Copy number variation analysis and methylome profiling of a GNAQ-mutant primary meningeal melanocytic tumor and its liver metastasis

Primary Meningeal Melanocytic Tumors of the Central Nervous System: A Review from the Ultra-Rare Brain Tumors Task Force of the European Network for Rare Cancers (EURACAN)

BACKGROUND

Primary meningeal melanocytic tumors are ultra-rare entities with distinct histological and molecular features compared with other melanocytic or pigmented lesions, such as brain and leptomeningeal metastases from metastatic melanoma.

METHODS

The European Network for Rare Cancers (EURACAN) Task Force on Ultra-Rare Brain Tumors (domain 10, subdomain 10) performed a literature review from January 1985 to December 2023 regarding the epidemiologic and clinical characteristics, histological and molecular features, radiological findings, and efficacy of local treatments (surgery and radiotherapy) and systemic treatments for these entities.

RESULTS

Molecular analysis can detect specific mutations, including GNAQ, GNA11, SF3B1, EIF1AX, BAP1, that are typically found in circumscribed primary meningeal melanocytic tumors and not in other melanocytic lesions, whereas NRAS and BRAF mutations are typical for diffuse primary meningeal melanocytic tumors. The neuroimaging of the whole neuroaxis suggests a melanocytic nature of a lesion, depicts its circumscribed or diffuse nature, but cannot predict the tumor's aggressiveness. Gross-total resection is the first choice in the case of circumscribed meningeal melanocytoma and melanoma; conversely, meningeal biopsy may be reserved for patients with diffuse and multinodular leptomeningeal spread to achieve a definitive diagnosis. High-dose radiotherapy is rarely indicated in diffuse melanocytic tumors except as palliative treatment to alleviate symptoms. Last, a definitive advantage of a specific systemic treatment could not be concluded, as most of the data available derive from case reports or small cohorts.

CONCLUSIONS

As primary meningeal melanocytic tumors are extremely rare, the correlations between the clinical characteristics, molecular profile, radiological findings at diagnosis and progression are weak, and poor evidence on the best therapeutic approach is available. There is a need to develop shared platforms and registries to capture more knowledge regarding these ultra-rare entities.

Clinicopathological features, current status, and progress of primary central nervous system melanoma diagnosis and treatment

Primary central nervous system (CNS) melanoma is an extremely rare condition, with an incidence rate of 0.01 per 100,000 individuals per year. Despite its rarity, the etiology and pathogenesis of this disease are not yet fully understood. Primary CNS melanoma exhibits highly aggressive biological behavior and presents clinically in a distinct manner from other types of melanomas. It can develop at any age, predominantly affecting the meninges as the primary site, with clinical symptoms varying depending on the neoplasm's location. Due to the lack of specificity in its presentation and the challenging nature of imaging diagnosis, distinguishing primary CNS melanoma from other CNS diseases. The combination of challenges in early detection, heightened tumor aggressiveness, and the obscured location of its origin contribute to an unfavorable prognostic outcome. Furthermore, there has been currently no consensus on a standardized treatment approach for primary CNS melanoma. Despite recent advancements in targeted therapy and immunotherapy for CNS melanoma, patients with primary CNS melanoma have limited treatment options due to their inadequate response to these therapies. Here, we provided a comprehensive summary of the epidemiology, clinical features, molecular pathological manifestations, and available diagnostic and therapeutic approaches of primary CNS melanoma. Additionally, we proposed potential therapeutic strategies for it.

How Should We Treat Meningeal Melanocytoma? A Retrospective Analysis of Potential Treatment Strategies

BACKGROUND

Meningeal melanocytomas (MM) are rare primary melanocytic tumors of the leptomeninges with an incidence of 1:10,000,000. Until now, there has been only sparse information about this tumor entity. Here, we provide a meta-analysis of all cases published in the English language since 1972.

METHODS

A literature review was performed using PubMed and Web of Science. All published cases were evaluated for location, sex, age, therapeutic approach, and outcome. In total, we included 201 patient cases in our meta-analysis.

RESULTS

The majority of MM was diagnosed more frequently in men between the third and fifth decade of life. Surgery is the preferred therapeutic approach, and total resection is associated with the best outcome. Patients with partial resection or tumor recurrence benefit from adjuvant radiotherapy, whereas chemo- or immunotherapies do not improve the disease course. Malignant transformation was described in 18 patients. Of these, 11 patients developed metastasis.

CONCLUSIONS

We present the first retrospective meta-analysis of all MM cases published in the English language, including an evaluation of different treatment strategies allowing us to suggest a novel treatment guideline highlighting the importance of total resection for recurrence-free survival and characterizing those cases which benefit from adjuvant radiotherapy.

Malignant Transformation and Metastatic Spread of Dumbbell-Shaped Meningeal Melanocytoma of the Cervical Spine: A Case Report and Literature Review

Background

Meningeal melanocytoma is a rare disease that originates from leptomeningeal melanocytes in the central nervous system. Meningeal melanocytoma is generally considered benign, and has a good prognosis following complete surgical resection. Reports of the malignant transformation and spread of these tumors are scarce.

Case Presentation

A 19 year old female presented with headache, progressive limb weakness, and dyspnea. Magnetic resonance imaging showed a dumbbell-shaped lesion at C1–C2 that was hyperintense on T1 weighted images and showed strong contrast enhancement. Total resection was achieved using a posterior midline approach. Post-operative pathology showed meningeal melanocytoma. The tumor recurred 9 months later with intracranial spread. Resection of the lesion revealed malignant transformation to meningeal melanoma.

Conclusion

Meningeal melanocytoma harbors malignant potential even with total resection. Radiotherapy could be considered to prevent disease recurrence and progression.

Case Report: Differential Genomics and Evolution of a Meningeal Melanoma Treated With Ipilimumab and Nivolumab

Primary melanocytic tumors of the CNS are extremely rare conditions, encompassing different disease processes including meningeal melanoma and meningeal melanocytosis. Its incidence range between 3-5%, with approximately 0.005 cases per 100,000 people. Tumor biological behavior is commonly aggressive, with poor prognosis and very low survivability, and a high recurrence rate, even after disease remission with multimodal treatments. Specific genetic alterations involving gene transcription, alternative splicing, RNA translation, and cell proliferation are usually seen, affecting genes like BRAF, TERT, GNAQ, SF3B1, and EIF1AX. Here we present an interesting case of a 59-year-old male presenting with neurologic symptoms and a further confirmed diagnosis of primary meningeal melanoma. Multiple therapy lines were used, including radiosurgery, immunotherapy, and chemotherapy. The patient developed two relapses and an evolving genetic makeup that confirmed the disease’s clonal origin. We also provide a review of the literature on the genetic basis of primary melanocytic tumors of the CNS.

Evaluating DNA Methylation, Gene Expression, Somatic Mutation, and Their Combinations in Inferring Tumor Tissue-of-Origin

Carcinoma of unknown primary (CUP) is a type of metastatic cancer, the primary tumor site of which cannot be identified. CUP occupies approximately 5% of cancer incidences in the United States with usually unfavorable prognosis, making it a big threat to public health. Traditional methods to identify the tissue-of-origin (TOO) of CUP like immunohistochemistry can only deal with around 20% CUP patients. In recent years, more and more studies suggest that it is promising to solve the problem by integrating machine learning techniques with big biomedical data involving multiple types of biomarkers including epigenetic, genetic, and gene expression profiles, such as DNA methylation. Different biomarkers play different roles in cancer research; for example, genomic mutations in a patient’s tumor could lead to specific anticancer drugs for treatment; DNA methylation and copy number variation could reveal tumor tissue of origin and molecular classification. However, there is no systematic comparison on which biomarker is better at identifying the cancer type and site of origin. In addition, it might also be possible to further improve the inference accuracy by integrating multiple types of biomarkers. In this study, we used primary tumor data rather than metastatic tumor data. Although the use of primary tumors may lead to some biases in our classification model, their tumor-of-origins are known. In addition, previous studies have suggested that the CUP prediction model built from primary tumors could efficiently predict TOO of metastatic cancers (Lal et al., 2013; Brachtel et al., 2016). We systematically compared the performances of three types of biomarkers including DNA methylation, gene expression profile, and somatic mutation as well as their combinations in inferring the TOO of CUP patients. First, we downloaded the gene expression profile, somatic mutation and DNA methylation data of 7,224 tumor samples across 21 common cancer types from the cancer genome atlas (TCGA) and generated seven different feature matrices through various combinations. Second, we performed feature selection by the Pearson correlation method. The selected features for each matrix were used to build up an XGBoost multi-label classification model to infer cancer TOO, an algorithm proven to be effective in a few previous studies. The performance of each biomarker and combination was compared by the 10-fold cross-validation process. Our results showed that the TOO tracing accuracy using gene expression profile was the highest, followed by DNA methylation, while somatic mutation performed the worst. Meanwhile, we found that simply combining multiple biomarkers does not have much effect in improving prediction accuracy.

A machine learning framework to trace tumor tissue-of-origin of 13 types of cancer based on DNA somatic mutation

Carcinoma of unknown primary (CUP), defined as metastatic cancers with unknown cancer origin, occurs in 3-5 per 100 cancer patients in the United States. Heterogeneity and metastasis of cancer brings great difficulties to the follow-up diagnosis and treatment for CUP. To find the tissue-of-origin (TOO) of the CUP, multiple methods have been raised. However, the accuracies for computed tomography (CT) and positron emission tomography (PET) to identify TOO were 20%-27% and 24%-40% respectively, which were not enough for determining targeted therapies. In this study, we provide a machine learning framework to trace tumor tissue origin by using gene length-normalized somatic mutation sequencing data. Somatic mutation data was downloaded from the Data Portal (Release 28) of the International Cancer Genome Consortium (ICGC), and 4909 samples for 13 cancers was used to identify primary site of cancers. Optimal results were obtained based on a 600-gene set by using the random forest algorithm with 10-fold cross-validation, and the average accuracy and F1-score were 0.8822 and 0.8886 respectively across 13 types of cancer. In conclusion, we provide an effective computational framework to infer cancer tissue-of-origin by combining DNA sequencing and machine learning techniques, which is promising in assisting clinical diagnosis of cancers.

Predicting Cancer Tissue-of-Origin by a Machine Learning Method Using DNA Somatic Mutation Data

Patients with carcinoma of unknown primary (CUP) account for 3-5% of all cancer cases. A large number of metastatic cancers require further diagnosis to determine their tissue of origin. However, diagnosis of CUP and identification of its primary site are challenging. Previous studies have suggested that molecular profiling of tissue-specific genes could be useful in inferring the primary tissue of a tumor. The purpose of this study was to evaluate the performance somatic mutations detected in a tumor to identify the cancer tissue of origin. We downloaded the somatic mutation datasets from the International Cancer Genome Consortium project. The random forest algorithm was used to extract features, and a classifier was established based on the logistic regression. Specifically, the somatic mutations of 300 genes were extracted, which are significantly enriched in functions, such as cell-to-cell adhesion. In addition, the prediction accuracy on tissue-of-origin inference for 3,374 cancer samples across 13 cancer types reached 81% in a 10-fold cross-validation. Our method could be useful in the identification of cancer tissue of origin, as well as the diagnosis and treatment of cancers.

TOOme: A Novel Computational Framework to Infer Cancer Tissue-of-Origin by Integrating Both Gene Mutation and Expression

Metastatic cancers require further diagnosis to determine their primary tumor sites. However, the tissue-of-origin for around 5% tumors could not be identified by routine medical diagnosis according to a statistics in the United States. With the development of machine learning techniques and the accumulation of big cancer data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), it is now feasible to predict cancer tissue-of-origin by computational tools. Metastatic tumor inherits characteristics from its tissue-of-origin, and both gene expression profile and somatic mutation have tissue specificity. Thus, we developed a computational framework to infer tumor tissue-of-origin by integrating both gene mutation and expression (TOOme). Specifically, we first perform feature selection on both gene expressions and mutations by a random forest method. The selected features are then used to build up a multi-label classification model to infer cancer tissue-of-origin. We adopt a few popular multiple-label classification methods, which are compared by the 10-fold cross validation process. We applied TOOme to the TCGA data containing 7,008 non-metastatic samples across 20 solid tumors. Seventy four genes by gene expression profile and six genes by gene mutation are selected by the random forest process, which can be divided into two categories: (1) cancer type specific genes and (2) those expressed or mutated in several cancers with different levels of expression or mutation rates. Function analysis indicates that the selected genes are significantly enriched in gland development, urogenital system development, hormone metabolic process, thyroid hormone generation prostate hormone generation and so on. According to the multiple-label classification method, random forest performs the best with a 10-fold cross-validation prediction accuracy of 96%. We also use the 19 metastatic samples from TCGA and 256 cancer samples downloaded from GEO as independent testing data, for which TOOme achieves a prediction accuracy of 89%. The cross-validation validation accuracy is better than those using gene expression (i.e., 95%) and gene mutation (53%) alone. In conclusion, TOOme provides a quick yet accurate alternative to traditional medical methods in inferring cancer tissue-of-origin. In addition, the methods combining somatic mutation and gene expressions outperform those using gene expression or mutation alone.