Predictors Of Disease-specific Survival Research Articles

Development of a novel staging classification for Siewert II adenocarcinoma of the esophagogastric junction after neoadjuvant chemotherapy.

Stage classification for Siewert II adenocarcinoma of the esophagogastric junction (AEG) treated with neoadjuvant chemotherapy (NAC) has not been established. To investigate the optimal stage classification for Siewert II AEG with NAC. A nomogram was established based on Cox regression model that analyzed variables associated with overall survival (OS) and disease-specific survival (DSS). The nomogram performance in terms of discrimination and calibration ability was evaluated using the likelihood-ratio test, Akaike information criterion, Harrell concordance index, time-receiver operating characteristic curve, and decision curve analysis. Data from 725 patients with Siewert type II AEG who underwent neoadjuvant therapy and gastrectomy were obtained from the Surveillance, Epidemiology, and End Results database. Univariate and multivariate analyses revealed that sex, marital status, race, ypT stage, and ypN stage were independent prognostic factors of OS, whereas sex, race, ypT stage, and ypN stage were independent prognostic factors for DSS. These factors were incorporated into the OS and DSS nomograms. Our novel nomogram model performed better in terms of OS and DSS prediction compared to the 8th American Joint Committee of Cancer pathological staging system for esophageal and gastric cancer. Finally, a user-friendly web application was developed for clinical use. The nomogram established specifically for patients with Siewert type II AEG receiving NAC demonstrated good prognostic performance. Validation using external data is warranted before its widespread clinical application.

Abstract 6262: Optimal cut-off age for AJCC staging system in Middle Eastern patients with differentiated thyroid cancer

Abstract Background: Differentiated Thyroid Cancer (DTC) has an indolent nature and generally has favorable prognosis. AJCC TNM classification is the most adopted system to evaluate the biological behavior of DTC. Age cut-off of 55 years has been included in the eighth edition of TNM staging system since it led to better prediction of disease specific survival (DSS) of patients with DTC. However, optimal age cut-off in DTC patients from Middle Eastern ethnicity has not been fully explored. We evaluated the optimal age cut-off for the TNM staging system to predict DSS in Middle Eastern patients with DTC. Methods: We retrospectively analyzed a large cohort of 1721 adult DTC patients who underwent surgery between 1990 to 2018. The optimal age cut-off value was determined using several age cut-offs (between 20 and 85 years) to assess DSS. Harrel’s C-Index, Akaike information criterion (AIC) and Bayesian Information Criterion (BIC) were used to assess the statistical model performance of the TNM staging system (eighth edition), with different age cut-offs for prediction of DSS. The proportion of variation explained (PVE) was also calculated to compare predictability of DSS in patients with DTC. Results: The median age of patients at diagnosis was 39.9 years (IQR 31.0 - 51.7 years) and 75.5% (1299/1721) were female. Median follow up was 9.3 years and 10 years DSS was 97.1%. For DTC overall, an age cut-off of 50 years had the best statistical model performance. On receiver operating characteristic curve analysis, the optimal age cut-off for prediction of DSS was 50.5 years (area under the curve = 0.872, p < 0.0001). Conclusion: In this large cohort of Middle Eastern DTC patients, an age cut-off of 50 years was more appropriate for TNM staging to achieve better predictability for DSS. Therefore, implementation of different age cut-off for DTC in Middle Eastern patients could improve the predictive value for TNM staging system, and allow for better therapeutic and surveillance approach for these patients. Citation Format: Sandeep K. Parvathareddy, Abdul K. Siraj, Padmanaban Annaiyappanaidu, Saeeda O. Ahmed, Nabil Siraj, Zeeshan Qadri, Saud Azam, Khawla S. Al-Kuraya. Optimal cut-off age for AJCC staging system in Middle Eastern patients with differentiated thyroid cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6262.

Radiomics analysis of baseline computed tomography to predict oncological outcomes in patients treated for resectable colorectal cancer liver metastasis.

The purpose of this study was to determine and compare the performance of pre-treatment clinical risk score (CRS), radiomics models based on computed (CT), and their combination for predicting time to recurrence (TTR) and disease-specific survival (DSS) in patients with colorectal cancer liver metastases. We retrospectively analyzed a prospectively maintained registry of 241 patients treated with systemic chemotherapy and surgery for colorectal cancer liver metastases. Radiomics features were extracted from baseline, pre-treatment, contrast-enhanced CT images. Multiple aggregation strategies were investigated for cases with multiple metastases. Radiomics signatures were derived using feature selection methods. Random survival forests (RSF) and neural network survival models (DeepSurv) based on radiomics features, alone or combined with CRS, were developed to predict TTR and DSS. Leveraging survival models predictions, classification models were trained to predict TTR within 18 months and DSS within 3 years. Classification performance was assessed with area under the receiver operating characteristic curve (AUC) on the test set. For TTR prediction, the concordance index (95% confidence interval) was 0.57 (0.57-0.57) for CRS, 0.61 (0.60-0.61) for RSF in combination with CRS, and 0.70 (0.68-0.73) for DeepSurv in combination with CRS. For DSS prediction, the concordance index was 0.59 (0.59-0.59) for CRS, 0.57 (0.56-0.57) for RSF in combination with CRS, and 0.60 (0.58-0.61) for DeepSurv in combination with CRS. For TTR classification, the AUC was 0.33 (0.33-0.33) for CRS, 0.77 (0.75-0.78) for radiomics signature alone, and 0.58 (0.57-0.59) for DeepSurv score alone. For DSS classification, the AUC was 0.61 (0.61-0.61) for CRS, 0.57 (0.56-0.57) for radiomics signature, and 0.75 (0.74-0.76) for DeepSurv score alone. Radiomics-based survival models outperformed CRS for TTR prediction. More accurate, noninvasive, and early prediction of patient outcome may help reduce exposure to ineffective yet toxic chemotherapy or high-risk major hepatectomies.

MRI-based radiomic signatures for pretreatment prognostication in cervical cancer.

Accurate pretherapeutic prognostication is important for tailoring treatment in cervical cancer (CC). To investigate whether pretreatment MRI-based radiomic signatures predict disease-specific survival (DSS) in CC. Retrospective. CC patients (n = 133) allocated into training(T) (nT = 89)/validation(V) (nV = 44) cohorts. T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) at 1.5T or 3.0T. Radiomic features from segmented tumors were extracted from T2WI and DWI (high b-value DWI and apparent diffusion coefficient (ADC) maps). Radiomic signatures for prediction of DSS from T2WI (T2rad ) and T2WI with DWI (T2 + DWIrad ) were constructed by least absolute shrinkage and selection operator (LASSO) Cox regression. Area under time-dependent receiver operating characteristics curves (AUC) were used to evaluate and compare the prognostic performance of the radiomic signatures, MRI-derived maximum tumor size ≤/> 4 cm (MAXsize ), and 2018 International Federation of Gynecology and Obstetrics (FIGO) stage (I-II/III-IV). Survival was analyzed using Cox model estimating hazard ratios (HR) and Kaplan-Meier method with log-rank tests. The radiomic signatures T2rad and T2 + DWIrad yielded AUCT /AUCV of 0.80/0.62 and 0.81/0.75, respectively, for predicting 5-year DSS. Both signatures yielded better or equal prognostic performance to that of MAXsize (AUCT /AUCV : 0.69/0.65) and FIGO (AUCT /AUCV : 0.77/0.64) and were significant predictors of DSS after adjusting for FIGO (HRT /HRV for T2rad : 4.0/2.5 and T2 + DWIrad : 4.8/2.1). Adding T2rad and T2 + DWIrad to FIGO significantly improved DSS prediction compared to FIGO alone in cohort(T) (AUCT 0.86 and 0.88 vs. 0.77), and FIGO with T2 + DWIrad tended to the same in cohort(V) (AUCV 0.75 vs. 0.64, p = 0.07). High radiomic score for T2 + DWIrad was significantly associated with reduced DSS in both cohorts. Radiomic signatures from T2WI and T2WI with DWI may provide added value for pretreatment risk assessment and for guiding tailored treatment strategies in CC.

Development and validation of a model for the prediction of disease-specific survival in patients with oral squamous cell carcinoma: based on random survival forest analysis.

To establish a model for predicting the disease-specific survival (DSS) of patients with oral squamous cell carcinoma (OSCC). Patients diagnosed with OSCC from the Surveillance, Epidemiology, and End Results (SEER) database were enrolled and randomly divided into development (n = 14,495) and internal validation cohort (n = 9625). Additionally, a cohort from a hospital located in Southeastern China was utilized for external validation (n = 582). TNM stage, adjuvant treatment, surgery, tumor sites, age, grade, and gender were used for RSF model construction based on the development cohort. The effectiveness of the model was confirmed through time-dependent ROC curves in different cohorts. The risk score exhibited an almost exponential increase in the hazard ratio of death due to OSCC. In development, internal, and external validation cohorts, the prognosis was significantly worse for patients in groups with higher risk scores (all log-rank P < 0.05). Based on RSF, a high-performance prediction model for OSCC prognosis was created and verified in this study.

Association of social vulnerability on survival, treatment, and presentation in oral cavity cancer.

This study utilized a population database to investigate how social environments are associated with outcomes including stage at diagnosis, multimodal treatment, and disease-specific survival for oral cavity squamous cell carcinomas. Retrospective analysis of adults with oral cavity squamous cell carcinoma between 2007 and 2016 from the Surveillance, Epidemiology, End Results (SEER) registry was performed. The CDC's social vulnerability index (SVI) was used to characterize social vulnerability at the county level. Predictors of disease-specific survival, stage at diagnosis, and use of multimodal therapy were identified using Cox regression and logistic regression. Our analysis included 17 043 patients. On adjusted models, patients in the highest SVI quartile (most social vulnerability) exhibited worse disease-specific survival compared to the lowest quartile (HR 1.24, 95% CI 1.12-1.37, p < 0.001), and were more likely to be diagnosed at later stages (OR 1.24, 95% CI 1.11-1.38, p < 0.001) and less likely to receive multimodal therapy (OR 0.84, 95% CI 0.77-0.99, p = 0.037). High social vulnerability was associated with worse disease-specific survival and disease presentation in oral cavity cancer patients.

Abstract 5375: Learned phenotypic embeddings enable scalable imputation of high-content molecular data elucidating prognostic chromatin signatures

Abstract Emerging high-content data modalities like functional genomics and spatial proteomics have enormous potential to reveal determinants of phenotypic plasticity that underlie variability in clinical outcomes, but to date these modalities are only collected in modestly sized research cohorts (&amp;lt; 200-400 patients), where we lack power to detect subtype-specific or prognostic signatures. To study intertumor heterogeneity on a much larger scale (&amp;gt;10,000 patients), we developed a machine learning framework based on self-supervised embeddings that allows scalable imputation of high-content data on large standard of care datasets. Our framework starts by learning a phenotypic embedding of tumor state based solely on H&amp;E histology images, allowing the embedding to be trained on large patient cohorts regardless of availability of molecular covariates. It then learns to predict genomic or proteomic labels from the lower-dimensional phenotypic embeddings. This model can be used for imputation in much larger cohorts, where only clinical outcome and histology are available. To demonstrate our method, we use the TCGA ATAC-seq data, which is available for 400 patients across 23 cancer types. By learning self-supervised embeddings of histology, our framework was able to impute ATAC-seq for 5,000 peaks in 11,000 patients across 31 cancer types, with high accuracy in held out samples (R2 = 0.61). To our knowledge, this represents the broadest available pan-cancer chromatin landscape. The imputed ATAC-seq reveals a subset of peaks that are significantly associated with overall survival (OS) in multiple cancer types (e.g., Breast (BC) ATAC-only HR 1.75, p: 8.6E-3). Genes proximal to these peaks are strongly enriched for well characterized oncogenes, and also several novel genes with functions in cellular metabolism and chromatin remodeling whose expression is not known to be prognostic in our disease settings. Finally, we developed models to predict OS from H&amp;E slide embeddings and from imputed ATAC-seq, both pan-cancer and in specific tumor types. Both models significantly outperform baseline stage and molecular subtype clinical risk predictors (e.g., BC baseline HR: 2.44, p: 3E-6 vs. embedding/imputed ATAC HR 3.78, 2E-9, p for improvement 2E-9) and, interestingly, we find that adding an ATAC-seq based risk score to an embedding-based risk score significantly improves disease-specific survival prediction (HCC embedding-only HR: 2.13, p: 8E-5 vs. HCC embedding/imputed ATAC HR: 2.65, p: 1E-6). This suggests that histopathology images are a rich source of prognostic information beyond that which is captured by traditional pathologist grading. Overall, our work highlights the ability to use self-supervised embeddings of histopathology to impute biological covariates on large, standard-of-care cohorts, empowering novel insights into disease mechanisms and patient outcome. Citation Format: Christopher S. Probert, Zachary R. McCaw, Navami Jain, Daphne Koller. Learned phenotypic embeddings enable scalable imputation of high-content molecular data elucidating prognostic chromatin signatures. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5375.

Epidemiological trends and novel prognostic evaluation approaches of patients with stage II-IV colorectal neuroendocrine neoplasms: A population-based study with external validation.

This study aimed to clarify the incidence trend of all-stage colorectal neuroendocrine neoplasms (CRNENs), overall survival (OS), and disease-specific survival (DSS) of patients with stage II-IV CRNENs, and to establish relevant nomograms for risk stratification. Among all patients diagnosed with CRNENs in the Surveillance, Epidemiology, and End Results (SEER) database from 1975 to 2019, temporal trends in incidence were assessed. Clinical data of 668 patients with stage II-IV CRNENs from 2010 to 2016 were extracted for survival analysis. Patients were randomly divided into a training cohort and a validation cohort at a ratio of 7:3. Univariate and multivariate cox regression analyses were utilized to identify independent prognostic factors affecting OS outcomes. Competing risk analysis was applied to investigate risk factors related to the DSS of CRNENs. Two nomograms specifically for OS and DSS were developed for patients with stage II-IV CRNENs, their prognostic capabilities were evaluated using calibration curves, receiver operating characteristic (ROC) curves, the time-dependent area under the curve (AUC), and decision-curve analysis (DCA). Our hospital's independent cohort of 62 patients with CRNENs was used as the external validation cohort. In the period of 1975-2019, the incidence of CRNENs increased steadily with an annual percentage change (APC) of 4.50 (95% confidence interval [CI]: 3.90-5.11, P < 0.05). In total, 668 patients with stage II-IV CRNENs were included in the survival analysis from 2010 and 2016. Independent adverse prognostic factors for both OS and DSS of CRNENs prior treatment included grade III/IV (HR for OS: 4.66, 95%CI: 2.92-7.42; HR for DSS: 4.79, 95%CI: 4.27-5.31), higher TNM stage ([stage III vs stage II] HR for OS: 2.22, 95%CI: 1.25-3.94; HR for DSS: 2.69, 95%CI: 1.96-3.42. [stage IV vs stage II] HR for OS: 3.99, 95%CI: 2.03-7.83; HR for DSS: 4.96, 95%CI: 4.14-5.78), liver metastasis (HR for OS: 1.61, 95%CI: 1.03-2.51; HR for DSS: 1.86, 95%CI: 1.39-2.32), and brain metastasis (HR for OS: 4.57, 95%CI: 1.66-12.58; HR for DSS: 5.01, 95%CI: 4.15-5.87). Advanced age was also identified as a risk factor for OS (HR: 2.03, 95%CI: 1.5-2.76) but not DSS. In terms of treatment, surgery can significantly prolong OS (HR: 0.62, 95%CI: 0.44-0.86) and DSS (HR: 0.67, 95%CI: 0.29-1.05), but chemotherapy and radiation failed to show significance. The respective nomograms for OS and DSS for stage II-IV CRNENs demonstrated high accuracy and robust prediction value in predicting 1-year, 3-year, and 5-year OS and DSS outcomes in training, internal validation, and external validation cohorts. Besides, two online tools regarding OS and DSS prediction were established, facilitating nomogram score calculation, risk group determination, as well as survival prediction for each individual patient. Over the past 40 years, the incidence of CRNENs presented increased steadily, along with improved survival outcomes. Grade III-IV, higher TNM stage, liver metastasis, brain metastasis, and without receiving surgery were found to be associated with worse OS and DSS. Advanced age was a risk factor for OS but not DSS. Nomograms for patients with stage II-IV stage CRNENs are capable of predicting the 1-, 3-, and 5-year OS and DSS rates with high accuracy, and realize risk stratification.

Evaluating Disease-specific Survival Prediction of Risk Stratification and TNM Systems in Differentiated Thyroid Cancer.

Many countries have national guidelines for the management of differentiated thyroid cancer (DTC), including a risk stratification system to predict recurrence of disease. Studies whether these guidelines could also have relevance, beyond their original design, in predicting survival are lacking. Additionally, no studies evaluated these international guidelines in the same population, nor compared them with the TNM system. Therefore, we investigated the prognostic value of 6 stratification systems used by 10 international guidelines, and the TNM system with respect to predicting disease-specific survival (DSS). We retrospectively studied adult patients with DTC from a Dutch university hospital. Patients were classified using the risk classification described in the British, Dutch, French, Italian, Polish, Spanish, European Society of Medical Oncology, European Thyroid Association, the 2009 and 2015 American Thyroid Association (ATA) guidelines, and the latest TNM system. DSS was analyzed using the Kaplan-Meier method, and the statistical model performance using the C-index, Akaike information criterion, Bayesian information criterion, and proportion of variance explained. We included 857 patients with DTC (79% papillary thyroid cancer, 21% follicular thyroid cancer). Median follow-up was 9 years, and 67 (7.8%) died because of DTC. The Dutch guideline had the worst statistical model performance, whereas the 2009 ATA/2014 British guideline had the best. However, the (adapted) TNM system outperformed all stratification systems. In a European population of patients with DTC, of 10 international guidelines using 6 risk of recurrence stratification systems and 1 mortality-based stratification system, our optimized age-adjusted TNM system (8th edition) outperformed all other systems.

Prognostic Involvement of Lymph Node Density in Oral Squamous Cell Carcinoma. A New Predictive Model

Lymph node density (LND) has been reported to be a significant predictor of survival in oral squamous cell carcinoma (OSCC). The aim of this study was to analyze LND as a prognostic factor in OSCC and create a predictive model that determines the probability of death in these patients. A retrospective cohort study was carried out with a cohort of patients who underwent cervical dissection and primary resection of OSCC between 1980 and 2020. The primary predictor variable in this study was LND, which is defined as the number of positive lymph nodes divided by the total number of lymph nodes removed. The cutoff values for prediction of disease-specific survival (DSS) were calculated by receiver operating characteristic curve analysis, which determined the best cutoff value was 0.07. Patients were divided into binary subgroups (low and high risk) using the best cutoff value of LND. The outcome variable was DSS, defined as the duration from the date of diagnosis to death due to OSCC and not due to other causes or secondary tumors. Other variables were type of neck dissection, postsurgical treatment, surgical margin, pathological T category stage, pathological N category (pN) stage, extranodal extension, perineural invasion, bone invasion, and presence of recurrence. A predictive model (score) was generated by selecting variables using a log-rank test and by using the Cox proportional-hazards regression (multivariate analysis). The sample consisted of 368 patients, 252 (68.5%) male and 116 (31.5%) female patients, with a mean age of 60.3years. According to the LND cutoff value, there were 289 patients with a low LND (≤0.07) and 79 with a high LND (>0.07). The univariate analysis showed LND as a significant predictor of DSS at 5years (67.1% in LND ≤ 0.07 vs 32.9% in LND > 0.07; P<.001). The Cox multivariate analysis identified LND (hazard ratio [HR]=27.2; 95% confidence interval [CI], 3.18-231; P=.002), recurrence (HR=4.45; 95% CI, 2.3-8.4; P<.001), and type of treatment (HR=0.52; 95% CI, 0.34-0.81; P<.001) as independent predictive factors for DSS. In the predictive model, the presence of recurrence was the most important factor with 8 points, whereas LND >0.07 contributed only 1 point; however, the 2 categories resulting from this limit were statistically significant. Our study demonstrates that LND is an additional prognostic factor in patients with a pN+disease. In addition, our predictive model could be useful in the therapeutic algorithm of OSCC patients, as it can predict the probability of death in these patients.

Neoadjuvant chemotherapy in patients with stage IVB uterine serous carcinoma: a Turkish multicentric study

The aim of this study was to evaluate the prognostic factors for and determine the effect of neoadjuvant chemotherapy (NACT) on oncologic outcome in stage IVB pure serous endometrial carcinoma patients who received taxane and platinum. Forty-two patients with 2009 International Federation of Gynecology and Obstetrics (FIGO) stage IVB uterine serous carcinoma were enrolled from six gynecologic oncology centers and a study group was created. The study group had a 2-year disease-free survival (DFS) of 32% and 2-year disease-specific survival (DSS) of 73%. On univariate analysis; lymphadenectomy (not performed vs. performed), paraaortic lymph node metastasis (positive vs. negative) and number of metastatic lymph node count (≤5 vs. >5) were found to have statistical significance for DFS (p < 0.001, p = 0.026 and p = 0.044, respectively). Adnexal metastasis (positive vs. negative) and type of cytoreductive surgery (maximal vs. optimal and suboptimal) had statistical significance for DSS (p = 0.041 and p = 0.015, respectively). Receiving NACT did not affect DFS and DSS in stage IVB uterine serous carcinoma patients. As our sample size was small, precise conclusions could not be made for suggesting the use of NACT in advanced stage uterine serous carcinoma. For more accurate results, more randomized controlled studies are needed in this patient group. IMPACT STATEMENT What is already known on this subject? Endometrial carcinoma is the most common type of gynecologic tract malignancies and usually it is diagnosed at early stages. Although the favorable prognosis, uterine serous carcinoma (USC), one of the rarest subtypes, has a poorer prognosis when compared to other histological subtypes. USC has a propensity to spread beyond pelvis. Due to this aggressive behavior, surgical intervention could not be feasible in advanced stage disease. What do the results of this study add? Our study evaluated the prognostic factors that affect survival in advanced stage USC patients. Also we investigated that neoadjuvant chemotherapy (NACT) could improve oncologic outcomes. Performing lymphadenectomy, presence of paraaortic lymph node and adnexal metastasis, number of metastatic lymph nodes and type of cytoreductive surgery improved survival in advanced stage USC patients. However, NACT did not have a statistical significance as a predictor for disease-specific survival (DSS) and disease-free survival (DFS). What are the implications of these findings for clinical practice and/or further research? Maximal surgical effort should be performed in advanced stage USC according to our results. On the other hand, NACT had no impact on DSS and DFS rates. For this reason, we could not be able to suggest the routine use of NACT in advanced stage USC. But more randomized controlled trials are warranted for confirmation of our results.

Characterization of sialylation-related long noncoding RNAs to develop a novel signature for predicting prognosis, immune landscape, and chemotherapy response in colorectal cancer.

Aberrant sialylation plays a key biological role in tumorigenesis and metastasis, including tumor cell survival and invasion, immune evasion, angiogenesis, and resistance to therapy. It has been proposed as a possible cancer biomarker and a potential therapeutic target of tumors. Nevertheless, the prognostic significance and biological features of sialylation-related long noncoding RNAs (lncRNAs) in colorectal cancer (CRC) remain unclear. This study aimed to develop a novel sialylation-related lncRNA signature to accurately evaluate the prognosis of patients with CRC and explore the potential molecular mechanisms of the sialylation-related lncRNAs. Here, we identified sialylation-related lncRNAs using the Pearson correlation analysis on The Cancer Genome Atlas (TCGA) dataset. Univariate and stepwise multivariable Cox analysis were used to establish a signature based on seven sialylation-related lncRNAs in the TCGA dataset, and the risk model was validated in the Gene Expression Omnibus dataset. Kaplan-Meier curve analysis revealed that CRC patients in the low-risk subgroup had a better survival outcome than those in the high-risk subgroup in the training set, testing set, and overall set. Multivariate analysis demonstrated that the sialylation-related lncRNA signature was an independent prognostic factor for overall survival, progression-free survival, and disease-specific survival prediction. The sialylation lncRNA signature-based nomogram exhibited a robust prognostic performance. Furthermore, enrichment analysis showed that cancer hallmarks and oncogenic signaling were enriched in the high-risk group, while inflammatory responses and immune-related pathways were enriched in the low-risk group. The comprehensive analysis suggested that low-risk patients had higher activity of immune response pathways, greater immune cell infiltration, and higher expression of immune stimulators. In addition, we determined the sialylation level in normal colonic cells and CRC cell lines by flow cytometry combined with immunofluorescence, and verified the expression levels of seven lncRNAs using real-time quantitative polymerase chain reaction. Finally, combined drug sensitivity analysis using the Genomics of Drug Sensitivity in Cancer, Cancer Therapeutics Response Portal, and Profiling Relative Inhibition Simultaneously in Mixtures indicated that the sialylation-related lncRNA signature could serve as a potential predictor for chemosensitivity. Collectively, this is the first sialylation lncRNA-based signature for predicting the prognosis, immune landscape, and chemotherapeutic response in CRC, and may provide vital guidance to facilitate risk stratification and optimize individualized therapy for CRC patients.

A new nomogram to predict oncological outcome in laryngeal and hypopharyngeal carcinoma patients after laryngopharyngectomy

BackgroundTo create nomograms for better prediction of the oncological outcome in advanced laryngeal (LxCAs) or hypopharyngeal (HpxCAs) cancer after laryngopharyngectomy.Materials239 patients who underwent total laryngectomy or laryngopharyngectomy due to LxCA (52.7%) or HpxCA (47.3%) were included in this study. Based on clinical risk factors (tumor site, lymph node involvement, salvage setting), we created nomograms for prediction of disease-specific survival (DSS) and disease-free survival (DFS).ResultsHpxCAs showed a higher rate of lymph node involvement (p < 0.001), a 2.47-fold higher risk of a 2nd head and neck cancer (p = 0.009) and significantly worse loco-regional control rates (p = 0.003) compared to LxCAs. Positive neck nodes and salvage procedures were associated with significantly worse outcome. Nomograms demonstrated that hypopharyngeal tumors with positive neck nodes in salvage situations had the worst oncological outcome with a 5-year DSS of 15–20%.ConclusionsThe oncological outcome is worse in hypopharyngeal carcinomas and could be easily quantified by our nomograms that are based on tumor site, lymph node involvement and salvage situation.

Malignant Myoepithelioma of the Head and Neck: Demographics, Clinicopathological Characteristics, Treatment, and Prognosis.

Malignant myoepithelioma of the head and neck (HNMM) is a rare malignancy, and its characteristics and survival rates have not been well-defined. This study aimed to define the epidemiology of HNMM and identify the prognostic factors associated with the disease. Data on all patients diagnosed with HNMM between 1991 and 2016 were gathered from the Surveillance Epidemiology and End Results (SEER) database. The demographics, clinicopathological characteristics, treatment, and prognoses of the patients were described. Cox regression analysis was used to identify the prognostic factors, and the prognostic nomograms for overall survival (OS) and disease-specific survival (DSS) were constructed. A total of 333 cases of HNMM were identified. The average age at diagnosis was 60.6 years, and 50.1% of the patients were men. After diagnosis, 46.2% of patients underwent surgery alone, 43.5% of patients underwent surgery and radiotherapy, and 3.6% of patients received only radiotherapy. Survival analysis showed that the 5-year OS and DSS for all HNMM patients were 69.7 and 82.1%, respectively. In the multivariate analysis model, the undifferentiated pathological grade (P <0.05) and M1 in the M category (P <0.01) were independent prognostic factors for poor OS and DSS, whereas the use of surgical resection was an independent favorable prognostic factor for both OS and DSS (P <0.05). The prognostic nomograms for OS and DSS prediction were constructed; the C-index values for OS and DSS prediction were 0.78 (95% CI 0.70–0.86) and 0.79 (95% CI 0.67–0.90), respectively. In conclusion, this SEER data-based study demonstrated that HNMM patients often had a favorable prognosis, and distant metastasis, pathological grade, and the use of surgery contributed to their survival. Furthermore, we developed a prognostic nomogram to predict OS and DSS for HNMM patients to aid physicians in the clinical management of this rare disease.

Intratumor Microbiome in Neuroendocrine Neoplasms: A New Partner of Tumor Microenvironment? A Pilot Study.

Neuroendocrine neoplasms (NENs) are rare neoplasms with heterogeneous clinical behavior. Alteration in human microbiota was reported in association with carcinogenesis in different solid tumors. However, few studies addressed the role of microbiota in NEN. We here aimed at evaluating the presence of bacterial infiltration in neuroendocrine tumoral tissue. To assess the presence of bacteria, 20 specimens from pancreatic NEN (pan-NEN) and 20 from intestinal NEN (I-NEN) were evaluated through Fluorescent In situ Hybridization and confocal microscopy. Demographic data, pre-operative investigations, operative findings, pathological diagnosis, follow-up, and survival data were evaluated. Among I-NEN, bacteria were detected in 15/20 (75%) specimens, with high variability in microbial distribution. In eight patients, a high infiltration of microorganisms was observed. Among pan-NEN, 18/20 (90%) showed microorganisms’ infiltration, with a homogeneous microbial distribution. Bacterial localization in pan-NEN was observed in the proximity of blood vessels. A higher bacterial infiltration in the tumoral specimen as compared with non-tumoral tissue was reported in 10/20 pan-NEN (50%). No significant differences were observed in mean bacterial count according to age, sex, ki67%, site, tumor stage. Mean bacterial count did not result to be a predictor of disease-specific survival. This preliminary study demonstrates the presence of a significant microbiota in the NEN microenvironment. Further research is needed to investigate the potential etiological or clinical role of microbiota in NEN.

Elevated Expression of Gamma-Glutamyl Hydrolase Is Associated With Poor Prognosis and Altered Immune Signature in Uterine Corpus Endometrial Carcinoma.

Uterine corpus endometrial carcinoma (UCEC) is a common malignant tumor of the female reproductive system with poor prognosis in advanced, recurrent, and metastatic cases. Identification of reliable molecular markers will help in the development of clinical strategies for early detection, diagnosis, and intervention. Gamma-glutamyl hydrolase (GGH) is a key enzyme in folate metabolism pathway. High expression of GGH is associated with severe clinicopathological features and poor prognosis of several cancers. High GGH expression is also related to cell resistance to antifolate drugs such as methotrexate. In this study we focused on the prognostic value of immunohistochemical GGH expression level in UCEC tissue and RNA-seq data from The Cancer Genome Atlas to establish associations with clinical features and outcomes. Further, we conducted comprehensive bioinformatics analyses to identify and functionally annotate differentially expressed genes (DEGs) associated with UCEC upregulation and assessed the effects of upregulation on immune infiltration. Both GGH mRNA and protein expression levels were elevated in tumor tissues, and higher expression was significantly associated with advanced clinicopathological features and poor prognosis by univariate analysis. Further multivariate analysis identified elevated GGH expression as an independent risk factor for poor outcome. Nomograms including GGH expression yielded a c-index for disease-specific survival prediction of 0.884 (95% confidence interval: 0.861–0.907). A total of 520 DEGs (111 upregulated and 409 downregulated) were identified between high and low GGH expression groups. Analysis using Gene ontology, Kyoto Encyclopedia of Genes and Genomes pathway, Gene set enrichment analysis, and protein‒protein interaction indicated significant associations of altered GGH expression with cell proliferation, immune response, and the occurrence and development of UCEC tumors. Finally, GGH expression level was associated with high Th2 cell and low natural killer CD56bright cell infiltration. Collectively, these findings indicate that GGH drives UCEC progression and could be a useful biomarker for survival prediction as well as a therapeutic target.

Utility of RGNEF in the Prediction of Clinical Prognosis in Patients with Rectal Cancer Receiving Preoperative Concurrent Chemoradiotherapy.

Rectal cancer is a heterogeneous malignancy with different clinical responses to preoperative concurrent chemoradiotherapy (CCRT). To discover the significant genes associated with CCRT response, we performed data mining of a transcriptomic dataset (GSE35452), including 46 rectal cancer patients who received preoperative CCRT and underwent standardized curative resection. We identified ARHGEF28 as the most significantly upregulated gene correlated with resistance to CCRT among the genes related to Rho guanyl-nucleotide exchange factor activity (GO:0005085). We enrolled 172 patients with rectal cancer receiving CCRT with radical surgery. The expression of ARHGEF28 encoded protein, Rho guanine nucleotide exchange factor (RGNEF), was assessed using immunohistochemistry. The results showed that upregulated RGNEF immunoexpression was considerably correlated with poor response to CCRT (p = 0.018), pre-CCRT positive nodal status (p = 0.004), and vascular invasion (p < 0.001). Furthermore, high RGNEF expression was significantly associated with worse local recurrence-free survival (p < 0.0001), metastasis-free survival (MeFS) (p = 0.0029), and disease-specific survival (DSS) (p < 0.0001). The multivariate analysis demonstrated that RGNEF immunoexpression status was an independent predictor of DSS (p < 0.001) and MeFS (p < 0.001). Using Gene Ontology enrichment analysis, we discovered that ARHGEF28 overexpression might be linked to Wnt/β-catenin signaling in rectal cancer progression. In conclusion, high RGNEF expression was related to unfavorable pathological characteristics and independently predicted worse clinical prognosis in patients with rectal cancer undergoing CCRT, suggesting its role in risk stratification and clinical decision making.

Clinical Outcomes of Taiwanese Patients with Resected Oral Cavity Squamous Cell Carcinoma Who Underwent Reconstruction with Free Versus Local Flaps.

We sought to compare the clinical outcomes of Taiwanese patients with resected oral cavity squamous cell carcinoma (OCSCC) who underwent reconstruction with free versus local flaps. From 2011 to 2017, we examined 8646 patients with first primary OCSCC who received surgery either with or without adjuvant therapy. Of these patients, 7297 and 1349 received free and local flap reconstruction, respectively. Two propensity score-matched groups of patients who underwent free versus local flap (n=1268 each) reconstructions were examined. Margin status was not included as a propensity score-matched variable. Compared with local flaps, patients who received free flaps had a higher prevalence of the following variables: male sex, age < 65years, pT3-4, pN1-3, p-Stage III-IV, depth ≥ 10mm, margin > 4mm, extranodal extension (ENE), and adjuvant therapy (all p<0.0001). Multivariable analysis identified the reconstruction method (local vs. free flaps, only overall survival [OS]), age ≥ 65years, pT3-4, pN1-3, p-Stage III-IV, depth ≥ 10mm (only OS), margins ≤ 4mm, and ENE as independent adverse prognosticators for disease-specific survival (DSS) and OS. The results of propensity score-matched analyses revealed that, compared with free flaps, patients who underwent local flap reconstruction showed less favorable 5-year DSS (hazard ratio [HR] 1.26, 82%/77%; p=0.0100) and OS (HR 1.21, 73%/68%; p=0.0079). After adjusting for covariates using multivariate models, and also by propensity score modeling, OCSCC patients who underwent free flap reconstruction showed a higher frequency of clear margins and a significant survival advantage compared with those who received local flaps.

Body composition is associated with operative and oncologic outcomes in the management of retroperitoneal and trunk soft tissue sarcoma

BackgroundSarcopenia, myosteatosis and obesity in cancer may confer negative clinical outcomes, but their prevalence and impact among patients with retroperitoneal and trunk soft tissue sarcoma have not been systematically studied. The aim of this study was to determine body composition among patients with retroperitoneal and trunk sarcoma, and assess impact on operative and oncologic outcomes. MethodsConsecutive patients undergoing treatment with curative intent from 2009 to 2019 were studied. Subcutaneous fat area and visceral fat areas, intramuscular adipose, lean body mass and fat mass were determined at diagnosis by CT at L3. Univariable and multivariable linear, logistic and Cox proportional hazards regression were performed. Results95 patients (43.2% retroperitoneal, 48.4% trunk, 46.3% multivisceral resection) were studied. Visceral obesity was evident in 47.4%. Postoperative morbidity occurred in 25.9%, with preoperative radiotherapy (OR10.53 [95% CI 1.08–102.39], P = 0.042) and fat mass (OR1.41 [1.12–1.79], P = 0.004) independently predictive on multivariable analysis, while intramuscular adipose independently predicted inpatient LOS (P < 0.001), wound infection (P = 0.024, OR1.20 [1.02–1.40]) and major postoperative morbidity (P = 0.027, OR1.15 [1.02–1.31]). Increasing fat mass, subcutaneous fat area and intramuscular adipose were associated with greater tumor size (all P < 0.01), while intramuscular adipose predicted disease progression during neoadjuvant therapy (P = 0.024), and independently predicted disease specific survival (DSS) (P = 0.005, HR1.11 [1.03–1.20]) and overall survival (OS) on multivariable analysis (P < 0.001, HR1.19 [1.08–1.31]). ConclusionVisceral obesity is common in retroperitoneal and trunk sarcoma, and measures of adiposity are associated with adverse operative, but not oncologic outcomes. Myosteatosis is independently associated with postoperative morbidity and adverse oncologic outcomes. Body composition may represent a marker of risk among patients with retroperitoneal and trunk sarcoma.

The log odds of positive lymph nodes predicts survival of advanced stage endometrial cancer: a retrospective analysis of 3230 patients in the SEER database

The log odds of positive lymph nodes predicts survival of advanced stage endometrial cancer: a retrospective analysis of 3230 patients in the SEER database