Dataset Information

Optimizing adaptive cancer therapy: dynamic programming and evolutionary game theory.

ABSTRACT: Recent clinical trials have shown that adaptive drug therapies can be more efficient than a standard cancer treatment based on a continuous use of maximum tolerated doses (MTD). The adaptive therapy paradigm is not based on a preset schedule; instead, the doses are administered based on the current state of tumour. But the adaptive treatment policies examined so far have been largely ad hoc. We propose a method for systematically optimizing adaptive policies based on an evolutionary game theory model of cancer dynamics. Given a set of treatment objectives, we use the framework of dynamic programming to find the optimal treatment strategies. In particular, we optimize the total drug usage and time to recovery by solving a Hamilton-Jacobi-Bellman equation. We compare MTD-based treatment strategy with optimal adaptive treatment policies and show that the latter can significantly decrease the total amount of drugs prescribed while also increasing the fraction of initial tumour states from which the recovery is possible. We conclude that the use of optimal control theory to improve adaptive policies is a promising concept in cancer treatment and should be integrated into clinical trial design.

SUBMITTER: Gluzman M

PROVIDER: S-EPMC7211445 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Optimizing adaptive cancer therapy: dynamic programming and evolutionary game theory.

Gluzman Mark M Scott Jacob G JG Vladimirsky Alexander A

Proceedings. Biological sciences 20200422 1925

Recent clinical trials have shown that adaptive drug therapies can be more efficient than a standard cancer treatment based on a continuous use of maximum tolerated doses (MTD). The adaptive therapy paradigm is not based on a preset schedule; instead, the doses are administered based on the current state of tumour. But the adaptive treatment policies examined so far have been largely ad hoc. We propose a method for systematically optimizing adaptive policies based on an evolutionary game theory ...[more]

PMID: 32315588

Similar Datasets

Project description:ImportanceNeoadjuvant therapy is increasingly used in localized pancreatic carcinoma, and survival is correlated with carbohydrate antigen 19-9 (CA19-9) levels and histopathologic response following neoadjuvant therapy. With several regimens now available, the choice of chemotherapy could be best dictated by response to neoadjuvant therapy (as measured by CA19-9 levels and/or pathologic response), a strategy defined herein as adaptive dynamic therapy.ObjectiveTo evaluate the association of adaptive dynamic therapy with oncologic outcomes in patients with surgically resected pancreatic cancer.Design, setting, and participantsThis retrospective cohort study included patients with localized pancreatic cancer who were treated with either gemcitabine/nab-paclitaxel or fluorouracil, leucovorin, irinotecan, and oxaliplatin (FOLFIRINOX) preoperatively between 2010 and 2019 at a high-volume tertiary care academic center. Participants were identified from a prospectively maintained database and had a median follow-up of 49 months. Data were analyzed from October 17 to November 24, 2020.ExposuresThe adaptive dynamic therapy group included 219 patients who remained on or switched to an alternative regimen as dictated by CA19-9 response and for whom the adjuvant regimen was selected based on CA19-9 and/or pathologic response. The nonadaptive dynamic therapy group included 103 patients who had their chemotherapeutic regimen selected independent of CA19-9 and/or tumoral response.Main outcomes and measuresPrognostic implications of dynamic perioperative therapy assessed through Kaplan-Meier analysis, Cox regression, and inverse probability weighted estimators.ResultsA total of 322 consecutive patients (mean [SD] age, 65.1 [9] years; 162 [50%] women) were identified. The adaptive dynamic therapy group, compared with the nonadaptive dynamic therapy group, had a more pronounced median (IQR) decrease in CA19-9 levels (-80% [-92% to -56%] vs -45% [-81% to -13%]; P < .001), higher incidence of complete or near-complete tumoral response (25 [12%] vs 2 [2%]; P = .007), and lower median (IQR) number of lymph node metastasis (1 [0 to 4] vs 2 [0 to 4]; P = .046). Overall survival was significantly improved in the dynamic group compared with the nondynamic group (38.7 months [95% CI, 34.0 to 46.7 months] vs 26.5 months [95% CI, 23.5 to 32.9 months]; P = .03), and on adjusted analysis, dynamic therapy was independently associated with improved survival (hazard ratio, 0.73; 95% CI, 0.53 to 0.99; P = .04). On inverse probability weighted analysis of 320 matched patients, the average treatment effect of dynamic therapy was to increase overall survival by 11.1 months (95% CI, 1.5 to 20.7 months; P = .02).Conclusions and relevanceIn this cohort study that sought to evaluate the role of adaptive dynamic therapy in localized pancreatic cancer, selecting a chemotherapeutic regimen based on response to preoperative therapy was associated with improved survival. These findings support an individualized and in vivo assessment of response to perioperative therapy in pancreatic cancer.

Dataset Information

Optimizing adaptive cancer therapy: dynamic programming and evolutionary game theory.

Publications

Optimizing adaptive cancer therapy: dynamic programming and evolutionary game theory.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets