Development and validation of polygenic risk scores for prediction of breast cancer and breast cancer subtypes in Chinese women

Utilizing polygenic risk score for breast cancer risk prediction in a Taiwanese population

BACKGROUND

Breast cancer has been the most frequently diagnosed cancer among women in Taiwan since 2003. While genetic variants play a significant role in the elevated risk of breast cancer, their implications have been less explored within Asian populations. Variant-based polygenic risk scores (PRS) have emerged as valuable tools for assessing the likelihood of developing breast cancer. In light of this, we attempted to establish a predictive breast cancer PRS tailored specifically for the Taiwanese population.

METHODS

The cohort analyzed in this study comprised 28,443 control subjects and 1501 breast cancer cases. These individuals were sourced from the Taiwan Precision Medicine Initiative (TPMI) array and the breast cancer registry lists at Taichung Veterans General Hospital (TCVGH). Utilizing the breast cancer-associated Polygenic Score (PGS) Catalog, we employed logistic regression to identify the most effective PRS for predicting breast cancer risk. Subsequently, we subjected the cohort of 1501 breast cancer patients to further analysis to investigate potential heterogeneity in breast cancer risk.

RESULTS

The Polygenic Score ID PGS000508 demonstrated a significant association with breast cancer risk in Taiwanese women with a 1.498-fold increase in cancer risk(OR = 1.498, 95 % CI(1.431-1.567, p=5.38×10^-68). Individuals in the highest quartile exhibited a substantially elevated risk compared to those in the lowest quartile, with an odds ratio (OR) of 3.11 (95 % CI: 2.70-3.59; p=1.15×10^-55). In a cohort of 1501 breast cancer cases stratified by PRS distribution, women in the highest quartile were diagnosed at a significantly younger age (p=0.003) compared to those in the lowest quartile. However, no significant differences were observed between PRS quartiles in relation to clinical stage (p=0.274), pathological stage (p=0.647), or tumor subtype distribution (p=0.244).

CONCLUSION

In our study, we pinpointed PGS000508 as a significant predictive factor for breast cancer risk in Taiwanese women. Furthermore, we found that a higher PGS000508 score was associated with younger age at the time of first diagnosis among the breast cancer cases examined.

Decoding core genes and intercellular communication in osteosarcoma: bioinformatic investigation and immune cell profiling for diagnostic and therapeutic insights

The study focusing on developing an artificial neural network (ANN) model in accordance with genetic characteristics of osteosarcoma (OS) to accurately speculate OS cases. In the present study, we identified 467 DEGs through differentially acting gene investigation and that 345 exist suppressed and 122 exist stimulated. The resultant of GO enrichment analysis displayed the functions mainly included T cell activation, secretory granule lumen, antioxidant property etc. The pathways identified in the differentially acting genes (DAGs) were greatly interacted with Phagosome, Staphylococcus aureus infection, Human T - cell leukemia virus 1 infection, etc. Next, we found out top ten hub DEGs (HDEGs) by PPI network analysis. In addition, through the validation of ANN itself and Test set samples, it was proved that the prediction performance of our constructed ANN model is accurate and reliable. Finally, the penetration of immune cells and its interaction with target CDEGs were examined, and variations in penetration of 22 types of immune cells amongst different classes were found, additionally correlation amongst immune cells and between immune cells and target CDEGs. Furthermore, we analyzed the expression of the top two CDEGs (YES1 and MFNG) in OS tissues and normal tissues, also the interrelationship among the activity of YES1 and MFNG in OS tissues and clinicopathological properties of OS cases. Furthermore, the correlation analysis between the top two CDEGs and immune infiltrating cells was performed in OS tissues. Our research results revealed that CDEGs-based ANN model is effective at predicting OS patients, which facilitates early diagnosis and treatment of OS.

Benefits and harms of polygenic risk scores in organised cancer screening programmes: a cost-effectiveness analysis

Background

While polygenic risk scores (PRS) could enable the streamlining of organised cancer screening programmes, its current discriminative ability is limited. We conducted a cost-effectiveness analysis to trade-off the benefits and harms of PRS-stratified cancer screening in China.

Methods

The validated National Cancer Center (NCC) modelling framework for six cancers (lung, liver, breast, gastric, colorectum, and oesophagus) was used to simulate cancer incidence, progression, stage-specific cancer detection, and risk of death. We estimated the number of cancer deaths averted, quality-adjusted life-years (QALY) gained, number needed to screen (NNS), overdiagnosis, and incremental cost-effectiveness ratio (ICER) of one-time PRS-stratified screening strategy (screening 25% of PRS-defined high-risk population) for a birth cohort at age 60 in 2025, compared with unstratified screening strategy (screening 25% of general population) and no screening strategy. We applied lifetime horizon, societal perspective, and 3% discount rate. An ICER less than $18,364 per QALY gained is considered cost-effective.

Findings

One-time cancer screening for population aged 60 was the most cost-effective strategy compared to screening at other ages. Compared with an unstratified screening strategy, the PRS-stratified screening strategy averted more cancer deaths (61,237 vs. 40,329), had a lower NNS to prevent one death (307 vs. 451), had a slightly higher overdiagnosis (14.1% vs. 13.8%), and associated with an additional 130,045 QALYs at an additional cost of $1942 million, over a lifetime horizon. The ICER for all six cancers combined was $14,930 per QALY gained, with the ICER varying from $7928 in colorectal cancer to $39,068 in liver cancer. ICER estimates were sensitive to changes in risk threshold and cost of PRS tools.

Interpretation

PRS-stratified screening strategy modestly improves clinical benefit and cost-effectiveness of organised cancer screening programmes. Reducing the costs of polygenic risk stratification is needed before PRS implementation.

Funding

The Chinese Academy of Medical Sciences, the Jing-jin-ji Special Projects for Basic Research Cooperation, and the Sanming Project of the Medicine in Shenzhen.

A Systematic Review and Critical Assessment of Breast Cancer Risk Prediction Tools Incorporating a Polygenic Risk Score for the General Population

Single nucleotide polymorphisms (SNPs) in the form of a polygenic risk score (PRS) have emerged as a promising factor that could improve the predictive performance of breast cancer (BC) risk prediction tools. This study aims to appraise and critically assess the current evidence on these tools. Studies were identified using Medline, EMBASE and the Cochrane Library up to November 2022 and were included if they described the development and/ or validation of a BC risk prediction model using a PRS for women of the general population and if they reported a measure of predictive performance. We identified 37 articles, of which 29 combined genetic and non-genetic risk factors using seven different risk prediction tools. Most models (55.0%) were developed on populations from European ancestry and performed better than those developed on populations from other ancestry groups. Regardless of the number of SNPs in each PRS, models combining a PRS with genetic and non-genetic risk factors generally had better discriminatory accuracy (AUC from 0.52 to 0.77) than those using a PRS alone (AUC from 0.48 to 0.68). The overall risk of bias was considered low in most studies. BC risk prediction tools combining a PRS with genetic and non-genetic risk factors provided better discriminative accuracy than either used alone. Further studies are needed to cross-compare their clinical utility and readiness for implementation in public health practices.

Breast cancer risk stratification using genetic and non-genetic risk assessment tools for 246,142 women in the UK Biobank

PURPOSE

The benefit of using individual risk prediction tools to identify high-risk individuals for breast cancer (BC) screening is uncertain, despite the personalized approach of risk-based screening.

METHODS

We studied the overlap of predicted high-risk individuals among 246,142 women enrolled in the UK Biobank. Risk predictors assessed include the Gail model (Gail), BC family history (FH, binary), BC polygenic risk score (PRS), and presence of loss-of-function (LoF) variants in BC predisposition genes. Youden J-index was used to select optimal thresholds for defining high-risk.

RESULTS

In total, 147,399 were considered at high risk for developing BC within the next 2 years by at least 1 of the 4 risk prediction tools examined (Gail2-year > 0.5%: 47%, PRS2-yea r > 0.7%: 30%, FH: 6%, and LoF: 1%); 92,851 (38%) were flagged by only 1 risk predictor. The overlap between individuals flagged as high-risk because of genetic (PRS) and Gail model risk factors was 30%. The best-performing combinatorial model comprises a union of high-risk women identified by PRS, FH, and, LoF (AUC2-year [95% CI]: 62.2 [60.8 to 63.6]). Assigning individual weights to each risk prediction tool increased discriminatory ability.

CONCLUSION

Risk-based BC screening may require a multipronged approach that includes PRS, predisposition genes, FH, and other recognized risk factors.

Genetic and lifestyle factors for breast cancer risk assessment in Southeast China

BACKGROUND

Despite the rising incidence and mortality of breast cancer among women in China, there are currently few predictive models for breast cancer in the Chinese population and with low accuracy. This study aimed to identify major genetic and life-style risk factors in a Chinese population for potential application in risk assessment models.

METHODS

A case-control study in southeast China was conducted including 1321 breast cancer patients and 2045 controls during 2013-2016, in which the data were randomly divided into a training set and a test set on a 7:3 scale. The association between genetic and life-style factors and breast cancer was examined using logistic regression models. Using AUC curves, we also compared the performance of the logistic model to machine learning models, namely LASSO regression model and support vector machine (SVM), and the scores calculated from CKB, Gail and Tyrer-Cuzick models in the test set.

RESULTS

Among all factors considered, the best model was achieved when polygenetic risk score, lifestyle, and reproductive factors were considered jointly in the logistic regression model (AUC = 0.73; 95% CI: 0.70-0.77). The models created in this study performed better than those using scores calculated from the CKB, Gail, and Tyrer-Cuzick models. However, the logistic model and machine learning models did not significantly differ from one another.

CONCLUSION

In summary, we have found genetic and lifestyle risk predictors for breast cancer with moderate discrimination, which might provide reference for breast cancer screening in southeast China. Further population-based studies are needed to validate the model for future applications in personalized breast cancer screening programs.

Adipose tissue-to-breast cancer crosstalk: Comprehensive insights

The review focuses on mechanistic evidence for the link between obesity and breast cancer. According to the IARC study, there is sufficient evidence that obesity is closely related to a variety of cancers. Among them, breast cancer is particularly disturbed by adipose tissue due to the unique histological structure of the breast. The review introduces the relationship between obesity and breast cancer from two aspects, including factors that promote tumorigenesis or metastasis. We summarize alterations in adipokines and metabolic pathways that contribute to breast cancer development. Breast cancer metastasis is closely related to obesity-induced pro-inflammatory microenvironment, adipose stem cells, and miRNAs. Based on the mechanism by which obesity causes breast cancer, we list possible therapeutic directions, including reducing the risk of breast cancer and inhibiting the progression of breast cancer. We also discussed the risk of autologous breast remodeling and fat transplantation. Finally, the causes of the obesity paradox and the function of enhancing immunity are discussed. Evaluating the balance between obesity-induced inflammation and enhanced immunity warrants further study.