Radiological Features of IDO1⁺/PDL1⁺ Lung Adenocarcinoma: A Retrospective Single-institution Study

Abstract

IDO1/PDL1 co-expression was significantly related to radiological invasiveness and malignancy in lung adenocarcinoma. This study may help select patients likely to benefit from combination therapy using immune-checkpoint inhibitors.

Tobacco carcinogen induces tryptophan metabolism and immune suppression via induction of indoleamine 2,3-dioxygenase 1

“…IDO1 is a promising target for anticancer drug development. 10 In NSCLC, IDO1 is positive in nearly 60% (229/388) of patients, 29 and its overexpression is associated with resistance to chemotherapy. 53 Meanwhile, coexpression of IDO1 and PD-L1 is detected in 28% (109/388) of NSCLC cases 29 and is associated with aggressive features of LUAD.…”

“… 10 In NSCLC, IDO1 is positive in nearly 60% (229/388) of patients, 29 and its overexpression is associated with resistance to chemotherapy. 53 Meanwhile, coexpression of IDO1 and PD-L1 is detected in 28% (109/388) of NSCLC cases 29 and is associated with aggressive features of LUAD. 54 IDO1 deficiency reduces lung tumor burden and improves survival in KRAS-induced lung carcinoma and breast carcinoma-derived pulmonary metastasis models, 55 and inhibition of IDO1 may overcome resistance to anti-PD1 treatment by blocking MDSCs.…”

“… 25 , 26 In NSCLC, IDO1 overexpression is correlated with former smokers, 27 and a high Trp/Kyn ratio is correlated with a low IDO1 expression level in never-smoker patients, 28 whereas coexpression of IDO1 and programmed cell death 1 ligand (PD-L1) is associated with smoking history. 29 Furthermore, a low Trp/Kyn ratio was associated with resistance to PD1 blockade therapies in patients with NSCLC. 30 Therefore, it is interesting to unveil the effects of tobacco smoke on IDO1 expression and Trp metabolism in NSCLC.…”

Molecular Imaging Assessment of Immunotherapy Treatment Response

“…The steady rise in artificial intelligence in radiology through deep learning applications, including computational neural networks, and the application of radiomics, presents opportunities to merge the technological advances of medical imaging and analysis and advances within clinical and biogenetic markers of tumor biology to advance imaging's ability to better assess tumor burden after immunotherapy. The development of radiomic signatures specific to immune responses after immunotherapy and of radiogenomics to understand and to correlate imaging findings with molecular features of tumors may help to predict treatment response and/or potential irAE that may arise with specific disease processes or immunotherapeutics 24–30. One major point that needs to be considered in the context of quantitative indices (including textural analysis through radiomics) is the dependence on the quality of PET images, which can cause results to vary distinctly between up-to-date imaging technology and equipment 10 or 20 years old 4.…”

“…The development of radiomic signatures specific to immune responses after immunotherapy and of radiogenomics to understand and to correlate imaging findings with molecular features of tumors may help to predict treatment response and/or potential irAE that may arise with specific disease processes or immunotherapeutics. [24][25][26][27][28][29][30] One major point that needs to be considered in the context of quantitative indices (including textural analysis through radiomics) is the dependence on the quality of PET images, which can cause results to vary distinctly between up-to-date imaging technology and equipment 10 or 20 years old. 4 These techniques have shown significant promise in improving disease assessment; however, further investigation is needed to standardize image acquisition protocols and to define the best features to investigate and how to extract that data to establish optimal workflow integration preferably in a vendor agnostic manner.…”