Data augmentation and multimodal learning for predicting drug response in patient-derived xenografts from gene expressions and histology images

e13572 Background: Prediction of drug response is a critical research area in precision oncology and has been previously explored with large drug screening studies of cancer cell lines (CCLs). Patient-derived xenografts (PDXs) are an appealing platform for preclinical drug studies because the in vivo environment of PDXs helps preserve tumor heterogeneity and usually better mimics drug response of patients with cancer compared to CCLs. Methods: We investigate multimodal neural network (NN) and data augmentation for drug response prediction in PDXs. The multimodal NN learns to predict response using drug descriptors, gene expressions (GE), and histology whole-slide images (WSIs) where the multi-modality refers to tumor features only. The NN uses late integration where separate subnetworks are used to encode the input feature types before concatenation and prediction layers. Median tumor volume per treatment group is assessed relative to the control group to create a binary variable representing response. The data include twelve single-drug and 36 drug-pair treatments resulting in 2,556 single-drug and 2,203 drug-pair response values. Pathology and omics data from 487 PDXs from NCI's Patient Derived Models Repository are used as tumor feature model inputs. We explore whether the integration of WSIs with GE improves predictions as compared with models that use GE alone. We use two methods to address the limited number of response values in the dataset: 1) homogenize drug representations which allows to combine single-drug and drug-pairs into a single dataset, 2) augment drug-pair samples by switching the order of drug features which doubles the sample size of all drug-pair samples. These methods enable us to combine single-drug and drug-pair treatments which results in 6,962 responses, allowing us to train multimodal and unimodal NNs without changing architectures or the dataset. Results: Prediction performance of three unimodal NNs which use GE (um1, um2, and um3) are compared to assess the contribution of data augmentation methods. NN um1 that uses the full dataset which includes the original and the augmented drug-pair treatments as well as single-drug treatments significantly outperforms NNs (p-values < 0.01) that ignore either the augmented drug-pairs (um2) or the single-drug treatments (um3). In assessing the contribution of multimodal learning, results show that the multimodal NN (mm) outperforms both unimodal NNs that ignore either the GE (um4) or the WSIs (um1). However, the improvement of mm over um1 is not statistically significant (p-value < 0.26). Conclusions: Our results show that data augmentation and integration of histology images and GE can help improve prediction performance of drug response in PDXs.[Table: see text]

BackgroundPredicting the drug response of a patient is important for precision oncology. In recent studies, multi-omics data have been used to improve the prediction accuracy of drug response. Although multi-omics data are good resources for drug response prediction, the large dimension of data tends to hinder performance improvement. In this study, we aimed to develop a new method, which can effectively reduce the large dimension of data, based on the supervised deep learning model for predicting drug response.ResultsWe proposed a novel method called Supervised Feature Extraction Learning using Triplet loss (Super.FELT) for drug response prediction. Super.FELT consists of three stages, namely, feature selection, feature encoding using a supervised method, and binary classification of drug response (sensitive or resistant). We used multi-omics data including mutation, copy number aberration, and gene expression, and these were obtained from cell lines [Genomics of Drug Sensitivity in Cancer (GDSC), Cancer Cell Line Encyclopedia (CCLE), and Cancer Therapeutics Response Portal (CTRP)], patient-derived tumor xenografts (PDX), and The Cancer Genome Atlas (TCGA). GDSC was used for training and cross-validation tests, and CCLE, CTRP, PDX, and TCGA were used for external validation. We performed ablation studies for the three stages and verified that the use of multi-omics data guarantees better performance of drug response prediction. Our results verified that Super.FELT outperformed the other methods at external validation on PDX and TCGA and was good at cross-validation on GDSC and external validation on CCLE and CTRP. In addition, through our experiments, we confirmed that using multi-omics data is useful for external non-cell line data.ConclusionBy separating the three stages, Super.FELT achieved better performance than the other methods. Through our results, we found that it is important to train encoders and a classifier independently, especially for external test on PDX and TCGA. Moreover, although gene expression is the most powerful data on cell line data, multi-omics promises better performance for external validation on non-cell line data than gene expression data. Source codes of Super.FELT are available at https://github.com/DMCB-GIST/Super.FELT.

Triple negative breast cancers (TNBCs) are a clinically and biologically aggressive breast cancer (BC) subtype; TNBC tumors have higher rates of metastasis, relapse and acquired/inherent drug resistance. Incidence and mortality rates of TNBC are stratified based on patient ethnicity - patients with African ancestry have higher mortality rates and diagnoses of invasive cancers compared to patients representing other ethnicities. Louisiana has a high proportion of African-American residents (32.7% in 2018), and New Orleans has among the highest incidences of TNBC in the country. Many of our patients present with TNBC tumors that are partially or completely resistant to neoadjuvant chemotherapies. There are currently no clinically approved targeted therapies for TNBC. Current therapeutic discovery focused TNBC research does not aptly address the knowledge gap regarding ethnic disparity in TNBC incidence/mortality rates and TNBC biology. To date, most TNBC-related research and knowledge has been acquired from Caucasian patients, although patients with African and Hispanic ancestries represent the majority of TNBC cases. Patient-derived xenografts (PDXs) are extensively used in BC research, as they mimic complex microanatomy, oncoarchitecture, and cell-cell/cell-stroma interactions of tumors. Here, we demonstrated the unique composition of PDX tumors is not dramatically affected by serial transplantation in mice, based on molecular phenotypes (examined using qRT-PCR and RNA sequencing) and the oncoarchitecture of the extracellular matrix (based on cryogenic scanning electron microscopy). Using these models in basic research facilitates translation of laboratory findings to the clinical setting, and dramatically enhanced drug discovery research. We have established over twelve TNBC PDX models, 90% of which represent patients of African ancestry, and most of which are resistant to neoadjuvant regimens. We focus on dissecting and evaluating kinase inhibitor/targeted drug response to various individual components (tumor cell biology, stroma, immune, extracellular matrix) of chemotherapy resistant TNBC tumors. Histone deacetylase inhibitors (DACi) are a promising therapeutic agent in TNBC systems; they have been shown to suppress tumorigenesis and metastasis in TNBC through suppression of the mesenchymal phenotype in cell line-based studies. In this study we utilized various TNBC PDX models (TU-BcX-2K1, -2O0, 4IC, -4M4, -4QAN, -4QX) to assess these findings in more translational systems. Interestingly, we showed that DACi effect on tumorigenesis and metastasis varied depending on specific TNBC PDXs utilized. These data implicate specific genes/signaling pathways exist in individual patient tumors that can predict tumor responsiveness to DACi. Preliminary data using the NCI oncology drug set implicated the MEK1/2 pathway contributed to sensitization of TNBC cells. Furthermore, we found a disconnect in gene expressions that were previously shown to be affected by DACi therapy (CDH1, VIM, ZEB1, ZEB2) in various derivations of PDX models (cells, PDX-Os, ex vivo, in vivo). These findings demonstrate that testing various derivations of PDX models is crucial to parsing out specific mechanisms of targeted therapies. Our methods presented here to assess targeted drug response and drug resistance using PDX models can be applied to any area of cancer research and is not limited to breast cancer. Citation Format: Margarite Matossian, Steven Elliott, Maryl Wright, Tiffany Chang, Madlin Alzoubi, Henri Wathieu, Rachel Sabol, Alex Alfortish, Hope Burks, Van Hoang, Deniz Ucar, Gabrielle Windsor, Thomas Yan, Jovanny Zabaleta, Fokhrul Hossain, Bruce Bunnell, Krzysztof Moroz, Arnold Zea, Adam Riker, Steven Jones, Elizabeth Martin, Lucio Miele, Bridgette Collins-Burow, Matthew Burow. Effect of histone deacetylase inhibitors on patient-derived neoadjuvant chemotherapy resistant triple negative breast cancer xenografts that represent understudied patients [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P6-03-17.

Introduction: Existing large-scale preclinical cancer drug response databases provide us with a great opportunity to identify and predict potentially effective drugs to combat cancers. Deep learning models built on these databases have been developed and applied to tackle the cancer drug-response prediction task. Their prediction has been demonstrated to significantly outperform traditional machine learning methods. However, due to the "black box" characteristic, biologically faithful explanations are hardly derived from these deep learning models. Interpretable deep learning models that rely on visible neural networks (VNNs) have been proposed to provide biological justification for the predicted outcomes. However, their performance does not meet the expectation to be applied in clinical practice. Methods: In this paper, we develop an XMR model, an eXplainable Multimodal neural network for drug Response prediction. XMR is a new compact multimodal neural network consisting of two sub-networks: a visible neural network for learning genomic features and a graph neural network (GNN) for learning drugs' structural features. Both sub-networks are integrated into a multimodal fusion layer to model the drug response for the given gene mutations and the drug's molecular structures. Furthermore, a pruning approach is applied to provide better interpretations of the XMR model. We use five pathway hierarchies (cell cycle, DNA repair, diseases, signal transduction, and metabolism), which are obtained from the Reactome Pathway Database, as the architecture of VNN for our XMR model to predict drug responses of triple negative breast cancer. Results: We find that our model outperforms other state-of-the-art interpretable deep learning models in terms of predictive performance. In addition, our model can provide biological insights into explaining drug responses for triple-negative breast cancer. Discussion: Overall, combining both VNN and GNN in a multimodal fusion layer, XMR captures key genomic and molecular features and offers reasonable interpretability in biology, thereby better predicting drug responses in cancer patients. Our model would also benefit personalized cancer therapy in the future.

Identifying stable EEG patterns over time for mental workload recognition using transfer DS-CNN framework

Building footprints provide essential information for mapping, disaster management, and other large-scale studies. Synthetic Aperture Radar (SAR) provides consistent data availability over optical images owing to its unique properties, which consequently makes it more challenging to interpret. Previous studies have demonstrated the success of automated methods using Convolutional Neural Networks to detect buildings in Very High Resolution (VHR) SAR images. However, the scarcity of such datasets that are available to the public can limit research progress in this field. We explored the impact of several data augmentation (DA) methods on the performance of building detection on a limited dataset of SAR images. Our results show that geometric transformations are more effective than pixel transformations. The former improves the detection of objects with different scale and rotation variations. The latter creates textural changes that help differentiate edges better, but amplifies non-object patterns, leading to increased false positive predictions. We experimented with applying DA at different stages and concluded that applying similar DA methods in training and inference showed the best performance compared with DA applied only during training. Some DA can alter key features of a building’s representation in radar images. Among them are vertical flips and quarter circle rotations, which yielded the worst performance. DA methods should be used in moderation to prevent unwanted transformations outside the possible object variations. Error analysis, either through statistical methods or manual inspection, is recommended to understand the bias presented in the dataset, which is useful in selecting suitable DAs. The findings from this study can provide potential guidelines for future research in selecting DA methods for segmentation tasks in radar imagery.

Using cancer models to validate drug targets, evaluate drug candidates, and support clinical trial design has been important parts of preclinical studies in cancer drug research. To translate cancer model studies into clinical studies, great efforts have been made to generate a large number of patient derived xenograft (PDX) tumor models in certain cancer types and to demonstrate their similarities to cancer patients in tumor growth, histopathology, tumor complexity, molecular features and drug responses. Recently, focus has been shifted to use cancer model populations to mimic clinical trial design and predict drug responses in clinical trials. We have developed over 1200 PDX models in multiple cancer types from naive or relapse tumor samples. Genomic profile and hotspot mutation analyses were performed to characterize drug targets and biomarkers used in clinical settings. Chemotherapies such as taxane and platinum, and targeted drugs such as cabozantinib, olaparib or sorafenib were tested at different doses and durations in PDX models such as lung cancer, gastric cancer or liver cancer. Drug response results from different regimens in PDX studies were analyzed by mRECIST method and compared with the corresponding results from clinical trials. Our results demonstrated that selection of PDX models with histopathology and genetic features matched to the corresponding patient population in clinical trials is important for treatment result prediction. Some widely used doses for chemos in preclinical studies need to be reduced to achieve consistency with clinical results. Longer treatment time and more models than those normally used in preclinical efficacy studies also improve prediction value especially in cancer types with higher heterogeneity. Overall benefits of a targeted drug combined with one chemo over its combination with another chemo can be more accurately reflected in a large PDX population. In contrast PDX models derived from naive patient samples showed not much difference from models derived from chemo resistant tumors in their responses to new targeted treatments. Drugs targeting RAS/RAF signaling, PI3K/AKT signaling or cell cycle showed more uncertainty in PDX models if single biomarkers were used for drug response prediction. In summary, a sufficient number of PDX models with pathological and molecular features similar to compositions of human cancer patients in clinical trials are necessary for using PDX mouse trial in predicting clinical outcome. Considerations should be given to mouse trial design similar to clinical trial design rather than traditional preclinical studies for targeting validation or proof-of-concept efficacy tests. Citation Format: Jingjing Jiang, Ying Yan, Tingting Tan, Wei Du, Jiali Gu, Ling Qiu, Katherine Ye, Zhenyu Gu. Considerations in PDX mouse trial design and their relevance to human clinical trial outcomes [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2175.

Deep neural networks (DNNs) often rely on massive labelled data for training, which is inaccessible in many applications. Data augmentation (DA) tackles data scarcity by creating new labelled data from available ones. Different DA methods have different mechanisms and therefore using their generated labelled data for DNN training may help improving DNN's generalisation to different degrees. Combining multiple DA methods, namely multi-DA, for DNN training, provides a way to further boost generalisation. Among existing multi-DA based DNN training methods, those relying on knowledge distillation (KD) have received great attention. They leverage knowledge transfer to utilise the labelled data sets created by multiple DA methods instead of directly combining them for training DNNs. However, existing KD-based methods can only utilise certain types of DA methods, incapable of making full use of the advantages of arbitrary DA methods. In this work, we propose a general multi-DA based DNN training framework capable to use arbitrary DA methods. To train a DNN, our framework replicates a certain portion in the latter part of the DNN into multiple copies, leading to multiple DNNs with shared blocks in their former parts and independent blocks in their latter parts. Each of these DNNs is associated with a unique DA and a newly devised loss that allows comprehensively learning from the data generated by all DA methods and the outputs from all DNNs in an online and adaptive way. The overall loss, i.e., the sum of each DNN's loss, is used for training the DNN. Eventually, one of the DNNs with the best validation performance is chosen for inference. We implement the proposed framework by using three distinct DA methods and apply it for training representative DNNs. Experimental results on the popular benchmarks of image classification demonstrate the superiority of our method to several existing single-DA and multi-DA based training methods.

Cancer organoids are heterogeneous 3D cellular clusters with complexities that mimic some characteristics of tumors in situ. Thus, assays performed with cancer organoids might enable better predictions of in vivo drug responses than those performed with cell monolayers. The National Cancer Institute (NCI) is developing a national repository of Patient-Derived (PD) models comprised of clinically annotated and molecularly characterized PD xenografts (PDXs), PD tumor cell lines (PDCs), and PD cancer organoids (PDOrgs) (https://pdmr.cancer.gov/). We evaluated the therapeutic activity of a panel of FDA-approved and investigational anticancer agents, including carboplatin, gemcitabine, paclitaxel, SN38, 5-FU, adavosertib, erlotinib, trametinib, and vemurafenib, against a cohort of PDCs, PDOrgs, and PDXs from solid tumors including colon, gastroesophageal, head and neck, NSCLC, pancreatic, bladder, and uterine cancers. Our goal was to investigate whether drug sensitivities determined using PDCs and PDOrgs correlate with responses observed in the matching PDXs. Cultures were exposed to anticancer agents at concentrations ranging from 1 pM to 100 µM for periods of 4 or 6 days. The data indicated that the GI50 values for PDOrgs were in overall agreement with in vivo PDX drug responses measured as relative median to event free survival (RMEFS), where an event is the median time (days) from treatment initiation to tumor volume quadrupling, calculated as median time to tumor volume quadrupling for treated animals/median time to tumor volume quadrupling for control animals. For both paclitaxel and trametinib, responses in PDOrgs, from most sensitive to most resistant, were similar to the corresponding PDXs. Drug sensitivities determined in PDC monolayers were less clearly related to in vivo PDX responses; particularly for PDCs treated with carboplatin, gemcitabine, and SN-38. This work is part of a larger effort to provide a rigorous comparison between fully characterized and annotated PDCs-PDOrgs-PDXs to assess the value of different in vitro model systems for the prediction of PDX drug responses. This research was supported [in part] by the Developmental Therapeutics Program in the Division of Cancer Treatment and Diagnosis of the National Cancer Institute. Funded by NCI Contract No. HHSN261200800001E. Citation Format: Petreena Campbell, Curtis Hose, Lara El Touny, Erik Harris, John Connelly, Carrie Bonomi, Kelly Dougherty, Savanna Styers, Abigail Walke, Jenna Moyer, Mariah Baldwin, Anna Wade, Michael Mullendore, Kaitlyn Arthur, Matthew Murphy, Kevin Plater, Marion Gibson, Joseph Geraghty, Michelle Gottholm-Ahalt, Tara Grinnage-Pulley, Tiffanie Chase, John Carter, Howard Stotler, Debbie Trail, Luke Stockwin, Dianne Newton, Yvonne Evrard, Melinda Hollingshead, Ralph E. Parchment, Nathan P. Coussens, Beverly A. Teicher, James H. Doroshow, Annamaria Rapisarda. Evaluation of patient-derived cell lines and cancer organoids for the prediction of drug responses in patient-derived xenograft models [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 3913.

Background: Breast cancer (BC) occurs in 1 of 8 women, often requiring debilitating surgery, chemotherapy or radiation for long term survival. Histologic and molecular biomarkers are used to classify BC according to defined subtypes which dictate the choice of targeted therapy or of non-targeted cytotoxic therapy. Despite high initial response rates, relapses are common for more aggressive tumors, and choosing the right therapy for each patient remains challenging. In vitro 3D BC models maintain biologic features that more closely resemble clinical disease than 2D models. However, many 3D models do not contain multiple cell types, are maintained in static culture conditions and rely on immortalized cell lines previously propagated in 2D culture conditions. To address these issues, we developed long term, 3D heterotypic BC microtumors, which recapitulate the dynamic interaction between stromal and epithelial components, retain subtype-specific biomarkers and demonstrate clinically-relevant drug response. We further demonstrated the value of developing non-lytic, label-free in situ analysis to monitor morphology and function of complex 3D microtumors over time. Materials & Methods: Er+, Her2+ or triple negative (TNBC) cell lines (MCF7, SKBR3, MDA-MB-231) or patient derived xenograft (PDX) cells were embedded with human mammary fibroblasts and adipose cells within a hydrogel encapsulated by a silk fibroin scaffold. Microtumors were maintained at least 4 weeks under perfusion flow utilizing the 3DKUBE™ and were characterized for cell morphology and phenotype (IHC), proliferation (PrestoBlue and PicoGreen), gene expression (qRT-PCR), redox ratio (multiphoton microscopy), and biomarker secretion (xMAP® multiplex immunoassay). Drug response profiling (DRP) was performed with tamoxifen, lapatinib and cisplatin. Results: 3D microtumors successfully recapitulated the morphology of primary BC predicted by molecular subtype and gene expression. Perfusion promoted cell proliferation and impacted redox ratio, gene expression, and biomarker secretion in comparison to static culture. Relative redox ratios of 3D microtumors were significantly different from those of cell lines in 2D (p<0.05). Perfusion, 3D conditions, Her2+ and TNBCs were independently associated with increased biomarker secretion, and both cell line and PDX microtumors had unique secretome signatures. PDX microtumors more accurately predicted drug response. Conclusions: Long-term, 3D heterotypic breast microtumors have unique metabolic and secretome signatures which are different than cells in 2D, and the microtumor morphology, metabolism and drug response can be monitored non-destructively in situ. Our ultimate goal is to develop these microtumors using primary human breast tumors for real time drug response profiling in the preclinical, co-clinical and clinical settings to improve outcomes for women with breast cancer. Citation Format: Tessa M. DesRochers, Stephen Shuford, Christina Mattingly, Terri Bruce, Zhiyi Liu, Kyle Quinn, Irene Georgakoudi, David L. Kaplan, David Orr, Howland E. Crosswell. Perfused 3D tri-culture breast cancer microtumors for accurate prediction of drug response. [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 320. doi:10.1158/1538-7445.AM2015-320

Background Recently, we established orthotopic neuroblastoma patient-derived xenografts (PDXs) which maintain the phenotypic, genomic, and stromal hallmarks of patient tumors. Here we examined how PDXs evolve following years of in vivo growth. Materials and Methods We established up to eight in vivo generations of neuroblastoma orthotopic PDXs through serial passaging in NSG mice. RNA sequencing, exome sequencing and SNP array analysis were used to analyze patient tumors and PDXs from different in vivo generations. Results Using SNP analysis, we found mostly a remarkable genomic stability at chromosomal level between patient tumors, early and late PDX generations. RNA-seq revealed that patient tumors expressed higher levels of genes involved in immune responses and ECM metabolism compared to PDXs. Different PDX samples clustered correctly into their respective tumor type. PDXs from all early generations did not separate from PDXs from late generations. Thus, gene expression levels are surprisingly often quite stable despite years of in vivo growth. To shed light on neuroblastoma intratumor heterogeneity, we implanted 10 different tumor fragments from a single patient tumor into mice. We classified the 10 mice into three groups based on the time periods required for tumor growth. RNA-seq showed that each of these groups had a distinct gene expression profile and pathways involved in neuroblastoma progression have been identified. Conclusions Neuroblastoma orthotopic PDXs are often very stable at chromosomal and gene expression levels despite years of in vivo growth. We utilized multiple PDXs to show functional intratumor heterogeneity coupled to distinct gene expression profile. Citation Format: Noémie Braekeveldt, Susanne Fransson, Kristoffer von Stedingk, Ingrid Öra, Rosa Noguera, Tommy Martinsson, David Gisselsson Nord, Sven Påhlman, Daniel Bexell. Evolution of neuroblastoma patient-derived orthotopic xenografts through space and time [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 1937. doi:10.1158/1538-7445.AM2017-1937

Rodent tumor models with histological and molecular resemblance of human tumors and improved predictive value for clinical drug response are highly desired for oncology drug discovery and development. Patient-derived xenograft (PDX) tumor models are believed to better preserve features of human malignancy than cancer cell line-derived xenograft models. We have established more than 170 PDX models from Asian-prevalent human tumors in SCID or nude mice. Here we report molecular and pharmacological profiling of a panel of pancreatic adenocarcinoma (PAC) and hepatocellular carcinoma (HCC) models. Among 13 PAC models, Sequenom analysis showed KRAS mutation in all models, p53 mutation in 3, p16/CDKN2A deletion in 9 and SMAD4 deletion in 4 models. Comparison of transcriptomes by Affymetrix U133Plus2 between the original tumor and derived xenografts at different passages (P1-P6) in one PAC model revealed a high degree of similarity (R2 =0.92-0.97). Also, the gene mutational status and histological characters remained unchanged across the parental tumor and different passages of the PDX model. Evaluation of response to Gemzar treatment (60 mpk, Q4D x 3) showed significant tumor regression in 6 PAC models and partial tumor growth inhibition in 4 PAC models. Notably, the regression cohort and the partial response cohort displayed differential gene expression patterns. In addition, primary tumor cells derived from PAC models via tissue digestion were tested in vitro with Gemzar. The in vitro sensitivity to Gemzar of derived cells correlated with in vivo response of the parental PAC models. Gene expression analysis in 11 HCC models found aberrant gene expression involving several signaling pathways such as WNT, EGF/IGF and TGF-β, which recapitulate the alteration of these pathways in human HCC. These HCC PDX models exhibited major features of three HCC subclasses defined by the study in human HCC: S1 with activation of WNT and TGF-β pathways, S2 with enriched AKT and MYC activation and S3 with β-catenin activation. The response of these models to Sorafenib was also examined and varying degrees of sensitivity to Sorafenib were observed. Together, we have successfully generated PDX tumor models that preserve the histological and biological properties of the original human tumor and represent a heterogeneous patient population with varying degree of response to current stand of care. These models are a relevant and powerful tool for evaluation of anticancer therapeutics. Whole genome sequencing and gene expression profiling by RNAseq on PDX models are being preformed. Citation Format: Xiaoran Qin, Zhonghua Tang, Gang Hu, Kedong Ouyang, Ke Wang, Fu Li, Fubo Xie, Qiuming Pan, Min Shi, Gang Zhao, Yixin Zhang, Chunchao Zhu, Danyi Wen, Weikang Tao. Establishment and molecular characterization of a panel of Asian patient-derived tumor xenograft models . [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 2779. doi:10.1158/1538-7445.AM2013-2779

Adenoid cystic carcinoma (ACC) is a rare, glandular cancer whose incidence rate and limited 2D cell culture capability make it difficult to study primary patient samples. Although well characterized ACC patient-derived xenografts (PDX) have proven to be a useful model to study disease mechanisms and therapeutic sensitives, animal drug studies are relatively low throughput, costly, and take months to accomplish. Ex vivo 3D cell culture can provide a high-throughput, less costly, and significantly faster platform for these drug studies but are hampered by the rarity of tissue. To address this unmet need for models of rare tumor types we developed 3D spheroid (3D-XPDXs™) and microtumor (3D-XPDXmt™) models of ACC using PDX as the primary tissue source. ACC PDX cells readily formed spheroids in our 3D-XPDXs™ platform, remained viable for up to 14 days, and maintained disease-relevant biomarkers such as MYB and c-kit. 3D-XPDXmt™ represent a more complex model of ACC by incorporating extracellular matrix. ACC 3D-XPDXmt™ displayed tumor-like morphologies concordant with the parental tumors and exhibited MYB and c-kit biomarker expression for up to 56 days in culture. Drug response profiling (DRP) was performed using the 3D-XPDXs™ ACC model with KIYATEC’s validated KIYA-PREDICT™ DRP platform. The screening panel consisted of drugs and drug-like compounds currently in use or under investigation for use in ACC, including broad-spectrum chemotherapies and targeted agents. Individualized drug responses were noted for each model as they exhibited differential sensitivities to DNA-damaging agents, microtubule stabilizers, and c-kit targeting kinase inhibitors mirroring the diversity of clinical outcomes. KIYA-PREDICT™ also identified drugs and drug-like compounds that uniformly inhibited viability. This is significant because there is currently no standard of care drugs for ACC, as few, if any have demonstrated homogenous responses in test populations. Monensin has been shown to inhibit activity of the MYB transcription factor, making it an attractive candidate drug for the treatment of ACCs which have a high prevalence of MYB activating alterations. In our study, monensin was effective in all three models with IC50 concentrations in the low micromolar range. These results correlate well with immunohistochemical staining of MYB in ACC 3D-XPDXs™ and 3D-XPDXmt™. Taken together, this data represents a new ex vivo 3D cell culture platform for the study of ACC biology and potential therapies. Citation Format: Melissa Millard, Teresa M. DesRochers, Michael J. Wick, Jeffrey Kaufman, Nicole Spardy Burr. Ex vivo 3D culture of adenoid cystic carcinoma PDX models recapitulate disease biomarkers and predict drug response [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 177.

Guanlin Li, Lemao Liu, Guoping Huang, Conghui Zhu, Tiejun Zhao. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.

Improving classification performance of motor imagery BCI through EEG data augmentation with conditional generative adversarial networks