response score
Recently Published Documents


TOTAL DOCUMENTS

149
(FIVE YEARS 84)

H-INDEX

17
(FIVE YEARS 5)

2022 ◽  
Author(s):  
Nickolay Khazanov ◽  
Melissa Shreve ◽  
Laura Lamb ◽  
Daniel Hovelson ◽  
Marc Matrana ◽  
...  

Abstract Pembrolizumab is approved in many advanced solid tumor types, however predictive biomarkers and the proportion of pembrolizumab-benefiting patients vary. Biomarkers beyond PD-L1 immunohistochemistry, microsatellite instability (MSI) status, and tumor mutation burden (TMB) may improve benefit prediction. Here, leveraging treatment data (time to next treatment [TTNT]) and comprehensive genomic and quantitative transcriptomic profiling on routine tumor tissue from 708 patients (24 tumor types) collected in an ongoing observational trial (NCT03061305), we report a multivariate, integrative predictor of pan-solid tumor pembrolizumab benefit. The Immune Response Score (IRS) model, which includes TMB and quantitative PD-1, PD-L2, ADAM12 and CD4 RNA expression, was confirmed as predictive through comparison of pembrolizumab TTNT with previous chemotherapy TTNT in a subset of 166 patients treated with both. Applying IRS to the entire NCT03061305 cohort (n=25,770 patients), 13.2-30.7% of patients (2.2-9.6% of patients outside of pembrolizumab approved tumor types [including TMB-High and MSI-High]) are predicted to benefit substantially from pembrolizumab. Hence, if prospectively validated, the IRS model may improve pembrolizumab benefit prediction across approved tumor types including patients outside of currently approved indications.


Blood ◽  
2021 ◽  
Vol 138 (Supplement 1) ◽  
pp. 3805-3805
Author(s):  
Nadine Abdallah ◽  
Eli Muchtar ◽  
Angela Dispenzieri ◽  
Morie A. Gertz ◽  
Prashant Kapoor ◽  
...  

Abstract Background: Systemic light chain (AL) amyloidosis is a plasma cell disorder characterized by multisystem deposition of misfolded immunoglobulin light chains produced by clonal plasma cells. Hematologic and organ responses with treatment have been shown to correlate with survival as early as 3 months from initiation of first-line treatment. Our group recently developed and validated a model integrating organ and hematologic responses for assessment of treatment outcomes at 6 months. Although current organ response criteria do not consider the depth of organ response, this has been shown to have prognostic utility in newly diagnosed patients. So, we designed this study to evaluate a composite hematologic and organ scoring system that also considers the depth of organ response at 1 to 6 months from initiation of first-line treatment. Methods: We included patients with AL amyloidosis who had at least one major organ involvement (cardiac, renal and/or liver involvement) and who had not started a second line treatment by 6 months from the time of initiating first-line treatment. For each patient, we calculated an organ response score and a hematologic response score at 1, 2, 3, 4, 5, and 6 months from initiation of first-line treatment. A score was assigned for each depth of hematologic response as follows: complete response=0, very good partial response=1, partial response=2, and no response/progressive disease=3). To calculate organ response, a score was assigned to each organ based on the depth of organ response (Muchtar et al. 2018): non-evaluable=0, complete response=1, very good partial response=2, partial response=3, and no response=4. The final organ response score was obtained by calculating the average of the individual involved organ scores. We then calculated the composite hematologic and organ response (HOR) score by adding the organ and hematologic responses at each interval and compared overall survival (OS) between patients with HOR score ≤ 5 (group 1) and those with score > 5 (group 2). Results: The cohort included 730 patients diagnosed with AL amyloidosis between February 10 th, 2006 and July 9 th, 2019. Median age was 63 (IQR: 56-69), and 65% were male. The involved light chain was Lambda in 75% of cases. Cardiac, renal, and liver involvement were found in 81%, 61%, and 17% of patients, respectively. Among all patients, 28% underwent autologous stem cell transplantation during their disease course. The median follow up in the entire cohort was 7.0 (95%CI: 6.4-8.0) years and OS was 3.6 (95%CI: 2.6-4.4) years. At 1 to 4 months, we observed a statistically significant difference in OS between patients with HOR score ≤5 vs. >5. However, there was no difference in OS between the 2 groups at 5 and 6 months. These results are presented in Table 1. Conclusion: A composite hematologic and organ response score that takes into consideration the depth of organ response can discriminate 2 groups of patients with distinct survival outcomes as early as 1 month from treatment initiation and maintains its predictive ability for up to 4 months. The lack of predictive ability beyond 4 months in this study may be due to limited sample size especially in group 2 as more patients achieve deeper responses with time. This approach can provide the basis for early changes in treatment but needs validation in future studies. Figure 1 Figure 1. Disclosures Dispenzieri: Janssen: Consultancy, Research Funding; Takeda: Research Funding; Sorrento Therapeutics: Consultancy; Oncopeptides: Consultancy; Pfizer: Research Funding; Alnylam: Research Funding. Gertz: Ionis Pharmaceuticals: Other: Advisory Board; Akcea Therapeutics, Ambry Genetics, Amgen Inc, Celgene Corporation, Janssen Biotech Inc, Karyopharm Therapeutics, Pfizer Inc (to Institution), Sanofi Genzyme: Honoraria; Aurora Biopharma: Other: Stock option; Akcea Therapeutics, Alnylam Pharmaceuticals Inc, Prothena: Consultancy; AbbVie Inc, Celgene Corporation: Other: Data Safetly & Monitoring. Kapoor: Ichnos Sciences: Research Funding; Karyopharm: Consultancy; Glaxo SmithKline: Research Funding; Pharmacyclics: Consultancy; Amgen: Research Funding; Cellectar: Consultancy; Sanofi: Consultancy; BeiGene: Consultancy; Regeneron Pharmaceuticals: Research Funding; Karyopharm: Research Funding; Sanofi: Research Funding; Takeda: Research Funding; AbbVie: Research Funding. Dingli: GSK: Consultancy; Sanofi: Consultancy; Janssen: Consultancy; Novartis: Research Funding; Apellis: Consultancy; Alexion: Consultancy. Kumar: Oncopeptides: Consultancy; Merck: Research Funding; Antengene: Consultancy, Honoraria; Bluebird Bio: Consultancy; Roche-Genentech: Consultancy, Research Funding; Astra-Zeneca: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Amgen: Consultancy, Research Funding; Novartis: Research Funding; Carsgen: Research Funding; Tenebio: Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; KITE: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; BMS: Consultancy, Research Funding; Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Beigene: Consultancy; Adaptive: Membership on an entity's Board of Directors or advisory committees, Research Funding; Sanofi: Research Funding; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding.


2021 ◽  
Vol 14 (1) ◽  
Author(s):  
M. Liontos ◽  
A. Andrikopoulou ◽  
K. Koutsoukos ◽  
C. Markellos ◽  
E. Skafida ◽  
...  

Abstract Background Neoadjuvant chemotherapy (NACT) followed by interval debulking surgery (IDS) is the recommended approach in patients with advanced epithelial ovarian cancer (EOC). However, most patients eventually relapse despite the initial high response rate to chemotherapy. Neutrophil-to-lymphocyte ratio is a well-known biomarker that reflects severe inflammation, critical illness, and mortality in various diseases. Chemotherapy response score (CRS) and neutrophil-to-lymphocyte ratio (NLR) have been identified as potential biomarkers of platinum resistance and disease prognosis. We retrospectively evaluated 132 patients with stage IIIc or IV ovarian/fallopian tube/primary peritoneal cancer who had received NACT followed by IDS from 01/01/2003 to 31/12/2018. CRS was assessed on omental specimens collected from IDS according to ICCR guidelines. Results Median age was 64.57 years (SD: 9.72; range 39.2–87.1). Most ovarian tumors were serous epithelial (90.9%; 120/132). An elevated NLR (defined as > 3) was observed in 72% (95/132) of patients in contrast with 28% (37/132) of patients characterized by low NLR status. Median PFS (mPFS) and median overall survival (mOS) were 13.05 months (95% CI: 11.42–14.67)) and 34.69 months (95% CI: 23.26–46.12) respectively. In univariate analysis, CRS3 score was significantly associated with prolonged mPFS (CRS1/2: 12.79 months vs CRS3: 17.7 months; P = 0.008). CRS score was not associated with mOS (P = 0.876). High NLR was not significantly associated with mPFS (P = 0.128), however it was significantly associated with poor mOS (P = 0.012). In multivariate analysis, only performance of surgery maintained its statistical significance with both PFS (P = 0.001) and OS (P = 0.008). Conclusion NLR could serve as a useful predictor of OS but not PFS in ovarian cancer patients receiving NACT. In accordance with our previous study, CRS score at omentum was found to be associated with PFS but not OS in ovarian cancer patients treated with NACT and IDS.


2021 ◽  
Author(s):  
Xiaoyan Li ◽  
Guoyin Li ◽  
Xiyang Tang ◽  
Yongsheng Zhou ◽  
Kaifu Zheng ◽  
...  

Abstract Background: In the past 10 years, the identification of new mutant genes involved in the pathogenesis of melanoma and the discovery of key immune checkpoints have promoted the development of targeted therapy and immunotherapy. There is no doubt that an important breakthrough has been made in the treatment of advanced or metastatic melanoma. However, the treatment of melanoma also faces many challenges. In addition to resistance to existing targeted therapies or immunotherapy, most patients do not respond to immunotherapy or have serious adverse reactions. At present, the value of existing biomarkers to predict treatment response and toxicity is still limited. Therefore, there is an urgent need to establish a convenient and reliable immunotherapy response prediction model in order to preliminarily clarify the population benefiting from immunotherapy.Results: We established a predictive model based on the expression values of five genes for patients with melanoma with an anti-PD1 immunotherapy response score. This model showed better predictive ability compared with other common immunotherapy predictors. Differences were found in the number of immune cells and the expression of common immune checkpoint genes between the high- and low-score groups. The model played a pivotal role in predicting renal cell carcinoma anti-PD1 immunotherapy response. Conclusions: The anti-PD1 immunotherapy response score prediction model for patients with melanoma showed good predictive power, thus having far-reaching significance for identifying people who benefited from anti-PD1 immunotherapy and reducing the potential toxicity of insensitive patients.


2021 ◽  
Author(s):  
Anmin Hu ◽  
Hui-Ping Li ◽  
Zhen Li ◽  
Zhongjun Zhang ◽  
Xiong-Xiong Zhong

Abstract Purpose: The aim of this study was to use machine learning to construct a model for the analysis of risk factors and prediction of delirium among ICU patients.Methods: We developed a set of real-world data to enable the comparison of the reliability and accuracy of delirium prediction models from the MIMIC-III database, the MIMIC-IV database and the eICU Collaborative Research Database. Significance tests, correlation analysis, and factor analysis were used to individually screen 80 potential risk factors. The predictive algorithms were run using the following models: Logistic regression, naive Bayesian, K-nearest neighbors, support vector machine, random forest, and eXtreme Gradient Boosting. Conventional E-PRE-DELIRIC and eighteen models, including all-factor (AF) models with all potential variables, characteristic variable (CV) models with principal component factors, and rapid predictive (RP) models without laboratory test results, were used to construct the risk prediction model for delirium. The performance of these machine learning models was measured by the area under the receiver operating characteristic curve (AUC) of tenfold cross-validation. The VIMs and SHAP algorithms, feature interpretation and sample prediction interpretation algorithms of the machine learning black box model were implemented.Results: A total of 78,365 patients were enrolled in this study, 22,159 of whom (28.28%) had positive delirium records. The E-PRE-DELIRIC model (AUC, 0.77), CV models (AUC, 0.77-0.93), CV models (AUC, 0.77-0.88) and RP models (AUC, 0.75-0.87) had discriminatory value. The random forest CV model found that the top five factors accounting for the weight of delirium were length of ICU stay, verbal response score, APACHE-III score, urine volume and hemoglobin. The SHAP values in the eXtreme Gradient Boosting CV model showed that the top three features that were negatively correlated with outcomes were verbal response score, urine volume, and hemoglobin; the top three characteristics that were positively correlated with outcomes were length of ICU stay, APACHE-III score, and alanine transaminase.Conclusion: Even with a small number of variables, machine learning has a good ability to predict delirium in critically ill patients. Characteristic variables provide direction for early intervention to reduce the risk of delirium.


2021 ◽  
pp. 101-110
Author(s):  
Kintan Utari ◽  
Neng Nenden Mulyaningsih ◽  
Irnin Agustina Dwi Astuti ◽  
Yoga Budi Bhakti ◽  
Zulherman Zulherman

In the material of thermodynamics, many physics equations make students abstract in understanding the material. It is necessary to have a learning media to be able to visualize the thermodynamics material. Matlab is usually used in numerical analysis. Currently, Matlab can be used as a medium for learning physics by students to understand the material well. This study aims to develop a physics calculator application on the subject of thermodynamics using Matlab software. The research method used in this research is the research development method with the development model of ADDIE (Analysis, Design, Development, Implementation and Evaluation). The instruments used in this study were a validation test questionnaire and a student response questionnaire. The analysis technique applied is the descriptive analysis and the average analysis techniques. The results developed in this study are in the form of a Physics calculator application that helps physics calculations on thermodynamic material. The average validation result by material experts and media experts is 83.1%, with the "Good" category. In other words, according to experts, this media is suitable for use in physics learning. This application also received a positive response from students, with an average student response score of 79.8%. This application is also equipped with materials, physics calculators, physics simulations and question exercises so that it is expected to help students understand physics learning in thermodynamics material.


Sign in / Sign up

Export Citation Format

Share Document