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Data_Figure 1_Impact of 17β‑HSD12, the 3‑ketoacyl‑CoA reductase of long‑chain fatty acid synthesis, on breast cancer cell proliferation and migration

Data of figure 1 from Impact of 17β‑HSD12, the 3‑ketoacyl‑CoA reductase of long‑chain fatty acid synthesis, on breast cancer cell proliferation and migration Dataset (doi: 10.1007/s00018-019-03227-w) contains the original figure as TIF-format (10.1007_s00018-019-03227-w_CMLS_Fig1). Corresponding raw data obtained from a) cellomics HTC array scan analysis provided as seven files in CSV format (31003A-179400_Date_examiner_17BHSD12_8_1_1-7), b) raw data obtained from proliferation investigation on xCELLigence provided as one (31003A-179400_date_examiner_17BHSD12_9_1_1) file in CSV format. All further experiment related information and subsequent data analysis provided as two meta-data-files as TXT format (31003A-179400_date_examiner_17BHSD12_8/9_1_M_1).

Data_supplemental figure 10_Impact of 17β‑HSD12, the 3‑ketoacyl‑CoA reductase of long‑chain fatty acid synthesis, on breast cancer cell proliferation and migration

Data of supplemental figure 10 from Impact of 17β‑HSD12, the 3‑ketoacyl‑CoA reductase of long‑chain fatty acid synthesis, on breast cancer cell proliferation and migration Dataset (doi: 10.1007/s00018-019-03227-w) contains the original figure as TIF-format (10.1194_jlr.M092908_Fig. S10). Corresponding raw data obtained from a/b) western blot and densitometry provided as 20 files in CSV format (31003A-179400_date_examiner_17BHSD12_2_27-30_1-5), c) mRNA content analyzed by RT-PCR provided as 10 files in CSV format (31003A-179400_date_examiner_17BHSD12_1_11-12_1-6). d) RT-PCR provided as 10 files in CSV format (31003A-179400_date_examiner_17BHSD12_1_13_1-4). All further experiment related information protocols and subsequent data analysis provided as meta-data-files (31003A-179400_date_examiner_17BHSD12_1/2_dataset_M_1) as TXT format and (31003A-179400_date_examiner_17BHSD12_2_dataset_M_2-3) as PNG format.

Data_supplemental figure 9_Impact of 17β‑HSD12, the 3‑ketoacyl‑CoA reductase of long‑chain fatty acid synthesis, on breast cancer cell proliferation and migration

Data of supplemental figure 9 from Impact of 17β‑HSD12, the 3‑ketoacyl‑CoA reductase of long‑chain fatty acid synthesis, on breast cancer cell proliferation and migration Dataset (doi: 10.1007/s00018-019-03227-w) contains the original figure as TIF-format (10.1194_jlr.M092908_Fig. S9). Corresponding raw data obtained from a) western blot and densitometry provided as four files in CSV format (31003A-179400_date_examiner_17BHSD12_2_23_1-5); b) Cellomics HTC array scan analysis provided as eight files in CSV format (31003A-179400_Date_examiner_17BHSD12_8_19-20_1-5); c) western blot and densitometry provided as four files in CSV format (31003A-179400_date_examiner_17BHSD12_2_26_1-3), d) mRNA content analyzed by RT-PCR provided as 10 files in CSV format (31003A-179400_date_examiner_17BHSD12_1_9-10_1-6), e) western blot and densitometry provided as four files in CSV format (31003A-179400_date_examiner_17BHSD12_2_25_1-3), f) western blot and densitometry provided as four files in CSV format (31003A-179400_date_examiner_17BHSD12_2_24_1-3) All further experiment related information protocols and subsequent data analysis provided as meta-data-files (31003A-179400_date_examiner_17BHSD12_1/2/8_dataset_M_1) as TXT format and (31003A-179400_date_examiner_17BHSD12_2_dataset_M_2-3) as PNG format.

Data_supplemental figure 8_Impact of 17β‑HSD12, the 3‑ketoacyl‑CoA reductase of long‑chain fatty acid synthesis, on breast cancer cell proliferation and migration

Data of supplemental figure 8 from Impact of 17β‑HSD12, the 3‑ketoacyl‑CoA reductase of long‑chain fatty acid synthesis, on breast cancer cell proliferation and migration Dataset (doi: 10.1007/s00018-019-03227-w) contains the original figure as TIF-format (10.1194_jlr.M092908_Fig. S8). Corresponding raw data obtained from a) cellomics HTC array scan analysis provided as five files in CSV format (31003A-179400_Date_examiner_17BHSD12_8_18_1-5); b) western blot and densitometry provided as four files in CSV format (31003A-179400_date_examiner_17BHSD12_2_22_1-4), investigation of migration on xCELLigence provided as four (31003A-179400_date_examiner_17BHSD12_9_5_1) files in CSV format. All further experiment related information protocols and subsequent data analysis provided as meta-data-files (31003A-179400_date_examiner_17BHSD12_2/8/9_dataset_M_1) as TXT format and (31003A-179400_date_examiner_17BHSD12_2_dataset_M_2-3) as PNG format.

Data_supplemental figure 5_Impact of 17β‑HSD12, the 3‑ketoacyl‑CoA reductase of long‑chain fatty acid synthesis, on breast cancer cell proliferation and migration

Data of supplemental figure 5 from Impact of 17β‑HSD12, the 3‑ketoacyl‑CoA reductase of long‑chain fatty acid synthesis, on breast cancer cell proliferation and migration Dataset (doi: 10.1007/s00018-019-03227-w) contains the original figure as TIF-format (10.1194_jlr.M092908_Fig. S5). Corresponding raw data from immunofluorescence measurements provided as three files (31003A-179400_20190528_MT, PST, ADU_17BHSD12_13_2_1-3) in png format. All further experiment related information protocols and subsequent data analysis provided as meta-data-file (31003A-179400_date_examiner_17BHSD12_13_2_M_1) as TXT format.

Data_supplemental figure 1_Impact of 17β‑HSD12, the 3‑ketoacyl‑CoA reductase of long‑chain fatty acid synthesis, on breast cancer cell proliferation and migration

Data of supplemental figure 1 from Impact of 17β‑HSD12, the 3‑ketoacyl‑CoA reductase of long‑chain fatty acid synthesis, on breast cancer cell proliferation and migration Dataset (doi: 10.1007/s00018-019-03227-w) contains the original figure as TIF-format (10.1194_jlr.M092908_Fig. S1). (31003A-179400_date_examiner_17BHSD12_2_14-17) PNG format. All further experiment related information protocols as meta-data-files (31003A-179400_date_examiner_17BHSD12_2_dataset_M_1) as TXT format.

Data_Figure 8_Impact of 17β‑HSD12, the 3‑ketoacyl‑CoA reductase of long‑chain fatty acid synthesis, on breast cancer cell proliferation and migration

Data of figure 8 from Impact of 17β‑HSD12, the 3‑ketoacyl‑CoA reductase of long‑chain fatty acid synthesis, on breast cancer cell proliferation and migration Dataset (doi: 10.1007/s00018-019-03227-w) contains the original figure as TIF-format (10.1194_jlr.M092908_Fig. 8). Corresponding raw data obtained from a1/2) Western blot and densitometry provided as three files in CSV format (31003A-179400_date_examiner_17BHSD12_2_9_1-3), mRNA content analyzed by RT-PCR provided as four files in CSV format (31003A-179400_date_examiner_17BHSD12_1_7_1-4); b) Western blot and densitometry provided as three files in CSV format (31003A-179400_date_examiner_17BHSD12_2_10-11_1-3); c1/2) mRNA content analyzed by RT-PCR provided as four files in CSV format (31003A-179400_date_examiner_17BHSD12_1_8_1-4), d1/2) Western blot and densitometry provided as seven files in CSV format (31003A-179400_date_examiner_17BHSD12_2_12-13_1-4). All further experiment related information protocols and subsequent data analysis provided as meta-data-files (31003A-179400_date_examiner_17BHSD12_2/1_dataset_M_1) as TXT format and (31003A-179400_date_examiner_17BHSD12_2_dataset_M_2-3) as PNG format.

Data_supplemental figure 4_Impact of 17β‑HSD12, the 3‑ketoacyl‑CoA reductase of long‑chain fatty acid synthesis, on breast cancer cell proliferation and migration

Data of supplemental figure 4 from Impact of 17β‑HSD12, the 3‑ketoacyl‑CoA reductase of long‑chain fatty acid synthesis, on breast cancer cell proliferation and migration Dataset (doi: 10.1007/s00018-019-03227-w) contains the original figure as TIF-format (10.1194_jlr.M092908_Fig. S4). Corresponding raw data from immunofluorescence measurements provided as three files (31003A-179400_date_examiner_17BHSD12_13_1_1-3) in png format. All further experiment related information protocols and subsequent data analysis provided as meta-data-file (31003A-179400_date_examiner_17BHSD12_13_1_M_1) as TXT format.

opencc-by-4.0May 2020View details →

TCGA breast and ovarian cancer PrediXcan models

TCGA breast and ovarian cancer PrediXcan models

opencc-zeroAug 2020View details →

Half-Dose Fulvestrant plus Anastrozole as a First-Line Treatment for Hormone Receptor-Positive Metastatic Breast Cancer: A Cost-Effectiveness Analysis

Abstract Objective The S0226 trial demonstrated that the combination of half-dose fulvestrant (FUL) and anastrozole (ANA) (F&A) caused a significant improvement in overall survival (OS) versus ANA monotherapy for first-line treatment of postmenopausal women with hormone receptor-positive metastatic breast cancer (PMW-MBC[HR+]). The objective of this study was to evaluate the cost-effectiveness of F&A in the first-line treatment for PMW-MBC(HR+) in China. Design We constructed a Markov model over a life-time horizon. The clinical outcomes and utility data were obtained from published literature. Cost data were obtained from official Chinese websites. Sensitivity analyses were performed to test result uncertainty. Setting Chinese health care system perspective. Population A hypothetical cohort of adult patients presenting with PMW-MBC(HR+). Interventions F&A compared with full-dose FUL and ANA monotherapy. Main outcome measures The main outcome of this study was the incremental cost-effectiveness ratio (ICER) and quality-adjusted life-years (QALY). Results ANA was estimated to have the lowest cost and minimum life years (LYs). The ICER of F&A versus ANA was $15,665.891/QALY with incremental cost and QALY of $12,401.120 and 0.792, respectively, which was less than the willingness-to-pay (WTP) of $29,383/QALY. Compared with F&A, FUL yielded a higher cost and a shorter lifetime; hence, it was identified as a dominated strategy. The univariate sensitivity analysis indicated the price of FUL was the most influential factor in our study. The probability that F&A was cost-effective at a threshold of $29,383/QALY in China was 86.5%. Conclusion F&A is a cost-effective alternative to FUL and ANA monotherapy for the first-line treatment of PMW-MBC(HR+) in China. F&A is a promising first-line treatment for PMW-MBC(HR+), and more research is needed to evaluate the economy of using F&A in other countries.

opencc-by-4.0Oct 2020View details →

Prognostic Significance of Deep Learning-Based Tumor-Infiltrating Lymphocyte Assessment for Triple-Negative Breast Cancer

The quantified manual and auto sTILs scores for the TNBC patiens from TCGA-BRCA cohort presented in the article "Prognostic Significance of Deep Learning-Based Tumor-Infiltrating Lymphocyte Assessment for Triple-Negative Breast Cancer" which is currently under review.

opencc-zeroNov 2020View details →

Towards establishing extracellular vesicle-associated RNAs as biomarkers for HER2+ breast cancer

Extracellular vesicles (EVs) are emerging as key players in breast cancer progression and hold immense promise as cancer biomarkers. However, difficulties in obtaining sufficient quantities of EVs for the identification of potential biomarkers hampers progress in this area. To circumvent this obstacle, we cultured BT-474 breast cancer cells in a two-chambered bioreactor with CDM-HD serum replacement to significantly improve the yield of cancer cell-associated EVs and eliminate bovine EV contamination. Cancer-relevant mRNAs BIRC5 (Survivin) and YBX1, as well as long-noncoding RNAs HOTAIR, ZFAS1, and AGAP2-AS1 were detected in BT-474 EVs by quantitative RT-PCR. Bioinformatics meta-analyses showed that BIRC5 and HOTAIR RNAs were substantially upregulated in breast tumours compared to non-tumour breast tissue, warranting further studies to explore their usefulness as biomarkers in patient EV samples. We envision this effective procedure for obtaining large amounts of cancer-specific EVs will accelerate discovery of EV-associated RNA biomarkers for cancers including HER2+ breast cancer.

opencc-by-4.0Jan 2021View details →

Raw single cell mass cytometry data from breast cancer patient derived xenografts

Raw single cell mass cytometry data from breast cancer patient derived xenografts. Data is organised in an expressioset R object

opencc-zeroDec 2015View details →

Data from: Outcome of breast cancer in Moroccan young women correlated to clinic-pathological features, risk factors and treatment: a comparative study of 716 cases in a single institution

Background: Breast cancer in young women is quite uncommon and shows more aggressive characteristics with major disparities between worldwide populations. Prognosis and outcome of breast cancer in young patients are widely studied, but still no consensus is available. Methods: We retrospectively included 716 cases of breast cancer women diagnosed in 2009 at the National Institute of Oncology of Rabat. Patients were divided into two groups according to their age: women aged ≤40 years (Group 1) and women aged >40 years (Group 2). Data were recorded from patients' medical files and analyzed using SPSS 13.0 software (IBM). Results: Young patients represent 24.9% of all patients with breast cancer. The comparison between the two groups displayed significant differences regarding nulliparity (p = 0.001) and progesterone receptor negativity (p = 0.01). Moreover, more progression (Metastases/Relapse) was registered in young women as compared to older women with breast cancer (p = 0.03). The estimated median follow-up period was 31 months. The 5-years Event-Free Survival (EFS) of patients with local disease was 64.6% in young women and 71.5% in older women with breast cancer (p = 0.04). Multivariate analysis in young women showed that nulliparity (HR: 7.2; 95%CI: 1.16–44.54; p = 0.03), T3 tumors (HR: 17.39; 95%CI: 1.74–173.34; p = 0.01) and negative PgR status (HR: 19.85; 95%CI: 1.07–366.54; p = 0.04) can be considered as risk factors for poorer event free survival while hormone therapy was associated with better EFS (HR: 0.11; 95%CI: 0.00–0.75; p = 0.03). In Group 2, multivariate analysis showed that patients with inflammatory breast cancer, N+ status, absence of radiotherapy, absence of chemotherapy, and absence of hormone therapy are at increased risk of recurrence. Conclusions: In Morocco, breast cancer is more frequent in young women as compared to western countries. Breast cancer in young women is more aggressive and is diagnosed late, leading to an intensive treatment. Moreover, the main factors associated with breast cancer development in young women would be hormonal and reproductive status. Analysis of other genetic biomarkers is needed to explain the high prevalence of breast cancer in young women to improve breast cancer management in Morocco

opencc-zeroDec 2018View details →

Data from: A clinical decision support system learned from data to personalize treatment recommendations towards preventing breast cancer metastasis

Objective: A Clinical Decision Support System (CDSS) that can amass Electronic Health Record (EHR) and other patient data holds promise to provide accurate classification and guide treatment choices. Our objective is to develop the Decision Support System for Making Personalized Assessments and Recommendations Concerning Breast Cancer Patients (DPAC), which is a CDSS learned from data that recommends the optimal treatment decisions based on a patient's features. Method: We developed a Bayesian network architecture called Causal Modeling with Internal Layers (CAMIL), and an algorithm called Treatment Feature Interactions (TFI), which learns from data the interactions needed in a CAMIL model. Using the TFI algorithm, we learned interactions for six treatments from the Lynn Sage Data Set (LSDS). We created a CAMIL model using these interactions, resulting in a DPAC which recommends treatments towards preventing 5-year breast cancer metastasis. Results: In a 5-fold cross-validation analysis, we compared the probability of being metastasis free in 5 years for patients who made decisions recommended by DPAC to those who did not. These probabilities are (the probability for those making the decisions appears first): chemotherapy (.938, .872); breast/chest wall radiation (.939, .902); nodal field radiation (.940, .784); antihormone (.941, .906); HER2 inhibitors (.934, .880); neadjuvant therapy (.931, .837). In an application of DPAC to the independent METABRIC dataset, the probabilities for chemotherapy were (.845, .788). Discussion: Patients who took the advice of DPAC had, as a group, notably better outcomes than those who did not. We conclude that DPAC is effective at amassing and analyzing data towards treatment recommendations. Some of the findings in DPAC are controversial. For example, DPAC says that chemotherapy increases the chances of metastasis for many node negative patients. This controversy shows the importance of developing a conclusive version of DPAC to ensure we provide patients with the best patient-specific treatment recommendations.

opencc-zeroDec 2014View details →

Data from: Comparison of Nottingham Prognostic Index and Adjuvant Online prognostic tools in young women with breast cancer: review of a single-institution experience

Objective: Accurately predicting the prognosis of young patients with breast cancer (<40 years) is uncertain since the literature suggests they have a higher mortality and that age is an independent risk factor. In this cohort study we considered two prognostic tools; Nottingham Prognostic Index and Adjuvant Online (Adjuvant!), in a group of young patients, comparing their predicted prognosis with their actual survival. Setting: North East England. Participants: Data was prospectively collected from the breast unit at a Hospital in Grimsby between January 1998 and December 2007. A cohort of 102 young patients with primary breast cancer was identified and actual survival data was recorded. The Nottingham Prognostic Index and Adjuvant! scores were calculated and used to estimate 10-year survival probabilities. Pearson's correlation coefficient was used to demonstrate the association between the Nottingham Prognostic Index and Adjuvant! scores. A constant yearly hazard rate was assumed to generate 10-year cumulative survival curves using the Nottingham Prognostic Index and Adjuvant! predictions. Results: Actual 10-year survival for the 92 patients who underwent potentially curative surgery for invasive cancer was 77.2% (CI 68.6% to 85.8%). There was no significant difference between the actual survival and the Nottingham Prognostic Index and Adjuvant! 10-year estimated survival, which was 77.3% (CI 74.4% to 80.2%) and 82.1% (CI 79.1% to 85.1%), respectively. The Nottingham Prognostic Index and Adjuvant! results demonstrated strong correlation and both predicted cumulative survival curves accurately reflected the actual survival in young patients. Conclusions: The Nottingham Prognostic Index and Adjuvant! are widely used to predict survival in patients with breast cancer. In this study no statistically significant difference was shown between the predicted prognosis and actual survival of a group of young patients with breast cancer.

opencc-zeroJun 2019View details →

Data from: Modifiable patient-related barriers and their association with breast cancer detection practices among Ugandan women without a diagnosis of breast cancer

Most women with breast cancer in sub-Saharan Africa (SSA) are diagnosed with late-staged disease. The current study assesses patient-related barriers among women from a general SSA population to better understand how patient-related barriers contribute to diagnostic delays. Using convenience-based sampling, 401 Ugandan women without breast cancer were surveyed to determine how prior participation in cancer detection practices correlate with patient-related barriers to prompt diagnosis. In a predominantly poor (76%) and rural population (75%), the median age of the participants was 38. Of the women surveyed, 155 (46%) had prior exposure to breast cancer education, 92 (27%) performed breast self-examination (BSE) and 68 (20%) had undergone a recent clinical breast examination (CBE), breast ultrasound or breast biopsy. The most commonly identified barriers to prompt diagnosis were knowledge deficits regarding early diagnosis (79%), economic barriers to accessing care (68%), fear (37%) and poor social support (24%). However, only women who reported knowledge deficits – a modifiable barrier – were less likely to participate in cancer detection practices (p<0.05). Women in urban and rural areas were similarly likely to report economic barriers, knowledge deficits and/or poor social support, but rural women were less likely than urban women to have received breast cancer education and/or perform BSE (p<0.001). Women who have had prior breast cancer education (p<0.001) and/or who perform BSE (p=0.02) were more likely to know where she can go to receive a diagnostic breast evaluation. These findings suggest that SSA countries developing early breast cancer detection programs should specifically address modifiable knowledge deficits among women less likely to achieve a diagnostic work-up to reduce diagnostic delays and improve breast cancer outcomes.

opencc-by-4.0Dec 2023View details →

Stiffness Regulates Breast Cancer Antitumor Immunity via COX2-FGF2 Pathway

The code for spatial analysis done with subset of samples form publication: Bassiouni R, Idowu MO, Gibbs LD, Robila V, Grizzard PJ, Webb MG, Song J, Noriega A, Craig DW, Carpten JD. Spatial Transcriptomic Analysis of a Diverse Patient Cohort Reveals a Conserved Architecture in Triple-Negative Breast Cancer. Cancer Res. 2023 Jan 4;83(1):34-48. doi: 10.1158/0008-5472.CAN-22-2682. PMID: 36283023; PMCID: PMC9812886.

opencc-by-4.0Nov 2024View details →

Breast cancer: CT and Structures. Free-breathing vs DIBH technique

Open the record for dataset details and reuse information.