bluuebunny/crossref_metadata_2025_split huggingface dataset:
Abstract PS3-25: Quantitation of serum thioredoxin 1 could mitigate difficulty to detect breast cancer from dense breasts by mammography
Authors: Suh Kyoung Hoon , Choi Bo Bae , Lee Youn Ju , Kim Je Ryoong , Lee Jin Sun , Kim Young 
Published: February 2021

Abstract
Background: Even though almost half of women have dense breasts (DB), the density of breasts has been an obstacle to detect breast cancer (BC) correctly by the gold standard of screening, mammography. It is strongly recommended to take consideration of breast density in analyzing mammogram. We have reported the possibility of serum level of thioredoxin 1 (Trx1) as a novel means to assess the risk of BC regardless of various conditions of breasts. In present study, we have evaluated how serum level of Trx1 can mitigate the difficulty to detect BC from dense breast by mammography. Methods: We have generated monoclonal antibodies against Trx1 and developed an ELISA kit that quantitates Trx1 in serum. The level of Trx1 was determined in each serum from BC patients (n=106) who had been confirmed as BC by various examinations including mammography as well as surgery. Each Trx1 level was analyzed with corresponding patient’s mammography results such as patterns, asymmetry, shapes, margin, and density. The analyzed results were compared with those from other biomarker tests including CA15-5 and CEA to evaluate the clinical validity of Trx1 in this study. Each test was duplicated two times, and test results were analyzed by ROC analysis, one-way ANOVA tests, and unpaired t-tests.Results: Most of BC patients (82.6%, n=86) was classified as mammography pattern 3 and 4, and BI-RADS score higher than 4 (74.0%, n=77). More than half of patients’ breast density was in high density group (54.8%, n=57), whereas 5.8% of iso density (n=6) and 39.4% of not determined (n=41). The blood Trx1 levels of patients were higher than the cut-off value of 14 U/ml indicating BC regardless of characteristics of breasts. Although other breast cancer biomarker tests such as CA15-3 and CEA could not identify BC, Trx1 level could correctly detect BC even in the cases that mammography could not show the mass clearly. The sensitivity/specificity of mammography and Trx1 test were 75.2%/74.0% and 96.2%/99.0%, respectively. When mammography and Trx1 test were combined, the whole sensitivity/specificity was 99.3%/100.0%. Conclusion: Even though it is obvious that larger size of further study necessary, the serum Trx1 level showed that it could identify BC in dense breasts easily. These results indicated that serum Trx1 level could efficiently mitigate the difficulty to detect BC from dense breasts by mammography. Keywords: Thioredoxin 1, Breast cancer, Dense breast, Diagnostics, Sensitivity, Specificity, Blood
Citation Format: Kyoung Hoon Suh, Bo Bae Choi, Youn Ju Lee, Je Ryoong Kim, Jin Sun Lee, Young Kim. Quantitation of serum thioredoxin 1 could mitigate difficulty to detect breast cancer from dense breasts by mammography [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 PS3-25.
Abstract PS4-43: Prediction of breast ductal carcinoma in situ recurrence using histomics analysis of stromal features from hematoxylin and eosin stain-based images
Authors: Upadhaya Taman , Balassanian Ronald , Morin Olivier , Park Catherine 
Published: February 2021

Abstract
OBJECTIVE
It remains a challenge to predict individual risk for recurrence after primary treatment of ductal carcinoma in situ (DCIS). While DCIS is contained within the duct, prior studies have pointed to the importance of stromal collagen in cancer progression. Using hematoxylin and eosin (H&E) stain-based images, we studied the collagen structure surrounding DCIS in a case-control cohort of women to determine whether stromal collagen could provide additional risk information.
METHODS
We present a quantitative histology image analysis pipeline (termed “histomics”) that consists of collagen segmentation, quantitative feature extraction and statistical analysis to predict recurrence. A cohort of 73 patients with DCIS (33 cases with recurrence matched with 40 controls) were retrospectively analyzed. H&E images were obtained from pathologic slides and regions-of-interest (ROI) of the collagen in the stroma adjacent to DCIS was automatically segmented using an in-house trained deep learning algorithm. A total of 123 histomics features (intensity, statistical and textural) were then extracted from the segmented ROI for each patient. Kruskal-Wallis test and receiver-operating characteristic methodology was used to assess performance of the features to differentiate patients with and without recurrence.
RESULTS
We found substantial variations in recorded H&E signal between samples, and the intensity and statistical features did not reach statistical significance. However, twelve percent of the textural features had an AUC between 0.62 to 0.61 with p-value between 0.06 to 0.08. Two textural histomics features in particular were significantly associated with DCIS patient recurrence; inverse variance (AUC: 0.64, p-value: 0.046) feature based on grey-level cooccurrences matrix (GLCM) and complexity (AUC 0.65, p-value: 0.025) feature based on neighborhood grey tone difference matrix (NGTDM).
CONCLUSION
Textural stromal features extracted from H&E image-based histomics indicate association of some features with tumor progression in DCIS. H&E image based histomics features can be a promising quantitative tool to develop predictive signatures of recurrence in patients with DCIS. We are processing additional patients through our quantitative image analysis pipeline to determine the significance of histomics features compared to conventional clinical features for the prediction of DCIS recurrence.
Citation Format: Taman Upadhaya, Ronald Balassanian, Olivier Morin, Catherine Park. Prediction of breast ductal carcinoma in situ recurrence using histomics analysis of stromal features from hematoxylin and eosin stain-based images [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 PS4-43.
Abstract PS5-41: Machine learning model of gut microbiota predicts neratinib induced diarrhea in patients with breast cancer
Authors: Wong Chi Wah , Yost Susan E. , Lee Jin Sun , Gillece John D. , Folkerts Megan , Reining Lauren , Highlander Sarah K. , Eftekhari Zahra , Mortimer Joanne , Yuan Yuan 
Published: February 2021

Abstract
Background: Neratinib is a potent small molecule tyrosine kinase inhibitor (TKI) of human epidermal growth factor receptors (HER1,2,4). One of the major side effects of neratinib is diarrhea. The human gut contains a dense microbiome ecosystem that is essential in maintaining a healthy host physiology, and its disruption may lead to increased risk of toxicities from cancer therapy. In this study, we aimed to develop a machine learning model based on analysis of gut microbiota data to predict neratinib-induced diarrhea.
Methods: Patients were enrolled in a phase II trial evaluating safety and tolerability of neratinib in older adults with HER2+ breast cancer (NCT02673398). Neratinib was administered as single agent, 240 mg oral daily in a 28-day cycle. Stool samples were collected at baseline and during treatment for 16S rRNA gene sequencing. Using microbial relative abundance data, we developed gradient-boosted tree models with two nested loops of cross validations to classify whether diarrhea would occur or not after treatment onset. For the inner validation loop, we used ten-fold cross validation to determine the optimal model from hyper-parameters including regularization. For the outer validation loop, we utilized a leave-one-patient-out cross validation to test this model on the hold-out patient’s baseline data and the predictions were used for model assessment.
Results: A total of 11 patients and 50 longitudinal stool samples were collected. The median age was 66 years. 73% developed grade ≥ 1 diarrhea attributed to neratinib. Shannon diversity index of gut microbiome was not associated with diarrhea. For predictive modeling, the outer validation loop Area Under the Receiver Operating Characteristic Curve (AUROC) and Area Under the Precision Recall Curve (AUPRC) were 0.92 and 0.97, respectively. The two most important taxa predictive of protection from diarrhea were Ruminiclostridium 9, and Bacteroides sp. HPS0048. We found that patients with a larger relative abundance of Ruminiclostridium 9 and Bacteroides sp. HPS0048 have reduced risk of neratinib-related diarrhea.
Conclusions: The machine learning model can identify breast cancer patients at risk of diarrhea prior to neratinib use. Future studies are required to validate this finding.
Citation Format: Chi Wah Wong, Susan E. Yost, Jin Sun Lee, John D. Gillece, Megan Folkerts, Lauren Reining, Sarah K. Highlander, Zahra Eftekhari, Joanne Mortimer, Yuan Yuan. Machine learning model of gut microbiota predicts neratinib induced diarrhea in patients with 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 PS5-41.
Abstract PS5-45: The impact of progesterone receptor negativity on oncologic outcomes in estrogen receptor positive breast cancer
Authors: Davey Matthew G , Ryan Éanna J , Folan Padraig J , O'Halloran Niamh , Boland Michael R , Barry Michael Kevin , Sweeney Karl J , Malone Carmel M , McLaughlin Ray J , Kerin Michael J , Lowery Aoife J 
Published: February 2021

Abstract
BACKGROUND: Estrogen receptor (ER) status provides invaluable prognostic and therapeutic information in breast cancer (BC). When clinical decision making is driven by ER status, the value of assessing progesterone receptor (PgR) status is less certain. AIM: To describe the clinicopathologic features of ER positive (ER+) /PgR negative (PgR-) BC and to determine the effect of PgR negativity on oncologic outcomes in ER+ disease.METHODS: Consecutive female patients with ER+ BC managed in a single institution between 2005-2015 were included. Clinicopathological features of PgR- BC were determined. Factors associated with PgR- disease were assessed using binary logistic regression. Oncological outcome was assessed using Kaplan-Meier curves and Cox regression analysis.RESULTS: 2660 patients were included with a median age of 59.6±13.3 years (21-99). Median follow-up was 97.2 months (3.0-181.2). 2208 cases were PgR+ (83.0%) and 452 were PgR- (17.0%). Being postmenopausal (OR:1.656, 95% Confidence interval (CI):1.249-2.195, P&lt;0.001), presenting with symptoms (OR:1.712, 95% CI:1.302-2.249, P&lt;0.001), invasive ductal subtype (OR:1.514, 95% CI:1.166-1.966, P=0.002) and grade 3 tumours (OR:2.198, 95% CI:1.683-2.870, P&lt;0.001) were all associated with PgR- status. In patients receiving neoadjuvant chemotherapy (n=308), pathological complete response rates were 10.1% (25/247) in patients with PgR+ disease, versus 18.0% in those with PgR- disease (11/61) (P=0.050). PgR negativity independently predicted worse disease-free (HR:1.632, 95%CI:1.209-2.204, P=0.001) and overall survival (OS) (HR:1.774, 95%CI:1.324-2.375, P&lt;0.001), as well as worse OS in ER+/HER2- disease (P=0.004). CONCLUSIONS: In ER+ disease, PgR- tumours have more aggressive clinicopathological features and worse oncological outcomes. Neoadjuvant and adjuvant therapeutic strategies should be tailored according to PgR status.
Citation Format: Matthew G Davey, Éanna J Ryan, Padraig J Folan, Niamh O'Halloran, Michael R Boland, Michael Kevin Barry, Karl J Sweeney, Carmel M Malone, Ray J McLaughlin, Michael J Kerin, Aoife J Lowery. The impact of progesterone receptor negativity on oncologic outcomes in estrogen receptor 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 PS5-45.
Abstract PS6-09: An AI-digital breast cancer risk discrimination platform (PreciseDx) using a representative H&E image and selected clinical variables accurately categorized patients with oncotype Dx low risk recurrence scores (RS)
Authors: Donovan Michael Joseph , Shpalensky Nina , Prastawa Marcel , Abishek SM , Scott Richard , Sawyer Mary , Zeineh Jack , Fernandez Gerardo 
Published: February 2021

Abstract
Background: Clinical practice guidelines emphasize the critical importance of grading and stage in breast cancer treatment. Although histologic grade is subjective, non-quantitative, skill-dependent, and oftentimes inaccurate it remains an independent prognostic feature and therefore plays a direct role in patient management including neoadjuvant therapy vs surgery, and interpretation of genomic studies. We developed an AI-based platform which combines digital H&E features with select clinical variables to assess risk of breast cancer recurrence and evaluated ability to predict Oncotype low risk RS categorization. Methods: Retrospective study to identify a subset of Mt. Sinai, NY invasive ductal breast cancer (IDC) patients from 2010-2016 with H&E stained slides, clinical features and OncotypeDx recurrence scores (RS). Recurrence endpoint(s): local- regional, distant-recurrence free and overall survival. Digital images generated with Philips scanning system; reviewed by two pathologist for tumor content and quality prior to image analysis and feature extraction. Support vector machine learning models were used for initial feature performance and final models generated. Positive predictive value (PPV), sensitivity (S) and likelihood ratios (LR) were used for performance. Results: 391 patients: mean age 57 years, 100% Stage I/II, 59% Grade 2, and 6% LN+ve 0-3; 97% IDC, 100% ER+ve, 94% PR+ve, 0% Her2 amplified; median follow-up 61 months; 323 (83%) low risk (&lt;/=25) RS and 68 (17%) high risk RS (&gt;25). There were 23 events (6%) and 13 (56%) were locoregional recurrence. PreciseDx model with age, and PR levels combined with imaging features reflective of mitotic activity and nuclear characteristics (clinical grade not selected) correctly identified LR RS categorization: PPV of 91%, [95CI 0.87-0.94], Se 92% [0.86-0.95] and positive / negative likelihood ratio : 6.8, and 0.5, respectively. Conclusions: Application of an AI-digital breast cancer risk assessment platform using only the H&E image and limited clinical data successfully classified low risk RS patients with high accuracy. Future models will extend outcome to 10 years and evaluate treatment selection and duration
Citation Format: Michael Joseph Donovan, Nina Shpalensky, Marcel Prastawa, SM Abishek, Richard Scott, Mary Sawyer, Jack Zeineh, Gerardo Fernandez. An AI-digital breast cancer risk discrimination platform (PreciseDx) using a representative H&E image and selected clinical variables accurately categorized patients with oncotype Dx low risk recurrence scores (RS) [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 PS6-09.
Abstract PS6-21: High expression of MicroRNA-223 indicates better prognosis in triple negative breast cancer
Authors: Chen Li , Zhu Xiuzhi , Yao Ling , Wang Zonghua 
Published: February 2021

Abstract
Background: MicroRNAs (miRNAs) can influence many biological processes and also show promise as biomarkers for cancer. Few researches have focused on the expression of microRNA-223 (miR-223) and its precise role in breast cancer (BC). We aimed to examine the expression level of miR-223 and further explored the prognostic value in BC.Methods: Tissue microarray-based miRNA detection in situ hybridization with LNA probe was used to detect miR-223 expression in 450 BC tissue. Overall survival (OS) and disease-free survival (DFS) were compared between two groups using the Kaplan-Meier method and Cox regression model.Results: Better OS and DFS were observed in the high-miR-223 expression patients compared to those of low expression patients (P &lt; 0.0001 and P = 0.017, respectively), especially in the triple-negative breast cancer (TNBC) subtype (P = 0.046and P &lt; 0.001, respectively). Combined with the univariate and multivariate Cox regression analysis, TNM stage (p = 0.008), molecular subtype (p =0.049) and miR-223 (p &lt; 0.001) were independently linked to OS and DFS. External validation was performed in METABRIC and TCGA database via online webtool and consistent with the data above.Conclusions: This study provided evidence that high-miR-223 expression at diagnosis was associated with better DFS and OS for BC, especially for the TNBC subtype. miR-223 is a valid and independent prognostic biomarker in BC.
Table Univariate and multivariate survival analysis for disease-free survivalUnivariate MultivariateVariableHR95% CIP valueHR95% CIP valueAge0.594≤40 vs &gt;601.3510.626-2.9140.44440-60 vs &gt;600.9940.548-1.8030.984Menopausal statuspre vs post0.7370.487-1.1130.147DifferentiationII vs III0.7300.457-1.1640.186TNM stage0.0080.008II vs I1.4210.860-2.3460.1701.4850.887-2.4850.132III vs I2.7581.446-5.2590.0022.8581.469-5.5630.002Molecular subtype0.1940.049Luminal B vs Luminal A1.1060.580-2.1070.7600.8130.415-1.5930.546HER2+ vs Luminal A0.9860.497-1.9560.9670.7010.337-1.4580.342Triple-negative vs Luminal A1.6260.930-2.8430.0881.4930.849-2.6260.164MicroRNA-223low vs high3.9602.201-7.126&lt;0.0013.7892.098-6.843&lt;0.001Abbreviations: CI, confidence interval; HR, hazard ratio.The covariates in the Cox model were all categorical variables, and the adjusted p value and HR were derived from the model.
Citation Format: Li Chen, Xiuzhi Zhu, Ling Yao, Zonghua Wang. High expression of MicroRNA-223 indicates better prognosis in triple negative 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 PS6-21.
Abstract PS6-44: The significance of body mass index and absolute lymphocyte count as a prognostic factor for disease-free survival in Korean breast cancer patients
Authors: Ko Sung Min 
Published: February 2021

Abstract
Introduction: The purpose of our study is to identify the association between body mass index (BMI) and absolute lymphocyte count (ALC), and to determine the effect of these two factors on the prognosis of breast cancer patients.Methods: We retrospectively identified 16,375 healthy female and 1,226 primary invasive breast cancer patients from Gangnam Severance Hospital. BMI and complete blood count (CBC) information at the time of diagnosed was collected. A multivariate Cox proportional hazard model was used to variable associated with disease-free survival (DFS).Results: BMI and ALC has positive correlation in breast cancer patients as well as health female (both P &lt; .001). However, the effect of BMI and ALC on prognosis was the opposite. Overweight and obese had worse DFS (HR, 2.00; 95% CIs, 1.36-2.96; P &lt; .001) than underweight or normal weight, but patients with high ALC had better DFS than those with low ALC (HR, 0.42; 95% CIs, 0.28-0.63; P &lt; .001). When the risk stratification group was divided according to BMI /ALC, the high-risk group who had high BMI and low ALC had worse DFS than low-risk group (HR, 2.61; 95% CIs, 1.51-4.51; P = .001). In subgroup analysis, Her2-overexpressing and early stage tumor was more affected by BMI/ALC.Conclusions: The impact of BMI and ALC on prognosis in breast cancer was the opposite although BMI and ALC had positive correlation. Patients with high BMI and low ALC had worse DFS than others. Weight management is essential to breast cancer patients, and additional study about correlation between BMI and ALC affecting prognosis is needed in the future.
Citation Format: Sung Min Ko. The significance of body mass index and absolute lymphocyte count as a prognostic factor for disease-free survival in Korean breast cancer patients [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 PS6-44.
Abstract PS6-48: Clinicopathologic features and follow-up outcomes of breast cancers with HER2 FISH group 3 Results: A single institution experience
Authors: Zhang Huina , Ajabnoor Rana , Hicks David , Turner Bradley 
Published: February 2021

Abstract
INTRODUCTION: There is dearth information on the long-term outcome and role of HER2 targeted therapy in breast cancer patients in the HER2 FISH group 3 category, which is defined as breast cancer with a HER2/CEP17 ratio &lt; 2 and HER2 copy number ≥ 6 signals/cell. In this study, we report the clinicopathologic features and outcomes of breast cancer patients in the HER2 FISH group 3 category at our institution. METHODS: We identified 52/2,874 (1.8%) breast cancer patients with HER2 FISH group 3 results between 1/2007 and 3/2020. 28 of these 52 patients had available detailed clinicopathologic and follow-up data, with an average follow-up of 38.5 months. RESULTS: Most of the cases with group 3 FISH results were high grade ductal carcinomas with positive hormonal receptor expressions and equivocal HER2 expression by immunohistochemistry. Among all the clinicopathologic variables, only tumor size (p=0.048) significantly contributed to the poor clinical outcomes. HER2 copy number failed to show any significant association with histologic grade, tumor size, clinical stage, hormonal receptor status or disease outcomes. There was no statistically significant difference in disease outcome between patients who were treated with HER2 targeted therapy and patients who did not receive HER2 targeted therapy, regardless of the HER2 copy number or clinical stage (Table 1). CONCLUSION: Our preliminary findings suggest that certain patients with HER2 FISH group 3 category breast cancer may not need HER-2 targeted therapy. Larger-scale studies are needed to further evaluate which HER2 FISH group 3 results are more likely to benefit from the HER-2 targeted therapy.
Table 1. Response to HER2 targeted therapy in HER2 FISH group 3 breast cancer patients based on HER2 copy numbers and clinical stageHER 2 targeted therapyp-valueGivenNot givenTotal populationGood outcome (n=21)1380.371Bad outcome (n=7)61HER2 copy number &lt; 10Good outcome (n=16)1060.366Bad outcome (n=7)61HER2 copy number ≥ 10Good outcome (n=5)321.000Bad outcome (n=0)00Stage I-IIGood outcome (n=16)970.737Bad outcome (n=3)21Stage III-IVGood outcome (n=5)410.343Bad outcome (n=4)40*Good outcome = No evidence of disease**Bad outcome = Local recurrence, distant metastasis, and/or died of disease
Citation Format: Huina Zhang, Rana Ajabnoor, David Hicks, Bradley Turner. Clinicopathologic features and follow-up outcomes of breast cancers with HER2 FISH group 3 Results: A single institution experience [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 PS6-48.