@article{fdi:010095006,
  title = {{L}everaging selection for function in tumor evolution : system-level cancer therapies},
  author = {{T}homas, {F}. and {C}app, {J}. {P}. and {D}ujon, {A}. {M}. and {M}arusyk, {A}. and {A}sselin, {K}. and {C}ampone, {M}. and {P}ujol, {P}. and {A}lix-{P}anabi{\`e}res, {C}. and {R}oche, {B}enjamin and {U}jvari, {B}. and {G}atenby, {R}. and {N}edelcu, {A}. {M}.},
  editor = {},
  language = {{ENG}},
  abstract = {{C}urrent cancer therapies often fail due to tumor heterogeneity and rapid resistance evolution. {A} new evolutionary framework, 'selection for function,' proposes that tumor progression is driven by group phenotypic composition ({GPC}) and its interaction with the microenvironment, not by individual cell traits. {T}his perspective opens new therapeutic avenues: targeting the tumor's functional networks rather than individual cells. {R}eal-time tracking of {GPC} changes could inform adaptive treatments, delaying progression and resistance. {B}y integrating evolutionary and ecological principles with conventional therapies, this strategy aims to transform cancer from a fatal to a manageable chronic disease. {C}rucially, it does not necessarily require new drugs but offers a way to repurpose existing therapies to impair a tumor's evolutionary potential. {B}y steering tumor evolution toward less aggressive states, this approach could improve prognosis and long-term patient survival compared to current methods. {W}e argue that leveraging {GPC} dynamics represents a critical, yet underexplored, opportunity in oncology.},
  keywords = {selection ; tumors ; evolution ; therapy},
  booktitle = {},
  journal = {{E}volution {M}edicine and {P}ublic {H}ealth},
  volume = {13},
  numero = {1},
  pages = {248--268},
  year = {2025},
  DOI = {10.1093/emph/eoaf022},
  URL = {https://www.documentation.ird.fr/hor/fdi:010095006},
}