The prognostic significance of focal adhesion kinase expression in stage I non-small-cell lung cancer

The prognostic significance of tumor budding and the expression of focal adhesion kinase and survivin in lung adenocarcinoma

OBJECTIVES

Adenocarcinoma is the tumor group with the highest incidence among lung cancers with poor prognosis. Tumor budding (TB) is the migration of single tumor cells or small clusters of cells from the neoplastic epithelium to the invasive front of the tumor. Focal adhesion kinase (FAK) and survivin are considered as poor prognostic factors in several tumors. Hence, we investigated TB, FAK, and survivin expression in lung adenocarcinoma.

METHODS

The study included 103 cases of lung adenocarcinoma in the resection materials. In tumoral tissues; TB was counted and scored in one high-power field (HPF), as low if <5 in 1 HPF and high if ≥5 in 1 HPF. FAK and survivin were studied immunohistochemically.

RESULTS

The mean number of TB in 1 HPF is 3.96 ± 2.8. Low-grade TB was observed in 45 (43.7 %) and high-grade TB was observed in 58 (56.3 %) patients. There was a positive correlation between TB and pT stage (p = 0.017), clinical stage (p = 0.002), lymphovascular invasion (p = 0.001), and perineural invasion (p = 0.045). The 4-year survival rate in patients was 90 % in those with low-grade TB and 60 % in those with high-grade TB (p = 0.001). FAK and survivin expressions were significantly increased in tumors with high-grade TB (p < 0.05).

CONCLUSION

A significant correlation was found between the grade of TB and pT stage, clinical stage, lymphovascular and perineural invasion in lung adenocarcinoma. TB can be considered as a histological parameter showing poor prognosis. It is thought that high expression of FAK and survivin also affect the prognosis in these patients by increasing TB.

Functional and clinical characteristics of focal adhesion kinases in cancer progression

Focal adhesion kinase (FAK) is a non-receptor tyrosine kinase and an adaptor protein that primarily regulates adhesion signaling and cell migration. FAK promotes cell survival in response to stress. Increasing evidence has shown that at the pathological level, FAK is highly expressed in multiple tumors in several systems (including lung, liver, gastric, and colorectal cancers) and correlates with tumor aggressiveness and patient prognosis. At the molecular level, FAK promotes tumor progression mainly by altering survival signals, invasive capacity, epithelial-mesenchymal transition, the tumor microenvironment, the Warburg effect, and stemness of tumor cells. Many effective drugs have been developed based on the comprehensive role of FAK in tumor cells. In addition, its potential as a tumor marker cannot be ignored. Here, we discuss the pathological and pre-clinical evidence of the role of FAK in cancer development; we hope that these findings will assist in FAK-based clinical studies.

Identifying cancer specific signaling pathways based on the dysregulation between genes

A large collection of studies has shown that the occurrence of cancer is related to the functional dysfunction of the pathways. Identification of cancer-related pathways could help researchers understand the mechanisms of complex diseases well. Whereas, most current signaling pathway analysis methods take no account of the gene interaction variations within pathways. Furthermore, considering that some pathways have connection with two or more cancer types, while some are likely to be cancer-type specific pathways. Identifying cancer-type specific pathways contributes to interpreting the different mechanisms of different cancer types. In this study, we first proposed a pathway analysis method named Pathway Analysis of Intergenic Regulation (PAIGR) to identify pathways with dysregulation between genes and compared the performance of this method with four existing methods on four colorectal cancer (CRC) datasets. The results showed that PAIGR could find cancer-related pathways more accurately. Moreover, in order to explore the relationship between the identified pathways and the cancer type, we constructed a pathway interaction network, in which nodes and edges represented pathways and interactions between pathways respectively. Highly connected pathways were considered to play a central role in an extensive range of biological processes, while sparsely connected pathways are considered to have certain specificity. Our results showed that pathways identified by PAIGR had a low nodal degree (i.e., a few numbers of interactions), which suggested that most of these pathways were cancer-type specific.

β-elemene inhibits non-small cell lung cancer cell migration and invasion by inactivating the FAK-Src pathway

Despite sustained effort, the prognosis of lung cancer remains poor and the therapeutic responses are limited. Cell movement ability is a prerequisite for lung cancer metastasis, which involves focal adhesion kinase (FAK)-mediated cell migration and invasion via complex formation with Src. Hence, FAK-Src signaling might be an effective target for anti-cancer treatment. β-elemene, the major component of elemene extracted from Curcuma Rhizoma, exhibits broad-spectrum anti-tumor properties. However, the role of β-elemene in lung cancer cell motility and its possible mechanism remain unknown. Herein, the role of β-elemene in the migration and invasion of two non-small cell lung cancer (NSCLC) cell lines was investigated by performing wound-healing and Transwell assays. The mRNA expression levels of genes associated with motility, including RhoA, Rac1, Cac42, matrix metalloprotease (MMP)2 and MMP9, were examined by reverse transcription-quantitative polymerase chain reaction. To determine whether β-elemene acts through FAK-Src signaling, western blotting was performed and the levels of phosphorylated FAK and Src were detected. The results indicated that β-elemene inhibited the migration and invasion of A549 and NCI-H1299 (H1299) cells, while the motility-associated genes were de-regulated following exposure to β-elemene. Furthermore, β-elemene decreased the activity of FAK and Src. Overall, these results suggest that β-elemene potentially inhibits NSCLC through FAK-Src signaling.

Prognostic and clinical significance of focal adhesion kinase expression in breast cancer: A systematic review and meta-analysis

Background

The prognostic significance of focal adhesion kinase (FAK) in breast cancer remains controversial. Here, we conducted a meta-analysis to explore the prognostic value of FAK expression in breast cancer.

Materials and methods

Possible prognostic significance of protein or mRNA expression of FAK in breast cancer was investigated with searches of electronic databases for relevant publications. Pooled hazard ratios (HRs) and odds ratios (ORs) with 95% confidence intervals (CIs) were extracted from eligible studies.

Results

A total of eight eligible studies which included 2604 participants were analyzed in this meta-analysis. Increased expression of FAK protein was found to significantly correlate with shorter overall survival (OS) (HR = 1.43, 95% CI: 1.12–1.83; P = 0.004), and not with disease-free survival (HR = 1.31, 95% CI: 0.92–1.85; P = 0.14). Elevated FAK protein expression was also associated with negative estrogen receptor (ER) expression (OR, 1.34; 95% CI, 1.06–1.68; P = 0.01), negative progesterone receptor (PR) expression (OR, 1.54; 95% CI, 1.22–1.93; P < 0.001), positive human epidermal growth factor receptor 2 (HER2) expression (OR, 1.64; 95% CI, 1.28–2.09; P < 0.001), triple-negative breast cancer (TNBC) (OR, 1.57; 95% CI, 1.14–2.17; P = 0.006), high nuclear grade (OR, 1.70; 95% CI, 1.05–2.78; P = 0.03), high Ki-67 expression level (OR, 2.87; 95% CI, 1.94–4.24; P < 0.001), and positive p53 status (OR, 2.28; 95% CI, 1.58–3.29; P < 0.001).

Conclusion

Our meta-analysis identifies an association between increased FAK protein expression and worse OS among breast cancer patients. Moreover, enhanced FAK expression is associated with negative ER expression, negative PR expression, positive HER2 expression, TNBC, high nuclear grade, high Ki-67 expression level, and positive p53 status in breast carcinoma.

Protein tyrosine kinase 2: a novel therapeutic target to overcome acquired EGFR-TKI resistance in non-small cell lung cancer

Background

Protein tyrosine kinase 2 (PTK2) expression has been reported in various types of human epithelial cancers including lung cancer; however, the role of PTK2 in epidermal growth factor receptor (EGFR)-mutant non-small cell lung cancer (NSCLC) has not been elucidated. We previously reported that pemetrexed-resistant NSCLC cell line PC-9/PEM also acquired EGFR-TKI resistance with constitutive Akt activation, but we could not find a therapeutic target.

Methods

Cell viability in EGFR-mutant NSCLC cell lines was measured by the WST-8 assay. Phosphorylation antibody array assay for receptor tyrosine kinases was performed in PC-9 and PC-9/PEM cell lines. We evaluated the efficacy of EGFR and PTK2 co-inhibition in EGFR-TKI-resistant NSCLC in vitro. Oral defactinib and osimertinib were administered in mice bearing subcutaneous xenografts to evaluate the efficacy of the treatment combination in vivo. Both the PTK2 phosphorylation and the treatment combination efficacy were evaluated in erlotinib-resistant EGFR-mutant NSCLC cell lines.

Results

PTK2 was hyperphosphorylated in PC-9/PEM. Defactinib (PTK2 inhibitor) and PD173074 (FGFR inhibitor) inhibited PTK2 phosphorylation. Combination of PTK2 inhibitor and EGFR-TKI inhibited Akt and induced apoptosis in PC-9/PEM. The combination treatment showed improved in vivo therapeutic efficacy compared to the single-agent treatments. Furthermore, erlotinib-resistant NSCLC cell lines showed PTK2 hyperphosphorylation. PTK2 inhibition in the PTK2 hyperphosphorylated erlotinib-resistant cell lines also recovered EGFR-TKI sensitivity.

Conclusion

PTK2 hyperphosphorylation occurs in various EGFR-TKI-resistant NSCLCs. Combination of PTK2 inhibitor and EGFR-TKI (defactinib and osimertinib) recovered EGFR-TKI sensitivity in the EGFR-TKI-resistant NSCLC. Our study result suggests that this combination therapy may be a viable option to overcome EGFR-TKI resistance in NSCLC.

Role of Focal Adhesion Kinase in Small-Cell Lung Cancer and Its Potential as a Therapeutic Target

Small-cell lung cancer (SCLC) represents 15% of all lung cancers and it is clinically the most aggressive type, being characterized by a tendency for early metastasis, with two-thirds of the patients diagnosed with an extensive stage (ES) disease and a five-year overall survival (OS) as low as 5%. There are still no effective targeted therapies in SCLC despite improved understanding of the molecular steps leading to SCLC development and progression these last years. After four decades, the only modest improvement in OS of patients suffering from ES-SCLC has recently been shown in a trial combining atezolizumab, an anti-PD-L1 immune checkpoint inhibitor, with carboplatin and etoposide, chemotherapy agents. This highlights the need to pursue research efforts in this field. Focal adhesion kinase (FAK) is a non-receptor protein tyrosine kinase that is overexpressed and activated in several cancers, including SCLC, and contributing to cancer progression and metastasis through its important role in cell proliferation, survival, adhesion, spreading, migration, and invasion. FAK also plays a role in tumor immune evasion, epithelial-mesenchymal transition, DNA damage repair, radioresistance, and regulation of cancer stem cells. FAK is of particular interest in SCLC, being known for its aggressiveness. The inhibition of FAK in SCLC cell lines demonstrated significative decrease in cell proliferation, invasion, and migration, and induced cell cycle arrest and apoptosis. In this review, we will focus on the role of FAK in cancer cells and their microenvironment, and its potential as a therapeutic target in SCLC.

Increased Expression and Activation of FAK in Small-Cell Lung Cancer Compared to Non-Small-Cell Lung Cancer

INTRODUCTION

Focal adhesion kinase (FAK) plays a crucial role in cancer development and progression. FAK is overexpressed and/or activated and associated with poor prognosis in various malignancies. However, in lung cancer, activated FAK expression and its prognostic value are unknown.

METHODS

FAK and activated FAK (phospho-FAK Y397) expressions were analyzed by multiplex immunofluorescence staining in formalin-fixed paraffin-embedded tissues from 95 non-small-cell lung cancer (NSCLC) and 105 small-cell lung cancer (SCLC) patients, and 37 healthy donors. The FAK staining score was defined as the percentage (%) of FAK-stained tumor area multiplied by (×) FAK mean intensity and phospho-FAK staining score as the (% of phospho-FAK-stained area of low intensity × 1) + (% of phospho-FAK-stained area of medium intensity × 2) + (% of the phospho-FAK-stained area of high intensity × 3). FAK and phospho-FAK staining scores were compared between normal, NSCLC, and SCLC tissues. They were also tested for correlations with patient characteristics and clinical outcomes.

RESULTS

The median follow-up time after the first treatment was 42.5 months and 6.4 months for NSCLC and SCLC patients, respectively. FAK and phospho-FAK staining scores were significantly higher in lung cancer than in normal lung and significantly higher in SCLC compared to NSCLC tissues (p < 0.01). Moreover, the ratio between phospho-FAK and FAK staining scores was significantly higher in SCLC than in NSCLC tissues (p < 0.01). However, FAK and activated FAK expression in lung cancer did not correlate with recurrence-free and overall survival in NSCLC and SCLC patients.

CONCLUSIONS

Total FAK and activated FAK expressions are significantly higher in lung cancer than in normal lung, and significantly higher in SCLC compared to NSCLC, but are not prognostic biomarkers in this study.

Diffusion-Weighted Magnetic Resonance Imaging for Early Detection of Chemotherapy Resistance in Non-Small Cell Lung Cancer

Background

The aim of this study was to examine the role of magnetic resonance imaging-diffusion weighted imaging (MRI-DWI) in the early detection of chemotherapy resistance in non-small cell lung cancer (NSCLC) patients.

Material/Methods

MRI-DWI and computed tomography (CT) were carried out in 75 patients with newly diagnostic NSCLC before and after first, second, fourth, and sixth cycles of chemotherapy. Resistance to chemotherapy was assessed based on the change in the largest tumor diameter after chemotherapy. Diffusion of water molecule in each lesion was quantitatively measured by apparent diffusion coefficient (ADC). The diagnostic results of DWI after first and second cycle of chemotherapy were analyzed by the area under receiver operating characteristics curve (ROC).

Results

Among the patients, 43 patients were chemo-resistance while 32 patients were chemo-sensitive. The ADC changing rate between second and first cycle of chemotherapy was significantly higher in chemo-sensitive patients compared with chemo-resistance patients (t=3.236, P=0.002). The ROC showed cutoff values of the ADC changing rate after first and second cycles of chemotherapy for resistance/sensitive discrimination were 23.6% and 5.56%, respectively. DWI after first and second cycles of therapy showed sensitivities of 55.8% and 55.8%, specificities of 65.6% and 87.5%, and area under ROC of 0.568 and 0.733, respectively.

Conclusions

ADC changing rate between first and second cycles of chemotherapy could sensitively distinguish chemo-sensitive and chemo-resistant tumors at earlier stages, which may direct treatment adjustment and improve the prognosis of patients.

CRISPR/Cas9: a powerful tool for identification of new targets for cancer treatment

Clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR associated nuclease 9 (Cas9), as a powerful genome-editing tool, has revolutionized genetic engineering. It is widely used to investigate the molecular basis of different cancer types. In this review, we present an overview of recent studies in which CRISPR/Cas9 has been used for the identification of potential molecular targets. Based on the collected data, we suggest here that CRISPR/Cas9 is an effective system to distinguish between mutant and wild-type alleles in cancer. We show that several new potential therapeutic targets, such as CD38, CXCR2, MASTL, and RBX2, as well as several noncoding (nc)RNAs have been identified using CRISPR/Cas9 technology. We also discuss the obstacles and challenges that we face for using CRISPR/Cas9 as a therapeutic.

Mifepristone inhibits proliferation, migration and invasion of HUUA cells and promotes its apoptosis by regulation of FAK and PI3K/AKT signaling pathway

Purpose

The aim was to investigate mifepristone effects on endometrial carcinoma and the related mechanism.

Methods

HHUA cells were treated with DMEM containing different concentrations of mifepristone. HHUA cells treated with 100 μmol/L mifepristone were named the Mifepristone group. HHUA cells co-transfected with pcDNA3.1-PI3K and pcDNA3.1-AKT overexpression vectors were treated with 100 μmol/L mifepristone and named the Mifepristone + PI3K/AKT group. mRNA expression was detected by quantitative reverse transcription PCR. Protein expression was performed by Western blot. Cell proliferation was conducted by MTT assay. Wound-healing assay was conducted. Transwell was used to detect cells migration and invasion. Apoptosis detection was performed by flow cytometry.

Results

Mifepristone inhibited HHUA cells proliferation in a dose-dependent manner. Compared with HHUA cells treated with 0 μmol/L mifepristone, HHUA cells treated by 50–100 μmol/L mifepristone had a lower wound-healing rate, a greater number of migrating and invasive cells (P<0.01), as well as a higher percentage of apoptotic cells and Caspase-3 expression (P<0.01). When HHUA cells were treated with 50–100 μmol/L of mifepristone, FAK, p-FAK, p-PI3K and p-AKT relative expression was all significantly lower than HHUA cells treated with 0 μmol/L of mifepristone (P<0.01). Compared with the Mifepristone group, HHUA cells of the Mifepristone + PI3K/AKT group had a lower cell growth inhibition rate and percentage of apoptotic cells (P<0.01).

Conclusion

Mifepristone inhibited HUUA cells proliferation, migration and invasion and promoted its apoptosis by regulation of FAK and PI3K/AKT signaling pathway.

Identification of differential protein-coding gene expressions in early phase lung adenocarcinoma

BACKGROUND

The diagnosis of early phase lung adenocarcinoma (LADC) is associated with therapeutic strategy, effect, and survival time. However, the sensitive biomarkers of early phase LADC are still unclear. This study aimed to identify protein-coding genes that can be used as biomarkers of early stage LADC.

METHODS

Gene microarray analysis was performed to identify key hub genes that show different expression in lung adenocarcinoma compared to normal tissues. The microarray data of lung adenocarcinoma in stages IA, IB, IIA, IIB, and normal tissues (GSE10072) were downloaded from a free online database, Gene Expression Omnibus (GEO).

RESULTS

A total of 572 differentially expressed genes (DEGs) were identified between early phase lung adenocarcinoma and normal tissues using R software. Database for Annotation, Visualization and Integrated Discovery online tools were used to obtain Gene Ontology analysis and the Kyoto Encyclopedia of Genes and Genomes was used to analyze DEGs. Cytoscape software was used to express the protein-protein interaction network. We found that some cancer-related Gene Ontology terms and pathways (e.g. cell adhesion, cell surface receptor signaling pathway, PI3K-Akt signaling pathway) were significantly enriched in DEGs.

CONCLUSION

Protein-coding genes JUN, FYN, CAV1, and SFN may play vital roles in the progress of early-stage lung adenocarcinoma. Consequently, through bioinformatics analysis, the key genes could be established to provide more potential references for the therapeutic targets of lung adenocarcinoma.

miR-1298 Inhibits Mutant KRAS-Driven Tumor Growth by Repressing FAK and LAMB3

Global miRNA functional screens can offer a strategy to identify synthetic lethal interactions in cancer cells that might be exploited therapeutically. In this study, we applied this strategy to identify novel gene interactions in KRAS-mutant cancer cells. In this manner, we discovered miR-1298, a novel miRNA that inhibited the growth of KRAS-driven cells both in vitro and in vivo Using miR-TRAP affinity purification technology, we identified the tyrosine kinase FAK and the laminin subunit LAMB3 as functional targets of miR-1298. Silencing of FAK or LAMB3 recapitulated the synthetic lethal effects of miR-1298 expression in KRAS-driven cancer cells, whereas coexpression of both proteins was critical to rescue miR-1298-induced cell death. Expression of LAMB3 but not FAK was upregulated by mutant KRAS. In clinical specimens, elevated LAMB3 expression correlated with poorer survival in lung cancer patients with an oncogenic KRAS gene signature, suggesting a novel candidate biomarker in this disease setting. Our results define a novel regulatory pathway in KRAS-driven cancers, which offers a potential therapeutic target for their eradication. Cancer Res; 76(19); 5777-87. ©2016 AACR.

Prognostic Value of Focal Adhesion Kinase (FAK) in Human Solid Carcinomas: A Meta-Analysis

Background

Recently, the number of reports on focal adhesion kinase (FAK) as a vital therapeutic target in solid carcinomas has increased; however, the prognostic role of FAK status remains poorly understood. This study aims to evaluate the prognostic effect of FAK by means of a meta-analysis.

Methods

We performed a systematic literature search in order to examine the correlation between expression of FAK and overall survival(OS). The hazard ratio (HR) of OS was used to measure survival. A random-effects model was used to pool study statistics. Sensitivity and publication bias analyses were also conducted.

Results

Thirty eligible studies involving 4702 patients were included. The median expression rate of FAK was 54%. Meta-analysis of the HRs demonstrated that high FAK expression was associated with worse OS (average HR = 2.073, 95%confidence interval[CI]:1.712–2.510, p = 0.000). Regarding cancer type, FAK was associated with worse OS in gastric cancer (HR = 2.646,95% CI:1.743–4.017, p = 0.000), hepatocellular carcinoma (HR = 1.788,95% CI:1.228–2.602, p = 0.002), ovarian cancer (HR = 1.815, 95% CI: 1.193–2.762, p = 0.005), endometrial cancer (HR = 4.149, 95% CI:2.832–6.079, p = 0.000), gliomas (HR = 2.650, 95% CI: 1.205–5.829, p = 0.015), and squamous cell carcinoma (HR = 1,696, 95% CI: 1.030–2.793, p = 0.038). No association was found between HR and disease staging according to our meta-regression analysis.

Conclusions

Our study shows that high expression of FAK is associated with a worse OS in patients with carcinomas, but the association between FAK and prognosis varies according to cancer type. The value of FAK status in clinical prognosis in cancer needs further research.

Signalling pathway impact analysis based on the strength of interaction between genes

Signalling pathway analysis is a popular approach that is used to identify significant cancer‐related pathways based on differentially expressed genes (DEGs) from biological experiments. The main advantage of signalling pathway analysis lies in the fact that it assesses both the number of DEGs and the propagation of signal perturbation in signalling pathways. However, this method simplifies the interactions between genes by categorising them only as activation (+1) and suppression (−1), which does not encompass the range of interactions in real pathways, where interaction strength between genes may vary. In this study, the authors used newly developed signalling pathway impact analysis (SPIA) methods, SPIA based on Pearson correlation coefficient (PSPIA), and mutual information (MSPIA), to measure the interaction strength between pairs of genes. In analyses of a colorectal cancer dataset, a lung cancer dataset, and a pancreatic cancer dataset, PSPIA and MSPIA identified more candidate cancer‐related pathways than were identified by SPIA. Generally, MSPIA performed better than PSPIA.

Subpathway Analysis based on Signaling-Pathway Impact Analysis of Signaling Pathway

Pathway analysis is a common approach to gain insight from biological experiments. Signaling-pathway impact analysis (SPIA) is one such method and combines both the classical enrichment analysis and the actual perturbation on a given pathway. Because this method focuses on a single pathway, its resolution generally is not very high because the differentially expressed genes may be enriched in a local region of the pathway. In the present work, to identify cancer-related pathways, we incorporated a recent subpathway analysis method into the SPIA method to form the “sub-SPIA method.” The original subpathway analysis uses the k-clique structure to define a subpathway. However, it is not sufficiently flexible to capture subpathways with complex structure and usually results in many overlapping subpathways. We therefore propose using the minimal-spanning-tree structure to find a subpathway. We apply this approach to colorectal cancer and lung cancer datasets, and our results show that sub-SPIA can identify many significant pathways associated with each specific cancer that other methods miss. Based on the entire pathway network in the Kyoto Encyclopedia of Genes and Genomes, we find that the pathways identified by sub-SPIA not only have the largest average degree, but also are more closely connected than those identified by other methods. This result suggests that the abnormality signal propagating through them might be responsible for the specific cancer or disease.

Classification of lung cancer using ensemble-based feature selection and machine learning methods

Lung cancer is one of the leading causes of death worldwide. There are three major types of lung cancers, non-small cell lung cancer (NSCLC), small cell lung cancer (SCLC) and carcinoid. NSCLC is further classified into lung adenocarcinoma (LADC), squamous cell lung cancer (SQCLC) as well as large cell lung cancer. Many previous studies demonstrated that DNA methylation has emerged as potential lung cancer-specific biomarkers. However, whether there exists a set of DNA methylation markers simultaneously distinguishing such three types of lung cancers remains elusive. In the present study, ROC (Receiving Operating Curve), RFs (Random Forests) and mRMR (Maximum Relevancy and Minimum Redundancy) were proposed to capture the unbiased, informative as well as compact molecular signatures followed by machine learning methods to classify LADC, SQCLC and SCLC. As a result, a panel of 16 DNA methylation markers exhibits an ideal classification power with an accuracy of 86.54%, 84.6% and a recall 84.37%, 85.5% in the leave-one-out cross-validation (LOOCV) and independent data set test experiments, respectively. Besides, comparison results indicate that ensemble-based feature selection methods outperform individual ones when combined with the incremental feature selection (IFS) strategy in terms of the informative and compact property of features. Taken together, results obtained suggest the effectiveness of the ensemble-based feature selection approach and the possible existence of a common panel of DNA methylation markers among such three types of lung cancer tissue, which would facilitate clinical diagnosis and treatment.