Histopathology‐guided mass spectrometry differentiates benign nevi from malignant melanoma

Abstract

Purpose: Distinguishing benign nevi from malignant melanoma using current histopathological criteria may be very challenging and is one the most difficult areas in dermatopathology. The goal of this study was to identify proteomic differences, which would more reliably differentiate between benign and malignant melanocytic lesions.Methods: We performed histolpathology -guided mass spectrometry (HGMS) profiling analysis on formalin-fixed, paraffin embedded tissue samples to identify differences at the proteomic level between different types of benign nevi and melanomas.A total of 756 cases, of which 357 cases of melanoma and 399 benign nevi, were included in the study. The specimens originated from both biopsies (376 samples) and tissue microarray (TMA) cores (380 samples). After obtaining mass spectra from each sample, classification models were built using a training set of biopsy specimens from 111 nevi and 100 melanomas. The classification algorithm developed on the training data set was validated on an independent set of 288 nevi and 257 melanomas from both biopsies and TMA cores.Results: In the melanoma cohort, 239/257 (93%) cases classified correctly in the validation set, 3/257 (1.2%) classified incorrectly, and 15/257 (5.8%) classified as indeterminate. In the cohort of nevi, 282/288 (98%) cases classified correctly, 1/288 (0.3%) classified incorrectly, and 5/288 (1.7%) were indeterminate. HGMS showed a sensitivity of 98.76% and specificity of 99.65% in determining benign vs malignant.Conclusion: HGMS proteomic analysis is an objective and reliable test with minimal tissue requirements, which can be a helpful ancillary test in the diagnosis of challenging melanocytic lesions. K E Y W O R D Sbenign nevus, histopathology-guided mass spectrometry, malignant melanoma, mass spectrometry, proteomics

Diagnosis of melanoma by imaging mass spectrometry: Development and validation of a melanoma prediction model

“…The final number of samples enrolled in this study is consistent with similar studies using this technology to classify melanoma. 23,24,28 The panel of dermatopathologists reviewed 16 biconcordant specimens and determined that two samples were unambiguous (included in the 333 triconcordant samples) and 14 were diagnostically challenging and excluded because of lack of consensus. We excluded 22 samples because of assay errors, and six samples did not meet study criteria upon pathological analysis.…”

“…Previous work demonstrates that this approach can accurately classify various cancers, including head and neck cancers, 20 ovarian cancer, 21 lung cancer, pancreatic cancer, and skin cancer. [22][23][24][25] Previous studies on the skin have shown proof-of-principle to distinguish spitzoid melanoma, 26 malignant melanoma, [27][28][29] and melanoma metastasis. 27,[30][31][32] Original studies used a reagent spotter to perform digestions.…”

“…23,24,27 Current technology uses homogeneous reagent coatings, helping collect data faster and from smaller regions of interest. 28 Despite these studies, there remains a need for more well-controlled studies and advanced bioinformatics approaches to translate the technology into clinical use. This report establishes MALDI IMS as a technology that accurately confirms the diagnosis of melanocytic neoplasms compared to triconcordant histopathological evaluation.…”

Robust subtyping of non‐small cell lung cancer whole sections through MALDI mass spectrometry imaging

“…Applying a classifier, trained on TMAs, to MALDI MSI measurements of whole sections can be difficult due to technical variations in the measurement process. While a somewhat similar approach has been recently taken to translate a classifier developed on melanoma biopsies to TMAs [13], a successful automatic classification of tissue whole sections from a model developed on TMAs has not been presented yet.…”

Are mass spectrometry imaging‐based diagnostics becoming reality?

“…While this is only one study with a small number of test samples, 16 in total, it shows great promise for adaptation to other MSI diagnostic studies. As has been previously shown, standardized sample preparation enables a high degree of reproducibility across both multiple sites [1] and time [5]. The use of robotics for sample preparation and automated image acquisition features such as Bruker's IntelliSlides, as well as streamlined analysis pipelines are helping to make the generation of high‐quality data collection accessible to a broader scientific community, not just to those who have spent years becoming experts in the field.…”