Background There is no standard treatment recommended at category 1 level in international guidelines for subsequent therapy after cyclin-dependent kinase 4/6 inhibitor (CDK4/6) based therapy. We aimed to evaluate which subsequent treatment oncologists prefer in patients with disease progression under CDKi. In addition, we aimed to show the effectiveness of systemic treatments after CDKi and whether there is a survival difference between hormonal treatments (monotherapy vs. mTOR-based). Methods A total of 609 patients from 53 centers were included in the study. Progression-free-survivals (PFS) of subsequent treatments (chemotherapy (CT, n:434) or endocrine therapy (ET, n:175)) after CDKi were calculated. Patients were evaluated in three groups as those who received CDKi in first-line (group A, n:202), second-line (group B, n: 153) and ≥ 3rd-line (group C, n: 254). PFS was compared according to the use of ET and CT. In addition, ET was compared as monotherapy versus everolimus-based combination therapy. Results The median duration of CDKi in the ET arms of Group A, B, and C was 17.0, 11.0, and 8.5 months in respectively; it was 9.0, 7.0, and 5.0 months in the CT arm. Median PFS after CDKi was 9.5 (5.0–14.0) months in the ET arm of group A, and 5.3 (3.9–6.8) months in the CT arm (p = 0.073). It was 6.7 (5.8–7.7) months in the ET arm of group B, and 5.7 (4.6–6.7) months in the CT arm (p = 0.311). It was 5.3 (2.5–8.0) months in the ET arm of group C and 4.0 (3.5–4.6) months in the CT arm (p = 0.434). Patients who received ET after CDKi were compared as those who received everolimus-based combination therapy versus those who received monotherapy ET: the median PFS in group A, B, and C was 11.0 vs. 5.9 (p = 0.047), 6.7 vs. 5.0 (p = 0.164), 6.7 vs. 3.9 (p = 0.763) months. Conclusion Physicians preferred CT rather than ET in patients with early progression under CDKi. It has been shown that subsequent ET after CDKi can be as effective as CT. It was also observed that better PFS could be achieved with the subsequent everolimus-based treatments after first-line CDKi compared to monotherapy ET.
In this work, breast cancer treatment methods are determined using data mining. For this purpose, software is developed to help to oncology doctor for the suggestion of application of the treatment methods about breast cancer patients. 462 breast cancer patient data, obtained from Ankara Oncology Hospital, are used to determine treatment methods for new patients. This dataset is processed with Weka data mining tool. Classification algorithms are applied one by one for this dataset and results are compared to find proper treatment method. Developed software program called as "Treatment Assistant" uses different algorithms (IB1, Multilayer Perception and Decision Table) to find out which one is giving better result for each attribute to predict and by using Java Net beans interface. Treatment methods are determined for the post surgical operation of breast cancer patients using this developed software tool. At modeling step of data mining process, different Weka algorithms are used for output attributes. For hormonotherapy output IB1, for tamoxifen and radiotherapy outputs Multilayer Perceptron and for the chemotherapy output decision table algorithm shows best accuracy performance compare to each other. In conclusion, this work shows that data mining approach can be a useful tool for medical applications particularly at the treatment decision step. Data mining helps to the doctor to decide in a short time.
Background: First-line treatments for metastatic pancreatic cancer are chemotherapy regimens consisting of 5-fluorouracil or gemcitabine; however, there are no biomarkers to help determine which patients might benefit from which treatment regimens. We aimed to show that microRNAs let-7c and 7d can be used as independent predictive biomarkers for metastatic pancreatic cancer. Methods: A total of 55 patients who had first-line chemotherapy with FOLFIRINOX or gemcitabine + capecitabine were included. Patients were divided into groups based on let-7c and let-7d levels and chemotherapy treatment as let-7c-7d high FOLFIRINOX, let-7c-7d high gemcitabine + capecitabine, let-7c-7d low FOLFIRINOX, and let-7c-7d low gemcitabine + capecitabine. Blood samples were taken from patients before chemotherapy for microRNA let-7c and 7d analysis. MicroRNA isolation was performed using a miRNeasy Serum/Plasma Kit and identified using spectrophotometric measurements. After isolation, microRNA was converted to cDNA using a microRNA cDNA Synthesis Kit with poly (A) polymerase tailing. The expression of microRNA was examined using quantitative real-time polymerase chain reaction. Results: The overall survival of patients who received FOLFIRINOX treatment with a high let-7c-7d level was statistically significantly longer than those who received gemcitabine + capecitabine with a high let-7c-7d level. In addition, patients with low let-7c expression receiving FOLFIRINOX progressed significantly 2.104 times earlier than patients with high let-7c expression receiving FOLFIRINOX. Conclusion: The serum MicroRNA let-7c level was found to be an independent predictive biomarker in the FOLFIRINOX treatment group.
Abstract Purpose:We evaluated the effect of pre-treatment inflammation response markers on overall survival (OS) and progression-free survival (PFS) in patients with locally advanced unresectable and metastatic gastric cancer. Material and Method:Patients with locally advanced unresectable and metastatic gastric cancer between January 2016 and December 2021 were included. Among these patients, 114 patients with ECOG (Eastern Cooperative Oncology Group) Performance status 0-2, who received at least one line of chemotherapy, had no comorbidities and brain metastases were included in the study. Pre-treatment platelet, lymphocyte, leukocyte, neutrophil, monocyte, albumin, C-reactive protein (CRP), lactatedehydrogenase (LDH) levels, histology types, age, surgical history, treatment history and ECOG Performance status were retrospectively analysed from their files. Threshold values of all values were determined by ROC analysis. Kaplan-Meier survival analyses were used for survival analyses. Hazardratio (HR) and confidence intervals (CI) of the factors affecting overall survival (OS) and progression-free survival (PFS) were calculated using Coxproportional-hazards model. Results:The median age of the patients was 63.5±11.9(28-80) years. Among the patients, 69(60.5%) were in metastatic stage. One hundred and six (93.0%) patients had poorly differentiated carcinoma histology. Progression developed in 88.6% (101) of patients and 98 patients (86%) were deceased. In the whole group, mPFS was 9.4+0.9 (95%CI 7.7-11.0) months and mOS was 14.1+1.6 (95%CI 10.8-17.2) months. When the Coxproportional-hazards model was used, the factors affecting OS were advanced age, metastatic stage, neutrophil lymphocyte ratio (NLR), platelet lymphocyte ratio (PLR), derived neutrophil lymphocyte ratio (dNLR) and lactate dehydrogenase (LDH), while the factors affecting PFS were advanced age, metastatic stage, NLR, dNLR and LDH. Conclusion: While NLR, PLR, dNLR, dNLR and LDH affect OS, LDH affects PFS. Systemic inflammatory markers of locally advanced unresectable and metastatic gastric cancers before chemotherapy can be used to predict prognosis.
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