Quantification and Optimization of Standard-of-Care Therapy to Delay the Emergence of Resistant Bone Metastatic Prostate Cancer.
Quantification and Optimization of Standard-of-Care Therapy to Delay the Emergence of Resistant Bone Metastatic Prostate Cancer.
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DOI：
10.3390/cancers13040677
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发表时间：
2021-02-08
期刊：
影响因子：
5.2
通讯作者：
Basanta D
中科院分区：
文献类型：
--
作者：
Araujo A;Cook LM;Frieling JS;Tan W;Copland JA 2nd;Kohli M;Gupta S;Dhillon J;Pow-Sang J;Lynch CC;Basanta D
使用第一原理方法，我们展示了如何通过使用常规测量来优化骨转移性前列腺癌（BMPCa）患者的标准治疗，以显着延迟耐药疾病的演变，从而可能延长患者的总体生存期。背景资料：骨转移性前列腺癌（BMPCa），尽管最初对雄激素剥夺治疗（ADT）有反应，但不可避免地变得耐药。最近的ADT联合化疗或新型激素治疗（NHT）的前期治疗临床试验延长了患者的总体生存期。这些结果表明，优化标准治疗以延迟进展性转移性疾病的出现具有显著潜力。研究方法：在这里，我们使用从醋酸阿比特龙/泼尼松治疗前后的人骨转移性活检中提取的数据来生成骨转移性前列腺癌的数学模型，该模型可以揭示治疗对疾病进展的影响。ADT和化疗耐药性方面的肿瘤内异质性来源于细胞水平的活检数据，允许模型根据生物学第一原理跟踪耐药表型对治疗的响应动力学，而不依赖于数据拟合。以前列腺特异性抗原（PSA）的产生为例，将这些细胞数据与肿瘤负荷的临床指标进行数学关联。结果如下：使用这种相关性，我们的模型概括了单独和独立的患者队列（n = 24）中个体患者对应用治疗的反应，并能够估计每例患者对ADT的初始耐药性。结合由患者特定的初始耐药性预测提供信息的干预决策算法，我们建议优化每位患者的治疗顺序，目标是延迟耐药性疾病的演变并限制癌细胞生长，为回顾性数据的改善提供证据。结论：我们的研究结果表明，如何最小的，但广泛可用的患者信息可以用于建模和跟踪BMPCa的进展在真实的时间，提供了一个临床相关的洞察患者特定的疾病的演变动力学，并提出新的治疗方案进行干预。试验注册：NCT # 01953640。资金：由NCI U 01（NCI）U 01 CA 202958 -01和莫菲特团队科学奖资助。CCL和DB部分由NCI PSON U 01（U 01 CA 244101）资助。AA部分由国防部前列腺癌研究计划（W81 XWH-15-1-0184）奖学金资助。LC部分由美国癌症协会的博士后奖学金（PF-13-175-01-CSM）资助。
Using a first-principles approach, we demonstrate how standard-of-care therapies for bone metastatic prostate cancer (BMPCa) patients can be optimized with the use of routine measurements to significantly delay the evolution of resistant disease, potentially extending overall patient survival. Background: Bone metastatic prostate cancer (BMPCa), despite the initial responsiveness to androgen deprivation therapy (ADT), inevitably becomes resistant. Recent clinical trials with upfront treatment of ADT combined with chemotherapy or novel hormonal therapies (NHTs) have extended overall patient survival. These results indicate that there is significant potential for the optimization of standard-of-care therapies to delay the emergence of progressive metastatic disease. Methods: Here, we used data extracted from human bone metastatic biopsies pre- and post-abiraterone acetate/prednisone to generate a mathematical model of bone metastatic prostate cancer that can unravel the treatment impact on disease progression. Intra-tumor heterogeneity in regard to ADT and chemotherapy resistance was derived from biopsy data at a cellular level, permitting the model to track the dynamics of resistant phenotypes in response to treatment from biological first-principles without relying on data fitting. These cellular data were mathematically correlated with a clinical proxy for tumor burden, utilizing prostate-specific antigen (PSA) production as an example. Results: Using this correlation, our model recapitulated the individual patient response to applied treatments in a separate and independent cohort of patients (n = 24), and was able to estimate the initial resistance to the ADT of each patient. Combined with an intervention-decision algorithm informed by patient-specific prediction of initial resistance, we propose to optimize the sequence of treatments for each patient with the goal of delaying the evolution of resistant disease and limit cancer cell growth, offering evidence for an improvement against retrospective data. Conclusions: Our results show how minimal but widely available patient information can be used to model and track the progression of BMPCa in real time, offering a clinically relevant insight into the patient-specific evolutionary dynamics of the disease and suggesting new therapeutic options for intervention. Trial registration: NCT # 01953640. Funding: Funded by an NCI U01 (NCI) U01CA202958-01 and a Moffitt Team Science Award. CCL and DB were partly funded by an NCI PSON U01 (U01CA244101). AA was partly funded by a Department of Defense Prostate Cancer Research Program (W81XWH-15-1-0184) fellowship. LC was partly funded by a postdoctoral fellowship (PF-13-175-01-CSM) from the American Cancer Society.
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影响因子：
3.9
作者：
Dehm SM;Tindall DJ
通讯作者：
Tindall DJ
影响因子：
8.4
作者：
Lipton, Allan;Fizazi, Karim;Jun, Susie
通讯作者：
Jun, Susie
DOI：
10.1016/s0140-6736(15)01037-5
发表时间：
2016-03-19
期刊：
Lancet (London, England)
影响因子：
--
作者：
James ND;Sydes MR;Clarke NW;Mason MD;Dearnaley DP;Spears MR;Ritchie AW;Parker CC;Russell JM;Attard G;de Bono J;Cross W;Jones RJ;Thalmann G;Amos C;Matheson D;Millman R;Alzouebi M;Beesley S;Birtle AJ;Brock S;Cathomas R;Chakraborti P;Chowdhury S;Cook A;Elliott T;Gale J;Gibbs S;Graham JD;Hetherington J;Hughes R;Laing R;McKinna F;McLaren DB;O'Sullivan JM;Parikh O;Peedell C;Protheroe A;Robinson AJ;Srihari N;Srinivasan R;Staffurth J;Sundar S;Tolan S;Tsang D;Wagstaff J;Parmar MK;STAMPEDE investigators
通讯作者：
STAMPEDE investigators
影响因子：
4.6
作者：
Jorfi S;Ansa-Addo EA;Kholia S;Stratton D;Valley S;Lange S;Inal J
通讯作者：
Inal J
DOI：
10.1056/nejmoa1702900
发表时间：
2017-07-27
期刊：
The New England journal of medicine
影响因子：
--
作者：
James ND;de Bono JS;Spears MR;Clarke NW;Mason MD;Dearnaley DP;Ritchie AWS;Amos CL;Gilson C;Jones RJ;Matheson D;Millman R;Attard G;Chowdhury S;Cross WR;Gillessen S;Parker CC;Russell JM;Berthold DR;Brawley C;Adab F;Aung S;Birtle AJ;Bowen J;Brock S;Chakraborti P;Ferguson C;Gale J;Gray E;Hingorani M;Hoskin PJ;Lester JF;Malik ZI;McKinna F;McPhail N;Money-Kyrle J;O'Sullivan J;Parikh O;Protheroe A;Robinson A;Srihari NN;Thomas C;Wagstaff J;Wylie J;Zarkar A;Parmar MKB;Sydes MR;STAMPEDE Investigators
通讯作者：
STAMPEDE Investigators