A network analysis to identify mediators of germline-driven differences in breast cancer prognosis

Escala-Garcia, Maria; Abraham, Jean; Andrulis, Irene L.; Anton‐Culver, Hoda; Arndt, Volker; Ashworth, Alan; Auer, Paul L.; Auvinen, Päivi; Beckmann, Matthias W.; Beesley, Jonathan; Behrens, Sabine; Benı́tez, Javier; Bermisheva, Marina; Blomqvist, Carl; Blot, William J.; Bogdanova, Natalia; Bojesen, Stig E.; Bolla, Manjeet K.; Børresen-Dale, Anne Lise; Brauch, Hiltrud; Brenner, Hermann; Brucker, Sara Y.; Burwinkel, Barbara; Caldas, Carlos; Canzian, Federico; Chang‐Claude, Jenny; Chanock, Stephen J.; Chin, Suet Feung; Clarke, Christine L.; Couch, Fergus J.; Cox, Angela; Cross, Simon S.; Czene, Kamila; Daly, Mary B.; Dennis, Joe; Devilee, Peter; Dunn, Janet A.; Dunning, Alison M.; Dwek, Miriam; Earl, Helena; Eccles, Diana; Eliassen, A. Heather; Ellberg, Carolina; Evans, D. Gareth; Fasching, Peter A.; Figueroa, Jonine D.; Flyger, Henrik; Gago‐Dominguez, Manuela; Gapstur, Susan M.; García‐Closas, Montserrat; García-Sáenz, José Ángel; Gaudet, Mia M.; George, Angela; Giles, Graham G.; Goldgar, David E.; González‐Neira, Anna; Grip, Mervi; Guénel, Pascal; Guo, Qi; Haiman, Christopher A.; Håkansson, Niclas; Hamann, Ute; Harrington, Patricia; Hiller, Louise; Hooning, Maartje J.; Hopper, John L.; Howell, Anthony; Huang, Chiun Sheng; Huang, Guanmengqian; Hunter, David J.; Jakubowska, Anna; John, Esther M.; Kaaks, Rudolf; Kapoor, Pooja Middha; Keeman, Renske; Kitahara, Cari M.; Koppert, Linetta B.; Kraft, Peter; Kristensen, Vessela N.; Lambrechts, Diether; Marchand, Loı̈c Le; Lejbkowicz, Flavio; Lindblom, Annika; Lubiński, Jan; Mannermaa, Arto; Manoochehri, Mehdi; Manoukian, Siranoush; Margolin, Sara; Martı́nez, Marı́a Elena; Maurer, Tabea; Mavroudis, Dimitriοs; Meindl, Alfons; Milne, Roger L.; Mulligan, Anna Marie; Neuhausen, Susan L.; Nevanlinna, Heli; Newman, William G.; Olshan, Andrew F.; Olson, Janet E.; Olsson, Håkan; Orr, Nick; Peterlongo, Paolo; Petridis, Christos; Prentice, Ross L.; Presneau, Nadège; Punie, Kevin; Ramachandran, Dhanya; Rennert, Gad; Romero, Atocha; Sachchithananthan, Mythily; Saloustros, Emmanouil; Sawyer, Elinor J.; Schmutzler, Rita K.; Schwentner, Lukas; Scott, Christopher; Simard, Jacques; Sohn, Christof; Southey, Melissa C.; Swerdlow, Anthony J.; Tamimi, Rulla M.; Tapper, William; Teixeira, Manuel R.; Terry, Mary Beth; Thorne, Heather; Tollenaar, Rob A.E.M.; Tomlinson, Ian; Troester, Melissa A.; Truong, Thérèse; Turnbull, Clare; Vachon, Celine M.; Kolk, Lizet E. van der; Wang, Qin; Winqvist, Robert; Wolk, Alicja; Yang, Xiaohong R.; Ziogas, Argyrios; Pharoah, Paul D.P.; Hall, Per; Wessels, Lodewyk; Chenevix-Trench, Georgia; Bader, Gary D.; Dörk, Thilo; Easton, Douglas F.; Canisius, Sander; Schmidt, Marjanka K.

doi:10.1038/s41467-019-14100-6

Cited by 32 publications

(25 citation statements)

References 80 publications

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“…Although several consortia have published on genetic variation that predicts survival in patients with breast cancer, no specific genetic markers have been identified that predict differences in survival metrics according to race. 13 - 16 There is limited evidence that genomic mutations in Chinese women with breast cancer differ significantly from those in White women, such as an increased prevalence of TP53 and AKT1 mutations, 4 which may translate into differences in oncologic outcomes.…”

Section: Discussionmentioning

confidence: 99%

Survival Differences in Chinese Versus White Women With Breast Cancer in the United States: A SEER-Based Analysis

Lim

Giannakeas

Narod

2020

JCO Global Oncology

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PURPOSE The affect of race on breast cancer prognosis is not well understood. We compared crude and adjusted breast cancer survival rates of Chinese women versus White women in the United States. METHODS We conducted a cohort study of Chinese and White women with breast cancer diagnosed between 2004 to 2015 in the SEER 18 registries database. We abstracted information on age at diagnosis, tumor size, grade, lymph node status, receptor status, surgical treatment, receipt of radiotherapy and chemotherapy, and death. We compared crude breast cancer–specific mortality between the two ethnic groups. We calculated adjusted hazard ratios (HRs) in a propensity-matched design using the Cox proportional hazards model. P < .05 was considered statistically significant. RESULTS There were 7,553 Chinese women (1.8%) and 414,618 White women (98.2%) with stage I-IV breast cancer in the SEER database. There were small differences in demographics, nodal burden, and clinical stage between Chinese and White women. Ten-year breast cancer–specific survival was 88.8% for Chinese women and 85.6% for White women (HR, 0.73; 95% CI, 0.67 to 0.80; P < .0001). In a propensity-matched analysis among women with stage I–IIIC breast cancer, the HR was 0.71 (95% CI, 0.62 to 0.81; P < .0001). Annual mortality rates in White women exceeded those in Chinese women for the first 9 years after diagnosis. CONCLUSION Chinese women in the United States have superior breast cancer–specific survival compared with White women. The reason for the observed difference is not clear. Differences in demographic and tumor features between Chinese and White women with breast cancer may contribute to the disparity, as may the possibility of intrinsic biologic differences.

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Section: Discussionmentioning

confidence: 99%

Survival Differences in Chinese Versus White Women With Breast Cancer in the United States: A SEER-Based Analysis

Lim

Giannakeas

Narod

2020

JCO Global Oncology

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“…We took the SNPs referred to in Table 1 as genetic instruments for each of the nine risk factors. For every SNP, we performed survival analyses to obtain survival estimates as described previously [ 23 ]. The analyses included the full OncoArray and iCOGS datasets.…”

Section: Methodsmentioning

confidence: 99%

“…We also aimed to investigate whether we could observe—or refute—an effect for the risk factors for which the association is not clear. We therefore performed a two-sample MR analysis using genetic variants and risk factor association summary estimates from the GWAS Catalog [ 22 ] and breast cancer survival summary estimates from the Breast Cancer Association Consortium (BCAC) cohort [ 23 ].…”

Section: Introductionmentioning

confidence: 99%

Breast cancer risk factors and their effects on survival: a Mendelian randomisation study

Escala-Garcia

Morra

Canisius

et al. 2020

BMC Med

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Background Observational studies have investigated the association of risk factors with breast cancer prognosis. However, the results have been conflicting and it has been challenging to establish causality due to potential residual confounding. Using a Mendelian randomisation (MR) approach, we aimed to examine the potential causal association between breast cancer-specific survival and nine established risk factors for breast cancer: alcohol consumption, body mass index, height, physical activity, mammographic density, age at menarche or menopause, smoking, and type 2 diabetes mellitus (T2DM). Methods We conducted a two-sample MR analysis on data from the Breast Cancer Association Consortium (BCAC) and risk factor summary estimates from the GWAS Catalog. The BCAC data included 86,627 female patients of European ancestry with 7054 breast cancer-specific deaths during 15 years of follow-up. Of these, 59,378 were estrogen receptor (ER)-positive and 13,692 were ER-negative breast cancer patients. For the significant association, we used sensitivity analyses and a multivariable MR model. All risk factor associations were also examined in a model adjusted by other prognostic factors. Results Increased genetic liability to T2DM was significantly associated with worse breast cancer-specific survival (hazard ratio [HR] = 1.10, 95% confidence interval [CI] = 1.03–1.17, P value [P] = 0.003). There were no significant associations after multiple testing correction for any of the risk factors in the ER-status subtypes. For the reported significant association with T2DM, the sensitivity analyses did not show evidence for violation of the MR assumptions nor that the association was due to increased BMI. The association remained significant when adjusting by other prognostic factors. Conclusions This extensive MR analysis suggests that T2DM may be causally associated with worse breast cancer-specific survival and therefore that treating T2DM may improve prognosis.

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“…Derived signatures can be validated in online (publicly) available datasets and in future studies. Genome-wide analysis of germ-line variations in almost 100,000 breast cancer patients in different cohorts revealed no major novel individual prognostic factors, whereas a network analysis identified the module 'cell growth and angiogenesis' to be prognostic for ER-but not ER+ breast cancer (101). One of the four components in this module was CHCHD4, which encodes a mitochondrial protein involved in HIF-1 stability and regulation of mitochondrial respiratory chain in tumor cell adaptation to hypoxia (33,102).…”

Section: Prognostic Markersmentioning

confidence: 99%

HIFs, angiogenesis, and metabolism: elusive enemies in breast cancer

Heer¹,

Jalving²,

Harris³

2020

Journal of Clinical Investigation

218

146

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Hypoxia-inducible factors (HIFs) and the HIF-dependent cancer hallmarks angiogenesis and metabolic rewiring are well-established drivers of breast cancer aggressiveness, therapy resistance, and poor prognosis. Targeting of HIF and its downstream targets in angiogenesis and metabolism has been unsuccessful so far in the breast cancer clinical setting, with major unresolved challenges residing in target selection, development of robust biomarkers for response prediction, and understanding and harnessing escape mechanisms. This Review discusses the pathophysiological role of HIFs, angiogenesis, and metabolism in breast cancer and the challenges of targeting these features in breast cancer patients. Rational therapeutic combinations, especially with immunotherapy and endocrine therapy, seem most promising in the clinical exploitation of the intricate interplay of HIFs, angiogenesis, and metabolism in breast cancer cells and the tumor microenvironment.

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A network analysis to identify mediators of germline-driven differences in breast cancer prognosis

Cited by 32 publications

References 80 publications

Survival Differences in Chinese Versus White Women With Breast Cancer in the United States: A SEER-Based Analysis

Survival Differences in Chinese Versus White Women With Breast Cancer in the United States: A SEER-Based Analysis

Breast cancer risk factors and their effects on survival: a Mendelian randomisation study

HIFs, angiogenesis, and metabolism: elusive enemies in breast cancer

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