Hypoxia-Related Radiomics and Immunotherapy Response: A Multicohort Study of Non-Small Cell Lung Cancer

Abstract

Background Immunotherapy yields survival benefit some advanced stage non-small cell lung cancer (NSCLC) patients. Since highly predictive biomarkers of immunotherapy response are an unmet clinical need, we utilized pre-treatment radiomics and clinical data to train and validate a parsimonious model associated with survival outcomes among NSCLC patients treated with immunotherapy. Methods Three cohorts of NSCLC patients treated with immunotherapy were analyzed: Training (N = 180), validation 1 (N = 90) and validation 2 (N = 62). The most informative clinical and radiomic features were subjected to decision tree analysis which stratified patients into risk groups of low-, moderate-, high-, and very-high-risk of death after initiation of immunotherapy. All statistical tests were two-sided. Results The very-high-risk group was associated with extremely poor overall survival (OS) in validation cohorts 1 (hazard ratio [HR]=5.35; 95% confidence interval [CI]=2.14–13.36, 1-year OS = 11.1%; 95% CI = 1.9–29.8, 3-year OS = 0%) and 2 (HR = 13.81; 95% CI = 2.58–73.93, 1-year OS = 47.6%; 95% CI = 18.2–72.4, 3-year OS = 0%) when compared to the low-risk group (HR = 1.00) in validation cohorts 1 (1-year OS = 85.0%; 95% CI = 60.4%–94.9%, 3-year OS = 38.9%; 95% CI = 17.1%–60.3%) and 2 (1-year OS = 80.2%; 95% CI = 40.3%–94.8% and 40.1%; 3-year OS = 40.1%; 95% CI = 1.3%–83.5%). The most informative radiomic feature, GLCM inverse difference, was positively associated with hypoxia-related carbonic anhydrase 9 using gene-expression profiling and Immunohistochemistry. Conclusion Utilizing standard-of-care imaging and clinical data, we identified and validated a novel parsimonious model associated with survival outcomes among NSCLC patients treated with immunotherapy. Based on this model, clinicians can identify patients that are unlikely to respond immunotherapy.

The Role of Radiomics in the Era of Immune Checkpoint Inhibitors: A New Protagonist in the Jungle of Response Criteria

“…As a result, tumor hypoxia is associated with clinical outcomes in cancer patients [ 44 ]. Recently, Tunali et al [ 45 ] have identified a significant association between gray-level co-occurrence matrix (GLCM) inverse difference and CAIX gene, which is implicated in pH regulation, allowing an acidic TME. Therefore, they suggested a potential application of these biomarkers in the context of immunotherapy, able to identify patients who are unlikely to respond to ICI.…”

Risk Assessment and Pancreatic Cancer: Diagnostic Management and Artificial Intelligence

“…The idea that imaging studies contain a great quantity of data, in the form of grey level patterns, which are imperceptible to the human eye, has become more and more interesting [303][304][305][306][307][308]. These texture features, when correlated with clinical-pathological data and outcomes, theoretically allow diagnostic and prognostic assessment, and could produce evidence-based clinical-decision support systems [309][310][311][312][313][314][315][316][317][318][319]. The main objective is to combine multimodal quantitative data with mathematical methods to provide clear and robust parameters allowing an outcome prediction.…”

Imaging approaches and radiomics: toward a new era of ultraprecision radioimmunotherapy?

“…91 This radiomic signature of TMB was associated with overall survival, in an independent cohort of NSCLC patients treated with immunotherapy (OS: HR=0.54; 95% CI 0.31 to 0.95; p=0.030, and PFS: HR=1.78; 95% CI 1.07 to 2.95; p=0.023). 91 Tunali et al 92 have developed a radiomic signature predicting overall survival of NSCLC patients treated with immunotherapy using a large cohort of 180 patients for training, and two cohorts of 90 and 62 patients for validation. Interestingly, the signature they identified, allowing discrimination of four risk groups was based on one radiomic feature: gray level co-occurrence matrix inverse difference, which has been associated with the expression of CAIX, an hypoxia-related gene in their analyses.…”

“…Interestingly, the signature they identified, allowing discrimination of four risk groups was based on one radiomic feature: gray level co-occurrence matrix inverse difference, which has been associated with the expression of CAIX, an hypoxia-related gene in their analyses. 92 Other radiomic studies, although conducted without immunotherapy, have also shown interesting results in large cohorts for the prediction of lymphocyte infiltration according to the Immunoscore on MRI images, 71 93 PD-L1 on either CT 94 95 or PET images, 70 or of microsatellite instability on CT or MRI images 96 97 and may also show promise for the prediction of response to immunotherapy. The diversity of biological mechanisms that can be assessed by radiomics is of definite interest for the development of new immunotherapy combinations such as myeloid-derived suppressor cells (MDSC)-targeting immunotherapy.…”

“…Interestingly, the signature they identified, allowing discrimination of four risk groups was based on one radiomic feature: gray level co-occurrence matrix inverse difference, which has been associated with the expression of CAIX, an hypoxia-related gene in their analyses. 92 …”