scholarly journals Integrating single cell sequencing with a spatial quantitative systems pharmacology model spQSP for personalized prediction of triple-negative breast cancer immunotherapy response

2021 ◽  
pp. 100002
Author(s):  
Shuming Zhang ◽  
Chang Gong ◽  
Alvaro Ruiz-Martinez ◽  
Hanwen Wang ◽  
Emily Davis-Marcisak ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Single Cell ◽  
Cancer Immunotherapy ◽  
Triple Negative Breast Cancer ◽  
Triple Negative ◽  
Systems Pharmacology ◽  
Quantitative Systems Pharmacology ◽  
Single Cell Sequencing ◽  
Systems Pharmacology Model

A Quantitative Systems Pharmacology Framework for Optimal Doxorubicin Granulocyte Colony-Stimulating Factor Regimens in Triple-Negative Breast Cancer

Pharmacology ◽  
2021 ◽  
pp. 1-9
Author(s):  
Rosalba Vivian Paredes Bonilla ◽  
Fahima Nekka ◽  
Morgan Craig
Keyword(s):  
Breast Cancer ◽  
Triple Negative Breast Cancer ◽  
Tumour Growth ◽  
Triple Negative ◽  
Effective Control ◽  
Systems Pharmacology ◽  
Granulocyte Colony Stimulating Factor ◽  
Colony Stimulating Factor ◽  
Quantitative Systems Pharmacology ◽  
Stimulating Factor

<b><i>Introduction:</i></b> To mitigate the risk of neutropenia during chemotherapy treatment of triple-negative breast cancer, prophylactic and supportive therapy with granulocyte colony-stimulating factor (G-CSF) is administered concomitant to chemotherapy. The proper timing of combined chemotherapy and G-CSF is crucial for treatment outcomes. <b><i>Methods:</i></b> Leveraging our established mathematical model of neutrophil production by G-CSF, we developed quantitative systems pharmacology (QSP) framework to investigate how modulating chemotherapy dose frequency and intensity can maximize antitumour effects. To establish schedules that best control tumour size while minimizing neutropenia, we combined Gompertzian tumour growth with pharmacokinetic/pharmacodynamic models of doxorubicin and G-CSF, and our QSP model of neutrophil production. <b><i>Results:</i></b> We optimized a range of chemotherapeutic cycle lengths and dose sizes to establish regimens that simultaneously reduced tumour burden while minimizing neutropenia. Our results suggest that cytotoxic chemotherapy with doxorubicin 45 mg/m<sup>2</sup> every 14 days provides effective control of tumour growth while mitigating neutropenic risks. <b><i>Conclusion:</i></b> This work suggests future avenues for optimal regimens of chemotherapy with prophylactic G-CSF support. Importantly, the algorithmic approach that we developed can aid in balancing the anticancer and the neutropenic effects of both drugs, and therefore contributes to rational considerations in clinical decision-making in triple-negative breast cancer.

scholarly journals Advances in Single-Cell Sequencing Technology and its Applications in Triple-Negative Breast Cancer

2021 ◽  
Author(s):  
Meng Li ◽  
Miaozhou Wang ◽  
Yanlin Xie ◽  
Tingting Yan ◽  
Yanfang Li
Keyword(s):  
Breast Cancer ◽  
Single Cell ◽  
Triple Negative Breast Cancer ◽  
Triple Negative ◽  
Current Knowledge ◽  
Resistance Mechanisms ◽  
Sequencing Technology ◽  
Single Cell Sequencing ◽  
Aggressive Subtype

Abstract Triple-negative breast cancer (TNBC) is the most aggressive subtype of breast cancer and is mainly treated with chemotherapy-based combination therapy. In recent years, the increasing development of single-cell sequencing (SCS) has become one of the most promising technologies in the field of biotechnology. The study of the heterogeneity of TNBC tumor cells using SCS will expand our current knowledge of metastasis, drug resistance mechanisms, mutations, and cloning in these cells; this will further guide clinical chemotherapy, targeted therapy, and immunotherapy. Relevant studies shown that SCS exactly plays an important role in clinical diagnosis and treatment. To highlight the role of SCS in the study of TNBC, we elaborate on the progress of research and the applications of SCS in TNBC.

scholarly journals Chemoresistance Evolution in Triple-Negative Breast Cancer Delineated by Single-Cell Sequencing

Cell ◽  
2018 ◽  
Vol 173 (4) ◽  
pp. 879-893.e13 ◽  
Author(s):  
Charissa Kim ◽  
Ruli Gao ◽  
Emi Sei ◽  
Rachel Brandt ◽  
Johan Hartman ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Single Cell ◽  
Triple Negative Breast Cancer ◽  
Triple Negative ◽  
Single Cell Sequencing

scholarly journals Quantitative systems pharmacology model predictions for efficacy of atezolizumab and nab-paclitaxel in triple-negative breast cancer

2021 ◽  
Vol 9 (2) ◽  
pp. e002100
Author(s):  
Hanwen Wang ◽  
Huilin Ma ◽  
Richard J Sové ◽  
Leisha A Emens ◽  
Aleksander S Popel
Keyword(s):  
Breast Cancer ◽  
Clinical Trials ◽  
Triple Negative Breast Cancer ◽  
Triple Negative ◽  
Predictive Biomarkers ◽  
Virtual Patient ◽  
Systems Pharmacology ◽  
Quantitative Systems Pharmacology ◽  
Patient Cohort ◽  
Model Predictions

BackgroundImmune checkpoint blockade therapy has clearly shown clinical activity in patients with triple-negative breast cancer, but less than half of the patients benefit from the treatments. While a number of ongoing clinical trials are investigating different combinations of checkpoint inhibitors and chemotherapeutic agents, predictive biomarkers that identify patients most likely to benefit remains one of the major challenges. Here we present a modular quantitative systems pharmacology (QSP) platform for immuno-oncology that incorporates detailed mechanisms of immune–cancer cell interactions to make efficacy predictions and identify predictive biomarkers for treatments using atezolizumab and nab-paclitaxel.MethodsA QSP model was developed based on published data of triple-negative breast cancer. With the model, we generated a virtual patient cohort to conduct in silico virtual clinical trials and make retrospective analyses of the pivotal IMpassion130 trial that led to the accelerated approval of atezolizumab and nab-paclitaxel for patients with programmed death-ligand 1 (PD-L1) positive triple-negative breast cancer. Available data from clinical trials were used for model calibration and validation.ResultsWith the calibrated virtual patient cohort based on clinical data from the placebo comparator arm of the IMpassion130 trial, we made efficacy predictions and identified potential predictive biomarkers for the experimental arm of the trial using the proposed QSP model. The model predictions are consistent with clinically reported efficacy endpoints and correlated immune biomarkers. We further performed a series of virtual clinical trials to compare different doses and schedules of the two drugs for simulated therapeutic optimization.ConclusionsThis study provides a QSP platform, which can be used to generate virtual patient cohorts and conduct virtual clinical trials. Our findings demonstrate its potential for making efficacy predictions for immunotherapies and chemotherapies, identifying predictive biomarkers, and guiding future clinical trial designs.

scholarly journals Systems pharmacology in combination with proteomics reveals underlying mechanisms of Xihuang pill against triple-negative breast cancer

Bioengineered ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 1170-1188
Author(s):  
Xingchao Xu ◽  
Jimei Zhang ◽  
Zhenhua Zhang ◽  
Meng Wang ◽  
Yaping Liu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Triple Negative Breast Cancer ◽  
Triple Negative ◽  
Systems Pharmacology ◽  
Underlying Mechanisms
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