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Huot JR, Livingston PD, Pin F, Thomas CR, Jamnick NA, Callaway CS, Bonetto A. Long-term Musculoskeletal Consequences of Chemotherapy in Pediatric Mice. FUNCTION 2024; 5:zqae011. [PMID: 38706958 PMCID: PMC11065107 DOI: 10.1093/function/zqae011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 02/08/2024] [Accepted: 03/04/2024] [Indexed: 05/07/2024] Open
Abstract
Thanks to recent progress in cancer research, most children treated for cancer survive into adulthood. Nevertheless, the long-term consequences of anticancer agents are understudied, especially in the pediatric population. We and others have shown that routinely administered chemotherapeutics drive musculoskeletal alterations, which contribute to increased treatment-related toxicity and long-term morbidity. Yet, the nature and scope of these enduring musculoskeletal defects following anticancer treatments and whether they can potentially impact growth and quality of life in young individuals remain to be elucidated. Here, we aimed at investigating the persistent musculoskeletal consequences of chemotherapy in young (pediatric) mice. Four-week-old male mice were administered a combination of 5-FU, leucovorin, irinotecan (a.k.a., Folfiri) or the vehicle for up to 5 wk. At time of sacrifice, skeletal muscle, bones, and other tissues were collected, processed, and stored for further analyses. In another set of experiments, chemotherapy-treated mice were monitored for up to 4 wk after cessation of treatment. Overall, the growth rate was significantly slower in the chemotherapy-treated animals, resulting in diminished lean and fat mass, as well as significantly smaller skeletal muscles. Interestingly, 4 wk after cessation of the treatment, the animals exposed to chemotherapy showed persistent musculoskeletal defects, including muscle innervation deficits and abnormal mitochondrial homeostasis. Altogether, our data support that anticancer treatments may lead to long-lasting musculoskeletal complications in actively growing pediatric mice and support the need for further studies to determine the mechanisms responsible for these complications, so that new therapies to prevent or diminish chemotherapy-related toxicities can be identified.
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Affiliation(s)
- Joshua R Huot
- Department of Anatomy, Cell Biology and Physiology, Indiana University School of Medicine, Indianapolis, IN, 46202 USA
- Simon Comprehensive Cancer Center, Indiana University School of Medicine, Indianapolis, IN, 46202 USA
- Indiana Center for Musculoskeletal Health, Indiana University School of Medicine, Indianapolis, IN, 46202 USA
- Department of Kinesiology, School of Health and Human Sciences, Indiana University Purdue University Indianapolis, IN, 46202 USA
| | - Patrick D Livingston
- Department of Kinesiology, School of Health and Human Sciences, Indiana University Purdue University Indianapolis, IN, 46202 USA
| | - Fabrizio Pin
- Department of Anatomy, Cell Biology and Physiology, Indiana University School of Medicine, Indianapolis, IN, 46202 USA
- Simon Comprehensive Cancer Center, Indiana University School of Medicine, Indianapolis, IN, 46202 USA
- Indiana Center for Musculoskeletal Health, Indiana University School of Medicine, Indianapolis, IN, 46202 USA
| | - Connor R Thomas
- Department of Anatomy, Cell Biology and Physiology, Indiana University School of Medicine, Indianapolis, IN, 46202 USA
| | - Nicholas A Jamnick
- Department of Pathology, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045 USA
| | - Chandler S Callaway
- Department of Pathology, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045 USA
| | - Andrea Bonetto
- Department of Pathology, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045 USA
- University of Colorado Comprehensive Cancer Center, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045 USA
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Runco DV, DiMeglio LA, Vanderpool CP, Han Y, Daggy J, Kelley MM, Mikesell R, Zimmers TA. Growth differentiation factor 15 (GDF15) elevation in children with newly diagnosed cancer. Front Oncol 2023; 13:1295228. [PMID: 38146512 PMCID: PMC10749306 DOI: 10.3389/fonc.2023.1295228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Accepted: 11/20/2023] [Indexed: 12/27/2023] Open
Abstract
Background Growth differentiation factor 15 (GDF15), an inflammatory marker and mediator of adult cancer cachexia, remains largely unexplored in children. GDF15 increases nausea, vomiting, and anorexia in cancer and contributes to malnutrition, with the potential to be a cachexia therapeutic target. No studies have examined GDF15 in children with newly diagnosed cancer. Our pilot study compares GDF15 in children with newly diagnosed cancer to age- and sex-matched controls and correlates levels with anthropometric measurements and quality of life (QOL). Methods Children with newly diagnosed cancer aged 2-21 years were enrolled with serum GDF15 ELISA, anthropometric measures [height, weight, and mid-upper arm circumference (MUAC)], and QOL assessments (using PedsQL™ Core and Gastrointestinal Modules), which were collected at baseline and repeated 3 months later. Serum GDF15 levels were obtained from age- and sex-matched controls for comparison. Results A total of 57 participants enrolled (N=30, cancer group; N=27, control group) with a median age of 8.8 years (IQR 5.6-15.9 years). The participants were primarily male (54.4%), white (82.5%), and non-Hispanic (82.5%). Cancer diagnoses included acute lymphoblastic leukemia (N=8), lymphoma (N=8), neuroblastoma (N=5), soft tissue tumors (N=4), acute myeloid leukemia (N=2), and single participants with brain, kidney, and bone tumors. Baseline GDF15 was higher in the cancer cohort compared to the control cohort (median=614.6pg/mL and 320.5pg/mL, respectively; p<0.001). When examining participants with evaluable baseline and 3-month follow-up GDF15 levels (N=18), GDF15 was not statistically different (median=657.1pg/mL and 675.3pg/mL, respectively; p=0.702). A total of 13 of the 30 participants and 21 caregivers completed the PedsQL™ Core and Gastrointestinal symptom modules. QOL scores did not differ significantly at 3-month follow-up compared to baseline, but diarrhea worsened (p=0.017). Median participant response for diarrhea at baseline was 92.9 (IQR=92.9-96.4; N=13), which was significantly better than the follow-up (median=78.6; IQR= 71.4-92.9; p=0.017). There were no correlations between change in height, weight, or MUAC and change in GDF15 levels (p=0.351, 0.920, and 0.269 respectively). Conclusion GDF15 was elevated in children with cancer at diagnosis compared to controls but did not correlate with anthropometric measurements or QOL. This pilot study will inform future prospective studies to better describe the natural history of GDF15 and its role in cachexia and as a potential therapeutic target.
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Affiliation(s)
- Daniel V. Runco
- Department of Pediatrics, Riley Hospital for Children at Indiana University Health, Indianapolis, IN, United States
- Division of Pediatric Hematology/Oncology, Department of Pediatrics, Indiana University School of Medicine, Indianapolis, IN, United States
- Indiana University Melvin and Bren Simon Comprehensive Cancer Center, Indiana University School of Medicine, Indianapolis, IN, United States
| | - Linda A. DiMeglio
- Department of Pediatrics, Riley Hospital for Children at Indiana University Health, Indianapolis, IN, United States
- Division of Pediatric Endocrinology and Diabetology, Department of Pediatrics, Indiana University School of Medicine, Indianapolis, IN, United States
| | - Charles P. Vanderpool
- Department of Pediatrics, Riley Hospital for Children at Indiana University Health, Indianapolis, IN, United States
- Division of Pediatric Gastroenterology, Hepatology and Nutrition, Department of Pediatrics, Indiana University School of Medicine, Indianapolis, IN, United States
| | - Yan Han
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN, United States
| | - Joanne Daggy
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN, United States
| | - Mary M. Kelley
- Department of Pediatrics, Riley Hospital for Children at Indiana University Health, Indianapolis, IN, United States
- Division of Pediatric Hematology/Oncology, Department of Pediatrics, Indiana University School of Medicine, Indianapolis, IN, United States
| | - Raya Mikesell
- Department of Pediatrics, Riley Hospital for Children at Indiana University Health, Indianapolis, IN, United States
- Division of Pediatric Hematology/Oncology, Department of Pediatrics, Indiana University School of Medicine, Indianapolis, IN, United States
| | - Teresa A. Zimmers
- Indiana University Melvin and Bren Simon Comprehensive Cancer Center, Indiana University School of Medicine, Indianapolis, IN, United States
- Department of Surgery, Indiana University School of Medicine, Indianapolis, IN, United States
- Indiana Center for Musculoskeletal Health, Indiana University School of Medicine, Indianapolis, IN, United States
- Department of Cell, Developmental and Cancer Biology, Knight Cancer Institute, Oregon Health and Science University, Portland, OR, United States
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Zapaishchykova A, Liu KX, Saraf A, Ye Z, Catalano PJ, Benitez V, Ravipati Y, Jain A, Huang J, Hayat H, Likitlersuang J, Vajapeyam S, Chopra RB, Familiar AM, Nabavidazeh A, Mak RH, Resnick AC, Mueller S, Cooney TM, Haas-Kogan DA, Poussaint TY, Aerts HJWL, Kann BH. Automated temporalis muscle quantification and growth charts for children through adulthood. Nat Commun 2023; 14:6863. [PMID: 37945573 PMCID: PMC10636102 DOI: 10.1038/s41467-023-42501-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Accepted: 10/12/2023] [Indexed: 11/12/2023] Open
Abstract
Lean muscle mass (LMM) is an important aspect of human health. Temporalis muscle thickness is a promising LMM marker but has had limited utility due to its unknown normal growth trajectory and reference ranges and lack of standardized measurement. Here, we develop an automated deep learning pipeline to accurately measure temporalis muscle thickness (iTMT) from routine brain magnetic resonance imaging (MRI). We apply iTMT to 23,876 MRIs of healthy subjects, ages 4 through 35, and generate sex-specific iTMT normal growth charts with percentiles. We find that iTMT was associated with specific physiologic traits, including caloric intake, physical activity, sex hormone levels, and presence of malignancy. We validate iTMT across multiple demographic groups and in children with brain tumors and demonstrate feasibility for individualized longitudinal monitoring. The iTMT pipeline provides unprecedented insights into temporalis muscle growth during human development and enables the use of LMM tracking to inform clinical decision-making.
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Affiliation(s)
- Anna Zapaishchykova
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Kevin X Liu
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Anurag Saraf
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Zezhong Ye
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Paul J Catalano
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Viviana Benitez
- Dana-Farber/Boston Children's Cancer and Blood Disorders Center, Harvard Medical School, Boston, MA, USA
| | - Yashwanth Ravipati
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Arnav Jain
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Julia Huang
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Hasaan Hayat
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- Michigan State University, East Lansing, MI, USA
| | - Jirapat Likitlersuang
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Sridhar Vajapeyam
- Dana-Farber/Boston Children's Cancer and Blood Disorders Center, Harvard Medical School, Boston, MA, USA
- Department of Radiology, Boston Children's Hospital, Boston, MA, USA
| | - Rishi B Chopra
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Ariana M Familiar
- Children's Hospital of Philadelphia, Philadelphia, USA
- University of Pennsylvania, Pennsylvania, USA
| | - Ali Nabavidazeh
- Children's Hospital of Philadelphia, Philadelphia, USA
- University of Pennsylvania, Pennsylvania, USA
| | - Raymond H Mak
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Adam C Resnick
- Children's Hospital of Philadelphia, Philadelphia, USA
- University of Pennsylvania, Pennsylvania, USA
| | - Sabine Mueller
- Department of Neurology, Neurosurgery and Pediatrics, University of California, San Francisco, USA
| | - Tabitha M Cooney
- Dana-Farber/Boston Children's Cancer and Blood Disorders Center, Harvard Medical School, Boston, MA, USA
| | - Daphne A Haas-Kogan
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Tina Y Poussaint
- Dana-Farber/Boston Children's Cancer and Blood Disorders Center, Harvard Medical School, Boston, MA, USA
- Department of Radiology, Boston Children's Hospital, Boston, MA, USA
| | - Hugo J W L Aerts
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Radiology and Nuclear Medicine, CARIM & GROW, Maastricht University, Maastricht, the Netherlands
| | - Benjamin H Kann
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA.
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.
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Franke J, Bishop C, Runco DV. Malnutrition screening and treatment in pediatric oncology: a scoping review. BMC Nutr 2022; 8:150. [PMID: 36550508 PMCID: PMC9773580 DOI: 10.1186/s40795-022-00643-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Accepted: 11/25/2022] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Malnutrition and cachexia during pediatric cancer treatment worsen toxicity and quality-of-life. Clinical practice varies with lack of standard malnutrition definition and nutrition interventions. This scoping review highlights available malnutrition screening and intervention data in childhood cancer and the need for standardizing assessment and treatment. METHODS Ovid Medline, CINAHL, and Cochrane Library were searched for studies containing malnutrition as the primary outcome with anthropometric, radiographic, or biochemical measurements. Secondary outcomes included validated nutritional assessment or screening tools. Two authors reviewed full manuscripts for inclusion. Narrative analysis was chosen over statistical analysis due to study heterogeneity. RESULTS The search yielded 234 articles and 17 articles identified from reference searching. Nine met inclusion criteria with six nutritional intervention studies (examining appetite stimulants, nutrition supplementation, and proactive feeding tubes) and three nutritional screening studies (algorithms or nutrition support teams) each with variable measures and outcomes. Both laboratory evaluations (albumin, prealbumin, total protein) and body measurement (weight loss, mid-upper arm circumference) were used. Studies demonstrated improved weight, without difference between formula or appetite stimulant used. Screening studies yielded mixed results on preventing weight loss, weight gain, and survival. CONCLUSION Our review demonstrated a paucity of evidence for malnutrition screening and intervention in pediatric cancer treatment. While a variety of malnutrition outcomes, interventions, and screening tools exist, nutritional interventions increased weight and decreased complications. Screening tools decreased malnutrition risk and may improve weight gain. Potential age- and disease-specific nutritional benefits and toxicities also exist, further highlighting the benefit of standardizing malnutrition definitions, screening, and interventions.
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Affiliation(s)
- Jessica Franke
- grid.421123.70000 0004 0413 3417Marian University College of Osteopathic Medicine, 3200 Cold Spring Rd, Indianapolis, IN 46222 USA
| | - Chris Bishop
- grid.421123.70000 0004 0413 3417Marian University College of Osteopathic Medicine, 3200 Cold Spring Rd, Indianapolis, IN 46222 USA
| | - Daniel V. Runco
- grid.257413.60000 0001 2287 3919Department of Pediatrics, Indiana University School of Medicine, 705 Riley Hospital Drive, ROC Suite 4340, Indianapolis, IN 46202 USA ,grid.414923.90000 0000 9682 4709Department of Pediatrics, Division of Hematology/Oncology, Riley Hospital for Children at Indiana University Health, Indianapolis, IN USA
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