Generalization optimizing machine learning to improve CT scan radiomics and assess immune checkpoint inhibitors’ response in non-small cell lung cancer: a multicenter cohort study

BackgroundRecent developments in artificial intelligence suggest that radiomics may represent a promising non-invasive biomarker to predict response to immune checkpoint inhibitors (ICIs). Nevertheless, validation of radiomics algorithms in independent cohorts remains a challenge due to variations in image acquisition and reconstruction. Using radiomics, we investigated the importance of scan normalization as part of a broader machine learning framework to enable model external generalizability to predict ICI response in non-small cell lung cancer (NSCLC) patients across different centers.MethodsRadiomics features were extracted and compared from 642 advanced NSCLC patients on pre-ICI scans using established open-source PyRadiomics and a proprietary DeepRadiomics deep learning technology. The population was separated into two groups: a discovery cohort of 512 NSCLC patients from three academic centers and a validation cohort that included 130 NSCLC patients from a fourth center. We harmonized images to account for variations in reconstruction kernel, slice thicknesses, and device manufacturers. Multivariable models, evaluated using cross-validation, were used to estimate the predictive value of clinical variables, PD-L1 expression, and PyRadiomics or DeepRadiomics for progression-free survival at 6 months (PFS-6).ResultsThe best prognostic factor for PFS-6, excluding radiomics features, was obtained with the combination of Clinical + PD-L1 expression (AUC = 0.66 in the discovery and 0.62 in the validation cohort). Without image harmonization, combining Clinical + PyRadiomics or DeepRadiomics delivered an AUC = 0.69 and 0.69, respectively, in the discovery cohort, but dropped to 0.57 and 0.52, in the validation cohort. This lack of generalizability was consistent with observations in principal component analysis clustered by CT scan parameters. Subsequently, image harmonization eliminated these clusters. The combination of Clinical + DeepRadiomics reached an AUC = 0.67 and 0.63 in the discovery and validation cohort, respectively. Conversely, the combination of Clinical + PyRadiomics failed generalizability validations, with AUC = 0.66 and 0.59.ConclusionWe demonstrated that a risk prediction model combining Clinical + DeepRadiomics was generalizable following CT scan harmonization and machine learning generalization methods. These results had similar performances to routine oncology practice using Clinical + PD-L1. This study supports the strong potential of radiomics as a future non-invasive strategy to predict ICI response in advanced NSCLC

Transcriptome-wide association study reveals candidate causal genes for lung cancer.

We have recently completed the largest GWAS on lung cancer including 29,266 cases and 56,450 controls of European descent. The goal of our study has been to integrate the complete GWAS results with a large-scale expression quantitative trait loci (eQTL) mapping study in human lung tissues (n = 1,038) to identify candidate causal genes for lung cancer. We performed transcriptome-wide association study (TWAS) for lung cancer overall, by histology (adenocarcinoma, squamous cell carcinoma and small cell lung cancer) and smoking subgroups (never- and ever-smokers). We performed replication analysis using lung data from the Genotype-Tissue Expression (GTEx) project. DNA damage assays were performed in human lung fibroblasts for selected TWAS genes. As expected, the main TWAS signal for all histological subtypes and ever-smokers was on chromosome 15q25. The gene most strongly associated with lung cancer at this locus using the TWAS approach was IREB2 (pTWAS = 1.09E-99), where lower predicted expression increased lung cancer risk. A new lung adenocarcinoma susceptibility locus was revealed on 9p13.3 and associated with higher predicted expression of AQP3 (pTWAS = 3.72E-6). Among the 45 previously described lung cancer GWAS loci, we mapped candidate target gene for 17 of them. The association AQP3-adenocarcinoma on 9p13.3 was replicated using GTEx (pTWAS = 6.55E-5). Consistent with the effect of risk alleles on gene expression levels, IREB2 knockdown and AQP3 overproduction promote endogenous DNA damage. These findings indicate genes whose expression in lung tissue directly influences lung cancer risk

Purine metabolism regulates DNA repair and therapy resistance in glioblastoma

Intratumoral genomic heterogeneity in glioblastoma (GBM) is a barrier to overcoming therapy resistance. Treatments that are effective independent of genotype are urgently needed. By correlating intracellular metabolite levels with radiation resistance across dozens of genomically-distinct models of GBM, we find that purine metabolites, especially guanylates, strongly correlate with radiation resistance. Inhibiting GTP synthesis radiosensitizes GBM cells and patient-derived neurospheres by impairing DNA repair. Likewise, administration of exogenous purine nucleosides protects sensitive GBM models from radiation by promoting DNA repair. Neither modulating pyrimidine metabolism nor purine salvage has similar effects. An FDA-approved inhibitor of GTP synthesis potentiates the effects of radiation in flank and orthotopic patient-derived xenograft models of GBM. High expression of the rate-limiting enzyme of de novo GTP synthesis is associated with shorter survival in GBM patients. These findings indicate that inhibiting purine synthesis may be a promising strategy to overcome therapy resistance in this genomically heterogeneous disease

Immune-mediated genetic pathways resulting in pulmonary function impairment increase lung cancer susceptibility

Impaired lung function is often caused by cigarette smoking, making it challenging to disentangle its role in lung cancer susceptibility. Investigation of the shared genetic basis of these phenotypes in the UK Biobank and International Lung Cancer Consortium (29,266 cases, 56,450 controls) shows that lung cancer is genetically correlated with reduced forced expiratory volume in one second (FEV1: r(g) = 0.098, p = 2.3 x 10(-8)) and the ratio of FEV1 to forced vital capacity (FEV1/FVC: r(g) = 0.137, p = 2.0 x 10(-12)). Mendelian randomization analyses demonstrate that reduced FEV1 increases squamous cell carcinoma risk (odds ratio (OR) = 1.51, 95% confidence intervals: 1.21-1.88), while reduced FEV1/FVC increases the risk of adenocarcinoma (OR = 1.17, 1.01-1.35) and lung cancer in never smokers (OR = 1.56, 1.05-2.30). These findings support a causal role of pulmonary impairment in lung cancer etiology. Integrative analyses reveal that pulmonary function instruments, including 73 novel variants, influence lung tissue gene expression and implicate immune-related pathways in mediating the observed effects on lung carcinogenesis

Application of a risk-management framework for integration of stromal tumor-infiltrating lymphocytes in clinical trials

Stromal tumor-infiltrating lymphocytes (sTILs) are a potential predictive biomarker for immunotherapy response in metastatic triple-negative breast cancer (TNBC). To incorporate sTILs into clinical trials and diagnostics, reliable assessment is essential. In this review, we propose a new concept, namely the implementation of a risk-management framework that enables the use of sTILs as a stratification factor in clinical trials. We present the design of a biomarker risk-mitigation workflow that can be applied to any biomarker incorporation in clinical trials. We demonstrate the implementation of this concept using sTILs as an integral biomarker in a single-center phase II immunotherapy trial for metastatic TNBC (TONIC trial, NCT02499367), using this workflow to mitigate risks of suboptimal inclusion of sTILs in this specific trial. In this review, we demonstrate that a web-based scoring platform can mitigate potential risk factors when including sTILs in clinical trials, and we argue that this framework can be applied for any future biomarker-driven clinical trial setting