Anaplastic Lymphoma Kinase Rearrangement Status Research Articles

Cancer organoid-based diagnosis reactivity prediction (CODRP) index-based anticancer drug sensitivity test in ALK-rearrangement positive non-small cell lung cancer (NSCLC)

BackgroundRecently, cancer organoid-based drug sensitivity tests have been studied to predict patient responses to anticancer drugs. The area under curve (AUC) or IC50 value of the dose-response curve (DRC) is used to differentiate between sensitive and resistant patient‘s groups. This study proposes a multi-parameter analysis method (cancer organoid-based diagnosis reactivity prediction, CODRP) that considers the cancer stage and cancer cell growth rate, which represent the severity of cancer patients, in the sensitivity test.MethodsOn the CODRP platform, patient-derived organoids (PDOs) that recapitulate patients with lung cancer were implemented by applying a mechanical dissociation method capable of high yields and proliferation rates. A disposable nozzle-type cell spotter with efficient high-throughput screening (HTS) has also been developed to dispense a very small number of cells due to limited patient cells. A drug sensitivity test was performed using PDO from the patient tissue and the primary cancer characteristics of PDOs were confirmed by pathological comparision with tissue slides.ResultsThe conventional index of drug sensitivity is the AUC of the DRC. In this study, the CODRP index for drug sensitivity test was proposed through multi-parameter analyses considering cancer cell proliferation rate, the cancer diagnosis stage, and AUC values. We tested PDOs from eight patients with lung cancer to verify the CODRP index. According to the anaplastic lymphoma kinase (ALK) rearrangement status, the conventional AUC index for the three ALK-targeted drugs (crizotinib, alectinib, and brigatinib) did not classify into sensitive and resistant groups. The proposed CODRP index-based drug sensitivity test classified ALK-targeted drug responses according to ALK rearrangement status and was verified to be consistent with the clinical drug treatment response.ConclusionsTherefore, the PDO-based HTS and CODRP index drug sensitivity tests described in this paper may be useful for predicting and analyzing promising anticancer drug efficacy for patients with lung cancer and can be applied to a precision medicine platform.

Deep learning based automated epidermal growth factor receptor and anaplastic lymphoma kinase status prediction of brain metastasis in non-small cell lung cancer.

The aim of this study was to investigate the feasibility of developing a deep learning (DL) algorithm for classifying brain metastases from non-small cell lung cancer (NSCLC) into epidermal growth factor receptor (EGFR) mutation and anaplastic lymphoma kinase (ALK) rearrangement groups and to compare the accuracy with classification based on semantic features on imaging. Data set of 117 patients was analysed from 2014 to 2018 out of which 33 patients were EGFR positive, 43 patients were ALK positive and 41 patients were negative for either mutation. Convolutional neural network (CNN) architecture efficient net was used to study the accuracy of classification using T1 weighted (T1W) magnetic resonance imaging (MRI) sequence, T2 weighted (T2W) MRI sequence, T1W post contrast (T1post) MRI sequence, fluid attenuated inversion recovery (FLAIR) MRI sequences. The dataset was divided into 80% training and 20% testing. The associations between mutation status and semantic features, specifically sex, smoking history, EGFR mutation and ALK rearrangement status, extracranial metastasis, performance status and imaging variables of brain metastasis were analysed using descriptive analysis [chi-square test (χ2)], univariate and multivariate logistic regression analysis assuming 95% confidence interval (CI). In this study of 117 patients, the analysis by semantic method showed 79.2% of the patients belonged to ALK positive were non-smokers as compared to double negative groups (P = 0.03). There was a 10-fold increase in ALK positivity as compared to EGFR positivity in ring enhancing lesions patients (P = 0.015) and there was also a 6.4-fold increase in ALK positivity as compared to double negative groups in meningeal involvement patients (P = 0.004). Using CNN Efficient Net DL model, the study achieved 76% accuracy in classifying ALK rearrangement and EGFR mutations without manual segmentation of metastatic lesions. Analysis of the manually segmented dataset resulted in improved accuracy of 89% through this model. Both semantic features and DL model showed comparable accuracy in classifying EGFR mutation and ALK rearrangement. Both methods can be clinically used to predict mutation status while biopsy or genetic testing is undertaken.

Predicting anaplastic lymphoma kinase rearrangement status in patients with non-small cell lung cancer using a machine learning algorithm that combines clinical features and CT images.

To develop an appropriate machine learning model for predicting anaplastic lymphoma kinase (ALK) rearrangement status in non-small cell lung cancer (NSCLC) patients using computed tomography (CT) images and clinicalfeatures. This study included 193 patients with NSCLC (154 in the training cohort, 39 in the validation cohort), 68 of whom tested positive for ALK rearrangements and 125 of whom tested negative. From the nonenhanced CT scans, 157 radiomic characteristics were extracted, and 8 clinical features were collected. Five machine learning (ML) models were assessed to find the best classification model for predicting ALK rearrangement status. A radiomic signature was developed using the least absolute shrinkage and selection operator (LASSO) algorithm. The predictive performance of the models based on radiomic features, clinical features, and their combination was assessed by receiver operating characteristic (ROC) curves. The support vector machine (SVM) model had the highest AUC of 0.914 for classification. The clinical features model had an AUC=0.805 (95% CI 0.731-0.877) and an AUC=0.735 (95% CI 0.566-0.863) in the training and validation cohorts, respectively. The CT image-based ML model had an AUC=0.953 (95% CI 0.913-1.0) in the training cohort and an AUC=0.890 (95% CI 0.778-0.971) in the validation cohort. Forpredicting ALK rearrangement status,theML model basedonCT images and clinical featuresperformedbetterthanthemodel basedononlyclinicalinformation or CT images, with an AUC of 0.965 (95% CI 0.826-0.882) in the primary cohort and an AUC of 0.914 (95% CI 0.804-0.893) in the validation cohort. Our findings revealed that ALK rearrangement status could be accurately predicted using an ML-based classification model based on CT images and clinical data.

IMPORTANCE of PRETREATMENT 18F-FDG PET/CT TEXTURE ANALYSIS in PREDICTING EGFR and ALK MUTATION in PATIENTS with NON-SMALL CELL LUNG CANCER.

Identification of anaplastic lymphoma kinase (ALK) and epidermal growth factor receptor (EGFR) mutation types is of great importance before treatment with tyrosine kinase inhibitors (TKIs). Radiomics is a new strategy for noninvasively predicting the genetic status of cancer. We aimed to evaluate the predictive power of 18F-FDG PET/CT-based radiomic features for mutational status before treatment in non-small cell lung cancer (NSCLC) and to develop a predictive model based on radiomic features. Images of patients who underwent 18F-FDG PET/CT for initial staging with the diagnosis of NSCLC between January 2015 and July 2020 were evaluated using LIFEx software. The region of interest (ROI) of the primary tumor was established and volumetric and textural features were obtained. Clinical data and radiomic data were evaluated with machine learning (ML) algorithms to create a model. For EGFR mutation prediction, the most successful machine learning algorithm obtained with GLZLM_GLNU and clinical data was Naive Bayes (AUC: 0.751, MCC: 0.347, acc: 71.4%). For ALK rearrangement prediction, the most successful machine learning algorithm obtained with GLCM_correlation, GLZLM_LZHGE and clinical data was evaluated as Naive Bayes (AUC: 0.682, MCC: 0.221, acc: 77.4%). In our study, we created prediction models based on radiomic analysis of 18F-FDG PET/CT images. Tissue analysis with ML algorithms are non-invasive methods for predicting ALK rearrangement and EGFR mutation status in NSCLC, which may be useful for targeted therapy selection in a clinical setting.

Predicting EGFR mutation, ALK rearrangement, and uncommon EGFR mutation in NSCLC patients by driverless artificial intelligence: a cohort study

BackgroundTimely identification of epidermal growth factor receptor (EGFR) mutation and anaplastic lymphoma kinase (ALK) rearrangement status in patients with non-small cell lung cancer (NSCLC) is essential for tyrosine kinase inhibitors (TKIs) administration. We aimed to use artificial intelligence (AI) models to predict EGFR mutations and ALK rearrangement status using common demographic features, pathology and serum tumor markers (STMs).MethodsIn this single-center study, demographic features, pathology, EGFR mutation status, ALK rearrangement, and levels of STMs were collected from Wuhan Union Hospital. One retrospective set (N = 1089) was used to train diagnostic performance using one deep learning model and five machine learning models, as well as the stacked ensemble model for predicting EGFR mutations, uncommon EGFR mutations, and ALK rearrangement status. A consecutive testing cohort (n = 1464) was used to validate the predictive models.ResultsThe final AI model using the stacked ensemble yielded optimal diagnostic performance with areas under the curve (AUC) of 0.897 and 0.883 for predicting EGFR mutation status and 0.995 and 0.921 for predicting ALK rearrangement in the training and testing cohorts, respectively. Furthermore, an overall accuracy of 0.93 and 0.83 in the training and testing cohorts, respectively, were achieved in distinguishing common and uncommon EGFR mutations, which were key evidence in guiding TKI selection.ConclusionsIn this study, driverless AI based on robust variables could help clinicians identify EGFR mutations and ALK rearrangement status and provide vital guidance in TKI selection for targeted therapy in NSCLC patients.

Spindle cell variant of epithelioid cell histiocytoma (spindle cell histiocytoma) with ALK gene fusions: Cases series and review of the literature.

Epithelioid fibrous histiocytoma (EFH) is an uncommon dermal neoplasm expressing anaplastic lymphoma kinase (ALK) protein. Rarely a histopathological variant of this entity exhibits exclusively spindle cells. We report three cases of EFH that do not completely fulfill phenotypic criteria featuring spindle cell morphology and expressing ALK protein. We also analyze the fusion partner genes rearranged with ALK in these cases. ALK expression and rearrangement status were evaluated by immunohistochemistry (IHC), fluorescence in situ hybridization (FISH), and next generation sequencing based gene fusion analysis. Three cases, all from females between 25 and 55 years old, have been biopsied from back, left arm, and thumb. All three cases showed tumor with exclusively spindle cell morphology without any epithelioid cells. The tumor cells exhibited strong ALK expression by IHC and FISH study confirmed ALK gene rearrangement in all three cases. DCTN1-ALK fusion was identified in two cases. EFH is not always purely epithelioid and its spindled cell variant, spindle cell histiocytoma, should be included in the differential diagnosis of superficial dermal spindled cell neoplasms. ALK immunostain is a useful diagnostic marker for this entity and further studies may be useful to investigate whether DCTN1-ALK fusion mutations are specific to EFH with spindled cell features.

Prevalence and Clinical Impact of Concomitant Mutations in Anaplastic Lymphoma Kinase Rearrangement Advanced Non-small-Cell Lung Cancer (Guangdong Association of Thoracic Oncology Study 1055).

Background: In patients with anaplastic lymphoma kinase (ALK) rearrangement-positive advanced non–small-cell lung cancer (NSCLC), ALK inhibitors are now the standard treatment, but their clinical efficacy varies widely for each patient. In this multicenter retrospective study, we evaluated the clinical efficacy of crizotinib according to the ALK rearrangement variants and concomitant mutations present.Patients and Methods: A total 132 patients with ALK rearrangement advanced NSCLC from 4 centers in Guangdong province, China were evaluated. All patients received crizotinib treatment and their ALK rearrangement status was identified by next-generation sequencing (NGS).Results: The median progression-free survival (PFS) in patients with EML4-ALK rearrangement (n = 121), non-EML4-ALK rearrangement (n = 5), and EML4-ALK arrangement accompanied by non-EML4-ALK rearrangement (n = 6) was 12.8, 7.5, and 7.4 months, respectively, with no significant difference between them (p = 0.1554). Similarly, among patients with various EML4-ALK variants (variant 1, variant 3a/b, and other variants), the median PFS values were again comparable. According to baseline NGS data, the median PFS in patients who had ALK rearrangement only, ALK rearrangement and concomitant tumor-suppressor gene mutations, and ALK rearrangement and concomitant oncogene mutations was 14.2, 10.9, and 4.9 months, respectively; (p = 0.0002). A multivariable analysis indicated that concomitant oncogene mutations and tumor-suppressor gene mutations were both negative factors influencing the efficacy of crizotinib in ALK rearrangement NSCLC.Conclusion: Concomitant oncogene mutations and tumor-suppressor gene mutations had negative effects on the efficacy of crizotinib, while various ALK variants had a similar influence.

Clinical, Conventional CT and Radiomic Feature-Based Machine Learning Models for Predicting ALK Rearrangement Status in Lung Adenocarcinoma Patients.

Objectives: To predict the anaplastic lymphoma kinase (ALK) mutations in lung adenocarcinoma patients non-invasively with machine learning models that combine clinical, conventional CT and radiomic features.Methods: This retrospective study included 335 lung adenocarcinoma patients who were randomly divided into a primary cohort (268 patients; 90 ALK-rearranged; and 178 ALK wild-type) and a test cohort (67 patients; 22 ALK-rearranged; and 45 ALK wild-type). One thousand two hundred and eighteen quantitative radiomic features were extracted from the semi-automatically delineated volume of interest (VOI) of the entire tumor using both the original and the pre-processed non-enhanced CT images. Twelve conventional CT features and seven clinical features were also collected. Normalized features were selected using a sequential of the F-test-based method, the density-based spatial clustering of applications with noise (DBSCAN) method, and the recursive feature elimination (RFE) method. Selected features were then used to build three predictive models (radiomic, radiological, and integrated models) for the ALK-rearranged phenotype by a soft voting classifier. Models were evaluated in the test cohort using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity, and the performances of three models were compared using the DeLong test.Results: Our results showed that the addition of clinical information and conventional CT features significantly enhanced the validation performance of the radiomic model in the primary cohort (AUC = 0.83–0.88, P = 0.01), but not in the test cohort (AUC = 0.80–0.88, P = 0.29). The majority of radiomic features associated with ALK mutations reflected information around and within the high-intensity voxels of lesions. The presence of the cavity and left lower lobe location were new imaging phenotypic patterns in association with ALK-rearranged tumors. Current smoking was strongly correlated with non-ALK-mutated lung adenocarcinoma.Conclusions: Our study demonstrates that radiomics-derived machine learning models can potentially serve as a non-invasive tool to identify ALK mutation of lung adenocarcinoma.

The role of PET/CT in determining egfr mutation and ALK rearrangement in patients with lung adenocarcinoma

Aim: The aim of the present study is to evaluate the role of tumor FDG uptake in a non-invasive method and the use of positron emission tomography / computed tomography (PET/CT) in estimating epidermal growth factor receptor (EGFR) mutations and anaplastic lymphoma kinase (ALK) rearrangements in cases of adenocarcinoma of the lung.Material and Methods: A total of 115 patients with the diagnosis of an adenocarcinoma of the lung that underwent F-18 FDG PET/CT for staging and whose EGFR mutation and ALK rearrangement status were analyzed were retrospectively analyzed. The association of the PET parameters (SUVmax, SUVmean, MTV and TLG) of primary tumor with the molecular profile was analyzed.Results: EGFR mutations (EGFR+ group) and ALK rearrangements (ALK+ group) were found in 15 (13%) and 10 (8.7%) of 115 patients; and 90 patients (78.3%) had no EGFR mutation or ALK rearrangement (EGFR/ALK-). EGFR mutations were found to be significantly higher among the never-smoked group (p=0.009). No significant association was identified between the SUVmean, MTV and TLG values and EGFR mutations; however, patients with a low SUVmax value were found to have a significantly higher rate of EGFR mutation (p=0.034). No statistically significant differences were found between the ALK+ and ALK- group in terms of age, sex, cigarette smoking status, tumor stage, or PET parameters.Conclusion: Our study results suggest that a low SUVmax value in lung adenocarcinomas is associated with EGFR mutation, although the diagnostic efficacy is not high.

Clinicopathological characteristics with EGFR, ALK, ROS1 genetic alternation and prognostic analysis of primary lymphoepithelioma-like carcinoma

Background: Primary lymphoepithelioma-like carcinoma (LELC) is a rare form of lung cancer, since genetic alternations participate in the carcinogenesis in lung cancer, whether critical driver genes such as ROS1 fusion, anaplastic lymphoma kinase (ALK) rearrangement and epidermal growth factor receptor ( EGFR ) mutation play roles in LELC remains unclear. Methods: We collected a total of 30 LELC samples for genetic and prognosis analysis retrospectively in Shanghai Chest Hospital, EGFR gene mutation, ALK rearrangement and ROS1 fusion status were extracted from digital database. Clinicopathological characteristics with genetic profiling and survival were analyzed. Results: All samples appeared negative for genes ( EGFR, ALK and ROS1 ) alternations detection. Female gender acted as an independently prognostic factor in poorer disease-free survival (DFS) (P=0.034), and tumors locate in the left lobe associate with worse DFS in univariate analysis (significant trend, P=0.051), moreover, serum positive NSE level also indicate a shorter DFS after adjustment (significant trend, P=0.057). Most of LELC are diagnosed at early stage, with 90.0% (27/30) patients obtained the opportunities for surgery. Conclusions: Since no classical genetic alternations appeared in present study, more investigations of other genes distributions should be explored in the future for better understanding of this rare subtype of lung cancer. Serum positive neuron-specific enolase (NSE) level seemed to be a prognostic biomarker for DFS in LELC patients.

Clinicopathological characteristics with EGFR, ALK, ROS1 genetic alternation and prognostic analysis of primary lymphoepithelioma-like carcinoma.

BackgroundPrimary lymphoepithelioma-like carcinoma (LELC) is a rare form of lung cancer, since genetic alternations participate in the carcinogenesis in lung cancer, whether critical driver genes such as ROS1 fusion, anaplastic lymphoma kinase (ALK) rearrangement and epidermal growth factor receptor (EGFR) mutation play roles in LELC remains unclear.MethodsWe collected a total of 30 LELC samples for genetic and prognosis analysis retrospectively in Shanghai Chest Hospital, EGFR gene mutation, ALK rearrangement and ROS1 fusion status were extracted from digital database. Clinicopathological characteristics with genetic profiling and survival were analyzed.ResultsAll samples appeared negative for genes (EGFR, ALK and ROS1) alternations detection. Female gender acted as an independently prognostic factor in poorer disease-free survival (DFS) (P=0.034), and tumors locate in the left lobe associate with worse DFS in univariate analysis (significant trend, P=0.051), moreover, serum positive NSE level also indicate a shorter DFS after adjustment (significant trend, P=0.057). Most of LELC are diagnosed at early stage, with 90.0% (27/30) patients obtained the opportunities for surgery.ConclusionsSince no classical genetic alternations appeared in present study, more investigations of other genes distributions should be explored in the future for better understanding of this rare subtype of lung cancer. Serum positive neuron-specific enolase (NSE) level seemed to be a prognostic biomarker for DFS in LELC patients.

A Proposal for Definition of Minimally Invasive Adenocarcinoma of the Lung Regardless of Tumor Size

Based on the current World Health Organization classification, adenocarcinoma with a predominantly lepidic pattern larger than 3.0 cm in diameter is considered to be invasive adenocarcinoma, specifically lepidic adenocarcinoma, even if it shows pathologic invasion of 0.5 cm or less. However, we sometimes encounter lepidic adenocarcinomas larger than 3.0 cm with a good prognosis as well as minimally invasive adenocarcinoma. Of 4,606 resected primary lung cancers at our institute between 1998 and 2011, this study focused on 18 lepidic adenocarcinomas with invasion of 0.5 cm or less. Clinicopathologic characteristics, including epidermal growth factor receptor mutation and anaplastic lymphoma kinase rearrangement status, and prognosis were evaluated. The median maximum pathologic tumor diameter was 3.4 cm (range, 3.1 to 4.7 cm) and the median maximum diameter of the invasive area within the tumor was 0.4 cm (range, 0 to 0.5 cm). No lymph node metastasis, vascular invasion, lymphatic permeation, or pleural invasion was pathologically confirmed in any of the 18 cases. Although epidermal growth factor receptor mutation was detected in 13 cases (72%), none of the patients showed anaplastic lymphoma kinase rearrangement. Recurrence was not observed in any of the 18 patients. The 5-year overall and cancer-specific survival rates were 94.4% and 100%, respectively. Lepidic adenocarcinoma with invasion of 0.5 cm or less had a good prognosis. Adenocarcinoma with a predominantly lepidic pattern and invasion of 0.5 cm or less can be regarded as minimally invasive adenocarcinoma even if the tumor is larger than 3.0 cm in diameter.

Clinicopathological Features and Therapeutic Responses of Chinese Patients with Advanced Lung Adenocarcinoma Harboring an Anaplastic Lymphoma Kinase Rearrangement

Background: Presence of anaplastic lymphoma kinase (ALK) rearrangement is an indication for crizotinib in the treatment of patients with advanced or metastatic lung adenocarcinoma. Here, we sought to elucidate the association between clinicopathological features and ALK rearrangement status in Chinese patients with advanced lung adenocarcinoma harboring an ALK rearrangement. Patients and Methods:ALK rearrangement status was determined using immunohistochemistry (IHC) in tumor tissues from 120 patients with advanced lung adenocarcinoma, and further assessed by fluorescence in situ hybridization (FISH) assay. The associations between ALK rearrangement status and clinicopathological features were analyzed. Results: According to IHC testing, the ALK-positive rate among the advanced lung adenocarcinoma patients was 6.67% (8/120). FISH validation found 5 patients with ALK rearrangement among the 8 IHC-positive cases. No significant difference was observed regarding age, sex, or smoking status between FISH-positive and -negative patients (p > 0.05). None of the 5 FISH-positive patients benefited from first-line chemotherapy. Conclusion: IHC can be used as a reliable method for ALK rearrangement screening in patients with lung adenocarcinoma, but further FISH validation is imperative. Presence of ALK rearrangement predicts a more aggressive biological behavior of the tumor and might be indicative of poor response to chemotherapy.

Detection of ALK rearrangements in lung cancer patients using a homebrew PCR assay.

Lung cancer patients with anaplastic lymphoma kinase (ALK) rearrangements are candidates for targeted therapeutics. However, patients must be tested with a companion diagnostic assay to realize their ALK rearrangement status. We analyzed the publicly available E-GEOD-31210 microarray dataset and identified a non-coding RNA, sweyjawbu, which is strongly associated with ALK rearrangements. We validated these results using quantitative real-time PCR in an independent cohort consisting of 4 cell lines and 83 clinical samples. We could differentiate between ALK rearrangement-positive and -negative lung cancer samples by comparing sweyjawbu expression. Additionally, ALK rearrangement status was determined by comparing the expression of the 5′ and 3′ regions of the ALK transcript or by detecting known ALK hybrid subtypes. Thus, using our homebrew PCR assay, we were able to accurately detect ALK rearrangements, which could be used for diagnostic screening of lung cancer patients. The prototype could potentially be transferred to an automatic multiplex PCR platform (FilmArray) to differentiate between ALK rearrangement-positive and -negative patients in point-of-care settings.

EML4-ALK Fusion Detected by RT-PCR Confers Similar Response to Crizotinib as Detected by FISH in Patients with Advanced Non-Small-Cell Lung Cancer

Reverse transcriptase polymerase chain reaction (RT-PCR) assay has been proved to have high sensitivity and specificity to detect anaplastic lymphoma kinase (ALK) rearrangements. The aim of this study was to investigate the response to crizotinib in patients of advanced non-small-cell lung cancer (NSCLC) with ALK rearrangements detected by RT-PCR. Only patients with advanced NSCLC who had their ALK rearrangement status detected by RT-PCR were included in this analysis. The utility of RT-PCR and fluorescence in situ hybridization (FISH) assay were compared in patients who were treated with crizotinib based on their positive ALK rearrangements. One thousand ten patients were included in this study. Among them, 104 patients were ALK RT-PCR positive and 53 of them received crizotinib treatment. Among 255 tumors simultaneously analyzed by FISH and RT-PCR, the latter successfully detected all the 25 tumors with arrangements, including two cases that were missed by FISH. The overall response rate and median progression-free survival of the 53 patients with ALK rearrangements who received crizotinib treatment were 60.4% (95% confidence interval [CI], 47.2-73.6) and 8.4 months (95% CI, 6.75-10.05), respectively, which were similar to the 21 patients detected by FISH with overall response rate of 57.1% (95% CI, 33.3-76.2; p = 0.799) and median progression-free survival of 7.4 months (95% CI, 4.43-10.38; p = 0.833) after crizotinib treatment. Interestingly, there were two patients responded to crizotinib had their ALK rearrangement detected by RT-PCR but not FISH. RT-PCR should be considered as an alternative/supplemental approach to detect ALK fusion oncogene in NSCLC patients who might benefit from crizotinib treatment.

Toward Personalized Medicine of Lung Cancer

We evaluated clinical parameters, histomorphology, and thyroid transcription factor 1 (TTF-1) immunoreactivity in 40 epidermal growth factor receptor (EGFR) mutation- and anaplastic lymphoma kinase (ALK) rearrangement-negative invasive pulmonary adenocarcinomas. Tumors were histomorphologically quantitated by a pulmonary pathologist and TTF-1 immunohistochemistry applied. EGFR mutation and ALK rearrangement status was determined with polymerase chain reaction/DNA sequencing and fluorescence in situ hybridization, respectively. Treatment response was related to type of treatment (P < .005) and clinical stage (P = .001). EGFR mutation- and ALK rearrangement-negative pulmonary adenocarcinomas containing papillary/micropapillary histology showed greater morphologic heterogeneity (P < .001), greater TTF-1 immunoreactivity (P = .004), and were more common in treatment responders (P < .05). These findings support that patients with pulmonary adenocarcinomas that are subject to nontargeted therapies may respond to treatment as a function of tumor cell differentiation with TTF-1 as a potential biomarker of this response.

Association of EGFR mutation or ALK rearrangement with expression of DNA repair and synthesis genes in never-smoker females with pulmonary adenocarcinoma.

10527 Background: Epidermal growth factor receptor (EGFR) mutation or anaplastic lymphoma kinase (ALK) rearrangement was found not only predict the efficacy of targeted drugs, but also associate with the efficacy of chemotherapy drugs in non-small cell lung cancer (NSCLC) patients. We investigated the relationship of EGFR mutation status or ALK rearrangement and DNA repair or synthesis genes, such as excision repair cross-complementing 1 (ERCC1), ribonucleotide reductase subunit M1 (RRM1), thymidylate synthetase (TS) and breast cancer gene one (BRCA1) gene expression, as a potential explanation for these observations. Methods: In this surgical series, 104 resected lung adenocarcinomas from nonsmoker females were analyzed concurrently for the EGFR mutation, ALK rearrangement status and mRNA expression of ERCC1, RRM1, TS and BRCA1 genes. EGFR mutation detection was performed by the method of ADx-ARMS, ALK rearrangement was detected by PCR and the mRNA expression of different genes were tested using the method of real-time PCR. Results: 73 (70.2%) patients harbored EGFR mutations and 10 (9.6%) had ALK rearrangement. The ERCC1 mRNA level in patients with EGFR mutation was 3.44±1.94×10-3 , which is significantly lower than in the patients with ALK positive and both negative(4.60±1.95×10-3 and 4.95±2.33×10-3 respectively, P=0.010). While the TS mRNA levels were significantly lower in the patients with EGFR mutation(1.15±1.38×10-3 VS 2.69±3.97×10-3, P=0.006) or ALK positive (1.21±0.78×10-3VS 2.69±3.97×10-3, P=0.020) than in patients with both negative. Conclusions: NSCLC specimens harboring activating EGFR mutations are more likely to express low ERCC1 and TS mRNA levels, while NSCLC patients with ALK rearrangement are more likely to express low TS mRNA levels, which could be helpful to select a proper chemotherapy regimen for NSCLC patients with known EGFR mutation or ALK fusion status.

Association of EGFR mutation or ALK rearrangement with expression of DNA repair and synthesis genes in never‐smoker women with pulmonary adenocarcinoma

Epidermal growth factor receptor (EGFR) mutation and anaplastic lymphoma kinase (ALK) rearrangement may predict the outcome of targeted drug therapy and also are associated with the efficacy of chemotherapy in patients with nonsmall cell lung cancer (NSCLC). The authors of this report investigated the relation of EGFR mutation or ALK rearrangement status and the expression of DNA repair or synthesis genes, including excision repair cross-complementing 1 (ERCC1), ribonucleotide reductase subunit M1 (RRM1), thymidylate synthetase (TS), and breast cancer-early onset (BRCA1), as a potential explanation for these observations. In total, 104 resected lung adenocarcinomas from women who were nonsmokers were analyzed concurrently for EGFR mutations, ALK rearrangements, and mRNA expression of the ERCC1, RRM1, TS, and BRCA1 genes. EGFR mutations were detected with a proprietary detection kit, ALK rearrangements were detected by polymerase chain reaction analysis, and genetic mRNA expression was detected by real-time polymerase chain reaction analysis. Of 104 patients, 73 (70.2%) had EGFR mutations, and 10 (9.6%) had ALK rearrangements. ERCC1 mRNA levels in patients who had EGFR mutations were 3.44 ± 1.94 × 10(-3) , which were significantly lower than the levels in patients who were positive for ALK rearrangements and in patients who were negative for both biomarkers (4.60 ± 1.95 × 10(-3) and 4.95 ± 2.33 × 10(-3) , respectively; P = .010). However, TS mRNA levels were significantly lower in patients who had EGFR mutations (1.15 ± 1.38 × 10(-3) vs 2.69 ± 3.97 × 10(-3) ; P = .006) or ALK rearrangements (1.21 ± 0.78 × 10(-3) vs 2.69 ± 3.97 × 10(-3) ; P = .020) than in patients who were negative for both biomarkers. NSCLC specimens that harbored activating EGFR mutations were more likely to express low ERCC1 and TS mRNA levels, whereas patients with NSCLC who had ALK rearrangement were more likely to express low TS mRNA levels.