A Molecule Based Nomogram Optimized the Prediction of Relapse in Stage I NSCLC

Abstract Background: Early-stage non-small cell lung cancer (NSCLC) is being diagnosed increasingly, and in 30% of diagnosed patients, recurrence will develop within 5 years. Thus, it is urgent to identify recurrence-related markers in order to optimize the management of patient-tailored therapeutics. The aim of the study was to develop a feasible tool to optimize the recurrence prediction of stage I NSCLC. Methods: The eligible datasets were downloaded from TCGA and GEO. In discovery phase, two algorithms, Least Absolute Shrinkage and Selector Operation and Support Vector Machine-Recursive Feature Elimination, were used to identify candidate genes. Recurrence associated signature was developed by penalized cox regression. The nomogram was constructed and further tested via two independent cohorts. Results: In this retrospective study, 14 eligible datasets and 7 published signatures were included. In discovery phase, 42 significant genes were highlighted as candidate predictors by two algorithms. A 13-gene based signature was generated by penalized cox regression categorized training cohort into high-risk and low-risk subgroups (HR = 8.873, 95% CI:4.228–18.480 P < 0.001). Furthermore, a nomogram integrating the recurrence related signature, age, and histology was developed to predict the recurrence-free survival in the training cohort, which performed well in the two external validation cohorts (concordance index: 0.737, 95%CI:0.732–0.742, P < 0.001; 0.666, 95%CI: 0.650–0.682, P < 0.001; 0.651, 95%CI:0.637–0.665, P < 0.001 respectively). Conclusions: The proposed nomogram is a promising tool for estimating recurrence free survival in stage I NSCLC, which might have tremendous value in guiding adjuvant therapy. Prospective studies are needed to test the clinical utility of the nomogram in individualized management of stage I NSCLC.

Download Full-text

CpG Methylation Signature Predicts Recurrence in Early-Stage Hepatocellular Carcinoma: Results From a Multicenter Study

Journal of Clinical Oncology ◽

10.1200/jco.2016.68.2153 ◽

2017 ◽

Vol 35 (7) ◽

pp. 734-742 ◽

Cited By ~ 53

Author(s):

Jiliang Qiu ◽

Baogang Peng ◽

Yunqiang Tang ◽

Yeben Qian ◽

Pi Guo ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Support Vector Machine ◽

Cox Regression ◽

Early Stage ◽

Cpg Methylation ◽

Recursive Feature Elimination ◽

Support Vector ◽

Training Phase ◽

Virus Surface ◽

Training Cohort

Purpose Early-stage hepatocellular carcinoma (E-HCC) is being diagnosed increasingly, and in one half of diagnosed patients, recurrence will develop. Thus, it is urgent to identify recurrence-related markers. We investigated the effectiveness of CpG methylation in predicting recurrence for patients with E-HCCs. Patients and Methods In total, 576 patients with E-HCC from four independent centers were sorted by three phases. In the discovery phase, 66 tumor samples were analyzed using the Illumina Methylation 450k Beadchip. Two algorithms, Least Absolute Shrinkage and Selector Operation and Support Vector Machine-Recursive Feature Elimination, were used to select significant CpGs. In the training phase, penalized Cox regression was used to further narrow CpGs into 140 samples. In the validation phase, candidate CpGs were validated using an internal cohort (n = 141) and two external cohorts (n = 191 and n =104). Results After combining the 46 CpGs selected by the Least Absolute Shrinkage and Selector Operation and the Support Vector Machine-Recursive Feature Elimination algorithms, three CpGs corresponding to SCAN domain containing 3, Src homology 3-domain growth factor receptor-bound 2-like interacting protein 1, and peptidase inhibitor 3 were highlighted as candidate predictors in the training phase. On the basis of the three CpGs, a methylation signature for E-HCC (MSEH) was developed to classify patients into high- and low-risk recurrence groups in the training cohort ( P < .001). The performance of MSEH was validated in the internal cohort ( P < .001) and in the two external cohorts ( P < .001; P = .002). Furthermore, a nomogram comprising MSEH, tumor differentiation, cirrhosis, hepatitis B virus surface antigen, and antivirus therapy was generated to predict the 5-year recurrence-free survival in the training cohort, and it performed well in the three validation cohorts (concordance index: 0.725, 0.697, and 0.693, respectively). Conclusion MSEH, a three-CpG–based signature, is useful in predicting recurrence for patients with E-HCC.

Download Full-text

Molecular profiling of early-stage colorectal cancer (CRC) using targeted next generation sequencing (NGS) to predict signatures of recurrence.

Journal of Clinical Oncology ◽

10.1200/jco.2020.38.4_suppl.242 ◽

2020 ◽

Vol 38 (4_suppl) ◽

pp. 242-242

Author(s):

Michael C. Burns ◽

Kristen Carroll ◽

Ryan Jones ◽

Masha Kocherginsky ◽

Kirsten Bell Burdett ◽

...

Keyword(s):

Adjuvant Chemotherapy ◽

Early Stage ◽

Late Recurrence ◽

Stage I ◽

Recurrence Free Survival ◽

Primary Tumors ◽

Free Survival ◽

Targeted Next Generation Sequencing ◽

Targeted Ngs ◽

Next Generation Sequencing Ngs

242 Background: While the 5-year recurrence rate in early stage CRC is low (12%) and there is currently limited role of adjuvant chemotherapy in such cases, a unique subset of patients (pts) will have late recurrences. To identify molecular signatures predictive of late recurrence after pts undergo intended curative resection, we employed a 22 targeted gene NGS panel in pts with early CRC. Association between mutation status and recurrence free survival (RFS) was analyzed. Methods: Pts with stage I-II CRC had their tumor prospectively sequenced between 09/2015-12/2018 by an ion torrent targeted 22 gene hotspot NGS panel, including KRAS, EGFR, BRAF, PIK3CA, AKT1, ERBB2/4, PTEN, NRAS, STK11, MAP2K1, ALK, DDR2, CTNNB1, MET, TP53, SMAD4, FBX7, NOTCH1, and FGFR1/2/3. Associations were analyzed with unadjusted p-values (p) and Benjamini & Hochberg adjusted (BHp) shown. Results: Clinical and pathologic data from 180 pts were analyzed: median age 66 (range 24-86), male (47%), stage I (41%), stage II (69%), left (54%) vs right (36%) sided primary tumors, and microsatellite stable (85%). 35 (19%) pts had adjuvant therapy (n = 21 rectal, n = 14 colon). Pathological mutations were found in 160 (89%) of pts, including TP53 (56%), KRAS (44%), PIK3CA (22%), BRAF (12%), SMAD4 (8%), MET (6%) and NRAS (3%). There was only 1 case of ERBB2 mutation. 33 pts (18%) had evidence of recurrence. 36 month RFS was 82%. Common sites of recurrence included liver (13 pts, 39%), lung (10 pts, 30%), and bone (2 pts, 6%). Alterations in MET cDNA and protein were associated with recurrence-free survival (RFS) (HR = 4.1; p =0.0026, BHp= 0.057). Interestingly, while TP53 mutations are typically associated with worse prognosis in metastatic colorectal cancers, it was not associated with RFS (HR = 0.8; p = 0.55, BHp= 0.98). There was also no association between the number of gene alterations and RFS (p = 0.45). Conclusions: These data highlight that targeted NGS tumor profiling of early stage CRC, including sequencing MET among other genes, may be utilized alongside known prognostic pathological factors to predict pts with a higher risk of recurrence and may facilitate tailored adjuvant chemotherapy to mitigate this risk.

Download Full-text

Development and validation of a CT-based radiomic model combined with margin-related radiomic features to distinguish precancerous lesions from early-stage lung adenocarcinoma.

Journal of Clinical Oncology ◽

10.1200/jco.2020.38.15_suppl.e21044 ◽

2020 ◽

Vol 38 (15_suppl) ◽

pp. e21044-e21044

Author(s):

Luyu Huang ◽

Haiyu Zhou ◽

Herui Yao ◽

Yunfang Yu ◽

Hongyuan Zhu ◽

...

Keyword(s):

Lung Adenocarcinoma ◽

Validation Cohort ◽

Early Stage ◽

Precancerous Lesions ◽

Recursive Feature Elimination ◽

Support Vector ◽

Combined Model ◽

Training Cohort ◽

Pulmonary Lesions ◽

Preoperative Ct

e21044 Background: The purpose of this study was to investigate whether the combined radiomic model based on tumor-associated and margin-related (5mm) radiomic features can effectively improve prediction performance of distinguishing precancerous lesions from early stage lung adenocarcinoma. Methods: 264 patients underwent preoperative chest CT in Guangdong Provincial People’s hospital from March 1, 2015 to December 31,2019 were sorted by three cohorts. All lesions were pathologically confirmed as precancerous lesions or Stage I lung adenocarcinoma and a total of 861 analyzable radiomic features were extracted from two segmented lesions including pulmonary lesions and margins, using PyRadiomics by two senior radiologists. In training cohort, 145 patients (70%) are selected randomly from the single-nodular patients (N = 207). As for the validation cohorts, the models were validated using the resting 62 patients from single-nodular cohort and multi-nodular cohort (n = 57) respectively. Least Absolute Shrinkage and Selector Operation and Support Vector Machine-Recursive Feature Elimination were used for feature selection. ROC analysis and AUC were used to evaluate the performance of three models which were developed by multiple logistic regression on distinguishing the precancerous lesions from early stage lung adenocarcinoma. Results: Selected features from pulmonary lesions and pericarcinous tissue were developed into two independent radiomic models and a combined model. Margin-related radiomic model performs well in three validation cohorts. The AUC Brock of single-nodular cohort in training cohort was 0.912 (95% CI: 0.876-0.948), while in single-nodular validation cohort was 0.93 (95% CI: 0.862-0.966). Multi-nodular validation cohort in this model shows an AUC of 0.891 (95% CI = 0.824–0.943). Comparing combined model and tumor-associated radiomic model, it is found that the AUC of combined model was improved from 0.865 (95% CI: 0.767-0.963) to 0.94 (95% CI: 0.767-0.963) for single-nodular validation cohort. Respectively, this combined model also performs well in multi-nodular validation cohort. Conclusions: This study demonstrated the potential of margin-related radiomic features based on preoperative CT scans to distinguish precancerous lesions from early stage lung adenocarcinoma. The constructed radiomic model provided an easy-to-use, preoperative tool for surgeons to develop accurate therapeutic strategies for multi-nodular patients.

Download Full-text

Utility of a metabolic-associated nomogram to predict the recurrence-free survival of stage I cervical cancer

Future Oncology ◽

10.2217/fon-2020-1024 ◽

2021 ◽

Vol 17 (11) ◽

pp. 1325-1337

Author(s):

Yan Zhang ◽

Huan Lu ◽

Jinjin Zhang ◽

Shixuan Wang

Keyword(s):

Cervical Cancer ◽

Risk Score ◽

Cox Regression ◽

Area Under The Curve ◽

Gene Expression Omnibus ◽

Stage I ◽

Recurrence Free Survival ◽

Free Survival ◽

Cox Regression Analysis ◽

Cox Analysis

Aims: To identify metabolism-associated genes (MAGs) that serve as biomarkers to predict prognosis associated with recurrence-free survival (RFS) for stage I cervical cancer (CC). Patients & methods: By analyzing the Gene Expression Omnibus (GEO) database for 258 cases of stage I CC via univariate Cox analysis, LASSO and multivariate Cox regression analysis, we unveiled 11 MAGs as a signature that was also validated using Kaplan–Meier and receiver operating characteristic analyses. In addition, a metabolism-related nomogram was developed. Results: High accuracy of this signature for prediction was observed (area under the curve at 1, 3 and 5 years was 0.964, 0.929 and 0.852 for the internal dataset and 0.759, 0.719 and 0.757 for the external dataset). The high-risk score group displayed markedly worse RFS than did the low-risk score group. The indicators performed well in our nomogram. Conclusions: We identified a novel signature as a biomarker for predicting prognosis and a nomogram to facilitate the individual management of stage I CC patients.

Download Full-text

Nomogram for Predicting Recurrence-Free Survival in Chinese Women with Endometrial Cancer after Initial Therapy: External Validation

Journal of Oncology ◽

10.1155/2020/2363545 ◽

2020 ◽

Vol 2020 ◽

pp. 1-11

Author(s):

Yuan Cheng ◽

Yangyang Dong ◽

Wenjuan Tian ◽

Hua Zhang ◽

Xiaoping Li ◽

...

Keyword(s):

Endometrial Cancer ◽

Chinese Women ◽

External Validation ◽

Figo Stage ◽

Peritoneal Cytology ◽

Tumor Diameter ◽

Recurrence Free Survival ◽

Free Survival ◽

Training Cohort ◽

Gynecology And Obstetrics

This study aimed at developing an available recurrence-free survival (RFS) model of endometrial cancer (EC) for accurate and individualized prognosis assessment. A training cohort of 520 women with EC who underwent initial surgical treatment and an external validation cohort of 445 eligible EC patients from 2006 to 2016 were analyzed retrospectively. Multivariable Cox proportional hazards regression models were used to develop nomograms for predicting recurrence. The concordance index (C-index) and the area under the receiver operating characteristic curve (AUC) were calculated to determine the discrimination of RFS prognostic scoring systems. Calibration plots were generated to examine the performance characteristics of the predictive nomograms. Regression analysis revealed that an advanced International Federation of Gynecology and Obstetrics (FIGO) stage, histological grade 3, primary tumor diameter ≥2 cm, and positive peritoneal cytology were independent prognostic factors for RFS in EC in the training set. The nomograms estimated RFS according to these four variables, with a C-index of 0.860, which was superior to that of FIGO stage (2009 criteria), at 0.809 (P=0.034), in the training cohort. Encouragingly, consistent results were observed in the validation set, with a C-index of 0.875 for the nomogram and a C-index of 0.833 for the FIGO staging (P=0.0137). Furthermore, the calibrations of the nomograms predicting 3- and 5-year RFS strongly corresponded to the actual survival outcome. In conclusion, this study developed an available nomogram with effective external validation and relatively appreciable discrimination and conformity for the accurate assessment of 3- and 5-year RFS in Chinese women with EC.

Download Full-text

Construction of methylation-associated nomogram for predicting the recurrence-free survival risk of stage I–III lung adenocarcinoma

Future Oncology ◽

10.2217/fon-2020-1270 ◽

2021 ◽

Author(s):

Youcai Liu ◽

Bin Wang ◽

Shiqiang Shi ◽

Zhaoxi Li ◽

Yajuan Wang ◽

...

Keyword(s):

Dna Methylation ◽

Lung Adenocarcinoma ◽

Operating Characteristic ◽

Cox Regression ◽

Stage I ◽

Recurrence Free Survival ◽

Characteristic Analysis ◽

Free Survival ◽

Selection Operator ◽

Survival Risk

Aim: The aim of our study was to investigate a methylation-associated predictor for prognosis in patients with stage I–III lung adenocarcinoma (LUAD). Methods: A DNA methylation-based signature was developed via univariate, least absolute shrinkage and selection operator and multivariate Cox regression models. Results: We identified a 14-site methylation signature that was correlated with recurrence-free survival of stage I–III lung adenocarcinoma patients. By receiver operating characteristic analysis, we showed the high ability of the 14-site methylation signature for predicting recurrence-free survival. In addition, the nomogram result showed a satisfactory predictive value. Conclusion: We successfully identified a DNA methylation-associated nomogram which can predict recurrence-free survival in patients with stage I–III lung adenocarcinoma.

Download Full-text

Development and Validation a Prediction Model to Recurrence in Borderline Ovarian Tumor Patients with Fertility-Preserving Surgery

10.21203/rs.3.rs-820789/v1 ◽

2021 ◽

Author(s):

Yunan He ◽

Shunjie Hu ◽

Jiaojiao Zhong ◽

Hui Yang ◽

Nianchun Shan

Keyword(s):

Prognostic Factors ◽

Ovarian Tumor ◽

Cox Regression ◽

Past History ◽

Borderline Ovarian Tumor ◽

Recurrence Free Survival ◽

Free Survival ◽

Training Cohort ◽

Cox Regression Analysis ◽

History Of

Abstract Background: To identify prognostic factors in patients with borderline ovarian tumor (BOT) and establish and validate a nomogram predicting recurrence in BOT patients treated with fertility-preserving surgery.Methods: Patients with BOT who underwent surgery at two institutions between January 2000 and June 2017 were included and categorized into training and validation cohorts. Univariate log-rank test and Cox regression analysis were performed in the training cohort to identify prognostic factors, and a nomogram was developed to predict the recurrence rate. The model was validated by calculating the C-index and drawing the calibration curve and receiver operating curve (ROC).Results: In the multivariate Cox regression analysis, practice period, past history of benign ovarian disease, past history of benign breast disease, elevated CA125 levels, elevated CA199 levels, surgical methods, greater omentum resection, FIGO stage, postoperative pregnancy, and re-operation were independently associated with recurrence-free survival (p<0.05). The aforementioned prognostic factors were used to develop a nomogram. The nomogram demonstrated a good ability to predict the risk of recurrence (training cohort C-index: 0.866, validation cohort C-index: 0.920). The calibration curve suggested that the predicted recurrence-free survival was closely related to the actual recurrence. ROC analysis showed that the nomogram had a good discriminatory power with the area under curve between 0.776 and 0.956. Conclusions: The nomogram can predict the 1-, 3-, and 5-year recurrence-free survival of BOT patients undergoing fertility-preserving surgery. The predictive model can help guide surgical plans, postoperative monitoring, and prognostic evaluation of BOT patients.

Download Full-text

Individualized Prediction of Stage I Colorectal Cancer Recurrence Using a Nomogram

10.21203/rs.3.rs-468523/v1 ◽

2021 ◽

Author(s):

Aobo Zhuang ◽

Dexiang Zhu ◽

Qi Lin ◽

Pingping Xu ◽

Guodong He ◽

...

Keyword(s):

Colorectal Cancer ◽

Recurrence Rate ◽

Cox Regression ◽

Radical Surgery ◽

Cancer Recurrence ◽

Stage I ◽

Recurrence Free Survival ◽

Curative Surgery ◽

Free Survival ◽

Preoperative Serum

Abstract Background Though the prognosis of stage l colorectal cancer (CRC) is suitable, some patients still recurrence and have a poor prognosis. Few prognostic risk models have been proposed. Therefore, we aimed to identify factors affecting the recurrence in patients with stage I CRC and develop a predictive nomogram. Methods The nomogram was based on a retrospective study on patients who underwent radical surgery for stage I CRC at Zhongshan Hospital (Shanghai, China) between August 2008 and December 2016. Predictive factors for recurrence were determined and a nomogram predicting recurrence-free survival was constructed based on Cox regression. This model was internally validated, and performance was evaluated through calibration plots. Results A total of 1,359 patients who underwent curative surgery for stage I CRC were enrolled. With the 62.0 months median follow-up time,71 (5.2%) experienced recurrence. The median time to recurrence was 24 months, 70% was diagnosed within three years after curative resection and 80% within 5 years. The 5-year cumulative recurrence rate was 5.0%, and the 10-year recurrence rate was 6.6%. In multivariate Cox analysis, age, preoperative serum CEA concentration, preoperative serum CA19-9 concentration, preoperative neutrophil-to-lymphocyte ratio, primary tumor location and lymphovascular invasion were the independent predictors of recurrence. A nomogram based on eight factors for recurrence-free survival was developed and internally validated. The concordance index of the nomogram was 0.716. Conclusions For stage I CRC, more than one in every twenty people may experience recurrence within 10 years after radical surgery. The nomogram we developed and internally validated might be helpfulhelpful in postoperative stage I CRC surveillance.

Download Full-text

Novel Gene Signatures Predictive of Patient Recurrence-Free Survival and Castration Resistance in Prostate Cancer

Cancers ◽

10.3390/cancers13040917 ◽

2021 ◽

Vol 13 (4) ◽

pp. 917

Author(s):

Jun A ◽

Baotong Zhang ◽

Zhiqian Zhang ◽

Hailiang Hu ◽

Jin-Tang Dong

Keyword(s):

Prostate Cancer ◽

Cox Regression ◽

Characteristic Curve ◽

Treatment Decision ◽

Rank Aggregation ◽

Lymph Node Status ◽

Calibration Plot ◽

Recurrence Free Survival ◽

Castration Resistance ◽

Free Survival

Molecular signatures predictive of recurrence-free survival (RFS) and castration resistance are critical for treatment decision-making in prostate cancer (PCa), but the robustness of current signatures is limited. Here, we applied the Robust Rank Aggregation (RRA) method to PCa transcriptome profiles and identified 287 genes differentially expressed between localized castration-resistant PCa (CRPC) and hormone-sensitive PCa (HSPC). Least absolute shrinkage and selection operator (LASSO) and stepwise Cox regression analyses of the 287 genes developed a 6-gene signature predictive of RFS in PCa. This signature included NPEPL1, VWF, LMO7, ALDH2, NUAK1, and TPT1, and was named CRPC-derived prognosis signature (CRPCPS). Interestingly, three of these 6 genes constituted another signature capable of distinguishing CRPC from HSPC. The CRPCPS predicted RFS in 5/9 cohorts in the multivariate analysis and remained valid in patients stratified by tumor stage, Gleason score, and lymph node status. The signature also predicted overall survival and metastasis-free survival. The signature’s robustness was demonstrated by the C-index (0.55–0.74) and the calibration plot in all nine cohorts and the 3-, 5-, and 8-year area under the receiver operating characteristic curve (0.67–0.77) in three cohorts. The nomogram analyses demonstrated CRPCPS’ clinical applicability. The CRPCPS thus appears useful for RFS prediction in PCa.

Download Full-text

Postoperative non-steroidal anti-inflammatory drug use and oncological outcomes of rectal cancer

BJS Open ◽

10.1093/bjsopen/zraa050 ◽

2021 ◽

Vol 5 (1) ◽

Author(s):

O Grahn ◽

M Lundin ◽

M-L Lydrup ◽

E Angenete ◽

M Rutegård

Keyword(s):

Rectal Cancer ◽

Propensity Score ◽

Propensity Score Matching ◽

Instrumental Variables ◽

Cox Regression ◽

Recurrence Free Survival ◽

Free Survival ◽

Oncological Outcomes ◽

Anti Inflammatory ◽

Instrumental Variables Approach

Abstract Background Non-steroidal anti-inflammatory drugs (NSAIDs) are known to suppress the inflammatory response after surgery and are often used for pain control. This study aimed to investigate NSAID use after radical surgical resection for rectal cancer and long-term oncological outcomes. Methods A cohort of patients who underwent anterior resection for rectal cancer between 2007 and 2013 in 15 hospitals in Sweden was investigated retrospectively. Data were obtained from the Swedish Colorectal Cancer Registry and medical records; follow-up was undertaken until July 2019. Patients who received NSAID treatment for at least 2 days after surgery were compared with controls who did not, and the primary outcome was recurrence-free survival. Cox regression modelling with confounder adjustment, propensity score matching, and an instrumental variables approach were used; missing data were handled by multiple imputation. Results The cohort included 1341 patients, 362 (27.0 per cent) of whom received NSAIDs after operation. In analyses using conventional regression and propensity score matching, there was no significant association between postoperative NSAID use and recurrence-free survival (adjusted hazard ratio (HR) 1.02, 0.79 to 1.33). The instrumental variables approach, including individual hospital as the instrumental variable and clinicopathological variables as co-variables, suggested a potential improvement in the NSAID group (HR 0.61, 0.38 to 0.99). Conclusion Conventional modelling did not demonstrate an association between postoperative NSAID use and recurrence-free survival in patients with rectal cancer, although an instrumental variables approach suggested a potential benefit.

Download Full-text