Multi-Modality Analysis Improves Survival Prediction in Enucleated Uveal Melanoma Patients

PURPOSE. Uveal melanoma (UM) is characterized by multiple chromosomal rearrangements and recurrent mutated genes. The aim of this study was to investigate if copy number variations (CNV) alone and in combination with other genetic and clinico-histopathological variables can be used to stratify for disease-free survival (DFS) in enucleated patients with UM. METHODS. We analyzed single nucleotide polymorphisms (SNP) array data of primary tumors and other clinical variables of 214 UM patients from the Rotterdam Ocular Melanoma Study (ROMS) cohort. Nonweighted hierarchical clustering of SNP array data was used to identify molecular subclasses with distinct CNV patterns. The subclasses associate with mutational status of BAP1, SF3B1, or EIF1AX. Cox proportional hazard models were then used to study the predictive performance of SNP array cluster-, mutation-, and clinico-histopathological data, and their combination for study endpoint risk. RESULTS. Five clusters with distinct CNV patterns and concomitant mutations in BAP1, SF3B1, or EIF1AX were identified. The sample’s cluster allocation contributed significantly to mutational status of samples in predicting the incidence of metastasis during a median of 45.6 (interquartile range [IQR]: 24.7–81.8) months of follow-up (P < 0.05) and vice versa. Furthermore, incorporating all data sources in one model yielded a 0.797 C-score during 100 months of follow-up. CONCLUSIONS. UM has distinct CNV patterns that correspond to different mutated driver genes. Incorporating clinico-histopathological, cluster and mutation data in the analysis results in good performance for UM-related DFS prediction

Is Tissue Still the Issue? The Promise of Liquid Biopsy in Uveal Melanoma

Uveal melanoma (UM) is the second most frequent type of melanoma. Therapeutic options for UM favor minimally invasive techniques such as irradiation for vision preservation. As a consequence, no tumor material is obtained. Without available tissue, molecular analyses for gene expression, mutation or copy number analysis cannot be performed. Thus, proper patient stratification is impossible and patients' uncertainty about their prognosis rises. Minimally invasive techniques have been studied for prognostication in UM. Blood-based biomarker analysis has become more common in recent years; however, no clinically standardized protocol exists. This review summarizes insights in biomarker analysis, addressing new insights in circulating tumor cells, circulating tumor DNA, extracellular vesicles, proteomics, and metabolomics. Additionally, medical imaging can play a significant role in staging, surveillance, and prognostication of UM and is addressed in this review. We propose that combining multiple minimally invasive modalities using tumor biomarkers should be the way forward and warrant more attention in the coming years.Patholog

Multi-Modality Analysis Improves Survival Prediction in Enucleated Uveal Melanoma Patients

textabstractPURPOSE. Uveal melanoma (UM) is characterized by multiple chromosomal rearrangements and recurrent mutated genes. The aim of this study was to investigate if copy number variations (CNV) alone and in combination with other genetic and clinico-histopathological variables can be used to stratify for disease-free survival (DFS) in enucleated patients with UM. METHODS. We analyzed single nucleotide polymorphisms (SNP) array data of primary tumors and other clinical variables of 214 UM patients from the Rotterdam Ocular Melanoma Study (ROMS) cohort. Nonweighted hierarchical clustering of SNP array data was used to identify molecular subclasses with distinct CNV patterns. The subclasses associate with mutational status of BAP1, SF3B1, or EIF1AX. Cox proportional hazard models were then used to study the predictive performance of SNP array cluster-, mutation-, and clinico-histopathological data, and their combination for study endpoint risk. RESULTS. Five clusters with distinct CNV patterns and concomitant mutations in BAP1, SF3B1, or EIF1AX were identified. The sample’s cluster allocation contributed significantly to mutational status of samples in predicting the incidence of metastasis during a median of 45.6 (interquartile range [IQR]: 24.7–81.8) months of follow-up (P < 0.05) and vice versa. Furthermore, incorporating all data sources in one model yielded a 0.797 C-score during 100 months of follow-up. CONCLUSIONS. UM has distinct CNV patterns that correspond to different mutated driver genes. Incorporating clinico-histopathological, cluster and mutation data in the analysis results in good performance for UM-related DFS prediction

Multi-Modality Analysis Improves Survival Prediction in Enucleated Uveal Melanoma Patients

PURPOSE. Uveal melanoma (UM) is characterized by multiple chromosomal rearrangements and recurrent mutated genes. The aim of this study was to investigate if copy number variations (CNV) alone and in combination with other genetic and clinico-histopathological variables can be used to stratify for disease-free survival (DFS) in enucleated patients with UM.METHODS. We analyzed single nucleotide polymorphisms (SNP) array data of primary tumors and other clinical variables of 214 UM patients from the Rotterdam Ocular Melanoma Study (ROMS) cohort. Nonweighted hierarchical clustering of SNP array data was used to identify molecular subclasses with distinct CNV patterns. The subclasses associate with mutational status of BAP1, SF3B1, or EIF1AX. Cox proportional hazard models were then used to study the predictive performance of SNP array cluster-, mutation-, and clinico-histopathological data, and their combination for study endpoint risk.RESULTS. Five clusters with distinct CNV patterns and concomitant mutations in BAP1, SF3B1, or EIF1AX were identified. The sample's cluster allocation contributed significantly to mutational status of samples in predicting the incidence of metastasis during a median of 45.6 (interquartile range [IQR]: 24.7-81.8) months of follow-up (P < 0.05) and vice versa. Furthermore, incorporating all data sources in one model yielded a 0.797 C-score during 100 months of follow-up.CONCLUSIONS. UM has distinct CNV patterns that correspond to different mutated driver genes. Incorporating clinico-histopathological, cluster and mutation data in the analysis results in good performance for UM-related DFS prediction.MTG