HIP1–ALK, a Novel Fusion Protein Identified in Lung Adenocarcinoma

Introduction:The most common mechanism underlying overexpression and activation of anaplastic lymphoma kinase (ALK) in non–small-cell lung carcinoma could be attributed to the formation of a fusion protein. To date, five fusion partners of ALK have been reported, namely, echinoderm microtubule associated protein like 4, tropomyosin-related kinase-fused gene, kinesin family member 5B, kinesin light chain 1, and protein tyrosine phosphatase, nonreceptor type 3.Methods:In this article, we report a novel fusion gene huntingtin interacting protein 1 (HIP1)–ALK, which is conjoined between the huntingtin-interacting protein 1 gene HIP1 and ALK. Reverse-transcriptase polymerase chain reaction and immunohistochemical analysis were used to detect this fusion gene’s transcript and protein expression, respectively. We had amplified the full-length cDNA sequence of this novel fusion gene by using 5′-rapid amplification of cDNA ends. The causative genomic translocation t(2;7)(p23;q11.23) for generating this novel fusion gene was verified by using genomic sequencing.Results:The examined adenocarcinoma showed predominant acinar pattern, and ALK immunostaining was localized to the cytoplasm, with intense staining in the submembrane region. In break-apart, fluorescence in situ hybridization analysis for ALK, split of the 5′ and 3′ probe signals, and isolated 3′ signals were observed. Reverse-transcriptase polymerase chain reaction revealed that the tumor harbored a novel fusion transcript in which exon 21 of HIP1 was fused to exon 20 of ALK in-frame.Conclusion:The novel fusion gene and its protein HIP1–ALK harboring epsin N-terminal homology, coiled-coil, juxtamembrane, and kinase domains, which could play a role in carcinogenesis, could become diagnostic and therapeutic target of the lung adenocarcinoma and deserve a further study in the future

Identification of Novel Reference Genes Using Multiplatform Expression Data and Their Validation for Quantitative Gene Expression Analysis

Normalization of mRNA levels using endogenous reference genes (ERGs) is critical for an accurate comparison of gene expression between different samples. Despite the popularity of traditional ERGs (tERGs) such as GAPDH and ACTB, their expression variability in different tissues or disease status has been reported. Here, we first selected candidate housekeeping genes (HKGs) using human gene expression data from different platforms including EST, SAGE, and microarray, and 13 novel ERGs (nERGs) (ARL8B, CTBP1, CUL1, DIMT1L, FBXW2, GPBP1, LUC7L2, OAZ1, PAPOLA, SPG21, TRIM27, UBQLN1, ZNF207) were further identified from these HKGs. The mean coefficient variation (CV) values of nERGs were significantly lower than those of tERGs and the expression level of most nERGs was relatively lower than high expressing tERGs in all dataset. The higher expression stability and lower expression levels of most nERGs were validated in 108 human samples including formalin-fixed paraffin-embedded (FFPE) tissues, frozen tissues and cell lines, through quantitative real-time RT-PCR (qRT-PCR). Furthermore, the optimal number of nERGs required for accurate normalization was as few as two, while four genes were required when using tERGs in FFPE tissues. Most nERGs identified in this study should be better reference genes than tERGs, based on their higher expression stability and fewer numbers needed for normalization when multiple ERGs are required

Correction: triple-negative, basal-like, and quintuple-negative breast cancers: better prediction model for survival

After the publication of this work [1], we found that there were some mistakes in calculating the percentage of composition in Table 1(1). Clinicopathologic characteristics of breast cancer subtypes. We are therefore providing the revised Table 1, with the updated data for rows Mucinous carcinoma, Metaplastic carcinoma and Others. In the sub-content of Table 1, Histological type, the total number of Others was corrected from 18 to 16, and the composition of Others type was slightly changed according to breast cancer subtypes. For IHC-Her2 subtype, the number of Others was changed from 4 to 3, and 6 cases which were previously unidentified were assigned to corresponding subtypes. One case to IHC-BLBC, 2 cases to IHC-QNBC/5NP and 3 cases to IHC-TNCB. There was no effect on statistical analysis with the correction.

Correction: triple-negative, basal-like, and quintuple-negative breast cancers: better prediction model for survival

After the publication of this work [1], we found that there were some mistakes in calculating the percentage of composition in Table 1(1). Clinicopathologic characteristics of breast cancer subtypes. We are therefore providing the revised Table 1, with the updated data for rows Mucinous carcinoma, Metaplastic carcinoma and Others. In the sub-content of Table 1, Histological type, the total number of Others was corrected from 18 to 16, and the composition of Others type was slightly changed according to breast cancer subtypes. For IHC-Her2 subtype, the number of Others was changed from 4 to 3, and 6 cases which were previously unidentified were assigned to corresponding subtypes. One case to IHC-BLBC, 2 cases to IHC-QNBC/5NP and 3 cases to IHC-TNCB. There was no effect on statistical analysis with the correction.

Triple-negative, basal-like, and quintuple-negative breast cancers: better prediction model for survival

Background: Triple-negative breast cancers (TNBCs) and basal-like breast cancers (BLBCs) are known as poor outcome subtypes with a lack of targeted therapy. Previous studies have shown conflicting results regarding the difference of prognostic significance between TNBCs and BLBCs. In this study, we aimed to characterize the prognostic features of TNBCs, in view of BLBCs and quintuple-negative breast cancers (QNBC/5NPs). Methods: Using tissue microarray-based immunohistochemical analysis, we categorized 951 primary breast cancers into four or five subtypes according to the expression of ER, PR, HER2, and basal markers (CK5/6, EGFR). Results: The results of this study showed that both TNBCs and BLBCs were associated with high histological and/ or nuclear grades. When the TNBCs are divided into two subtypes by the presence of basal markers, the clinicopathologic characteristics of TNBCs were mainly maintained in the BLBCs. The 5-subgrouping was the better prediction model for both disease free and overall survival in breast cancers than the 4-subgrouping. After multivariate analysis of TNBCs, the BLBCs did not have a worse prognosis than the QNBC/5NPs. Interestingly, the patients with BLBCs showed significant adjuvant chemotherapy benefit. In addition, QNBC/5NPs comprised about 6~8% of breast cancers in publicly available breast cancer datasets Conclusion: The QNBC/5NP subtype is a worse prognostic subgroup of TNBCs, especially in higher stage and this result may be related to adjuvant chemotherapy benefit of BLBCs, calling for caution in the identification of subgroups of patients for therapeutic classification

A prognostic model for lymph node-negative breast cancer patients based on the integration of proliferation and immunity

A model for a more precise prognosis of the risk of relapse is needed to avoid overtreatment of lymph node-negative breast cancer patients. A large derivation data set (n = 684) was generated by pooling three independent breast cancer expression microarray data sets. Two major prognostic factors, proliferation and immune response, were identified among genes showing significant differential expression levels between the good outcome and poor outcome groups. For each factor, four proliferation-related genes (p-genes) and four immunity-related genes (i-genes) were selected as prognostic genes, and a prognostic model for lymph node-negative breast cancer patients was developed using a parametric survival analysis based on the lognormal distribution. The p-genes showed a predominantly negative correlation (coefficient: -0.603) with survival time, while the i-genes showed a positive correlation (coefficient: 0.243), reflecting the beneficial effect of the immune response against deleterious proliferative activity. The prognostic model shows that approximately 54% of lymph node-negative breast cancer patients were predicted to be distant metastasis-free for more than 5 years with at least 85% survival probability. The prognostic model showed a robust and high prognostic performance (HR 2.85-3.45) through three external validation data sets. Based on the integration of proliferation and immunity, the new prognostic model is expected to improve clinical decision making by providing easily interpretable survival probabilities at any time point and functional causality of the predicted prognosis with respect to proliferation and immune response

Precision medicine approaches to lung adenocarcinoma with concomitant MET and HER2 amplification

Abstract Background Patient-derived xenograft (PDX) models are important tools in precision medicine and for the development of targeted therapies to treat cancer patients. This study aimed to evaluate our precision medicine strategy that integrates genomic profiling and preclinical drug-screening platforms, in order to personalize cancer treatments using PDX models. Methods We performed array-comparative genomic hybridization, microarray, and targeted next-generation sequencing analyses, in order to determine the oncogenic driver mutations. PDX cells were obtained from PDXs and subsequently screened in vitro with 17 targeted agents. Results PDX tumors recapitulated the histopathologic and genetic features of the patient tumors. Among the samples from lung cancer patients that were molecularly-profiled, copy number analysis identified unique focal MET amplification in one sample, 033 T, without RTK/RAS/RAF oncogene mutations. Although HER2 amplification in 033 T was not detected in the cancer panel, the selection of HER2-amplified clones was found in PDXs and PDX cells. Additionally, MET and HER2 overexpression were found in patient tumors, PDXs, and PDX cells. Crizotinib or EGFR tyrosine kinase inhibitor treatments significantly inhibited cell growth and impaired tumor sphere formation in 033 T PDX cells. Conclusions We established PDX cell models using surgical samples from lung cancer patients, and investigated their preclinical and clinical implications for personalized targeted therapy. Additionally, we suggest that MET and EGFR inhibitor-based therapy can be used to treat MET and HER2-overexpressing lung cancers, without receptor tyrosine kinase /RAS/RAF pathway alterations

Chronic TGF beta stimulation promotes the metastatic potential of lung cancer cells by Snail protein stabilization through integrin beta 3-Akt-GSK3 beta signaling

Chronic exposure to TGFβ, a frequent occurrence for tumor cells in the tumor microenvironment, confers more aggressive phenotypes on cancer cells by promoting their invasion and migration while at the same time increasing their resistance to the growth-inhibitory effect of TGFβ. In this study, a transdifferentiated (TD) A549 cell model, established by chronically exposing A549 cells to TGFβ, showed highly invasive phenotypes in conjunction with attenuation of Smad-dependent signaling. We show that Snail protein, the mRNA expression of which strongly correlates with a poor prognosis in lung cancer patients, was highly stable in TD cells after TGFβ stimulation. The increased protein stability of Snail in TD cells correlated with elevated inhibitory phosphorylation of GSK3β, resulting from the high Akt activity. Notably, integrin β3, whose expression was markedly increased upon sustained exposure to TGFβ, was responsible for the high Akt activity as well as the increased Snail protein stability in TD cells. Consistently, clinical database analysis on lung cancer patients revealed a negative correlation between overall survival and integrin β3 mRNA levels. Therefore, we suggest that the integrin β3-Akt-GSK3β signaling axis plays an important role in non-canonical TGFβ signaling, determining the invasive properties of tumor cells chronically exposed to TGFβ.