MALDI imaging mass spectrometry differentiates basal cell carcinoma from trichoblastoma and trichoepithelioma: A proof of principle study

Abstract

Background: Basal cell carcinoma (BCC) comprises a large portion of dermatopathology specimens; however, benign mimics such as trichoblastoma/trichoepithelioma (TB/TE) place accurate diagnosis at risk and consequently lead to inappropriate clinical management and overuse of healthcare resources. This study aims to address the challenges of traditional histopathological evaluation by utilizing matrix-assisted laser desorption ionization imaging mass spectrometry (MALDI IMS).

Methods and findings: Formalin-fixed paraffin-embedded BCC and TB/TE tissue blocks were taken from archival tissue. A cohort of 69 BCC and TB/TE specimens were identified, each having three concordant diagnoses given by Dermatopathologists after a blinded analysis. H&E stained sections of each specimen were imaged for pathological analysis and uploaded to a digital annotation software with the following classifications: BCC, TB, TE, BCC stroma, TB stroma, and TE stroma. Mass spectra were collected from unstained serial sections guided by the areas annotated by the Dermatopathologists on the H&E stained serial sections. Before informatics, the data from the cohort were divided randomly into a training set (n = 55) and a validation set (n = 14). Prediction models were developed using a support vector machine (SVM) classification model from the training set data. The platform predicted BCC and TB/TE in model 2 (tumor nests alone) with a sensitivity of 98.9% (95% CI 98.3-99.4%) and specificity of 88.4% (95% CI 78.4-94.5%) at the spectral level in the validation set. Model 1 (stroma alone) had a sensitivity of 46.1% (95% CI 43.0-49.1%) and specificity of 99.2% (95% CI 97.1-99.9%). A combined model 3 (tumor nests and stroma) had a sensitivity of 90.26% (95% CI 89.1%-91.3%) and a specificity of 97.1% (95% CI 94.6% to 98.7%). The limitations of this study included a small sample set, which included easily identifiable cases obtained from a single tissue source.

Conclusions: Our study proves that BCC and TB/TE exhibit different proteomic profiles that one can use to enable accurate differential diagnosis. While our findings are not yet validated for clinical use, this merits further research to support IMS as an ancillary diagnostic tool for adequately and efficiently identifying the most common cutaneous malignancy in the United States. We recommend that future studies obtain a more extensive set of histologically challenging cases from multiple institutions and adequate clinical follow-up to confirm diagnostic accuracy.

Fig 1. Basal cell carcinoma with a nodular growth pattern showing nested growth pattern of basaloid nests within the dermis and no epidermal connection (A, H&E 20X), the nests grow in an expansile fashion in the dermis and induce some fibrotic changes in the background stroma (B, H&E 100X).

On closer inspection, the basaloid nests show peripheral palisading, with apoptotic bodies and mitotic figures, along with some deposition of myxoid substance (C, H&E 200X). An example of trichoblastoma showing the basaloid nests within the dermis with no overlying epidermal connection (D, H&E 20X), Similar to basal cell carcinoma, trichoblastoma also grows in the form of expansile basaloid nests in the dermis with peripheral palisading; however, the difference is mostly subtle histologically represented by a spindled stroma with clefting within the stroma and minimal mitotic activity (E, H&E 100X). The presence of papillary mesenchymal bodies is a helpful feature in trichoblastoma/trichoepithelioma (F, H&E 200X).

Fig 2. Workflow for histology-directed IMS.

A) Cases are selected, and blocks are sectioned to include a slide for annotation and a slide for IMS. A pathologist annotates regions of interest registered to the section for IMS. Mass spectra are collected from the area annotated. B) Mass spectra are binned based on the annotations by pathologists and subjected to machine learning, which develops classifiers based on spectral features. The model can classify novel data and generate predictions based on spectral features.

Fig 3. The top row demonstrates a case of Trichoepithelioma, and the lower row represents a case of Basal cell carcinoma.

Note the different generated spectra from these two tumors despite the histopathologic overlap (x-axis representing m/z (mass-to-charge) and y-axis representing ion intensity). Mass Spectra are displayed from m/z 500–2100 and represent a subset of the mass range collected.