Projects

Genome Institute of Singapore, A*STAR 

Laboratory of Women's Health and Genetics

Research that changes how we think about breast cancer. 

Research that is understood by patients. 

Research that addresses unmet needs of survivors and caregivers.

Identification of women at high-risk of breast cancer in Singapore:

Towards more effective primary prevention

Funded by the National Research Foundation Singapore (NRF-NRFF2017-02) awarded to J Li

Currently, breast cancer screening is based on an age-based model – all women above age 40 are recommended to attend mammography screening. Early detection of breast cancer is known to save lives. However, the benefit of mammography screening in Singapore is negated by low participation rate and adherence – only 66% of the target group of women ever had a mammogram, and half of them do not come back for screening at regular intervals.

The effectiveness of breast cancer prevention in terms of cost and lives saved can be substantially enhanced through targeting prevention at those most likely to benefit. The goal is that targeted prevention will become available for the Singapore population. Identifying high-risk women will mitigate the rising incidence of breast cancer in Singapore in two ways: targeted early prevention and early diagnosis.

The success of my project will contribute towards the understanding of several important risk factors such as mammographic density and the less explored genetics of rare variants in breast cancer. I envision an ideal screening program that incorporates the new knowledge generated through my research: At their first mammographic screening, women would be asked to have their individual risk of breast cancer assessed. Information obtained from consenting women about their genetic profile and other risk factors such as life style, family history, reproductive history, breast density, would be integrated into the online comprehensive risk prediction tool. Application of this tool would thus allow the assignment of preventive measures or treatment interventions on the basis of individual characteristics, which can result in better outcomes than the use of the same strategy for everyone. Recommendations about age at onset and the frequency of mammographic screening could be calibrated to an individual woman's risk of breast cancer. Targeted reminders can be sent to high-risk women to return for regular screenings.

The country’s small size and political stability makes it possible to adjust rapidly to the changing reality of the health economy, and the country has the potential to take the lead in a paradigm shift in breast cancer screening, by taking a risk-based approach over the conventional age-based approach to screening. 


Selected publications:

Childhood body size in association with adult health  

Supported by Karolinska Institutets Research Foundation Grants 2016-2017 (2016fobi47643) awarded to J Li

Close to 1 in 5 children aged 7-9 years old in Sweden are overweight (Obes Rev 12:305-14, 2011). The extra pounds put a child at greater risk of being overweight or obese when they get older, which may in turn confer a greater risk of poor health in adulthood (Circulation 99:1471-6, 1999, Prev Med 22:167-77, 1993, Int J Cancer 112:348-51, 2004). For example, childhood obesity has been linked to cardiovascular diseases and certain cancers (e.g. breast cancer) later on in life (Int J Epidemiol 25:829-34, 1996, Cancer Res 74:235-42, 2014, Breast Cancer Res 12:R23, 2010). Since there is a strong association between childhood body mass index and adult levels, it is conceivable that thin children are less likely to be on the heavy side in adulthood, but little light has been shed on this front. To a lesser extent than obesity, underweight adults have also been observed to be associated with higher rates of morbidity and mortality.

Childhood weight issues represent relevant economic and health care burden on families and the community. With the emergence of phenomenal nation-wide cohort data, it is timely and pertinent to examine the effects of childhood body size in both extremes and adult health. The specific aims are to investigate the: 

1) Relation of childhood body size to adult lifestyle, reproductive health, behavior and quality of life

2) Panorama of diseases in adulthood associated with childhood body size

3) Change in body size during childhood and adolescence and health consequences in adulthood

Impact: We believe that a better understanding of the childhood body size defining long-term health outcomes and quality of life in adults will give us a head start in individual-focused interventions by means of nutrition or physical activity, as well as increase the cost-effectiveness of the health care system. 


Related publications:

1.  Shawon, M.S.R., Eriksson, M. & Li, J. Body size in early life and risk of breast cancer. Breast Cancer Res 19, 84 (2017). (IF 2016: 6.345)

2. Li, J., Eriksson, M., He, W., Hall, P. & Czene, K. Associations between childhood body size and seventeen adverse outcomes: analysis of 65,057 European women. Sci Rep 7, 16917 (2017). (IF 2016: 4.259)

3. Li, J., Humphreys, K., Eriksson, L., Czene, K., Liu, J. & Hall, P. Effects of childhood body size on breast cancer tumour characteristics. Breast Cancer Res 12, R23 (2010). (IF 2016: 6.345)

Genetic correlation between immune-mediated diseases and breast cancer risk 

Supported by Åke Wibergs stiftelse (M16-0019) awarded to J Li

Breast cancer is a complex disease. Despite it being a success story of the genome-wide association study (GWAS) era, only ~28% of familial risk is explained by common variants identified by concerted efforts involving over 100000 samples, with a further ~20% due to higher penetrance loci (Michailidou, K. et al. Nat Genet 47, 373-80, 2015 and Michailidou, K. et al. Nat Genet 45, 353-61, 361e1-2, 2013). Currently, the identification of genetic markers for breast cancer places heavy weight on statistical significance (the genome-wide significance threshold is set at 5×10-8). However, there might be biologically meaningful markers lost to noise. 

Although the cancer immunosurveillance hypothesis has been around for several decades, there is still ongoing debate on whether cancer prevention is a primary function of the immune system. A strong immune system has been postulated to confer protection against cancer  (Corthay, A. Front Immunol 5, 197, 2014). However, it is also possible that the inflammatory responses necessary for enabling an immune reaction may backfire and promote cancer initiation (David, H. Pathol Res Pract 183, 356-64, 1988). Accumulated clinical and experimental data indicate that the outcome of an immune response toward an evolving breast neoplasm is largely determined by the type of immune response elicited (reviewed in DeNardo, D.G. & Coussens, L.M. Breast Cancer Res 9, 212, 2007).

To understand the relationship between immune response and breast cancer risk, we ask the following questions: 

1) Is there a genetic overlap between immune-mediated diseases and breast cancer? 

2) Can we then identify more breast cancer loci using an alternative candidate gene approach (prioritizing overlapping variants between the two diseases)?

The identification of novel genetic markers through indirect methods will improve genetic profiling and enable targeted breast-cancer prediction, detection and treatment for those who need it most.


Related publications:

1.  Yang, H., He, W., Eriksson, M., Li, J., Holowko, N., Chiesa, F., Hall, P. & Czene, K. Inherited factors contribute to an inverse association between preeclampsia and breast cancer. Breast Cancer Res 20, 6 (2018). (IF 2016: 6.345)

2. Ugalde-Morales, E., Li, J., Humphreys, K., Ludvigsson, J.F., Yang, H., Hall, P. & Czene, K. Common shared genetic variation behind decreased risk of breast cancer in celiac disease. Sci Rep 7, 5942 (2017). (IF 2016: 4.259)

3. Yang, H., Brand, J.S., Li, J., Ludvigsson, J.F., Ugalde-Morales, E., Chiesa, F., Hall, P. & Czene, K. Risk and predictors of psoriasis in patients with breast cancer: a Swedish population-based cohort study. BMC Med 15, 154 (2017). (IF 2016: 7.901)

Benign breast diseases as an intermediate phenotype of breast cancer 

Supported by Ollie och Elof Ericssons Stiftelse för Vetenskaplig Forskning awarded to J Li

Gene effects can show greater penetrance at the level of an intermediate phenotype than at the level of a more complex clinical disorder. In such cases, the genetic association may be stronger with the intermediate phenotype in individuals who do not have the clinical diagnosis. While there are many examples of such shared genetic basis in psychiatric diseases (Tan, H.Y., Callicott, J.H. & Weinberger, D.R. Mol Psychiatry 13, 233-8, 2008), the only analogous intermediate phenotype of breast cancer is mammographic density, which is the proportion of radiologically dense tissue on a mammogram (Lindstrom, S. et al. Nat Commun 5, 5303, 2014, Lindstrom, S. et al. Nat Genet 43, 185-7, 2011, Stone, J. et al. Cancer Res 75, 2457-67, 2015, Varghese, J.S. et al. Cancer Res 72, 1478-84, 2012). Intermediate phenotypes such as dense breasts typically occur more commonly than breast cancer, and the identification of markers for such intermediate phenotypes can possibly also predict breast cancer risk. Quantification of the genetic overlap between candidate intermediate phenotypes and breast cancer will also help to speculate the genetic phylogeny of the disease and understand how breast cancer develops biologically. 

Among disorders of the breast, there are invasive breast cancers, breast cancer in situ (CIS, non-invasive, Stage 0), and a third group known as benign breast diseases (BBD). Both CIS and BBD are important risk factors for a later invasive breast cancer, which can develop in either breast (Hartmann, L.C. et al. N Engl J Med 353, 229-37, 2005). Today, CIS accounts for around ~10% of all diagnosed breast cancer tumors in countries with mammography screening programmes. BBDs are non-cancerous conditions. They make up the largest group of breast disorders and can be found in most women (American Cancer Society).

Although BBD is potentially a strong candidate as an intermediate phenotype of breast cancer, it is highly heterogeneous group, and not all subtypes are associated with elevated breast cancer risk. Currently, BBD is grouped into three broad categories based their morphology: non-proliferative, proliferative without atypia (structural abnormality), and proliferative with atypia. The associated breast cancer risk is commonly reported to be between 2 to 4-fold for women diagnosed with proliferative BBDs compared to those without (Hartmann, L.C. et al. N Engl J Med 353, 229-37, 2005 and Guray, M. & Sahin, A.A. Oncologist 11, 435-49, 2006) However, there is no consensus on how to categorize the many BBD, especially some rarer conditions, into these three groups. It is today not possible to predict accurately which women with BBD will develop invasive disease later.

To address the inadequacy of current classifications of BBD (in order to study it as an intermediate phenotype), my specific aims include:

1) Understanding risk factors associated with BBD, and how these correlate with breast cancer

2) Using genetic information to classify BBD in an unsupervised manner (based purely on genotypes, no prior assignment of BBD subtypes)

3) Identifying genetic markers of relevant BBD categories 


Related publications:

1. Li, J., Humphreys, K., Ho, P.J., Eriksson, M., Darai-Ramqvist, E., Lindström, L.S., Hall, P. & Czene, K. Family History, Reproductive, and Lifestyle Risk Factors for Fibroadenoma and Breast Cancer. JNCI Cancer Spectrum 2(2018). 

2. Petridis, C., Brook, M.N., Shah, V., Kohut, K., Gorman, P., Caneppele, M., Levi, D., Papouli, E., Orr, N., Cox, A., Cross, S.S., Dos-Santos-Silva, I., Peto, J., Swerdlow, A., Schoemaker, M.J., Bolla, M.K., Wang, Q., Dennis, J., Michailidou, K., Benitez, J., Gonzalez-Neira, A., Tessier, D.C., Vincent, D., Li, J., Figueroa, J., Kristensen, V., Borresen-Dale, A.L., Soucy, P., Simard, J., Milne, R.L., Giles, G.G., Margolin, S., Lindblom, A., Bruning, T., Brauch, H., Southey, M.C., Hopper, J.L., Dork, T., Bogdanova, N.V., Kabisch, M., Hamann, U., Schmutzler, R.K., Meindl, A., Brenner, H., Arndt, V., Winqvist, R., Pylkas, K., Fasching, P.A., Beckmann, M.W., Lubinski, J., Jakubowska, A., Mulligan, A.M., Andrulis, I.L., Tollenaar, R.A., Devilee, P., Le Marchand, L., Haiman, C.A., Mannermaa, A., Kosma, V.M., Radice, P., Peterlongo, P., Marme, F., Burwinkel, B., van Deurzen, C.H., Hollestelle, A., Miller, N., Kerin, M.J., Lambrechts, D., Floris, G., Wesseling, J., Flyger, H., Bojesen, S.E., Yao, S., Ambrosone, C.B., Chenevix-Trench, G., Truong, T., Guenel, P., Rudolph, A., Chang-Claude, J., Nevanlinna, H., Blomqvist, C., Czene, K., Brand, J.S., Olson, J.E., Couch, F.J., Dunning, A.M., Hall, P., Easton, D.F., Pharoah, P.D., Pinder, S.E., Schmidt, M.K., Tomlinson, I., Roylance, R., Garcia-Closas, M. & Sawyer, E.J. Genetic predisposition to ductal carcinoma in situ of the breast. Breast Cancer Res 18, 22 (2016). (IF 2016: 6.345)

3. Sawyer, E., Roylance, R., Petridis, C., Brook, M.N., Nowinski, S., Papouli, E., Fletcher, O., Pinder, S., Hanby, A., Kohut, K., Gorman, P., Caneppele, M., Peto, J., Dos Santos Silva, I., Johnson, N., Swann, R., Dwek, M., Perkins, K.A., Gillett, C., Houlston, R., Ross, G., De Ieso, P., Southey, M.C., Hopper, J.L., Provenzano, E., Apicella, C., Wesseling, J., Cornelissen, S., Keeman, R., Fasching, P.A., Jud, S.M., Ekici, A.B., Beckmann, M.W., Kerin, M.J., Marme, F., Schneeweiss, A., Sohn, C., Burwinkel, B., Guenel, P., Truong, T., Laurent-Puig, P., Kerbrat, P., Bojesen, S.E., Nordestgaard, B.G., Nielsen, S.F., Flyger, H., Milne, R.L., Perez, J.I., Menendez, P., Benitez, J., Brenner, H., Dieffenbach, A.K., Arndt, V., Stegmaier, C., Meindl, A., Lichtner, P., Schmutzler, R.K., Lochmann, M., Brauch, H., Fischer, H.P., Ko, Y.D., Network, G., Nevanlinna, H., Muranen, T.A., Aittomaki, K., Blomqvist, C., Bogdanova, N.V., Dork, T., Lindblom, A., Margolin, S., Mannermaa, A., Kataja, V., Kosma, V.M., Hartikainen, J.M., Chenevix-Trench, G., Investigators, K., Lambrechts, D., Weltens, C., Van Limbergen, E., Hatse, S., Chang-Claude, J., Rudolph, A., Seibold, P., Flesch-Janys, D., Radice, P., Peterlongo, P., Bonanni, B., Volorio, S., Giles, G.G., Severi, G., Baglietto, L., McLean, C.A., Haiman, C.A., Henderson, B.E., Schumacher, F., Le Marchand, L., Simard, J., Goldberg, M.S., Labreche, F., Dumont, M., Kristensen, V., Winqvist, R., Pylkas, K., Jukkola-Vuorinen, A., Kauppila, S., Andrulis, I.L., Knight, J.A., Glendon, G., Mulligan, A.M., Devillee, P., Tollenaar, R.A., Seynaeve, C.M., Kriege, M., Figueroa, J., Chanock, S.J., Sherman, M.E., Hooning, M.J., Hollestelle, A., van den Ouweland, A.M., van Deurzen, C.H., Li, J., Czene, K., Humphreys, K., Cox, A., Cross, S.S., Reed, M.W., Shah, M., Jakubowska, A., Lubinski, J., Jaworska-Bieniek, K., Durda, K., Swerdlow, A., Ashworth, A., Orr, N., Schoemaker, M., Couch, F.J., Hallberg, E., Gonzalez-Neira, A., Pita, G., Alonso, M.R., Tessier, D.C., Vincent, D., Bacot, F., Bolla, M.K., Wang, Q., Dennis, J., Michailidou, K., Dunning, A.M., Hall, P., Easton, D., Pharoah, P., Schmidt, M.K., Tomlinson, I. & Garcia-Closas, M. Genetic predisposition to in situ and invasive lobular carcinoma of the breast. PLoS Genet 10, e1004285 (2014). (IF 2016: 6.100)

Interval breast cancer

Funded by a UNESCO-L'Oréal International Fellowship awarded to J Li.

What? Recent large-scale genotyping experiments have found a total of 77 common variants implicated in the development of breast cancer (according to the definition of loci by Nature Genetics editor Orli Bahcall).

However, the disease does not always behave in a uniform way. Some cancers, such as interval breast cancers, which are detected within two years of a negative mammogram, are associated with more aggressive tumour characteristics and worse prognosis. Previously, the prostate cancer group in the Department of Medical Epidemiology and Biostatistics (MEB) at Karolinska Institutet, led by Professor Henrik Grönberg, has demonstrated in a Proceedings of National Academy of Science article (Xu et al, 2009) that inherited variants predisposing to aggressive but not indolent prostate cancer exists in the genome. As women with interval cancers were twice as likely to have a personal of family history of breast cancer, it is likely that there exist inherited variants that predispose a woman to the more aggressive form of the disease. The aim of the project is to go beyond studying risk to identifying inherited genetic variants that predispose women to the more aggressive form of the disease, or alter one’s prognosis.

So what? When aggressive breast cancers get diagnosed, it may be too advanced for effective treatment. Mammography screening is also currently of limited help in detecting interval cancers, which are typically more aggressive in nature. Germline genetic markers can be used to overcome this situation because they do not measure a tumour-derived product and can be informative years before cancer develops. Identifying survival-associated inherited variants can also help to elucidate the most important drivers of breast cancer, representing ideal targets for tailored therapeutics, and may also enhance our current prognostic capabilities.

What has been done so far? To show evidence that interval cancers are genetically different from screen-detected cancers, we have recently submitted a report on an analysis based on 77 common genetic variants (single nucleotide polymorphisms, SNPs) which reflect the state of the art of breast cancer genome-wide association studies. Previous studies of breast cancer have shown that patients whose tumours are detected by mammography screening (screen-detected) have a more favourable disease than interval cancers, which develop within the time interval between screening mammograms. Whilst Mavaddat et al. (JNCI-14-0479, under revision) showed in a report that a polygenic risk score constructed using the 77 SNPs could be used for risk stratification of breast cancer, we took it one step further to evaluate whether the polygenic risk score is differentially associated with screen-detected and interval cancers. 

Whilst much work has been performed to compare the clinic-pathological characteristics, molecular biomarkers and survival outcomes of screen-detected and interval cancers, there is yet a study which examines for germline genetic variation between the two diagnostic sub-phenotypes of breast cancer. Within the available 77-SNP portfolio, we observed disparate associations between screen-detected and interval cancers, suggesting that the two cancers detected by different diagnostic methods are indeed distinct in both underlying genetics and biology. Replications from two independent studies provide further evidence that screen-detected and interval cancers have different genetic profiles.

At a tipping point of genetic discoveries for breast cancer, a study that looks into stratifying the disease further into distinct subtypes is timely. The examination of possible genetic differences between cancers diagnosed by different methods of detection is also novel. The major strength of this study is the extensive national registry data available in Sweden, and that the study population represents a large sample of the Swedish population. We have also incorporated the use of the most comprehensive, updated and validated list of breast cancer susceptibility loci to date in the construction of polygenic risk score. The subject matter is important and adds to understanding of the genesis of the more aggressive interval breast cancer, including refining knowledge of the role of previously identified breast cancer susceptibility genetic variants in this sub-phenotype. To our knowledge, this is the first report looking into the genetic differences between screen-detected and interval cancers.


Selected publications:

1.  Li, J., Ugalde-Morales, E., Wen, W.X., Decker, B., Eriksson, M., Torstensson, A., Christensen, H.N., Dunning, A.M., Allen, J., Luccarini, C., Pooley, K.A., Simard, J., Dorling, L., Easton, D.F., Teo, S.H., Hall, P. & Czene, K. Differential Burden of Rare and Common Variants on Tumor Characteristics, Survival, and Mode of Detection in Breast Cancer. Cancer Res 78, 6329-6338 (2018). (IF 2017: 9.130)

2. Strand, F., Humphreys, K., Eriksson, M., Li, J., Andersson, T.M., Tornberg, S., Azavedo, E., Shepherd, J., Hall, P. & Czene, K. Longitudinal fluctuation in mammographic percent density differentiates between interval and screen-detected breast cancer. Int J Cancer 140, 34-40 (2017). (IF 2016: 6.543)

3. Li, J., Ivansson, E., Klevebring, D., Tobin, N.P., Lindstrom, L.S., Holm, J., Prochazka, G., Cristando, C., Palmgren, J., Tornberg, S., Humphreys, K., Hartman, J., Frisell, J., Rantalainen, M., Lindberg, J., Hall, P., Bergh, J., Gronberg, H. & Czene, K. Molecular differences between screen-detected and interval breast cancers are largely explained by PAM50 subtypes. Clin Cancer Res (2016). (IF 2016:  9.619)

4. Li, J., Holm, J., Bergh, J., Eriksson, M., Darabi, H., Lindstrom, L.S., Tornberg, S., Hall, P. & Czene, K. Breast cancer genetic risk profile is differentially associated with interval and screen-detected breast cancers. Ann Oncol 26, 517-22 (2015). (IF 2016: 11.855)

5. Holm, J., Humphreys, K., Li, J., Ploner, A., Cheddad, A., Eriksson, M., Tornberg, S., Hall, P. & Czene, K. Risk factors and tumor characteristics of interval cancers by mammographic density. J Clin Oncol 33, 1030-7 (2015). (IF 2016: 24.008)

6. Lindstrom, L.S., Li, J., Lee, M., Einbeigi, Z., Hartman, M., Hall, P. & Czene, K. Prognostic information of a previously diagnosed sister is an independent prognosticator for a newly diagnosed sister with breast cancer. Ann Oncol 25, 1966-72 (2014). (IF 2016: 11.855)

Mammographic density as a risk factor of breast cancer

Funded by an A*STAR Graduate Scholarship (Overseas) Award and an A*STAR Joint Council (JCO) Career Development Award (CDA), awarded to J Li.

Towards the end of my PhD I was involved in the development of a novel thresholding method (1) to automatically measure mammographic density, one of the strongest risk factors for development of breast cancer. Screening mammography is a fundamental tool in the detection of early breast cancers. However, the clinical relevance of mammographic screening has potential to assume more importance if the proportion of dense tissue on a mammogram, known to be associated with a 4-6 fold increase in breast cancer risk, can be quantified objectively on a high-throughput setting. 

The fully-automated thresholding program has since been applied to upwards of 40,000 pre- and post-diagnostic images in the Swedish CAHRES study, which resulted in an article in the Journal of Clinical Oncology (2). We followed nearly 1,300 breast cancer patients (who survived long enough to get a post-treatment mammogram and thus have the opportunity to benefit from tamoxifen therapy) for 15 years and found that a 20% reduction in mammographic density, comparing the base line and first follow up mammogram, reduced the risk of breast cancer specific death by approximately 50%. 

We then addressed in a further study one of the possible reasons as to why some women experience a decrease in mammographic density and a dramatic influence on risk and prognosis of breast cancer while others do not. In this exploratory candidate gene study, we looked at genetic polymorphisms of the Cytochrome P450 2D6 (CYP2D6) enzyme, which metabolizes tamoxifen to clinically active metabolites. Our hypothesis is that only women who are able to metabolize the prodrug, tamoxifen, to the active metabolites, 4-hydroxytamoxifen (afimoxifen) and N-desmethyl-4-hydroxytamoxifen (endoxifen), would experience a decrease in density and a parallel effect on breast cancer risk and prognosis. As hypothesized, we found that functionality of CYP2D6 was significantly associated with change in mammographic density (3). 

Since mammograms are images of the breast, I also started a project to look at total breast area on a mammogram as a more accurate proxy to breast size as compared to bra band size. It is conceivably difficult to judge breast size using bra size due to poor fit, unreliable bra size charts, lack of an industry standard, and possible “vanity sizing”. While it could be argued at the positioning of the breast and differential compression force applied during mammography introduce variability in the breast area measured, we believe that the objectivity of an area-based measure performs better than other proxies for breast size. This is the first GWAS to analyze mammographic breast area as an unbiased measure for breast size, and we reported a new locus at 22q13 (rs5995871, P = 3.2 × 10−8) (4).

Many prolific new collaborations were set up using this new method (5-19).


Related publications:

1.  Li, J., Szekely, L., Eriksson, L., Heddson, B., Sundbom, A., Czene, K., Hall, P. & Humphreys, K. High-throughput mammographic-density measurement: a tool for risk prediction of breast cancer. Breast Cancer Res 14, R114 (2012). (IF 2016: 6.345)

2. Li, J., Humphreys, K., Eriksson, L., Edgren, G., Czene, K. & Hall, P. Mammographic density reduction is a prognostic marker of response to adjuvant tamoxifen therapy in postmenopausal patients with breast cancer. J Clin Oncol 31, 2249-56 (2013). (IF 2016: 24.008)

3. Li, J., Czene, K., Brauch, H., Schroth, W., Saladores, P., Li, Y., Humphreys, K. & Hall, P. Association of CYP2D6 metabolizer status with mammographic density change in response to tamoxifen treatment. Breast Cancer Res 15, R93 (2013). (IF 2016: 6.345)

4. Li, J., Foo, J.N., Schoof, N., Varghese, J.S., Fernandez-Navarro, P., Gierach, G.L., Quek, S.T., Hartman, M., Nord, S., Kristensen, V.N., Pollan, M., Figueroa, J.D., Thompson, D.J., Li, Y., Khor, C.C., Humphreys, K., Liu, J., Czene, K. & Hall, P. Large-scale genotyping identifies a new locus at 22q13.2 associated with female breast size. J Med Genet 50, 666-73 (2013). (IF 2016: 5.451)

5. Sovio, U., Li, J., Aitken, Z., Humphreys, K., Czene, K., Moss, S., Hall, P., McCormack, V. & dos-Santos-Silva, I. Comparison of fully and semi-automated area-based methods for measuring mammographic density and predicting breast cancer risk. Br J Cancer 110, 1908-16 (2014). (IF 2016: 6.176)

6. Sandberg, M.E., Li, J., Hall, P., Hartman, M., dos-Santos-Silva, I., Humphreys, K. & Czene, K. Change of mammographic density predicts the risk of contralateral breast cancer--a case-control study. Breast Cancer Res 15, R57 (2013). (IF 2016: 6.345)

7. Aylward, S., Hadjiiski, L.M., Law, Y.N., Lieng, M.K., Li, J. & Khoo, D.A.-A. Automated breast tissue density assessment using high order regional texture descriptors in mammography. 9035, 90351Q (2014). 

8. Brand, J.S., Czene, K., Shepherd, J.A., Leifland, K., Heddson, B., Sundbom, A., Eriksson, M., Li, J., Humphreys, K. & Hall, P. Automated measurement of volumetric mammographic density: a tool for widespread breast cancer risk assessment. Cancer Epidemiol Biomarkers Prev 23, 1764-72 (2014). (IF 2016: 4.142)

9. Brand, J.S., Humphreys, K., Thompson, D.J., Li, J., Eriksson, M., Hall, P. & Czene, K. Volumetric mammographic density: heritability and association with breast cancer susceptibility loci. J Natl Cancer Inst 106(2014). (IF 2016: 12.589)

10. Cheddad, A., Czene, K., Eriksson, M., Li, J., Easton, D., Hall, P. & Humphreys, K. Area and volumetric density estimation in processed full-field digital mammograms for risk assessment of breast cancer. PLoS One 9, e110690 (2014). (IF 2016: 2.806)

11. Cheddad, A., Czene, K., Shepherd, J.A., Li, J., Hall, P. & Humphreys, K. Enhancement of mammographic density measures in breast cancer risk prediction. Cancer Epidemiol Biomarkers Prev 23, 1314-23 (2014). (IF 2016: 4.142)

12. Couwenberg, A.M., Verkooijen, H.M., Li, J., Pijnappel, R.M., Charaghvandi, K.R., Hartman, M. & van Gils, C.H. Assessment of a fully automated, high-throughput mammographic density measurement tool for use with processed digital mammograms. Cancer Causes Control 25, 1037-43 (2014). (IF 2016: 2.510)

13. Eng, A., Gallant, Z., Shepherd, J., McCormack, V., Li, J., Dowsett, M., Vinnicombe, S., Allen, S. & dos-Santos-Silva, I. Digital mammographic density and breast cancer risk: a case-control study of six alternative density assessment methods. Breast Cancer Res 16, 439 (2014). (IF 2016: 6.345)

14. Brand, J.S., Li, J., Humphreys, K., Karlsson, R., Eriksson, M., Ivansson, E., Hall, P. & Czene, K. Identification of two novel mammographic density loci at 6Q25.1. Breast Cancer Res 17, 75 (2015). (IF 2016: 6.345)

15. Lee, C.P., Choi, H., Soo, K.C., Tan, M.H., Chay, W.Y., Chia, K.S., Liu, J., Li, J. & Hartman, M. Mammographic Breast Density and Common Genetic Variants in Breast Cancer Risk Prediction. PLoS One 10, e0136650 (2015). (IF 2016: 2.806)

16. Mariapun, S., Li, J., Yip, C.H., Taib, N.A. & Teo, S.H. Ethnic differences in mammographic densities: an Asian cross-sectional study. PLoS One 10, e0117568 (2015). (IF 2016: 2.806)

17. Busana, M.C., De Stavola, B.L., Sovio, U., Li, J., Moss, S., Humphreys, K. & Dos-Santos-Silva, I. Assessing within-woman changes in mammographic density: a comparison of fully versus semi-automated area-based approaches. Cancer Causes Control 27, 481-91 (2016). (IF 2016: 2.510)

18. Khoo, D.A.-A., Li, J., Czene, K., Hall, P., Humphreys, K. & Law, Y.N. A Combined Segmentation and Registration Framework for Bilateral and Temporal Mammogram Analysis. Journal of Medical Imaging and Health Informatics 6, 380-386 (2016). 

19. Eriksson, M., Li, J., Leifland, K., Czene, K. & Hall, P. A comprehensive tool for measuring mammographic density changes over time. Breast Cancer Res Treat 169, 371-379 (2018). (IF 2016: 3.626)