In a substudy to Karma, scientists will study why it is more common among women with high mammographic density to develop breast cancer. The study will investigate breast tissue biopsies from women with low breast density and compare this with biopsies from women with dense breasts.

Karma Normal is a collaboration between researchers at the Karolinska Institute and physicians at the mammography clinic in Helsingborg Hospital. The study will invite randomly selected women in the Helsingborg area who already participates in Karma.

Medical doctors take biopsies from healthy breasts using the same procedures as in investigating a breast lump. The biopsies are analyzed with different methods which will present a picture of what the tissue looks like. The researchers will look into any cell composition that represents density and any composition that represents changes that would lead to a cancer later on in life.

The aim is to understand what gives rise to the density and what cell types and genes are influencing development of cancer, and hopefully to prevent breast cancer in the future.

Breast cancer has steadily increased over the past 40 years. In Sweden, one woman per hour is diagnosed with breast cancer and every six hours a woman dies from the disease. The need to prevent the onset of breast cancer is high.

Therefore researchers at the Karolinska Institute in collaboration with four hospitals in Sweden perform the largest cancer study ever in Sweden – Karma.  The objective of this study is to significantly reduce the incidence of breast cancer. Karma will identify high risk women based on lifestyle, genetics, mammographic morphology and other markers. When we can assess the individual risk for breast cancer, the next step is to tailor a preventive treatment for each woman based on the treatments available to them at the time.

Thanks to the 1000 women who voluntarily choose to join the Karma study every week, we have today reached a major milestone with 50,000 participating women!

Karma study has been made possible thanks to a donation of 50 million SEK from Märit and Hans Rausing’s Initiative Against Breast Cancer.

Mammographic density (MD) is a strong, independent risk factor of breast cancer, but measuring MD is time-consuming and reader-dependent. Objective MD measurement in a high-throughput fashion would enable its wider use as a biomarker for breast cancer. We use a public domain image processing software for the fully automated analysis of MD and penalised regression to construct a measure which mimics a well-established semi-automated measure (Cumulus). We also describe measures which incorporate additional features of mammographic images for improving the risk associations of MD and breast cancer risk.

We randomly partitioned our dataset into a training set for model building (733 cases, 748 controls) and a test set for model assessment (765 cases, 747 controls). Pearson’s product moment correlation coefficient (r) was used to compare the MD measurements by Cumulus and our automated measure which mimics Cumulus. The likelihood ratio test was used to validate the performance of logistic regression models for breast cancer risk, which included our measure capturing additional information in mammographic images.

We observed a high correlation between the Cumulus measure and our measure mimicking Cumulus (r = 0.884, 95% CI: 0.872 to 0.894) in an external test set. Adding a variable, which includes extra information to percent density, significantly improved the fit of the logistic regression model of breast cancer risk (P=0.0002).

Our results demonstrate the potential to facilitate the integration of mammographic density measurements into large-scale research studies and subsequently into clinical practice.

Li J, Szekely L, Eriksson L, Heddson B, Sundbom A, Czene K, Hall P, Humphreys K.
Breast Cancer Res. 2012 Jul 30;14(4):R114.

The elucidation of breast cancer subgroups and their molecular drivers requires integrated views of the genome and transcriptome from representative numbers of patients. We present an integrated analysis of copy number and gene expression in a discovery and validation set of 997 and 995 primary breast tumours, respectively, with long-term clinical follow-up. Inherited variants (copy number variants and single nucleotide polymorphisms) and acquired somatic copy number aberrations (CNAs) were associated with expression in ∼40% of genes, with the landscape dominated by cis- and trans-acting CNAs. By delineating expression outlier genes driven in cis by CNAs, we identified putative cancer genes, including deletions in PPP2R2A, MTAP and MAP2K4. Unsupervised analysis of paired DNA-RNA profiles revealed novel subgroups with distinct clinical outcomes, which reproduced in the validation cohort. These include a high-risk, oestrogen-receptor-positive 11q13/14 cis-acting subgroup and a favourable prognosis subgroup devoid of CNAs. Trans-acting aberration hotspots were found to modulate subgroup-specific gene networks, including a TCR deletion-mediated adaptive immune response in the ‘CNA-devoid’ subgroup and a basal-specific chromosome 5 deletion-associated mitotic network. Our results provide a novel molecular stratification of the breast cancer population, derived from the impact of somatic CNAs on the transcriptome.

Curtis C, Shah SP, Chin SF, Turashvili G, Rueda OM, Dunning MJ, Speed D, Lynch AG, Samarajiwa S, Yuan Y, Gräf S, Ha G, Haffari G, Bashashati A, Russell R, McKinney S; METABRIC Group; Co-chairs, Caldas C, Aparicio S; Writing committee, Curtis C, Shah SP, Caldas C, Aparicio S; Steering committee, Brenton JD, Ellis I, Huntsman D, Pinder S, Purushotham A, Murphy L, Caldas C, Aparicio S; Tissue and clinical data source sites:; University of Cambridge/Cancer Research UK Cambridge Research Institute, Caldas C, Bardwell H, Chin SF, Curtis C, Ding Z, Gräf S, Jones L, Liu B, Lynch AG, Papatheodorou I, Sammut SJ, Wishart G; British Columbia Cancer Agency, Aparicio S, Chia S, Gelmon K, Huntsman D, McKinney S, Speers C, Turashvili G, Watson P; University of Nottingham, Ellis I, Blamey R, Green A, Macmillan D, Rakha E; King’s College London, Purushotham A, Gillett C, Grigoriadis A, Pinder S, di Rinaldis E, Tutt A; Manitoba Institute of Cell Biology, Murphy L, Parisien M, Troup S; Cancer genome/transcriptome characterization centres:; University of Cambridge/Cancer Research UK Cambridge Research Institute, Caldas C, Chin SF, Chan D, Fielding C, Maia AT, McGuire S, Osborne M, Sayalero SM, Spiteri I, Hadfield J; British Columbia Cancer Agency, Aparicio S, Turashvili G, Bell L, Chow K, Gale N, Huntsman D, Kovalik M, Ng Y, Prentice L; Data analysis subgroup:; University of Cambridge/Cancer Research UK Cambridge Research Institute, Caldas C, Tavaré S, Curtis C, Dunning MJ, Gräf S, Lynch AG, Rueda OM, Russell R, Samarajiwa S, Speed D, Markowetz F, Yuan Y, Brenton JD; British Columbia Cancer Agency, Aparicio S, Shah SP, Bashashati A, Ha G, Haffari G, McKinney S, Langerød A, Green A, Provenzano E, Wishart G, Pinder S, Watson P, Markowetz F, Murphy L, Ellis I, Purushotham A, Børresen-Dale AL, Brenton JD, Tavaré S, Caldas C, Aparicio S.

1] Department of Oncology, University of Cambridge, Hills Road, Cambridge CB2 2XZ, UK [2] Cancer Research UK, Cambridge Research Institute, Li Ka Shing Centre, Robinson Way, Cambridge CB2 0RE, UK [3] Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California 90033, USA (Ch.C.); University College London, Genetics Institute, WC1E 6BT, UK (D.S.). [4].

Nature. 2012 Apr 18. doi: 10.1038/nature10983. [Epub ahead of print]

The 19p13.1 breast cancer susceptibility locus is a modifier of breast cancer risk in BRCA1 mutation carriers and is also associated with risk of ovarian cancer. Here we investigated 19p13.1 variation and risk of breast cancer subtypes, defined by estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor-2 (HER2) status, using 48,869 breast cancer cases and 49,787 controls from the Breast Cancer Association Consortium (BCAC). Variants from 19p13.1 were not associated with breast cancer overall or with ER-positive breast cancer but were significantly associated with ER-negative breast cancer risk [rs8170 Odds Ratio (OR)=1.10, 95% Confidence Interval (CI) 1.05 – 1.15, p=3.49 x 10-5] and triple negative (TN) (ER, PR and HER2 negative) breast cancer [rs8170 OR=1.22, 95% CI 1.13 – 1.31, p=2.22 x 10-7]. However, rs8170 was no longer associated with ER-negative breast cancer risk when TN cases were excluded [OR=0.98, 95% CI 0.89 – 1.07, p=0.62]. In addition, a combined analysis of TN cases from BCAC and the Triple Negative Breast Cancer Consortium (TNBCC) (n=3,566) identified a genome-wide significant association between rs8170 and TN breast cancer risk [OR=1.25, 95% CI 1.18 – 1.33, p=3.31 x 10-13]. Thus, 19p13.1 is the first triple negative-specific breast cancer risk locus and the first locus specific to a histological subtype defined by ER, PR, and HER2 to be identified. These findings provide convincing evidence that genetic susceptibility to breast cancer varies by tumor subtype and that triple negative tumors and other subtypes likely arise through distinct etiologic pathways.

Stevens KN, Fredericksen Z, Vachon CM, Wang X, Margolin S, Lindblom A, Nevanlinna H, Greco D, Aittomäki K, Blomqvist C, Chang-Claude J, Vrieling A,Flesch-Janys D, Sinn HP, Wang-Gohrke S, Nickels S, Brauch H, Ko YD, Fischer HP, Network TG, Schmutzler RK, Meindl A, Bartram CR, Schott S, Engel C,Godwin AK, Weaver J, Pathak HB, Sharma P, Brenner H, Muller H, Arndt V, Stegmaier C, Miron P, Yannoukakos D, Stavropoulou A, Fountzilas G, Gogas HJ,Swann R, Dwek M, Perkins KA, Milne RL, Benítez J, Zamora MP, Ignacio Arias Pérez J, Bojesen SE, Nielsen SF, Nordestgaard BG, Flyger H, Guénel P, Truong T, Menegaux F, Cordina-Duverger E, Burwinkel B, Marmé F, Schneeweiss A, Sohn C, Sawyer E, Tomlinson I, Kerin MJ, Peto J, Johnson N, Fletcher O, Dos Santos Silva I, Fasching PA, Beckmann MW, Hartmann A, Ekici AB, Lophatananon A, Muir K, Puttawibul P, Wiangnon S, Schmidt MK, Broeks A, Braaf LM,Rosenberg EH, Hopper JL, Apicella C, Park DJ, Southey MC, Swerdlow AJ, Ashworth A, Orr N, Schoemaker MJ, Anton-Culver H, Ziogas A, Bernstein L, Clarke Dur C, Shen CY, Yu JC, Hsu HM, Hsiung CN, Hamann U, Dünnebier T, Rüdiger T, Ulrich Ulmer H, Pharoah PD, Dunning AM, Humphreys MK, Wang Q, Cox A,Cross SS, Reed MW, Hall P, Czene K, et al.
To the article … 

Breast cancer is the most common cancer among women. To date, 22 common breast cancer susceptibility loci have been identified accounting for ∼8% of the heritability of the disease. We attempted to replicate 72 promising associations from two independent genome-wide association studies (GWAS) in ∼70,000 cases and ∼68,000 controls from 41 case-control studies and 9 breast cancer GWAS. We identified three new breast cancer risk loci at 12p11 (rs10771399; P = 2.7 × 10(-35)), 12q24 (rs1292011; P = 4.3 × 10(-19)) and 21q21 (rs2823093; P = 1.1 × 10(-12)). rs10771399 was associated with similar relative risks for both estrogen receptor (ER)-negative and ER-positive breast cancer, whereas the other two loci were associated only with ER-positive disease. Two of the loci lie in regions that contain strong plausible candidate genes: PTHLH (12p11) has a crucial role in mammary gland development and the establishment of bone metastasis in breast cancer, and NRIP1 (21q21) encodes an ER cofactor and has a role in the regulation of breast cancer cell growth.

Ghoussaini M, Fletcher O, Michailidou K, Turnbull C, Schmidt MK, Dicks E, Dennis J, Wang Q, Humphreys MK, Luccarini C, Baynes C, Conroy D, Maranian M, Ahmed S, Driver K, Johnson N, Orr N, Dos Santos Silva I, Waisfisz Q, Meijers-Heijboer H, Uitterlinden AG, Rivadeneira F; Netherlands Collaborative Group on Hereditary Breast and Ovarian Cancer (HEBON), Hall P, Czene K, et al.
To the article …

The Karma Research Platform (beta) is now up and running. As of today, any scientist with sound science can get access to the world’s best-characterized breast cancer cohort.

“We are proud to present the Karma Research Platform. With more than 20,000 participants and a steady inflow of more than 800 women per week I believe the Karma Research Platform will become a game-changer for breast cancer research” says Professor Per Hall, Karma PI.

Interested scientists will get access after an inquiry has been sent to Karma’s Research Platform scientific manager. Then, in just a few simple steps the researcher can look at the collected Karma material, select interesting variables and values, modulate the material and finally export selected variables.

“With the Karma Research Platform a scientist can quickly find out how Karma can strengthen his or her research with access to unprecedented real life data and bio-samples” continues Per Hall. “We strongly believe that by sharing data freely, research leading to reduced mortality and incidence in breast cancer will come faster and better.”

To get access to Karma Research Platform, please follow this link.