journal article·2024-07Open access

A PET‐Surrogate Signature for the Interrogation of the Metabolic Status of Breast Cancers

Stefano Confalonieri, Bronislava Matoskova, Rosa Pennisi, Flavia Martino, Agnese De Mario, Giorgia Miloro, Francesca Montani, Luca Rotta, Mahila Esmeralda Ferrari, Laura Gilardi, Francesco Ceci, Chiara Maria Grana, Rosario Rizzuto, Cristina Mammucari, Pier Paolo Di Fiore, Letizia Lanzetti

Advanced Science· Volume 11· Issue 28

Abstract

Abstract Metabolic alterations in cancers can be exploited for diagnostic, prognostic, and therapeutic purposes. This is exemplified by 18F‐fluorodeoxyglucose (FDG)‐positron emission tomography (FDG‐PET), an imaging tool that relies on enhanced glucose uptake by tumors for diagnosis and staging. By performing transcriptomic analysis of breast cancer (BC) samples from patients stratified by FDG‐PET, a 54‐gene signature (PETsign) is identified that recapitulates FDG uptake. PETsign is independently prognostic of clinical outcome in luminal BCs, the most common and heterogeneous BC molecular subtype, which requires improved stratification criteria to guide therapeutic decision‐making. The prognostic power of PETsign is stable across independent BC cohorts and disease stages including the earliest BC stage, arguing that PETsign is an ab initio metabolic signature. Transcriptomic and metabolomic analysis of BC cells reveals that PETsign predicts enhanced glycolytic dependence and reduced reliance on fatty acid oxidation. Moreover, coamplification of PETsign genes occurs frequently in BC arguing for their causal role in pathogenesis. CXCL8 and EGFR signaling pathways feature strongly in PETsign, and their activation in BC cells causes a shift toward a glycolytic phenotype. Thus, PETsign serves as a molecular surrogate for FDG‐PET that could inform clinical management strategies for BC patients.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Cancer, Hypoxia, and Metabolism · Cancer, Lipids, and Metabolism · Medical Imaging Techniques and Applications

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

IEO European Institute of Oncology IRCCS Via Ripamonti 435 Milan 20141 Italy

Department of Oncology University of Torino Medical School Candiolo Turin 10060 Italy

Candiolo Cancer Institute FPO‐IRCCS Str. Provinciale 142 km 3.95, Candiolo Turin 10060 Italy

Department of Biomedical Sciences University of Padua Via U. Bassi 58/B Padua 35131 Italy

Department of Oncology and Haemato‐Oncology University of Milan Milan 20142 Italy

Funding

Associazione Italiana per la Ricerca sul Cancro

References (59)

Show source-provided references
1. 10.1158/2159-8290.CD-21-105910.1158/2159-8290.CD-21-1059 ↗
2. Warburg O. · Biochem. Z. · 1923 · 142 · 317
3. 10.1038/s42255-020-0172-210.1038/s42255-020-0172-2 ↗
4. 10.1038/nrc303810.1038/nrc3038 ↗
5. 10.1126/sciadv.160020010.1126/sciadv.1600200 ↗
6. 10.2967/jnumed.108.05718210.2967/jnumed.108.057182 ↗
7. 10.1097/MD.000000000002674510.1097/MD.0000000000026745 ↗
8. 10.1097/MD.000000000001631310.1097/MD.0000000000016313 ↗
9. 10.3109/1042819090344095310.3109/10428190903440953 ↗
10. 10.1155/2021/431229610.1155/2021/4312296 ↗
11. 10.1186/s40064-015-1521-610.1186/s40064-015-1521-6 ↗
12. 10.3322/caac.2166010.3322/caac.21660 ↗
13. 10.1016/j.annonc.2021.06.02310.1016/j.annonc.2021.06.023 ↗
14. 10.1309/AJCPIV9IQ1MRQMOO10.1309/AJCPIV9IQ1MRQMOO ↗
15. 10.1038/nature1141210.1038/nature11412 ↗
16. 10.1093/annonc/mdv22110.1093/annonc/mdv221 ↗
17. 10.1200/JCO.2015.65.228910.1200/JCO.2015.65.2289 ↗
18. 10.1056/NEJMoa04158810.1056/NEJMoa041588 ↗
19. 10.1093/annonc/mdv29810.1093/annonc/mdv298 ↗
20. 10.1073/pnas.19136709810.1073/pnas.191367098 ↗
21. 10.1056/NEJMoa170183010.1056/NEJMoa1701830 ↗
22. Avril N. · J. Nucl. Med. · 2001 · 42 · 9
23. 10.2214/AJR.11.713010.2214/AJR.11.7130 ↗
24. 10.1016/j.cpet.2023.04.00410.1016/j.cpet.2023.04.004 ↗
25. 10.1007/s40336-021-00426-z10.1007/s40336-021-00426-z ↗
26. 10.1186/s13058-016-0793-210.1186/s13058-016-0793-2 ↗
27. 10.2967/jnumed.115.15413810.2967/jnumed.115.154138 ↗
28. 10.1038/nature1098310.1038/nature10983 ↗
29. 10.1038/ncomms1147910.1038/ncomms11479 ↗
30. 10.1016/j.cell.2015.09.03310.1016/j.cell.2015.09.033 ↗
31. 10.1016/j.cell.2018.02.05210.1016/j.cell.2018.02.052 ↗
32. 10.1016/j.ejca.2022.01.00310.1016/j.ejca.2022.01.003 ↗
33. 10.1038/s41467-020-16836-y10.1038/s41467-020-16836-y ↗
34. 10.1016/j.ebiom.2019.02.03610.1016/j.ebiom.2019.02.036 ↗
35. 10.1158/0008-5472.CAN-15-050610.1158/0008-5472.CAN-15-0506 ↗
36. 10.1093/nar/gkac100010.1093/nar/gkac1000 ↗
37. 10.1038/s41586-019-1186-310.1038/s41586-019-1186-3 ↗
38. 10.1038/s41591-019-0404-810.1038/s41591-019-0404-8 ↗
39. 10.1038/s41586-018-0409-310.1038/s41586-018-0409-3 ↗
40. 10.1038/s41587-019-0037-y10.1038/s41587-019-0037-y ↗
41. 10.1074/jbc.M115.66049810.1074/jbc.M115.660498 ↗
42. 10.1186/1471-2164-9-37510.1186/1471-2164-9-375 ↗
43. 10.1056/NEJMoa05293310.1056/NEJMoa052933 ↗
44. 10.1186/s12885-018-4388-410.1186/s12885-018-4388-4 ↗
45. 10.1007/s00018-023-04902-910.1007/s00018-023-04902-9 ↗
46. 10.1016/j.cmet.2020.10.01210.1016/j.cmet.2020.10.012 ↗
47. 10.3390/cancers1513339010.3390/cancers15133390 ↗
48. 10.1016/j.phrs.2019.10451110.1016/j.phrs.2019.104511 ↗
49. 10.1038/3502109310.1038/35021093 ↗
50. 10.1016/j.ctrv.2016.12.01010.1016/j.ctrv.2016.12.010 ↗
51. 10.1016/j.clbc.2023.05.01210.1016/j.clbc.2023.05.012 ↗
52. 10.1016/j.cyto.2022.15582810.1016/j.cyto.2022.155828 ↗
53. 10.1007/s11739-023-03309-510.1007/s11739-023-03309-5 ↗
54. 10.1016/j.celrep.2021.10927510.1016/j.celrep.2021.109275 ↗
55. 10.1186/gb-2009-10-3-r2510.1186/gb-2009-10-3-r25 ↗
56. 10.1186/1471-2105-12-32310.1186/1471-2105-12-323 ↗
57. 10.1158/2159-8290.CD-12-009510.1158/2159-8290.CD-12-0095 ↗
58. 10.1126/scisignal.200408810.1126/scisignal.2004088 ↗
59. 10.1093/bioinformatics/btp61610.1093/bioinformatics/btp616 ↗