Cell-to-cell heterogeneity can substantially impact drug response, especially for monoclonal antibody (mAb) therapies that may exhibit variability in both delivery (pharmacokinetics) and action (pharmacodynamics) within solid tumors. However, it has traditionally been difficult to examine the kinetics of mAb delivery at a single-cell level and in a manner that enables controlled dissection of target-dependent and -independent behaviors. To address this issue, here we developed an in vivo confocal (intravital) microscopy approach to study single-cell mAb pharmacology in a mosaic xenograft comprising a mixture of cancer cells with variable expression of the receptor HER2. As a proof-of-principle, we applied this model to trastuzumab therapy, a HER2-targeted mAb widely used for treating breast and gastric cancer patients. Trastuzumab accumulated to a higher degree in HER2-over expressing tumor cells compared to HER2-low tumor cells (~5:1 ratio at 24 h after administration) but importantly, the majority actually accumulated in tumor-associated phagocytes. For example, 24 h after IV administration over 50% of tumoral trastuzumab was found in phagocytes whereas at 48 h it was >80%. Altogether, these results reveal the dynamics of how phagocytes influence mAb behavior in vivo, and demonstrate an application of intravital microscopy for quantitative single-cell measurement of mAb distribution and retention in tumors with heterogeneous target expression. © 2019 International Society for Advancement of Cytometry Key terms tumor associated macrophage; metastatic breast cancer; human epidermal growth factor receptor 2/HER2/ERBB2; Fc-receptor; antibody-dependent cellular phagocytosis

Successful mapping of cancer dependencies requires conducting genetic and drug screens on a diversity of models. However, the difficulty in generating long-term models of many cancers limits the share of patient samples that can be studied. Such long-term models have likely also lost the cellular heterogeneity present in the original tumor due to in vitro propagation. To overcome these limitations, we are developing image-based ex vivo cancer biosensors from early patient material. Using freshly received gastroesophageal cancer ascites, we are optimizing perturbation methods and utilizing single-cell transcriptomics and label-free microscopy to infer a subpopulation-specific vulnerability profile. We show that label-free microscopy can infer cell identity and viability in heterogeneous early patient samples. Additionally, early drug perturbation recapitulates observations made in established gastroesophageal cancer organoids. Successful implementation of ex vivo biosensors will expand the cancer dependency space by making perturbational studies accessible to more diverse samples, and by identifying and validating hits in a more immediate setting to the original tumor. Citation Format: Mushriq Al-Jazrawe, Csaba Molnar, Niklas Rindtorff, Pinar Eser, Sean Misek, Maria Alimova, Oana Ursu, William Colgan, Adel Attari, Natalie Tsang, Paula Keskula, Carmen Rios, Moony Tseng, Anne Carpenter, James McFarland, Adam Bass, Samuel Klempner, Jesse Boehm. Evaluating dependencies by rapid image-based ex vivo cancer biosensors [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr PO-093.

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.

Functional genomics has held great promise for mapping cancer dependencies and identifying new therapeutic targets in cancer cell lines. However, this remains a major challenge for prostate cancer (PCa) due to the lack of cancer cell models and efficient optimization of pooled genetic perturbation screens. The Broad Cancer Cell Line Factory (CCLF) is attempting to fulfill this unmet need by generating novel prostate cancer cell models and uncovering prostate specific lineage dependencies. Encouragingly, Dr. Yu Chen at Memorial Sloan Kettering Cancer Center has developed several 3D cancer organoids from advanced prostate cancer. Although the success rate needs improvement for primary PCa, over 12 long-term models have been established by this group. In collaboration with Chen Lab, we aim to expand the prostate cell model collection by using PCa organoids and patient-derived normal cells. We can then apply these models for genetic perturbation profiling to unravel prostate lineage specific dependencies. Many historical cancer cell lines and cancer organoids were not compatible with genome-scale CRISPR screening due to the slow cellular growth. Here we first optimized the media conditions in these PCa organoids by using empirical media screening assay conditions through our HYBRID technology. The HYBRID technology allows for the systemic evaluation of 64 media conditions at once, so that we can perform RNAseq analysis to identify the conditions most physiologically resembling the PCa environment with the maximum doubling time. We have further developed our high throughput screening strategies for organoid culture using an optimized protocol to study 3D growth patterns in a 96-well format using the Incucyte S3 System. Next, to map out all possible dependencies, we are testing the feasibility of a pooled genome scale CRISPR screen. In our preliminary assay development results, we demonstrated the reproducible viral infectibility in several organoids ranging from 40-70% infection rate. We anticipate that these genome-wide CRISPR data in PCa organoid and normal prostate cell models can be integrated and analyzed with 4 previous PCa cell lines screened in the Broad Dependency Map to point out more prostate lineage specific genetic vulnerabilities and possible therapeutic targets. Citation Format: Adel Attari, Sean Misek, Pinar Eser, Alexa Yeagley, Yu Chen, Jesse Boehm, Yuen-Yi (Moony) Tseng. Genome-scale CRISPR screen finds prostate lineage specific dependencies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2975.