The SFI Centre for Research Training in Genomics Data Sciences, National University of Ireland Galway, Galway, Republic of Ireland.
Bioinformatics and Biostatistics Research Cluster, School of Mathematics, Statistics and Applied Mathematics, National University of Ireland Galway, Galway, Republic of Ireland.
PLoS One. 2021 Feb 3;16(2):e0245042. doi: 10.1371/journal.pone.0245042. eCollection 2021.
Breast cancer is the leading cause of cancer related death among women. Breast cancers are generally diagnosed and treated based on clinical and histopathological features, along with subtype classification determined by the Prosigna Breast Cancer Prognostic Gene Signature Assay (also known as PAM50). Currently the copy number alteration (CNA) landscape of the tumour is not considered. We set out to examine the role of genomic instability (GI) in breast cancer survival since CNAs reflect GI and correlate with survival in other cancers. We focused on the 70% of breast cancers classified as luminal and carried out a comprehensive survival and association analysis using Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) data to determine whether CNA Score Quartiles derived from absolute CNA counts are associated with survival. Analysis revealed that patients diagnosed with luminal A breast cancer have a CNA landscape associated with disease specific survival, suggesting that CNA Score can provide a statistically robust prognostic factor. Furthermore, stratification of patients into subtypes based on gene expression has shown that luminal A and B cases overlap, and it is in this region we largely observe luminal A cases with reduced survival outlook. Therefore, luminal A breast cancer patients with quantitatively elevated CNA counts may benefit from more aggressive therapy. This demonstrates how individual genomic landscapes can facilitate personalisation of therapeutic interventions to optimise survival outcomes.
乳腺癌是女性癌症相关死亡的主要原因。乳腺癌的诊断和治疗通常基于临床和组织病理学特征,以及通过 Prosigna 乳腺癌预后基因签名分析(也称为 PAM50)确定的亚型分类。目前,肿瘤的拷贝数改变(CNA)图谱不被考虑。我们着手研究基因组不稳定性(GI)在乳腺癌生存中的作用,因为 CNA 反映了 GI 并与其他癌症的生存相关。我们专注于 70%分类为 luminal 的乳腺癌,并使用乳腺癌国际联合会的分子分类学(METABRIC)数据进行全面的生存和关联分析,以确定是否可以根据绝对 CNA 计数得出的 CNA 评分四分位数与生存相关。分析表明,诊断为 luminal A 乳腺癌的患者具有与疾病特异性生存相关的 CNA 图谱,这表明 CNA 评分可以提供一个统计学上稳健的预后因素。此外,基于基因表达对患者进行亚型分层表明,luminal A 和 B 病例重叠,并且在这个区域,我们主要观察到 luminal A 病例的生存前景降低。因此,定量升高 CNA 计数的 luminal A 乳腺癌患者可能受益于更积极的治疗。这表明个体基因组图谱如何促进治疗干预的个性化,以优化生存结果。