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Tesi etd-04202016-100309


Thesis type
Tesi di dottorato di ricerca
Author
SAMEEN, SHEEMA
URN
etd-04202016-100309
Title
Hybrid Modeling of Cancer Drug Resistance Mechanisms
Settore scientifico disciplinare
INF/01
Corso di studi
INFORMATICA
Commissione
tutor Prof. Barbuti, Roberto
tutor Prof. Milazzo, Paolo
tutor Prof. Cerone, Antonio
Parole chiave
  • Hybrid modeling
  • colorectal cancer
  • lung cancer
Data inizio appello
15/05/2016;
Consultabilità
completa
Riassunto analitico
Cancer is a multi-scale disease and its overwhelming complexity depends upon the multiple<br>interwind events occurring at both molecular and cellular levels, making it very difficult<br>for therapeutic advancements in cancer research. The resistance to cancer drugs is a<br>significant challenge faced by scientists nowadays. The roots of the problem reside not<br>only at the molecular level, due to multiple type of mutations in a single tumor, but also<br>at the cellular level of drug interactions with the tumor. Tumor heterogeneity is the term<br>used by oncologists for the involvement of multiple mutations in the development of a<br>tumor at the sub-cellular level. The mechanisms for tumor heterogeneity are rigorously<br>being explored as a reason for drug resistance in cancer patients. It is important to observe<br>cell interactions not only at intra-tumoral level, but it is also essential to study the drug<br>and tumor cell interactions at cellular level to have a complete picture of the mechanisms<br>underlying drug resistance.<br>The multi-scale nature of cancer drug resistance problem require modeling approaches<br>that can capture all the multiple sub-cellular and cellular interaction factors with respect to<br>di erent scales for time and space. Hybrid modeling offers a way to integrate both discrete<br>and continuous dynamics to overcome this challenge. This research work is focused on the<br>development of hybrid models to understand the drug resistance behaviors in colorectal<br>and lung cancers. The common thing about the two types of cancer is that they both have<br>di erent mutations at epidermal growth factor receptors (EGFRs) and they are normally<br>treated with anti-EGFR drugs, to which they develop resistances with the passage of time.<br>The acquiring of resistance is the sign of relapse in both kind of tumors.<br>The most challenging task in colorectal cancer research nowadays is to understand the<br>development of acquired resistance to anti-EGFR drugs. The key reason for this problem is<br>the KRAS mutations appearance after the treatment with monoclonal antibodies (moAb).<br>A hybrid model is proposed for the analysis of KRAS mutations behavior in colorectal<br>cancer with respect to moAb treatments. The colorectal tumor hybrid model is represented<br>as a single state automata, which shows tumor progression and evolution by means of<br>mathematical equations for tumor sub-populations, immune system components and drugs<br>for the treatment. The drug introduction is managed as a discrete step in this model.<br>To evaluate the drug performance on a tumor, equations for two types of tumors cells<br>are developed, i.e KRAS mutated and KRAS wild-type. Both tumor cell populations<br>were treated with a combination of moAb and chemotherapy drugs. It is observed that<br>even a minimal initial concentration of KRAS mutated cells before the treatment has the ability to make the tumor refractory to the treatment. Moreover, a small population of<br>KRAS mutated cells has a strong influence on a large number of wild-type cells by making<br>them resistant to chemotherapy. Patient&#39;s immune responses are specifically taken into<br>considerations and it is found that, in case of KRAS mutations, the immune strength does<br>not affect medication efficacy. Finally, cetuximab (moAb) and irinotecan (chemotherapy)<br>drugs are analyzed as first-line treatment of colorectal cancer with few KRAS mutated<br>cells. Results show that this combined treatment could be only effective for patients with<br>high immune strengths and it should not be recommended as first-line therapy for patients<br>with moderate immune strengths or weak immune systems because of a potential risk of<br>relapse, with KRAS mutant cells acquired resistance involved with them.<br>Lung cancer is more complicated then colorectal cancer because of acquiring of multiple<br>resistances to anti-EGFR drugs. The appearance of EGFR T790M and KRAS mutations<br>makes tumor resistant to a geftinib and AZD9291 drugs, respectively. The hybrid model for<br>lung cancer consists of two non-resistant and resistant states of tumor. The non-resistant<br>state is treated with geftinib drug until resistance to this drug makes tumor regrowth<br>leading towards the resistant state. The resistant state is treated with AZD9291 drug for<br>recovery. In this model the complete resistant state due to KRAS mutations is ignored<br>because of the unavailability of parameter information and patient data. Each tumor state<br>is evaluated by mathematical differential equations for tumor growth and progression. The<br>tumor model consists of four tumor sub-population equations depending upon the type<br>of mutations. The drug administration in this model is also managed as a discrete step<br>for exact scheduling and dosages. The parameter values for the model are obtained by<br>experiments performed in the laboratory. The experimental data is only available for<br>the tumor progression along with the geftinib drug. The model is then fine tuned for<br>obtaining the exact tumor growth patterns as observed in clinic, only for the geftinib<br>drug. The growth rate for EGFR T790M tumor sub-population is changed to obtain the<br>same tumor progression patterns as observed in real patients. The growth rate of mutations<br>largely depends upon the immune system strength and by manipulating the growth rates<br>for different tumor populations, it is possible to capture the factor of immune strength of<br>the patient. The fine tuned model is then used to analyze the effect of AZD9291 drug<br>on geftinib resistant state of the tumor. It is observed that AZD9291 could be the best<br>candidate for the treatment of the EGFR T790M tumor sub-population.<br>Hybrid modeling helps to understand the tumor drug resistance along with tumor<br>progression due to multiple mutations, in a more realistic way and it also provides a way<br>for personalized therapy by managing the drug administration in a strict pattern that<br>avoid the growth of resistant sub-populations as well as target other populations at the<br>same time. The only key to avoid relapse in cancer is the personalized therapy and the<br>proposed hybrid models promises to do that.
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