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Background: HER2+ breast cancer (BC) is a heterogeneous disease. Despite the novel targeted therapies against HER2, 20% of patients relapse. In this context, the identification of prognostic and predictor biomarkers is needed.MicroRNAs are involved in BC development and prognosis. We aim at assessing the prognostic significance of two micro-RNAs (named as A and B) in HER2+ BC tissue, to propose signature to predict relapse in this setting of patients.Methods: This study was conducted in a cohort of 46 non-consecutive HER2+ BC patients diagnosed at Hospital Clínico València-INCLIVA. All BC patients received standard treatment for localized disease. RNA was isolated from formalin-fixed paraffin-embedded primary tumor and retro-transcribed to cDNA. MicroRNA expression levels were measured by real-time qPCR and results were normalized according to the expression of housekeeping mir-16 miRNA.Non-parametric Mann-Whitney U test was used to ascertain the statistical significance of differences in miRNA expression levels between disease-free and relapse. Receiver operator characteristics (ROC) curves were constructed and AUC, accuracy, specificity and sensitivity, were calculated as a biomarker performance parameter. The best cutoff value was established based on the highest value obtained in ROC curve analysis according to the maximization of the sum of sensitivity and specificity. Kaplan-Meier curves were plotted for disease-free survival (DFS) based on the best cutoff value. As both microRNA showed high correlation (r=0.85), a genetic signature was developed using Principal Component Analysis (PCA) and extracting the first component. MicroRNA targeted genes were downloaded from miRTarBase (release 7.0). Cytoscape (v3.8.0) and ClueGo (v2.5.6) and were used to perform functional enrichment analysis with the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database. To select significant terms/pathways, p-values were adjusted by the Benjamni-Hochberg method (p Citation Format: Anna Adam-Artigues, Juan Antonio Carbonell-Asíns, Sheila Zúñiga-Trejos, Santiago Moragón, Maria Teresa Martínez, Cristina Hernando, Eduardo Tormo, Octavio Burgués, Ana Lluch, Begoña Bermejo, Pilar Eroles, Juan Miguel Cejalvo. Identification of a novel two-microRNA signature for recurrence prediction in HER2 positive breast cancer [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PS19-20.

Background: Colorectal cancer (CRC) is one of the most fatal malignancies worldwide, and this is in part due to high rates of tumor recurrence in these patients. Currently, TNM staging remains the gold standard for predicting prognosis and recurrence in CRC patients; however, this approach is inadequate for the identification of high-risk patients who have the highest likelihood of disease recurrence. Recent evidence has revealed that enhancer RNAs (eRNAs) represent a higher level of cellular regulation and their expression is frequently dysregulated in several cancers including CRC. However, the clinical significance of eRNAs as recurrence predictor biomarkers in CRC remains unexplored; which was the primary aim of the present study. Methods: We performed a systematic analysis of eRNA expression profiles in a cohort of 379 colon cancer (CC) and 140 rectal cancer (RC) patients, from the TCGA dataset. By using rigorous biomarker discovery approaches, by splitting the entire dataset into a training and the testing cohort, we identified an eRNA signature for recurrence prediction in CRC. Kaplan-Meier analysis, multivariate and LASSO Cox regression models were used to evaluate the associations of discovered biomarkers with patient cancer recurrence. The performance of the prioritized biomarker panel was first trained and compared against key clinicopathological factors using receiver operating characteristics (ROC) curves analysis, followed by independent validation in the testing cohort. Results: The genome-wide eRNA profiling resulted in the identification of a 22-eRNA in patients with CC, and a 19-eRNA panel in RC. These biomarker panels performed robustly in stratifying low and high-risk CC (P= 7.29e-05) and RC (P = 6.81e-03) patients with recurrence. Multivariate Cox regression analysis demonstrated that both biomarker panels were independent predictors of recurrence, and significantly superior than TNM staging in CC (HR = 11.89, P = 9.54e-04) and RC (HR = 3.91, P = 3.52e-02). Importantly, ROC analysis revealed that both panels exhibited excellent recurrence prediction accuracy in CC (AUC = 0.83; 95% CI: 0.74-0.93) and RC (AUC = 0.81; 95% CI: 0.72-0.92) patients. Subsequently, a combination signature which included the eRNA panels along with TNM staging achieved an even greater predictive accuracy in patients with CC (AUC=0.85) and RC (AUC=0.83). Conclusions: Herein, we report a novel eRNA signature for predicting recurrence in patients with CRC. Pending validation in independent clinical cohorts, these biomarkers have the potential to improve current risk stratification approaches for guiding precision oncology treatments in patients suffering from this lethal malignancy. Citation Format: Divya Sahu, Ajay Goel. Transcriptomic profiling identifies an enhancer RNA signature for recurrence prediction in colorectal cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 650.

Background: Diffuse type gastric cancer (DGC), represented by low sensitivity to 5-fluorouracil based chemotherapy and poor prognosis, is a heterogenous malignancy in which patient subsets exhibit diverse oncological risk-profiles. The current risk stratification criteria based on the TNM staging system are inadequate in predicting the prognosis and in guiding optimal treatment in patients with DGC. Our study aimed to develop molecular biomarkers for prognostic risk-stratification in patients with DGC. Methods: We undertook a systematic and comprehensive discovery and validation effort to identify recurrence prediction biomarkers by analyzing genome-wide transcriptomic expression profiling data from 157 patients with DGC (GSE62254, GSE13861, and TCGA-STAD). Rigorous bioinformatic approaches were used to identify biomarker panels, followed by evaluation of their performance in two independent clinical cohorts (a training cohort, n=180; a validation cohort, n=74) using qRT-PCR assays. Furthermore, using Cox proportional hazard and Kaplan-Meier analyses, the clinical significance of the biomarkers was evaluated for their ability to predict recurrence individually, and when analyzed in combination with key prognostic factors. Results: Genome-wide transcriptomic profiling identified a 7-gene panel for robust prediction of recurrence in DGC patients (AUC=0.91; 95% CI: 0.83-0.96), which was successfully validated in an independent dataset (AUC=0.86; 95% CI: 0.66-0.96). Examination of 180 tissue specimens from a training cohort allowed us to establish a risk prediction model (AUC=0.78; 95% CI: 0.71-0.84), which was subsequently validated in an independent cohort of 74 GC patients (AUC=0.83; 95% CI: 0.72-0.90). The Kaplan-Meier analyses exhibited a consistently superior performance of our 7-gene risk-prediction model in the identification of high and low-risk patient subgroups. Interestingly, the performance of our gene panel to predict recurrence was significantly improved when combined with the tumor stage, in both clinical cohorts (AUC=0.83; 95% CI: 0.76-0.88 - training cohort; AUC=0.89; 95% CI: 0.80-0.95 - validation cohort). Finally, for an easier clinical translation, we established a nomogram that comprised of the 7-gene panel and the tumor stage, which robustly predicted prognosis in patients with DGC. Conclusions: We developed a novel gene expression-based risk-stratification model for predicting recurrence and the identification of high-risk patients, which could facilitate individualized treatments and improving the survival outcomes in patients suffering from DGC. Citation Format: Inseob Lee, Divya Sahu, Hoon Hur, Jeong-Hwan Yook, Byung-Sik Kim, Ajay Goel. Discovery and validation of a gene expression signature for recurrence prediction in high-risk diffuse-type gastric cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 654.

Purpose: Colorectal cancer (CRC) is one of the leading causes of cancer-related deaths worldwide. In 2016, there were estimated 95,270 newly diagnosed CRC cases, and 49,190 deaths from this disease in the United States. Survival of patients is closely associated with the tumor stage at the time of diagnosis as 5-year relative survival rates range from 65% for all stages, 90% for localized primary tumor, 71% for regional metastasis and 13% for distant metastasis. Post-surgery, adjuvant therapy is only recommended to those with high risk stage II, as well as stage III and IV tumors. However, approximately 40-50% of the patients undergo curative surgery only and 20-30% that are treated with adjuvant chemotherapy, eventually relapse and experience a metastatic disease and eventual death. The current gold standard TNM (Tumor, Node, Metastasis) staging for determining the prognosis of CRC patients remains inadequate at identification of high-risk stage II and III patients that have a high potential of developing tumor recurrence. MicroRNAs (miRNAs) play an important role in CRC development and are emerging as important disease biomarkers. Therefore, in this study we sought to determine the prognostic potential of the miRNAs using a systematic, genome-wide biomarker discovery approach, followed by validation of biomarkers in multiple patient cohorts Experimental design: Three independent publicly available genome-wide miRNA expression datasets were used for miRNA biomarker discovery (n=158) and validation (n=109 and n=40) of this miRNA signature for recurrence prediction in stage II and III CRC using multivariate Cox regression analysis. The eight gene miRNA signature discovered from the genome-wide analysis was analytically validated in two independent patient cohorts (n=127 and n=96) using Taqman-based RT-PCR assays. Results: The genome-wide comprehensive analysis led to the identification of an eight gene miRNA classifier that significantly predicted recurrence free survival (RFS) in training (log-rank p=0.003) and two independent validation cohorts (log-rank p Conclusions: In conclusion, we have identified a novel miRNA-based classifier, which is robustly predictive of poor prognosis in patents with stage II/III CRC, and might facilitate identification and stratification of high-risk patients that are candidates for adjuvant chemotherapy and clinical surveillance. Citation Format: Raju Kandimalla, Feng Gao, Naoki Takahashi, Yasuhide Yamada, Takatoshi Matsuyama, Toshiyaki Ishikawa, Xin Wang, Ajay Goel. Genome-wide discovery and identification of a novel microrna signature for recurrence prediction in colorectal cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 5721. doi:10.1158/1538-7445.AM2017-5721

Hormone receptor positive breast cancer patients have relatively good prognosis after tamoxifen treatment but still about 40% of patients experience treatment failure (1). Recently microRNAs (miRNAs) have been spotlighted as a new biomarker for predicting prognosis and treatment response. The purpose of this study is to identify microRNAs (miRNAs) that related with distant recurrence after tamoxifen and standard treatment and then build an early recurrence prediction model. Methods: Our study measured the expression of 5 miRNAs (miR-134, miR-125b-5P, miRNA-30a, miR-10a-5p, and miR-222) that are selected as a tamoxifen resistant related miRNA by previous micro-array experiment and paper review. 176 FFPE tumor tissues of 176 patients who have hormone receptor positive breast cancer with tamoxifen were measured miRNA expression by quantitative real-time PCR (qRT-PCR). Results: We identified all 5 miRNAs are up-regulated in distant recurrence cases within 5 years after surgery. Other clinico-pathological parameters that are tumor grade, pathologic stage, HER2 statue and P53 status didn't have any specific correlation with expression of miRNAs. The results suggest that distant recurrence during tamoxifen treatment might be affected by tamoxifen resistant related miRNAs. Kaplan-Meier survival analyses based on expression cut-offs determined by receiver characteristics curves show high expression of miR-134, miR-125b-5P, miRNA-30a, miR-10a-5p, and miR-222 were associated with short relapse-free time(log-rank P = 0.006, P Citation Format: Aeree Kim, Chungyeul Kim. Recurrence prediction model using microRNA expression in hormone receptor positive and tamoxifen treated breast cancer. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 3980. doi:10.1158/1538-7445.AM2015-3980

Recurrence Prediction Model for Early Gastric Cancer Joo Hoon Kim, Hyo Song Kim, Kiyeol Kim, MD, 5 Ji Fu Lai, MD, Hyun Cheol Chung, MD, 2,3,4 Sung Hoon Noh, MD 1,2,3 Sun Young Rha, MD, 2,3,4 1Cancer Metastasis Research Center, 2National Biochip Research Center, 3Brain Korea 21 Project for Medical Science, 4Yonsei Cancer Center, Yonsei Cancer Research Institute, 5Department of Internal Medicine, 1 Department of Surgery, Yonsei University College of Medicine 5 Oral cancer research institute, Purpose: Recurrence in early gastric cancer (EGC) after curative resection is rare, and in which type of EGC would recur is not well studied. We tried to make a predicting system for recurrence in EGC after curative resection. Methods: From January 1987 to April 2005, 2923 patients with EGC underwent R0 curative resection in at Severance Hospital, Yonsei University were enrolled. Logistic regression was performed to identify independent risk factors for recurrence in EGC. Nomogram was developed on the basis of the Cox regression. The concordance index (c-index) was used for accuracy measure, with bootstrapping to correct for optimistic bias. Calibration plots were constructed. Results: Out of enrolled 2923 patients, 79 patients were recurred (2.7%). The median time to recurrence was 20.5 months, and among them early recurrence developed within 2 years after surgery were 60.7%. In logistic results, lymph node metastasis and elevated gross type were independent risk factors for overall recurrence; whereas overall recurrence rate was higher in patients with both identified risk factors (17.5%). Meanwhile, male gender, elevated gross type and lymph node metastasis were significantly associated with early recurrence, and in patients with all identified risk factors above, the early recurrence rate was 12.2%. Nomogram for predicting the disease-free survival after curative surgery was constructed. Its c-index was 0.79 and the calibration appeared to be accurate as internal validation. Conclusion: Recurrence in EGC after R0 resection is predictable by using common clinical characteristics. Analyzing the combination of independent risk factors, patients in high risk of early and overall recurrence could be identified. An internally validated nomogram is established to predict disease-free survival accurately and individually. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 892.