Skip to main content
Top
Published in: Breast Cancer Research 1/2024

Open Access 01-12-2024 | Breast Cancer | Research

Metabolic adaptation towards glycolysis supports resistance to neoadjuvant chemotherapy in early triple negative breast cancers

Authors: Françoise Derouane, Manon Desgres, Camilla Moroni, Jérôme Ambroise, Martine Berlière, Mieke R. Van Bockstal, Christine Galant, Cédric van Marcke, Marianela Vara-Messler, Stefan J. Hutten, Jos Jonkers, Larissa Mourao, Colinda L. G. J. Scheele, Francois P. Duhoux, Cyril Corbet

Published in: Breast Cancer Research | Issue 1/2024

Login to get access

Abstract

Background

Neoadjuvant chemotherapy (NAC) is the standard of care for patients with early-stage triple negative breast cancers (TNBC). However, more than half of TNBC patients do not achieve a pathological complete response (pCR) after NAC, and residual cancer burden (RCB) is associated with dismal long-term prognosis. Understanding the mechanisms underlying differential treatment outcomes is therefore critical to limit RCB and improve NAC efficiency.

Methods

Human TNBC cell lines and patient-derived organoids were used in combination with real-time metabolic assays to evaluate the effect of NAC (paclitaxel and epirubicin) on tumor cell metabolism, in particular glycolysis. Diagnostic biopsies (pre-NAC) from patients with early TNBC were analyzed by bulk RNA-sequencing to evaluate the predictive value of a glycolysis-related gene signature.

Results

Paclitaxel induced a consistent metabolic switch to glycolysis, correlated with a reduced mitochondrial oxidative metabolism, in TNBC cells. In pre-NAC diagnostic biopsies from TNBC patients, glycolysis was found to be upregulated in non-responders. Furthermore, glycolysis inhibition greatly improved response to NAC in TNBC organoid models.

Conclusions

Our study pinpoints a metabolic adaptation to glycolysis as a mechanism driving resistance to NAC in TNBC. Our data pave the way for the use of glycolysis-related genes as predictive biomarkers for NAC response, as well as the development of inhibitors to overcome this glycolysis-driven resistance to NAC in human TNBC patients.
Appendix
Available only for authorised users
Literature
1.
go back to reference Cardoso F, Kyriakides S, Ohno S, Penault-Llorca F, Poortmans P, Rubio IT, et al. Early breast cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol. 2019;30(10):1674.PubMedCrossRef Cardoso F, Kyriakides S, Ohno S, Penault-Llorca F, Poortmans P, Rubio IT, et al. Early breast cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol. 2019;30(10):1674.PubMedCrossRef
2.
go back to reference Early Breast Cancer Trialists' Collaborative G. Long-term outcomes for neoadjuvant versus adjuvant chemotherapy in early breast cancer: Meta-analysis of individual patient data from ten randomised trials. Lancet Oncol. 2018;19(1):27–39. Early Breast Cancer Trialists' Collaborative G. Long-term outcomes for neoadjuvant versus adjuvant chemotherapy in early breast cancer: Meta-analysis of individual patient data from ten randomised trials. Lancet Oncol. 2018;19(1):27–39.
3.
go back to reference Yau C, Osdoit M, van der Noordaa M, Shad S, Wei J, de Croze D, et al. Residual cancer burden after neoadjuvant chemotherapy and long-term survival outcomes in breast cancer: A multicentre pooled analysis of 5161 patients. Lancet Oncol. 2022;23(1):149–60.PubMedCrossRef Yau C, Osdoit M, van der Noordaa M, Shad S, Wei J, de Croze D, et al. Residual cancer burden after neoadjuvant chemotherapy and long-term survival outcomes in breast cancer: A multicentre pooled analysis of 5161 patients. Lancet Oncol. 2022;23(1):149–60.PubMedCrossRef
4.
go back to reference Cortazar P, Zhang L, Untch M, Mehta K, Costantino JP, Wolmark N, et al. Pathological complete response and long-term clinical benefit in breast cancer: The CTNeoBC pooled analysis. Lancet. 2014;384(9938):164–72.PubMedCrossRef Cortazar P, Zhang L, Untch M, Mehta K, Costantino JP, Wolmark N, et al. Pathological complete response and long-term clinical benefit in breast cancer: The CTNeoBC pooled analysis. Lancet. 2014;384(9938):164–72.PubMedCrossRef
5.
go back to reference Symmans WF, Wei C, Gould R, Yu X, Zhang Y, Liu M, et al. Long-term prognostic risk after neoadjuvant chemotherapy associated with residual cancer burden and breast cancer subtype. J Clin Oncol. 2017;35(10):1049–60.PubMedPubMedCentralCrossRef Symmans WF, Wei C, Gould R, Yu X, Zhang Y, Liu M, et al. Long-term prognostic risk after neoadjuvant chemotherapy associated with residual cancer burden and breast cancer subtype. J Clin Oncol. 2017;35(10):1049–60.PubMedPubMedCentralCrossRef
6.
go back to reference Balko JM, Giltnane JM, Wang K, Schwarz LJ, Young CD, Cook RS, et al. Molecular profiling of the residual disease of triple-negative breast cancers after neoadjuvant chemotherapy identifies actionable therapeutic targets. Cancer Discov. 2014;4(2):232–45.PubMedCrossRef Balko JM, Giltnane JM, Wang K, Schwarz LJ, Young CD, Cook RS, et al. Molecular profiling of the residual disease of triple-negative breast cancers after neoadjuvant chemotherapy identifies actionable therapeutic targets. Cancer Discov. 2014;4(2):232–45.PubMedCrossRef
7.
go back to reference Kim C, Gao R, Sei E, Brandt R, Hartman J, Hatschek T, et al. Chemoresistance evolution in triple-negative breast cancer delineated by single-cell sequencing. Cell. 2018;173(4):879-93 e13.PubMedPubMedCentralCrossRef Kim C, Gao R, Sei E, Brandt R, Hartman J, Hatschek T, et al. Chemoresistance evolution in triple-negative breast cancer delineated by single-cell sequencing. Cell. 2018;173(4):879-93 e13.PubMedPubMedCentralCrossRef
8.
go back to reference Almendro V, Cheng YK, Randles A, Itzkovitz S, Marusyk A, Ametller E, et al. Inference of tumor evolution during chemotherapy by computational modeling and in situ analysis of genetic and phenotypic cellular diversity. Cell Rep. 2014;6(3):514–27.PubMedPubMedCentralCrossRef Almendro V, Cheng YK, Randles A, Itzkovitz S, Marusyk A, Ametller E, et al. Inference of tumor evolution during chemotherapy by computational modeling and in situ analysis of genetic and phenotypic cellular diversity. Cell Rep. 2014;6(3):514–27.PubMedPubMedCentralCrossRef
9.
go back to reference Yates LR, Gerstung M, Knappskog S, Desmedt C, Gundem G, Van Loo P, et al. Subclonal diversification of primary breast cancer revealed by multiregion sequencing. Nat Med. 2015;21(7):751–9.PubMedPubMedCentralCrossRef Yates LR, Gerstung M, Knappskog S, Desmedt C, Gundem G, Van Loo P, et al. Subclonal diversification of primary breast cancer revealed by multiregion sequencing. Nat Med. 2015;21(7):751–9.PubMedPubMedCentralCrossRef
11.
go back to reference Chang CA, Jen J, Jiang S, Sayad A, Mer AS, Brown KR, et al. Ontogeny and vulnerabilities of drug-tolerant persisters in HER2+ breast cancer. Cancer Discov. 2022;12(4):1022–45.PubMedPubMedCentralCrossRef Chang CA, Jen J, Jiang S, Sayad A, Mer AS, Brown KR, et al. Ontogeny and vulnerabilities of drug-tolerant persisters in HER2+ breast cancer. Cancer Discov. 2022;12(4):1022–45.PubMedPubMedCentralCrossRef
12.
go back to reference Hangauer MJ, Viswanathan VS, Ryan MJ, Bole D, Eaton JK, Matov A, et al. Drug-tolerant persister cancer cells are vulnerable to GPX4 inhibition. Nature. 2017;551(7679):247–50.ADSPubMedPubMedCentralCrossRef Hangauer MJ, Viswanathan VS, Ryan MJ, Bole D, Eaton JK, Matov A, et al. Drug-tolerant persister cancer cells are vulnerable to GPX4 inhibition. Nature. 2017;551(7679):247–50.ADSPubMedPubMedCentralCrossRef
13.
go back to reference Risom T, Langer EM, Chapman MP, Rantala J, Fields AJ, Boniface C, et al. Differentiation-state plasticity is a targetable resistance mechanism in basal-like breast cancer. Nat Commun. 2018;9(1):3815.ADSPubMedPubMedCentralCrossRef Risom T, Langer EM, Chapman MP, Rantala J, Fields AJ, Boniface C, et al. Differentiation-state plasticity is a targetable resistance mechanism in basal-like breast cancer. Nat Commun. 2018;9(1):3815.ADSPubMedPubMedCentralCrossRef
14.
go back to reference Sharma SV, Lee DY, Li B, Quinlan MP, Takahashi F, Maheswaran S, et al. A chromatin-mediated reversible drug-tolerant state in cancer cell subpopulations. Cell. 2010;141(1):69–80.PubMedPubMedCentralCrossRef Sharma SV, Lee DY, Li B, Quinlan MP, Takahashi F, Maheswaran S, et al. A chromatin-mediated reversible drug-tolerant state in cancer cell subpopulations. Cell. 2010;141(1):69–80.PubMedPubMedCentralCrossRef
15.
go back to reference Zawistowski JS, Bevill SM, Goulet DR, Stuhlmiller TJ, Beltran AS, Olivares-Quintero JF, et al. Enhancer remodeling during adaptive bypass to MEK inhibition is attenuated by pharmacologic targeting of the P-TEFb complex. Cancer Discov. 2017;7(3):302–21.PubMedPubMedCentralCrossRef Zawistowski JS, Bevill SM, Goulet DR, Stuhlmiller TJ, Beltran AS, Olivares-Quintero JF, et al. Enhancer remodeling during adaptive bypass to MEK inhibition is attenuated by pharmacologic targeting of the P-TEFb complex. Cancer Discov. 2017;7(3):302–21.PubMedPubMedCentralCrossRef
16.
go back to reference Gong Y, Ji P, Yang YS, Xie S, Yu TJ, Xiao Y, et al. Metabolic-pathway-based subtyping of triple-negative breast cancer reveals potential therapeutic targets. Cell Metab. 2021;33(1):51-64 e9.PubMedCrossRef Gong Y, Ji P, Yang YS, Xie S, Yu TJ, Xiao Y, et al. Metabolic-pathway-based subtyping of triple-negative breast cancer reveals potential therapeutic targets. Cell Metab. 2021;33(1):51-64 e9.PubMedCrossRef
17.
go back to reference Roshanzamir F, Robinson JL, Cook D, Karimi-Jafari MH, Nielsen J. Metastatic triple negative breast cancer adapts its metabolism to destination tissues while retaining key metabolic signatures. Proc Natl Acad Sci USA. 2022;119(35):e2205456119.PubMedPubMedCentralCrossRef Roshanzamir F, Robinson JL, Cook D, Karimi-Jafari MH, Nielsen J. Metastatic triple negative breast cancer adapts its metabolism to destination tissues while retaining key metabolic signatures. Proc Natl Acad Sci USA. 2022;119(35):e2205456119.PubMedPubMedCentralCrossRef
18.
go back to reference Shen S, Vagner S, Robert C. Persistent cancer cells: The deadly survivors. Cell. 2020;183(4):860–74.PubMedCrossRef Shen S, Vagner S, Robert C. Persistent cancer cells: The deadly survivors. Cell. 2020;183(4):860–74.PubMedCrossRef
19.
go back to reference Goncalves AC, Richiardone E, Jorge J, Polonia B, Xavier CPR, Salaroglio IC, et al. Impact of cancer metabolism on therapy resistance—clinical implications. Drug Resist Updates. 2021;59:100797.CrossRef Goncalves AC, Richiardone E, Jorge J, Polonia B, Xavier CPR, Salaroglio IC, et al. Impact of cancer metabolism on therapy resistance—clinical implications. Drug Resist Updates. 2021;59:100797.CrossRef
20.
go back to reference Fox DB, Garcia NMG, McKinney BJ, Lupo R, Noteware LC, Newcomb R, et al. NRF2 activation promotes the recurrence of dormant tumour cells through regulation of redox and nucleotide metabolism. Nat Metab. 2020;2(4):318–34.PubMedPubMedCentralCrossRef Fox DB, Garcia NMG, McKinney BJ, Lupo R, Noteware LC, Newcomb R, et al. NRF2 activation promotes the recurrence of dormant tumour cells through regulation of redox and nucleotide metabolism. Nat Metab. 2020;2(4):318–34.PubMedPubMedCentralCrossRef
21.
go back to reference Goldman A, Khiste S, Freinkman E, Dhawan A, Majumder B, Mondal J, et al. Targeting tumor phenotypic plasticity and metabolic remodeling in adaptive cross-drug tolerance. Sci Signal. 2019;12(595):eaas8779.PubMedPubMedCentralCrossRef Goldman A, Khiste S, Freinkman E, Dhawan A, Majumder B, Mondal J, et al. Targeting tumor phenotypic plasticity and metabolic remodeling in adaptive cross-drug tolerance. Sci Signal. 2019;12(595):eaas8779.PubMedPubMedCentralCrossRef
22.
go back to reference Echeverria GV, Ge Z, Seth S, Zhang X, Jeter-Jones S, Zhou X, et al. Resistance to neoadjuvant chemotherapy in triple-negative breast cancer mediated by a reversible drug-tolerant state. Sci Transl Med. 2019;11(488):eaav0936.PubMedPubMedCentralCrossRef Echeverria GV, Ge Z, Seth S, Zhang X, Jeter-Jones S, Zhou X, et al. Resistance to neoadjuvant chemotherapy in triple-negative breast cancer mediated by a reversible drug-tolerant state. Sci Transl Med. 2019;11(488):eaav0936.PubMedPubMedCentralCrossRef
23.
go back to reference Symmans WF, Peintinger F, Hatzis C, Rajan R, Kuerer H, Valero V, et al. Measurement of residual breast cancer burden to predict survival after neoadjuvant chemotherapy. J Clin Oncol. 2007;25(28):4414–22.PubMedCrossRef Symmans WF, Peintinger F, Hatzis C, Rajan R, Kuerer H, Valero V, et al. Measurement of residual breast cancer burden to predict survival after neoadjuvant chemotherapy. J Clin Oncol. 2007;25(28):4414–22.PubMedCrossRef
24.
go back to reference Cancer Genome Atlas N. Comprehensive molecular portraits of human breast tumours. Nature. 2012;490(7418):61–70. Cancer Genome Atlas N. Comprehensive molecular portraits of human breast tumours. Nature. 2012;490(7418):61–70.
25.
go back to reference Hatzis C, Pusztai L, Valero V, Booser DJ, Esserman L, Lluch A, et al. A genomic predictor of response and survival following taxane-anthracycline chemotherapy for invasive breast cancer. JAMA. 2011;305(18):1873–81.PubMedPubMedCentralCrossRef Hatzis C, Pusztai L, Valero V, Booser DJ, Esserman L, Lluch A, et al. A genomic predictor of response and survival following taxane-anthracycline chemotherapy for invasive breast cancer. JAMA. 2011;305(18):1873–81.PubMedPubMedCentralCrossRef
26.
go back to reference Park YH, Lal S, Lee JE, Choi YL, Wen J, Ram S, et al. Chemotherapy induces dynamic immune responses in breast cancers that impact treatment outcome. Nat Commun. 2020;11(1):6175.ADSPubMedPubMedCentralCrossRef Park YH, Lal S, Lee JE, Choi YL, Wen J, Ram S, et al. Chemotherapy induces dynamic immune responses in breast cancers that impact treatment outcome. Nat Commun. 2020;11(1):6175.ADSPubMedPubMedCentralCrossRef
27.
go back to reference Marine JC, Dawson SJ, Dawson MA. Non-genetic mechanisms of therapeutic resistance in cancer. Nat Rev Cancer. 2020;20(12):743–56.PubMedCrossRef Marine JC, Dawson SJ, Dawson MA. Non-genetic mechanisms of therapeutic resistance in cancer. Nat Rev Cancer. 2020;20(12):743–56.PubMedCrossRef
28.
go back to reference Carey LA, Dees EC, Sawyer L, Gatti L, Moore DT, Collichio F, et al. The triple negative paradox: Primary tumor chemosensitivity of breast cancer subtypes. Clin Cancer Res. 2007;13(8):2329–34.PubMedCrossRef Carey LA, Dees EC, Sawyer L, Gatti L, Moore DT, Collichio F, et al. The triple negative paradox: Primary tumor chemosensitivity of breast cancer subtypes. Clin Cancer Res. 2007;13(8):2329–34.PubMedCrossRef
29.
go back to reference Stine ZE, Schug ZT, Salvino JM, Dang CV. Targeting cancer metabolism in the era of precision oncology. Nat Rev Drug Discov. 2022;21(2):141–62.PubMedCrossRef Stine ZE, Schug ZT, Salvino JM, Dang CV. Targeting cancer metabolism in the era of precision oncology. Nat Rev Drug Discov. 2022;21(2):141–62.PubMedCrossRef
30.
go back to reference Vander Linden C, Corbet C. Reconciling environment-mediated metabolic heterogeneity with the oncogene-driven cancer paradigm in precision oncology. Semin Cell Dev Biol. 2020;98:202–10.PubMedCrossRef Vander Linden C, Corbet C. Reconciling environment-mediated metabolic heterogeneity with the oncogene-driven cancer paradigm in precision oncology. Semin Cell Dev Biol. 2020;98:202–10.PubMedCrossRef
31.
go back to reference Cheung SM, Husain E, Masannat Y, Miller ID, Wahle K, Heys SD, et al. Lactate concentration in breast cancer using advanced magnetic resonance spectroscopy. Br J Cancer. 2020;123(2):261–7.PubMedPubMedCentralCrossRef Cheung SM, Husain E, Masannat Y, Miller ID, Wahle K, Heys SD, et al. Lactate concentration in breast cancer using advanced magnetic resonance spectroscopy. Br J Cancer. 2020;123(2):261–7.PubMedPubMedCentralCrossRef
32.
go back to reference Rizwan A, Serganova I, Khanin R, Karabeber H, Ni X, Thakur S, et al. Relationships between LDH-A, lactate, and metastases in 4T1 breast tumors. Clin Cancer Res. 2013;19(18):5158–69.PubMedCrossRef Rizwan A, Serganova I, Khanin R, Karabeber H, Ni X, Thakur S, et al. Relationships between LDH-A, lactate, and metastases in 4T1 breast tumors. Clin Cancer Res. 2013;19(18):5158–69.PubMedCrossRef
33.
go back to reference He M, Jin Q, Chen C, Liu Y, Ye X, Jiang Y, et al. The miR-186-3p/EREG axis orchestrates tamoxifen resistance and aerobic glycolysis in breast cancer cells. Oncogene. 2019;38(28):5551–65.PubMedCrossRef He M, Jin Q, Chen C, Liu Y, Ye X, Jiang Y, et al. The miR-186-3p/EREG axis orchestrates tamoxifen resistance and aerobic glycolysis in breast cancer cells. Oncogene. 2019;38(28):5551–65.PubMedCrossRef
35.
go back to reference Lee KM, Giltnane JM, Balko JM, Schwarz LJ, Guerrero-Zotano AL, Hutchinson KE, et al. MYC and MCL1 cooperatively promote chemotherapy-resistant breast cancer stem cells via regulation of mitochondrial oxidative phosphorylation. Cell Metab. 2017;26(4):633-47 e7.PubMedPubMedCentralCrossRef Lee KM, Giltnane JM, Balko JM, Schwarz LJ, Guerrero-Zotano AL, Hutchinson KE, et al. MYC and MCL1 cooperatively promote chemotherapy-resistant breast cancer stem cells via regulation of mitochondrial oxidative phosphorylation. Cell Metab. 2017;26(4):633-47 e7.PubMedPubMedCentralCrossRef
36.
go back to reference Cho N, Im SA, Cheon GJ, Park IA, Lee KH, Kim TY, et al. Integrated (18)F-FDG PET/MRI in breast cancer: Early prediction of response to neoadjuvant chemotherapy. Eur J Nucl Med Mol Imaging. 2018;45(3):328–39.PubMedCrossRef Cho N, Im SA, Cheon GJ, Park IA, Lee KH, Kim TY, et al. Integrated (18)F-FDG PET/MRI in breast cancer: Early prediction of response to neoadjuvant chemotherapy. Eur J Nucl Med Mol Imaging. 2018;45(3):328–39.PubMedCrossRef
37.
go back to reference Garcia Vicente AM, Cruz Mora MA, Leon Martin AA, Munoz Sanchez Mdel M, Relea Calatayud F, Van Gomez LO, et al. Glycolytic activity with 18F-FDG PET/CT predicts final neoadjuvant chemotherapy response in breast cancer. Tumour Biol. 2014;35(11):11613–20.PubMedCrossRef Garcia Vicente AM, Cruz Mora MA, Leon Martin AA, Munoz Sanchez Mdel M, Relea Calatayud F, Van Gomez LO, et al. Glycolytic activity with 18F-FDG PET/CT predicts final neoadjuvant chemotherapy response in breast cancer. Tumour Biol. 2014;35(11):11613–20.PubMedCrossRef
38.
go back to reference Koolen BB, Pengel KE, Wesseling J, Vogel WV, Vrancken Peeters MJ, Vincent AD, et al. FDG PET/CT during neoadjuvant chemotherapy may predict response in ER-positive/HER2-negative and triple negative, but not in HER2-positive breast cancer. Breast. 2013;22(5):691–7.PubMedCrossRef Koolen BB, Pengel KE, Wesseling J, Vogel WV, Vrancken Peeters MJ, Vincent AD, et al. FDG PET/CT during neoadjuvant chemotherapy may predict response in ER-positive/HER2-negative and triple negative, but not in HER2-positive breast cancer. Breast. 2013;22(5):691–7.PubMedCrossRef
39.
go back to reference Barrio JR, Huang SC, Satyamurthy N, Scafoglio CS, Yu AS, Alavi A, et al. Does 2-FDG PET accurately reflect quantitative in vivo glucose utilization? J Nucl Med. 2020;61(6):931–7.PubMedPubMedCentralCrossRef Barrio JR, Huang SC, Satyamurthy N, Scafoglio CS, Yu AS, Alavi A, et al. Does 2-FDG PET accurately reflect quantitative in vivo glucose utilization? J Nucl Med. 2020;61(6):931–7.PubMedPubMedCentralCrossRef
40.
go back to reference Derouane F, van Marcke C, Berliere M, Gerday A, Fellah L, Leconte I, et al. Predictive biomarkers of response to neoadjuvant chemotherapy in breast cancer: Current and future perspectives for precision medicine. Cancers (Basel). 2022;14(16):3876.PubMedPubMedCentralCrossRef Derouane F, van Marcke C, Berliere M, Gerday A, Fellah L, Leconte I, et al. Predictive biomarkers of response to neoadjuvant chemotherapy in breast cancer: Current and future perspectives for precision medicine. Cancers (Basel). 2022;14(16):3876.PubMedPubMedCentralCrossRef
41.
go back to reference Liu C, Li Y, Wei M, Zhao L, Yu Y, Li G. Identification of a novel glycolysis-related gene signature that can predict the survival of patients with lung adenocarcinoma. Cell Cycle. 2019;18(5):568–79.PubMedPubMedCentralCrossRef Liu C, Li Y, Wei M, Zhao L, Yu Y, Li G. Identification of a novel glycolysis-related gene signature that can predict the survival of patients with lung adenocarcinoma. Cell Cycle. 2019;18(5):568–79.PubMedPubMedCentralCrossRef
42.
go back to reference Wang ZH, Zhang YZ, Wang YS, Ma XX. Identification of novel cell glycolysis related gene signature predicting survival in patients with endometrial cancer. Cancer Cell Int. 2019;19:296.PubMedPubMedCentralCrossRef Wang ZH, Zhang YZ, Wang YS, Ma XX. Identification of novel cell glycolysis related gene signature predicting survival in patients with endometrial cancer. Cancer Cell Int. 2019;19:296.PubMedPubMedCentralCrossRef
43.
go back to reference Zhu J, Wang S, Bai H, Wang K, Hao J, Zhang J, et al. Identification of five glycolysis-related gene signature and risk score model for colorectal cancer. Front Oncol. 2021;11:588811.PubMedPubMedCentralCrossRef Zhu J, Wang S, Bai H, Wang K, Hao J, Zhang J, et al. Identification of five glycolysis-related gene signature and risk score model for colorectal cancer. Front Oncol. 2021;11:588811.PubMedPubMedCentralCrossRef
44.
go back to reference Yao Y, Xu X, Yang L, Zhu J, Wan J, Shen L, et al. Patient-derived organoids predict chemoradiation responses of locally advanced rectal cancer. Cell Stem Cell. 2020;26(1):17-26 e6.PubMedCrossRef Yao Y, Xu X, Yang L, Zhu J, Wan J, Shen L, et al. Patient-derived organoids predict chemoradiation responses of locally advanced rectal cancer. Cell Stem Cell. 2020;26(1):17-26 e6.PubMedCrossRef
45.
go back to reference Ooft SN, Weeber F, Dijkstra KK, McLean CM, Kaing S, van Werkhoven E, et al. Patient-derived organoids can predict response to chemotherapy in metastatic colorectal cancer patients. Sci Transl Med. 2019;11(513):eaay2574.PubMedCrossRef Ooft SN, Weeber F, Dijkstra KK, McLean CM, Kaing S, van Werkhoven E, et al. Patient-derived organoids can predict response to chemotherapy in metastatic colorectal cancer patients. Sci Transl Med. 2019;11(513):eaay2574.PubMedCrossRef
46.
go back to reference Ganesh K, Wu C, O’Rourke KP, Szeglin BC, Zheng Y, Sauve CG, et al. A rectal cancer organoid platform to study individual responses to chemoradiation. Nat Med. 2019;25(10):1607–14.PubMedPubMedCentralCrossRef Ganesh K, Wu C, O’Rourke KP, Szeglin BC, Zheng Y, Sauve CG, et al. A rectal cancer organoid platform to study individual responses to chemoradiation. Nat Med. 2019;25(10):1607–14.PubMedPubMedCentralCrossRef
47.
go back to reference Vlachogiannis G, Hedayat S, Vatsiou A, Jamin Y, Fernandez-Mateos J, Khan K, et al. Patient-derived organoids model treatment response of metastatic gastrointestinal cancers. Science. 2018;359(6378):920–6.ADSPubMedPubMedCentralCrossRef Vlachogiannis G, Hedayat S, Vatsiou A, Jamin Y, Fernandez-Mateos J, Khan K, et al. Patient-derived organoids model treatment response of metastatic gastrointestinal cancers. Science. 2018;359(6378):920–6.ADSPubMedPubMedCentralCrossRef
48.
go back to reference Campaner E, Zannini A, Santorsola M, Bonazza D, Bottin C, Cancila V, et al. Breast cancer organoids model patient-specific response to drug treatment. Cancers (Basel). 2020;12(12):3869.PubMedCrossRef Campaner E, Zannini A, Santorsola M, Bonazza D, Bottin C, Cancila V, et al. Breast cancer organoids model patient-specific response to drug treatment. Cancers (Basel). 2020;12(12):3869.PubMedCrossRef
49.
go back to reference Guillen KP, Fujita M, Butterfield AJ, Scherer SD, Bailey MH, Chu Z, et al. A human breast cancer-derived xenograft and organoid platform for drug discovery and precision oncology. Nat Cancer. 2022;3(2):232–50.PubMedPubMedCentralCrossRef Guillen KP, Fujita M, Butterfield AJ, Scherer SD, Bailey MH, Chu Z, et al. A human breast cancer-derived xenograft and organoid platform for drug discovery and precision oncology. Nat Cancer. 2022;3(2):232–50.PubMedPubMedCentralCrossRef
50.
go back to reference Sachs N, de Ligt J, Kopper O, Gogola E, Bounova G, Weeber F, et al. A living biobank of breast cancer organoids captures disease heterogeneity. Cell. 2018;172(1–2):373-86 e10.PubMedCrossRef Sachs N, de Ligt J, Kopper O, Gogola E, Bounova G, Weeber F, et al. A living biobank of breast cancer organoids captures disease heterogeneity. Cell. 2018;172(1–2):373-86 e10.PubMedCrossRef
51.
go back to reference Shu D, Shen M, Li K, Han X, Li H, Tan Z, et al. Organoids from patient biopsy samples can predict the response of BC patients to neoadjuvant chemotherapy. Ann Med. 2022;54(1):2581–97.PubMedPubMedCentralCrossRef Shu D, Shen M, Li K, Han X, Li H, Tan Z, et al. Organoids from patient biopsy samples can predict the response of BC patients to neoadjuvant chemotherapy. Ann Med. 2022;54(1):2581–97.PubMedPubMedCentralCrossRef
52.
go back to reference Richiardone E, Van den Bossche V, Corbet C. Metabolic studies in organoids: Current applications. Oppor Chall Organoids. 2022;1(1):85–105.CrossRef Richiardone E, Van den Bossche V, Corbet C. Metabolic studies in organoids: Current applications. Oppor Chall Organoids. 2022;1(1):85–105.CrossRef
53.
go back to reference Okkelman IA, Neto N, Papkovsky DB, Monaghan MG, Dmitriev RI. A deeper understanding of intestinal organoid metabolism revealed by combining fluorescence lifetime imaging microscopy (FLIM) and extracellular flux analyses. Redox Biol. 2020;30:101420.PubMedCrossRef Okkelman IA, Neto N, Papkovsky DB, Monaghan MG, Dmitriev RI. A deeper understanding of intestinal organoid metabolism revealed by combining fluorescence lifetime imaging microscopy (FLIM) and extracellular flux analyses. Redox Biol. 2020;30:101420.PubMedCrossRef
54.
go back to reference Okkelman IA, Foley T, Papkovsky DB, Dmitriev RI. Live cell imaging of mouse intestinal organoids reveals heterogeneity in their oxygenation. Biomaterials. 2017;146:86–96.PubMedCrossRef Okkelman IA, Foley T, Papkovsky DB, Dmitriev RI. Live cell imaging of mouse intestinal organoids reveals heterogeneity in their oxygenation. Biomaterials. 2017;146:86–96.PubMedCrossRef
55.
go back to reference Singh SP, Gao Y, Singh LD, Kunapuli SP, Ravindra R. Role of microtubules in glucose uptake by C6 glioma cells. Pharmacol Toxicol. 1998;83(2):83–9.PubMedCrossRef Singh SP, Gao Y, Singh LD, Kunapuli SP, Ravindra R. Role of microtubules in glucose uptake by C6 glioma cells. Pharmacol Toxicol. 1998;83(2):83–9.PubMedCrossRef
57.
go back to reference Landau BR, Laszlo J, Stengle J, Burk D. Certain metabolic and pharmacologic effects in cancer patients given infusions of 2-deoxy-d-glucose. J Natl Cancer Inst. 1958;21(3):485–94.PubMed Landau BR, Laszlo J, Stengle J, Burk D. Certain metabolic and pharmacologic effects in cancer patients given infusions of 2-deoxy-d-glucose. J Natl Cancer Inst. 1958;21(3):485–94.PubMed
Metadata
Title
Metabolic adaptation towards glycolysis supports resistance to neoadjuvant chemotherapy in early triple negative breast cancers
Authors
Françoise Derouane
Manon Desgres
Camilla Moroni
Jérôme Ambroise
Martine Berlière
Mieke R. Van Bockstal
Christine Galant
Cédric van Marcke
Marianela Vara-Messler
Stefan J. Hutten
Jos Jonkers
Larissa Mourao
Colinda L. G. J. Scheele
Francois P. Duhoux
Cyril Corbet
Publication date
01-12-2024
Publisher
BioMed Central
Published in
Breast Cancer Research / Issue 1/2024
Electronic ISSN: 1465-542X
DOI
https://doi.org/10.1186/s13058-024-01788-8

Other articles of this Issue 1/2024

Breast Cancer Research 1/2024 Go to the issue
Webinar | 19-02-2024 | 17:30 (CET)

Keynote webinar | Spotlight on antibody–drug conjugates in cancer

Antibody–drug conjugates (ADCs) are novel agents that have shown promise across multiple tumor types. Explore the current landscape of ADCs in breast and lung cancer with our experts, and gain insights into the mechanism of action, key clinical trials data, existing challenges, and future directions.

Dr. Véronique Diéras
Prof. Fabrice Barlesi
Developed by: Springer Medicine