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Expression of ceramide anabolizing and catabolizing enzymes in MIN and CIN colorectal cells upon DL-PDMP treatment.

UGCG and SGMS1 knockdown efficiencies with different shRNA and siRNA.

Supplementary Tables 1-6

DL-PDMP inhibits the proliferation of myeloid leukemia cell lines.

DL-PDMP does not affect proliferation of MEFs and colorectal cancer cells.

DL-PDMP treatment does not cause weight loss in mice.

Aneuploidy levels in Bub1b H/H and Cdc20 AAA/AAA MEFs.

Aneuploidy, a hallmark of cancer cells, poses an appealing opportunity for cancer treatment and prevention strategies. Using a cell-based screen to identify small molecules that could selectively kill aneuploid cells, we identified the compound N-[2-hydroxy-1-(4-morpholinylmethyl)-2-phenylethyl]-decanamide monohydrochloride (DL-PDMP), an antagonist of UDP-glucose ceramide glucosyltransferase. DL-PDMP selectively inhibited proliferation of aneuploid primary mouse embryonic fibroblasts and aneuploid colorectal cancer cells. Its selective cytotoxic effects were based on further accentuating the elevated levels of ceramide, which characterize aneuploid cells, leading to increased apoptosis. We observed that DL-PDMP could also enhance the cytotoxic effects of paclitaxel, a standard-of-care chemotherapeutic agent that causes aneuploidy, in human colon cancer and mouse lymphoma cells. Our results offer pharmacologic evidence that the aneuploid state in cancer cells can be targeted selectively for therapeutic purposes, or for reducing the toxicity of taxane-based drug regimens. Cancer Res; 77(19); 5272–86. ©2017 AACR.

Ovarian cancer has few known risk factors, hampering identification of high-risk women. We assessed the association of prediagnostic plasma metabolites (N = 420) with risk of epithelial ovarian cancer, including both borderline and invasive tumors. A total of 252 cases and 252 matched controls from the Nurses' Health Studies were included. Multivariable logistic regression was used to estimate ORs and 95% confidence intervals (CI), comparing the 90th–10th percentile in metabolite levels, using the permutation-based Westfall and Young approach to account for testing multiple correlated hypotheses. Weighted gene coexpression network analysis (WGCNA; n = 10 metabolite modules) and metabolite set enrichment analysis (n = 23 metabolite classes) were also evaluated. An increase in pseudouridine levels from the 10th to the 90th percentile was associated with a 2.5-fold increased risk of overall ovarian cancer (OR = 2.56; 95% CI, 1.48–4.45; P = 0.001/adjusted P = 0.15); a similar risk estimate was observed for serous/poorly differentiated tumors (n = 176 cases; comparable OR = 2.38; 95% CI, 1.33–4.32; P = 0.004/adjusted P = 0.55). For nonserous tumors (n = 34 cases), pseudouridine and C36:2 phosphatidylcholine plasmalogen had the strongest statistical associations (OR = 9.84; 95% CI, 2.89–37.82; P < 0.001/adjusted P = 0.07; and OR = 0.11; 95% CI, 0.03–0.35; P < 0.001/adjusted P = 0.06, respectively). Five WGCNA modules and 9 classes were associated with risk overall at FDR ≤ 0.20. Triacylglycerols (TAG) showed heterogeneity by tumor aggressiveness (case-only heterogeneity P < 0.0001). The TAG association with risk overall and serous tumors differed by acyl carbon content and saturation. In summary, this study suggests that pseudouridine may be a novel risk factor for ovarian cancer and that TAGs may also be important, particularly for rapidly fatal tumors, with associations differing by structural features.Significance:Pseudouridine represents a potential novel risk factor for ovarian cancer and triglycerides may be important particularly in rapidly fatal ovarian tumors.

Sphingolipid composition of aneuploid and euploid cell lines upon DL-PDMP treatment.

Supplementary data on Study population, Metabolite profiling, and Statistical methods

Supplementary Figures 1-8

Table S1: Summary of compound effect in aneuploid Ts13 MEF compared to euploid wild-type MEF. Table S2: shRNA and siRNA sequences used in this study. Table S3: Primers used for quantitative Real-Time PCR.

Transcriptional states in pancreatic cancer can stratify patients by response to chemotherapy and clinical outcomes. These include the classical and basal-like states as well as a newly identified neural-like progenitor (NRP) state, which we have previously found to be enriched in primary patient tumors treated with neoadjuvant chemotherapy and radiotherapy. While several transcription factor drivers of classical and basal-like identity have been described, key regulators of the NRP state are unknown. Through in silico approaches, we identified candidate transcription factors of the NRP state, including GLIS3, a Krüppel-like zinc finger protein that mediates neuroendocrine fate during pancreatic development and differentiation of human embryonic stem cells into posterior neural progenitor cells. Our understanding of biologic and clinically-relevant attributes of transcriptional cell states remains limited by state-specific biases in various preclinical models. Existing human cell lines maintained as two-dimensional cultures tend to preferentially represent the basal-like state, whereas human three-dimensional organoid models grown in standard culture conditions best reflect the classical state. These phenotypes are therefore impacted by culture conditions as well as underlying genetic features. Furthermore, most murine pancreatic cancer models do not fully reflect the classical vs. basal-like state heterogeneity observed in humans. To enable systematic study of the classical, basal-like and NRP phenotypes, we developed isogenic KP (KrasG12D/+;Trp53FL/FL) murine organoids with a germline dCas9-VPR system to enable facile overexpression of state-specific transcription factors through CRISPR activation approaches. Quantitative PCR, RNA-sequencing, and proteomics confirmed Gata6, deltaN Trp63, and Glis3 as drivers of classical, basal-like, and NRP identity, respectively. DeltaN Trp63 organoids were further differentiated by loss of luminal morphology. Pairwise comparisons of global transcriptional alterations suggest the greatest similarities between the Gata6- and Glis3-overexpressed models, which is consistent with enhanced associations between classical and NRP states in patient tumors. Finally, although basal-like and NRP states are associated with poorer response to multi-agent chemotherapy, state-specific therapeutic sensitivities to other treatments remain incompletely defined. We therefore performed drug sensitivity assays with a panel of targeted therapies and unveiled state-specific sensitivities. These data were corroborated by drug sensitivity profiling of human patient-derived organoids and cell lines. Taken together, these results suggest a framework for defining cell state-specific vulnerabilities that may aid in stratifying and treating pancreatic cancer patients with new therapies. Citation Format: Jimmy A. Guo, Jennifer Su, Ananya Jambhale, Julien Dilly, Connor J. Hennessey, Carina Shiau, Patrick Yu, Steven Wang, Junning Wang, Laleh Abbassi, James Neiswender, Tate Bertea, Annan Yang, Qijia Yu, Peter Westcott, Jason Schenkel, Daniel Y. Kim, Hannah I. Hoffman, Grissel Cervantes Jaramillo, Giselle A. Uribe, Westley W. Wu, Arnav Mehta, David Ting, Julian A. Pacheco, Amy Deik, Clary Clish, Francisca Vazquez, Brian Wolpin, Aviv Regev, William A. Freed-Pastor, Joseph D. Mancias, Tyler Jacks, William L. Hwang, Andrew J. Aguirre. Systematic dissection of transcriptional states in pancreatic cancer [abstract]. In: Proceedings of the AACR Special Conference on Pancreatic Cancer; 2022 Sep 13-16; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2022;82(22 Suppl):Abstract nr A052.

Most deaths from cancer are explained by metastasis, and yet large-scale metastasis research has been impractical due to the inherent scale limitation of the in vivo models. Here we introduce an in vivo barcoding strategy capable of determining the metastatic potential of human cancer cell lines in murine xenografts at scale. We validated the robustness, scalability and reproducibility of the method, and applied it to 500 cell lines spanning 21 solid cancer types. We created a first-generation Metastasis Map (MetMap) that reveals organ-specific patterns of metastasis and allows relating those patterns to clinical and genomic features. We demonstrated the utility of MetMap by exploring the molecular basis of breast cancers capable of metastasizing to the brain - a principal cause of death in these patients. Breast cancers that were brain metastatic had unexpected genetic, expression, and metabolic evidence of altered lipid metabolism. Perturbing lipid metabolism curbed brain metastasis development and limited the outgrowth of cancer cells in the brain, suggesting a therapeutic strategy to combat the disease. These results illustrated the utility of MetMap as a step towards next-generation approach for high-throughput metastasis research. Citation Format: Xin Jin, Zelalem Demere, Karthik Nair, Ahmed Ali, Gino B. Ferraro, Ted Natoli, Amy Deik, Lia Petronio, Andrew A. Tang, Cong Zhu, Li Wang, Danny Rosenberg, Vamsi Mangena, Jennifer Roth, Kwanghun Chung, Rakesh K. Jain, Clary B. Clish, Matthew G. Vander Heiden, Todd R. Golub. A metastasis map of human cancer cell lines [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 NG10.

Brain metastases are refractory to therapies that otherwise control systemic disease in patients with human epidermal growth factor receptor 2 (HER2+) breast cancer, and the unique brain microenvironment contributes to this therapy resistance. Nutrient availability can vary across tissues, therefore metabolic adaptations required for breast cancer growth in the brain microenvironment may also introduce liabilities that can be exploited for therapy. Here, we assessed how metabolism differs between breast tumors growing in the brain versus extracranial sites and found that fatty acid synthesis is elevated in breast tumors growing in the brain. We determine that this phenotype is an adaptation to decreased lipid availability in the brain relative to other tissues, which results in a site-specific dependency on fatty acid synthesis for breast tumors growing at this site. Genetic or pharmacological inhibition of fatty acid synthase (FASN) reduces HER2+ breast tumor growth in the brain, demonstrating that differences in nutrient availability across metastatic sites can result in targetable metabolic dependencies. Citation Format: Gino B. Ferraro, Ahmed Ali, Alba Luengo, David P. Kodack, Amy Deik, Keene L. Abbott, Divya Bezwada, Landry Blanc, Brendan Prideaux, Xin Jin, Jessica M. Possada, Jiang Chen, Christopher R. Chin, Zohreh Amoozgar, Raphael Ferreira, Ivy Chen, Kamila Naxerova, Christopher Ng, Anna M. Westermark, Mark Duquette, Sylvie Roberge, Costas A. Lyssiotis, Dan G. Duda, Todd R. Golub, Shawn M. Davidson, Dai Fukumura, Véronique A. Dartois, Clary B. Clish, Matthew G. Vander Heiden, Rakesh K. Jain. Fatty acid synthesis is required for breast cancer brain metastasis [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 90.

Most deaths from cancer are explained by metastasis, and yet large-scale metastasis research has been impractical due to the complexity of in vivo models. Here, we introduce an in vivo barcoding strategy capable of determining the metastatic potential of human cancer cell lines in murine xenografts at scale. We validated the robustness, scalability and reproducibility of the method, and applied it to 500 cell lines spanning 21 solid cancer types. We created a first-generation Metastasis Map (MetMap) that reveals organ-specific patterns of metastasis and allows relating those patterns to clinical and genomic features. We demonstrated the utility of MetMap by exploring the molecular basis of breast cancers capable of metastasizing to the brain – a principal cause of death in these patients. We found that breast cancers capable of metastasizing to the brain had unexpected evidence of altered lipid metabolism. Perturbing lipid metabolism curbed brain metastasis development, suggesting a therapeutic strategy to combat the disease and demonstrating the utility of MetMap as a public resource to support metastasis research. Citation Format: Xin Jin, Zelalem Demere, Karthik Nair, Ahmed Ali, Gino B. Ferraro, Ted Natoli, Amy Deik, Lia Petronio, Andrew A. Tang, Cong Zhu, Li Wang, Danny Rosenberg, Vamsi Mangena, Jennifer Roth, Kwanghun Chung, Rakesh K. Jain, Clary B. Clish, Matthew G. Vander Heiden, Todd R. Golub. MetMap: A map of metastatic potential of human cancer cell lines [abstract]. In: Abstracts: AACR Special Virtual Conference on Epigenetics and Metabolism; October 15-16, 2020; 2020 Oct 15-16. Philadelphia (PA): AACR; Cancer Res 2020;80(23 Suppl):Abstract nr PO-031.