Background

Individualising treatment in breast cancer requires effective predictive biomarkers. While relatively few genomic aberrations are clinically relevant, there is a need for characterising patients across different subtypes to identify actionable alterations.

Methods

We identified genomic alterations in 49 potentially actionable genes for which drugs are available either clinically or via clinical trials. We explored the landscape of mutations and copy number alterations (CNAs) in actionable genes in seven breast cancer subtypes utilising The Cancer Genome Atlas. To dissect the genomic complexity, we analysed the patterns of co-occurrence and mutual exclusivity in actionable genes.

Results

We found that >30% of tumours harboured putative actionable events that are targetable by currently available drugs. We identified genes that had multiple targetable alterations, representing candidate targets for combination therapy. Genes predicted to be drivers in primary breast tumours fell into five categories: mTOR pathway, immune checkpoints, oestrogen signalling, tumour suppression and DNA damage repair. Our analysis also revealed that CNAs in 34/49 (69%) and mutations in 13/49 (26%) genes were significantly associated with gene expression, validating copy number events as a dominant oncogenic mechanism in breast cancer.

Conclusion

These results may enable the acceleration of personalised therapy and improve clinical outcomes in breast cancer.

中文翻译：

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乳腺癌临床相关基因的推定可操作改变的鉴定

背景

乳腺癌的个体化治疗需要有效的预测生物标志物。虽然与临床相关的基因组畸变相对较少，但需要对不同亚型的患者进行特征分析，以确定可操作的改变。

方法

我们确定了 49 个潜在可作用基因的基因组改变，这些基因的药物可在临床上或通过临床试验获得。我们利用癌症基因组图谱探索了七种乳腺癌亚型中可操作基因的突变和拷贝数改变 (CNA)。为了剖析基因组的复杂性，我们分析了可操作基因的共现和相互排斥的模式。

结果

我们发现超过 30% 的肿瘤存在可被当前可用药物靶向的假定可操作事件。我们鉴定了具有多个可靶向改变的基因，代表了联合治疗的候选靶点。预测原发性乳腺肿瘤驱动基因分为五类：mTOR 通路、免疫检查点、雌激素信号、肿瘤抑制和 DNA 损伤修复。我们的分析还显示，34/49 (69%) 基因中的 CNA 和 13/49 (26%) 基因中的突变与基因表达显着相关，验证了拷贝数事件是乳腺癌的主要致癌机制。

结论

这些结果可能有助于加速个性化治疗并改善乳腺癌的临床结果。