The prognostic association of SPAG5 gene expression in breast cancer patients with systematic therapy

Background: Despite much effort on the treatment of breast cancer over the decades, a great uncertainty regarding the appropriate molecular biomarkers and optimal therapeutic strategy still exists. This research was performed to analyze the association of SPAG5 gene expression with clinicopathological factors and survival outcomes. Methods: We used a breast cancer database including 5667 patients with a mean follow-up of 69 months. Kaplan-Meier survival analyses for relapse free survival (RFS), overall survival (OS), and distant metastasis-free survival (DMFS) were performed. In addition, ROC analysis was performed to validate SPAG5 as a prognostic candidate gene. Results: Mean SPAG5 expression value was significantly higher with some clinicopathological factors that resulted in tumor promotion and progression, including poor differentiated type, HER2 positive or TP53 mutated breast cancer. Based on ROC-analysis SPAG 5 is a suitable prognostic marker of poor survival. In patients who received chemotherapy alone, SPAG5 had only a moderate and not significant predictive impact on survival outcomes. However, in hormonal therapy, high SPAG5 expression could strongly predict prognosis with detrimental RFS (HR = 1.57, 95% CI 1.2-2.06, p = 0.001), OS (HR = 2, 95% CI 1.05-3.8, p = 0.03) and DMFS (HR = 2.36, 95% CI 1.57-3.54, p < 0.001), respectively. In addition, SPAG5 could only serve as a survival predictor in ER+, but not ER- breast cancer patients. Patients might also be at an increased risk of relapse despite being diagnosed with a lower grade cancer (well differentiated type). Conclusions: SPAG5 could be used as an independent prognostic and predictive biomarker that might have clinical utility, especially in ER+ breast cancer patients who received hormonal therapy. © 2019 The Author(s)

Methylation Markers of Early-Stage Non-Small Cell Lung Cancer

Despite of intense research in early cancer detection, there is a lack of biomarkers for the reliable detection of malignant tumors, including non-small cell lung cancer (NSCLC). DNA methylation changes are common and relatively stable in various types of cancers, and may be used as diagnostic or prognostic biomarkers.We performed DNA methylation profiling of samples from 48 patients with stage I NSCLC and 18 matching cancer-free lung samples using microarrays that cover the promoter regions of more than 14,500 genes. We correlated DNA methylation changes with gene expression levels and performed survival analysis.We observed hypermethylation of 496 CpGs in 379 genes and hypomethylation of 373 CpGs in 335 genes in NSCLC. Compared to adenocarcinoma samples, squamous cell carcinoma samples had 263 CpGs in 223 hypermethylated genes and 513 CpGs in 436 hypomethylated genes. 378 of 869 (43.5%) CpG sites discriminating the NSCLC and control samples showed an inverse correlation between CpG site methylation and gene expression levels. As a result of a survival analysis, we found 10 CpGs in 10 genes, in which the methylation level differs in different survival groups.We have identified a set of genes with altered methylation in NSCLC and found that a minority of them showed an inverse correlation with gene expression levels. We also found a set of genes that associated with the survival of the patients. These newly-identified marker candidates for the molecular screening of NSCLC will need further analysis in order to determine their clinical utility

Influence of common genetic variation on lung cancer risk: meta-analysis of 14 900 cases and 29 485 controls

Recent genome-wide association studies (GWASs) have identified common genetic variants at 5p15.33, 6p21-6p22 and 15q25.1 associated with lung cancer risk. Several other genetic regions including variants of CHEK2 (22q12), TP53BP1 (15q15) and RAD52 (12p13) have been demonstrated to influence lung cancer risk in candidate- or pathway-based analyses. To identify novel risk variants for lung cancer, we performed a meta-analysis of 16 GWASs, totaling 14 900 cases and 29 485 controls of European descent. Our data provided increased support for previously identified risk loci at 5p15 (P = 7.2 × 10−16), 6p21 (P = 2.3 × 10−14) and 15q25 (P = 2.2 × 10−63). Furthermore, we demonstrated histology-specific effects for 5p15, 6p21 and 12p13 loci but not for the 15q25 region. Subgroup analysis also identified a novel disease locus for squamous cell carcinoma at 9p21 (CDKN2A/p16INK4A/p14ARF/CDKN2B/p15INK4B/ANRIL; rs1333040, P = 3.0 × 10−7) which was replicated in a series of 5415 Han Chinese (P = 0.03; combined analysis, P = 2.3 × 10−8). This large analysis provides additional evidence for the role of inherited genetic susceptibility to lung cancer and insight into biological differences in the development of the different histological types of lung cance

Rare variants of large effect in BRCA2 and CHEK2 affect risk of lung cancer.

We conducted imputation to the 1000 Genomes Project of four genome-wide association studies of lung cancer in populations of European ancestry (11,348 cases and 15,861 controls) and genotyped an additional 10,246 cases and 38,295 controls for follow-up. We identified large-effect genome-wide associations for squamous lung cancer with the rare variants BRCA2 p.Lys3326X (rs11571833, odds ratio (OR) = 2.47, P = 4.74 × 10(-20)) and CHEK2 p.Ile157Thr (rs17879961, OR = 0.38, P = 1.27 × 10(-13)). We also showed an association between common variation at 3q28 (TP63, rs13314271, OR = 1.13, P = 7.22 × 10(-10)) and lung adenocarcinoma that had been previously reported only in Asians. These findings provide further evidence for inherited genetic susceptibility to lung cancer and its biological basis. Additionally, our analysis demonstrates that imputation can identify rare disease-causing variants with substantive effects on cancer risk from preexisting genome-wide association study data

Identification of lung cancer histology-specific variants applying Bayesian framework variant prioritization approaches within the TRICL and ILCCO consortia

Large-scale genome-wide association studies (GWAS) have likely uncovered all common variants at the GWAS significance level. Additional variants within the suggestive range (0.0001> P > 5x10(-8)) are, however, still of interest for identifying causal associations. This analysis aimed to apply novel variant prioritization approaches to identify additional lung cancer variants that may not reach the GWAS level. Effects were combined across studies with a total of 33456 controls and 6756 adenocarcinoma (AC; 13 studies), 5061 squamous cell carcinoma (SCC; 12 studies) and 2216 small cell lung cancer cases (9 studies). Based on prior information such as variant physical properties and functional significance, we applied stratified false discovery rates, hierarchical modeling and Bayesian false discovery probabilities for variant prioritization. We conducted a fine mapping analysis as validation of our methods by examining top-ranking novel variants in six independent populations with a total of 3128 cases and 2966 controls. Three novel loci in the suggestive range were identified based on our Bayesian framework analyses: KCNIP4 at 4p15.2 (rs6448050, P = 4.6x10(-7)) and MTMR2 at 11q21 (rs10501831, P = 3.1x10(-6)) with SCC, as well as GAREM at 18q12.1 (rs11662168, P = 3.4x10(-7)) with AC. Use of our prioritization methods validated two of the top three loci associated with SCC (P = 1.05x10(-4) for KCNIP4, represented by rs9799795) and AC (P = 2.16x10(-4) for GAREM, represented by rs3786309) in the independent fine mapping populations. This study highlights the utility of using prior functional data for sequence variants in prioritization analyses to search for robust signals in the suggestive range