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Precision oncology requires identifying and understanding of cancer genome changes in a patient tumor tissue and finding the best cancer therapy targeting the changes. Although many cancer gene targets have been validated so far, next-generation genomic profile analyses have uncovered much more cancer gene variants with unconfirmed functions. Developing methods to functionally evaluate mutations/variants and understand their roles in cancer development and drug responses, such as drug resistance or synthetic lethality, will be critical in cancer treatment decision support. In addition, in some clinical cases, multiple treatment choices such as multiple drug combinations exist. Developing cancer models which can test multiple treatments will provide direct comparison of those therapies and select the best options. At GenenDesign, we have performed drug tests on mouse “avatars”, which are also known as Patient-Derived Xenograft (PDX) models. They are personalized cancer models derived from patient tumor samples with cancer mutation profiles and drug responses very similar to the corresponding cancer patients. Drug screenings were carried out in avatars by testing chemotherapies or targeted drugs against specific cancer gene mutations and variants. Selected drugs or drug combinations from avatar studies have been applied to corresponding patients with highly consistent treatment outcome. From genomic profile analysis of our near 1500 PDX tumor models in cancer types such as lung, colorectal, gastric, liver, and esophageal, we are able to identify a large number of cancer gene mutations/variants, gene fusions, as well as gene copy number and RNA expression changes in major cancer signal pathways such as EGFR, Her2, c-Met/ALK, Ras/Raf, FGFRs, PI3K/Akt, Wnt, Notch, DNA repair, cell cycle regulation, angiogenesis. Many of these gene aberrations are potential drug targets and have been functionally tested in PDX models with approved drugs or clinical drug candidates. The mutation/variant information and drug response information generated from PDX models have been organized into our Precision Cancer Information Lab database. Patient tumor DNA test results have been used for searching genetically matched PDX models in our database. Once matched PDX models are identified, the available drug response information can be used as evidence for clinical treatment decision. In addition, the matched PDX models can also been used to test more treatment options such as different combinations and new clinical drug candidates. Citation Format: Jingjing Jiang, Tengfei Yu, Ying Yan, Wei Du, Tingting Tan, Xuqin Yang, Jiali Gu, Ling Qiu, Xin K. Ye, Zhenyu Gu. In vivo functional analyses of cancer gene variants for cancer driver identification and drug discovery [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 3139. doi:10.1158/1538-7445.AM2017-3139

e23140 Background: Clinical trial to evaluate the safety and efficacy of an oncology drug candidate in general is a long and expensive process required for regulatory approval in drug development. High failure rates and difficulties to predict the outcome of clinical trials have wasted the resources and hindered the progress to bring more cancer drugs to patients. PDX models have been widely used recently in preclinical efficacy studies of new cancer agents. Comparing with tumor cell line derived xenografts, PDX models showed more similarities to cancer patients in tumor pathological features, molecular changes and drug responses. More importantly, the large number of available PDX models make it feasible to do preclinical tests in large scales which can represent cancer complexity similar to clinical trials with cancer patients. Methods: At GenenDesign, we have established over 1000 PDX tumor models in various cancer types and derived about 200 acquired drug resistance models. In our in house studies, we have tested near 40 treatments with standard of cares (SoCs) and late stage clinical drug candidates either as mono-therapies or combination therapies, and have generated over 3300 drug response datasets. Many tests with targeted drugs are based on cancer gene aberrations as predictive biomarkers. The drug responses were analyzed by Modifying Response Evaluation Criteria in Solid Tumors (mRECIST) method. Results: By comparing the mRECIST scores in mouse trials with results from about two dozens of historical clinical trials at phase 3 or phase 2, we found that there are high correlations in the rates of objective response (OR), stable disease (SD) and progressive disease (PD) between the mouse trials and their counterpart clinical trials. Progression-free survival also showed similar patterns in these analyses. Conclusions: PDX based mouse trials have advantages in flexibility of study design, quickness in testing new drug candidates and direct comparison of multiple drug treatments in the same models. It can provide valuable information on clinical trial outcome prediction and support clinical study design and patient selection.

Cancer is a heterogeneous disease with various molecular lesions and drug response profiles within the same tumor type. Patient stratification in clinical trials based on molecular features has contributed to recent success of several targeted cancer drugs on molecular aberrations such as BCR-ABL translocation in CML, Her2 amplification in breast and gastric cancers, EGFR mutations and ALK fusions in lung cancer and BRAF mutation in melanoma. However, in most cancers, the molecular features which can be used for patient stratification are not as simple as a single genetic aberration. Multiple drug resistance mechanisms caused by various mutations in cancer signaling pathways can also increase the uncertainty of clinical outcomes. To increase the chance of success in human clinical studies, patient-derived xenograft (PDX) clinical trials (PCTs) have increasingly been used for predictive biomarker validation, resistance mechanism investigation and combination therapy selection. PDX tumor models have been demonstrated to have high correlations with human patients in tumor pathology, molecular characteristics and drug responses. Large scale PCTs have also shown consistency in results when compared to related human clinical trials. At GenenDesign, we have established over 1000 PDX tumor models and more than 100 resistance models against various cancer drugs. Many of these PDX models have been characterized at RNA/Exome sequence, gene expression, gene copy number and hot-spot mutation levels. We carried out our PDX clinical trials by testing multiple approved drugs and clinical drug candidates such as targeted inhibitors against FGFRs, c-Met/ALK, HER2, EGFR, cell cycle regulators, Ras/Raf pathway, PI3K/Akt pathway, as well as chemotherapy drugs in biomarker-driven multi-drug multi-arm expanded PDX clinical trials. So far, we have accumulated more than 3000 efficacy data sets and associated PD samples. Analysis of drug response and associated genomic information from PDX clinical trials yielded rich information for predictive biomarker identification and validation. At the same time, many potential resistance mechanisms were also revealed. These information can make human clinical trial better prepared, more efficient and focused. More importantly, testing of a targeted drug with multiple chemotherapies in the same models can also provide guidance on future combination selection strategy. Citation Format: Jingjing Jiang, Tengfei Yu, Ying Yan, Wei Du, Tingting Tan, Xuqin Yang, Jiali Gu, Xin K. Ye, Zhenyu Gu. Patient stratification and drug combination strategy based on drug response and genomic information from PDX clinical trials (PCTs). [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 395.

The main purpose of precision medicine is to find the right drug for the right patient at the right time. Current progress in cancer drug target identification and development of new targeted drugs has presented many successful examples. However, for majority of patients, finding treatments based on the unique mutations in their own tumor cells are still hard. New approaches for precision medicine are in great needs and thus being investigated. A mouse “avatar”, also known as Patient-Derived Xenograft (PDX) model, is a personalized cancer model derived from a patient tumor sample and used to test different drugs for the patient. “GIFTS” (or Genomic Information Fitting based Therapeutics Selection) is a method with which a patient's cancer genomic information is compared to PDX model genomic profiles to find the best genomic fit and related therapeutic options in GenenDesign Drug Response Database and Genomic Information database. So far we have successfully derived more than 1000 PDX models from patient tumor tissues of various cancer types including lung, gastric, liver, esophageal, colorectal, pancreatic and many other cancers. In some cases, both patients and their avatars were tested with same cancer drugs including targeted drugs and chemotherapies. By comparing drug responses in mouse avatars with patient clinical results, we found high correlations between them in both sensitivity and unresponsiveness. We have also developed a bioinformatics algorithm to analyze PDX model genomic and drug response information. Both drug sensitivity and resistance biomarkers have been used in matching cancer patient genomic profiles to those of PDX models in GenenDesign database as a part of our GIFTS method. Our preliminary results show that there are high degree of similarities in drug response profiles between patients and their matched PDX models. Avatar and GIFTS methods can provide predictions of therapeutic effectiveness on both targeted drugs and chemotherapies. Avatar is a drug screening based method, while GIFTS is a genomic profile matching based method. Both methods are especially useful for patients, whose tumors could not be treated with known targeted drugs. Citation Format: Jingjing Jiang, Ying Yan, Zhongguang Luo, Jia He, Tengfei Yu, Wei Du, Xuqin Yang, Jiali Gu, Xin K. Ye, Guanglei Zhuang, Jie Liu, Zhenyu Gu. Prediction and selection of cancer drug treatments using personalized tumor models or models with matching genomic profiles. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 3982.

Companion diagnostics are assays intended to find the right drug for the right patient in targeted cancer therapy. Biomarker discovery is the first step in companion diagnostics development. Currently great efforts have been devoted to biomarker discovery and validation through analysis of clinical samples. However, the quantity and quality of tumor samples from drug candidate treated patients are very limited during drug development, which has posed challenges for fast and cost-effective drug-diagnostic co-development. Patient derived xenograft (PDX) tumor models have been demonstrated to represent cancer complexity and heterogeneity known in patients. Drug targets and responses to targeted drugs in PDX models show high correlation to those in cancer patients. We have established over 950 PDX tumor models and around 100 resistance models to drugs of interest. To evaluate mouse trials with PDX models as biomarker discovery platform, we have been testing PDX models with SOCs and clinical drug candidates such as targeted inhibitors against FGFRs, c-Met/ALK, HER2, EGFR, cell cycle regulators, Ras/Raf pathway, PI3K/Akt pathway, as well as chemotherapy drugs in a biomarker-driven multi-drug multi-arm clinical trial setting, and have accumulated more than 1500 data sets and associated PD samples. Through analysis of drug response data and genomic profile data from PDX models, we show that many of the drug sensitivity biomarkers and drug resistance biomarkers identified in clinical studies can be faithfully found in PDX studies. Moreover, some novel predictive biomarkers for candidate drugs in relatively early clinical stage have also been identified and will be further validated in future clinical studies. The availability of a large number of PDX models, the flexibility of designing studies and the quickness in testing new drug candidates make this type of mouse trial and its derived data sets a useful platform for biomarker discovery in companion diagnostics development. Citation Format: Jingjing Jiang, Tengfei Yu, Ying Yan, Wei Du, Tingting Tang, Xuqin Yang, Jiali Gu, Liang Hua, Xin Katherine Ye, Zhenyu Gu. Accelerating biomarker discovery for companion diagnostics through mouse trials with PDX models in clinical settings. [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2015 Nov 5-9; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2015;14(12 Suppl 2):Abstract nr A32.

Liver cancer is one of the leading causes of cancer mortality worldwide. Hepatocelluar carcinoma (HCC) is the most common form of liver cancer, followed by intrahepatic cholangiocarcinoma (IHCC). HCC has dismal clinical outcome, whereas the prognosis is even worse for IHCC as it is more difficult to diagnose and to treat comparing to HCC. Surgical resection and local ablation remain the top choices of therapy for early liver cancer while chemoembolization-TACE is commonly used to treat intermediate HCC. Sorafenib is the only FDA approved target therapy for advanced HCC and its clinical utility in IHCC is still being examined in clinical trials. On-going clinical trials are also testing therapeutic modalities against oncogenic pathways including RTK signaling pathways, PI3K/Ras pathways and the angiogenesis pathway. PDX tumor models recapitulate the clinical complexity of the original human cancers. At GenenDesign, we have established over 800 PDX tumor models and conducted extensive drug response tests in a mouse trial format. GenenDesign liver cancer PDX tumor panel comprises of 36 HCC and over 10 IHCC models. Our in-house mouse trials in liver PDX tumor models include treatment with sorafenib as well as XL184, a multi-kinase inhibitor currently being evaluated in clinical trials. In addition, crizotinib (a cMET inhibitor) and AZD4547 (an FGFR inhibitor) as mono-therapies as well as in combination with sorafenib are also tested in search of therapeutic signals. From these studies, we have identified both drug sensitive and de novo drug resistant models. Through long-term treatment, acquired resistance models and reversible resistance models are also established. Currently, GD liver cancer PDX tumors are being analyzed by genetic and genomic profiling (hot-spot mutational analysis, exome-seq, RNA-seq, SNP array and gene expression array). Bioinformatics analysis is on-going to identify genomic signatures with the potential as predictive biomarkers for sorafenib and other targeted therapies in liver cancer. Together with genomic profiling, signal search in PDX mouse trials promise to be effective in generating preclinical datasets to facilitate clinical trial designs. Citation Format: Tengfei Yu, Ying Yan, Wei Du, Liang Hua, Xuqin Yang, Tingting Tan, Jiali Gu, Jingjing Jiang, Xin K. Ye, Zhenyu Gu. Effect of target therapies in liver cancer PDX tumor models: Response, resistance and predictive biomarkers. [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 1476. doi:10.1158/1538-7445.AM2015-1476

Patient derived xenograft (PDX) tumor models have been proved to recapitulate the complexity and heterogeneity of their corresponding human tumors by phenotypic and genomic characterization, and thus become to be widely used in recent years in preclinical setting to facilitate drug discovery, translational studies and clinical trials support. Results from increasing number of preclinical studies, especially mouse trials, with PDX models in the past several years, have also demonstrated close correlation between drug response profiles with PDX models and clinical outcomes. GenenDesign has established over 800 PDX tumor models and derived around 100 resistance models to drugs of interest. Genomic profiling data of PDX models are acquired at hot-spot mutation, gene expression, gene copy number and RNA/Exome sequence levels. Through our in-house efforts, PDX models of different tumor types were tested with related SOCs and clinical candidates in biomarker-driven multi-drug multi-arm clinical trial settings. So far, more than 1200 data sets have been generated, including responses to targeted inhibitors against HER2, EGFR, FGFRs, c-Met/ALK, cell cycle regulators, Ras/Raf pathway, PI3K/Akt pathway, epigenetic targets, as well as chemotherapy drugs. In this study, we use bioinformatic tools to compare the genomic profiles of NSCLC PDX models based on their response profiles to multiple chemotherapy drugs. Biomarker signatures associated with SOC treatment sensitivity or resistance are revealed from bioinformatic analysis and being tested with both new NSCLC PDX models and clinical cohorts. The combination of genomic profiles and drug response information to multiple chemo/targeted therapies of over 800 PDX models at GenenDesign would help biomarker discovery for companion diagnosis to meet the increasing needs for precision medicine. Citation Format: Jingjing Jiang, Tengfei Yu, Ying Yan, Wei Du, Tingting Tan, Xuqin Yang, Jiali Gu, Liang Hua, Katherine Xin Ye, Zhenyu Gu. Biomarker discovery through bioinformatic analysis of genomic profiles of PDX models with different responses to cancer therapies. [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 3222. doi:10.1158/1538-7445.AM2015-3222

From Chinese cancer patients, close to 600 patient derived xenograft (PDX) tumor models have been established (> P3, three passages in mice) at GenenDesign through serial passages in the immune-compromised nude mice. The major collection of GenenDesign PDX tumor model platform represents cancer types that are prevalent in Asian patients, including gastric cancer (> 200 models), esophageal cancer (>100 models), liver cancer (∼50 models), pancreatic cancer (>60 models) and lung cancer (> 80 models). Establishment of variant PDX models from the same patient tumor is on-going to support translational studies of tumor heterogeneity. Initial characterization indicates that the mouse PDX models have captured the major histopathological characteristics of the original human tumors. Reproducible growth curves for PDX models (>P3) support their usage in efficacy analysis of anti-cancer therapeutic agents. Response curves to SoC (standard of care) chemotherapies such as Paclitaxel for lung cancer, FOLFOX for gastric cancer and Sorafenib for liver cancer have been established in the PDX tumor models, providing a baseline for further investigation of novel therapies in a combination setting. On-going molecular characterization including oncogene mutational analysis and target specific IHC and FISH analysis has identified panels of PDX tumor models with aberrations in key oncogenic signaling pathways, including lung panels with EGFR overexpression or KRAS mutations, gastric panels with FGFR2 amplification or being HER2 positive, lung and gastric panels with cMET overexpression. Testing of Herceptin in the gastric HER2 positive tumor panel resulted in observations similar to that from the ToGA trial. At the same time, Herceptin resistant PDX tumor variants (de novo or acquired) were identified or established. The PDX tumor model panels facilitate translational studies in a “mouse trial” format in a setting similar to clinical trials to test patient stratification strategies and drug response predictive biomarkers for emerging therapeutic modalities. Citation Format: Ying Yan, Tengfei Yu, Wei Du, Guosheng Tong, Yuefei Yang, Tingting Tan, Xuqin Yang, Zhenhua Liu, Jiali Gu, Liang Hua, Wei Zhang, Xin K. Ye, Zhenyu Gu. A patient derived xenograft tumor model platform for “mouse trials”. [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 1210. doi:10.1158/1538-7445.AM2014-1210

Anti-cancer drugs, either targeted therapies or cytotoxic chemotherapies, have proven to be effective in treating certain cancer patients. However, in most cases, tumors recur and become resistant to the treatment after a period of time. There are urgent needs to understand the underlying drug resistance mechanisms, to discover drug resistance targets and drug resistance biomarkers and to develop new therapies or combination therapies to tackle this widely occurring clinical problem. Currently the main approaches to study cancer drug resistance include analyzing clinical samples and developing drug resistance models in vitro. Numerous potential resistance mechanisms have been revealed. However, validation of these findings in a clinical-like setting and to test therapies in preclinical studies requires in vivo tumor models of drug resistance. At GenenDesign, we have developed cancer drug resistance PDX tumor models through short term drug testing or long term treatment of xenograft tumor mice. Cancer drugs investigated in our studies include major classes of targeted therapeutic modalities such as Her2 inhibitors, EGFR inhibitors, FGFR inhibitors and cMet/ALK inhibitors, as well as several standard of care (SoC) chemotherapies. From these studies, we have identified de novo resistance models, acquired resistance models and reversible resistance models in multiple cancer types including lung cancer and gastric cancer. In analyzing more than a dozen of Her2 positive but Herceptin resistance PDX models, we have uncovered molecular abnormalities such as Pten deletion, PI3K mutation, amplification of EGFR, cMet and cyclin E, which have been reported previously to be associated with Herceptin resistance in early studies. Studies are on-going to test whether combination therapies will be effective in overcoming Herceptin resistance. In drug response profiling of our PDX models, we also found wide spread phenotypical and functional heterogeneity in individual tumors. The heterogeneity within each tumor, in some cases, contributed to evolving of drug resistance from initially responsive tumors. Citation Format: Tengfei Yu, Ying Yan, Wei Du, Yuefei Yang, Tingting Tan, Xuqin Yang, Jiali Gu, Liang Hua, Xin K. Ye, Zhenyu Gu. Studying cancer drug resistance in patient derived xenograft tumor models. [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 1212. doi:10.1158/1538-7445.AM2014-1212