Small Cell Variant of Metastatic Melanoma: A Mimicker of Lymphoblastic Leukemia/Lymphoma

The Great Impostor: A challenging case of small-cell melanoma with isolated adrenal metastasis

Small-cell melanoma masquerading as an adrenal non-Hodgkin lymphoma. The index report illustrates the deceptive cytomorphologic features of a small cell type malignant melanoma metastatic to the adrenal gland. The diagnosis was confirmed by performing immunocytochemistry on the cell block sections. The key cytomorphologic mimics and their distinctive features have also been highlighted.

Dynamic thresholding and tissue dissociation optimization for CITE-seq identifies differential surface protein abundance in metastatic melanoma

Multi-omics profiling by CITE-seq bridges the RNA-protein gap in single-cell analysis but has been largely applied to liquid biopsies. Applying CITE-seq to clinically relevant solid biopsies to characterize healthy tissue and the tumor microenvironment is an essential next step in single-cell translational studies. In this study, gating of cell populations based on their transcriptome signatures for use in cell type-specific ridge plots allowed identification of positive antibody signals and setting of manual thresholds. Next, we compare five skin dissociation protocols by taking into account dissociation efficiency, captured cell type heterogeneity and recovered surface proteome. To assess the effect of enzymatic digestion on transcriptome and epitope expression in immune cell populations, we analyze peripheral blood mononuclear cells (PBMCs) with and without dissociation. To further assess the RNA-protein gap, RNA-protein we perform codetection and correlation analyses on thresholded protein values. Finally, in a proof-of-concept study, using protein abundance analysis on selected surface markers in a cohort of healthy skin, primary, and metastatic melanoma we identify CD56 surface marker expression on metastatic melanoma cells, which was further confirmed by multiplex immunohistochemistry. This work provides practical guidelines for processing and analysis of clinically relevant solid tissue biopsies for biomarker discovery.

Neurosarcomatous amelanotic transformation of malignant melanoma presenting as malignant periopheral nerve sheath tumor: Rare case report

RATIONALE

Malignant melanoma (MM) is notorious for its remarkable morphological variation and aberrant histopathological patterns. In addition, Malignant Periopheral Nerve Sheath Tumor (MPNST) is an uncommon but aggressive soft tissue sarcoma. Because of the common embryological origin of melanocytes and Schwann cells in the neural crest, discriminating between a particular type of MM and MPNST can be difficult, particularly when they are amelanotic. Our goal is to increase awareness among clinicians of the rare variations of MM and the importance of medical history in improving the accuracy of the final clinical diagnosis.

PATIENT CONCERNS

A 68-year-old man was admitted to the hospital due to pain in his right ankle, which had persisted for 8 months, along with swelling for 4 months. Medical history revealed delayed healing of right plantar for 5 years after a traumatic injury.

DIAGNOSES

The ankle mass was initially diagnosed as MPNST through biopsy. After reviewing the patient's medical history and receiving the final pathological report following amputation, we have revised the diagnosis to metastatic amelanotic desmoplastic melanoma in the ankle part and lentigo maligna melanoma in the plantar part. This is due to both lesions displaying positive markers or mutated genes in immunohistology and Gene Mutation Detection, indicating homology between the 2 tumors.

INTERVENTIONS

Due to the malignant characteristics of the tumor and the patient's wishes, amputation of the right lower leg was carried out.

OUTCOMES

Subsequently, the patient was treated with interferon-γ and immunosuppressant PD-1 inhibitor, and survived for 1 year after amputation.

LESSONS

Clinical data, immunohistochemisty biomarkers and genes detection results can serve as valuable evidence for pathologists and clinicians in identifying the disease process. Collaborative efforts between clinicians and scientists are crucial in order to identify specific markers that can effectively differentiate between the 2 tumors, thereby enhancing the conclusiveness of the diagnosis.

Predicting melanoma survival and metastasis with interpretable histopathological features and machine learning models

Introduction

Melanoma is the fifth most common cancer in US, and the incidence is increasing 1.4% annually. The overall survival rate for early-stage disease is 99.4%. However, melanoma can recur years later (in the same region of the body or as distant metastasis), and results in a dramatically lower survival rate. Currently there is no reliable method to predict tumor recurrence and metastasis on early primary tumor histological images.

Methods

To identify rapid, accurate, and cost-effective predictors of metastasis and survival, in this work, we applied various interpretable machine learning approaches to analyze melanoma histopathological H&E images. The result is a set of image features that can help clinicians identify high-risk-of-metastasis patients for increased clinical follow-up and precision treatment. We use simple models (i.e., logarithmic classification and KNN) and "human-interpretable" measures of cell morphology and tissue architecture (e.g., cell size, staining intensity, and cell density) to predict the melanoma survival on public and local Stage I-III cohorts as well as the metastasis risk on a local cohort.

Results

We use penalized survival regression to limit features available to downstream classifiers and investigate the utility of convolutional neural networks in isolating tumor regions to focus morphology extraction on only the tumor region. This approach allows us to predict survival and metastasis with a maximum F1 score of 0.72 and 0.73, respectively, and to visualize several high-risk cell morphologies.

Discussion

This lays the foundation for future work, which will focus on using our interpretable pipeline to predict metastasis in Stage I & II melanoma.

Cytologic features of small cell melanoma

Small cell melanoma (SCM) is an aggressive variant of malignant melanoma (MM), which has been rarely described in the cytology literature. The aim of this study was to describe the clinical and cytologic features of a series of cases of metastatic SCM with discussion of the differential diagnosis of metastatic SCM diagnosed by fine-needle aspiration (FNA). A retrospective review of cases was performed, identifying two FNA cases and one core biopsy with touch preparation of metastatic SCM. Clinical presentation, cytomorphology features, ancillary tests, and final diagnoses were documented and analyzed. Patients ranged in age from 69 to 85 years-old. Cytomorphologic features included the presence of a monomorphic population of dispersed small round blue cells, with scant cytoplasm, high nuclear to cytoplasmic ratios, dense nuclear chromatin, and inconspicuous nucleoli. Acinar like arrangement (n = 2) and nuclear molding (n = 1) were also present. All cases showed diffuse positivity for the melanocytic markers SOX10 and Melan A by immunohistochemistry (IHC). Expression of neuroendocrine markers was variable. Diagnosing metastatic SCM at unusual anatomic sites by FNA cytology is a challenging task, especially in patients without known prior history of melanoma. Cytomorphology of SCM is unique, differing from conventional MM in many aspects, including the presence of acinar formations and a lack of typical melanoma features, such as large cells, intracytoplasmic melanin, and macronucleoli. IHC is critical for establishing the diagnosis of SCM.