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Abstract Background Intratumoral heterogeneity emerges from accumulating genetic and epigenetic changes during tumorigenesis, which may contribute to therapeutic failure and drug resistance. However, the lack of a quick and convenient approach to determine the intratumoral epigenetic heterogeneity (eITH) limit the application of eITH in clinical settings. Here, we aimed to develop a tool that can evaluate the eITH using the DNA methylation profiles from bulk tumors. Methods Genomic DNA of three laser micro-dissected tumor regions, including digestive tract surface, central bulk, and invasive front, was extracted from formalin-fixed paraffin-embedded sections of colorectal cancer patients. The genome-wide methylation profiles were generated with methylation array. The most variable methylated probes were selected to construct a DNA methylation-based heterogeneity (MeHEG) estimation tool that can deconvolve the proportion of each reference tumor region with the support vector machine model-based method. A PCR-based assay for quantitative analysis of DNA methylation (QASM) was developed to specifically determine the methylation status of each CpG in MeHEG assay at single-base resolution to realize fast evaluation of epigenetic heterogeneity. Results In the discovery set with 79 patients, the differentially methylated CpGs among the three tumor regions were found. The 7 most representative CpGs were identified and subsequently selected to develop the MeHEG algorithm. We validated its performance of deconvolution of tumor regions in an independent cohort. In addition, we showed the significant association of MeHEG-based epigenetic heterogeneity with the genomic heterogeneity in mutation and copy number variation in our in-house and TCGA cohorts. Besides, we found that the patients with higher MeHEG score had worse disease-free and overall survival outcomes. Finally, we found dynamic change of epigenetic heterogeneity based on MeHEG score in cancer cells under the treatment of therapeutic drugs. Conclusion By developing a 7-loci panel using a machine learning approach combined with the QASM assay for PCR-based application, we present a valuable method for evaluating intratumoral heterogeneity. The MeHEG algorithm offers novel insights into tumor heterogeneity from an epigenetic perspective, potentially enriching current knowledge of tumor complexity and providing a new tool for clinical and research applications in cancer biology.

Abstract 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.

Abstract Background Microwave ablation (MWA) is widely used to eliminate colorectal liver metastases (CRLM). However, the risk of tumor recurrence is difficult to predict due to lack of reliable clinical and biological markers. Elevation of gamma-glutamyl transferase (GGT) and aspartate transaminase (AST) provides signals for liver inflammation and cancer progression. The present study evaluated the association between pre-ablation GGT to AST ratio index (GSR) and hepatic recurrence in patients with CRLM after MWA. Methods A retrospectively analyzed 192 CRLM patients who underwent MWA from January 2013 to December 2017. Pre-ablation GSR was classified into high (≤ 2.34) or low (> 2.34) using the upper quartile value. The prognostic value of GSR and other risk factors for liver progression-free survival (LPFS) and cancer-specific survival (CSS) were evaluated by univariate and multivariate analyses. Results High GSR was significantly associated with males (P = 0.041), the presence of cholelithiasis (P = 0.012), but not pre-ablation chemotherapy (P = 0.355), which caused significantly increased levels of GGT (P = 0.015) and AST (P = 0.008). GSR showed a significant association with LPFS and CSS through univariate analysis (P = 0.002 and 0.006) and multivariate analysis (P = 0.043 and 0.037). The subgroup analysis demonstrated no interaction between GSR and all variables except for distribution in the sub-analysis of LPFS. Conclusions Our findings suggest that the pre-ablation GSR can be considered as a promising prognostic indicator for poor prognosis of patients with CRLM underwent MWA. Trial registration Not applicable.

Abstract Background DNA methylation is one of the most promising biomarkers in predicting the prognosis of colorectal cancer (CRC). We aimed to develop a DNA methylation biomarker that could evaluate the prognosis of CRC. Methods A promising DNA methylation biomarker was developed by hypermethylated genes in cancer tissue that were identified from Illumina EPIC methylation arrays. A cohort comprising 30 pairs of snap-frozen tumor tissue and adjacent normal tissue was used for correlation analysis between the methylation and expression status of the marker. The other cohort comprising 254 formalin-fixed paraffin-embedded (FFPE) tumor tissue from 254 CRC patients was used for prognosis analysis. Results Regulating synaptic membrane exocytosis 2 (RIMS2) was hypermethylated and lowly expressed in CRC comparing to adjacent normal tissue. Hypermethylation of RIMS2 in CRC was correlated with less frequent KRAS mutant and high differentiation. RIMS2 promoter methylation showed independent predictive value for survival outcome (P = 0.015, HR 1.992, 95% CI [(1.140–3.48)]), and a combination of RIMS2 methylation with KRAS status could predict prognosis better. Conclusions RIMS2 is frequently hypermethylated in CRC, which can silence the expression of RIMS2. RIMS2 methylation is a novel biomarker for predicting the prognosis of CRC.

Abstract Background We aimed to develop a radiomic model based on pre-treatment computed tomography (CT) to predict the pathological complete response (pCR) in patients with rectal cancer after neoadjuvant treatment and tried to integrate our model with magnetic resonance imaging (MRI)-based radiomic signature. Methods This was a secondary analysis of the FOWARC randomized controlled trial. Radiomic features were extracted from pre-treatment portal venous-phase contrast-enhanced CT images of 177 patients with rectal cancer. Patients were randomly allocated to the primary and validation cohort. The least absolute shrinkage and selection operator regression was applied to select predictive features to build a radiomic signature for pCR prediction (rad-score). This CT-based rad-score was integrated with clinicopathological variables using gradient boosting machine (GBM) or MRI-based rad-score to construct comprehensive models for pCR prediction. The performance of CT-based model was evaluated and compared by receiver operator characteristic (ROC) curve analysis. The LR (likelihood ratio) test and AIC (Akaike information criterion) were applied to compare CT-based rad-score, MRI-based rad-score and the combined rad-score. Results We developed a CT-based rad-score for pCR prediction and a gradient boosting machine (GBM) model was built after clinicopathological variables were incorporated, with improved AUCs of 0.997 [95% CI 0.990–1.000] and 0.822 [95% CI 0.649–0.995] in the primary and validation cohort, respectively. Moreover, we constructed a combined model of CT- and MRI-based radiomic signatures that achieve better AIC (75.49 vs. 81.34 vs.82.39) than CT-based rad-score (P = 0.005) and MRI-based rad-score (P = 0.003) alone did. Conclusions The CT-based radiomic models we constructed may provide a useful and reliable tool to predict pCR after neoadjuvant treatment, identify patients that are appropriate for a 'watch and wait' approach, and thus avoid overtreatment. Moreover, the CT-based radiomic signature may add predictive value to the MRI-based models for clinical decision making.

Abstract Background Neurotrophic tropomyosin receptor kinases (NTRKs) are a gene family function as oncogene or tumor suppressor gene in distinct cancers. We aimed to investigate the methylation and expression profiles and prognostic value of NTRKs gene in colorectal cancer (CRC). Methods An analysis of DNA methylation and expression profiles in CRC patients was performed to explore the critical methylations within NTRKs genes. The methylation marker was validated in a retrospectively collected cohort of 229 CRC patients and tested in other tumor types from TCGA. DNA methylation status was determined by quantitative methylation-specific PCR (QMSP). Results The profiles in six CRC cohorts showed that NTRKs gene promoter was more frequently methylated in CRC compared to normal mucosa, which was associated with suppressed gene expression. We identified a specific methylated region within NTRK3 promoter targeted by cg27034819 and cg11525479 that best predicted survival outcome in CRC. NTRK3 promoter methylation showed independently predictive value for survival outcome in the validation cohort (P = 0.004, HR 2.688, 95% CI [1.355, 5.333]). Based on this, a nomogram predicting survival outcome was developed with a C-index of 0.705. Furthermore, the addition of NTRK3 promoter methylation improved the performance of currently-used prognostic model (AIC: 516.49 vs 513.91; LR: 39.06 vs 43.64, P = 0.032). Finally, NTRK3 promoter methylation also predicted survival in other tumors, including pancreatic cancer, glioblastoma and stomach adenocarcinoma. Conclusions This study highlights the essential value of NTRK3 methylation in prognostic evaluation and the potential to improve current prognostic models in CRC and other tumors.

Abstract Background The incidence of anal squamous cell carcinoma (SCC) has been steadily growing globally in the past decade. Clinical data on anal SCC from China are rare. We conducted this study to describe the clinical and epidemiological characteristics of anal SCC in China and explore prognostic factors of outcomes among patients with anal SCC. Methods We audited demographic characteristics, relevant symptoms, risk factors, treatment modalities and outcomes for patients diagnosed with anal SCC at 11 medical institutions in China between January 2007 and July 2018. Results A total of 144 patients (109 females) were diagnosed with SCC during this period. Median age at initial diagnosis was 52.0 (interquartile range: 46.0–61.8) years. The most common symptoms were bleeding (n = 93, 64.6%), noticing a lump (n = 49, 34.0%), and pain (n = 47, 32.6%). The proportion of patients at the American Joint Committee on Cancer (AJCC) stages I-IV were 10 (6.9%), 22 (15.3%), 61 (42.4%) and 8 (5.6%), respectively, and AJCC stages in 43 (29.9%) patients were unknown. Thirty-six patients (25.0%) underwent abdominoperineal resection initially. Univariable analysis showed that T stage predicted recurrence-free survival (RFS) (Hazard ratio [HR] = 3.03, 95% Confidence interval [CI]: 1.10–8.37, p = 0.032), and age group (HR = 2.90, 95% CI: 1.12–7.49, p = 0.028), AJCC stage (HR = 4.56, 95% CI: 1.02–20.35, p = 0.046), and N stage (HR = 3.05, 95% CI: 1.07–8.74, p = 0.038) predicted overall survival (OS). Conclusions T stage was identified as prognostic factor of RFS, and age, AJCC stage, and N stage were identified as prognostic factors of OS. Improving symptom awareness and earlier presentation among patients potentially at risk for anal SCC should be encouraged. Familiarity with the standard treatment among health care providers in China should be further improved.

Abstract Background The clinical factors that accurately predict the response to preoperative treatment in rectal cancer were yet unknown. The carcinoembryonic antigen (CEA) clearance pattern during neoadjuvant treatment has been developed and the predictive value explored in rectal cancer patients with elevated CEA levels (> 5 ng/mL). Methods The training cohort was derived from the FOWARC prospective phase III trial, and 71/483 eligible patients were included. The validation cohort consisted of 75/587 consecutive rectal cancer patients from Xiangya Hospital between 2014 and 2015. The kinetic changes in serum CEA were measured at different time points during the neoadjuvant treatment. An exponential trend line was drawn using the CEA values. The patients were categorized into two groups based on the R2 value of the trend line, which indicates the correlation coefficient between the exponential graph and measured CEA values: exponential decrease group (0.9