by Ricardo Muniz, FAPESP

Cachexia classification models. A Decision tree generated using Classification And Regression Trees (CART). The bottom boxes indicate hazard ratios, the number of patients at risk in each leaf, and the percentage of patients in each leaf. This analysis was performed using the discovery set samples. B Kaplan–Meier survival curve generated by Cutoff Finder tool (red-curve: PMA > 0.71 and low-risk group; black-curve: PMA < 0.71 and high-risk group) using samples from the discovery set. C AUC-ROC curve demonstrating the specificity and sensitivity of PMA cutoffs in indicating low muscle mass (determined by third lumbar skeletal muscle index cutoffs [4]) in the discovery set. D Kaplan–Meier survival curve comparing LM (low-muscularity patients) and HM (high-muscularity patients) using samples from the validation set. Red-curve: PMA > 0.71 and low-risk group; Black-curve: PMA < 0.71 and high-risk group The resulting P-values for the log-rank test are shown. E Kaplan–Meier survival curve comparing low- and high-muscularity (LM and HM, respectively) patients by combining the discovery and validation datasets. Red-curve: PMA > 0.71 and low-risk group; Black-curve: PMA < 0.71 and high-risk group. The resulting P-values for the log-rank test are shown. HR: hazard ratio; CI: 95% confidence interval. Credit: Journal of Translational Medicine (2023). DOI: 10.1186/s12967-023-03901-5

Cachexia is a syndrome characterized by severe loss of weight and muscle mass. It is present in approximately half of all lung cancer patients and is particularly damaging in cases of non-small cell lung cancer. Early detection is important for prognostic purposes and as a basis for optimal decisions on treatment.

Weakness due to cachexia hinders everyday activities, causes pain, and increases the difficulty of coping with the disease and the side effects of its treatment. In lung cancer patients, the main hazard is acute respiratory failure.

Sarah Santiloni Cury, a postdoctoral fellow at São Paulo State University's Botucatu Institute of Biosciences (IBB-UNESP) in Brazil, studies strategies for early diagnosis of cachexia and is first author of an article on a novel method of predicting the syndrome published in the Journal of Translational Medicine.

Her work won an award from the European Molecular Biology Organization (EMBO) and the Federation of European Biochemical Societies (FEBS) in 2020 at an event focusing on artificial intelligence and machine learning in cancer research.

"Multiple screening tools are used to measure muscle loss. One possibility is computed tomography [CT] for muscle quantification at the third lumbar vertebra [L3]. However, CT scans of non-small cell lung cancer patients do not usually include L3," she said.

Some researchers have surmounted this limitation by analyzing the pectoralis muscle area (PMA). PMA quantification is associated with clinical outcomes, but each study uses different calculations and cutoffs, and the best cutoff for the purposes of classifying cachectic patients based on CTs had yet to be defined.

Cury and her group demonstrated that PMA alone can serve as a cachexia predictor in these patients. They used machine learning to build a muscle loss prediction model based on muscularity, clinical data, and the transcriptional profile from the tumor microenvironment.

They first measured PMA in 211 non-small cell lung cancer patients using publicly available CT scans from The Cancer Imaging Archive (TCIA). Cutoffs were established using machine learning algorithms (CART and Cutoff Finder) applied to PMA, clinical data and survival data.

"We evaluated the efficacy of our model using a validation set comprising 36 patients treated at UNESP's Botucatu Medical School," said Robson Francisco Carvalho, last author of the article. Carvalho is a professor at IBB-UNESP and supervised Cury's research.

"This study represents an important advance in our understanding of cachexia in lung cancer patients. We identified potential novel mediators and biomarkers of the syndrome using CT scans and molecular analysis for early diagnosis. These discoveries will serve as a basis for future research on therapeutic strategies and help medical teams devise better ongoing patient care," Carvalho said.

Research results previously published by the group showed a link between the presence of certain cancer biomarkers and the risk of developing cachexia. "Of the types of tumor that frequently produce cachexia, we found lung cancer to be associated with increased expression of specific factors that contribute to loss of muscle mass. Some of these factors act on cell surface receptors in muscle tissue, contributing to the loss," Carvalho said.

Tumor RNA sequencing revealed 90 upregulated secretory genes that potentially interact with muscle cell receptors in low-muscularity patients and ultimately result in muscle wasting. It also identified cell types in the tumor microenvironment that contributed by secreting cytokines or other cachexia-inducing factors.

Digital cytometry based on these patients' gene expression profile revealed high proportions of CD8+ T cells, a specific type of lymphocyte. Although these cells are frequently associated with intensive anti-cancer activity, in this case the researchers found that they may be associated with a worse prognosis. Other researchers had shown that CD8+ T cells lead to adipose tissue wasting in cachexia associated with chronic infection, but the link with lung cancer had not yet been established.

In sum, the study identified parameters for predicting cachexia and cells that may be associated with the syndrome, paving the way for the development of novel therapies. The tumor microenvironment is complex, however, and more research is necessary, the researchers stress.

More information: Sarah Santiloni Cury et al, Low muscle mass in lung cancer is associated with an inflammatory and immunosuppressive tumor microenvironment, Journal of Translational Medicine (2023). DOI: 10.1186/s12967-023-03901-5

Journal information: Journal of Translational Medicine 

Provided by FAPESP