Patient-calibrated agent-based modelling of ductal carcinoma in situ (DCIS): From microscopic measurements to macroscopic predictions of clinical progression

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• 作者：Paul Macklin^a ; ¹ ; ² ; ^{Paul.Macklin@usc.edu} ; ; Mary E. Edgerton^b ; Alastair M. Thompson^b ; ^c ; Vittorio Cristini^d ; ²
• 关键词：Agent-based model ; Ductal carcinoma in situ ; Patient-specific calibration ; Biomechanics ; Calcification
• 刊名：Journal of Theoretical Biology
• 出版年：2012
• 期刊代码：79_00225193
• 类别：mt
• 出版时间：21 May, 2012
• 卷：301
• 期：Complete
• 页码：122-140
• 文件大小：2129 K

摘要

Ductal carcinoma in situ (DCIS)鈥攁 significant precursor to invasive breast cancer鈥攊s typically diagnosed as microcalcifications in mammograms. However, the effective use of mammograms and other patient data to plan treatment has been restricted by our limited understanding of DCIS growth and calcification. We develop a mechanistic, agent-based cell model and apply it to DCIS. Cell motion is determined by a balance of biomechanical forces. We use potential functions to model interactions with the basement membrane and amongst cells of unequal size and phenotype. Each cell's phenotype is determined by genomic/proteomic- and microenvironment-dependent stochastic processes. Detailed 鈥渟ub-models鈥?describe cell volume changes during proliferation and necrosis; we are the first to account for cell calcification.

We introduce the first patient-specific calibration method to fully constrain the model based upon clinically-accessible histopathology data. After simulating 45 days of solid-type DCIS with comedonecrosis, the model predicts: necrotic cell lysis acts as a biomechanical stress relief and is responsible for the linear DCIS growth observed in mammography; the rate of DCIS advance varies with the duct radius; the tumour grows 7-10 mm per year鈥攃onsistent with mammographic data; and the mammographic and (post-operative) pathologic sizes are linearly correlated鈥攊n quantitative agreement with the clinical literature. Patient histopathology matches the predicted DCIS microstructure: an outer proliferative rim surrounds a stratified necrotic core with nuclear debris on its outer edge and calcification in the centre. This work illustrates that computational modelling can provide new insight on the biophysical underpinnings of cancer. It may 1 day be possible to augment a patient's mammography and other imaging with rigorously-calibrated models that help select optimal surgical margins based upon the patient's histopathologic data.