Project description:Melanomas are the deadliest skin cancers, in part due to cellular plasticity and heterogeneity. Within tumors, cells coexist in different mutable phenotypes that exhibit differential functional properties and drug responses. The definition of these phenotypic states has been challenging to rigorously define with conventional marker-based methods, and more high-parameter molecular methods are cell-destructive, labor-intensive, and can take days to weeks to obtain a readout. To overcome these technical and practical limitations, we utilized the Deepcell platform to perform real-time classification of unlabeled melanoma cells into Melanocytic and Mesenchymal phenotypes. We used 19 patient-derived cell lines with known Melanocytic or Mesenchymal transcription scores to develop the ‘Melanoma Phenotype Classifier’ to phenotype melanoma cells based on morphology alone. A Classifier accuracy of >88% was achieved, and morphology analysis of the images revealed distinct morphotypes for each phenotype, highlighting distinct morphological differences. To further link phenotypic state with multi-dimensional morphological profiles, we performed genetic and chemical perturbations known to shift the phenotypic state. The AI Classifier successfully predicted shifts in phenotype driven by the perturbations. These results further demonstrate how phenotype is linked to distinct morphological changes that are detectable by AI. Lastly, we applied the Melanoma Phenotype Classifier to dissociated biopsy samples, which revealed phenotypic heterogeneity that was confirmed by single cell RNASeq. This work establishes a link between morphology and Melanoma phenotype, and lays the groundwork for the use of morphology as a label-free method of phenotyping viable melanoma cells combined with additional analyses.
Project description:Analysis of DNA from fixed tissues specimens of 58 primary uveal melanomas, with known clinical outcome, to determine gene copy number variations that were associated with survival. Abstract: Uveal melanomas can be stratified into subgroups with high or low risk of metastatic death, according to the presence of gross chromosomal abnormalities. Where a monosomy 3 uveal melanoma is detected, patient survival at three years is reduced to 50%. However, approximately 5% of patients with a disomy 3 tumour ultimately develop metastasis, and a further 5% of monosomy 3 uveal melanoma patients’ exhibit disease-free survival for more than five years. Despite extensive knowledge of the chromosomal abnormalities occurring in uveal melanoma, the genes driving metastasis are not well defined. Gene copy number variations occurring in a well-characterised cohort of 58 formalin-fixed, paraffin-embedded uveal melanoma samples were identified using the Affymetrix SNP 6.0 whole genome microarray. Four genetic sub-groups of primary uveal melanoma were represented in the patient cohort: 1) disomy 3 with long-term survival; 2) metastasizing disomy 3; 3) metastasizing monosomy 3; and 4) monosomy 3 with long-term survival. Cox regression and Kaplan-Meier survival analysis identified three genes that were associated with differences in patient survival. Patients with an amplification of CNKSR3 (6q) or RIPK1 (6p) demonstrated longer survival than those with gene deletions or no copy number change (log rank, p=0.022 and p<0.001, respectively). Conversely, those patients with an amplification of PENK (8q) showed reduced survival (log rank p<0.001). CNKSR3, RIPK1 and PENK are novel candidate metastasis regulatory genes in uveal melanoma. This is the first report of amplification of a specific gene on 6p that is associated with improved uveal melanoma patient survival and suggests that the development of uveal melanomas with a propensity to metastasise may be limited by genes on 6p. 58 samples in total. Ten disomy 3 with long-term survival. Fifteen disomy 3 with metastasising. Seventeen monosomy 3 with long-term survival. Sixteen monosomy 3 metastasising.
Project description:Analysis of DNA from fixed tissues specimens of 58 primary uveal melanomas, with known clinical outcome, to determine gene copy number variations that were associated with survival. Abstract: Uveal melanomas can be stratified into subgroups with high or low risk of metastatic death, according to the presence of gross chromosomal abnormalities. Where a monosomy 3 uveal melanoma is detected, patient survival at three years is reduced to 50%. However, approximately 5% of patients with a disomy 3 tumour ultimately develop metastasis, and a further 5% of monosomy 3 uveal melanoma patients’ exhibit disease-free survival for more than five years. Despite extensive knowledge of the chromosomal abnormalities occurring in uveal melanoma, the genes driving metastasis are not well defined. Gene copy number variations occurring in a well-characterised cohort of 58 formalin-fixed, paraffin-embedded uveal melanoma samples were identified using the Affymetrix SNP 6.0 whole genome microarray. Four genetic sub-groups of primary uveal melanoma were represented in the patient cohort: 1) disomy 3 with long-term survival; 2) metastasizing disomy 3; 3) metastasizing monosomy 3; and 4) monosomy 3 with long-term survival. Cox regression and Kaplan-Meier survival analysis identified three genes that were associated with differences in patient survival. Patients with an amplification of CNKSR3 (6q) or RIPK1 (6p) demonstrated longer survival than those with gene deletions or no copy number change (log rank, p=0.022 and p<0.001, respectively). Conversely, those patients with an amplification of PENK (8q) showed reduced survival (log rank p<0.001). CNKSR3, RIPK1 and PENK are novel candidate metastasis regulatory genes in uveal melanoma. This is the first report of amplification of a specific gene on 6p that is associated with improved uveal melanoma patient survival and suggests that the development of uveal melanomas with a propensity to metastasise may be limited by genes on 6p.
Project description:Identification of genomic characteristics in a cohorte of human cutaneous primary melanoma associated with a distant metastasis free survival.
Project description:Microarray analysis was used to determine the expression of 12,000 genes in a set of 50 gliomas, 28 glioblastomas and 22 anaplastic oligodendrogliomas. Supervised learning approaches were used to build a two-class prediction model based on a subset of 14 glioblastomas and 7 anaplastic oligodendrogliomas with classic histology. A 20-feature k-nearest neighbor model correctly classified 18 of the 21 classic cases in leave-one-out cross-validation when compared with pathological diagnoses. This model was then used to predict the classification of clinically common, histologically nonclassic samples. When tumors were classified according to pathology, the survival of patients with nonclassic glioblastoma and nonclassic anaplastic oligodendroglioma was not significantly different (P = 0.19). However, class distinctions according to the model were significantly associated with survival outcome (P = 0.05). This class prediction model was capable of classifying high-grade, nonclassic glial tumors objectively and reproducibly. Moreover, the model provided a more accurate predictor of prognosis in these nonclassic lesions than did pathological classification. These data suggest that class prediction models, based on defined molecular profiles, classify diagnostically challenging malignant gliomas in a manner that better correlates with clinical outcome than does standard pathology.
Project description:Microarray analysis was used to determine the expression of 12,000 genes in a set of 50 gliomas, 28 glioblastomas and 22 anaplastic oligodendrogliomas. Supervised learning approaches were used to build a two-class prediction model based on a subset of 14 glioblastomas and 7 anaplastic oligodendrogliomas with classic histology. A 20-feature k-nearest neighbor model correctly classified 18 of the 21 classic cases in leave-one-out cross-validation when compared with pathological diagnoses. This model was then used to predict the classification of clinically common, histologically nonclassic samples. When tumors were classified according to pathology, the survival of patients with nonclassic glioblastoma and nonclassic anaplastic oligodendroglioma was not significantly different (P = 0.19). However, class distinctions according to the model were significantly associated with survival outcome (P = 0.05). This class prediction model was capable of classifying high-grade, nonclassic glial tumors objectively and reproducibly. Moreover, the model provided a more accurate predictor of prognosis in these nonclassic lesions than did pathological classification. These data suggest that class prediction models, based on defined molecular profiles, classify diagnostically challenging malignant gliomas in a manner that better correlates with clinical outcome than does standard pathology. louis-00379 Assay Type: Gene Expression Provider: Affymetrix Array Designs: HG_U95Av2 Organism: Homo sapiens (ncbitax) Material Types: total_RNA, synthetic_RNA, organism_part, whole_organism Disease States: Classic anaplastic oligodendroglioma, Non-classic glioblastoma, Classic glioblastoma, Non-classic anaplastic oligodendroglioma
Project description:We utilize the transcriptional effects of BETi in melanoma and identify AMIGO2 as a direct target gene essential for melanoma cell survival both in vitro and in vivo. We further map the enhancer landscape of NHM and melanooma and show that genes regulated by super enhancers are expressed in higher levels, exihibit higher sensitivity to BETi, and over expressed in melanoma relative to NHM. In melanoma, AMIGO2 is regulated by super enhancers, which upon BETi lose their BRD2/BRD4 enrichment, resulting in AMIGO2 silencing.