AI 中文总结
本文提出逆向项目反应理论，将癌症类型视为受试者、药物视为项目，在GDSC2数据上估计耐药性与药物活性，在碎片化缺失下优于简单平均，实现鲁棒排序。
AI 中文摘要
我们将逆向项目反应理论（IRT）引入药物基因组学药物反应分析，将癌症类型视为具有耐药能力的潜在“受试者”，将药物视为具有逃避难度的“项目”。应用于来自癌症药物敏感性基因组学（GDSC2）数据库的242,036项药物敏感性测量，该模型在共享潜在尺度上估计癌症类型水平的体外耐药性和药物水平的广泛活性。在四种缺失机制下的验证表明，逆向IRT比简单平均更能恢复全数据潜在排序，在MCAR、癌症偏倚和药物偏倚稀疏性下，60%缺失时Delta-rho优势为+0.089至+0.095。留出预测证实IRT在五种评估方法中取得最佳Brier分数。自举置信区间显示28种癌症类型中有19种具有稳定的耐药/敏感分类。跨平台PRISM复制显示82%的方向一致性但较弱的秩相关（rho=0.25），表明贡献在于碎片化评估下的方法学鲁棒性，而非通用的临床耐药性排行榜。
英文摘要
We introduce reverse Item Response Theory (IRT) to pharmacogenomic drug-response analysis by treating cancer types as latent "subjects" with resistance ability and drugs as "items" with evasion difficulty. Applied to 242,036 drug sensitivity measurements from the Genomics of Drug Sensitivity in Cancer (GDSC2) database, the model estimates cancer-type-level in-vitro resistance and drug-level broad activity on a shared latent scale. Validation across four missingness regimes demonstrates that reverse IRT better recovers the full-data latent ranking than simple averaging, with advantages of Delta-rho = +0.089 to +0.095 at 60% missingness under MCAR, cancer-biased, and drug-biased sparsity. Held-out prediction confirms IRT achieves the best Brier score among five evaluated methods. Bootstrap confidence intervals show 19 of 28 cancer types have stable resistant/sensitive classifications. Cross-platform PRISM replication shows 82% directional agreement but weak rank-order correlation (rho = 0.25), indicating the contribution is methodological robustness under fragmented evaluation, not a universal clinical resistance leaderboard.
Comments8 pages, 4 figures, 3 tables
