Purpose

Genomic analysis of individual patients is now affordable, and therapies targeting specific molecular aberrations are being tested in clinical trials. Genomically-informed therapy is relevant to many clinical domains, but is particularly applicable to cancer treatment. However, even specialized clinicians need help to interpret genomic data, to navigate the complicated space of clinical trials, and to keep up with the rapidly expanding biomedical literature. To quantitate the cognitive load on treating clinicians, we attempt to quantitate the rate of change in potential treatment options for patients considering genomically-relevant and genomically-selected therapy for cancer.

Materials and methods

To this end, we analyzed patient-specific reports generated by a precision oncology decision support team (PODS) at a large academic cancer center. Two types of potential treatment options were analyzed: FDA-approved genomically-relevant and genomically-selected therapies and therapies available via clinical trials. We focused on two clinically-actionable alterations: ERBB2 (Her2/neu; amplified vs. non-amplified) and BRAF mutation (V600 vs. non-V600). To determine changes in available treatment options, we grouped patients into similar groups by disease site (ERBB2: breast, gastric and “other”; BRAF: melanoma, non-melanoma).

Results

A total of 2927 reports for 2366 unique patients were generated 8/2016-12/2018. Reports included 9902 gene variants and 150 disease classifications. BRAF mutation and ERBB2 amplification were annotated with therapeutic options in 270 reports (225 unique patients). The median survival time of a therapeutic option was nine months.

Conclusion

When compared to “traditional” clinical practice guideline recommendations, treatment options for personalized cancer therapy change seven times more rapidly; partly due to change in knowledge and partly due to logistics such as clinical trial availability.

中文翻译：

---

研究性治疗方案的变化率：对大型精准肿瘤学决策支持工作的报告进行分析。

目的

现在可以对个体患者进行基因组分析，并且针对特定分子畸变的疗法正在临床试验中进行测试。基因组学治疗与许多临床领域相关，但特别适用于癌症治疗。然而，即使是专业的临床医生也需要帮助来解释基因组数据，驾驭复杂的临床试验空间，并跟上快速扩展的生物医学文献。为了量化治疗临床医生的认知负荷，我们尝试量化考虑基因组相关和基因组选择的癌症治疗的患者的潜在治疗方案的变化率。

材料和方法

为此，我们分析了大型学术癌症中心的精准肿瘤决策支持团队 (PODS) 生成的针对患者的特定报告。分析了两种类型的潜在治疗方案： FDA 批准的基因组相关和基因组选择的疗法以及通过临床试验提供的疗法。我们重点关注两种临床上可行的改变：ERBB2（Her2/neu；扩增与非扩增）和BRAF突变（V600与非V600）。为了确定可用治疗方案的变化，我们根据疾病部位将患者分为相似的组（ERBB2：乳腺癌、胃癌和“其他”；BRAF：黑色素瘤、非黑色素瘤）。

结果

2016 年 8 月至 2018 年 12 月，共生成了 2366 名独特患者的 2927 份报告。报告包括 9902 个基因变异和 150 个疾病分类。270 份报告（225 名独特患者）中对BRAF突变和ERBB2扩增进行了治疗选择注释。治疗方案的中位生存时间为九个月。

结论

与“传统”临床实践指南建议相比，个性化癌症治疗的治疗方案变化速度快七倍；部分是由于知识的变化，部分是由于临床试验可用性等后勤工作。