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Molecular profiles of response to neoadjuvant chemoradiotherapy in oesophageal cancers to develop personalized treatment strategies.

ABSTRACT: Identification of molecular predictive markers of response to neoadjuvant chemoradiation could aid clinical decision-making in patients with localized oesophageal cancer. Therefore, we subjected pretreatment biopsies of 75 adenocarcinoma (OAC) and 16 squamous cell carcinoma (OSCC) patients to targeted next-generation DNA sequencing, as well as biopsies of 85 OAC and 20 OSCC patients to promoter methylation analysis of eight GI-specific genes, and subsequently searched for associations with histopathological response and disease-free (DFS) and overall survival (OS). Thereby, we found that in OAC, CSMD1 deletion (8%) and ETV4 amplification (5%) were associated with a favourable histopathological response, whereas SMURF1 amplification (5%) and SMARCA4 mutation (7%) were associated with an unfavourable histopathological response. KRAS (15%) and GATA4 (7%) amplification were associated with shorter OS. In OSCC, TP63 amplification (25%) and TFPI2 (10%) gene promoter methylation were associated with an unfavourable histopathological response and shorter DFS (TP63) and OS (TFPI2), whereas CDKN2A deletion (38%) was associated with prolonged OS. In conclusion, this study identified candidate genetic biomarkers associated with response to neoadjuvant chemoradiotherapy in patients with localized oesophageal cancer.

SUBMITTER: de Klerk LK

PROVIDER: S-EPMC8024738 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundExcessive skeletal muscle loss during neoadjuvant concurrent chemoradiotherapy (NACRT) is significantly related to survival outcomes of oesophageal cancer. However, the conventional method for measuring skeletal muscle mass requires computed tomography (CT) images, and the calculation process is labour-intensive. In this study, we built machine-learning models to predict excessive skeletal muscle loss, using only body mass index data and blood laboratory test results.MethodsWe randomly split the data of 232 male patients treated with NACRT for oesophageal cancer into the training (70%) and test (30%) sets for 1000 iterations. The naive random over sampling method was applied to each training set to adjust for class imbalance, and we used seven different machine-learning algorithms to predict excessive skeletal muscle loss. We used five input variables, namely, relative change percentage in body mass index, albumin, prognostic nutritional index, neutrophil-to-lymphocyte ratio, and platelet-to-lymphocyte ratio over 50 days. According to our previous study results, which used the maximal χ2 method, 10.0% decrease of skeletal muscle index over 50 days was determined as the cut-off value to define the excessive skeletal muscle loss.ResultsThe five input variables were significantly different between the excessive and the non-excessive muscle loss group (all P < 0.001). None of the clinicopathologic variables differed significantly between the two groups. The ensemble model of logistic regression and support vector classifier showed the highest area under the curve value among all the other models [area under the curve = 0.808, 95% confidence interval (CI): 0.708-0.894]. The sensitivity and specificity of the ensemble model were 73.7% (95% CI: 52.6%-89.5%) and 74.5% (95% CI: 62.7%-86.3%), respectively.ConclusionsMachine learning model using the ensemble of logistic regression and support vector classifier most effectively predicted the excessive muscle loss following NACRT in patients with oesophageal cancer. This model can easily screen the patients with excessive muscle loss who need an active intervention or timely care following NACRT.

Project description:Oesophageal adenocarcinoma (OAC) is the sixth most common cause of cancer deaths worldwide, and the 5-year survival rate for patients diagnosed with the disease is approximately 17%. The standard of care for locally advanced disease is neoadjuvant chemotherapy or, more commonly, combined neoadjuvant chemoradiation therapy (neo-CRT) prior to surgery. Unfortunately, ~60-70% of patients will fail to respond to neo-CRT. Therefore, the identification of biomarkers indicative of patient response to treatment has significant clinical implications in the stratification of patient treatment. Furthermore, understanding the molecular mechanisms underpinning tumour response and resistance to neo-CRT will contribute towards the identification of novel therapeutic targets for enhancing OAC sensitivity to CRT. MicroRNAs (miRNA/miR) function to regulate gene and protein expression and play a causal role in cancer development and progression. MiRNAs have also been identified as modulators of key cellular pathways associated with resistance to CRT. Here, to identify miRNAs associated with resistance to CRT, pre-treatment diagnostic biopsy specimens from patients with OAC were analysed using miRNA-profiling arrays. In pre-treatment biopsies miR-330-5p was the most downregulated miRNA in patients who subsequently failed to respond to neo-CRT. The role of miR-330 as a potential modulator of tumour response and sensitivity to CRT in OAC was further investigated in vitro. Through vector-based overexpression the E2F1/p-AKT survival pathway, as previously described, was confirmed as a target of miR-330 regulation. However, miR-330-mediated alterations to the E2F1/p-AKT pathway were insufficient to significantly alter cellular sensitivity to chemotherapy (cisplatin and 5-flurouracil). In contrast, silencing of miR-330-5p enhanced, albeit subtly, cellular resistance to clinically relevant doses of radiation. This study highlights the need for further investigation into the potential of miR-330-5p as a predictive biomarker of patient sensitivity to neo-CRT and as a novel therapeutic target for manipulating cellular sensitivity to neo-CRT in patients with OAC.

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Molecular profiles of response to neoadjuvant chemoradiotherapy in oesophageal cancers to develop personalized treatment strategies.

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