Idiopathic Pulmonary Fibrosis (IPF) is a lethal lung disease of unknown etiology. A major limitation in transcriptomic profiling of lung tissue in IPF has been a dependence on snap-frozen fresh tissues (FF). In this project we sought to determine whether genome scale transcript profiling using RNA Sequencing (RNA-Seq) could be applied to archived Formalin-Fixed Paraffin-Embedded (FFPE) IPF tissues. We isolated total RNA from 7 IPF and 5 control FFPE lung tissues and performed 50 base pair paired-end sequencing on Illumina 2000 HiSeq. TopHat2 was used to map sequencing reads to the human genome. On average ~62 million reads (53.4% of ~116 million reads) were mapped per sample. 4,131 genes were differentially expressed between IPF and controls (1,920 increased and 2,211 decreased (FDR < 0.05). We compared our results to differentially expressed genes calculated from a previously published dataset generated from FF tissues analyzed on Agilent microarrays (GSE47460). The overlap of differentially expressed genes was very high (760 increased and 1,413 decreased, FDR < 0.05). Only 92 differentially expressed genes changed in opposite directions. Pathway enrichment analysis performed using MetaCore confirmed numerous IPF relevant genes and pathways including extracellular remodeling, TGF-beta, and WNT. Gene network analysis of MMP7, a highly differentially expressed gene in both datasets, revealed the same canonical pathways and gene network candidates in RNA-Seq and microarray data. For validation by NanoString nCounter® we selected 35 genes that had a fold change of 2 in at least one dataset (10 discordant, 10 significantly differentially expressed in one dataset only and 15 concordant genes). High concordance of fold change and FDR was observed for each type of the samples (FF vs FFPE) with both microarrays (r = 0.92) and RNA-Seq (r = 0.90) and the number of discordant genes was reduced to four. Our results demonstrate that RNA sequencing of RNA obtained from archived FFPE lung tissues is feasible. The results obtained from FFPE tissue are highly comparable to FF tissues. The ability to perform RNA-Seq on archived FFPE IPF tissues should greatly enhance the availability of tissue biopsies for research in IPF.

Background: Idiopathic pulmonary fibrosis (IPF) has common risk factors with cancer and significant similarities in the pathobiology process, both diseases having poor outcomes. Immune checkpoint PD-L1 has become the target of checkpoint inhibitory therapy that unleashes antitumor T cells and has revolutionized cancer treatment. This is a pilot study exploring membrane immune checkpoint PD-L1 expression in human IPF lung tissue samples and its soluble form, soluble PD-L1 (sPD-L1) plasma concentrations in IPF patients, in order to investigate potential role of PD-L1 as an IPF biomarker. Methods: Twelve human IPF lung tissue samples (formalin-fixed, paraffin-embedded) obtained by surgical biopsy, have been tested for PD-L1 expression by PD-L1 IHC 22C3 pharmDx assay, while plasma samples for examination of sPD-L1 forms, PD-L1 (B7-H1/CD274) blood concentration, originated from 23 patients with IPF who did not undergo surgical biopsy. Results: Membrane PD-L1 expression in IPF lung tissue samples was positive to overexpression of PD-L1 in 9 samples out of 12. Only very few cells in the interstitium have shown a discrete PD-L1 expression, but not of a membrane type. As for sPD-L1 forms, we have found elevated concentrations of sPD-L1 in the serum of IPF patients 314.3 ng/L (117.7–483.1 ng/L), significantly higher compared with healthy control group 91.0 ng/L (52.4–119.7 ng/L), P<0.01. Conclusions: For IPF with PD-L1 expression on alveolar macrophages, further studies are necessary to elucidate this phenomenon. Serum sPD-1/PD-L1 is easily detected in clinical practice and should be further evaluated as a potential prognostic or/and predictive biomarker in IPF.

Background: The objective of this prospective study was to evaluate whether soluble programmed cell death- 1/programmed cell death-ligand 1 (PD-1/PD-L1) and serum amyloid A1 (SAA1) are potential diagnostic, predictive or prognostic biomarkers in lung cancer. Methods: Lung cancer patients (n=115) with advanced metastatic disease, 101 with non-small cell lung cancer, NSCLC (77 EGFR wild-type NSCLC patients on chemotherapy, 15 EGFR mutation positive adenocarcinoma patients, 9 patients with mPD-L1 Expression ≥50% NSCLC – responders to immunotherapy), and 14 patients with small cell lung cancer (SCLC) were examined. ELISA method was used to determine sPD-L1 and SAA1 concentrations in patients’ plasma. Results: Significantly higher blood concentrations of sPD-L1 and SAA1 were noted in lung cancer patients compared with a healthy control group. In PD-L1+ NSCLC patients, a significantly higher sPD-L1 level was noticed compared to any other lung cancer subgroup, as well as the highest average SAA1 value compared to other subgroups. Conclusions: It seems that sPD-1/PD-L1 might be a potential biomarker, prognostic and/or predictive, particularly in patients treated with immunotherapy. Serum amyloid A1 has potential to act as a good predictor of patients’ survival, as well as a biomarker of a more advanced disease, with possibly good capability to predict the course of disease measured at different time points.

Background and objectives: The objective of this research was to analyze the correlation of the neutrophil-to-lymphocyte ratio (NLR), C-reactive protein (CRP), soluble programmed cell death ligand 1 (sPD-L1), and Schlafen 11 (SLFN11) with the response to first-line chemotherapy in a cohort of small cell lung cancer (SCLC) patients, and to determine their potential as predictive serum biomarkers. Materials and Methods: A total of 60 SCLC patients were included. Blood samples were taken to determine CRP, sPD-L1, and SLFN11 levels. The first sampling was performed before the start of chemotherapy, the second after two cycles, and the third after four cycles of chemotherapy. Results: The patients who died earlier during the study had NLR and SLFN11 concentrations significantly higher compared to the survivor group. In the group of survivors, after two cycles of chemotherapy, the NLR ratio decreased significantly (p < 0.01), but after four cycles, the NLR ratio increased (p < 0.05). Their serum SLFN11 concentration increased significantly (p < 0.001) after two cycles of chemotherapy, but after four cycles, the level of SLFN11 fell significantly (p < 0.01). CRP, NLR, and SLFN11 were significant predictors of patient survival according to Kaplan–Meier analysis. The combination of inflammatory parameters and SLFN11 with a cutoff value above the 75th percentile of the predicted probability was associated with significantly lower overall survival in SCLC patients (average survival of 3.6 months vs. 4.8 months). Conclusion: The combination of inflammatory markers and the levels of two specific proteins (sPD-L1, SLFN11) could potentially serve as a non-invasive biomarker for predicting responses to DNA-damaging therapeutic agents in SCLC.

Thymic epithelial tumors (TETs) include several anterior mediastinal malignant tumours: thymomas, thymic carcinomas and thymic neuroendocrine cancers. There is significant variety in the biologic features and clinical course of TETs and many attempts have been made to identify target genes for successful therapy of TETs. Next generation sequencing (NGS) represents a huge advancement in diagnostics and these new molecular technologies revealed that thymic neoplasms have the lowest tumor mutation burden among all adult malignant tumours with a different pattern of molecular aberrations in thymomas and thymic carcinomas. As for the PD-L1 expression in tumor cells in thymoma and thymic carcinoma, it varies a lot in published studies, with findings of PD-L1 expression from 23% to 92% in thymoma and 36% to 100% in thymic carcinoma. When correlated PD-L1 expression with disease stage some controversial results were obtained, with no association with tumor stage in most studies. This is, at least in part, explained by the fact that several diverse PD-L1 immunohistochemical tests were used in each trial, with four different antibodies (SP142, SP263, 22C3, and 28-8), different definition of PD-L1 positivity and cutoff values throughout the studies as well. There is a huge interest in using genomic features to produce predictive genomic-based immunotherapy biomarkers, particularly since recent data suggest that certain tumor-specific genomic alterations, either individually or in combination, appear to influence immune checkpoint activity and better responses as the outcome, so as such in some cancer types they may complement existing biomarkers to improve the selection criteria for immunotherapy.