Predict Disease Recurrence Research Articles

Clinicopathological utility of miR-203a-3p in diagnosing colorectal cancer.

Biomarkers, such as microRNAs, are helpful in diagnosing colorectal cancer, regulating disease progression, predicting disease recurrence, and determining therapy success. This research aimed to look at the clinicopathological characteristics of serum miRNA-203a-3p expression in colorectal cancer patients. This case-control study was conducted on 43 patients with colorectal cancer and 43 healthy individuals. After RNA extraction, cDNA was synthesized. The expression of miR-203a-3p was measured using RT-qPCR. Demographic and histochemical data were extracted from patient documents. SPSS and GraphPad Prism software were used to analyze the data. The expression of miR-203a-3p in CRC patients was 2.39 times lower than in the control group (p < 0.0001). The miR-203a-3p expression was significantly lower in the CRC tumor stages, tumor grades, and lymph node metastasis compared to the control group (p < 0.0001 each). The ROC curves showed that the AUC was 0.73, and the best cut-point based on the Youden index was 0.3954, 0.7105, 0.5087, and 0.4868 for detecting colorectal cancer (p = 0.0002), tumor grade (p = 0.006), tumor stage (p = 0.001), and lymph node metastasis (p = 0.0011) compared to the control group, respectively. The binary logistic regression analysis was performed on the correlation between BMI, smoking, and cancer inheritance with miR-203a-3p in cancer and control groups. This study's findings revealed that serum miR-203a-3p is a fair non-invasive molecular biomarker for diagnosing and progressing tumor grade, tumor stage, and lymph node metastasis in colorectal cancer. However, further research with higher statistical numbers is needed to strengthen the correlation and be used for diagnostic applications.

Targeting cyclin-dependent kinase 7-association between CDK7 and pMED1 expression in prostate cancer tissue.

Cyclin-dependent kinase (CDK) 7-mediated phosphorylation of Mediator-complex subunit 1 (MED1) enhances androgen receptor (AR) activity in prostate cancer (PCa). Hyperactive AR-signalling plays a key role for the development of castration resistance. Several CDK7 inhibitors are currently under investigation in Phase I/II trials addressing solid tumours, including PCa. Aim of this study was to characterize the CDK7/phospho-(p)MED1 axis in human tissue. Immunohistochemistry was performed on 595 PCa samples including 394 primary tumour foci obtained by radical prostatectomy (RP), 64 advanced or recurrent tumours obtained by palliative transurethral resection of the prostate (pTUR), 65 lymph node metastases (LNM), 35 distant metastases (DM) and 36 benign samples. CDK7 is expressed in 79.3% of PCa tissues and protein levels are significantly higher in LNM, pTUR and DM and lower in benign tissues compared to primary tumours. CDK7 and pMED1 expression show strong positive correlation. High expression of CDK7 associated with shorter 5-year biochemical recurrence-free-survival (63.0% vs. 85.0%) and reduced survival persists when adjusted for T-Stage, nodal status, resection boundaries, grade group and pre-operative prostate-specific antigen in multivariate Cox-regression (hazard ratio 4.30; 95% CI, 1.43 to 12,40, P = 0.007). High CDK7 and pMED1 levels correlate with nuclear AR expression. CDK7 positive tumours harbour higher Ki67 expression indices and show more frequently positive ERG (ETS-related gene)-status. In conclusion, CDK7 is frequently expressed in human PCa and predicts disease recurrence after RP. Therapeutical inhibition of CDK7 might be a promising approach in treatment of advanced PCa.

External validation of the VENUSS prognostic model to predict recurrence after surgery in non-metastatic papillary renal cell carcinoma: A multi-institutional analysis.

Recently, VENUSS (VEnous extension, NUclear Grade, Size, Stage), as a prognostic model, was defined to predict disease recurrence (DR) after curative surgery of non-metastatic papillary renal cell carcinoma (papRCC). This study aimed to validate the VENUSS prognostic model in a large multi-institutional European cohort of patients with histopathologically proven papRCC after curative surgery for non-metastatic disease. Overall, 980 patients undergoing partial or radical nephrectomy for sporadic, unilateral and non-metastatic papRCC between 1987 and 2020 were included from 7 European tertiary institutions. The primary outcome was the prediction of DR by VENUSS score and VENUSS risk groups. Chi-square, Kruskal-Wallis, Cox-regression and Kaplan-Meier survival analyses were used in statistical methods. The Concordance (C) Index was calculated to assess model's discriminatory power. The median age was 64 (IQR:55-70) years and 82.6 % (n = 809) of patients were male. Median VENUSS score was 2 (IQR: 0-4), and 62.9 % (n = 617), 23.9 % (n = 234) and 13.2 % (n = 129) of patients was classified into low, intermediate and high risk according to the VENUSS model, respectively. At a median follow-up of 48 (IQR:23-88) months, the disease recurred in 6.6%, 18.8% and 63.8%, and the 5-year recurrence-free survival was 93.8%, 80.7% and 26.7% in low, intermediate and high-risk groups, respectively. (P < 0.001) Each increase in VENUSS score had 1.52-fold (95%CI:1.45-1.60, P < 0.001) DR risk. Compared with the VENUSS low risk, the intermediate risk had a 2.91-fold increased DR risk (95%CI:1.90-4.46, P < 0.001) and 17.9-fold (95%CI:12.25-26.25, P < 0.001) in high risk, while it was 6.07-fold greater in high risk vs. intermediate risk (95%CI:4.17-8.83, P < 0.001). The discrimination was 81.2% (95%CI:77.5%-84.8%) for the VENUSS score, and 78.6% (95%CI:74.8%-82.4%) for VENUSS risk groups, respectively. Both the VENUSS score and groups were well calibrated. This contemporary multi-institutional European large dataset validated the use of VENUSS score and VENUSS risk groups on the prediction of DR after curative surgery in patients with non-metastatic papRCC. The VENUSS prognostic model can provide valuable information for patient counselling, follow-up and patient selection for adjuvant trials.

The R.E.N.A.L score’s relevance in determining perioperative and oncological outcomes: a Middle-Eastern tertiary care center experience

ABSTRACT Objective The aim of this study is to evaluate the significance of the R.E.N.A.L nephrometry scoring system in predicting perioperative and oncological outcomes and determining the surgical approach of choice for kidney tumors. Patients and Methods: Our study retrospectively reviewed outcomes from the year 2002 to 2017. Mann-Whitney U test was used to compare continuous variables and chi-square test was used to compare categorical variables. Kaplan-Meier estimates and multivariable cox proportional hazard regression were performed to determine an association between the different R.E.N.A.L categories and disease recurrence or mortality. Results A total of 325 patients underwent kidney surgery The most common R.E.N.A.L score category in our cohort study was intermediate (41.2%), followed by low, (33.2%) and high (25.5%). Patients with a high R.E.N.A.L score had worse perioperative outcomes compared to those with a low R.E.N.A.L score. High R.E.N.A.L score patients were 3 times more likely to receive blood transfusions compared to those with a low R.E.N.A.L score (19.4% vs 6.3%, p = 0.018), and a statistically significant longer hospital length of stay was also observed between the two groups (median 4.5 vs 4 days, p = 0.0419). In addition, the only predictor of disease recurrence or mortality was a high R.E.N.A.L score (Hazard Ratio (HR) 3.65, 95% Confidence Interval (CI) 1.05–12.7, p = 0.041). Conclusion Our study sheds light on the use of R.E.N.A.L nephrometry score in predicting perioperative, postoperative, and oncological outcomes. Such findings may play a role in optimizing surgical approaches and pre-operative patient counseling.

Surface-Enhanced Raman Spectroscopy of Pretreated Plasma Samples Predicts Disease Recurrence in Muscle-Invasive Bladder Cancer Patients Undergoing Neoadjuvant Chemotherapy and Radical Cystectomy.

ObjectiveTo explore the value of surface-enhanced Raman spectroscopy analysis of pretreated plasma samples in prediction of bladder cancer (BCa) recurrence after neoadjuvant chemotherapy (NAC) and radical cystectomy (RC).Patients and MethodsSERS was used to analyze plasma samples collected before biopsy and treatment in BCa patients undergoing NAC and RC. The value of clinicopathological parameters and distinctive SERS peaks in the prediction of disease recurrence were analyzed in Cox regression proportional hazard analysis and Log rank test. Principal component analysis and linear discriminant analysis (PCA-LDA) were employed to process spectral data and construct diagnostic algorithms.ResultsA total of 88 patients with 440 plasma SERS spectra were collected. The SRES spectra from recurrent patients were compared with patients who remained recurrence free. The SERS demonstrated higher levels of circulating free nucleic acid components in recurrent population, which is represented by significantly higher intensities at SERS peaks of 725 cm−1, 1328 cm−1 and 1455 cm−1. The SERS also detected significantly lower levels of tryptophan shown as lower significantly intensities at the 1558 cm−1, which is proved to be an independent predictor of BCa recurrence. The addition of SERS peaks of 1558 cm−1 to classic clinicopathological predictors including pathological tumor stage, lymph node metastasis and pathological downstaging can significantly enhance the power of the predictive model from 0.66 to 0.76 in the area under curve (AUC) of receiver operating characteristic (ROC) curves. Meanwhile, the PCA-LDA diagnostic model based on SERS spectra reveals a high accuracy of 85.2% in prediction of disease recurrence and the AUC of 0.92 in the ROC curve. When validated in the leave-one-out cross-validation method, the accuracy of the model remained 84.1%.ConclusionWe show that SERS analysis of plasma before NAC treatment can accurately classify patients with different risks of disease recurrence after surgery and improve the power of clinicopathological predictive models, thus refining clinical decision-making.

GRade, Age, Nodes, and Tumor (GRANT) compared with Leibovich score to predict survival in localized renal cell carcinoma: A nationwide study.

ObjectiveTo examine the performance of Leibovich score versus GRade, Age, Nodes, and Tumor score in predicting disease recurrence in renal cell carcinoma.MethodsIn total, 7653 patients diagnosed with renal cell carcinoma from 2010 to 2018 were captured in the nationwide DaRenCa database; 2652 underwent radical or partial nephrectomy and had full datasets regarding the GRade, Age, Nodes, and Tumor score and Leibovich score. Discrimination was assessed with a Cox regression model. The results were evaluated with concordance index analysis.ResultsMedian follow‐up was 40 months (interquartile range 24–56). Recurrence occurred in 17%, and 15% died. A significant proportion of patients (36%) had missing data for the calculation of the Leibovich score. Among 1957 clear cell renal cell carcinoma patients the distribution of GRade, Age, Nodes, and Tumor score of 0, 1, 2, or 3/4 was 21%, 56%, 21% and 1.4%, respectively, and for Leibovich score of low/intermediate/high this was 47%, 36% and 18%, respectively. A similar distribution was seen in 655 non‐clear cell patients. Both Leibovich and GRade, Age, Nodes, and Tumor scores performed well in predicting outcomes for the favorable patient risk groups. The Leibovich score was better at predicting recurrence‐free survival (concordance index 0.736 versus 0.643), but not overall survival (concordance index 0.657 versus 0.648). Similar results were obtained in non‐clear cell renal cell carcinoma.ConclusionGRade, Age, Nodes, and Tumor and Leibovich scores were validated in clear cell and non‐clear cell renal cell carcinoma. Leibovich score outperformed the GRade, Age, Nodes, and Tumor score in predicting recurrence‐free survival and should remain the standard approach to risk stratify patients during follow‐up when all data are available.

Tumor mutational burden and somatic mutation status to predict disease recurrence in advanced melanoma.

Tumor mutational burden (TMB) has recently been identified as a biomarker of response to immune checkpoint inhibitors in many cancers, including melanoma. Co-assessment of TMB with inflammatory markers and genetic mutations may better predict disease outcomes. The goal of this study was to evaluate the potential for TMB and somatic mutations in combination to predict the recurrence of disease in advanced melanoma. A retrospective review of 85 patients with stage III or IV melanoma whose tumors were analyzed by next-generation sequencing was conducted. Fisher's exact test was used to assess differences in TMB category by somatic mutation status as well as recurrence locations. Kaplan-Meier estimates and Cox-proportional regression model were used for survival analyses. The most frequently detected mutations were TERT (32.9%), CDKN2A (28.2%), KMT2 (25.9%), BRAF V600E (24.7%), and NRAS (24.7%). Patients with TMB-L + BRAFWT status were more likely to have a recurrence [hazard ratio (HR), 3.43; confidence interval (CI), 1.29-9.15; P = 0.01] compared to TMB-H + BRAF WT. Patients with TMB-L + NRASmut were more likely to have a recurrence (HR, 5.29; 95% CI, 1.44-19.45; P = 0.01) compared to TMB-H + NRAS WT. TMB-L tumors were associated with local (P = 0.029) and in-transit (P = 0.004) recurrences. Analysis of TMB alone may be insufficient in understanding the relationship between melanoma's molecular profile and the body's immune system. Classification into BRAFmut, NRASmut, and tumor mutational load groups may aid in identifying patients who are more likely to have disease recurrence in advanced melanoma.

Comparison of Different Machine Learning Models in Prediction of Postirradiation Recurrence in Prostate Carcinoma Patients.

After primary treatment of localized prostate carcinoma (PC), up to a third of patients have disease recurrence. Different predictive models have already been used either for initial stratification of PC patients or to predict disease recurrence. Recently, artificial intelligence has been introduced in the diagnosis and management of PC with a potential to revolutionize this field. The aim of this study was to analyze machine learning (ML) classifiers in order to predict disease progression in the moment of prostate-specific antigen (PSA) elevation during follow-up. The study cohort consisted of 109 PC patients treated with external beam radiotherapy alone or in combination with androgen deprivation therapy. We developed and evaluated the performance of two ML algorithms based on artificial neural networks (ANN) and naïve Bayes (NB). Of all patients, 72.5% was randomly selected for a training set while the remaining patients were used for testing of the models. The presence/absence of disease progression was defined as the output variable. The input variables for models were conducted from the univariate analysis preformed among two groups of patients in the training set. They included two pretreatment variables (UICC stage and Gleason's score risk group) and five posttreatment variables (nadir PSA, time to nadir PSA, PSA doubling time, PSA velocity, and PSA in the moment of disease reevaluation). The area under the receiver operating characteristic curve, sensitivity, specificity, positive predictive value, negative predictive value, and predictive accuracy was calculated to test the models' performance. The results showed that specificity was similar for both models, while NB achieved better sensitivity then ANN (100.0% versus 94.4%). The ANN showed an accuracy of 93.3%, and the matching for NB model was 96.7%. In this study, ML classifiers have shown potential for application in routine clinical practice during follow-up when disease progression was suspected.

Pan-immune-inflammation value independently predicts disease recurrence in patients with Merkel cell carcinoma

PurposeWe aimed to determine whether the pan-immune-inflammation value (PIV) of patients with Merkel cell carcinoma (MCC) at primary diagnosis differs from controls and whether it is associated with disease stage and outcome.MethodsIn this retrospective study, we recruited MCC patients with stage I–III. PIV was calculated from absolute complete blood cell counts obtained within one week at MCC diagnosis as follows: [neutrophils (103/mm3) × platelets (103/mm3) × monocytes (103/mm3)]/lymphocytes (103/mm3). As controls, we studied age–gender-matched cutaneous melanoma (CM, stage I–III) patients and healthy controls (HC). Univariate and multivariate statistics were used.ResultsThe median PIV in MCC patients was significantly increased compared to both CM patients as well as healthy controls. PIV of MCC patients in stage II and III was significantly higher compared to stage I patients. ROC analysis revealed that MCC recurrence was significantly associated with a PIV greater than 372 [p < 0.0001, Youden index 0.58; hazard ratio: 4 (95% confidence interval: 1.7 to 9.2)]. In multivariate analysis, only a PIV greater than 372 and higher MCC stage were determined as independent predictors for disease recurrence.ConclusionWe determined, for the first time, the prognostic ability of the promising blood-based biomarker PIV in MCC patients and observed that PIV is increased in MCC patients in dependence on disease stage and independently predicts MCC recurrence.

Machine learning and magnetic resonance imaging radiomics for predicting human papilloma virus status and prognostic factors in oropharyngeal squamous cell carcinoma.

We attempted to predict pathological factors and treatment outcomes using machine learning and radiomic features extracted from preoperative magnetic resonance imaging (MRI) of oropharyngeal squamous cell carcinoma (OPSCC) patients. The medical records and imaging data of 155 patients who were diagnosed with OPSCC were analyzed retrospectively. The logistic regression model showed that the area under the receiver operating characteristic curve (AUC) of the model was 0.792 in predicting human papilloma virus (HPV) status. The LightGBM model showed an AUC of 0.8333 in predicting HPV status. The performance of the logistic model in predicting lymphovascular invasion, extracapsular nodal spread, and metastatic lymph nodes showed AUC values of 0.7871, 0.6713, and 0.6638, respectively. In predicting disease recurrence, the LightGBM model showed an AUC of 0.8571. In predicting patient death, the logistic model showed an AUC of 0.8175. A machine learning model using MRI radiomics showed satisfactory performance in predicting pathologic factors and treatment outcomes of OPSCC patients.

Changes in beta-catenin expression and activation during progression of primary sclerosing cholangitis predict disease recurrence

Primary sclerosing cholangitis (PSC) is a rare, chronic, cholestatic liver disease characterized by progressive inflammation and fibrosis of the bile ducts. We have previously demonstrated the importance of Wnt/β-catenin signaling in mouse models of PSC. In this study, we wished to determine the clinical relevance of β-catenin localization in patient samples. In livers explanted from patients diagnosed with PSC, the majority (12/16; 75%) lacked β-catenin protein expression. Biopsies from patients post-transplant were classified as recurrent or non-recurrent based on pathology reports and then scored for β-catenin activation as a function of immunohistochemical localization. Despite lack of statistical significance, patients with recurrent primary disease (n = 11) had a greater percentage of samples with nuclear, transcriptionally active β-catenin (average 58.8%) than those with no recurrence (n = 10; 40.53%), while non-recurrence is correlated with β-catenin staining at the cell surface (average 52.63% for non-recurrent vs. 27.34% for recurrent), as determined by three different methods of analysis. β-catenin score and years-to-endpoint are both strongly associated with recurrence status (p = 0.017 and p = 0.00063, respectively). Finally, there was significant association between higher β-catenin score and increased alkaline phosphatase, a marker of biliary injury and disease progression. Thus, β-catenin expression and activation changes during the progression of PSC, and its localization may be a useful prognostic tool for predicting recurrence of this disease.

Molecular Markers in Guiding Lung Cancer Diagnosis and Treatment

Background: Lung cancer has the highest mortality rates and one of the lowest 5-year survival rates amongst cancer types in the world. Although there are constant advancements in treatment, the overall prognosis for lung cancer continues to be poor. In order to achieve early detection and personalized targeted treatment, an effective method is needed to make prognostic and treatment decisions. Methods: A thorough literature search was conducted to identify tumor tissue, blood, and expired breath markers that have been discovered in lung cancer. Articles were chosen by determining main markers holding promise for future clinical use. Results: Data suggests significance in using tumor tissue markers as promising diagnostic, prognostic and predictive of treatment response and outcome. Epidermal Growth Factor Receptor (EGFR) and Anaplastic Lymphoma Kinase (ALK) and ROS-1 biomarkers can be used to decide to treat with EGFR-TKI and ALK-TKI, respectively. KRAS and p53 mutations suggest a likelihood of developing EGFR-TKI resistance. And c-MET is showing pertinence in predicting disease recurrence. Blood and expired breath markers are two more novel sources for biomarkers that is gaining more ground in lung cancer research. Circulating tumor cells (CTC) and DNA (ctDNA) were shown to be important markers in lung cancer prognosis and treatment response prediction. Circulating tumor cells suggest negative prognosis and increased likelihood of recurrence, while ctDNA data indicates use in treatment monitoring to help make decisions without keeping patients on disagreeable therapies. Volatile organic compounds (VOC) are the least studied, but investigators have noticed changes in VOC profiles between healthy and lung cancer patients. Blood and expired breath markers continue to be studied as these would be a welcome alternative to invasive biopsies. Recently there had been interest in using specific tumor biomarkers for imaging to localize tumors and determine disease progression. Conclusions: Years of research have elucidated multiple candidates as biomarkers found in tumor tissues, circulation, and even in exhaled air. Although more studies need to be performed on some markers mentioned in this review, such as EGFR, KRAS, ALK, and ROS-1, there is enough evidence for some use of these biomarkers to guide decisions in clinic, as well as evidence for promising future developments.

Novel Therapeutic Targets and Biomarkers Associated with Prostate Cancer-Associated Fibroblasts (CAFs).

Despite advances in treatment, prostate cancer remains a significant cause of morbidity and mortality worldwide. While the vast majority of prostate cancer research has centered on malignant epithelial cells, the tumor mi croenvironment (TME) has recently become increasingly recognized as an important regulator of tumor progression and response to treatment. Among the diverse cell types within the TME, stromal fibroblasts, in particular cancer-associated fibroblasts (CAFs), play an important role in prostate cancer progression. This is highlighted by the prognostic value of CAF markers in prostate cancer, which can predict disease recurrence, metastasis, and patient survival. There are also an increasing number of studies that demonstrate the critical role of CAFs in mediating response to specific therapies and CAF signaling pathways as potential therapeutic targets. However, further investigation into the mechanisms that underpin the interactions between cancer cells and CAFs are required to develop novel therapeutic approaches and identify predictive and prognostic biomarkers in CAFs. In this review, we discuss the current knowledge of CAF-dependent regulatory pathways in prostate tumorigenesis and their prognostic and therapeutic potential. Furthermore, we explore the emerging models and technologies that are likely to progress this field of research in terms of discovery and translation to the clinic.

Prognostic factors for recurrence in patients with surgically resected non-small cell lung cancer.

Patients with lung cancer receive treatment according to National Cancer Comprehensive Network (NCCN) standards. However, disease recurrence is reported in about 30% of patients during the first five years. Our study aimed to establish independent predictors of lung cancer recurrence. 104 patients with definitive treatment for non-small-cell lung carcinoma receiving standard adjuvant chemotherapy in the period 2014-2018 in our cancer center were retrospectively reviewed. The prognostic significance of five routine immunohistochemical (IHC) markers was examined. During the follow-up period disease recurrence occurred in 42 (40.4%) of the 104 enrolled patients. The median recurrence-free survival was 56.3 months, range 4-84.0 months (95% CI = 46.866-65.683). The recurrence-free survival rate was 58.8%. The frequencies of locoregional recurrence, lung recurrence, kidney, bone, lymph nodes of the neck, liver, and brain recurrence were 23.8%, 21.5%, 16.7%, 9.5%, 9.5%, 9.5% and 9.5%, respectively. Using the Cox regression model, category T, histological differentiation, and smoking status were identified as independent predictors of disease recurrence. The studied biological markers (PD-L1, Ki67, p53, epidermal growth factor receptor, and ALK) did not help the model predict disease recurrence. For statistical reliability, it is necessary to conduct a study on a larger cohort of patients and compare the mutual influence of several biomarkers.

Circulating metabolites as a concept beyond tumor biology determining disease recurrence after resection of colorectal liver metastasis.

Micro-metastatic growth is considered the main source of early cancer recurrence. Nutritional and microenvironmental components are increasingly recognized to play a significant role in the liver. We explored the predictive potential of preoperative plasma metabolites for postoperative disease recurrence in colorectal cancer liver metastasis (CRCLM) patients. All included patients (n=71) had undergone R0 liver resection for colorectal cancer liver metastasis in the years between 2012 and 2018. Preoperative blood samples were collected and assessed for 180 metabolites using a preconfigured mass-spectrometry kit (Biocrates Absolute IDQ p180 kit). Postoperative disease-free (DFS) and overall survival (OS) were prospectively recorded. Patients that recurred within 6 months after surgery were defined as "high-risk" and, subsequently, a three-metabolite model was created which can assess DFS in our collective. Multiple lysophosphatidylcholines (lysoPCs) and phosphatidylcholines (PCs) significantly predicted disease recurrence within 6 months (strongest: PC aa C36:1 AUC=0.83, p=0.003, PC ae C34:0 AUC=0.83, p=0.004 and lysoPC a C18:1 AUC=0.8, p=0.006). High-risk patients had a median DFS of 183 days versus 522 days in low-risk population (p=0.016, HR=1.98 95% CI 1.16-4.35) with a 6 months recurrence rate of 47.6% versus 4.7%, outperforming routine predictors of oncological outcome. Circulating metabolites identified CRCLM patients at highest risk for 6 months disease recurrence after surgery. Our data also suggests that circulating metabolites might play a significant pathophysiological role in micro-metastatic growth and concomitant early tumor recurrences after liver resection. However, the clinical applicability and performance of this proposed metabolomic concept needs to be independently validated in future studies.

Evaluating the Association between Clinical Characteristics with Progression-Free Survival and 3-Year Survival in Patients with Epithelial Ovarian Cancer

Background: This study was conducted to determine the association between clinical characteristics, progression-free survival (PFS), and 3-year survival in patients with epithelial ovarian cancer who underwent surgery in 2016-2017 at RSUD Dr. Soetomo. This study was carried out with the hope of contributing to services for patients with epithelial ovarian cancer to improve outcomes at RSUD Dr. Soetomo. Methods: This retrospective analytic study used medical record data. Survival analysis was done employing Kaplan-Meier and log-rank tests, while Cox regression was utilized to analyze characteristics, recurrence, and mortality. Results: In 2016-2017, 56 patients with epithelial ovarian cancer met the inclusion criteria. Clinical characteristics of residue, stage had significant associations with PFS (P-value of 0.007 and P-value of 0.005, respectively). Residue, stage, histopathology, and the number of chemotherapy cycles had significant associations with 3-year survival (P-value of 0.001, P-value of &lt; 0.001, P-value of &lt; 0.001, P-value of 0.031, respectively). Recurrence and stage had a significant association with the following hazard ratios: stage I HR: 1 (CI 95%, P-value 0.145), stage II HR: 6.5 (CI 95% 0.6–74.7, P-value 0.134), stage III HR: 12.2 (CI 95% 1.4–105.4, P-value 0.061), and stage IV HR: 10.4 (CI 95% 0.8–120.8, P-value 0.061). Mortality had significant associations with stage, histopathology, and the number of chemotherapy cycles, with hazard ratios as follows: stage IV HR: 43.6 (CI 95% 4.5–417.9, P-value 0.001), seromucinous histopathology HR: 20.1 (CI 95% 0.9–408.6, P-value 0.026), chemotherapy cycles &lt; 3 HR: 3.6 (CI 95% 1.2–11.5, P-value 0.459), and &gt; 3 HR: 1 (CI 95%, P-value 0.028).Conclusions: Residue and stage had statistically significant associations with PFS and can be predictors for disease recurrence. Residue, stage, histopathology, number of chemotherapy cycles had significant associations with 3-year survival, but only the latter three characteristics can be predictors for mortality

Exploring the association with disease recurrence of miRNAs predictive of colorectal cancer.

Disease recurrence after surgery is a crucial predictor of poor prognosis in colorectal cancer, where disseminated disease at the time of intervention can also be observed in localized early-stage cases. We evaluated the ability to predict disease recurrence of miRNAs from two signatures that we have found linked to the presence of colorectal cancer (CL signature) or adenoma (HgA signature) in higher-risk subjects. miRNAs from the signatures were studied longitudinally by quantitative real-time polymerase chain reaction in plasma from 24 patients with resectable colorectal cancer collected at the time of surgery and during scheduled follow-up across 36 months. Patients either showed relapse within 36 months (alive with disease (AWD)), or remained disease-free (no evidence of disease (NED)) for the same period. Although the signatures did not predict recurrence, expression of the miRNAs from the CL signature decreased 1 year after surgery, and one miRNA of the signature, miR-378a-3p, almost reached significance in the NED subgroup (Wilcoxon signed-rank test: p-value = 0.078). Also, miR-335-5p from the HgA signature was higher in AWD patients before surgery (Kruskal-Wallis test: p-value = 0.019). These data, although from a small cohort of patients, support the possible use of miRNAs as non-invasive biomarkers in liquid biopsy-based tests to identify patients at risk of relapse and to monitor them during follow-up.

Validation of MiROvaR, a microRNA-based predictor of early relapse in early stage epithelial ovarian cancer as a new strategy to optimise patients' prognostic assessment

AimEarly-stage epithelial ovarian cancer (eEOC) patients have a generally favorable prognosis but unpredictable recurrence. Accurate prediction of risk of relapse is still a major concern, essentially to avoid overtreatment. Our robust tissue-based miRNA signature named MiROvaR, predicting early EOC recurrence in mostly advanced-stage EOC patients, is here challenged in an independent cohort to extend its classifying ability in the early-stage EOC setting. MethodsWe retrospectively selected patients who underwent comprehensive surgical staging at our institution including stages from IA to IIB. miRNA expression profile was analysed in 89 cases and MiROvaR algorithm was applied using the previously validated cut-off for patients' classification. The primary endpoint was progression-free survival (PFS) at 5 years. Complete follow-up time (median = 112 months) was also considered as secondary analysis. ResultsMiROvaR was assessable on 87 cases (19 events of disease progression) and classified 68 (78%) low-risk and 19 (22%) high-risk patients. Recurrence rate at primary end-point was 39% for high-risk patients as compared to 9.5% for low-risk ones. Accordingly, their Kaplan–Meier PFS curves were significantly different at both primary and secondary analysis (p = 0.0006 and p = 0.03, respectively). While none of the prominent clinical variables had prognostic relevance, MiROvaR significantly predicted disease recurrence at the 5-year assessment (primary endpoint analysis; HR:5.43, 95%CI:1.82–16.1, p = 0.0024; AUC = 0.78, 95%CI:0.53–0.82) and at complete follow-up time (HR:2.67, 95%CI:1.04–6.8, p = 0.041; AUC:0.68, 95%CI:0.52–0.82). ConclusionsWe validated MiROvaR performance in identifying at diagnosis eEOC patients' at higher risk of early relapse thus enabling selection of the most effective therapeutic approach.

EZH2 Protein Expression in Triple-negative Breast Cancer Treated With Neoadjuvant Chemotherapy: An Exploratory Study of Association With Tumor Response and Prognosis.

Neaodjuvant chemotherapy is used to treat high risk triple-negative breast cancer (TNBC). Residual cancer burden (RCB) is used to predict risk of relapse after neoadjuvant chemotherapy (NAC); however, it cannot predict disease recurrence with certainty. EZH2 is a targetable oncogenic protein overexpressed in TNBC and associated with metastasis and stem cell expansion. We quantified EZH2 protein expression in TNBC before NAC to examine potential utility as a predictive and prognostic biomarker. We retrospectively identified 63 patients with localized TNBC treated with NAC. We quantified EZH2 nuclear expression in pretherapy biopsies using a score which included intensity and percent of positive cells at each intensity. EZH2 expression was evaluated as a continuous variable and dichotomized at a score of 210. Logistic regression analysis was used to determine association between EZH2 expression and RCB, tumor-infiltrating lymphocytes, clinicopathologic features and disease-free survival. There was no significant association between EZH2 score and posttreatment RCB class evaluated as a continuous variable (P=0.831) or dichotomized at 210 (P=0.546). On multivariable logistic regression, adjusted for covariates including RCB, EZH2 >210 was associated with development of metastasis (odds ratio=14.35, 95% confidence interval: 2.69-76.66; P=0.002). Logistic regression was run with EZH2 scores as a continuous variable and increased EZH2 score was associated with metastasis (odds ratio=1.10, 95% confidence interval: 1.00-1.03; P=0.047). In our study of TNBC treated with NAC, high EZH2 expression in pretherapy core biopsies was significantly associated with metastatic recurrence independent of RCB. The potential value of EZH2 as a biomarker to improve stratification of outcome after NAC should be explored further.

Tryptase and IL-33 in Bronchoalveolar Lavage Fluid May Predict the Types of Eosinophilic Pneumonia and Disease Recurrence

Introduction: Eosinophilic pneumonia (EP) is characterized by a marked accumulation of eosinophils in the lungs and blood. Eosinophils and mast cells play an important role in the pathogenesis of EP via release of biomarkers such as tryptase and interleukin (IL)-33. However, the potential role of these biomarkers is not fully understood. Objectives: We aimed to evaluate the differences among the levels of tryptase and IL-33 in bronchoalveolar lavage fluid (BALF) from several types of EP. We evaluated the differences between the levels of these biomarkers in the recurrent and nonrecurrent cases. Method: We prospectively collected the clinical data of patients with EP, diagnosed between 2006 and 2015 in our institution. Bronchoscopy was performed before steroid treatment; BALF was collected. The clinical characteristics of EP patients and the levels of tryptase and IL-33 in BALF were evaluated. Results: We enrolled 15 patients with chronic EP (CEP), 5 with acute EP (AEP), 10 with drug-induced EP, and 6 with angiitis-related EP. Tryptase levels in the CEP group were significantly higher than that in the drug-induced EP group (p = 0.048), while the AEP group had the highest IL-33 levels. Recurrence of EP was noted in 67% of patients with CEP. The levels of tryptase and IL-33 were notably higher in the recurrent cases than that in the nonrecurrent CEP group (p = 0.004, p = 0.04, respectively). Furthermore, there was a positive correlation between the levels of tryptase and IL-33 in the BALF of patients with CEP (ρ = 0.69, p = 0.004). Conclusions: Tryptase and IL-33 in BALF are useful biomarkers for the assessment of EP types. These biomarkers could be used to predict disease recurrence.