Genomic Classification and Individualized Prognosis in Multiple Myeloma.
Обработка статьи · этапы, ошибки и управление
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Maura Francesco, Rajanna Arjun Raj, Ziccheddu Bachisio, Poos Alexandra M, Derkach Andriy, Maclachlan Kylee, Durante Michael, Diamond Benjamin, Papadimitriou Marios, Davies Faith, Boyle Eileen M, Walker Brian, Hultcrantz Malin, Silva Ariosto, Hampton Oliver, Teer Jamie K, Siegel Erin M, Bolli Niccolò, Jackson Graham H, Kaiser Martin, Pawlyn Charlotte, Cook Gordon, Kazandjian Dickran, Stein Caleb, Chesi Marta, Bergsagel Leif, Mai Elias K, Goldschmidt Hartmut, Weisel Katja C, Fenk Roland, Raab Marc S, Van Rhee Fritz, Usmani Saad, Shain Kenneth H, Weinhold Niels, Morgan Gareth, Landgren Ola
Аннотация
METHODS: To decipher and predict the molecular and clinical heterogeneity of NDMM, we assembled a series of 1,933 patients with available clinical, genomic, and therapeutic data.
RESULTS: Leveraging a comprehensive catalog of genomic drivers, we identified 12 groups, expanding on previous gene expression-based molecular classifications. To build a model predicting individualized risk in NDMM (IRMMa), we integrated clinical, genomic, and treatment variables. To correct for time-dependent variables, including high-dose melphalan followed by autologous stem-cell transplantation (HDM-ASCT), and maintenance therapy, a multi-state model was designed. The IRMMa model accuracy was significantly higher than all comparator prognostic models, with a c-index for OS of 0.726, compared with International Staging System (ISS; 0.61), revised-ISS (0.572), and R2-ISS (0.625). Integral to model accuracy was 20 genomic features, including 1q21 gain/amp, del 1p, TP53 loss, NSD2 translocations, APOBEC mutational signatures, and copy-number signatures (reflecting the complex structural variant chromothripsis). IRMMa accuracy and superiority compared with other prognostic models were validated on 256 patients enrolled in the GMMG-HD6 (ClinicalTrials.gov identifier: NCT02495922) clinical trial. Individualized patient risks were significantly affected across the 12 genomic groups by different treatment strategies (ie, treatment variance), which was used to identify patients for whom HDM-ASCT is particularly effective versus patients for whom the impact is limited.
CONCLUSION: Integrating clinical, demographic, genomic, and therapeutic data, to our knowledge, we have developed the first individualized risk-prediction model enabling personally tailored therapeutic decisions for patients with NDMM.