Inflammation-associated intramyocellular lipid alterations in human pancreatic cancer cachexia

Systemic metabolic crosstalk as driver of cancer cachexia

Cachexia is a complex metabolic disorder characterized by negative energy balance due to increased consumption and lowered intake, leading to progressive tissue wasting and inefficient energy distribution. Once considered as passive bystander, metabolism is now acknowledged as a regulator of biological functions and disease progression. This shift in perspective mirrors the evolving understanding of cachexia itself, no longer viewed merely as a secondary consequence of cancer but as an active process. However, metabolic dysregulations in cachexia are currently studied in an organ-specific manner, failing to be fully integrated into a comprehensive framework that explains their functional roles in disease progression. Thus, in this review, we aim to provide a general overview of the various metabolic alterations with a potential systemic impact.

Cancer cachexia: multilevel metabolic dysfunction

Cancer cachexia is a complex metabolic disorder marked by unintentional body weight loss or 'wasting' of body mass, driven by multiple aetiological factors operating at various levels. It is associated with many malignancies and significantly contributes to cancer-related morbidity and mortality. With emerging recognition of cancer as a systemic disease, there is increasing awareness that understanding and treatment of cancer cachexia may represent a crucial cornerstone for improved management of cancer. Here, we describe the metabolic changes contributing to body wasting in cachexia and explain how the entangled action of both tumour-derived and host-amplified processes induces these metabolic changes. We discuss energy homeostasis and possible ways that the presence of a tumour interferes with or hijacks physiological energy conservation pathways. In that context, we highlight the role played by metabolic cross-talk mechanisms in cachexia pathogenesis. Lastly, we elaborate on the challenges and opportunities in the treatment of this devastating paraneoplastic phenomenon that arise from the complex and multifaceted metabolic cross-talk mechanisms and provide a status on current and emerging therapeutic approaches.

Interpretable machine learning model based on clinical factors for predicting muscle radiodensity loss after treatment in ovarian cancer

PURPOSE

Muscle radiodensity loss after surgery and adjuvant chemotherapy is associated with poor outcomes in ovarian cancer. Assessing muscle radiodensity is a real-world clinical challenge owing to the requirement for computed tomography (CT) with consistent protocols and labor-intensive processes. This study aimed to use interpretable machine learning (ML) to predict muscle radiodensity loss.

METHODS

This study included 723 patients with ovarian cancer who underwent primary debulking surgery and platinum-based chemotherapy between 2010 and 2019 at two tertiary centers (579 in cohort 1 and 144 in cohort 2). Muscle radiodensity was assessed from pre- and post-treatment CT acquired with consistent protocols, and a decrease in radiodensity ≥ 5% was defined as loss. Six ML models were trained, and their performances were evaluated using the area under the curve (AUC) and F1-score. The SHapley Additive exPlanations (SHAP) method was applied to interpret the ML models.

RESULTS

The CatBoost model achieved the highest AUC of 0.871 (95% confidence interval, 0.870-0.874) and F1-score of 0.688 (95% confidence interval, 0.685-0.691) among the models in the training set and outperformed in the external validation set, with an AUC of 0.839 and F1-score of 0.673. Albumin change, ascites, and residual disease were the most important features associated with a higher likelihood of muscle radiodensity loss. The SHAP force plot provided an individualized interpretation of model predictions.

CONCLUSION

An interpretable ML model can assist clinicians in identifying ovarian cancer patients at risk of muscle radiodensity loss after treatment and understanding the contributors of muscle radiodensity loss.

Is Lipid Metabolism of Value in Cancer Research and Treatment? Part I- Lipid Metabolism in Cancer

For either healthy or diseased organisms, lipids are key components for cellular membranes; they play important roles in numerous cellular processes including cell growth, proliferation, differentiation, energy storage and signaling. Exercise and disease development are examples of cellular environment alterations which produce changes in these networks. There are indications that alterations in lipid metabolism contribute to the development and progression of a variety of cancers. Measuring such alterations and understanding the pathways involved is critical to fully understand cellular metabolism. The demands for this information have led to the emergence of lipidomics, which enables the large-scale study of lipids using mass spectrometry (MS) techniques. Mass spectrometry has been widely used in lipidomics and allows us to analyze detailed lipid profiles of cancers. In this article, we discuss emerging strategies for lipidomics by mass spectrometry; targeted, as opposed to global, lipid analysis provides an exciting new alternative method. Additionally, we provide an introduction to lipidomics, lipid categories and their major biological functions, along with lipidomics studies by mass spectrometry in cancer samples. Further, we summarize the importance of lipid metabolism in oncology and tumor microenvironment, some of the challenges for lipodomics, and the potential for targeted approaches for screening pharmaceutical candidates to improve the therapeutic efficacy of treatment in cancer patients.