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Research article
Shahin Sayed
Aga Khan University - Kenya
Corresponding Author
ORCiD: https://orcid.org/0000-0002-3472-511X
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background
Few studies have investigated risk factor heterogeneity by molecular subtypes in indigenous African populations where prevalence of traditional breast cancer (BC) risk factors, genetic background, and environmental exposures show marked differences compared to European ancestry populations.
Methods
We conducted a case-only analysis of 838 pathologically confirmed BC cases recruited from 5 groups of public, faith-based and private institutions across Kenya between March 2012 to May 2015. Centralized pathology review and immunohistochemistry (IHC) for key markers (ER, PR, HER2, EGFR, CK5-6, and Ki67) was performed to define subtypes. Risk factor data was collected at time of diagnosis through a questionnaire. Multivariable polytomous logistic regression models were used to determine associations between BC risk factors and tumor molecular subtypes, adjusted for clinical characteristics and risk factors.
Results
The median age at menarche and first pregnancy were 14 and 21 years, median number of children was 3 and breastfeeding duration was 62 months per child. Distribution of molecular subtypes for luminal A, luminal B, HER2-enriched, and Triple Negative (TN) breast cancers was 34.8%, 35.8%, 10.7%, and 18.6%, respectively. After adjusting for covariates, compared to patients with ER positive tumors, ER negative patients were more likely to have higher parity (OR=2.03, 95% CI= (1.11, 3.72), p=0.021, comparing ≥5 to <2 children) and younger age at first pregnancy (ORtrend=0.77, 95% CItrend=0.61, 0.98, Ptrend=0.032, comparing older to younger age). Compared to patients with luminal A tumors, luminal B patients were more likely to have lower parity (OR=0.45, 95% CI= 0.23, 0.87, p=0.018, comparing ≥5 to <2 children); HER2-enriched patients were less likely to be obese (OR=0.36, 95% CI=0.16, 0.81, p=0.013) or older age at menopause (OR=0.38, 95% CI=0.15, 0.997, p=0.049). Body mass index (BMI), either overall or by menopausal status, did not vary significantly by ER status. Overall, cumulative or average breastfeeding duration did not vary significantly across subtypes.
Conclusions
In Kenya, we found associations between parity-related risk factors and ER status consistent with observations in European ancestry populations, but differing associations with BMI and breastfeeding. Inclusion of diverse populations in cancer etiology studies are needed to develop population and subtype specific risk prediction/prevention strategies.
Keywords
Breast cancer, molecular sub types, risk factors, Kenya, sub-Saharan Africa
Figure 1
Figure 2
This is a list of supplementary files associated with this preprint. Click to download.
- SupplementaryTable1.Classificationsofhospitals1215.docx
Supplementary Table 1. Classifications of the five hospital groups
- SupplementaryTable2.RiskfactorsandPRandHER2.docx
Supplementary Table 2. Associations between breast cancer risk factors and PR and HER2 status in Kenyan breast cancer patients (N=838)
- SupplementaryTable3.KeyRiskfactorsandERbyhospitals.docx
Supplementary Table 3. Associations of key risk factors with ER status by hospitals (N=838)
- SupplementaryTable4.BMIandHER2byhospitals.docx
Supplementary Table 4. Associations between BMI and HER2 status stratified by hospitals (N=821)
- SupplementaryTable5KeyriskfactorsERstatusbyage.docx
Supplementary Table 5. Associations between key risk factors and ER status by age at diagnosis (N=834)
- SupplementaryTable6RFandtumorsubtypesstratified.docx
Supplementary Table 6. Associations between breast cancer risk factors and tumor molecular subtypes in Kenyan breast cancer patients (N=776)
- SupplementaryFigure1.BMIandHER2byhospital.docx
Supplementary Figure 1. Associations between BMI and HER2 status stratified by hospital groups
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Review #2 received
Received 14 Mar, 2021
Reviewer #1 agreed
On 04 Mar, 2021
Reviewers invited
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Reviews received
Received 04 Mar, 2021
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Editor assigned
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This preprint is under consideration at Breast Cancer Research. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research article
Shahin Sayed
Aga Khan University - Kenya
Corresponding Author
ORCiD: https://orcid.org/0000-0002-3472-511X
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Review
Version (no preprint)
Posted 04 May, 2021
Editor invited
On 04 May, 2021
Editor assigned
On 04 May, 2021
Submission checks complete
On 04 May, 2021
This version was not preprinted
Version 1
Posted 03 Mar, 2021
No community comments so far
Editorial decision: Minor Revision
On 30 Mar, 2021
Review #1 received
Received 24 Mar, 2021
Reviewer #2 agreed
On 14 Mar, 2021
Review #2 received
Received 14 Mar, 2021
Reviewer #1 agreed
On 04 Mar, 2021
Reviewers invited
Invitations sent on 04 Mar, 2021
Reviews received
Received 04 Mar, 2021
Editor invited
On 26 Feb, 2021
Editor assigned
On 24 Feb, 2021
Submission checks complete
On 24 Feb, 2021
First submitted
On 21 Feb, 2021
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Cancer Biology
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background
Few studies have investigated risk factor heterogeneity by molecular subtypes in indigenous African populations where prevalence of traditional breast cancer (BC) risk factors, genetic background, and environmental exposures show marked differences compared to European ancestry populations.
Methods
We conducted a case-only analysis of 838 pathologically confirmed BC cases recruited from 5 groups of public, faith-based and private institutions across Kenya between March 2012 to May 2015. Centralized pathology review and immunohistochemistry (IHC) for key markers (ER, PR, HER2, EGFR, CK5-6, and Ki67) was performed to define subtypes. Risk factor data was collected at time of diagnosis through a questionnaire. Multivariable polytomous logistic regression models were used to determine associations between BC risk factors and tumor molecular subtypes, adjusted for clinical characteristics and risk factors.
Results
The median age at menarche and first pregnancy were 14 and 21 years, median number of children was 3 and breastfeeding duration was 62 months per child. Distribution of molecular subtypes for luminal A, luminal B, HER2-enriched, and Triple Negative (TN) breast cancers was 34.8%, 35.8%, 10.7%, and 18.6%, respectively. After adjusting for covariates, compared to patients with ER positive tumors, ER negative patients were more likely to have higher parity (OR=2.03, 95% CI= (1.11, 3.72), p=0.021, comparing ≥5 to <2 children) and younger age at first pregnancy (ORtrend=0.77, 95% CItrend=0.61, 0.98, Ptrend=0.032, comparing older to younger age). Compared to patients with luminal A tumors, luminal B patients were more likely to have lower parity (OR=0.45, 95% CI= 0.23, 0.87, p=0.018, comparing ≥5 to <2 children); HER2-enriched patients were less likely to be obese (OR=0.36, 95% CI=0.16, 0.81, p=0.013) or older age at menopause (OR=0.38, 95% CI=0.15, 0.997, p=0.049). Body mass index (BMI), either overall or by menopausal status, did not vary significantly by ER status. Overall, cumulative or average breastfeeding duration did not vary significantly across subtypes.
Conclusions
In Kenya, we found associations between parity-related risk factors and ER status consistent with observations in European ancestry populations, but differing associations with BMI and breastfeeding. Inclusion of diverse populations in cancer etiology studies are needed to develop population and subtype specific risk prediction/prevention strategies.
Figure 1
Figure 2
This is a list of supplementary files associated with this preprint. Click to download.
- SupplementaryTable1.Classificationsofhospitals1215.docx
Supplementary Table 1. Classifications of the five hospital groups
- SupplementaryTable2.RiskfactorsandPRandHER2.docx
Supplementary Table 2. Associations between breast cancer risk factors and PR and HER2 status in Kenyan breast cancer patients (N=838)
- SupplementaryTable3.KeyRiskfactorsandERbyhospitals.docx
Supplementary Table 3. Associations of key risk factors with ER status by hospitals (N=838)
- SupplementaryTable4.BMIandHER2byhospitals.docx
Supplementary Table 4. Associations between BMI and HER2 status stratified by hospitals (N=821)
- SupplementaryTable5KeyriskfactorsERstatusbyage.docx
Supplementary Table 5. Associations between key risk factors and ER status by age at diagnosis (N=834)
- SupplementaryTable6RFandtumorsubtypesstratified.docx
Supplementary Table 6. Associations between breast cancer risk factors and tumor molecular subtypes in Kenyan breast cancer patients (N=776)
- SupplementaryFigure1.BMIandHER2byhospital.docx
Supplementary Figure 1. Associations between BMI and HER2 status stratified by hospital groups
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