<div>Abstract<p>Lung adenocarcinoma (LUAD) is a heterogeneous group of tumors associated with different survival rates, even when detected at an early stage. Here, we aim to investigate the biological determinants of early LUAD indolence or aggressiveness using radiomics as a surrogate of behavior. We present a set of 92 LUAD patients with data collected across different methodologies. Patients were risk-stratified using the Computed Tomography–based Score Indicative of Lung cancer Aggression (SILA) tool (0=least aggressive, 1= most aggressive). We grouped the patients as indolent (x <= 0.4, n=14), intermediate (0.4 > x <= 0.6, n=27) and aggressive (0.6 > x <= 1, n=52). Using CyTOF, we identified subpopulations with high HLA-DR expression that were associated with indolent behavior. In the RNA-Seq dataset, pathways related to immune response were associated with indolent behavior, while pathways associated with cell cycle and proliferation were associated with aggressive behavior. We extracted quantitative radiomics features from the CT scans of the patients. Integrating these datasets we identified four feature signatures and four patient clusters that were associated with survival. Using single-cell RNA-Seq, we found that indolent tumors had significantly more T cells and less B cells than aggressive tumors, and that the latter had a higher abundance of regulatory T cells and T helpers. In conclusion, we were able to uncover a correspondence between radiomics and tumor biology, which could improve the discrimination between indolent and aggressive LUAD tumors, enhance our knowledge in the biology of these tumors and offer novel and personalized avenues for intervention.</p></div>

Lung adenocarcinoma (LUAD) is a heterogeneous group of tumors associated with different survival rates, even when detected at an early stage. Here, we aim to investigate the biological determinants of early LUAD indolence or aggressiveness using radiomics as a surrogate of behavior. We present a set of 92 LUAD patients with data collected across different methodologies. Patients were risk-stratified using the Computed Tomography–based Score Indicative of Lung cancer Aggression (SILA) tool (0=least aggressive, 1= most aggressive). We grouped the patients as indolent (x &lt;= 0.4, n=14), intermediate (0.4 &gt; x &lt;= 0.6, n=27) and aggressive (0.6 &gt; x &lt;= 1, n=52). Using CyTOF, we identified subpopulations with high HLA-DR expression that were associated with indolent behavior. In the RNA-Seq dataset, pathways related to immune response were associated with indolent behavior, while pathways associated with cell cycle and proliferation were associated with aggressive behavior. We extracted quantitative radiomics features from the CT scans of the patients. Integrating these datasets we identified four feature signatures and four patient clusters that were associated with survival. Using single-cell RNA-Seq, we found that indolent tumors had significantly more T cells and less B cells than aggressive tumors, and that the latter had a higher abundance of regulatory T cells and T helpers. In conclusion, we were able to uncover a correspondence between radiomics and tumor biology, which could improve the discrimination between indolent and aggressive LUAD tumors, enhance our knowledge in the biology of these tumors and offer novel and personalized avenues for intervention.

<p>Supplementary Figures S1-S21</p>

<p>Supplementary tables</p>

<p>Supplementary Figures S1-S21</p>