scholarly journals Computational modeling of ovarian cancer dynamics suggests optimal strategies for therapy and screening

2021 ◽  
Vol 118 (25) ◽  
pp. e2026663118
Author(s):  
Shengqing Gu ◽  
Stephanie Lheureux ◽  
Azin Sayad ◽  
Paulina Cybulska ◽  
Liat Hogen ◽  
...  
Keyword(s):  
Stochastic Models ◽  
Clinical Course ◽  
Optimal Strategies ◽  
Tumor Burden ◽  
Mathematical Framework ◽  
Debulking Surgery ◽  
Primary Debulking Surgery ◽  
Key Issues ◽  
Parameter Values ◽  
Cancer Dynamics

High-grade serous tubo-ovarian carcinoma (HGSC) is a major cause of cancer-related death. Treatment is not uniform, with some patients undergoing primary debulking surgery followed by chemotherapy (PDS) and others being treated directly with chemotherapy and only having surgery after three to four cycles (NACT). Which strategy is optimal remains controversial. We developed a mathematical framework that simulates hierarchical or stochastic models of tumor initiation and reproduces the clinical course of HGSC. After estimating parameter values, we infer that most patients harbor chemoresistant HGSC cells at diagnosis and that, if the tumor burden is not too large and complete debulking can be achieved, PDS is superior to NACT due to better depletion of resistant cells. We further predict that earlier diagnosis of primary HGSC, followed by complete debulking, could improve survival, but its benefit in relapsed patients is likely to be limited. These predictions are supported by primary clinical data from multiple cohorts. Our results have clear implications for these key issues in HGSC management.

ATL

2018 ◽  
Vol 28 (8) ◽  
pp. 1498-1506 ◽  
Author(s):  
Tatyana V. Gorodnova ◽  
Khristina B. Kotiv ◽  
Alexandr O. Ivantsov ◽  
Olga N. Mikheyeva ◽  
Galina I. Mikhailiuk ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Neoadjuvant Chemotherapy ◽  
Tumor Cells ◽  
Mitomycin C ◽  
Pathologic Complete Response ◽  
Complete Response ◽  
Tumor Burden ◽  
Debulking Surgery ◽  
Tumor Spread ◽  
Primary Debulking Surgery

ObjectivesCisplatin and mitomycin C exert high activity towards BRCA1-deficient cells. This study aimed to evaluate the efficacy of a combination of these drugs in hereditary BRCA1-associated ovarian cancer (OC).MethodsTwelve OC patients, who could not be treated by primary debulking surgery owing to extensive tumor spread, were given neoadjuvant cisplatin (100 mg/m2) and mitomycin C (10 mg/m2) every 4 weeks for 3 (n = 9), 2 (n = 2), or 4 (n = 1) cycles.ResultsThe decrease of tumor burden and complete surgical cytoreduction were achieved in all patients. Pathologic complete response, defined as the absence of tumor cells in surgically removed tissues, was observed in 2 (17%) of 12 cases. Retrospective analysis of 62 OC in BRCA1 mutation carriers subjected to conventional neoadjuvant chemotherapy schemes revealed 36 objective tumor responses (58%) and 37 instances (60%) of complete cytoreductive surgery; however, none of these patients demonstrated pathologic complete response.ConclusionsThe combination of cisplatin plus mitomycin C showed promising results in BRCA1-driven OC and therefore deserves further clinical evaluation.

scholarly journals Computational Modeling of Ovarian Cancer Reveals Optimal Strategies for Therapy and Screening

2019 ◽  
Author(s):  
Shengqing Gu ◽  
Stephanie Lheureux ◽  
Azin Sayad ◽  
Paulina Cybulska ◽  
Liat Ben-David Hogen ◽  
...  
Keyword(s):  
Adjuvant Chemotherapy ◽  
Clinical Data ◽  
Evolutionary Dynamics ◽  
Optimal Strategies ◽  
Residual Tumor ◽  
Optimal Order ◽  
Mathematical Framework ◽  
Primary Debulking Surgery ◽  
Interval Surgery ◽  
Neo Adjuvant Chemotherapy

AbstractHigh-grade serous tubo-ovarian carcinoma (HGSC) is a major cause of cancer-related death. Whether treatment order—primary debulking surgery followed by adjuvant chemotherapy (PDS) or neo-adjuvant chemotherapy with interval surgery (NACT)—affects outcome is controversial. We developed a mathematical framework that holds for hierarchical or stochastic models of tumor initiation and reproduces HGSC clinical course. After estimating parameter values, we infer that most patients harbor chemo-resistant HGSC cells at diagnosis, and that if complete debulking (<1 mm residual tumor) can be achieved, PDS is superior to NACT due to better depletion of resistant cells. We further predict that earlier diagnosis of primary HGSC, followed by complete debulking, could improve survival, but its benefit in relapsed patients is likely to be limited. Our predictions are supported by primary clinical data from multiple cohorts. Our results have clear implications for these key issues in HGSC management.Significance StatementThe optimal order and timing of surgery and chemotherapy, and the potential benefits of earlier diagnosis of HGSC, remain controversial. We developed a mathematical framework of tumor dynamics to address such issues, populated the model with primary clinical data and reliably recapitulated clinical observations. Our model prospectively predicts that: (1) PDS is superior to NACT when complete debulking is feasible; (2) timely adjuvant chemotherapy is critical for the outcome of PDS with <1mm, but not >1mm, residual tumors; (3) earlier detection of relapse is unlikely to be beneficial with current therapies; (4) earlier detection of primary HGSC, followed by complete debulking, could have substantial benefit. Our model provides insights into the evolutionary dynamics of HGSC, argues for new clinical trials to optimize HGSC therapy, and is potentially applicable to other tumor types.

scholarly journals Computing Optimal Properties of Drugs Using Mathematical Models of Single Channel Dynamics

2018 ◽  
Vol 6 (1) ◽  
pp. 41-64 ◽  
Author(s):  
Aslak Tveito ◽  
Mary M. Maleckar ◽  
Glenn T. Lines
Keyword(s):  
Differential Equations ◽  
Markov Model ◽  
Stochastic Models ◽  
Markov Models ◽  
Single Channel ◽  
Probability Density Functions ◽  
Density Functions ◽  
Mathematical Framework ◽  
Wild Type ◽  
Channel Dynamics

AbstractSingle channel dynamics can be modeled using stochastic differential equations, and the dynamics of the state of the channel (e.g. open, closed, inactivated) can be represented using Markov models. Such models can also be used to represent the effect of mutations as well as the effect of drugs used to alleviate deleterious effects of mutations. Based on the Markov model and the stochastic models of the single channel, it is possible to derive deterministic partial differential equations (PDEs) giving the probability density functions (PDFs) of the states of the Markov model. In this study, we have analyzed PDEs modeling wild type (WT) channels, mutant channels (MT) and mutant channels for which a drug has been applied (MTD). Our aim is to show that it is possible to optimize the parameters of a given drug such that the solution of theMTD model is very close to that of the WT: the mutation’s effect is, theoretically, reduced significantly.We will present the mathematical framework underpinning this methodology and apply it to several examples. In particular, we will show that it is possible to use the method to, theoretically, improve the properties of some well-known existing drugs.

Primary debulking surgery versus neoadjuvant chemotherapy in stage IV ovarian cancer

2011 ◽  
Vol 120 ◽  
pp. S12-S13 ◽  
Author(s):  
J. Rauh-Hain ◽  
W. Growdon ◽  
N. Rodriguez ◽  
A. Goodman ◽  
D. Boruta ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Neoadjuvant Chemotherapy ◽  
Stage Iv ◽  
Debulking Surgery ◽  
Primary Debulking Surgery
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