Gene Amplification Status Research Articles

Application of the FISH method and high-density SNP arrays to assess genetic changes in neuroblastoma-research by one institute.

Neuroblastoma is the most common extracranial solid tumor in children. Amplification of the MYCN gene has been observed in approximately 20%-30% of tumors. It is strongly correlated with advanced-stage disease, rapid tumor progression, resistance to chemotherapy and poor outcomes independent of patient age and stage of advanced disease. MYCN amplification identifies high-risk patients. To assess neuroblastoma tumors with MYCN amplification we used paraffin-embedded tissue sections in 57 patients and intraoperative tumor imprints in 10 patients by fluorescence in situ hybridization (FISH). Positive results for MYCN amplification have been observed in twelve patients' paraffin-embedded tissue sections and in three patients' intraoperative tumor imprints, which represents 22.4% of all patients tested in the analysis. Fluorescence in situ hybridization is a highly sensitive and useful technique for detecting MYCN amplification on paraffin-embedded tissue sections of neuroblastoma tumors and intraoperative tumor imprints thus facilitating therapeutic decisions based on the presence or absence of this important biologic marker. The presence of structural changes, regardless of MYCN gene amplification status, influences the clinical behavior of neuroblastoma. High-Density SNP Arrays have emerged as the perfect tools for detecting these changes due to their exceptional accuracy, sensitivity and ability to analyze copy number and allele information. Consequently, they are proven to be highly valuable in the genomic diagnosis of immature neuroectodermal tumors.

EGFR, HER2, and MET gene amplification and protein expression profiles in biliary tract cancer and their prognostic significance.

There are limited conventional chemotherapy options for biliary tract cancers (BTCs), a heterogenous group of lethal, rare malignancies. The receptor tyrosine kinase (RTK) is closely associated with the progression of human malignancies through the regulation of cell cycle. Overexpression or amplification of RTKs has been investigated as a potential biomarker and therapeutic target in BTC; herein, we investigate the value of such interventions. Overexpression of RTK proteins was examined by immunohistochemistry in 193 BTC samples, of which 137 were gallbladder carcinoma, 29 were perihilar cholangiocarcinoma, and 27 were intrahepatic cholangiocarcinoma. Silver in situ hybridization of MET and HER2 was performed to assess gene amplification. In the entire cancer group, gallbladder, perihilar, and intrahepatic, MET amplification rates were 15.7%, 19.0%, 3.4%, and 14.8%, respectively, and of HER2 amplification rates were 22.4%, 27.2%, 17.2%, and 3.7%, respectively. MET and HER2 protein expressions were significantly correlated with their gene amplification status. RTKs were significantly associated with adverse clinicopathologic features such as advanced pT category and lymph node metastasis. Overall survival was significantly shorter in MET-amplified (P = .024) and EGFR-overexpressed cases (P = .045). Recurrence-free survival was significantly correlated with HER2-amplified (P = .038) and EGFR-overexpressed cases (P = .046) in all patient groups. Overall and recurrence-free survival were significantly shorter in patients who were double positive for HER2 and EGFR. Our data suggested that MET, HER2, and EGFR might be potential therapeutic targets and that their co-expression is a strong prognostic factor for BTCs.

The Expression and Amplification of HER2 Has a Significant Impact on the Prognosis of Endometrial Carcinoma in Korean Patients.

Objective: The purpose of this study was to analyze the protein overexpression and gene amplification of HER2 in endometrial carcinoma (EC) and to evaluate its role as a prognostic factor in Korean women. Methods: A tissue microarray (TMA) was constructed from samples from 191 patients with diverse histologic types of EC. HER2 protein expression and gene amplification status were analyzed using immunohistochemistry (IHC) and silver in situ hybridization (SISH), respectively. All patients were treated and followed up at a single tertiary medical center in Seoul, Korea, between July 2009 and October 2020. Results: In terms of histological type, among the 191 EC patients, 157 had endometrioid carcinoma, nine had uterine serous papillary carcinoma (USPC), one had clear cell carcinoma, one had squamous cell carcinoma, eight had mixed carcinoma, and 15 had uterine carcinosarcoma (UC). HER2 protein overexpression was observed in eight of the 191 (4.2%) EC patients; of these patients, five had IHC scores of 2+, and three had IHC scores of 3+. The HER2 overexpression rates of USPC, UC, and endometrioid carcinomas were 33.3%, 26.6%, and 0.6%, respectively. HER2 protein overexpression was significant in USPC and UC tissues (p < 0.000) and was associated with poor overall survival (OS) (p < 0.001). HER2 gene amplification was confirmed in seven of 184 patients (3.8%), including three patients with USPC and four patients with UC. OS was significantly shorter in patients who had HER2 amplification (p < 0.001). On multivariate analysis, HER2 expression and HER2 amplification were statistically significantly associated with worse OS (p = 0.006). However, HER2 expression without amplification was not statistically associated with OS (p = 0.993). Conclusions: HER2 protein overexpression and gene amplification are significantly correlated with shorter OS in Korean women. HER2 can be considered an important predictor of survival outcomes in EC patients.

TM4SF19 controls GABP-dependent YAP transcription in head and neck cancer under oxidative stress conditions

Tobacco and alcohol are risk factors for human papillomavirus-negative head and neck squamous cell carcinoma (HPV- HNSCC), which arises from the mucosal epithelium of the upper aerodigestive tract. Notably, despite the mutagenic potential of smoking, HPV- HNSCC exhibits a low mutational load directly attributed to smoking, which implies an undefined role of smoking in HPV- HNSCC. Elevated YAP (Yes-associated protein) mRNA is prevalent in HPV- HNSCC, irrespective of the YAP gene amplification status, and the mechanism behind this upregulation remains elusive. Here, we report that oxidative stress, induced by major risk factors for HPV- HNSCC such as tobacco and alcohol, promotes YAP transcription via TM4SF19 (transmembrane 4 L six family member 19). TM4SF19 modulates YAP transcription by interacting with the GABP (Guanine and adenine-binding protein) transcription factor complex. Mechanistically, oxidative stress induces TM4SF19 dimerization and topology inversion in the endoplasmic reticulum membrane, which in turn protects the GABPβ1 subunit from proteasomal degradation. Conversely, depletion of TM4SF19 impairs the survival, proliferation, and migration of HPV- HNSCC cells, highlighting the potential therapeutic relevance of targeting TM4SF19. Our findings reveal the roles of the key risk factors of HPV- HNSCC in tumor development via oxidative stress, offering implications for upcoming therapeutic approaches in HPV- HNSCC.

Decreased HER2 expression in endometrial cancer following anti-HER2 therapy.

Trastuzumab has demonstrated clinical efficacy in the treatment of HER2-positive serous endometrial cancer (EC), which led to its incorporation into standard-of-care management of this aggressive disease. Acquired resistance remains an important challenge, however, and its underlying mechanisms in EC are unknown. To define the molecular changes that occur in response to anti-HER2 therapy in EC, targeted next-generation sequencing (NGS), HER2 immunohistochemistry (IHC), and fluorescence in situ hybridization (FISH) were performed on pre- and post-treatment tumour samples from 14 patients with EC treated with trastuzumab or trastuzumab emtansine. Recurrent tumours after anti-HER2 therapy acquired additional genetic alterations compared with matched pre-treatment ECs and frequently showed decreased HER2 protein expression by IHC (7/14, 50%). Complete/near-complete absence of HER2 protein expression (score 0/1+) observed post-treatment (4/14, 29%) was associated with retained HER2 gene amplification (n = 3) or copy number neutral status (n = 1). Whole-exome sequencing performed on primary and recurrent tumours from the latter case, which exhibited genetic heterogeneity of HER2 amplification in the primary tumour, revealed selection of an early HER2-non-amplified clone following therapy. Our findings demonstrate that loss of target expression, by selection of HER2-non-amplified clones or, more commonly, by downregulation of expression, may constitute a mechanism of resistance to anti-HER2 therapy in HER2-positive EC. © 2023 The Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Clinico-Genomics Model to Predict Survival in Non-Metastatic Breast Cancer: Comparing the Performance of Machine Learning Models

Breast cancer is a heterogenous spectrum of disease with variable outcome. This heterogeneity of outcome could be driven by underlying heterogeneity in the genotypes. There remains an unmet need of defining prognosis based on underlying genetic heterogeneity. The primary objective of this study was to build a combined clinic-genomics model to predict overall survival (OS) in patients with non-metastatic breast cancer. The Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) database is a combined Canada-UK Project which contains information on 31 demographic, clinical and treatment characteristics in addition to m-RNA level z-score for 331 genes for 1904 breast cancer patients. We excluded patients with metaplastic breast cancer (n = 1) or unknown histologic subtype (n = 13), and those with missing information on breast surgery (n = 22) and cellularity (n = 54). Demographic characteristics included age of the patient and menopausal status while clinical characteristics included tumor stage, number of positive lymph nodes, pathologic grade, cellularity, receptor status for estrogen and progesterone receptors, and HER2neu gene amplification status. Treatment included type of breast cancer surgery, chemotherapy, hormone therapy, and radiation therapy. The data was split randomly at an 80:20 ratio into training and testing dataset. Subsequently, two cross-validated supervised machine learning algorithms were applied to build the prognostic model - elastic net logistic regression and random forest classifier. We compared the performance of the models in the testing dataset using area under receiver operating curve (AUC). All analysis were done using sparklyr - an R interface to Apache SparkTM. Overall, 1814 patients were included in the analysis. The AUC for elastic net logistic regression model and random forest classifier was 0.74, and 0.73, respectively. The sensitivity and specificity of the elastic net logistic regression model was 0.84 and 0.45, respectively while those for the random forest classifier was 0.83 and 0.51, respectively. STAT5A, CASP8, HSD17b11, and CDKN2c were common to the list of important genes predicting OS in both models while age, lymph node count, Nottingham Prognostic Index (NPI), and type of breast surgery were common to the list of clinical factors predicting OS in both models. This study shows that a multivariable prognostic model with combination of clinical and demographic parameters with Z-score expression data can predict for overall survival.

ERBB2 Amplification and HER2 Expression in Salivary Duct Carcinoma: Evaluation of Scoring Guidelines and Potential for Expanded Anti-HER2 Therapy

Salivary duct carcinoma (SDC) is aggressive with limited therapeutic options. A subset of SDC display human epidermal growth factor receptor 2 (HER2) protein overexpression by immunohistochemistry, and some show ERBB2 gene amplification. Guidelines for HER2 scoring are not firmly established. Recent advances in breast carcinoma have established a role for anti-HER2 therapies in lesions with low HER2 expression lacking ERBB2 amplification. Delineating HER2 staining patterns in SDC is critical for evaluating anti-HER2 treatments. In total, 53 cases of SDC resected at our institution between 2004 and 2020 were identified. Androgen receptor (AR) and HER2 immunohistochemistry and ERBB2 fluorescence in situ hybridization were performed in all cases. AR expression was scored for percentage positive cells and categorized as positive (>10% of cells), low positive (1%-10%), or negative (<1%). HER2 staining levels and patterns were recorded, scored using 2018 ASCO/CAP guidelines, and categorized into HER2-positive (3+ or 2+ with ERBB2 amplification), HER2-low (1+ or 2+ without ERBB2 amplification), HER2-very low (faint staining in <10% of cells), or HER2-absent types. Clinical parameters and vital status were recorded. Median age was 70 years, with a male predominance. ERBB2-amplified tumors (11/53; 20.8%) presented at lower pT stages (pTis/pT1/pT2; P = .005, Fisher exact test) and more frequently had perineural invasion (P = .007, Fisher exact test) compared with ERBB2 nonamplified tumors; no other pathologic features differed significantly by gene amplification status. In addition, 2+ HER2 staining by 2018 ASCO/CAP criteria was most common (26/53; 49%); only 4 cases (8%) were HER2-absent status; 3+ HER2 staining was found in 9 tumors, and all were ERBB2 amplified. Six patients with HER2-expressing tumors received trastuzumab therapy, including 2 with ERBB2-amplified tumors. Overall survival and recurrence-free survival did not differ significantly based on ERBB2 status. This work suggests that 2018 ASCO/CAP guidelines for HER2 evaluation in breast carcinoma could be applied to SDC. Our findings also show broad overexpression of HER2 in SDC raising the possibility that more patients may benefit from anti-HER2–directed therapies.

Deep learning to automatically evaluate HER2 gene amplification status from fluorescence in situ hybridization images

Human epidermal growth factor receptor 2 (HER2) gene amplification helps identify breast cancer patients who may respond to targeted anti-HER2 therapy. This study aims to develop an automated method for quantifying HER2 fluorescence in situ hybridization (FISH) signals and improve the working efficiency of pathologists. An Aitrox artificial intelligence (AI) model based on deep learning was constructed, and a comparison between the AI model and traditional manual counting was performed. In total, 918 FISH images from 320 consecutive invasive breast cancers were analysed and automatically classified into 5 groups according to the 2018 ASCO/CAP guidelines. The overall classification accuracy was 85.33% (157/184) with a mean average precision of 0.735. In Group 5, the most common group, the consistency was as high as 95.90% (117/122), while the consistency was low in the other groups due to the limited number of cases. The causes of this inconsistency, including clustered HER2 signals, coarse CEP17 signals and some section quality problems, were analysed. The developed AI model is a reliable tool for evaluating HER2 amplification statuses, especially for breast cancer in Group 5; additional cases from multiple centres could further improve the accuracy achieved for other groups.

Prediction of MYCN Gene Amplification in Pediatric Neuroblastomas: Development of a Deep Learning-Based Tool for Automatic Tumor Segmentation and Comparative Analysis of Computed Tomography-Based Radiomics Features Harmonization.

MYCN oncogene amplification is closely linked to high-grade neuroblastoma with poor prognosis. Accurate quantification is essential for risk assessment, which guides clinical decision making and disease management. This study proposes an end-to-end deep-learning framework for automatic tumor segmentation of pediatric neuroblastomas and radiomics features-based classification of MYCN gene amplification. Data from pretreatment contrast-enhanced computed tomography scans and MYCN status from 47 cases of pediatric neuroblastomas treated at a tertiary children's hospital from 2009 to 2020 were reviewed. Automated tumor segmentation and grading pipeline includes (1) a modified U-Net for tumor segmentation; (2) extraction of radiomic textural features; (3) feature-based ComBat harmonization for removal of variabilities across scanners; (4) feature selection using 2 approaches, namely, (a) an ensemble approach and (b) stepwise forward-and-backward selection method using logistic regression classifier; and (5) radiomics features-based classification of MYCN gene amplification using machine learning classifiers. Median train/test Dice score for modified U-Net was 0.728/0.680. The top 3 features from the ensemble approach were neighborhood gray-tone difference matrix (NGTDM) busyness, NGTDM strength, and gray-level run-length matrix (GLRLM) low gray-level run emphasis, whereas those from the stepwise approach were GLRLM low gray-level run emphasis, GLRLM high gray-level run emphasis, and NGTDM coarseness. The top-performing tumor classification algorithm achieved a weighted F1 score of 97%, an area under the receiver operating characteristic curve of 96.9%, an accuracy of 96.97%, and a negative predictive value of 100%. Harmonization-based tumor classification improved the accuracy by 2% to 3% for all classifiers. The proposed end-to-end framework achieved high accuracy for MYCN gene amplification status classification.

A Multiparameter Molecular Classifier to Predict Response to Neoadjuvant Lapatinib plus Trastuzumab without Chemotherapy in HER2+ Breast Cancer.

Clinical trials reported 25% to 30% pathologic complete response (pCR) rates in HER2+ patients with breast cancer treated with anti-HER2 therapies without chemotherapy. We hypothesize that a multiparameter classifier can identify patients with HER2-"addicted" tumors who may benefit from a chemotherapy-sparing strategy. Baseline HER2+ breast cancer specimens from the TBCRC023 and PAMELA trials, which included neoadjuvant treatment with lapatinib and trastuzumab, were used. In the case of estrogen receptor-positive (ER+) tumors, endocrine therapy was also administered. HER2 protein and gene amplification (ratio), HER2-enriched (HER2-E), and PIK3CA mutation status were assessed by dual gene protein assay (GPA), research-based PAM50, and targeted DNA-sequencing. GPA cutoffs and classifier of response were constructed in TBCRC023 using a decision tree algorithm, then validated in PAMELA. In TBCRC023, 72 breast cancer specimens had GPA, PAM50, and sequencing data, of which 15 had pCR. Recursive partitioning identified cutoffs of HER2 ratio ≥ 4.6 and %3+ IHC staining ≥ 97.5%. With PAM50 and sequencing data, the model added HER2-E and PIK3CA wild-type (WT). For clinical implementation, the classifier was locked as HER2 ratio ≥ 4.5, %3+ IHC staining ≥ 90%, and PIK3CA-WT and HER2-E, yielding 55% and 94% positive (PPV) and negative (NPV) predictive values, respectively. Independent validation using 44 PAMELA cases with all three biomarkers yielded 47% PPV and 82% NPV. Importantly, our classifier's high NPV signifies its strength in accurately identifying patients who may not be good candidates for treatment deescalation. Our multiparameter classifier differentially identifies patients who may benefit from HER2-targeted therapy alone from those who need chemotherapy and predicts pCR to anti-HER2 therapy alone comparable with chemotherapy plus dual anti-HER2 therapy in unselected patients.

Development and Evaluation of MR-Based Radiogenomic Models to Differentiate Atypical Lipomatous Tumors from Lipomas

The aim of this study was to develop and validate radiogenomic models to predict the MDM2 gene amplification status and differentiate between ALTs and lipomas on preoperative MR images. MR images were obtained in 257 patients diagnosed with ALTs (n = 65) or lipomas (n = 192) using histology and the MDM2 gene analysis as a reference standard. The protocols included T2-, T1-, and fat-suppressed contrast-enhanced T1-weighted sequences. Additionally, 50 patients were obtained from a different hospital for external testing. Radiomic features were selected using mRMR. Using repeated nested cross-validation, the machine-learning models were trained on radiomic features and demographic information. For comparison, the external test set was evaluated by three radiology residents and one attending radiologist. A LASSO classifier trained on radiomic features from all sequences performed best, with an AUC of 0.88, 70% sensitivity, 81% specificity, and 76% accuracy. In comparison, the radiology residents achieved 60-70% accuracy, 55-80% sensitivity, and 63-77% specificity, while the attending radiologist achieved 90% accuracy, 96% sensitivity, and 87% specificity. A radiogenomic model combining features from multiple MR sequences showed the best performance in predicting the MDM2 gene amplification status. The model showed a higher accuracy compared to the radiology residents, though lower compared to the attending radiologist.

Using publicly available datasets to identify population-based transcriptomic landscape contributing to the aggressiveness of breast cancer in young women.

Introduction: Although the risk of breast cancer increases with advancing age, some regions have larger number of young breast cancer patients (≤45years-old), such as the Middle East, Eastern Asia, and North Africa, with more aggressive and poorly differentiated tumors. We aimed to conduct an in-silico analysis in an attempt to understand the aggressive nature of early-onset breast cancer, and to identify potential drivers of early-onset breast cancer using gene expression profiling datasets in a population-dependent manner. Methods: Functional genomics experiments data were acquired from cBioPortal database for cancer genomics, followed by the stratification of patients based on the age at representation of breast cancer and race. Differential gene expression analysis and gene amplification status analysis were carried out, followed by hub gene, transcription factor, and signalling pathway identification. Results: PAM50 subtype analysis revealed that young patients (≤45years-old) had four-fold more basal tumors and worst progression-free survival (median of 101months), compared with the 45-65years group (median of 168months). Fourteen genes were amplified in more than 14% of patients with an early-onset breast cancer. Interestingly, FREM2, LINC00332, and LINC00366 were exclusively amplified in younger patients. Gene expression data from three different populations (Asian, White, and African) revealed a unique transcriptomic profile of young patients, which was also reflected on the PAM50 subtype analysis. Our data indicates a higher tendency of young African patients to develop basal tumors, while young Asian patients are more prone to developing Luminal A tumors. Most genes that were found to be upregulated in younger patients are involved in important signaling pathways that promote cancer progression and metastasis, such as MAPK pathway, Reelin pathway and the PI3K/Akt pathway. Conclusion: This study provides strong evidence that the molecular profile of tumors derived from young breast cancer patients of different populations is unique and may explain the aggressiveness of these tumors, stressing the need to conduct population- based multi-omic analyses to identify the potential drivers for tumorigenesis and molecular profiles of young breast cancer patients.

Using Deep Learning to Predict Final HER2 Status in Invasive Breast Cancers That are Equivocal (2+) by Immunohistochemistry.

Invasive breast carcinomas are routinely tested for HER2 using immunohistochemistry (IHC), with reflex in situ hybridization (ISH) for those scored as equivocal (2+). ISH testing is expensive, time-consuming, and not universally available. In this study, we trained a deep learning algorithm to directly predict HER2 gene amplification status from HER2 2+ IHC slides. Data included 115 consecutive cases of invasive breast carcinoma scored as 2+ by IHC that had follow-up HER2 ISH testing. An external validation data set was created from 36 HER2 IHC slides prepared at an outside institution. All internal IHC slides were digitized and divided into training (80%), and test (20%) sets with 5-fold cross-validation. Small patches (256×256 pixels) were randomly extracted and used to train convolutional neural networks with EfficientNet B0 architecture using a transfer learning approach. Predictions for slides in the test set were made on individual patches, and these predictions were aggregated to generate an overall prediction for each slide. This resulted in a receiver operating characteristic area under the curve of 0.83 with an overall accuracy of 79% (sensitivity=0.70, specificity=0.82). Analysis of external validation slides resulted in a receiver operating characteristic area under the curve of 0.79 with an overall accuracy of 81% (sensitivity=0.50, specificity=0.82). Although the sensitivity and specificity are not high enough to negate the need for reflexive ISH testing entirely, this approach may be useful for triaging cases more likely to be HER2 positive and initiating treatment planning in centers where HER2 ISH testing is not readily available.

Addition of Chromosome 17 Polysomy and HER2 Amplification Status Improves the Accuracy of Clinicopathological Factor-Based Progression Risk Stratification and Tumor Grading of Non-Muscle-Invasive Bladder Cancer.

Simple SummaryThe addition of chromosome 17 polysomy/HER2 amplification status to the updated EAU and AUA scores improves their accuracy, allowing molecular reclassification of EAU high-risk NMIBCs and G2 tumors. Based on this, we propose to reclassify the non-HER2 amplified, non-polysomic EAU 3–4 (high- and very high-risk) cases to the EAU 2 (intermediate) risk group to prevent unnecessarily strict follow-up and treatment for these patients. Furthermore, to classify Chr17 polysomic and/or HER2 amplified G2 tumors as high-grade (HG) and non-HER2 amplified, non-polysomic G2 tumors as low-grade (LG) NMIBCs (G1 and G3 tumors remain graded as low- and high-grade, respectively). Thus, the implementation of Chr17 polysomy/HER2 amplification testing would provide an immediate and simple solution to further refine the prognostic risk assessment of NMIBCs in the uro-oncology practice.Progression of non-muscle-invasive bladder cancer (NMIBC) to muscle-invasive disease (MIBC) significantly worsens life expectancy. Its risk can be assessed by clinicopathological factors according to international guidelines. However, additional molecular markers are needed to refine and improve the prediction. Therefore, in the present study, we aimed to predict the progression of NMIBCs to MIBC by assessing p53 expression, polysomy of chromosome 17 (Chr17) and HER2 status in the tissue specimens of the tumors of 90 NMIBC patients. Median follow-up was 77 months (range 2–158). Patients with Chr17 polysomy or HER2 gene amplification had a higher rate of disease progression (hazard ratio: 7.44; p < 0.001 and 4.04; p = 0.033, respectively; univariate Cox regression). Multivariable Cox regression models demonstrated that the addition of either Chr17 polysomy or HER2 gene amplification status to the European Association of Urology (EAU) progression risk score increases the c-index (from 0.741/EAU/ to 0.793 and 0.755, respectively), indicating that Chr17 polysomy/HER2 amplification status information improves the accuracy of the EAU risk table in predicting disease progression. HER2/Chr17 in situ hybridization can be used to select non-progressive cases not requiring strict follow-up, by reclassifying non-HER2-amplified, non-polysomic NMIBCs from the high- and very high-risk groups of EAU to the intermediate-risk group.

CT-based morphologic and radiomics features for the classification of MYCN gene amplification status in pediatric neuroblastoma.

MYCN onco-gene amplification in neuroblastoma confers patients to the high-risk disease category for which prognosis is poor and more aggressive multimodal treatment is indicated. This retrospective study leverages machine learning techniques to develop a computed tomography (CT)-based model incorporating semantic and non-semantic features for non-invasive prediction of MYCN amplification status in pediatric neuroblastoma. From 2009 to 2020, 54 pediatric patients treated for neuroblastoma at a specialized children's hospital with pre-treatment contrast-enhanced CT and MYCN status were identified (training cohort, n = 44; testing cohort, n = 10). Six morphologic features and 107 quantitative gray-level texture radiomics features extracted from manually drawn volume-of-interest were analyzed. Following feature selection and class balancing, the final predictive model was developed with eXtreme Gradient Boosting (XGBoost) algorithm. Accumulated local effects (ALE) plots were used to explore main effects of the predictive features. Tumor texture maps were also generated for visualization of radiomics features. One morphologic and 2 radiomics features were selected for model building. The XGBoost model from the training cohort yielded an area under the receiver operating characteristics curve (AUC-ROC) of 0.930 (95% CI, 0.85-1.00), optimized F1-score of 0.878, and Matthews correlation coefficient (MCC) of 0.773. Evaluation on the testing cohort returned AUC-ROC of 0.880 (95% CI, 0.64-1.00), optimized F1-score of 0.933, and MCC of 0.764. ALE plots and texture maps showed higher "GreyLevelNonUniformity" values, lower "Strength" values, and higher number of image-defined risk factors contribute to higher predicted probability of MYCN amplification. The machine learning model reliably classified MYCN amplification in pediatric neuroblastoma and shows potential as a surrogate imaging biomarker.

HOXA5 is amplified in glioblastoma stem cells and promotes tumor progression by transcriptionally activating PTPRZ1

Although the tumorigenic potential of glioma stem cells (GSCs) is associated with multiple molecular alterations, the gene amplification status of GSCs has not been elucidated. Overexpression of HomeoboxA5 (HOXA5) is associated with increased glioma malignancy. In this study, we identify the gene amplification and protein overexpression of HOXA5 in GSCs and its function in regulating GSC maintenance and the downstream transcriptional effector, to explore the significance of HOXA5 amplification/overexpression for GSC identification and prognostic determination. The HOXA5 gene is significantly amplified in glioblastoma (GBM) and is an independent prognostic factor for predicting worse patient outcomes. Specifically, HOXA5 gene amplification and the resultant protein overexpression are correlated with increased proportions of GSCs and enhanced self-renewal/invasiveness of these cells. Disruption of HOXA5 expression impairs GSC survival and GBM tumor propagation. Mechanistically, HOXA5 directly binds to the promoter region of protein tyrosine phosphatase receptor type Z1 (PTPRZ1), thereby upregulating this gene for GSC maintenance. Suppression of PTPRZ1 largely compromises the pro-tumoral effect of HOXA5 on GSCs. In summary, HOXA5 amplification serves as a genetic biomarker for predicting worse GBM outcome, by enhancing PTPRZ1-mediated GSC survival.

Alpha-actinin-4 (ACTN4) gene amplification is a predictive biomarker for adjuvant chemotherapy with tegafur/uracil in stage I lung adenocarcinomas.

Although adjuvant tegafur/uracil (UFT) is recommended for patients with completely resected stage I non‐small‐cell lung cancer (NSCLC) in Japan, only one‐third of cases has received adjuvant chemotherapy (ADJ) according to real‐world data. Therefore, robust predictive biomarkers for selecting ADJ or observation (OBS) without ADJ are needed. Patients who underwent complete resection of stage I lung adenocarcinoma with or without adjuvant UFT were enrolled. The status of ACTN4 gene amplification was analyzed by FISH. Statistical analyses to determine whether the status of ACTN4 gene amplification affected recurrence‐free survival (RFS) were carried out. Formalin‐fixed, paraffin‐embedded samples from 1136 lung adenocarcinomas were submitted for analysis of ACTN4 gene amplification. Ninety‐nine (8.9%) of 1114 cases were positive for ACTN4 gene amplification. In the subgroup analysis of patients aged 65 years or older, the ADJ group had better RFS than the OBS group in the ACTN4‐positive cohort (hazard ratio [HR], 0.084, 95% confidence interval [CI], 0.009‐0.806; P = .032). The difference in RFS between the ADJ group and the OBS group was not significant in ACTN4‐negative cases (all ages: HR, 1.214; 95% CI, 0.848‐1.738; P = .289). Analyses of ACTN4 gene amplification contributed to the decision regarding postoperative ADJ for stage I lung adenocarcinomas, preventing recurrence, improving the quality of medical care, preventing the unnecessary side‐effects of ADJ, and saving medical costs.

Computer-aided scoring of erb-b2 receptor tyrosine kinase 2 (HER2) gene amplification status in breast cancer

BackgroundIdentification of HER2 protein overexpression and/or amplification of the HER2 gene are required to qualify breast cancer patients for HER2 targeted therapies. In situ hybridization (ISH) assays that identify HER2 gene amplification function as a stand-alone test for determination of HER2 status and rely on the manual quantification of the number of HER2 genes and copies of chromosome 17 to determine HER2 amplification. MethodsTo assist pathologists, we have developed the uPath HER2 Dual ISH Image Analysis for Breast (uPath HER2 DISH IA) algorithm, as an adjunctive aid in the determination of HER2 gene status in breast cancer specimens. The objective of this study was to compare uPath HER2 DISH image analysis vs manual read scoring of VENTANA HER2 DISH-stained breast carcinoma specimens with ground truth (GT) gene status as the reference. Three reader pathologists reviewed 220, formalin-fixed, paraffin-embedded (FFPE) breast cancer cases by both manual and uPath HER2 DISH IA methods. Scoring results from manual read (MR) and computer-assisted scores (image analysis, IA) were compared against the GT gene status generated by consensus of a panel of pathologists. The differences in agreement rates of HER2 gene status between manual, computer-assisted, and GT gene status were determined. ResultsThe positive percent agreement (PPA) and negative percent agreement (NPA) rates for image analysis (IA) vs GT were 97.2% (95% confidence interval [CI]: 95.0, 99.3) and 94.3% (95% CI: 90.8, 97.3) respectively. Comparison of agreement rates showed that the lower bounds of the 95% CIs for the difference of PPA and NPA for IA vs MR were –0.9% and –6.2%, respectively. Further, inter- and intra-reader agreement rates in the IA method were observed with point estimates of at least 96.7%. ConclusionsOverall, our data show that the uPath HER2 DISH IA is non-inferior to manual scoring and supports its use as an aid for pathologists in routine diagnosis of breast cancer.

Establishment of Rapid and Accurate Screening System for Molecular Target Therapy of Osteosarcoma.

Introduction Comprehensive analyses using clinical sequences subcategorized osteosarcoma (OS) into several groups according to the activated signaling pathways. Mutually exclusive co-occurrences of gene amplification (PDGFRA/KIT/KDR, VEGFA/CCND3, and MDM2/CDK4) have been identified in approximately 40% of OS, representing candidate subsets for clinical evaluation of additional therapeutic options. Thus, it would be desirable to evaluate the specific gene amplification before starting therapy in patients with OS. Materials and Methods This is a retrospective study. We examined 13 cases of clinical OS samples using NanoString-based copy number variation (CNV) analysis. Decalcification and chemotherapeutic effects on this analysis were also assessed. Results First, the accuracy of this system was validated by showing that amplification/deletion data obtained from this system using various types of cancer cell lines almost perfectly matched to that from the Cancer Cell Line Encyclopedia (CCLE). We identified potentially actionable alterations in CDK4/MDM2 amplification in 10% of samples and potential additional therapeutic targets (PDGFRA/KIT/KDR and VEGFA/CCND3) in 20% of samples, which is consistent with the reported frequencies. Furthermore, this assay could identify these potential therapeutic targets regardless of the sample status (frozen vs formalin-fixed paraffin-embedded [FFPE] tissues). Conclusion We established a NanoString-based rapid and cost-effective method with a short turnaround time (TAT) to examine gene amplification status in OS. This CNV analysis using FFPE samples is recommended where the histological evaluation of viable tumor cells is possible, especially for tumors after chemotherapy with higher chemotherapeutic effects.

Fibroblast growth factor (FGF), FGF receptor (FGFR), and cyclin D1 (CCND1) DNA methylation in head and neck squamous cell carcinomas is associated with transcriptional activity, gene amplification, human papillomavirus (HPV) status, and sensitivity to tyrosine kinase inhibitors

BackgroundDysregulation of fibroblast growth factor receptor (FGFR) signaling pathway has been observed in head and neck squamous cell carcinoma (HNSCC) and is a promising therapeutic target for selective tyrosine kinase inhibitors (TKIs). Potential predictive biomarkers for response to FGFR-targeted therapies are urgently needed. Understanding the epigenetic regulation of FGF pathway related genes, i.e. FGFRs, FGFs, and CCND1, could enlighten the way towards biomarker-selected FGFR-targeted therapies.MethodsWe performed DNA methylation analysis of the encoding genes FGFR1, FGFR2, FGFR3, FGFR4, FGF1-14, FGF16-23, and CCND1 at single CpG site resolution (840 CpG sites) employing The Cancer Genome Research Atlas (TCGA) HNSCC cohort comprising N = 530 tumor tissue and N = 50 normal adjacent tissue samples. We correlated DNA methylation to mRNA expression with regard to human papilloma virus (HPV) and gene amplification status. Moreover, we investigated the correlation of methylation with sensitivity to the selective FGFR inhibitors PD 173074 and AZD4547 in N = 40 HPV(−) HNSCC cell lines.ResultsWe found sequence-contextually nuanced CpG methylation patterns in concordance with epigenetically regulated genes. High methylation levels were predominantly found in the promoter flank and gene body region, while low methylation levels were present in the central promoter region for most of the analyzed CpG sites. FGFRs, FGFs, and CCND1 methylation differed significantly between tumor and normal adjacent tissue and was associated with HPV and gene amplification status. CCND1 promoter methylation correlated with CCND1 amplification. For most of the analyzed CpG sites, methylation levels correlated to mRNA expression in tumor tissue. Furthermore, we found significant correlations of DNA methylation of specific CpG sites with response to the FGFR1/3–selective inhibitors PD 173074 and AZD4547, predominantly within the transcription start site of CCND1.ConclusionsOur results suggest an epigenetic regulation of CCND1, FGFRs, and FGFs via DNA methylation in HNSCC and warrants further investigation of DNA methylation as a potential predictive biomarker for response to selective FGFR inhibitors in clinical trials.