The impact of normal tissue density on tumor growth and evolution in a 3D whole-tumor model of lung cancer
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Serial low-dose computed tomography (LDCT) scans in patients who are diagnosed with lung cancer during screening offer a history of the densities of tumors and the tissues that surround them during carcinogenesis and cancer progression. We built a CT-scan resolution computational model to explore how variations in lung tissue density impact tumor growth and evolution in non-small cell lung cancer (NSCLC). Our findings indicate that tumors spread more rapidly through denser tissues when they upregulate glycolytic pathways and acid production, whilst tumors spread more rapidly through sparser tissues when they upregulate angiogenesis. We used data and images from the National Lung Screening Trial to calibrate our model for untreated lung cancer growth in patients and corroborated our findings in low-density environments.
Significance
Our lung lesion model supports prior studies that find tumors tend to “speciate” into angiogenic or glyoclytic phenotypes. We demonstrate that these evolutionary strategies may in part be driven by the surrounding normal tissue density. We also suggest predictive biomarkers of tumor phenotype so that these evolutionary strategies may be detected and targeted in patients.
