Clinical parameters combined with radiomics features to predict the efficacy of immunotherapy for advanced non-small cell lung cancer.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e21187-e21187
Author(s):  
Qian Zhao ◽  
Linlin Wang
Keyword(s):  
Predictive Models ◽  
Predictive Value ◽  
Mixed Model ◽  
Proportional Hazards ◽  
Prediction Models ◽  
Image Features ◽  
Cox Proportional Hazards ◽  
Lasso Regression ◽  
Discriminative Ability ◽  
3D Slicer

e21187 Background: Many tools have been developed to predict the efficacy of immunotherapy, such as LIPI, EPSILoN and mLIPI scores. The aim of this study was to determine the ability of to predict outcomes in Chinese aNSCLC patients treated with ICIs.With the development of imaging technology, radiomics has increasingly received a great deal of attention. We use 3D-slicer to delineat the ROI in the patient's CT image, extract a large number of features, and further screen out the radiomics features that have predictive value for the outcome. Methods: We retrospectively enrolled 317 patients with histologically proven aNSCLC (IIIB–IV) treated with ICIs. The discriminative ability of the predictive models was evaluated by AUC in the ROC analysis. Patients were randomly divided into training and validation cohorts using a 2：1 ratio. We used the semi-automatic segmentation method of the 3D-slicer platform to delineate the ROI of the tumor’s lesion area and extract 854 image features for each patient. In the training set of patients, the LASSO algorithm was used to screen the radiomic features.Hosmer-Lemeshow tests (H-L tests) were conducted to determine the fit of the prediction models. PFS and OS curves were generated using the Kaplan-Meier method and differences were assessed using log-rank tests. Univariate and multivariate analyses were performed using Cox proportional-hazards regression models. The glmnet R package was used for the LASSO regression method. The rms, Hmisc R packages used for C-index. Results: Among the 317 patients included in the study, the median OS and PFS were 14.2 months and 5.6 months, respectively and the ORR was 23.1%. The AUC values of LIPI, mLIPI, and EPSILoN scores for predicting PFS were 0.649 (95% CI: 0.588–0.709), 0.765 (95% CI: 0.713–0.818]), and 0.637 (95% CI: 0.567–0.698), respectively (P < 0.001 for all models). The AUC value of mLIPI scores was significantly higher than that of LIPI and EPSILoN scores (P < 0.05). The C-index of mLIPI was 0.645 (95% CI: 0.617-0.673). In this study, 5 radiomics features with predictive value were selected from radiomics features. The C-indexs of the radscore were 0.643 (95% CI: 0.602–0.684) and 0.632 (95% CI: 0.571–0.693). Then we combined mLIPI and radscore to obtain a mixed model. The C-index of the combination mLIPI scores with radscore for predicting PFS was 0.810 (95% CI: 0.770–0.849) and 0.706（CI: 0.633–0.778) in the training and validation cohorts, respectively. Conclusions: By externally validating LIPI, mLIPI, and EPSILoN scores, we found that all three of these predictive models could identify different prognostic subsets of patients treated with ICIs to statistically significant degrees. We also found that mLIPI had the highest accuracy among the three models. With the addition of radiomics features, the prediction performance of the mixed model has been further improved.

Integrative tumor and immune cell multi-omic analyses to predict melanoma response to immune checkpoint blockade.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. 10009-10009
Author(s):  
Valsamo Anagnostou ◽  
Daniel C. Bruhm ◽  
Noushin Niknafs ◽  
James R White ◽  
John-William Sidhom ◽  
...  
Keyword(s):  
Clinical Outcome ◽  
B Cell ◽  
Predictive Models ◽  
Immune Checkpoint ◽  
Proportional Hazards ◽  
Immune Checkpoint Blockade ◽  
Cox Proportional Hazards ◽  
Checkpoint Blockade ◽  
Therapeutic Decisions ◽  
Tumor Biopsies

10009 Background: The complex crosstalk between tumor and immune cells during immune checkpoint blockade mandates the development of integrated models to interpret the antitumor immune response and predict clinical outcome. Methods: We performed comprehensive genomic, transcriptomic and T cell repertoire analyses on tumor biopsies from 64 patients with advanced melanoma receiving nivolumab +/- ipilimumab on CheckMate-038 (NCT01621490). Tumor biopsies were obtained at baseline and 2-4 weeks on therapy. Machine learning and Cox proportional hazards regression analyses were employed to integrate multi-omics features in predictive models of response, defined by RECISTv1.1 as complete and partial response, and survival (PFS and OS). Results: Responding patients had a higher tumor mutation burden (TMB) than non-responders. Expressed TMB more accurately predicted overall survival than genomic TMB (log rank p = 0.028 vs 0.078). High tumor aneuploidy was associated with worse prognosis especially for the patients in the nivolumab + ipilimumab group (log rank p = 0.01). TCR sequencing of paired tumors before and on-treatment revealed that responders had a significantly higher number of unique TCR clones at baseline and more clonotypic shifts on-treatment (p = 0.0018). Gene rearrangement analyses using transcriptome data identified a higher number of rearrangements involving immunoglobulin (Ig) genes in baseline tumors from responders. Deconvolution of transcriptomic data confirmed an enrichment in tumor associated B cells in baseline tumors of responders, suggesting that pre-existing B cell infiltration is a predictor of clinical outcome. Random forests were utilized to integrate Ig rearrangements, expressed TMB and tumor aneuploidy, into a predictive model of response that was superior to TMB (AUC = 0.89 and 0.65 respectively). Multivariate Cox proportional hazards analysis incorporating the same features was utilized to generate a risk score for each patient; those with high risk scores had a significantly shorter PFS compared to low risk patients (median PFS 1.45 months vs 29.01 months, log rank p = 3.4e-06, HR = 9.18, 95% CI: 3.14-26.85). Conclusions: Our findings highlight the multi-faceted interactions between the tumor and the immune system and the importance of pre-existing T and B cell immunity in driving clinical response and PFS after immune checkpoint blockade, laying the groundwork for integration of genomic and immune features into predictive models that may ultimately optimize therapeutic decisions.

scholarly journals Prognostic Nomogram for the Overall Survival of Patients with Newly Diagnosed Multiple Myeloma

2019 ◽  
Vol 2019 ◽  
pp. 1-10
Author(s):  
Yue Zhang ◽  
Xiao-Lei Chen ◽  
Wen-Ming Chen ◽  
He-Bing Zhou
Keyword(s):  
Multiple Myeloma ◽  
Overall Survival ◽  
Proportional Hazards ◽  
Predictive Accuracy ◽  
Cox Proportional Hazards ◽  
Serum Lactate Dehydrogenase ◽  
Newly Diagnosed ◽  
Discriminative Ability ◽  
Calibration Curves ◽  
Staging Systems

To establish a nomogram for predicting the overall survival (OS) of patients with newly diagnosed multiple myeloma (MM), 304 patients with newly diagnosed MM were recruited between June 1, 2010, and June 30, 2015, from the Beijing Chaoyang Hospital, Capital Medical University, and randomly divided into training (n=214) and validation (n=90) cohorts. The Kaplan-Meier method and the Cox proportional hazards regression model were used to evaluate the prognostic effects of multiple clinical and laboratory parameters on survival. Significant prognostic factors were combined to build a nomogram. The discriminative ability and predictive accuracy of the nomogram were evaluated using the index of concordance (C-index) and calibration curves and compared with the five staging systems currently used for MM. Multivariate analysis of the training cohort revealed that the age at diagnosis, clonal bone marrow plasma cells, serum lactate dehydrogenase, serumβ2-microglobulin, and del (17p) were independent risk factors for OS and were used to establish the nomogram. The C-index value of the nomogram for predicting OS was 0.749, which was significantly higher than the C-indices of the five most common staging systems currently used for MM. In the validation cohort, the C-index for nomogram-based predictions was 0.711 for OS, and the nomogram discrimination was better than the above mentioned five staging systems (P<0.001). All calibration curves revealed good consistency between predicted and actual survivals. The proposed nomogram is more accurate in predicting the prognoses of patients with newly diagnosed MM.

Phenotypic expression of Bax is a predictive marker of 5-fluorouracil treatment in colorectal cancer

2006 ◽  
Vol 24 (18_suppl) ◽  
pp. 3605-3605
Author(s):  
U. Manne ◽  
C. Suarez-Cuervo ◽  
N. C. Jhala ◽  
J. Posey ◽  
C. B. Herring ◽  
...  
Keyword(s):  
Overall Survival ◽  
Predictive Value ◽  
Proportional Hazards ◽  
Phenotypic Expression ◽  
Tumor Stage ◽  
Cox Proportional Hazards ◽  
Nuclear Accumulation ◽  
Adjuvant Therapies ◽  
Post Surgery ◽  
Significant Difference

3605 Background: The anti-tumor activity of 5-fluorouracil (5-FU) has been related to its ability to induce apoptosis. Therefore, we assessed the predictive value of phenotypic expression of key apoptotic molecules in colorectal adenocarcinomas (CRC) of patients treated with 5-FU-based adjuvant therapies. Methods: Archival tissues of CRCs from 56 patients who received a complete regimen of 5-FU-based chemotherapy after surgery, and 56 age, gender, ethnicity, tumor stage, tumor location, and tumor differentiation matched patients who had undergone only surgery (without any pre- or post-surgery chemo- or radiation therapy), were evaluated for immunophenotypic expression of Bax, Bcl-2 and nuclear accumulation of p53 (p53nac). Immunophenotypic expression of these markers was categorized into low and high expressors and cut-point values were determined based on their expression in benign epithelium. These tumors Ability of these markers in predicting the efficacy of 5-FU-based treatment was assessed by estimating overall survival utilizing the Kaplan-Meier and Cox proportional hazards methods. Results: There was no significant difference in overall survival rates between the two patient categories (logrank P=0.487). However, a better survival was observed for patients who received chemotherapy when their CRCs lacked Bax expression; in contrast, patients with high Bax expression CRCs had worse survival when they received chemotherapy (logrank P=0.016). Surgically treated patients with CRCs lacked Bax expression had 5.33 times higher mortality than those with high Bax expression (HR, 5.33; CI: 1.78–15.94), when controlled for tumor stage and other confounding variables. Bcl-2 or p53nac had no predictive value in either patient group. Conclusions: These preliminary findings are the first to provide good evidence to suggest that patients with CRCs lacking Bax phenotypic expression benefit from 5-FU-based adjuvant therapies but not those with high Bax expression (Supported by NIH/NCI R01-CA98932–01). No significant financial relationships to disclose.

Germline polymorphisms in genes maintaining replication fork to predict the efficacy of oxaliplatin and irinotecan in metastatic colorectal cancer (mCRC) patients enrolled in MAVERICC trial.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 3139-3139
Author(s):  
Hiroyuki Arai ◽  
Yi Xiao ◽  
Jingyuan Wang ◽  
Francesca Battaglin ◽  
Natsuko Kawanishi ◽  
...  
Keyword(s):  
Predictive Value ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Multivariable Analysis ◽  
Cox Proportional Hazards ◽  
Dna Lesions ◽  
Cox Proportional Hazards Model ◽  
Replication Forks ◽  
Univariable Analysis ◽  
Snp Interaction

3139 Background: Protection of replication forks is critical for the survival of cancer cells. Chemotherapeutic drugs such as oxaliplatin and irinotecan can impede the progression of replication forks by inducing DNA lesions, which cause fork collapse and generate double-strand breaks. We hypothesized that functional genetic variants in genes involved in the maintenance of replication forks may predict the efficacy of cytotoxic drugs in mCRC patients. Methods: We analyzed genomic and clinical data from MAVERICC, a phase II trial which compared mFOLFOX6 and FOLFIRI in combination with bevacizumab in untreated mCRC patients. Genomic DNA extracted from blood samples was genotyped using an OncoArray (Illumina, Inc., San Diego, CA, USA). Candidate six missense single nucleotide polymorphisms (SNPs) ( SLFN11 rs9898983, SLFN11 rs12453150, RPA1 rs5030749, MCM3 rs2230240, TIMELESS rs2291739, and TIMELESS rs774047) were tested for association with progression-free survival (PFS) and overall survival (OS), using Cox proportional hazards model. To confirm the predictive value, the treatment-by-SNP interaction was tested. Results: A total of 324 patients were available for the SNP analyses (mFOLFOX6 plus bevacizumab arm [OHP arm]: n = 161; FOLFIRI plus bevacizumab arm [IRI arm]: n = 163). In the OHP arm, univariable analysis showed a significantly better PFS in patients with G/G genotype of TIMELESS rs2291739 compared to those with any A allele, and in patients with T/T genotype of TIMELESS rs774047 compared to those with any C allele. However, neither of these SNP’s associations were confirmed by multivariable analysis: TIMELESS rs2291739 (any A allele vs G/G, hazard ratio [HR] = 0.60, 95% confidence interval [CI] = 0.31–1.17, p = 0.12) and TIMELESS rs774047 (any C allele vs T/T, HR = 0.74, 95% CI = 0.41–1.36, p = 0.33). In the IRI arm, univariable analysis showed a significantly worse OS in patients with G/G genotype of TIMELESS rs2291739 compared to those with any A allele, and in patients with T/T genotype of TIMELESS rs774047 compared to those with any C allele. Multivariable analysis confirmed the significant associations in these SNPs: TIMELESS rs2291739 (any A allele vs G/G, HR = 3.06, 95% CI = 1.49–6.25, p < 0.01) and TIMELESS rs774047 (any C allele vs T/T, HR = 2.95, 95% CI = 1.43–6.08, p < 0.01). Treatment-by-SNP interaction test confirmed the significant predictive value of both SNPs, both on PFS and OS. Conclusions: Germline polymorphisms in the TIMELESS gene involved in the protection of replication forks may predict efficacy of oxaliplatin and irinotecan in mCRC patients. Our novel findings warrant further validation studies.

scholarly journals Performance of Atrial Fibrillation Risk Prediction Models in Over Four Million Individuals

2020 ◽  
Author(s):  
Shaan Khurshid ◽  
Uri Kartoun ◽  
Jeffrey M. Ashburner ◽  
Ludovic Trinquart ◽  
Anthony Philippakis ◽  
...  
Keyword(s):  
Heart Failure ◽  
Atrial Fibrillation ◽  
Risk Prediction ◽  
Proportional Hazards ◽  
Prediction Models ◽  
Predictive Accuracy ◽  
Health Management ◽  
Cox Proportional Hazards ◽  
Risk Scores ◽  
Office Visits

Background - Atrial fibrillation (AF) is associated with increased risks of stroke and heart failure. Electronic health record (EHR) based AF risk prediction may facilitate efficient deployment of interventions to diagnose or prevent AF altogether. Methods - We externally validated an EHR atrial fibrillation (EHR-AF) score in IBM Explorys Life Sciences, a multi-institutional dataset containing statistically de-identified EHR data for over 21 million individuals ("Explorys Dataset"). We included individuals with complete AF risk data, ≥2 office visits within two years, and no prevalent AF. We compared EHR-AF to existing scores including CHARGE-AF, C 2 HEST, and CHA 2 DS 2 -VASc. We assessed association between AF risk scores and 5-year incident AF, stroke, and heart failure using Cox proportional hazards modeling, 5-year AF discrimination using c-indices, and calibration of predicted AF risk to observed AF incidence. Results - Of 21,825,853 individuals in the Explorys Dataset, 4,508,180 comprised the analysis (age 62.5, 56.3% female). AF risk scores were strongly associated with 5-year incident AF (hazard ratio [HR] per standard deviation [SD] increase 1.85 using CHA 2 DS 2 -VASc to 2.88 using EHR-AF), stroke (1.61 using C 2 HEST to 1.92 using CHARGE-AF), and heart failure (1.91 using CHA 2 DS 2 -VASc to 2.58 using EHR-AF). EHR-AF (c-index 0.808 [95%CI 0.807-0.809]) demonstrated favorable AF discrimination compared to CHARGE-AF (0.806 [0.805-0.807]), C 2 HEST (0.683 [0.682-0.684]), and CHA 2 DS 2 -VASc (0.720 [0.719-0.722]). Of the scores, EHR-AF demonstrated the best calibration to incident AF (calibration slope 1.002 [0.997-1.007]). In subgroup analyses, AF discrimination using EHR-AF was lower in individuals with stroke (c-index 0.696 [0.692-0.700]) and heart failure (0.621 [0.617-0.625]). Conclusions - EHR-AF demonstrates predictive accuracy for incident AF using readily ascertained EHR data. AF risk is associated with incident stroke and heart failure. Use of such risk scores may facilitate decision-support and population health management efforts focused on minimizing AF-related morbidity.

scholarly journals Prognostic role of BNP in children undergoing surgery for congenital heart disease: analysis of prediction models incorporating standard risk factors

2015 ◽  
Vol 53 (11) ◽  
Author(s):  
Massimiliano Cantinotti ◽  
Raffaele Giordano ◽  
Marco Scalese ◽  
Sabrina Molinaro ◽  
Francesca della Pina ◽  
...  
Keyword(s):  
Cardiac Surgery ◽  
Risk Prediction ◽  
Proportional Hazards ◽  
Prediction Models ◽  
Proportional Hazards Model ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Pediatric Cardiac Surgery ◽  
Hazards Model ◽  
Hazard Ratios

AbstractThe routine use of brain natriuretic peptide (BNP) in pediatric cardiac surgery remains controversial. Our aim was to test whether BNP adds information to predict risk in pediatric cardiac surgery.In all, 587 children undergoing cardiac surgery (median age 6.3 months; 1.2–35.9 months) were prospectively enrolled at a single institution. BNP was measured pre-operatively, on every post-operative day in the intensive care unit, and before discharge. The primary outcome was major complications and length ventilator stay >15 days. A first risk prediction model was fitted using Cox proportional hazards model with age, body surface area and Aristotle score as continuous predictors. A second model was built adding cardiopulmonary bypass time and arterial lactate at the end of operation to the first model. Then, peak post-operative log-BNP was added to both models. Analysis to test discrimination, calibration, and reclassification were performed.BNP increased after surgery (p<0.001), peaking at a mean of 63.7 h (median 36 h, interquartile range 12–84 h) post-operatively and decreased thereafter. The hazard ratios (HR) for peak-BNP were highly significant (first model HR=1.40, p=0.006, second model HR=1.44, p=0.008), and the log-likelihood improved with the addition of BNP at 12 h (p=0.006; p=0.009). The adjunction of peak-BNP significantly improved the area under the ROC curve (first model p<0.001; second model p<0.001). The adjunction of peak-BNP also resulted in a net gain in reclassification proportion (first model NRI=0.089, p<0.001; second model NRI=0.139, p=0.003).Our data indicates that BNP may improve the risk prediction in pediatric cardiac surgery, supporting its routine use in this setting.

Predictive models for identifying risk of readmission after index hospitalization for heart failure: A systematic review

2018 ◽  
Vol 17 (8) ◽  
pp. 675-689 ◽  
Author(s):  
Satish M Mahajan ◽  
Paul Heidenreich ◽  
Bruce Abbott ◽  
Ana Newton ◽  
Deborah Ward
Keyword(s):  
Heart Failure ◽  
Predictive Models ◽  
Complex Disease ◽  
Prediction Models ◽  
Predictive Accuracy ◽  
External Validation ◽  
Cox Proportional Hazards ◽  
Adverse Outcomes ◽  
Electronic Data ◽  
The Past

Aims: Readmission rates for patients with heart failure have consistently remained high over the past two decades. As more electronic data, computing power, and newer statistical techniques become available, data-driven care could be achieved by creating predictive models for adverse outcomes such as readmissions. We therefore aimed to review models for predicting risk of readmission for patients admitted for heart failure. We also aimed to analyze and possibly group the predictors used across the models. Methods: Major electronic databases were searched to identify studies that examined correlation between readmission for heart failure and risk factors using multivariate models. We rigorously followed the review process using PRISMA methodology and other established criteria for quality assessment of the studies. Results: We did a detailed review of 334 papers and found 25 multivariate predictive models built using data from either health system or trials. A majority of models was built using multiple logistic regression followed by Cox proportional hazards regression. Some newer studies ventured into non-parametric and machine learning methods. Overall predictive accuracy with C-statistics ranged from 0.59 to 0.84. We examined significant predictors across the studies using clinical, administrative, and psychosocial groups. Conclusions: Complex disease management and correspondingly increasing costs for heart failure are driving innovations in building risk prediction models for readmission. Large volumes of diverse electronic data and new statistical methods have improved the predictive power of the models over the past two decades. More work is needed for calibration, external validation, and deployment of such models for clinical use.

scholarly journals SOURCE: Prediction Models for Overall Survival in Patients With Metastatic and Potentially Curable Esophageal and Gastric Cancer

2021 ◽  
Vol 19 (4) ◽  
pp. 403-410
Author(s):  
Héctor G. van den Boorn ◽  
Ameen Abu-Hanna ◽  
Nadia Haj Mohammad ◽  
Maarten C.C.M. Hulshof ◽  
Suzanne S. Gisbertz ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Overall Survival ◽  
Proportional Hazards ◽  
Prediction Models ◽  
Metastatic Cancer ◽  
Cox Proportional Hazards ◽  
Future Research ◽  
Survival Prediction ◽  
Netherlands Cancer Registry ◽  
Treatment Type

Background: Personalized prediction of treatment outcomes can aid patients with cancer when deciding on treatment options. Existing prediction models for esophageal and gastric cancer, however, have mostly been developed for survival prediction after surgery (ie, when treatment has already been completed). Furthermore, prediction models for patients with metastatic cancer are scarce. The aim of this study was to develop prediction models of overall survival at diagnosis for patients with potentially curable and metastatic esophageal and gastric cancer (the SOURCE study). Methods: Data from 13,080 patients with esophageal or gastric cancer diagnosed in 2015 through 2018 were retrieved from the prospective Netherlands Cancer Registry. Four Cox proportional hazards regression models were created for patients with potentially curable and metastatic esophageal or gastric cancer. Predictors, including treatment type, were selected using the Akaike information criterion. The models were validated with temporal cross-validation on their C-index and calibration. Results: The validated model’s C-index was 0.78 for potentially curable gastric cancer and 0.80 for potentially curable esophageal cancer. For the metastatic models, the c-indices were 0.72 and 0.73 for esophageal and gastric cancer, respectively. The 95% confidence interval of the calibration intercepts and slopes contain the values 0 and 1, respectively. Conclusions: The SOURCE prediction models show fair to good c-indices and an overall good calibration. The models are the first in esophageal and gastric cancer to predict survival at diagnosis for a variety of treatments. Future research is needed to demonstrate their value for shared decision-making in clinical practice.

Multiparametric analysis of magnetic resonance images for glioma grading and patient survival time prediction

2011 ◽  
Vol 52 (9) ◽  
pp. 1052-1060 ◽  
Author(s):  
Benjamón Garzín ◽  
Kyrre E Emblem ◽  
Kim Mouridsen ◽  
Baard Nedregaard ◽  
Paulina Due-Tønnessen ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Survival Time ◽  
Predictive Value ◽  
Magnetic Resonance Images ◽  
Image Features ◽  
Cox Proportional Hazards ◽  
Survival Prediction ◽  
Dynamic Susceptibility ◽  
Post Contrast ◽  
Time Prediction

Background A systematic comparison of magnetic resonance imaging (MRI) options for glioma diagnosis is lacking. Purpose To investigate multiple MR-derived image features with respect to diagnostic accuracy in tumor grading and survival prediction in glioma patients. Material and Methods T1 pre- and post-contrast, T2 and dynamic susceptibility contrast scans of 74 glioma patients with histologically confirmed grade were acquired. For each patient, a set of statistical features was obtained from the parametric maps derived from the original images, in a region-of-interest encompassing the tumor volume. A forward stepwise selection procedure was used to find the best combinations of features for grade prediction with a cross-validated logistic model and survival time prediction with a cox proportional-hazards regression. Results Presence/absence of enhancement paired with kurtosis of the FM (first moment of the first-pass curve) was the feature combination that best predicted tumor grade (grade II vs. grade III-IV; median AUC = 0.96), with the main contribution being due to the first of the features. A lower predictive value (median AUC = 0.82) was obtained when grade IV tumors were excluded. Presence/absence of enhancement alone was the best predictor for survival time, and the regression was significant ( P < 0.0001). Conclusion Presence/absence of enhancement, reflecting transendothelial leakage, was the feature with highest predictive value for grade and survival time in glioma patients.

Health-related quality of life (HRQoL) and pain progression with enzalutamide (ENZ) in metastatic hormone-sensitive prostate cancer (mHSPC) from the ARCHES study.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. 5044-5044
Author(s):  
Arnulf Stenzl ◽  
Curtis Dunshee ◽  
Ugo De Giorgi ◽  
Boris Alekseev ◽  
Taro Iguchi ◽  
...  
Keyword(s):  
Repeated Measures ◽  
Mixed Model ◽  
Proportional Hazards ◽  
Short Form ◽  
Progression Free Survival ◽  
Brief Pain Inventory ◽  
Cox Proportional Hazards ◽  
Pain Severity ◽  
Significant Difference ◽  
Patient Reported

5044 Background: The Phase 3 ARCHES trial (NCT02677896) evaluated the efficacy and safety of ENZ + androgen deprivation therapy (ADT) vs placebo (PBO) + ADT in 1150 men with mHSPC. Here we report patient-reported outcome (PRO) data using Functional Assessment of Cancer Therapy-Prostate (FACT-P) and Brief Pain Inventory Short Form (BPI-SF). Methods: FACT-P and BPI-SF were assessed at baseline (BL), week (wk) 13, and then every 12 wks until disease progression. Longitudinal changes were assessed using mean scores and mixed-model repeated measures; lower BPI-SF scores represent less pain/interference; higher FACT-P scores represent better HRQoL. Time from BL to first deterioration in PRO score was assessed by Kaplan-Meier estimates and Cox proportional hazards models. Clinically meaningful difference was defined by change from baseline ≥10 for FACT-P total and ≥2 for worst pain/severity. Results: PRO instrument completion rates were high (88−96%) up to wk 73. At BL, men in both arms were generally asymptomatic and reported good HRQoL (FACT-P total: ENZ + ADT, 113.9; PBO + ADT, 112.7) and low pain (worst pain [item 3]: ENZ + ADT, 1.80; PBO + ADT, 1.77). HRQoL and pain scores remained stable over time and there were no clinically meaningful differences between groups in change from BL to wk 73. The proportion of men with no change or improvement in PRO scores (67–88%) was similar in both groups at all time points up to wk 73. There was no significant difference between arms for time to deterioration in FACT-P total (HR 0.90 [95% CI] (0.74, 1.09); p = 0.2998). ENZ + ADT significantly delayed time to pain progression for worst pain (HR 0.82 [0.69, 0.98]; p = 0.0322) and pain severity (HR 0.79 [0.65, 0.97]; p = 0.0209) vs PBO + ADT. Conclusions: Men with mHSPC were generally asymptomatic and had high levels of HRQoL and low levels of pain at BL, likely due to most men initiating ADT several months prior to study entry. No clinically meaningful differences in HRQoL were observed between ENZ and PBO. The prolongation in radiographic progression-free survival observed with ENZ + ADT was accompanied by a significantly prolonged time to progression of worst pain and pain severity vs PBO + ADT. Clinical trial information: NCT02677896.

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