Deciphering tertiary lymphoid structure heterogeneity reveals prognostic signature and therapeutic potentials for colorectal cancer: a multicenter retrospective cohort study.

BACKGROUND: Tertiary lymphoid structures (TLSs) exert a crucial role in the tumor microenvironment (TME), impacting tumor development, immune escape, and drug resistance. Nonetheless, the heterogeneity of TLSs in colorectal cancer (CRC) and their impact on prognosis and treatment response remain unclear. METHODS: We collected genome, transcriptome, clinicopathological information, and digital pathology images from multiple sources. An unsupervised clustering algorithm was implemented to determine diverse TLS patterns in CRC based on the expression levels of 39 TLS signature genes (TSGs). Comprehensive explorations of heterogeneity encompassing mutation landscape, TME, biological characteristics, response to immunotherapy, and drug resistance were conducted using multi-omics data. TLSscore was then developed to quantitatively assess TLS patterns of individuals for further clinical applicability. RESULTS: Three distinct TLS patterns were identified in CRC. Cluster 1 exhibited upregulation of proliferation-related pathways, high metabolic activity, and intermediate prognosis, while Cluster 2 displayed activation of stromal and carcinogenic pathways and a worse prognosis. Both Cluster 1 and Cluster 2 may potentially benefit from adjuvant chemotherapy. Cluster 3, characterized by the activation of immune regulation and activation pathways, demonstrated a favorable prognosis and enhanced responsiveness to immunotherapy. We subsequently employed a regularization algorithm to construct the TLSscore based on 9 core genes. Patients with lower TLSscore trended to prolonged prognosis and a more prominent presence of TLSs, which may benefit from immunotherapy. Conversely, those with higher TLSscore exhibited increased benefits from adjuvant chemotherapy. CONCLUSIONS: We identified distinct TLS patterns in CRC and characterized their heterogeneity through multi-omics analyses. The TLSscore held promise for guiding clinical decision-making and further advancing the field of personalized medicine in CRC.

The 3-Dimensional Intelligent Structured Light Technique: A New Registration Method in Stereotactic Neurosurgery.

BACKGROUND AND OBJECTIVES: Surface-based facial scanning registration emerged as an essential registration method in the robot-assisted neuronavigation surgery, providing a marker-free way to align a patient's facial surface with the imaging data. The 3-dimensional (3D) structured light was developed as an advanced registration method based on surface-based facial scanning registration. We aspire to introduce the 3D structured light as a new registration method in the procedure of the robot-assisted neurosurgery and assess the accuracy, efficiency, and safety of this method by analyzing the relative operative results. METHODS: We analyzed the results of 47 patients who underwent Ommaya reservoir implantation (n = 17) and stereotactic biopsy (n = 30) assisted by 3D structured light at our hospital from January 2022 to May 2023. The accuracy and additional operative results were analyzed. RESULTS: For the Ommaya reservoir implantation, the target point error was 3.2 ± 2.2 mm and the entry point error was 3.3 ± 2.4 mm, while the operation duration was 35.8 ± 8.3 minutes. For the stereotactic biopsy, the target point error was 2.3 ± 1.3 mm and the entry point error was 2.7 ± 1.2 mm, while the operation duration was 24.5 ± 6.3 minutes. CONCLUSION: The 3D structured light technique reduces the patients' discomfort and offers the advantage of a simpler procedure, which can improve the clinical efficiency with the sufficient accuracy and safety to meet the clinical requirements of the puncture and navigation.

Localizing seizure onset zone by a cortico-cortical evoked potentials-based machine learning approach in focal epilepsy.

OBJECTIVE: We aimed to develop a new approach for identifying the localization of the seizure onset zone (SOZ) based on corticocortical evoked potentials (CCEPs) and to compare the connectivity patterns in patients with different clinical phenotypes. METHODS: Fifty patients who underwent stereoelectroencephalography and CCEP procedures were included. Logistic regression was used in the model, and six CCEP metrics were input as features: root mean square of the first peak (N1RMS) and second peak (N2RMS), peak latency, onset latency, width duration, and area. RESULTS: The area under the curve (AUC) for localizing the SOZ ranged from 0.88 to 0.93. The N1RMS values in the hippocampus sclerosis (HS) group were greater than that of the focal cortical dysplasia (FCD) IIa group (p < 0.001), independent of the distance between the recorded and stimulated sites. The sensitivity of localization was higher in the seizure-free group than in the non-seizure-free group (p = 0.036). CONCLUSIONS: This new method can be used to predict the SOZ localization in various focal epilepsy phenotypes. SIGNIFICANCE: This study proposed a machine-learning approach for localizing the SOZ. Moreover, we examined how clinical phenotypes impact large-scale abnormality of the epileptogenic networks.

Hypometabolic patterns are related to post-surgical seizure outcomes in focal cortical dysplasia: A semi-quantitative study.

OBJECTIVE: Fluorine-18-fluorodeoxyglucose-positron emission tomography (FDG-PET) is routinely used for presurgical evaluation in many epilepsy centers. Hypometabolic characteristics have been extensively examined in prior studies, but the metabolic patterns associated with specific pathological types of drug-resistant epilepsy remain to be fully defined. This study was developed to explore the relationship between metabolic patterns or characteristics and surgical outcomes in type I and II focal cortical dysplasia (FCD) patients based on results from a large cohort. METHODS: Data from individuals who underwent epilepsy surgery from 2014 to 2019 with a follow-up duration of over 3 years and a pathological classification of type I or II FCD in our hospital were retrospectively analyzed. Hypometabolic patterns were quantitatively identified via statistical parametric mapping (SPM) and qualitatively analyzed via visual examination of PET-MRI co-registration images. Univariate analyses were used to explore the relationship between metabolic patterns and surgical outcomes. RESULTS: In total, this study included data from 210 patients. Following SPM calculations, four hypometabolic patterns were defined including unilobar, multi-lobar, and remote patterns as well as cases where no pattern was evident. In type II FCD patients, the unilobar pattern was associated with the best surgical outcomes (p = 0.014). In visual analysis, single gyrus (p = 0.032) and Clear-cut hypometabolism edge (p = 0.040) patterns exhibited better surgery outcomes in the type II FCD group. CONCLUSIONS: PET metabolic patterns are well-correlated with the prognosis of type II FCD patients. However, similar correlations were not observed in type I FCD, potentially owing to the complex distribution of the epileptogenic region. PLAIN LANGUAGE SUMMARY: In this study, we demonstrated that FDG-PET was a crucial examination for patients with FCD, which was a common cause of epilepsy. We compared the surgical prognosis for patients with different hypometabolism distribution patterns and found that clear and focal abnormal region in PET was correlated with good surgical outcome in type II FCD patients.

Knowledge-embedded spatio-temporal analysis for euploidy embryos identification in couples with chromosomal rearrangements.

BACKGROUND: The goal of the assisted reproductive treatment is to transfer one euploid blastocyst and to help infertile women giving birth one healthy neonate. Some algorithms have been used to assess the ploidy status of embryos derived from couples with normal chromosome, who subjected to preimplantation genetic testing for aneuploidy (PGT-A) treatment. However, it is currently unknown whether artificial intelligence model can be used to assess the euploidy status of blastocyst derived from populations with chromosomal rearrangement. METHODS: From February 2020 to May 2021, we collected the whole raw time-lapse videos at multiple focal planes from in vitro cultured embryos, the clinical information of couples, and the comprehensive chromosome screening results of those blastocysts that had received PGT treatment. Initially, we developed a novel deep learning model called the Attentive Multi-Focus Selection Network (AMSNet) to analyze time-lapse videos in real time and predict blastocyst formation. Building upon AMSNet, we integrated additional clinically predictive variables and created a second deep learning model, the Attentive Multi-Focus Video and Clinical Information Fusion Network (AMCFNet), to assess the euploidy status of embryos. The efficacy of the AMCFNet was further tested in embryos with parental chromosomal rearrangements. The receiver operating characteristic curve (ROC) was used to evaluate the superiority of the model. RESULTS: A total of 4112 embryos with complete time-lapse videos were enrolled for the blastocyst formation prediction task, and 1422 qualified blastocysts received PGT-A ( n = 589) or PGT for chromosomal structural rearrangement (PGT-SR, n = 833) were enrolled for the euploidy assessment task in this study. The AMSNet model using seven focal raw time-lapse videos has the best real-time accuracy. The real-time accuracy for AMSNet to predict blastocyst formation reached above 70% on the day 2 of embryo culture, and then increased to 80% on the day 4 of embryo culture. Combing with 4 clinical features of couples, the AUC of AMCFNet with 7 focal points increased to 0.729 in blastocysts derived from couples with chromosomal rearrangement. CONCLUSION: Integrating seven focal raw time-lapse images of embryos and parental clinical information, AMCFNet model have the capability of assessing euploidy status in blastocysts derived from couples with chromosomal rearrangement.

A prognostic model for ovarian neoplasms established by an integrated analysis of 1580 transcriptomic profiles.

Even after debulking surgery combined with chemotherapy or new adjuvant chemotherapy paired with internal surgery, the average year of disease free survival in advanced ovarian cancer was approximately 1.7 years1. The development of a molecular predictor of early recurrence would allow for the identification of ovarian cancer (OC) patients with high risk of relapse. The Ovarian Cancer Disease Free Survival Predictor (ODFSP), a predictive model constructed from a special set of 1580 OC tumors in which gene expression was assessed using both microarray and sequencing platforms, was created by our team. To construct gene expression barcodes that were resistant to biases caused by disparate profiling platforms and batch effects, we employed a meta-analysis methodology that was based on the binary gene pair technique. We demonstrate that ODFSP is a reliable single-sample predictor of early recurrence (1 year or less) using the largest pool of OC transcriptome data sets available to date. The ODFSP model showed significantly high prognostic value for binary recurrence prediction unaffected by clinicopathologic factors, with a meta-estimate of the area under the receiver operating curve of 0.64 (P = 4.6E-05) and a D-index (robust hazard ratio) of 1.67 (P = 9.2E-06), respectively. GO analysis of ODFSP's 2040 gene pairs (collapsed to 886 distinct genes) revealed the involvement in small molecular catabolic process, sulfur compound metabolic process, organic acid catabolic process, sulfur compound biosynthetic process, glycosaminoglycan metabolic process and aminometabolic process. Kyoto encyclopedia of genes and genomes pathway analysis of ODFSP's signature genes identified prominent pathways that included cAMP signaling pathway and FoxO signaling pathway. By identifying individuals who might benefit from a more aggressive treatment plan or enrolment in a clinical trial but who will not benefit from standard surgery or chemotherapy, ODFSP could help with treatment decisions.

Senescence-based colorectal cancer subtyping reveals distinct molecular characteristics and therapeutic strategies.

Cellular senescence has been listed as a hallmark of cancer, but its role in colorectal cancer (CRC) remains unclear. We comprehensively evaluated the transcriptome, genome, digital pathology, and clinical data from multiple datasets of CRC patients and proposed a novel senescence subtype for CRC. Multi-omics data was used to analyze the biological features, tumor microenvironment, and mutation landscape of senescence subtypes, as well as drug sensitivity and immunotherapy response. The senescence score was constructed to better quantify senescence in each patient for clinical use. Unsupervised learning revealed three transcriptome-based senescence subtypes. Cluster 1, characterized by low senescence and activated proliferative pathways, was sensitive to chemotherapeutic drugs. Cluster 2, characterized by intermediate senescence and high immune infiltration, exhibited significant immunotherapeutic advantages. Cluster 3, characterized by high senescence, high immune, and stroma infiltration, had a worse prognosis and maybe benefit from targeted therapy. We further constructed a senescence scoring system based on seven senescent genes through machine learning. Lower senescence scores were highly predictive of longer disease-free survival, and patients with low senescence scores may benefit from immunotherapy. We proposed the senescence subtypes of CRC and our findings provide potential treatment interventions for each CRC senescence subtype to promote precision treatment.

Aberrant methylation in neurofunctional gene serves as a hallmark of tumorigenesis and progression in colorectal cancer.

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.

An immune, stroma, and epithelial-mesenchymal transition-related signature for predicting recurrence and chemotherapy benefit in stage II-III colorectal cancer.

BACKGROUND: Debates exist on the treatment decision of the stage II/III colorectal cancer (CRC) due to the insufficiency of the current TNM stage-based risk stratification system. Epithelial-mesenchymal transition (EMT) and tumor microenvironment (TME) have both been linked to CRC progression in recent studies. We propose to improve the prognosis prediction of CRC by integrating TME and EMT. METHODS: In total, 2382 CRC patients from seven datasets and one in-house cohort were collected, and 1640 stage II/III CRC patients with complete survival information and gene expression profiles were retained and divided into a training cohort and three independent validation cohorts. Integrated analysis of 398 immune, stroma, and epithelial-mesenchymal transition (ISE)-related genes identified an ISE signature independently associated with the recurrence of CRC. The underlying biological mechanism of the ISE signature and its influence on adjuvant chemotherapy was further explored. RESULTS: We constructed a 26-gene signature which was significantly associated with poor outcome in Training cohort (p < 0.001, HR [95%CI] = 4.42 [3.25-6.01]) and three independent validation cohorts (Validation cohort-1: p < 0.01, HR [95%CI] = 1.70 [1.15-2.51]; Validation cohort-2: p < 0.001, HR [95% CI] = 2.30 [1.67-3.16]; Validation cohort-3: p < 0.01, HR [95% CI] = 2.42 [1.25-4.70]). After adjusting for known clinicopathological factors, multivariate cox analysis confirmed the ISE signature's independent prognostic value. Subgroup analysis found that stage III patients with low ISE score might benefit from adjuvant chemotherapy (p < 0.001, HR [95%CI] = 0.15 [0.04-0.55]). Hypergeometric test and enrichment analysis revealed that low-risk group was enriched in thr immune pathway while high-risk group was associated with the EMT pathway and CMS4 subtype. CONCLUSION: We proposed an ISE signature for robustly predicting the recurrence of stage II/III CRC and help treatment decision by identifying patients who will not benefit from current standard treatment.

CT-based radiogenomic analysis dissects intratumor heterogeneity and predicts prognosis of colorectal cancer: a multi-institutional retrospective study.

BACKGROUND: This study aimed to develop a radiogenomic prognostic prediction model for colorectal cancer (CRC) by investigating the biological and clinical relevance of intratumoural heterogeneity. METHODS: This retrospective multi-cohort study was conducted in three steps. First, we identified genomic subclones using unsupervised deconvolution analysis. Second, we established radiogenomic signatures to link radiomic features with prognostic subclone compositions in an independent radiogenomic dataset containing matched imaging and gene expression data. Finally, the prognostic value of the identified radiogenomic signatures was validated using two testing datasets containing imaging and survival information collected from separate medical centres. RESULTS: This multi-institutional retrospective study included 1601 patients (714 females and 887 males; mean age, 65 years ± 14 [standard deviation]) with CRC from 5 datasets. Molecular heterogeneity was identified using unsupervised deconvolution analysis of gene expression data. The relative prevalence of the two subclones associated with cell cycle and extracellular matrix pathways identified patients with significantly different survival outcomes. A radiogenomic signature-based predictive model significantly stratified patients into high- and low-risk groups with disparate disease-free survival (HR = 1.74, P = 0.003). Radiogenomic signatures were revealed as an independent predictive factor for CRC by multivariable analysis (HR = 1.59, 95% CI:1.03-2.45, P = 0.034). Functional analysis demonstrated that the 11 radiogenomic signatures were predominantly associated with extracellular matrix and immune-related pathways. CONCLUSIONS: The identified radiogenomic signatures might be a surrogate for genomic signatures and could complement the current prognostic strategies.

Predicting prognosis and immunotherapy response among colorectal cancer patients based on a tumor immune microenvironment-related lncRNA signature.

Long non-coding RNAs (lncRNAs) remodel the tumor immune microenvironment (TIME) by regulating the functions of tumor-infiltrating immune cells. It remains uncertain the way that TIME-related lncRNAs (TRLs) influence the prognosis and immunotherapy response of colorectal cancer (CRC). Aiming at providing survival and immunotherapy response predictions, a CRC TIME-related lncRNA signature (TRLs signature) was developed and the related potential regulatory mechanisms were explored with a comprehensive analysis on gene expression profiles from 97 immune cell lines, 61 CRC cell lines and 1807 CRC patients. Stratifying CRC patients with the TRLs signature, prolonged survival was observed in the low-risk group, while the patients in the high-risk group had significantly higher pro-tumor immune cells infiltration and higher immunotherapy response rate. Through the complex TRLs-mRNA regulation network, immunoregulation pathways and immunotherapy response pathways were found to be differently activated between the groups. In conclusion, the CRC TRLs signature is capable of making prognosis and immunotherapy response predictions, which may find application in stratifying patients for immunotherapy in the bedside.

A Transcription Factor Signature Can Identify the CMS4 Subtype and Stratify the Prognostic Risk of Colorectal Cancer.

Background: Colorectal cancer (CRC) is a heterogeneous disease, and current classification systems are insufficient for stratifying patients with different risks. This study aims to develop a generalized, individualized prognostic consensus molecular subtype (CMS)-transcription factors (TFs)-based signature that can predict the prognosis of CRC. Methods: We obtained differentially expressed TF signature and target genes between the CMS4 and other CMS subtypes of CRC from The Cancer Genome Atlas (TCGA) database. A multi-dimensional network inference integrative analysis was conducted to identify the master genes and establish a CMS4-TFs-based signature. For validation, an in-house clinical cohort (n = 351) and another independent public CRC cohort (n = 565) were applied. Gene set enrichment analysis (GSEA) and prediction of immune cell infiltration were performed to interpret the biological significance of the model. Results: A CMS4-TFs-based signature termed TF-9 that includes nine TF master genes was developed. Patients in the TF-9 high-risk group have significantly worse survival, regardless of clinical characteristics. The TF-9 achieved the highest mean C-index (0.65) compared to all other signatures reported (0.51 to 0.57). Immune infiltration revealed that the microenvironment in the high-risk group was highly immune suppressed, as evidenced by the overexpression of TIM3, CD39, and CD40, suggesting that high-risk patients may not directly benefit from the immune checkpoint inhibitors. Conclusions: The TF-9 signature allows a more precise categorization of patients with relevant clinical and biological implications, which may be a valuable tool for improving the tailoring of therapeutic interventions in CRC patients.

Multi-Size Deep Learning Based Preoperative Computed Tomography Signature for Prognosis Prediction of Colorectal Cancer.

Background: Preoperative and postoperative evaluation of colorectal cancer (CRC) patients is crucial for subsequent treatment guidance. Our study aims to provide a timely and rapid assessment of the prognosis of CRC patients with deep learning according to non-invasive preoperative computed tomography (CT) and explore the underlying biological explanations. Methods: A total of 808 CRC patients with preoperative CT (development cohort: n = 426, validation cohort: n = 382) were enrolled in our study. We proposed a novel end-to-end Multi-Size Convolutional Neural Network (MSCNN) to predict the risk of CRC recurrence with CT images (CT signature). The prognostic performance of CT signature was evaluated by Kaplan-Meier curve. An integrated nomogram was constructed to improve the clinical utility of CT signature by combining with other clinicopathologic factors. Further visualization and correlation analysis for CT deep features with paired gene expression profiles were performed to reveal the molecular characteristics of CRC tumors learned by MSCNN in radiographic imaging. Results: The Kaplan-Meier analysis showed that CT signature was a significant prognostic factor for CRC disease-free survival (DFS) prediction [development cohort: hazard ratio (HR): 50.7, 95% CI: 28.4-90.6, p < 0.001; validation cohort: HR: 2.04, 95% CI: 1.44-2.89, p < 0.001]. Multivariable analysis confirmed the independence prognostic value of CT signature (development cohort: HR: 30.7, 95% CI: 19.8-69.3, p < 0.001; validation cohort: HR: 1.83, 95% CI: 1.19-2.83, p = 0.006). Dimension reduction and visualization of CT deep features demonstrated a high correlation with the prognosis of CRC patients. Functional pathway analysis further indicated that CRC patients with high CT signature presented down-regulation of several immunology pathways. Correlation analysis found that CT deep features were mainly associated with activation of metabolic and proliferative pathways. Conclusions: Our deep learning based preoperative CT signature can effectively predict prognosis of CRC patients. Integration analysis of multi-omic data revealed that some molecular characteristics of CRC tumor can be captured by deep learning in CT images.

DNA Repair-Related Gene Signature in Predicting Prognosis of Colorectal Cancer Patients.

Background: Increasing evidence have depicted that DNA repair-related genes (DRGs) are associated with the prognosis of colorectal cancer (CRC) patients. Thus, the aim of this study was to evaluate the impact of DNA repair-related gene signature (DRGS) in predicting the prognosis of CRC patients. Method: In this study, we retrospectively analyzed the gene expression profiles from six CRC cohorts. A total of 1,768 CRC patients with complete prognostic information were divided into the training cohort (n = 566) and two validation cohorts (n = 624 and 578, respectively). The LASSO Cox model was applied to construct a prediction model. To further validate the clinical significance of the model, we also validated the model with Genomics of Drug Sensitivity in Cancer (GDSC) and an advanced clear cell renal cell carcinoma (ccRCC) immunotherapy data set. Results: We constructed a prognostic DRGS consisting of 11 different genes to stratify patients into high- and low-risk groups. Patients in the high-risk groups had significantly worse disease-free survival (DFS) than those in the low-risk groups in all cohorts [training cohort: hazard ratio (HR) = 2.40, p < 0.001, 95% confidence interval (CI) = 1.67-3.44; validation-1: HR = 2.20, p < 0.001, 95% CI = 1.38-3.49 and validation-2 cohort: HR = 2.12, p < 0.001, 95% CI = 1.40-3.21). By validating the model with GDSC, we could see that among the chemotherapeutic drugs such as oxaliplatin, 5-fluorouracil, and irinotecan, the IC50 of the cell line in the low-risk group was lower. By validating the model with the ccRCC immunotherapy data set, we can clearly see that the overall survival (OS) of the objective response rate (ORR) with complete response (CR) and partial response (PR) in the low-risk group was the best. Conclusions: DRGS is a favorable prediction model for patients with CRC, and our model can predict the response of cell lines to chemotherapeutic agents and potentially predict the response of patients to immunotherapy.

The growth pattern of liver metastases on MRI predicts early recurrence in patients with colorectal cancer: a multicenter study.

OBJECTIVES: The multicenter study aimed to explore the relationship between the growth pattern of liver metastases on preoperative MRI and early recurrence in patients with colorectal cancer liver metastases (CRCLM) after surgery. METHODS: A total of 348 CRCLM patients from 3 independent centers were enrolled, including 130 patients with 339 liver metastases in the primary cohort and 218 patients in validation cohorts. Referring to the gross classification of hepatocellular carcinoma (HCC), the growth pattern of each liver metastasis on MRI was classified into four types: rough, smooth, focal extranodular protuberant (FEP), and nodular confluent (NC). Disease-free survival (DFS) curve was constructed using the Kaplan-Meier method. RESULTS: In primary cohort, 42 (12.4%) of the 339 liver metastases were rough type, 237 (69.9%) were smooth type, 29 (8.6%) were FEP type, and 31 (9.1%) were NC type. Those patients with FEP- and/or NC-type liver metastases had shorter DFS than those without such metastases (p < 0.05). However, there were no significant differences in DFS between patients with rough- and smooth-type liver metastases and those without such metastases. The patients with FEP- and/or NC-type liver metastases also had shorter DFS than those without such metastases in two external validation cohorts. In addition, 40.5% of high-risk-type (FEP and NC) liver metastases converted to low-risk types (rough and smooth) after neoadjuvant chemotherapy. CONCLUSION: The FEP- and NC-type liver metastases were associated with early recurrence, which may facilitate the clinical treatment of CRCLM patients. KEY POINTS: • In the primary cohort, patients with FEP- and NC-type metastases had shorter disease-free survival (DFS) and a higher intrahepatic recurrence rate than patients without such metastases in the liver. • In the primary cohort, there were no significant differences in DFS or intrahepatic recurrence rate between patients with rough- and smooth-type metastases and those without such metastases in the liver. • High-risk patients had shorter DFS and a higher intrahepatic recurrence rate than low-risk patients in primary and external validation cohorts.

The Addition of Preoperative Radiation Is Insufficient for Lateral Pelvic Control in a Subgroup of Patients With Low Locally Advanced Rectal Cancer: A Post Hoc Study of a Randomized Controlled Trial.

BACKGROUND: The local recurrence of rectal cancer has been improved by total mesorectal excision following neoadjuvant chemoradiotherapy. However, in patients with low locally advanced rectal cancer, lateral pelvic recurrence remains to be addressed. OBJECTIVE: This study aimed to determine the efficiency of neoadjuvant radiotherapy in addressing lateral pelvic recurrence and which subgroup of patients might be optimal to receive lateral lymph node dissection. DESIGN: The MRI/CT images were reassessed for lateral lymph node status. The lateral lymph nodes with short axis ≥5 mm and ≥4 mm were considered positive in pretreatment and restaging MRI/CT. SETTING: This was a post hoc analysis of a prospective randomized controlled trial (FOWARC, NCT01211210). PATIENTS: A total of 495 patients with stage II or III rectal adenocarcinoma were included in the original trial. According to the excluding criteria, the finally included population consists of 253 patients; of these, 195 patients received neoadjuvant chemoradiotherapy and 94 received chemotherapy alone. MAIN OUTCOMES AND MEASURES: The primary outcome was the 5-year lateral pelvic recurrence rate. RESULTS: Compared with patients receiving chemotherapy alone, patients receiving additional radiotherapy had a marginal significance of lower lateral pelvic recurrence rate (6.6% vs 13.0%; p = 0.051). In the subset with pretreatment positive lateral lymph nodes, patients had a lateral pelvic recurrence rate of 22.6% and 45.1% after neoadjuvant chemoradiotherapy and chemotherapy alone. Of note, 34.9% of the pretreatment positive lateral lymph nodes were persistent after neoadjuvant chemoradiotherapy, culminating in a lateral pelvic recurrence rate of 63.3%. LIMITATIONS: This is a post hoc analysis, and only the patients from the leading center were included, which limited the sample size. In addition, the lateral lymph node dissection was not performed in this cohort. CONCLUSIONS: The addition of radiotherapy in neoadjuvant regimens could not address lateral pelvic recurrence adequately. Some subgroups of patients might need additional dissection. See Video Abstract at http://links.lww.com/DCR/B613. LA INCLUSION DE LA RADIOTERAPIA PREOPERATORIA ES INSUFICIIENTE EN EL CONTROL PLVICO LATERAL EN UN SUBGRUPO DE PACIENTES CON CNCER DE RECTO INFERIOR LOCALMENTE AVANZADO UN ESTUDIO POSTHOC CONTROLADO Y RANDOMIZADO: ANTECEDENTES:La recurrencia local del cancer de recto ha disminuido al efectuar una excision mesorrectal total seguida de quimioradioterapia neoadyuvante. No obstante, en pacientes con cancer de tercio inferior de recto avanzado localmente, aún está por controlarse la recurrencia pélvicaOBJETIVOS:Determinar la eficacia de la radioterapia neoadyuvante en el control de la recurrencia pélvica lateral y en que subgrupo de pacientes sería conveniente efecutar una excisión lateral de las cadenas ganglionares.DISEÑO:Se reevaluaron las imágenes tomográficas y de resonancia magnética del status de las cadenas ganglionares linfáticas laterales. Los ganglios linfáticos laterales con un eje-corto > 5 mm y ≥ 4 mm se consideraron como positivos previo al tratamiento y reestadificados con RM y TAC respectivamente.ESCENARIO:Es un análisis post hoc de un studio prospectivo randomizado controlado (FOWARC, NCT01211210).PACIENTESSe incluyeron un total de 495 pacientes en estdio II o III con adenomcarcinoma rectal en el estudio original. De acuerdo a los criterios de exclusión, la población final incluida consistió en 253 pacientes; de estos, 195 recibieron quimioradioterapia neoadyuvante y 94 quimioterapia sola.EVALUACION DE LOS RESULTADOS PRINCIPALES:El parámetro mas importante fue la tasa de recurrencia pélvica lateral a cinco años.RESULTADOS:En comparación con los pacientes que recibieron quimioterapia sola, aquellos que además fueron sometidos a radioterapia adicional presentaron un margen significativo de menor tasa de recurrencia pélvica lateral (6.6% vs. 13.0%; p=0.051). En el grupo de pacientes con ganglios linfáticos laterales positivos, los enfermos presentaron una tasa de recurrencia pélvica lateral de 22.6% y 45.1% después de quimioradiaterapia neoadyuvante en comparación con quimioterapia sola respectivamente. Cabe mencionar que el 34.9% de los pacientes con ganglios linfáticos laterales positivos antes del tratamiento persistieron después de la quimioradioterapia neoadyuvante, reportándose finalmente una recurrencia pélvica lateral de un 63.3%.LIMITACIONES:Se trata de un análisis posthoc y solo los pacientes del hospital fueron incluidos, lo que limita el tamaño de la muestra. Además, no se efectuó la disección de los ganglios linfáticos laterales en este grupo.CONCLUSIONES:La radioterapia en los esquemas de neoadyuvancia no logran controlar la recurrencia pélvica lateral en forma adecuada. Algunos subgrupos de pacientes podría requerir de disección adicional. Consulte Video Resumen en http://links.lww.com/DCR/B613.

Identification of Transcriptional Variation in Aortic Remodeling Using a Murine Transverse Aortic Constriction (TAC) Model.

Arterial remodeling is a major pathological consequence of hypertension, which is recognized as the most common chronic non-communicable disease. However, the detailed mechanism of how arterial remodeling is induced by hypertension has not yet been fully elucidated. Evaluating the transcriptional changes in arterial tissue in response to elevated blood pressure at an early stage may provide new insights and identify novel therapeutic candidates in preventing arterial remodeling. Here, we used the ascending aorta of the transverse aortic constriction (TAC) model to induce arterial remodeling in C57BL/6 male mice. Age-matched mice were subjected to sham surgery as controls. The TAC model was only considered successful if the mice conformed to the criteria (RC/LC blood flow velocity with 5-10-fold change) 1 week after the surgery. Two weeks after surgery, the ascending aorta developed severe remodeling in TAC mice as compared to the sham group. High throughput sequencing was then applied to identify differentially expressed (DE) transcripts. In silicon analysis were then performed to systematically network transcriptional changes. A total of 1,019 mRNAs were significantly changed between TAC and the sham group at the transcriptional level. GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) analysis revealed that stress/stimulus/immune-related biological processes played a crucial role during arterial remodeling. Our data provide a comprehensive understanding of global gene expression changes in the TAC model, which suggests that targeting inflammation and vascular smooth cell transformation are potential therapeutic strategies to interfere with the aortic remodeling at an early stage in the development of hypertension.

Epigenetic Inactivation of α-Internexin Accelerates Microtubule Polymerization in Colorectal Cancer.

DNA methylation contributes to malignant transformation, but little is known about how the methylation drives colorectal cancer evolution at the early stages. Here we identify aberrant INA (α-internexin) gene methylation in colon adenoma and adenocarcinoma by filtering data obtained from a genome-wide screen of methylated genes. The gene encoding INA, a type IV intermediate filament, was frequently hypermethylated in CpG islands located in the promoter region. This hypermethylation preferentially occurred in large tumors and was a prognostic marker for poor overall survival in patients with colorectal cancer. This type of epigenetic alteration silenced INA expression in both adenoma and adenocarcinoma tissues. Gene silencing of INA in colorectal cancer cells increased cell proliferation, migration, and invasion. Restored INA expression blocked migration and invasion in vitro and reduced lung metastasis in vivo. Mechanistically, INA directly inhibited microtubule polymerization in vitro and decreased intracellular microtubule plus-end assembly rates. A peptide array screen surveying the tubulin-binding sites in INA identified a tubulin-binding motif located in the N-terminal head domain that plays a tumor-suppressive role by binding to unpolymerized tubulins and impeding microtubule polymerization. Thus, epigenetic inactivation of INA is an intermediate filament reorganization event that is essential to accelerate microtubule polymerization in the early stages of colorectal cancer. SIGNIFICANCE: This work provides insight into the epigenetic inactivation of INA, a novel identified tumor suppressor, which increases microtubule polymerization during colorectal cancer progression.

High platelet-to-lymphocyte ratio predicts improved survival outcome for perioperative NSAID use in patients with rectal cancer.

PURPOSE: Nonsteroidal anti-inflammatory drugs (NSAIDs) have been shown to block tumor-associated inflammation in rectal cancer. However, the perioperative use of NSAIDs remains controversial. This study was designed to investigate whether the perioperative use of NSAIDs influences outcomes and to provide a predictive marker to identify patients who would benefit from NSAIDs. METHODS: We enrolled 515 patients with stage I to III rectal cancer in this retrospective study. Patients were classified into the NSAID and non-NSAID groups according to their perioperative use of NSAIDs. The whole cohort was stratified by platelet-to-lymphocyte ratio (PLR). The primary endpoints were disease-free survival (DFS) and overall survival (OS). RESULTS: The NSAID group had a 12.6% lower risk of recurrence than the non-NSAID group (P = 0.015), while the association with survival was nonsignificant. In the high-PLR subset, the NSAID group had a 17.3% lower risk of recurrence (P = 0.003) and a better DFS (P = 0.033) outcome than the non-NSAID group. Multivariate analysis confirmed this independent significant association with DFS (P = 0.023). In the low-PLR subset, the association of NSAID use with survival was nonsignificant. CONCLUSION: Perioperative use of NSAIDs was associated with improved survival outcomes in rectal cancer patients with high PLR.

A Metabolism-Related Radiomics Signature for Predicting the Prognosis of Colorectal Cancer.

Background: Radiomics refers to the extraction of a large amount of image information from medical images, which can provide decision support for clinicians. In this study, we developed and validated a radiomics-based nomogram to predict the prognosis of colorectal cancer (CRC). Methods: A total of 381 patients with colorectal cancer (primary cohort: n = 242; validation cohort: n = 139) were enrolled and radiomic features were extracted from the vein phase of preoperative computed tomography (CT). The radiomics score was generated by using the least absolute shrinkage and selection operator algorithm (LASSO). A nomogram was constructed by combining the radiomics score with clinicopathological risk factors for predicting the prognosis of CRC patients. The performance of the nomogram was evaluated by the calibration curve, receiver operating characteristic (ROC) curve and C-index statistics. Functional analysis and correlation analysis were used to explore the underlying association between radiomic feature and the gene-expression patterns. Results: Five radiomic features were selected to calculate the radiomics score by using the LASSO regression model. The Kaplan-Meier analysis showed that radiomics score was significantly associated with disease-free survival (DFS) [primary cohort: hazard ratio (HR): 5.65, 95% CI: 2.26-14.13, P < 0.001; validation cohort: HR: 8.49, 95% CI: 2.05-35.17, P < 0.001]. Multivariable analysis confirmed the independent prognostic value of radiomics score (primary cohort: HR: 5.35, 95% CI: 2.14-13.39, P < 0.001; validation cohort: HR: 5.19, 95% CI: 1.22-22.00, P = 0.026). We incorporated radiomics signature with the TNM stage to build a nomogram, which performed better than TNM stage alone. The C-index of the nomogram achieved 0.74 (0.69-0.80) in the primary cohort and 0.82 (0.77-0.87) in the validation cohort. Functional analysis and correlation analysis found that the radiomic signatures were mainly associated with metabolism related pathways. Conclusions: The radiomics score derived from the preoperative CT image was an independent prognostic factor and could be a complement to the current staging strategies of colorectal cancer.