Gaffin Center for Neuro-Oncology, Sharett Institute for Oncology, Hadassah Medical Center and Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel.
The Wohl Institute for Translational Medicine, Hadassah Medical Center and Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel.
Sci Rep. 2024 Sep 20;14(1):21962. doi: 10.1038/s41598-024-73027-1.
Intratumoral heterogeneity is an important clinical challenge because low burden clones expressing specific genetic alterations drive therapeutic resistance mechanisms. We have developed CAVE (cancer-associated variant enrichment), a gene-agnostic computational tool to identify specific enrichment of low-burden cancer driver variants in next-generation sequencing (NGS) data. For this study, CAVE was applied to TP53 in chronic lymphocytic leukemia (CLL) as a cancer model. Indeed, as TP53 mutations are part of treatment decision-making algorithms and low-burden variants are frequent, there is a need to distinguish true variants from background noise. Recommendations have been published for reliable calling of low-VAF variants of TP53 in CLL and the assessment of the background noise for each platform is essential for the quality of the testing. CAVE is able to detect specific enrichment of low-burden variants starting at variant allele frequencies (VAFs) as low as 0.3%. In silico TP53 dependent and independent analyses confirmed the true driver nature of all these variants. Orthogonal validation using either ddPCR or NGS analyses of follow-up samples confirmed variant identification. CAVE can be easily deployed in any cancer-related NGS workflow to detect the enrichment of low-burden variants of clinical interest.
肿瘤内异质性是一个重要的临床挑战，因为表达特定遗传改变的低负担克隆会驱动治疗抵抗机制。我们开发了 CAVE（癌症相关变异富集），这是一种基因不可知的计算工具，用于识别下一代测序（NGS）数据中低负担癌症驱动变异的特异性富集。在这项研究中，CAVE 被应用于慢性淋巴细胞白血病（CLL）中的 TP53 作为癌症模型。事实上，由于 TP53 突变是治疗决策算法的一部分，并且低负担变异很常见，因此需要将真正的变异与背景噪声区分开来。已经为在 CLL 中可靠地调用低 VAF 的 TP53 变体以及评估每个平台的背景噪声发布了建议，这对于测试质量至关重要。CAVE 能够检测到低至 0.3%的变异等位基因频率（VAF）的低负担变体的特异性富集。基于 TP53 的计算机分析和独立分析证实了所有这些变体的真正驱动性质。使用 ddPCR 或后续样本的 NGS 分析进行正交验证确认了变体的识别。CAVE 可以轻松部署在任何与癌症相关的 NGS 工作流程中，以检测临床相关的低负担变体的富集。
