with the area of the lesion being biopsied. To date, no biomarkers or tools are used clinically to triage oral LGDs. This study utilizes a combination of DNA ploidy and chromatin organization (CO) scores from cells obtained from lesion brushings to identify oral LGDs at high-risk of progression. A total of 130 lesion brushings from patients with oral LGDs were selected of which 16 (12.3%) lesions progressed to severe dysplasia or cancer. DNA ploidy and CO scores were analyzed from nuclear features measured by our in-house DNA image cytometry (DNA-ICM) system and used to classify brushings into low risk and high risk. A total of 57 samples were classified as high-risk of which 13 were progressors. High-risk DNA brushing was significant for progression (P = 0.001) and grade of dysplasia (P = 0.004). Multivariate analysis showed high-risk DNA brushing showed 5.1 to 8-fold increased risk of progression, a stronger predictor than dysplasia grading and lesion clinical features. DNA-ICM can serve as a non-invasive, high throughput tool to identify high-risk lesions several years prior to transformation. This will help clinicians focus on such lesions while low-risk lesions may be spared from unnecessary biopsies.

Abstract 2732: Multiplexed immunohistochemistry and deep learning: a new approach to spatially map the tumor microenvironment

The usual standard of care for assessing if a patient has cancer, its stage and its likely future biological behavior is visual examination of one or more H&E and/or Immunohistochemistry(IHC) stained sections. The paradigm of digital pathology has changed, moving from single-marker IHC towards multiplexed labeling, increasing the need for more advanced techniques that can be easily integrated in routine clinical pathology. Although recent advances in multiple immunostaining have enabled characterization of several parameters on a single tissue section. For a higher dimensional chromogen based methodology, we have developed a multiplexed IHC procedure combining multiple labels per round, enabling analysis of complex immune cell population's on a single slide through consecutive cycles of staining, destaining, hyperspectral imaging and spectral unmixing of the chromogen biomarkers in each round. Not only does this allow us to completely visualize and spatially map the tumor microenvironment(TME), but through the application of AI we can further recognize common histological features present in the tissue. Robust, accurate, segmentation of cell nuclei for overlapping nuclei is one of the most significant unsolved issues in digital pathology. Analyzing the cell-cell interactions between immune and tumor cells and identifying clinically relevant patterns may improve patient outcomes while informing on the likelihood of success of possible treatments. By combining a multiplexed IHC technique which enables the detection of multiple markers on a single slide with deep learning segmentation methods to segment every individual cell nuclei in tissue sections with an accuracy comparable to human annotation, we can improve the accuracy of tissue classification based upon the measured characteristics of these cells and their spatial organization. The two techniques joined can be scaled up to the entire tissue section level, providing an accurate spatial cell level representation of the tissue.

Citation Format: Kouther Noureddine, Paul Gallagher, Martial Guillaud, Calum MacAulay. Multiplexed immunohistochemistry and deep learning: a new approach to spatially map the tumor microenvironment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2732.

Abstract 2539: DNA content and chromatin texture measurement to predict malignant transformation in low-grade oral dysplasia

Objectives: The key to reducing oral cancer morbidity and mortality is the early identification of oral potentially malignant lesions (OPMLs) at high risk of malignant transformation (MT). The gold standard to assess OPML risk is the grade of dysplasia. Risk prediction of low-grade dysplasias (LGDs) is challenging as only a small proportion progress. Currently no tools or biomarkers are used clinically to predict MT. Abnormal DNA content has shown to be a marker of malignancy in various sites including the oral cavity. Changes in nuclear morphology and chromatin texture have also been observed in cells with normal DNA content adjacent to cancerous fields as a result of genetic or epigenetic alterations that drive MT. The aim of this study is to use OPML lesion brushings to measure nuclear DNA content and morphometric features to identify oral LGDs at high risk of MT.

Methods: Patients with oral LGDs consented into the British Columbia Oral Cancer Prediction Longitudinal Study were included in the study. Inclusion criteria included patients with mild or moderate dysplasia with lesion brushings prior to or concurrent to biopsy dates and had more than 6 months of follow up. Outcome was defined as progression to severe dysplasia or cancer. Thin monolayer slides were prepared and stained using Feulgen Thionin. Slides were scanned using the Cancer Imaging Scanner at BC Cancer using machine learning algorithms to measure DNA amount and over 120 nuclear morphometric features. Linear discriminant function (LDF) analysis of nuclear features from normal diploid cells were used to train classifiers to differentiate progressing from non-progressing lesions. Results: A total of 143 lesion brushings from 140 patients were selected. Forty-nine mild, 48 moderate, and 46 lesions with a previous history of dysplasia at the same site were included of which 16 (11%) progressed. DNA content of each nucleus was measured by a normalized scale known as DNA Index (DI) and were classified into groups: diploid (0.9 &lt;DI&lt;1.1), tetraploid (1.8&lt;DI&lt;2.2), proliferating (1.2&lt;DI&lt;1.7) and aneuploid (DI&gt;2.5). Abnormal DNA content threshold was determined using combinations of cell frequencies/percentage of each group. A lesion was considered at high-risk of MT when it showed 5 or more non-diploid cells (DI&gt;1.2). 12 out of 16 progressors and 41 out of 116 non-progressors showed high-risk DNA content. Preliminary results from LDF show that chromatin texture features best differentiated progressing cells from non-progressing. Analysis to determine the best threshold for chromatin texture features is ongoing.

Conclusion: This suggests that chromatin texture changes can be observed in cells with normal DNA content during MT. Together, they can serve as a quick, non-invasive, cost effective tool to triage high risk OPMLs to cancer care centres for monitoring and early intervention. A larger sample size is required to validate our results.

Citation Format: Madhurima Datta, Denise M. Laronde, Miriam Rosin, Anita Carraro, Korbelik Jagoda, Alan Harrison, Zhaoyang Chen, Martial Guillaud. DNA content and chromatin texture measurement to predict malignant transformation in low-grade oral dysplasia [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2539.

Abstract 2813: A multiplexed, multispectral approach to analyzing the immune microenvironment of oral potentially malignant lesions

Objectives: Tissue sections of oral potentially malignant lesions (OPML) can be used not only to provide pathological assessment (diagnosis) but could also be used to analyze the interactions between cellular populations, signaling molecules, and structural proteins that impact the clinical course of disease. However, much of this information remains unpacked due to methodological limitations. Traditional immunohistochemistry (IHC) limits the number of proteins able to be simultaneously analyzed within a tissue section and is also prone to human error in its analysis. Newer multiplexed IHC (mIHC) methods involving repeated cycles of staining allow for quantification of a greater number of markers; however, tissue integrity may be compromised, and complexity is added to the interpretation of the results. There is a need to develop an immunostaining method that overcomes these barriers.

Hypothesis: mIHC and an in-house Hyperspectral Cell Sociology (HCS) platform will allow for robust and detailed investigation of the immune microenvironment of OPML compared to traditional IHC staining and scoring techniques.

Methods: Automated mIHC staining with a seven immune marker panel was completed on annotated formalin-fixed paraffin-embedded OPML tissue. A cocktail of three primary antibodies was applied, then antigens and chromogens stripped using SDS-glycine before second round staining with four antibodies and a hematoxylin counterstain was completed. Slides were then digitally imaged, regions of interest selected in conjunction with an oral pathologist and staining quantified computationally using the HCS platform. Traditional IHC was completed on a randomized subset of cases using sequentially cut tissue sections and a double-staining technique. Scoring was completed by two blinded clinicians.

Results: One cycle of multiplexed staining and de-staining allowed for the detection of seven markers on one section while minimizing loss of tissue integrity. The HCS platform captures a single tissue section at multiple wavelengths, enabling the unmixing of multiple overlapping, colocalized chromogens. The resulting set of images displayed each stain separately, allowing for nuclei segmentation and the generation of a map of the epithelium and underlying connective tissue. True positive cells for each stain were demarcated on this map, allowing for investigation of marker positivity, co-positivity, cell to cell spatial relationships, and layer-based analysis, compared to cell count and density analyses obtained with traditional IHC.

Conclusion: The immune microenvironment contains a wealth of information pertaining to the biology, pathogenesis, and outcome of disease. Multiplexed staining and imaging methods to analyze and unpack this information is of great clinical utility.

Citation Format: Iris Lin, Kouther Noureddine, Paul Gallagher, Martial Guillaud, Lewei Zhang, Leigha Rock, Miriam Rosin, Denise Laronde. A multiplexed, multispectral approach to analyzing the immune microenvironment of oral potentially malignant lesions [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2813.

Assessment of internal refractive index profile of stochastically inhomogeneous nuclear models via analysis of two-dimensional optical scattering patterns

SIGNIFICANCE

Optical scattering signals obtained from tissue constituents contain a wealth of structural information. Conventional intensity features, however, are mostly dictated by the overall morphology and mean refractive index of these constituents, making it very difficult to exclusively sense internal refractive index fluctuations.

AIM

We perform a systematic analysis to elucidate how changes in internal refractive index profile of cell nuclei can best be detected via optical scattering.

APPROACH

We construct stochastically inhomogeneous nuclear models and numerically simulate their azimuth-resolved scattering patterns. We then process these two-dimensional patterns with the goal of identifying features that directly point to subnuclear structure.

RESULTS

Azimuth-dependent intensity variations over the side scattering range provide significant insights into subnuclear refractive index profile. A particular feature we refer to as contrast ratio is observed to be highly sensitive to the length scale and extent of refractive index fluctuations; further, this feature is not susceptible to changes in the overall size and mean refractive index of nuclei, thereby allowing for selective tracking of subnuclear structure that can be linked to chromatin distribution.

CONCLUSIONS

Our analysis will potentially pave the way for scattering-based assessment of chromatin reorganization that is considered to be a key hallmark of precancer progression.

Abstract PO-043: Combining multiplexed immunohistochemistry and deep learning to spatially map the tumor microenvironment

The tumour microenvironment(TME) is a highly complex mixture containing epithelium, stroma and a diverse network of immune cells and the spatial organization of these immune cells within the TME reflects a crucial process in anti-tumor immunity. The usual standard of care for assessing if a patient has cancer, its stage and its likely future biological behaviour is visual examination of one or more H&E and/or Immunohistochemistry(IHC) stained sections. The paradigm of digital pathology has changed, moving from single-marker IHC towards multiplexed labeling, increasing the need for more advanced techniques that can be easily integrated in routine clinical pathology. Although recent advances in multiple immunostaining have enabled characterization of several parameters on a single tissue section. For a higher dimensional chromogen based methodology, we have developed a multiplexed IHC procedure combining multiple labels per round with several sequential rounds, where multiple is 12-14 chromogen based antibodies on a single tissue section. Thus, enabling analysis of complex immune cell population’s on a single slide through consecutive cycles of staining, destaining, hyperspectral imaging and spectral unmixing of the chromogen biomarkers in each round. The process presented is using absorption microscopy, enabling these images to be done in a reasonable time frame. Not only does this allow us to completely visualize and spatially map the TME, but through the application of AI we can further recognize common histological features. Stoichiometric DNA following mIHC, improves nuclei identification so we can identify various cell populations present in the tissue. Thus, providing us with an accurate spatial cell level representation of any tissue section using only a single staining process. Although there have been several uprising technologies which characterize the complexities of the TME, our process is capable of doing so in a shorter time frame and at a reduced cost using absorption based methods. Robust, accurate, segmentation of cell nuclei for overlapping nuclei is one of the most significant unsolved issues in digital pathology. By combining a multiplexed IHC technique which enables the detection of multiple markers on a single slide with deep learning segmentation methods to segment every individual cell nuclei in tissue sections with an accuracy comparable to human annotation. We can analyze the cell-cell interactions between immune and tumour cells and identify clinically relevant patterns that may improve patients outcome by informing on the likelihood of success of possible treatments. These two techniques joined can be scaled up to the entire tissue section level, improving our understanding of the biological aggressiveness of specific cancers and enabling an accurate spatial cell level representation of the tissue.

Citation Format: Kouther Noureddine, Paul Gallagher, Martial Guillaud, Calum MacAulay. Combining multiplexed immunohistochemistry and deep learning to spatially map the tumor microenvironment [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr PO-043.

Abstract PO-009: Towards solving overlapping nuclei segmentation: Sequential CNNs for one to many mapping of pixels to objects

Automated cell nucleus segmentation is key to quantifying cell features and functionality to identify the disease state of tissue and its likely biological future development. Despite considerable advances in automated segmentation, it is a challenging task to split overlapping clustered nuclei where pixels can belong to more than one nuclei. To substantially improve upon current cell/nuclei segmentation methods our group has developed a promising nuclear segmentation method using sequential rounds of modified UNet CNNs which we denote SUNet. This sequential application of two separate CNNs is required to allow the reparsing the original image into multiple overlapping sub images which explicitly allows for the SUNet approach to perform a one pixel mapping to many objects that is missing from other CNN based segmentation methods in Digital Pathology. We initially constructed two human annotated 8000 overlapping nuclei training sets one each from lung and prostate tissue sections quantitatively stained for DNA. These were used to train two SUNets. Their performance was evaluated on ~20,000 nuclei from multiple tissues for each tissue type. The average correct visual nuclei segmentation rate was observed to be ~84%. A subset of the training images were hand annotated by multiple observers and the Jaccard similarity coefficient (JSC) was used to quantify the similarity between these annotations. Four human observers have annotated the same 1065 nuclei from 100 nuclei clusters. For the 6 pairwise comparisons of these individuals, the average JSC was found to be 0.69 (range 0.63-0.75). This low number reflects the inability of humans to exactly trace the same boundary by hand and that not all observers recognized the same nuclei within the highly complex clusters. The SUNets JSC was similar to the average JSC and within the range of the human observers. Indicating that for these images the SUNet performs as well as a human at segmenting nuclei within complex clusters of nuclei. Comparing the performance of SUNet with a published mask_RCNN based nuclei segmentation and a recently published nucleAIzer method on a non-complex (not a lot of overlapping nuclei) image; we find the three are almost equivalent. However, the two published methods miss a few overlapping nuclei that are found by SUNet. On images with highly complex overlapping clusters of nuclei SUnet correctly segments more (10-20%, image complexity dependent) nuclei than the other methods in areas of high complexity. In addition, SUNet recognizes and segments large numbers of nuclei completely missed by the other methods. An added benefit of allowing a one to many assignment of pixels, is that it correctly preserves the complete shape of the nuclei as it is able to assign the same pixel(s) in an image to the multiple nuclei involved in the area of their overlap as apposed to assigning it to one of the nuclei involved. In summary, the sequential CNN proposed approach can segment nuclei in complex cluster with an accuracy equivalent to human observers.

Citation Format: Calum E. MacAulay, Kouther Noureddine, Martial Guillaud, Paul Gallagher. Towards solving overlapping nuclei segmentation: Sequential CNNs for one to many mapping of pixels to objects [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr PO-009.

An improved algorithm using a Health Canada-approved DNA-image cytometry system for non-invasive screening of high-grade oral lesions

BACKGROUND

DNA-image cytometry (DNA-ICM) is able to detect gross alterations of cellular DNA-content representing aneuploidy, a biomarker of malignancy. A Health Canada-approved DNA-ICM system, ClearCyte^® in combination with a cytopathologist's review, has demonstrated high sensitivity (89%) and specificity (97%) in identifying high-grade oral lesions. The study objective was to create an improved automated algorithm (iClearcyte) and test its robustness in differentiating high grade from benign reactive oral lesions without a cytopathologist's input.

METHODS

A set of 214 oral brushing samples of oral cancer (n = 92), severe dysplasia (n = 20), reactive lesions (n = 52), and normal samples (n = 50) were spun down onto slides and stained using Feulgen-Thionin reaction. Following ClearCyte^® scan, nuclear features were calculated, and nuclei categorized into "diploid," "hyperdiploid," "tetraploid," and "aneuploid" DNA ploidy groups by the ClearCyte^® software. The samples were randomized into training and test sets (70:30) based on patient's age, sex, tobacco use, and lesion site risk. The training set was used to create a new algorithm which was then validated using the remaining samples in the test set, where sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated.

RESULTS

The proposed iClearCyte algorithm (>1 "aneuploid" cell or ≥ 1.7% combined "hyperdiploid" and "tetraploid" nuclei frequency) identified high-grade samples with sensitivity, specificity, PPV, and NPV of 100.0%, 86.7%, 89.7%, and 100.0%, respectively, in the test set.

CONCLUSION

The iClearCyte test has potential to serve as a robust non-invasive automated oral cancer screening tool promoting early oral cancer detection and decreasing the number of unnecessary invasive biopsies.

Abstract PO-082: Combining multiplex immunohistochemistry and deep learning: A new approach to tracing the tumour microenvironment

The tumour microenvironment(TME) is a highly complex mixture containing epithelium, stroma and a diverse network of immune cells and the spatial organization of these immune cells within the TME reflects a crucial process in anti-tumor immunity. Not all tumour cells interact similarly; micro-environmental features are often key indicators of treatment response and patient outcome. The usual standard of care for assessing if a patient has cancer, its stage and its likely future biological behaviour is visual examination of one or more H&E and/or Immunohistochemistry(IHC) stained sections. The paradigm of digital pathology has changed, moving from single-¬marker IHC towards multiplexed labeling, increasing the need for more advanced techniques that can be easily integrated in routine clinical pathology. Although recent advances in multiple immunostaining have enabled characterization of several parameters on a single tissue section. For a higher dimensional chromogen based methodology, we have developed a multiplexed IHC procedure combining multiple labels per round with multiple sequential rounds, where multiple is 12-14 chromogen based antibodies on a single tissue section. Thus, enabling analysis of complex immune cell population’s on a single slide through consecutive cycles of staining, destaining, hyperspectral imaging and spectral unmixing of the chromogen biomarkers in each round. That the process presented is chromogen based (absorption microscopy) means that high throughput imaging of 12-14 markers across entire slides is feasible in a reasonable time frame. Robust, accurate, segmentation of cell nuclei for touching/overlapping nuclei is one of the most significant unsolved issues in digital pathology. Analyzing cell-cell interactions between immune and tumour cells and identifying clinically relevant patterns may improve patients outcome by informing on the likelihood of success of possible treatments. By combining a multiplexed IHC technique which enables the detection of multiple markers on a single slide with deep learning segmentation methods to recognize/segment every individual cell nuclei in tissue sections with an accuracy comparable to human annotation, we can improve the accuracy of tissue classification based upon the measured characteristics of these cells and their spatial organization. These two techniques joined can be scaled up to the entire tissue section level, improving our understanding of the biological aggressiveness of specific cancers and enabling an accurate spatial cell level representation of the tissue.

Citation Format: Kouther Noureddine, Paul Gallagher, Martial Guillaud, Calum MacAulay. Combining multiplex immunohistochemistry and deep learning: A new approach to tracing the tumour microenvironment [abstract]. In: Proceedings of the AACR Virtual Special Conference on Tumor Heterogeneity: From Single Cells to Clinical Impact; 2020 Sep 17-18. Philadelphia (PA): AACR; Cancer Res 2020;80(21 Suppl):Abstract nr PO-082.

Multi-scale tissue architecture analysis of favorable-risk prostate cancer: Correlation with biochemical recurrence

Purpose

Prostate cancer (PCa) with biopsy-based grade group (GG) 1 or 2 characteristics has a favorable outcome, yet some cases still progress after radical prostatectomy and present with biochemical recurrence (BCR). We hypothesized that the multi-scale tissue architecture (MSTA) analysis score would correlate with the aggressive PCa phenotype and could be used as a tool for risk assessment to improve the management of patients with favorable-risk PCa.

Materials and Methods

MSTA was evaluated in needle-biopsy samples from 115 patients with favorable-risk PCa, as defined by GG1 and GG2, a prostate-specific antigen (PSA) level of <10 ng/mL, a clinical stage of cT1c to cT2b, and general Gleason GG (GGG) and expert pathologist-assessed GG (EGG). Algorithms based on Voronoi diagrams were applied to all Feulgen-thionin-stained diagnostic areas. One hundred tissue architecture features were calculated and an MSTA score, a linear combination of the most discriminant features, was generated. Correlation of MSTA score with BCR and other clinical variables was investigated.

Results

In a univariate regression model, EGG, clinical stage, and MSTA were significant predictors of BCR (respective p-values: 0.0016, 0.016, and 0.028). Survival analysis showed that patients with a high MSTA score were more likely to experience BCR than were patients with a low MSTA score (odds ratio, 2.9). Combining MSTA with GG assessment resulted in a significant stratification of risk for BCR.

Conclusions

MSTA score could be used as an objective adjunct risk stratification tool to pathologist assessments and could improve the management of patients with favorable-risk PCa.

Factors Associated with Failure of Botulinum Toxin Injection in Adductor Spasmodic Dysphonia

Objective:

Electromyography (EMG) Guided botulinum toxin (BTX) injection is considered first-line treatment for adductor spasmodic dysphonia (SD). Failure rate can range between 6% and 29%. Study objective was to determine which factors were associated with failure.

Methods:

This was a retrospective review conducted at a tertiary, academic center. Adductor SD patients presenting for BTX injections from August 2017 to October 2018 were eligible. Age, gender, Voice Handicap Index (VHI-10), Consensus Auditory-Perceptual Evaluation of Voice (CAPE-V), number of injections, disease duration, unilateral/bilateral injection, right/left injection, dose quantity, body mass index (BMI), professional voice user, employment, psychiatric comorbidity, breathiness, and dysphagia were investigated. Outcomes included failure as defined by the patient and dosage change. Univariate and multivariate statistical analysis was conducted.

Results:

Sixty seven out of 564 injections (12%) were categorized as failure by 131 patients. In multivariate analysis, dosage change was associated with shorter duration of good effect ( P < .001), BTX dose ( P = .016), breathiness ( P < .001), bilateral injection ( P = .024), dysphagia ( P = .012) and professional voice user ( P = .021). Failure was associated with first injection with a new physician ( P < .001), professional voice user P < .001) and lack of breathiness ( P = .003). Failure rate was not associated with age, gender, VHI-10, CAPE-V, disease duration, left/right injection, dose quantity, BMI, psychiatric comorbidity, and dysphagia.

Conclusion:

Failure rate was 12% and associated with patients’ first injection with a physician, professional voice user, and lack of breathiness. Dosage change occurred in 29% of injections and was associated with injection side effects, bilateral injections, BTX dose, professional voice user, and shorter duration of good effect. Level of evidence: 3

Cervical cytology reproducibility and associated clinical and demographic factors

BACKGROUND

Although the Pap test has been the standard screening method for cervical precancer/cancer detection, it has been criticized for having a relatively low sensitivity and a low reproducibility between pathologists. There is limited knowledge about inter-rater agreement and what clinical and demographic factors are associated with disagreements between pathologists reading the same Pap smear.

METHODS

This study aimed to assess inter- and intra- rater agreement of the Pap smear in 1619 cytologic slides with biopsy confirmation, using kappa statistics. Clinical and demographic factors associated with higher odds of inter-rater agreement were also examined and stratified by histologic diagnosis grade.

RESULTS

Using a five grade classification system, the overall kappa statistics for total, inter-rater, and intra-rater samples were 0.62, 0.57, and 0.88 (unweighted) and 0.83, 0.81, and 0.95 (weighted), respectively. In stratified analyses by histologic grade, total kappas ranged from 0.40 (atypia) to 0.64 (human papilloma virus/CIN 1). Factors such as referral for abnormal Pap test (diagnostic vs screening population), recruiting site, and parity were found to be associated with higher agreement between the two cytologic readings.

CONCLUSIONS

We observed relatively higher levels of agreement compared with other studies. However, variability was considerable and agreement was generally moderate, suggesting that cervical screening test accuracy and reproducibility needs to be improved.

Racial/ethnic variation in the prevalence of vaccine-related human papillomavirus genotypes

Objective: There are currently three licensed human papillomavirus (HPV) vaccines that protect against cervical cancer. Here we compare the prevalence of bi-, quadri-, and nonavalent vaccine-related HPV genotypes in a multi-ethnic sample of non-Hispanic white, non-Hispanic black, Hispanic, and Asian women. Design: Patients in this analysis (n = 419) represent a subset of women with a previous abnormal Pap test participating in a clinical trial. HPV genotyping was conducted using the Roche Linear Array. Prevalent HPV genotypes 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). Results: The prevalence of HPV genotypes covered by the bi-/quadrivalent vaccines was lowest among non-Hispanic black (15%) and Hispanic women (20%), compared to non-Hispanic white (38%) and Asian women (38%). Across all racial/ethnic groups, a large proportion of infections (38%-49%) were with genotypes included in the nonavalent vaccine. However, the prevalence of HPV genotypes not covered by any vaccine was significantly higher among non-Hispanic black (36%) and Hispanic women (42%), compared to non-Hispanic white (24%) and Asian women (16%) (p < 0.001). Racial/ethnic differences in HPV genotype prevalence were observed when controlling for demographic and sexual behavior characteristics, as well as when restricting the analysis to women with CIN 2+. Conclusion: Our data suggest racial/ethnic differences in the prevalence of vaccine-related HPV genotypes. In particular, non-Hispanic black and Hispanic women had the lowest prevalence of HPV genotypes covered by the bi-/quadrivalent vaccines. While a large proportion of their infections were covered by the nonavalent vaccine, non-Hispanic black and Hispanic women also had the highest prevalence of HPV genotypes not covered by any vaccine.

The role of DNA image cytometry in screening oral potentially malignant lesions using brushings: A systematic review

It is believed that the majority of oral cancers develop from oral potentially malignant lesions (OPML). Though they can be easily detected during screening, risk stratification is difficult. During screening clinicians often find it difficult to distinguish OPMLs from benign lesions, and predicting OPML at risk of malignant transformation is particularly challenging. DNA aneuploidy has been known to be a marker of malignancy in a number of sites including the oral cavity. We performed a systematic review to evaluate the effectiveness of DNA-ICM using brushings in differentiating OPMLs from benign/inflammatory lesions during screening and in predicting malignant transformation. MEDLINE, Pubmed, EMBASE electronic databases were systematically searched using a combination of keywords and subject headings. A total of 11 articles satisfied our inclusion criteria. These studies reported a wide range of sensitivity (16-96.4%) and specificity (90-100%) due to the differences in study design, definitions of high risk or low risk lesions and DNA-ICM protocol used. No long-term longitudinal studies were identified to assess the role of DNA-ICM using brushings in predicting malignant transformation. No studies evaluated the role of DNA-ICM in community screening settings. A number of studies combined DNA-ICM with other techniques like cytology or argyrophilic nucleolar organizer region counts leading to improved test results. In spite of DNA aneuploidy being accepted as a marker of malignancy, there is limited evidence of DNA-ICM using brushings being successful as an adjunct oral cancer screening tool. Longitudinal studies and large community screening studies need to be undertaken to draw stronger conclusion.

Abstract 4223: Conducting community oral cancer screening among South Asians in British Columbia

Introduction: Globally, more than 300,000 cases of oral cancer are diagnosed annually. South Asian countries, such as India, bear the brunt of this disease due to rampant use of chewing tobacco, betel quid and areca nut. BC has a high proportion of South Asian immigrants. Oral cancer has a high mortality rate (~50% 5-year survival) due to the advanced stage at which it is often diagnosed. It is purported that the majority of oral cancers develop from oral potentially malignant lesions (OPML). While lesions can be easily detected by oral health care providers, it can be challenging to differentiate benign lesions from OPML. DNA aneuploidy has been shown to be an effective marker to predict malignant transformation in OPML. Quantitative cytology (QC) studies DNA content (ploidy) and nuclear morphometric changes within the cell. The aim of this project is to assess the need for oral cancer screening in South Asians in BC and to validate QC as an adjunct screening device in a predominantly South Asian community screening setting to assess its effectiveness in identifying high-risk lesions among visually suspicious lesions.

Methods: Demographic information (gender, age, country of birth, ethnicity, risk habit information and dental usage) were collected at the time of screening. Extraoral, intraoral and fluorescence visualization (FV) examinations were conducted. Buccal mucosal brushings were collected from each participant. Brushings were also collected from lesions or areas that had a loss of fluorescence. Thin-layer cytology slides were prepared and stained using Feulgen Eosin. Slides were scanned using the Cancer Imaging Scanner at BC Cancer using machine learning classification algorithms to identify single, in-focus epithelial nucleus. Cells are classified based on DNA ploidy and malignant associated changes.

Results: 307 participants were screened of which 303 were eligible. More than 99% of the participants were South Asian or Asian. 104 (34%) lesions were documented: 45 (15%) were high risk (white or red lesions, lichen planus (LP)) and 59 (19%) were low risk (trauma, candidiasis, aphthous ulcers). Twenty participants (7%) were suspected to have high-risk OPMLs (not LP): 12 were referred directly to our Next Gen Oral Dysplasia clinic for biopsy, while 8 were reassessed at 3 weeks. Chewing tobacco was found to be strongly associated with lesion presence (p&lt;0.01). QC analysis is ongoing for 320 samples. To date, 5 biopsies have been performed resulting in 1 mild dysplasia, 1 severe dysplasia and 3 hyperplasia.

Conclusion: South Asians in BC were found to be at high risk for OPMLs. QC may help to improve the sensitivity and specificity of oral cancer screening by distinguishing false FV positive inflammatory lesions from high-risk lesions.

Citation Format: Leigha D. Rock, Madhurima Datta, Denise M. Laronde, Anita Carraro, Jagoda Korbelik, Alan Harrison, Martial Guillaud. Conducting community oral cancer screening among South Asians in British Columbia [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 4223.

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&lt;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 (&lt;30), a significant proportion of older women (&gt;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.

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.