Dataset Information

Nuclear expression of Gli-1 is predictive of pathologic complete response to chemoradiation in trimodality treated oesophageal cancer patients.

ABSTRACT:

Background

Predictive biomarkers or signature(s) for oesophageal cancer (OC) patients undergoing preoperative therapy could help administration of effective therapy, avoidance of ineffective ones, and establishment new strategies. Since the hedgehog pathway is often upregulated in OC, we examined its transcriptional factor, Gli-1, which confers therapy resistance, we wanted to assess Gli-1 as a predictive biomarker for chemoradiation response and validate it.

Methods

Untreated OC tissues from patients who underwent chemoradiation and surgery were assessed for nuclear Gli-1 by immunohistochemistry and labelling indices (LIs) were correlated with pathologic complete response (pathCR) or ResultsInitial 60 patients formed the discovery set (TDS) and then unique 167 patients formed the validation set (TVS). 16 (27%) patients in TDS and 40 (24%) patients in TVS achieved a pathCR. Nuclear Gli-1 LIs were highly associated with pathCR based on the fitted logistic regression models (P<0.0001) in TDS and TVS. The areas under the curve (AUCs) for receiver-operating characteristics (ROCs) based on a fitted model were 0.813 (fivefold cross validation (0.813) and bootstrap resampling (0.816) for TDS and 0.902 (fivefold cross validation (0.901) and bootstrap resampling (0.902)) for TVS. Our preclinical (including genetic knockdown) studies with FU or radiation resistant cell lines demonstrated that Gli-1 indeed mediates therapy resistance in OC.

Conclusions

Our validated data in OC show that nuclear Gli-1 LIs are predictive of pathCR after chemoradiation with desirable sensitivity and specificity.

SUBMITTER: Wadhwa R

PROVIDER: S-EPMC5572179 | biostudies-literature | 2017 Aug

REPOSITORIES: biostudies-literature

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Publications

Nuclear expression of Gli-1 is predictive of pathologic complete response to chemoradiation in trimodality treated oesophageal cancer patients.

Wadhwa Roopma R Wang Xuemei X Baladandayuthapani Veerabhadran V Liu Bin B Shiozaki Hironori H Shimodaira Yusuke Y Lin Quan Q Elimova Elena E Hofstetter Wayne L WL Swisher Stephen G SG Rice David C DC Maru Dipen M DM Kalhor Neda N Bhutani Manoop S MS Weston Brian B Lee Jeffrey H JH Skinner Heath D HD Scott Ailing W AW Kaya Dilsa Mizrak DM Harada Kazuto K Berry Donald D Song Shumei S Ajani Jaffer A JA

British journal of cancer 20170720 5

<h4>Background</h4>Predictive biomarkers or signature(s) for oesophageal cancer (OC) patients undergoing preoperative therapy could help administration of effective therapy, avoidance of ineffective ones, and establishment new strategies. Since the hedgehog pathway is often upregulated in OC, we examined its transcriptional factor, Gli-1, which confers therapy resistance, we wanted to assess Gli-1 as a predictive biomarker for chemoradiation response and validate it.<h4>Methods</h4>Untreated OC ...[more]

PMID: 28728163

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Project description:Purpose Pathologic complete response (pCR) is a critical factor in determining whether patients with rectal cancer (RC) should have surgery after neoadjuvant chemoradiotherapy (nCRT). Currently, a pathologist's histological analysis of surgical specimens is necessary for a reliable assessment of pCR. Machine learning (ML) algorithms have the potential to be a non-invasive way for identifying appropriate candidates for non-operative therapy. However, these ML models' interpretability remains challenging. We propose using explainable boosting machine (EBM) to predict the pCR of RC patients following nCRT. Methods A total of 296 features were extracted, including clinical parameters (CPs), dose-volume histogram (DVH) parameters from gross tumor volume (GTV) and organs-at-risk, and radiomics (R) and dosiomics (D) features from GTV. R and D features were subcategorized into shape (S), first-order (L1), second-order (L2), and higher-order (L3) local texture features. Multi-view analysis was employed to determine the best set of input feature categories. Boruta was used to select all-relevant features for each input dataset. ML models were trained on 180 cases from our institution, with 37 cases from RTOG 0822 clinical trial serving as the independent dataset for model validation. The performance of EBM in predicting pCR on the test dataset was evaluated using ROC AUC and compared with that of three state-of-the-art black-box models: extreme gradient boosting (XGB), random forest (RF) and support vector machine (SVM). The predictions of all black-box models were interpreted using Shapley additive explanations. Results The best input feature categories were CP+DVH+S+R_L1+R_L2 for all models, from which Boruta-selected features enabled the EBM, XGB, RF, and SVM models to attain the AUCs of 0.820, 0.828, 0.828, and 0.774, respectively. Although EBM did not achieve the best performance, it provided the best capability for identifying critical turning points in response scores at distinct feature values, revealing that the bladder with maximum dose >50 Gy, and the tumor with maximum2DDiameterColumn >80 mm, elongation <0.55, leastAxisLength >50 mm and lower variance of CT intensities were associated with unfavorable outcomes. Conclusions EBM has the potential to enhance the physician's ability to evaluate an ML-based prediction of pCR and has implications for selecting patients for a “watchful waiting” strategy to RC therapy.

Project description:PURPOSE:To examine the value of early changes in quantitative diffusion-weighted imaging and 18F-fluorodeoxyglucose positron emission tomography/computed tomography (FDG-PET/CT) for discriminating complete pathologic response (pCR) to chemoradiation in esophageal cancer. METHODS AND MATERIALS:Twenty esophageal cancer patients treated with chemoradiation followed by surgery were prospectively enrolled. Patients underwent magnetic resonance imaging and FDG-PET/CT scans at baseline, interim (2 weeks after chemoradiation start), and first follow-up. On the basis of pathologic findings at surgery, patients were categorized into tumor regression groups (TRG1, TRG2, and TRG3+). Distributions of summary statistics in apparent diffusion coefficient (ADC) and FDG-PET at baseline and relative changes at interim and follow-up scans were compared between pCR/TRG1 and non-pCR/TRG2+ groups and across readers. Receiver operating characteristics were evaluated for summary measures to characterize discrimination of pCR from non-pCR. RESULTS:Relative changes in tumor volume ADC (ΔADC) mean and 25th and 10th percentiles from baseline to interim were able to completely discriminate (area under the curve = 1, P < .0011) between pCR and non-pCR (thresholds = 27.7%, 29.2%, and 32.1%, respectively) and were found to have high interreader reliability (95% limits of agreement of 1.001, 0.944, and 0.940, respectively). Relative change in total lesion glycolysis (TLG) from baseline to interim was significantly different among pCR and non-pCR groups (P=.0117) and yielded an area under the curve of 0.947 (95% confidence interval 0.8505-1.043). An optimal threshold of 59% decrease in TLG provided optimal sensitivity (specificity) of 1.000 (0.867). Changes in ADC summary measures were negatively correlated with that of TLG (Spearman, -0.495, P=.027). CONCLUSIONS:Quantitative volume ΔADC and TLG during treatment may serve as early imaging biomarkers for discriminating pathologic response to chemoradiation in esophageal cancer. Validation of these data in larger, prospective, multicenter studies is essential.

Project description:BackgroundTo investigate the association of axillary pathologic complete response (pCR) rate among breast cancer patients with pCR after neoadjuvant chemotherapy (NCT).MethodsThe retrospective clinical data of 1,903 patients who were treated with NCT between March, 2010 and December, 2018, were collected from one Chinese database and analyzed. The correlations between clinicopathological characteristics and breast pCR with axillary pCR were calculated by χ2 test. Binary logistic regression analysis was used for multivariate analysis. The relative risk of positive axillary nodes after NCT in patients with and without breast pCR was analyzed using a Cochran-Mantel-Haenszel (CMH) test stratified by initial N stage and tumor subtype.ResultsThe rate of axillary pCR was increased in the cases with initial cN0, Ki67 high expression, HR+HER2+/HR-HER2+/TN subtypes, and breast pCR. After NCT, the relative risk of nodal disease burden was 4.81 in patients without breast pCR compared with patients with breast pCR. The relative risk of positive nodal status in patients with cN0, cN1, cN2, and cN3 disease without vs. with breast pCR was 6.45, 4.88, 5.69 and 6.24, respectively. The relative risk of positive nodal status in patients with HR+HER2-, HR+HER2+, HR-HER2+, and TN disease was 4.02, 4.50, 3.82 and 4.18, respectively. Of cN0 patients with breast pCR, only 4 out of 44 (9%) with HER2-positive disease had 1 or 2 axillary lymph node metastases at final surgical pathology compared to 30 out of 98 (31%) of those without breast pCR. There was no evidence of positive nodal residue among all 21 patients (100%) with TN disease compared to 65% (36 of 55) of patients without breast pCR.ConclusionsNodal status is strongly correlated with breast pCR after NCT. Patients with initial cN0/1 TN/HER2 positive disease who achieve breast pCR at surgery have a low risk of nodal metastasis. These results suggest that the failure rate of missing positive lymph nodes among those patients was very low and that it is safe for such patients to undergo sentinel lymph node biopsy (SLNB) after NCT. This study also provides a theoretical basis for clinical trials focused on the avoidance of axillary surgery in selected patients.

Project description:AimsA complete metabolic response (CMR) on early post-treatment 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) is a positive prognostic factor for cervical cancer patients treated with definitive chemoradiation, but long-term outcomes of this group of patients are unknown. Patterns of failure and risk subgroups are identified.Materials and methodsPatients who received curative-intent chemoradiation from 1998 to 2018 for International Federation of Gynecology and Obstetrics (FIGO) stage IB1-IVA cervical cancer and had a CMR on post-treatment FDG-PET within 5 months of treatment completion were included. Cox proportional hazards models determined factors associated with locoregional and distant failure. Kaplan-Meier estimates of freedom from any recurrence (FFR) of patient subgroups were compared with Log-rank tests.ResultsThere were 402 patients with a CMR after chemoradiation on FDG-PET. Initial T stage was T1 (38%)/T2 (40%)/T3 (20%)/T4 (2%); initial FDG-avid nodal status was no nodes (50%)/pelvic lymph nodes (40%)/pelvic and para-aortic lymph nodes (10%). After a median follow-up of 6 years, 109 (27%) recurred. The pattern of recurrence was locoregional (27%), distant (61%) or both (12%). No factors were associated with locoregional failure. Distant recurrence was more likely in patients with T3-4 lesions (hazard ratio = 2.4, 95% confidence interval 1.5-3.8) and involvement of pelvic (hazard ratio = 1.6, 95% confidence interval 1.0-2.7) or para-aortic lymph nodes (hazard ratio = 2.7, 95% confidence interval 1.4-5.0) at diagnosis. The 5-year FFR rates for T1-2 patients with no nodes, pelvic nodes alone or para-aortic nodes at diagnosis were 85, 76 and 62%, respectively (P = 0.04, none versus para-aortic nodes). The 5-year FFR for T3-4 patients with no nodes, pelvic nodes alone or para-aortic nodes at diagnosis were 68, 56 and 25%, respectively (P = 0.09, none versus para-aortic nodes).ConclusionsT3-4 tumours and para-aortic nodal involvement at diagnosis are poor prognostic factors, even after a CMR following chemoradiation.

Project description:BackgroundAnti-EGFR-based therapies have limited success in HNSCC patients. Predictive biomarkers are greatly needed to identify the patients likely to be benefited from these targeted therapies. Here, we present the prognostic and predictive association of biomarkers in HPV-negative locally advanced (LA) HNSCC patients.MethodsTreatment-naive tumour tissue samples of 404 patients, a subset of randomised Phase 3 trial comparing cisplatin radiation (CRT) versus nimotuzumab plus cisplatin radiation (NCRT) were analysed to evaluate the expression of HIF1α, EGFR and pEGFR by immunohistochemistry and EGFR gene copy change by FISH. Progression-free survival (PFS), locoregional control (LRC) and overall survival (OS) were estimated by Kaplan-Meier method. Hazard ratios were estimated by Cox proportional hazard models.ResultsBaseline characteristics of the patients were balanced between two treatment groups (CRT vs NCRT) and were representative of the trial cohort. The median follow-up was of 39.13 months. Low HIF1α was associated with better PFS [HR (95% CI) = 0.62 (0.42-0.93)], LRC [HR (95% CI) = 0.56 (0.37-0.86)] and OS [HR (95% CI) = 0.63 (0.43-0.93)] in the CRT group. Multivariable analysis revealed HIF1α as an independent negative prognostic biomarker. For patients with high HIF1α, NCRT significantly improved the outcomes [PFS:HR (95% CI) = 0.55 (0.37-0.82), LRC:HR (95% CI) = 0.55 (0.36-0.85) and OS:HR (95% CI) = 0.54 (0.36-0.81)] compared to CRT. While in patients with low HIF1α, no difference in the clinical outcomes was observed between treatments. Interaction test suggested a predictive value of HIF1α for OS (P = 0.008).ConclusionsHigh HIF1α expression is a predictor of poor clinical response to CRT in HPV-negative LA-HNSCC patients. These patients with high HIF1α significantly benefited with the addition of nimotuzumab to CRT.Clinical trial registrationRegistered with the Clinical Trial Registry of India (Trial registration identifier-CTRI/2014/09/004980).

Dataset Information

Nuclear expression of Gli-1 is predictive of pathologic complete response to chemoradiation in trimodality treated oesophageal cancer patients.

Background

Methods

Conclusions

Publications

Nuclear expression of Gli-1 is predictive of pathologic complete response to chemoradiation in trimodality treated oesophageal cancer patients.

Similar Datasets

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