Publications by authors named "Nicola J Camp"

145 Publications

Shared genomic segment analysis in a large high-risk chronic lymphocytic leukemia pedigree implicates in inherited risk.

J Transl Genet Genom 2021 15;5:189-199. Epub 2021 Jun 15.

Huntsman Cancer Institute, University of Utah, Salt Lake City, UT 84112, USA.

Aim: Chronic lymphocytic leukemia (CLL) has been shown to cluster in families. First-degree relatives of individuals with CLL have an ~8 fold increased risk of developing the malignancy. Strong heritability suggests pedigree studies will have good power to localize pathogenic genes. However, CLL is relatively rare and heterogeneous, complicating ascertainment and analyses. Our goal was to identify CLL risk loci using unique resources available in Utah and methods to address intra-familial heterogeneity.

Methods: We identified a six-generation high-risk CLL pedigree using the Utah Population Database. This pedigree contains 24 CLL cases connected by a common ancestor. We ascertained and genotyped eight CLL cases using a high-density SNP array, and then performed shared genomic segment (SGS) analysis - a method designed for extended high-risk pedigrees that accounts for heterogeneity.

Results: We identified a genome-wide significant region ( = 1.9 × 10, LOD-equivalent 5.6) at 2q22.1. The 0.9 Mb region was inherited through 26 meioses and shared by seven of the eight genotyped cases. It sits within a ~6.25 Mb locus identified in a previous linkage study of 206 small CLL families. Our narrow region intersects two genes, including which is highly expressed in CLL cells and implicated in maintenance and progression.

Conclusion: SGS analysis of an extended high-risk CLL pedigree identified the most significant evidence to-date for a 0.9 Mb CLL disease locus at 2q22.1, harboring This discovery contributes to a growing literature implicating in inherited risk to CLL. Investigation of the segregating haplotype in the pedigree will be valuable for elucidating risk variant(s).
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http://dx.doi.org/10.20517/jtgg.2021.05DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8341589PMC
June 2021

Genetic determinants of multiple myeloma risk within the Wnt/beta-catenin signaling pathway.

Cancer Epidemiol 2021 08 30;73:101972. Epub 2021 Jun 30.

Department of Lymphoma/Myeloma, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, United States. Electronic address:

Background: Aberrant Wnt/beta-catenin pathway activation is implicated in Multiple Myeloma (MM) development, but little is known if genetic variants within this pathway contribute to MM susceptibility.

Methods: We performed a discovery candidate pathway analysis in 269 non-Hispanic white MM cases and 272 controls focusing on 171 variants selected from 26 core genes within the Wnt/beta-catenin pathway. Significant candidate variants (P < 0.05) were selected for validation in internal and external non-Hispanic white populations totaling 818 cases and 1209 controls. We also examined significant variants in non-Hispanic black and Hispanic case/control study populations to identify potential differences by race/ethnicity. Possible biological functions of candidate variants were predicted in silico.

Results: Seven variants were significantly associated with MM risk in non-Hispanic whites in the discovery population, of which LRP6:rs7966410 (OR: 0.57; 95 % CI: 0.38-0.88; P = 9.90 × 10) and LRP6:rs7956971 (OR: 0.64; 95 % CI: 0.44-0.95; P = 0.027) remained significant in the internal and external populations. CSNK1D:rs9901910 replicated among all three racial/ethnic groups, with 2-6 fold increased risk of MM (OR: 2.40; 95 % CI: 1.67-3.45; P = 2.43 × 10 - non-Hispanic white; OR: 6.42; 95 % CI: 2.47-16.7; P = 3.14 × 10 - non-Hispanic black; OR: 4.31; 95 % CI: 1.83-10.1; P = 8.10 × 10 - Hispanic). BTRC:rs7916830 was associated with a significant 37 % and 24 % reduced risk of MM in the non-Hispanic white (95 % CI: 0.49-0.82; P = 5.60 × 10) and non-Hispanic Black (95 % CI: 0.60-0.97; P = 0.028) population, respectively. In silico tools predicted that these loci altered function through via gene regulation.

Conclusion: We identified several variants within the Wnt/beta-catenin pathway associated with MM susceptibility. Findings of this study highlight the potential genetic role of Wnt/beta-catenin signaling in MM etiology among a diverse patient population.
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http://dx.doi.org/10.1016/j.canep.2021.101972DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8351401PMC
August 2021

Genetic determinants of multiple myeloma risk within the Wnt/beta-catenin signaling pathway.

Cancer Epidemiol 2021 08 30;73:101972. Epub 2021 Jun 30.

Department of Lymphoma/Myeloma, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, United States. Electronic address:

Background: Aberrant Wnt/beta-catenin pathway activation is implicated in Multiple Myeloma (MM) development, but little is known if genetic variants within this pathway contribute to MM susceptibility.

Methods: We performed a discovery candidate pathway analysis in 269 non-Hispanic white MM cases and 272 controls focusing on 171 variants selected from 26 core genes within the Wnt/beta-catenin pathway. Significant candidate variants (P < 0.05) were selected for validation in internal and external non-Hispanic white populations totaling 818 cases and 1209 controls. We also examined significant variants in non-Hispanic black and Hispanic case/control study populations to identify potential differences by race/ethnicity. Possible biological functions of candidate variants were predicted in silico.

Results: Seven variants were significantly associated with MM risk in non-Hispanic whites in the discovery population, of which LRP6:rs7966410 (OR: 0.57; 95 % CI: 0.38-0.88; P = 9.90 × 10) and LRP6:rs7956971 (OR: 0.64; 95 % CI: 0.44-0.95; P = 0.027) remained significant in the internal and external populations. CSNK1D:rs9901910 replicated among all three racial/ethnic groups, with 2-6 fold increased risk of MM (OR: 2.40; 95 % CI: 1.67-3.45; P = 2.43 × 10 - non-Hispanic white; OR: 6.42; 95 % CI: 2.47-16.7; P = 3.14 × 10 - non-Hispanic black; OR: 4.31; 95 % CI: 1.83-10.1; P = 8.10 × 10 - Hispanic). BTRC:rs7916830 was associated with a significant 37 % and 24 % reduced risk of MM in the non-Hispanic white (95 % CI: 0.49-0.82; P = 5.60 × 10) and non-Hispanic Black (95 % CI: 0.60-0.97; P = 0.028) population, respectively. In silico tools predicted that these loci altered function through via gene regulation.

Conclusion: We identified several variants within the Wnt/beta-catenin pathway associated with MM susceptibility. Findings of this study highlight the potential genetic role of Wnt/beta-catenin signaling in MM etiology among a diverse patient population.
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http://dx.doi.org/10.1016/j.canep.2021.101972DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8351401PMC
August 2021

Genetic determinants of multiple myeloma risk within the Wnt/beta-catenin signaling pathway.

Cancer Epidemiol 2021 08 30;73:101972. Epub 2021 Jun 30.

Department of Lymphoma/Myeloma, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, United States. Electronic address:

Background: Aberrant Wnt/beta-catenin pathway activation is implicated in Multiple Myeloma (MM) development, but little is known if genetic variants within this pathway contribute to MM susceptibility.

Methods: We performed a discovery candidate pathway analysis in 269 non-Hispanic white MM cases and 272 controls focusing on 171 variants selected from 26 core genes within the Wnt/beta-catenin pathway. Significant candidate variants (P < 0.05) were selected for validation in internal and external non-Hispanic white populations totaling 818 cases and 1209 controls. We also examined significant variants in non-Hispanic black and Hispanic case/control study populations to identify potential differences by race/ethnicity. Possible biological functions of candidate variants were predicted in silico.

Results: Seven variants were significantly associated with MM risk in non-Hispanic whites in the discovery population, of which LRP6:rs7966410 (OR: 0.57; 95 % CI: 0.38-0.88; P = 9.90 × 10) and LRP6:rs7956971 (OR: 0.64; 95 % CI: 0.44-0.95; P = 0.027) remained significant in the internal and external populations. CSNK1D:rs9901910 replicated among all three racial/ethnic groups, with 2-6 fold increased risk of MM (OR: 2.40; 95 % CI: 1.67-3.45; P = 2.43 × 10 - non-Hispanic white; OR: 6.42; 95 % CI: 2.47-16.7; P = 3.14 × 10 - non-Hispanic black; OR: 4.31; 95 % CI: 1.83-10.1; P = 8.10 × 10 - Hispanic). BTRC:rs7916830 was associated with a significant 37 % and 24 % reduced risk of MM in the non-Hispanic white (95 % CI: 0.49-0.82; P = 5.60 × 10) and non-Hispanic Black (95 % CI: 0.60-0.97; P = 0.028) population, respectively. In silico tools predicted that these loci altered function through via gene regulation.

Conclusion: We identified several variants within the Wnt/beta-catenin pathway associated with MM susceptibility. Findings of this study highlight the potential genetic role of Wnt/beta-catenin signaling in MM etiology among a diverse patient population.
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http://dx.doi.org/10.1016/j.canep.2021.101972DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8351401PMC
August 2021

Sequencing at lymphoid neoplasm susceptibility loci maps six myeloma risk genes.

Hum Mol Genet 2021 Jun;30(12):1142-1153

Huntsman Cancer Institute, University of Utah, Salt Lake City, UT 84112, USA.

Inherited genetic risk factors play a role in multiple myeloma (MM), yet considerable missing heritability exists. Rare risk variants at genome-wide association study (GWAS) loci are a new avenue to explore. Pleiotropy between lymphoid neoplasms (LNs) has been suggested in family history and genetic studies, but no studies have interrogated sequencing for pleiotropic genes or rare risk variants. Sequencing genetically enriched cases can help discover rarer variants. We analyzed exome sequencing in familial or early-onset MM cases to identify rare, functionally relevant variants near GWAS loci for a range of LNs. A total of 149 distinct and significant LN GWAS loci have been published. We identified six recurrent, rare, potentially deleterious variants within 5 kb of significant GWAS single nucleotide polymorphisms in 75 MM cases. Mutations were observed in BTNL2, EOMES, TNFRSF13B, IRF8, ACOXL and TSPAN32. All six genes replicated in an independent set of 255 early-onset MM or familial MM or precursor cases. Expansion of our analyses to the full length of these six genes resulted in a list of 39 rare and deleterious variants, seven of which segregated in MM families. Three genes also had significant rare variant burden in 733 sporadic MM cases compared with 935 control individuals: IRF8 (P = 1.0 × 10-6), EOMES (P = 6.0 × 10-6) and BTNL2 (P = 2.1 × 10-3). Together, our results implicate six genes in MM risk, provide support for genetic pleiotropy between LN subtypes and demonstrate the utility of sequencing genetically enriched cases to identify functionally relevant variants near GWAS loci.
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http://dx.doi.org/10.1093/hmg/ddab066DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8188404PMC
June 2021

Common genetic polymorphisms contribute to the association between chronic lymphocytic leukaemia and non-melanoma skin cancer.

Int J Epidemiol 2021 Aug;50(4):1325-1334

Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA.

Background: Epidemiological studies have demonstrated a positive association between chronic lymphocytic leukaemia (CLL) and non-melanoma skin cancer (NMSC). We hypothesized that shared genetic risk factors between CLL and NMSC could contribute to the association observed between these diseases.

Methods: We examined the association between (i) established NMSC susceptibility loci and CLL risk in a meta-analysis including 3100 CLL cases and 7667 controls and (ii) established CLL loci and NMSC risk in a study of 4242 basal cell carcinoma (BCC) cases, 825 squamous cell carcinoma (SCC) cases and 12802 controls. Polygenic risk scores (PRS) for CLL, BCC and SCC were constructed using established loci. Logistic regression was used to estimate odds ratios (ORs) and 95% confidence intervals (CIs).

Results: Higher CLL-PRS was associated with increased BCC risk (OR4th-quartile-vs-1st-quartile = 1.13, 95% CI: 1.02-1.24, Ptrend = 0.009), even after removing the shared 6p25.3 locus. No association was observed with BCC-PRS and CLL risk (Ptrend = 0.68). These findings support a contributory role for CLL in BCC risk, but not for BCC in CLL risk. Increased CLL risk was observed with higher SCC-PRS (OR4th-quartile-vs-1st-quartile = 1.22, 95% CI: 1.08-1.38, Ptrend = 1.36 × 10-5), which was driven by shared genetic susceptibility at the 6p25.3 locus.

Conclusion: These findings highlight the role of pleiotropy regarding the pathogenesis of CLL and NMSC and shows that a single pleiotropic locus, 6p25.3, drives the observed association between genetic susceptibility to SCC and increased CLL risk. The study also provides evidence that genetic susceptibility for CLL increases BCC risk.
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http://dx.doi.org/10.1093/ije/dyab042DOI Listing
August 2021

Expression quantitative trait loci of genes predicting outcome are associated with survival of multiple myeloma patients.

Int J Cancer 2021 07 30;149(2):327-336. Epub 2021 Mar 30.

Department of Clinical Immunology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark.

Gene expression profiling can be used for predicting survival in multiple myeloma (MM) and identifying patients who will benefit from particular types of therapy. Some germline single nucleotide polymorphisms (SNPs) act as expression quantitative trait loci (eQTLs) showing strong associations with gene expression levels. We performed an association study to test whether eQTLs of genes reported to be associated with prognosis of MM patients are directly associated with measures of adverse outcome. Using the genotype-tissue expression portal, we identified a total of 16 candidate genes with at least one eQTL SNP associated with their expression with P < 10 either in EBV-transformed B-lymphocytes or whole blood. We genotyped the resulting 22 SNPs in 1327 MM cases from the International Multiple Myeloma rESEarch (IMMEnSE) consortium and examined their association with overall survival (OS) and progression-free survival (PFS), adjusting for age, sex, country of origin and disease stage. Three polymorphisms in two genes (TBRG4-rs1992292, TBRG4-rs2287535 and ENTPD1-rs2153913) showed associations with OS at P < .05, with the former two also associated with PFS. The associations of two polymorphisms in TBRG4 with OS were replicated in 1277 MM cases from the International Lymphoma Epidemiology (InterLymph) Consortium. A meta-analysis of the data from IMMEnSE and InterLymph (2579 cases) showed that TBRG4-rs1992292 is associated with OS (hazard ratio = 1.14, 95% confidence interval 1.04-1.26, P = .007). In conclusion, we found biologically a plausible association between a SNP in TBRG4 and OS of MM patients.
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http://dx.doi.org/10.1002/ijc.33547DOI Listing
July 2021

Natural history of monoclonal B-cell lymphocytosis among relatives in CLL families.

Blood 2021 04;137(15):2046-2056

Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD.

Chronic lymphocytic lymphoma (CLL) has one of the highest familial risks among cancers. Monoclonal B-cell lymphocytosis (MBL), the precursor to CLL, has a higher prevalence (13%-18%) in families with 2 or more members with CLL compared with the general population (5%-12%). Although, the rate of progression to CLL for high-count MBLs (clonal B-cell count ≥500/µL) is ∼1% to 5%/y, no low-count MBLs have been reported to progress to date. We report the incidence and natural history of MBL in relatives from CLL families. In 310 CLL families, we screened 1045 relatives for MBL using highly sensitive flow cytometry and prospectively followed 449 of them. MBL incidence was directly age- and sex-adjusted to the 2010 US population. CLL cumulative incidence was estimated using Kaplan-Meier survival curves. At baseline, the prevalence of MBL was 22% (235/1045 relatives). After a median follow-up of 8.1 years among 449 relatives, 12 individuals progressed to CLL with a 5-year cumulative incidence of 1.8%. When considering just the 139 relatives with low-count MBL, the 5-year cumulative incidence increased to 5.7%. Finally, 264 had no MBL at baseline, of whom 60 individuals subsequently developed MBL (2 high-count and 58 low-count MBLs) with an age- and sex-adjusted incidence of 3.5% after a median of 6 years of follow-up. In a screening cohort of relatives from CLL families, we reported progression from normal-count to low-count MBL to high-count MBL to CLL, demonstrating that low-count MBL precedes progression to CLL. We estimated a 1.1% annual rate of progression from low-count MBL, which is in excess of that in the general population.
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http://dx.doi.org/10.1182/blood.2020006322DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8057266PMC
April 2021

Breast Cancer Risk Factors and Survival by Tumor Subtype: Pooled Analyses from the Breast Cancer Association Consortium.

Cancer Epidemiol Biomarkers Prev 2021 04 26;30(4):623-642. Epub 2021 Jan 26.

Gynaecology Research Unit, Hannover Medical School, Hannover, Germany.

Background: It is not known whether modifiable lifestyle factors that predict survival after invasive breast cancer differ by subtype.

Methods: We analyzed data for 121,435 women diagnosed with breast cancer from 67 studies in the Breast Cancer Association Consortium with 16,890 deaths (8,554 breast cancer specific) over 10 years. Cox regression was used to estimate associations between risk factors and 10-year all-cause mortality and breast cancer-specific mortality overall, by estrogen receptor (ER) status, and by intrinsic-like subtype.

Results: There was no evidence of heterogeneous associations between risk factors and mortality by subtype ( > 0.30). The strongest associations were between all-cause mortality and BMI ≥30 versus 18.5-25 kg/m [HR (95% confidence interval (CI), 1.19 (1.06-1.34)]; current versus never smoking [1.37 (1.27-1.47)], high versus low physical activity [0.43 (0.21-0.86)], age ≥30 years versus <20 years at first pregnancy [0.79 (0.72-0.86)]; >0-<5 years versus ≥10 years since last full-term birth [1.31 (1.11-1.55)]; ever versus never use of oral contraceptives [0.91 (0.87-0.96)]; ever versus never use of menopausal hormone therapy, including current estrogen-progestin therapy [0.61 (0.54-0.69)]. Similar associations with breast cancer mortality were weaker; for example, 1.11 (1.02-1.21) for current versus never smoking.

Conclusions: We confirm associations between modifiable lifestyle factors and 10-year all-cause mortality. There was no strong evidence that associations differed by ER status or intrinsic-like subtype.

Impact: Given the large dataset and lack of evidence that associations between modifiable risk factors and 10-year mortality differed by subtype, these associations could be cautiously used in prognostication models to inform patient-centered care.
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http://dx.doi.org/10.1158/1055-9965.EPI-20-0924DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8026532PMC
April 2021

Breast Cancer Risk Genes - Association Analysis in More than 113,000 Women.

N Engl J Med 2021 02 20;384(5):428-439. Epub 2021 Jan 20.

The authors' affiliations are as follows: the Centre for Cancer Genetic Epidemiology, Departments of Public Health and Primary Care (L.D., S. Carvalho, J.A., K.A.P., Q.W., M.K.B., J.D., B.D., N. Mavaddat, K. Michailidou, A.C.A., P.D.P.P., D.F.E.) and Oncology (C.L., P.A.H., C. Baynes, D.M.C., L.F., V.R., M. Shah, P.D.P.P., A.M.D., D.F.E.), University of Cambridge, Cambridge, the Centre for Genomic and Experimental Medicine, MRC Institute of Genetics and Molecular Medicine (A. Campbell, D.J.P.), and the Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology (D.J.P.), University of Edinburgh, the Cancer Research UK Edinburgh Centre (D.A.C., J.F.), and the Usher Institute of Population Health Sciences and Informatics, University of Edinburgh Medical School (A. Campbell, J.F.), Edinburgh, the Divisions of Informatics, Imaging, and Data Sciences (E.F.H.), Cancer Sciences (A. Howell), Population Health, Health Services Research, and Primary Care (A. Lophatananon, K. Muir), and Evolution and Genomic Sciences, School of Biological Sciences (W.G.N., E.M.V., D.G.E.), University of Manchester, the NIHR Manchester Biomedical Research Unit (E.F.H.) and the Nightingale Breast Screening Centre, Wythenshawe Hospital (E.F.H., H.I.), Academic Health Science Centre and North West Genomics Laboratory Hub, and the Manchester Centre for Genomic Medicine, St. Mary's Hospital, Manchester University NHS Foundation Trust (W.G.N., E.M.V., D.G.E.), Manchester, the School of Cancer and Pharmaceutical Sciences, Comprehensive Cancer Centre, Guy's Campus, King's College London, London (E.J.S.), the Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham (I.T.), and the Wellcome Trust Centre for Human Genetics and Oxford NIHR Biomedical Research Centre, University of Oxford, Oxford (I.T.) - all in the United Kingdom; the Human Genotyping-CEGEN Unit, Human Cancer Genetic Program (A.G.-N., M.R.A., N.Á., B.H., R.N.-T.), and the Human Genetics Group (V.F., A.O., J.B.), Spanish National Cancer Research Center, Centro de Investigación en Red de Enfermedades Raras (A.O., J.B.), Servicio de Oncología Médica, Hospital Universitario La Paz (M.P.Z.), and Molecular Oncology Laboratory, Hospital Clinico San Carlos, Instituto de Investigación Sanitaria del Hospital Clínico San Carlos (M. de la Hoya), Madrid, the Genomic Medicine Group, Galician Foundation of Genomic Medicine, Instituto de Investigación Sanitaria de Santiago de Compostela, Complejo Hospitalario Universitario de Santiago (A. Carracedo, M.G.-D.), and Centro de Investigación en Red de Enfermedades Raras y Centro Nacional de Genotipado, Universidad de Santiago de Compostela (A. Carracedo), Santiago de Compostela, the Oncology and Genetics Unit, Instituto de Investigacion Sanitaria Galicia Sur, Xerencia de Xestion Integrada de Vigo-Servizo Galeo de Saúde, Vigo (J.E.C.), and Servicio de Cirugía General y Especialidades, Hospital Monte Naranco, Oviedo (J.I.A.P.) - all in Spain; the Division of Oncology and Pathology, Department of Clinical Sciences Lund, Lund University, Lund (C. Wahlström, J.V., M.L., T. Törngren, Å.B., A.K.), the Department of Oncology, Örebro University Hospital, Örebro (C. Blomqvist), and the Departments of Medical Epidemiology and Biostatistics (K.C., M.E., M.G., P. Hall, W.H., K.H.), Oncology, Södersjukhuset (P. Hall, S. Margolin), Molecular Medicine and Surgery (A. Lindblom), and Clinical Science and Education, Södersjukhuset (S. Margolin, C. Wendt), Karolinska Institutet, and the Department of Clinical Genetics, Karolinska University Hospital (A. Lindblom), Stockholm - all in Sweden; the Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD (M.T.P., C.F., G.C.-T., A.B.S.), the Cancer Epidemiology Division, Cancer Council Victoria (G.G.G., R.J.M., R.L.M.), the Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health (G.G.G., R.J.M., R.L.M.), and the Department of Clinical Pathology (M.C.S.), University of Melbourne, Anatomical Pathology, Alfred Hospital (C.M.), and the Cancer Epidemiology Division, Cancer Council Victoria (M.C.S.), Melbourne, VIC, and Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC (G.G.G., M.C.S., R.L.M.) - all in Australia; the Division of Molecular Pathology (R.K., S. Cornelissen, M.K.S.), Family Cancer Clinic (F.B.L.H., L.E.K.), Department of Epidemiology (M.A.R.), and Division of Psychosocial Research and Epidemiology (M.K.S.), the Netherlands Cancer Institute-Antoni van Leeuwenhoek Hospital, Amsterdam, Division Laboratories, Pharmacy and Biomedical Genetics, Department of Genetics, University Medical Center, Utrecht (M.G.E.M.A.), the Department of Clinical Genetics, Erasmus University Medical Center (J.M.C., A.M.W.O.), and the Department of Medical Oncology, Family Cancer Clinic, Erasmus MC Cancer Institute (B.A.M.H.-G., A. Hollestelle, M.J.H.), Rotterdam, the Department of Clinical Genetics, Maastricht University Medical Center, Maastricht (E.B.G.G.), the Departments of Human Genetics (I.M.M.L., M.P.G.V., P.D.), Clinical Genetics (C.J.A.), and Pathology (P.D.), Leiden University Medical Center, Leiden, the Department of Human Genetics, Radboud University Medical Center, Nijmegen (A.R.M.), and the Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen (J.C.O.) - all in the Netherlands; the Cancer Genetics and Comparative Genomics Branch, National Human Genome Research Institute (B.D.), and the Division of Cancer Epidemiology and Genetics, National Cancer Institute (T.A., S.J.C., X.R.Y., M.G.-C.), National Institutes of Health, Bethesda, MD; the Department of Pathology, Brigham and Women's Hospital, Harvard Medical School (B.D.), and the Department of Nutrition, Harvard T.H. Chan School of Public Health (R.M.V.D.), Boston; the Departments of Clinical Genetics (K.A.), Oncology (C. Blomqvist), and Obstetrics and Gynecology (H.N., M. Suvanto), Helsinki University Hospital, University of Helsinki, Helsinki, and the Unit of Clinical Oncology, Kuopio University Hospital (P. Auvinen), the Institute of Clinical Medicine, Oncology (P. Auvinen), the Translational Cancer Research Area (J.M.H., V.-M.K., A. Mannermaa), and the Institute of Clinical Medicine, Pathology, and Forensic Medicine (J.M.H., V.-M.K., A. Mannermaa), University of Eastern Finland, and the Biobank of Eastern Finland, Kuopio University Hospital (V.-M.K., A. Mannermaa), Kuopio - both in Finland; the N.N. Alexandrov Research Institute of Oncology and Medical Radiology, Minsk, Belarus (N.N.A., N.V.B.); the Department of Gynecology and Obstetrics and Institute of Clinical Molecular Biology, University Hospital of Schleswig-Holstein, Campus Kiel, Christian-Albrechts University Kiel, Kiel (N.A.), the Institute of Medical Biometry and Epidemiology (H. Becher) and Cancer Epidemiology Group (T.M., J.C.-C.), University Cancer Center Hamburg, University Medical Center Hamburg-Eppendorf, Hamburg, the Department of Gynecology and Obstetrics (M.W.B., P.A.F., L.H.) and Institute of Human Genetics (A.B.E.), University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nuremberg, Comprehensive Cancer Center Erlangen-European Metropolitan Region of Nuremberg, Erlangen, the Division of Cancer Epidemiology (S.B., A. Jung, P.M.K., J.C.-C.), Molecular Epidemiology Group, C080 (B. Burwinkel, H.S.), Division of Pediatric Neurooncology (A.F.), and Molecular Genetics of Breast Cancer (U.H., M.M., M.U.R., D.T.), German Cancer Research Center, Molecular Biology of Breast Cancer, University Women's Clinic Heidelberg, University of Heidelberg (B. Burwinkel, A.S., H.S.), Hopp Children's Cancer Center (A.F.), Faculty of Medicine, University of Heidelberg (P.M.K.), and National Center for Tumor Diseases, University Hospital and German Cancer Research Center (A.S., C.S.), Heidelberg, the Department of Radiation Oncology (N.V.B., M. Bremer, H.C.) and the Gynecology Research Unit (N.V.B., T.D., P. Hillemanns, T.-W.P.-S., P.S.), Hannover Medical School, Hannover, the Institute of Human Genetics, University of Münster, Münster (N.B.-M.), Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, Stuttgart (H. Brauch, W.-Y.L.), iFIT-Cluster of Excellence, University of Tübingen, and the German Cancer Consortium, German Cancer Research Center, Partner Site Tübingen (H. Brauch), and the University of Tübingen (W.-Y.L.), Tübingen, Institute for Prevention and Occupational Medicine of the German Social Accident Insurance, Institute of the Ruhr University Bochum, Bochum (T.B.), Institute for Medical Informatics, Statistics, and Epidemiology, University of Leipzig, Leipzig (C.E.), Center for Hereditary Breast and Ovarian Cancer (E.H., R.K.S.) and Center for Integrated Oncology (E.H., R.K.S.), Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, the Department of Internal Medicine, Evangelische Kliniken Bonn, Johanniter Krankenhaus, Bonn (Y.-D.K.), the Department of Gynecology and Obstetrics, University of Munich, Campus Großhadern, Munich (A. Meindl), and the Institute of Pathology, Städtisches Klinikum Karlsruhe, Karlsruhe (T.R.) - all in Germany; the Gynecological Cancer Registry, Centre Georges-François Leclerc, Dijon (P. Arveux), and the Center for Research in Epidemiology and Population Health, Team Exposome and Heredity, INSERM, University Paris-Saclay, Villejuif (E.C.-D., P.G., T. Truong) - both in France; the Institute of Biochemistry and Genetics, Ufa Federal Research Center of the Russian Academy of Sciences (M. Bermisheva, E.K.), the Department of Genetics and Fundamental Medicine, Bashkir State University (E.K., D.P., Y.V.), and the Ufa Research Institute of Occupational Health and Human Ecology (Y.V.), Ufa, Russia; the Department of Genetics and Pathology (K.B., A. Jakubowska, J. Lubiński, K.P.) and the Independent Laboratory of Molecular Biology and Genetic Diagnostics (A. Jakubowska), Pomeranian Medical University, Szczecin, Poland; the Copenhagen General Population Study, the Department of Clinical Biochemistry (S.E.B., B.G.N.), and the Department of Breast Surgery (H.F.), Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, and the Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen (S.E.B., B.G.N.) - both in Denmark; the Division of Cancer Prevention and Genetics, European Institute of Oncology Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) (B. Bonanni), the Unit of Medical Genetics, Department of Medical Oncology and Hematology, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano (S. Manoukian), the Genome Diagnostics Program, FIRC Institute of Molecular Oncology (P.P.), and the Unit of Molecular Bases of Genetic Risk and Genetic Testing, Department of Research, Fondazione IRCCS Istituto Nazionale dei Tumori (P.R.), Milan; the Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital-Radiumhospitalet (A.-L.B.-D., G.I.G.A., V.N.K.), and the Institute of Clinical Medicine, Faculty of Medicine, University of Oslo (A.-L.B.-D., V.N.K.), Oslo; Medical Faculty, Universidad de La Sabana (I.B.), and the Clinical Epidemiology and Biostatistics Department (F.G.) and Institute of Human Genetics (D.T.), Pontificia Universidad Javeriana, Bogota, Colombia; the Department of Internal Medicine and Huntsman Cancer Institute, University of Utah (N.J.C., M.J.M., J.A.W.), and the Intermountain Healthcare Biorepository and Department of Pathology, Intermountain Healthcare (M.H.C.), Salt Lake City; the David Geffen School of Medicine, Department of Medicine Division of Hematology and Oncology, University of California, Los Angeles (P.A.F.), and Moores Cancer Center (M.G.-D., M.E.M.) and the Department of Family Medicine and Public Health (M.E.M.), University of California San Diego, La Jolla; the Departments of Medical Oncology (V.G., D.M.) and Pathology (M.T.), University Hospital of Heraklion, Heraklion, and the Department of Oncology, University Hospital of Larissa, Larissa (E.S.) - both in Greece; the Fred A. Litwin Center for Cancer Genetics, Lunenfeld-Tanenbaum Research Institute of Mount Sinai Hospital (G.G., I.L.A.), the Departments of Laboratory Medicine and Pathobiology (A.M.M.) and Molecular Genetics (I.L.A.), University of Toronto, and the Laboratory Medicine Program, University Health Network (A.M.M.), Toronto, and the Genomics Center, Centre Hospitalier Universitaire de Québec-Université Laval Research Center, Québec City, QC (J.S.) - both in Canada; the Department of Electron Microscopy and Molecular Pathology (A. Hadjisavvas, K.K., M.A.L.), the Cyprus School of Molecular Medicine (A. Hadjisavvas, K.K., M.A.L., K. Michailidou), and the Biostatistics Unit (K. Michailidou), Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus; the Saw Swee Hock School of Public Health (M. Hartman, R.M.V.D.) and the Department of Medicine, Yong Loo Lin School of Medicine (R.M.V.D.), National University of Singapore, the Department of Surgery, National University Health System (M. Hartman, J. Li), and the Human Genetics Division, Genome Institute of Singapore (J. Li), Singapore; the Department of Mathematical Sciences, Faculty of Science and Engineering, University of Nottingham Malaysia (W.K.H.), and the Breast Cancer Research Programme, Cancer Research Malaysia (W.K.H., P.S.N., S.-Y.Y., S.H.T.), Selangor, and the Breast Cancer Research Unit, Cancer Research Institute (N.A.M.T.), and the Department of Surgery, Faculty of Medicine (N.A.M.T., P.S.N., S.H.T.), University Malaya, Kuala Lumpur - both in Malaysia; Surgery, School of Medicine, National University of Ireland, Galway (M.J.K., N. Miller); the Department of Surgery, Daerim Saint Mary's Hospital (S.-W.K.), the Department of Surgery, Ulsan University College of Medicine and Asan Medical Center (J.W.L.), the Department of Surgery, Soonchunhyang University College of Medicine and Soonchunhyang University Hospital (M.H.L.), Integrated Major in Innovative Medical Science, Seoul National University College of Medicine (S.K.P.), and the Cancer Research Institute, Seoul National University (S.K.P.), Seoul, South Korea; the Department of Basic Sciences, Shaukat Khanum Memorial Cancer Hospital and Research Center, Lahore, Pakistan (M.U.R.); and the National Cancer Institute, Ministry of Public Health, Nonthaburi, Thailand (S.T.).

Background: Genetic testing for breast cancer susceptibility is widely used, but for many genes, evidence of an association with breast cancer is weak, underlying risk estimates are imprecise, and reliable subtype-specific risk estimates are lacking.

Methods: We used a panel of 34 putative susceptibility genes to perform sequencing on samples from 60,466 women with breast cancer and 53,461 controls. In separate analyses for protein-truncating variants and rare missense variants in these genes, we estimated odds ratios for breast cancer overall and tumor subtypes. We evaluated missense-variant associations according to domain and classification of pathogenicity.

Results: Protein-truncating variants in 5 genes (, , , , and ) were associated with a risk of breast cancer overall with a P value of less than 0.0001. Protein-truncating variants in 4 other genes (, , , and ) were associated with a risk of breast cancer overall with a P value of less than 0.05 and a Bayesian false-discovery probability of less than 0.05. For protein-truncating variants in 19 of the remaining 25 genes, the upper limit of the 95% confidence interval of the odds ratio for breast cancer overall was less than 2.0. For protein-truncating variants in and , odds ratios were higher for estrogen receptor (ER)-positive disease than for ER-negative disease; for protein-truncating variants in , , , , , and , odds ratios were higher for ER-negative disease than for ER-positive disease. Rare missense variants (in aggregate) in , , and were associated with a risk of breast cancer overall with a P value of less than 0.001. For , , and , missense variants (in aggregate) that would be classified as pathogenic according to standard criteria were associated with a risk of breast cancer overall, with the risk being similar to that of protein-truncating variants.

Conclusions: The results of this study define the genes that are most clinically useful for inclusion on panels for the prediction of breast cancer risk, as well as provide estimates of the risks associated with protein-truncating variants, to guide genetic counseling. (Funded by European Union Horizon 2020 programs and others.).
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http://dx.doi.org/10.1056/NEJMoa1913948DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7611105PMC
February 2021

21-gene recurrence score testing utilization among older women from different races: A population-based study.

J Geriatr Oncol 2021 03 7;12(2):206-211. Epub 2020 Jul 7.

Utah Cancer Registry, 250 East 200 South, Room 1375, Salt Lake City, UT 84111, United States of America; Department of Internal Medicine, Division of Epidemiology, University of Utah Health, 295 Chipeta Building, Salt Lake City, UT 84108, United States of America. Electronic address:

Objectives: The influence of older age at diagnosis in combination with race/ethnicity on utilization and results of the 21-gene recurrence score (RS) assay for breast cancer (BC) patients is not fully understood. Our objectives were to evaluate the utilization of RS among older women with BC, the likelihood of a high-risk RS, and factors associated with breast cancer-specific mortality (BCSM) among older patients across different races.

Materials And Methods: We utilized the Surveillance, Epidemiology, and Results (SEER) database with linked RS results to evaluate women with estrogen receptor-positive BC diagnosed 2004-2015. Multivariable logistic regression was used to describe the differences in utilization of RS testing and the association of high-risk RS according to patient characteristics. The Cox proportional hazards model was used to analyze factors associated with BCSM.

Results: We found that 20.4% (109,244/536,555) of all women ≥18 and 14.3% (33,584/235,171) of women ≥65 underwent RS testing. Non-whites had lower odds of RS testing at younger ages whereas among women ≥65 there was no significant difference. After taking into account stage and grade, being ≥65 reduced the odds of high-risk RS in all races except American Indian/Alaskan Native. Age ≥ 65 was independently associated with increased hazard BCSM. Among women ≥65 with high-risk RS, chemotherapy was associated with lower hazard of BCSM in all races.

Conclusions: Older women are less likely to be tested for RS, but also less likely to have high-risk RS. Older women with high-risk RS, when given chemotherapy have reduced BCSM across all races.
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http://dx.doi.org/10.1016/j.jgo.2020.06.004DOI Listing
March 2021

Assessment of polygenic architecture and risk prediction based on common variants across fourteen cancers.

Nat Commun 2020 07 3;11(1):3353. Epub 2020 Jul 3.

Section of Epidemiology and Biostatistics, Leeds Institute of Cancer and Pathology, University of Leeds, Leeds, UK.

Genome-wide association studies (GWAS) have led to the identification of hundreds of susceptibility loci across cancers, but the impact of further studies remains uncertain. Here we analyse summary-level data from GWAS of European ancestry across fourteen cancer sites to estimate the number of common susceptibility variants (polygenicity) and underlying effect-size distribution. All cancers show a high degree of polygenicity, involving at a minimum of thousands of loci. We project that sample sizes required to explain 80% of GWAS heritability vary from 60,000 cases for testicular to over 1,000,000 cases for lung cancer. The maximum relative risk achievable for subjects at the 99th risk percentile of underlying polygenic risk scores (PRS), compared to average risk, ranges from 12 for testicular to 2.5 for ovarian cancer. We show that PRS have potential for risk stratification for cancers of breast, colon and prostate, but less so for others because of modest heritability and lower incidence.
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http://dx.doi.org/10.1038/s41467-020-16483-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7335068PMC
July 2020

Coinherited genetics of multiple myeloma and its precursor, monoclonal gammopathy of undetermined significance.

Blood Adv 2020 06;4(12):2789-2797

Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, Rochester, MN.

So far, 23 germline susceptibility loci have been associated with multiple myeloma (MM) risk. It is unclear whether the genetic variation associated with MM susceptibility also predisposes to its precursor, monoclonal gammopathy of undetermined significance (MGUS). Leveraging 2434 MM cases, 754 MGUS cases, and 2 independent sets of controls (2567/879), we investigated potential shared genetic susceptibility of MM and MGUS by (1) performing MM and MGUS genome-wide association studies (GWAS); (2) validating the association of a polygenic risk score (PRS) based on 23 established MM loci (MM-PRS) with risk of MM, and for the first time with MGUS; and (3) examining genetic correlation of MM and MGUS. Heritability and genetic estimates yielded 17% (standard error [SE] ±0.04) and 15% (SE ±0.11) for MM and MGUS risk, respectively, and a 55% (SE ±0.30) genetic correlation. The MM-PRS was associated with risk of MM when assessed continuously (odds ratio [OR], 1.17 per SD; 95% confidence interval [CI], 1.13-1.21) or categorically (OR, 1.70; 95% CI, 1.38-2.09 for highest; OR, 0.71; 95% CI, 0.55-0.90 for lowest compared with middle quintile). The MM-PRS was similarly associated with MGUS (OR, 1.19 per SD; 95% CI, 1.14-1.26 as a continuous measure, OR, 1.77, 95%CI: 1.29-2.43 for highest and OR, 0.70, 95%CI: 0.50-0.98 for lowest compared with middle quintile). MM and MGUS associations did not differ by age, sex, or MM immunoglobulin isotype. We validated a 23-SNP MM-PRS in an independent series of MM cases and provide evidence for its association with MGUS. Our results suggest shared common genetic susceptibility to MM and MGUS.
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http://dx.doi.org/10.1182/bloodadvances.2020001435DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7322948PMC
June 2020

Lipid Trait Variants and the Risk of Non-Hodgkin Lymphoma Subtypes: A Mendelian Randomization Study.

Cancer Epidemiol Biomarkers Prev 2020 05 27;29(5):1074-1078. Epub 2020 Feb 27.

Emory University, Atlanta, Georgia.

Background: Lipid traits have been inconsistently linked to risk of non-Hodgkin lymphoma (NHL). We examined the association of genetically predicted lipid traits with risk of diffuse large B-cell lymphoma (DLBCL), chronic lymphocytic leukemia (CLL), follicular lymphoma (FL), and marginal zone lymphoma (MZL) using Mendelian randomization (MR) analysis.

Methods: Genome-wide association study data from the InterLymph Consortium were available for 2,661 DLBCLs, 2,179 CLLs, 2,142 FLs, 824 MZLs, and 6,221 controls. SNPs associated ( < 5 × 10) with high-density lipoprotein (HDL, = 164), low-density lipoprotein (LDL, = 137), total cholesterol (TC, = 161), and triglycerides (TG, = 123) were used as instrumental variables (IV), explaining 14.6%, 27.7%, 16.8%, and 12.8% of phenotypic variation, respectively. Associations between each lipid trait and NHL subtype were calculated using the MR inverse variance-weighted method, estimating odds ratios (OR) per standard deviation and 95% confidence intervals (CI).

Results: HDL was positively associated with DLBCL (OR = 1.14; 95% CI, 1.00-1.30) and MZL (OR = 1.09; 95% CI, 1.01-1.18), while TG was inversely associated with MZL risk (OR = 0.90; 95% CI, 0.83-0.99), all at nominal significance ( < 0.05). A positive trend was observed for HDL with FL risk (OR = 1.08; 95% CI, 0.99-1.19; = 0.087). No associations were noteworthy after adjusting for multiple testing.

Conclusions: We did not find evidence of a clear or strong association of these lipid traits with the most common NHL subtypes. While these IVs have been previously linked to other cancers, our findings do not support any causal associations with these NHL subtypes.

Impact: Our results suggest that prior reported inverse associations of lipid traits are not likely to be causal and could represent reverse causality or confounding.
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http://dx.doi.org/10.1158/1055-9965.EPI-19-0803DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7196490PMC
May 2020

Family Study Designs Informed by Tumor Heterogeneity and Multi-Cancer Pleiotropies: The Power of the Utah Population Database.

Cancer Epidemiol Biomarkers Prev 2020 04 25;29(4):807-815. Epub 2020 Feb 25.

Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah.

Background: Previously, family-based designs and high-risk pedigrees have illustrated value for the discovery of high- and intermediate-risk germline breast cancer susceptibility genes. However, genetic heterogeneity is a major obstacle hindering progress. New strategies and analytic approaches will be necessary to make further advances. One opportunity with the potential to address heterogeneity via improved characterization of disease is the growing availability of multisource databases. Specific to advances involving family-based designs are resources that include family structure, such as the Utah Population Database (UPDB). To illustrate the broad utility and potential power of multisource databases, we describe two different novel family-based approaches to reduce heterogeneity in the UPDB.

Methods: Our first approach focuses on using pedigree-informed breast tumor phenotypes in gene mapping. Our second approach focuses on the identification of families with similar pleiotropies. We use a novel network-inspired clustering technique to explore multi-cancer signatures for high-risk breast cancer families.

Results: Our first approach identifies a genome-wide significant breast cancer locus at 2q13 [ = 1.6 × 10, logarithm of the odds (LOD) equivalent 6.64]. In the region, and are of particular interest, key cytokine genes involved in inflammation. Our second approach identifies five multi-cancer risk patterns. These clusters include expected coaggregations (such as breast cancer with prostate cancer, ovarian cancer, and melanoma), and also identify novel patterns, including coaggregation with uterine, thyroid, and bladder cancers.

Conclusions: Our results suggest pedigree-informed tumor phenotypes can map genes for breast cancer, and that various different cancer pleiotropies exist for high-risk breast cancer pedigrees.

Impact: Both methods illustrate the potential for decreasing etiologic heterogeneity that large, population-based multisource databases can provide.
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http://dx.doi.org/10.1158/1055-9965.EPI-19-0912DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7168701PMC
April 2020

Harnessing Population Pedigree Data and Machine Learning Methods to Identify Patterns of Familial Bladder Cancer Risk.

Cancer Epidemiol Biomarkers Prev 2020 05 25;29(5):918-926. Epub 2020 Feb 25.

Population Sciences, Huntsman Cancer Institute at the University of Utah, Salt Lake City, Utah.

Background: Relatives of patients with bladder cancer have been shown to be at increased risk for kidney, lung, thyroid, and cervical cancer after correcting for smoking-related behaviors that may concentrate in some families. We demonstrate a novel approach to simultaneously assess risks for multiple cancers to identify distinct multicancer configurations (multiple different cancer types that cluster in relatives) surrounding patients with familial bladder cancer.

Methods: This study takes advantage of a unique population-level data resource, the Utah Population Database (UPDB), containing vast genealogy and statewide cancer data. Familial risk is measured using standardized incidence risk (SIR) ratios that account for sex, age, birth cohort, and person-years of the pedigree members.

Results: We identify 1,023 families with a significantly higher bladder cancer rate than population controls (familial bladder cancer). Familial SIRs are then calculated across 25 cancer types, and a weighted Gower distance with K-medoids clustering is used to identify familial multicancer configurations (FMC). We found five FMCs, each exhibiting a different pattern of cancer aggregation. Of the 25 cancer types studied, kidney and prostate cancers were most commonly enriched in the familial bladder cancer clusters. Laryngeal, lung, stomach, acute lymphocytic leukemia, Hodgkin disease, soft-tissue carcinoma, esophageal, breast, lung, uterine, thyroid, and melanoma cancers were the other cancer types with increased incidence in familial bladder cancer families.

Conclusions: This study identified five familial bladder cancer FMCs showing unique risk patterns for cancers of other organs, suggesting phenotypic heterogeneity familial bladder cancer.

Impact: FMC configurations could permit better definitions of cancer phenotypes (subtypes or multicancer) for gene discovery and environmental risk factor studies.
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http://dx.doi.org/10.1158/1055-9965.EPI-19-0681DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7196496PMC
May 2020

Genetically Determined Height and Risk of Non-hodgkin Lymphoma.

Front Oncol 2019 28;9:1539. Epub 2020 Jan 28.

Interdisciplinary Department of Medicine, University of Bari, Bari, Italy.

Although the evidence is not consistent, epidemiologic studies have suggested that taller adult height may be associated with an increased risk of some non-Hodgkin lymphoma (NHL) subtypes. Height is largely determined by genetic factors, but how these genetic factors may contribute to NHL risk is unknown. We investigated the relationship between genetic determinants of height and NHL risk using data from eight genome-wide association studies (GWAS) comprising 10,629 NHL cases, including 3,857 diffuse large B-cell lymphoma (DLBCL), 2,847 follicular lymphoma (FL), 3,100 chronic lymphocytic leukemia (CLL), and 825 marginal zone lymphoma (MZL) cases, and 9,505 controls of European ancestry. We evaluated genetically predicted height by constructing polygenic risk scores using 833 height-associated SNPs. We used logistic regression to estimate odds ratios (OR) and 95% confidence intervals (CI) for association between genetically determined height and the risk of four NHL subtypes in each GWAS and then used fixed-effect meta-analysis to combine subtype results across studies. We found suggestive evidence between taller genetically determined height and increased CLL risk (OR = 1.08, 95% CI = 1.00-1.17, = 0.049), which was slightly stronger among women (OR = 1.15, 95% CI: 1.01-1.31, = 0.036). No significant associations were observed with DLBCL, FL, or MZL. Our findings suggest that there may be some shared genetic factors between CLL and height, but other endogenous or environmental factors may underlie reported epidemiologic height associations with other subtypes.
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http://dx.doi.org/10.3389/fonc.2019.01539DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6999122PMC
January 2020

Fine-mapping of 150 breast cancer risk regions identifies 191 likely target genes.

Nat Genet 2020 01 7;52(1):56-73. Epub 2020 Jan 7.

Unit of Medical Genetics, Department of Medical Oncology and Hematology, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, Milan, Italy.

Genome-wide association studies have identified breast cancer risk variants in over 150 genomic regions, but the mechanisms underlying risk remain largely unknown. These regions were explored by combining association analysis with in silico genomic feature annotations. We defined 205 independent risk-associated signals with the set of credible causal variants in each one. In parallel, we used a Bayesian approach (PAINTOR) that combines genetic association, linkage disequilibrium and enriched genomic features to determine variants with high posterior probabilities of being causal. Potentially causal variants were significantly over-represented in active gene regulatory regions and transcription factor binding sites. We applied our INQUSIT pipeline for prioritizing genes as targets of those potentially causal variants, using gene expression (expression quantitative trait loci), chromatin interaction and functional annotations. Known cancer drivers, transcription factors and genes in the developmental, apoptosis, immune system and DNA integrity checkpoint gene ontology pathways were over-represented among the highest-confidence target genes.
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http://dx.doi.org/10.1038/s41588-019-0537-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6974400PMC
January 2020

Genetic overlap between autoimmune diseases and non-Hodgkin lymphoma subtypes.

Genet Epidemiol 2019 10 13;43(7):844-863. Epub 2019 Aug 13.

Medicina Traslazionale, Università del Piemonte Orientale, Vercelli, Italy.

Epidemiologic studies show an increased risk of non-Hodgkin lymphoma (NHL) in patients with autoimmune disease (AD), due to a combination of shared environmental factors and/or genetic factors, or a causative cascade: chronic inflammation/antigen-stimulation in one disease leads to another. Here we assess shared genetic risk in genome-wide-association-studies (GWAS). Secondary analysis of GWAS of NHL subtypes (chronic lymphocytic leukemia, diffuse large B-cell lymphoma, follicular lymphoma, and marginal zone lymphoma) and ADs (rheumatoid arthritis, systemic lupus erythematosus, and multiple sclerosis). Shared genetic risk was assessed by (a) description of regional genetic of overlap, (b) polygenic risk score (PRS), (c)"diseasome", (d)meta-analysis. Descriptive analysis revealed few shared genetic factors between each AD and each NHL subtype. The PRS of ADs were not increased in NHL patients (nor vice versa). In the diseasome, NHLs shared more genetic etiology with ADs than solid cancers (p = .0041). A meta-analysis (combing AD with NHL) implicated genes of apoptosis and telomere length. This GWAS-based analysis four NHL subtypes and three ADs revealed few weakly-associated shared loci, explaining little total risk. This suggests common genetic variation, as assessed by GWAS in these sample sizes, may not be the primary explanation for the link between these ADs and NHLs.
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http://dx.doi.org/10.1002/gepi.22242DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6763347PMC
October 2019

Shared genomic segments in high-risk multigenerational pedigrees with gastroschisis.

Birth Defects Res 2019 12 5;111(20):1655-1664. Epub 2019 Aug 5.

Department of Internal Medicine and Huntsman Cancer Institute, University of Utah School of Medicine, Salt Lake City, Utah.

Objectives: Gastroschisis remains an etiologic dilemma. We posit that an underlying genetic susceptibility either separately or coupled with a periconceptional environmental exposure stimulates an inflammatory response resulting in gastroschisis. To investigate the genetic link, we applied shared genomic segment (SGS) analysis, a novel approach to discover chromosomal segments that inherit in high-risk multigenerational pedigrees.

Methods: We studied pedigrees containing distantly related children with gastroschisis originating from a common ancestor. We used the Illumina OmniExpress genotyping array with >700,000 SNPs. Samples from 40 affected children in 13 pedigrees (≥3 affected children) were genotyped to generate the high-density SNP data necessary to perform SGS analysis. Assessment of significance in SGS was determined empirically using simulations based on precise pedigree structure and modeling linkage disequilibrium (LD) for SNPs in the general population to properly account for genetic architecture. The LD model was estimated from the 1000 Genome Project using the same set of SNPs. Genome-wide significance thresholds were determined for each pedigree.

Results: We identified six pedigrees that contained genome-wide statistically significant SGS regions inherited from a common founder. These regions were different in each pedigree, all contained immune pathway genes.

Discussion: The genome-wide significant regions support a genetic susceptibility for gastroschisis. The regions are compelling candidates for regionally focused genome sequencing, enabling the discovery of coding or noncoding (e.g., regulatory) risk variants, the latter of which are unlikely to be found using conventional exomic/gene-focused approaches. This technique provides a comprehensive and focused genomic interrogation that will help to advance our understanding of gastroschisis.
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http://dx.doi.org/10.1002/bdr2.1567DOI Listing
December 2019

Association of elevated serumfree light chains with chronic lymphocytic leukemia and monoclonal B-cell lymphocytosis.

Blood Cancer J 2019 08 5;9(8):59. Epub 2019 Aug 5.

Division of Epidemiology, Department of Health Sciences, Mayo Clinic, Rochester, MN, USA.

Chronic lymphocytic leukemia (CLL) and its precursor, monoclonal B-cell lymphocytosis (MBL), are heritable. Serumfree light-chain (sFLC) measures are a prognostic factor for CLL, but their role in susceptibility to CLL is not clear. We investigated differences between sFLC measurements in pre-treatment serum from five groups to inform the association of sFLC with familial and sporadic CLL: (1) familial CLL (n = 154), (2) sporadic CLL (n = 302), (3) familial MBL (n = 87), (4) unaffected first-degree relatives from CLL/MBL families (n = 263), and (5) reference population (n = 15,396). The percent of individuals having elevated monoclonal and polyclonal sFLCs was compared using age-stratified and age- and sex-adjusted logistic regression models. In age groups >50 years, monoclonal sFLC elevations were increased in sporadic and familial CLL cases compared to the reference population (p's < 0.05). However, there were no statistically significant differences in sFLC monoclonal or polyclonal elevations between familial and sporadic CLL cases (p's > 0.05). Unaffected relatives and MBL cases from CLL/MBL families, ages >60 years, showed elevated monoclonal sFLC, compared to the reference population (p's < 0.05). This is the first study to demonstrate monoclonal sFLC elevations in CLL cases compared to controls. Monoclonal sFLC levels may provide additional risk information in relatives of CLL probands.
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http://dx.doi.org/10.1038/s41408-019-0220-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6683199PMC
August 2019

Elevated IgM and abnormal free light chain ratio are increased in relatives from high-risk chronic lymphocytic leukemia pedigrees.

Blood Cancer J 2019 02 26;9(3):25. Epub 2019 Feb 26.

University of Utah School of Medicine, Salt Lake City, UT, 84112, USA.

Abnormal serum immunoglobulin (Ig) free light chains (FLC) are established biomarkers of early disease in multiple B-cell lymphoid malignancies, including chronic lymphocytic leukemia (CLL). Heavy chains have also been shown to be biomarkers in plasma cell disorders. An unanswered question is whether these Ig biomarkers are heritable, i.e., influenced by germline factors. CLL is heritable but highly heterogeneous. Heritable biomarkers could elucidate steps of disease pathogenesis that are affected by germline factors, and may help partition heterogeneity and identify genetic pleiotropies across malignancies. Relatives in CLL pedigrees present an opportunity to identify heritable biomarkers. We compared FLCs and heavy chains between relatives in 23 high-risk CLL pedigrees and population controls. Elevated IgM (eIgM) and abnormal FLC (aFLC) ratio was significantly increased in relatives, suggesting that these Ig biomarkers are heritable and could offer risk stratification in pedigree relatives. Within high-risk CLL pedigrees, B-cell lymphoid malignancies were five times more prevalent in close relatives of individuals with eIgM, prostate cancer was three times more prevalent in relatives of individuals with aFLC, and monoclonal B-cell lymphocytosis increased surrounding individuals with normal Ig levels. These different clustering patterns suggest Ig biomarkers have the potential to partition genetic heterogeneity in CLL and provide insight into distinct heritable pleiotropies associated with CLL.
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http://dx.doi.org/10.1038/s41408-019-0186-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6391432PMC
February 2019

Predictors of Response Outcomes for Research Recruitment Through a Central Cancer Registry: Evidence From 17 Recruitment Efforts for Population-Based Studies.

Am J Epidemiol 2019 05;188(5):928-939

Division of Epidemiology, Department of Internal Medicine, School of Medicine, University of Utah, Salt Lake City, Utah.

When recruiting research participants through central cancer registries, high response fractions help ensure population-based representation. We conducted multivariable mixed-effects logistic regression to identify case and study characteristics associated with making contact with and obtaining cooperation of Utah cancer cases using data from 17 unique recruitment efforts undertaken by the Utah Cancer Registry (2007-2016) on behalf of the following studies: A Population-Based Childhood Cancer Survivors Cohort Study in Utah, Comparative Effectiveness Analysis of Surgery and Radiation for Prostate Cancer (CEASAR Study), Costs and Benefits of Follow-up Care for Adolescent and Young Adult Cancers, Study of Exome Sequencing for Head and Neck Cancer Susceptibility Genes, Genetic Epidemiology of Chronic Lymphocytic Leukemia, Impact of Remote Familial Colorectal Cancer Risk Assessment and Counseling (Family CARE Project), Massively Parallel Sequencing for Familial Colon Cancer Genes, Medullary Thyroid Carcinoma (MTC) Surveillance Study, Osteosarcoma Surveillance Study, Prostate Cancer Outcomes Study, Risk Education and Assessment for Cancer Heredity Project (REACH Project), Study of Shared Genomic Segment Analysis and Tumor Subtyping in High-Risk Breast-Cancer Gene Pedigrees, Study of Shared Genomic Segment Analysis for Localizing Multiple Myeloma Genes. Characteristics associated with lower odds of contact included Hispanic ethnicity (odds ratio (OR) = 0.34, 95% confidence interval (CI): 0.27, 0.41), nonwhite race (OR = 0.46, 95% CI: 0.35, 0.60), and younger age at contact. Years since diagnosis was inversely associated with making contact. Nonwhite race and age ≥60 years had lower odds of cooperation. Study features with lower odds of cooperation included longitudinal design (OR = 0.50, 95% CI: 0.41, 0.61) and study brochures (OR = 0.70, 95% CI: 0.54, 0.90). Increased odds of cooperation were associated with including a questionnaire (OR = 3.19, 95% CI: 1.54, 6.59), postage stamps (OR = 1.60, 95% CI: 1.21, 2.12), and incentives (OR = 1.62, 95% CI: 1.02, 2.57). Among cases not responding after the first contact, odds of eventual response were lower when >10 days elapsed before subsequent contact (OR = 0.71, 95% CI: 0.59, 0.85). Obtaining high response is challenging, but study features identified in this analysis support better results when recruiting through central cancer registries.
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http://dx.doi.org/10.1093/aje/kwz011DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6494669PMC
May 2019

Re-interpretation of PAM50 gene expression as quantitative tumor dimensions shows utility for clinical trials: application to prognosis and response to paclitaxel in breast cancer.

Breast Cancer Res Treat 2019 May 23;175(1):129-139. Epub 2019 Jan 23.

Huntsman Cancer Institute, University of Utah, Salt Lake City, USA.

Background: We recently showed PAM50 gene expression data can be represented by five quantitative, orthogonal, multi-gene breast tumor traits. These novel tumor 'dimensions' were superior to categorical intrinsic subtypes for clustering in high-risk breast cancer pedigrees, indicating potential to represent underlying genetic susceptibilities and biological pathways. Here we explore the prognostic and predictive utility of these dimensions in a sub-study of GEICAM/9906, a Phase III randomized prospective clinical trial of paclitaxel in breast cancer.

Methods: Tumor dimensions, PC1-PC5, were calculated using pre-defined coefficients. Univariable and multivariable Cox proportional hazards (PH) models for disease-free survival (DFS) were used to identify associations between quantitative dimensions and prognosis or response to the addition of paclitaxel. Results were illustrated using Kaplan-Meier curves.

Results: Dimensions PC1 and PC5 were associated with DFS (Cox PH p = 6.7 [Formula: see text] 10 and p = 0.036), remaining significant after correction for standard clinical-pathological prognostic characteristics. Both dimensions were selected in the optimal multivariable model, together with nodal status and tumor size (Cox PH p = 1.4 [Formula: see text] 10). Interactions with treatment were identified for PC3 and PC4. Response to paclitaxel was restricted to tumors with low PC3 and PC4 (log-rank p = 0.0021). Women with tumors high for PC3 or PC4 showed no survival advantage.

Conclusions: Our proof-of-concept application of quantitative dimensions illustrated novel findings and clinical utility beyond standard clinical-pathological characteristics and categorical intrinsic subtypes for prognosis and predicting chemotherapy response. Consideration of expression data as quantitative tumor dimensions offers new potential to identify clinically important patient subsets in clinical trials and advance precision medicine.
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http://dx.doi.org/10.1007/s10549-018-05097-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6491406PMC
May 2019

Re-interpretation of PAM50 gene expression as quantitative tumor dimensions shows utility for clinical trials: application to prognosis and response to paclitaxel in breast cancer.

Breast Cancer Res Treat 2019 May 23;175(1):129-139. Epub 2019 Jan 23.

Huntsman Cancer Institute, University of Utah, Salt Lake City, USA.

Background: We recently showed PAM50 gene expression data can be represented by five quantitative, orthogonal, multi-gene breast tumor traits. These novel tumor 'dimensions' were superior to categorical intrinsic subtypes for clustering in high-risk breast cancer pedigrees, indicating potential to represent underlying genetic susceptibilities and biological pathways. Here we explore the prognostic and predictive utility of these dimensions in a sub-study of GEICAM/9906, a Phase III randomized prospective clinical trial of paclitaxel in breast cancer.

Methods: Tumor dimensions, PC1-PC5, were calculated using pre-defined coefficients. Univariable and multivariable Cox proportional hazards (PH) models for disease-free survival (DFS) were used to identify associations between quantitative dimensions and prognosis or response to the addition of paclitaxel. Results were illustrated using Kaplan-Meier curves.

Results: Dimensions PC1 and PC5 were associated with DFS (Cox PH p = 6.7 [Formula: see text] 10 and p = 0.036), remaining significant after correction for standard clinical-pathological prognostic characteristics. Both dimensions were selected in the optimal multivariable model, together with nodal status and tumor size (Cox PH p = 1.4 [Formula: see text] 10). Interactions with treatment were identified for PC3 and PC4. Response to paclitaxel was restricted to tumors with low PC3 and PC4 (log-rank p = 0.0021). Women with tumors high for PC3 or PC4 showed no survival advantage.

Conclusions: Our proof-of-concept application of quantitative dimensions illustrated novel findings and clinical utility beyond standard clinical-pathological characteristics and categorical intrinsic subtypes for prognosis and predicting chemotherapy response. Consideration of expression data as quantitative tumor dimensions offers new potential to identify clinically important patient subsets in clinical trials and advance precision medicine.
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http://dx.doi.org/10.1007/s10549-018-05097-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6491406PMC
May 2019

Pooled study of occupational exposure to aromatic hydrocarbon solvents and risk of multiple myeloma.

Occup Environ Med 2018 11 18;75(11):798-806. Epub 2018 Aug 18.

Department of Public Health and Clinical Molecular Medicine, University of Cagliari, Cagliari, Italy.

Objectives: To investigate the association between occupational exposure to aromatic hydrocarbon solvents and risk of multiple myeloma (MM) in a large, consortium-based study.

Methods: We pooled data on 2854 cases and 10 743 controls from nine studies participating in the InterLymph consortium. Occupational exposures to benzene, toluene and xylene were assigned by a job-exposure matrix, coupled with 'correction' of exposure probability by self-reported or expert-assessed exposure from the individual studies. Cumulative intensity was calculated as the job-specific exposure intensity multiplied by job duration, summed across jobs. Associations were estimated using logistic regression, with inclusion of covariates for study matching factors and other potential confounders. We repeated our main analysis using random-effects meta-analysis to evaluate heterogeneity of effect.

Results: Benzene, toluene and xylene were each associated with MM. For the three solvents, the highest quartile of high-probability cumulative intensity exposure (vs unexposed) was associated with 42% to 63% increased risks of MM. Associations with toluene and xylene exposures were fairly consistent and robust to sensitivity analyses. The estimated effect for benzene was moderately heterogeneous between the studies. Each solvent's association with MM was stronger for exposure occurring within 20 years of diagnosis than with exposure lagged by more than 20 years.

Conclusions: Our study adds important evidence for a role of aromatic hydrocarbon solvents in causation of MM. The difficulty in disentangling individual compounds in this group and a lack of data on potential carcinogenicity of toluene and xylene, in widespread current use, underscore a need for further epidemiological evaluation.
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http://dx.doi.org/10.1136/oemed-2018-105154DOI Listing
November 2018

HLA Class I and II Diversity Contributes to the Etiologic Heterogeneity of Non-Hodgkin Lymphoma Subtypes.

Cancer Res 2018 07 7;78(14):4086-4096. Epub 2018 May 7.

Department of Public Health, Clinical and Molecular Medicine, University of Cagliari, Cagliari, Italy.

A growing number of loci within the human leukocyte antigen (HLA) region have been implicated in non-Hodgkin lymphoma (NHL) etiology. Here, we test a complementary hypothesis of "heterozygote advantage" regarding the role of HLA and NHL, whereby HLA diversity is beneficial and homozygous HLA loci are associated with increased disease risk. HLA alleles at class I and II loci were imputed from genome-wide association studies (GWAS) using SNP2HLA for 3,617 diffuse large B-cell lymphomas (DLBCL), 2,686 follicular lymphomas (FL), 2,878 chronic lymphocytic leukemia/small lymphocytic lymphomas (CLL/SLL), 741 marginal zone lymphomas (MZL), and 8,753 controls of European descent. Both DLBCL and MZL risk were elevated with homozygosity at class I HLA-B and -C loci (OR DLBCL = 1.31, 95% CI = 1.06-1.60; OR MZL = 1.45, 95% CI = 1.12-1.89) and class II HLA-DRB1 locus (OR DLBCL = 2.10, 95% CI = 1.24-3.55; OR MZL = 2.10, 95% CI = 0.99-4.45). Increased FL risk was observed with the overall increase in number of homozygous HLA class II loci ( trend < 0.0001, FDR = 0.0005). These results support a role for HLA zygosity in NHL etiology and suggests that distinct immune pathways may underly the etiology of the different NHL subtypes. HLA gene diversity reduces risk for non-Hodgkin lymphoma. .
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http://dx.doi.org/10.1158/0008-5472.CAN-17-2900DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6065509PMC
July 2018

Association of polygenic risk score with the risk of chronic lymphocytic leukemia and monoclonal B-cell lymphocytosis.

Blood 2018 06 19;131(23):2541-2551. Epub 2018 Apr 19.

Huntsman Cancer Institute and Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT.

Inherited loci have been found to be associated with risk of chronic lymphocytic leukemia (CLL). A combined polygenic risk score (PRS) of representative single nucleotide polymorphisms (SNPs) from these loci may improve risk prediction over individual SNPs. Herein, we evaluated the association of a PRS with CLL risk and its precursor, monoclonal B-cell lymphocytosis (MBL). We assessed its validity and discriminative ability in an independent sample and evaluated effect modification and confounding by family history (FH) of hematological cancers. For discovery, we pooled genotype data on 41 representative SNPs from 1499 CLL and 2459 controls from the InterLymph Consortium. For validation, we used data from 1267 controls from Mayo Clinic and 201 CLL, 95 MBL, and 144 controls with a FH of CLL from the Genetic Epidemiology of CLL Consortium. We used odds ratios (ORs) to estimate disease associations with PRS and c-statistics to assess discriminatory accuracy. In InterLymph, the continuous PRS was strongly associated with CLL risk (OR, 2.49; = 4.4 × 10). We replicated these findings in the Genetic Epidemiology of CLL Consortium and Mayo controls (OR, 3.02; = 7.8 × 10) and observed high discrimination (c-statistic = 0.78). When jointly modeled with FH, PRS retained its significance, along with FH status. Finally, we found a highly significant association of the continuous PRS with MBL risk (OR, 2.81; = 9.8 × 10). In conclusion, our validated PRS was strongly associated with CLL risk, adding information beyond FH. The PRS provides a means of identifying those individuals at greater risk for CLL as well as those at increased risk of MBL, a condition that has potential clinical impact beyond CLL.
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http://dx.doi.org/10.1182/blood-2017-11-814608DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5992865PMC
June 2018

Reparameterization of PAM50 Expression Identifies Novel Breast Tumor Dimensions and Leads to Discovery of a Genome-Wide Significant Breast Cancer Locus at .

Cancer Epidemiol Biomarkers Prev 2018 06 12;27(6):644-652. Epub 2018 Apr 12.

Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah.

Breast tumor subtyping has failed to provide impact in susceptibility genetics. The PAM50 assay categorizes breast tumors into: Luminal A, Luminal B, HER2-enriched and Basal-like. However, tumors are often more complex than simple categorization can describe. The identification of heritable tumor characteristics has potential to decrease heterogeneity and increase power for gene finding. We used 911 sporadic breast tumors with PAM50 expression data to derive tumor dimensions using principal components (PC). Dimensions in 238 tumors from high-risk pedigrees were compared with the sporadic tumors. Proof-of-concept gene mapping, informed by tumor dimension, was performed using Shared Genomic Segment (SGS) analysis. Five dimensions (PC1-5) explained the majority of the PAM50 expression variance: three captured intrinsic subtype, two were novel (PC3, PC5). All five replicated in 745 TCGA tumors. Both novel dimensions were significantly enriched in the high-risk pedigrees (intrinsic subtypes were not). SGS gene-mapping in a pedigree identified a 0.5 Mb genome-wide significant region at This region segregated through 32 meioses to 8 breast cancer cases with extreme PC3 tumors ( = 2.6 × 10). PC analysis of PAM50 gene expression revealed multiple independent, quantitative measures of tumor diversity. These tumor dimensions show evidence for heritability and potential as powerful traits for gene mapping. Our study suggests a new approach to describe tumor expression diversity, provides new avenues for germline studies, and proposes a new breast cancer locus. Similar reparameterization of expression patterns may inform other studies attempting to model the effects of tumor heterogeneity. .
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http://dx.doi.org/10.1158/1055-9965.EPI-17-0887DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5984724PMC
June 2018

Germline Lysine-Specific Demethylase 1 () Mutations Confer Susceptibility to Multiple Myeloma.

Cancer Res 2018 05 20;78(10):2747-2759. Epub 2018 Mar 20.

University of Chicago, Chicago, Illinois.

Given the frequent and largely incurable occurrence of multiple myeloma, identification of germline genetic mutations that predispose cells to multiple myeloma may provide insight into disease etiology and the developmental mechanisms of its cell of origin, the plasma cell (PC). Here, we identified familial and early-onset multiple myeloma kindreds with truncating mutations in lysine-specific demethylase 1 (LSD1/KDM1A), an epigenetic transcriptional repressor that primarily demethylates histone H3 on lysine 4 and regulates hematopoietic stem cell self-renewal. In addition, we found higher rates of germline truncating and predicted deleterious missense KDM1A mutations in patients with multiple myeloma unselected for family history compared with controls. Both monoclonal gammopathy of undetermined significance (MGUS) and multiple myeloma cells have significantly lower KDM1A transcript levels compared with normal PCs. Transcriptome analysis of multiple myeloma cells from KDM1A mutation carriers shows enrichment of pathways and MYC target genes previously associated with myeloma pathogenesis. In mice, antigen challenge followed by pharmacologic inhibition of KDM1A promoted PC expansion, enhanced secondary immune response, elicited appearance of serum paraprotein, and mediated upregulation of MYC transcriptional targets. These changes are consistent with the development of MGUS. Collectively, our findings show that KDM1A is the first autosomal-dominant multiple myeloma germline predisposition gene providing new insights into its mechanistic roles as a tumor suppressor during post-germinal center B-cell differentiation. KDM1A is the first germline autosomal dominant predisposition gene identified in multiple myeloma and provides new insights into multiple myeloma etiology and the mechanistic role of KDM1A as a tumor suppressor during post-germinal center B-cell differentiation. .
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http://dx.doi.org/10.1158/0008-5472.CAN-17-1900DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5955848PMC
May 2018
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