Digital morphometry of tumor nuclei correlates to BAP-1 status, monosomy 3, gene expression class and survival in uveal melanoma

Highlights

• Twelve cell size and shape features were analyzed digitally in uveal melanoma.

• The average number of cells analyzed in each of 27 tumors, was 1957 (SD 349).

• Interobserver concordance was ≥85% for all morphometric variables (κ 0.70–0.93).

• Tumor nuclei size correlated to BAP-1 protein expression, BAP-1 mutation status, monosomy 3 and gene expression class.

• Patients had significantly shorter survival if their tumor cells had large nuclei.

Abstract

Cytologic features such as the shape and size of tumor cells can predict metastatic death in uveal melanoma and other cancers but suffer from poor reproducibility. In this study, we investigate the interobserver concordance of digital morphometry, and correlate the results with BRCA associated protein-1 (BAP-1) expression and BAP-1 gene mutation status, monosomy 3, gene expression classifications and patient survival in uveal melanoma. The average number of cells analyzed in each of 107 tumors, was 1957 (SD 349). Mean time consumption was less than 2.5 min per tumor. Identical morphometric classification was obtained for ≥85% of tumors in all twelve evaluated morphometric variables (κ 0.70–0.93). The mean nucleus area, nucleus perimeter, nucleus max caliper and nucleus to cell area ratio were significantly greater in tumors with low BAP-1 expression and gene expression class 2. Patients had significantly shorter survival if their tumors had low BAP-1 (Log-Rank p = 0.002), gene expression class 2 (p = 0.004), long nucleus perimeters (p = 0.031), long nucleus max calipers (p = 0.029) and high mean nucleus to cell area ratios (p = 0.041) as defined in a training cohort and then tested in a validation cohort. Long nucleus perimeters and long nucleus max calipers correlated with monosomy 3 (Pearson Chi-Square p = 0.006 and p = 0.009, respectively). Long nucleus perimeters also correlated with BAP-1 mutation (p = 0.017). We conclude that digital morphometry can be fast and highly reproducible, that for the first time, morphometry parameters can be objectively quantitated in thousands of cells at a time in sub-μm resolutions, and that variables describing the shape and size tumor nuclei correlate to BAP-1 status, monosomy 3, gene expression class as well as patient survival.

Introduction

Uveal melanoma is the most common primary intraocular malignancy in adults (Singh et al., 2014). Less than 5% of patients have clinically detectable metastases at the time of diagnosis (Singh et al., 2014). At a later stage however, up to 45% of patients will develop metastases even if the eye containing the tumor has been removed (Kujala et al., 2003). Once macrometastases develop, there is no effective treatment and median patient survival is only 4–12 months (Carvajal et al., 2016; Augsburger et al., 2009).

Several methods for prognostication are in clinical use. Tumor thickness, diameter, location in the eye and presence of distant metastases determine tumor stage (Kivelä et al., 2017; Arnljots et al., 2018). Loss of chromosome 3 has a high positive and negative predictive value for metastasis (Bornfeld et al., 1996). Commercial gene tests based on the expression of 12 classifier genes have been developed and show excellent prognostic utility in separation of class 1 tumors with low metastatic risk from class 2 tumors with high metastatic risk (Onken et al., 2012). Furthermore, we have previously shown the prognostic utility of manual (Szalai et al., 2018) and digital image analysis-based (Stålhammar et al., 2019a) determination of the level of nuclear BAP-1 (nBAP-1) expression.

In 1931, Callender described six types of uveal melanoma based on cytologic features such as cell shape and the size of the nucleus (Callender, 1931). The original classification could accurately predict metastatic death, but suffered from substantial intra- and interobserver discordance (Gamel et al., 1992; Coleman et al., 1996). After several modifications, the morphological classification of uveal melanoma now rely on assessments of the proportion of epitheloid tumor cells (McLean et al., 1983; Seddon et al., 1987). Examination of cytological features still require a high level of cytologic expertise and suffer from poor reproducibility (Gamel et al., 1992). Computer-assisted methods have therefore been proposed as a way of facilitating these assessments. In 1982, Gamel et al. found that 13 of 18 nuclear and nucleolar features correlated significantly with patient mortality when evaluated with a digitizer superimposed on microscopic images at a rate of 100 cells per hour (Gamel et al., 1982). Since then, computers have improved manyfold in terms of their computing power, cost and the number and scope of software applications and we can now analyze a dozen of variables or more in thousands of cells per minute on inexpensive off-the-shelf laptop computers (Stålhammar et al., 2016, 2018).

Consequently, we see an opportunity to analyze cell morphometry features with digital image analysis and compare these to other prognostic factors including nBAP-1 expression in uveal melanoma patients from one American and one European referral center.