Piotrowska et al

Piotrowska et al. initiation for this study cohort. Our results also demonstrate that this growth rate of the resistant population is usually highly correlated to the time to tumor progression. These estimates of the size of the resistant and persistent tumor cell population during TKI treatment can inform combination treatment strategies such as multi-agent schedules or a combination of targeted agents and radiotherapy. Introduction The emergence of resistance to targeted agents such as small molecule tyrosine kinase inhibitors (TKI) is one of the key challenges in the effort to control cancer. A significant research effort focuses on the mechanisms of resistance (1-4), in KX-01-191 order to determine the pathways by which resistance arises and identify strategies to prevent its emergence. An important question of clinical relevance in this context is the temporal evolution of drug resistance. Resistance may arise either from sub-clones or during therapy (5-9). This distinction is important for the administration of targeted agents in clinical practice and the combination of targeted agents with other treatment modalities, such as surgery, radiotherapy or thermal ablation in non-metastatic disease. From a statistical perspective the probability of pre-existing resistance in a macroscopic tumor is high, due to the large number of cell divisions necessary to reach a tumor of detectable size (10-12). This holds when phenomena such as stochastic drift and variations in fitness conferred with mutations are taken into account (13,14). Pre-existing resistance has been well studied in non-small cell lung cancer (NSCLC) patients treated with TKI targeting the epidermal growth factor receptor (EGFR-TKIs) especially for T790M, a common resistance-conferring mutation to first-generation EGFR-TKIs. It can develop via distinct evolutionary paths (5) from a reservoir a drug-tolerant persister cells (8), and has been found in 1C25% of patients pre-treatment KX-01-191 and correlated with shorter time to disease progression (15,16). Even though some of these results have been attributed to measurement artifacts (17), it seems beyond doubt that pre-existing resistance occurs in some patients. In general, a tumors genomic instability fosters a genetic diversity which is the underlying driver for its heterogeneity, which in general leads to inferior outcomes when treated with targeted agents. This is not restricted to EGFR/EGFRT790M, but can be observed in a variety of activating mutations and resistance mechanisms, as recently comprehensively reviewed (18-20). It has been demonstrated that allelic frequencies of specific mutations compared to the abundance of the activating EGFR mutation can predict greater tumor volume response (20,21). Piotrowska et al. recently also showed that EGFRT790M-positive and -negative clones do co-exist in patients, and that the changes in their relative abundance reflects the response to various targeted therapies (22). Another path to resistance is through evolution from drug-resistant persister cells (23,24), which has been shown to lead to resistance to EGFR-TKIs via the T790M mutation (5,8). While the evolution of acquired resistance is being thoroughly investigated versus resistance as the predominant cause of progression in patients on targeted therapy, and there is little clinical evidence to support one hypothesis over the other. One of the main reasons for this uncertainty is the fact that biopsies only provide a limited window, in space as well as in time, into the process of resistance development and are unlikely to detect small populations of resistant clones and populations in EGFR-mutant lung cancer patients during treatment with TKIs. We propose a general model and two restricted models describing only resistance and only resistance. The goal is to estimate the sizes of these populations based on the macroscopic behavior of tumor burden, making as few assumptions as possible about the mechanisms of resistance KX-01-191 themselves. We apply the models to tumor volume trajectories of NSCLC patients undergoing treatment with EGFR-TKIs and progressing.Based on these results we conducted a robustness analysis and assumed a range of three values for the growth of the persister population: two, four or eight times slower than the tumor before therapy. time of treatment initiation for this study cohort. Our results also demonstrate that the growth rate of the resistant population is highly correlated to the time to tumor progression. These estimates of the size of the resistant and persistent tumor cell population during TKI treatment can inform combination treatment strategies such as multi-agent schedules or a combination of targeted agents and radiotherapy. Introduction The emergence of resistance to targeted agents such as small molecule tyrosine kinase inhibitors (TKI) is one of the key challenges in the effort to control cancer. A significant research effort focuses on the mechanisms of resistance (1-4), in order to determine the pathways by which resistance arises and identify strategies to prevent its KX-01-191 emergence. An important question of clinical relevance in this context is the temporal evolution of drug resistance. Resistance may arise either from sub-clones or during therapy (5-9). This distinction is important for the administration of targeted agents in clinical practice and the combination of targeted agents with other treatment modalities, such as surgery, radiotherapy or thermal ablation in non-metastatic disease. From a statistical perspective the probability of pre-existing resistance in a macroscopic tumor is high, due to the large number of cell divisions necessary to reach a tumor of detectable size (10-12). This holds when phenomena such as stochastic drift and variations in fitness conferred with mutations are taken into account (13,14). Pre-existing resistance has been well studied in non-small cell lung cancer (NSCLC) patients treated with TKI targeting the epidermal growth factor receptor (EGFR-TKIs) especially for T790M, a common resistance-conferring mutation to first-generation EGFR-TKIs. It can develop via KX-01-191 distinct evolutionary paths (5) from a reservoir a drug-tolerant persister cells (8), and has been found in 1C25% of individuals pre-treatment and correlated with shorter time to disease progression (15,16). Even though some of these results have been attributed to measurement artifacts (17), it seems beyond doubt that pre-existing resistance occurs in Rabbit Polyclonal to CYSLTR1 some patients. In general, a tumors genomic instability fosters a genetic diversity which is the underlying driver for its heterogeneity, which in general leads to substandard results when treated with targeted providers. This is not restricted to EGFR/EGFRT790M, but can be seen in a variety of activating mutations and resistance mechanisms, as recently comprehensively examined (18-20). It has been shown that allelic frequencies of specific mutations compared to the abundance of the activating EGFR mutation can forecast greater tumor volume response (20,21). Piotrowska et al. recently also showed that EGFRT790M-positive and -bad clones do co-exist in individuals, and that the changes in their relative abundance displays the response to numerous targeted treatments (22). Another path to resistance is definitely through development from drug-resistant persister cells (23,24), which has been shown to lead to resistance to EGFR-TKIs via the T790M mutation (5,8). While the development of acquired resistance is being thoroughly investigated versus resistance as the predominant cause of progression in individuals on targeted therapy, and there is little clinical evidence to support one hypothesis on the additional. One of the main reasons for this uncertainty is the truth that biopsies only provide a limited windows, in space as well as in time, into the process of resistance development and are unlikely to detect small populations of resistant clones and populations in EGFR-mutant lung malignancy individuals during treatment with TKIs. We propose a general model and two restricted models describing only resistance and only resistance. The goal is to estimate the sizes of these populations based on the macroscopic behavior of tumor burden, making as few assumptions as you possibly can about the mechanisms of resistance themselves. We apply the models to tumor volume trajectories of NSCLC individuals undergoing treatment with EGFR-TKIs and progressing having a T790M mutation. Materials and Methods Patient populace and data acquisition We recognized metastatic T790M on medical molecular profiling of tumor biopsy at progression Absence of additional treatments during the observation period.