A Nine-Gene Signature for Predicting the Response to Preoperative Chemoradiotherapy in Patients with Locally Advanced Rectal Cancer

ZNF37A downregulation promotes TNFRSF6B expression and leads to therapeutic resistance to concurrent chemoradiotherapy in rectal cancer patients

The identification a signature comprising a group of genes as markers of cancer response to chemoradiotherapy would be more appropriate and effective for predicting chemoradiotherapy efficacy. This study investigated the differentially expressed genes (DEGs) related to chemoradiotherapy resistance and established a multigene expression model for predicting the sensitivity of rectal cancer to chemoradiotherapy in rectal cancer patients, elucidated the mechanism of resistance to synchronized chemoradiotherapy. The genome-wide expression profiling microarray were performed in the tissues of 81 rectal cancer patients before neoadjuvant therapy to analyze and discover DEGs related to chemoradiotherapy resistance, and the results were verified in 45 rectal cancer patients, and finally a 20-gene signature was proposed to be a predictor of chemoradiotherapy response. Molecular biology experiments revealed that zinc finger protein 37A (ZNF37A) downregulation leads to therapeutic resistance. This study identified a 20-gene signature with group of genes can help predict the response to chemoradiotherapy of rectal cancer patients. ZNF37A demonstrated a statistically significant correlation with sensitivity to chemoradiotherapy and survival in patients with LARC who underwent chemoradiotherapy. The findings revealed that ZNF37A bound to the tumor necrosis factor receptor superfamily member 6B (TNFRSF6B) promoter region, thereby suppressing its transcriptional activity. Reduced expression of ZNF37A induces chemoradiation resistance by inhibiting apoptosis in colorectal cancer (CRC) cells. TNFRSF6B Knockdown restored the sensitivity of CRC to chemoradiotherapy. ZNF37A is an effective modulator of chemoradiotherapy response in rectal cancer. These findings elucidate the molecular mechanism underlying chemoradiotherapy resistance and provide potential applications for individualized clinical therapy.

Artificial Intelligence-Driven Volumetric Analysis of Muscle Mass as a Predictor of Tumor Response to Neoadjuvant Chemoradiotherapy in Patients with Rectal Cancer

Background/Objectives: Artificial intelligence (AI)-based volumetric measurements for assessing sarcopenia are expected to offer comprehensive insight into three-dimensional muscle volume and distribution. Therefore, we investigated the role of sarcopenia using computed tomography (CT)-based automated AI volumetric muscle measurements in predicting neoadjuvant chemoradiotherapy (nCRT) response and prognosis in patients with rectal cancer who underwent nCRT. Methods: We retrospectively analyzed the data of patients who underwent nCRT followed by curative resection between March 2010 and August 2021. Sarcopenia was defined using the Q1 cutoff value of the volumetric skeletal muscle index (SMI). The association between pre-nCRT volumetric sarcopenia and nCRT response was analyzed using logistic regression. A Cox proportional hazards model was used to identify the prognostic value of the pre- and post-nCRT volumetric SMIs. Results: Notably, 22 (25.6%) of the 86 patients had volumetric sarcopenia. The sarcopenia group showed a poorer nCRT response than the non-sarcopenia group. Pre-nCRT sarcopenia was a significant predictor of poor nCRT response (OR, 0.34 [95% CI, 0.12-0.96]; p = 0.041). Furthermore, an increased volumetric SMI during nCRT was a more significant prognostic factor on recurrence-free survival (aHR, 0.26 [95% CI, 0.08-0.83]; p = 0.023) and overall survival (aHR, 0.41 [95% CI, 0.17-0.99]; p = 0.049) than a decreased SMI. Conclusions: Volumetric sarcopenia can be used to predict poor nCRT response. A reduction in volumetric sarcopenia can be a poor prognostic factor in patients with rectal cancer who undergo nCRT.

A 10-Gene Signature to Predict the Prognosis of Early-Stage Triple-Negative Breast Cancer

PURPOSE

Triple-negative breast cancer (TNBC) is a particularly challenging subtype of breast cancer, with a poorer prognosis compared to other subtypes. Unfortunately, unlike luminal-type cancers, there is no validated biomarker to predict the prognosis of patients with early-stage TNBC. Accurate biomarkers are needed to establish effective therapeutic strategies.

MATERIALS AND METHODS

In this study, we analyzed gene expression profiles of tumor samples from 184 TNBC patients (training cohort, n=76; validation cohort, n=108) using RNA sequencing.

RESULTS

By combining weighted gene expression, we identified a 10-gene signature (DGKH, GADD45B, KLF7, LYST, NR6A1, PYCARD, ROBO1, SLC22A20P, SLC24A3, and SLC45A4) that stratified patients by risk score with high sensitivity (92.31%), specificity (92.06%), and accuracy (92.11%) for invasive disease-free survival. The 10-gene signature was validated in a separate institution cohort and supported by meta-analysis for biological relevance to well-known driving pathways in TNBC. Furthermore, the 10-gene signature was the only independent factor for invasive disease-free survival in multivariate analysis when compared to other potential biomarkers of TNBC molecular subtypes and T-cell receptor β diversity. 10-gene signature also further categorized patients classified as molecular subtypes according to risk scores.

CONCLUSION

Our novel findings may help address the prognostic challenges in TNBC and the 10-gene signature could serve as a novel biomarker for risk-based patient care.

Identification and validation of a machine learning model of complete response to radiation in rectal cancer reveals immune infiltrate and TGFβ as key predictors

BACKGROUND

It is uncertain which biological features underpin the response of rectal cancer (RC) to radiotherapy. No biomarker is currently in clinical use to select patients for treatment modifications.

METHODS

We identified two cohorts of patients (total N = 249) with RC treated with neoadjuvant radiotherapy (45Gy/25) plus fluoropyrimidine. This discovery set included 57 cases with pathological complete response (pCR) to chemoradiotherapy (23%). Pre-treatment cancer biopsies were assessed using transcriptome-wide mRNA expression and targeted DNA sequencing for copy number and driver mutations. Biological candidate and machine learning (ML) approaches were used to identify predictors of pCR to radiotherapy independent of tumour stage. Findings were assessed in 107 cases from an independent validation set (GSE87211).

FINDINGS

Three gene expression sets showed significant independent associations with pCR: Fibroblast-TGFβ Response Signature (F-TBRS) with radioresistance; and cytotoxic lymphocyte (CL) expression signature and consensus molecular subtype CMS1 with radiosensitivity. These associations were replicated in the validation cohort. In parallel, a gradient boosting machine model comprising the expression of 33 genes generated in the discovery cohort showed high performance in GSE87211 with 90% sensitivity, 86% specificity. Biological and ML signatures indicated similar mechanisms underlying radiation response, and showed better AUC and p-values than published transcriptomic signatures of radiation response in RC.

INTERPRETATION

RCs responding completely to chemoradiotherapy (CRT) have biological characteristics of immune response and absence of immune inhibitory TGFβ signalling. These tumours may be identified with a potential biomarker based on a 33 gene expression signature. This could help select patients likely to respond to treatment with a primary radiotherapy approach as for anal cancer. Conversely, those with predicted radioresistance may be candidates for clinical trials evaluating addition of immune-oncology agents and stromal TGFβ signalling inhibition.

FUNDING

The Stratification in Colorectal Cancer Consortium (S:CORT) was funded by the Medical Research Council and Cancer Research UK (MR/M016587/1).

Stratification to Neoadjuvant Radiotherapy in Rectal Cancer by Regimen and Transcriptional Signatures

Response to neoadjuvant radiotherapy (RT) in rectal cancer has been associated with immune and stromal features that are captured by transcriptional signatures. However, how such associations perform across different chemoradiotherapy regimens and within individual consensus molecular subtypes (CMS) and how they affect survival remain unclear. In this study, gene expression and clinical data of pretreatment biopsies from nine cohorts of primary rectal tumors were combined (N = 826). Exploratory analyses were done with transcriptomic signatures for the endpoint of pathologic complete response (pCR), considering treatment regimen or CMS subtype. Relevant findings were tested for overall survival and recurrence-free survival. Immune and stromal signatures were strongly associated with pCR and lack of pCR, respectively, in RT and capecitabine (Cap)/5-fluorouracil (5FU)-treated patients (N = 387), in which the radiosensitivity signature (RSS) showed the strongest association. Upon addition of oxaliplatin (Ox; N = 123), stromal signatures switched direction and showed higher chances to achieve pCR than without Ox (p for interaction 0.02). Among Cap/5FU patients, most signatures performed similarly across CMS subtypes, except cytotoxic lymphocytes that were associated with pCR in CMS1 and CMS4 cases compared with other CMS subtypes (p for interaction 0.04). The only variables associated with survival were pCR and RSS. Although the frequency of pCR across different chemoradiation regimens is relatively similar, our data suggest that response rates may differ depending on the biological landscape of rectal cancer. Response to neoadjuvant RT in stroma-rich tumors may potentially be improved by the addition of Ox. RSS in preoperative biopsies provides predictive information for response specifically to neoadjuvant RT with 5FU.

SIGNIFICANCE

Rectal cancers with stromal features may respond better to RT and 5FU/Cap with the addition of Ox. Within patients not treated with Ox, high levels of cytotoxic lymphocytes associate with response only in immune and stromal tumors. Our analyses provide biological insights about the outcome by different radiotherapy regimens in rectal cancer.

An immune-related gene prognostic prediction risk model for neoadjuvant chemoradiotherapy in rectal cancer using artificial intelligence

Background

This study aimed to establish and validate a prognostic model based on immune-related genes (IRGPM) for predicting disease-free survival (DFS) in patients with locally advanced rectal cancer (LARC) undergoing neoadjuvant chemoradiotherapy, and to elucidate the immune profiles associated with different prognostic outcomes.

Methods

Transcriptomic and clinical data were sourced from the Gene Expression Omnibus (GEO) database and the West China Hospital database. We focused on genes from the RNA immune-oncology panel. The elastic net approach was employed to pinpoint immune-related genes significantly impacting DFS. We developed the IRGPM for rectal cancer using the random forest technique. Based on the IRGPM, we calculated prognostic risk scores to categorize patients into high-risk and low-risk groups. Comparative analysis of immune characteristics between these groups was conducted.

Results

In this study, 407 LARC samples were analyzed. The elastic net identified a signature of 20 immune-related genes, forming the basis of the IRGPM. Kaplan-Meier survival analysis revealed a lower 5-year DFS in the high-risk group compared to the low-risk group. The receiver operating characteristic (ROC) curve affirmed the model's robust predictive capability. Validation of the model was performed in the GSE190826 cohort and our institution's cohort. Gene expression differences between high-risk and low-risk groups predominantly related to cytokine-cytokine receptor interactions. Notably, the low-risk group exhibited higher immune scores. Further analysis indicated a greater presence of activated B cells, activated CD8 T cells, central memory CD8 T cells, macrophages, T follicular helper cells, and type 2 helper cells in the low-risk group. Additionally, immune checkpoint analysis revealed elevated PDCD1 expression in the low-risk group.

Conclusions

The IRGPM, developed through random forest and elastic net methodologies, demonstrates potential in distinguishing DFS among LARC patients receiving standard treatment. Notably, the low-risk group, as defined by the IRGPM, showed enhanced activation of adaptive immune responses within the tumor microenvironment.

Linking gene expression to clinical outcomes in pediatric Crohn's disease using machine learning

Pediatric Crohn's disease (CD) is characterized by a severe disease course with frequent complications. We sought to apply machine learning-based models to predict risk of developing future complications in pediatric CD using ileal and colonic gene expression. Gene expression data was generated from 101 formalin-fixed, paraffin-embedded (FFPE) ileal and colonic biopsies obtained from treatment-naïve CD patients and controls. Clinical outcomes including development of strictures or fistulas and progression to surgery were analyzed using differential expression and modeled using machine learning. Differential expression analysis revealed downregulation of pathways related to inflammation and extra-cellular matrix production in patients with strictures. Machine learning-based models were able to incorporate colonic gene expression and clinical characteristics to predict outcomes with high accuracy. Models showed an area under the receiver operating characteristic curve (AUROC) of 0.84 for strictures, 0.83 for remission, and 0.75 for surgery. Genes with potential prognostic importance for strictures (REG1A, MMP3, and DUOX2) were not identified in single gene differential analysis but were found to have strong contributions to predictive models. Our findings in FFPE tissue support the importance of colonic gene expression and the potential for machine learning-based models in predicting outcomes for pediatric CD.

Identification of Novel Biomarkers for Response to Preoperative Chemoradiation in Locally Advanced Rectal Cancer with Genetic Algorithm-Based Gene Selection

BACKGROUND

The conventional treatment for patients with locally advanced colorectal tumors is preoperative chemo-radiotherapy (PCRT) preceding surgery. This treatment strategy has some long-term side effects, and some patients do not respond to it. Therefore, an evaluation of biomarkers that may help predict patients' response to PCRT is essential.

METHODS

We took advantage of genetic algorithm to search the space of possible combinations of features to choose subsets of genes that would yield convenient performance in differentiating PCRT responders from non-responders using a logistic regression model as our classifier.

RESULTS

We developed two gene signatures; first, to achieve the maximum prediction accuracy, the algorithm yielded 39 genes, and then, aiming to reduce the feature numbers as much as possible (while maintaining acceptable performance), a 5-gene signature was chosen. The performance of the two gene signatures was (accuracy = 0.97 and 0.81, sensitivity = 0.96 and 0.83, and specificity = 86 and 0.77) using a logistic regression classifier. Through analyzing bias and variance decomposition of the model error, we further investigated the involved genes by discovering and validating another 28-gene signature which possibly points towards two different sub-systems involved in the response of the patients to treatment.

CONCLUSIONS

Using genetic algorithm as our gene selection method, we have identified two groups of genes that can differentiate PCRT responders from non-responders in patients of the studied dataset with considerable performance.

IMPACT

After passing standard requirements, our gene signatures may be applicable as a robust and effective PCRT response prediction tool for colorectal cancer patients in clinical settings and may also help future studies aiming to further investigate involved pathways gain a clearer picture for the course of their research.

Exploring novel genetic and hematological predictors of response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer

Introduction: The standard treatment for locally advanced rectal cancer (LARC) is neoadjuvant chemoradiotherapy (nCRT). To select patients who would benefit the most from nCRT, there is a need for predictive biomarkers. The aim of this study was to evaluate the role of clinical, pathological, radiological, inflammation-related genetic, and hematological parameters in the prediction of post-nCRT response. Materials and methods: In silico analysis of published transcriptomics datasets was conducted to identify candidate genes, whose expression will be measured using quantitative Real Time PCR (qRT-PCR) in pretreatment formaline-fixed paraffin-embedded (FFPE) samples. In this study, 75 patients with LARC were prospectively included between June 2020-January 2022. Patients were assessed for tumor response in week 8 post-nCRT with pelvic MRI scan and rigid proctoscopy. For patients with a clinical complete response (cCR) and initially distant located tumor no immediate surgery was suggested ("watch and wait" approach). The response after surgery was assessed using histopathological tumor regression grading (TRG) categories from postoperative specimens by Mandard. Responders (R) were defined as patients with cCR without operative treatment, and those with TRG 1 and TRG 2 postoperative categories. Non-responders (NR) were patients classified as TRG 3-5. Results: Responders group comprised 35 patients (46.6%) and NR group 53.4% of patients. Analysis of published transcriptomics data identified genes that could predict response to treatment and their significance was assessed in our cohort by qRT-PCR. When comparison was made in the subgroup of patients who were operated (TRG1 vs. TRG4), the expression of IDO1 was significantly deregulated (p < 0.05). Among hematological parameters between R and NR a significant difference in the response was detected for neutrophil-to-monocyte ratio (NMR), initial basophil, eosinophil and monocyte counts (p < 0.01). According to MRI findings, non-responders more often presented with extramural vascular invasion (p < 0.05). Conclusion: Based on logistic regression model, factors associated with favorable response to nCRT were tumor morphology and hematological parameters which can be easily and routinely derived from initial laboratory results (NMR, eosinophil, basophil and monocyte counts) in a minimally invasive manner. Using various metrics, an aggregated score of the initial eosinophil, basophil, and monocyte counts demonstrated the best predictive performance.

Clinical Significance of NKD Inhibitor of WNT Signaling Pathway 1 (NKD1) in Glioblastoma

Introduction. As the most malignant type of gliomas, glioblastoma is characterized with disappointing prognosis. Here, we aimed to investigate expression and function of NKD inhibitor of Wnt signaling pathway 1 (NKD1), an antagonist of Wnt-beta-catenin signaling pathways, in glioblastoma. Methods. The mRNA level of NKD1 was firstly retrieved from TCGA glioma dataset to evaluate its correlation with clinical characteristics and its value in prognosis prediction. Then, its protein expression level in glioblastoma was tested by immunohistochemistry staining in a retrospectively cohort collected from our medical center (n = 66). Univariate and multivariate survival analyses were conducted to assess its effect on glioma prognosis. Two glioblastoma cell lines, U87 and U251, were used to further investigate the tumor-related role of NKD1 through overexpression strategy in combination with cell proliferation assays. Immune cell enrichment in glioblastoma and its correlation with NKD1 level was finally assessed using bioinformatics analyses. Results. NKD1 shows a lower expression level in glioblastoma compared to that in the normal brain or other glioma subtypes, which is independently correlated to a worse prognosis in both the TCGA cohort and our retrospective cohort. Overexpressing NKD1 in glioblastoma cell lines can significantly attenuate cell proliferation. In addition, expression of NKD1 in glioblastoma is negatively correlated to the T cell infiltration, indicating it may have crosstalk with the tumor immune microenvironment. Conclusions. NKD1 inhibits glioblastoma progression and its downregulated expression indicates a poor prognosis.

Identification of a 5-gene-based signature to predict prognosis and correlate immunomodulators for rectal cancer

BACKGROUND

Specific tumor markers have yet to be identified in rectal cancer. This study aims to identify a novel genetic signature in rectal cancer to provide clues for survival and immunotherapy.

METHODS

DEGs were obtained from two GEO datasets of rectal cancer. By using data from TCGA and GSE133057, two cohorts of rectal cancer were applied to establish and evaluate the signature. A nomogram was constructed for training and validation. We integrated the risk-score with clinicopathological features and assessed its interplay with immune cells and molecules. Finally, our study performed functional annotations, gene-targeted miRNAs, and single-cell analysis.

RESULTS

A total of 468 DEGs were identified, and a signature consisting of 5 genes (CLIC5, ENTPD8, PACSIN3, HGD, and GNG7) was selected to calculate the risk-score. The model exhibited high performance in time-dependent ROC and a nomogram. Further results showed that overall survival was significantly worse in the high-risk group. As an independent prognostic factor, the risk-score was associated with vascular invasion. There was a dramatic difference in nonregulatory CD4+ and CD8+ T cells between the high and low-risk groups, and the 5 genes were correlated with immune inhibitors. There was a considerable difference in autophagy, immune, cell cycle, infection, and apoptosis-associated terms and pathways in GO and KEGG. The functional states of differentiation, apoptosis, and quiescence were closely related to the 5-gene signature in single-cell analysis.

CONCLUSION

Our results suggest that the signature could serve as a novel prognostic biomarker in rectal cancer, which might benefit decision-making regarding immunotherapy.

Exome and Tissue-Associated Microbiota as Predictive Markers of Response to Neoadjuvant Treatment in Locally Advanced Rectal Cancer

The clinical and pathological responses to multimodal neoadjuvant therapy in locally advanced rectal cancers (LARCs) remain unpredictable, and robust biomarkers are still lacking. Recent studies have shown that tumors present somatic molecular alterations related to better treatment response, and it is also clear that tumor-associated bacteria are modulators of chemotherapy and immunotherapy efficacy, therefore having implications for long-term survivorship and a good potential as the biomarkers of outcome. Here, we performed whole exome sequencing and 16S ribosomal RNA (rRNA) amplicon sequencing from 44 pre-treatment LARC biopsies from Argentinian and Brazilian patients, treated with neoadjuvant chemoradiotherapy or total neoadjuvant treatment, searching for predictive biomarkers of response (responders, n = 17; non-responders, n = 27). In general, the somatic landscape of LARC was not capable to predict a response; however, a significant enrichment in mutational signature SBS5 was observed in non-responders (p = 0.0021), as well as the co-occurrence of APC and FAT4 mutations (p < 0.05). Microbiota studies revealed a similar alpha and beta diversity of bacteria between response groups. Yet, the linear discriminant analysis (LDA) of effect size indicated an enrichment of Hungatella, Flavonifractor, and Methanosphaera (LDA score ≥3) in the pre-treatment biopsies of responders, while non-responders had a higher abundance of Enhydrobacter, Paraprevotella (LDA score ≥3) and Finegoldia (LDA score ≥4). Altogether, the evaluation of these biomarkers in pre-treatment biopsies could eventually predict a neoadjuvant treatment response, while in post-treatment samples, it could help in guiding non-operative treatment strategies.

External Validation of a Radiomics Model for the Prediction of Complete Response to Neoadjuvant Chemoradiotherapy in Rectal Cancer

Objective: Our objective was to develop a radiomics model based on magnetic resonance imaging (MRI) and contrast-enhanced computed tomography (CE-CT) to predict pathological complete response (pCR) to neoadjuvant treatment in locally advanced rectal cancer (LARC). Material: All patients treated for a LARC with neoadjuvant CRT and subsequent surgery in two separate institutions between 2012 and 2019 were considered. Both pre-CRT pelvic MRI and CE-CT were mandatory for inclusion. The tumor was manually segmented on the T2-weighted and diffusion axial MRI sequences and on CE-CT. In total, 88 radiomic parameters were extracted from each sequence using the Miras© software, with a total of 822 features by patient. The cohort was split into training (Institution 1) and testing (Institution 2) sets. The ComBat and Synthetic Minority Over-sampling Technique (SMOTE) approaches were used to account for inter-institution heterogeneity and imbalanced data, respectively. We selected the most predictive characteristics using Spearman's rank correlation and the Area Under the ROC Curve (AUC). Five pCR prediction models (clinical, radiomics before and after ComBat, and combined before and after ComBat) were then developed on the training set with a neural network approach and a bootstrap internal validation (n = 1000 replications). A cut-off maximizing the model's performance was defined on the training set. Each model was then evaluated on the testing set using sensitivity, specificity, balanced accuracy (Bacc) with the predefined cut-off. Results: Out of the 124 included patients, 14 had pCR (11.3%). After ComBat harmonization, the radiomic and the combined models obtained a Bacc of 68.2% and 85.5%, respectively, while the clinical model and the pre-ComBat combined achieved respective Baccs of 60.0% and 75.5%. Conclusions: After correction of inter-site variability and imbalanced data, addition of radiomic features enhances the prediction of pCR after neoadjuvant CRT in LARC.

A Unique Immune-Related Gene Signature Represents Advanced Liver Fibrosis and Reveals Potential Therapeutic Targets

Innate and adaptive immune responses are critically associated with the progression of fibrosis in chronic liver diseases. In this study, we aim to identify a unique immune-related gene signature representing advanced liver fibrosis and to reveal potential therapeutic targets. Seventy-seven snap-frozen liver tissues with various chronic liver diseases at different fibrosis stages (1: n = 12, 2: n = 12, 3: n = 25, 4: n = 28) were subjected to expression analyses. Gene expression analysis was performed using the nCounter PanCancer Immune Profiling Panel (NanoString Technologies, Seattle, WA, USA). Biological meta-analysis was performed using the CBS Probe PINGSTM (CbsBioscience, Daejeon, Korea). Using non-tumor tissues from surgically resected specimens, we identified the immune-related, five-gene signature (CHIT1_FCER1G_OSM_VEGFA_ZAP70) that reliably differentiated patients with low- (F1 and F2) and high-grade fibrosis (F3 and F4; accuracy = 94.8%, specificity = 91.7%, sensitivity = 96.23%). The signature was independent of all pathological and clinical features and was independently associated with high-grade fibrosis using multivariate analysis. Among these genes, the expression of inflammation-associated FCER1G, OSM, VEGFA, and ZAP70 was lower in high-grade fibrosis than in low-grade fibrosis, whereas CHIT1 expression, which is associated with fibrogenic activity of macrophages, was higher in high-grade fibrosis. Meta-analysis revealed that STAT3, a potential druggable target, highly interacts with the five-gene signature. Overall, we identified an immune gene signature that reliably predicts advanced fibrosis in chronic liver disease. This signature revealed potential immune therapeutic targets to ameliorate liver fibrosis.

Epigenetic DNA Modifications Upregulate SPRY2 in Human Colorectal Cancers

Conventional wisdom is that Sprouty2 (SPRY2), a suppressor of Receptor Tyrosine Kinase (RTK) signaling, functions as a tumor suppressor and is downregulated in many solid tumors. We reported, for the first time, that increased expression of SPRY2 augments cancer phenotype and Epithelial-Mesenchymal-Transition (EMT) in colorectal cancer (CRC). In this report, we assessed epigenetic DNA modifications that regulate SPRY2 expression in CRC. A total of 4 loci within SPRY2 were evaluated for 5mC using Combined Bisulfite Restriction Analysis (COBRA). Previously sequenced 5hmC nano-hmC seal data within SPRY2 promoter and gene body were evaluated in CRC. Combined bioinformatics analyses of SPRY2 CRC transcripts by RNA-seq/microarray and 450K methyl-array data archived in The Cancer Genome Atlas (TCGA) and GEO database were performed. SPRY2 protein in CRC tumors and cells was measured by Western blotting. Increased SPRY2 mRNA was observed across several CRC datasets and increased protein expression was observed among CRC patient samples. For the first time, SPRY2 hypomethylation was identified in adenocarcinomas in the promoter and gene body. We also revealed, for the first time, increases of 5hmC deposition in the promoter region of SPRY2 in CRC. SPRY2 promoter hypomethylation and increased 5hmC may play an influential role in upregulating SPRY2 in CRC.

A 41-Gene Pair Signature for Predicting the Pathological Response of Locally Advanced Rectal Cancer to Neoadjuvant Chemoradiation

Background and Purpose: Pathological response status is a standard reference for the early evaluation of the effect of neoadjuvant chemoradiation (nCRT) on locally advanced rectal cancer (LARC) patients. Various patients respond differently to nCRT, but identifying the pathological response of LARC to nCRT remains a challenge. Therefore, we aimed to identify a signature that can predict the response of LARC to nCRT. Material and Methods: The gene expression profiles of 111 LARC patients receiving fluorouracil-based nCRT were used to obtain gene pairs with within-sample relative expression orderings related to pathological response. These reversal gene pairs were ranked according to the mean decrease Gini index provided by the random forest algorithm to obtain the signature. This signature was verified in two public cohorts of 46 and 42 samples, and a cohort of 33 samples measured at our laboratory. In addition, the signature was used to predict disease-free survival benefits in a series of colorectal cancer datasets. Results: A 41-gene pair signature (41-GPS) was identified in the training cohort with an accuracy of 84.68% and an area under the receiver operating characteristic curve (AUC) of 0.94. In the two public test cohorts, the accuracy was 93.37 and 73.81%, with AUCs of 0.97 and 0.86, respectively. In our dataset, the AUC was 0.80. The results of the survival analysis show that 41-GPS plays an effective role in identifying patients who will respond to nCRT and have a better prognosis. Conclusion: The signature consisting of 41 gene pairs can robustly predict the clinical pathological response of LARC patients to nCRT.

Validation of Gene Expression-Based Predictive Biomarkers for Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer

BACKGROUND

Neoadjuvant chemoradiotherapy (nCRT) followed by surgery is widely used for patients with locally advanced rectal cancer. However, response to nCRT varies substantially among patients, highlighting the need for predictive biomarkers that can distinguish non-responsive from responsive patients before nCRT. This study aimed to build novel multi-gene assays for predicting nCRT response, and to validate our signature and previously-reported signatures in multiple independent cohorts.

METHODS

Three microarray datasets of pre-therapeutic biopsies containing a total of 61 non-responders and 53 responders were used as the discovery cohorts to screen for genes that were consistently associated with nCRT response. The predictive values of signatures were tested in a meta-analysis using six independent datasets as the validation cohorts, consisted of a total of 176 non-responders and 99 responders.

RESULTS

We identified four genes, including BRCA1, GPR110, TNIK, and WDR4 in the discovery cohorts. Although our 4-gene signature and nine published signatures were evaluated, they were unable to predict nCRT response in the validation cohorts.

CONCLUSIONS

Although this is one of the largest studies addressing the validity of gene expression-based classifiers using pre-treatment biopsies from patients with rectal cancer, our findings do not support their clinically meaningful values to be predictive of nCRT response.

Comprehensive Analysis of Mutation-Based and Expressed Genes-Based Pathways in Head and Neck Squamous Cell Carcinoma

Over- or under-expression of mRNA results from genetic alterations. Comprehensive pathway analyses based on mRNA expression are as important as single gene level mutations. This study aimed to compare the mutation- and mRNA expression-based signaling pathways in head and neck squamous cell carcinoma (HNSCC) and to match these with potential drug or druggable pathways. Altogether, 93 recurrent/metastatic HNSCC patients were enrolled. We performed targeted gene sequencing using Illumina HiSeq-2500 for NGS, and nanostring nCounter® for mRNA expression; mRNA expression was classified into over- or under-expression groups based on the expression. We investigated mutational and nanostring data using the CBSJukebox® system, which is a big-data driven platform to analyze druggable pathways, genes, and protein-protein interaction. We calculated a Treatment Benefit Prediction Score (TBPS) to identify suitable drugs. By mapping the high score interaction genes to identify druggable pathways, we found highly related signaling pathways with mutations. Based on the mRNA expression and interaction gene scoring model, several pathways were found to be associated with over- and under-expression. Mutation-based pathways were associated with mRNA under-expressed genes-based pathways. These results suggest that HNSCCs are mainly caused by the loss-of-function mutations. TBPS found several matching drugs such as immune checkpoint inhibitors, EGFR inhibitors, and FGFR inhibitors.

Significance of TERT Genetic Alterations and Telomere Length in Hepatocellular Carcinoma

Simple Summary

Telomerase reverse transcriptase (TERT) mutations are the most frequent genetic alterations in hepatocellular carcinoma (HCC). However, integrative analysis studies of TERT-telomere signaling during hepatocarcinogenesis are lacking. In this study, we investigated the clinicopathological association and prognostic value of TERT gene alterations and telomere length in HCC patients undergoing hepatectomy as well as transarterial chemotherapy (TACE). We found that there are eight key TERT-interacting genes and higher TERT expression and longer telomere length in HCC. We also found TERT-telomeric signals related to correlation with tumor differentiation and stage progression. TERT promoter mutations were an independent predictor of worse overall survival after hepatectomy, while TERT expression independently predicted worse time to progression after TACE. Telomere length was also associated with survival in TACE-treated patients. These findings suggest that TERT-telomere signals might be useful biomarkers for HCC, but the prognostic values may differ with tumor characteristics and treatment.

Abstract

Telomerase reverse transcriptase (TERT) mutations are reportedly the most frequent somatic genetic alterations in hepatocellular carcinoma (HCC). An integrative analysis of TERT-telomere signaling during hepatocarcinogenesis is lacking. This study aimed to investigate the clinicopathological association and prognostic value of TERT gene alterations and telomere length in HCC patients undergoing hepatectomy as well as transarterial chemotherapy (TACE). TERT promoter mutation, expression, and telomere length were analyzed by Sanger sequencing and real-time PCR in 305 tissue samples. Protein–protein interaction (PPI) analysis was performed to identify a set of genes that physically interact with TERT. The PPI analysis identified eight key TERT-interacting genes, namely CCT5, TUBA1B, mTOR, RPS6KB1, AKT1, WHAZ, YWHAQ, and TERT. Among these, TERT was the most strongly differentially expressed gene. TERT promoter mutations were more frequent, TERT expression was significantly higher, and telomere length was longer in tumors versus non-tumors. TERT promoter mutations were most frequent in HCV-related HCCs and less frequent in HBV-related HCCs. TERT promoter mutations were associated with higher TERT levels and longer telomere length and were an independent predictor of worse overall survival after hepatectomy. TERT expression was positively correlated with tumor differentiation and stage progression, and independently predicted shorter time to progression after TACE. The TERT-telomere network may have a crucial role in the development and progression of HCC. TERT-telomere abnormalities might serve as useful biomarkers for HCC, but the prognostic values may differ with tumor characteristics and treatment.

Pre-Existing Tumoral B Cell Infiltration and Impaired Genome Maintenance Correlate with Response to Chemoradiotherapy in Locally Advanced Rectal Cancer

Locally advanced rectal cancer (LARC) remains a medical challenge. Reliable biomarkers to predict which patients will significantly respond to neoadjuvant chemoradiotherapy (nCRT) have not been identified. We evaluated baseline genomic and transcriptomic features to detect differences that may help predict response to nCRT. Eligible LARC patients received nCRT (3D-LCRT 50.4 Gy plus capecitabine 825 mg/m2/bid), preceded by three cycles of CAPOX in high systemic-relapse risk tumors, and subsequent surgery. Frozen tumor biopsies at diagnosis were sequenced using a colorectal cancer panel. Transcriptomic data was used for pathway and cell deconvolution inferential algorithms, coupled with immunohistochemical validation. Clinical and molecular data were analyzed according to nCRT outcome. Pathways related to DNA repair and proliferation (p < 0.005), and co-occurrence of RAS and TP53 mutations (p = 0.001) were associated with poor response. Enrichment of expression signatures related to enhanced immune response, particularly B cells and interferon signaling (p < 0.005), was detected in good responders. Immunohistochemical analysis of CD20+ cells validated the association of good response with B cell infiltration (p = 0.047). Findings indicate that the presence of B cells is associated with successful tumor regression following nCRT in LARC. The prevalence of simultaneous RAS and TP53 mutations along with a proficient DNA repair system that may counteract chemoradio-induced DNA damage was associated with poor response.

Pre-Treatment T2-WI Based Radiomics Features for Prediction of Locally Advanced Rectal Cancer Non-Response to Neoadjuvant Chemoradiotherapy: A Preliminary Study

Locally advanced rectal cancer (LARC) response to neoadjuvant chemoradiotherapy (nCRT) is very heterogeneous and up to 30% of patients are considered non-responders, presenting no tumor regression after nCRT. This study aimed to determine the ability of pre-treatment T2-weighted based radiomics features to predict LARC non-responders. A total of 67 LARC patients who underwent a pre-treatment MRI followed by nCRT and total mesorectal excision were assigned into training (n = 44) and validation (n = 23) groups. In both datasets, the patients were categorized according to the Ryan tumor regression grade (TRG) system into non-responders (TRG = 3) and responders (TRG 1 and 2). We extracted 960 radiomic features/patient from pre-treatment T2-weighted images. After a three-step feature selection process, including LASSO regression analysis, we built a radiomics score with seven radiomics features. This score was significantly higher among non-responders in both training and validation sets (p < 0.001 and p = 0.03) and it showed good predictive performance for LARC non-response, achieving an area under the curve (AUC) = 0.94 (95% CI: 0.82–0.99) in the training set and AUC = 0.80 (95% CI: 0.58–0.94) in the validation group. The multivariate analysis identified the radiomics score as an independent predictor for the tumor non-response (OR = 6.52, 95% CI: 1.87–22.72). Our results indicate that MRI radiomics features could be considered as potential imaging biomarkers for early prediction of LARC non-response to neoadjuvant treatment.