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A mathematical model of tumor regression and recurrence after therapeutic oncogene inactivation.

ABSTRACT: The targeted inactivation of individual oncogenes can elicit regression of cancers through a phenomenon called oncogene addiction. Oncogene addiction is mediated by cell-autonomous and immune-dependent mechanisms. Therapeutic resistance to oncogene inactivation leads to recurrence but can be counteracted by immune surveillance. Predicting the timing of resistance will provide valuable insights in developing effective cancer treatments. To provide a quantitative understanding of cancer response to oncogene inactivation, we developed a new 3-compartment mathematical model of oncogene-driven tumor growth, regression and recurrence, and validated the model using a MYC-driven transgenic mouse model of T-cell acute lymphoblastic leukemia. Our mathematical model uses imaging-based measurements of tumor burden to predict the relative number of drug-sensitive and drug-resistant cancer cells in MYC-dependent states. We show natural killer (NK) cell adoptive therapy can delay cancer recurrence by reducing the net-growth rate of drug-resistant cells. Our studies provide a novel way to evaluate combination therapy for personalized cancer treatment.

SUBMITTER: Hori SS

PROVIDER: S-EPMC7809285 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

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A mathematical model of tumor regression and recurrence after therapeutic oncogene inactivation.

Hori Sharon S SS Tong Ling L Swaminathan Srividya S Liebersbach Mariola M Wang Jingjing J Gambhir Sanjiv S SS Felsher Dean W DW

Scientific reports 20210114 1

The targeted inactivation of individual oncogenes can elicit regression of cancers through a phenomenon called oncogene addiction. Oncogene addiction is mediated by cell-autonomous and immune-dependent mechanisms. Therapeutic resistance to oncogene inactivation leads to recurrence but can be counteracted by immune surveillance. Predicting the timing of resistance will provide valuable insights in developing effective cancer treatments. To provide a quantitative understanding of cancer response t ...[more]

PMID: 33446671

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Project description:Cancers can exhibit marked tumor regression after oncogene inhibition through a phenomenon called "oncogene addiction." The ability to predict when a tumor will exhibit oncogene addiction would be useful in the development of targeted therapeutics. Oncogene addiction is likely the consequence of many cellular programs. However, we reasoned that many of these inputs may converge on aggregate survival and death signals. To test this, we examined conditional transgenic models of K-ras(G12D)--or MYC-induced lung tumors and lymphoma combined with quantitative imaging and an in situ analysis of biomarkers of proliferation and apoptotic signaling. We then used computational modeling based on ordinary differential equations (ODEs) to show that oncogene addiction could be modeled as differential changes in survival and death intracellular signals. Our mathematical model could be generalized to different imaging methods (computed tomography and bioluminescence imaging), different oncogenes (K-ras(G12D) and MYC), and several tumor types (lung and lymphoma). Our ODE model could predict the differential dynamics of several putative prosurvival and prodeath signaling factors [phosphorylated extracellular signal-regulated kinase 1 and 2, Akt1, Stat3/5 (signal transducer and activator of transcription 3/5), and p38] that contribute to the aggregate survival and death signals after oncogene inactivation. Furthermore, we could predict the influence of specific genetic lesions (p53⁻/⁻, Stat3-d358L, and myr-Akt1) on tumor regression after oncogene inactivation. Then, using machine learning based on support vector machine, we applied quantitative imaging methods to human patients to predict both their EGFR genotype and their progression-free survival after treatment with the targeted therapeutic erlotinib. Hence, the consequences of oncogene inactivation can be accurately modeled on the basis of a relatively small number of parameters that may predict when targeted therapeutics will elicit oncogene addiction after oncogene inactivation and hence tumor regression.

Project description:BackgroundEffective management of patients with borderline ovarian tumor (BOT) requires the timely identification of those at a higher risk of recurrence. Artificial neural networks have been successfully used in many areas of clinical event prediction, significantly affecting clinical decisions and practice.ObjectiveWe developed and validated a novel clinical model based on neural multi-task logistic regression (N-MTLR) for predicting recurrence in patients with BOT who underwent initial surgeries, and compared its prediction performance with that of the Cox regression model.MethodsThis retrospective study included 736 patients diagnosed with BOT from May 2011 to August 2022, with 84 recurrences. The synthetic minority oversampling technique (SMOTE) was used to balance the minority group such that the two patient types were 1:1. Using random sampling, the SMOTE-balanced dataset was divided into 80% of the sample (1043 patients) as the training set and 20% (261 patients) as the validation set. Both N-MTLR and Cox regression models were trained on the training set using SMOTE and evaluated on the validation set using the time-dependent area under the receiver operating characteristic curve (tdAUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy.ResultsAmong the 736 enrolled patients, only 84 (11.41%) were diagnosed with BOT recurrence. Using SMOTE, the balanced dataset (1304 patients) contained equal numbers of patients (652 patients) in both recurrence and non-recurrence groups. Multivariate Cox regression analysis of the training set revealed that independent risk factors for BOT recurrence were premenopause, laparoscopic surgery, tumor rupture, advanced clinical stage, undissected lymph nodes, bilateral tumors, and fertility-sparing surgery (FSS). The N-MTLR model was constructed by correlation screening of 34 features in the training set, and 10 variables were screened including FSS, completeness of surgery, comorbidities, International Federation of Gynecology and Obstetrics (FIGO) staging, age, omentectomy, lymphadenectomy, parity, menopausal status, and peritoneal implantation. The N-MTLR model outperformed the Cox regression model in terms of AUC, accuracy, specificity, PPV, and NPV at the quartiles of follow-ups (2, 4, and 7 years).ConclusionsThe N-MTLR model effectively predicts BOT recurrence. Identifying high-risk recurrence groups in patients with BOT can facilitate close monitoring, suitable treatment, and an opportune time for intervention.

Dataset Information

A mathematical model of tumor regression and recurrence after therapeutic oncogene inactivation.

Publications

A mathematical model of tumor regression and recurrence after therapeutic oncogene inactivation.

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