Integrating molecular profiles into clinical frameworks through the Molecular Oncology Almanac to prospectively guide precision oncology.
Integrating molecular profiles into clinical frameworks through the Molecular Oncology Almanac to prospectively guide precision oncology.
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DOI：
10.1038/s43018-021-00243-3
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发表时间：
2021-10
期刊：
影响因子：
22.7
通讯作者：
Van Allen, Eliezer M.
中科院分区：
文献类型：
--
作者：
Reardon, Brendan;Moore, Nathanael D.;Moore, Nicholas S.;Kofman, Eric;AlDubayan, Saud H.;Cheung, Alexander T. M.;Conway, Jake;Elmarakeby, Haitham;Imamovic, Alma;Kamran, Sophia C.;Keenan, Tanya;Keliher, Daniel;Konieczkowski, David J.;Liu, David;Mouw, Kent W.;Park, Jihye;Vokes, Natalie, I;Dietlein, Felix;Van Allen, Eliezer M.
单基因变体（“一阶”）基因组改变的肿瘤分子谱为潜在的治疗方法提供了信息。这种一阶事件和全局分子特征（例如突变特征）之间的相互作用越来越多地与临床结果相关，但这些“二阶”改变尚未在临床解释算法和知识库中考虑。我们介绍了分子肿瘤学年鉴（MOAlmanac），一个配对的临床解释算法和知识库，使多模态基因组学数据的综合解释，即时护理决策和翻译假设生成。我们在多个回顾性队列中将MOAlmanac基准为一阶解释方法，并观察到越来越多的临床假设，从分子特征评估和细胞系匹配。当应用于前瞻性精确肿瘤学试验队列时，MOAlmanac为每位患者指定了两种治疗的中位数，并确定了47%患者的治疗策略。总体而言，我们提出了一种开源的计算方法，用于个体化分子谱的综合临床解释。
Tumor molecular profiling of single gene-variant (“first-order”) genomic alterations informs potential therapeutic approaches. Interactions between such first-order events and global molecular features (e.g. mutational signatures) are increasingly associated with clinical outcomes, but these “second-order” alterations are not yet accounted for in clinical interpretation algorithms and knowledge bases. We introduce the Molecular Oncology Almanac (MOAlmanac), a paired clinical interpretation algorithm and knowledge base to enable integrative interpretation of multimodal genomics data for point-of-care decision-making and translational hypothesis generation. We benchmarked MOAlmanac to a first-order interpretation method across multiple retrospective cohorts and observed an increased number of clinical hypotheses, from evaluation of molecular features and profile-to-cell line matchmaking. When applied to a prospective precision oncology trial cohort, MOAlmanac nominated a median of two therapies per patient and identified therapeutic strategies administered in 47% of patients. Overall, we present an open-source computational method for integrative clinical interpretation of individualized molecular profiles.
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影响因子：
3.7
作者：
Chatterjee P;Choudhary GS;Sharma A;Singh K;Heston WD;Ciezki J;Klein EA;Almasan A
通讯作者：
Almasan A
影响因子：
64.8
作者：
Ghandi, Mahmoud;Huang, Franklin W.;Sellers, William R.
通讯作者：
Sellers, William R.
影响因子：
28.2
作者：
Chang MT;Bhattarai TS;Schram AM;Bielski CM;Donoghue MTA;Jonsson P;Chakravarty D;Phillips S;Kandoth C;Penson A;Gorelick A;Shamu T;Patel S;Harris C;Gao J;Sumer SO;Kundra R;Razavi P;Li BT;Reales DN;Socci ND;Jayakumaran G;Zehir A;Benayed R;Arcila ME;Chandarlapaty S;Ladanyi M;Schultz N;Baselga J;Berger MF;Rosen N;Solit DB;Hyman DM;Taylor BS
通讯作者：
Taylor BS
影响因子：
2.1
作者：
Olshen, AB;Venkatraman, ES;Wigler, M
通讯作者：
Wigler, M
影响因子：
14.9
作者：
Karczewski KJ;Weisburd B;Thomas B;Solomonson M;Ruderfer DM;Kavanagh D;Hamamsy T;Lek M;Samocha KE;Cummings BB;Birnbaum D;The Exome Aggregation Consortium;Daly MJ;MacArthur DG
通讯作者：
MacArthur DG