Dataset Information

Pretreatment 18F-FDG Uptake Heterogeneity Predicts Treatment Outcome of First-Line Chemotherapy in Patients with Metastatic Triple-Negative Breast Cancer.

ABSTRACT: BACKGROUND:Intratumoral heterogeneity of 18F-fluorodeoxyglucose (18F-FDG) uptake in primary tumor has proven to be a surrogate marker for predicting treatment outcome in various tumors. However, the value of intraindividual heterogeneity in metastatic diseases remains unknown. The aim of this study was to evaluate pretreatment positron emission tomography/computed tomography (PET/CT) 18F-FDG-based heterogeneity for the prediction of first-line treatment outcome in metastatic triple-negative breast cancer (mTNBC). MATERIALS AND METHODS:mTNBC patients from three clinical trials (NCT00601159, NCT01287624, and NCT02341911) with whole-body 18F-FDG PET/CT scan before first-line gemcitabine/platinum were included. Heterogeneity index (HI) and the maximum of FDG uptake (MAX) across total metastatic lesions (-T) on baseline PET/CT scans were assessed. HI was measured by MAX divided by the minimum FDG uptake across metastatic lesions. Optimal cutoffs were determined by time-dependent receiver operator characteristics (ROC) analysis. Progression-free survival (PFS) and overall survival (OS) were estimated by Kaplan-Meier method and compared by log-rank test. RESULTS:A total of 42 mTNBC patients were included in this study. The median PFS of patients with high HI-T (>1.9) and high MAX-T (>10.5) was significantly shorter than patients with low HI-T (<1.9; p = .049) and low MAX-T (<10.5; p = .001). In terms of OS, only high MAX-T was significant for poorer outcome (p = .013). ROC curve analysis confirmed the predictive value of MAX and HI in mTNBC patients. Area under the ROC curve for MAX-T and HI-T was 0.75 and 0.65, indicating a higher predictive accuracy than conventional clinical risk factors. CONCLUSION:HI and MAX measured among metastatic lesions on pretreatment 18F-FDG PET/CT scans could be potential predicators for first-line treatment outcome in patients with mTNBC. IMPLICATIONS FOR PRACTICE:Intratumoral heterogeneity of 18F-fluorodeoxyglucose (FDG) uptake in primary tumor has proven to be a robust surrogate predictive marker. A novel positron emission tomography/computed tomography (PET/CT) parameter-heterogeneity index (HI) to quantify the heterogeneous characteristics of metastatic disease is proposed. Triple-negative breast cancer (TNBC) is a highly heterogeneous disease and remains a clinical challenge. The predictive performance of HI, along with the maximum FDG uptake (MAX), measured on pretreatment PET/CT scans in patients with metastatic TNBC was evaluated. Results indicate that HI and MAX may serve as applicable imaging predicators for treatment outcome of metastatic TNBC in clinical practice.

SUBMITTER: Gong C

PROVIDER: S-EPMC6263118 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Pretreatment <sup>18</sup>F-FDG Uptake Heterogeneity Predicts Treatment Outcome of First-Line Chemotherapy in Patients with Metastatic Triple-Negative Breast Cancer.

Gong Chengcheng C Ma Guang G Hu Xichun X Zhang Yingjian Y Wang Zhonghua Z Zhang Jian J Zhao Yannan Y Li Yi Y Xie Yizhao Y Yang Zhongyi Z Wang Biyun B

The oncologist 20180806 10

<h4>Background</h4>Intratumoral heterogeneity of <sup>18</sup>F-fluorodeoxyglucose (<sup>18</sup>F-FDG) uptake in primary tumor has proven to be a surrogate marker for predicting treatment outcome in various tumors. However, the value of intraindividual heterogeneity in metastatic diseases remains unknown. The aim of this study was to evaluate pretreatment positron emission tomography/computed tomography (PET/CT) <sup>18</sup>F-FDG-based heterogeneity for the prediction of first-line treatment o ...[more]

PMID: 30082489

Similar Datasets

Project description:BACKGROUND:Chemotherapy resistance is a great obstacle in effective treatment for metastatic triple negative breast cancer (TNBC). The ability to predict chemotherapy response would allow chemotherapy administration to be directed toward only those patients who would benefit, thus maximizing treatment efficiency. Differentially expressed plasma proteins may serve as putative biomarkers for predicting chemotherapy outcomes. PATIENTS AND METHODS:In this study, 26 plasma samples (10 samples with partial response (S) and 16 samples with progression disease (R)) from patients with metastatic TNBC were measured by Tandem Mass Tag (TMT)-based proteomics analysis to identify differentially expressed proteins between the S and R group. Potential proteinswere validated with enzyme-linked immunosorbent assay (ELISA) in another 67 plasma samples. RESULTS:A total of 320 plasma proteins were identified, and statistical analysis showed that 108 proteins were significantly dysregulated between R and S groups in the screening stage. Bioinformatics revealed relevant pathways and regulatory networks of the differentially expressed proteins. Three differentially expressed proteins were validated by ELISA with 67 samples from TNBC patients. The R group had significantly higher plasma CAMK2A level than the S group (P=0.0074). The ROC curve analysis showed an AUC of 0.708, with sensitivity 48.4% and specificity 86.1%. In multivariate logistic regression analysis, the level of plasma CAMK2A was also significant for chemotherapeutic response (P=0.009, OR=0.152). Furthermore, the patients with higher CAMK2A level had shorter OS than those with lower CAMK2A level, which amounted to 13.9 and 28.9 months, respectively (P=0.034). In the multivariate Cox regression analysis, CAMK2A level still had significant effect on OS (P=0.031, HR=1.865). CONCLUSION:TMT-based proteomic analysis was able to identify potential biomarkers in plasma that predicted chemotherapy resistance in the metastatic TNBC. The plasma of CAMK2A level may serve as apotential predictive and prognostic biomarker for chemotherapy in metastatic TNBC.

Project description:Purpose: Building a universal genomic signature predicting the intensity of FDG uptake in diverse metastatic tumors may allow us to understand better the biological processes underlying this phenomenon and their requirements of glucose uptake. Methods: A balanced training set (n=71) of metastatic tumors including some of the most frequent histologies, with matched PET/CT quantification measurements and whole human genome gene expression microarrays, was used to build the signature. Selection of microarray features was carried out exclusively on the basis of their strong association with FDG uptake (as measured by SUVmean35) by means of univariate linear regression. A thorough bioinformatics study of these genes was performed and multivariable models were built by fitting several state of the art regression techniques to the training set for comparison. Results: The 909 probes with the strongest association with the SUVmean35 (comprising 742 identifiable genes and 62 probes not matched to a symbol) were used to build the signature. Partial Least Squares using 3 components (PLS-3) was the best performing model in the training dataset cross-validation (Root Mean Square Error, RMSE=0.443) and was validated further in an independent validation dataset (n=13) obtaining a performance within the 95% CI of that obtained in the training dataset (RMSE=0.645). Significantly overrepresented biological processes correlating with the SUVmean35 were identified beyond glycolysis, such as ribosome biogenesis and DNA replication (correlating with a higher SUVmean35), and cytoskeleton reorganization and autophagy (correlating with a lower SUVmean35), among others. Conclusions: PLS-3 is a signature predicting accurately the intensity of FDG uptake in diverse metastatic tumors. FDG-PET might help in the design of specific targeted therapies directed to counteract the identified malignant biological processes more likely activated in a tumor as inferred from the SUVmean35 and also from its variations in response to antineoplastic treatments.

Dataset Information

Pretreatment 18F-FDG Uptake Heterogeneity Predicts Treatment Outcome of First-Line Chemotherapy in Patients with Metastatic Triple-Negative Breast Cancer.

Publications

Pretreatment <sup>18</sup>F-FDG Uptake Heterogeneity Predicts Treatment Outcome of First-Line Chemotherapy in Patients with Metastatic Triple-Negative Breast Cancer.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets

Dataset Information

Pretreatment 18F-FDG Uptake Heterogeneity Predicts Treatment Outcome of First-Line Chemotherapy in Patients with Metastatic Triple-Negative Breast Cancer.

Publications

Pretreatment &lt;sup&gt;18&lt;/sup&gt;F-FDG Uptake Heterogeneity Predicts Treatment Outcome of First-Line Chemotherapy in Patients with Metastatic Triple-Negative Breast Cancer.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets

Pretreatment <sup>18</sup>F-FDG Uptake Heterogeneity Predicts Treatment Outcome of First-Line Chemotherapy in Patients with Metastatic Triple-Negative Breast Cancer.