Next Article in Journal
Efficacy of Immune Checkpoint Inhibitors vs. Tyrosine Kinase Inhibitors/Everolimus in Adjuvant Renal Cell Carcinoma: Indirect Comparison of Disease-Free Survival
Previous Article in Journal
Biomarker Analysis from a Phase I/Ib Study of Regorafenib and Nivolumab in Mismatch Repair-Proficient Advanced Refractory Colorectal Cancer
Previous Article in Special Issue
Developing Cardio-Oncology Programs in the New Era: Beyond Ventricular Dysfunction Due to Cancer Treatments
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Geriatric Assessment of Older Patients Receiving Trabectedin in First-Line Treatment for Advanced Soft Tissue Sarcomas: The E-TRAB Study from The German Interdisciplinary Sarcoma Group (GISG-13)

1
Sarcoma Unit, Mannheim Cancer Center (MCC), Mannheim University Medical Center, University of Heidelberg, 68167 Mannheim, Germany
2
Department of Oncology and Palliative Care, HELIOS Klinikum Bad Saarow, 15526 Bad Saarow, Germany
3
Department of Internal Medicine C, University Medicine Greifswald, 17475 Greifswald, Germany
4
Department of Medical Oncology and Hematology, Kantonsspital St. Gallen, 9007 St. Gallen, Switzerland
5
Medical Department I, University Hospital Carl Gustav Carus, Dresden University of Technology, 01307 Dresden, Germany
6
Department of Hematology and Oncology, Paracelsus Medical University, 90419 Nuremberg, Germany
7
Department of Medical Oncology, Bern University Hospital, University of Bern, 3010 Bern, Switzerland
8
Department of Hematology, Oncology and Palliative Medicine, Vivantes Klinikum Spandau, 13585 Berlin, Germany
9
Clinical Division of Oncology, Department of Internal Medicine, Medical University Graz, 8036 Graz, Austria
10
Clinic for Internal Medicine III, University Hospital Ulm, 89081 Ulm, Germany
11
Vienna General Hospital (AKH), Medizinische Universität Wien, 1090 Vienna, Austria
12
Department of Internal Medicine V, Heidelberg University Hospital, 69120 Heidelberg, Germany
13
Department of Oncology, Helios Klinikum Berlin-Buch, 13125 Berlin, Germany
14
Division of Surgical Oncology and Thoracic Surgery, University Medical Center Mannheim, Mannheim Medical Faculty, University of Heidelberg, 68167 Mannheim, Germany
15
Onkologischer Schwerpunkt am Oskar-Helene Heim, 14195 Berlin, Germany
*
Author to whom correspondence should be addressed.
Cancers 2024, 16(3), 558; https://doi.org/10.3390/cancers16030558
Submission received: 29 December 2023 / Revised: 25 January 2024 / Accepted: 26 January 2024 / Published: 28 January 2024

Abstract

:

Simple Summary

Older patients ≥ 55 years represent more than 50% of all sarcoma patients and have a worse prognosis compared to younger patients. Age alone should not be a reason to deprive patients of standard treatment with chemotherapy and surgery, provided they are functionally fit enough and willing to take the risk of adverse events. The E-TRAB study evaluated the feasibility and prognostic value of a comprehensive geriatric assessment representing different domains such as activities of daily living, co-morbidities and Patient-Reported Outcomes (PROs) for quality of life (QoL) and treatment tolerability. The study included 69 patients with soft tissue sarcoma (STS) between 55 and 88 years who were unsuited for treatment with anthracyclines and ifosfamide and received trabectedin as first-line therapy. Results show evidence that specific geriatric screening instruments could help predict or limit adverse treatment effects and, thereby, optimize treatment strategies in older STS patients.

Abstract

E-TRAB was a non-interventional, prospective trial investigating the feasibility and predictive value of geriatric assessments (GA) in older STS patients treated with trabectedin as first-line therapy. Primary endpoints were overall survival (OS), quality of life and individual clinical benefit assessed by the patient-reported outcome measures QLQ-C30 and PRO-CTCAE. Further, several GA tools were applied and correlated with clinical outcomes and treatment-related toxicities. The final analyses included 69 patients from 12 German-speaking sites. The median age of patients was 78 years (range: 55 to 88). Baseline data on PROs and GA identified a diverse population of older patients with respect to their global health status, although a large proportion of them suffered from limitations, required geriatric help and had a high risk of morbidity. The Cancer and Age Research Group (CARG) score classified 38%, 29% and 23% of the patients with low, intermediate and high risks for therapy-related side effects, respectively. Median OS was 11.2 months [95%CI: 5.6; 19.4]. The study confirmed that trabectedin as first-line treatment in older patients with STS has an acceptable and manageable safety profile. Potential prognostic factors for clinical outcome and therapy-related toxicity were identified among the GA tools. Long Timed Up and Go (TUG) showed a significant correlation to OS and early death, whereas a high CARG score (>9) was associated with an increase in unplanned hospitalizations and the incidence of toxicities grade ≥ 3.

1. Introduction

Soft tissue sarcomas (STS) are a heterogeneous group of tumors arising mainly from the embryonic mesoderm and can be localized anywhere in the body. They comprise more than 150 different histological tumor entities exhibiting great differences in terms of clinical behavior, pathogenesis and genetic alterations [1]. All over the world, especially in industrialized countries, the number of older people is constantly increasing. In the German Cancer Registry, almost 50% of sarcomas occur beyond the age of 65 years [2], with typical features such as a higher proportion of complex karyotypes and a higher rate of adverse prognostic factors [3].
If diagnosed at an early stage and complete surgical removal of all tumor manifestations can be achieved, the prognosis of STS is favorable [4]. However, in up to 50% of patients, distant metastases occur. In previous years, the median overall survival (OS) in the metastatic setting was reported to be 12 to 15 months [5,6]. More recently, median OS appears to have improved and ranged from 20 to 24 months in randomized trials [7].
With few effective targeted treatments available for most advanced and/or metastatic STS, doxorubicin and ifosfamide—which have been used for more than 30 years—still remain the backbone of systemic chemotherapy with well-known side effects such as neutropenia, thrombocytopenia and nephrotoxicity [8]. In most cases, patients with advanced STS have a poor prognosis, and the primary goal of treatment is disease control and palliation. Trabectedin [9] and pazopanib [10] were introduced beyond first-line therapy and significantly enriched the therapeutic armamentarium for patients with STS. Trabectedin (Yondelis®) is the first anticancer marine-derived drug, approved in the European Union in 2007 and currently in close to 80 other countries for the treatment of adults with advanced STS after the failure of anthracycline and ifosfamide or for those patients who are unsuited to receive these agents, with a comparatively improved toxicity profile.
Previous clinical trials conducted in Germany specifically studied the older STS patient population by evaluating pazopanib and trofosfamide in first-line treatment in comparison to standard single-agent therapy with doxorubicin [11,12]. There are isolated retrospective reports in the literature on the use of trabectedin in older patients [13,14]; however, this patient population has not been studied prospectively or in first-line treatment with trabectedin yet.
The use of adequate geriatric assessment tools and predictive tools for estimating the expected chemotherapy toxicity should help when deciding whether or not to use chemotherapy in older patients [15]. On the other hand, several clinical studies demonstrated that the integration of geriatric assessment-guided interventions significantly reduces the toxic effects of cancer treatments [16,17]. However, although geriatric assessment has been implemented as a standard of care in guidelines for treating older cancer patients [18], it is still rarely used in daily clinical practice. There are concerns among oncologists that geriatric assessment can only be implemented at great expense and does not add any value. In fact, many are not yet aware of validated geriatric assessment tools [19,20]. The collection of further evidence-based data obtained through clinical trials may improve the existing tools and raise awareness of their usefulness in the standard of care for older cancer patients and could help these tools find their way into clinical practice.
The German Interdisciplinary Sarcoma Group (GISG) predominantly evaluates questions with an interdisciplinary focus in the early stages of clinical testing (www.gisg.de (accessed on 29 December 2023)). GISG-13 (E-TRAB) focused on establishing a geriatric assessment in patients aged ≥55 years receiving trabectedin as first-line treatment for advanced and/or metastatic STS. In this study, a comprehensive geriatric assessment (GA) was carried out, including the following domains: Instrumental Activities of Daily Living (IADL), Mini Nutritional Assessment (MNA), Charlson Comorbidity Index (CCI), Geriatric Depression Scale (GDS), Timed up and Go (TUG). Additionally, predictive data of two independent geriatric screening instruments (G8 and the Cancer and Age Research Group [CARG] prediction tool) in regards to unexpected hospitalization, the occurrence of toxicities grade ≥ 3 and early death during the first six months of treatment were also implemented. Furthermore, an explorative analysis of Patient-Reported Outcomes (PRO) was performed using the 30-item core European Organization for the Research and Treatment of Cancer (EORTC) Quality of Live Questionnaire (EORTC QLQ-30) as well as the core version of the Patient-Reported Outcomes of the Common Terminology Criteria for Adverse Events (PRO-CTCAE) [21]. E-TRAB was a D.A.CH. project that recruited patients from Germany, Austria and Switzerland (ClinicalTrials.gov Identifier: NCT03022448).

2. Materials and Methods

E-TRAB was designed as a non-interventional, prospective, international, phase IV study to identify measures of feasibility and effectiveness for advanced and/or metastatic STS patients unsuited (for example, due to cardiac comorbidities) to receive standard first-line chemotherapy and/or ≥60 years and treated with trabectedin. To this end, PROs, GA and geriatric screening instruments, as well as records of disease progression and tolerability of therapy, were evaluated. The study was performed at 12 German-speaking study centers across Germany, Switzerland and Austria. According to the non-interventional nature of the study, all treatment decisions and diagnostic and therapeutic procedures followed the routine clinical practice and were at the discretion of the investigator.
The study was conducted in accordance with the ethical standards as laid down in the 1964 Declaration of Helsinki and its later amendments, guidelines for Good Clinical Practice and local regulations on clinical trials. The study was approved by each study site’s independent ethics committee. All patients signed an informed consent document prior to inclusion in the study.
Patients ≥ 60 years of age with a histologic diagnosis of advanced or metastatic STS of intermediate or high grade were eligible for the study. A limited number of patients < 60 years were allowed but had to be unsuited for treatment with anthracyclines and ifosfamide. All patients were suitable for first-line treatment with trabectedin according to the local Summary of Product characteristics (SmPC). Patients with contraindications for trabectedin, as outlined in the SmPC, were excluded.
The dosage and frequency of trabectedin treatment followed the specifications given in the SmPC and the standard medical care depending on the patient’s condition. The recommended dose of trabectedin for the treatment of STS is 1.5 mg/m2 body surface area administered as an intravenous infusion with a three-week interval between the cycles. Any dose modifications and changes in the dosing interval were in accordance with the SmPC and the investigator’s best clinical judgment. The number of treatment cycles was not restricted, and treatment was continued as long as the investigator felt there was benefit for the patient, even in the presence of apparent disease progression or until withdrawal of consent by the patient. After the end of treatment, follow-up assessments were scheduled at 3- to 6-month intervals. A summary of the study design is given in Figure 1.
The primary endpoints of the study were OS and QoL as measured by PROs. PROs as a subjective measure of clinical benefit were assessed using QLQ-C30 [22] and 18 items PRO-CTCAE [21]. After scoring according to the EORTC QLQ-C30 manual, all of the scales and single-item measures range in score from 0 to 100. A high score for the global health status/QoL represents a high QoL. A high score for a functional scale represents a high/healthy level of functioning, but a high score for a symptom scale/item represents a high level of symptomatology/problems.
As secondary endpoint, a self-administered GA was performed using several domains, including IADL [23], MNA [24], CCI [25], GDS [26], TUG [27]. Further, the predictive value of two different geriatric screening instruments (G8 [28], CARG prediction tool [29]) with regard to unplanned hospitalizations, occurrence of grade ≥ 3 toxicities and early death (within first six months after start of treatment) was assessed. The relationship between these assessments and disease-related endpoints such as OS, progression-free survival (PFS), overall response rate (ORR), disease control rate (DCR), time to onset of response, response duration and safety variables was examined.
OS was defined as the time between the start of trabectedin treatment and patient death from any cause, while PFS was defined as the time interval between the first administration of trabectedin and the date of disease progression or death, regardless of cause (whichever occurred first). Disease was regarded as progressed if either “disease progression” was recorded at the end of treatment, found by overall response assessment, or an adverse event (AE) occurred that indicated disease progression. Disease progression also included events of death. Patients without disease progression, being alive or considered lost to follow-up by the date of the database lock, were censored with the date of last contact. ORR was defined as the percentage of patients who achieved complete (CR) or partial response (PR) according to Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1 [30], whereas DCR was defined as the percentage of patients with radiological CR, PR or stable disease (SD).
AEs were reported according to CTCAE version 4.0 and their relationship to trabectedin. Treatment-related AEs were followed until resolution or start of new therapy. AEs and serious AEs (SAEs) were recorded until 30 days after the last dose.
Statistical analyses were performed using the R software Version 4.1.1. Analyses were based on the intention-to-treat (ITT) population, which included all enrolled patients who provided informed consent and received at least one dose of trabectedin. All parameters were analyzed descriptively. Categorical variables were reported as absolute and relative frequencies, and continuous variables were described by number of observations, median and range (minimum to maximum). Frequency tables were provided for categorical variables and checked for dependencies by Fisher’s exact test. Approximate 95% confidence intervals (CI) were calculated for estimated rates (e.g., tumor control rate). Time-to-event endpoints (i.e., PFS and OS) were analyzed according to the Kaplan–Meier method and compared using the log-rank test. Cox regression for time-to-event outcomes and logistic regression for binary outcomes such as unplanned hospitalizations, occurrence of toxicities of grade ≥ 3 and early death were applied to assess the effects of the following parameters: age (<78 vs. ≥78 years), sex (male vs. female), ECOG PS (0 vs. 1 vs. 2), QLQ summary score (≤72.2 vs. >72.2), IADL (<1 vs. ≥1), MNA (normal vs. risk of malnutrition vs. malnutrition), CCI (≤2 vs. >2), GDS (normal vs. light to moderate), G8 score (≤14 vs. >14), CARG (low vs. intermediate vs. high), TUG (no vs. moderate restriction). For specifications of the different categories, please refer to the parameters listed in Table 1. Firstly, univariate regression was performed, including only one of these factors. Only factors with a significant effect in the univariate model at 10% significance level were considered in the multivariate model. The multivariate regression model was reduced by stepwise selection (mix of backward and forward selection) to relevant factors. For all analyses, p-value < 0.05 was considered statistically significant.

3. Results

3.1. Patient Disposition and Baseline Characteristics

Between 2 February 2017 and 31 December 2021, 70 patients were enrolled in 12 study centers in Austria, Germany and Switzerland, and 69 of them received at least one dose of trabectedin. All patients under trabectedin treatment were included in the ITT population (Figure 2).
Demographic and baseline characteristics are summarized in Table 1. Sixty-nine patients included in the ITT had a median age of 78 years (range: 55 to 88 years) at the time of inclusion in the study. Moreover, 56 patients were ≥70 years and 54 patients were >70 years; only two patients were younger than 60 years. Overall, 43.5% of patients were female and 56.5% were male. Twenty-two of the patients had an ECOG PS of 0, 38 had an ECOG PS of 1 and 9 patients had an ECOG PS of 2. The baseline data of PROs and GAs identified a diverse population of older patients with respect to their global health status, although a large proportion of them suffered from restrictions, required geriatric help and had a high risk of morbidity. Nonetheless, according to the CARG score, the risk for side effects was assessed as low or intermediate for 38% and 29%, respectively, of the population included in the study.

3.2. Extent of Exposure

Overall, the mean time on treatment with trabectedin was 3.6 months (range: 0 to 22.7 months). Study participants received a median of three cycles, with 24 (34.8%) patients receiving ≥6 cycles and up to a maximum of 21 cycles (2 patients, 2.9%). The median cumulative total dose was 4.20 mg/m2 (range: 0.94 to 30.0 mg/m2). Dose reductions were reported for 29 (42.0%) patients with a median of two (range: 1 to 10) cycles before the first reduction. Treatment interruptions were reported for 10 (14.5%) patients with a median of two (range: 1 to 5) cycles before the first interruption. Sixty-one (88.4%) patients discontinued treatment with trabectedin for the following reasons: disease progression (n = 27, 44.3%), death (n = 10, 16.4%), withdrawal of consent (n = 9, 14.8%), side effects (n = 9, 14.8%), investigator´s or sponsor´s decision (n = 5, 8.2%) and non-compliance (n = 1, 1.6%).

3.3. Primary Endpoints QLQ-C30 and PRO-CTCAE

Overall, 67 (97.1%) patients provided valid answers to the QLQ-C30 questionnaire at the baseline visit (start of treatment if available, screening otherwise) and 29 (42.0%) at the end of treatment (EOT). Figure 3 illustrates the changes in QLQ-C30 sum score and global health status during treatment with trabectedin. Corresponding figures for the individual functional and symptom scales/items are provided in the supplementary materials (Figures S1 and S2). A change in score of >10 was regarded as clinically relevant deterioration or improvement. Among the patients with valid entries at baseline and EOT, three (4.3% of the ITT population) and five (7.2%) patients reported an improvement with regard to the QLQ-C30 sum score and global health status, respectively. No change was reported by 12 (17.4%) and 9 (13%) patients, respectively, whereas 14 (20.3%) patients felt that their health status had deteriorated. Overall, the median time to deterioration varied between 3.2 and 18 weeks for the different QLQ-C30 scales (see Figure 4 and Table 2).
The PRO-CTCAE questionnaire provides a more differentiated record of symptoms since it covers different parameters such as intensity, frequency and interference with daily life (see in Figure 5 the extracted data for pain. Further Bar plots by question are provided in the supplementary materials (Figure S3).
The questionnaire was completed by 66 (95.7%) patients at baseline and by 29 (42.0%) patients at EOT.

3.4. Clinical Outcomes

The median OS was estimated at 11.2 months [95%CI: 5.6; 19.4] (Figure 6). In total, 56 events of disease progression, including cases of death, were observed until the end of follow-up. The median PFS was estimated at 2.5 months [95%CI: 1.8; 4.3] (Figure 6).
Univariate Cox regression of OS with different factors revealed that the hazard ratios (HR) of MNA malnutrition and TUG moderate restriction were significantly different from 1 at the 10% level. However, in the multivariate model none of the possible predictors had an HR significantly different from 1 at the 5% level of significance (Table 3). Univariate Cox regression analysis for PFS revealed no significant HR for any of the predictors.
None of the patients achieved overall CR or PR at any visit. Thus, the ORR is 0 for all visits. The overall DCR (either CR, PR or SD) was 23.2% [95% CI: 0.130; 0.334]. Univariate logistic regression analysis showed no significant effect on DCR for any of the parameters tested.
The median time to onset of response was estimated at 6.92 weeks [95% CI: 3.99; 8.99]. The univariate Cox regression model showed evidence that the chance for disease control (i.e., reaching CR, PR or SD) is higher in the group with QLQ-C30 sum score of ≥72.2 than in the group with a sum score of <72.2 (Table 3). This corresponds to an earlier onset of response. The median time to onset of response for the groups with a sum score of <72.2 and ≥72.2 was 8.99 weeks [95% CI: 6.99; 10.13] and 4.13 weeks [95% CI: 3.85; 6.85], respectively. However, these data should be interpreted with caution as tumor assessment was performed as per investigators’ discretion and not at pre-defined time points throughout the study.
The median duration of response (patients with at least SD) was estimated at 27.3 weeks [95% CI: 9.43; 56]. In the multivariate model, none of the HRs was significantly different from 1. In the single Cox regression models, both MNA risk of malnutrition and a high-risk CARG sore had a significant effect on the duration of response. Here, the median duration of response was significantly shorter than in the other categories. However, due to the low number of patients, the results have to be interpreted with caution.

3.5. Predictive Value of G8 and CARG

The correlation between G8 and CARG sum scores was weak (Spearman correlation = 0.196), indicating that G8 and CARG predict different aspects of disease development. Regarding unplanned hospital admissions, the OR of a high CARG score vs. low CARG score is significantly different from 1, i.e., the odds of experiencing an unplanned hospitalization were five times higher for patients with a high CARG score compared to those with a low CARG score. All other variables investigated do not appear to be predictive of the occurrence of unplanned hospitalizations (Table 4).
Regarding the occurrence of grade ≥ 3 toxicities, the OR for G8 was not significantly different from 1, indicating that these factors do not have a significant effect. The OR for a high CARG score (>9) was 7.941, indicating a clear trend although not reaching statistical significance. The odds for the occurrence of a toxicity of at least grade 3 were 3.77 times higher in the group of patients with ECOG PS Grade 1 than in the subgroup of patients with ECOG PS Grade 0. All other variables investigated were not predictive of the occurrence of toxicities grade ≥ 3 (Table 4). It has to be noted that the overall number of toxicities grade ≥ 3 was low, which may reduce the power to reveal significant relationships.
A moderate restriction in TUG significantly increases the chance of an early death. The odds for an early death were 5.75 times higher for patients with a moderate restriction than for patients with no restriction in TUG. All other variables investigated do not appear to have an effect on early death (Table 4).

3.6. Safety

A total of 54 (78.3%) patients had at least one grade ≥ 3 AE. The most common (≥10% of patients) grade 3/4 AEs were decreased neutrophil count (n = 8, 11.6% of patients) and anemia (n = 7, 10.1% of patients). Eleven (15.9%) patients experienced grade 5 AEs, namely disease progression (n = 3), acute kidney injury (n = 1), infection (n = 1), liver injury (n = 1), pneumonia (n = 1), pulmonary embolism (n = 1), renal failure (n = 1), sepsis (n = 1) and vaginal hemorrhage (n = 1). For 47 (68.1%) patients, at least one SAE was reported.
A total of 55 (79.7%) patients had at least one trabectedin-related adverse drug reaction (ADR) of any grade, 35 (50.7%) of whom experienced grade ≥ 3 ADRs (Table 5). Sixteen (23.2%) patients had grade ≥ 3 ADRs leading to dose modifications, including drug withdrawal, and 22 (31.9%) of them experienced an SAE that was classified as trabectedin-related. No new toxicities or unexpected safety concerns were identified for trabectedin.

4. Discussion

There are only very few studies specifically focusing on the toxicity and activity of individual chemotherapy regimens given as first-line treatment to older STS patients. A prospective trial evaluating daily treatment with oral cyclophosphamide and prednisolone in patients aged ≥ 65 years unsuitable for conventional chemotherapy with doxorubicin and/or ifosfamide resulted in low toxicity and an ORR of 27% with a median PFS of 6.8 months [31]. A phase II study evaluated trabectedin as first-line treatment in patients with advanced STS unfit to receive standard chemotherapy for reasons such as stable arrhythmia, previous myocardial infarction and/or age ≥ 80 years [32].
In Germany, two first-line studies with a focus on systemic therapies in STS patients older than 60 years were completed [11,12]. These and other systemic first-line treatment studies are summarized in Table 6.
In regard to second- and later-line treatments of older patients with advanced STS, Le Cesne et al. conducted a retrospective pooled analysis of five phase II trials [13] and concluded that trabectedin is a feasible treatment in young and older patients with STS, comparable with that observed in the overall population, with an acceptable and manageable safety profile.
A subgroup analysis of 131 patients aged ≥65 years and enrolled in the phase III, randomized ET743-SAR-3007 study [35,37] of trabectedin vs. dacarbazine in patients with advanced liposarcoma or leiomyosarcoma also corroborated the previous observations showing that trabectedin not only significantly improved disease control over dacarbazine but also derived a similar benefit observed in younger patients [35]. In older patients, compared with dacarbazine, trabectedin significantly prolonged median PFS: 4.9 vs. 1.5 months (HR 0.40, p = 0.0002; overall population: 4.2 vs. 1.5 months, HR 0.55, p < 0.001) and showed a trend toward prolonged median OS (15.1 vs. 8.0 months; HR 0.72, p = 0.18; overall population: 13.7 vs. 13.1 months, HR 0.93, p = 0.49) [38].
It was hypothesized that for the patient population included in the herein-reported non-interventional E-TRAB study, the OS will not be beneath 10 months, and the QoL outcomes will be in the same range as for the standard therapy. In fact, a median PFS of 2.5 months and a median OS of 11.2 months are comparable with the populations of older patients receiving trabectedin in other studies (see Table 6 and [39]).
The E-TRAB trial investigated the feasibility and prognostic value of two geriatric screening instruments (G8 and CARG) and a comprehensive GA represented by different domains such as activities of daily living, co-morbidities and others (IADL, MNA, CCI, GDS, TUG), as well as PROs for QoL (QLQ-C30) and treatment tolerability (PRO-CTCAE). Results showed an overall good compliance of patients and oncologists to perform PROs and GAs. Regression analyses identified some of the parameters as potential prognostic factors for clinical outcomes and toxicity of therapy. Interestingly, age was not among these prognostic factors. Instead, there was evidence of impaired performance status or geriatric or functional status being of prognostic value. This has also been reported by Hamacher and colleagues investigating the role of geriatric variables in elderly patients with STS on toxicity and clinical outcomes in a post-hoc analysis of the EPAZ trial [40]. In this study, the prognostic factor of geriatric screening elements on PFS and OS was demonstrated, outlining the importance of geriatric screening and further investigation in clinical trials. In our study, univariate regression analysis revealed a significant correlation between moderate mobility restrictions, as identified by the TUG test, and OS, as well as early death. This is in line with data published by Soubeyran et al., who demonstrated that a long time in the TUG test is associated with a higher risk of early death [41].
The CARG score is a brief and validated tool to stratify patients into groups for low, medium, or high risk of severe chemotherapy toxicity. It includes five key geriatric assessment domains, selected laboratory values, age, tumor type and treatment intensity and was identified as a useful and feasible tool for supporting clinical decision-making [42,43]. In our study, a high CARG score (>9) was associated with a significant increase in unplanned hospitalizations and a trend for higher incidences of toxicities grade ≥ 3, while the G8 did not show this. Regarding the G8, this is in contrast to the findings of Lodewijckx and colleagues [44].
According to Chiusole and colleagues, CGA (comprehensive geriatric assessment) and oncological multidimensional prognostic index are prognostic values for the survival of patients with metastatic STS and are valuable tools to identify frail and high-risk patients who could benefit from an individualized oncogeriatric management approach to optimize treatment-related survival and reduce toxicity [45]. Further, results from the INTEGERATE study demonstrated that CGA led to better QoL and healthcare delivery in older patients receiving systemic anticancer treatment [36].
Data from our study using CGA in STS patients show evidence for a higher incidence of unplanned hospitalizations and toxicities grade ≥ 3 in patients with a high CARG score, indicating that patients at risk can be identified early and subjected to individualized therapy with reduced toxicity.
Although the study was limited by its non-interventional design that did not allow aligning time points for treatments and outcome assessments and finally included only a relatively small number of patients, it generated valuable data under real-life conditions in everyday clinical practice. Data confirmed that trabectedin as first-line treatment in older patients with STS has an acceptable and manageable safety profile. It must, however, be borne in mind that after 8 weeks of treatment, about 50% of the patients developed deterioration of their QoL. The study underlines the feasibility and important role of PROs and CGA as valuable tools for monitoring and even predicting treatment tolerability and efficacy, thus supporting individualized therapies that reconcile the side effects of chemotherapy and the QoL of older patients.

5. Conclusions

In conclusion, patients with STS should be treated in referral centers to ensure proper expertise with interdisciplinary treatment protocols. Older patients, who represent more than 50% of all sarcoma patients, have a worse prognosis compared to younger patients and are included only in a very small percentage of clinical trials. Whenever possible, they should be included in clinical studies, provided they are functionally fit enough and willing to take the risk of adverse events. Age alone should not be a reason to deprive patients of standard treatment with chemotherapy and surgery. Specific geriatric screening instruments, such as those implemented in the E-TRAB (GISG-13) trial, may help stratify patients to predict or limit adverse treatment effects and could be an important tool in optimizing treatment strategies for older patients with STS.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/cancers16030558/s1, Figure S1: Changes in QLQ-C30 functional scales over the treatment period; Figure S2: Changes in QLQ-C30 symptom scales/items over the treatment period; Figure S3: PRO-CTCAE Bar plots by question.

Author Contributions

Conceptualization, B.K., P.H. and M.K.S.; methodology, M.K.S.; formal analysis and data curation, B.K. and M.K.S.; investigation, B.K., D.P., A.K. (Attila Kollar), P.R., S.R., G.E., M.A., V.G., T.B., W.E., A.K. (Annegret Kunitz) and C.R.; resources, B.K.; writing—original draft preparation, B.K. and M.K.S.; writing—review and editing, All; supervision, B.K. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by PharmaMar, S.A., Madrid, Spain.

Institutional Review Board Statement

All study procedures were carried out in accordance with the Declaration of Helsinki and its later amendments and local regulations on clinical trials and were approved by the institutional review boards of each participating center (Ethik-Kommission II, Medizinische Fakultät Mannheim der Ruprechts-Karls-Universität Heidelberg, 2016-630N-MA).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data underlying this article will be shared upon reasonable request to the corresponding author.

Acknowledgments

This study was organized by GWT-TUD GmbH, Dresden, Germany. The authors acknowledge Dorothea Schipp for assisting with the statistical analyses and Antje Gasparic for providing writing and editorial assistance for the manuscript (funded by PharmaMar, S.A.).

Conflicts of Interest

B.K. and P.H. received honoraria for an advisory role from PharmaMar. D.P. reports institutional fees from Roche (advisory role), Boehringer Ingelheim (advisory role), PharmaMar (advisory role, lecture fee), Blueprint Medicines (lecture fee) and research grants from Lilly, PharmaMar, Novartis, Clinigen, BMS, EUSA Pharma, Roche, outside the submitted work. C.R. received consulting and advisory role fees from Pfizer, Bristol-Myers Squibb and MSD Oncology. S.R. received payments for consultancy, congress and travel expenses from PharmaMar. T.B. received lecture fees from PharmaMar. P.R. received honoraria for an advisory role from Bayer, Novartis, Roche, Deciphera, Mundibiopharma, PharmaMar, Blueprint Medicines, GSK, Boehringer Ingelheim and lecture fees from Clinigen, Deciphera, PharmaMar and Boehringer Ingelheim. M.K.S. received institutional research grants and travel expenses from PharmaMar. The other authors indicated no financial relationships.

References

  1. International Agency for Research on Cancer; World Health Organization; International Academy of Pathology. WHO Classification of Tumours of Soft Tissue and Bone Tumours, 5th ed.; IARC: Lyon, France, 2020; Volume 3.
  2. Ressing, M.; Wardelmann, E.; Hohenberger, P.; Jakob, J.; Kasper, B.; Emrich, K.; Eberle, A.; Blettner, M.; Zeissig, S.R. Strengthening health data on a rare and heterogeneous disease: Sarcoma incidence and histological subtypes in Germany. BMC Public Health 2018, 18, 235. [Google Scholar] [CrossRef] [PubMed]
  3. Bourcier, K.; Dinart, D.; Le Cesne, A.; Honore, C.; Meeus, P.; Blay, J.Y.; Michot, A.; Le Loarer, F.; Italiano, A. Outcome of Patients with Soft-Tissue Sarcomas: An Age-Specific Conditional Survival Analysis. Oncologist 2019, 24, e559–e564. [Google Scholar] [CrossRef] [PubMed]
  4. Lahat, G.; Dhuka, A.R.; Lahat, S.; Lazar, A.J.; Lewis, V.O.; Lin, P.P.; Feig, B.; Cormier, J.N.; Hunt, K.K.; Pisters, P.W.; et al. Complete soft tissue sarcoma resection is a viable treatment option for select elderly patients. Ann. Surg. Oncol. 2009, 16, 2579–2586. [Google Scholar] [CrossRef] [PubMed]
  5. Karavasilis, V.; Seddon, B.M.; Ashley, S.; Al-Muderis, O.; Fisher, C.; Judson, I. Significant clinical benefit of first-line palliative chemotherapy in advanced soft-tissue sarcoma: Retrospective analysis and identification of prognostic factors in 488 patients. Cancer 2008, 112, 1585–1591. [Google Scholar] [CrossRef] [PubMed]
  6. Bucklein, V.; Limmroth, C.; Kampmann, E.; Schuebbe, G.; Issels, R.; Roeder, F.; Angele, M.; Durr, H.R.; Knosel, T.; Abdel-Rahman, S.; et al. Ifosfamide, Carboplatin, and Etoposide (ICE) in Combination with Regional Hyperthermia as Salvage Therapy in Patients with Locally Advanced Nonmetastatic and Metastatic Soft-Tissue Sarcoma. Sarcoma 2020, 2020, 6901678. [Google Scholar] [CrossRef] [PubMed]
  7. Tap, W.D.; Wagner, A.J.; Schoffski, P.; Martin-Broto, J.; Krarup-Hansen, A.; Ganjoo, K.N.; Yen, C.C.; Abdul Razak, A.R.; Spira, A.; Kawai, A.; et al. Effect of Doxorubicin Plus Olaratumab vs Doxorubicin Plus Placebo on Survival in Patients With Advanced Soft Tissue Sarcomas: The ANNOUNCE Randomized Clinical Trial. JAMA 2020, 323, 1266–1276. [Google Scholar] [CrossRef] [PubMed]
  8. Judson, I.; Verweij, J.; Gelderblom, H.; Hartmann, J.T.; Schoffski, P.; Blay, J.Y.; Kerst, J.M.; Sufliarsky, J.; Whelan, J.; Hohenberger, P.; et al. Doxorubicin alone versus intensified doxorubicin plus ifosfamide for first-line treatment of advanced or metastatic soft-tissue sarcoma: A randomised controlled phase 3 trial. Lancet Oncol. 2014, 15, 415–423. [Google Scholar] [CrossRef]
  9. Demetri, G.D.; Chawla, S.P.; von Mehren, M.; Ritch, P.; Baker, L.H.; Blay, J.Y.; Hande, K.R.; Keohan, M.L.; Samuels, B.L.; Schuetze, S.; et al. Efficacy and safety of trabectedin in patients with advanced or metastatic liposarcoma or leiomyosarcoma after failure of prior anthracyclines and ifosfamide: Results of a randomized phase II study of two different schedules. J. Clin. Oncol. 2009, 27, 4188–4196. [Google Scholar] [CrossRef]
  10. van der Graaf, W.T.; Blay, J.Y.; Chawla, S.P.; Kim, D.W.; Bui-Nguyen, B.; Casali, P.G.; Schoffski, P.; Aglietta, M.; Staddon, A.P.; Beppu, Y.; et al. Pazopanib for metastatic soft-tissue sarcoma (PALETTE): A randomised, double-blind, placebo-controlled phase 3 trial. Lancet 2012, 379, 1879–1886. [Google Scholar] [CrossRef]
  11. Grunwald, V.; Karch, A.; Schuler, M.; Schoffski, P.; Kopp, H.G.; Bauer, S.; Kasper, B.; Lindner, L.H.; Chemnitz, J.M.; Crysandt, M.; et al. Randomized Comparison of Pazopanib and Doxorubicin as First-Line Treatment in Patients with Metastatic Soft Tissue Sarcoma Age 60 Years or Older: Results of a German Intergroup Study. J. Clin. Oncol. 2020, 38, 3555–3564. [Google Scholar] [CrossRef]
  12. Hartmann, J.T.; Kopp, H.G.; Gruenwald, V.; Piperno-Neumann, S.; Kunitz, A.; Hofheinz, R.; Mueller, L.; Geissler, M.; Horger, M.; Fix, P.; et al. Randomised phase II trial of trofosfamide vs. doxorubicin in elderly patients with untreated metastatic soft-tissue sarcoma. Eur. J. Cancer 2020, 124, 152–160. [Google Scholar] [CrossRef] [PubMed]
  13. Cesne, A.L.; Judson, I.; Maki, R.; Grosso, F.; Schuetze, S.; Mehren, M.V.; Chawla, S.P.; Demetri, G.D.; Nieto, A.; Tanovic, A.; et al. Trabectedin is a feasible treatment for soft tissue sarcoma patients regardless of patient age: A retrospective pooled analysis of five phase II trials. Br. J. Cancer 2013, 109, 1717–1724. [Google Scholar] [CrossRef] [PubMed]
  14. Jones, R.L. Sarcomas and old age: Few options for such a large patient population. Future Oncol. 2019, 15, 11–15. [Google Scholar] [CrossRef] [PubMed]
  15. Hentschel, L.; Rentsch, A.; Lenz, F.; Hornemann, B.; Schmitt, J.; Baumann, M.; Ehninger, G.; Schuler, M. A Questionnaire Study to Assess the Value of the Vulnerable Elders Survey, G8, and Predictors of Toxicity as Screening Tools for Frailty and Toxicity in Geriatric Cancer Patients. Oncol. Res. Treat. 2016, 39, 210–216. [Google Scholar] [CrossRef] [PubMed]
  16. Li, D.; Sun, C.L.; Kim, H.; Soto-Perez-de-Celis, E.; Chung, V.; Koczywas, M.; Fakih, M.; Chao, J.; Cabrera Chien, L.; Charles, K.; et al. Geriatric Assessment-Driven Intervention (GAIN) on Chemotherapy-Related Toxic Effects in Older Adults with Cancer: A Randomized Clinical Trial. JAMA Oncol. 2021, 7, e214158. [Google Scholar] [CrossRef] [PubMed]
  17. Mohile, S.G.; Mohamed, M.R.; Xu, H.; Culakova, E.; Loh, K.P.; Magnuson, A.; Flannery, M.A.; Obrecht, S.; Gilmore, N.; Ramsdale, E.; et al. Evaluation of geriatric assessment and management on the toxic effects of cancer treatment (GAP70+): A cluster-randomised study. Lancet 2021, 398, 1894–1904. [Google Scholar] [CrossRef] [PubMed]
  18. Mohile, S.G.; Dale, W.; Somerfield, M.R.; Schonberg, M.A.; Boyd, C.M.; Burhenn, P.S.; Canin, B.; Cohen, H.J.; Holmes, H.M.; Hopkins, J.O.; et al. Practical Assessment and Management of Vulnerabilities in Older Patients Receiving Chemotherapy: ASCO Guideline for Geriatric Oncology. J. Clin. Oncol. 2018, 36, 2326–2347. [Google Scholar] [CrossRef]
  19. Gajra, A.; Jeune-Smith, Y.; Fortier, S.; Feinberg, B.; Phillips, E., Jr.; Balanean, A.; Klepin, H.D. The Use and Knowledge of Validated Geriatric Assessment Instruments Among US Community Oncologists. JCO Oncol. Pract. 2022, 18, e1081–e1090. [Google Scholar] [CrossRef]
  20. Dale, W. Why Is Geriatric Assessment so Infrequently Used in Oncology Practices? The Ongoing Issue of Nonadherence to This Standard of Care for Older Adults with Cancer. JCO Oncol. Pract. 2022, 18, 475–477. [Google Scholar] [CrossRef]
  21. Dueck, A.C.; Mendoza, T.R.; Mitchell, S.A.; Reeve, B.B.; Castro, K.M.; Rogak, L.J.; Atkinson, T.M.; Bennett, A.V.; Denicoff, A.M.; O’Mara, A.M.; et al. Validity and Reliability of the US National Cancer Institute’s Patient-Reported Outcomes Version of the Common Terminology Criteria for Adverse Events (PRO-CTCAE). JAMA Oncol. 2015, 1, 1051–1059. [Google Scholar] [CrossRef]
  22. Aaronson, N.K.; Ahmedzai, S.; Bergman, B.; Bullinger, M.; Cull, A.; Duez, N.J.; Filiberti, A.; Flechtner, H.; Fleishman, S.B.; de Haes, J.C.; et al. The European Organization for Research and Treatment of Cancer QLQ-C30: A quality-of-life instrument for use in international clinical trials in oncology. J. Natl. Cancer Inst. 1993, 85, 365–376. [Google Scholar] [CrossRef] [PubMed]
  23. Lawton, M.P.; Brody, E.M. Assessment of older people: Self-maintaining and instrumental activities of daily living. Gerontologist 1969, 9, 179–186. [Google Scholar] [CrossRef] [PubMed]
  24. Vellas, B.; Guigoz, Y.; Garry, P.J.; Nourhashemi, F.; Bennahum, D.; Lauque, S.; Albarede, J.L. The Mini Nutritional Assessment (MNA) and its use in grading the nutritional state of elderly patients. Nutrition 1999, 15, 116–122. [Google Scholar] [CrossRef] [PubMed]
  25. Charlson, M.E.; Pompei, P.; Ales, K.L.; MacKenzie, C.R. A new method of classifying prognostic comorbidity in longitudinal studies: Development and validation. J. Chronic Dis. 1987, 40, 373–383. [Google Scholar] [CrossRef] [PubMed]
  26. Yesavage, J.A.; Brink, T.L.; Rose, T.L.; Lum, O.; Huang, V.; Adey, M.; Leirer, V.O. Development and validation of a geriatric depression screening scale: A preliminary report. J. Psychiatr. Res. 1982, 17, 37–49. [Google Scholar] [CrossRef] [PubMed]
  27. Podsiadlo, D.; Richardson, S. The timed “Up & Go”: A test of basic functional mobility for frail elderly persons. J. Am. Geriatr. Soc. 1991, 39, 142–148. [Google Scholar] [CrossRef] [PubMed]
  28. Bellera, C.A.; Rainfray, M.; Mathoulin-Pelissier, S.; Mertens, C.; Delva, F.; Fonck, M.; Soubeyran, P.L. Screening older cancer patients: First evaluation of the G-8 geriatric screening tool. Ann. Oncol. 2012, 23, 2166–2172. [Google Scholar] [CrossRef]
  29. Hurria, A.; Togawa, K.; Mohile, S.G.; Owusu, C.; Klepin, H.D.; Gross, C.P.; Lichtman, S.M.; Gajra, A.; Bhatia, S.; Katheria, V.; et al. Predicting chemotherapy toxicity in older adults with cancer: A prospective multicenter study. J. Clin. Oncol. 2011, 29, 3457–3465. [Google Scholar] [CrossRef]
  30. Eisenhauer, E.A.; Therasse, P.; Bogaerts, J.; Schwartz, L.H.; Sargent, D.; Ford, R.; Dancey, J.; Arbuck, S.; Gwyther, S.; Mooney, M.; et al. New response evaluation criteria in solid tumours: Revised RECIST guideline (version 1.1). Eur. J. Cancer 2009, 45, 228–247. [Google Scholar] [CrossRef]
  31. Mir, O.; Domont, J.; Cioffi, A.; Bonvalot, S.; Boulet, B.; Le Pechoux, C.; Terrier, P.; Spielmann, M.; Le Cesne, A. Feasibility of metronomic oral cyclophosphamide plus prednisolone in elderly patients with inoperable or metastatic soft tissue sarcoma. Eur. J. Cancer 2011, 47, 515–519. [Google Scholar] [CrossRef]
  32. Grosso, F.; D’Ambrosio, L.; Zucchetti, M.; Ibrahim, T.; Tamberi, S.; Matteo, C.; Rulli, E.; Comandini, D.; Palmerini, E.; Baldi, G.G.; et al. Pharmacokinetics, safety, and activity of trabectedin as first-line treatment in elderly patients who are affected by advanced sarcoma and are unfit to receive standard chemotherapy: A phase 2 study (TR1US study) from the Italian Sarcoma Group. Cancer 2020, 126, 4726–4734. [Google Scholar] [CrossRef]
  33. Younger, E.; Litiere, S.; Le Cesne, A.; Mir, O.; Gelderblom, H.; Italiano, A.; Marreaud, S.; Jones, R.L.; Gronchi, A.; van der Graaf, W.T.A. Outcomes of Elderly Patients with Advanced Soft Tissue Sarcoma Treated with First-Line Chemotherapy: A Pooled Analysis of 12 EORTC Soft Tissue and Bone Sarcoma Group Trials. Oncologist 2018, 23, 1250–1259. [Google Scholar] [CrossRef] [PubMed]
  34. Yousaf, N.; Harris, S.; Martin-Liberal, J.; Stanway, S.; Linch, M.; Ifijen, M.; Al Muderis, O.; Khabra, K.; Fisher, C.; Noujaim, J.; et al. First line palliative chemotherapy in elderly patients with advanced soft tissue sarcoma. Clin. Sarcoma Res. 2015, 5, 10. [Google Scholar] [CrossRef]
  35. Jones, R.L.; Demetri, G.D.; Schuetze, S.M.; Milhem, M.; Elias, A.; Van Tine, B.A.; Hamm, J.; McCarthy, S.; Wang, G.; Parekh, T.; et al. Efficacy and tolerability of trabectedin in elderly patients with sarcoma: Subgroup analysis from a phase III, randomized controlled study of trabectedin or dacarbazine in patients with advanced liposarcoma or leiomyosarcoma. Ann. Oncol. 2018, 29, 1995–2002. [Google Scholar] [CrossRef]
  36. Soo, W.K.; King, M.T.; Pope, A.; Parente, P.; Darzins, P.; Davis, I.D. Integrated Geriatric Assessment and Treatment Effectiveness (INTEGERATE) in older people with cancer starting systemic anticancer treatment in Australia: A multicentre, open-label, randomised controlled trial. Lancet Healthy Longev. 2022, 3, e617–e627. [Google Scholar] [CrossRef] [PubMed]
  37. Demetri, G.D.; von Mehren, M.; Jones, R.L.; Hensley, M.L.; Schuetze, S.M.; Staddon, A.; Milhem, M.; Elias, A.; Ganjoo, K.; Tawbi, H.; et al. Efficacy and Safety of Trabectedin or Dacarbazine for Metastatic Liposarcoma or Leiomyosarcoma After Failure of Conventional Chemotherapy: Results of a Phase III Randomized Multicenter Clinical Trial. J. Clin. Oncol. 2016, 34, 786–793. [Google Scholar] [CrossRef] [PubMed]
  38. Patel, S.; von Mehren, M.; Reed, D.R.; Kaiser, P.; Charlson, J.; Ryan, C.W.; Rushing, D.; Livingston, M.; Singh, A.; Seth, R.; et al. Overall survival and histology-specific subgroup analyses from a phase 3, randomized controlled study of trabectedin or dacarbazine in patients with advanced liposarcoma or leiomyosarcoma. Cancer 2019, 125, 2610–2620. [Google Scholar] [CrossRef]
  39. Grunwald, V.; Pink, D.; Egerer, G.; Schalk, E.; Augustin, M.; Deinzer, C.K.W.; Kob, V.; Reichert, D.; Kebenko, M.; Brandl, S.; et al. Trabectedin for Patients with Advanced Soft Tissue Sarcoma: A Non-Interventional, Prospective, Multicenter, Phase IV Trial. Cancers 2022, 14, 5234. [Google Scholar] [CrossRef]
  40. Hamacher, R.; Liu, X.; Schuler, M.K.; Hentschel, L.; Schoffski, P.; Kopp, H.G.; Bauer, S.; Kasper, B.; Lindner, L.; Chemnitz, J.M.; et al. A post hoc analysis of the EPAZ trial: The role of geriatric variables in elderly soft tissue sarcoma patients on toxicity and outcome. Eur. J. Cancer 2023, 181, 145–154. [Google Scholar] [CrossRef]
  41. Soubeyran, P.; Fonck, M.; Blanc-Bisson, C.; Blanc, J.F.; Ceccaldi, J.; Mertens, C.; Imbert, Y.; Cany, L.; Vogt, L.; Dauba, J.; et al. Predictors of early death risk in older patients treated with first-line chemotherapy for cancer. J. Clin. Oncol. 2012, 30, 1829–1834. [Google Scholar] [CrossRef]
  42. Ortland, I.; Mendel Ott, M.; Kowar, M.; Sippel, C.; Jaehde, U.; Jacobs, A.H.; Ko, Y.D. Comparing the performance of the CARG and the CRASH score for predicting toxicity in older patients with cancer. J. Geriatr. Oncol. 2020, 11, 997–1005. [Google Scholar] [CrossRef]
  43. Nishijima, T.F.; Deal, A.M.; Williams, G.R.; Sanoff, H.K.; Nyrop, K.A.; Muss, H.B. Impact of the Cancer and Aging Research Group score and treatment intensity on survival and toxicity outcomes in older adults with advanced noncolorectal gastrointestinal cancers. Cancer 2022, 128, 1929–1936. [Google Scholar] [CrossRef] [PubMed]
  44. Lodewijckx, E.; Kenis, C.; Flamaing, J.; Debruyne, P.; De Groof, I.; Focan, C.; Cornelis, F.; Verschaeve, V.; Bachmann, C.; Bron, D.; et al. Unplanned hospitalizations in older patients with cancer: Occurrence and predictive factors. J. Geriatr. Oncol. 2021, 12, 368–374. [Google Scholar] [CrossRef] [PubMed]
  45. Chiusole, B.; Tortorelli, I.; Galiano, A.; Murtas, F.; Ahcene-Djaballah, S.; Tierno, G.; Bergo, E.; Banzato, A.; Gatti, M.; Di Maggio, A.; et al. Management and Outcomes of Older Patients (Age ≥ 70 Years) with Advanced Soft Tissue Sarcoma and Role of Geriatric Assessment and Oncological Multidimensional Prognostic Index (Onco-MPI) in a Real-World Setting. Cancers 2023, 15, 1043. [Google Scholar] [CrossRef] [PubMed]
Figure 1. Scheme of study design. ICF—Informed consent; ECOG PS—Eastern Cooperative Oncology Group Performance Status; EoS—End of study.
Figure 1. Scheme of study design. ICF—Informed consent; ECOG PS—Eastern Cooperative Oncology Group Performance Status; EoS—End of study.
Cancers 16 00558 g001
Figure 2. Consort chart.
Figure 2. Consort chart.
Cancers 16 00558 g002
Figure 3. Changes in QLQ-C30 sum score and global health status over the treatment period. A change in score of >10 is regarded as clinically relevant deterioration or improvement. Please note that EOT is not an exactly defined time point and differs in time from patient to patient.
Figure 3. Changes in QLQ-C30 sum score and global health status over the treatment period. A change in score of >10 is regarded as clinically relevant deterioration or improvement. Please note that EOT is not an exactly defined time point and differs in time from patient to patient.
Cancers 16 00558 g003
Figure 4. QLQ-C30 sum score: median time to deterioration, Kaplan–Meier estimates and 95% confidence intervals.
Figure 4. QLQ-C30 sum score: median time to deterioration, Kaplan–Meier estimates and 95% confidence intervals.
Cancers 16 00558 g004
Figure 5. PRO-CTCAE: Change in pain frequency, intensity and interference with daily life during treatment with trabectedin. Please note that EOT is not an exactly defined time point and differs in time from patient to patient.
Figure 5. PRO-CTCAE: Change in pain frequency, intensity and interference with daily life during treatment with trabectedin. Please note that EOT is not an exactly defined time point and differs in time from patient to patient.
Cancers 16 00558 g005
Figure 6. Kaplan–Meier plots of overall survival (left) and progression-free survival (right) (given are survival probabilities ± 95% confidence interval).
Figure 6. Kaplan–Meier plots of overall survival (left) and progression-free survival (right) (given are survival probabilities ± 95% confidence interval).
Cancers 16 00558 g006
Table 1. Patient demographics and baseline characteristics.
Table 1. Patient demographics and baseline characteristics.
ParameterITT (n = 69)
Age at study entry (years)Median (range)78 (55–88)
   <60 yearsn (%)2 (2.9%)
   ≥60 yearsn (%)67 (97.1%)
Gender
   Femalen (%)30 (43.5%)
   Malen (%)39 (56.5%)
ECOG PS
   Grade 0n (%)22 (31.9%)
   Grade 1n (%)38 (55.1%)
   Grade 2n (%)9 (13.0%)
QLQ-C30 score 1
   Summary scoreMedian (range)72.2 (22.3–99.5) (n = 67)
   Global health statusMedian (range)58.3 (8.33–100) (n = 66)
Functional scales
   Physical functioningMedian (range)66.7 (6.67–100) (n = 67)
   Role functioningMedian (range)66.7 (0–100) (n = 67)
   Emotional functioningMedian (range)66.7 (0–100) (n = 67)
   Cognitive functioningMedian (range)100 (16.7–100) (n = 67)
   Social functioningMedian (range)66.7 (0–100) (n = 67)
Symptom scales/items
   FatigueMedian (range)44.4 (0–100) (n = 67)
   Nausea and vomitingMedian (range)0 (0–100) (n = 67)
   PainMedian (range)33.3 (0–100) (n = 67)
   DyspneaMedian (range)33.3 (0–100) (n = 67)
   InsomniaMedian (range)33.3 (0–100) (n = 67)
   Appetite lossMedian (range)33.3 (0–100) (n = 67)
   ConstipationMedian (range)33.3 (0–100) (n = 67)
   DiarrheaMedian (range)0 (0–100) (n = 67)
   Financial difficultiesMedian (range)0 (0–100) (n = 67)
IADL total scoreMedian (range)8 (2–8) (n = 68)
   Patients with restrictions (<8)n (%)32 (46.4%)
   Patients without restrictions (8)n (%)36 (52.2%)
MNA total scoreMedian (range)27 (11–33) (n = 68)
   Normal (≥24)n (%)55 (79.7%)
   Risk of malnutrition (17–23.5)n (%)10 (14.5)
   Malnutrition (<17)n (%)3 (4.3%)
CCI total scoreMedian (range)8 (0–14) (n = 59)
   Low mortality risk (≤2)n (%)12 (17.4%)
   High mortality risk (>2)n (%)47 (68.1%)
GDS total scoreMedian (range)3 (0–10) (n = 65)
   Normal (0–5)n (%)59 (85.5%)
   Light-to-moderate depression (6–10)n (%)6 (8.7%)
   Severe depression (11–15)n (%)0
G8 total scoreMedian (range)13 (5–16) (n = 68)
   Geriatric care needed (≤14)n (%)50 (72.5%)
   No geriatric care needed (>14)n (%)18 (26.1%)
TUG (Time in s)Median (range)8.0 (1–17) (n = 54)
   Severe mobility restriction (≥30 s)n (%)0
   Significant mobility restriction (20–29 s)n (%)0
   Moderate mobility restriction (11–19 s)n (%)15 (21.7%)
   No mobility restriction (<10 s)n (%)39 (56.5%)
CARG total scoreMedian (range)7.0 (0–18) (n = 62)
   Low risk for side effects (0–5)n (%)26 (37.7%)
   Intermediate risk for side effects (6–9)n (%)20 (29.0%)
   High risk for side effects (>9)n (%)16 (23.2%)
1 For QLQ-C30, all of the scales and single-item measures range in score from 0 to 100. A high score for the global health status/QoL represents a high QoL. A high score for a functional scale represents a high/healthy level of functioning, but a high score for a symptom scale/item represents a high level of symptomatology/problems.
Table 2. Individual QLQ-C30 scales: time to deterioration.
Table 2. Individual QLQ-C30 scales: time to deterioration.
ScaleNumber of EventsMedian Time to Deterioration (Weeks) [95% CI]
QLQ-C30 sum score367.14 [5.000; 12.994]
Global health status/QoL357.14 [6.000; 12.857]
Physical functioning385.86 [3.714; 9.000]
Role functioning394.00 [3.143; 6.292]
Emotional functioning299.00 [6.292; 22.000]
Cognitive functioning336.86 [6.000; 9.143]
Social functioning326.29 [3.149; 18.006]
Fatigue463.15 [3.006; 5.000]
Nausea and vomiting317.14 [4.286; 16.994]
Pain2813.00 [9.994; 20.137]
Dyspnea2314.29 [9.000; n.a.]
Insomnia297.14 [6.994; 18.006]
Appetite loss319.00 [6.006; 16.994]
Constipation249.57 [6.994; n.a.]
Diarrhea2218.01 [10.006; n.a.]
Financial difficulties15n.a. [18.006; n.a.]
n.a. not applicable.
Table 3. Univariate logistic regression.
Table 3. Univariate logistic regression.
OSTime to Onset of ResponseDuration of Response
FactorHR [95% CI], p-ValueHR [95% CI], p-ValueHR [95% CI], p-Value
Age ≥ 781.130 [0.610; 2.095], 0.6972.251 [0.788; 6.430], 0.1300.700 [0.182; 2.691], 0.604
Sex (male)1.169 [0.634; 2.155], 0.6170.942 [0.339; 2.617], 0.9091.068 [0.259; 4.401], 0.928
ECOG PS Grade 11.270 [0.634; 2.544], 0.5010.951 [0.299; 3.028], 0.9331.081 [0.205; 5.700], 0.927
ECOG PS Grade 21.664 [0.643; 4.309], 0.2942.263 [0.409; 12.530], 0.3500.230 [0.017; 3.114], 0.269
QLQ-C30 summary score > 72.20.698 [0.377; 1.292], 0.2525.462 [1.325; 22.51], 0.0192.192 [0.505; 9.517], 0.295
IADL at least one restriction1.516 [0.831; 2.763], 0.1751.241 [0.446; 3.452], 0.6800.858 [0.244; 3.014], 0.811
MNA malnutrition2.663 [0.627; 11.310], 0.1851.226 [0.149; 10.097], 0.8500.873 [0.099; 7.693], 0.902
MNA risk of malnutrition2.288 [0.917; 5.714], 0.0760.787 [0.215; 2.880], 0.7176.232 [1.022; 37.996], 0.047
CCI > 2 (high mortality risk)0.966 [0.403; 2.316], 0.9371.113 [0.298; 4.152], 0.8740.311 [0.056; 1.740], 0.184
GDS moderate1.764 [0.688; 4.522], 0.2372.966 [0.331; 26.61], 0.3313.534 [0.366; 34.100], 0.275
G8 ≤ 141.025 [0.524; 2.005], 0.9430.717 [0.252; 2.037], 0.5321.907 [0.489; 7.446], 0.353
CARG intermediate (6–9)1.298 [0.633; 2.661], 0.4761.100 [0.328; 3.688], 0.8780.217 [0.024; 1.992], 0.177
CARG high (>9)0.752 [0.318; 1.778], 0.5160.483 [0.119; 1.965], 0.31010.12 [1.042; 98.337], 0.046
TUG moderate restriction1.987 [0.971; 4.063], 0.0600.602 [0.126; 2.865], 0.5241.691 [0.188; 15.250], 0.639
OS—overall survival; HR—hazard ratio; CI—confidence interval.
Table 4. Univariate logistic regression.
Table 4. Univariate logistic regression.
Unplanned HospitalizationsOccurrence of Toxicities Grade ≥ 3Early Death
FactorOR [95% CI], p-ValueOR [95% CI], p-ValueOR [95% CI], p-Value
G8 > 141.105 [0.373; 3.273], 0.8570.709 [0.173; 2.900], 0.6320.867 [0.294; 2.559], 0.796
CARG intermediate (6–9)0.955 [0.296; 3.078], 0.9382.118 [0.543; 8.262], 0.2801.048 [0.325; 3.378], 0.938
CARG high (>9)5.056 [1.159; 22.060], 0.0317.941 [0.898; 70.216], 0.0620.857 [0.246; 2.983], 0.809
ECOG PS 10.714 [0.591; 4.970], 0.3213.771 [1.048; 13.569], 0.0421.533 [0.532; 4.422], 0.429
ECOG PS 20.800 0.168; 3.799], 0.7792.000 [0.332; 12.046], 0.4490.500 [0.099; 2.522], 0.401
IADL at least one restriction1.491 [0.565; 3.933], 0.4192.692 [0.751; 9.649], 0.1282.083 [0.788; 5.506], 0.139
MNA risk of malnutrition0.387 [0.033; 4.526], 0.4492.512 [0.289; 21.8], 0.4044.462 [0.868; 22.941], 0.073
MNA malnutrition1.806 [0.422; 7.730], 0.4250.558 [0.047; 6.693], 0.6452.231 [0.191; 26.062], 0.522
TUG moderate restriction2.783 [0.675; 11.48], 0.157not estimable5.750 [1.394; 23.716], 0.016
OR—odds ratio, CI—confidence interval.
Table 5. Treatment-related adverse drug reactions (ADRs) in at least ≥3% of patients and all grade-5 ADRs as reported by the investigators (all treated patients, n = 69) 1.
Table 5. Treatment-related adverse drug reactions (ADRs) in at least ≥3% of patients and all grade-5 ADRs as reported by the investigators (all treated patients, n = 69) 1.
Treatment-Related ADR as Per NCI-CTC, Worst Case Per Patient (≥3% of Patients)Grade 1
n = 28
Grade 2
n = 23
Grade 3
n = 31
Grade 4
n = 7
Grade 5
n = 2
Total
n = 55
n%n%n%n%n%n%
Nausea1420.357.257.2----2434.8
Fatigue1318.857.2------1826.1
Anemia68.734.357.2----1420.3
WBC decreased57.245.822.934.3--1420.3
ALAT increased45.845.834.311.4--1217.4
AST increased913.0--22.9----1115.9
Neutrophil count decreased--22.957.234.3--1014.5
Vomiting710.122.9------10 214.5
Constipation34.3--68.7----913.0
GGT increased57.222.922.9----913.0
Platelet count decreased57.211.4--22.9--811.6
Decreased appetite68.711.4------710.1
Acute kidney injury11.411.434.311.4--68.7
AP increased34.3--34.3----68.7
Abdominal pain34.311.411.4----57.2
Edema peripheral45.811.4------57.2
Hypoalbuminemia34.311.4------45.8
Hepatotoxicity--11.422.9----34.3
Liver disorder--11.422.9----34.3
Liver injury--------11.411.4
Sepsis--------11.411.4
1 The percentages relate to the number of patients in the Safety Analysis Set (all treated patients). 2 For one event of vomiting, the grade was not specified. NCI-CTC—National Cancer Institute Common Toxicity Criteria; ADR—adverse drug reaction; WBC—white blood cell count; ALAT—alanine aminotransferase; ASAT—aspartate aminotransferase; GGT—gamma-glutamyltransferase; AP—alkaline phosphatase.
Table 6. Summary of first-line studies in older patients.
Table 6. Summary of first-line studies in older patients.
ReferenceTreatmentnAge [Years]ORR [%]Median PFS [Months]Median OS [Months]
E-TRAB studyTrabectedin69Median: 7823.2 (DCR)2.511.2
Younger et al. [33]Doxorubicin-based chemotherapies348>6514.93.510.8
Yousaf et al. [34]Doxorubicin or others120>6520n.e.6.5
Mir et al. [31]Cyclophosphamide27≥65276.8n.e.
Grosso et al. [32]Trabectedin24≥8062
(DCR)
49
Hartmann et al. [12]Trofosfamide80≥606.62.812.3
Grünwald et al. [11]Pazopanib81≥60n.e.4.412.3
Jones et al. [35,36]Trabectedin94>6594.915.1
ORR—overall response rate; DCR—disease control rate; PFS—progression-free survival; OS—overall survival; n.e.—not evaluated.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Kasper, B.; Pink, D.; Rothermundt, C.; Richter, S.; Augustin, M.; Kollar, A.; Kunitz, A.; Eisterer, W.; Gaidzik, V.; Brodowicz, T.; et al. Geriatric Assessment of Older Patients Receiving Trabectedin in First-Line Treatment for Advanced Soft Tissue Sarcomas: The E-TRAB Study from The German Interdisciplinary Sarcoma Group (GISG-13). Cancers 2024, 16, 558. https://doi.org/10.3390/cancers16030558

AMA Style

Kasper B, Pink D, Rothermundt C, Richter S, Augustin M, Kollar A, Kunitz A, Eisterer W, Gaidzik V, Brodowicz T, et al. Geriatric Assessment of Older Patients Receiving Trabectedin in First-Line Treatment for Advanced Soft Tissue Sarcomas: The E-TRAB Study from The German Interdisciplinary Sarcoma Group (GISG-13). Cancers. 2024; 16(3):558. https://doi.org/10.3390/cancers16030558

Chicago/Turabian Style

Kasper, Bernd, Daniel Pink, Christian Rothermundt, Stephan Richter, Marinela Augustin, Attila Kollar, Annegret Kunitz, Wolfgang Eisterer, Verena Gaidzik, Thomas Brodowicz, and et al. 2024. "Geriatric Assessment of Older Patients Receiving Trabectedin in First-Line Treatment for Advanced Soft Tissue Sarcomas: The E-TRAB Study from The German Interdisciplinary Sarcoma Group (GISG-13)" Cancers 16, no. 3: 558. https://doi.org/10.3390/cancers16030558

APA Style

Kasper, B., Pink, D., Rothermundt, C., Richter, S., Augustin, M., Kollar, A., Kunitz, A., Eisterer, W., Gaidzik, V., Brodowicz, T., Egerer, G., Reichardt, P., Hohenberger, P., & Schuler, M. K. (2024). Geriatric Assessment of Older Patients Receiving Trabectedin in First-Line Treatment for Advanced Soft Tissue Sarcomas: The E-TRAB Study from The German Interdisciplinary Sarcoma Group (GISG-13). Cancers, 16(3), 558. https://doi.org/10.3390/cancers16030558

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop