Mitochondrial metabolism is an attractive target for cancer therapy1,2. Reprogramming metabolic pathways could improve the ability of metabolic inhibitors to suppress cancers with limited treatment options, such as triple-negative breast cancer (TNBC)1,3. Here we show that BTB and CNC homology1 (BACH1)4, a haem-binding transcription factor that is increased in expression in tumours from patients with TNBC, targets mitochondrial metabolism. BACH1 decreases glucose utilization in the tricarboxylic acid cycle and negatively regulates transcription of electron transport chain (ETC) genes. BACH1 depletion by shRNA or degradation by hemin sensitizes cells to ETC inhibitors such as metformin5,6, suppressing growth of both cell line and patient-derived tumour xenografts. Expression of a haem-resistant BACH1 mutant in cells that express a short hairpin RNA for BACH1 rescues the BACH1 phenotype and restores metformin resistance in hemin-treated cells and tumours7. Finally, BACH1 gene expression inversely correlates with ETC gene expression in tumours from patients with breast cancer and in other tumour types, which highlights the clinical relevance of our findings. This study demonstrates that mitochondrial metabolism can be exploited by targeting BACH1 to sensitize breast cancer and potentially other tumour tissues to mitochondrial inhibitors.
This is a preview of subscription content, access via your institution
Open Access articles citing this article.
Journal of Experimental & Clinical Cancer Research Open Access 10 November 2023
Hypoxia-induced ALDH3A1 promotes the proliferation of non-small-cell lung cancer by regulating energy metabolism reprogramming
Cell Death & Disease Open Access 20 September 2023
Heme biosensor-guided in vivo pathway optimization and directed evolution for efficient biosynthesis of heme
Biotechnology for Biofuels and Bioproducts Open Access 01 March 2023
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 51 print issues and online access
$199.00 per year
only $3.90 per issue
Rent or buy this article
Prices vary by article type
Prices may be subject to local taxes which are calculated during checkout
All data are available from the authors upon reasonable request. Additional material including source data is available online.
Vander Heiden, M. G. & DeBerardinis, R. J. Understanding the intersections between metabolism and cancer biology. Cell 168, 657–669 (2017).
Weinberg, S. E. & Chandel, N. S. Targeting mitochondria metabolism for cancer therapy. Nat. Chem. Biol. 11, 9–15 (2015).
DeBerardinis, R. J. & Chandel, N. S. Fundamentals of cancer metabolism. Sci. Adv. 2, e1600200 (2016).
Oyake, T. et al. Bach proteins belong to a novel family of BTB-basic leucine zipper transcription factors that interact with MafK and regulate transcription through the NF-E2 site. Mol. Cell. Biol. 16, 6083–6095 (1996).
Wheaton, W. W. et al. Metformin inhibits mitochondrial complex I of cancer cells to reduce tumorigenesis. eLife 3, e02242 (2014).
Birsoy, K. et al. Metabolic determinants of cancer cell sensitivity to glucose limitation and biguanides. Nature 508, 108–112 (2014).
Ogawa, K. et al. Heme mediates derepression of Maf recognition element through direct binding to transcription repressor Bach1. EMBO J. 20, 2835–2843 (2001).
Bianchini, G., Balko, J. M., Mayer, I. A., Sanders, M. E. & Gianni, L. Triple-negative breast cancer: challenges and opportunities of a heterogeneous disease. Nat. Rev. Clin. Oncol. 13, 674–690 (2016).
Dangi-Garimella, S. et al. Raf kinase inhibitory protein suppresses a metastasis signalling cascade involving LIN28 and let-7. EMBO J. 28, 347–358 (2009).
Yun, J. et al. Signalling pathway for RKIP and Let-7 regulates and predicts metastatic breast cancer. EMBO J. 30, 4500–4514 (2011).
Lee, U. et al. A prognostic gene signature for metastasis-free survival of triple negative breast cancer patients. PLoS ONE 8, e82125 (2013).
Liang, Y. et al. Transcriptional network analysis identifies BACH1 as a master regulator of breast cancer bone metastasis. J. Biol. Chem. 287, 33533–33544 (2012).
Sun, J. et al. Hemoprotein Bach1 regulates enhancer availability of heme oxygenase-1 gene. EMBO J. 21, 5216–5224 (2002).
Cancer Genome Atlas Network. Comprehensive molecular portraits of human breast tumours. Nature 490, 61–70 (2012).
Curtis, C. et al. The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups. Nature 486, 346–352 (2012).
Wang, Y. et al. Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer. Lancet 365, 671–679 (2005).
Schmidt, M. et al. The humoral immune system has a key prognostic impact in node-negative breast cancer. Cancer Res. 68, 5405–5413 (2008).
Minn, A. J. et al. Genes that mediate breast cancer metastasis to lung. Nature 436, 518–524 (2005).
Igarashi, K. et al. Multivalent DNA binding complex generated by small Maf and Bach1 as a possible biochemical basis for β-globin locus control region complex. J. Biol. Chem. 273, 11783–11790 (1998).
Lee, J. et al. Network of mutually repressive metastasis regulators can promote cell heterogeneity and metastatic transitions. Proc. Natl Acad. Sci. USA 111, E364–E373 (2014).
Barski, A. et al. High-resolution profiling of histone methylations in the human genome. Cell 129, 823–837 (2007).
Liu, X., Romero, I. L., Litchfield, L. M., Lengyel, E. & Locasale, J. W. Metformin targets central carbon metabolism and reveals mitochondrial requirements in human cancers. Cell Metab. 24, 728–739 (2016).
Schell, J. C. & Rutter, J. The long and winding road to the mitochondrial pyruvate carrier. Cancer Metab. 1, 6 (2013).
Kim, J. W., Tchernyshyov, I., Semenza, G. L. & Dang, C. V. HIF-1-mediated expression of pyruvate dehydrogenase kinase: a metabolic switch required for cellular adaptation to hypoxia. Cell Metab. 3, 177–185 (2006).
Jitrapakdee, S., Vidal-Puig, A. & Wallace, J. C. Anaplerotic roles of pyruvate carboxylase in mammalian tissues. Cell. Mol. Life Sci. 63, 843–854 (2006).
Gui, D. Y. et al. Environment dictates dependence on mitochondrial complex I for NAD+ and aspartate production and determines cancer cell sensitivity to metformin. Cell Metab. 24, 716–727 (2016).
Murai, M. & Miyoshi, H. Current topics on inhibitors of respiratory complex I. Biochim. Biophys. Acta Bioenerg. 1857, 884–891 (2016).
Dowling, R. J. O. et al. Metformin pharmacokinetics in mouse tumors: implications for human therapy. Cell Metab. 23, 567–568 (2016).
Chandel, N. S. et al. Are metformin doses used in murine cancer models clinically relevant? Cell Metab. 23, 569–570 (2016).
Klil-Drori, A. J., Azoulay, L. & Pollak, M. N. Cancer, obesity, diabetes, and antidiabetic drugs: is the fog clearing? Nat. Rev. Clin. Oncol. 14, 85–99 (2017).
Emami Riedmaier, A., Fisel, P., Nies, A. T., Schaeffeler, E. & Schwab, M. Metformin and cancer: from the old medicine cabinet to pharmacological pitfalls and prospects. Trends Pharmacol. Sci. 34, 126–135 (2013).
Liang, H. & Ward, W. F. PGC-1α: a key regulator of energy metabolism. Adv. Physiol. Educ. 30, 145–151 (2006).
Zenke-Kawasaki, Y. et al. Heme induces ubiquitination and degradation of the transcription factor Bach1. Mol. Cell. Biol. 27, 6962–6971 (2007).
Dhar, G. J., Bossenmaier, I., Petryka, Z. J., Cardinal, R. & Watson, C. J. Effects of hematin in hepatic porphyria. Further studies. Ann. Intern. Med. 83, 20–30 (1975).
Suzuki, H. et al. Heme regulates gene expression by triggering Crm1-dependent nuclear export of Bach1. EMBO J. 23, 2544–2553 (2004).
Zhang, X. et al. A renewable tissue resource of phenotypically stable, biologically and ethnically diverse, patient-derived human breast cancer xenograft models. Cancer Res. 73, 4885–4897 (2013).
Efron, B. & Tibshirani, R. On testing the significance of sets of genes. Ann. Appl. Stat. 1, 107–129 (2007).
Cerami, E. et al. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov. 2, 401–404 (2012).
Gao, J. et al. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci. Signal. 6, pl1 (2013).
Colaprico, A. et al. TCGAbiolinks: an R/Bioconductor package for integrative analysis of TCGA data. Nucleic Acids Res. 44, e71 (2016).
Young, M. D., Wakefield, M. J., Smyth, G. K. & Oshlack, A. Gene ontology analysis for RNA-seq: accounting for selection bias. Genome Biol. 11, R14 (2010).
Wan, Q. et al. BioXpress: an integrated RNA-seq-derived gene expression database for pan-cancer analysis. Database 2015, bav019 (2015).
Wu, J. et al. BCIP: a gene-centered platform for identifying potential regulatory genes in breast cancer. Sci. Rep. 7, 45235 (2017).
Louie, S. M. et al. GSTP1 Is a driver of triple-negative breast cancer cell metabolism and pathogenicity. Cell Chem. Biol. 23, 567–578 (2016).
Benjamin, D. I. et al. Ether lipid generating enzyme AGPS alters the balance of structural and signaling lipids to fuel cancer pathogenicity. Proc. Natl Acad. Sci. USA 110, 14912–14917 (2013).
Gao, X. et al. Serine availability influences mitochondrial dynamics and function through lipid metabolism. Cell Reports 22, 3507–3520 (2018).
Liu, J. & Locasale, J. W. Metabolomics: A primer. Trends Biochem. Sci. 42, 274–284 (2017).
We thank G. Greene and members of his laboratory for sharing the PDX tumors. This study was supported by NIH R01CA184494 (M.R.R.), R01GM121735 (M.R.R.), NIH R01CA172667 (D.K.N.), DoD Breakthrough Breast Cancer BC161588 (J. Lee), NIH 1R01AI131267 (M.G.B.), 1R56ES028149 (M.G.B.) and NIH R01CA193256 (J.W.L.). We also thank G. Balazsi and members of the Rosner laboratory for careful reading of the manuscript.
Nature thanks Matthew J. Ellis, Kazuhiko Igarashi and the other anonymous reviewer(s) for their contribution to the peer review of this work.
The authors declare no competing interests.
Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended data figures and tables
Extended Data Fig. 1 BACH1 expression is high in patients with TNBC and suppresses expression of ETC genes at their promoter.
a, Left, BACH1 expression levels (determined by RNA-seq) with respect to relative DNA copy-number alterations in TCGA breast cancers (n = 1105). Middle, BACH1 expression (RNA-seq) in TNBC (n = 83) or basal (n = 98) breast cancers compared to non-TNBC (n = 734) or non-basal (n = 424) breast cancers using Pam50 classification of TCGA data. Right, breast cancer subtypes classified by Pam50 (n = 522 total, n = 98 basal, n = 58 HER2-enriched, n = 231 luminal-A, n = 127 luminal-B, n = 8 normal-like). Two-tailed t-test. b, BACH1 expression levels (by RNA-seq) in patients with TNBC compared to patients that did not have TNBC, using the datasets of patients with breast cancer of METABRIC (n = 2509), GSE2034 (n = 286) and GSE11121 (n = 200). Two-tailed t-test. c, Gene Ontology terms as determined by gene set analysis for cell components that are positively correlated with BACH1 depletion based on microarray analysis of BM1-shBACH1 cell transcripts. n = 3 biologically independent samples, FDR-corrected P < 0.05. d, Left, relative mRNA levels of mitochondrial inner membrane genes in MB436-shBACH1 cells (two shBACH1 vectors, clone 1, clone 2) compared to the wild type control (MB436-shCont). Data are mean ± s.e.m., n = 3 biologically independent samples, two-tailed t-test. Right, representative western blots of mitochondrial genes using MB436-shBACH1 or control cell lysates. Each experiment was repeated independently three times with similar results. Band density quantification is shown below the blots. e, Schematic showing proximal BACH1 binding on the promoter regions of mitochondrial membrane genes. TSS, transcription start site. Arrows, primers used for ChIP-PCR. f, ChIP assays showing relative fold enrichment of BACH1 recruitment to the HMOX1 promoter using BACH1-depleted TNBC (BM1 and MB436) or control cells. g, h, ChIP assays showing fold enrichment of BACH1 and H3K27me3 recruitment to the mitochondrial membrane genes in low-BACH1-expressing MB468 and MB436 cells. For ChIP assays in f–h, data are mean ± s.e.m., n = 3 biologically independent samples, two-tailed t-test. i, KEGG pathways demonstrating the negative correlation between BACH1 expression and oxidative phosphorylation in all patients with breast cancer (n = 1,105, left) and patients with TNBC (n = 119, right). FDR values (−log10(FDR)) are generated in the R package GOseq using the default program Wallenius P values with Benjamini−Hochberg-corrected P values. j, Expression of ETC genes (COX15, ATP5D and ATP5G2 (also known as ATP5MC2)) in TNBC compared to tumours from patients that did not have TNBC using multiple breast cancer datasets: METABRIC (TNBC n = 319, non-TNBC n = 1661), GSE2034 (TNBC n = 54, non-TNBC n = 232) and GSE11121 (TNBC n = 33, non-TNBC n = 150). P values are determined by two-tailed t-test. k, Co-expression plots of UQCRC1 or ATP5D and BACH1 in TCGA breast cancer (n = 1105) or TNBC (n = 115) dataset. Pearson’s and Spearman’s correlation coefficients are shown.
a, Measurement of OCR and ECAR in BM1 or MB436 cells expressing control or shBACH1. Data are mean ± s.e.m., n = 6 biologically independent samples, unpaired two-tailed t-test. b, Relative abundance of steady-state metabolites in BM1-shBACH1 or control cells cultured with DMEM (glucose, 10 mM) measured by mass spectrometry. Pyr, pyruvate; Lac, lactate. Data are mean ± s.e.m., n = 5 biologically independent samples, two-tailed t-test. c, d, Fractional isotopic incorporation of [U-13C6]-glucose (c) or [U-13C5]-glutamine (d) into the metabolites in glycolysis and the TCA cycle are shown. Data are mean ± s.e.m., n = 4 biologically independent samples, two-tailed t-test. M indicates number of carbons labelled. Fraction is ratio of isotopologues to sum of all isotopologues. e, Relative mRNA and protein levels of PDH and PDK genes in MB436-shBACH1 cells compared to controls. qRT–PCR data are mean ± s.e.m., n = 3 biologically independent samples, two-tailed t-test. Representative images of western blots are shown. Band density quantification is shown below the blots. Each experiment was repeated independently three times with similar results. f, ChIP assays showing fold enrichment of BACH1 recruitment to promoters of PDK genes using MB436 and MB468 cells. Data are mean ± s.e.m., n = 3 biological replicates per cell line, two-tailed t-test. g, Relative mRNA levels of pyruvate carboxylase (PC) in shBACH1 cells compared to control. Data are mean ± s.e.m., n = 3 biologically independent samples. NS, not significant by two-tailed student’s t-test.
Extended Data Fig. 3 BACH1 levels determine response to ETC inhibitor treatment in breast cancer cells.
a, Cellular growth (per cent confluency) of BACH1-depleted cells (BM1-shBACH1 or MB436-shBACH1) or their controls treated with vehicle (veh), metformin (met), rotenone (rot) or antimycin A (ant). b, c, Relative cell viability (%) of BACH1-depleted cells (BM1-shBACH1 or MB436-shBACH1) or their controls treated with vehicle, metformin, rotenone or antimycin A. d, e, Cellular growth (per cent confluency) (d) or cell viability (%) (e) of low-BACH1 (MB468), medium-BACH1 (MB436) or high-BACH1 (BM1)-expressing TNBC cells treated with vehicle (control), 1 mM metformin, or 1–10 mM metformin. f, Cell viability (%) of non-malignant mammary epithelial cells (MCF10A and 184A1) treated with vehicle, metformin, rotenone or antimycin A. For cell viability and growth assays in a and d, values are mean ± s.e.m., n = 6 biologically independent samples, unpaired two-tailed t-test. Arrow indicates the time at which inhibitors were added. For cell viability assays in b, c, e and f, cells were incubated for 48 h after addition of inhibitors and stained with CaAM for 1 h.
a, Cellular growth (per cent confluency) of BM1-shBACH1 or control cells treated with vehicle or metformin in growth medium containing glucose (1 mM) and supplemented with or without pyruvate (2.5 mM). b, Relative NAD+/NADH ratios in BACH1-depleted BM1 cells treated with pyruvate (2.5 mM) and/or metformin (5 mM) for 24 h. Data are mean ± s.e.m., n = 3 biologically independent samples, two-tailed t-test. c, Representative western blots of COX15, UQCRC1 and α-tubulin using BM1-shBACH1 cell lysates transfected with siCOX15 (150 nM), siUQCRC1 (150 nM), and siControl (150 nM). Each experiment was repeated independently two times with similar results. d, Cellular growth (per cent confluence) of BM1-shBACH1 cells transfected with siRNA for COX15 and/or UQCRC1 and treated with vehicle or metformin (10 mM). For a and d, data are mean ± s.e.m., n = 6 biologically independent samples, unpaired two-tailed t-test between vehicle-treated and metformin (10 mM)-treated group. e, Relative OCT1 (also known as SLC22A1, left), PPARG and PGC1A (also known as PPARGC1A, right) mRNA levels in BM1-shBACH1 cells. Data are mean ± s.e.m., n = 3 biologically independent samples.
Extended Data Fig. 5 Hemin treatment of cells expressing wild-type BACH1 or hemin-resistant BACH1(mut).
a, Left, cellular growth (per cent confluence) of BM1 and MB436 cells treated with hemin (10, 20, 40 or 80 μM) as indicated. Right, representative western blots of BACH1 from MB436 cells after treatment with hemin (10–40 μM) for 24 h (see also Fig. 3d). Each experiment was repeated independently three times with similar results. b, Relative mRNA levels of mitochondrial membrane genes in MB436 cells treated with vehicle or hemin (20 μM) for 48 h. and representative western blots. Data are mean ± s.e.m., n = 3 biologically independent samples, two-tailed t-test. Band density quantification is shown below the blots. c, Measurement of OCR or ECAR of BM1 cells treated with vehicle or hemin (full data from Fig. 3b). d, Cell viability (%) of BM1 cells treated with vehicle, hemin (20 μM) or ETC inhibitors (metformin, rotenone or antimycin A) for 48 h. e, Cell viability (%) or cell growth (per cent confluence) of MB436 cells treated with vehicle, hemin (20 μM) or metformin (1 mM). f, Representative western blots from MB436-shBACH1 cells transiently transfected with Bach1mut (100 ng) and treated with vehicle or hemin (10, 20, 40 or 80 μM) for 48 h. Each experiment was repeated independently three times with similar results. g, Measurement of OCR in BM1 or MB436 cells stably expressing shControl, shBACH1 or shBACH1 + Bach1mut vectors. h, Relative mRNA levels of mitochondrial genes in BM1-shBACH1 cells, shCont cells, or BM1-shBACH1 cells transfected with BACH1(mut). Data are mean ± s.e.m., n = 3 biologically independent samples, two-tailed t-test. i, Left, cell viability (%) of BM1-shBACH1 cells transfected with BACH1(mut) and then treated with hemin (20 μM) or vehicle for 48 h. Right, representative western blots showing BACH1(mut) from cells treated with vehicle or hemin. j, Measurement of OCR and ECAR of BM1-shBACH1 cells expressing BACH1(mut) pre-treated with hemin. Conditions for OCR and ECAR, and statistics are the same as in Extended Data Fig. 2a. Data are mean ± s.e.m., n = 6 biologically independent samples. k, Cell viability (%) of MB436 cells stably expressing shRNA-resistant BACH1(WT), BACH1(mut) or shCont vectors treated with vehicle, hemin (20 μM) or metformin (5 mM) for 48 h. Representative western blots of BACH1 expression are shown. Each experiment was repeated independently three times with similar results. For growth and viability assays in a, d, e, i, j and k, data are mean ± s.e.m., n = 4 biologically independent samples), unpaired two-tailed t-test.
a, Tumour weights and volumes of mice injected with MB436-shBACH1 or control cells (left, n = 6–7 per group) or BM1-shBACH1 or control cells (right, n = 8–10 per group) and treated with vehicle or metformin. Data are mean ± s.e.m., unpaired two-tailed t-test. b, Tumour images of representative mice in each treatment group of mouse models. Scale bar, 1 cm. c, Primary tumour (%) indicates the ratio of mice with tumours or tumour-free upon metformin treatment compared to the total number of mice per treatment group at the end of experiment. d, Body weights of mice monitored before and after treatment of hemin and metformin. Arrow indicates initiation of hemin (H) or metformin (M) treatment. e, Relative mRNA expression of PDK and PDH mRNAs in tumours from MB436-shBACH1 xenograft mice by qRT–PCR. Data are mean ± s.e.m., n = 2 per group. f, Representative western blots of total PDH, BACH1 and mitochondrial membrane proteins (COX15, SLC25A15, NDUFA9) using MB436-shBACH1 or control tumour lysates. Each experiment was repeated independently three times with similar results. g, Lung metastases from mice with MB436-shBACH1 or control xenograft tumours. Lung tissues sectioned and H & E-stained to visualize and count lung metastases in mice. n = 5 mice per group. Data are mean ± s.e.m., two-tailed unpaired t-test. h, Representative lung metastasis images. Arrow indicates tumour metastases with a scale bar (1000 μm).
Extended Data Fig. 7 Combination treatment using hemin and metformin suppresses growth of tumours through BACH1 in multiple TNBC mouse models.
a, Left, monitoring of BACH1 degradation by hemin treatment assayed by western blotting using tumour lysates. Mice (n = 2 per treatment group) injected with BM1 cells (2 × 106 cells) for 4 weeks to form tumours were treated with 25 mg kg−1 or 50 mg kg−1 hemin for the indicated times. Right, representative western blots showing relative BACH1 expression using tumours from mice treated with hemin for the experiments (see Fig. 4a–d). Western blotting experiments were repeated at least twice with similar results. b, Schematic depicting experimental plans with time line for cancer cell injection, hemin treatment (50 mg kg−1 day−1) or metformin treatment (200–300 mg kg−1 day−1) using TNBC mouse models. c, Representative western blots showing relative BACH1 expression from xenograft tumour models using tumours derived from BM1 cells, MB436 cells or two independent patients (n = 2 biologically independent samples). Western blotting experiments were repeated twice with similar results. d, Relative tumour volumes of BM1 or PDX (no. 4195) mouse xenograft monitored weekly during treated with vehicle, hemin or metformin. Tumour volume data are mean ± s.e.m., two-way ANOVA with multiple comparisons. BM1 tumours (vehicle n = 10, hemin n = 10, metformin n = 10, hemin + metformin n = 5) or PDX tumours (vehicle n = 9, hemin n = 10, metformin n = 8, hemin + metformin n = 7). e, Tumour weights, collected and measured at the end of the treatment using hemin and metformin of MB436, BM1-xenograft, or two PDX models (no. 2147 and no. 4195). Data are mean ± s.e.m. with P values using unpaired two-tailed t-test. f, Representative tumour images from each treatment group of MB436, BM1-xenograft or two PDX models are shown. Scale bar. 1 cm. g, Representative western blots of BACH1 using tumour lysates from mice xenografted with BM1-shCont, BM1-shBACH1 expressing BACH1(mut) or BM1-shBACH1 expressing wild-type BACH1. h, Tumour weights from mice xenografted with MB436-shBACH1 cells expressing BACH1(mut) and treated with vehicle, hemin or metformin. Data are mean ± s.e.m., two-tailed t-test. i, j, Tumour growth of BM1 BACH1(mut) (vehicle n = 5, hemin n = 4, metformin n = 5, hemin + metformin n = 5) or wild-type BACH1 xenografts (vehicle n = 5, hemin n = 4, metformin n = 4, hemin + metformin n = 5) treated with vehicle or hemin and representative tumour images from each treatment group of mice. Scale bar, 1 cm. Representative western blots showing BACH1 expression in multiple mouse tumour lysates.
a, Distribution of BACH1 expression in TNBC. Clinical and RNA-seq data associated with the TCGA cohort of patients with breast cancer were accessed at https://www.cbioportal.org/. Out of all provisional cases (n = 1,105), breast cancer samples (n = 914) that had clinical information regarding receptor status of ER, PR (also known as PGR) and HER2 (also known as ERBB2) based on immunohistochemistry analysis as well as RNA-seq data for BACH1-related genes were analysed. The TNBC subgroup among these 914 samples were identified as samples that are negative for all three receptors (n = 115). If the immunohistochemistry results were positive, indeterminate or equivocal for any of the three receptors, those samples were grouped in non-TNBC (n = 799). BACH1 status of the samples were based on an arbitrary 0.5 cut-off for the z-score transformed RNA-seq expression values for the BACH1 gene (245 BACH1-high cases with z-score >0.5; 301 BACH1-low cases with z-score <0.5). b, Frequency (%) of patient tumours with overexpression of BACH1 compared to their matched normal tissues across multiple TCGA cancer types. Numbers of patients relative to healthy controls are indicated in the plot. c, Enriched BACH1 expression (RNA-seq) in TCGA provisional cancer datasets. Red bar indicates median BACH1 expression level in breast cancers. d, Extended plots from KEGG pathway analyses in Fig. 4f, carried out using DAVID using Benjamini-corrected P values (FDR), of genes that are negatively correlated with BACH1 expression. The top eight most-significantly enriched pathways with FDR values (−log(FDR)) are shown for each cancer type: colorectal, liver, lung, skin, ovary, pancreas, prostate and TNBC. e, Co-expression plots of UQCRC1 and BACH1 in TCGA cancers such as prostate (n = 497), skin (n = 472), liver (n = 371) and colon (n = 379). Pearson’s (<−0.3) and Spearman’s (<−0.3) correlation coefficients are shown.
a–d, Heat maps demonstrating upregulation (red) or downregulation (blue) of BACH1 and ETC genes across TCGA tumours (a, prostate carcinoma TCGA provisional, n = 497; b, patients with skin cutaneous cancer TCGA provisional, n = 472; c, patients with liver cancer TCGA provisional, n = 371; d, patients with colorectal cancer TCGA provisional, n = 379).
About this article
Cite this article
Lee, J., Yesilkanal, A.E., Wynne, J.P. et al. Effective breast cancer combination therapy targeting BACH1 and mitochondrial metabolism. Nature 568, 254–258 (2019). https://doi.org/10.1038/s41586-019-1005-x
This article is cited by
Heme biosensor-guided in vivo pathway optimization and directed evolution for efficient biosynthesis of heme
Biotechnology for Biofuels and Bioproducts (2023)
Journal of Experimental & Clinical Cancer Research (2023)
Hypoxia-induced ALDH3A1 promotes the proliferation of non-small-cell lung cancer by regulating energy metabolism reprogramming
Cell Death & Disease (2023)
Specific regulation of BACH1 by the hotspot mutant p53R175H reveals a distinct gain-of-function mechanism
Nature Cancer (2023)
Journal of Cancer Research and Clinical Oncology (2023)