For many decades, the most common type of anti-cancer treatment has been conventional chemotherapy; however, their broad-based mechanisms (e.g. DNA-alkylating agents) usually lead to severe systemic side effects. Within the last decade, treatment has migrated towards therapies that inhibit molecules with a role in tumor growth or progression, but are not often found in normal cells. Tumor-specific drugs help reduce the systemic toxicity that accompanied older treatments.
Today, targeted therapies represent an integrative approach to cancer therapy that has already led to break-through clinical responses in specific subsets of cancers. This has been particularly evident with several recently developed kinase inhibitors that target EGFR, BCR-ABL, HER2, ALK, VEGFR, mTOR, JAK2, and BRAF. The discovery of signalling pathways associated with states of “oncogene addiction,” has allowed scientifically guided drug discovery strategies to exploit specific tumor cell vulnerabilities opening up a new paradigm of personalized cancer therapy.
The opportunity to select defined patient populations by incorporating tailored diagnostic tests and biomarkers to identify those most likely to benefit from these targeted therapies has been received with much enthusiasm. However, these “smart” therapies also suffer from the same major limitation associated with traditional chemotherapy drugs—the duration of any observed clinical benefit is invariably short lived, due to the relatively rapid acquisition of drug resistance. This results in clinical relapse and ultimately failure in the management of the disease.
Identifying the specific molecular mechanisms of resistance to chemotherapeutics has been very challenging, in part due to the relatively nonspecific nature of the anti-tumor mechanisms associated with these drugs. As a result, the discovery of second-generation chemotherapeutics that can effectively treat such acquired chemo-drug resistance has been limited. In contrast, the mechanisms of acquired resistance to “pathway-targeted” drugs—for example tyrosine kinase inhibitors (TKIs)—have been more tractable to some degree. In a few cases, the discovery of such mechanisms has already led to the development of follow-on drugs (e.g. nilotinib in chronic myeloid leukemia) specifically designed to overcome the acquired resistance.
As more mechanisms of acquired resistance are unravelled, this opens up further opportunities to develop new drugs targeting the root cause of the resistance process. In conjunction with this, there is a pressing need for more clinically-relevant and predictive pre-clinical models to address the high attrition rates of agents entering clinical trials and also for the evaluation of new agents in models that replicate the acquired resistance mechanisms.
All new agents entering Phase 1 clinical trials will be tested in cancer patients that most likely have become resistant to a range of targeted and chemotherapeutic agents, making drug-naïve preclinical models inappropriate and poorly predictive. Standard cell-derived xenograft (CDX) models use cell lines that are maintained in long-term passage on plastic; these have a poor success rate in predicting clinical efficacy and have adapted to grow independently of the tumor microenvironment, resulting in models with genetic and phenotypic characteristics distinct from that seen in the clinic.
Patient-derived xenograft tumors (PDX) are being increasingly used to improve and refine preclinical modelling and provide a more relevant heterogeneous system, in which human tumor and stromal cells are in close co-operation. Maintaining the human microenvironment in such models also sustains molecular, genetic, and histological heterogeneity of the original tumors. Collectively these important features help to ensure authentic responses to current targeted agents or chemotherapeutics as well as sustaining the properties of defined tumor subsets reflective of those seen in the clinic.
Case study
Erlotinib (Tarceva) and gefitinib (Iressa) inhibit the epidermal growth factor receptor (EGFR) kinase and are first-line agents used to treat non–small cell lung cancer (NSCLC) patients that have activating mutations in the EGFR gene. Whilst most NSCLC patients with activating EGFR mutations will respond to EGFR tyrosine kinase inhibitors (TKIs), the tumors will ultimately become resistant to treatment with progression of disease occurring in patients typically around 10–16 months after commencing. In about 50% of these cases, resistance is due to the occurrence of a secondary mutation in EGFR (T790M). The mechanisms that contribute to resistance in the remaining tumors include amplification of MET kinase (5% to 10%) and mutations in downstream signalling such as PIK3CA (<5%). EGFR inhibitor resistant models are needed to explore the additional mechanisms of resistance and for testing new agents and/or combination strategies to delay/combat the emergence of resistance.
LION102 is a proprietary Caucasian NSCLC (adenocarcinoma) PDX model derived by PRECOS Ltd. with an activating EGFR mutation (L858R) which is maintained in serial passage subcutaneously in vivo admixed with a human stromal cell component.
LION102 demonstrates strong sensitivity (regression at 25 mg/kg and 50 mg/kg erlotinib) which is consistent with responses seen in the clinic for patients bearing this EGFR mutation. Mutational status (exon 21, L858R) and response to EGFRi was maintained through cryopreservation and subsequent resuscitation. Empirical tolerance to EGFRi, compared to tumor naïve model, was established through repeated dosing cycles followed by outgrowth. At present with 3 cycles of EGFR inhibitor treatment, resistance to EGFR TKIs is emerging. The HCC827 NSCLC cell line which also has an activating EGFR mutation (exon 19 E746_A750del) showed resistance to treatment following repeated dosing in vitro, with an elevation of cMET gene copy number, suggesting homogeneous population of cells can be used to model acquired resistance, however the PDX model would be more clinical relevant and valuable in examining the mechanisms of resistance in the clinic as well as assessing new agents and combination strategies.
Filed Under: Drug Discovery
