MétaCan
Menu
Retour à la cohorte
Enregistrement W4403007843 · doi:10.3322/caac.21867

Breast cancer: The good, the bad, and an important call to effective risk reduction strategies

2024· editorial· en· W4403007843 sur OpenAlex

Pourquoi ce travail est dans la base

Une base qui oublie comment elle a trouvé un travail ne peut pas être vérifiée. Voici les voies qui ont admis celui-ci.

aboutLe titre ou le résumé porte un signal canadien du lexique géographique.
no affAucune affiliation canadienne : ce travail est invisible pour une base fondée sur la seule affiliation.
Aucune affiliation canadienne. Une base fondée sur la seule affiliation (le devis habituel) n'aurait jamais vu ce travail. C'est l'un des travaux qui justifient l'inversion de la base.

Notice bibliographique

RevueCA A Cancer Journal for Clinicians · 2024
Typeeditorial
Langueen
DomaineMedicine
ThématiqueCancer Risks and Factors
Établissements canadiensnon disponible
Organismes subventionnairesNational Cancer InstituteCancer Prevention and Research Institute of TexasBreast Cancer Research Foundation
Mots-clésMedicineBreast cancerReduction (mathematics)CancerInternal medicine

Résumé

récupéré en direct d'OpenAlex

The 2024 Breast Cancer Statistics highlight a few interesting trends: breast cancer incidence is increasing, there is a greater increase in younger women, and most of this increase is driven by early stage diagnosis and hormone receptor (HR)-positive disease.1 In addition, compared with other racial groups, women with Asian American/Pacific Islander (AAPI) heritage have a greater increase in breast cancer; and, despite overall declining death rates from breast cancer, Black women continue to have higher mortality compared with White women. Let us consider these findings in more detail. Breast cancer incidence in the United States briefly decreased in the early 2000s, possibly related to a decline in the use of hormone-replacement therapy, but it has since shown an increase of approximately 1% per year. This increase is associated with HR-positive breast cancers and is mostly seen in younger women. A potential contributing factor for this association may be a decrease in the number of live births.2 Another possibility may be the greater incidence of young-onset HR-positive breast cancer in Indian and Chinese women.3-5 AAPI women have a greater increase in breast cancer incidence, which is largely noted in Asian women immigrating to the United States rather than Asian women born in the United States.6 Compared with Asian American women born in the United States, Asian American women who have immigrated to the United States and have lived more than 50% of their life in the United States, on average, are three times more likely to be diagnosed with breast cancer.6 Specifically for Indian women, there has been a rise in breast cancer incidence by almost 50% between 1965 and 1985,4 and Chinese women are projected to have a rise in breast cancer incidence by greater than 11% by 2030.7 In the past 35 years, breast cancer mortality rates have decreased by 44%. This decrease is attributed to early diagnosis stemming from nationwide screening recommendations as well as treatment advances in all disease stages.8 Based on simulation models, 25% of the reduction in mortality rates comes from screening mammography, 29% comes from treatment advances in metastatic disease, and 47% comes from treatment advances in early stage disease. The addition of trastuzumab has increased survival in metastatic HER2-positive breast cancer by 16 months9 and, in early stage disease, by 26%–37%.10-12 Most recently, the addition of CDK4/6 inhibitors has broken the 5-year barrier in median overall survival in metastatic HR-positive breast cancer,13 suggesting that mortality rates will continue to show an improvement in patient survival in subsequent iterations of the breast cancer statistics. Nonetheless, as we celebrate overall improvements in survival, mortality rates in Black women remain unchanged. Black women are less likely to be diagnosed with early stage disease, have a higher incidence of triple-negative breast cancer (TNBC)—a more virulent breast cancer subtype—and have lower survival rates regardless of subtype. The underlying cause of this difference is likely multifactorial. Studies have demonstrated that Black women are more likely to have delays in initiating endocrine therapy.14 However, there may also be differences in tumor biology contributing to outcome disparities. Data from adjuvant genomic trials in early stage, HR-positive breast cancer have shown that Black women have breast tumors with higher proliferation indices; and, despite a higher level of compliance with endocrine therapy, they had worse outcomes.15, 16 Studies in Black women have also shown differences in tumor microenvironment after neoadjuvant chemotherapy, suggesting a higher propensity for developing metastatic disease.17 For women with TNBC, data suggest that Black women have less frequent use of chemotherapy18 as well as a higher incidence of the basal-like molecular subtype,19, 20 which may contribute to a worse prognosis. So how can we truly affect breast cancer outcomes? How can our dream of ending breast cancer become a reality? As cancer metastasizes, it mutates and develops subclones.21 Trying to cure clinical metastatic disease with a high tumor burden is not very feasible and is something we cannot promise to patients. We seem to be playing catchup with the disease. Therefore, we believe that early detection, effective risk reduction strategies, and movement of effective therapies from the metastatic setting to early stage disease, in which tumor burden and drug-resistant clones are significantly reduced, are key to getting closer to a cure. Early detection can be achieved through effective risk assessment and universal screening programs. Mammography screening increased in the United States from 29% in 1987 to 70% in 2000 among women aged 50 years and older and has since remained relatively stable.22 During the coronavirus disease 2019 pandemic, screening mammograms decreased by 44%,23 and data suggest that this may have a small impact on future breast cancer mortality.24 Although screening guidelines exist, personalized screening based on risk assessment is key. For example, given the higher prevalence of TNBC in Black women, they may benefit from earlier breast cancer screening,25 and the type of screening modality may need to be taken into consideration because Black women have higher breast density compared with White women.26 Several risk assessment models have been developed, but their accuracy has been moderate, producing areas under the curve less than 0.8, even when incorporating polygenic risk scores.27 There is heightened interest in the use of artificial intelligence to improve risk assessment models, and improving their accuracy is imperative.28 Genetic testing for the identification of high-penetrant and moderately-penetrant genes is an important component of risk assessment. Currently available guidelines for genetic testing rely heavily on family history and can miss up to 50% of mutation-positive individuals.29, 30 Unfortunately, calls for universal genetic testing have not gained traction.31 Risk reduction can be achieved by tackling modifiable risk factors as well as investing in chemoprevention. Modifiable risk factors include postmenopausal obesity,32-34 the use of hormone-replacement therapy,35 alcohol consumption,36 smoking,37 and lack of exercise.38 Public health policy focused on education, taxation, and price regulation can have a positive impact on several of these factors.39 In addition, treatments like ionizing chest wall radiation and anthracyclines have been associated with an increased risk for breast cancer.40, 41 The medical community has made efforts to avoid therapies with such long-term toxicities by minimizing their use and advocating for less toxic alternatives according to available treatment guidelines.42 Chemoprevention has not been an effective strategy to date. Oral estrogen-targeting agents, such as tamoxifen and aromatase inhibitors, can reduce breast cancer incidence by 50%; however, they only prevent HR-positive breast cancers and have no impact on overall survival.43-45 These agents are also associated with side effects that limit their use in the prevention setting, resulting in underutilization.46 Prevention trials are also challenging to perform because of the large number of patients and the length of follow-up required. Therefore, efforts should be made to identify surrogate end points, which will allow for shorter and more cost-effective clinical trials in patients at high risk for breast cancer. Dose optimization is also critical to assess. We cannot assume that treatment and prevention doses should be the same. Efforts have been made in this space, and low-dose tamoxifen has been shown to be an effective chemopreventive strategy.47 Chemoprevention strategies for TNBC and HER2-positive breast cancer are lacking, although several ongoing trials may change the landscape in the future.48 As we celebrate our victories, we should also take a moment to reflect on our failures. The lack of racially diverse groups in clinical trials, inequities in care, and little progress in risk reduction is affecting breast cancer outcomes. As breast cancer incidence is rising by 1% annually, and disproportionately so in Hispanic and AAPI women, we should work smarter as a community and learn from past successes and failures to improve care for ALL of our patients. We acknowledge the assistance of Dr Saba Shaikh with article preparation. This work is supported by the National Cancer Institute (NCI; Grant P30 CA142543, Grant NCI R01CA224899, and NCI Breast Specialized Program of Research Excellence Grant P50 CA098131; Carlos L. Arteaga), the Cancer Prevention & Research Institute of Texas (Grant RR170061, Carlos L. Arteaga), the Susan G. Komen Breast Cancer Foundation (Grant SAB1800010, Carlos L. Arteaga), the Breast Cancer Research Foundation (Grant DRC-20-001, Carlos L. Arteaga), The Dolores Knes Fund (Virginia G. Kaklamani), and the NCI (Grant R01CA277498-02, Virginia G. Kaklamani). Virgina G. Kaklamani reports research grants from Eisai; and personal/consulting fees from AstraZeneca, Daiichi Sankyo Company, Eli Lilly & Company, Genentech, Gilead Sciences (aka Gilead Foundation), Menarini, Novartis, Puma Biotechnology Inc., Pfizer Canada Inc., Seagen Inc., and Tersera outside the submitted work. Carlos L. Arteaga reports research grants from Eli Lilly & Company, Laboratorios Pfizer Ltda., Novartis, and Takeda Oncology; personal/consulting fees from Arvinas, AstraZeneca, Athenex Pharmaceutical Division LLC, Daiichi Sankyo Company, Ely Lilly & Company, Immunomedics Inc., Merck, Novartis, OrigiMed, Sanofi Pasteur Inc., Puma Biotechnology, Susan G. Komen for the Cure, Taiho Oncology Inc., and Takeda Oncology outside the submitted work; and holds minor stock options in Provista. Breast Cancer Research Foundation, Grant/Award Number: DRC-20-001; National Cancer Institute, Grant/Award Numbers: P30 CA142543, P50 CA098131, R01CA224899, R01CA277498-02; Susan G. Komen, Grant/Award Number: SAB1800010; Cancer Prevention and Research Institute of Texas, Grant/Award Number: RR170061

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Intégrité de la recherche
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Éditorial · Signal consensuel: Éditorial
Score de désaccord entre enseignants0,064
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0020,000
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0000,000
Études des sciences et des technologies0,0010,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0010,003
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,015
Tête enseignante GPT0,395
Écart entre enseignants0,380 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle