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Local and Metastatic Relapse Features in Patients After a Primary Soft Tissue Sarcoma: Advocating for a Better-Tailored Follow-Up.

ABSTRACT: Background: No consensus exists on how to follow patients after complete remission of a primary Soft Tissue Sarcoma (STS). Studying relapse features could help tailor guidelines for follow-up. Patients and Methods: Patients in complete remission after initial management of a localized STS at Institut Bergonié who presented a first local and/or metastatic relapse between January 1995 and July 2015 were eligible. Characteristics of relapse diagnosis were retrospectively collected. Results: 359 patients met inclusion criteria. 197 and 187 patients presented a local relapse and a metastatic relapse, respectively. In group 1 (limbs/trunk wall) and 2 (trunk/gynecological/other location), local relapse was diagnosed on clinical symptoms in 89 and 44% of cases, first detected by the patient himself in 68.5 and 34% of cases, and outside a planned visit in 67 and 36% of cases, respectively. In patients with metastatic relapse, diagnosis was made during a planned visit in 63% of cases, and by imaging in 62% of cases. Median survival after relapse was not different whether the first local relapse was diagnosed clinically or by imaging (44 [95%CI: 28-69.8] vs. 57 months [95%CI: 33.9-84.5], p = 0.35) but was longer if diagnosis of metastatic relapse was made on planned chest-CT scan rather than chest X-ray (58 [95%CI: 35.5-103.9] vs. 25 months [95%CI: 16.5-32.6], p < 0.05). Conclusion: Patient's education for regular clinical examination can be recommended for follow-up of local relapses after a primary STS of the limbs or superficial trunk. Modeling studies aiming at better understanding and predicting tumor biology to improve tailoring STS patients' follow-up are warranted.

SUBMITTER: Blaye C

PROVIDER: S-EPMC6614176 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Local and Metastatic Relapse Features in Patients After a Primary Soft Tissue Sarcoma: Advocating for a Better-Tailored Follow-Up.

Blaye Céline C Kind Michele M Stoeckle Eberhard E Brouste Véronique V Kantor Guy G Le Loarer François F Italiano Antoine A Toulmonde Maud M

Frontiers in oncology 20190702

<b>Background:</b> No consensus exists on how to follow patients after complete remission of a primary Soft Tissue Sarcoma (STS). Studying relapse features could help tailor guidelines for follow-up. <b>Patients and Methods:</b> Patients in complete remission after initial management of a localized STS at Institut Bergonié who presented a first local and/or metastatic relapse between January 1995 and July 2015 were eligible. Characteristics of relapse diagnosis were retrospectively collected. <b ...[more]

PMID: 31312612

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Project description:Objective: Trabectedin is an anti-cancer drug commonly used for the treatment of patients with metastatic soft tissue sarcoma (mSTS). Despite its recognized efficacy, significant variability in pharmacological response has been observed among mSTS patients. To address this issue, this pharmacometabolomics study aimed to identify pre-dose plasma metabolomics signatures that can explain individual variations in trabectedin pharmacokinetics and overall clinical response to treatment. Methods: In this study, 40 mSTS patients treated with trabectedin administered by 24 h-intravenous infusion at a dose of 1.5 mg/m2 were enrolled. The patients' baseline plasma metabolomics profiles, which included derivatives of amino acids and bile acids, were analyzed using multiple reaction monitoring LC-MS/MS together with their pharmacokinetics profile of trabectedin. Multivariate Partial least squares regression and univariate statistical analyses were utilized to identify correlations between baseline metabolite concentrations and trabectedin pharmacokinetics, while Partial Least Squares-Discriminant Analysis was employed to evaluate associations with clinical response. Results: The multiple regression model, derived from the correlation between the AUC of trabectedin and pre-dose metabolomics, exhibited the best performance by incorporating cystathionine, hemoglobin, taurocholic acid, citrulline, and the phenylalanine/tyrosine ratio. This model demonstrated a bias of 4.6% and a precision of 17.4% in predicting drug AUC, effectively accounting for up to 70% of the inter-individual pharmacokinetic variability. Through the use of Partial least squares-Discriminant Analysis, cystathionine and hemoglobin were identified as specific metabolic signatures that effectively distinguish patients with stable disease from those with progressive disease. Conclusions: The findings from this study provide compelling evidence to support the utilization of pre-dose metabolomics in uncovering the underlying causes of pharmacokinetic variability of trabectedin, as well as facilitating the identification of patients who are most likely to benefit from this treatment. Graphical Abstract

Dataset Information

Local and Metastatic Relapse Features in Patients After a Primary Soft Tissue Sarcoma: Advocating for a Better-Tailored Follow-Up.

Publications

Local and Metastatic Relapse Features in Patients After a Primary Soft Tissue Sarcoma: Advocating for a Better-Tailored Follow-Up.

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