Genomic Classification of Cutaneous Melanoma

By Rehan Akbani, K. Akdemir, B. A. Aksoy, Monique Albert, Adrian Ally, S. Amin, H. Arachchi, Arshi Arora, J. Auman, Brenda Ayala and 342 more. Cell, 2015

2,764 citations recorded by the source corpus.

SUMMARY We describe the landscape of genomic alterations in cutaneous melanomas through DNA, RNA, and protein-based analysis of 333 primary and/or metastatic melanomas from 331 patients. We establish a framework for genomic classification into one of four subtypes based on the pattern of the most prevalent significantly mutated genes: mutant BRAF, mutant RAS, mutant NF1, and Triple-WT (wild-type). Integrative analysis reveals enrichment of KIT mutations and focal amplifications and complex structural rearrangements as a feature of the Triple-WT subtype. We found no significant outcome correlation with genomic classification, but samples assigned a transcriptomic subclass enriched for immune gene expression associated with lymphocyte infiltrate on pathology review and high LCK protein expression, a T cell marker, were associated with improved patient survival. This clinicopathological and multidimensional analysis suggests that the prognosis of melanoma patients with regional metastases is influenced by tumor stroma immunobiology, offering insights to further personalize therapeutic decision-making.

Subfields: Cancer Research, Molecular Biology, Oncology

View Paper: https://www.semanticscholar.org/paper/2505fe509225f7d26acc3f4106ecee083bdcfb3d

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In Cancer Research

1. M. Lawrence
2. Peter W. Laird
3. Gad Getz
4. Moritz Gerstung
5. M. Wiznerowicz
6. Houtan Noushmehr

In Molecular Biology

1. Katherine A. Hoadley
2. M. Wiznerowicz
3. Chip Stewart
4. Peter W. Laird
5. Gad Getz
6. Ronglai Shen
7. Moritz Gerstung
8. Li Ding

In Oncology

1. Peter W. Laird
2. Gad Getz
3. Timothy A. Chan

Citations in the corpus

• Artificial intelligence in histopathology: enhancing cancer research and clinical oncology (2022, background): “In the last decades, genomic, transcriptomic and proteomic analyses have become a focus of cancer research 6–11 and subsequently began to enter clinical routine 12 (Fig.”
• Scalable Open Science Approach for Mutation Calling of Tumor Exomes Using Multiple Genomic Pipelines (2018, positive): “We observed that skin and lung cancers (SKCM, lung squamous cell carcinoma, and lung adenocarcinoma) had the largest median number of SNVs per sample, consistent with previous publications (Akbani et al., 2015; Collis-son et al., 2014; Hammerman et al., 2012) (Figure 2).”
• Comprehensive Analysis of Hypermutation in Human Cancer (2017, positive): “…as those by The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC), have confirmed frequent hypermutation in melanoma (Akbani et al., 2015), lung (Govindan et al., 2012), and bladder cancer (Weinstein et al., 2014) and uncovered rare cases of hypermutation in other…”