PO.BCS01.13 · 生物信息与计算
预测、扰动与处理：一个用于识别合成致死的系统生物学流程
Predict, perturb, and process: A systems biology pipeline for identifying synthetic lethality
作者与单位 Authors & Affiliations
摘要 Abstract
中文摘要
合成致死原理为癌症治疗带来了巨大希望，但识别合成致死对却面临巨大挑战。合成致死指两个基因的缺失导致细胞死亡，而单独缺失任一基因则不会——它难以检测，因为组合扰动技术效率低下，且搜索空间远超当前实验系统的能力：19,000个蛋白编码基因提供了超过1.8亿个候选基因对。为解决这一问题，我们将网络驱动的预测模型与In4mer CRISPR/Cas12a组合敲除平台相结合，以进行可操作的两两敲除筛选，具有高概率识别合成致死。即便有了这些进展，用于在这些筛选中量化遗传相互作用（GI）的有效方法仍然有限。为弥补这一空白，我们开发了GRAPE（Genetic interaction Regression Analysis of Pairwise Effects，成对效应的遗传相互作用回归分析），一种基于回归的新型方法，用于分析全对全基因敲除（KO）文库设计中的GI。GRAPE从多重CRISPR阵列推断单基因KO适应性，预测组合基因KO适应性，并识别偏离该预期的合成致死对。由于不存在用于基准测试的金标准，我们构建了一个模拟框架以系统评估GRAPE，证明其在计算效率、适应性以及精确率-召回率表现方面优于现有方法。利用网络驱动的实验设计、In4mer筛选和GRAPE分析，我们在12种不同的癌症细胞系中筛选了参与受体酪氨酸激酶（RTK）信号传导的206个基因的所有两两组合、DNA损伤应答（DDR）通路中167个基因的所有配对，以及超过4,000个旁系同源基因对。我们的DDR结果与其他研究报告的发现高度一致，而我们的RTK网络为ER介导的蛋白修饰和致癌信号依赖性提供了新的见解。总体而言，我们的工作证实了我们能够预测和检测全局性及背景特异性的遗传相互作用，推进了功能基因组学和癌症靶点发现的最新进展。
查看英文原文 English abstract
The synthetic lethality principle holds great promise for cancer therapy, but identifying synthetic lethals presents enormous challenges. Synthetic lethality, where loss of two genes causes cell death, but loss of either gene alone does not, is difficult to assay because combinatorial perturbation technologies are inefficient and because the search space is well beyond the capacity of current experimental systems: 19,000 protein coding genes offer more than 180 million candidate gene pairs. To address this, we combine network-driven predictive models with the In4mer CRISPR/Cas12a combinatorial knockout platform to conduct tractable pairwise knockout screens with high probability of identifying synthetic lethals.Even with these advances, effective methods for quantifying GIs in these screens remain limited. To address this gap, we developed GRAPE (Genetic interaction Regression Analysis of Pairwise Effects), a novel regression-based method for analyzing GIs in all-by-all gene knockout (KO) library designs. GRAPE infers single-gene KO fitness from multiplex CRISPR arrays, predicts combinatorial gene KO fitness, and identifies synthetic lethals that deviate from this expectation. Since no gold-standard exists for benchmarking, we built a simulation framework to systematically evaluate GRAPE, demonstrating improved computational efficiency, adaptability, and precision-recall performance over existing methods.Using the network-driven experimental design, In4mer screening, and GRAPE analysis, we screened all pairwise combinations of 206 genes involved in receptor tyrosine kinase (RTK) signaling, all pairs of 167 genes in DNA damage response (DDR) pathways, and over 4,000 paralog pairs across 12 diverse cancer cell lines. Our DDR results closely align with findings reported by other studies, while our RTK network provides novel insight into ER-mediated protein modification and oncogenic signaling dependencies. Overall, our efforts confirm that we can predict and detect both global and background-specific genetic interactions, advancing the state of the art in functional genomics and cancer target finding.
利益披露 Disclosure
J. Chou, None..
C. Lin, None..
I. Gheorghe, None..
S. Alibai, None..
S. Kim, None..
L. Wilson, None..
X. Ma, None.