A simple two-gene prognostic model for adenocarcinoma of the lung

Radiomics and artificial intelligence for precision medicine in lung cancer treatment

Lung cancer is the leading cause of cancer-related deaths worldwide. It exhibits, at the mesoscopic scale, phenotypic characteristics that are generally indiscernible to the human eye but can be captured non-invasively on medical imaging as radiomic features, which can form a high dimensional data space amenable to machine learning. Radiomic features can be harnessed and used in an artificial intelligence paradigm to risk stratify patients, and predict for histological and molecular findings, and clinical outcome measures, thereby facilitating precision medicine for improving patient care. Compared to tissue sampling-driven approaches, radiomics-based methods are superior for being non-invasive, reproducible, cheaper, and less susceptible to intra-tumoral heterogeneity. This review focuses on the application of radiomics, combined with artificial intelligence, for delivering precision medicine in lung cancer treatment, with discussion centered on pioneering and groundbreaking works, and future research directions in the area.

Apolipoprotein C1 (APOC1) as a novel diagnostic and prognostic biomarker for lung cancer: A marker phase I trial

BACKGROUND

Tumor cells continuously evolve over time in response to host pressures. However, explanations as to how tumor cells are influenced by the inflammatory tumor microenvironment over time are, to date, poorly defined. We hypothesized that prognostic biomarkers could be obtained by exploring the expression of inflammation-associated genes between early and late stage lung cancer tumor samples.

METHODS

Candidate inflammation-associated genes, apolipoprotein C-1 (APOC1), MMP1, KMO)1, CXCL5, CXCL)7, IL-1α, IL-1β, TNF-α and IL-6 were verified by real-time quantitative polymerase chain reaction. Gene expression profiles and immunofluorescence staining of 30 lung cancer tissues were compared.

RESULTS

Expressions of APOC1 and IL-6 mRNA on tumor tissues in late stage disease were significantly higher than in early stage lung cancer samples. Immunofluorescence staining of tumor samples showed that the expression of APOC1 gradually increased from early to late stage in lung cancer patients. The expression levels of IL-6 and APOC1 in tumor samples were positively correlated; however, no prognostic value of APOC1 can be identified in serum samples.

CONCLUSIONS

We found that the level of tumor APOC1 was highly expressed in late stage lung cancer. Further research is warranted to determine the molecular mechanisms underlying the cross talk of APOC1 and IL-6 in tumor progression. An expanded sample size marker phase II study may lead to the discovery of new lung cancer therapeutics targeting APOC1.

Down-regulation of SIX3 is associated with clinical outcome in lung adenocarcinoma

BACKGROUND

Lung cancer is a common cancer and the leading cause of cancer-related death worldwide. SIX3 is a human homologue of the highly conserved sine oculis gene family essential during embryonic development in vertebrates, and encodes a homeo-domain containing transcription factor. Little is known about the role of SIX3 in human tumorigenesis. This study is to assess the expression/function of SIX3 and the significance of SIX3 as a prognostic biomarker in lung adenocarcinoma.

METHODS

Quantitative real-time RT-PCR was used to analyze SIX3 mRNA expression and quantitative methylation specific PCR (MSP) was used to examine promoter methylation. MTS and colony formation assays were performed to examine cell proliferation. Wound healing assays were used to assess cell migration, and microarrays were utilized to examine genes regulated by SIX3 in lung cancer cells. Association of SIX3 expression levels with clinical outcomes of patients with lung adenocarcinoma was evaluated using the Kaplan-Meier method and a multivariate Cox proportional hazards regression model.

RESULTS

SIX3 was down-regulated in lung adenocarcinoma tissues compared to their matched adjacent normal tissues, and this down-regulation was associated with methylation of the SIX3 promoter. SIX3 was also methylation-silenced in lung cancer cell lines. Restoration of SIX3 in lung cancer cells lacking endogenous SIX3 suppressed cell proliferation and migration, and downregulated a number of genes involved in proliferation and metastasis such as S100P, TGFB3, GINS3 and BAG1. Moreover, SIX3 mRNA expression was associated with significantly improved overall survival (OS) and progression-free survival (PFS) in adenocarcinoma patients and patients with bronchioloalveolar carcinoma (BAC) features.

CONCLUSIONS

SIX3 may play an important role as a novel suppressor in human lung cancer. SIX3 has potential as a novel prognostic biomarker for patients with lung adenocarcinomas.

Growth of lung cancer cells in three-dimensional microenvironments reveals key features of tumor malignancy

Cultured human lung cancer cell lines have been used extensively to dissect signaling pathways underlying cancer malignancy, including proliferation and resistance to chemotherapeutic agents. However, the ability of malignant cells to grow and metastasize in vivo is dependent upon specific cell-cell and cell-extracellular matrix (ECM) interactions, many of which are absent when cells are cultured on conventional tissue culture plastic. Previous studies have found that breast cancer cell lines show differential growth morphologies in three-dimensional (3D) gels of laminin-rich (lr) ECM, and that gene expression patterns associated with organized cell structure in 3D lrECM were associated with breast cancer patient prognosis. We show here that established lung cancer cell lines also can be classified by growth in lrECM into different morphological categories and that transcriptional alterations distinguishing growth on conventional tissue culture plastic from growth in 3D lrECM are reflective of tissue-specific differentiation. We further show that gene expression differences that distinguish lung cell lines that grow as smooth vs. branched structures in 3D lrECM can be used to stratify adenocarcinoma patients into prognostic groups with significantly different outcome, defining phenotypic response to 3D lrECM as a potential surrogate of lung cancer malignancy.

Genomic medicine in non-small cell lung cancer: paving the path to personalized care

Lung cancer is the commonest cause of cancer-related mortality and non-small cell lung cancer (NSCLC) accounts for 80% of all lung cancer. The prognosis of NSCLC remains poor across all stages, despite advances in staging techniques and treatments. The findings of recent high-throughput mRNA microarray studies have shown potential in refining current NSCLC diagnosis, classification, prognosis and treatment paradigms. Emerging microarray studies of microRNA, DNA copy number and methylation profiles are also providing novel insights into the biology of NSCLC. Currently there are several challenges, such as the reproducibility and cost of microarray platforms that will need to be addressed prior to the implementation of these genomic technologies to routine thoracic oncology practice. In addition, genomic tests (such as prognosis and prediction gene expression signatures) will need to be validated in well designed prospective studies that aim to answer clinically relevant questions. If successful, the integration of microarray-based genomic information with existing clinicopathological models may enhance the ability of clinicians to match the most effective treatment to an individual patient. Such a strategy may improve survival and reduce treatment-related morbidity in NSCLC patients.

Downregulation of EMX2 is associated with clinical outcomes in lung adenocarcinoma patients

BACKGROUND

The 5-year survival rate for stage I non-small-cell lung cancer (NSCLC) of 50% to 70% indicates that our current staging methods do not adequately predict outcome. Empty spiracles homeobox 2 (EMX2) is a homeo-domain-containing transcription factor that regulates a key developmental pathway known to promote lung tumorigenesis. This study assessed the significance of EMX2 as a prognostic biomarker in lung adenocarcinoma including bronchioloalveolar carcinoma (BAC).

PATIENTS AND METHODS

144 patients with lung adenocarcinoma undergoing surgical resection were studied. Quantitative real-time reverse transcriptase polymerase chain reaction and Immunohistochemistry were used to analyze EMX2 mRNA and protein expression, respectively. Association of EMX2 mRNA expression levels with clinical outcomes was evaluated using the Kaplan-Meier method and a multivariate Cox proportional hazards regression model.

RESULTS

EMX2 mRNA expression was significantly downregulated in lung adenocarcinoma compared with matched adjacent normal tissue (P < .001). EMX2 protein expression was similarly found to be downregulated in lung adenocarcinoma. The EMX2-high mRNA expressing group had statistically significant better overall survival (OS) than the EMX2-low mRNA expressing group (P = .005). Subgroup analysis also demonstrated improved survival in stage I patients (P = .01) and patients with BAC (P = .03). Lastly, the EMX2-high mRNA expressing group had statistically significant better recurrence-free survival (RFS) than the EMX2-low mRNA expression group in patients with adenocarcinoma (P < .001).

CONCLUSION

EMX2 expression is downregulated in lung adenocarcinoma. Low EMX2 mRNA expression is significantly associated with decreased OS and RFS in patients with lung adenocarcinoma, particularly with stage I disease and BAC.

Discovering Disease-specific Biomarker Genes for Cancer Diagnosis and Prognosis

The large amounts of microarray data provide us a great opportunity to identify gene expression profiles (GEPs) in different tissues or disease states. Disease-specific biomarker genes likely share GEPs that are distinct in disease samples as compared with normal samples. The similarity of the GEPs may be evaluated by Pearson Correlation Coefficient (PCC) and the distinctness of GEPs may be assessed by Kolmogorov-Smirnov distance (KSD). In this study, we used the PCC and KSD metrics for GEPs to identify disease-specific (cancer-specific) biomarkers. We first analyzed and compared GEPs using microarray datasets for smoking and lung cancer. We found that the number of genes with highly different GEPs between comparing groups in smoking dataset was much larger than that in lung cancer dataset; this observation was further verified when we compared GEPs in smoking dataset with prostate cancer datasets. Moreover, our Gene Ontology analysis revealed that the top ranked biomarker candidate genes for prostate cancer were highly enriched in molecular function categories such as ‘cytoskeletal protein binding’ and biological process categories such as ‘muscle contraction’. Finally, we used two genes, ACTC1 (encoding an actin subunit) and HPN (encoding hepsin), to demonstrate the feasibility of diagnosing and monitoring prostate cancer using the expression intensity histograms of marker genes. In summary, our results suggested that this approach might prove promising and powerful for diagnosing and monitoring the patients who come to the clinic for screening or evaluation of a disease state including cancer.

The role of gene expression profiling in early-stage non-small cell lung cancer

For patients with identical clinical-pathological characteristics or the same stage of lung cancer, great uncertainties remain regarding how some patients will be cured while other patients will have cancer recurrence, metastasis, or death after surgical resection. Identification of patients at high risk of recurrence, those who are unlikely to respond to specific chemotherapeutic agents, is the rationale for measuring specific biochemical markers. Thus, main investigational studies nowadays are focused in identifying molecular markers of recurrence, beyond pathologic stage, after surgical treatment and factors that can predict a benefit from adjuvant chemotherapy in poor prognosis subgroups, to individualize treatments. Advances in genomics and proteomics have generated many candidate markers with potential clinical value. Gene expression profiling (GEP) by microarray or real-time quantitative reverse-transcriptase polymerase chain reaction (qRT-PCR) can be useful in the classification or prognosis of various types of cancer, including lung cancer. A number of prognostic gene expression signatures have been reported to predict survival in non-small cell lung cancer (NSCLC). In this review, we focus on the role of GEP in early-stage NSCLC as predictive and prognostic biomarker and its potential use for a 'personalized' medicine in the years to come.

Genomic alterations of chromosome region 11p as predictive marker by array comparative genomic hybridization in lung adenocarcinoma patients

Array comparative genomic hybridization (aCGH) provides a method to quantitatively measure the changes of DNA copy number with an extremely high resolution and to map them directly onto the complete linear genome sequences. In this study, we used aCGH to compare genomic alterations in fresh-frozen lung cancer tissues of 21 adenocarcinomas (AdCCs) (11 early relapse and 10 nonrelapse) and identified genomic alterations that showed significant by different frequency between early relapse and nonrelapse AdCCs. Twelve clones were identified by the false discovery rate (FDR) test, and Kaplan-Meier analyses were selected as predictive markers. The significant gain clones were found in 11p (11p15.4, 11p15.1, and 11p13). When the cutoff value was 2, study of the association between candidate clones and relapse prediction revealed that early relapse and nonrelapse groups were most effectively separated. To further validate the gain of chromosome 11p region that was identified by array CGH, fluorescence in situ hybridization (FISH) was performed. To further confirm the results of aCGH, copy number changes of cancer-related candidate genes in AdCC patients were compared by real-time quantitative polymerase chain reaction. Array CGH and real-time quantitative polymerase chain reaction data were found to correspond to delineated DNA copy number changes. Genomic alterations of chromosome 11p region in AdCC patients were observed with aCGH, and a relapsable marker was identified in the nonrelapse group. This marker could be useful in stratifying patient groups according to likelihood of relapse for adjuvant treatment after surgical resection.

High-resolution computed tomography screening for lung cancer: unexpected findings and new controversies regarding adenocarcinogenesis

CONTEXT

Recent advances in human imaging technologies reawakened interest in lung cancer screening. Although historic and current preliminary and noncontrolled studies have not shown a decrease in lung cancer mortality in screened populations, many explanations have been proffered while the lung cancer community awaits the results of several large controlled population studies.

OBJECTIVE

To critically review the current model of adenocarcinoma development against the background of lung cancer screening results combined with observational pathologic and radiographic studies.

DATA SOURCES

Published articles pertaining to lung cancer screening, lung adenocarcinoma pathology, and radiology accessible through PubMed form the basis for this review.

CONCLUSIONS

The current adenocarcinogenesis model is probably valid for many but not all lung adenocarcinomas. Screening data combined with radiographic and pathologic studies suggest that not all lung adenocarcinomas are clinically aggressive, and it is uncertain whether all aggressive adenocarcinomas arise from identified precursors.

Microarray-based cancer prediction using soft computing approach

One of the difficulties in using gene expression profiles to predict cancer is how to effectively select a few informative genes to construct accurate prediction models from thousands or ten thousands of genes. We screen highly discriminative genes and gene pairs to create simple prediction models involved in single genes or gene pairs on the basis of soft computing approach and rough set theory. Accurate cancerous prediction is obtained when we apply the simple prediction models for four cancerous gene expression datasets: CNS tumor, colon tumor, lung cancer and DLBCL. Some genes closely correlated with the pathogenesis of specific or general cancers are identified. In contrast with other models, our models are simple, effective and robust. Meanwhile, our models are interpretable for they are based on decision rules. Our results demonstrate that very simple models may perform well on cancerous molecular prediction and important gene markers of cancer can be detected if the gene selection approach is chosen reasonably.

beta(2)microglobulin mRNA expression levels are prognostic for lymph node metastasis in colorectal cancer patients

Colorectal cancer (CRC) is the fourth most common non-cutaneous malignancy in the United States and the second most frequent cause of cancer-related death. One of the most important determinants of CRC survival is lymph node metastasis. To determine whether molecular markers might be prognostic for lymph node metastases, we measured by quantitative real-time RT–PCR the expression levels of 15 cancer-associated genes in formalin-fixed paraffin-embedded primary tissues derived from stage I–IV CRC patients with (n=20) and without (n=18) nodal metastases. Using the mean of the 15 genes as an internal reference control, we observed that low expression of β₂microglobulin (B2M) was a strong prognostic indicator of lymph node metastases (area under the curve (AUC)=0.85; 95% confidence interval (CI)=0.69–0.94). We also observed that the expression ratio of B2M/Spint2 had the highest prognostic accuracy (AUC=0.87; 95% CI=0.71–0.96) of all potential two-gene combinations. Expression values of Spint2 correlated with the mean of the entire gene set at an R² value of 0.97, providing evidence that Spint2 serves not as an independent prognostic gene, but rather as a reliable reference control gene. These studies are the first to demonstrate a prognostic role of B2M at the mRNA level and suggest that low B2M expression levels might be useful for identifying patients with lymph node metastasis and/or poor survival.