|
1
|
Lindström HJG, de Wijn AS, Friedman R. Interplay of mutations, alternate mechanisms, and treatment breaks in leukaemia: Understanding and implications studied with stochastic models. Comput Biol Med 2024; 169:107826. [PMID: 38101118 DOI: 10.1016/j.compbiomed.2023.107826] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 11/08/2023] [Accepted: 12/04/2023] [Indexed: 12/17/2023]
Abstract
Bcr-Abl1 kinase domain mutations are the most prevalent cause of treatment resistance in chronic myeloid leukaemia (CML). Alternate resistance pathways nevertheless exist, and cell line experiments show certain patterns in the gain, and loss, of some of these alternate adaptations. These adaptations have clinical consequences when the tumour develops mechanisms that are beneficial to its growth under treatment, but slow down its growth when not treated. The results of temporarily halting treatment in CML have not been widely discussed in the clinic and there is no robust theoretical model that could suggest when such a pause in therapy can be tolerated. We constructed a dynamic model of how mechanisms such as Bcr-Abl1 overexpression and drug transporter upregulation evolve to produce resistance in cell lines, and investigate its behaviour subject to different treatment schedules, in particular when the treatment is paused ('drug holiday'). Our study results suggest that the presence of additional resistance mechanisms creates an environment which favours mutations that are either preexisting or occur late during treatment. Importantly, the results suggest the existence of tumour drug addiction, where cancer cells become dependent on the drug for (optimal) survival, which could be exploited through a treatment holiday. All simulation code is available at https://github.com/Sandalmoth/dual-adaptation.
Collapse
MESH Headings
- Humans
- Fusion Proteins, bcr-abl/genetics
- Fusion Proteins, bcr-abl/metabolism
- Fusion Proteins, bcr-abl/therapeutic use
- Protein Kinase Inhibitors/pharmacology
- Drug Resistance, Neoplasm
- Mutation
- Leukemia, Myelogenous, Chronic, BCR-ABL Positive/drug therapy
- Leukemia, Myelogenous, Chronic, BCR-ABL Positive/genetics
- Leukemia, Myelogenous, Chronic, BCR-ABL Positive/pathology
Collapse
Affiliation(s)
- H Jonathan G Lindström
- Department of Chemistry and Biomedical Sciences, Linnaeus University, Kalmar, SE-39182, Sweden
| | - Astrid S de Wijn
- Department of Mechanical and Industrial Engineering, , Norwegian University of Science and Technology, Trondheim, 7491, Norway
| | - Ran Friedman
- Department of Chemistry and Biomedical Sciences, Linnaeus University, Kalmar, SE-39182, Sweden.
| |
Collapse
|
|
2
|
Vakil V, Trappe W. Drug-Resistant Cancer Treatment Strategies Based on the Dynamics of Clonal Evolution and PKPD Modeling of Drug Combinations. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:1603-1614. [PMID: 33326383 DOI: 10.1109/tcbb.2020.3045315] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
A method for determining a dosage strategy is proposed to combat drug resistance in tumor progression. The method is based on a dynamic model for the clonal evolution of cancerous cells and considers the Pharmacokinetic/Pharmacodynamic (PKPD) modeling of combination therapy. The proposed mathematical representation models the dynamic and kinetic effects of multiple drugs on the number of cells while considering potential mutations and assuming that no cross-resistance arises. An optimization problem is then proposed to minimize the total number of cancerous cells in a finite treatment period given a limited number of treatments. The dosage schedule, including the amount of each drug to be administered and the timing, is found by solving the optimization problem. This treatment schedule is constrained to achieve a target minimum effectiveness, while also ensuring that the concentration of the drugs, individually and totally, does not exceed a prescribed toxicity threshold. The proposed optimization problem is represented as a Complementary Geometric Programming (CGP) problem. The results show that the solution of the optimization problem for combination therapy is the dosing schedule that leads to tumor eradication at the end of the treatment period. The results also investigate the tumor dynamics for all mutation types when undergoing treatment, showing that single drug therapies can fail to combat the emergence of resistance, while optimized combination therapies can reduce the amount of all mutation types during the course of treatment, thereby combating resistance.
Collapse
|
|
3
|
Suresh S, Raghavendran S, Selvaraj S. Combining Evolution and Cancer Therapy: A Review of the Mathematical
Approach. CURRENT CANCER THERAPY REVIEWS 2022. [DOI: 10.2174/1573394717666210922151146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/09/2022]
Abstract
:
Conventional cancer therapy kills tumors by applying the maximum tolerable dose of
therapy. However, it leads to the development of tumoral heterogeneity and resistance, hence leading
to therapy failure and progression. It is necessary to design therapies keeping in mind the evolutionary
dynamics of tumors to minimize resistance and delay progression. Mathematical models
are of great importance in oncology as they assist in the recreation of the tumor microenvironment,
predict the outcomes of treatment strategies and elucidate fundamentals of tumor growth and resistance
development. The body of literature covering models which incorporate evolutionary dynamics
is vast. This paper provides an overview of existing models of “evolutionary therapy”, including
ordinary differential equations, fitness, and probability functions.
Collapse
Affiliation(s)
- Sruthi Suresh
- School of Chemical & Biotechnology, SASTRA Deemed University, Thanjavur, India
| | | | - Stalin Selvaraj
- School of Chemical & Biotechnology, SASTRA Deemed University, Thanjavur, India
| |
Collapse
|
|
4
|
Komarova NL, Boland CR, Goel A, Wodarz D. Aspirin and the chemoprevention of cancers: A mathematical and evolutionary dynamics perspective. WILEY INTERDISCIPLINARY REVIEWS. SYSTEMS BIOLOGY AND MEDICINE 2020; 12:e1487. [PMID: 32163237 PMCID: PMC7486281 DOI: 10.1002/wsbm.1487] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/25/2019] [Revised: 02/10/2020] [Accepted: 02/19/2020] [Indexed: 12/16/2022]
Abstract
Epidemiological data indicate that long-term low dose aspirin administration has a protective effect against the occurrence of colorectal cancer, both in sporadic and in hereditary forms of the disease. The mechanisms underlying this protective effect, however, are incompletely understood. The molecular events that lead to protection have been partly defined, but remain to be fully characterized. So far, however, approaches based on evolutionary dynamics have not been discussed much, but can potentially offer important insights. The aim of this review is to highlight this line of investigation and the results that have been obtained. A core observation in this respect is that aspirin has a direct negative impact on the growth dynamics of the cells, by influencing the kinetics of tumor cell division and death. We discuss the application of mathematical models to experimental data to quantify these parameter changes. We then describe further mathematical models that have been used to explore how these aspirin-mediated changes in kinetic parameters influence the probability of successful colony growth versus extinction, and how they affect the evolution of the tumor during aspirin administration. Finally, we discuss mathematical models that have been used to investigate the selective forces that can lead to the rise of mismatch-repair deficient cells in an inflammatory environment, and how this selection can be potentially altered through aspirin-mediated interventions. This article is categorized under: Models of Systems Properties and Processes > Mechanistic Models Analytical and Computational Methods > Analytical Methods Analytical and Computational Methods > Computational Methods.
Collapse
Affiliation(s)
- Natalia L Komarova
- Department of Mathematics, University of California Irvine, Irvine, California, USA
| | - C Richard Boland
- Department of Medicine, UCSD School of Medicine, San Diego, California, USA
| | - Ajay Goel
- Department of Molecular Diagnostics and Experimental Therapeutics, Beckman Research Institute, City of Hope Comprehensive Cancer Center, Biomedical Research Center, Monrovia, California, USA
| | - Dominik Wodarz
- Department of Population Health and Disease Prevention, Program in Public Health, Susan and Henry Samueli College of Health Sciences, University of California Irvine, Irvine, California, USA
| |
Collapse
|
|
5
|
G. Lindström HJ, Friedman R. The effects of combination treatments on drug resistance in chronic myeloid leukaemia: an evaluation of the tyrosine kinase inhibitors axitinib and asciminib. BMC Cancer 2020; 20:397. [PMID: 32380976 PMCID: PMC7204252 DOI: 10.1186/s12885-020-06782-9] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2019] [Accepted: 03/23/2020] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Chronic myeloid leukaemia is in principle a treatable malignancy but drug resistance is lowering survival. Recent drug discoveries have opened up new options for drug combinations, which is a concept used in other areas for preventing drug resistance. Two of these are (I) Axitinib, which inhibits the T315I mutation of BCR-ABL1, a main source of drug resistance, and (II) Asciminib, which has been developed as an allosteric BCR-ABL1 inhibitor, targeting an entirely different binding site, and as such does not compete for binding with other drugs. These drugs offer new treatment options. METHODS We measured the proliferation of KCL-22 cells exposed to imatinib-dasatinib, imatinib-asciminib and dasatinib-asciminib combinations and calculated combination index graphs for each case. Moreover, using the median-effect equation we calculated how much axitinib can reduce the growth advantage of T315I mutant clones in combination with available drugs. In addition, we calculated how much the total drug burden could be reduced by combinations using asciminib and other drugs, and evaluated which mutations such combinations might be sensitive to. RESULTS Asciminib had synergistic interactions with imatinib or dasatinib in KCL-22 cells at high degrees of inhibition. Interestingly, some antagonism between asciminib and the other drugs was present at lower degrees on inhibition. Simulations revealed that asciminib may allow for dose reductions, and its complementary resistance profile could reduce the risk of mutation based resistance. Axitinib, however, had only a minor effect on T315I growth advantage. CONCLUSIONS Given how asciminib combinations were synergistic in vitro, our modelling suggests that drug combinations involving asciminib should allow for lower total drug doses, and may result in a reduced spectrum of observed resistance mutations. On the other hand, a combination involving axitinib was not shown to be useful in countering drug resistance.
Collapse
MESH Headings
- Antineoplastic Combined Chemotherapy Protocols/pharmacology
- Axitinib/administration & dosage
- Cell Line, Tumor
- Computer Simulation
- Dasatinib/administration & dosage
- Drug Discovery/methods
- Drug Resistance, Neoplasm/genetics
- Drug Synergism
- Humans
- Imatinib Mesylate/administration & dosage
- Leukemia, Myelogenous, Chronic, BCR-ABL Positive/drug therapy
- Leukemia, Myelogenous, Chronic, BCR-ABL Positive/genetics
- Leukemia, Myelogenous, Chronic, BCR-ABL Positive/metabolism
- Mutation
- Niacinamide/administration & dosage
- Niacinamide/analogs & derivatives
- Pyrazoles/administration & dosage
Collapse
Affiliation(s)
| | - Ran Friedman
- Department of Chemistry and Biomedical Sciences, Linnæus University, Kalmar, 391 82 Sweden
| |
Collapse
|
|
6
|
Chahal KK, Li J, Kufareva I, Parle M, Durden DL, Wechsler-Reya RJ, Chen CC, Abagyan R. Nilotinib, an approved leukemia drug, inhibits smoothened signaling in Hedgehog-dependent medulloblastoma. PLoS One 2019; 14:e0214901. [PMID: 31539380 PMCID: PMC6754133 DOI: 10.1371/journal.pone.0214901] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2019] [Accepted: 09/04/2019] [Indexed: 01/21/2023] Open
Abstract
Dysregulation of the seven-transmembrane (7TM) receptor Smoothened (SMO) and other components of the Hedgehog (Hh) signaling pathway contributes to the development of cancers including basal cell carcinoma (BCC) and medulloblastoma (MB). However, SMO-specific antagonists produced mixed results in clinical trials, marked by limited efficacy and high rate of acquired resistance in tumors. Here we discovered that Nilotinib, an approved inhibitor of several kinases, possesses an anti-Hh activity, at clinically achievable concentrations, due to direct binding to SMO and inhibition of SMO signaling. Nilotinib was more efficacious than the SMO-specific antagonist Vismodegib in inhibiting growth of two Hh-dependent MB cell lines. It also reduced tumor growth in subcutaneous MB mouse xenograft model. These results indicate that in addition to its known activity against several tyrosine-kinase-mediated proliferative pathways, Nilotinib is a direct inhibitor of the Hh pathway. The newly discovered extension of Nilotinib's target profile holds promise for the treatment of Hh-dependent cancers.
Collapse
Affiliation(s)
- Kirti Kandhwal Chahal
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego (UCSD), La Jolla, California, United States of America
- Department of Pharmaceutical Sciences, G.J. University of Science and Technology, Hisar, India
| | - Jie Li
- Department of Neurosurgery, Minneapolis, Minnesota, United States of America
| | - Irina Kufareva
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego (UCSD), La Jolla, California, United States of America
| | - Milind Parle
- Department of Pharmaceutical Sciences, G.J. University of Science and Technology, Hisar, India
| | - Donald L. Durden
- Department of Pediatrics, Moores Cancer Center, School of Medicine, UCSD and Rady Children’s Hospital, San Diego, La Jolla, California, United States of America
| | - Robert J. Wechsler-Reya
- Tumor Initiation and Maintenance Program, NCI-Designated Cancer Center, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, California, United States of America
| | - Clark C. Chen
- Department of Neurosurgery, Minneapolis, Minnesota, United States of America
| | - Ruben Abagyan
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego (UCSD), La Jolla, California, United States of America
| |
Collapse
|
|
7
|
Gleixner KV, Sadovnik I, Schneeweiss M, Eisenwort G, Byrgazov K, Stefanzl G, Berger D, Herrmann H, Hadzijusufovic E, Lion T, Valent P. A kinase profile-adapted drug combination elicits synergistic cooperative effects on leukemic cells carrying BCR-ABL1 T315I in Ph+ CML. Leuk Res 2019; 78:36-44. [PMID: 30711891 DOI: 10.1016/j.leukres.2018.12.013] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2018] [Revised: 12/23/2018] [Accepted: 12/27/2018] [Indexed: 11/30/2022]
Abstract
In chronic myeloid leukemia (CML), resistance against second-generation tyrosine kinase inhibitors (TKI) remains a serious clinical challenge, especially in the context of multi-resistant BCR-ABL1 mutants, such as T315I. Treatment with ponatinib may suppress most of these mutants, including T315I, but is also associated with a high risk of clinically relevant side effects. We screened for alternative treatment options employing available tyrosine kinase inhibitors (TKI) in combination. Dasatinib and bosutinib are two second-generation TKI that bind to different, albeit partially overlapping, spectra of kinase targets in CML cells. This observation prompted us to explore anti-leukemic effects of the combination dasatinib + bosutinib in highly resistant primary CML cells, various CML cell lines (K562, K562R, KU812, KCL22) and Ba/F3 cells harboring various BCR-ABL1 mutant-forms. We found that bosutinib synergizes with dasatinib in inducing growth inhibition and apoptosis in all CML cell lines and in Ba/F3 cells exhibiting BCR-ABL1T315I. Clear synergistic effects were also observed in primary CML cells in all patients tested (n = 20), including drug-resistant cells carrying BCR-ABL1T315I. Moreover, the drug combination produced cooperative or even synergistic apoptosis-inducing effects on CD34+/CD38- CML stem cells. Finally, we found that the drug combination is a potent approach to block the activity of major additional CML targets, including LYN, KIT and PDGFRα. Together, bosutinib and dasatinib synergize in producing anti-leukemic effects in drug-resistant CML cells. Whether such cooperative TKI effects also occur in vivo in patients with drug-resistant CML, remains to be determined in forthcoming studies.
Collapse
Affiliation(s)
- Karoline V Gleixner
- Department of Internal Medicine I, Division of Hematology & Hemostaseology, Medical University of Vienna, Austria; Ludwig Boltzmann Institute for Hematology and Oncology, Medical University of Vienna, Austria
| | - Irina Sadovnik
- Department of Internal Medicine I, Division of Hematology & Hemostaseology, Medical University of Vienna, Austria
| | - Mathias Schneeweiss
- Ludwig Boltzmann Institute for Hematology and Oncology, Medical University of Vienna, Austria
| | - Gregor Eisenwort
- Department of Internal Medicine I, Division of Hematology & Hemostaseology, Medical University of Vienna, Austria; Ludwig Boltzmann Institute for Hematology and Oncology, Medical University of Vienna, Austria
| | | | - Gabriele Stefanzl
- Department of Internal Medicine I, Division of Hematology & Hemostaseology, Medical University of Vienna, Austria; Ludwig Boltzmann Institute for Hematology and Oncology, Medical University of Vienna, Austria
| | - Daniela Berger
- Department of Internal Medicine I, Division of Hematology & Hemostaseology, Medical University of Vienna, Austria
| | - Harald Herrmann
- Ludwig Boltzmann Institute for Hematology and Oncology, Medical University of Vienna, Austria; Department of Radiation Therapy, Medical University of Vienna, Austria
| | - Emir Hadzijusufovic
- Department of Internal Medicine I, Division of Hematology & Hemostaseology, Medical University of Vienna, Austria; Ludwig Boltzmann Institute for Hematology and Oncology, Medical University of Vienna, Austria; Department/Clinic for Companion Animals and Horses, Clinic for Small Animals, Clinical Unit of Internal Medicine, University of Veterinary Medicine Vienna, Austria
| | - Thomas Lion
- Ludwig Boltzmann Institute for Hematology and Oncology, Medical University of Vienna, Austria; Children's Cancer Research Institute (CCRI), Vienna, Austria; Department of Pediatrics, Medical University of Vienna, Austria
| | - Peter Valent
- Department of Internal Medicine I, Division of Hematology & Hemostaseology, Medical University of Vienna, Austria; Ludwig Boltzmann Institute for Hematology and Oncology, Medical University of Vienna, Austria.
| |
Collapse
|
|
8
|
Chen JCH, Chuang HY, Liao YJ, Hsu FT, Chen YC, Wang WH, Hwang JJ. Enhanced cytotoxicity of human hepatocellular carcinoma cells following pretreatment with sorafenib combined with trichostatin A. Oncol Lett 2018; 17:638-645. [PMID: 30655811 DOI: 10.3892/ol.2018.9582] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2016] [Accepted: 04/27/2018] [Indexed: 12/14/2022] Open
Abstract
Trichostatin A (TSA), a hydroxamate histone deacetylase inhibitor, is a compound that has been identified to induce anticancer activity. The aim of the present study was to investigate whether sorafenib, in combination with TSA, was able to augment the anticancer effects of TSA, identifying an optimum treatment time plan and the potential underlying molecular mechanisms involved in human hepatocellular carcinoma (HCC) in vitro. Huh7/nuclear factor-κB (NF-κB)-luc2 cells were treated with TSA or sorafenib alone, or sorafenib, prior to, in combination with or following TSA treatment. Huh7/NF-κB-luc2 cell viability following TSA treatment was determined using an MTT assay, and NF-κB activity was analyzed. In addition, the expression levels of NF-κB-regulated downstream effector proteins were assayed by western blotting. Inhibitors of mitogen-activated protein kinases (MAPKs), protein kinase B (AKT) and mutant inhibitor of NF-κBα (IκBαM) vectors were used to confirm the function of the NF-κB signal transduction pathways in response to the effects of sorafenib combined with TSA against HCC. The results of the present study indicated that pre-treatment with sorafenib followed by TSA inhibited the cell viability compared with other treatment modalities, and prevented TSA-induced extracellular-signal-regulated kinase (ERK)/NF-κB activity and expression of downstream effector proteins. It was further demonstrated that IκBαM vector sensitized Huh7/NF-κB-luc2 cells to TSA, thus it was possible to reverse TSA-induced NF-κB activity using PD98059, a MAPK/ERK kinase inhibitor. In conclusion, sorafenib pre-treatment may increase the efficacy of subsequent TSA treatment in HCC. Furthermore, sorafenib pre-treatment is hypothesized to sensitize HCC to TSA via the inhibition of the MEK/ERK/NF-κB signal transduction pathway.
Collapse
Affiliation(s)
- John Chun-Hao Chen
- Department of Biomedical Imaging and Radiological Sciences, National Yang Ming University, Taipei 112, Taiwan, R.O.C.,Department of Radiation Oncology, Mackay Memorial Hospital, Taipei 251, Taiwan, R.O.C
| | - Hui-Yen Chuang
- Department of Biomedical Imaging and Radiological Sciences, National Yang Ming University, Taipei 112, Taiwan, R.O.C
| | - Yi-Jen Liao
- School of Medical Laboratory and Biotechnology, Taipei Medical University, Taipei 110, Taiwan, R.O.C
| | - Fei-Ting Hsu
- Department of Medical Imaging, Taipei Medical University Hospital, Taipei 110, Taiwan, R.O.C
| | - Yen-Chung Chen
- Department of Pathology, National Yang Ming University Hospital, Yilan 260, Taiwan, R.O.C
| | - Wei-Hsun Wang
- Department of Orthopedic Surgery, Changhua Christian Hospital, Changhua 500, Taiwan, R.O.C
| | - Jeng-Jong Hwang
- Department of Biomedical Imaging and Radiological Sciences, National Yang Ming University, Taipei 112, Taiwan, R.O.C
| |
Collapse
|
|
9
|
Wodarz D, Goel A, Boland CR, Komarova NL. Effect of aspirin on tumour cell colony formation and evolution. J R Soc Interface 2018; 14:rsif.2017.0374. [PMID: 28878032 DOI: 10.1098/rsif.2017.0374] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2017] [Accepted: 08/14/2017] [Indexed: 12/21/2022] Open
Abstract
Aspirin is known to reduce the risk of colorectal cancer (CRC) incidence, but the underlying mechanisms are not fully understood. In a previous study, we quantified the in vitro growth kinetics of different CRC tumour cell lines treated with varying doses of aspirin, measuring the rate of cell division and cell death. Here, we use these measured parameters to calculate the chances of successful clonal expansion and to determine the evolutionary potential of the tumour cell lines in the presence and absence of aspirin. The calculations indicate that aspirin increases the probability that a single tumour cell fails to clonally expand. Further, calculations suggest that aspirin increases the evolutionary potential of an expanding tumour cell colony. An aspirin-treated tumour cell population is predicted to result in the accumulation of more mutations (and is thus more virulent and more difficult to treat) than a cell population of the same size that grew without aspirin. This indicates a potential trade-off between delaying the onset of cancer and increasing its evolutionary potential through chemoprevention. Further work needs to investigate to what extent these findings apply to in vivo settings, and to what degree they contribute to the epidemiologically documented aspirin-mediated protection.
Collapse
Affiliation(s)
- Dominik Wodarz
- Department of Ecology and Evolutionary Biology, University of California, Irvine, CA 92617, USA .,Department of Mathematics, University of California, Rowland Hall, Irvine, CA 92617, USA
| | - Ajay Goel
- Center for Gastroenterological Research, Baylor Research Institute and Sammons Cancer Center, Baylor University Medical Center, Dallas TX, USA
| | - C Richard Boland
- University of California San Diego, 9500 Gilman Drive, La Jolla CA 92093, USA
| | - Natalia L Komarova
- Department of Ecology and Evolutionary Biology, University of California, Irvine, CA 92617, USA.,Department of Mathematics, University of California, Rowland Hall, Irvine, CA 92617, USA
| |
Collapse
|
|
10
|
Woywod C, Gruber FX, Engh RA, Flå T. Dynamical models of mutated chronic myelogenous leukemia cells for a post-imatinib treatment scenario: Response to dasatinib or nilotinib therapy. PLoS One 2017; 12:e0179700. [PMID: 28678800 PMCID: PMC5497988 DOI: 10.1371/journal.pone.0179700] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2016] [Accepted: 06/02/2017] [Indexed: 01/05/2023] Open
Abstract
Targeted inhibition of the oncogenic BCR-ABL1 fusion protein using the ABL1 tyrosine kinase inhibitor imatinib has become standard therapy for chronic myelogenous leukemia (CML), with most patients reaching total and durable remission. However, a significant fraction of patients develop resistance, commonly due to mutated ABL1 kinase domains. This motivated development of second-generation drugs with broadened or altered protein kinase selectivity profiles, including dasatinib and nilotinib. Imatinib-resistant patients undergoing treatment with second-line drugs typically develop resistance to them, but dynamic and clonal properties of this response differ. Shared, however, is the observation of clonal competition, reflected in patterns of successive dominance of individual clones. We present three deterministic mathematical models to study the origins of clinically observed dynamics. Each model is a system of coupled first-order differential equations, considering populations of three mutated active stem cell strains and three associated pools of differentiated cells; two models allow for activation of quiescent stem cells. Each approach is distinguished by the way proliferation rates of the primary stem cell reservoir are modulated. Previous studies have concentrated on simulating the response of wild-type leukemic cells to imatinib administration; our focus is on modelling the time dependence of imatinib-resistant clones upon subsequent exposure to dasatinib or nilotinib. Performance of the three computational schemes to reproduce selected CML patient profiles is assessed. While some simple cases can be approximated by a basic design that does not invoke quiescence, others are more complex and require involvement of non-cycling stem cells for reproduction. We implement a new feedback mechanism for regulation of coupling between cycling and non-cycling stem cell reservoirs that depends on total cell populations. A bifurcation landscape analysis is also performed for solutions to the basic ansatz. Computational models reproducing patient data illustrate potential dynamic mechanisms that may guide optimization of therapy of drug resistant CML.
Collapse
Affiliation(s)
- Clemens Woywod
- Centre for Theoretical and Computational Chemistry, Chemistry Department, University of Tromsø - The Arctic University of Norway, N-9037 Tromsø, Norway
- * E-mail:
| | - Franz X. Gruber
- NORSTRUCT, Chemistry Department, University of Tromsø - The Arctic University of Norway, N-9037 Tromsø, Norway
| | - Richard A. Engh
- NORSTRUCT, Chemistry Department, University of Tromsø - The Arctic University of Norway, N-9037 Tromsø, Norway
| | - Tor Flå
- Centre for Theoretical and Computational Chemistry, Chemistry Department, University of Tromsø - The Arctic University of Norway, N-9037 Tromsø, Norway
- Mathematics Department, University of Tromsø - The Arctic University of Norway, N-9037 Tromsø, Norway
| |
Collapse
|
|
11
|
Affiliation(s)
- Ivana Bozic
- Program for Evolutionary Dynamics and
- Department of Mathematics, Harvard University, Cambridge, Massachusetts 02138
- Department of Applied Mathematics, University of Washington, Seattle, Washington 98195
| | - Martin A. Nowak
- Program for Evolutionary Dynamics and
- Department of Mathematics, Harvard University, Cambridge, Massachusetts 02138
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, Massachusetts 02138
| |
Collapse
|
|
12
|
Soyer N, Uysal A, Tombuloglu M, Sahin F, Saydam G, Vural F. Allogeneic stem cell transplantation in chronic myeloid leukemia patients: Single center experience. World J Hematol 2017; 6:1-10. [DOI: 10.5315/wjh.v6.i1.1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/23/2016] [Revised: 10/21/2016] [Accepted: 01/03/2017] [Indexed: 02/05/2023] Open
Abstract
Chronic myeloid leukemia (CML) is a myeloproliferative disease which leads the unregulated growth of myeloid cells in the bone marrow. It is characterized by the presence of Philadelphia chromosome. Reciprocal translocation of the ABL gene from chromosome 9 to 22 t (9; 22) (q34; q11.2) generate a fusion gene (BCR-ABL). BCR-ABL protein had constitutive tyrosine kinase activity that is a primary cause of chronic phase of CML. Tyrosine kinase inhibitors (TKIs) are now considered standard therapy for patients with CML. Even though, successful treatment with the TKIs, allogeneic stem cell transplantation (ASCT) is still an important option for the treatment of CML, especially for patients who are resistant or intolerant to at least one second generation TKI or for patients with blastic phase. Today, we know that there is no evidence for increased transplant-related toxicity and negative impact of survival with pre-transplant TKIs. However, there are some controversies about timing of ASCT, the optimal conditioning regimens and donor source. Another important issue is that BCR-ABL signaling is not necessary for survival of CML stem cell and TKIs were not effective on these cells. So, ASCT may play a role to eliminate CML stem cells. In this article, we review the diagnosis, management and treatment of CML. Later, we present our center’s outcomes of ASCT for patients with CML and then, we discuss the place of ASCT in CML treatment in the TKIs era.
Collapse
|
|
13
|
He Q, Zhu J, Dingli D, Foo J, Leder KZ. Optimized Treatment Schedules for Chronic Myeloid Leukemia. PLoS Comput Biol 2016; 12:e1005129. [PMID: 27764087 PMCID: PMC5072565 DOI: 10.1371/journal.pcbi.1005129] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2016] [Accepted: 09/02/2016] [Indexed: 11/17/2022] Open
Abstract
Over the past decade, several targeted therapies (e.g. imatinib, dasatinib, nilotinib) have been developed to treat Chronic Myeloid Leukemia (CML). Despite an initial response to therapy, drug resistance remains a problem for some CML patients. Recent studies have shown that resistance mutations that preexist treatment can be detected in a substantial number of patients, and that this may be associated with eventual treatment failure. One proposed method to extend treatment efficacy is to use a combination of multiple targeted therapies. However, the design of such combination therapies (timing, sequence, etc.) remains an open challenge. In this work we mathematically model the dynamics of CML response to combination therapy and analyze the impact of combination treatment schedules on treatment efficacy in patients with preexisting resistance. We then propose an optimization problem to find the best schedule of multiple therapies based on the evolution of CML according to our ordinary differential equation model. This resulting optimization problem is nontrivial due to the presence of ordinary different equation constraints and integer variables. Our model also incorporates drug toxicity constraints by tracking the dynamics of patient neutrophil counts in response to therapy. We determine optimal combination strategies that maximize time until treatment failure on hypothetical patients, using parameters estimated from clinical data in the literature. Targeted therapy using imatinib, nilotinib or dasatinib has become standard treatment for chronicle myeloid leukemia. A minority of patients, however, fail to respond to treatment or relapse due to drug resistance. One primary driving factor of drug resistance are point mutations within the driving oncogene. Laboratory studies have shown that different leukemic mutants respond differently to different drugs, so a promising way to improve treatment efficacy is to combine multiple targeted therapies. We build a mathematical model to predict the dynamics of different leukemic mutants with imatinib, nilotinib and dasatinib, and employ optimization techniques to find the best treatment schedule of combining the three drugs sequentially. Our study shows that the optimally designed combination therapy is more effective at controlling the leukemic cell burden than any monotherapy under a wide range of scenarios. The structure of the optimal schedule depends heavily on the mutant types present, growth kinetics of leukemic cells and drug toxicity parameters. Our methodology is an important step towards the design of personalized optimal therapeutic schedules for chronicle myeloid leukemia.
Collapse
Affiliation(s)
- Qie He
- Department of Industrial and Systems Engineering, University of Minnesota, Minneapolis, MN, USA
| | - Junfeng Zhu
- Department of Industrial and Systems Engineering, University of Minnesota, Minneapolis, MN, USA
| | - David Dingli
- Department of Hematology, Mayo Clinic, Rochester, MN, USA
| | - Jasmine Foo
- Department of Mathematics, University of Minnesota, Minneapolis, MN
| | - Kevin Zox Leder
- Department of Industrial and Systems Engineering, University of Minnesota, Minneapolis, MN, USA
| |
Collapse
|
|
14
|
Lindsay D, Garvey CM, Mumenthaler SM, Foo J. Leveraging Hypoxia-Activated Prodrugs to Prevent Drug Resistance in Solid Tumors. PLoS Comput Biol 2016; 12:e1005077. [PMID: 27560187 PMCID: PMC4999195 DOI: 10.1371/journal.pcbi.1005077] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2016] [Accepted: 07/25/2016] [Indexed: 11/21/2022] Open
Abstract
Experimental studies have shown that one key factor in driving the emergence of drug resistance in solid tumors is tumor hypoxia, which leads to the formation of localized environmental niches where drug-resistant cell populations can evolve and survive. Hypoxia-activated prodrugs (HAPs) are compounds designed to penetrate to hypoxic regions of a tumor and release cytotoxic or cytostatic agents; several of these HAPs are currently in clinical trial. However, preliminary results have not shown a survival benefit in several of these trials. We hypothesize that the efficacy of treatments involving these prodrugs depends heavily on identifying the correct treatment schedule, and that mathematical modeling can be used to help design potential therapeutic strategies combining HAPs with standard therapies to achieve long-term tumor control or eradication. We develop this framework in the specific context of EGFR-driven non-small cell lung cancer, which is commonly treated with the tyrosine kinase inhibitor erlotinib. We develop a stochastic mathematical model, parametrized using clinical and experimental data, to explore a spectrum of treatment regimens combining a HAP, evofosfamide, with erlotinib. We design combination toxicity constraint models and optimize treatment strategies over the space of tolerated schedules to identify specific combination schedules that lead to optimal tumor control. We find that (i) combining these therapies delays resistance longer than any monotherapy schedule with either evofosfamide or erlotinib alone, (ii) sequentially alternating single doses of each drug leads to minimal tumor burden and maximal reduction in probability of developing resistance, and (iii) strategies minimizing the length of time after an evofosfamide dose and before erlotinib confer further benefits in reduction of tumor burden. These results provide insights into how hypoxia-activated prodrugs may be used to enhance therapeutic effectiveness in the clinic. It has been suggested that one key factor driving the emergence of drug resistance is the spatial heterogeneity in the distribution of drug and oxygen throughout a tumor due to disorganized tumor vasculatures. Researchers have developed a class of novel drugs that penetrate to hypoxic regions where they are activated to kill tumor cells. The inclusion of these drugs, called hypoxia-activated prodrugs (HAPs) alongside standard therapies in combination may be the key to long-term tumor control or eradication. However, identifying the right timing and administration sequence of combination therapies is an extremely difficult task, and the time and human costs of clinical trials to investigate even a few options is often prohibitive. In this work we design a mathematical model based upon evolutionary principles to investigate the potential of combining HAPs with standard targeted therapy for a specific example in non-small cell lung cancer. We formulate novel toxicity constraints from existing clinical data to estimate the shape of the tolerated drug combination treatment space. We find that (i) combining these therapies delays resistance longer than any monotherapy schedule with either evofosfamide or erlotinib alone, and (ii) the best strategy for combination involves single doses of each drug sequentially administered in an alternating sequence. These model predictions of tumor dynamics during treatment provide insight into the role of the tumor microenvironment in combination therapy and identify treatment hypotheses for further experimental and clinical testing.
Collapse
Affiliation(s)
- Danika Lindsay
- School of Mathematics, University of Minnesota, Minneapolis, Minnesota, United States of America
| | - Colleen M. Garvey
- Lawrence J. Ellison Institute for Transformative Medicine, University of Southern California, Los Angeles, California, United States of America
| | - Shannon M. Mumenthaler
- Lawrence J. Ellison Institute for Transformative Medicine, University of Southern California, Los Angeles, California, United States of America
- * E-mail: (SMM); (JF)
| | - Jasmine Foo
- School of Mathematics, University of Minnesota, Minneapolis, Minnesota, United States of America
- * E-mail: (SMM); (JF)
| |
Collapse
|
|
15
|
Abstract
Cancer drug resistance leading to therapeutic failure in the treatment of many cancers encompasses various mechanisms and may be intrinsic relying on the patient's genetic makeup or be acquired by tumors that are initially sensitive to cancer drugs. All in all, it may be responsible for treatment failure in over 90 % of patients with metastatic cancer. Cancer drug resistance, in particular acquired resistance, may stem from the micro-clonality/micro-genetic heterogeneity of the tumors whereby, among others, the following mechanisms may entail resistance: altered expression of drug influx/efflux transporters in the tumor cells mediating lower drug uptake and/or greater efflux of the drug; altered role of DNA repair and impairment of apoptosis; role of epigenomics/epistasis by methylation, acetylation, and altered levels of microRNAs leading to alterations in upstream or downstream effectors; mutation of drug targets in targeted therapy and alterations in the cell cycle and checkpoints; and tumor microenvironment that are briefly reviewed.
Collapse
Affiliation(s)
- José Rueff
- Centre for Toxicogenomics and Human Health, Genetics, Oncology and Human Toxicology, NOVA Medical School/Faculdade de Ciências Médicas, Universidade Nova de Lisboa, Rua Câmara Pestana 6, 1150-008, Lisbon, Portugal.
| | - António Sebastião Rodrigues
- Centre for Toxicogenomics and Human Health, Genetics, Oncology and Human Toxicology, NOVA Medical School/Faculdade de Ciências Médicas, Universidade Nova de Lisboa, Rua Câmara Pestana 6, 1150-008, Lisbon, Portugal
| |
Collapse
|
|
16
|
Clapp GD, Lepoutre T, El Cheikh R, Bernard S, Ruby J, Labussière-Wallet H, Nicolini FE, Levy D. Implication of the Autologous Immune System in BCR–ABL Transcript Variations in Chronic Myelogenous Leukemia Patients Treated with Imatinib. Cancer Res 2015; 75:4053-62. [DOI: 10.1158/0008-5472.can-15-0611] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2015] [Accepted: 07/29/2015] [Indexed: 11/16/2022]
|
|
17
|
Liu J, Wang Z. Diverse array-designed modes of combination therapies in Fangjiomics. Acta Pharmacol Sin 2015; 36:680-8. [PMID: 25864646 PMCID: PMC4594182 DOI: 10.1038/aps.2014.125] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/30/2014] [Accepted: 10/30/2014] [Indexed: 12/11/2022]
Abstract
In line with the complexity of disease networks, diverse combination therapies have been demonstrated potential in the treatment of different patients with complex diseases in a personal combination profile. However, the identification of rational, compatible and effective drug combinations remains an ongoing challenge. Based on a holistic theory integrated with reductionism, Fangjiomics systematically develops multiple modes of array-designed combination therapies. We define diverse "magic shotgun" vertical, horizontal, focusing, siege and dynamic arrays according to different spatiotemporal distributions of hits on targets, pathways and networks. Through these multiple adaptive modes for treating complex diseases, Fangjiomics may help to identify rational drug combinations with synergistic or additive efficacy but reduced adverse side effects that reverse complex diseases by reconstructing or rewiring multiple targets, pathways and networks. Such a novel paradigm for combination therapies may allow us to achieve more precise treatments by developing phenotype-driven quantitative multi-scale modeling for rational drug combinations.
Collapse
Affiliation(s)
- Jun Liu
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing 100700, China
| | - Zhong Wang
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing 100700, China
| |
Collapse
|
|
18
|
Abstract
Drug resistance is a fundamental problem in the treatment of cancer since cancer that becomes resistant to the available drugs may leave the patient with no therapeutic alternatives. In this chapter, we consider the dynamics of drug resistance in blood cancer and the related issue of the dynamics of cancer stem cells. After describing the main types of chemotherapeutic agents available for cancer treatment, we review the different mechanisms of drug resistance development. Various mathematical models of drug resistance found in the literature are then reviewed. Given the well-known hierarchy of the hematopoietic system, it is critical to focus on those cells that have the ability to self-renew, since these will be the only cells able to induce long-term drug resistance. Thus, a recent mathematical model taking into account the complex dynamics of the leukemic stem-like cells is described. The chapter closes with a few applications of this model to chronic myeloid leukemia.
Collapse
Affiliation(s)
- Cristian Tomasetti
- Johns Hopkins School of Medicine, 550 North Broadway, Suite 1103, Baltimore, MD 21205, USA,
| |
Collapse
|
|
19
|
Fu F, Nowak MA, Bonhoeffer S. Spatial heterogeneity in drug concentrations can facilitate the emergence of resistance to cancer therapy. PLoS Comput Biol 2015; 11:e1004142. [PMID: 25789469 PMCID: PMC4366398 DOI: 10.1371/journal.pcbi.1004142] [Citation(s) in RCA: 91] [Impact Index Per Article: 9.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2014] [Accepted: 01/20/2015] [Indexed: 02/06/2023] Open
Abstract
Acquired resistance is one of the major barriers to successful cancer therapy. The development of resistance is commonly attributed to genetic heterogeneity. However, heterogeneity of drug penetration of the tumor microenvironment both on the microscopic level within solid tumors as well as on the macroscopic level across metastases may also contribute to acquired drug resistance. Here we use mathematical models to investigate the effect of drug heterogeneity on the probability of escape from treatment and the time to resistance. Specifically we address scenarios with sufficiently potent therapies that suppress growth of all preexisting genetic variants in the compartment with the highest possible drug concentration. To study the joint effect of drug heterogeneity, growth rate, and evolution of resistance, we analyze a multi-type stochastic branching process describing growth of cancer cells in multiple compartments with different drug concentrations and limited migration between compartments. We show that resistance is likely to arise first in the sanctuary compartment with poor drug penetrations and from there populate non-sanctuary compartments with high drug concentrations. Moreover, we show that only below a threshold rate of cell migration does spatial heterogeneity accelerate resistance evolution, otherwise deterring drug resistance with excessively high migration rates. Our results provide new insights into understanding why cancers tend to quickly become resistant, and that cell migration and the presence of sanctuary sites with little drug exposure are essential to this end. Failure of cancer therapy is commonly attributed to the outgrowth of pre-existing resistant mutants already present prior to treatment, yet there is increasing evidence that the tumor microenvironment influences cell sensitivity to drugs and thus mediates the evolution of resistance during treatment. Here, we take into consideration important aspects of the tumor microenvironment, including spatial drug gradients and differential rates of cell proliferation. We show that the dependence of fitness on space together with cell migration facilitates the emergence of acquired resistance. Our analysis indicates that resistant cells that are selected for in compartments with high concentrations are likely to disseminate from sanctuary sites where they first acquire resistance preceding migration. The results suggest that it would be helpful to improve clinical outcomes by combining targeted therapy with anti-metastatic treatment aimed at constraining cell motility as well as by enhancing drug transportation and distribution throughout all metastatic compartments.
Collapse
Affiliation(s)
- Feng Fu
- Theoretical Biology Group, Institute of Integrative Biology, ETH Zurich, Zurich, Switzerland
- * E-mail:
| | - Martin A. Nowak
- Program for Evolutionary Dynamics, Department of Organismic and Evolutionary Biology, Department of Mathematics, Harvard University, Cambridge, Massachusetts, United States of America
| | - Sebastian Bonhoeffer
- Theoretical Biology Group, Institute of Integrative Biology, ETH Zurich, Zurich, Switzerland
| |
Collapse
|
|
20
|
Abstract
Recently, there has been significant activity in the mathematical community, aimed at developing quantitative tools for studying leukemia and lymphoma. Mathematical models have been applied to evaluate existing therapies and to suggest novel therapies. This article reviews the recent contributions of mathematical modeling to leukemia and lymphoma research. These developments suggest that mathematical modeling has great potential in this field. Collaboration between mathematicians, clinicians, and experimentalists can significantly improve leukemia and lymphoma therapy.
Collapse
Affiliation(s)
- Geoffrey Clapp
- Department of Mathematics, University of Maryland, College Park, MD 20742
| | - Doron Levy
- Department of Mathematics, University of Maryland, College Park, MD 20742; Center for Scientific Computation and Mathematical Modeling (CSCAMM), University of Maryland, College Park, MD 20742, USA
| |
Collapse
|
|
21
|
Zhu Y, Pan L, Hong M, Liu W, Qiao C, Li J, Qian S. The combination therapy of imatinib and dasatinib achieves long-term molecular response in two imatinib-resistant and dasatinibintolerant patients with advanced chronic myeloid leukemia. J Biomed Res 2014; 30:525-528. [PMID: 27924071 PMCID: PMC5138586 DOI: 10.7555/jbr.30.20130172] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2013] [Revised: 03/27/2014] [Accepted: 08/05/2014] [Indexed: 11/03/2022] Open
Abstract
For patients with chronic myeloid leukemia (CML) failing imatinib therapy, second-generation tyrosine kinase inhibitors (TKIs) are recommended. Here, we describe two patients with advanced CML who failed imatinib therapy and did not tolerate the recommended dose of dasatinib, but then achieved a major molecular response with the combination of imatinib and dasatinib with no significant extramedullary toxicity. Our observations suggest that combination of TKIs may provide an additive/synergistic antileukemic effect.
Collapse
Affiliation(s)
- Yu Zhu
- Department of Hematology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, China
| | - Liangqin Pan
- Department of Hematology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, China
| | - Ming Hong
- Department of Hematology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, China
| | - Weixing Liu
- Department of Hematology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, China
| | - Chun Qiao
- Department of Hematology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, China
| | - Jianyong Li
- Department of Hematology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, China
| | - Sixuan Qian
- Department of Hematology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, China;
| |
Collapse
|
|
22
|
Foo J, Michor F. Evolution of acquired resistance to anti-cancer therapy. J Theor Biol 2014; 355:10-20. [PMID: 24681298 PMCID: PMC4058397 DOI: 10.1016/j.jtbi.2014.02.025] [Citation(s) in RCA: 180] [Impact Index Per Article: 16.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2013] [Revised: 02/19/2014] [Accepted: 02/20/2014] [Indexed: 12/21/2022]
Abstract
Acquired drug resistance is a major limitation for the successful treatment of cancer. Resistance can emerge due to a variety of reasons including host environmental factors as well as genetic or epigenetic alterations in the cancer cells. Evolutionary theory has contributed to the understanding of the dynamics of resistance mutations in a cancer cell population, the risk of resistance pre-existing before the initiation of therapy, the composition of drug cocktails necessary to prevent the emergence of resistance, and optimum drug administration schedules for patient populations at risk of evolving acquired resistance. Here we review recent advances towards elucidating the evolutionary dynamics of acquired drug resistance and outline how evolutionary thinking can contribute to outstanding questions in the field.
Collapse
Affiliation(s)
- Jasmine Foo
- School of Mathematics, University of Minnesota, Minneapolis, MN 55455, USA.
| | - Franziska Michor
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Biostatistics, Harvard School of Public Health, Boston, MA 02215, USA.
| |
Collapse
|
|
23
|
Jabbour EJ, Cortes JE, Kantarjian HM. Tyrosine kinase inhibition: a therapeutic target for the management of chronic-phase chronic myeloid leukemia. Expert Rev Anticancer Ther 2013; 13:1433-1452. [PMID: 24236822 PMCID: PMC4181370 DOI: 10.1586/14737140.2013.859074] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Chronic myeloid leukemia (CML) is a hematologic neoplasm with a progressive, ultimately terminal, disease course. In most cases, CML arises owing to the aberrant formation of a chimeric gene for a constitutively active tyrosine kinase. Inhibition of the signaling activity of this kinase has proved to be a highly successful treatment target, transforming the prognosis of patients with CML. New tyrosine kinase inhibitors continue to improve the management of CML, offering alternative options for those resistant to or intolerant of standard tyrosine kinase inhibitors. Here we review the pathobiology of CML and explore emerging strategies to optimize the management of chronic-phase CML, particularly first-line treatment.
Collapse
Affiliation(s)
- Elias J Jabbour
- The University of Texas, MD Anderson Cancer Center, Houston, TX, USA
| | - Jorge E Cortes
- The University of Texas, MD Anderson Cancer Center, Houston, TX, USA
| | | |
Collapse
|
|
24
|
Chen JH, Kuo YH, Luh HP. Optimal policies of non-cross-resistant chemotherapy on Goldie and Coldman's cancer model. Math Biosci 2013; 245:282-98. [PMID: 23927854 DOI: 10.1016/j.mbs.2013.07.020] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2012] [Revised: 07/19/2013] [Accepted: 07/26/2013] [Indexed: 12/14/2022]
Abstract
Mathematical models can be used to study the chemotherapy on tumor cells. Especially, in 1979, Goldie and Coldman proposed the first mathematical model to relate the drug sensitivity of tumors to their mutation rates. Many scientists have since referred to this pioneering work because of its simplicity and elegance. Its original idea has also been extended and further investigated in massive follow-up studies of cancer modeling and optimal treatment. Goldie and Coldman, together with Guaduskas, later used their model to explain why an alternating non-cross-resistant chemotherapy is optimal with a simulation approach. Subsequently in 1983, Goldie and Coldman proposed an extended stochastic based model and provided a rigorous mathematical proof to their earlier simulation work when the extended model is approximated by its quasi-approximation. However, Goldie and Coldman's analytic study of optimal treatments majorly focused on a process with symmetrical parameter settings, and presented few theoretical results for asymmetrical settings. In this paper, we recast and restate Goldie, Coldman, and Guaduskas' model as a multi-stage optimization problem. Under an asymmetrical assumption, the conditions under which a treatment policy can be optimal are derived. The proposed framework enables us to consider some optimal policies on the model analytically. In addition, Goldie, Coldman and Guaduskas' work with symmetrical settings can be treated as a special case of our framework. Based on the derived conditions, this study provides an alternative proof to Goldie and Coldman's work. In addition to the theoretical derivation, numerical results are included to justify the correctness of our work.
Collapse
Affiliation(s)
- Jeng-Huei Chen
- Department of Mathematical Sciences, National Chengchi University, Taipei, Taiwan.
| | | | | |
Collapse
|
|
25
|
|
|
26
|
Shi M, Lou B, Ji J, Shi H, Zhou C, Yu Y, Liu B, Zhu Z, Zhang J. Synergistic antitumor effects of dasatinib and oxaliplatin in gastric cancer cells. Cancer Chemother Pharmacol 2013; 72:35-44. [PMID: 23712327 DOI: 10.1007/s00280-013-2166-1] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2013] [Accepted: 04/12/2013] [Indexed: 01/13/2023]
Abstract
PURPOSE The aim of this study is to investigate whether dasatinib, a Src inhibitor, has the synergistic effect with oxaliplatin in treating gastric cancer cells. METHODS The baseline levels of total Src and p-Src in 10 human gastric cancer cell lines and gastric mucosa epithelial cell line GES-1 were detected by Western blot (WB). The changes of Src and p-Src expression after oxaliplatin exposure were evaluated by WB. The combination indices and clonogenic assay were used to evaluate the synergistic effects of dasatinib with oxaliplatin on cell growth and proliferation in vitro. Gastric cancer xenografts in nude mice were established and treated by oxaliplatin with or without dasatinib. The tumor growth curves were calculated and the impacts of different treatment on the tumor proliferation and src protein expression in gastric cancer xenografts were determined by immunohistochemistry staining and WB. RESULTS The different levels of Src expression in gastric cancer cells were related with their different sensitivity to oxaliplatin. The expression of p-Src, but not total Src, was elevated after oxaliplatin exposure both in vitro and in vivo. Dasatinib could dramatically inhibit p-Src expression, and combination indices demonstrated that dasatinib and oxaliplatin were synergistic in inhibiting gastric cancer cell growth. Dasatinib plus oxaliplatin were more effective in inhibiting clone formation than oxaliplatin or dasatinib monotherapy in clonogenic assay. The tumor volume and tumor weight of xenografts were significantly lower in doublet treatment group than those in single-agent treatment groups. CONCLUSIONS Dasatinib plays synergistic role with oxaliplatin in inhibiting gastric cancer cell growth both in vitro and in vivo, via inhibiting Src activity stimulated by oxaliplatin.
Collapse
Affiliation(s)
- Min Shi
- Department of Surgery, Shanghai Institute of Digestive Surgery, Rui Jin Hospital, Shanghai Jiaotong University School of Medicine, No. 197 Ruijin er Road, Shanghai, 200025, People's Republic of China
| | | | | | | | | | | | | | | | | |
Collapse
|
|
27
|
Bozic I, Reiter JG, Allen B, Antal T, Chatterjee K, Shah P, Moon YS, Yaqubie A, Kelly N, Le DT, Lipson EJ, Chapman PB, Diaz LA, Vogelstein B, Nowak MA. Evolutionary dynamics of cancer in response to targeted combination therapy. eLife 2013; 2:e00747. [PMID: 23805382 PMCID: PMC3691570 DOI: 10.7554/elife.00747] [Citation(s) in RCA: 424] [Impact Index Per Article: 35.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2013] [Accepted: 05/20/2013] [Indexed: 12/16/2022] Open
Abstract
In solid tumors, targeted treatments can lead to dramatic regressions, but responses are often short-lived because resistant cancer cells arise. The major strategy proposed for overcoming resistance is combination therapy. We present a mathematical model describing the evolutionary dynamics of lesions in response to treatment. We first studied 20 melanoma patients receiving vemurafenib. We then applied our model to an independent set of pancreatic, colorectal, and melanoma cancer patients with metastatic disease. We find that dual therapy results in long-term disease control for most patients, if there are no single mutations that cause cross-resistance to both drugs; in patients with large disease burden, triple therapy is needed. We also find that simultaneous therapy with two drugs is much more effective than sequential therapy. Our results provide realistic expectations for the efficacy of new drug combinations and inform the design of trials for new cancer therapeutics. DOI:http://dx.doi.org/10.7554/eLife.00747.001 As medicine becomes increasingly personalized, more and more emphasis is being placed on the development of therapies that target specific cancer-causing mutations. But while many of these drugs are effective in the short term, and do extend patient lives, tumors tend to evolve resistance to them within a few months. The key problem is that large tumors are genetically diverse. This means that for any given treatment, there is likely to be a small population of cells within the tumor that is resistant to the effects of the drug. When the drug is given to a patient, these cells will survive and multiply and this will lead ultimately to treatment failure. Given that a single drug is therefore highly unlikely to eradicate a tumor, combinations of two or more drugs may offer a higher chance of cure. This approach has been effective in the treatment of HIV as well as certain forms of leukemia. Here, Bozic et al. present a mathematical model designed to predict the effects of combination targeted therapies on tumors, based on the data obtained from 20 melanoma (skin cancer) patients. Their model revealed that if even 1 of the 6.6 billion base pairs of DNA present in a human diploid cell has undergone a mutation that confers resistance to each of two drugs, treatment with those drugs will not lead to sustained improvement for the majority of patients. This confirms the need to develop drugs that target distinct pathways. The model also reveals that combination therapy with two drugs given simultaneously is far more effective than sequential therapy where the drugs are used one after the other. Indeed, the model of Bozic et al. indicates that sequential treatment offers no chance of a cure, even when there are no cross-resistance mutations present, whereas combination therapy offers some hope of a cure, even in the presence of cross-resistance mutations. By emphasizing the need to develop drugs that target distinct pathways, and to administer them in combination rather than sequentially, the study by Bozic et al. offers valuable advice for drug development and the design of clinical trials, as well as for clinical practice. DOI:http://dx.doi.org/10.7554/eLife.00747.002
Collapse
Affiliation(s)
- Ivana Bozic
- Program for Evolutionary Dynamics , Harvard University , Cambridge , United States ; Department of Mathematics , Harvard University , Cambridge , United States
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|
|
28
|
Is Imatinib Maintenance Required for Patients with Relapse Chronic Myeloid Leukemia Post-Transplantation Obtaining CMR? A Pilot Retrospective Investigation. PLoS One 2013; 8:e65981. [PMID: 23823695 PMCID: PMC3688864 DOI: 10.1371/journal.pone.0065981] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2013] [Accepted: 04/29/2013] [Indexed: 11/22/2022] Open
Abstract
Imatinib can induce complete molecular remission (CMR) in relapse chronic myelogenous leukemia (CML) after allogeneic hematopoietic stem cell transplantation, but it is indefinite whether imatinib is required to maintain CMR. We retrospectively reviewed 37 relapse CML post-transplants treated with imatinib (n = 20) or donor lymphocyte infusion (DLI) (n = 17). The rate of CMR was 85% and 76.47% (P = 0.509) and treatment-related mortality was 0% and 29.4% (P = 0.019), respectively, in imatinib and DLI groups. Fifteen patients obtaining CMR voluntarily ceased imatinib, and did not experience relapse. The 8-year overall survival (OS) after relapse was 85%±8% and 40.3±12.1% (P = 0.017), and disease-free survival (DFS) after relapse was 85%±8% and 40.3±12.1% (P = 0.011), respectively, in imatinib and DLI groups. Imatinib resulted in higher OS and DFS than that of DLI in relapse CML. Imatinib maintenance might not be required for patients with relapse CML post-transplants after they achieved full donor chimerism and CMR.
Collapse
|
|
29
|
AMALA KOMPELLA, RAO ABHUJANGA, GORANTLA BHARATHI, GONDI CHRISTOPHERS, RAO JASTIS. Design, synthesis and preclinical evaluation of NRC-AN-019. Int J Oncol 2012; 42:168-78. [DOI: 10.3892/ijo.2012.1697] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2012] [Accepted: 10/12/2012] [Indexed: 11/06/2022] Open
|
|
30
|
Bozic I, Allen B, Nowak MA. Dynamics of targeted cancer therapy. Trends Mol Med 2012; 18:311-6. [PMID: 22595628 DOI: 10.1016/j.molmed.2012.04.006] [Citation(s) in RCA: 46] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2012] [Revised: 04/08/2012] [Accepted: 04/16/2012] [Indexed: 01/25/2023]
Abstract
Targeted cancer therapies offer renewed hope for an eventual 'cure for cancer'. At present, however, their success is often compromised by the emergence of resistant tumor cells. In many cancers, patients initially respond to single therapy treatment but relapse within months. Mathematical models of somatic evolution can predict and explain patterns in the success or failure of anticancer drugs. These models take into account the rate of cell division and death, the mutation rate, the size of the tumor, and the dynamics of tumor growth including density limitations caused by geometric and metabolic constraints. As more targeted therapies become available, mathematical modeling will provide an essential tool to inform the design of combination therapies that minimize the evolution of resistance.
Collapse
Affiliation(s)
- Ivana Bozic
- Program for Evolutionary Dynamics, Harvard University, Cambridge, MA 02138, USA
| | | | | |
Collapse
|
|
31
|
Tang M, Foo J, Gönen M, Guilhot J, Mahon FX, Michor F. Selection pressure exerted by imatinib therapy leads to disparate outcomes of imatinib discontinuation trials. Haematologica 2012; 97:1553-61. [PMID: 22419579 DOI: 10.3324/haematol.2012.062844] [Citation(s) in RCA: 34] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
Abstract
BACKGROUND Chronic myeloid leukemia is successfully managed by imatinib therapy, but the question remains whether treatment must be administered indefinitely. Imatinib discontinuation trials have led to two distinct outcomes: about 60% of patients experienced disease relapse within 6 months of treatment cessation, while the remaining 40% remained disease-free throughout the duration of follow-up. We aimed to investigate the mechanisms underlying these disparate clinical outcomes. DESIGN AND METHODS We utilized molecular data from the "Stop Imatinib" trial together with a mathematical framework of chronic myeloid leukemia, based on a four-compartment model that can explain the kinetics of the molecular response to imatinib. This approach was complemented by statistical analyses to estimate system parameters and investigate whether chronic myeloid leukemia can be cured by imatinib therapy alone. RESULTS We found that there are insufficient follow-up data from the "Stop Imatinib" trial in order to conclude whether the absence of a relapse signifies cure of the disease. We determined that selection of less aggressive leukemic phenotypes by imatinib therapy recapitulates the trial outcomes. This postulated mechanism agrees with the observation that most patients who have a complete molecular response after discontinuation of imatinib continue to harbor minimal residual disease, and might work in concert with other factors suppressing leukemic cell expansion when the tumor burden remains low. CONCLUSIONS Our analysis provides evidence for a mechanistic model of chronic myeloid leukemia selection by imatinib treatment and suggests that it may not be safe to discontinue therapy outside a clinical trial.
Collapse
Affiliation(s)
- Min Tang
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA 02115, USA
| | | | | | | | | | | |
Collapse
|
|
32
|
Tomasetti C. On the probability of random genetic mutations for various types of tumor growth. Bull Math Biol 2012; 74:1379-95. [PMID: 22311065 DOI: 10.1007/s11538-012-9717-1] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2011] [Accepted: 01/13/2012] [Indexed: 11/24/2022]
Abstract
In this work, we consider the problem of estimating the probability for a specific random genetic mutation to be present in a tumor of a given size. Previous mathematical models have been based on stochastic methods where the tumor was assumed to be homogeneous and, on average, growing exponentially. In contrast, we are able to obtain analytical results for cases where the exponential growth of cancer has been replaced by other, arguably more realistic types of growth of a heterogeneous tumor cell population. Our main result is that the probability that a given random mutation will be present by the time a tumor reaches a certain size, is independent of the type of curve assumed for the average growth of the tumor, at least for a general class of growth curves. The same is true for the related estimate of the expected number of mutants present in a tumor of a given size, if mutants are indeed present.
Collapse
Affiliation(s)
- Cristian Tomasetti
- Department of Biostatistics, Harvard University, and Dana-Farber Cancer Institute, CLS11007, 450 Brookline Ave, Boston, MA 02215, USA.
| |
Collapse
|
|
33
|
Zhu GR, Ji O, Ji JM, Zhang YC, Wu Y, Yu H, Jiang PJ, Shen Q. Combining nilotinib and imatinib improves the outcome of imatinib-resistant blast phase CML. Acta Haematol 2012; 127:152-5. [PMID: 22286512 DOI: 10.1159/000333107] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2011] [Accepted: 08/22/2011] [Indexed: 12/11/2022]
Abstract
Imatinib resistance is an important hurdle in the treatment of chronic myeloid leukemia (CML), and CML patients with this drug resistance are often given a dismal prognosis. In this case report, an imatinib-refractory blast phase CML patient was treated with a combination of imatinib and nilotinib. A complete hematologic response was achieved within 3 months, the drug combination was well tolerated, and there was a relatively long bone-marrow complete remission. These results suggest that combining imatinib and nilotinib treatment may improve the outcome of imatinib-resistant CML patients in the blast phase. We hypothesize regarding the possible mechanism for the effectiveness of the drug combination by reviewing the recent literature.
Collapse
Affiliation(s)
- Guang-Rong Zhu
- Department of Hematology, First Affiliated Hospital of Nanjing University of Chinese Medicine, China
| | | | | | | | | | | | | | | |
Collapse
|
|
34
|
Werner B, Lutz D, Brümmendorf TH, Traulsen A, Balabanov S. Dynamics of resistance development to imatinib under increasing selection pressure: a combination of mathematical models and in vitro data. PLoS One 2011; 6:e28955. [PMID: 22216147 PMCID: PMC3245228 DOI: 10.1371/journal.pone.0028955] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2011] [Accepted: 11/17/2011] [Indexed: 12/20/2022] Open
Abstract
In the last decade, cancer research has been a highly active and rapidly evolving scientific area. The ultimate goal of all efforts is a better understanding of the mechanisms that discriminate malignant from normal cell biology in order to allow the design of molecular targeted treatment strategies. In individual cases of malignant model diseases addicted to a specific, ideally single oncogene, e.g. Chronic myeloid leukemia (CML), specific tyrosine kinase inhibitors (TKI) have indeed been able to convert the disease from a ultimately life-threatening into a chronic disease with individual patients staying in remission even without treatment suggestive of operational cure. These developments have been raising hopes to transfer this concept to other cancer types. Unfortunately, cancer cells tend to develop both primary and secondary resistance to targeted drugs in a substantially higher frequency often leading to a failure of treatment clinically. Therefore, a detailed understanding of how cells can bypass targeted inhibition of signaling cascades crucial for malignant growths is necessary. Here, we have performed an in vitro experiment that investigates kinetics and mechanisms underlying resistance development in former drug sensitive cancer cells over time in vitro. We show that the dynamics observed in these experiments can be described by a simple mathematical model. By comparing these experimental data with the mathematical model, important parameters such as mutation rates, cellular fitness and the impact of individual drugs on these processes can be assessed. Excitingly, the experiment and the model suggest two fundamentally different ways of resistance evolution, i.e. acquisition of mutations and phenotype switching, each subject to different parameters. Most importantly, this complementary approach allows to assess the risk of resistance development in the different phases of treatment and thus helps to identify the critical periods where resistance development is most likely to occur.
Collapse
Affiliation(s)
- Benjamin Werner
- Evolutionary Theory Group, Max-Planck-Institute for Evolutionary Biology, Plön, Germany.
| | | | | | | | | |
Collapse
|
|
35
|
Abstract
Large-scale cancer genomics, proteomics and RNA-sequencing efforts are currently mapping in fine detail the genetic and biochemical alterations that occur in cancer. However, it is becoming clear that it is difficult to integrate and interpret these data and to translate them into treatments. This difficulty is compounded by the recognition that cancer cells evolve, and that initiation, progression and metastasis are influenced by a wide variety of factors. To help tackle this challenge, the US National Cancer Institute Physical Sciences-Oncology Centers initiative is bringing together physicists, cancer biologists, chemists, mathematicians and engineers. How are we beginning to address cancer from the perspective of the physical sciences?
Collapse
Affiliation(s)
- Franziska Michor
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, Massachusetts 02115, USA. michor@jimmy. harvard.edu
| | | | | | | |
Collapse
|
|
36
|
Komarova NL. Mathematical modeling of cyclic treatments of chronic myeloid leukemia. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2011; 8:289-306. [PMID: 21631131 DOI: 10.3934/mbe.2011.8.289] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/30/2023]
Abstract
Cyclic treatment strategies in Chronic Myeloid Leukemia (CML) are characterized by alternating applications of two (or more) different drugs, given one at a time. One of the main causes for treatment failure in CML is the generation of drug resistance by mutations of cancerous cells. We use mathematical methods to develop general guidelines on optimal cyclic treatment scheduling, with the aim of minimizing the resistance generation. We define a condition on the drugs' potencies which allows for a relatively successful application of cyclic therapies. We find that the best strategy is to start with the stronger drug, but use longer cycle durations for the weaker drug. We further investigate the situation where a degree of cross-resistance is present, such that certain mutations cause cells to become resistant to both drugs simultaneously.
Collapse
Affiliation(s)
- Natalia L Komarova
- Department of Mathematics, University of California Irvine, Irvine CA 92697, United States.
| |
Collapse
|
|
37
|
Garland P, Apperley J. Nilotinib: evaluation and analysis of its role in chronic myeloid leukemia. Future Oncol 2011; 7:201-18. [DOI: 10.2217/fon.10.174] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Nilotinib, formally known as AMN107, is a second-generation tyrosine kinase inhibitor, rationally designed from its revolutionary parent compound imatinib, to produce a 30–40-fold enhancement in the inhibition of the BCR–ABL1-derived oncoprotein associated with chronic myeloid leukemia. In clinical trials, nilotinib has proven to be a useful agent in the treatment of imatinib-refractory disease and was initially approved by both the US FDA and EMA in 2007 for use in adults as a second-line therapy. More recently, data from the first randomized controlled trials of the front-line use of nilotinib in newly diagnosed patients with chronic phase chronic myeloid leukemia have demonstrated superiority in the rates of major molecular responses at 12 months over the gold standard–imatinib 400 mg. As such, in June 2010, the FDA granted accelerated approval for its use in newly diagnosed Philadelphia chromosome-positive chronic myeloid leukemia. Nilotinib is well tolerated, with a favorable side-effect profile. With the emergence of supportive trial data, it is likely to have a leading role both in the front-line management of newly presenting patients and in the second-line treatment of patients resistant to or intolerant of imatinib and other second-line agents.
Collapse
Affiliation(s)
- Paula Garland
- Department of Hematology, Imperial College London, Hammersmith Hospital, London, UK
| | | |
Collapse
|
|
38
|
Tomasetti C, Levy D. An elementary approach to modeling drug resistance in cancer. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2010; 7:905-18. [PMID: 21077714 PMCID: PMC3877932 DOI: 10.3934/mbe.2010.7.905] [Citation(s) in RCA: 39] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/12/2023]
Abstract
Resistance to drugs has been an ongoing obstacle to a successful treatment of many diseases. In this work we consider the problem of drug resistance in cancer, focusing on random genetic point mutations. Most previous works on mathematical models of such drug resistance have been based on stochastic methods. In contrast, our approach is based on an elementary, compartmental system of ordinary differential equations. We use our very simple approach to derive results on drug resistance that are comparable to those that were previously obtained using much more complex mathematical techniques. The simplicity of our model allows us to obtain analytic results for resistance to any number of drugs. In particular, we show that the amount of resistance generated before the start of the treatment, and present at some given time afterward, always depends on the turnover rate, no matter how many drugs are simultaneously used in the treatment.
Collapse
Affiliation(s)
- Cristian Tomasetti
- Department of Mathematics and Center for Scientific Computation and Mathematical Modeling, University of Maryland, College Park, MD 20742, United States.
| | | |
Collapse
|
|
39
|
Katouli AA, Komarova NL. Optimizing combination therapies with existing and future CML drugs. PLoS One 2010; 5:e12300. [PMID: 20808800 PMCID: PMC2925944 DOI: 10.1371/journal.pone.0012300] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2010] [Accepted: 07/21/2010] [Indexed: 01/14/2023] Open
Abstract
Small-molecule inhibitors imatinib, dasatinib and nilotinib have been developed to treat Chromic Myeloid Leukemia (CML). The existence of a triple-cross-resistant mutation, T315I, has been a challenging problem, which can be overcome by finding new inhibitors. Many new compounds active against T315I mutants are now at different stages of development. In this paper we develop an algorithm which can weigh different combination treatment protocols according to their cross-resistance properties, and find the protocols with the highest probability of treatment success. This algorithm also takes into account drug toxicity by minimizing the number of drugs used, and their concentration. Although our methodology is based on a stochastic model of CML microevolution, the algorithm itself does not require measurements of any parameters (such as mutation rates, or division/death rates of cells), and can be used by medical professionals without a mathematical background. For illustration, we apply this algorithm to the mutation data obtained in [1], [2].
Collapse
MESH Headings
- Algorithms
- Antineoplastic Agents/administration & dosage
- Antineoplastic Agents/therapeutic use
- Antineoplastic Combined Chemotherapy Protocols
- Drug Resistance, Multiple/genetics
- Drug Resistance, Neoplasm/genetics
- Humans
- Leukemia, Myelogenous, Chronic, BCR-ABL Positive/drug therapy
- Leukemia, Myelogenous, Chronic, BCR-ABL Positive/genetics
- Leukemia, Myelogenous, Chronic, BCR-ABL Positive/therapy
- Models, Biological
- Mutation
- Probability
- Stochastic Processes
- Treatment Outcome
Collapse
Affiliation(s)
- Allen A. Katouli
- Department of Mathematics, University of California Irvine, Irvine, California, United States of America
| | - Natalia L. Komarova
- Department of Mathematics, University of California Irvine, Irvine, California, United States of America
- * E-mail:
| |
Collapse
|
|
40
|
Katouli AA, Komarova NL. The worst drug rule revisited: mathematical modeling of cyclic cancer treatments. Bull Math Biol 2010; 73:549-84. [PMID: 20396972 DOI: 10.1007/s11538-010-9539-y] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2009] [Accepted: 03/23/2010] [Indexed: 12/11/2022]
Abstract
In drug treatments of cancer, cyclic treatment strategies are characterized by alternating applications of two (or more) different drugs, given one at a time. One of the main problems of drug treatment in cancer is associated with the generation of drug resistance by mutations of cancerous cells. We use mathematical methods to develop general guidelines on optimal cyclic treatment scheduling, with the aim of minimizing the resistance generation. We define a condition on the drugs' potencies which allows for a relatively successful application of cyclic therapies. We find that the best strategy is to start with the stronger drug, but use longer cycle durations for the weaker drug. We further investigate the situation where a degree of cross-resistance is present, such that certain mutations cause cells to become resistant to both drugs simultaneously. We show that the general rule (best-drug-first, worst-drug-longer) is unchanged by the presence of cross-resistance. We design a systematic method to test all strategies and come up with the optimal timing and drug order. The role of various constraints in the optimal therapy design, and in particular, suboptimal treatment durations and drug toxicity, is considered. The connection with the "worst drug rule" of Day (Cancer Res. 46:3876, 1986b) is discussed.
Collapse
Affiliation(s)
- Allen A Katouli
- Department of Mathematics, University of California Irvine, Irvine, CA 92697, USA
| | | |
Collapse
|
|
41
|
Wachsberger P, Burd R, Ryan A, Daskalakis C, Dicker AP. Combination of Vandetanib, Radiotherapy, and Irinotecan in the LoVo Human Colorectal Cancer Xenograft Model. Int J Radiat Oncol Biol Phys 2009; 75:854-61. [DOI: 10.1016/j.ijrobp.2009.06.016] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2009] [Revised: 05/09/2009] [Accepted: 06/09/2009] [Indexed: 12/14/2022]
|