Cachexia index in predicting outcomes among patients receiving immune checkpoint inhibitor treatment for metastatic renal cell carcinoma

Aslan, Volkan; Kılıç, Atiye Cenay Karabörk; Sütçüoğlu, Osman; Eraslan, Emrah; Bayrak, Ahmet; Oksuzoglu, Omur Berna; Tahtacı, Gözde; Özdemir, Nuriye; Üner, Aytuğ; Günel, Nazan; Özet, Ahmet; Yazıcı, Ozan

doi:10.1016/j.urolonc.2022.07.018

Cited by 13 publications

(7 citation statements)

References 22 publications

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“…Three studies in total reported the relationship between body fat and ICI efficacy with conflicting results: two papers with larger sample sizes (n=251 and n=79) demonstrated a positive correlation with survival, while one study reported no significant correlation. Subcutaneous adipose tissue was measured in two papers ( 27 , 30 ) with diverse findings: SFI and SFA failed to show significant predictive value in these two studies, whereas SAT% was demonstrated as a positive prognostic factor for patients’ OS and PFS. One paper ( 27 ) investigated the predictive value of visceral adipose tissue using VFA but found no significant correlation with survival.TFI was showed in one study ( 32 ) to be significantly associated with OS and PFS, while the body composition risk score applied in the same study was a poor prognostic factor for OS, PFS and clinical benefit (CB).…”

Section: Resultsmentioning

confidence: 99%

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Imaging-based adipose biomarkers for predicting clinical outcomes of cancer patients treated with immune checkpoint inhibitors: a systematic review

Pei,

Xie,

Liu

et al. 2023

Front. Oncol.

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BackgroundSince the application of Immune checkpoint inhibitors (ICI), the clinical outcome for metastatic cancer has been greatly improved. Nevertheless, treatment response varies in patients, making it urgent to identify patients who will receive clinical benefits after ICI therapy. Adipose body composition has proved to be associated with tumor response. In this systematic review, we aimed to summarize the current evidence on imaging adipose biomarkers that predict clinical outcomes in patients treated with ICI in various cancer types.MethodsEmbase and PubMed were searched from database inception to 1st February 2023. Articles included investigated the association between imaging-based adipose biomarkers and the clinical outcomes of patients treated with ICI. The methodological quality of included studies was evaluated through Newcastle- Ottawa Quality Assessment Scale and Radiomics Quality Score tools. ResultsTotally, 22 studies including 2256 patients were selected. Non-small cell lung cancer (NSCLC) had the most articles (6 studies), followed by melanoma (5 studies), renal cell carcinoma (RCC) (3 studies), urothelial carcinoma (UC) (2 studies), head and neck squamous cell carcinoma (HNSCC) (1 study), gastric cancer (1 study) and liver cancer (1 study). The remaining 3 studies investigated metastatic solid tumors including various types of cancers. Adipose biomarkers can be summarized into 5 categories, including total fat, visceral fat, subcutaneous fat, intramuscular fat and others, which exerted diverse correlations with patients’ prognosis after being treated with ICI in different cancers. Most biomarkers of body fat were positively associated with survival benefits. Nevertheless, more total fat was predictable of worse outcomes in NSCLC, while inter-muscular fat was associated with poor clinical benefits in UC. ConclusionThere is relatively well-supported evidence for imaging-based adipose biomarkers to predict the clinical outcome of ICI. In general, most of the studies show that adipose tissue is positively correlated with clinical outcomes. This review summarizes the significant biomarkers proven by researches for each cancer type. Further validation and large independent prospective cohorts are needed in the future. The protocol of this systematic review has been registered at the International Prospective Register of Systematic Reviews (http://www.crd.york.ac.uk/PROSPERO, registration no: CRD42023401986).

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Section: Resultsmentioning

confidence: 99%

“…After removing 302 duplicates, 1108 articles were screened by abstract and title, and only 71 articles were retained for full-text screening. Ultimately, 22 studies were included for analysis, with 49 studies excluded (21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31).…”

Section: Resultsmentioning

confidence: 99%

Imaging-based adipose biomarkers for predicting clinical outcomes of cancer patients treated with immune checkpoint inhibitors: a systematic review

Pei,

Xie,

Liu

et al. 2023

Front. Oncol.

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“…Cancer cachexia, skeletal muscle loss, and sarcopenia affect the efficacy and continuation of chemotherapy. 26 , 27 , 28 , 29 , 30 , 31 , 32 Conversely, chemotherapy reportedly exacerbates skeletal muscle wasting in patients having cancer. 33 This association accordingly points to the need for simultaneous monitoring and intervention in the management of chemotherapy-induced adverse events and cancer cachexia.…”

Section: The Role Of Pharmacists For Patients With Cancer Cachexiamentioning

confidence: 99%

The role of pharmacists in multimodal cancer cachexia care

Fujii,

Yamada,

Iihara

et al. 2023

Asia-Pacific Journal of Oncology Nursing

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“…The prevalence of cachexia among people with cancer ranges between 40% and 80%, and it is a marker of poor prognosis and a direct cause of approximately 20% of cancer deaths 34 . Cachexia correlates with shorter survival in several cancer types, 35–37 and signs of cachexia at baseline have been shown to be predictive of treatment resistance 38,39 . Disease improvement due to treatment response can ameliorate cachexia, simultaneously reducing systemic clearance of therapeutic antibodies with time 40 .…”

Section: Mechanisms Of Time‐dependent Clearancementioning

confidence: 99%

“… 34 Cachexia correlates with shorter survival in several cancer types, 35 , 36 , 37 and signs of cachexia at baseline have been shown to be predictive of treatment resistance. 38 , 39 Disease improvement due to treatment response can ameliorate cachexia, simultaneously reducing systemic clearance of therapeutic antibodies with time. 40 This results in a time‐dependent increase in drug exposure that is more pronounced among responding patients as their underlying disease and cachexia improves.…”

Section: Mechanisms Of Time‐dependent Clearancementioning

confidence: 99%

Time‐dependent clearance can confound exposure–response analysis of therapeutic antibodies: A comprehensive review of the current literature

Proctor,

Wong

2023

Clinical Translational Sci

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Exposure–response (ER) analysis is used to optimize dose and dose regimens during clinical development. Characterization of relationships between drug exposure and efficacy or safety outcomes can be utilized to make dose adjustments that improve patient response. Therapeutic antibodies typically show predictable pharmacokinetics (PK) but can exhibit clearance that decreases over time due to treatment. Moreover, time‐dependent changes in clearance are frequently associated with drug response, with larger decreases in clearance and increased exposure seen in patients who respond to treatment. This often confounds traditional ER analysis, as drug response influences exposure rather than the reverse. In this review, we survey published population PK analyses for reported time‐dependent drug clearance effects across 158 therapeutic antibodies approved or in regulatory review. We describe the mechanisms by which time‐dependent clearance can arise, and evaluate trends in frequency, magnitude, and time scale of changes in clearance with respect to indication, mechanistic interpretation of time‐dependence, and PK modeling techniques employed. We discuss the modeling and simulation strategies commonly used to characterize time‐dependent clearance, and examples where time‐dependent clearance has impeded ER analysis. A case study using population model simulation was explored to interrogate the impact of time‐dependent clearance on ER analysis and how it can lead to spurious conclusions. Overall, time‐dependent clearance arises frequently among therapeutic antibodies and has spurred erroneous conclusions in ER analysis. Appropriate PK modeling techniques aid in identifying and characterizing temporal shifts in exposure that may impede accurate ER assessment and successful dose optimization.

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Cachexia index in predicting outcomes among patients receiving immune checkpoint inhibitor treatment for metastatic renal cell carcinoma

Cited by 13 publications

References 22 publications

Imaging-based adipose biomarkers for predicting clinical outcomes of cancer patients treated with immune checkpoint inhibitors: a systematic review

Imaging-based adipose biomarkers for predicting clinical outcomes of cancer patients treated with immune checkpoint inhibitors: a systematic review

The role of pharmacists in multimodal cancer cachexia care

Time‐dependent clearance can confound exposure–response analysis of therapeutic antibodies: A comprehensive review of the current literature

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