Spatially resolved metabolomics to discover tumor-associated metabolic alterations

Sun, Chenglong; Li, Tiegang; Song, Xiaowei; Huang, Luojiao; Zang, Qingce; Xu, Jing; Bi, Nan; Jiao, Guanggen; Hao, Yanzeng; Chen, Yanhua; Zhang, Ruiping; Luo, Zhigang; Li, Xin; Wang, Lühua; Wang, Zhonghua; Song, Yongmei; He, Jie; Abliz, Zeper

doi:10.1073/pnas.1808950116

Cited by 271 publications

(203 citation statements)

References 43 publications

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“…5A) showed that of the 38 up-regulated metabolites in ZD:CTRL esophagus, about 1/3 (32%) were associated with metabolic alteration in amino acid (AA) metabolism. Differences in AA levels have been reported in many human cancers, including ESCC (35,36,40). Six AAs (lysine, histidine, proline, serine, tyrosine, glycine) are similarly up-regulated in human ESCC (36).…”

Section: Zd:ctrl Rat Esophagus Exhibits a Tumor-associated Metabolomementioning

confidence: 89%

“…Most metabolomics analyses in ESCC had been performed on serum/plasma from patients, with limited ESCC tissue analyses (35,36). Based on P value < 0.05, our differential analysis identified 69 significantly altered metabolites (38 up-and 31 down-regulated) in the high ESCC-burden ZD:CTRL esophagus vs. ESCC-free ZS:CTRL.…”

Section: Zd:ctrl Rat Esophagus Exhibits a Tumor-associated Metabolomementioning

confidence: 99%

“…S4C). Because polyamine biosynthesis is altered in human ESCC (35), the result that the level of putrescine was reduced by 48% with a P value approaching significance (P = 0.0625) (SI Appendix, Fig. S3D) suggests that putrescine could be a metabolite biomarker in ESCC treatment.…”

Section: Zd:ctrl Rat Esophagus Exhibits a Tumor-associated Metabolomementioning

confidence: 99%

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Abrogation of esophageal carcinoma development in miR-31 knockout rats

Fong

Taccioli

Palamarchuk

et al. 2020

Proc. Natl. Acad. Sci. U.S.A.

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MicroRNA-31 (miR-31) is overexpressed in esophageal squamous cell carcinoma (ESCC), a deadly disease associated with dietary Zn deficiency and inflammation. In a Zn deficiency-promoted rat ESCC model with miR-31 up-regulation, cancer-associated inflammation, and a high ESCC burden followingN-nitrosomethylbenzylamine (NMBA) exposure, systemic antimiR-31 delivery reduced ESCC incidence from 85 to 45% (P= 0.038) and miR-31 gene knockout abrogated development of ESCC (P= 1 × 10−6). Transcriptomics, genome sequencing, and metabolomics analyses in these Zn-deficient rats revealed the molecular basis of ESCC abrogation by miR-31 knockout. Our identification of EGLN3, a known negative regulator of nuclear factor κB (NF-κB), as a direct target of miR-31 establishes a functional link between oncomiR-31, tumor suppressor target EGLN3, and up-regulated NF-κB–controlled inflammation signaling. Interaction among oncogenic miR-31, EGLN3 down-regulation, and inflammation was also documented in human ESCCs. miR-31 deletion resulted in suppression of miR-31–associated EGLN3/NF-κB–controlled inflammatory pathways. ESCC-free, Zn-deficient miR-31−/−rat esophagus displayed no genome instability and limited metabolic activity changes vs. the pronounced mutational burden and ESCC-associated metabolic changes of Zn-deficient wild-type rats. These results provide conclusive evidence that miR-31 expression is necessary for ESCC development.

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Section: Zd:ctrl Rat Esophagus Exhibits a Tumor-associated Metabolomementioning

confidence: 89%

Section: Zd:ctrl Rat Esophagus Exhibits a Tumor-associated Metabolomementioning

confidence: 99%

See 1 more Smart Citation

Abrogation of esophageal carcinoma development in miR-31 knockout rats

Fong

Taccioli

Palamarchuk

et al. 2020

Proc. Natl. Acad. Sci. U.S.A.

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“…Thus, although we recapitulated known metabolic features of prostate cancer, other metabolic dysregulation may be missed. This can be addressed in the future as spatially-resolved proteomic 87 and metabolomic 88 technologies improve and such data can be incorporated into the metabolic network model. Indeed, new technologies already enable simultaneous measurement of spatially-resolved transcriptome and proteome 89 .…”

Section: Discussionmentioning

confidence: 99%

Spatial modeling of prostate cancer metabolic gene expression reveals extensive heterogeneity and selective vulnerabilities

Wang

Ruzzo

2020

Sci Rep

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Spatial heterogeneity is a fundamental feature of the tumor microenvironment (TME), and tackling spatial heterogeneity in neoplastic metabolic aberrations is critical for tumor treatment. Genomescale metabolic network models have been used successfully to simulate cancer metabolic networks. However, most models use bulk gene expression data of entire tumor biopsies, ignoring spatial heterogeneity in the TME. To account for spatial heterogeneity, we performed spatially-resolved metabolic network modeling of the prostate cancer microenvironment. We discovered novel malignantcell-specific metabolic vulnerabilities targetable by small molecule compounds. We predicted that inhibiting the fatty acid desaturase SCD1 may selectively kill cancer cells based on our discovery of spatial separation of fatty acid synthesis and desaturation. We also uncovered higher prostaglandin metabolic gene expression in the tumor, relative to the surrounding tissue. Therefore, we predicted that inhibiting the prostaglandin transporter SLCO2A1 may selectively kill cancer cells. Importantly, SCD1 and SLCO2A1 have been previously shown to be potently and selectively inhibited by compounds such as CAY10566 and suramin, respectively. We also uncovered cancer-selective metabolic liabilities in central carbon, amino acid, and lipid metabolism. Our novel cancer-specific predictions provide new opportunities to develop selective drug targets for prostate cancer and other cancers where spatial transcriptomics datasets are available.Cancer cells reprogram their metabolism to fulfill the energetic and biosynthetic needs of proliferation, invasion and migration 1 . This is exemplified in prostate cancer, the second most common cancer in American men after melanoma 2 . Previous studies have uncovered profound metabolic dysregulation in multiple pathways, particularly in fatty acid and lipid metabolism 3,4 . Discovering novel cancer-specific metabolic aberrations has significant translational applications, because cancer-associated metabolic dysfunctions can be exploited to advance cancer detection (e.g., 18 F-FDG (Fludeoxyglucose) imaging based on elevated glycolysis in cancer 5 ) and treatment (e.g., L-asparaginase in treating acute lymphoblastic leukemia 6 ).Cancer metabolic reprograming is profoundly impacted by spatial heterogeneity, a fundamental feature of the tumor microenvironment (TME) 7 . Heterogeneous distributions of blood vessels and stromal tissues create uneven spatial gradients of nutrients and metabolic byproducts, which significantly shape the phenotypes of many cell types in the TME 8 . Recent technologies, such as spatial transcriptomics 9,10 and Slide-seq 11 have enabled transcriptomic profiling of hundreds of locations within tissue sections with high spatial resolution (2-100 μm), and have been used to study multiple types of malignancies, including prostate cancer, breast cancer, pancreatic cancer, and melanoma 9,10,12-14 . These spatially-resolved datasets provide novel opportunities to dissect spatial metabolic heterogeneity i...

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“…Some health states-especially in their early stages-may not be reflected in biofluid metabolite levels; in the case of cancer, this may occur due to immune suppression 48 . Individual responses to a given cancer (or other disorder) may be highly variable, and even tumor region specific 49 , possibly obfuscating any chemical signal in a biofluid. Large and diverse cohorts may minimize such variability-perhaps one reason why the large, 1005-person cancer study 25 performed so well.…”

Section: Discussionmentioning

confidence: 99%

Predicting human health from biofluid-based metabolomics using machine learning

Evans

Duvallet

Oberst

et al. 2020

Preprint

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Biofluid-based metabolomics enables the profiling of thousands of molecules and has the potential to provide highly accurate, minimally invasive diagnostics for a range of health conditions. However, typical metabolomics studies focus on only a few statistically significant features. We study the applicability of machine learning for health state-prediction across 35 human mass spectrometry-based metabolomics studies. Models trained on all features outperform those using only significant features and frequently provide high predictive performance across nine health states, despite disparate experimental conditions and disease contexts. Combining data from different experimental settings (e.g. sample type, instrument, chromatography) within a study minimally alters predictive performance, suggesting information overlap between different methods. Using only non-significant features, we still often obtain high predictive performance. To facilitate further advances, we provide all data online. This work highlights the applicability of biofluid-based metabolomics with data-driven analysis for health state diagnostics.

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Spatially resolved metabolomics to discover tumor-associated metabolic alterations

Cited by 271 publications

References 43 publications

Abrogation of esophageal carcinoma development in miR-31 knockout rats

Abrogation of esophageal carcinoma development in miR-31 knockout rats

Spatial modeling of prostate cancer metabolic gene expression reveals extensive heterogeneity and selective vulnerabilities

Predicting human health from biofluid-based metabolomics using machine learning

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