Neoadjuvant chemotherapy is noninferior to chemoradiotherapy for early-onset locally advanced rectal cancer in the FOWARC trial.

BACKGROUND: The early-onset rectal cancer with rapidly increasing incidence is considered to have distinct clinicopathological and molecular profiles with high-risk features. This leads to challenges in developing specific treatment strategies for early-onset rectal cancer patients and questions of whether early-onset locally advanced rectal cancer (LARC) needs aggressive neoadjuvant treatment. METHODS: In this post hoc analysis of FOWARC trial, we investigated the role of preoperative radiation in early-onset LARC by comparing the clinicopathological profiles and short-term and long-term outcomes between the early-onset and late-onset LARCs. RESULTS: We revealed an inter-tumor heterogeneity of clinical profiles and treatment outcomes between the early-onset and late-onset LARCs. The high-risk features were more prevalent in early-onset LARC. The neoadjuvant radiation brought less benefits of tumor response and more risk of complications in early-onset group (pCR: OR = 3.75, 95% CI = 1.37-10.27; complications: HR = 11.35, 95% CI = 1.46-88.31) compared with late-onset group (pCR: OR = 5.33, 95% CI = 1.83-15.58; complications: HR = 5.80, 95% CI = 2.32-14.49). Furthermore, the addition of radiation to neoadjuvant chemotherapy didn't improve long-term OS (HR = 1.37, 95% CI = 0.49-3.87) and DFS (HR = 1.05, 95% CI = 0.58-1.90) for early-onset patients. CONCLUSION: Preoperative radiation plus chemotherapy may not be superior to the chemotherapy alone in the early-onset LARC. Our findings provide insight into the treatment of early-onset LARC by interrogating the aggressive treatment and alternative regimens.

Radiogenomic profiling of global DNA methylation associated with molecular phenotypes and immune features in glioma.

BACKGROUND: The radiogenomic analysis has provided valuable imaging biomarkers with biological insights for gliomas. The radiogenomic markers for molecular profile such as DNA methylation remain to be uncovered to assist the molecular diagnosis and tumor treatment. METHODS: We apply the machine learning approaches to identify the magnetic resonance imaging (MRI) features that are associated with molecular profiles in 146 patients with gliomas, and the fitting models for each molecular feature (MoRad) are developed and validated. To provide radiological annotations for the molecular profiles, we devise two novel approaches called radiomic oncology (RO) and radiomic set enrichment analysis (RSEA). RESULTS: The generated MoRad models perform well for profiling each molecular feature with radiomic features, including mutational, methylation, transcriptional, and protein profiles. Among them, the MoRad models have a remarkable performance in quantitatively mapping global DNA methylation. With RO and RSEA approaches, we find that global DNA methylation could be reflected by the heterogeneity in volumetric and textural features of enhanced regions in T2-weighted MRI. Finally, we demonstrate the associations of global DNA methylation with clinicopathological, molecular, and immunological features, including histological grade, mutations of IDH and ATRX, MGMT methylation, multiple methylation-high subtypes, tumor-infiltrating lymphocytes, and long-term survival outcomes. CONCLUSIONS: Global DNA methylation is highly associated with radiological profiles in glioma. Radiogenomic global methylation is an imaging-based quantitative molecular biomarker that is associated with specific consensus molecular subtypes and immune features.