| 1. |
- Del Castillo Velasco-Herrera, Martin, et al.
(författare)
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Comparative genomics reveals that loss of lunatic fringe (LFNG) promotes melanoma metastasis
- 2018
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Ingår i: Molecular Oncology. - : Wiley. - 1574-7891. ; 12:2, s. 239-255
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Tidskriftsartikel (refereegranskat)abstract
- Metastasis is the leading cause of death in patients with advanced melanoma, yet the somatic alterations that aid tumour cell dissemination and colonisation are poorly understood. Here, we deploy comparative genomics to identify and validate clinically relevant drivers of melanoma metastasis. To do this, we identified a set of 976 genes whose expression level was associated with a poor outcome in patients from two large melanoma cohorts. Next, we characterised the genomes and transcriptomes of mouse melanoma cell lines defined as weakly metastatic, and their highly metastatic derivatives. By comparing expression data between species, we identified lunatic fringe (LFNG), among 28 genes whose expression level is predictive of poor prognosis and whose altered expression is associated with a prometastatic phenotype in mouse melanoma cells. CRISPR/Cas9-mediated knockout of Lfng dramatically enhanced the capability of weakly metastatic melanoma cells to metastasise in vivo, a phenotype that could be rescued with the Lfng cDNA. Notably, genomic alterations disrupting LFNG are found exclusively in human metastatic melanomas sequenced as part of The Cancer Genome Atlas. Using comparative genomics, we show that LFNG expression plays a functional role in regulating melanoma metastasis.
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| 2. |
- Garg, Manik, et al.
(författare)
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Tumour gene expression signature in primary melanoma predicts long-term outcomes
- 2021
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Ingår i: Nature Communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 12:1
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Tidskriftsartikel (refereegranskat)abstract
- Adjuvant systemic therapies are now routinely used following resection of stage III melanoma, however accurate prognostic information is needed to better stratify patients. We use differential expression analyses of primary tumours from 204 RNA-sequenced melanomas within a large adjuvant trial, identifying a 121 metastasis-associated gene signature. This signature strongly associated with progression-free (HR = 1.63, p = 5.24 × 10−5) and overall survival (HR = 1.61, p = 1.67 × 10−4), was validated in 175 regional lymph nodes metastasis as well as two externally ascertained datasets. The machine learning classification models trained using the signature genes performed significantly better in predicting metastases than models trained with clinical covariates (pAUROC = 7.03 × 10−4), or published prognostic signatures (pAUROC < 0.05). The signature score negatively correlated with measures of immune cell infiltration (ρ = −0.75, p < 2.2 × 10−16), with a higher score representing reduced lymphocyte infiltration and a higher 5-year risk of death in stage II melanoma. Our expression signature identifies melanoma patients at higher risk of metastases and warrants further evaluation in adjuvant clinical trials.
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| 3. |
- Godson, Lucy, et al.
(författare)
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Immune subtyping of melanoma whole slide images using multiple instance learning
- 2024
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Ingår i: Medical Image Analysis. - : ELSEVIER. - 1361-8415 .- 1361-8423. ; 93
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Tidskriftsartikel (refereegranskat)abstract
- Determining early-stage prognostic markers and stratifying patients for effective treatment are two key challenges for improving outcomes for melanoma patients. Previous studies have used tumour transcriptome data to stratify patients into immune subgroups, which were associated with differential melanoma specific survival and potential predictive biomarkers. However, acquiring transcriptome data is a time-consuming and costly process. Moreover, it is not routinely used in the current clinical workflow. Here, we attempt to overcome this by developing deep learning models to classify gigapixel haematoxylin and eosin (H&E) stained pathology slides, which are well established in clinical workflows, into these immune subgroups. We systematically assess six different multiple instance learning (MIL) frameworks, using five different image resolutions and three different feature extraction methods. We show that pathology-specific self-supervised models using 10x resolution patches generate superior representations for the classification of immune subtypes. In addition, in a primary melanoma dataset, we achieve a mean area under the receiver operating characteristic curve (AUC) of 0.80 for classifying histopathology images into 'high' or 'low immune' subgroups and a mean AUC of 0.82 in an independent TCGA melanoma dataset. Furthermore, we show that these models are able to stratify patients into 'high' and 'low immune' subgroups with significantly different melanoma specific survival outcomes (log rank test, P < 0.005). We anticipate that MIL methods will allow us to find new biomarkers of high importance, act as a tool for clinicians to infer the immune landscape of tumours and stratify patients, without needing to carry out additional expensive genetic tests.
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| 4. |
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| 5. |
- Nsengimana, Jérémie, et al.
(författare)
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Independent replication of a melanoma subtype gene signature and evaluation of its prognostic value and biological correlates in a population cohort.
- 2015
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Ingår i: Oncotarget. - : Impact Journals, LLC. - 1949-2553. ; 6:13, s. 11683-11693
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Tidskriftsartikel (refereegranskat)abstract
- Development and validation of robust molecular biomarkers has so far been limited in melanoma research. In this paper we used a large population-based cohort to replicate two published gene signatures for melanoma classification. We assessed the signatures prognostic value and explored their biological significance by correlating them with factors known to be associated with survival (vitamin D) or etiological routes (nevi, sun sensitivity and telomere length). Genomewide microarray gene expressions were profiled in 300 archived tumors (224 primaries, 76 secondaries). The two gene signatures classified up to 96% of our samples and showed strong correlation with melanoma specific survival (P=3x10-4), Breslow thickness (P=5x10-10), ulceration (P=9.x10-8) and mitotic rate (P=3x10-7), adding prognostic value over AJCC stage (adjusted hazard ratio 1.79, 95%CI 1.13-2.83), as previously reported. Furthermore, molecular subtypes were associated with season-adjusted serum vitamin D at diagnosis (P=0.04) and genetically predicted telomere length (P=0.03). Specifically, molecular high-grade tumors were more frequent in patients with lower vitamin D levels whereas high immune tumors came from patients with predicted shorter telomeres. Our data confirm the utility of molecular biomarkers in melanoma prognostic estimation using tiny archived specimens and shed light on biological mechanisms likely to impact on cancer initiation and progression.
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| 6. |
- Thakur, Rohit, et al.
(författare)
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Transcriptomic analysis reveals prognostic molecular signatures of stage I melanoma
- 2019
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Ingår i: Clinical Cancer Research. - 1078-0432. ; 25:24, s. 7424-7435
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Tidskriftsartikel (refereegranskat)abstract
- Purpose: Previously identified transcriptomic signatures have been based on primary and metastatic melanomas with relatively few American Joint Committee on Cancer (AJCC) stage I tumors, given difficulties in sampling small tumors. The advent of adjuvant therapies has highlighted the need for better prognostic and predictive biomarkers, especially for AJCC stage I and stage II disease. Experimental Design: A total of 687 primary melanoma transcriptomes were generated from the Leeds Melanoma Cohort (LMC). The prognostic value of existing signatures across all the AJCC stages was tested. Unsupervised clustering was performed, and the prognostic value of the resultant signature was compared with that of sentinel node biopsy (SNB) and tested as a biomarker in three published immunotherapy datasets. Results: Previous Lund and The Cancer Genome Atlas signatures predicted outcome in the LMC dataset (P = 10¯8 to 10¯4) but showed a significant interaction with AJCC stage (P = 0.04) and did not predict outcome in stage I tumors (P = 0.3–0.7). Consensus-based classification of the LMC dataset identified six classes that predicted outcome, notably in stage I disease. LMC class was a similar indicator of prognosis when compared with SNB, and it added prognostic value to the genes reported by Gerami and colleagues. One particular LMC class consistently predicted poor outcome in patients receiving immunotherapy in two of three tested datasets. Biological characterization of this class revealed high JUN and AXL expression and evidence of epithelial-to-mesenchymal transition. Conclusions: A transcriptomic signature of primary melanoma was identified with prognostic value, including in stage I melanoma and in patients undergoing immunotherapy.
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