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Overlapped genes between Genes upregulated in Ptenpc-/-Smad4pc-/- tumors as compared to Ptenpc-/- tumors ({greater than or equal to}2 fold) and genes upregulated in GFP+ tumors cells from Ptenpc-/-Smad4pc-/- mice as compared to Tomato+ cells ({greater than or equal to}4 fold).

Markers used in CyTOF analysis.

Genes upregulated in Ptenpc-/-Smad4pc-/- tumors as compared to Ptenpc-/- tumors ({greater than or equal to}2 fold).

Detailed pathology description of the Gr1 treated mice and Cxcr2 inhibitor treated mice.

Table S4. Genes upregulated in GFP+ tumors cells from Ptenpc-/-Smad4pc-/- mice as compared to Tomato+ cells ({greater than or equal to}4 fold).

Supplementary Figure S1. CyTOF analysis of biological samples from Ptenpc-/-Smad4pc-/- mice (Related to Figure 2). Supplementary Figure S2. Strategy used for MDSCs Isolation (Related to Figure 3). Supplementary Figure S3. Treatment scheme for Gr-1 antibody, peptibody, and Cxcr2 inhibitor SB225002. Supplementary Figure S4. IHC staining of Ki67, CD45, Vimentin, Smooth muscle actin (SMA) and Trichrome staining of mouse prostate tissues treated with IgG control or Gr1 antibody. Supplementary Figure S5. The top 10 differentially expressed genes in Ptenpc-/-Smad4pc-/- tumors as compared to Ptenpc-/- tumors, identified by microarray analysis (n=5). Figure S6. Top 10 activated oncogenic signatures identified by GSEA analysis in Ptenpc-/- Smad4pc-/- tumors as compared to Ptenpc-/- tumors (n=5). Figure S7. Clustering of primary prostate tumors from Wallace et al into MDSC-high and MDSC-low subtypes.

Detailed information for the YAP1 IHC staining in human prostate cancers.

The Broad Cancer Cell Line Factory (CCLF) aims to increase the number and representation of in vitro/ex vivo cell models for common and rare cancer types. Neuroendocrine tumor (NET) cell model derivation is one of the CCLFs focus because it lacks well-characterized, publicly available models.Two major barriers existed in deriving NET cell models, including how to collect sufficient patient tumor tissue samples for the model derivation pilot and how to systematically iterate model derivation strategies since there is no prior knowledge for NET cell model generation success. Thus, we partnered with the MD Anderson Cancer Center, the Dana-Farber Cancer Institute, and the Rare Cancer Research Foundation to collect patient tissue samples. All patient’s NET tissues were sequenced with a targeted Pan-Cancer panel to ensure high tumor content. To reduce fibroblast outgrowth, we combined an empirical rich media matrix (HYBRID technology) with a 3D spheroid culture system to initiate one sample in 16-64 conditions. The growing cultures at passage 3-5 were genomically credentialed to ensure the driver events matched with the original patient tissue. So far, we have received more than 70 NET samples. While several derived models are still under culture, we successfully generated 5 genomically verified NET tumor models, including small intestinal, pancreas, and liver subtypes. To phenotypically characterize these NET models, neuroendocrine biomarkers such as chromogranin A, synaptophysin, SSTR2, and VMAT 1/2 were also evaluated using qRT-PCR and ELISA. We observed that these NET spheroid models display long doubling times (2-4 weeks) at later passages which limits their utility for large scale perturbation experiments and model sharing capability with the research community. While we are currently working on several strategies to improve the propagation ability in these models, 1 (out of 5) model has reached passage 15 with a 3 day doubling time. Genomic studies, such as RNAseq, will be performed to address the model transcriptome changes after overcoming growth plateaus. Here we showed that it is feasible to derive NET models from patient biospecimens using our HYBRID strategy. As we expand our NET cohort, we will further refine disease-specific model generation protocols for different NET types. Our goal is to share our model generation experience and make these tumor cell models publicly available to the research community in order to accelerate cancer research. Citation Format: Adel Attari, Madison Liistro, Barbara Van Hare, Jennifer Chan, Emma Coleman, Tim Heffernan, Bianca Amador, Matthew Meyerson, Jesse Boehm, William Sellers, Yuen-Yi (Moony) Tseng. A systemic model derivation platform for generating 3D neuroendocrine tumor cell spheroids to accelerate cancer research [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 197.

TransPoC is a not-for-profit research organization that will deliver open-access, large-scale translational oncology platforms to enable greater clinical proof-of-concept success for new cancer therapies. TransPoC will comprise three platforms: 1. CPN - Cancer Cell “PoC” Network for screening compounds against 1000+ genomically-characterized cell lines; 2. MPN- Mouse “PoC” Network - a multi-site platform for mouse preclinical trials using genomically-characterized Patient-Derived Xenograft (PDX) models; 3. BioIT - analysis and integration of genomic information and pharmacological profiling data. Here we present an overview of the Mouse “PoC” Network, define a path to implementation of multi-center pre-clinical trials in mice and describe a pilot study to demonstrate the feasibility of implementing such a network. PDX models are increasingly used in pre-clinical studies as they capture and retain the histological, molecular, and genetic heterogeneity of the original tumor compared to cell line derived xenografts and are therefore a closer representation of a patient's tumor in situ. To enable transformative preclinical studies, models need to be characterized in a manner similar to tumor samples in The Cancer Genome Atlas and the International Cancer Genome Consortium, and must be assembled in sufficient quantity to capture clinically relevant major cancer (sub)types. To achieve this, TransPoC is building a global network of mouse PDX “hospitals” with genomic and metabolomic profiles characterized in a consistent manner. In addition, each mouse hospital will utilize common SOPs to generate comparable pharmacology data sets across sites that will include testing standard of care agents. BioIT will enable deep interrogation of data sets and provide pipelines for pharmacogenomics correlates of response to both standard and novel agents. To date, the network has collated over 2,000 PDX models and will enable sponsors to execute multi-center pre-clinical trials in a manner similar to those used in multi-institutional cooperative clinical trials. To demonstrate the viability of MPN, a pilot study has been initiated at 6 sites located in Canada, Italy, China and USA to evaluate the activity of MEK and RAF inhibitors against a panel of BRAF/KRAS mutant melanoma and colorectal cancer PDX models. An update on the initial tolerability, PK/PD/efficacy studies and molecular characterization of PDX models in the network will be presented. TransPoC continues to recruit new sites and characterize their PDX models for incorporation into MPN for use by TransPoC sponsors. Through this effort TransPoC enables rapid assessment of standard and novel investigational therapies to determine their therapeutic potential for translation to clinical trials with a mission to improve the chance of observing clinical proof-of-concept. Citation Format: Peter G. Smith, David Sutton, Andrea Bertotti, Livio Trusolino, Susan Airhart, Ming S. Tsao, Bradly G. Wouters, S. Gail Eckhardt, Lai Wang, Tim Heffernan, David Verbel, Andrea Gerken, Peter Fekkes, Lihua Yu, Lihua Yu, Markus Warmuth. Translational Proof-of-Concept (TransPoC), a not-for-profit research organization enabling access to large-scale translational oncology platforms: The Patient-Derived Xenograft network. [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 1191. doi:10.1158/1538-7445.AM2014-1191