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.

Ex vivo cancer cell models provide the starting material for in-depth mechanistic studies of cancer. However, the clinical/histopathologic, biomarker, and genetic heterogeneity of breast cancer has not been well represented in the current breast cancer cell model collection. While published breast cancer model generation protocols have been helpful, their high failure rates indicate the urgent need to improve model derivation efficiency. Here, the Broad Cancer Cell Line Factory (CCLF) project presents a novel model derivation technology to generate breast and ovarian cancer organoids with a high success rate by leveraging the paracrine support from historical cancer cell lines. We observed that most established breast cancer cell lines can grow in a simple basal media with 10% fetal bovine serum; We hypothesized that historical cell lines may secrete vital growth factors that support breast cancer cells' survival and growth. To test this, we randomly selected a pool of 20 breast cancer cell lines, collected its conditioned media (CM20) and incorporated the CM20 as a supplement into our empirical rich media matrix (HYBRID, 16 mixed media conditions) with a Matrigel culturing system. Three-dimensional (3D) structures formed at Day 14-21 in the CM20 supplementary conditions compared to conditions without CM20 and only organoids with the CM20 supplement could be propagated to passage 5 and beyond. We performed pan-cancer targeted sequencing to evaluate tumor content of these organoids at passage 5 with paired tumor tissues. In our first 10 attempts, 95% of organoid cultures were genomically verified as high purity tumor models, indicating the CM20 is essential to enrich breast cancer cell growth in an in vitro culturing setting. We applied the CM20 to ovarian cancers and observed a similar success rate suggesting a tissue-specific supporting manner. We tested conditioned media collected from other historical cancer cell lines but the breast/ovarian cancer organoid growth effect was not recapitulated. Importantly, when testing the individual breast cancer cell lines from the pool of 20, we discovered one cell line to be supporting the effect. More biochemistry work is needed to dissect the possible factors secreted by the line and molecular mechanisms of cancer cell survivors but preliminary data suggests the secretion factors are most likely proteins. We generated 27 breast/ovarian cancer cell models using this technology and RNAseq data shows the breast cancer organoids still express their expected molecular subtype markers. 22 breast/ovarian cancer organoids have been propagated long-term with 17(out of 22) deposited to ATCC. Overall, this method provides an efficient model generation rate for female cancers. We anticipate that this method will not only allow us to quickly increase breast cancer cell model diversity but shed light on a new direction for breast cancer dependencies Citation Format: Rebecca Deasy, Xin Jin, Pierre Michel Jean Beltran, Adel Atari, Madison Liistro, Cheryl Thompson, Stefanie Avril, Julie Boerner, Payal Pradhan, Samuel Klempner, Keith Ligon, William Sellers, Steven Carr, Todd Golub, Yuen-Yi (Moony) Tseng. The efficient utilization of paracrine support from established cell lines for breast/ovarian cancer model generation [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 3088.