
==== Front
J Cancer Res Clin Oncol
J Cancer Res Clin Oncol
Journal of Cancer Research and Clinical Oncology
0171-5216
1432-1335
Springer Berlin Heidelberg Berlin/Heidelberg

5812
10.1007/s00432-024-05812-4
Research
Low skeletal muscle mass predicts melanoma-specific survival in melanoma patients treated with adjuvant immune checkpoint blockade
http://orcid.org/0000-0002-1651-7114
Mengoni Miriam miriam.mengoni@med.ovgu.de

1
http://orcid.org/0000-0002-6954-0633
Braun Andreas Dominik 1
Hinnerichs Mattes Simon 2
Aghayev Anar 2
http://orcid.org/0000-0001-7146-0934
Tüting Thomas 1
http://orcid.org/0000-0002-9273-3943
Surov Alexey 3
1 https://ror.org/03m04df46 grid.411559.d 0000 0000 9592 4695 Department of Dermatology, University Hospital Magdeburg, Leipziger Straße 44, 39120 Magdeburg, Germany
2 https://ror.org/03m04df46 grid.411559.d 0000 0000 9592 4695 Department for Radiology and Nuclear Medicine, University Hospital Magdeburg, Magdeburg, Germany
3 https://ror.org/04tsk2644 grid.5570.7 0000 0004 0490 981X Department of Radiology, Neuroradiology and Nuclear Medicine, Johannes Wesling University Hospital, Ruhr University Bochum, Bochum, Germany
25 5 2024
25 5 2024
2024
150 5 27531 3 2024
20 5 2024
© The Author(s) 2024
https://creativecommons.org/licenses/by/4.0/ Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Purpose

Adjuvant immunotherapy with immune checkpoint blockade(ICB) has greatly reduced the risk of recurrence and metastatic spread in early and advanced melanoma. However, not all patients benefit from adjuvant treatment: many patients show disease recurrence despite therapy, while those without recurrence harbor the risk for potentially irreversible adverse events. Biomarkers to select patients benefitting most from adjuvant therapy are currently lacking. As body composition assessment using CT images has shown promising results as a prognostic biomarker in stage IV melanoma, we aim to study the applicability of body composition parameters also in adjuvant melanoma treatment.

Methods

We analyze body composition features via CT scans in a retrospective cohort of 109 patients with resected stage IIB-IV melanoma receiving an adjuvant first-line treatment with ICB in our department. In this analysis, we focus on the impact of body composition, especially the presence of low skeletal muscle mass (LSMM), on patients’ survival and occurrence of adverse events (AEs).

Results

In uni- and multivariate analyses, we identify an association between CT-measured LSMM and melanoma-specific survival in patients treated with adjuvant ICB. Furthermore, LSMM is associated with a lower risk for therapy-related AEs, especially hypothyroidism, fatigue, and xerostomia. Conventional serological biomarkers e.g. S100 and LDH and measures of adipose tissue compartments did not show a correlation with survival or the occurrence of AEs.

Conclusions

LSMM constitutes a novel biomarker for melanoma-specific survival in patients treated with adjuvant ICB.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00432-024-05812-4.

Keywords

Immunotherapy
Body composition
Low skeletal muscle mass
Melanoma
Adjuvant therapy
http://dx.doi.org/10.13039/501100003042 Else Kröner-Fresenius-Stiftung 2017_Kolleg.07; TP4 2017_Kolleg.07; TP3 Mengoni Miriam Braun Andreas Dominik http://dx.doi.org/10.13039/501100006693 Arbeitsgemeinschaft Dermatologische Forschung Universitätsklinikum Magdeburg (8973)Open Access funding enabled and organized by Projekt DEAL.

issue-copyright-statement© Springer-Verlag GmbH Germany, part of Springer Nature 2024
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pmcIntroduction

The success of immune checkpoint blockade (ICB) for patients with unresectable or metastatic melanoma has promoted the use of these agents also as adjuvant therapies for melanoma. Several clinical trials have successfully demonstrated the efficacy of PD1-based ICB, leading to the approval of nivolumab and pembrolizumab for the adjuvant treatment of melanoma, and the establishment of these therapies as the current standard of care (Eggermont et al. 2022; Larkin et al. 2023). The introduction of adjuvant ICB in increasingly early stages of disease has drastically increased the number of patients eligible for therapy (Luke et al. 2022; Kirkwood et al. 2023). Moreover, early-stage patients harbor a smaller individual risk for disease recurrence or progression, while the risk for irreversible adverse events (AEs) remains comparable to patients with advanced disease (Robert et al. 2019; Larkin et al. 2019). This inherently unfavorable risk-reward ratio of adjuvant therapy highlights the clinical need to identify patients who benefit most from ICB. However, despite this clinical need, biomarkers established in routine patient care are currently lacking.

Alterations of the physical constitution including syndromes such as cachexia have been described as common events in patients with solid cancers (Argilés et al. 2023). Furthermore, connections between body composition and response to modern treatments have been described in a pooled meta-analysis of melanoma patients (McQuade et al. 2018). In this work, adipose patients harboring a body mass index (BMI)  > 25 showed an improved survival under treatment with ICB. This effect was most pronounced in male patients with mild obesity (Naik et al. 2019). Interestingly, a high serum creatinine serving as a surrogate parameter for skeletal muscle mass, was also associated with longer survival (Naik et al. 2019). These results indicate a profound impact of body composition on treatment responses in patients with metastatic disease. How these results translate to the adjuvant treatment of melanoma is currently unknown.

The clinical interest in body composition has catalyzed the development of morphometric measures to quantify different tissue compartments as potential biomarkers of treatment response or occurrence of AEs. Due to the availability of CT scans in routine patient care, methods have been developed to facilitate accurate measurements of tissue compartments such as muscle mass and adipose tissue from single CT slices (Zopfs et al. 2020). In early studies, the prognostic value of sarcopenia as the reduction of muscle mass on CT images has been demonstrated in patients with respiratory or gastrointestinal solid tumors (Prado et al. 2008). Furthermore, visceral and subcutaneous adipose tissue abundance have been identified as favorable prognostic factors in melanoma patients with metastatic disease (Lee et al. 2022; Mengoni et al. 2023), further highlighting the use of radiologic measures of body composition as biomarkers. Again, the applicability of these results for patients in an adjuvant setting receiving ICB remains unknown.

In the current work, we therefore assess the association of body composition on outcome of a representative cohort of melanoma patients receiving ICB and analyze the impact of body composition on the occurrence of AEs.

Materials and methods

Study cohort selection

This retrospective study was approved by the institutional ethics committee of the Otto-von-Guericke University Magdeburg (Approval number 145/21, Ethics Committee, Otto-von-Guericke University Magdeburg, Germany). All patients with resected, stage IIB-IV melanoma treated with PD1-based adjuvant immunotherapy as first-line treatment at the Dermatology Department of the University Hospital Magdeburg from the years 2014 until 2023 were retrospectively assessed. All patients included into the present study underwent contrast-enhanced CT scans at baseline diagnosis in a time course of maximum 120 days before treatment initiation. Patients with prior melanoma-related therapies (except adjuvant interferon-alpha), secondary malignancies, no available CT staging at baseline as well as prior or concurrent immunosuppression were excluded from this trial. All patients received treatment until recurrence and/or disease progression according to the Response Evaluation Criteria in Solid Tumor (RECIST 1.1) criteria (Eisenhauer et al. 2009) or occurrence of inacceptable toxicity. All patients, including those who discontinued therapy, were included in the analysis to prevent attrition bias.

Image acquisition

All CT images were acquired pretherapeutically on a multidetector CT scanner (Siemens Somatom Definition AS+, Siemens Healthineers, Germany, or Canon Aquilion Prime, Canon Medical Systems Corporation, Japan). Imaging was performed in supine position of patients with a standardized CT protocol (acquisition slice thickness of 1 mm with 5 mm reconstructions, tube voltage 120 kV with automatic tube current modulation, pitch factor 1.2, collimation 0.6 mm, application of 90 mL i.v. contrast medium (300 ml/mg Accupaque, GE Healthcare, USA)). All CT scans analyzed in this study were acquired in the portal venous phase.

Segmentation and radiologic measurements of body composition parameters

All images were analyzed by trained radiologists who were blinded to the clinical course of the patients. For analysis, images were viewed in the soft tissue window. Total, subcutaneous, intermuscular and visceral adipose tissue as well as skeletal muscle cross-sectional areas were assessed semiautomatically on the height of the mid third lumbar vertebra (L3) using standard Hounsfield unit ranges (adipose tissue: − 190 to − 30 HU: skeletal muscle:− 29 to + 150 HU) (Richards et al. 2012). Tissue area and density were measured using ImageJ (version 1.48). Representative images describing the segmentation procedure are shown in Supplementary Fig.1. The skeletal muscle index (SMI) was calculated as the ratio of smooth muscle area (SMA) and the body height squared (muscle area/height2). In our current work, we assessed low skeletal muscle mass (LSMM) CT-based with the SMI threshold proposed by Prado et al.: < 52.4 cm2/m2 for male patients and < 38.5 cm2/m2 for female patients (Prado et al. 2008). The visceral to subcutaneous fat ratio (VSR) was obtained by dividing the visceral adipose tissue area with the subcutaneous adipose tissue area. For the VSR, a threshold of 1.1 was used. Adipose tissue gauge indices were calculated as described previously by our group (tissue area * tissue density/patient height) (Mengoni et al. 2023).

Clinical data collection

Demographic and treatment data were compiled from electronic medical records. The following parameters were examined and included in the analysis: age, sex, body weight, height, tumor stage according to the American Joint Committee on Cancer Staging system (8th edition) (Gershenwald et al. 2017), laboratory parameters (lactate dehydrogenase (LDH), S100) and therapeutic agents used. As cutoff-values for S100 and LDH, the upper reference levels supplied by the assay manufacturer (0.11 µg/l for S100 and 3.75 µmol/s*l for LDH) were used. Additionally, dates of first dosing, disease recurrence and/or progression, death, cause of death and last follow-up were assembled to calculate the recurrence-free survival (RFS) and melanoma-specific survival (MSS) as primary outcome parameters of this study.

Statistical analysis

Kaplan–Meier estimators were utilized for univariate analysis, and statistical significance was tested using a logrank-test. Furthermore, multivariate Cox regression models were fitted and adjusted for age. Analyses were conducted for both MSS and RFS. Missing values were omitted from analyses. Statistical significance of differences for adverse events were tested using Fisher’s exact test. Analyses were performed using python with standard library modules and the packages lifelines and scipy. p-values < 0.05 were considered statistically significant.

Results

Study cohort characteristics

We enrolled 109 patients with resected, stage IIB-IV melanoma in the current analysis who initiated adjuvant ICB for one year in our department between 2014 and 2023. The cohort consisted of 61 male and 48 female patients with a median age of 63 years. 63 patients received pembrolizumab, 41 patients nivolumab and 5 patients received other PD1-based therapy regiments. Patients were stratified by the presence or absence of low skeletal muscle mass (LSMM) as defined by Prado et al. (Prado et al. 2008). Patients with and without LSMM differed significantly in body weight, whereas all other characteristics were comparable between the two subgroups. The median follow-up of our cohort was 32.7 months, and 31.5 months for patients without disease recurrence. The full cohort characteristics are shown in Table 1.Table 1 Study cohort characteristics

Characteristic	Total (n = 109)	No LSMM (n = 67)	LSMM (n = 42)	p	
Median age* [years]	63 (50–75)	58 (50–72)	70 (55–77)	0.16	
Sex	0.23	
 Sex: male	61 (56.0%)	34 (50.7%)	27 (64.3%)	
 Sex: female	48 (44.0%)	33 (49.3%)	15 (35.7%)	
Therapeutic agent	0.68	
 Therapeutic agent: Pembrolizumab	63 (57.8%)	38 (56.7%)	25 (59.5%)	
 Therapeutic agent: Nivolumab	41 (37.6%)	25 (37.3%)	16 (38.1%)	
 Therapeutic agent: other PD1-based	5 (4.6%)	4 (6.0%)	1 (2.4%)	
Stage at therapy initiation	0.62	
 N/A	1 (0.9%)	1 (0.9%)	0 (0.0%)	
 Stage IIB	1 (0.9%)	1 (1.5%)	0 (0.0%)	
 Stage IIC	3 (2.8%)	2 (3.0%)	1 (2.4%)	
 Stage IIIA	11 (10.2%)	7 (10.6%)	4 (9.5%)	
 Stage IIIB	30 (27.8%)	15 (22.7%)	15 (35.7%)	
 Stage IIIC	55 (50.9%)	36 (54.5%)	19 (45.2%)	
 Stage IIID	5 (4.6%)	4 (6.1%)	1 (2.4%)	
 Stage IV	3 (2.8%)	1 (1.5%)	2 (4.8%)	
Median weight* [kg]	80 (69–93)	85 (72–93)	79 (66–87)	0.03	
Median height* [cm]	172 (166–180)	170 (165–178)	172 (168–180)	0.17	
Median S100* [µg/l]	0.057 (0.042–0.076)	0.059 (0.042–0.078)	0.056 (0.04–0.075)	0.43	
Median LDH* [µmol/s*l]	3.38 (2.96–3.773)	3.4 (3.105–3.773)	3.35 (2.95–3.72)	0.79	
Data are presented as absolute number of patients with percentage in parentheses, with the exception for marked values (*), where interquartile ranges are given in parentheses

LDH lactate dehydrogenase, LSMM low skeletal muscle mass, N/A not available

The presence of low skeletal muscle mass is associated with reduced melanoma-specific survival

We first analyzed the impact of LSMM on MSS. In our cohort, patients with LSMM revealed a significantly shorter MSS compared to patients without LSMM (Fig. 1a). Since obesity as measured by a BMI > 25 has been described to be associated with a favorable outcome of patients receiving ICB, we furthermore assessed the BMI in our cohort. While patients with a BMI > 25 showed a trend towards longer MSS, this effect was not statistically significant in a univariate analysis (Fig. 1b). Interestingly, the conventional serological biomarkers S100 and LDH failed to predict MSS in our cohort (Fig. 1c, d). A multivariate cox regression model for MSS substantiated the finding of LSMM as a predictor of worse outcome, revealing a hazard ratio of 6.82 (p = 0.02, Fig. 1e, Table 2). Other body composition measures were not associated with MSS (Supplementary Fig.2). In summary, these data identify LSMM as a novel biomarker for MSS, outperforming conventional biomarkers S100 and LDH in our adjuvant cohort.Fig. 1 Low skeletal muscle mass outperforms conventional serological tumor markers as predictor of poor prognosis. a–d show Kaplan–Meier curves of melanoma-specific survival stratified by low skeletal muscle mass (a), BMI (b), S100 (c) and LDH (d). p-values were calculated by logrank test. e shows a multivariate regression analysis of body composition parameters and MSS. Each square represents the log(HR) for the coefficients, the whiskers denote the 95% confidence interval. BMI body mass index, HR hazard ratio, LDH lactate dehydrogenase, LSMM low skeletal muscle mass, MSS Melanoma-specific survival, SAT subcutaneous adipose tissue, VAT visceral adipose tissue, VSR visceral to subcutaneous fat ratio

Table 2 Multivariate cox regression model of the association between melanoma-specific or recurrence-free survival and body composition features

Characteristic	Melanoma-specific survival	Recurrence-free survival	
Hazard ratio (95% CI)	p-value	Hazard ratio (95% CI)	p-value	
LSMM	6.82 (1.36–34.28)	0.02	1.21 (0.59–2.51)	0.60	
SAT	2.34 (0.23–24.20)	0.47	4.42 (0.55–35.53)	0.16	
VSR	1.55 (0.26–9.23)	0.63	0.80 (0.35–1.82)	0.59	
BMI	1.01 (0.83–1.21)	0.95	1.01 (0.92–1.11)	0.89	
Sex	0.96 (0.17–5.50)	0.96	0.72 (0.32–1.61)	0.42	
VAT	0.27 (0.03–2.21)	0.22	1.35 (0.46–3.95)	0.58	
Shown are hazard ratios and p-values of a multivariate Cox regression model for MSS (left) or RFS (right). Both models were adjusted for age and sex

BMI body mass index, CI confidence interval, LSMM low skeletal muscle mass, SAT subcutaneous adipose tissue, VAT visceral adipose tissue, VSR visceral to subcutaneous fat ratio

The presence of LSMM does not predict recurrence-free survival

We furthermore assessed the relationship between the presence of LSMM and RFS. Whereas the RFS in our cohort was comparable to previously published results for melanoma patients treated with adjuvant ICB (Eggermont et al. 2022; Larkin et al. 2023), patients with LSMM surprisingly did not differ in their RFS compared to patients without LSMM in a univariate analysis of our cohort (Fig. 2a). Similarly, neither BMI nor the serological parameters S100 or LDH significantly predicted RFS in our patient cohort (Fig. 2b–d). In a multivariate cox regression model, no parameter reached statistical significance (Fig. 2e, Table 2). Again, no other body composition parameter was associated with RFS in our cohort (Supplementary Fig. 3).Fig. 2 Low skeletal muscle mass does not predict recurrence-free survival (RFS). a–d show Kaplan–Meier curves of RFS stratified by low skeletal muscle mass (a), BMI (b), S100 (c) and LDH (d). p-values were calculated by logrank test. e shows a multivariate regression analysis of body composition parameters and RFS. Each square represents the log(HR) for the coefficients, the whiskers denote the 95% confidence interval. BMI body mass index, HR hazard ratio, LDH lactate dehydrogenase, LSMM low skeletal muscle mass, RFS recurrence-free survival, SAT subcutaneous adipose tissue, VAT visceral adipose tissue, VSR visceral to subcutaneous fat ratio

The presence of LSMM is associated with a lower risk of adverse events

Next, we investigated the association of body composition features with AEs in our cohort. We observed a total of 159 AEs in our cohort. Of these 82 events were of grade 1, 63 events of grade 2, 12 events of grade 3 and 2 events of grade 4. Interestingly, patients with LSMM harbored fewer occurrences of hypothyroidism, fatigue, and xerostomia (Table 3). The incidence of any AE or severe AEs of grade 3 or 4 did not significantly differ between patients with or without LSMM (any grade p = 0.68, grade 3–4 p = 0.25).Table 3 Adverse events

Adverse event (AE)	Total	No LSMM	LSMM	p-value	
Hypothyroidism	24.0 (22.0%)	17.0 (31.5%)	7.0 (12.7%)	0.0221	
Fatigue	21.0 (18.3%)	15.0 (27.8%)	6.0 (10.9%)	0.030379	
Hyperthyroidism	14.0 (12.8%)	10.0 (18.5%)	4.0 (7.3%)	0.093089	
Pruritus	10.0 (9.2%)	5.0 (9.3%)	5.0 (9.1%)	1.000000	
Diarrhea	10.0 (9.2%)	5.0 (9.3%)	5.0 (9.1%)	1.000000	
Arthritis	9.0 (8.3%)	7.0 (13.0%)	2.0 (3.6%)	0.093269	
Rash maculopapular	9.0 (8.3%)	3.0 (5.6%)	6.0 (10.9%)	0.489	
Skin hypopigmentation	7.0 (6.4%)	4.0 (7.4%)	3.0 (5.5%)	0.716	
ALAT increased	6.0 (5.5%)	3.0 (5.6%)	3.0 (5.5%)	1.000	
Sarcoid-like reaction	6.0 (5.5%)	3.0 (5.3%)	3.0 (5.5%)	1.000000	
Dry mouth, xerostomia	5.0 (4.6%)	5.0 (9.3%)	0.0 (0.0%)	0.027070	
ASAT increased	5.0 (4.6%)	3.0 (5.3%)	2.0 (3.6%)	0.678654	
Eczema	5.0 (4.6%)	2.0 (3.7%)	3.0 (5.5%)	1.000000	
Colitis	4.0 (3.7%)	1.0 (1.9%)	3.0 (5.5%)	0.618023	
Hyperglycemia	3.0 (2.8%)	2.0 (3.7%)	1.0 (1.8%)	0.618023	
Pancreatitis	3.0 (2.8%)	2.0 (3.7%)	1.0 (1.8%)	0.618023	
Pneumonitis	3.0 (2.8%)	1.0 (1.9%)	2.0 (3.6%)	1.000000	
Dry skin	3.0 (2.8%)	2.0 (3.7%)	1.0 (1.8%)	0.618023	
Arthralgia	2.0 (1.8%)	1.0 (1.9%)	1.0 (1.8%)	1.000000	
Mucositis oral	2.0 (1.8%)	1.0 (1.9%)	1.0 (1.8%)	1.000000	
Hypophysitis	1.0 (0.9%)	0.0 (0.0%)	1.0 (1.8%)	1.000000	
Bronchial infection	1.0 (0.9%)	0.0 (0.0%)	1.0 (1.8%)	1.000000	
Bullous dermatitis	1.0 (0.9%)	0.0 (0.0%)	1.0 (1.8%)	1.000000	
Erectile dysfunction	1.0 (0.9%)	1.0 (1.9%)	0.0 (0.0%)	0.495413	
Neutrophil count decreased	1.0 (0.9%)	1.0 (1.9%)	0.0 (0.0%)	0.495413	
Headache	1.0 (0.9%)	1.0 (1.9%)	0.0 (0.0%)	0.495413	
Pancreatic enzymes decreased	1.0 (0.9%)	1.0 (1.9%)	0.0 (0.0%)	0.495413	
Alopecia	1.0 (0.9%)	0.0 (0.0%)	1.0 (1.8%)	1.000000	
Shown are absolute number of events of adverse events (AEs) observed in the study, stratified also by the presence or absence of low skeletal muscle mass (LSMM). p-values shown are between cohorts of patients with and without LSMM as tested by Fisher’s exact test

ALAT alanine aminotransferase, ASAT aspartate aminotransferase, LSMM low skeletal muscle mass

Discussion

In our current study, we describe the presence of LSMM as a risk factor for melanoma patients treated with adjuvant PD1-based ICB. To our knowledge, this is the first study examining the effect of body composition measures on the efficacy and tolerability of adjuvant ICB in melanoma patients of stages IIB-IV. Interestingly, pre-treatment LSMM was associated with a reduced MSS, whereas other parameters of body composition and routine serologic biomarkers (S100, LDH) did not correlate with patient outcome. At the same time, patients with LSMM less frequently experienced adverse events such as hypothyroidism under ICB. Since hypothyroidism and other immune-related adverse events have been described as favorable prognostic factors (Dawidowska et al. 2022), this further substantiates a potential immunosuppressive impact of LSMM. Previous reports have linked the presence of LSMM with poor prognosis also in stage III melanoma without adjuvant therapy (Sabel et al. 2011; Youn et al. 2022), suggesting a broader applicability of LSMM as a risk factor in melanoma. LSMM has also been identified as a predictor of worse outcome and increased frequency of adverse events in other cancer entities including patients with colon cancer receiving adjuvant chemotherapy (Jung et al. 2015). The mechanism how LSMM or other body composition features influence immune responses are currently unclear. Recent meta-analyses have observed sex-specific differences of body composition a predictor of improved outcome in melanoma under ICB, in which obesity served as a favorable indicator only in male, but not female patients (Trinkner et al. 2023). The interdependence between sex and body composition suggests that skeletal muscle and adipose tissue might alter hormonal responses governing anti-tumoral immune responses.

While the presence of LSMM predicted poor MSS in our cohort, we did not observe a difference for RFS in patients with LSMM. Similar discrepancies between PFS and overall survival have been described in meta-analyses of solid cancer clinical trials (Belin et al. 2020; Pasalic et al. 2020). These differences were not attributable to methodological errors, but rather caused by the longer post-progression survival enabled by novel therapies and additional second-line therapy options (Amir et al. 2012; Hess et al. 2019).

A major advantage of LSMM as a biomarker for melanoma is the simplicity of calculation, which is possible from routinely acquired CT images readily available for most patients. Automated systems assessing various body composition measures including LSMM are under active development (Lee et al. 2018; Graffy et al. 2019), further facilitating the applicability of our results into patient care. Moreover, the precision of body composition measurements might be further enhanced by deep-learning based volumetric models (Koitka et al. 2021).

The limited cohort size of our study poses a key limitation of our work. Moreover, the retrospective study design impedes the analysis of AEs since these are frequently underreported in retrospective analyses. Nonetheless, our results on adjuvant patients are consistent with reports from metastatic disease, substantiating the validity of our results. Further prospective trials using larger cohorts are required to fully uncover the usability of LSMM and other body composition parameters as predictors in adjuvant melanoma patients receiving ICB.

Conclusion

We present the first study evaluating body composition features as biomarkers for melanoma patients treated with adjuvant ICB and identify LSMM as a predictive indicator of reduced melanoma-specific survival. LSMM was associated with a lower risk for AEs.

Supplementary Information

Below is the link to the electronic supplementary material.Supplementary Figure 1: Body composition assessment. Exemplary description of the segmentation procedure. Shown are CT images on the height of the mid third lumbar vertebra of a patient with low skeletal muscle mass (LSMM) (a) and without LSMM (b). Greyscale image is shown left, corresponding segmentation results are shown right. The subcutaneous adipose tissue area is marked in blue, visceral adipose tissue area in yellow, intramuscular adipose tissue area in green and skeletal muscle area in red. (TIF 4201 KB)

Supplementary Figure 2: Univariate analysis of body composition parameters for melanoma-specific survival (MSS). Shown are Kaplan-Meier curves of melanoma-specific survival stratified by SAT, VAT, VSR, SATGI, VATGI, TATGI and IMATGI. p-values were calculated by logrank test. IMATGI, intermuscular adipose tissue gauge index, SAT, subcutaneous adipose tissue, SATGI, subcutaneous adipose tissue gauge index, TATGI, total adipose tissue gauge index, VAT, visceral adipose tissue, VATGI, visceral adipose tissue gauge index, VSR, visceral to subcutaneous fat ratio. (TIF 2444 KB)

Supplementary Figure 3: Univariate analysis of body composition parameters for recurrence-free survival (RFS). Shown are Kaplan-Meier curves of recurrence-free survival stratified by SAT, VAT, VSR, SATGI, VATGI, TATGI and IMAGTI. p-values were calculated by logrank test. IMATGI, intermuscular adipose tissue gauge index, SAT, subcutaneous adipose tissue, SATGI, subcutaneous adipose tissue gauge index, TATGI, total adipose tissue gauge index, VAT, visceral adipose tissue, VATGI, visceral adipose tissue gauge index, VSR, visceral to subcutaneous fat ratio. (TIF 2472 KB)

Abbreviations

AE Adverse event

BMI Body mass index

CI Confidence interval

CT Computed tomography

DLT Dose-limiting toxicity

HR Hazard ratio

ICB Immune checkpoint blockade

IMATGI Intermuscular adipose tissue gauge index

IQR Interquartile range

LDH Lactate dehydrogenase

LSMM Low skeletal muscle mass

MSS Melanoma-specific survival

PFS Progression-free survival

RECIST Response evaluation criteria in solid tumor

RFS Recurrence-free survival

SAT Subcutaneous adipose tissue

SATGI Subcutaneous adipose tissue gauge index

SMI Skeletal muscle index

SMA Smooth muscle area

TAT Total adipose tissue

TATGI Total adipose tissue gauge index

VAT Visceral adipose tissue

VATGI Visceral adipose tissue gauge index

VSR Visceral to subcutaneous fat ratio

Author contributions

MM performed the acquisition of clinical data. MSH and AA performed the measurement of body composition parameters under the supervision of AS. MM and ADB analyzed and interpreted the data and performed statistical analysis and data visualization. MM, ADB and AS drafted the manuscript. AS and TT contributed to the study conceptualization. All authors read and approved the final manuscript.

Funding

Open Access funding enabled and organized by Projekt DEAL. ADB and MM were funded by the Else Kröner Forschungskolleg Magdeburg (grant numbers 2017_Kolleg.07; TP3 and TP4). MM was supported by the Clinician Scientist Program of the Deutsche Stiftung Dermatologie e.V. (Deutsche Dermatologische Gesellschaft e.V. [DDG; https://derma.de/stipendien-forschungspreise]/Arbeitsgemeinschaft Dermatologische Forschung e.V. [ADF; https://www.adf-online.de]). The Clinican Scientist Program of the DDG is kindly supported by: Abbvie Germany, Allmirall, Janssen-Cilag, Leo Pharma, Lilly and Novartis.

Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Declarations

Conflict of interest

Unrelated to this work, MM has received travel grants and honoraria for presentations from Kyowa Kirin, Novartis Pharma GmbH, Pierre Fabre Pharma GmbH, Stemline Therapeutics and Sun Pharmaceutical Industries, Inc. MM served as consultant for Bristol-Myers Squibb and Merck Sharp & Dohme. Also unrelated to this manuscript, ADB served as consultant for Merck Sharp & Dohme and has received travel grants from Novartis Pharma GmbH. MSH, AA, TT and AS declare they have no financial interests.

Ethics approval

This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of the Otto-von-Guericke University Magdeburg, Germany (Approval number 145/21).

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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