Araujo Arturo, Cook Leah M, Frieling Jeremy S, Tan Winston, Copland John A, Kohli Manish, Gupta Shilpa, Dhillon Jasreman, Pow-Sang Julio, Lynch Conor C, Basanta David
Integrated Mathematical Oncology Department, Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA.
School of Arts, University of Roehampton, London SW15 5PU, UK.
Cancers (Basel). 2021 Feb 8;13(4):677. doi: 10.3390/cancers13040677.
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.
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.
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.
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.
NCT # 01953640.
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.
骨转移性前列腺癌（BMPCa）尽管最初对雄激素剥夺疗法（ADT）有反应，但不可避免地会产生耐药性。最近关于ADT联合化疗或新型激素疗法（NHTs）进行 upfront 治疗的临床试验延长了患者的总生存期。这些结果表明，优化标准治疗方案以延迟进行性转移性疾病的出现具有巨大潜力。
在此，我们使用从醋酸阿比特龙/泼尼松治疗前后的人骨转移活检中提取的数据，生成了一个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。关于ADT和化疗耐药性的肿瘤内异质性来自细胞水平的活检数据，使该模型能够从生物学第一原理追踪耐药表型对治疗的反应动态，而无需依赖数据拟合。以前列腺特异性抗原（PSA）产生为例，这些细胞数据在数学上与肿瘤负荷的临床指标相关联。
利用这种相关性，我们的模型在一个单独且独立的患者队列（n = 24）中重现了个体患者对应用治疗的反应，并能够估计每个患者对ADT的初始耐药性。结合基于患者初始耐药性的特定预测的干预决策算法，我们建议为每个患者优化治疗顺序，以延迟耐药疾病的进展并限制癌细胞生长，为基于回顾性数据的改进提供证据。
我们的结果表明，如何利用最少但广泛可用的患者信息实时建模和追踪BMPCa的进展，为该疾病患者特异性进化动态提供临床相关见解，并提出新的干预治疗选择。
NCT编号# 01953640。
由美国国立癌症研究所（NCI）U01（NCI）U01CA202958 - 01和莫菲特团队科学奖资助。CCL和DB部分由NCI PSON U01（U01CA244101）资助。AA部分由国防部前列腺癌研究计划（W81XWH - 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。
