Mutually exclusive genetic interactions and gene essentiality shape the genomic landscape of primary melanoma

Birkeälv, Sofia; Harland, Mark; Matsuyama, Larissa Satiko Alcântara Sekimoto; Rashid, Mamun; Mehta, Ishan; Laye, Jonathan P.; Haase, Kerstin; Mell, Tracey; Iyer, Vivek; Robles‐Espinoza, Carla Daniela; McDermott, Ultan; Loo, Peter Van; Kuijjer, Marieke L.; Possik, Patrícia A.; Engler, Silvya Stuchi Maria; Bishop, D. Timothy; Newton‐Bishop, Julia; Adams, David J.

doi:10.1002/path.6019

Cited by 3 publications

(2 citation statements)

References 64 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The detection in one patient each of multiple concurrent BRAF ctDNA mutations and multiple concurrent NRAS ctDNA mutations, as well as concurrent BRAF and NRAS mutations in 24% of patients, is interesting. Despite the high mutational burden of melanomas from sun-exposed tissues, concurrent BRAF and NRAS hotspot mutations appeared relatively uncommon and appeared to be anti-correlated in the initial TCGA analysis of melanoma [ 1 ], as well as in more recent analyses of primary melanoma [ 84 ]. However, tumours carrying both BRAF and NRAS hotspot mutations are found [ 1 , 85 , 86 ].…”

Section: Discussionmentioning

confidence: 99%

“…4–5). The prevalence of multiple concurrent driver mutations (especially in BRAF and NRAS genes) is surprising and as noted above, has not been reported consistently in large melanoma tumour sequencing studies [ 1 , 84 ]. Whether multiple concurrent sub-clonal driver mutations, including those that appear to emerge over the course of treatment as identified here, are in fact more common than previously recognised, or are an artefact of CHIP that for some reason we could not detect, requires further investigation in larger cohorts.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Dynamic ctDNA Mutational Complexity in Patients with Melanoma Receiving Immunotherapy

et al. 2023

View full text Add to dashboard Cite

Background Circulating tumour DNA (ctDNA) analysis promises to improve the clinical care of people with cancer, address health inequities and guide translational research. This observational cohort study used ctDNA to follow 29 patients with advanced-stage cutaneous melanoma through multiple cycles of immunotherapy. Method A melanoma-specific ctDNA next-generation sequencing (NGS) panel, droplet digital polymerase chain reaction (ddPCR) and mass spectrometry analysis were used to identify ctDNA mutations in longitudinal blood plasma samples from Aotearoa New Zealand (NZ) patients receiving immunotherapy for melanoma. These technologies were used in conjunction to identify the breadth and complexity of tumour genomic information that ctDNA analysis can reliably report. Results During the course of immunotherapy treatment, a high level of dynamic mutational complexity was identified in blood plasma, including multiple BRAF mutations in the same patient, clinically relevant BRAF mutations emerging through therapy and co-occurring sub-clonal BRAF and NRAS mutations. The technical validity of this ctDNA analysis was supported by high sample analysis–reanalysis concordance, as well as concordance between different ctDNA measurement technologies. In addition, we observed > 90% concordance in the detection of ctDNA when using cell-stabilising collection tubes followed by 7-day delayed processing, compared with standard EDTA blood collection protocols with rapid processing. We also found that the undetectability of ctDNA at a proportion of treatment cycles was associated with durable clinical benefit (DCB). Conclusion We found that multiple ctDNA processing and analysis methods consistently identified complex longitudinal patterns of clinically relevant mutations, adding support for expanded clinical trials of this technology in a variety of oncology settings. Supplementary Information The online version contains supplementary material available at 10.1007/s40291-023-00651-4.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Dynamic ctDNA Mutational Complexity in Patients with Melanoma Receiving Immunotherapy

et al. 2023

View full text Add to dashboard Cite

show abstract

Interferon regulatory factor 4 modulates epigenetic silencing and cancer‐critical pathways in melanoma cells

Sobhiafshar,

Çakici,

Yilmaz

et al. 2024

Molecular Oncology

View full text Add to dashboard Cite

Interferon regulatory factor 4 (IRF4) was initially identified as a key controller in lymphocyte differentiation and function, and subsequently as a dependency factor and therapy target in lymphocyte‐derived cancers. In melanocytes, IRF4 takes part in pigmentation. Although genetic studies have implicated IRF4 in melanoma, how IRF4 functions in melanoma cells has remained largely elusive. Here, we confirmed prevalent IRF4 expression in melanoma and showed that high expression is linked to dependency in cells and mortality in patients. Analysis of genes activated by IRF4 uncovered, as a novel target category, epigenetic silencing factors involved in DNA methylation (DNMT1, DNMT3B, UHRF1) and histone H3K27 methylation (EZH2). Consequently, we show that IRF4 controls the expression of tumour suppressor genes known to be silenced by these epigenetic modifications, for instance cyclin‐dependent kinase inhibitors CDKN1A and CDKN1B, the PI3–AKT pathway regulator PTEN, and primary cilium components. Furthermore, IRF4 modulates activity of key downstream oncogenic pathways, such as WNT/β‐catenin and AKT, impacting cell proliferation and survival. Accordingly, IRF4 modifies the effectiveness of pertinent epigenetic drugs on melanoma cells, a finding that encourages further studies towards therapeutic targeting of IRF4 in melanoma.

show abstract

Melanoma classification using generative adversarial network and proximal policy optimization

Ju,

Lin,

Lee

et al. 2024

Photochem & Photobiology

View full text Add to dashboard Cite

In oncology, melanoma is a serious concern, often arising from DNA changes caused mainly by ultraviolet radiation. This cancer is known for its aggressive growth, highlighting the necessity of early detection. Our research introduces a novel deep learning framework for melanoma classification, trained and validated using the extensive SIIM‐ISIC Melanoma Classification Challenge‐ISIC‐2020 dataset. The framework features three dilated convolution layers that extract critical feature vectors for classification. A key aspect of our model is incorporating the Off‐policy Proximal Policy Optimization (Off‐policy PPO) algorithm, which effectively handles data imbalance in the training set by rewarding the accurate classification of underrepresented samples. In this framework, the model is visualized as an agent making a series of decisions, where each sample represents a distinct state. Additionally, a Generative Adversarial Network (GAN) augments training data to improve generalizability, paired with a new regularization technique to stabilize GAN training and prevent mode collapse. The model achieved an F‐measure of 91.836% and a geometric mean of 91.920%, surpassing existing models and demonstrating the model's practical utility in clinical environments. These results demonstrate its potential in enhancing early melanoma detection and informing more accurate treatment approaches, significantly advancing in combating this aggressive cancer.

show abstract

Mutually exclusive genetic interactions and gene essentiality shape the genomic landscape of primary melanoma

Cited by 3 publications

References 64 publications

Dynamic ctDNA Mutational Complexity in Patients with Melanoma Receiving Immunotherapy

Dynamic ctDNA Mutational Complexity in Patients with Melanoma Receiving Immunotherapy

Interferon regulatory factor 4 modulates epigenetic silencing and cancer‐critical pathways in melanoma cells

Melanoma classification using generative adversarial network and proximal policy optimization

Contact Info

Product

Resources

About