Differentially Expressed Genes Involved in Primary Resistance to Immunotherapy in Patients with Advanced-Stage Pulmonary Cancer.

In the last few years, nivolumab has become the standard of care for advanced-stage lung cancer patients. Unfortunately, up to 60% of patients do not respond to this treatment. In our study, we identified variations in gene expression related to primary resistance to immunotherapy. Bronchoscopy biopsies were obtained from advanced non-small cell lung cancer (NSCLC) patients previously characterized as responders or non-responders after nivolumab treatment. Ten tumor biopsies (from three responders and seven non-responders) were analyzed by the differential expression of 760 genes using the NanoString nCounter platform. These genes are known to be involved in the response to anti-PD1/PD-L1 therapy. All the patients were treated with nivolumab. Examining the dysregulated expression of 24 genes made it possible to predict the response to nivolumab treatment. Supervised analysis of the gene expression profile (GEP) revealed that responder patients had significantly higher levels of expression of CXCL11, NT5E, KLRK1, CD3G, GZMA, IDO1, LCK, CXCL9, GNLY, ITGAL, HLA-DRB1, CXCR6, IFNG, CD8A, ITK, B2M, HLA-B, and HLA-A than did non-responder patients. In contrast, PNOC, CD19, TP73, ARG1, FCRL2, and PTGER1 genes had significantly lower expression levels than non-responder patients. These findings were validated as predictive biomarkers in an independent series of 201 patients treated with nivolumab (22 hepatocellular carcinomas, 14 non-squamous cell lung carcinomas, 5 head and neck squamous cell carcinomas, 1 ureter/renal pelvis carcinoma, 120 melanomas, 4 bladder carcinomas, 31 renal cell carcinomas, and 4 squamous cell lung carcinomas). ROC curve analysis showed that the expression levels of ITK, NT5E, ITGAL, and CD8A were the best predictors of response to nivolumab. Further, 13/24 genes showed an adverse impact on overall survival (OS) in an independent, large series of patients with NSCLC (2166 cases). In summary, we found a strong association between the global GEP of advanced NSCLC and the response to nivolumab. The classification of NSCLC patients based on GEP enabled us to identify those patients who genuinely benefited from treatment with immune checkpoint inhibitors (ICIs). We also demonstrated that abnormal expression of most of the markers comprising the genomic signature has an adverse influence on OS, making them significant markers for therapeutic decision-making. Additional prospective studies in larger series of patients are required to confirm the clinical utility of these biomarkers.

The Impact of Liquid Biopsies Positive for EGFR Mutations on Overall Survival in Non-Small Cell Lung Cancer Patients.

In recent years, non-small cell lung cancer treatment has been revolutionized. EGFR tyrosine kinase inhibitors and our improved understanding of its alterations have driven new diagnostic strategies. Liquid biopsies have emerged as a useful tool in these contexts, showing potential utility in early diagnosis combined with low-dose CT scans, as well as potential in monitoring treatment response and predicting the development of patients. We studied the circulating tumor DNA (ctDNA) of 38 EGFR-mutated non-small cell lung cancer patients at diagnosis in different moments of their disease by liquid biopsy techniques. Our results show that mean overall survival was significantly lower when a liquid biopsy was positive for the detection of EGFR mutations compared with wild-type patients in their liquid biopsy in both univariate (29 ± 4 vs. 104 ± 19 months; p = 0.004) and multivariate analysis (p = 0.008). Taking this into consideration, liquid biopsies could be key to improving the control of this disease.

iCEMIGE: Integration of CEll-morphometrics, MIcrobiome, and GEne biomarker signatures for risk stratification in breast cancers.

BACKGROUND: The development of precision medicine is essential for personalized treatment and improved clinical outcome, whereas biomarkers are critical for the success of precision therapies. AIM: To investigate whether iCEMIGE (integration of CEll-morphometrics, MIcro biome, and GEne biomarker signatures) improves risk stratification of breast cancer (BC) patients. METHODS: We used our recently developed machine learning technique to identify cellular morphometric biomarkers (CMBs) from the whole histological slide images in The Cancer Genome Atlas (TCGA) breast cancer (TCGA-BRCA) cohort. Multivariate Cox regression was used to assess whether cell-morphometrics prognosis score (CMPS) and our previously reported 12-gene expression prognosis score (GEPS) and 15-microbe abundance prognosis score (MAPS) were independent prognostic factors. iCEMIGE was built upon the sparse representation learning technique. The iCEMIGE scoring model performance was measured by the area under the receiver operating characteristic curve compared to CMPS, GEPS, or MAPS alone. Nomogram models were created to predict overall survival (OS) and progress-free survival (PFS) rates at 5- and 10-year in the TCGA-BRCA cohort. RESULTS: We identified 39 CMBs that were used to create a CMPS system in BCs. CMPS, GEPS, and MAPS were found to be significantly independently associated with OS. We then established an iCEMIGE scoring system for risk stratification of BC patients. The iGEMIGE score has a significant prognostic value for OS and PFS independent of clinical factors (age, stage, and estrogen and progesterone receptor status) and PAM50-based molecular subtype. Importantly, the iCEMIGE score significantly increased the power to predict OS and PFS compared to CMPS, GEPS, or MAPS alone. CONCLUSION: Our study demonstrates a novel and generic artificial intelligence framework for multimodal data integration toward improving prognosis risk stratification of BC patients, which can be extended to other types of cancer.

Factors influencing malignant evolution and long-term survival in solitary fibrous tumours of the pleura.

Solitary pleuro-pulmonary fibrous tumours are relatively uncommon neoplasms that are difficult to manage therapeutically and which, cytogenetically, have been poorly studied. The aim of the present work was to analyse the characteristics of a series of consecutive operated solitary pleural fibrous tumours in an attempt to discover a malignant pattern of evolution. This was a retrospective observational study of 19 cases. Samples were studied for clinical, histological, immunohistochemical and cytogenetic characteristics (aCGH, FISH). Descriptive statistics were used: the Kapplan-Meyer log-rank test and the Cox-regression model for survival analysis. Analysis of malignant evolution was achieved using 2x2 tables; significant factors were included in a binary logistic regression model. Parietal pleural implantation of the primary tumour, high mib1 expression, and low p53 expression were seen to be statistically significant factors for survival. We recommend a close follow-up for patients with a malignant primary tumour and low p53 expression and a regular long-term follow-up for benign primary tumours with a high mib1 index, high positive p53, and deletions. These findings need confirmation in more extensive series.