Cyrus Mirsaidi

Abstract Patient-derived xenograft (PDX) models have been established from the world's largest living tumor bank at Molecular Response and used to mimic clinical trial settings with patients with heterogeneous populations and heterogeneous disease within oncology indications for a faster and less expensive path to test novel compounds in a more predictive preclinical setting. Overall, the collection is comprised of 144 000 tumor samples corresponding to 70 000 unique patients and continues to grow with new patient samples added routinely. For several patients, multiple samples were collected at different times (before and after treatment) or at different locations (primary/ metastatic). This allows evaluation of drug efficiency in pre and post treatment application which mimics a clinical setting as well the ability to monitor the effect of drug on the primary tumor and a matched metastatic tumor. For banked tumors, we have histo-pathological and molecular data as well as FFPE slides, DNA, RNA and chemosensitivity data from the original patient. MRL currently has over 350 PDX models in progress and has established >300 unique PDX models in multiple indications. Many established PDX models have confirmed histology and mutational profiling (Next-Gen sequencing) to demonstrate preservation of the biological characteristics of these models to the original patient sample as well as between passages within the model. MRL's vast collection of patient samples allows clients the ability to focus on unique subsets of patients for a custom model build or the ability to choose from our extensive list of established PDX models to further drug discovery of novel compounds. Citation Format: Thomas Broudy, Jill Ricono, Colleen Scott, Praveen Nair, Jayant Thatte, Cyrus Mirsaidi. Molecular Response LLC tumor bank and patient-derived tumor xenograft models: a powerful translational engine for discovery and development of novel oncology therapeutics. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 3224. doi:10.1158/1538-7445.AM2015-3224

Abstract With a 7% likelihood of regulatory approval, oncology drug registration failure rates lead all therapeutic areas. Further complicating the drug development process, preclinical oncology models poorly reflect tumor tissue biology and produce a high level of false positive data. Yet, monocellular two-dimensional (2D) tissue culture remains the preferred platform for most laboratory preclinical studies. The popularity of 2D tissue culture was driven predominantly by fast and dependable proliferation of human tumor cells rather than alignment to the pathophysiology of human cancer. As a consequence of this artificial selection bias, many registered oncology drugs today are highly toxic with a propensity to attack any dividing cell (i.e., non-targeted). Fortunately, recent advances in genomics, metabolomics and immunology have aided in development of new targeted agents, many of which have achieved regulatory approval. Nevertheless, rational drug combinations and identification of clinically meaningful drug targets persist as major challenges in oncology, and each can be meaningfully addressed using more diverse and biologically relevant preclinical models. Ex vivo tumor tissue transplant models, such as patient-derived xenografts (PDX) offer greater model diversity and more faithfully reflect patient tumor genetics. However, cost and scalability barriers tend to limit widespread and practical model utility. In addition, there remains a strong selection bias for autocrine human tumors that can quickly adapt to a cross-species mouse host. As a practical alternative to PDX models, we established, serially propagated and molecularly characterized 300 ex vivo 3D (3DX) models spanning 15 tumor indications. Given patient tumor seeding success rates of over 95%, the 3DX-TGA model enabled diverse pharmacologic evaluation for the vast majority of cancer patient models attempted. After achieving sufficient tumor biomass, typically within 7 to 8 weeks, thirty-one drugs were screened for proliferation and viability endpoints over a three-log dose range. Resistance and sensitivity profiles mirrored population-based response rates for indication-aligned FDA-registered drugs. Taken together, the 3DX-TGA model represents a powerful preclinical ex vivo model that: (1.) more faithfully recapitulates human tumor biology, (2.) can be scaled at a fraction of time and cost compared to PDX models, and (3.) provide a superior screening platform for novel drugs and drug-drug combinations. Citation Format: Praveen Nair, Dileep Nair, Kaede Hinata, Cyrus Mirsaidi, Junjie Wu, Yong Hu, Brett M. Hall. Ex vivo three-dimensional tumor growth assay: 3DX-TGA [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 5767. doi:10.1158/1538-7445.AM2017-5767

Abstract Small cell lung cancer comprises 15-20% of all lung cancer cases, and is more invasive and has a higher rate of proliferation with respect to non-small cell lung cancer, leading to a higher mortality rate. Most cases are responsive to chemotherapy, however there is a high rate of recurrence with treated patients and those in advanced stage of the disease often have a refractory response to treatment. As such, the need for new treatment modalities is critical. Use of patient-derived tumor xenograft (PDX) models as clinically-relevant preclinical model for novel drug development has gained widespread adoption in recent years. In previous work, we have reported using the Molecular Response tumor bank, comprising >144,000 viable tumor specimens, to establish more than 100 PDX models of various cancer types. In this work, we describe the development of 8 new SCLC PDX models established from a collection of >300 small cell lung cancer specimens, many of which come from prior-treated and metastatic patients. We report a comprehensive characterization of these models, including: histopathology, immunohistochemistry, and mutation analysis by next-generation sequencing. Additionally, we have evaluated functional response of these models with in vivo pharmacology studies. Current studies are underway to derive correlations between in vivo drug response and mutational status of these models. Our data strongly suggests the potential to use these unique PDX models to aid in efforts in drug development efforts in oncology. Citation Format: Patrick Carlson, Jill Ricono, Chelsea Mullins, Thomas Broudy, Cyrus Mirsaidi, Praveen Nair. Establishment of patient-derived xenograft (PDX) models for small cell lung cancer (SCLC) as a preclinical platform for drug development. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 1189. doi:10.1158/1538-7445.AM2014-1189

Abstract Despite best efforts in the treatment of gastrointestinal stromal tumors (GIST), patients continue to face a poor prognosis. That said, introduction of imatinib into clinical practice has vastly improved outcomes for KIT positive patients. However, as with many kinases, resistance has developed into a clinical dilemma, rendering the drug ineffective. A number of resistance mutations have previously been identified including the well known D816 activating mutation. To overcome this challenge, we have established a set of GIST patient derived xenograft models to recapitulate the original patient tumor, including genetics of resistance. In this work, we show four primary GIST samples which were selected based on mutational and clinical profile. PDX models were developed in immunocompromised mice and were further characterized by immunohistochemistry, additional sequencing and pharmacological efficacy. We then evaluated the efficacy of chemotherapeutic agents in these models. We describe imatinib resistance mutations, and demonstrate in vivo efficacy of dasatinib over imatinib in the resistant GIST PDX model. Take together, this data validates these GIST PDX models as a novel platform for the evaluation of new drug candidates to better delay and circumvent resistance now found in the clinic. Citation Format: Chelsea Mullins, Jill Ricono, Patrick Carson, Gaston Habets, Rafe Shellooe, Hoa Nguyen, Thomas Broudy, Cyrus Mirsaidi, Praveen Nair. A patient derived xenograft (PDX) platform for development of next generation KIT kinase inhibitors in imatinib-resistant gastrointestinal stromal tumors (GIST). [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 1224. doi:10.1158/1538-7445.AM2014-1224