Fractal analysis of nuclear histology integrates tumor and stromal features into a single prognostic factor of the oral cancer microenvironment

BMC Cancer

Research Article

Kelly Guggisberg¹  Nigel T Brockton²  Pinaki Bose³  Mauro Tambasco⁴  Joseph C Dort⁵  Steven C Nakoneshny⁵  Elizabeth Kornaga⁶  Alexander C Klimowicz⁷ 

[1] Department of Anatomic Pathology, Calgary Laboratory Services, Rockyview General Hospital, T2V 1P9, Calgary, Alberta, Canada;Department of Cancer Epidemiology and Prevention Research, CancerControl Alberta, Alberta Health Services, T2N 2T9, Calgary, Alberta, Canada;Department of Oncology, University of Calgary, Calgary, Canada;Current Address: Canada’s Michael Smith Genome Sciences Centre, British Columbia Cancer Agency, Vancouver, British Columbia, Canada;Department of Physics, San Diego State University, 92182-1233, San Diego, California, USA;Department of Surgery, Division of Otolaryngology-Head and Neck Surgery, University of Calgary, T2N 4Z6, Calgary, Alberta, Canada;Functional Tissue Imaging Unit, Translational Laboratories, Tom Baker Cancer Centre, T2N 4N2, Calgary, Alberta, Canada;Immunology and Inflammation Research, Boehringer Ingelheim Pharmaceuticals, Inc, 06877, Ridgefield, Connecticut, USA;

关键词: Fractal Dimension;    Tumor Microenvironment;    Oral Squamous Cell Carcinoma;    Oral Squamous Cell Carcinoma;    Lymphocytic Infiltration;   

DOI  :  10.1186/s12885-015-1380-0

 received in 2014-12-21, accepted in 2015-04-28,  发布年份 2015

来源: Springer

【 摘 要 】

BackgroundThe lack of prognostic biomarkers in oral squamous cell carcinoma (OSCC) has hampered treatment decision making and survival in OSCC remains poor. Histopathological features are used for prognostication in OSCC and, although useful for predicting risk, manual assessment of histopathology is subjective and labour intensive. In this study, we propose a method that integrates multiple histopathological features of the tumor microenvironment into a single, digital pathology-based biomarker using nuclear fractal dimension (nFD) analysis.MethodsOne hundred and seven consecutive OSCC patients diagnosed between 1998 and 2006 in Calgary, Canada were included in the study. nFD scores were generated from DAPI-stained images of tissue microarray (TMA) cores. Ki67 protein expression was measured in the tumor using fluorescence immunohistochemistry (IHC) and automated quantitative analysis (AQUA®). Lymphocytic infiltration (LI) was measured in the stroma from haematoxylin-eosin (H&E)-stained TMA slides by a pathologist.ResultsTwenty-five (23.4%) and 82 (76.6%) patients were classified as high and low nFD, respectively. nFD was significantly associated with pathological tumor-stage (pT-stage; P = 0.01) and radiation treatment (RT; P = 0.01). High nFD of the total tumor microenvironment (stroma plus tumor) was significantly associated with improved disease-specific survival (DSS; P = 0.002). No association with DSS was observed when nFD of either the tumor or the stroma was measured separately. pT-stage (P = 0.01), pathological node status (pN-status; P = 0.02) and RT (P = 0.03) were also significantly associated with DSS. In multivariate analysis, nFD remained significantly associated with DSS [HR 0.12 (95% CI 0.02-0.89, P = 0.04)] in a model adjusted for pT-stage, pN-status and RT. We also found that high nFD was significantly associated with high tumor proliferation (P < 0.0001) and high LI (P < 0.0001), factors that we and others have shown to be associated with improved survival in OSCC.ConclusionsWe provide evidence that nFD analysis integrates known prognostic factors from the tumor microenvironment, such as proliferation and immune infiltration, into a single digital pathology-based biomarker. Prospective validation of our results could establish nFD as a valuable tool for clinical decision making in OSCC.

【 授权许可】

Unknown   
© Bose et al.; licensee BioMed Central. 2015. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.