Dataset Information

Liu2019 - Logistic Regression models to predict intrinsic resistance to anti-PD1 ICB in Ipilimumab treated and Ipilimumab naive patients with metastatic melanoma.

ABSTRACT: In this comprehensive study, the authors have developed concise models integrating clinical, genomic and transcriptomic features to predict intrinsic resistance to anti-PD1 Immune Checkpoint Blockade (ICB) treatment in individual tumors. It's important to note that their validation was performed in smaller, independent cohorts, constrained by data availability. The authors have developed two Logistic Regression based models for Ipilimumab treated and Ipilimumab naive patients with metastatic melanoma. The main predictive features for the Ipilimumab treated patients are MHC-II HLA, LDH at treatment initiation and the presence of lymph node metastases (LN met), chosen using forward selection methodology. The main predictive features for the Ipilimumab naive patients are tumor heterogeneity, tumor ploidy and tumor purity, chosen using forward selection methodology. Please note that in these models, the output ‘1’ means progressive disease (PD) and ‘0’ means non-PD. The original GitHub repository can be accessed at https://github.com/vanallenlab/schadendorf-pd1

SUBMITTER: Divyang Deep Tiwari

PROVIDER: MODEL2310150001 | BioModels | 2023-11-17

REPOSITORIES: BioModels

ACCESS DATA

Dataset's files

Source:

			Action	DRS
	MODEL2310150001?filename=Ipilimumab_naive.onnx	Other
	MODEL2310150001?filename=Ipilimumab_treated.onnx	Other
	MODEL2310150001?filename=PD1_melanoma_LR.csv	Csv
	MODEL2310150001?filename=Reproduced_Figures.pdf	Pdf
	MODEL2310150001?filename=docker_predict.zip	Other

Items per page:

1 - 5 of 7

Publications

Integrative molecular and clinical modeling of clinical outcomes to PD1 blockade in patients with metastatic melanoma.

Liu David D Schilling Bastian B Liu Derek D Sucker Antje A Livingstone Elisabeth E Jerby-Arnon Livnat L Zimmer Lisa L Gutzmer Ralf R Satzger Imke I Loquai Carmen C Grabbe Stephan S Vokes Natalie N Margolis Claire A CA Conway Jake J He Meng Xiao MX Elmarakeby Haitham H Dietlein Felix F Miao Diana D Tracy Adam A Gogas Helen H Goldinger Simone M SM Utikal Jochen J Blank Christian U CU Rauschenberg Ricarda R von Bubnoff Dagmar D Krackhardt Angela A Weide Benjamin B Haferkamp Sebastian S Kiecker Felix F Izar Ben B Garraway Levi L Regev Aviv A Flaherty Keith K Paschen Annette A Van Allen Eliezer M EM Schadendorf Dirk D

Nature medicine 20191202 12

Immune-checkpoint blockade (ICB) has demonstrated efficacy in many tumor types, but predictors of responsiveness to anti-PD1 ICB are incompletely characterized. In this study, we analyzed a clinically annotated cohort of patients with melanoma (n = 144) treated with anti-PD1 ICB, with whole-exome and whole-transcriptome sequencing of pre-treatment tumors. We found that tumor mutational burden as a predictor of response was confounded by melanoma subtype, whereas multiple novel genomic and transc ...[more]

PMID: 31792460

Similar Datasets

Project description:Ribba2012 - Low-grade gliomas, tumour growth inhibition model Using longitudinal mean tumour diameter (MTD) data, this model describe the size evolution of low-grade glioma (LGG) in patients treated with chemotherapy or radiotherapy. This model is described in the article: A tumour growth inhibition model for low-grade glioma treated with chemotherapy or radiotherapy Ribba B, Kaloshi G, Peyre M, Ricard D, Calvez V, Tod M, Cajavec-Bernard B, Idbaih A, Psimaras D, Dainese L, Pallud J, Cartalat-Carel S, Delattre JY, Honnorat J, Grenier E, Ducray F. Clin. Cancer Res. 2012 Sep; 18(18): 5071-5080 Abstract: PURPOSE: To develop a tumor growth inhibition model for adult diffuse low-grade gliomas (LGG) able to describe tumor size evolution in patients treated with chemotherapy or radiotherapy. EXPERIMENTAL DESIGN: Using longitudinal mean tumor diameter (MTD) data from 21 patients treated with first-line procarbazine, 1-(2-chloroethyl)-3-cyclohexyl-l-nitrosourea, and vincristine (PCV) chemotherapy, we formulated a model consisting of a system of differential equations, incorporating tumor-specific and treatment-related parameters that reflect the response of proliferative and quiescent tumor tissue to treatment. The model was then applied to the analysis of longitudinal tumor size data in 24 patients treated with first-line temozolomide (TMZ) chemotherapy and in 25 patients treated with first-line radiotherapy. RESULTS: The model successfully described the MTD dynamics of LGG before, during, and after PCV chemotherapy. Using the same model structure, we were also able to successfully describe the MTD dynamics in LGG patients treated with TMZ chemotherapy or radiotherapy. Tumor-specific parameters were found to be consistent across the three treatment modalities. The model is robust to sensitivity analysis, and preliminary results suggest that it can predict treatment response on the basis of pretreatment tumor size data. CONCLUSIONS: Using MTD data, we propose a tumor growth inhibition model able to describe LGG tumor size evolution in patients treated with chemotherapy or radiotherapy. In the future, this model might be used to predict treatment efficacy in LGG patients and could constitute a rational tool to conceive more effective chemotherapy schedules. This model is hosted on BioModels Database and identified by: BIOMD0000000521 . To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

Project description:Zhu2015 - Combined gemcitabine and birinapant in pancreatic cancer cells - basic PD model Mathematical model to illustrate the effectiveness of combination chemotherapy involving gemcitabine and birinapant against pancreatic cancer. This model is described in the article: Mechanism-based mathematical modeling of combined gemcitabine and birinapant in pancreatic cancer cells. Zhu X, Straubinger RM, Jusko WJ. J Pharmacokinet Pharmacodyn 2015 Oct; 42(5): 477-496 Abstract: Combination chemotherapy is standard treatment for pancreatic cancer. However, current drugs lack efficacy for most patients, and selection and evaluation of new combination regimens is empirical and time-consuming. The efficacy of gemcitabine, a standard-of-care agent, combined with birinapant, a pro-apoptotic antagonist of Inhibitor of Apoptosis Proteins (IAPs), was investigated in pancreatic cancer cells. PANC-1 cells were treated with vehicle, gemcitabine (6, 10, 20 nM), birinapant (50, 200, 500 nM), and combinations of the two drugs. Temporal changes in cell numbers, cell cycle distribution, and apoptosis were measured. A basic pharmacodynamic (PD) model based on cell numbers, and a mechanism-based PD model integrating all measurements, were developed. The basic PD model indicated that synergistic effects occurred in both cell proliferation and death processes. The mechanism-based model captured key features of drug action: temporary cell cycle arrest in S phase induced by gemcitabine alone, apoptosis induced by birinapant alone, and prolonged cell cycle arrest and enhanced apoptosis induced by the combination. A drug interaction term Ψ was employed in the models to signify interactions of the combination when data were limited. When more experimental information was utilized, Ψ values approaching 1 indicated that specific mechanisms of interactions were captured better. PD modeling identified the potential benefit of combining gemcitabine and birinapant, and characterized the key interaction pathways. An optimal treatment schedule of pretreatment with gemcitabine for 24-48 h was suggested based on model predictions and was verified experimentally. This approach provides a generalizable modeling platform for exploring combinations of cytostatic and cytotoxic agents in cancer cell culture studies. This model is hosted on BioModels Database and identified by: BIOMD0000000668. To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8. To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

Project description:Zhu2015 - combined gemcitabine and birinapant in pancreatic cancer cells - mechanistic PD model Mechanistic mathematical model to illustrate the effectiveness of combination chemotherapy involving gemcitabine and birinapant against pancreatic cancer. This model is described in the article: Mechanism-based mathematical modeling of combined gemcitabine and birinapant in pancreatic cancer cells. Zhu X, Straubinger RM, Jusko WJ. J Pharmacokinet Pharmacodyn 2015 Oct; 42(5): 477-496 Abstract: Combination chemotherapy is standard treatment for pancreatic cancer. However, current drugs lack efficacy for most patients, and selection and evaluation of new combination regimens is empirical and time-consuming. The efficacy of gemcitabine, a standard-of-care agent, combined with birinapant, a pro-apoptotic antagonist of Inhibitor of Apoptosis Proteins (IAPs), was investigated in pancreatic cancer cells. PANC-1 cells were treated with vehicle, gemcitabine (6, 10, 20 nM), birinapant (50, 200, 500 nM), and combinations of the two drugs. Temporal changes in cell numbers, cell cycle distribution, and apoptosis were measured. A basic pharmacodynamic (PD) model based on cell numbers, and a mechanism-based PD model integrating all measurements, were developed. The basic PD model indicated that synergistic effects occurred in both cell proliferation and death processes. The mechanism-based model captured key features of drug action: temporary cell cycle arrest in S phase induced by gemcitabine alone, apoptosis induced by birinapant alone, and prolonged cell cycle arrest and enhanced apoptosis induced by the combination. A drug interaction term Ψ was employed in the models to signify interactions of the combination when data were limited. When more experimental information was utilized, Ψ values approaching 1 indicated that specific mechanisms of interactions were captured better. PD modeling identified the potential benefit of combining gemcitabine and birinapant, and characterized the key interaction pathways. An optimal treatment schedule of pretreatment with gemcitabine for 24-48 h was suggested based on model predictions and was verified experimentally. This approach provides a generalizable modeling platform for exploring combinations of cytostatic and cytotoxic agents in cancer cell culture studies. This model is hosted on BioModels Database and identified by: BIOMD0000000669. To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8. To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.