Survival prediction for patients with lung adenocarcinoma: A prognostic risk model based on gene mutations

The impact of misclassification errors on the performance of biomarkers based on next-generation sequencing, a simulation study

The development of next-generation sequencing (NGS) opens opportunities for new applications such as liquid biopsy, in which tumor mutation genotypes can be determined by sequencing circulating tumor DNA after blood draws. However, with highly diluted samples like those obtained with liquid biopsy, NGS invariably introduces a certain level of misclassification, even with improved technology. Recently, there has been a high demand to use mutation genotypes as biomarkers for predicting prognosis and treatment selection. Many methods have also been proposed to build classifiers based on multiple loci with machine learning algorithms as biomarkers. How the higher misclassification rate introduced by liquid biopsy will affect the performance of these biomarkers has not been thoroughly investigated. In this paper, we report the results from a simulation study focused on the clinical utility of biomarkers when misclassification is present due to the current technological limit of NGS in the liquid biopsy setting. The simulation covers a range of performance profiles for current NGS platforms with different machine learning algorithms and uses actual patient genotypes. Our results show that, at the high end of the performance spectrum, the misclassification introduced by NGS had very little effect on the clinical utility of the biomarker. However, in more challenging applications with lower accuracy, misclassification could have a notable effect on clinical utility. The pattern of this effect can be complex, especially for machine learning-based classifiers. Our results show that simulation can be an effective tool for assessing different scenarios of misclassification.

Ribonucleotide reductase regulatory subunit M2 (RRM2) as a potential sero-diagnostic biomarker in non-small cell lung cancer

OBJECTIVES

Non-small cell lung cancer (NSCLC) is a major cause of cancer-related death worldwide. Most cases are diagnosed at an advanced stage using current tumor markers. Here, we aimed to identify potential novel potential biomarkers for NSCLC.

MATERIAL/METHODS

Four independent datasets from the Gene Expression Omnibus database were analyzed. The relative expression of ribonucleotide reductase regulatory subunit M2 (RRM2) mRNA in 30 paired of NSCLC paired tissues was measured by reverse transcription quantitative PCR. Serum levels of cytokeratin fragment 21-1 (CYFRA21-1), pro-gastrin-releasing peptide (ProGRP), carcinoembryonic antigen (CEA), and neuron-specific enolase (NSE) were measured using electrochemiluminescence immunoassays, and serum RRM2 levels were evaluated by an enzyme-linked immunosorbent assay.

RESULTS

The mRNA expression level of RRM2 was significantly increased in most NSCLC lesions compared to para-adjacent tissues. Serum RRM2 levels in NSCLC patients were significantly elevated compared to healthy controls and were also associated with distant metastasis and histological type, but not with tumor size or lymph node metastasis. Receiver operating characteristic curve analysis showed a higher diagnostic ratio for NSCLC using RRM2 alone compared to other traditional tumor markers.

CONCLUSIONS

RRM2 is a potential sero-diagnostic biomarker for NSCLC.

Genetic characterization of primary and metastatic high-grade serous ovarian cancer tumors reveals distinct features associated with survival

High-grade serous ovarian cancer (HGSC) is the most lethal histotype of ovarian cancer and the majority of cases present with metastasis and late-stage disease. Over the last few decades, the overall survival for patients has not significantly improved, and there are limited targeted treatment options. We aimed to better characterize the distinctions between primary and metastatic tumors based on short- or long-term survival. We characterized 39 matched primary and metastatic tumors by whole exome and RNA sequencing. Of these, 23 were short-term (ST) survivors (overall survival (OS) < 3.5 years) and 16 were long-term (LT) survivors (OS > 5 years). We compared somatic mutations, copy number alterations, mutational burden, differential gene expression, immune cell infiltration, and gene fusion predictions between the primary and metastatic tumors and between ST and LT survivor cohorts. There were few differences in RNA expression between paired primary and metastatic tumors, but significant differences between the transcriptomes of LT and ST survivors in both their primary and metastatic tumors. These findings will improve the understanding of the genetic variation in HGSC that exist between patients with different prognoses and better inform treatments by identifying new targets for drug development.

A radiomics nomogram prediction for survival of patients with "driver gene-negative" lung adenocarcinomas (LUAD)

BACKGROUND

To study the role of computed tomography (CT)-derived radiomics features and clinical characteristics on the prognosis of "driver gene-negative" lung adenocarcinoma (LUAD) and to explore the potential molecular biological which may be helpful for patients' individual postoperative care.

METHODS

A total of 180 patients with stage I-III "driver gene-negative" LUAD in the First Affiliated Hospital of Sun Yat-Sen University from September 2003 to June 2015 were retrospectively collected. The Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression model was used to screen radiomics features and calculated the Rad-score. The prediction performance of the nomogram model based on radiomics features and clinical characteristics was validated and then assessed with respect to calibration. Gene set enrichment analysis (GSEA) was used to explore the relevant biological pathways.

RESULTS

The radiomics and the clinicopathological characteristics were combined to construct a nomogram resulted in better performance for the estimation of OS (C-index: 0.815; 95% confidence interval [CI]: 0.756-0.874) than the clinicopathological nomogram (C-index: 0.765; 95% CI: 0.692-0.837). Decision curve analysis demonstrated that in terms of clinical usefulness, the radiomics nomogram outperformed the traditional staging system and the clinicopathological nomogram. The clinical prognostic risk score of each patient was calculated based on the radiomics nomogram and divided by X-tile into high-risk (> 65.28) and low-risk (≤ 65.28) groups. GSEA results showed that the low-risk score group was directly related to amino acid metabolism, and the high-risk score group was related to immune and metabolism pathways.

CONCLUSIONS

The radiomics nomogram was promising to predict the prognosis of patients with "driver gene-negative" LUAD. The metabolism and immune-related pathways may provide new treatment orientation for this genetically unique subset of patients, which may serve as a potential tool to guide individual postoperative care for those patients.

The prognostic value of histopathology in invasive lung adenocarcinoma: a comparative review of the main proposed grading systems

INTRODUCTION

An accurate histological evaluation of invasive lung adenocarcinoma is essential for a correct clinical and pathological definition of the tumour. Different grading systems have been proposed to predict the prognosis of invasive lung adenocarcinoma.

AREAS COVERED

Invasive non mucinous lung adenocarcinoma is often morphologically heterogeneous, consisting of complex combinations of architectural patterns with different proportions. Several grading systems for non-mucinous lung adenocarcinoma have been proposed, being the main based on architectural differentiation and the predominant growth pattern. Herein we perform a thorough review of the literature using PubMed, Scopus and Web of Science and we highlight the peculiarities and the differences between the main grading systems and compare the data about their prognostic value. In addition, we carried out an evaluation of the proposed grading systems for less common histological variants of lung adenocarcinoma, such as fetal adenocarcinoma and invasive mucinous adenocarcinoma.

EXPERT OPINION

The current IASLC grading system, based on the combined score of predominant growth pattern plus high-grade histological pattern, shows the stronger prognostic significance than the previous grading systems in invasive non mucinous lung adenocarcinoma.

Identification of mutational signature for lung adenocarcinoma prognosis and immunotherapy prediction

There is no robust genomic signature to predict the prognosis of patients with early-stage lung adenocarcinoma (LUAD). It was known that clonal heterogeneity was closely associated to tumour progression and prognosis prediction. Herein, using stage I patients from The Cancer Genome Atlas, we identified the clonal/subclonal events of each gene and preselected a set of genes with prognosis-specific mutation patterns based on a robust published transcriptomic prognostic signature. Subsequently, we constructed a mutational prognostic signature (MPS), whose prognostic performance was independently validated in two datasets of stage I samples. The predicted high-risk patients had significantly higher immune cell infiltration, along with higher expression of cytotoxic and immune checkpoint genes, and an integrated dataset with 88 samples confirmed that high-risk patients could benefit from immunotherapy. The developed MPS can identify the high-risk patients with stage I LUAD and improve individualised treatment planning of high-risk patients who might benefit from immunotherapy. KEY MESSAGES: We creatively developed a prognostic signature (57-MPS) based on clonal diversity. The high-risk samples displayed an underlying immunosuppressive mechanism. 57-MPS improved the predictive performance of PD-L1 for immunotherapy.

What Is New in Biomarker Testing at Diagnosis of Advanced Non-Squamous Non-Small Cell Lung Carcinoma? Implications for Cytology and Liquid Biopsy

The discovery and clinical validation of biomarkers predictive of the response of non-squamous non-small-cell lung carcinomas (NS-NSCLC) to therapeutic strategies continue to provide new data. The evaluation of novel treatments is based on molecular analyses aimed at determining their efficacy. These tests are increasing in number, but the tissue specimens are smaller and smaller and/or can have few tumor cells. Indeed, in addition to tissue samples, complementary cytological and/or blood samples can also give access to these biomarkers. To date, it is recommended and necessary to look for the status of five genomic molecular biomarkers (EGFR, ALK, ROS1, BRAFV600, NTRK) and of a protein biomarker (PD-L1). However, the short- and more or less long-term emergence of new targeted treatments of genomic alterations on RET and MET, but also on others’ genomic alteration, notably on KRAS, HER2, NRG1, SMARCA4, and NUT, have made cellular and blood samples essential for molecular testing. The aim of this review is to present the interest in using cytological and/or liquid biopsies as complementary biological material, or as an alternative to tissue specimens, for detection at diagnosis of new predictive biomarkers of NS-NSCLC.

Recent updates in thoracic SMARCA4-deficient undifferentiated tumor

Germline inactivating mutations in SMARCA4 (SWI/SNF-related, matrix-associated, actin-dependent regulator of chromatin, subfamily A, member 4) gene encoding for BRG1 (Brahma related gene-1) are the molecular drivers in small cell carcinoma of ovary, hypercalcemic type (SCCOHT) and in malignant rhabdoid tumors (MRT) that occur in the context of rhabdoid tumor predisposition syndrome-type 2. Somatic SMARCA4 mutations and/or loss of BRG1 have been identified in a variety of adult-onset epithelial and mesenchymal neoplasms. Among thoracic tumors, these include subsets of smoking-related non-small cell lung carcinoma (NSCLC) and a relatively rare, newly recognised tumor entity: thoracic SMARCA4-deficient undifferentiated tumor (SMARCA4-UT). Less than 100 cases of SMARCA4-UT have been reported to date. They present as large compressive and infiltrative mediastinal, lung and/or pleural masses in middle-aged male smokers. They are undifferentiated tumors composed of sheets of small/epithelioid and/or rhabdoid tumor cells variably expressing epithelial markers and consistently showing loss of BRG1 and the closely related protein, Brahma (BRM). Frequent expression of stem cell markers (SOX2, CD34, SALL4) is noted. Despite gene expression profiles similar to MRTs and SCCOHT, they show striking genomic overlap with SMARCA4-mutant NSCLC with frequent TP53, STK11, KEAP1, and KRAS mutations, high tumor mutation burden (TMB), and presence of smoking related molecular signatures in tumor cells. SMARCA4-UT show uniformly poor survival and are irresponsive to conventional therapies. Immunotherapy responses are variable but promising, although PDL1 expression appears to be of poor predictive value. Drugs exploiting genetic and epigenetic mechanisms of SMARCA4 antagonism hold promise for future targeted therapies.

A prognostic predictive model constituted with gene mutations of APC, BRCA2, CDH1, SMO, and TSC2 in colorectal cancer

Background

Colorectal cancer (CRC) is a malignant tumor that seriously threatens human health. A CRC predictive model can be used as an effective method to provide an appropriate treatment for CRC patients.

Methods

A total of 34 CRC patients were enrolled in this study. After performing 1000-gene panel targeted next-generation sequencing (NGS), high-frequency mutation genes were screened, and their functional terms and pathways were enriched. In The Cancer Genome Atlas (TCGA) CRC cases, the risk factors for overall survival (OS) were screened by univariate and multivariate analysis, and a predictive model was constructed and verified. Subsequently, the relationship among mutation status, gene expression, methylation level, and OS was analyzed to explore the molecular mechanism of CRC progression.

Results

A total of 26 high-frequency mutation genes were screened, which were mainly enriched in breast cancer and proteoglycans in cancer pathways. The clinical parameters of age, stage, recurrence and metastasis, the mutation status of APC, BRCA2, CDH1, SMO, and TSC2 were identified as risk factors for the construction of the predictive model. The areas under the receiver operating characteristic curve were 0.734, 0.754, 0.774, and 0.74 for 1-, 3-, 5- and 7-year survival in the model group, respectively.

Conclusions

We identified several mutated genes and clinical parameters affecting OS and established a model to better predict the OS of CRC patients.

TSC2 Mutations Were Associated with the Early Recurrence of Patients with HCC Underwent Hepatectomy

Purpose

To explore the value of Tuberous sclerosis complex 2 (TSC2) mutations in evaluating the early recurrence of hepatocellular carcinoma (HCC) patients underwent hepatectomy.

Patients and Methods

A total of 183 HCC patients were enrolled. Next-generation sequencing was performed on tumor tissues to analyze genomic alterations, tumor mutational burden and variant allele fraction (VAF). The associations between TSC2 mutations and recurrence rate within 1 year, RFS and OS after hepatectomy were analyzed.

Results

Our results showed that TSC2 mutation frequency in HCC was 12.6%. Compared to patients without TSC2 mutation, the proportion of microvascular invasion (MVI) and Edmondson grade III–IV was significantly higher in patients with a TSC2 mutation (p<0.05). The VAF of mutated TSC2 was higher in patients with maximum diameter of tumor >5cm or MVI than that of other patients (p<0.05). The frequency of TP53 mutation was significantly higher in patients with a TSC2 mutation than those without TSC2 mutation (p=0.003). Follow-up analysis showed that patients with a TSC2 mutation had significantly higher recurrence rate within 1 year (p=0.015) and poorer median recurrence-free survival (RFS) (p=0.010) than patients without TSC2 mutation. TSC2 mutations did not significantly affect overall survival of patients (p=0.480). The multivariate analysis results showed that the Barcelona Clinic Liver Cancer (BCLC) B-C stage, TSC2 mutations and preoperative serum alpha-fetoprotein level ≥400μg/L were independently associated with recurrence within 1 year after hepatectomy (HR=8.628, 95% CI: 3.836–19.405, p=0.000; HR=3.885, 95% CI: 1.295–11.653, p=0.015; HR=2.327, 95% CI: 1.018–5.323, p=0.045; respectively), and poorer RFS after hepatectomy (HR=3.070, 95% CI: 1.971–4.783, p=0.000; HR=1.861, 95% CI: 1.061–3.267, p=0.030; HR=1.715, 95% CI: 1.093–2.693, p=0.019; respectively).

Conclusion

TSC2 mutations were significantly associated with MVI in liver para-carcinoma tissue and Edmondson grade III–IV in patients with HCC and were independently associated with recurrence within 1 year and poorer RFS after hepatectomy. The TSC2 mutation may be a potential predictor for early recurrence in HCC patients underwent hepatectomy.

Prognostic Prediction Using a Stemness Index-Related Signature in a Cohort of Gastric Cancer

Background

With characteristic self-renewal and multipotent differentiation, cancer stem cells (CSCs) have a crucial influence on the metastasis, relapse and drug resistance of gastric cancer (GC). However, the genes that participates in the stemness of GC stem cells have not been identified.

Methods

The mRNA expression-based stemness index (mRNAsi) was analyzed with differential expressions in GC. The weighted gene co-expression network analysis (WGCNA) was utilized to build a co-expression network targeting differentially expressed genes (DEG) and discover mRNAsi-related modules and genes. We assessed the association between the key genes at both the transcription and protein level. Gene Expression Omnibus (GEO) database was used to validate the expression levels of the key genes. The risk model was established according to the least absolute shrinkage and selection operator (LASSO) Cox regression analysis. Furthermore, we determined the prognostic value of the model by employing Kaplan-Meier (KM) plus multivariate Cox analysis.

Results

GC tissues exhibited a substantially higher mRNAsi relative to the healthy non-tumor tissues. Based on WGCNA, 17 key genes (ARHGAP11A, BUB1, BUB1B, C1orf112, CENPF, KIF14, KIF15, KIF18B, KIF4A, NCAPH, PLK4, RACGAP1, RAD54L, SGO2, TPX2, TTK, and XRCC2) were identified. These key genes were clearly overexpressed in GC and validated in the GEO database. The protein-protein interaction (PPI) network as assessed by STRING indicated that the key genes were tightly connected. After LASSO analysis, a nine-gene risk model (BUB1B, NCAPH, KIF15, RAD54L, KIF18B, KIF4A, TTK, SGO2, C1orf112) was constructed. The overall survival in the high-risk group was relatively poor. The area under curve (AUC) of risk score was higher compared to that of clinicopathological characteristics. According to the multivariate Cox analysis, the nine-gene risk model was a predictor of disease outcomes in GC patients (HR, 7.606; 95% CI, 3.037-19.051; P < 0.001). We constructed a prognostic nomogram with well-fitted calibration curve based on risk score and clinical data.

Conclusion

The 17 mRNAsi-related key genes identified in this study could be potential treatment targets in GC treatment, considering that they can inhibit the stemness properties. The nine-gene risk model can be employed to predict the disease outcomes of the patients.