Utility of CD8 score by automated quantitative image analysis in head and neck squamous cell carcinoma
Introduction
Tumor infiltrating lymphocytes (TILs) have become an important measurement in many organ sites as their presence correlates with prognosis and for some tumors TILs also have therapeutic implications [1]. Among these TILs, CD8 T cells have been shown to be tightly associated with cancer patient survival and the predominant effector for cancer immunotherapy [2], [3]. Cytotoxic CD8 T cells are able to directly kill neoplastic cells through direct lysis. Accordingly, many immunotherapy approaches have sought to target and enhance tumor-reactive CD8 T cells [3], [4].
For head and neck squamous cell carcinoma (HNSCC) high numbers of tumor infiltrating CD8 T cells in the tumor microenvironment (TME) are associated with oropharyngeal localization, limited tumor growth and a lower incidence of smoking [5], [6]. Patients with HNSCC that exhibit high infiltration of CD8 T cells also demonstrate a significantly better outcome [5], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16]. In HNSCC human papillomavirus (HPV) infection is associated with better prognosis, which appears in part to be due to enhanced immune activation in the TME, and especially enhanced infiltration of CD8 T cells [16], [17], [18]. Given the direct relationship between TILs and HNSCC, a direct method to assess an immune response within HNSCC would be desirable [19].
The assessment of the CD8 T cells in patients afflicted with HNSCC has been attempted using flow cytometry either on procured tissue biopsies or from peripheral blood [8], [20]. Attaining such a “CD8 score” may be of value to help determine prognosis and treatment before definitive surgical resection. An additional benefit of employing flow cytometry for this purpose is the multidimensional interrogation of immune cells that can be performed by this method. However, there are conflicting studies about whether systemic CD8 T cell levels correlate with tissue CD8 T cell infiltration.
Since there are no distinct morphologic features of CD8 T lymphocytes present in hematoxylin and eosin (H&E) stained tissues, an immunohistochemical (IHC) stain for CD8 T cells is necessary. The assessment of CD8 T cells within immunostained tissue sections has generally been reported in quantitative and semi-quantitative fashions. No specific method has yet been recommended to assess CD8 T cells and thus each published study assessing TILs in HNSCC has analyzed CD8 infiltrates using slightly different methods [19]. One method frequently exploited included counting the number of CD8 T cells in various high power fields and then averaging those values across these fields [7], [9], [10], [11], [13]. Many studies relied on manual counts from acquired still images or examining stained samples directly with a conventional light microscope. One group of authors utilized software (Count from Biomas) to perform cell counting [11], [21]. The output of prior quantification studies in TILs in HNSCC has typically been reported as an absolute count of T cells or percentage/ratio to other immune cells. Few studies have provided quantification in the form of density (i.e. number of CD8 T cells per area of measurement) [8], [22]. Using digital imaging, image analysis can be leveraged to perform this quantification [23].
Additionally, it is hard to extrapolate findings from different publications as these studies often quantified cells in different regions of tissue sections. The three main areas generally scored include the intratumoral/intraepithelial/tumor-host, peritumoral/tumor-stroma, and peripheral tumor compartments [5], [6], [7], [12]. Specific descriptions of these tissue areas are lacking in these studies. Nevertheless, most investigations focused predominantly on the intratumoral component. Moreover, some of these studies were based on tissue microarrays while others used whole tissue sections. To the best of our knowledge, no investigators have employed automated image analysis of whole slide images to determine CD8 scores in oropharyngeal HNSCC.
The aim of this study was to develop and validate an image analysis algorithm to automatically quantify CD8 T cells in whole slide images acquired from immunohistochemically stained tissue sections from patients with HNSCC.
Section snippets
Methods
This study was approved by the University of Pittsburgh’s Institutional review Board (IRB PRO 15070630) and Yale Human Investigation Committee protocol #9505008219 from Yale University.
The immunohistochemistry clone for CD8 was C8/144B (DAKO, Carpenteria, CA). Pre-treatment of tissue was performed and antibody was diluted to a concentration of 1:40. DAB (3,3′-diaminobenzidine) was used as the color reagent for visualization.
The best approach to validate a new assay is to compare it to a
CD8 algorithm performance
Manual CD8 cell count for TMA cores ranged from 0 cells to 946 cells/core. The linear correlation between the manual count and the unadjusted cytoplasmic algorithm for the TMA was 0.861. The linear correlation between the manual count and the customized CD8 algorithm for the TMA was 0.943. The linear correlation between the customized CD8 algorithm performed on the digitized TMA slide at Leica in Dublin, Ireland and the same customized algorithm performed at UPMC on the TMA digital slide was
Discussion
Numerous studies have revealed that the infiltration of CD8 T cells within tumor sections in many organ system tumors, including HNSCC, has both prognostic and potentially therapeutic implications. One impediment to the routine evaluation of infiltrating of CD8 T cells within tumors is the availability of a reproducible and reliable method to quantify the amount of CD8 T cells within tissue. Different techniques of scoring CD8 T cell density (e.g. flow cytometry, western blots, morphological
Conclusion
In summary, we report the development and validation of an image analysis algorithm that can be utilized to automate CD8 T cell density evaluation for oropharyngeal HNSCC. Employing brightfield analysis and immunohistochemistry of entire tumor sections instead of specific tumor subcompartments permits this strategy to be easily adopted in routine clinical practice by many pathology laboratories.
Conflicts of interest
Dr. Liron Pantanowitz is on the medical Advisory board for Leica.
Acknowledgements
Colleen Vrbin (President of Analytical Insights, LLC) for performing the statistical analysis for the HNSCC cohort.
Head and Neck SPORE (P50 CA097190), and UPMC Hillman Cancer Center Support Grant (P30 CA047904).
Lindsey Seigh for her technical help in the UPMC Image Analysis Laboratory.
This work was made possible by grant funding including the Yale SPORE in Lung Cancer, P50-CA196530, the Yale Cancer Center Support Grant, P30-CA016359, and a sponsored research agreement from Ultivue to Dr. Rimm.
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