L Rocha Heber, Aguilar Boris, Getz Michael, Shmulevich Ilya, Macklin Paul
Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA.
Institute for Systems Biology, Seattle, WA, USA.
NPJ Syst Biol Appl. 2024 Dec 4;10(1):144. doi: 10.1038/s41540-024-00472-z.
Metastasis is the leading cause of death in patients with cancer, driving considerable scientific and clinical interest in immunosurveillance of micrometastases. We investigated this process by creating a multiscale mathematical model to study the interactions between the immune system and the progression of micrometastases in general epithelial tissue. We analyzed the parameter space of the model using high-throughput computing resources to generate over 100,000 virtual patient trajectories. We demonstrated that the model could recapitulate a wide variety of virtual patient trajectories, including uncontrolled growth, partial response, and complete immune response to tumor growth. We classified the virtual patients and identified key patient parameters with the greatest effect on the simulated immunosurveillance. We highlight the lessons derived from this analysis and their impact on the nascent field of cancer patient digital twins (CPDTs). While CPDTs could enable clinicians to systematically dissect the complexity of cancer in each individual patient and inform treatment choices, our work shows that key challenges remain before we can reach this vision. In particular, we show that there remain considerable uncertainties in immune responses, unreliable patient stratification, and unpredictable personalized treatment. Nonetheless, we also show that in spite of these challenges, patient-specific models suggest strategies to increase control of clinically undetectable micrometastases even without complete parameter certainty.
转移是癌症患者死亡的主要原因，引发了对微转移免疫监视的大量科学和临床关注。我们通过创建一个多尺度数学模型来研究这一过程，以探讨免疫系统与一般上皮组织中微转移进展之间的相互作用。我们利用高通量计算资源分析了模型的参数空间，生成了超过10万条虚拟患者轨迹。我们证明该模型可以重现各种各样的虚拟患者轨迹，包括肿瘤的无控制生长、部分缓解以及对肿瘤生长的完全免疫反应。我们对虚拟患者进行了分类，并确定了对模拟免疫监视影响最大的关键患者参数。我们强调了从该分析中获得的经验教训及其对新兴的癌症患者数字孪生（CPDT）领域的影响。虽然CPDT可以使临床医生系统地剖析每个患者癌症的复杂性并为治疗选择提供依据，但我们的工作表明，在实现这一愿景之前仍存在关键挑战。特别是，我们表明在免疫反应、不可靠的患者分层和不可预测的个性化治疗方面仍存在相当大的不确定性。尽管如此，我们也表明，尽管存在这些挑战，但即使没有完全确定的参数，针对特定患者的模型仍能提出增加对临床不可检测微转移控制的策略。
