Integrated Metabolic and Gene Expression Proling Reveals New Therapeutic Modalities for Rapidly Proliferating Breast Cancers

Chengheng Liao University of Texas Southwestern Medical Center Cherise Ryan Glodowski University of North Carolina School of Medicine Cheng Fan University of North Carolina School of Medicine Juan Liu Duke University Kevin Raynard Mott University of North Carolina School of Medicine Akash Kaushik University of Texas Southwestern Medical Center Hieu Vu University of Texas Southwestern Medical Center Jason Locasale Duke University https://orcid.org/0000-0002-7766-3502 Samuel K McBrayer University of Texas Southwestern Medical Center Ralph DeBerardinis The University of Texas Southwestern Medical Center https://orcid.org/0000-0002-2705-7432 Charles Perou (  [email protected] ) University of North Carolina School of Medicine Qing Zhang (  [email protected] ) University of Texas Southwestern Medical Center https://orcid.org/0000-0002-6595-8995

Research Article

Keywords: Metabolic Dysregulation, Patient Prognosis, Genomic Biomarker

Posted Date: August 6th, 2021

DOI: https://doi.org/10.21203/rs.3.rs-117384/v2 License:   This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License Integrated Metabolic and Gene Expression Profiling Reveals New Therapeutic

Modalities for Rapidly Proliferating Breast Cancers

Chengheng Liao1,7, Cherise Ryan Glodowski2,7, Cheng Fan3, Juan Liu4, Kevin R. Mott2,

Akash Kaushik5, Hieu Vu5, Jason W. Locasale4, Samuel K. McBrayer5, Ralph J.

DeBerardinis5,6, Charles M. Perou2,3,*, and Qing Zhang1,*

1 Department of Pathology, University of Texas Southwestern Medical Center, Dallas, TX

75390, USA

2 Department of Genetics, University of North Carolina School of Medicine, Chapel Hill,

NC 27599, USA

3 Lineberger Comprehensive Cancer Center, University of North Carolina School of

Medicine, Chapel Hill, NC 27599, USA

4 Department of Pharmacology and Cancer Biology, Duke University School of Medicine,

Durham, NC 27710, USA.

5 Children's Medical Center Research Institute, University of Texas Southwestern Medical

Center, 5323 Harry Hines Boulevard, Dallas, TX 75390, USA.

6 Howard Hughes Medical Institute, University of Texas Southwestern Medical Center,

5323 Harry Hines Boulevard, Dallas, TX 75390, USA.

7 These authors contributed equally

* Co-corresponding authors (Q.Z or C.P):

Qing Zhang, Ph.D, Email: [email protected]

Charles Perou, Ph.D, Email: [email protected]

Running title: Metabolic profiling in breast cancer.

1

Summary

This study provides a new stratification of breast tumor samples based on integrated metabolic and gene expression profiling, which guides the selection of newly effective therapeutic strategies targeting breast cancer subsets.

Abstract

Metabolic dysregulation, although a prominent feature in breast cancer, remains under- characterized in patient tumors. By performing untargeted metabolomics analyses on triple-negative breast cancer (TNBC) and Estrogen Receptor (ER) positive patient breast tumors, as well as TNBC patient-derived xenografts (PDXs), we identified two major metabolic groups independent of breast cancer histological subtypes: a

“Nucleotide/Carbohydrate-Enriched” group and a “Lipid/Fatty Acid-Enriched” group. Cell lines grown in vivo more faithfully recapitulate the metabolic profiles of patient tumors.

Integrated metabolic and gene expression analyses reveal genes that strongly correlate with metabolic dysregulation and predict patient prognosis. As a proof-of-principle, targeting Nucleotide/Carbohydrate-Enriched TNBC cell line or PDX xenografts with a pyrimidine biosynthesis inhibitor, and/or a glutaminase inhibitor, led to therapeutic efficacy.

In addition, the pyrimidine biosynthesis inhibitor presents better therapeutic outcomes than chemotherapy agents in multiple murine TNBC models. Our study provides a new stratification of breast tumor samples based on integrated metabolic and gene expression profiling, which guides the selection of newly effective therapeutic strategies targeting rapidly proliferating breast cancer subsets. In addition, we develop a public, interactive data visualization portal (http://brcametab.org) based on the data generated from this study.

2

Introduction

Breast cancer is the most commonly diagnosed malignancy and 2nd most frequent cause of cancer-associated death for women worldwide (Sung et al., 2021). Triple-negative breast cancer (TNBC), which accounts 15-20% all breast cancers (Anders and Carey,

2009), is a clinically defined breast cancer subtype lacking estrogen and progesterone hormone receptor (ER and PR) expression and human epidermal growth factor receptor

2 (HER2) gene amplification (Anders and Carey, 2009). TNBC is featured by clinically aggressive and heterogeneous, highly metastatic, and shorter survival times compared to other subtypes such as ER+ and HER2+ breast cancers (Anders and Carey, 2009). The absence of a valuable therapeutic target in TNBC leaves the patients with limited treatment options, which is mostly focused upon canonical chemotherapy. Therefore, there is an urgent need to identify novel targets in TNBC.

Tumor cells often undergo metabolic alterations to fulfill the high demands of uncontrolled cell proliferation and tumorigenesis, which is a hallmark of cancer (Hanahan and

Weinberg, 2011; Ward and Thompson, 2012). Therefore, tumor cell metabolism can be considered cancer’s Achilles’ heel and is a proven target of successful therapies

(Kroemer and Pouyssegur, 2008; Pavlova and Thompson, 2016; Vander Heiden, 2011).

Cancer metabolic alterations include reprogramming of glycolysis, glutaminyl’s, oxidative phosphorylation (OXPHOS), fatty acid metabolism, etc., which provide essential energy, biosynthesis intermediates, division and redox homeostasis for tumor growth (Pavlova and Thompson, 2016). Therefore, a better understanding of breast tumor cell metabolism can potentially lead to novel therapeutic strategies through targeting specific metabolic pathways dysregulated in tumors while sparing the normal cells. Recently, several studies

3 suggest that there is a unique metabolic dependence in subsets of TNBCs, including

TNBCs in patients undergoing chemotherapy or displaying PI3K/Akt hyper-activation

(Brown et al., 2017a; Lien et al., 2016; Mathur et al., 2017). However, it remains unclear how we can potentially target the specific metabolic vulnerabilities in TNBC compared to other subtypes of breast cancer. In addition, given the well-known heterogeneity of TNBC, it remains unclear whether we can stratify TNBC patients according to their metabolic abnormalities and design rational metabolic therapeutic modalities against subsets of

TNBC.

Our study comprehensively investigated the metabolic phenotypes, as well as gene expression phenotypes, of breast tumor patient samples, TNBC patient-derived xenografts (PDXs), and cell lines grown in vitro and in vivo from representative breast cancer subtypes including TNBC and ER+ breast cancers. In this array of systematic analyses, we identified the specific metabolites that are enriched in ER+ tumors and

TNBCs. We also examined the correlation between metabolites and gene expression features. Lastly, we performed functional validation experiments by targeting the dysregulated metabolic pathways pharmacologically in multiple TNBC models.

Results

Metabolic Profiling of Patient Breast Cancer Tumors and TNBC PDXs

To systemically examine the metabolic profiles in TNBC and ER+ breast cancer, we obtained 15 TNBC and 9 ER+ flash frozen breast tumor samples; we also collected 7 flash frozen tumor samples from 2 TNBC PDX models (WHIM2 and WHIM30) grown in NOD

Scid Gamma-deficient (NSG) mice (Li et al., 2013) (see Table S1 for details). We first extracted polar metabolites from a portion of these tumors followed by untargeted 4 metabolomics analysis by liquid chromatography, high-resolution mass spectrometry

(LC-MS) (Fig. 1A). A total of 301 metabolites were detected in all 31 tumor samples with

263 well annotated by the Kyoto Encyclopedia of Genes and Genomes (KEGG) database

(Kanehisa and Goto, 2000) or the Human Metabolome Database (HMDB) (Wishart et al.,

2018) (Fig. 1B). Inquiring whether the global metabolic profiling could reveal differences in distinct breast cancer subsets, we performed an unsupervised hierarchical clustering using the 263 annotated metabolites data from the 31 tumor samples (Fig. 1C; Table S2).

The cluster divided these patients into two distinctive metabolic clusters (Fig. 1C).

Intriguingly, 7/15 TNBC tumors clustered with all of ER+ breast tumors (Cluster 1) while all TNBC PDXs tumors grouped with 8/15 TNBC tumor samples (Cluster 2). The clustering pattern for these breast tumor samples suggests metabolic heterogeneity of the TNBC tumors and a more homogenous feature of ER+ tumors in terms of metabolite abundance. We also performed mRNA sequencing (mRNAseq) on all 31 tumor samples and applied PAM50 subtyping based on gene expression to further interrogate the tumor molecular features (Parker et al., 2009). As expected, all TNBC PDXs and most TNBC tumors were categorized as basal-like breast cancer (9 out of 15), and most basal-like

TNBCs (i.e., 6/9), together with the 7 basal-like PDXs, were present in the Cluster 2 (Fig.

1C), while all of the ER+ and some of TNBC from Cluster 1 were luminal or normal-like subtype by the PAM50 analysis (Fig. 1C). We then performed a supervised statistical analysis (SAM (Significance Analysis of Microarrays), see Methods for details) to the metabolite data to identify the significantly altered metabolites between the two metabolic clusters (Fig. S1A). Among the 263 annotated metabolites, 93 were significantly higher in

5

Cluster 1 (35.3%; q<0.05, SAM), and 90 were significantly higher in Cluster 2 (34.2%; q<0.05, SAM) (Fig. 1D; Fig. S1, B and C).

To systematically investigate the metabolic alterations in the breast cancer patient tumors and PDXs stratified by the metabolic clusters, we performed a KEGG pathway-based differential abundance (DA) analysis (Hakimi et al., 2016) using the metabolites significantly varied between Cluster 1 and 2 tumors. The DA scores were calculated based on the average, gross changes for all metabolites in a pathway. Given the small number detected by LC-MS but important role of some metabolites in some pathway, pathways with at least 2 detectable metabolites were included for the DA analysis and this captured 49 metabolic pathways as being differentially abundant with a broad metabolic pathway category (Fig. 1E). Interestingly, most of the pathways elevated in the

Cluster 1 tumors were lipid/fatty acid metabolism and some amino acid metabolism pathways such as glycine, serine and threonine metabolism, whereas pathways elevated in the Cluster 2 tumors were significantly enriched with pyrimidine and purine metabolism, energy production and carbohydrate metabolism (Fig. 1E). We also conducted integrated pathway enrichment and topology analysis (PA) by MetaboAnalyst (Chong et al., 2019), which considered the metabolite impact in each pathway and found some common pathways from both DA and PA analyses in both clusters (Fig. S2, A and B). Additionally, these pathway-based analyses were further illustrated by the differential abundance of individual key metabolites for each pathway category in these two clusters (Fig. 1C, F-I;

Fig. S2, C-F). Given the overall enrichment of lipid/fatty acid metabolites in Cluster 1 and nucleotide/carbohydrate metabolites in Cluster 2 (Fig. 1C, F-H), we annotated these clusters “Lipid/Fatty Acid-Enriched” for Cluster 1 and “Nucleotide/Carbohydrate-Enriched”

6 for Cluster 2 to highlight the metabolic features in these groups of patient tumors and

PDXs. We also obtained 9 flash frozen normal breast tissues and performed metabolic profiling (Fig. S3A) and found that these normal breast tissue samples clustered together with ER+ breast tumor samples (Cluster 1) and did not change the overall distribution of the tumor sample clustering pattern or pathway enrichment in two clusters (Fig. S3, B-D).

Taken together, our metabolic profiling in breast cancer patient tumors clearly classifies a subset of tumors in the Nucleotide/Carbohydrate-Enriched cluster enriched for basal- like/TNBC patient tumors and distinct from the Lipid/Fatty Acid-Enriched cluster that contained the other subtypes of breast cancers (and true normal).

Distinctive Metabolism of Breast Cancer Cell Lines Grown In Vivo Versus In Vitro

Next, we asked whether the metabolic alteration observed in breast cancer patients was represented in cell lines in a subtype-specific and growth-context manner. To this end, we obtained representative breast cancer cell lines from 4 ER+ (MCF-7, T47D, ZR-75-1 and HCC1428) and 4 TNBC (MDA-MB-231, MDA-MB-468, HCC70 and HCC1806) cell lines. These cell lines were either implanted orthotopically in NSG mice (in vivo growth, n=3 mice/line) as described previously (Zhang et al., 2015; Zhang et al., 2009) or cultured in parallel in petri dishes (in vitro growth, n=2 biological duplicates/line). When cell line xenograft tumors reached a common size (~0.5 cm in diameter), tumors were harvested and underwent untargeted metabolomics analysis as described above (Fig. 2A; Table

S3). It is known that there are metabolic differences for cell lines grown in vivo versus in vitro (Schimke, 1964), and therefore we first examined the data with principal component analysis (PCA), in which the samples were clearly separated by the in vitro and in vivo growth condition (Fig. 2B). Unsupervised hierarchical clustering further showed distinctive

7 metabolic profiles between the cell lines grown in in vitro versus in vivo (Fig. 2C), regardless of subtypes of these cells (Fig. S4, A and B). By comparing the metabolic pathway in these two cell growth conditions, metabolites significantly accumulated in vitro were enriched in several metabolic pathways, including amino acid metabolism (arginine biosynthesis, alanine, aspartate and glutamate metabolism, etc.), TCA cycle, and glycolysis/gluconeogenesis (Fig. 2D). Conversely, metabolic pathways enriched in vivo included nucleotide metabolism and fatty acid metabolism (biosynthesis of unsaturated fatty acid and glycerolipid metabolism) (Fig. 2E). Further examination of representative metabolites revealed that glucose, pyruvate, 2-oxoglutarate, succinate, glutamine, and other amino acids were strongly enriched in cells grown in vitro (Fig. 2F) versus many pyrimidine and purine nucleotides which were significantly higher for cells grown in vivo

(Fig. 2G). This is expected given that most of these components (such as glucose, glutamine, pyruvate and amino acids) are abundant in the typical DMEM/RPMI cell growth media and readily utilized by cells in the 2-D in vitro growth condition. We also performed analysis to compare in vivo versus in vitro within each breast cancer cell subtype and found very similar pathway enrichment results (Fig. S4, C-F). These findings suggested that in vitro growth dramatically changed the metabolism compared to the in vivo growth in these breast cancer cells, irrespective of the subtype.

Metabolic Profiling of Cell Line Xenograft Grown In Vivo Reveals Similar Metabolite

Enrichment as in Patients

Next, we combined all the metabolomics data from the patient, PDX, and in vitro and in vivo cell lines to examine the metabolic profile. Overall, global unsupervised clustering of all samples suggests that the metabolism in cell lines grown in vivo and PDX tumors is

8 more representative of breast tumors from patients (Fig. 3A). Therefore, we focused our analyses on the data of cell lines grown in vivo. We performed supervised comparison between the ER+ and TNBC cell lines that revealed 43 and 45 differential abundant metabolites enriched in ER+ and TNBC cells, respectively (Fig. 3B). PCA and hierarchical clustering analyses by these metabolites suggested two distinct groups between the ER+ and TNBC in vivo cells (Fig. 3, C and D). Pathway analysis for each cell line subtype did not fully recapitulate the metabolic pathway enrichment results from patient tumors (Fig.

S5, A and B), which is possibly due to the relatively fewer metabolites significantly altered between the ER+ and TNBC cells, and/or an overall highly proliferative character for these cancer cell lines grown in mice. However, the abundance of some metabolites revealed high similarity between the ER+ and TNBC subtypes, as we observed in the metabolic clusters from patient tumors (Fig. 3, E and F; Fig. S5, C-E). These results suggest that pathways such as glucose, glutamine, and lipid/fatty acid metabolism were faithfully altered between the ER+ and TNBC subtypes of breast cancer patients and cell lines grown in vivo. Therefore, the cell line in vivo model partially mimics the metabolism seen in actual tumor specimens.

Integrated Analysis of Patient Metabolomics and Transcriptomics Data Reveals

Valuable Metabolite and Gene Signatures

Unique gene expression patterns, including PAM50 subtype and proliferation signatures, show prognostic value for breast cancer patients (Nielsen et al., 2010; Parker et al., 2009).

To rigorously evaluate possible relationships between metabolite and gene expression profiles (Fig. 4A), we first calculated median values of the Lipid/Fatty Acid-Enriched metabolite signature from Cluster 1 (n=93 metabolites, Fig. S1B) and

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Nucleotide/Carbohydrate-Enriched metabolite signature from Cluster 2 (n=90 metabolites,

Fig. S1C) for each of the patient samples, and then compared these values according to their PAM50 subtype. For our data set of 31 tumors and 9 normal breast tissue samples, the Lipid/Fatty Acid-Enriched feature was higher in Luminal A and B samples compared with the basal-like samples, and was especially high in normal-like and true normal breast samples (Fig. 4B). Conversely, the Nucleotide/Carbohydrate-Enriched feature displayed significantly higher values in basal-like subtype tumors (Fig. 4B). Noted that there are 4

TNBC tumors in Cluster 1 with normal-like PAM50 subtype (Fig. 1C) whereas with a

Lipid/Fatty Acid-Enriched metabolite profile, further suggesting the potential of using these metabolite signatures to stratify TNBC patients and predicting subtype heterogeneity. Given the known association between PAM50 subtypes and proliferation rates (Nielsen et al., 2010), we next split these samples into tertiles (high, medium, and low) based on the median values of the two metabolite signatures, then plotted gene expression-based proliferation signature values (Fig. 4C). This analysis showed that a high Nucleotide/Carbohydrate-Enriched metabolite profile was significantly associated with high proliferation signature values, while the Lipid/Fatty Acid-Enriched metabolite profile correlated with low proliferation potential. To validate the metabolomic and genomic PAM50 profile relationships, we used genomic and metabolomics data from 32 human breast tumor and 6 normal breast tissue samples from a previous study by Brauer et al. (Brauer et al., 2013) as a validation data set. We used the previously published

PAM50 subtype classifications (Brauer et al., 2013) and determined proliferation scores as well as metabolite scores with two clusters as described in the methods. Of the metabolite signatures from our study, 34 out of the 90 metabolites for the

10

Nucleotide/Carbohydrate-Enriched metabolite signature and 22 out of 93 metabolites for the Lipid/Fatty Acid-Enriched metabolite signature were present in the validation data set.

Using all 32 tumor and 6 normal samples (n=38), the Nucleotide/Carbohydrate-Enriched metabolite signature showed statistically significant differences by expression subtype

(p<0.01) and was high in the basal-like tumors in this validation set, which is consistent with the results from our data (Fig. S6A). However, the Lipid/Fatty Acid-Enriched signature did not show statistically significant differences in PAM50 subtypes (Fig. S6A), possibly reflecting the small numbers matched metabolites in this validation set (22/93).

We further split the 32 tumor samples into tertiles (high, medium, and low) based on the median values of the Nucleotide/Carbohydrate-Enriched signature and proliferation score, which again showed higher values in highly proliferating tumors (Fig. S6B), thus validating the predictive value of these signatures on a second specimen test set.

To identify genes that correlate with the metabolite signatures, we performed a supervised gene expression analysis based on the metabolic clustering (Fig. 4, A and D).

Supervised analysis of gene expression by SAM based on the two metabolic clusters each gave rise to distinctive gene expression profiles and groups of differentially expressed (DE) genes (q<0.05, SAM) (Fig. 4D; Fig. S6C). We herein define these genes as “Lipid/Fatty Acid-Enriched” or “Nucleotide/Carbohydrate-Enriched” associated gene signatures. For the Nucleotide/Carbohydrate-Enriched genes from Cluster 2, this analysis gave 2908 DE genes; when examing the top 200 DE genes, these genes were homogenously and highly expressed in Cluster 2 versus Cluster 1 tumors (Fig. S6D).

Gene ontology (GO) analysis using these top 200 DE genes, suggested they correlated strongly with biological processes including cell division, DNA replication, cell cycle, and

11 cell proliferation (Fig. 4E). Importantly, when analyzed using the 1100 patients in the

TCGA Breast Cancer data set (Cancer Genome Atlas, 2012), this top 200 gene mRNA profile correlated with multiple previously defined proliferation profiles, including the aforementioned PAM50 proliferation signature used in Fig. 4C and Fig. S6B (Pearson correlation 0.93). Next, we performed a similar analysis using the Lipid/Fatty Acid-

Enriched metabolite profile and identified 6224 DE genes as being associated with this feature (q<0.05, SAM). Examining the Lipid/Fatty Acid-Enriched metabolite profile associated top 200 genes (Fig. S6E) by GO analysis revealed that they were involved in angiogenesis, cellular response to hormone stimulus, and response to progesterone (Fig.

4F). These represent some of the canonical pathways generally enriched in the luminal breast cancers (Osborne and Schiff, 2011).

To determine whether these 200 gene signatures predict patient prognosis in breast cancer, we extracted gene expression data from the METABRIC (Curtis et al., 2012)

(n=1992), Harrell 2011 (Harrell et al., 2012) (n=855), and SCAN-B data sets (Brueffer et al., 2018) (n=2969), then calculated the median expression of the

Nucleotide/Carbohydrate-Enriched 200 DE genes followed by Kaplan-Meier (K-M) survival analysis according to tertiles of expression (Fig. 4G). High expression of these

200 genes strongly correlated with worse prognosis, which is expected given their high accordance to known proliferation signatures and the strong, known prognostic value of proliferation features in breast cancer (Gatza et al., 2014; Nielsen et al., 2010).

Interestingly, by performing survival analysis in the aforementioned patient datasets when using the Lipid/Fatty Acid-Enriched 200 DE genes, we found that high expression of these genes predicted better prognosis (Fig. 4H).

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Pyrimidine Metabolism and Glutaminolysis Pathways are Elevated in TNBC

We further probed the relationships between gene expression and metabolite levels using the identified DE genes within each metabolic clusters (Fig. 4D) and performed metabolic focused gene set-based pathway analysis by the DAVID bioinformatics tools (Huang et al., 2009). Interestingly, although there were more DE genes identified in Cluster 1 than

Cluster 2 (2908 versus 6224, Fig. 4D), we found that more metabolic pathways were significantly enriched in the samples from Cluster 2 than Cluster 1 (Fig. 5A). Notably, pyrimidine and purine metabolism, carbon metabolism, and the energy production related pathways were highly enriched in Cluster 2 while many lipid/fatty acid metabolism related pathways were enriched in Cluster 1, suggesting the metabolic gene expression is associated with the metabolite abundance in the patient samples. This finding was further demonstrated by the joint-pathway analysis function from MetaboAnalyst using the significantly accumulated metabolites and upregulated DE genes from each cluster (Fig.

S7, A and B). Among these analyses, we found that the pyrimidine metabolism was the top pathway (Fig. 5A; Fig. S7B), and its related metabolic genes were upregulated in

Cluster 2 TNBC and basal-like subtype tumors (Fig. 5, B-D; Fig. S7B). It is also worth noting that pyrimidine biosynthesis was previously reported to be upregulated in response to chemotherapy exposure in TNBC (Brown et al., 2017a). We also noticed that in all cases of metabolic profiling analyses, TNBC patient tumors in Cluster 2, PDXs, and TNBC cell lines grown in vivo had marked high levels of glutamate and low levels of glutamine

(Fig. 1I and 3E). Some of the key glutamine metabolism and transport enzymes gene expression such as the glutaminase (GLS) was upregulated in the Cluster 2 TNBC tumors

(Fig. 5, C-E), suggesting the TNBC tumors maintains high level of glutamine metabolism

13 compared to ER+ breast tumors. In addition, PTEN mutant TNBC cells were dependent on glutamine flux through the de novo pyrimidine synthesis pathway, which rendered these cells to be sensitive to DHODH inhibitor treatment (Mathur et al., 2017). Indeed, our steady-state metabolite profiling in patient tumors suggested that Cluster 2 TNBC tumors maintained high levels of glutaminolysis state with high levels of TCA cycle intermediates, glutamate and other downstream metabolites including arginine, proline, reduced glutathione (GSH), and high levels of de novo pyrimidine biosynthesis intermediates, concomitant with low level of glutamine and aspartate in these tumors (Fig.

5F). By examing the relative ratio both in the patient tumors and cell lines grown in vivo, we showed higher levels of glutamate/glutamine (Fig. 5G), carbamoyl- aspartate/glutamine (Fig. 5H), and carbamoyl-aspartate/aspartate (Fig. 5I) in TNBC versus ER+ breast cancer. These results suggest that the glutaminolysis and pyrimidine metabolism pathways are elevated in TNBC.

It is worth noting that glutamine-dependent catabolism feeds multiple biosynthetic pathways including nucleotides, lipids, TCA cycle, and others (Altman et al., 2016), and a recent study showed that glutamine metabolism was the major energy and nucleotide resources for cancer cells (Reinfeld et al., 2021). To confirm that the glutaminolysis and pyrimidine metabolism are directly from the glutamine metabolism in TNBC, we performed in vivo isotope tracing experiment in a basal-like TNBC cell xenograft model.

13 Bolus intravenous injection of C5-glutamine through tail vein within 45 mins (Leone et al., 2019) led to a rapid glutamine pool labeling in the HCC1806 xenograft tumors (Fig.

5J). Notably, bolus infusion also revealed significant pool labeling of glutamate and aspartate but not other amino acids (Fig. 5J). We also noticed high levels of labelled TCA

14 cycle intermediates such as succinate, fumarate, and citrate, but minimal labeling of GSH or GSSG (Fig. 5K). Intriguingly, some of the pyrimidine nucleotides and nucleotide sugars

13 were also labelled in the carbon backbone through C5-glutamine infusion, which may largely be due to the indirect fueling from labelled aspartate (Fig. 5L). To directly confirm the fueling from glutamine to de novo pyrimidine biosynthesis, we also bolus infused the

15 HCC1806 xenograft with N2-glutamine and observed significant labelling of the pyrimidine nucleotides in 45 mins (Fig. 5M). Altogether, these results demonstrate that glutamine catabolism-based pyrimidine metabolism and glutaminolysis pathways are highly required in TNBC.

Combinatorial Targeting of Pyrimidine and Glutamine Metabolism in TNBC Models

Next, we sought to explore therapeutic opportunities by targeting the pyrimidine and/or glutamine metabolism in TNBC models. To target glutamine metabolism, we used the drug CB-839 to inhibit the GLS enzyme, and to target pyrimidine metabolism, we used the drug Brequinar to inhibit DHODH, the enzyme that converts dihydroorotate to orotate

(Fig. 6A). To understand how glutamine metabolism changes in TNBC cells upon CB-839

13 treatment, we bolus-infused the HCC1806 tumors with C5-glutamine with or without CB-

839 treatment. We found significant decrease of the labeled glutamate (Fig. 6B) and its downstream metabolites related to glutamate metabolism (Fig. 5F), including aspartate, proline, and arginine biosynthesis intermediates (Fig. 6B), as well as TCA cycle intermediates and glutathione (Fig. 6C) by CB-839 treatment. Similarly, we bolus-infused

15 the HCC1806 tumors with N2-glutamine with or without Brequinar treatment and found decrease of labeled intermediates from de novo pyrimidine synthesis pathway, including

15 orotate or UMP by Brequinar treatment (Fig. 6D). These data suggest on-target effects of these drug treatments in our TNBC model.

To examine the therapeutic efficacy of targeting pyrimidine and glutamine metabolism in vivo, we first separately implanted two different basal-like TNBC cell lines, fast-growing

HCC1806 and slower-growing MDA-MB-468, orthotopically into the mammary fat pad in

NSG mice (Fig. 6, E and J). Upon tumor growth to approximately 150 mm3 for HCC1806 and 50 mm3 for MDA-MB-568, mice were randomized into control, CB-839, Brequinar, and CB-839 and Brequinar combined treatment groups. CB-839 was administered continuously in a CB-839 formulated chow (1400mg/kg diet dose) and Brequinar was given intraperitoneally (20mg/kg body weight) twice a week (Fig. 6, E and J). We monitored the tumor growth over time and recorded the survival days upon tumors reaching the end point (2 cm in diameter).

For the HCC1806 xenografts, we did not observe overt toxicity reflected by relatively stable body weight for these single and combination treatments (Fig. S8A). CB-839 or

Brequinar single treatment led to a decrease in HCC1806 tumor growth (Fig. 6F), and improved survival compared to the control group (Fig. 6G). The combination of both drugs further suppressed the tumor growth over time and significantly prolonged the survival compared to the control or any single treatment group (Fig. 6, F and G). In order to examine the effect of the combination drug treatment on tumor metabolism in vivo, we treated mice with established HCC1806 orthotopic tumors (tumor volume ~1000 mm3) with or without combination treatment for 10 days followed by tumor collection and targeted metabolite analysis by LC-MS. We found that the metabolites closely related to the CB-839 or Brequinar inhibited pathways were significantly changed by the 16 combination drug treatment (Fig. 6A), e.g., glutamine was accumulated, whereas its downstream metabolites including glutamate, TCA cycle intermediates, GSH, and GSSG were decreased by the combined drug treatment (Fig. 6H). On the other hand, the immediate upstream substrate metabolite of DHODH enzyme, dihydroorotate was dramatically accumulated concomitant with decreased downstream pyrimidine nucleotides (Fig. 6I). Therefore, not only did these analyses demonstrate a drug on-target effect, but also provide a proof-of-principle for combining the CB-839 with Brequinar in vivo. We repeated the same set of xenograft experiments above in another TNBC/basal like breast cancer cell line MDA-MB-468 and found a very similar pattern (Fig.6, J-N; Fig.

S8B, S9, A-F).

To further test the efficacy of these drugs in a more physiologically relevant model, we treated WHIM2, a representative basal-like TNBC PDX model (Ding et al., 2010) (Fig.

6O). Our results showed that Brequinar showed anti-tumor efficacy, and that the combined treatment caused decreased tumor growth and prolonged survival when compared to control group or CB-839 treatment group (Fig. 6, P and Q). To further address the metabolite change in tumors over time, metabolites in the PDX tumors with acute (24 hour) or middle-term (15 days) single drug treatment were measured, which further demonstrated the on-target efficacy of these drugs reflected by related metabolite change (Fig. 6, R and S).

Comparison of Pyrimidine Metabolism Inhibition with Standard Chemotherapy

Previous studies targeted the pyrimidine synthesis in the PTEN-mutant or chemotherapy resistant tumors, which showed promising therapeutic outcomes in TNBC (Brown et al.,

2017b; Mathur et al., 2017). Given that pyrimidine metabolism inhibition by Brequinar

17 showed significant antitumor efficacy in our TNBC tumor models, we further sought to compare the therapeutic efficacy of Brequinar with standard chemotherapy in TNBC in immune competent models. The Trp53-null genetically engineered transplant mouse model, which produces phenotypically and genomically diverse tumors, can be classified into three major subtypes/classes based on gene expression profiles: p53null-Basal,

Claudin-low, and p53null-Luminal (Pfefferle et al., 2013). Here, we utilized 5 validated p53-null serial transplant TNBC models, namely 2336R, 9263-3, 2224L, 2225L, and T-

11, to test the efficacy of Brequinar and traditional chemotherapy. In the chemotherapy

(Carbo/Tax) treatment group, tumor bearing mice were intraperitoneally administered

Paclitaxel (10mg/kg) and Carboplatin (50 mg/kg) once per week (Fig. 7A). Tumor growth was monitored and tumor volume was measured upon the control group reached tumor burden, and survival studies were conducted with continued drug treatment over time.

We found that each p53-null murine tumor model showed different levels of sensitivity to

Brequinar or Carbo/Tax treatment (Fig. 7, B-F). For the Brequinar treatment, all models

(2336R, 9263-3R, 2224L, 2225L, and T-11) were sensitive with significantly decreased tumor volume and prolonged survival (Fig. 7, B-F). However, with the Carbo/Tax treatment, 3 models (2336R, 2225L, and T-11) were sensitive (Fig. 7, D-F) and 2 models

(9263-3R, 2224L) were resistant (Fig. 7, B and C). More importantly, Brequinar treatment showed better therapeutic outcomes than Carbo/Tax in these models and less toxicity

(Fig. 7, B-F). These data suggest that pyrimidine metabolism inhibition by Brequinar shows comparable, or even better, antitumor activity compared with a standard chemotherapy doublet in this array of heterogenous murine TNBC models.

Discussion

18

Our study provides a comprehensive metabolic profiling and gene expression analysis of breast cancers comparing TNBC and ER+ patient tumors, PDXs, and cell lines in vivo.

Based on the data generated from this study, we developed a public, interactive data visualization portal (http://brcametab.org). This portal enables users to interactively explore associations between metabolite abundances and gene expression level within different clinical and PAM50 subtypes of breast cancer samples. We found two distinctive groups of patient samples stratified by unsupervised analyses of the untargeted metabolite data. One of these groups, which was comprised of normal breast tissues,

ER+ and some TNBC tumor samples, was denoted the “Lipid/Fatty Acid-Enriched” metabolite phenotype because of its high enrichment for lipid/fatty acid related metabolism pathways. Of note, the majority of TNBC tumors and all PDXs tested were featured in the second group, denoted “Nucleotide/Carbohydrate-Enriched” because of its high levels of nucleotide, carbohydrate, and energy metabolism pathways.

Furthermore, we found very a strong gene expression signature correlated with each metabolite signature, suggesting that these metabolites may be used as a strong predictor for high cell proliferation rates in breast cancer patients. Therapeutically, we tested the efficacy in different models of TNBC xenografts by blocking two metabolic pathways (pyrimidine metabolism and glutamine/glutamate metabolism) that are robustly altered and upregulated in TNBCs. We also provided the rationale for inhibiting these two pathways through combination treatments, showing improved therapeutic outcomes when compared with single drug treatment in vivo.

Previous research has attempted to identify therapeutic vulnerabilities in metabolic pathways in TNBCs. For example, by using cell line models grown in vitro, researchers

19 showed that a core set of TCA cycle and fatty acid pathways could be important for TNBC cell line survival (Lanning et al., 2017). The potential caveat is that this study only used the cell line grown in vitro, which, as we show in our current study, displayed distinctly different metabolic profiles when compared to breast tumor patient samples or even these same cell lines grown in vivo (Fig. 3A). Another study using representative mouse breast cancer models, including PyMT, Wnt1, Neu and C3-TAg model, showed that C3-TAg, a mouse TNBC tumor line with gene expression similar to human basal-like subtype tumors

(Pfefferle et al., 2013), displayed decreased lipids and -glutamyl amino acids with increased glycogen metabolites (Dai et al., 2018). This study suggests that increased glutathione production or decreased glutathione breakdown may be important in TNBC, which is consistent with the findings in our study (Fig. 1E and Fig. S7B). Another study used 204 ER+ and 67 ER- breast tumors for metabolomics and revealed that 19 metabolites showed different levels between these two clinical subtypes (Budczies et al.,

2013). The metabolite changes included increased beta-alanine, 2-hydroxyglutarate (2-

HG), glutamate, and decreased glutamine in the ER- breast tumors. This latter finding is consistent with our finding that glutamate metabolism is enriched in TNBC breast tumors.

Another study using a limited number of TCGA breast tumor samples showed that ER- breast tumors displayed high levels of 2-HG and tryptophan metabolite kynurenine (Tang et al., 2014). Our study also revealed significant increase in 2-HG in TNBC tumors, PDXs, as well as TNBC cells lines grown in vivo (Fig. 3E; and Fig. S2C). It was also reported that Warburg-like metabolism was enriched in breast tumors exemplified by the increase

Glut-1 expression (Brauer et al., 2013; Sun et al., 2020). In accordance with this finding, we also found increased levels of lactate in TNBC tumors and PDXs compared to ER+

20 breast tumors. Lactate is one of the most important metabolites involved in glycolysis and has been reported to drive cancer progression in different cancer settings (Faubert et al.,

2017; Hirschhaeuser et al., 2011; Tasdogan et al., 2020). Its enrichment in the TNBC samples cross-validated our metabolomics results. Besides these, we also see that other metabolites involved in glutamate and pyrimidine metabolism were increased in TNBC tumors, including glutamate, succinate, fumarate, UMP, UDP, CDP, and dUMP, etc.

Therefore, it is reasonable to speculate that these metabolites may promote cell proliferation in a similar fashion as lactate. Future investigation will need to be carried out to examine their roles in driving TNBC tumor progression.

Importantly, our study here is one of the very few that have integrated metabolomics and gene expression analysis in breast cancer. It has long been debated whether metabolite dysregulation may play a role in driving breast cancer or whether metabolites may be just the product of dysregulated cell proliferation. Here, we performed integrated analysis for the metabolomics and gene expression for TNBC breast tumors and PDXs, which showed promising implications. First, hierarchical clustering of the patient metabolomics data divided our tumor samples into two distinctive clusters (i.e., one cluster enriched with nucleotides and another in lipids/fatty acids). The small subset of TNBC patient tumors clustered with ER+ or normal tissues showed a different metabolic pattern compared with the other TNBC or PDXs tumors, an observation that highlights how the clinically defined

TNBC group is diverse on a molecular level, and that analyses strictly using clinically defined groups can mask important heterogeneity in TNBC. Second, the cluster enriched with TNBC and the two PDXs showed distinctive gene expression differences compared to the other cluster enriched with ER+ breast tumors, suggesting we can further use a

21 gene expression profile to define these metabolically distinct patient subsets. It is important to note that in the top 200 gene signature comparison between metabolite- defined clusters, the gene signature derived from the Nucleotide/Carbohydrate-Enriched cluster correlated highly with many previously defined cell proliferation signatures that contain many of the enzymes for pyrimidine and purine synthesis, thus showing very different and objective ‘omics’ methods each landing on a common and strong biological phenotype.

Chemotherapy still remains the standard of care for TNBC patients, although new immunotherapy treatments are beginning to have a role in the metastatic setting.

Chemotherapy is effective in early stage TNBC, whereas late stage patients and/or patients that develop metastases are more resistant to chemotherapy, leading to a poor survival times (Garrido-Castro et al., 2019). It is imperative to identify new therapeutic vulnerabilities in TNBC that may be able to, either alone or in combination, improve the survival rate for these patients. Although dysregulated metabolism is considered to be important for cancer progression, including in TNBCs, it lacks systemic characterization.

In our study, we performed systemic profiling of metabolomics and gene expression for relevant TNBC tumor samples as well as PDXs. We identified that pyrimidine and glutamine/glutamate metabolism are enriched in TNBC tumors, and that directly interfering in these pathways using targeted agents inhibited TNBC models growth. In conclusion, we identify two therapeutic vulnerabilities present within the often-deadly

TNBC cells, and a biomarker in the form of both metabolic and genetic signatures, to identify these patients. Future studies will explore the possibilities of using these inhibitors, alone, together, and/or in combination with chemotherapy, to examine whether

22 specifically targeting pyrimidine and/or glutamate metabolism can be used as a true biologically targeted therapeutic approach for patients with TNBC.

Materials and Methods

Patient Samples

Breast cancer tumors and normal breast tissue samples were obtained from patients from the UNC Tissue Procurement Facility. Primary tumor and normal tissues were obtained, flash frozen, and then used for metabolite profiling and RNA extraction and sequencing.

These tumor specimens were cut in half, and half used to make RNA, and half used for metabolite extraction. These patient samples were de-identified and anonymized. An

Institutional Review Board (IRB) exemption has been obtained in this study and thus this was considered non-Human Subjects Research.

Cell Culture

MDA-MB-231 and MCF-7 cells were cultured in Dulbecco’s Modified Eagle’s Medium

(DMEM) (Gibco 11965118) supplemented with 10% fetal bovine serum (FBS) and 1% penicillin-streptomycin. T47D, HCC1428, HCC70, HCC1806, ZR-75-1, MDA-MB-468 cells were cultured in 10% FBS, 1% P penicillin-streptomycin RPMI 1640 (Gibco

11875093). All cell lines were obtained from ATCC. Cells were used for experiments within 10-20 passages from thawing. All cells were authenticated via short tandem repeat testing. Mycoplasma detection was routinely performed to ensure cells were not infected with mycoplasma by using MycoAlert Detection kit (Lonza, LT07-218).

23

Drugs

Brequinar (HY-108325) and CB-839 (Synonyms: Telaglenastat, HY-12248) were purchased from MedChem Express. Brequinar was dissolved in 10% DMSO and 90% sterilized corn oil. CB-839 drug was formulated in the special diet made by Research

Diets Inc. which containing 1400 mg/kg diet dose of CB-839 drug. Paclitaxel and

Carboplatin were purchased from Henry Schein Medical.

Metabolite Extraction

The flash frozen patient tumors, normal breast tissues, PDX tumors, and cell line xenograft tumors were first cut in small pieces on dry ice, then 30 to 100 mg sections were weighed in a new 2 mL Eppendorf tube. 500 μL 80% (vol/vol) methanol (pre-cooled in −80 °C) was added to each tissue sample. The tissue chunk was further broken down on ice to form an even suspension by using a rotor-stator homogenizer (TissueRuptor,

Qiagen). An additional 500 μL pre-cooled 80% methanol was added in each tube and vortexed rigorously for 1 min. After incubation on ice for an additional 10 min, the tissue extract was centrifuged at 20,000 g at 4 °C for 15 min. The supernatant, normalized to the amount weighed of the samples, was transferred into clean Eppendorf tubes. For metabolite extraction from cell lines grown in vitro, cell lines were grown in 6-well plates for 2 days to 90% confluence. The culture medium was completely removed, cells were rinsed twice with 2 mL of cold normal saline solution to remove the medium residue completely. Cells were immediately placed on dry ice, followed by the addition of 1 ml 80%

(vol/vol) methanol (pre-cooled in −80 °C) to each well. After incubation in −80 °C for 15 min, cells were scraped into 80% methanol on dry ice, transferred to Eppendorf tubes,

24 and centrifuged at 20,000 g for 15 min at 4°C. The supernatant was normalized to cell number and transfer into Eppendorf tubes. Finally, the tubes were speed vacuum dried at room temperature then store dry pellet in -80 °C freezer for further LC-MS analysis.

Metabolomics/Metabolite Analysis

The dry pellets were reconstituted into 30 μL sample solvent (water:methanol:acetonitrile,

2:1:1, v/v) and 3 μL was further analyzed by liquid chromatography-mass spectrometry

(LC-MS). Ultimate 3000 UHPLC (Dionex) was coupled to Q Exactive Plus-Mass spectrometer (QE-MS, Thermo Scientific) for metabolite profiling. A hydrophilic interaction chromatography method (HILIC) employing an Xbridge amide column (100 x

2.1 mm i.d., 3.5 μm; Waters) was used for polar metabolite separation. Detailed LC method was described previously (Liu et al., 2014) except that mobile phase A was replaced with water containing 5 mM ammonium acetate (pH 6.8). The QE-MS was equipped with a HESI probe with related parameters set as below: heater temperature,

120 °C; sheath gas, 30; auxiliary gas, 10; sweep gas, 3; spray voltage, 3.0 kV for the positive mode and 2.5 kV for the negative mode; capillary temperature, 320 °C; S-lens,

55; A scan range (m/z) of 70 to 900 was used in positive mode from 1.31 to 12.5 minutes.

For negative mode, a scan range of 70 to 900 was used from 1.31 to 6.6 minutes and then 100 to 1,000 from 6.61 to 12.5 minutes; resolution: 70000; automated gain control

(AGC), 3 × 106 ions. Customized mass calibration was performed before data acquisition.

LC-MS peak extraction and integration were performed using commercially available software Sieve 2.2 (Thermo Scientific). The integrated peak area was used to represent

25 the relative abundance of each metabolite in different samples. The missing values were handled as described in a previous study (Liu et al., 2014).

Metabolomics Data Analysis

The 40 patient and PDX samples were extracted, normalized by weight and measured by

LC-MS at the same time. The metabolomics data was combined in some cases by removing any missing data and keep only the common metabolites that detected in all samples. The annotated metabolites from Kyoto Encyclopedia of Genes and Genomes

(KEGG) Compound database (www.genome.jp/kegg) (Kanehisa and Goto, 2000) and human metabolome database (HMDB; www.hmdb.ca) (Wishart et al., 2018) were used for further analysis. The data matrix was then normalized in the following sequential steps: two-based log transformation, row median centering then followed by column standardization. All Hierarchical clustering was performed using the normalized data by the Cluster 3.0 software and visualized by Java TreeView (1.1.6r4). Hierarchical clustering was performed using the following parameters: similarity measure: Euclidean distance; clustering algorithm: Centroid linkage. Principal component analysis (PCA) and

KEGG-based pathway enrichment analysis and pathway topology analysis was applied using the MetaboAnalyst 5.0 (https://www.metaboanalyst.ca/) (Chong et al., 2019).

Differential Abundance (DA) Score

The differential abundance (DA) score calculation was described in a previous study with some modifications (Hakimi et al., 2016). The DA score captures the tendency for a pathway to have increased levels of metabolites, relative to a control group. Briefly, the

26 detected metabolites were first assigned to different metabolic pathway categories based on the KEGG pathways by MetaboAnalyst 5.0 software (Chong et al., 2019). Then a statistical analysis method (SAM analysis has been used in this study; see Statistical

Analysis section for detail) was applied to determine which metabolites are significantly increased or decreased in abundance in a pathway between different sample groups.

Then, after determining the significant increased/decreased metabolites, the DA score was calculated based on the following formular:

DA score = (number of metabolites increased - number of metabolites decreased) /

number of measured metabolites in a pathway

Thus, the DA score varies from -1 to 1. A score of 1 indicates that all metabolites in a pathway increased in abundance, while a score of -1 indicates that all metabolites decreased. Consider a limited detection capability by LC-MS but important role of metabolites for some pathways. Pathways with no less than 2 detected metabolites were used for DA score calculation.

RNA Sequencing

Total RNA was isolated from tumors, normal breast tissues, and PDXs using the RNeasy

Plus Mini kit (Qiagen), with additional on-column DNase treatment to eliminate traces of genomic DNA. mRNAseq libraries were made from total RNA using the Illumina TruSeq mRNA sample preparation kit and sequenced on an Illumina HiSeq 2500 using a 2x50bp configuration. Purity-filtered reads were aligned to the human reference GRCh38/hg38 genome using Spliced Transcripts Aligned to a Reference (STAR) version 2.4.2a1.

Transcript (GENCODE v22) abundance estimates were generated by Salmon version

27

0.6.02 in ‘-quant’ mode, based on the STAR alignments. Raw read counts for all RNAseq samples were normalized to a fixed upper quartile3. RNAseq normalized gene counts were then log2 transformed (zeros were unchanged), and genes were filtered for those expressed in 70% of samples.

PAM50 Subtyping

To determine the intrinsic subtypes, we used clinical biomarker statuses and RNAseq gene expression data from 40 samples that included 9 normal breast tissues, 9 ER+ breast tumor samples, 15 TNBC (ER-/HER2-/PR-) breast tumor samples, and 7 individual tumors from 2 TNBC patient-derived xenografts (WHIM2 and WHIM30). To obtain the subtype-related biomarkers from our mRNAseq gene expression data, we first used an

ER/HER2 subgroup-specific gene normalization method, using the IHC status assigned to each sample. This normalization was done prior to applying the PAM50 predictor to correct differences in the biological composition and any technical bias between the gene expression of our 40 study samples derived from RNAseq and the Agilent Human

Microarrays used to create the original PAM50 UNC232 training set (Parker et al., 2009).

After labeling samples with their ER/HER2 status, we then extracted the ER/HER2 subgroup-specific percentile centering genes, with the percentile determined from the subgroup of the clinicopathological classifications (See detailed method description in supplemental methods) (Zhao et al., 2015). We then normalized the expression values of the PAM50 genes present in our 40 samples. After gene normalization, we applied the

PAM50 predictor (Parker et al., 2009). This calculated the correlation coefficient to the

28

PAM50 centroids, and allowed us to assign following intrinsic molecular subtypes to each sample: Basal-like, HER2-Enriched, Luminal A, Luminal B, and Normal-like signatures.

Proliferation Signature and Calculation

We determined proliferation scores for each of our 40 tumor samples using the Zhao et al (2015) PAM50 subtyping method (Zhao et al., 2015), which outputs a proliferation score for each sample by utilizes a proliferation signature is based on the average expression of 11 key proliferation genes linked to cell cycle progression described by Nielson et al

(2010) (Nielsen et al., 2010). This proliferation signature is known to provide robust, prognostic information for disease-free survival (Nielson et al., 2010). To replicate this proliferation score on our Brauer et al (2013) validation test set, the expression values for the 10/11 expressed proliferation score genes (CDC20, KNTC2, MKI67, PTTG1, RRM2,

TYMS, UBE2C, BIRC5, CCNB1, CDCA1) were median-averaged and used as the proliferation scores for correlating metabolite signatures with proliferation potential.

In Vivo Isotope Tracing

Six-week old female NOD SCID Gamma mice (NSG, Jackson lab) were injected with

1×106 viable HCC1806 cells orthotopically into the mammary fat pad. Upon tumors establishment (~1000 mm3), mice were treated with CB-839 for 3 days or Brequinar for 3 doses of injections before the tracing experiment. The in vivo isotope tracing experiment

13 15 was described previously (Leone et al., 2019). Briefly, C5- or N2-glutamine (Cambridge

13 15 Isotope Laboratories) were dissolved in sterilized saline. 200uL of 0.2M C5- or N2- glutamine solution were injected 3 times with 15-minute interval into the tail vein under

29 anesthesia. Mice were euthanized via cervical dislocation at 45 min after first injection.

Tumor tissue was rapidly harvested and snap frozen in liquid nitrogen and stored at -80 prior to metabolite extraction. ℃

Treatment in Orthotopic Tumor Xenografts, TNBC PDXs, and Trp53-null Transplant

Model

All orthotopic tumor xenograft animal experiments were in compliance with National

Institutes of Health guidelines and were approved by the University of Texas

Southwestern Medical Center Animal Care and Use Committee, and all TNBC PDX and

Trp53-null transplant model experiments were performed in accordance with approved

University of North Carolina (UNC) Institutional Animal Care and Use Committee protocols.

Orthotopic Tumor Xenograft

Six-week old female NOD SCID Gamma mice (NSG, Jackson lab) were used for xenograft studies. Approximately 1×106 viable HCC1806 or MDA-MB-468 cells were resuspended a mixture of 50 μL Matrigel (Corning, 354234) and 50 μL FBS-free growth medium and injected orthotopically into the mammary fat pad of each mouse. Treatment was started when HCC1806 tumors reached the volume of approximately 150 mm3 and

MDA-MB-468 tumors reached the volume of approximately 50 mm3, considering tumorigenesis ability of each cell line.

TNBC PDX and Trp53-null Transplant Model

30

The TNBC PDX model for treatments used in this study was the WHIM2, obtained from the Washington University in St Louis MO. The NSG mice (NOD SCID GAMMA mice) were obtained from the Jackson Laboratory or supplied in-house by the UNC Animal

Services Core (ASC). The Trp53-null transplant model has phenotypically and genomically diverse tumors that can be classified into three major subtypes/classes based on gene expression profiles: p53null Basal, Claudin-low and p53null-Luminal

(Pfefferle et al., 2013). The five validated BALB/c Trp53-null serial transplant models used in this study were 2336R, 9263-3, 2224L, 2225L, and T-11. The Balb/c female mice used were obtained from the Jackson Laboratory.

Tumor Transplantation for TNBC PDX and Trp53-null Transplant Model

Tumors were digested with the Miltenyi tumor dissociation kit to establish cell aggregate suspensions. Cell aggregates were subsequently washed in Hank’s Balanced Salt solution containing 2% FBS (HF Media) and resuspended in HF media with 50% Matrigel prior to transplant into cohort mice. Mice were briefly anesthetized with 2% isoflurane and tumor cells were injected into the inguinal mammary fat pad. Mice were followed 2-3 times weekly with caliper measurement for the establishment of tumors and upon reaching a diameter of 0.5 cm were randomly assigned into either treatment or control groups.

Treatment with CB-839 and Brequinar

Prior to treatment, the tumor-bearing mouse cohorts for the orthotopic xenograft, TNBC

PDX, and Trp53-null murine tumors were divided into four groups by randomization.

Tumor-bearing mice in the treatment group were continuously administered a specially

31 formulated chow containing 1400 mg/kg diet dose of CB-839 or an intraperitoneal (I.P.) injection of 20 mg/kg Brequinar every 3 or 4 days, or a combination of both. Tumor size was measured by caliper and body weight was recorded every 3 or 4 days. Tumor volumes were calculated with the formula: volume=(L×W2)/2, where L is the tumor length and W is the tumor width measured in millimeters. For the survival study, mice were sacrificed when tumor reached 2 cm in any dimension.

Treatment with Brequinar and Carbo/Tax

Prior to treatment, the tumor-bearing Trp53-null mice were divided into four groups by randomization. Mice in the Carbo/Tax treatment group were administered intraperitoneal injections of Paclitaxel (10mg/kg dose) and Carboplatin (50 mg/kg dose) once per week.

Tumor bearing mice in the Brequinar treatment group were administered an intraperitoneal injection of Brequinar drug (20mg/kg dose) twice per week. All mice were observed for overall condition and weighed bi-weekly. Throughout the treatment period caliper tumor measurements for all mice groups continued at a 2-3 per week frequency until the conclusion of the study when mice reached tumor burden.

Statistical Analysis

A statistical method specialized for multi-testing, SAM (Significance Analysis of

Microarrays) (Tusher et al., 2001) was applied to identify differential abundant metabolites or differentially expressed genes in the patient samples. Specifically, we used SAM to examine the correlation between abundance of each metabolite compounds or mRNA expression of each genes between different sample groups based on the unsupervised

32 clustering. For all SAM analyses, distribution-independent ranking tests (based on the

Wilcoxon test) and the sample-wise permutation (default by the samr package) were used to ascertain significance (false discovery rate, FDR<0.05). The SAM analysis gives a list of significantly upregulated (positive log fold change) or downregulated (negative log fold change) features between different sample groups with a q-value<0.05 were considered statistically significance.

All other statistical analysis was conducted using Prism 8.0 (GraphPad Software). All graphs depict mean ± SEM unless otherwise indicated. Statistical significances are denoted as not significant (ns; P>0.05), *P<0.05, **P<0.01, ***P<0.001, ****P<0.0001.

The numbers of experiments are noted in figure legends. To assess the statistical significance of single metabolites between two groups, we used unpaired two-tail student’s t-test. For animal experiments comparing more than two conditions, differences were tested by a one-way ANOVA followed by Dunnett’s or Tukey’s multiple comparison tests.

Data Availability Statement

The FASTQ files from RNAseq data and the star-salmon upper quartile normalized gene expression matrix are being deposited into the dbGAP (dbGaP Study Accession: phs002396.v1.p1) and GEO database. The accession number will be available before publication. The full set of metabolomics data can be accessed from the Tables S2-3, or from corresponding authors (Dr. Qing Zhang, [email protected] or Dr.

Charles Perou, [email protected]) upon request.

33

Acknowledgments

We thank all members of the Zhang and Perou laboratories for helpful discussions and suggestions. This work was supported by Cancer Prevention and Research Institute of

Texas (Q. Zhang, CPRIT, RR190058) and ACS Research Scholar Award (Q. Zhang,

RSG-18-059-01-TBE), NCI Breast SPORE program (C.M. Perou, P50-CA58223),

National Cancer Institute (C.M. Perou, R01-CA148761) and BCRF (C.M. Perou).

Financial support: This work was supported by Cancer Prevention and Research

Institute of Texas (Q. Zhang, CPRIT, RR190058) and ACS Research Scholar Award (Q.

Zhang, RSG-18-059-01-TBE), NCI Breast SPORE program (C.M. Perou, P50CA58223),

National Cancer Institute (Q.Z, R01CA256833. C.M. Perou, R01CA148761) and BCRF

(C.M. Perou).

Author contributions

C.M.P., Q.Z., and C.L. participated in the conception and design of the experiments. C.L. performed the metabolite extraction and data interpretation. C.L. and A.K. performed the isotope tracing experiments. J.L. and H. V. performed the metabolite LC/MS measurement and annotation. C.R.G. and C.L. performed the RNA-sequencing and analysis. C.L. and K.R.M. performed the animal study. C.F. performed the data processing, bioinformatic and statistical analysis. R.J.D., J.W.L. and S.K.M. provided critical advice and comments. C.L., Q.Z., C.R.G., and C.M.P. wrote and revised the paper with comments from all authors.

34

Competing interests: C.M.P is an equity stock holder and consultant of BioClassifier

LLC; C.M.P is also listed an inventor on patent applications on the Breast PAM50

Subtyping assay.

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38

Figure legends

Figure 1. Metabolic profiling in tumors from breast cancer patient and PDXs.

(A) Schematic of the experimental design for untargeted global metabolomics analysis in

ER+, TNBC breast cancer patient tumors and two TNBC patient-derived xenografts

(PDXs) tumors. (B) Pie chart shows the number of metabolites identified and annotated in all samples. The annotated metabolites all with a registered ID either from the Kyoto

Encyclopedia of Genes and Genomes (KEGG) database or the Human Metabolome

Database (HMDB) which can be further used for pathway analysis. (C) Unsupervised hierarchical clustering heatmap examines global metabolic profiles in 31 ER+/TNBC patient tumor samples and PDX tumors by using the normalized metabolite intensity. Note the cluster divides the samples in two major metabolic clusters (Cluster 1 and Cluster 2).

Clinical subtype and PAM50 subtype for each sample are showed. Selected classes of metabolites are highlighted. (D) Pie chart shows the metabolites that are significant increase in the Cluster 1 or Cluster 2 samples based on the SAM analysis (q-value<0.05).

(E) KEGG pathway-based differential abundance (DA) analysis using the significant changed metabolites between Cluster 1 and 2. The DA score reflects the average, gross changes for all detected metabolites in a pathway. A score of 1 indicates all measured metabolites in the pathway increase in the Cluster 2 versus Cluster 1, and score of -1 indicates all measured metabolites in a pathway decrease. Pathways with no less than two measured metabolites were used for DA score calculation based on the detection capability for some important pathways. (F-I) Relative normalized intensity levels of representative metabolites, involved in energy production (F), nucleotides (G), lipids/fatty acids (H), and amino acids (I), that were differentially abundant between metabolic

39 clusters from panel (C). Data are represented as mean ± standard error of the mean

(SEM), two-tailed Student’s t-test; *p<0.05, **p <0.01, ***p<0.001, ****p<0.0001.

Figure 2. Distinctive metabolism of cell lines grown in vivo versus in vitro.

(A) Schematic of the experimental design for metabolic profiling of breast cancer cell lines grown in vitro and in vivo condition. Cell lines grown in vivo were conducted by growing and collecting tumors from three individual mice, cell line grown in vitro were performed in parallel in biological duplicates. (B) Principal component analysis (PCA) of individual samples for the normalized metabolomics data in cell line grown in vivo and in vitro. (C)

Unsupervised hierarchical clustering heatmap of normalized metabolomics data in cell line grown in vivo and in vitro. (D and E) Histograms of metabolic pathway enrichment using statistical differed metabolites (SAM, q-value <0.05) that high in cells grown in vitro

(D) or high in cells grown in vitro (E). All statistically significant (p<0.05) pathways were showed. (F and G) Relative normalized intensity levels of representative metabolites that are significantly increased in cell line grown in vitro (F) or in vivo (G). Data are represented as mean ± standard error of the mean (SEM), two-tailed Student’s t-test; *p<0.05,

**p<0.01, ***p<0.001, ****p<0.0001.

Figure 3. Cell line grown in vivo reveals similar metabolites enrichment as in patients.

(A) Unsupervised hierarchical clustering heatmap of the normalized metabolomics data by combining all cell line in vitro and in vivo, patient, PDXs samples. (B) Pie chart shows the metabolites that are significant increase in the ER+ or TNBC cell derived xenograft

40 tumors based on the SAM analysis (q-value<0.05). (C and D) PCA analysis (C) and unsupervised hierarchical clustering heatmap (D) of the ER+ and TNBC cell line derived xenograft tumors using the significant changed metabolites. (E and F) Relative normalized intensity levels of representative metabolites that are significantly changed in cell line grown in vitro versus in vivo. Data are represented as mean ± standard error of the mean (SEM), two-tailed Student’s t-test; *p<0.05, **p<0.01, ***p<0.001, ****p<0.0001, not significant (ns).

Figure 4. Integrated analysis of transcriptional and metabolic signatures in patient tumors.

(A) Schematic of the correlation analysis between metabolomics and gene expression data. (B) The PAM50 subtype correlation with the metabolite signatures from Cluster 1 and Cluster 2 patient samples from this study. (C) Correlation of proliferation score with the tertile median level of the two metabolite signatures from Cluster 1 and Cluster 2 patient samples. (D) A supervised clustering heatmap of the differentially expressed (DE) genes that were highly expressed in the Cluster 1 and Cluster 2 samples. DE genes were identified by SAM analysis between the Cluster 1 and Cluster 2 samples with q- value<0.05. (E and F) Histogram of gene ontology (GO) analysis showing the top 200 genes that are upregulated in Cluster 2 (E) or Cluster 1 (F). Top 15 pathways with statistical significance (p<0.05) were showed. (G and H) Kaplan-Meier plotters generated by the median expression of the top 200 genes from Cluster 2 (G) or Cluster 1 (H) in three different databases.

41

Figure 5. Pyrimidine metabolism and glutaminolysis are elevated in TNBC.

(A) Metabolic pathway-based gene set enrichment analysis using the DE genes (q- value<0.05, SAM) within each metabolic cluster. All statistically significant (p<0.05) pathways were showed. (B) Heatmap showed relative expression of genes related to pyrimidine metabolism in the patient tumors and PDXs stratified by the metabolic clusters.

Genes upregulated in Cluster 2 samples are shown with statistic markers. (C) A schematic shows the key genes for the initial steps of glutaminolysis and de novo pyrimidine biosynthesis. (D) Relative gene expression of the key genes between Cluster

1 and 2 samples. (E) Heatmap shows relative expression of genes related to glutamine metabolism and transport in the patient tumors and PDXs stratified by the metabolic clusters. Genes upregulated in Cluster 2 samples are shown with statistic markers. (F)

Schematic of key metabolites denotes metabolite abundance in tumors in Cluster 1 or

Cluster 2. Significant abundant metabolites from SAM analysis (q-value <0.05) were highlighted, with metabolite in red accumulated in cluster 2 or in blue accumulated in

13 15 cluster 1. The expected major labeling patterns of some metabolites from C5- or N2- glutamine isotope tracing are also showed. (G-I) Relative abundance ratio of the downstream and upstream metabolites from glutamate to glutamine (G), carbamoyl- aspartate to glutamine (H), and carbamoyl-aspartate to aspartate (I), indicating the metabolic flow at the initial steps in the glutaminolysis and pyrimidine metabolism pathways in patient (left) or cell line in vivo (right) samples. (J-L) Contribution of glutamine carbon to amino acids (J), TCA cycle and glutathione (K), and nucleotides (L) in

13 HCC1806 TNBC xenograft tumors (N=4) infused with C5-glutamine in 45 mins. (M)

42

Contribution of glutamine nitrogen to nucleotides in HCC1806 TNBC xenograft tumors

15 (N=6) infused with N2-glutamine in 45 mins.

Data are represented as mean ± standard error of the mean (SEM), two-tailed Student’s t-test; *p<0.05, **p<0.01, ***p<0.001, ****p<0.0001, ns, not significant.

Figure 6. Combination targeting the pyrimidine and glutamate metabolism in TNBC cell line xenograft and PDX model.

(A) A schematic shows the key enzymes targeted by CB-839 and Brequinar in the glutamine and pyrimidine metabolism pathway and related key metabolites. (B-C)

Contribution of glutamine carbon to amino acids (B), the TCA cycle and glutathione (C) in HCC1806 tumors obtained from vehicle- and CB839-treated mice (N=4/group) at 45

13 min of C5-glutamine bolus infusion. (D) Contribution of glutamine nitrogen to pyrimidine nucleotides in HCC1806 tumors obtained from 3 doses of vehicle- and Brequinar-treated

15 mice (N=5/group) at 45 min of N2-glutamine bolus infusion. The expected major labeling

13 15 patterns of some metabolites from C5- or N2-glutamine isotope tracing are showed in

Figure 5F. (E-G) Treatment strategy (E), tumor growth measured by volume (F) and survival (G) of the HCC1806 xenograft cohort (N=10/group). (H and I) Related abundance of metabolites related to the glutamine metabolism (H) or pyrimidine metabolism (I) between the control and combination treatment group in HCC1806 xenograft tumors.

Mice treated with the drugs for 10 days, then the tumors harvested for metabolite analysis.

(J-L) Treatment strategy (J), tumor growth measured by volume (K) and harvest tumor weight at 28 day (L) of the MDA-MB-468 xenograft cohort (Control, N=10; CB-839, N=10;

Brequinar, N=9; Combined, N=8). (M and N) Related abundance of key metabolites in

43 the glutamine metabolism (M) or pyrimidine metabolism (N) pathways in MDA-MB-468 xenograft tumors in different treatment for 28 days. (O-Q) Treatment strategy (O), survival

(P) tumor growth measured by volume (Q) of the WHIM2 PDX xenograft cohort (Control,

N=12; CB-839, N=5; Brequinar, N=8; Combined, N=5). (R and S) Related abundance of key metabolites in the glutamine metabolism (R) or pyrimidine metabolism (S) pathways in the WHIM2 PDX xenograft tumors in different treatment group. Tumors harvested for metabolite analysis at 24 hours and 15 days after the treatment. Data are represented as mean ± standard error of the mean (SEM), two-tailed Student’s t-test; *p<0.05, **p<0.01,

***p<0.001, ****p<0.0001, not significant (*ns).

Figure 7. Comparison of pyrimidine metabolism inhibition with standard chemotherapy.

(A) Treatment strategy in five p53null TNBC murine tumor line xenografts, mice were treated with standard-of-care chemotherapy (Carboplatin+Paclitaxel) or Brequinar at indicated dose and time. (B-F) Therapeutic outcome in terms of tumor volume change

(left) or survival of the cohort (right) in the 2224L (B), 9263-3R (C), 2225L (D), T11 (E), and 2336R (F) tumor model. Data are represented as mean ± standard error of the mean

(SEM), two-tailed Student’s t-test.

44

Figure 1

A B 2 TNBC PDXs (n=7) D Patients with Metabolites detected Metabolites abundance 38 breast cancer Metabolite LC/MS in all samples (q < 0.05, SAM) extraction acquisition Data analysis 80 93 High in Cluster 1 Annotated 263 Unannotated High in Cluster 2 90 No changed TNBC tumors (n=15) & ER+ tumors (n=9) Total=301 Total=263

C Cluster 1 Cluster 2 E Pathways enriched in Metabolite enriched in Cluster 1 Cluster 2 Metabolic Pathways

Fatty acid biosynthesis 4 4 0

15

12

11 14

13 Linoleic acid metabolism 2 2 0

-8

-1

-9

-6

-7

-5

-4 -3

-2 Vitamin B6 metabolism 2 1 0

TNBC-

TNBC-

TNBC-8

TNBC-

TNBC-6

TNBC-3

TNBC-

TNBC-1

WHIM2-3

WHIM2-2

WHIM2-1

WHIM30-4

WHIM30-3

WHIM30-2

WHIM30-1

TNBC-7

TNBC-

TNBC-9

TNBC-2

TNBC-5

TNBC-4

TNBC1-0

ER

ER

ER

ER

ER

ER

ER

ER ER Tissue type Sphingolipid metabolism 2 1 0 PAM50 type Normalized Primary bile acid biosynthesis 2 1 0 abundance Biosynthesis of unsaturated fatty acids 7 4 1 Glycine, serine and threonine metabolism 11 6 2 3.0 Glyoxylate and dicarboxylate metabolism 9 5 2 1.5 D-Glutamine and D-glutamate metabolism 0 3 2 1 Porphyrin and chlorophyll metabolism 3 2 1 -1.5 Valine, leucine and isoleucine degradation -3.0 4 2 1 Valine, leucine and isoleucine biosynthesis 5 2 1 Nicotinate and nicotinamide metabolism Tissue type 6 2 1 Histidine metabolism 6 2 1 ER+ Phenylalanine metabolism 4 1 1 Galactose metabolism TNBC 4 1 1 Glycerolipid metabolism 3 1 1 WHIM30-PDX Fatty acid degradation 3 1 1 Pathway category WHIM2-PDX Nitrogen metabolism 2 1 1 Arginine biosynthesis 10 4 5 Energy metabolism Citrate cycle (TCA cycle) 8 2 3 Nucleotide metabolism PAM50 type Tyrosine metabolism 7 2 3 Purine metabolism 19 6 9 Lipid metabolism Normal-like Pentose phosphate pathway 6 2 3 Luminal A Glycolysis / Gluconeogenesis 6 2 3 Amino acid metabolism Cysteine and methionine metabolism 11 3 5 Luminal B beta-Alanine metabolism 5 1 2 Carbohydrate metabolism HER2 Alanine, aspartate and glutamate metabolism 14 4 7 Aminoacyl-tRNA biosynthesis 18 4 8 Metabolism of cofactors, Basal-like Pantothenate and CoA biosynthesis 8 2 4 vitamins and others Tryptophan metabolism 8 1 3 Metabolite category Taurine and hypotaurine metabolism 4 1 2 Arginine and proline metabolism 9 2 5 Energy production related Fructose and mannose metabolism 6 2 4 Nucleotides Butanoate metabolism 3 1 2 Lysine degradation Lipids/Fatty acids 3 1 2 Inositol phosphate metabolism 3 0 1 Amino acids Glutathione metabolism 10 1 5 Pentose and glucuronate interconversions 5 1 3 Pyruvate metabolism 5 1 3 Pyrimidine metabolism 19 2 11 Glycerophospholipid metabolism 8 1 5 Riboflavin metabolism 2 0 1 Phenylalanine, tyrosine and tryptophan biosynthesis 2 0 1 Biotin metabolism 2 0 1 Starch and sucrose metabolism 2 0 1 Amino sugar and nucleotide sugar metabolism 10 2 8 Ascorbate and aldarate metabolism 4 0 3 Phosphonate and phosphinate metabolism 2 0 2

-1.0 -0.5 0.0 0.5 1.0 Differential Abundance Score

F Energy production G Nucleotides

2 **** 2 **** 1.0 ** 0.4 * 0.4 * 1.0 ** 2 ** 4 **** 2 * 2 * 2 *** 2 *

1 1 0.5 0.5 1 2 1 1 1 1 0.0 0.0 0 0 0 0 0.0 0.0 0 0 0 0 -1 -1 -1 -0.4 -0.4 -2 -1 -0.5 -0.5 -1 -2 -2 -1 -1 -2

-3 -2 -1.0 -0.8 -0.8 -1.0 -2 -4 -3 -2 -2 -3 Normalized intensity Normalized intensity Glucose Pyruvate Lactate Fumarate Malate Succinate UMP dUMP UDP AMP GMP GDP H Lipids/Fatty acids I Amino acids

1.5 ** 2 * 1.5 **** 2 ** 1.5 **** 1.0 ** 1.0 *** 1.0 **** 1.0 **** 1.0 *** 1.5 * 2 *** 1.0 1.0 1.0 1 1 0.5 0.5 0.5 0.5 0.5 1.0 1 0.5 0.5 0.5 0.0 0.0 0.5 0 0.0 0 0.0 0 0.0 0.0 0.0 0.0 -0.5 -0.5 0.0 -1 -0.5 -0.5 -0.5 -1 -1 -0.5 -0.5 -0.5 -1.0 -1.0 -1.0 -1.0 -1.0 -0.5 -2

-1.5 -2 -1.5 -2 -1.5 -1.0 -1.0 -1.5 -1.0 -1.5 -1.0 -3 Normalized intensity Normalized intensity Palmitate Palmitoleate Adipic acid Tetradecenoate Laurate Myristate Glutamate Glutamine Methionine Lysine Glycine Glutathione

Cluster 1 Cluster 2 Figure 2

A B 10 In vitro Breast cancer cell line In vivo growth Tumor In vivo Metabolite LC/MS acquisition Data analysis + collection ER TNBC extraction T47D MDA-MB-231 MCF-7 MDA-MB-468 HCC1428 HCC70 ZR-75-1 HCC1806

In vitro growth 2 (14.7 %) PC -5 0 5

C

10 - Growth condition -10 -5 0 5 In vitro PC 1 (49.7 %) In vivo D Pathway high in vitro (all cell lines) Normalized Arginine biosynthesis abundance Citrate cycle (TCA cycle) 3.0 Alanine, aspartate and glutamate metabolism 1.5 Phenylalanine metabolism 0 Aminoacyl-tRNA biosynthesis -1.5 Pyrimidine metabolism -3.0 Valine, leucine and isoleucine biosynthesis Vitamin B6 metabolism Riboflavin metabolism Phenylalanine, tyrosine and tryptophan biosynthesis Tyrosine metabolism D-Glutamine and D-glutamate metabolism Glycolysis / Gluconeogenesis 012345 -log10[P value] E Pathway high in vivo (all cell lines)

Purine metabolism Pyrimidine metabolism Glycine, serine and threonine metabolism Biosynthesis of unsaturated fatty acids Glycerophospholipid metabolism Phosphonate and phosphinate metabolism Taurine and hypotaurine metabolism Pantothenate and CoA biosynthesis

0 1 2 3 4 5 -log10[P value]

F 1.5 **** 1.5 **** 2 **** 1.0 **** 0.4 **** 0.2 * 0.6 **** 1.0 **** 0.4 1.0 1.0 0.5 0.2 0.1 0.5 0.5 0.5 1 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0 -0.2 -0.5 -0.2 -0.1 -0.5 -0.5 -0.5 -0.4

-1.0 -1.0 -1 -1.0 -0.4 -0.2 -0.6 -1.0 Normalized intensity Glucose Pyruvate 2-oxoglutarate Succinate cis-Aconitate Fumarate Glutamine Arginine In vitro In vivo G 1.0 **** 0.5 **** 1.0 **** 1.0 ** 0.4 **** 1.0 **** 1.5 *** 1.0 **** 0.5 0.5 0.2 0.5 1.0 0.0 0.0 0.0 0.5 0.0 0.5 0.5 0.0 -0.5 -0.5 -0.5 0.0 -0.2 0.0 0.0 -1.0 -1.0 -0.4 -0.5 -0.5 -1.5 -1.0 -1.5 -0.5 -0.6 -1.0 -1.0 -0.5 Normalized intensity AMP ADP UMP UDP Guanine Adenine Cytosine Uracil Figure 3

A B D

Metabolites abundance ER+ vs TNBC (q < 0.05, SAM) Sample Category Cell type Cell line in vitro ER+ Cell line in vivo 43 TNBC PDXs TNBC in Cluster 2 Normalized 45 abundance Normal, ER+, and 172 TNBC in Cluster 1 3.0 1.5 0 Normalized -1.5 abundance Total=260 -3.0 3.0 1.5 High in ER+ cells 0 High in TNBC cells B -1.5 -3.0 No changed

C

ER+ TNBC MDA-MB-468

ZR-75-1 MDA-MB-231 HCC1428 HCC70 Cell line T47D PC 2 (11.8 %) PC MCF-7

HCC1806

-5 0 5

10 10 - -10 -5 0 5 10 PC 1 (39.4 %)

ns E 1.0 * 1.0 1.0 **** 0.3 *** 0.4 ** 0.4 * 1.0 ** 0.5 * 2 ** 0.5 0.2 0.2 0.2 1 0.5 0.5 0.0 0.1 0.5 0.0 0.0 0.0 0 0.0 0.0 0.0 0.0 -0.5 -0.5 -0.1 -0.2 -0.2 -1

-1.0 -0.5 -0.5 -0.2 -0.4 -0.4 -0.5 -1.0 -2 Normalized intensity Glucose 2-Oxoglutarate 2-Hydroxyglutarate Glutamate Glutamine Arspartate Carbamoyl Uracil Adenosine -aspartate F 0.6 **** 0.6 **** 0.6 **** 0.6 **** 0.6 **** 1.0 *** 0.4 **** 0.4 *** 0.4 0.4 0.2 0.3 0.3 0.3 0.5 0.2 + 0.2 0.2 ER 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 TNBC -0.2 -0.3 -0.3 -0.3 -0.2 -0.2 -0.5 -0.2 -0.6 -0.6 -0.6 -0.4 -0.4 -1.0 -0.4 -0.4 Normalized intensity Octanoyl Lauroyl Butyryl Decanoyl Tetradecanoyl Tiglyl 3-hydroxy-isovaleryl Mevalonate carnitine carnitine carnitine carnitine carnitine carnitine carnitine Figure 4

Metabolomics 2 TNBC PDXs (n=7) A Patients with Unsupervised metabolite enriched PAM50 subtype/ breast cancer metabolic clustering signatures in clusters proliferation correlation

Metabolite-gene 31 tumor association samples TNBC tumors (n=15) Supervised gene Top200 gene Breast cancer & ER+ tumors (n=9) expression profiling signatures in clusters patient prognosis RNA-seq

B Cluster 1 metabolite signature Cluster 2 metabolite signature C Cluster 1 metabolite signature Cluster 2 metabolite signature 0.6 p−value = 2.7e−06 p−value = 1.8e−06 0.6 0.4 0.5 0.5 0.4 0.2 0.2 0.0 0.0 0.0 0.0 -0.2

median median value -0.2

median median value -0.4 -0.5 -0.5

Proliferationscore Proliferationscore Metabolitesignature -0.4 Metabolitesignature -0.6 p−value = 8e−07 p−value = 2.5e−08 -0.6 -0.8 -1.0 -1.0 High Medium Low High Medium Low

Cluster 2 metabolite signature Cluster 1 Cluster 2

D E associated top 200 genes _GO term

15

12

11

14

13

-8

-1

-9

-6

-7

-5

-4 -3

-2 cell division

TNBC-

TNBC-

TNBC-8

TNBC-

TNBC-6

TNBC-3

TNBC-

TNBC-1

WHIM2-3

WHIM2-2

WHIM2-1

WHIM30-4

WHIM30-3

WHIM30-2

WHIM30-1

TNBC-7

TNBC-

TNBC-9

TNBC-2

TNBC-5

TNBC-4

TNBC1-0

ER

ER

ER

ER

ER

ER

ER

ER ER Tissue type mitotic nuclear division PAM50 type sister chromatid cohesion DNA replication chromosome segregation G2/M transition of mitotic cell cycle mitotic sister chromatid segregation regulation of cell cycle DNA repair anaphase-promoting complex-dependent catabolic process in cluster 2 2 cluster in G1/S transition of mitotic cell cycle (q < 0.05, SAM) 0.05, < (q cell proliferation mitotic cytokinesis 2908 genes genes 2908 upregulated microtubule-based movement mitotic metaphase plate congression 0 10 20 30 40 50 -log10[P value] Cluster 1 metabolite signature F associated top 200 genes_GO term angiogenesis regulation of smooth muscle contraction cellular response to hormone stimulus vasculogenesis (q < 0.05, SAM) 0.05, < (q regulation of cardiac conduction negative chemotaxis response to progesterone lipid transport

6224 genes genes 6224 upregulated cluster 1 in leukocyte migration positive regulation of cell migration neural crest cell migration establishment of endothelial barrier Normalized Tissue type PAM50 type cell surface receptor signaling pathway muscle contraction expression ER+ Normal-like 1.0 negative regulation of angiogenesis 0.5 TNBC Luminal A 0 WHIM30-PDX Luminal B 02468 -0.5 -log10[P value] -1.0 WHIM2-PDX HER2 Basal-like G Cluster 2 metabolite signature associated top 200 genes H Cluster 1 metabolite signature associated top 200 genes METABRIC Harrell Sweden METABRIC Harrell Sweden

low 160/658 low 104/285 low 44/856 low 282/657 low 144/285 low 97/856

med 256/656 med 120/285 med 84/856 med 241/657 med 127/285 med 74/856

OS (Probability) OS

OS (Probability) OS

MFS (Probability) MFS

MFS (Probability) MFS DRFS (Probability) DRFS

high 301/657 high 152/285 high 90/857 high 194/657 high 105/285 (Probability) DRFS high 47/857

Log rank p = 4.88e-15 Log rank p = 5.45e-07 Log rank p = 0.000105 Log rank p = 7.88e-06 Log rank p = 8.02e-05 Log rank p = 0.000161

0.0 0.2 0.4 0.6 0.8 1.0

0.0 0.2 0.4 0.6 0.8 1.0 0.6 0.7 0.8 0.9 1.0

0.6 0.7 0.8 0.9 1.0

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 86420 10 0 50 100 150 200 0 500 1000 1500 0 642 8 10 0 50 100 150 200 0 500 1000 1500 Years Months Days Years Months Days Figure 5

A Enriched in Cluster 1 Enriched in Cluster 2 B Cluster 1 Cluster 2 NT5C3A** Pyrimidine metabolism CTPS1**** POLA1** Carbon metabolism POLA2** POLD1*** Purine metabolism POLD3* Oxidative phosphorylation POLE3** POLE**** Cysteine and methionine metabolism NME1**** Glycosphingolipid biosynthesis NME2*** POLR1A** Pyruvate metabolism POLR1B*** Aminoacyl-tRNA biosynthesis POLR1C*** Normalized POLR2C** expression One carbon pool by folate POLR2D**** 1.0 Citrate cycle (TCA cycle) POLR2F**** POLR2H**** 0.5 Terpenoid backbone biosynthesis POLR2I** 0 POLR2J**** -0.5 N-Glycan biosynthesis POLR2K** -1.0 Pentose phosphate pathway POLR3C* POLR3G**** Regulation of lipolysis in adipocytes CAD** CMPK2 Arachidonic acid metabolism DCK Choline metabolism in cancer DTYMK**** Pyrimidine Pyrimidine metabolism ENTPD6*** Histidine metabolism PNPT1**** Significant alerted Significant alerted metabolic pathway Sphingolipid signaling pathway PRIM1* PRIM2**** beta-Alanine metabolism RRM1* Valine, leucine and isoleucine degradation RRM2**** TK1**** Ether lipid metabolism TYMS** ZNRD1*** 8 6 42 0 2 4 6 8 UMPS** UCK2** DHODH -log10[P value] CAD 4 ** 4 ** E Cluster 1 Cluster 2 CDGlutamine Carbamoyl P 3 3 GLS ** CAD GFPT2 GLS 2 2 ASNSD1 N-Carbamoyl-aspartate SLC7A6 1 1 CAD ** Normalized CAD PHGDH **** expression Glutamate 0 0 GMPS ** 1.0 Dihydroorotate GLS CAD Cluster 1 GGH **** Cluster 2 CTPS1 **** 0.5 DHODH ns PPAT *** 0 2.0 2.0 ** SLC7A5 **** -0.5 Orotate SLC7A11 ** -1.0 1.5 1.5 transport SLC38A7 *** UMPS SLC38A3 * 1.0 1.0 GFPT1 Orotidine 5P mRNARelative levels SLC1A5 0.5 0.5 ASNS ****

Glutamine Glutamine metabolism & SLC7A9 * UMPS 0.0 0.0 F UMP DHODH UMPS G Glutamate/Glutamine H Carbamoyl-aspartate/Glutamine I Carbamoyl-aspartate/Aspartate Glutamine Carbamoyl-phosphate Aspartate 15 **** 10 ** 0.04 * 0.05 ** 0.04 ** 0.12 * Proline 8 0.04 Glutamate Carbamoyl-aspartate 0.03 0.03 10 0.08 6 0.03 N-Acetyl-glutamate Dihydroorotate 0.02 0.02 GSH 4 0.02 5 0.04 0.01 0.01 GSSG 2 0.01 Arginine α-KG Orotate

Succinate Abundance ratio

Abundance ratio 0 0 0.00 0.00 Abundance0.00 ratio 0.00 + + + Orotidine 5’-phosphate ER ER ER Fumarate TNBC TNBC TNBC Cluster 1 Cluster 2 Cluster 1 Cluster 2 Cluster 1 Cluster 2 Patient Cell line in vivo Patient Cell line in vivo Patient Cell line in vivo Citrate Malate UMP Uridine Lactate UDP JKLMAmino acids TCA cycle& Nucleotides& Nucleotides Uracil Aspartate Glutathione Nucleotide sugar pyruvate UTP 100 100 100 M+6 100 Dihydrouracil M+5 M+5 M+5 80 80 80 M+5 80 Frucose-6P M+4 M+4 M+4 UDP-GlcNAc CTP M+4 60 M+3 60 M+3 60 60 M+3

UDP-glucose M+3 -Glutamine

-Glutamine

Glucose-6P 5 M+2 M+2 M+2

UDP-xylose CDP dUDP 40 40 40 2 40 C M+2 N

UDP-glucuronate M+1 M+1 M+1 13 20 20 20 M+1 15 20 Glucose CMP dUMP M+0 M+0 M+0

0 0 0 M+0 0 from

from

Metabolite annotation Labelling patterns Fractional enrichment (%)

Fractional enrichment (%) IMP CTP ADP 13 GSH UDP AMPGMP CDP UMPAMP GDP high in Cluster 1 No change C AlanineSerine MalateCitrate GSSG high in Cluster 2 Not detected 15N-amide GlutamineGlutamateAspartate Succinate UDP-glucoseUDP-GlcNAc Figure 6

A B Amino acids CDTCA cycle & Glutathione Nucleotides Glutamine Carbamoyl P 80 **** 80 80 ** Vehicle Vehicle Vehicle CB-839 GLS Carbamoyl-aspartate ** 60 **** CB-839 60 *** CB-839 60 Brequinar Dihydroorotate Glutamate 40 40 40 DHODH Brequinar *** ns ** **

-glutamine20 M+5 **

-glutamine M+5 **** 20 -glutamine20 M+2

5

5 Orotate 2 **

C

C **** **** N

13

13 TCA Orotidine 5P 0 0 15 0

% Enrichment relative to

% Enrichment relative to cycle % Enrichment relative to Pyrimidine nucleotides GSH M+5 UMP M+1CMP M+1 2-HG M+5 GSSG M+5 Proline M+5 Malate M+4Citrate M+4 Orotate M+1 OrnithineCitrulline M+5 M+5 GlutamateAspartate M+5 M+4 Succinate M+4 E F HCC1806 xenograft G HCC1806 xenograft survival Brequinar (20mg/kg, I.P., twice/week) ) 4000 3 Control CB-839 (1400 mg/kg, chow, daily) 100 Control CB-839 3000 CB-839 ***

Brequinar ****

Day 0 Mice Brequinar ****

2000 **** survival Combined 50 NSG Combined **** mice 1000

HCC1806 Monitor tumor growth Percent survival 0 0

xenograft Tumor volume (mm 0102030405060 0204060 Days on treatment Days on treatment H Glutamine Glutamate M alate Succinate GSH GSSG I Dihydroorotate UMP UDP CDP 3 *** 1.2 *** 1.2 **** 1.2 ** 1.2 * 1.2 * 1600 **** 1.2 * 1.2 * 1.2 *** Control Control 1200 2 0.8 0.8 0.8 0.8 0.8 Combined 0.8 0.8 0.8 Combined 800 1 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 400

0 0.0 0.0 0.0 0.0 0.0 0 0.0 0.0 0.0

Relative abundance

Relative abundance

K MDA-MB-468 xenograft MDA-MB-468 Xenograft M J L Glutamine Glutamate

Brequinar (20mg/kg, I.P., twice/week) ) 250 **** ns 3 0.20 **** ** Control 4 ns 1.5 ns CB-839 (1400 mg/kg, chow, daily) **** ns

200 CB-839 **** ** *** ****

ns 0.15 ** ****

ns 3 150 Brequinar 1.0 Day 0 Day 28 Combined 0.10 100 2 NSG 0.5 mice Metabolite 50 0.05 1 analysis

MDA-MB-468 Monitor tumor 0 Tumor weight (g) 0.00 0 0.0 Tumor volume (mm xenograft growth & weight 0 10 20 30 Relative abundance Control CB-839 Brequinar Combined Days on treatment Control CB-839 BrequinarCombined WHIM-2 PDX N Carbamoyl-aspartate Dihydroorotate OPQWHIM-2 PDX survival

) 6000 **** ** Brequinar (20mg/kg, I.P., twice/week) 3

ns **** 150 120 *** 100 Control ns ns** ** CB-839 (1400 mg/kg, chow, daily) * ns * ***

* CB-839 ** ns

90 4000 ns 100 Brequinar * Day 0 Mice 60 50 survival Combined 50 NSG 2000 30 mice

Percent survival

0 0 WHIM-2 PDX Monitor tumor volume 0 0 Tumor volume (mm Relative abundance 0102030 Control CB-839 Brequinar Combined Days on treatment Control CB-839 BrequinarCombined R Glutamine Glutamate M alate Aspartate Dihydroorotate S Dihydroorotate UMP CMP Uridine ns ns ns ns ns 6 ** 1.2 ns 1.5 ** 1.5 ** 4 250 *** 2.0 1.5 3 * ** * * Control *** * ** * Control 3 200 1.5 4 1.0 1.0 CB-839 24h 1.0 2 Brequinar 24h 0.8 150 2 CB-839 15d 1.0 Brequinar 15d 100 2 0.4 0.5 0.5 0.5 1 1 50 0.5

0 0.0 0.0 0.0 0 0 0.0 0.0 0

Relative abundance Relative abundance Figure 7

A B 2224L tumor at 14 day Survival of 2224L tumor line

) p=0.56 Carboplatin+Paclitaxel (50 mg/kg+10mg/kg, I.P., once/week ) p<0.0001 3 5000 Brequinar (20mg/kg, I.P., twice/week) p<0.0001 p<0.0001 100 Untreated (n=7) 4000 p=0.53

Balb/c mice 3000 Brequinar (n=8) p<0.0001 50 2000 Carbo/Tax (n=8) Day 0 1000

Five p53null TNBC murine Monitor tumor volume 0 Percent survival 0

tumor line xenografts and survival Tumor volume (mm 0102030 Survival (Days) Untreated Brequinar Carbo/Tax

C 9263-3R tumor at 14 day Survival of 9263-3R Tumor Line D 2225L tumor at 14 day Survival of 2225L Tumor Line

)

) p=0.94 p<0.0001 p<0.0001

3

3 5000 p=0.019 5000 p=0.0028 p=0.0020 p<0.0001 p=0.0011 100 100 Untreated (n=7) Untreated (n=7) p=0.0008

4000 p=0.59 4000 p=0.043 3000 Brequinar (n=8) 3000 Brequinar (n=8) p<0.0001 50 50 2000 2000 Carbo/Tax (n=8) Carbo/Tax (n=8) 1000 1000

Percent survival 0 Percent survival 0 0 0

Tumor volume (mm Tumor volume (mm 05101520 0102030 Survival (Days) Survival (Days) Untreated Brequinar Carbo/Tax Untreated Brequinar Carbo/Tax

E T11 tumor at 14 day Survival of T11 Tumor Line F 2336R tumor at 16 day Survival of 2336R Tumor Line

) p=0.0006 ) p<0.0001 p=0.0024

3 3 5000 5000 p=0.065 p=0.0001 p=0.14 100 p=0.0031 p=0.16 100

Untreated (n=7) Untreated (n=7) p=0.0005

4000 p=0.30 4000 p=0.0007 3000 Brequinar (n=8) p=0.008 3000 Brequinar (n=8) 50 2000 2000 50 Carbo/Tax (n=8) Carbo/Tax (n=8) 1000 1000

Percent survival 0 0 0 Percent survival 0

Tumor volume (mm 0510152025 Tumor volume (mm 01020304050 Survival (Days) Survival Untreated Brequinar Carbo/Tax Untreated Brequinar Carbo/Tax Figure S1

A Cluster 1 vs Cluster 2 SAM out 6 90 metabolites increased in cluster 2 (q-value < 0.05) Legend for panel B and C

Tissue type PAM50 type Normalized ER+ Normal-like abundance TNBC Luminal A 3.0 1.5 WHIM30-PDX Luminal B Observed Observed score 93 metabolites 0 WHIM2-PDX HER2 -1.5 increased in cluster 1 -3.0

-6 -4 -2 0 2(q 4 -value < 0.05) Basal-like -8

-2 -1 0 1 2 Expected score

B Cluster 1 Cluster 2 C Cluster 1 Cluster 2

15 15

12 12

11 11

14 14

13 13

-8 -8

-1 -1

-9 -9

-6 -6

-7 -7

-5 -5

-4 -4

-3 -3

-2 -2

TNBC- TNBC-

TNBC- TNBC-

TNBC-8 TNBC-8

TNBC- TNBC-

TNBC-6 TNBC-6

TNBC-3 TNBC-3

TNBC- TNBC-

TNBC-1 TNBC-1

WHIM2-3 WHIM2-3

WHIM2-2 WHIM2-2

WHIM2-1 WHIM2-1

WHIM30-4 WHIM30-4

WHIM30-3 WHIM30-3

WHIM30-2 WHIM30-2

WHIM30-1 WHIM30-1

TNBC-7 TNBC-7

TNBC- TNBC-

TNBC-9 TNBC-9

TNBC-2 TNBC-2

TNBC-5 TNBC-5

TNBC-4 TNBC-4

TNBC1-0 TNBC1-0

ER ER

ER ER

ER ER

ER ER

ER ER

ER ER

ER ER

ER ER

ER ER

2-methylglutaconic acid CMP-n-trimethyl-2-aminoethylphosphonate l-erythrulose coenzyme a fructoseglycine dUMP alpha-d-glucose l-cystathionine pyruvate n2-formyl-n1-(5-phospho-d-ribosyl)glycinamide l-alanyl-l-leucine sedoheptulose 1,7-bisphosphate (r)-mevalonate uracil 4-hydroxy-2-nonenal/3,4-epoxynonanal guanine 3-ureidoisobutyrate/glycylsarcosine cdpcholine l-glutamine l-cysteine 4-hydroperoxy-2-nonenal beta-d-fructose 1,6-bisphosphate acetylcarnosine n1,n12-diacetylspermidine adipic acid l-fucose 1-phosphate laurate GMP perillic acid diphosphate 5,6-dihydrothymine CMP-2-aminoethylphosphonate 3-hydroxyhexadecenoylcarnitine ADP-mannose/GDP-l-fucose nonanoate 15(s)-hydroxyeicosatrienoic acid 9(10)-EpOME/12(13)-EpOME xanthosine trans-4,5-epoxy-2(e)-decenal riboflavin creatinine UDP-n-acetyl-alpha-d-glucosamine n-phosphocreatinate UDP-d-glucuronate methylimidazoleacetic acid l-methionine perillyl aldehyde glutathione spermidine dialdehyde-2 l-cysteinylglycine 9,10-hydroxyoctadec-12(z)-enoate 5-methylthioadenosine 13-carboxy-gama-tocopherol UMP phenylacetate/(4-hydroxyphenyl)acetaldehyde deoxyinosine palmitate UDP-d-glucose/UDP-d-galactose w-hydroxydecanoicacid n(omega)-(l-arginino)succinate 3-keto-beta-d-galactose/l-gulonolactone n6n6n6-trimethyl-l-lysine limonene/(+)-alpha-pinene hypotaurine pseudoecgonine/ecgonine GDP-d-mannose linoleic acid/linoelaidic acid cdp-ethanolamine oleic acid/elaidate/trans-vaccenate UDP-alpha-d-xylose trimethylamine n-oxide NADH palmitoleate n-acetyl-l-glutamate myristate l-glutamate caprate l-4-hydroxyglutamate semialdehyde 25-hydroxyvitamin d3-26-23-lactone tetracosatetraenoyl carnitine 2-oxoglutarate(2-) 2-hydroxyglutarate 3-dehydro-l-gulonate/d-glucuronate GDP adenosine lysine hexadecanedioic acid butyryl carnitine pentadecanoate phosphodimethylethanolamine naphthalene n-acetylneuraminate 9-phosphate dodecanedioic acid UDP azelaic acid 1-methylpyrrolinium CTP xanthine l-fucose/l-fuculose AMP adrenochrome/n-benzoylglycinate d-glucosamine 6-phosphate GTP choline phosphate 3-methyl-2-oxobutanoate propionyl-carnitine 4-hydroxybenzoate/gentisate aldehyde n-methylethanolamine phosphate n(2)-acetyl-l-ornithine CDP margarate lactate (-)-salsolinol/nn-dimethyldopaminequinone-1 succinate(2-) o-acetylcarnitine adrenaline/l-normetanephrine taurodeoxycholic acid/taurochenodeoxycholate nicotinamide bilirubin stearoylcarnitine methylisocitric acid/2-methylcitrate gulonate sulfoglycolithocholate 90 metabolites increased in cluster 2 (q-value < 0.05) 5,6-dihydrouracil/l-3-cyanoalanine

93 metabolites increased increased 93 1 cluster metabolites in0.05) (q-value < 12S-HHT s-adenosyl-l-methionine cholesterol sulfate acetamidopropanal 4-pyridoxate CMP stearate l-proline 3-deoxy-d-glycero-d-galacto-2-nonulosonic acid gama-l-glutamyl-l-alanine/5-l-glutamyl-l-alanine-1 nn-dimethylglycine 5-hydroxyindoleacetate l-xylonate/l-lyxonate (4-hydroxy-3-methoxyphenyl)acetaldehyde trans-caffeate/3-(4-hydroxyphenyl)pyruvate l-kynurenine/formyl-5-hydroxykynurenamine l-cysteate 6-phosphogluconic acid d-glycerate n-carbamoyl-l-aspartate 3-methyl-2-oxopentanoate/4-methyl-2-oxopentanoate l-isoleucine/l-leucine choline dUDP decanoyl carnitine ADP 3-hydroxy-n6n6n6-trimethyl-l-lysine l-phenylalanine 3-hydroxybutyrylcarnitine itaconate/mesaconate NAD+ arginine glycylproline/l-prolinylglycine sn-glycero-3-phosphocholine l-iditol/d-glucitol/galactitol sedoheptulose 7-phosphate glycine fructosyllysine inosine 5-methoxyindoleacetate 4-hydroxyphenylacetate/2-hydroxyphenylacetate dihydroxyacetone phosphate eicosenoic acid 8,11,14-eicosatrienoic acid (dgla) d-erythrose 4-phosphate fumarate adenine n-acetyl-l-aspartate/2-amino-3-oxoadipate methionine sulfoxide malate(2-) octanoyl carnitine/l-octanoylcarnitine thymine/imidazol-4-ylacetate ATP 2-deoxycytidine aspartate d-glucuronate 1-phosphate n-formylanthranilate/noradrenochrome l-serine gama-l-glutamyl-l-alpha-aminobutyrate Figure S1. Top metabolites signatures enriched in breast cancer patients stratified by hierarchical clustering. (A) Significance Analysis for Microarrays (SAM) analysis revealed differential abundant metabolites (q-value < 0.05) that altered in Cluster 1 or Cluster2 samples. (B and C) Heatmap of top 93 metabolites enriched in cluster 1 (B) and top 90 metabolites enriched in cluster 2 (C). Figure S2

Pathway enriched in Cluster 1 Pathway enriched in Cluster 2

AB 6

Pyrimidine metabolism 5 Amino sugar and nucleotide sugar metabolism Glycine, serine and Alanine, aspartate and threonine metabolism glutamate metabolism Glyoxylate and dicarboxylate metabolism Arginine metabolism

Purine metabolism Ascorbate and

-Log(P) -Log(P) aldarate metabolism Linoleic acid metabolism Glutathione metabolism

Alanine, aspartate and

glutamate metabolism

0 1 2 3 4 5 6 0 1 2 3 4 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 Pathway Impact Pathway Impact Energy production Nucleotides C D ns ns 2 * 3 * 4 * 3 **** 2.0 ** 1.5 **** 3 1.5 1 2 2 2 1.5 1.0 2 1.0 0 0 1 1 1.0 0.5 1 0.5 Cluster 1 -1 -2 0.5 0 0.0 0 0 0.0 -2 -4 0.0 -1 -0.5 Cluster 2 -3 -1 -6 -1 -0.5 -0.5 -2 -1.0 -4 -2 -8 -2 -1.0 -1.0 -3 -1.5 2-oxoglutarate 2-hydroxyglutarate N-Carbamoyl Uracil Dihydrouracil Guanine CDP CMP -aspartate E Lipids/Fatty acids 1.0 *** 2 * 4 * 2 **** 3 * 3 2 0.5 1 1 0.0 2 1 Cluster 1 0 1 0 0 -0.5 0 -1 Cluster 2 -1 -1 -1.0 -1 -2 -1.5 -2 -2 -2 -3

3-Hydroxycapric linoleic acid/ Hexadecanedioic 9(10)-EpOME Oleic acid Normalized Normalized intensity Normalized intensity acid linoelaidic acid acid /Elaidate

Amino acids F ns ns 0.6 *** 0.4 ** 10 *** 4 *** 0.5 ** 1.0 0.5 0.2 0.3 5 2 0.5 0.0 0 0.0 0.0 0.0 -0.2 0 0.0 -0.4 -2 -0.5 -0.5 -0.3 -5 -0.5 -0.6 -4 -0.6 -0.8 -10 -6 -1.0 -1.0 -1.0 Normalized Normalized intensity Proline Phenylalanine Cystathionine Cysteine Leucine/ Arginine Tyrosine Cluster 1 Isoleucine Cluster 2 ns ns ns ns ns ns ns 0.4 1.0 1.0 1.0 0.6 0.5 0.5 0.2 0.5 0.5 0.5 0.3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -0.5 -0.2 -0.5 -0.5 -0.4 -0.5 -0.5 -1.0 -0.3 -1.0 -0.6 -1.0 -1.0 -1.5 -0.6 -1.5 -1.0

Normalized Normalized intensity Valine Serine Threonine Aspartate Asparagine Tryptophan Alanine

Figure S2. Metabolic pathways and representative metabolites between Cluster 1 and Cluster 2 samples. (A and B) Pathway enrichment and topology analysis by MetaboAnalyst 5.0 using the top 93 metabolites enriched in cluster 1 (A) or top 93 metabolites enriched in cluster 2 (B). Representative pathways with p-value < 0.05 were annotated. The Kyoto Encyclopedia of Genes and Genome (KEGG) compound database was used as the reference metabolic pathway database. (C-F) Relative normalized intensity levels of representative metabolites involved in energy production (C), nucleotides (D), lipids/fatty acids (E), and amino acids (F) were showed. Data are represented as mean ± standard error of the mean (SEM), two-tailed Student’s t-test; *p<0.05, **p <0.01, ***p<0.001, ****p<0.0001. Figure S3 A 2 TNBC PDXs (n=7) Patients with Metabolite breast cancer extraction Data acquisition Data analysis

TNBC tumors (n=15) & ER+ tumors (n=9)

Normal tissues (n=9) Cluster 1 Cluster 2

B C Pathway enriched in Cluster 1

15

12

14

11

10

13

-9

-1

-8

-4

-7

-5

-6 -3

-2 Glycine, serine and threonine metabolism

TNBC-7

TNBC-

TNBC-

TNBC-

TNBC-8

TNBC-

TNBC-6

TNBC-3

TNBC-1

WHIM2-3

WHIM2-2

WHIM2-1

WHIM30-4

WHIM30-3

WHIM30-2

WHIM30-1

TNBC-9

TNBC-2

ER

TNBC-5

TNBC-4

TNBC-

ER

ER

Normal-6

Normal-2

Normal-9

Normal-5

Normal-7

Normal-3

Normal-1

TNBC-

ER

Normal-4

ER

ER

Normal-8

ER

ER ER Tissue type PAM50 type Linoleic acid metabolism

Glyoxylate and dicarboxylate metabolism

D Pathway enriched in Cluster 2 Pyrimidine metabolism

Alanine, aspartate and glutamate metabolism

Arginine biosynthesis

Amino sugar and nucleotide sugar metabolism

Purine metabolism Ascorbate and aldarate metabolism Glutathione metabolism Riboflavin metabolism

Normalized Tissue type PAM50 type Metabolite category abundance Normal Normal-like Energy production related 3.0 ER+ Luminal A Nucleotides 1.5 0 TNBC Luminal B Lipids/Fatty acids -1.5 WHIM30-PDX HER2 Amino acids -3.0 WHIM2-PDX Basal-like Figure S3. Metabolic profiling in tumors from breast cancer patient and PDXs. (A) Schematic of the experimental design for untargeted global metabolomics analysis in normal tissue, ER+, TNBC breast caner patient tumors and two TNBC patient-derived xenografts (PDXs) tumors. (B) Unsupervised hierarchical clustering heatmap examines global metabolic profiles in 40 normal tissue, ER+/TNBC patient tumor samples and PDX tumors by using the normalized metabolite intensity. Note the normal tissues grouped with the ER+ tumors in Cluster 1. (C and D) Pathway enrichment and topology analysis by MetaboAnalyst 5.0 using the top metabolites enriched in cluster 1 (C) cluster 2 (D). Representative pathways with p-value < 0.05 were annotated. Figure S4 A B ER+ cell line: TNBC cell line:

Normalized Normalized abundance abundance 3.0 3.0 1.5 1.5 0 0 -1.5 -1.5 -3.0 -3.0

Growth condition Growth condition In vitro In vitro In vivo In vivo

In vitro In vivo In vitro In vivo C D Pathway high in vitro (ER+) Pathway high in vivo (ER+)

Arginine biosynthesis Pyrimidine metabolism Phenylalanine metabolism Glycine, serine and threonine metabolism Citrate cycle (TCA cycle) Purine metabolism Valine, leucine and isoleucine biosynthesis Ascorbate and aldarate metabolism Alanine, aspartate and glutamate metabolism Pantothenate and CoA biosynthesis Pyrimidine metabolism Biosynthesis of unsaturated fatty acids Phenylalanine, tyrosine and tryptophan biosynthesis Glycerophospholipid metabolism Aminoacyl-tRNA biosynthesis Amino sugar and nucleotide sugar metabolism D-Glutamine and D-glutamate metabolism Phosphonate and phosphinate metabolism Glycolysis / Gluconeogenesis Nicotinate and nicotinamide metabolism Glutathione metabolism Alanine, aspartate and glutamate metabolism Tyrosine metabolism Glutathione metabolism

01234 01234 -log10[P value] -log10[P value]

E Pathway high in vitro (TNBC) F Pathway high in vivo (TNBC)

Arginine biosynthesis Purine metabolism Citrate cycle (TCA cycle) Glycerophospholipid metabolism Alanine, aspartate and glutamate metabolism Biosynthesis of unsaturated fatty acids Pyrimidine metabolism Glycine, serine and threonine metabolism Vitamin B6 metabolism Pyrimidine metabolism Glutathione metabolism Nicotinate and nicotinamide metabolism p < 0.05 Phenylalanine metabolism Linoleic acid metabolism Riboflavin metabolism Phosphonate and phosphinate metabolism D-Glutamine and D-glutamate metabolism Alanine, aspartate and glutamate metabolism Glycolysis / Gluconeogenesis Valine, leucine and isoleucine biosynthesis Tyrosine metabolism Taurine and hypotaurine metabolism Valine, leucine and isoleucine biosynthesis Amino sugar and nucleotide sugar metabolism

0 1 2 3 4 5 0 2 4 6 -log10[P value] -log10[P value]

Figure S4. Metabolic profiling shows dramatic metabolic change in cell lines grown in vivo and in vitro. (A and B) Unsupervised hierarchical clustering heatmap of global metabolites in ER+ (A) or TNBC (B) cell lines grown in vivo and in vitro.(C and D) Histograms of metabolic pathway enrichment using statistical differed metabolites (SAM, q-value < 0.05) that high in ER+ cells grown in vitro (C) and in vivo (D). (E and F) Histograms of metabolic pathway enrichment using statistical differed metabolites (SAM, q-value < 0.05) that high in TNBC cells grown in vitro (E) and in vivo (F). Figure S5

A Pathway high in ER+ cells B Pathway high in TNBC cells

Alanine, aspartate and glutamate metabolism Arginine biosynthesis Arginine biosynthesis Linoleic acid metabolism Cysteine and methionine metabolism D-Glutamine and D-glutamate metabolism Amino sugar and nucleotide sugar metabolism Ascorbate and aldarate metabolism Purine metabolism Alanine, aspartate and glutamate metabolism Pyrimidine metabolism Butanoate metabolism Nicotinate and nicotinamide metabolism 0123 -log10[P value] 0123 C -log10[P value] ns ns ns ns ns ns 0.6 0.4 0.4 0.4 0.1 0.1 0.4 0.2 0.2 0.2 0.2 0.0 ER+ 0.0 0.0 0.0 0.0 0.0 -0.2 TNBC -0.2 -0.2 -0.2 -0.1 -0.1 -0.4 -0.4 -0.4 -0.6 -0.4

Normalized Normalized intensity Pyruvate Lactate Fumarate M alate Succinate citrate/isocitrate

D ns ns ns ns 0.6 * 1.0 0.5 0.6 0.3 0.4 0.4 0.2 0.5 ER+ 0.2 0.0 0.2 0.1 0.0 0.0 0.0 TNBC 0.0 -0.5 -0.2 -0.1 -0.5 -0.2 -0.4 -0.2 -0.4 -1.0 -1.0 -0.6 -0.3

Normalized Normalized intensity Plam itate Palmitoleate Tetradecenoate Laurate Myristate

ns ns ns ns ns ns ns ns ns E 0.4 1.0 1.0 0.6 1.5 1 0.6 1.0 1.0 0.2 0.5 0.4 1.0 0 0.4 0.5 0.5 0.0 0.5 0.2 0.2 0.0 0.5 -1 0.0 0.0 -0.2 0.0 0.0 0.0 -0.4 -0.5 -0.2 0.0 -2 -0.2 -0.5 -0.5 -0.6 -0.5 -1.0 -0.4 -0.5 -3 -0.4 -1.0 -1.0 ER+

Normalized Normalized intensity UMP UDP UTP CMP CDP CTP dUMP dUDP Dihydrouracil TNBC ns ns ns ns ns ns 0.4 * 0.8 0.2 0.6 0.4 0.2 0.2 * 0.2 ** 0.4 0.2 0.6 0.4 0.1 0.2 0.0 0.2 0.1 0.1 0.0 0.4 -0.2 0.2 0.0 0.0 0.0 0.0 -0.2 0.2 0.0 0.0 -0.1 -0.2 -0.4 0.0 -0.4 -0.2 -0.1 -0.1 -0.2 -0.4 -0.2 -0.6 -0.2 -0.6 -0.4 -0.2 -0.2 -0.3 -0.6 -0.8 -0.4 -0.8 -0.6 -0.4 -0.3 -0.3 -0.4 -0.8

Normalized Normalized intensity AMP IMP GMP UDP ADP Guanine UDP-D-glucose UDP-GlcNAc UDP-glucuronate

Figure S5. Metabolic profiling shows dramatic metabolic change in cell lines grown in vivo and in vitro. (A and B) Histograms of metabolic pathway enrichment using statistical differed metabolites (SAM, q-value < 0.05) that high in ER+ (A) or TNBC (B) cell lines grown in vivo. (C-E) Relative normalized intensity levels of representative metabolites involved in energy production (C), lipids/fatty acids (D), and nucleotides (E) were showed. Data are represented as mean ± standard error of the mean (SEM), two-tailed Student’s t-test; *p<0.05, **p <0.01, ***p<0.001, ****p<0.0001. Figure S6

A Cluster 1 metabolite signature Cluster 2 metabolite signature B Cluster 1 metabolite signature Cluster 2 metabolite signature

0.6 0.5 -1 0.4 -1

0.2 -2 0.0 -2 0.0

-3 -3 median median value

median median value -0.2 Proliferation scoreProliferation

-0.5 Proliferationscore Metabolitesignature Metabolitesignature -0.4 p−value = 0.11 p−value = 0.0094 -4 p−value = 0.028 -4 p−value = 0.003

High Medium Low High Medium Low

Validation Data Set (Brauer et al., 2013) C DE Cluster 1 Cluster 2 NCCRP1 Cluster 1 Cluster 2 DAAM2-AS1 GINS4 CYP4F12 APBA2 ERCC6L FPGT-TNNI3K PREX2 ERICH5 MAD2L1 KLF3-AS1 ROBO4 Cluster 1 vs Cluster 2 SAM out BARD1 ORC6 ATP1B2 EBF2 BAK1 FANCA CAPN11 ADRA2A CHAF1B PIMREG LIN7A FLRT2 2908 genes upregulated C17orf53 UHRF1 EGR3 PDE1A GPRIN1 HJURP FOSB LDB2 WDR62 RRM2 DUSP1 RUNX1T1 in cluster 2 HSPA12B 5 EEF1AKMT4 ASF1B EGR1 ALG3 SPC24 MYOM1 GNG11 (q-value < 0.05) CDT1 AURKB PRKN NDN PKMYT1 CKS2 APH1B JAM3 TEDC2 BRIP1 NAALAD2 RAMP2 TUBG1 CIP2A FAM172A LRRC70 TONSL DIAPH3 PDK4 AOC3 RECQL4 KPNA2 FABP4 VWF CDCA4 CLSPN PLIN1 C16orf45

0 HASPIN ORC1 CEP68 HGF POLQ KNL1 DIXDC1 PDGFD MASTL E2F8 PRCD EDNRB SRPK1 E2F2 ANKRD29 EBF1 NCAPG2 PCLAF LOC101928307 ECSCR CIT NEIL3 BMX RASIP1

MND1 LOC100288637 TCEAL7 FZD4 Observed Observed score ESPL1 EZH2 DNAJC18 TAL1 6224 genes upregulated NUF2 SPAG5 ACKR1 KCND3 -5 CDC25A CENPF MEOX2 SLC7A2 SPC25 CHEK1 KIF17 SEMA6B in cluster 1 STIL KIF15 SELP MICU3 RAD54L CDCA3 BTNL9 ITM2A (q-value < 0.05) SKA3 FEN1 RAPGEF3 NRIP2 PTTG1 PARPBP SOX18 LEPR E2F7 TICRR CD300LG CLDN11 -3-2 -1 0 1 2 3 PRR11 FANCI GPIHBP1 FHL5 SGO1 SHCBP1 ATOH8 GALNT15 KIFC1 CENPN AGAP11 PHLDB2 Expected score MYBL2 PBK ASPA PRICKLE2 HMMR RAD51AP1 ITGA7 PELI2 DTL ZWINT AVPR2 INPP4B KIF18B CCNE1 NPR1 FRY TROAP AUNIP LINC01537 CORO2B CDC6 PIF1 CAV1 CCNDBP1 FOXM1 POC1A TSPAN7 ARL15 MKI67 CDCA7 FAM110D LRRN4CL CENPE DDIAS GSTM5 ADCYAP1R1 RACGAP1 ZNF695 EEPD1 CHL1 ARHGAP11A TACC3 DLG2 CHRDL1 IQGAP3 RFC4 NDNF MYRIP BIRC5 SPDL1 CTTNBP2 STARD9 AURKA JPT1 CIRBP RNF125 CKAP2L H2AFZ NOSTRIN PTH1R TOP2A PSMD14 CFAP69 SEMA3D CDC25C CBX2 C7 LRRTM2 KIF20A LMNB2 CCL14 ADAMTS9-AS2 LMNB1 INCENP GRID1 WDR86 CDC45 WDR4 TXNIP C2orf40 CDC20 HCCS ZNF366 TRABD2B CDCA8 MCM2 USHBP1 GPR17 ANLN HDGF F10 FAXDC2 ASPM RCOR2 FXYD1 LIFR EXO1 SAPCD2 PDE2A HRC BUB1 UBE2SP2 CAVIN2 TNS2 TPX2 UBE2S CLDN5 MYO15B CCNB2 UBE2SP1 KANK3 PLCXD3 MCM10 DEPDC1B PGM5 AMY2B KIF14 TIMELESS ADAM33 ANKRD65 CDCA5 E2F3 ABCA10 AASS DEPDC1 RPS10-NUDT3 ABCA8 FAM198B-AS1 KIF2C SNRPC FAM162B RERG KIF11 MSL3P1 KL NOVA1 NEK2 TIMM23 MYCT1 PHYHIP KIF4A ATP5MC3 MMRN2 PCDHB5 CCNB1 TK1 DIPK2B CADM3 GINS1 NME1 MYH11 C1QTNF7 CDCA2 EME1 CBX7 NOVA2 GTSE1 CDK16 INMT FAM47E NCAPG PSMD4 STEAP4 CYB5D2 NUSAP1 SAP30 TLL1 SEMA3E UBE2C MAGEF1 FAM13C PDLIM3 MELK LRFN1 JAM2 CASQ2 RAD51 MRPL12 EMCN ACOX2 CEP55 H2AFX TEK LINC00982 NDC80 PYCR1 PTGER3 IGFN1 CDK1 SHMT2 HSPB6 MAPKBP1 BUB1B FAM208B MRGPRF INKA1 NCAPH CDK2AP1 CARMN NKAPL KIF23 PRAME KLHDC1 RUNDC3B CENPI PDIA4 UBAP1L ABCB1 CENPA CHAC1 CCDC8 ADRB2 XRCC2 RNFT2 CNN1 LRRC2 TTK SIM2 LMOD1 IL33 SKA1 TUBBP1 LIMS2 GIMAP1 DLGAP5 GLDC RGS5 COX4I2 E2F1 MYO3A CCM2L NAP1L2 CCNA2 KIF20B ARHGAP6 PIWIL2 PLK4 HMGA1 ABCA9 GAS2 FANCB CBS ABCA6 MASP2 PLK1 OLA1P1 ARHGEF15 MOCS1 SLC7A11 PPP2R2B MUC16 CES1

-1.0 3.01.50-1.5-3.01.00.50-0.5 -1.0 3.01.50-1.5-3.01.00.50-0.5 Normalized mRNA expression Normalized mRNA expression Figure S6. Integration analysis of transcriptional and metabolic signatures in patient tumors. (A) The PAM50 subtype correlation with the metabolite signatures from Cluster 1 and Cluster 2 patient samples in a validation data set of breast cancer patient samples from Brauer et al., 2013. (B) Correlation of proliferation score with the tertile median level of the two metabolite signatures from Cluster 1 and Cluster 2 patient samples in the validation data set of breast cancer patient samples. (C) Significance Analysis for Microarrays (SAM) analysis revealed differential expressed genes (q-value < 0.05) that altered in Cluster 1 or Cluster2 samples. (D and E) Heatmap of the top 200 genes that were highly expressed in Cluster 2 (D) or Cluster 1 (E) samples. Figure S7

A Metabolome-Transcriptome B Metabolome-Transcriptome joint-pathway analysis joint-pathway analysis

Pathway enriched in Cluster 1 (p<0.05) Pathway enriched in Cluster 2 (p<0.05) Ether lipid metabolism Cysteine and methionine metabolism Purine metabolism Pyruvate metabolism Phosphatidylinositol signaling system Pyrimidine metabolism Linoleic acid metabolism Glycolysis or Gluconeogenesis Glycerophospholipid metabolism Citrate cycle (TCA cycle) Nitrogen metabolism Lysine degradation Inositol phosphate metabolism Fructose and mannose metabolism Glycerolipid metabolism Glutathione metabolism Histidine metabolism Arginine biosynthesis Arachidonic acid metabolism Phosphonate and phosphinate metabolism Mucin type O-glycan biosynthesis Purine metabolism beta-Alanine metabolism Arginine and proline metabolism Lysine degradation Amino sugar and nucleotide sugar metabolism Nicotinate and nicotinamide metabolism Pentose phosphate pathway Thiamine metabolism Aminoacyl-tRNA biosynthesis alpha-Linolenic acid metabolism Glycerophospholipid metabolism D-Glutamine and D-glutamate metabolism One carbon pool by folate Sphingolipid metabolism Alanine, aspartate and glutamate metabolism Retinol metabolism 0 1 2 3 4 5 012345 -log10[P value] -log10[P value]

Pathway category Energy metabolism Amino acid metabolism Nucleotide metabolism Carbohydrate metabolism Lipid metabolism Metabolism of cofactors, vitamins and others

Figure S7. Integration analysis of transcriptional and metabolic signatures in patient tumors. (A and B) Metabolome-transcriptome joint-pathway analysis from MetabAnalyst 5.0 using the significantly accumulated metabolites and upregulated DE genes from the Cluster 1 (A) or Cluster 2 (B). The Kyoto Encyclopedia of Genes and Genome (KEGG) compound database was used as the reference metabolic pathway database. Significant enriched pathways (p-value<0.05) were showed. Figure S8

A B

HCC1806 xenograft MDA-MB-468 Xenograft

150 Control # 1 2 3 4 5 6 7 8 9 10 CB-839 Control Brequinar CB-839 100 Combined Brequinar

Combined 50 0102030

Percentage of initial weight Days on treatment

Figure S8. Targeting the pyrimidine and glutamate metabolism in TNBC cell line xenograft and PDX model. (A) Mouse body weight change during the treatment of the HCC1806 xenograft cohort. (B) Image of tumors after dissection at day 28 of the MDA-MB-468 xenograft cohort. Figure S9

A 2-Oxoglutarate Succinate Fumarate Malate Aspartate GSH GSSG N-acetyl-glutamate 2.0 2.0 1.5 1.5 2.5 2.0 1.5 1.5

1.5 1.5 2.0 1.5 1.0 1.0 1.0 1.0 1.5 1.0 1.0 1.0 1.0 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5

Relative abundance0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

CB-839 CB-839 CB-839 CB-839 CB-839 CB-839 CB-839 CB-839 UntreatedBrequinarCombined UntreatedBrequinarCombined UntreatedBrequinarCombined UntreatedBrequinarCombined UntreatedBrequinarCombined UntreatedBrequinarCombined UntreatedBrequinarCombined UntreatedBrequinarCombined

B UMP UDP UDP CMP CMP dUMP dUDP Uridine 1.5 2.0 2.5 1.5 2.0 2.5 3 1.5

1.5 2.0 1.5 2.0 1.0 1.0 2 1.0 1.5 1.5 1.0 1.0 1.0 1.0 0.5 0.5 1 0.5 0.5 0.5 0.5 0.5

Relative abundance0.0 0.0 0.0 0.0 0.0 0.0 0 0.0

CB-839 CB-839 CB-839 CB-839 CB-839 CB-839 CB-839 CB-839 UntreatedBrequinarCombined UntreatedBrequinarCombined UntreatedBrequinarCombined UntreatedBrequinarCombined UntreatedBrequinarCombined UntreatedBrequinarCombined UntreatedBrequinarCombined UntreatedBrequinarCombined

Changed by CB-839 treatment C Untreated D CB-839 Pyrimidine metabolism Brequinar Purine metabolism Combined Arginine biosynthesis Alanine, aspartate and glutamate metabolism Vitamin B6 metabolism Pentose phosphate pathway

PC 2 (17 %) Aminoacyl-tRNA biosynthesis Glycine, serine and threonine metabolism D-Glutamine and D-glutamate metabolism

-2 -1 0 1 2 p<0.05 Nitrogen metabolism

-4-2 0 2 4 0123 -log [P value] PC 1 (53.3 %) 10 E F Changed by Brequinar treatment Changed by Combination treatment

Citrate cycle (TCA cycle) Alanine, aspartate and glutamate metabolism Alanine, aspartate and glutamate metabolism Arginine biosynthesis Arginine biosynthesis Pyrimidine metabolism Pyrimidine metabolism Aminoacyl-tRNA biosynthesis Glutathione metabolism Pantothenate and CoA biosynthesis Cysteine and methionine metabolism Glutathione metabolism Valine, leucine and isoleucine biosynthesis D-Glutamine and D-glutamate metabolism Taurine and hypotaurine metabolism Purine metabolism Vitamin B6 metabolism beta-Alanine metabolism Pentose phosphate pathway Arginine and proline metabolism 0 1 2 3 4 0 1 2 3 4 5 -log [P value] -log10[P value] 10

Figure S9. Metabolomics analysis of the drug treatment by targeting the pyrimidine and glutamate metabolism. (A and B) Relative intensity level of downstream metabolites involved in glutamine/glutamate metabolism (A) and pyrimidine metabolism (B) in the tumors of MDA-MB-468 xenograft treated with drug for 28 days. (C) Principal component analysis (PCA) using metabolomics data of MDA-MB-468 xenograft tumors with indicated treatment. (D-F) Histogram of metabolic pathway enrichment using statistical differed metabolites (SAM, q-value < 0.05) that changed in CB-839 (D), Brequinar (E), and combination (F) treated tumors versus control tumors in MDA-MB-468 xenograft.