scholarly journals Plasma extracellular vesicle long RNA profiles in the diagnosis and prediction of treatment response for breast cancer

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Yonghui Su ◽  
Yuchen Li ◽  
Rong Guo ◽  
Jingjing Zhao ◽  
Weiru Chi ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Patients ◽  
Treatment Efficacy ◽  
Breast Imaging ◽  
Early Stage ◽  
Neoadjuvant Treatment ◽  
Extracellular Vesicle ◽  
Breast Cancer Patients ◽  
Mtorc1 Signaling ◽  
Efficacy Prediction

AbstractA large number RNAs are enriched and stable in extracellular vesicles (EVs), and they can reflect their tissue origins and are suitable as liquid biopsy markers for cancer diagnosis and treatment efficacy prediction. In this study, we used extracellular vesicle long RNA (exLR) sequencing to characterize the plasma-derived exLRs from 112 breast cancer patients, 19 benign patients and 41 healthy participants. The different exLRs profiling was found between the breast cancer and non-cancer groups. Thus, we constructed a breast cancer diagnostic signature which showed high accuracy with an area under the curve (AUC) of 0.960 in the training cohort and 0.900 in the validation cohort. The signature was able to identify early stage BC (I/II) with an AUC of 0.940. Integrating the signature with breast imaging could increase the diagnosis accuracy for breast cancer patients. Moreover, we enrolled 58 patients who received neoadjuvant treatment and identified an exLR (exMSMO1), which could distinguish pathological complete response (pCR) patients from non-pCR with an AUC of 0.790. Silencing MSMO1 could significantly enhance the sensitivity of MDA-MB-231 cells to paclitaxel and doxorubicin through modulating mTORC1 signaling pathway. This study demonstrated the value of exLR profiling to provide potential biomarkers for early detection and treatment efficacy prediction of breast cancer.

scholarly journals Plasma Extracellular Vesicle Long RNA Profiles in the Diagnosis and Prediction of Treatment Response for Breast Cancer

2020 ◽  
Author(s):  
Yonghui Su ◽  
Jingjing Zhao ◽  
Rong Guo ◽  
Hongyan Lai ◽  
Weiru Chi ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neoadjuvant Chemotherapy ◽  
Cancer Patients ◽  
Treatment Efficacy ◽  
Validation Cohort ◽  
Extracellular Vesicle ◽  
Support Vector ◽  
Breast Cancer Patients ◽  
Training Cohort ◽  
Efficacy Prediction

Abstract Background: The utility of extracellular vesicle long RNAs (exLRs) as noninvasive biomarkers in breast cancer remains elusive. The purpose of this study was to explore the potential of exLRs as clinically actionable biomarkers for breast cancer diagnosis, classification, and neoadjuvant therapy efficacy prediction. Methods: One hundred and seventy-two participants, including 112 breast cancer patients, 19 benign patients and 41 healthy controls, were enrolled in this case-control study. The exLR profile of the plasma samples was analyzed by exLR sequencing. The d-signature was identified using a support vector machine algorithm with a training cohort (n=120) and was validated using an internal validation cohort (n=52). Treatment efficacy prediction was conducted with 48 patients who received neoadjuvant chemotherapy.Results: We constructed a breast cancer diagnostic signature that showed high accuracy with an area under the curve (AUC) of 0.960 in the training cohort and 0.900 in the validation cohort. The signature was able to identify early stage BC (I/II) with an AUC of 0.940. Integrating the signature could increase the diagnosis accuracy by up to 91.9% for breast cancer patients with the corresponding predictive results based on the Breast Imaging Reporting and Data System classification of 4 or 5. Moreover, the exLRs could provide a strong indication of the breast cancer subtypes, and exMSMO1 is employable as a predictive biomarker in response to neoadjuvant chemotherapy.Conclusions: This study demonstrated the value of exLR profiling to provide potential biomarkers for early detection and treatment efficacy prediction of breast cancer.

scholarly journals Breast Cancer Patients’ Treatment Expectations after Exposure to the Decision Aid Program Adjuvant Online: The Influence of Numeracy

2010 ◽  
Vol 30 (4) ◽  
pp. 464-473 ◽  
Author(s):  
Isaac M. Lipkus ◽  
Ellen Peters ◽  
Gretchen Kimmick ◽  
Vlayka Liotcheva ◽  
Paul Marcom
Keyword(s):  
Breast Cancer ◽  
Cancer Patients ◽  
Treatment Efficacy ◽  
Decision Aid ◽  
Early Stage ◽  
Combined Therapy ◽  
Breast Cancer Patients ◽  
Treatment Expectations ◽  
Free Survival ◽  
Perceived Benefit

The decision aid called ‘‘Adjuvant Online’’ (Adjuvant! for short) helps breast cancer patients make treatment decisions by providing numerical estimates of treatment efficacy (e.g., 10-y relapse or survival). Studies exploring how patients’ numeracy interacts with the estimates provided by Adjuvant! are lacking. Pooling across 2 studies totaling 105 women with estrogen receptor—positive, early-stage breast cancer, the authors explored patients’ treatment expectations, perceived benefit from treatments, and confidence of personal benefit from treatments. Patients who were more numerate were more likely to provide estimates of cancer-free survival that matched the estimates provided by Adjuvant! for each treatment option compared with patients with lower numeracy (odds ratios of 1.6 to 2.4). As estimates of treatment efficacy provided by Adjuvant! increased, so did patients’ estimates of cancer-free survival (0.37 > rs > 0.48) and their perceptions of treatment benefit from hormonal therapy (rs = 0.28) and combined therapy (rs = 0.27). These relationships were significantly more pronounced for those with higher numeracy, especially for perceived benefit of combined therapy. Results suggest that numeracy influences a patient’s ability to interpret numerical estimates of treatment efficacy from decision aids such as Adjuvant!.

scholarly journals Usefulness of 3D-surgical guides in breast conserving surgery after neoadjuvant treatment

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Han Shin Lee ◽  
Hee Jeong Kim ◽  
Il Yong Chung ◽  
Jisun Kim ◽  
Sae Byul Lee ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Patients ◽  
Neoadjuvant Treatment ◽  
Breast Conserving Surgery ◽  
Resection Margins ◽  
Breast Cancer Patients ◽  
Original Tumor ◽  
Surgical Guides ◽  
Neoadjuvant Systemic Therapy ◽  
Tumor Area

AbstractWe used 3D printed-breast surgical guides (3DP-BSG) to designate the original tumor area from the pre-treatment magnetic resonance imaging (MRI) during breast-conserving surgery (BCS) in breast cancer patients who received neoadjuvant systemic therapy (NST). Targeting the original tumor area in such patients using conventional localization techniques is difficult. For precise BCS, a method that marks the tumor area found on MRI directly to the breast is needed. In this prospective study, patients were enrolled for BCS after receiving NST. Partial resection was performed using a prone/supine MRI-based 3DP-BSG. Frozen biopsies were analyzed to confirm clear tumor margins. The tumor characteristics, pathologic results, resection margins, and the distance between the tumor and margin were analyzed. Thirty-nine patients were enrolled with 3DP-BSG for BCS. The median nearest distance between the tumor and the resection margin was 3.9 cm (range 1.2–7.8 cm). Frozen sections showed positive margins in 4/39 (10.3%) patients. Three had invasive cancers, and one had carcinoma in situ; all underwent additional resection. Final pathology revealed clear margins. After 3-year surveillance, 3/39 patients had recurrent breast cancer. With 3DP-BSG for BCS in breast cancer patients receiving NST, the original tumor area can be identified and marked directly on the breast, which is useful for surgery. Trial Registration: Clinical Research Information Service (CRIS) Identifier Number: KCT0002272. First registration number and date: No. 1 (27/04/2016).

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