Hemoglobin level trajectories in the early treatment period are related with survival outcomes in patients with breast cancer

Lee, Chia-Lin; Tsai, Chun‐Hao; Yeh, Dah Cherng; Lin, Chi; Li, Yu Fen; Tzeng, Huey En

doi:10.18632/oncotarget.13679

Cited by 9 publications

(6 citation statements)

References 45 publications

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“…In this study, low hemoglobin levels at baseline were associated with reduced TTF. Moreover, previous studies associated the low hemoglobin levels including anemia with poor survival prognosis in breast cancer patients [ 25 – 28 ]. Thus, low hemoglobin levels may be a robust prognostic indicator for reduced TTF for breast cancer patients.…”

Section: Discussionmentioning

confidence: 99%

Effectiveness and safety of eribulin in Japanese patients with HER2-negative, advanced breast cancer: a 2-year post-marketing observational study in a real-world setting

et al. 2020

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Background Data on eribulin as the first-or second-line treatment in a clinical setting, especially the overall survival (OS) of patients, are scarce. Therefore, we assessed the effectiveness and safety of eribulin as the first-, second-, and third-or later-line treatments in patients with human epidermal growth factor receptor 2 (HER2)-negative advanced breast cancer in Japan. Methods This multicenter, prospective, post-marketing, observational study enrolled patients from September 2014 to February 2016 in Japan and followed them for 2 years. Patients were categorized by eribulin use into the first-, second-, and third-or later-line treatment groups. Results Of 651 registered patients, 637 patients were included in the safety and effectiveness analysis. In all, first-, second-, and third or later-line treatment groups, median OS (95% confidence interval) were 15.6 (13.8-17.6), 22.8 (17.3-31.0), 16.3 (12.4-19.9), and 12.6 (11.2-15.1) months and time to treatment failure (TTF) (95% confidence interval) were 4.2 (3.7-4.4), 5.2 (3.7-5.9), 4.2 (3.7-5.1), and 3.8 (3.5-4.2) months, respectively. Prolonged TTF was associated with complications of diabetes and the development of peripheral neuropathy after eribulin treatment, according to multivariate Cox regression analysis. Grade ≥ 3 adverse drug reactions (ADRs) were reported in 61.7% of the patients. Neutropenia (49.5%) was the most common grade ≥ 3 ADR in all groups. Conclusions The effectiveness and safety results of eribulin as the first-or second-line treatment were favorable. Thus, these suggest eribulin may be a first-line treatment candidate for patients with HER2-negative advanced breast cancer in Japan.

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Section: Discussionmentioning

confidence: 99%

Effectiveness and safety of eribulin in Japanese patients with HER2-negative, advanced breast cancer: a 2-year post-marketing observational study in a real-world setting

et al. 2020

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“…In some cancers such as breast cancer and lung cancer, patients’ hemoglobin levels are related to treatment outcomes and survival. 13,14 In cervical cancer, anemia is associated with poor local disease control and a decreased survival rate. 15,16 Improving hemoglobin levels in patients with cervical cancer may help achieve better outcomes.…”

Section: Discussionmentioning

confidence: 99%

Predictive value of hemoglobin, platelets, and D-dimer for the survival of patients with stage IA1 to IIA2 cervical cancer: a retrospective study

Shou

Zhu

2021

J Int Med Res

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Objective Coagulation indexes may be useful survival biomarkers for cervical cancer. This study evaluated the ability of hemoglobin, red blood cells (RBCs), platelets, and D-dimer levels to predict post-hysterectomy survival outcomes in patients with stage IA1 to IIA2 cervical cancer. Methods In this retrospective study, coagulation-related indexes were compared between the anemia and non-anemia groups. Independent variables were analyzed by the Cox proportional hazards model. Survival was assessed by the Kaplan–Meier method with the log-rank test. Mortality predictions were evaluated by receiver operating characteristic curves. Results Among this study’s 1088 enrolled patients, 152 had anemia. The 10-year overall survival and recurrence-free survival rates were 90.8% and 86.5%, respectively. Hemoglobin, RBC, and the rate of abnormal platelet counts were significantly lower in the anemia group. Abnormal preoperative D-dimer was an independent factor for recurrence-free survival. Receiver operating characteristic curves showed that D-dimer had area under the curve of 0.734 (cut-off value: 0.685, sensitivity: 85.7%, and specificity: 64.0%). Hemoglobin and platelets had areas under the curves of 0.487 and 0.462, respectively. Conclusion Preoperative D-dimer was the most effective prognostic predictor for patients with cervical cancer. The prognosis of patients with cervical cancer was poorer if their D-dimer levels were >0.685 mg/L.

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“…Hemoglobin (Hb) levels had been reported by a number of studies to be associated with treatment outcomes and survival of patients of various cancer types (57)(58)(59) including breast cancer (60). It was reported that anemia in cancer patients is a prognostic factor associated with shorter survival (61).…”

Section: Hemoglobin Statusmentioning

confidence: 99%

A Knowledge-Based Discovery Approach Couples Artificial Neural Networks With Weight Engineering to Uncover Immune-Related Processes Underpinning Clinical Traits of Breast Cancer

et al. 2022

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Immune-related processes are important in underpinning the properties of clinical traits such as prognosis and drug response in cancer. The possibility to extract knowledge learned by artificial neural networks (ANNs) from omics data to explain cancer clinical traits is a very attractive subject for novel discovery. Recent studies using a version of ANNs called autoencoders revealed their capability to store biologically meaningful information indicating that autoencoders can be utilized as knowledge discovery platforms aside from their initial assigned use for dimensionality reduction. Here, we devise an innovative weight engineering approach and ANN platform called artificial neural network encoder (ANNE) using an autoencoder and apply it to a breast cancer dataset to extract knowledge learned by the autoencoder model that explains clinical traits. Intriguingly, the extracted biological knowledge in the form of gene–gene associations from ANNE shows immune-related components such as chemokines, carbonic anhydrase, and iron metabolism that modulate immune-related processes and the tumor microenvironment play important roles in underpinning breast cancer clinical traits. Our work shows that biological “knowledge” learned by an ANN model is indeed encoded as weights throughout its neuronal connections, and it is possible to extract learned knowledge via a novel weight engineering approach to uncover important biological insights.

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Hemoglobin level trajectories in the early treatment period are related with survival outcomes in patients with breast cancer

Cited by 9 publications

References 45 publications

Effectiveness and safety of eribulin in Japanese patients with HER2-negative, advanced breast cancer: a 2-year post-marketing observational study in a real-world setting

Effectiveness and safety of eribulin in Japanese patients with HER2-negative, advanced breast cancer: a 2-year post-marketing observational study in a real-world setting

Predictive value of hemoglobin, platelets, and D-dimer for the survival of patients with stage IA1 to IIA2 cervical cancer: a retrospective study

A Knowledge-Based Discovery Approach Couples Artificial Neural Networks With Weight Engineering to Uncover Immune-Related Processes Underpinning Clinical Traits of Breast Cancer

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