Background: Oncological sigmoid and rectal resections are accompanied with substantial risk of anastomotic leakage. Preoperative risk assessment and patient selection remain difficult, highlighting... Show moreBackground: Oncological sigmoid and rectal resections are accompanied with substantial risk of anastomotic leakage. Preoperative risk assessment and patient selection remain difficult, highlighting the importance of finding easy-to-use parameters. This study evaluates the prognostic value of contrast-enhanced (CE) computed tomography (CT)-based muscle measurements for predicting anastomotic leakage. Methods: Patients that underwent oncological sigmoid and rectal resections in the LUMC between 2016 and 2020 were included. Preoperative CE-CT scans, were analyzed using Vitrea software to measure total abdominal muscle area (TAMA) and total psoas area (TPA). Muscle areas were standardized using patient's height into: psoas muscle index (PMI) and skeletal muscle index (SMI) (cm(2)/m(2)). Results: In total 46 patients were included, of which 13 (8.9%) suffered from anastomotic leakage. Patients with anastomotic leakage had a significantly lower PMI (22.1 vs. 25.1, p < 0.01) and SMI (41.8 vs. 46.6, p < 0.01). After adjusting for confounders (age and comorbidity), lower PMI (odds ratio [OR]: 0.85, 95% confidence interval [CI] 0.71-0.99, p = 0.03) and SMI (OR: 0.93, 95%CI 0.86-0.99, p = 0.02) were both associated with anastomotic leakage. Conclusion: This study showed that lower PMI and SMI were associated with anastomotic leakage. These results indicate that preoperative CT-based muscle measurements can be used as prognostic factor for risk stratification for anastomotic leakage. Show less
Diederiks, N.; Ravensbergen, C.J.; Treep, M.; Wezel, M. van; Kuruc, M.; Ruhaak, L.R.; ... ; Mesker, W.E. 2023
In the pursuit of personalized diagnostics and tailored treatments, quantitative protein tests contribute to a more precise definition of health and disease. The development of new quantitative... Show moreIn the pursuit of personalized diagnostics and tailored treatments, quantitative protein tests contribute to a more precise definition of health and disease. The development of new quantitative protein tests should be driven by an unmet clinical need and performed in a collaborative effort that involves all stakeholders. With regard to the analytical part, mass spectrometry (MS)-based platforms are an excellent tool for quantification of specific proteins in body fluids, for example focused on cancer. The obtained readouts have great potential in deter-mining tumor aggressiveness to facilitate treatment decisions, and can furthermore be used to monitor patient response. Internationally standardized TNM classifications of malignant tumors are beneficial for diagnosis, however treatment outcome and survival of cancer patients is poorly predicted. To this end, the importance of the tumor microenvironment has endorsed the introduction of the tumor-stroma ratio as a prognostic parameter in solid primary tumor types. Currently, the stromal content of tumor tissues is determined via routine diagnostic pathology slides. With the development of liquid chromatography (LC)-MS methods we aim at quantification of tumor-stroma specific proteins in body fluids. In this mini-review the analytical aspect of this developmental trajectory is further detailed. Show less
Ravensbergen, C.J.; Kuruc, M.; Polack, M.; Crobach, S.; Putter, H.; Gelderblom, H.; ... ; Mesker, W.E. 2022
Simple Summary Liquid biopsy offers a novel minimally invasive approach to tumor sampling and is believed to capture a comprehensive overview of the molecular tumor landscape. However, current... Show moreSimple Summary Liquid biopsy offers a novel minimally invasive approach to tumor sampling and is believed to capture a comprehensive overview of the molecular tumor landscape. However, current liquid biopsy analytes in cancer are principally derived from the malignant cells without regard to the tumor microenvironment. The Stroma Liquid Biopsy(TM) (SLB) proteomics panel contains proteins from key stromal pathways in cancer and was designed to address the tumor microenvironment in liquid biopsy. We aimed to explore and characterize SLB panel constituents using an in-silico transcriptomics approach in colon cancer. Additionally, the association between the SLB panel constituents and histologic intratumoral stromal content, a poor prognostic tumor characteristic, was investigated. This explorative study presents an alternative workflow to gene signature development and provides a molecular characterization of the SLB panel. We believe that our findings contribute to the ever-increasing appreciation of the tumor microenvironment in cancer. Liquid biopsy has emerged as a novel approach to tumor characterization, offering advantages in sample accessibility and tissue heterogeneity. However, as mutational analysis predominates, the tumor microenvironment has largely remained unacknowledged in liquid biopsy research. The current work provides an explorative transcriptomic characterization of the Stroma Liquid Biopsy(TM) (SLB) proteomics panel in colon carcinoma by integrating single-cell and bulk transcriptomics data from publicly available repositories. Expression of SLB genes was significantly enriched in tumors with high histologic stromal content in comparison to tumors with low stromal content (median enrichment score 0.308 vs. 0.222, p = 0.036). In addition, we identified stromal-specific and epithelial-specific expression of the SLB genes, that was subsequently integrated into a gene signature ratio. The stromal-epithelial signature ratio was found to have prognostic significance in a discovery cohort of 359 colon adenocarcinoma patients (OS HR 2.581, 95%CI 1.567-4.251, p < 0.001) and a validation cohort of 229 patients (OS HR 2.590, 95%CI 1.659-4.043, p < 0.001). The framework described here provides transcriptomic evidence for the prognostic significance of the SLB panel constituents in colon carcinoma. Plasma protein levels of the SLB panel may reflect histologic intratumoral stromal content, a poor prognostic tumor characteristic, and hence provide valuable prognostic information in liquid biopsy. Show less
The best current biomarker strategies for predicting response to immune checkpoint inhibitor (ICI) therapy fail to account for interpatient variability in response rates. The histologic tumor... Show moreThe best current biomarker strategies for predicting response to immune checkpoint inhibitor (ICI) therapy fail to account for interpatient variability in response rates. The histologic tumor-stroma ratio (TSR) quantifies intratumoral stromal content and was recently found to be predictive of response to neoadjuvant therapy in multiple cancer types. In the current work, we predicted the likelihood of ICI therapy responsivity of 335 therapy-naive colon adenocarcinoma tumors from The Cancer Genome Atlas, using bioinformatics approaches. The TSR was scored on diagnostic tissue slides, and tumor-infiltrating immune cells (TIICs) were inferred from transcriptomic data. Tumors with high stromal content demonstrated increased T regulatory cell infiltration (p = 0.014) but failed to predict ICI therapy response. Consequently, we devised a hybrid tumor microenvironment classification of four stromal categories, based on histological stromal content and transcriptomic-deconvoluted immune cell infiltration, which was associated with previously established transcriptomic and genomic biomarkers for ICI therapy response. By integrating these biomarkers, stroma-low/immune-high tumors were predicted to be most responsive to ICI therapy. The framework described here provides evidence for expansion of current histological TIIC quantification to include the TSR as a novel, easy-to-use biomarker for the prediction of ICI therapy response. Show less
