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Tesi etd-04202016-100309

Thesis type
Tesi di dottorato di ricerca
Hybrid Modeling of Cancer Drug Resistance Mechanisms
Settore scientifico disciplinare
Corso di studi
tutor Prof. Barbuti, Roberto
tutor Prof. Milazzo, Paolo
tutor Prof. Cerone, Antonio
Parole chiave
  • Hybrid modeling
  • colorectal cancer
  • lung cancer
Data inizio appello
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.