scholarly journals Identification of an eight-lncRNA prognostic model for breast cancer using WGCNA network analysis and a Cox‑proportional hazards model based on L1-penalized estimation

2019 ◽  
Author(s):  
Zhenbin Liu ◽  
Menghu Li ◽  
Qi Hua ◽  
Yanfang Li ◽  
Gang Wang
Keyword(s):  
Breast Cancer ◽  
Network Analysis ◽  
Prognostic Model ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Penalized Estimation ◽  
Model Based ◽  
Hazards Model

Comorbidities and Overall Survival In Myelodysplastic Syndromes (MDS): Development of a Prognostic Model Incorporating IPSS and Age with ACE-27 Comorbidity Index

Blood ◽  
2010 ◽  
Vol 116 (21) ◽  
pp. 605-605 ◽  
Author(s):  
Kiran Naqvi ◽  
Maria E Suarez-Almazor ◽  
Sagar Sardesai ◽  
Jeong Oh ◽  
Carlos Vigil ◽  
...  
Keyword(s):  
Median Survival ◽  
Prognostic Model ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Risk Group ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Hazards Model ◽  
Kaplan Meier ◽  
Comorbidity Scores

Abstract Abstract 605 Background: Cancer patients often experience comorbidities that may affect their therapeutic options, prognosis, and outcome. Limited studies have evaluated the characteristics and impact of comorbidities in MDS. The aim of this study was to determine the effect of comorbidities on the survival of patients with MDS. Methods: We reviewed the medical records of 600 consecutive MDS patients who presented to MD Anderson Cancer Center from 01–2002 to 06–2004. The Adult Comorbidity Evaluation-27 (ACE-27), a validated 27-item comorbidity index for cancer patients, was used to assess the severity of comorbid conditions. Data on demographic characteristics, International Prognostic Scoring System (IPSS), stem cell transplant (SCT) and outcomes (leukemic transformation and survival) was collected. Kaplan-Meier methods and log-rank tests were used to assess survival. Multivariate analysis was performed using the Cox Proportional Hazards Model. A prognostic model incorporating baseline comorbidities with age and IPSS was developed to predict survival. A score point for each significant factor (age, IPSS and ACE-27 comorbidity score) was obtained by dividing respective coefficients from the multivariate model by 0.3 and rounding to the nearest integer. Results: Of the 600 patients included in this study, 400 (65.7%) were male, and 518 (87.1%) were white; median age at presentation was 66.6 years (range 17.3 – 93.5); median duration of follow-up was 14.8 months (range 0–88). The ACE-27 comorbidity scores were as follows: none, 137 patients (28.8%); mild, 254 (42.3%); moderate, 127 (21.2%); and severe, 82 (13.7%). Four hundred and fifty six (76.0%) patients died, 123 (20.5%) suffered leukemic transformation and 51 (8.5%) patients underwent SCT. Overall median survival using the Kaplan-Meier method was 18.6 months. Median survival according to ACE-27 scores was: 31.8, 16.8, 15.2 and 9.7 months for none, mild, moderate and severe comorbidity scores respectively (p < 0.0001). The adjusted hazards ratios from the multivariate Cox Proportional Hazards Model were 1.3, 1.6 and 2.3 for mild, moderate and severe comorbidity scores when adjusted for age and IPSS (p < 0.0001). A final prognostic model incorporating comorbidity score with age and IPSS was developed. A risk score was derived based on the regression coefficients from the final multivariate model. The score points assigned were: Age > 65 years=2; IPSS of Intermediate-2= 2 and High= 3; ACE-27 score of mild or moderate= 1 and Severe= 3. Based upon their risk scores, patients were categorized into 3 groups: low (0 - 1), intermediate (2 - 4) and high (5 – 8). Almost 50% of the patients in our study were noted to be in the intermediate category with a median survival of 23 months. The model confirmed a better survival in patients in low risk group of 43 months versus 9 months in the high risk group (p < 0.001). Conclusion: Comorbidities had a significant impact on the survival of patients with myelodysplastic syndrome. Patients with higher ACE-27 comorbidity scores had a shorter survival than those with no comorbidity, independent of their age and the IPSS risk group. A comprehensive assessment of comorbidity is therefore needed to determine the prognosis in patients with MDS. Our newly developed prognostic model helps predict survival in such patients based on their comorbidities. Disclosures: No relevant conflicts of interest to declare.

scholarly journals Antibiotics modulate neoadjuvant therapy efficiency in patients with breast cancer: a pilot analysis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Xi Zhang ◽  
Long Yu ◽  
Jiajie Shi ◽  
Sainan Li ◽  
Shiwei Yang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neoadjuvant Therapy ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Antibiotic Administration ◽  
Hazards Model ◽  
Kaplan Meier ◽  
The Impact

AbstractMounting evidence suggests that microbiota dysbiosis caused by antibiotic administration is a risk factor for cancer, but few research reports focus on the relationships between antibiotics and chemotherapy efficiency. We evaluated the influence of antibiotic administration on neoadjuvant therapy efficacy in patients with breast cancer (BC) in the present study. BC patients were stratified into two groups: antibiotic-treated and control based on antibiotic administration within 30 days after neoadjuvant therapy initiation. Disease-free survival (DFS) and overall survival (OS) were assessed using the Kaplan–Meier method, and the Cox proportional hazards model was used for multivariate analyses. The pathologic complete response rate of the control group was significantly higher than that of the antibiotic-treated group (29.09% vs. 10.20%, p = 0.017). Further univariate analysis with Kaplan–Meier calculations demonstrated that antibiotic administration was strongly linked with both reduced DFS (p = 0.04) at significant statistical levels and OS (p = 0.088) at borderline statistical levels. Antibiotic administration was identified as a significant independent prognostic factor for DFS [hazard ratio (HR) 3.026, 95%, confidence interval (CI) 1.314–6.969, p = 0.009] and OS (HR 2.836, 95% CI 1.016–7.858, p = 0.047) by Cox proportional hazards model analysis. Antibiotics that initiated reduced efficiency of chemotherapy were more noticeable in the HER2-positive subgroup for both DFS (HR 5.51, 95% CI 1.77–17.2, p = 0.003) and OS (HR 7.0395% CI 1.94–25.53, p = 0.003), as well as in the T3-4 subgroup for both DFS (HR 20.36, 95% CI 2.41–172.07, p = 0.006) and OS (HR 13.45, 95% CI 1.39–130.08, p = 0.025) by stratified analysis. Antibiotic administration might be associated with reduced efficacy of neoadjuvant therapy and poor prognosis in BC patients. As a preliminary study, our research made preparations for further understanding and large-scale analyses of the impact of antibiotics on the efficacy of neoadjuvant therapy.

Prognostic Factors and Survival Outcomes among Patients with Breast Cancer and Brain Metastases at Diagnosis: A National Cancer Database Analysis

Oncology ◽  
2021 ◽  
Vol 99 (5) ◽  
pp. 280-291
Author(s):  
Brittney S. Zimmerman ◽  
Danielle Seidman ◽  
Krystal P. Cascetta ◽  
Meng Ru ◽  
Erin Moshier ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Brain Metastases ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Breast Cancer Diagnosis ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
National Cancer Database ◽  
Hazards Model ◽  
Sites Of Metastases

Introduction: The aim of this study was to assess for clinicopathologic and socioeconomic features that predict improved survival for patients with advanced breast cancer with synchronous brain metastases at diagnosis. Methods: We utilized the National Cancer Database (NCDB) to identify all patients with brain metastases present at diagnosis, with adequate information on receptor status (ER, PR, Her2), clinical T stage of cT1-4, clinical M1, with 3,943 patients available for analysis. The association between brain metastases patterns and patient/disease variables was examined by robust Poisson regression model. Cox proportional hazards model was used to quantify the associations between overall survival (OS) and these variables. Results: In univariable analysis, OS was significantly associated with the number of sites of metastases (p < 0.0001). Patients with 2 or more additional extracranial sites of metastases had significantly worse OS (median 8.8 months, 95% confidence interval [CI] 7.8, 9.9) than patients with brain metastases only (median OS 10.6 months, 95% CI 9.4, 12.9) or brain metastases plus one other extracranial site of metastases (median OS 13.1 months, 95% CI 11.8, 14.4). Risk factors which predicted poor prognosis included triple-negative disease, high comorbidity score, poorly differentiated tumors, invasive lobular histology, multi-organ involvement of metastases, and government or lack of insurance. Factors which improve survival include younger age and Hispanic race. Discussion/Conclusion: Using a large NCDB, we identified various factors associated with prognosis for patients with brain metastases at the time of breast cancer diagnosis. Insurance status and related socioeconomic challenges provide potential areas for improvement in care for these patients. This information may help stratify patients into prognostic categories at the time of diagnosis to improve treatment plans.

scholarly journals A novel 14-gene signature for overall survival in lung adenocarcinoma based on the Bayesian hierarchical Cox proportional hazards model

2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Na Sun ◽  
Jiadong Chu ◽  
Wei Hu ◽  
Xuanli Chen ◽  
Nengjun Yi ◽  
...  
Keyword(s):  
Lung Adenocarcinoma ◽  
Prognostic Model ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
External Validation ◽  
Gene Signature ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Bayesian Hierarchical ◽  
Hazards Model

AbstractThere have been few investigations of cancer prognosis models based on Bayesian hierarchical models. In this study, we used a novel Bayesian method to screen mRNAs and estimate the effects of mRNAs on the prognosis of patients with lung adenocarcinoma. Based on the identified mRNAs, we can build a prognostic model combining mRNAs and clinical features, allowing us to explore new molecules with the potential to predict the prognosis of lung adenocarcinoma. The mRNA data (n = 594) and clinical data (n = 470) for lung adenocarcinoma were obtained from the TCGA database. Gene set enrichment analysis (GSEA), univariate Cox proportional hazards regression, and the Bayesian hierarchical Cox proportional hazards model were used to explore the mRNAs related to the prognosis of lung adenocarcinoma. Multivariate Cox proportional hazard regression was used to identify independent markers. The prediction performance of the prognostic model was evaluated not only by the internal cross-validation but also by the external validation based on the GEO dataset (n = 437). With the Bayesian hierarchical Cox proportional hazards model, a 14-gene signature that included CPS1, CTPS2, DARS2, IGFBP3, MCM5, MCM7, NME4, NT5E, PLK1, POLR3G, PTTG1, SERPINB5, TXNRD1, and TYMS was established to predict overall survival in lung adenocarcinoma. Multivariate analysis demonstrated that the 14-gene signature (HR 3.960, 95% CI 2.710–5.786), T classification (T1, reference; T3, HR 1.925, 95% CI 1.104–3.355) and N classification (N0, reference; N1, HR 2.212, 95% CI 1.520–3.220; N2, HR 2.260, 95% CI 1.499–3.409) were independent predictors. The C-index of the model was 0.733 and 0.735, respectively, after performing cross-validation and external validation, a nomogram was provided for better prediction in clinical application. Bayesian hierarchical Cox proportional hazards models can be used to integrate high-dimensional omics information into a prediction model for lung adenocarcinoma to improve the prognostic prediction and discover potential targets. This approach may be a powerful predictive tool for clinicians treating malignant tumours.

Tamoxifen, hot flashes, and breast cancer recurrence: Support for pharmacogenetics

2007 ◽  
Vol 25 (18_suppl) ◽  
pp. 500-500 ◽  
Author(s):  
J. Mortimer ◽  
S. Flatt ◽  
B. Parker ◽  
E. Gold ◽  
J. P. Pierce
Keyword(s):  
Breast Cancer ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Hot Flashes ◽  
Tumor Stage ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Adjuvant Tamoxifen ◽  
Hazards Model ◽  
Night Sweats

500 Background: Knowledge of the pharmacogenetics of the CYP2D6 enzyme has been shown to correlate with the efficacy of adjuvant tamoxifen. Women who are ‘extensive metabolizers” of CYP2D6 have an improved relapse free survival and experience more hot flashes than women who have impaired metabolism (Goetz, JCO 2005;23:9312–18). We hypothesized that the development of hot flashes on adjuvant tamoxifen was an indicator of drug metabolism and would correlate with a more favorable outcome than women who did not experience hot flashes. Methods: The WHEL trial enrolled 3,088 breast cancer survivors with stages I (T1c)-IIIA breast cancer, within 2–48 months of initial diagnosis, and age < 75 years to either a dietary intervention (n=1,537) or a control group (n=1,551). Data on the primary tumor, cancer treatment, disease status, and quality of life measures were collected at baseline and annually. Bivariate associations of vasomotor symptoms with age, race/ethnicity, menopausal status, cancer stage, ER and PR status, and time since diagnosis were tested using chi-square tests for categorical and t-tests for continuous variables. A left-truncated Cox proportional hazards model tested the association between recurrence-free survival and hot flashes, adjusting for tumor stage and grade and patient age. Women who died without a new breast cancer event were censored at their date of death; those without a new breast cancer event were censored at December 1, 2006 or the date of their most recent self-report of their breast cancer status. Results: The study sample includes 864 women treated with adjuvant tamoxifen 78% who reported hot flashes, and 69% of those reporting hot flashes also reported night sweats; 4% reported night sweats without hot flashes, and 18% reported neither hot flashes nor night sweats. A delayed entry Cox proportional hazards model adjusting for tumor stage and grade showed that those reporting hot flashes had a hazard ratio of 0.51 of recurrence during the follow-up period (95% CI 0.32–0.79) and that hot flashes were more predictive of outcome for tamoxifen treated patients than were age, grade, hormone receptor status, or stage II cancer. Conclusions: Our results contribute to the data that suggest tamoxifen side effects and efficacy may relate to an individual’s pharmacogenetics. No significant financial relationships to disclose.

Which benefit from subsequent chemotherapy lines beyond the second for women with metastatic breast cancer? Evidence from a single-center retrospective analysis of survivorship.

2011 ◽  
Vol 29 (27_suppl) ◽  
pp. 255-255
Author(s):  
G. Bernardo ◽  
R. Palumbo ◽  
A. Bernardo ◽  
C. Teragni ◽  
F. Sottotetti ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Metastatic Breast Cancer ◽  
Retrospective Analysis ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Univariate Analysis ◽  
Metastatic Breast ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Hazards Model

255 Background: Although the true impact of chemotherapy (CT) in metastatic breast cancer (MBC) is still debated, in the routine clinical practice an increasing number of women asking for further treatment after progression receive subsequent CT lines. This study aimed to assess which benefit could be brought by the succession of CT lines in patients treated for MBC and to identify women who benefit from these treatments. Methods: This retrospective analysis included 980 women treated with CT for MBC at our Institution over a 7-year period (May 1999-July 2006). With overall survival (OS) data updated at December 1, 2008, the median follow-up was 125 months (range 48-192), OS and time to treatment failure (TTF) were calculated according to the Kaplan-Meyer method for each CT line. Cox proportional hazards model was used to identify factors that could influence TTF and OS. Results: Median OS evaluated from day 1 of each CT line decreased with the line number from 34.8 months (980 patients, 1st line, range 4-208) to 22.6 months (838 patients, 2nd line), 14.6 months (684 patients, 3rd line), 12.4 months (302 patients, 4th line), 9.4 months (88 patients, 5th line), 8.2 months (45 patients, seven or more lines). Median TTF ranged from 9.2 months to 7.8 and 6.4 months for the first, second and third line, respectively, with no significant decrease observed beyond the 3rd line (median 5.2 months, range 4.8-6.2). In univariate analysis factors positively linked to a longer duration of TTF for each CT line were positive hormonal receptor status, absence of liver metastasis, adjuvant CT exposure, response to CT for the metastatic disease; in the multivariate analysis the duration of TTF for each CT line was the only one factor with significant impact on survival benefit for subsequent treatments (p<0.001). Conclusions: CT beyond the 2nd line may be beneficial in a significant subset of women treated for MBC, with improved TTF and OS. These findings could help physician in planning an appropriate strategy of subsequent schedules for women with symptomatic MBC who responded to their 1st line CT, while non responder patients should be considered for clinical trials.

scholarly journals Risk factors associated with major adverse cardiac and cerebrovascular events following percutaneous coronary intervention: a 10-year follow-up comparing random survival forest and Cox proportional-hazards model

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Maryam Farhadian ◽  
Sahar Dehdar Karsidani ◽  
Azadeh Mozayanimonfared ◽  
Hossein Mahjub
Keyword(s):  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Coronary Intervention ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Hazards Model ◽  
Cerebrovascular Events ◽  
Long Term Follow Up ◽  
Stent Length

Abstract Background Due to the limited number of studies with long term follow-up of patients undergoing Percutaneous Coronary Intervention (PCI), we investigated the occurrence of Major Adverse Cardiac and Cerebrovascular Events (MACCE) during 10 years of follow-up after coronary angioplasty using Random Survival Forest (RSF) and Cox proportional hazards models. Methods The current retrospective cohort study was performed on 220 patients (69 women and 151 men) undergoing coronary angioplasty from March 2009 to March 2012 in Farchshian Medical Center in Hamadan city, Iran. Survival time (month) as the response variable was considered from the date of angioplasty to the main endpoint or the end of the follow-up period (September 2019). To identify the factors influencing the occurrence of MACCE, the performance of Cox and RSF models were investigated in terms of C index, Integrated Brier Score (IBS) and prediction error criteria. Results Ninety-six patients (43.7%) experienced MACCE by the end of the follow-up period, and the median survival time was estimated to be 98 months. Survival decreased from 99% during the first year to 39% at 10 years' follow-up. By applying the Cox model, the predictors were identified as follows: age (HR = 1.03, 95% CI 1.01–1.05), diabetes (HR = 2.17, 95% CI 1.29–3.66), smoking (HR = 2.41, 95% CI 1.46–3.98), and stent length (HR = 1.74, 95% CI 1.11–2.75). The predictive performance was slightly better by the RSF model (IBS of 0.124 vs. 0.135, C index of 0.648 vs. 0.626 and out-of-bag error rate of 0.352 vs. 0.374 for RSF). In addition to age, diabetes, smoking, and stent length, RSF also included coronary artery disease (acute or chronic) and hyperlipidemia as the most important variables. Conclusion Machine-learning prediction models such as RSF showed better performance than the Cox proportional hazards model for the prediction of MACCE during long-term follow-up after PCI.

scholarly journals Elevated plasma growth and differentiation factor 15 predicts incident anemia in older adults aged 60 years and older

2020 ◽  
Author(s):  
Yuko Yamaguchi ◽  
Marta Zampino ◽  
Toshiko Tanaka ◽  
Stefania Bandinelli ◽  
Yusuke Osawa ◽  
...  
Keyword(s):  
Older Adults ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Differentiation Factor ◽  
Hazards Model ◽  
Increased Risk ◽  
The Relationship

Abstract Background Anemia is common in older adults and associated with greater morbidity and mortality. The causes of anemia in older adults have not been completely characterized. Although elevated circulating growth and differentiation factor 15 (GDF-15) has been associated with anemia in older adults, it is not known whether elevated GDF-15 predicts the development of anemia. Methods We examined the relationship between plasma GDF-15 concentrations at baseline in 708 non-anemic adults, aged 60 years and older, with incident anemia during 15 years of follow-up among participants in the Invecchiare in Chianti (InCHIANTI) Study. Results During follow-up, 179 (25.3%) participants developed anemia. The proportion of participants who developed anemia from the lowest to highest quartile of plasma GDF-15 was 12.9%, 20.1%, 21.2%, and 45.8%, respectively. Adults in the highest quartile of plasma GDF-15 had an increased risk of developing anemia (Hazards Ratio 1.15, 95% Confidence Interval 1.09, 1.21, P&lt;.0001) compared to those in the lower three quartiles in a multivariable Cox proportional hazards model adjusting for age, sex, serum iron, soluble transferrin receptor, ferritin, vitamin B12, congestive heart failure, diabetes mellitus, and cancer. Conclusions Circulating GDF-15 is an independent predictor for the development of anemia in older adults.

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