A Transcriptome-Based Precision Oncology Platform for Patient-Therapy Alignment in a Diverse Set of Treatment-Resistant Malignancies.
A Transcriptome-Based Precision Oncology Platform for Patient-Therapy Alignment in a Diverse Set of Treatment-Resistant Malignancies.
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DOI：
10.1158/2159-8290.cd-22-1020
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发表时间：
2023-06-02
期刊：
影响因子：
28.2
通讯作者：
Califano, Andrea
中科院分区：
文献类型：
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作者：
Mundi, Prabhjot S.;Cruz, Filemon S. Dela;Grunn, Adina;Diolaiti, Daniel;Mauguen, Audrey;Rainey, Allison R.;Guillan, Kristina;Siddiquee, Armaan;You, Daoqi;Realubit, Ronald;Karan, Charles;Ortiz, Michael V.;Douglass, Eugene F.;Accordino, Melissa;Mistretta, Suzanne;Brogan, Frances;Bruce, Jeffrey N.;Caescu, Cristina I.;Carvajal, Richard D.;Crew, Katherine D.;Decastro, Guarionex;Heaney, Mark;Henick, Brian S.;Hershman, Dawn L.;Hou, June Y.;Iwamoto, Fabio M.;Jurcic, Joseph G.;Kiran, Ravi P.;Kluger, Michael D.;Kreisl, Teri;Lamanna, Nicole;Lassman, Andrew B.;Lim, Emerson A.;Manji, Gulam A.;Mckhann, Guy M.;Mckiernan, James M.;Neugut, Alfred I.;Olive, Kenneth P.;Rosenblat, Todd;Schwartz, Gary K.;Shu, Catherine A.;Sisti, Michael B.;Tergas, Ana;Vattakalam, Reena M.;Welch, Mary;Wenske, Sven;Wright, Jason D.;Canoll, Peter;Hibshoosh, Hanina;Kalinsky, Kevin;Aburi, Mahalaxmi;Sims, Peter A.;Alvarez, Mariano J.;Kung, Andrew L.;Califano, Andrea
预测体内对抗肿瘤药物的反应仍然是一个难以捉摸的挑战。我们对两种基于转录组的精确癌症医学方法进行了首次评估，以预测肿瘤对一系列临床相关肿瘤学药物的敏感性，这些药物的作用机制我们在同源细胞系中进行了实验评估。我们招募了在组织学上不同、预后不佳的恶性肿瘤患者，这些患者在多种治疗方法上取得了进展，并开发了低传代、患者来源的异种移植模型，用于验证35种患者特定的药物预测。识别单个主调节(MR)蛋白的高亲和力抑制物的OncoTarget和识别逆转超连接MR模块转录活性的药物OncoTreat都产生了非常显著的30天疾病控制率(分别为68%和91%)。此外，在18种肿瘤治疗预测药物中，15种在体内诱导了预测的MR模块活性反转。预测药物的效果明显优于被选为非预测对照的抗肿瘤药物，这表明这些方法可能对现有的PCM方法有实质性的补充作用，案例研究也证明了这一点。
Predicting in vivo response to antineoplastics remains an elusive challenge. We performed first-of-kind evaluation of two transcriptome-based precision cancer medicine methodologies to predict tumor sensitivity to a comprehensive repertoire of clinically relevant oncology drugs, whose mechanism-of-action we experimentally assessed in cognate cell lines. We enrolled patients with histologically distinct, poor prognosis malignancies who had progressed on multiple therapies, and developed low-passage, patient-derived xenograft models that were used to validate 35 patient-specific drug predictions. Both OncoTarget, which identifies high-affinity inhibitors of individual Master Regulator (MR) proteins, and OncoTreat, which identifies drugs that invert the transcriptional activity of hyper-connected MR modules, produced highly significant 30-day disease control rates (68% and 91%, respectively). Moreover, of 18 OncoTreat-predicted drugs, 15 induced the predicted MR-module activity inversion in vivo. Predicted drugs significantly outperformed antineoplastic drugs selected as unpredicted controls, suggesting these methods may substantively complement existing PCM approaches, as also illustrated by a case study.