Identification of genetic variations associated with drug resistance in non-small cell lung cancer patients undergoing systemic treatment

Serum Protein Fishing for Machine Learning-Boosted Diagnostic Classification of Small Nodules of Lung

Diagnosis of benign and malignant small nodules of the lung remains an unmet clinical problem which is leading to serious false positive diagnosis and overtreatment. Here, we developed a serum protein fishing-based spectral library (ProteoFish) for data independent acquisition analysis and a machine learning-boosted protein panel for diagnosis of early Non-Small Cell Lung Cancer (NSCLC) and classification of benign and malignant small nodules. We established an extensive NSCLC protein bank consisting of 297 clinical subjects. After testing 5 feature extraction algorithms and six machine learning models, the Lasso algorithm for a 15-key protein panel selection and Random Forest was chosen for diagnostic classification. Our random forest classifier achieved 91.38% accuracy in benign and malignant small nodule diagnosis, which is superior to the existing clinical assays. By integrating with machine learning, the 15-key protein panel may provide insights to multiplexed protein biomarker fishing from serum for facile cancer screening and tackling the current clinical challenge in prospective diagnostic classification of small nodules of the lung.

Identification of molecular biomarkers associated with non-small-cell lung carcinoma (NSCLC) using whole-exome sequencing

OBJECTIVES

Significant progress has been made in the treatment of patients with pulmonary adenocarcinoma (ADCA) based on molecular profiling. However, no such molecular target exists for squamous cell carcinoma (SQCC). An exome sequence may provide new markers for personalized medicine for lung cancer patients of all subtypes. The current study aims to discover new genetic markers that can be used as universal biomarkers for non-small cell lung cancer (NSCLC).

METHODS

WES of 19 advanced NSCLC patients (10 ADCA and 9 SQCC) was performed using Illumina HiSeq 2000. Variant calling was performed using GATK HaplotypeCaller and then the impacts of variants on protein structure or function were predicted using SnpEff and ANNOVAR. The clinical impact of somatic variants in cancer was assessed using cancer archives. Somatic variants were further prioritized using a knowledge-driven variant interpretation approach. Sanger sequencing was used to validate functionally important variants.

RESULTS

We identified 24 rare single-nucleotide variants (SNVs) including 17 non-synonymous SNVs, and 7 INDELs in 18 genes possibly linked to lung carcinoma. Variants were classified as known somatic (n= 10), deleterious (n= 8), and variant of uncertain significance (n= 6). We found TBP and MPRIP genes exclusively associated with ADCA subtypes, FBOX6 with SQCC subtypes and GPRIN2, KCNJ18 and TEKT4 genes mutated in all the patients. The Sanger sequencing of 10 high-confidence somatic SNVs showed 100% concordance in 7 genes, and 80% concordance in the remaining 3 genes.

CONCLUSIONS

Our bioinformatics analysis identified KCNJ18, GPRIN2, TEKT4, HRNR, FOLR3, ESSRA, CTBP2, MPRIP, TBP, and FBXO6 may contribute to progression in NSCLC and could be used as new biomarkers for the treatment. The mechanism by which GPRIN2, KCNJ12, and TEKT4 contribute to tumorigenesis is unclear, but our results suggest they may play an important role in NSCLC and it is worth investigating in future.

Effects of p450 Polymorphisms on the Clinical Outcomes of Gefitinib Treatment in Patients with Epidermal Growth Factor Receptor Mutation-Positive Non-Small Cell Lung Cancer

Aims: In this study, we determined whether different genotypes of drug-metabolizing enzymes are associated with the therapeutic effects of gefitinib in non-small cell lung cancer (NSCLC). Methods: A retrospective analysis of 112 patients with stage III or IV NSCLC was performed. The clinical characteristics of these patients, including progression-free survival (PFS), outcome of gefitinib treatment, and relationship between the genotypes of rs1065852/rs2242480 and prognosis, were analyzed. Results: The rs1065852 CT/TT genotype was associated with worse prognosis than the CC type (p = 0.0306), and the median PFS was lower than that with the CC type (287 days vs. 350 days). Compared with those with CC+CC genotypes, individuals carrying T alleles (CT/TT+CT/TT) at rs1065852/rs2242480 had a poorer prognosis, and the median PFS of CT/TT+CT/TT at rs1065852/rs2242480 was significantly lower than that of the CC+CC type (188 days vs. 444.5 days). Conclusions: Genotypes of the drug-metabolizing enzymes rs1065852 and rs2242480 have an impact on the prognosis of patients with NSCLC treated with gefitinib.

FCGR3A: A new biomarker with potential prognostic value for prostate cancer

To screen target gene cluster by bioinformatics analysis and verify them by in vitro experiment and clinicopathological correlation analysis. We try to find a new biomarker with prognostic value for prostate cancer (PCa). 42 candidate marker genes were constructed by protein protein interaction (PPI) network and enriched by KEGG pathway to find out the gene cluster we are interested in. Prognostic model was established to preliminarily analyze the prognostic value of this gene cluster in PCa, and Cox risk regression was used for comparative analysis. Immunohistochemistry was used to detect the expression of each gene in clinical tissue microarray. Finally, we analyzed the correlation between each gene and their clinicopathological features of PCa combined with TCGA clinical data. Based on the analysis of PPI and KEGG, we found the target gene cluster (FCGR3A, HAVCR2, CCR7 and CD28). Prognostic model analysis showed that this gene cluster had the ability to predict biochemical recurrence, and the survival rate and ROC analysis showed favorable prediction effect. Univariate Cox regression analysis showed that the risk scores of Gleason score (GS), T stage, N stage and PSA were significantly different (P<0.05), and the risk ratio of high expression was 2.30 times that of low expression (P=0.004). However, it was not statistically significant in multivariate Cox regression analysis (P>0.05). The results of tissue microarray showed that FCGR3A and HAVCR2 were highly expressed in PCa (P<0.01), while the expression of CCR7 and CD28 had no significant difference (P>0.05). Kaplan-Meier analysis showed that there was significant difference in BCR free survival of FCGR3A and HAVCR2 (FCGR3A, P=0.010; HAVCR2, P=0.018), while the expression of CCR7 and CD28 had no significant difference on the survival and prognosis of PCa patients (P>0.05). TCGA clinical data analysis found that the expression of FCGR3A had a unique correlation with the clinicopathological features of PCa, which was closely related to the tumor stage. The expression of FCGR3A is related to BCR free survival of PCa patients. Therefore, FCGR3A is a new biomarker with potential prognostic value of PCa.

Development and Validation of an ADME-Related Gene Signature for Survival, Treatment Outcome and Immune Cell Infiltration in Head and Neck Squamous Cell Carcinoma

ADME genes are a set of genes which are involved in drug absorption, distribution, metabolism, and excretion (ADME). However, prognostic value and function of ADME genes in head and neck squamous cell carcinoma (HNSCC) remain largely unclear. In this study, we established an ADME-related prognostic model through the least absolute shrinkage and selection operator (LASSO) analysis in the Cancer Genome Atla (TCGA) training cohort and its robustness was validated by TCGA internal validation cohort and a Gene Expression Omnibus (GEO) external cohort. The 14-gene signature stratified patients into high- or low-risk groups. Patients with high-risk scores exhibited significantly poorer overall survival (OS) and disease-free survival (DFS) than those with low-risk scores. Receiver operating characteristic (ROC) curve analysis was used to confirm the signature’s predictive efficacy for OS and DFS. Furthermore, gene ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway analyses showed that immune-related functions and pathways were enriched, such as lymphocyte activation, leukocyte cell-cell adhesion and T-helper cell differentiation. The Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) and other analyses revealed that immune cell (especially B cell and T cell) infiltration levels were significantly higher in the low-risk group. Moreover, patients with low-risk scores were significantly associated with immunotherapy and chemotherapy treatment benefit. In conclusion, we constructed a novel ADME-related prognostic and therapeutic biomarker associated with immune cell infiltration of HNSCC patients.

Effects of ABCB1 DNA methylation in donors on tacrolimus blood concentrations in recipients following liver transplantation

Aims

To investigate the effects of ABCB1 DNA methylation in donors on individual differences in tacrolimus blood concentrations following liver transplantation.

Methods

Twenty‐three donor liver samples carrying the CYP3A5*3/*3 genotype were classified into 2 groups based on their initial tacrolimus blood concentrations (C₀ >10 μg/L or <5 μg/L) following liver transplantation. ABCB1 mRNA levels in liver tissues and HepG2 cells were determined by quantitative reverse transcriptase polymerase chain reaction. DNA methylation status in liver tissues and HepG2 cells was determined using Illumina 850 methylation chip sequencing technology and pyrosequencing. 5‐Aza‐2dC was used to reverse methylation in HepG2 cells. Intracellular tacrolimus concentrations were determined by liquid mass spectrometry.

Results

Genome‐wide methylation sequencing and pyrosequencing analyses showed that the methylation levels of 3 ABCB1 CpG sites (cg12501229, cg00634941 and cg05496710) were significantly different between groups with different tacrolimus concentration/dose (C₀/D) ratios. ABCB1 mRNA expression in donor livers was found to be positively correlated with tacrolimus C₀/D ratio (R = .458, P < .05). After treatment with 5‐Aza‐2‐Dc, the methylation levels of the ABCB1 CpG sites in HepG2 cells significantly decreased, and this was confirmed by pyrosequencing; there was also a significant increase in ABCB1 transcription, which induced a decrease in intracellular tacrolimus concentrations.

Conclusion

ABCB1 CpG site methylation affects tacrolimus metabolism in humans by regulating ABCB1 expression. Therefore, ABCB1 DNA methylation in donor livers might be an important epigenetic factor that affects tacrolimus blood concentrations following liver transplantation.