Biomarkers for Predicting the Response to Radiation-Based Neoadjuvant Therapy in Rectal Cancer

Chen, Yuhong; Yang, Bicheng; Chen, Mingyang; Li, Zhaojun; Liao, Zhengyin

doi:10.31083/j.fbl2707201

Cited by 5 publications

(4 citation statements)

References 163 publications

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“…Based on earlier findings, approximately 40% of patients with rectal cancer do not respond to NCRT, with certain cases exhibiting disease progression while others showing a slight regression to stable disease. 14 In our patient cohort, a higher proportion of patients (67.1%) were classified as exhibiting poor response (TRG 2-3). This suggests that the therapeutic effect of NCRT may be significantly reduced in MAC patients.…”

Section: Factormentioning

confidence: 70%

Predictive value of circulating lymphocyte subsets and inflammatory indexes for neoadjuvant chemoradiotherapy response in rectal mucinous adenocarcinoma patients: A machine learning approach

Lin,

Sun,

Jiang

et al. 2024

Cancer Medicine

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IntroductionIn this study, we aimed to evaluate the predictive value of circulating lymphocyte subsets and inflammatory indexes in response to neoadjuvant chemoradiotherapy (NCRT) in patients with rectal mucinous adenocarcinomas (MACs).MethodsRectal MAC patients who underwent NCRT and curative resection at Fujian Medical University Union Hospital's Department of Colorectal Surgery between 2016 and 2020 were included in the study. Patients were categorized into good and poor response groups based on their pathological response to NCRT. An independent risk factor‐based nomogram model was constructed by utilizing multivariate logistic regression analysis. Additionally, the extreme gradient boosting (XGB) algorithm was applied to build a machine learning (ML)‐based predictive model. Feature importance was quantified using the Shapley additive explanations method.ResultsOut of the 283 participants involved in this research, 190 (67.1%) experienced an unfavorable outcome. To identify the independent risk factors, logistic regression analysis was performed, considering variables such as tumor length, pretreatment clinical T stage, PNI, and Th/Tc ratio. Subsequently, a nomogram model was constructed, achieving a C‐index of 0.756. The ML model exhibited higher prediction accuracy than the nomogram model, achieving an AUROC of 0.824 in the training set and 0.762 in the tuning set. The top five important parameters of the ML model were identified as the Th/Tc ratio, neutrophil to lymphocyte, Th lymphocytes, Gross type, and T lymphocytes.ConclusionRadiochemotherapy sensitivity is markedly influenced by systemic inflammation and lymphocyte‐mediated immune responses in rectal MAC patients. Our ML model integrating clinical characteristics, circulating lymphocyte subsets, and inflammatory indexes is a potential assessment tool that can provide a reference for individualized treatment for rectal MAC patients.

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Section: Factormentioning

confidence: 70%

Predictive value of circulating lymphocyte subsets and inflammatory indexes for neoadjuvant chemoradiotherapy response in rectal mucinous adenocarcinoma patients: A machine learning approach

Lin,

Sun,

Jiang

et al. 2024

Cancer Medicine

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“…Many papers were unsuitable and attempted to identify a biomarker within pre-treatment samples that reliably predicts clinical response to neoadjuvant-radiotherapy-based strategies. This body of literature has previously been reviewed and will not be further discussed here [ 7 , 9 , 10 , 15 , 16 ]. Instead, this review will focus on 12 papers identifying gene-expression changes induced by neoadjuvant-radiotherapy-based strategies in rectal cancer using irradiated and non-irradiated samples.…”

Section: Resultsmentioning

confidence: 99%

Radiation-induced changes in gene expression in rectal cancer specimens

Hillson,

McCulloch,

Edwards

et al. 2024

Clin Transl Oncol

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Purpose The standard-of-care for locally advanced rectal cancer is radiotherapy-based neoadjuvant therapy followed by surgical resection. This article reviews the evidence of molecular changes at the transcriptome level induced through radiotherapy in rectal cancer. Methods The PubMed search “(radiation OR radiotherapy) cancer (transcriptome OR “gene expression”) rectal” was used. The studies taken forward utilised gene-expression data on both pre-treatment and post-treatment rectal adenocarcinoma biospecimens from patients treated with RT-based neoadjuvant strategies. Results Twelve publications met the review criteria. There was variation in approaches in terms of design, patient population, cohort size, timing of the post-radiotherapy sampling and method of measuring gene expression. Most of the post-treatment biospecimen retrievals were at resection. The literature indicates a broad upregulation of immune activity through radiotherapy using gene-expression data. Conclusion Future studies would benefit from standardised prospective approaches to sampling to enable the inclusion of timepoints relevant to the tumour and immune response.

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“…Proteomic and metabolomic approaches could be applied to prediction of the response to selected therapeutic strategies and monitoring the progression of disease ( 9 – 11 ). Although RT has been used extensively for a variety of tumors, little progress has been made in predicting and monitoring treatment outcomes after RT ( 12 ). There are only a few studies concerning proteomic or metabolomic profiling of tissue or serum/plasma from rectal cancer patients with various RT outcomes ( 13 – 18 ).…”

Section: Introductionmentioning

confidence: 99%

Proteomic and metabolomic signatures of rectal tumor discriminate patients with different responses to preoperative radiotherapy

Wojakowska,

Marczak,

Zeman

et al. 2024

Front. Oncol.

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BackgroundNeoadjuvant radiotherapy (neo-RT) is widely used in locally advanced rectal cancer (LARC) as a component of radical treatment. Despite the advantages of neo-RT, which typically improves outcomes in LARC patients, the lack of reliable biomarkers that predict response and monitor the efficacy of therapy, can result in the application of unnecessary aggressive therapy affecting patients’ quality of life. Hence, the search for molecular biomarkers for assessing the radio responsiveness of this cancer represents a relevant issue.MethodsHere, we combined proteomic and metabolomic approaches to identify molecular signatures, which could discriminate LARC tumors with good and poor responses to neo-RT.ResultsThe integration of data on differentially accumulated proteins and metabolites made it possible to identify disrupted metabolic pathways and signaling processes connected with response to irradiation, including ketone bodies synthesis and degradation, purine metabolism, energy metabolism, degradation of fatty acid, amino acid metabolism, and focal adhesion. Moreover, we proposed multi-component panels of proteins and metabolites which could serve as a solid base to develop biomarkers for monitoring and predicting the efficacy of preoperative RT in rectal cancer patients.ConclusionWe proved that an integrated multi-omic approach presents a valid look at the analysis of the global response to cancer treatment from the perspective of metabolomic reprogramming.

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Biomarkers for Predicting the Response to Radiation-Based Neoadjuvant Therapy in Rectal Cancer

Cited by 5 publications

References 163 publications

Predictive value of circulating lymphocyte subsets and inflammatory indexes for neoadjuvant chemoradiotherapy response in rectal mucinous adenocarcinoma patients: A machine learning approach

Predictive value of circulating lymphocyte subsets and inflammatory indexes for neoadjuvant chemoradiotherapy response in rectal mucinous adenocarcinoma patients: A machine learning approach

Radiation-induced changes in gene expression in rectal cancer specimens

Proteomic and metabolomic signatures of rectal tumor discriminate patients with different responses to preoperative radiotherapy

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