Clinicopathological Risk Research Articles

Comprehensive analysis of clinicopathologic and sonographic features in thyroid cancer with skip lymph node metastasis: establish and assessment of a prediction nomogram

Lateral Lymph Node Metastasis (LLNM) is common in Papillary Thyroid Carcinoma (PTC) and is associated with a poor prognosis. LLNM without central lymph node metastasis as skip metastasis is not common. We aimed to investigate clinicopathologic and sonographic risk factors for skip metastasis in PTC patients, and to establish a nomogram for predicting the possibility of skip metastasis in order to determine the therapeutic strategy. We retrospectively reviewed the data of 1037 PTC patients who underwent surgery from 2016 to 2020 at a single institution. Univariate and multivariate analyses were used to identify the clinicopathologic and preoperative sonographic risk factors of skip metastasis. A nomogram including the risk factors for predicting skip metastasis was further developed and validated. The incidence of skip metastasis was 10.7%. The univariate and multivariate analyses suggested that gender (p= 0.001), tumor location (p= 0.000), extrathyroidal extension (p= 0.000), and calcification (p= 0.000) were independent risk factors. For papillary thyroid microcarcinoma, tumor location (p= 0.000) and calcification (p= 0.001) were independent risk factors. A nomogram according to the clinicopathologic and sonographic predictors was developed. The receiver operating characteristic curve indicated that AUC was 0.824 and had an excellent consistency. The calibration plot analysis showed a good performance and clinical utility of the model. Decision curve analysis revealed it was clinically useful. A nomogram for predicting the probability of skip metastasis was developed, which exhibited a favorable predictive value and consistency. For the female PTC patient, tumor located at the upper pole is more likely to have skip metastasis. Surgeons and sonographers should pay close attention to the patients who have the risk factors.Evidence level: This article’s evidence level is 3. Level 3 evidence is derived from non-randomized, controlled clinical trials. In this study, patients who receive an intervention are compared to a control group. Authors may detect a statistically significant and clinically relevant outcome.

Invasive Breast Cancer with HER2 ≥4.0 and

To investigate the HER2 status and clinicopathological features in invasive breast cancer with HER2 ≥4.0 and <6.0, which has always been controversial. Forty breast cancer cases with HER2 ≥4.0 and <6.0 by fluorescence in situ hybridization (FISH) were collected and classified into two groups based on the HRE2/CEP17 ratio (Group A: ≥2.0, n=22; Group B: <2.0, n=18). Clinicopathological characteristics, HER2 status, risk classification, and molecular typing were further analyzed and compared by 21-Gene expression assay and MammaPrint plus BluePrint test. The majority of cases in both groups were invasive carcinoma (NOS), with histological grade II, HR+, Ki-67 ≥20%, HER2 2+, and a high risk of recurrence, although younger patients and lymph node metastases were more common in Group A. Surprisingly, all HR+ breast cancers in both groups were classified as luminal-type, HR- cases were all basal-type or unknown, and the index of HER2 in all cases was <0.000 using the BluePrint test, which indicated that HER2 status should be negative. Furthermore, the level of HER2 mRNA expression in all cases of both groups was <10.7, which was defined as HER2 negative by the 21-Gene expression assay. In addition, 10 patients of Group A received anti-HER2 neoadjuvant therapy; only one patient with HR- achieved Grade 5 based on the Miller-Payne system, whereas none of the patients achieved pathological complete response (pCR) based on the Residual Cancer Burden system. Group A breast cancer, which has always been unquestionably diagnosed as HER2 amplification, was more likely to be HER2 negative and derived less benefit from anti-HER2 neoadjuvant chemotherapy. Group A breast cancer should be distinguished from classical HER2-positive breast cancers when assessing HER2 FISH, and a larger cohort of Group A patients should be included in further studies.

Clinicopathological Risk Factors of Unfavorable Outcomes in Vietnamese Women with Primary Invasive Breast Cancer: A Retrospective Cohort Study.

The rate of unfavorable outcomes, such as recurrence and death, in women with invasive breast cancer varies widely across countries and populations. Identifying those with high-risk profiles is critical so that early detection, prediction, and intervention can be made to improve their survival rate. Therefore, our study evaluated the rate of unfavorable outcomes and its association with clinicopathological characteristics in Vietnamese women with primary invasive breast cancer. A retrospective open cohort study was conducted on Vietnamese women with invasive breast cancer who underwent a mastectomy and were regularly followed up by the hospitals. Kaplan-Meier method was used to estimate the rate of unfavorable outcomes to take into account the follow-up time of each patient. Univariate and multiple Cox regression analyses were conducted to examine the associations between unfavorable outcomes and clinicopathological characteristics. Among 204 women included in the data analysis, the mean age was 54.4 ± 10.9 years. The majority of patients were diagnosed with early-stage (76.5%) or locally advanced (22.5%) breast cancer. The 5-year rate of unfavorable outcomes was 12.8%, and the 8-year rate was 31.7%. Patients with advanced stages had a higher risk of unfavorable outcomes compared to those with early stages (IA, IIA, T2N1). Patients with lymph node metastases and those with triple-negative molecular classification had significantly higher rates of unfavorable outcomes. Although Vietnamese women with breast cancer have a relatively low rate of unfavorable outcomes compared to other countries, findings from this study emphasize the importance of early detection and underscore the need for targeted interventions for patients with advanced stages, lymph node metastases, and triple-negative breast cancer to optimize their treatment, outcomes, and overall prognosis.

Radiomic signatures based on pretreatment 18F-FDG PET/CT, combined with clinicopathological characteristics, as early prognostic biomarkers among patients with invasive breast cancer.

The aim of this study was to investigate the predictive role of fluorine-18 fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) in the prognostic risk stratification of patients with invasive breast cancer (IBC). To achieve this, we developed a clinicopathologic-radiomic-based model (C-R model) and established a nomogram that could be utilized in clinical practice. We retrospectively enrolled a total of 91 patients who underwent preoperative 18F-FDG PET/CT and randomly divided them into training (n=63) and testing cohorts (n=28). Radiomic signatures (RSs) were identified using the least absolute shrinkage and selection operator (LASSO) regression algorithm and used to compute the radiomic score (Rad-score). Patients were assigned to high- and low-risk groups based on the optimal cut-off value of the receiver operating characteristic (ROC) curve analysis for both Rad-score and clinicopathological risk factors. Univariate and multivariate Cox regression analyses were performed to determine the association between these variables and progression-free survival (PFS) or overall survival (OS). We then plotted a nomogram integrating all these factors to validate the predictive performance of survival status. The Rad-score, age, clinical M stage, and minimum standardized uptake value (SUVmin) were identified as independent prognostic factors for predicting PFS, while only Rad-score, age, and clinical M stage were found to be prognostic factors for OS in the training cohort. In the testing cohort, the C-R model showed superior performance compared to single clinical or radiomic models. The concordance index (C-index) values for the C-R model, clinical model, and radiomic model were 0.816, 0.772, and 0.647 for predicting PFS, and 0.882, 0.824, and 0.754 for OS, respectively. Furthermore, decision curve analysis (DCA) and calibration curves demonstrated that the C-R model had a good ability for both clinical net benefit and application. The combination of clinicopathological risks and baseline PET/CT-derived Rad-score could be used to evaluate the prognosis in patients with IBC. The predictive nomogram based on the C-R model further enhanced individualized estimation and allowed for more accurate prediction of patient outcomes.

Lung surveillance following colorectal cancer pulmonary metastasectomy: Utilization of clinicopathologic risk factors to guide strategy

BackgroundAppropriately selected patients clearly benefit from resection of colorectal cancer (CRC) pulmonary metastases (PMs). However, there remains equipoise surrounding optimal chest surveillance strategies following pulmonary metastasectomy. We aimed to identify risk factors that may inform chest surveillance in this population. MethodsPatients who underwent CRC pulmonary metastasectomy were identified from a single institution’s prospectively maintained surgical database. Clinicopathologic and genomic characteristics were collected. Patients were stratified by diagnosis of subsequent PM within 6 months of the index lung resection. Multivariate modeling was used to evaluate risk factors. ResultsA total of 197 patients met the study’s inclusion criteria, of whom 52.3% (n = 103) developed subsequent PM, at a median of 9.51 months following the index metastasectomy. Patients with KRAS alterations (odds ratio [OR], 3.073; 95% confidence interval [CI], 1.363-6.926; P = .007), TP53 alterations (OR, 3.109; 95% CI, 1.318-7.341; P = .010) were found to be at risk of PM diagnosis within 6 months of the index metastasectomy, while those with an APC alteration (OR, .218; 95% CI, 0.080-0.598; P = .003) were protected. Moreover, patients who received systemic therapy within 3 months of the initial PM diagnosis also were more likely to develop early lung recurrence (OR, 2.105; 95% CI, 0.971-4.563; P = .059). ConclusionsPatients with KRAS alterations, TP53 alterations, and no APC alterations developed early recurrence in the lung following pulmonary metastasectomy, as did those who received chemotherapy after their initial PM diagnosis. As such, these groups benefit from early lung imaging after metastasectomy, as chest surveillance protocols should be based on patient-centered clinicopathologic and genomic risk factors.

Development of a Nomogram to Predict Postoperative Peritoneal Metastasis of Colon Cancer.

The aim of this study was to determine the clinicopathological and radiological risk factors for postoperative peritoneal metastasis and develop a prediction model for the early detection of peritoneal metastasis in patients with colon cancer. We included 174 patients with colon cancer. The clinicopathological and radiological data were retrospectively analyzed. A Cox proportional hazards regression model was used to identify risk factors for postoperative peritoneal metastasis. Based on these risk factors, a nomogram was developed. At a median follow-up of 63 months, 43 (24.7%) patients developed peritoneal metastasis. Six independent risk factors (hazards ratio [95% confidence interval]) were identified for postoperative peritoneal metastasis: abdominopelvic fluid (2.12 [1.02-4.40]; P = 0.04), longer maximum tumor length (1.02 [1.00-1.03]; P = 0.02), pN1 (2.50 [1.13-5.56]; P = 0.02), pN2 (4.45 [1.77-11.17]; P = 0.02), nonadenocarcinoma (2.75 [1.18-6.38]; P = 0.02), and preoperative carcinoembryonic antigen levels ≥5 ng/mL (3.08 [1.50-6.30]; P < 0.01). A clinicopathological-radiological model was developed based on these factors. The model showed good discrimination (concordance index, 0.798 [0.723-0.876]; P < 0.001) and was well-calibrated. The developed clinicopathological-radiological nomogram may assist clinicians in identifying patients at high risk of postoperative peritoneal metastasis.

Updates on Triple-Negative Breast Cancer in Type 2 Diabetes Mellitus Patients: From Risk Factors to Diagnosis, Biomarkers and Therapy.

Triple-negative breast cancer (TNBC) is usually the most malignant and aggressive mammary epithelial tumor characterized by the lack of expression for estrogen receptors and progesterone receptors, and the absence of epidermal growth factor receptor (HER)2 amplification. Corresponding to 15-20% of all breast cancers and well-known by its poor clinical outcome, this negative receptor expression deprives TNBC from targeted therapy and makes its management therapeutically challenging. Type 2 diabetes mellitus (T2DM) is the most common ageing metabolic disorder due to insulin deficiency or resistance resulting in hyperglycemia, hyperinsulinemia, and hyperlipidemia. Due to metabolic and hormonal imbalances, there are many interplays between both chronic disorders leading to increased risk of breast cancer, especially TNBC, diagnosed in T2DM patients. The purpose of this review is to provide up-to-date information related to epidemiology and clinicopathological features, risk factors, diagnosis, biomarkers, and current therapy/clinical trials for TNBC patients with T2DM compared to non-diabetic counterparts. Thus, in-depth investigation of the diabetic complications on TNBC onset, development, and progression and the discovery of biomarkers would improve TNBC management through early diagnosis, tailoring therapy for a better outcome of T2DM patients diagnosed with TNBC.

Clinicopathological characteristics, risk factors and prognostic value of intrarenal vascular lesions in IgA nephropathy

BackgroundThe aim was to study clinicopathological characteristics, risk factors and renal outcome in IgA nephropathy (IgAN) patients with vascular lesions. MethodsWe enrolled a Chinese cohort with 458 biopsy-confirmed primary IgAN patients for a retrospective analysis. They were divided into three groups according to vascular lesions: no vascular lesions (n = 239), arterio-/arteriolosclerosis (n = 181) and microangiopathic lesions (n = 38). The clinicopathological features and renal outcome were recorded. In univariate and multivariate models, association between vascular lesions and renal outcome and vascular lesions associated clinical factors were analyzed. ResultsPatients with vascular lesions presented worse clinical characteristics with regard to blood pressure and kidney function, and segmental glomerulosclerosis (S1), tubular atrophy/interstitial fibrosis (T1/2) and lymphocytes and monocytes infiltration were more common. Furthermore, older age, hyperuricemia, proteinuria, global glomerulosclerosis and endocapillary hypercellularity (E1) were more severe in patients with simple arterio-/arteriolosclerosis. By multivariate logistic regression, age, MAP and eGFR were significantly associated with vascular lesions. Vascular lesions, especially arterio-/arteriolosclerosis, were significantly associated with poorer renal survival in IgAN patients, and renal survival was similar whether patients with arterio-/arteriolosclerosis received immunosuppressive therapy. In addition to eGFR, arterio-/arteriolosclerosis, along with arterial intimal fibrosis, was an independent predictor for renal survival in multivariate Cox analyses. ConclusionIgAN patients with vascular lesions, especially with arterio-/arteriolosclerosis, presented more severe clinicopathological features. Renal function, blood pressure and age contributed to distinguishing patients with vascular lesions. Arterio-/arteriolosclerosis lesions were associated with poorer renal survival.

A radiomics model based on preoperative gadoxetic acid-enhanced magnetic resonance imaging for predicting post-hepatectomy liver failure in patients with hepatocellular carcinoma.

Post-hepatectomy liver failure (PHLF) is a fatal complication after liver resection in patients with hepatocellular carcinoma (HCC). It is of clinical importance to estimate the risk of PHLF preoperatively. This study aimed to develop and validate a prediction model based on preoperative gadoxetic acid-enhanced magnetic resonance imaging to estimate the risk of PHLF in patients with HCC. A total of 276 patients were retrospectively included and randomly divided into training and test cohorts (194:82). Clinicopathological variables were assessed to identify significant indicators for PHLF prediction. Radiomics features were extracted from the normal liver parenchyma at the hepatobiliary phase and the reproducible, robust and non-redundant ones were filtered for modeling. Prediction models were developed using clinicopathological variables (Clin-model), radiomics features (Rad-model), and their combination. The PHLF incidence rate was 24% in the whole cohort. The combined model, consisting of albumin-bilirubin (ALBI) score, indocyanine green retention test at 15min (ICG-R15), and Rad-score (derived from 16 radiomics features) outperformed the Clin-model and the Rad-model. It yielded an area under the receiver operating characteristic curve (AUC) of 0.84 (95% confidence interval (CI): 0.77-0.90) in the training cohort and 0.82 (95% CI: 0.72-0.91) in the test cohort. The model demonstrated a good consistency by the Hosmer-Lemeshow test and the calibration curve. The combined model was visualized as a nomogram for estimating individual risk of PHLF. A model combining clinicopathological risk factors and radiomics signature can be applied to identify patients with high risk of PHLF and serve as a decision aid when planning surgery treatment in patients with HCC.

Multimodal recurrence scoring system for prediction of clear cell renal cell carcinoma outcome: a discovery and validation study

Improved markers for predicting recurrence are needed to stratify patients with localised (stage I-III) renal cell carcinoma after surgery for selection of adjuvant therapy. We developed a novel assay integrating three modalities-clinical, genomic, and histopathological-to improve the predictive accuracy for localised renal cell carcinoma recurrence. In this retrospective analysis and validation study, we developed a histopathological whole-slide image (WSI)-based score using deep learning allied to digital scanning of conventional haematoxylin and eosin-stained tumour tissue sections, to predict tumour recurrence in a development dataset of 651 patients with distinctly good or poor disease outcome. The six single nucleotide polymorphism-based score, which was detected in paraffin-embedded tumour tissue samples, and the Leibovich score, which was established using clinicopathological risk factors, were combined with the WSI-based score to construct a multimodal recurrence score in the training dataset of 1125 patients. The multimodal recurrence score was validated in 1625 patients from the independent validation dataset and 418 patients from The Cancer Genome Atlas set. The primary outcome measured was the recurrence-free interval (RFI). The multimodal recurrence score had significantly higher predictive accuracy than the three single-modal scores and clinicopathological risk factors, and it precisely predicted the RFI of patients in the training and two validation datasets (areas under the curve at 5 years: 0·825-0·876 vs 0·608-0·793; p<0·05). The RFI of patients with low stage or grade is usually better than that of patients with high stage or grade; however, the RFI in the multimodal recurrence score-defined high-risk stage I and II group was shorter than in the low-risk stage III group (hazard ratio [HR] 4·57, 95% CI 2·49-8·40; p<0·0001), and the RFI of the high-risk grade 1 and 2 group was shorter than in the low-risk grade 3 and 4 group (HR 4·58, 3·19-6·59; p<0·0001). Our multimodal recurrence score is a practical and reliable predictor that can add value to the current staging system for predicting localised renal cell carcinoma recurrence after surgery, and this combined approach more precisely informs treatment decisions about adjuvant therapy. National Natural Science Foundation of China, and National Key Research and Development Program of China.

Demographic and Clinicopathological Associated Risk Factors in Oral Cancer in Rajasthan: A Retrospective Study

Demographic and Clinicopathological Associated Risk Factors in Oral Cancer in Rajasthan: A Retrospective Study

Genomic signature to guide adjuvant chemotherapy treatment decisions for early breast cancer patients in France: a cost-effectiveness analysis.

Chemotherapy (CT) is commonly used as an adjuvant treatment for women with early breast cancer (BC). However, not all patients benefit from CT, while all are exposed to its short- and long-term toxicity. The Oncotype DX® test assesses cancer-related gene expression to estimate the risk of BC recurrence and predict the benefit of chemotherapy. The aim of this study was to estimate, from the French National Health Insurance (NHI) perspective, the cost-effectiveness of the Oncotype DX® test compared to standard of care (SoC; involving clinicopathological risk assessment only) among women with early, hormone receptor-positive, human epidermal growth factor receptor 2-negative BC considered at high clinicopathological risk of recurrence. Clinical outcomes and costs were estimated over a lifetime horizon based on a two-component model that comprised a short-term decision tree representing the adjuvant treatment choice guided by the therapeutic decision support strategy (Oncotype DX® test or SoC) and a Markov model to capture long-term outcomes. In the base case, the Oncotype DX® test reduced CT use by 55.2% and resulted in 0.337 incremental quality-adjusted life-years gained and cost savings of €3,412 per patient, compared with SoC. Being more effective and less costly than SoC, Oncotype DX® testing was the dominant strategy. Widespread implementation of Oncotype DX® testing would improve patient care, provide equitable access to more personalized medicine, and bring cost savings to the health system.

18F-FDG PET/CT radiomics predicts brain metastasis in I-IIIA resected Non-Small cell lung cancer

ObjectiveTo establish 18F-FDG PET/CT radiomics model for predicting brain metastasis in non-small cell lung cancer (NSCLC) patients. MethodsThis research comprised 203 NSCLC patients who had received surgical therapy at two institutions. To identify independent predictive factors of brain metastasis, metabolic indicators, CT features, and clinical features were investigated. A prediction model was established by incorporating radiomics signature and clinicopathological risk variables. The suggested model’s performance was assessed from the perspective of discrimination, calibration, and clinical application. ResultsThe C-indices of the PET/CT radiomics model in the training, internal validation, and external validation cohorts were 0.911, 0.825 and 0.800, respectively. According to the multivariate analysis, neuron-specific enolase (NSE) and air bronchogram were independent risk factors for brain metastasis (BM). Furthermore, the combined model integrating radiomics and clinicopathological characteristics related to brain metastasis performed better in terms of prediction, with C-indices of 0.927, 0.861, and 0.860 in the training, internal validation, and external validation cohorts, respectively. The decision curve analysis (DCA) suggested that the PET/CT nomogram was clinically beneficial. ConclusionsA predictive algorithm based on PET/CT imaging information and clinicopathological features may accurately predict the probability of brain metastasis in NSCLC patients following surgery. This presented doctors with a unique technique for screening NSCLC patients at high risk of brain metastasis.

Prognostic value of computed tomography radiomics features in patients with gastric neuroendocrine neoplasm.

The present study aimed to investigate the clinical prognostic significance of radiomics signature (R-signature) in patients with gastric neuroendocrine neoplasm (GNEN). A retrospective study of 182 patients with GNEN who underwent dual-phase enhanced computed tomography (CT) scanning was conducted. LASSO-Cox regression analysis was used to screen the features and establish the arterial, venous and the arteriovenous phase combined R-signature, respectively. The association between the optimal R-signature with the best prognostic performance and overall survival (OS) was assessed in the training cohort and verified in the validation cohort. Univariate and multivariate Cox regression analysis were used to identify the significant factors of clinicopathological characteristics for OS. Furthermore, the performance of a combined radiomics-clinical nomogram integrating the R-signature and independent clinicopathological risk factors was evaluated. The arteriovenous phase combined R-signature had the best performance in predicting OS, and its C-index value was better than the independent arterial and venous phase R-signature (0.803 vs 0.784 and 0.803 vs 0.756, P<0.001, respectively). The optimal R-signature was significantly associated with OS in the training cohort and validation cohort. GNEN patients could be successfully divided into high and low prognostic risk groups with radiomics score median. The combined radiomics-clinical nomogram combining this R-signature and independent clinicopathological risk factors (sex, age, treatment methods, T stage, N stage, M stage, tumor boundary, Ki67, CD56) exhibited significant prognostic superiority over clinical nomogram, R-signature alone, and traditional TNM staging system (C-index, 0.882 vs 0.861, 882 vs 0.803, and 0.882 vs 0.870 respectively, P<0.001). All calibration curves showed remarkable consistency between predicted and actual survival, and decision curve analysis verified the usefulness of the combined radiomics-clinical nomogram for clinical practice. The R-signature could be used to stratify patients with GNEN into high and low risk groups. Furthermore, the combined radiomics-clinical nomogram provided better predictive accuracy than other predictive models and might aid clinicians with therapeutic decision-making and patient counseling.

Signature construction and molecular subtype identification based on immune-related genes for better prediction of prognosis in hepatocellular carcinoma

ObjectiveHepatocellular carcinoma (HCC) immunotherapy is a focus of current research. We established a model that can effectively predict the prognosis and efficacy of HCC immunotherapy by analyzing the immune genes of HCC.MethodsThrough the data mining of hepatocellular carcinoma in The Cancer Genome Atlas (TCGA), the immune genes with differences in tumor and normal tissues are screened, and then the univariate regression analysis is carried out to screen the immune genes with differences related to prognosis. The prognosis model of immune related genes is constructed by using the minimum absolute contraction and selection operator (lasso) Cox regression model in the TCGA training set data, The risk score of each sample was calculated, and the survival was compared with the Kaplan Meier curve and the receiver operating characteristic (ROC) curve to evaluate the predictive ability. Data sets from ICGC and TCGA were used to verify the reliability of signatures. The correlation between clinicopathological features, immune infiltration, immune escape and risk score was analyzed.ResultsSeven immune genes were finally determined as the prognostic model of liver cancer. According to these 7 genes, the samples were divided into the high and low risk groups, and the results suggested that the high-risk group had a poorer prognosis, lower risk of immune escape, and better immunotherapy effect. In addition, the expression of TP53 and MSI was positively correlated in the high-risk group. Consensus clustering was performed to identify two main molecular subtypes (named clusters 1 and 2) based on the signature. It was found that compared with cluster 1, better survival outcome was observed in cluster 2.ConclusionSignature construction and molecular subtype identification of immune-related genes could be used to predict the prognosis of HCC, which may provide a specific reference for the development of novel biomarkers for HCC immunotherapy.

Establishment of a prognostic model to predict chemotherapy response and identification of RAC3 as a chemotherapeutic target in bladder cancer.

Cisplatin-based chemotherapy is considered the primary treatment option for patients with advanced bladder cancer (BCa). However, the objective response rate to chemotherapy is often unsatisfactory, leading to a poor 5-year survival rate. Furthermore, current strategies for evaluating chemotherapy response and prognosis are limited and inefficient. In this study, we aimed to address these challenges by establishing a chemotherapy response type gene (CRTG) signature consisting of 9 genes and verified the prognostic value of this signature using TCGA and GEO BCa cohorts. The risk scores based on the CRTG signature were found to be associated with advanced clinicopathological status and demonstrated favorable predictive power for chemotherapy response in the TCGA cohort. Meanwhile, tumors with high risk scores exhibited a tendency toward a "cold tumor" phenotype. These tumors showed a low abundance of T cells, CD8+ T cells and cytotoxic lymphocytes, along with a high abundance of cancer-associated fibroblasts. Moreover, they displayed higher mRNA levels of these immune checkpoints: CD200, CD276, CD44, NRP1, PDCD1LG2 (PD-L2), and TNFSF9. Furthermore, we developed a nomogram that integrated the CRTG signature with clinicopathologic risk factors. This nomogram proved to be a more effective tool for predicting the prognosis of BCa patients. Additionally, we identified Rac family small GTPase 3 (RAC3) as a biomarker in our model. RAC3 was found to be overexpressed in chemoresistant BCa tissues and enhance the chemotherapeutic resistance of BCa cells in vitro and in vivo by regulating the PAK1-ERK1/2 pathway. In conclusion, our study presents a novel CRTG model for predicting chemotherapy response and prognosis in BCa. We also highlight the potential of combining chemotherapy with immunotherapy as a promising strategy for chemoresistant BCa and that RAC3 might be a latent target for therapeutic intervention.

Endometrial carcinoma: association between mutational status, sites of metastasis, recurrence, and correlation with overall survival.

To investigate the association between sites of endometrial carcinoma (EC) recurrence and metastases, mutational status, race, and overall survival (OS). This single-center retrospective study evaluated patients with biopsy-proven EC that underwent genomic molecular testing between January 2015 and July 2021. Association between genomic profile and sites of metastases or recurrence was performed using Pearson's chi-squared or Fisher exact test. Survival curves for ethnicity and race, mutations, sites of metastases or recurrence were estimated using the Kaplan-Meier method. Univariable and multivariable Cox proportional hazard regression models were used. The study included 133 women [median age 64 years (IQR 57-69)]. The most common mutation was TP53 (65/105 patients, 62%). The most common site of metastasis was the peritoneum (35/43, 81%). The most common recurrence was in lymph nodes (34/75, 45%). Mutations of TP53 and PTEN were significantly associated with Black women (p = 0.048, p = 0.004, respectively). In the univariable Cox regression analyses, TP53 mutation and presence of recurrence or metastases to the peritoneum were associated with lower OS (HR 2.1; 95% CI 1.1, 4.3; p = 0.03/ HR 2.9; 95% CI 1.6, 5.4; p = 0.0004; respectively). On multivariable Cox proportional hazards model ER expression (HR 0.4; 95% CI 0.22, 0.91; p = 0.03), peritoneal recurrence or metastases (HR 3.55; 95% CI 1.67, 7.57; p = 0.001), and Black race (HR 2.2; 95% CI 1.1, 4.6; p = 0.03) were significant independent predictors of OS. The integration of EC mutational status and clinicopathological risk assessment demonstrated potential implications on the patterns of metastasis, recurrence, and OS.

Identification of N7-methylguanosine-related lncRNAs for the risk stratification of hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is one of the most common malignancies in the world. The N7-methylguanosine (m7G) modification is related to the biological processes and regulation of various diseases. This study investigated the role and predictive value of m7G-related long non-coding RNAs (lncRNAs) in HCC. HCC patients were clustered by consensus clustering, and a prognostic signature was developed using Least Absolute Shrinkage and Selection Operator (LASSO)-Cox regression analysis. The immune landscape and clinicopathological features of the distinct clusters and subgroups were investigated. A total of 32 m7G-related lncRNAs were confirmed to be prognostic lncRNAs. Two molecular clusters showed significant differences in terms of their clinicopathological features, prognoses, and immune checkpoint gene (ICG) expression levels. Cluster II was associated with upregulated ICG expression and poor overall survival (OS). The Cancer Genome Atlas training cohort was then used to create an m7G-related lncRNA signature for predicting OS. The signature exhibited excellent predictive performance in the training, test, and all cohorts. The high-risk patients had worse clinical outcomes than the low-risk patients. Further study revealed that this signature was an independent prognostic indicator, and a predictive nomogram was developed based on the clinicopathological features and risk score. In addition, we discovered that this model was correlated with ICG expression and tumor immune cell infiltration. Our findings demonstrated that m7G-related lncRNAs are associated with the tumor immune landscape and prognosis and can serve as independent prognostic markers for HCC. These findings provide new insights into the functions of m7G-related lncRNAs in HCC.

Real-world characteristics and treatment pattern of patients with newly diagnosed endometrial cancer in China.

e17613 Background: Endometrial cancer (EC) is the second most common gynecologic cancer in China with an increasing incidence. Currently, there is no comprehensive profile of EC patients in China. This real-world multicenter retrospective study aimed to depict the characteristics and treatment patterns in newly diagnosed EC patients in China. Methods: Medical records of newly diagnosed EC at 8 hospitals between January 1, 2015 and December 31, 2019 were retrospectively reviewed. The demographic and clinical characteristics at diagnosis as well as the treatment received within 6 months after diagnosis were collected. Results: A total of 1397 patients were enrolled in the study. The average age was 53.5 years. The number of patients (n=1256) classified as stage I, II, III, and IV was 885 (70.5%), 141 (11.2%), 207 (16.5%) and 23 (1.8%), respectively. The number of patients (n=1340) presented with low-grade (G1-2) and high-grade (G3) tumors was 924 (74.0%) and 324 (26.0%), respectively. Invasion of lymphovascular space was detected in 237 (17.0%) out of 1397 patients. Among 1389 patients with pathological data, pure endometrioid carcinoma (90.3%) and serous carcinoma (5.1%) accounted for the majority of pathological types. Estrogen receptor (ER) expression was positive in 91.0% (977/1074) of patients and progesterone-receptor (PR) expression was positive in 85.2% (904/1061) of patients. Mismatch repair deficient/microsatellite instability-high (dMMR/MSI-H) was identified in 26.6% (107/402) of patients. Among 1393 patients with treatment information, surgery was performed in 99.6% of patients and 66.9% received adjuvant treatment following surgery according to clinicopathological risk factors. The percentage of patients receiving adjuvant chemotherapy, adjuvant radiotherapy, and adjuvant chemoradiotherapy was 36.7%, 7.3%, and 22.0%, respectively. Among 306 patients who received chemoradiotherapy, 47.1% underwent concurrent chemoradiotherapy, 31.0% underwent sandwich chemoradiotherapy, and 21.9% underwent sequential chemoradiotherapy. Conclusions: To date, this is the first real-world study with largest sample size of EC patients in China. EC is frequently diagnosed at early 50s in Chinese patients, which is at a younger age compared with European and American population. Newly diagnosed EC patients in China are primarily treated with surgery. Chemotherapy and radiotherapy remain the main adjuvant treatment after surgery. MSI-H/dMMR patients accounted for about 1/4 of newly diagnosed patients, for whom immunotherapy may open up better prognosis in the future.

Does RSClin provide additional information over classic clinico-pathologic scores (PREDICT 2.1, Influence, CTS-5)? A TEAM Pathology substudy.

533 Background: Gene-expression profiling tests (e.g. Oncotype, Mammaprint, Prosignia, Breast Cancer Index and EndoPredict) are widely used in the care of patients with early-stage hormone positive breast cancer (node negative or 1-3 lymph nodes). However, these tests are resource intensive, and few studies have compared their value with either free and widely available clinico-pathologic risk calculators (e.g. PREDICT 2.1, INFLUENCE 2.0, and CTS-5) or tools that combine genomic testing with clinico-pathologic data (e.g. RSClin). The TEAM pathology substudy population was used to compare these different predicted model scores with outcomes. Methods: The TEAM pathology study consists of 3284 postmenopausal hormone positive breast cancer patients treated with either exemestane or tamoxifen followed by exemestane. Accrual was from 2001 to 2006. Genes comprising the multi-parametric Oncotype Dx were used to train signatures to create true assay results. Patient data was then used to calculate recurrence scores through various tools. This included clinico-pathologic models and their respective endpoints PREDICT 2.1 (overall survival at 5 years), INFLUENCE 2.0 (distant metastasis at 5 years), CTS-5 (distant recurrence risk at year 5-10), the purely genomic Oncotype Dx trained results (distant recurrence risk at 9 years), as well as the new clinico-pathologic RSClin (distant recurrence risk at 10 years). We compared the level of association between these predictive model scores using Spearman correlation coefficients. The prognostic ability of each model was contrasted for each outcome using Harrell’s C-statistic. Results: Results were available for CTS-5 (3022 patients), INFLUENCE 2.0 (3485 patients), ODx-trained (3825 patients), and RSClin (3029 patients). Correlation coefficients showed low correlation between Influence ( r= 0.25) and CTS-5 ( r= 0.17) with Oncotype-Dx trained results, and high correlation between RSClin ( r = 0.84) and Oncotype-Dx trained results. The concordance index was similar (0.65 to 0.68) for all models with distant metastasis-free survival as the outcome. Analysis is ongoing and further results will be available at the time of presentation. Conclusions: Other clinico-pathologic tools such as Influence 2.0 and CTS-5 have good prognostic ability when compared to Oncotype Dx-trained results and RSClin. [Table: see text]