Assessment of Breast Cancer Immunohistochemical Properties with Demographics and Pathological Features; A Retrospective Study

Background: Breast cancer is considered the most common malignant disease in the female population. It is known as an emerging epidemy with a great burden on women's health, which can be associated with poor outcomes. Some factors including histological type, immunohistochemistry (IHC), tumor grade, and tumor size can have effects on breast cancer. Objectives: This study aimed at assessing the effects of mentioned factors on IHC type of breast cancer. Methods: This retrospective cross-sectional study was conducted on 142 patients, who were referred to one of the referral centers for breast cancer in Mashhad. Information including age, histological type, familial history, menopause status, tumor grade, tumor size, and IHC properties was collected from the patient’s medical records. Allred score was used for reporting hormonal status. The data were analyzed by version 26 of SPSS software. Results: The mean age of patient was 50.2 ± 12.7. The frequency of luminal A and luminal B type was calculated as 29.7 and 18.9%, respectively. In addition, triple-negative IHC type has a prevalence of 24.3% and HER2 had a prevalence of 27%. There were no significant differences between age (P = 0.34), familial history (P = 0.42), menopause (P = 0.36), histological type (invasive: P = 0.11, in situ: P = 0.45), and IHC properties. However, tumor diameter (P = 0.0001) and tumor grading (P = 0.002) had significant association with IHC properties. Conclusions: Factors including tumor size and pathological grade can have effects on the gene expression properties of breast cancers. Luminal IHC type A is more common in breast cancer and is associated with better outcomes. However, age, histological type, familial history, and menopause status had no effects on the IHC properties of breast cancer.

Critical review of oncology clinical trial design under non-proportional hazards

In trials of novel immuno-oncology drugs, the proportional hazards (PH) assumption often does not hold for the primary time-to-event (TTE) efficacy endpoint, likely due to the unique mechanism of action of these drugs. In practice, when it is anticipated that PH may not hold for the TTE endpoint with respect to treatment, the sample size is often still calculated under the PH assumption, and the hazard ratio (HR) from the Cox model is still reported as the primary measure of the treatment effect. Sensitivity analyses of the TTE data using methods that are suitable under non-proportional hazards (non-PH) are commonly pre-planned. In cases where a substantial deviation from the PH assumption is likely, we suggest designing the trial, calculating the sample size and analyzing the data, using a suitable method that accounts for non-PH, after gaining alignment with regulatory authorities. In this comprehensive review article, we describe methods to design a randomized oncology trial, calculate the sample size, analyze the trial data and obtain summary measures of the treatment effect in the presence of non-PH. For each method, we provide examples of its use from the recent oncology trials literature. We also summarize in the Appendix some methods to conduct sensitivity analyses for overall survival (OS) when patients in a randomized trial switch or cross-over to the other treatment arm after disease progression on the initial treatment arm, and obtain an adjusted or weighted HR for OS in the presence of cross-over. This is an example of the treatment itself changing at a specific point in time - this cross-over may lead to a non-PH pattern of diminishing treatment effect.

Survival probability and prognostic factors of Iranian breast cancer patients using cure rate model

Breast cancer, the major concern of the global health, is the fifth cause of death of women in Iran. In this longitudinal study, 3048 cases of breast cancer who visited and were treated at the Cancer Research Center in Shahid Beheshti University, were studied. During study, 518 patients died. The overall survival rate of 1, 5, 10, 15, and 20-year were 95%, 76%, 59%, 47% and 46%, respectively. A significant relation was observed between survival time and the variables such as age, size of tumor, number of lymph nodes, stage, grade, and lymphovascular invasion.

Comparison Cure Rate Models by DIC Criteria in Breast Cancer Data

Background: One of the malignant tumors is Breast Cancer (BC) that starts in the cells of breast. There is many models for survival analysis of patients such as Cox PH model, Parametric models etc. But some disease are that all of patients will not experience main event then usual survival model is inappropriate. In addition, In the presence of cured patients, if researcher can specify distribution of survival time, usually cure rate models are preferable to parametric models. Distribution of Survival time can be Weibull, Log normal, Logistic, Gamma and so. Comparison of Weibull, Log normal and Logistic distribution for finding the best distribution of survival time is purpose of this study. Material and Methods: Among 787 patients with BC by Cancer Research Center recognized and followed from 1985 until 2013. Variables stage of cancer, age at diagnosis, tumor size and Number of Removed Positive Lymph Nodes (NRPLN) for fitting Cure rate model were selected. The best model selected with DIC criteria. All analysis were performed using SAS 9.2. Results: Mean (SD) of age was 48.47 (11.49) years and Mean of survival time and Maximum follow up time was 326 and 55.12 months respectively. During following patients, 145 (18.4%) patients died from BC and others survived (censored). Also, 1-year, 5-year and 10-year survival rate was 94, 77 and 56 percent respectively. Log normal model with smaller DIC were selected and fitted. All of mentioned variables in the model were significant on cure rate. Conclusion: This study indicated that survival time of BC followed from Log normal distribution in the best way.

Long-Term Disease-Free Survival of Non-Metastatic Breast Cancer Patients in Iran: A Survival Model with Competing Risks Taking Cure Fraction and Frailty into Account

Introduction: Survival modeling is a very important tool to detect risk factors and provide a basis for health care planning. However, cancer data may have properties leading to distorted results with routine methods. Therefore, this study aimed to cover specific factors (competing risk, cure fraction and heterogeneity) with a real dataset of Iranian breast cancer patients using a competing risk-cure-frailty model. Materials and methods: For this historical cohort study, information for 550 Iranian breast cancer patients who underwent surgery for tumor removal from 2001 to 2007 and were followed up to March 2017, was analyzed using R 3.2 software. Results: In contrast to T-stage and N-stage, hormone receptor status did not have any significant effect on the cure fraction (long-term disease-free survival). However, T-stage, N-stage and hormone receptor status all had a significant effect on short-term disease-free survival so that the hazard of loco-regional relapse or distant metastasis in cases positive for a hormone receptor was only 0.3 times that for their negative hormone receptor counterparts. The likelihood of locoregional relapse in the first quartile of follow up was nearly twice that of other quartiles. The least cumulative incidence of time to locoregional relapse was for cases with a positive hormone receptor, low N stage and low T stage. The effect of frailty term was significant in this study and a model with frailty appeared more appropriate than a model without, based on the Akaike information criterion (AIC); values for the frailty model and one without the frailty parameter were 1370.39 and 1381.46, respectively. Conclusions: The data from this study indicate ae necessity to consider competing risk, cure fraction and heterogeneity in survival modeling. The competing risk-cure-frailty model can cover complex situations with survival data.