Genome-wide association studies have found SNPs at 17q22 to be associated with breast cancer risk. To identify potential causal variants related to breast cancer risk, we performed a high resolution fine-mapping analysis that involved genotyping 517 SNPs using a custom Illumina iSelect array (iCOGS) followed by imputation of genotypes for 3,134 SNPs in more than 89,000 participants of European ancestry from the Breast Cancer Association Consortium (BCAC). We identified 28 highly correlated common variants, in a 53 Kb region spanning two introns of the STXBP4 gene, that are strong candidates for driving breast cancer risk (lead SNP rs2787486 (OR = 0.92; CI 0.90-0.94; P = 8.96 × 10(-15))) and are correlated with two previously reported risk-associated variants at this locus, SNPs rs6504950 (OR = 0.94, P = 2.04 × 10(-09), r(2) = 0.73 with lead SNP) and rs1156287 (OR = 0.93, P = 3.41 × 10(-11), r(2) = 0.83 with lead SNP). Analyses indicate only one causal SNP in the region and several enhancer elements targeting STXBP4 are located within the 53 kb association signal. Expression studies in breast tumor tissues found SNP rs2787486 to be associated with increased STXBP4 expression, suggesting this may be a target gene of this locus.

Although 70-80% of newly diagnosed ovarian cancer patients respond to first-line therapy, almost all relapse and five-year survival remains below 50%. One strategy to increase five-year survival is prolonging time to relapse by improving first-line therapy response. However, no biomarker today can accurately predict individual response to therapy. In this study, we present analytical and prospective clinical validation of a new test that utilizes primary patient tissue in 3D cell culture to make patient-specific response predictions prior to initiation of treatment in the clinic. Test results were generated within seven days of tissue receipt from newly diagnosed ovarian cancer patients obtained at standard surgical debulking or laparoscopic biopsy. Patients were followed for clinical response to chemotherapy. In a study population of 44, the 32 test-predicted Responders had a clinical response rate of 100% across both adjuvant and neoadjuvant treated populations with an overall prediction accuracy of 89% (39 of 44, p < 0.0001). The test also functioned as a prognostic readout with test-predicted Responders having a significantly increased progression-free survival compared to test-predicted Non-Responders, p = 0.01. This correlative accuracy establishes the test's potential to benefit ovarian cancer patients through accurate prediction of patient-specific response before treatment.

We propose an effective machine learning approach to identify group of interacting single nucleotide polymorphisms (SNPs), which contribute most to the breast cancer (BC) risk by assuming dependencies among BCAC iCOGS SNPs. We adopt a gradient tree boosting method followed by an adaptive iterative SNP search to capture complex non-linear SNP-SNP interactions and consequently, obtain group of interacting SNPs with high BC risk-predictive potential. We also propose a support vector machine formed by the identified SNPs to classify BC cases and controls. Our approach achieves mean average precision (mAP) of 72.66, 67.24 and 69.25 in discriminating BC cases and controls in KBCP, OBCS and merged KBCP-OBCS sample sets, respectively. These results are better than the mAP of 70.08, 63.61 and 66.41 obtained by using a polygenic risk score model derived from 51 known BC-associated SNPs, respectively, in KBCP, OBCS and merged KBCP-OBCS sample sets. BC subtype analysis further reveals that the 200 identified KBCP SNPs from the proposed method performs favorably in classifying estrogen receptor positive (ER+) and negative (ER-) BC cases both in KBCP and OBCS data. Further, a biological analysis of the identified SNPs reveals genes related to important BC-related mechanisms, estrogen metabolism and apoptosis.

High-risk mutations in several genes predispose to both colorectal cancer (CRC) and endometrial cancer (EC). We therefore hypothesised that some lower-risk genetic variants might also predispose to both CRC and EC. Using CRC and EC genome-wide association series, totalling 13,265 cancer cases and 40,245 controls, we found that the protective allele [G] at one previously-identified CRC polymorphism, rs2736100 near TERT, was associated with EC risk (odds ratio (OR) = 1.08, P = 0.000167); this polymorphism influences the risk of several other cancers. A further CRC polymorphism near TERC also showed evidence of association with EC (OR = 0.92; P = 0.03). Overall, however, there was no good evidence that the set of CRC polymorphisms was associated with EC risk, and neither of two previously-reported EC polymorphisms was associated with CRC risk. A combined analysis revealed one genome-wide significant polymorphism, rs3184504, on chromosome 12q24 (OR = 1.10, P = 7.23 × 10(-9)) with shared effects on CRC and EC risk. This polymorphism, a missense variant in the gene SH2B3, is also associated with haematological and autoimmune disorders, suggesting that it influences cancer risk through the immune response. Another polymorphism, rs12970291 near gene TSHZ1, was associated with both CRC and EC (OR = 1.26, P = 4.82 × 10(-8)), with the alleles showing opposite effects on the risks of the two cancers.

The risk of breast cancer associated with CHEK2:c.1100delC is 2-threefold but higher in carriers with a family history of breast cancer than without, suggesting that other genetic loci in combination with CHEK2:c.1100delC confer an increased risk in a polygenic model. Part of the excess familial risk has been associated with common low-penetrance variants. This study aimed to identify genetic loci that modify CHEK2:c.1100delC-associated breast cancer risk by searching for candidate risk alleles that are overrepresented in CHEK2:c.1100delC carriers with breast cancer compared with controls. We performed whole-exome sequencing in 28 breast cancer cases with germline CHEK2:c.1100delC, 28 familial breast cancer cases and 70 controls. Candidate alleles were selected for validation in larger cohorts. One recessive synonymous variant, rs16897117, was suggested, but no overrepresentation of homozygous CHEK2:c.1100delC carriers was found in the following validation. Furthermore, 11 non-synonymous candidate alleles were suggested for further testing, but no significant difference in allele frequency could be detected in the validation in CHEK2:c.1100delC cases compared with familial breast cancer, sporadic breast cancer and controls. With this method, we found no support for a CHEK2:c.1100delC-specific genetic modifier. Further studies of CHEK2:c.1100delC genetic modifiers are warranted to improve risk assessment in clinical practice.

Successful development of novel therapies requires that clinical trials are conducted in patient cohorts with the highest benefit-to-risk ratio. Population-based biobanks with comprehensive health and genetic data from large numbers of individuals hold promise to facilitate identification of trial participants, particularly when interventions need to start while symptoms are still mild, such as for Alzheimer's disease (AD). This study describes a process for clinical recall studies from FinnGen. We demonstrate the feasibility to systematically ascertain customized clinical data from FinnGen participants with ICD10 diagnosis of AD or mild cognitive disorder (MCD) in a single-center cross-sectional study testing blood-based biomarkers and cognitive functioning in-person, computer-based and remote. As a result, 19% (27/140) of a pre-specified FinnGen subcohort were successfully recalled and completed the study. Hospital records largely validated registry entries. For 8/12 MCD patients, other reasons than AD were identified as underlying diagnosis. Cognitive measures correlated across platforms, with highest consistencies for dementia screening (r = 0.818) and semantic fluency (r = 0.764), respectively, for in-person versus telephone-administered tests. Glial fibrillary acidic protein (GFAP) (p < 0.002) and phosphorylated-tau 181 (pTau-181) (p < 0.020) most reliably differentiated AD from MCD participants. We conclude that informative, customized clinical recall studies from FinnGen are feasible.

Several known breast cancer susceptibility genes encode proteins involved in DNA damage response (DDR) and are characterized by rare loss-of-function mutations. However, these explain less than half of the familial cases. To identify novel susceptibility factors, 39 rare truncating mutations, identified in 189 Northern Finnish hereditary breast cancer patients in parallel sequencing of 796 DDR genes, were studied for disease association. Mutation screening was performed for Northern Finnish breast cancer cases (n = 578-1565) and controls (n = 337-1228). Mutations showing potential cancer association were analyzed in additional Finnish cohorts. c.7253dupT in TEX15, encoding a DDR factor important in meiosis, associated with hereditary breast cancer (p = 0.018) and likely represents a Northern Finnish founder mutation. A deleterious c.2715 + 1G > A mutation in the Fanconi anemia gene, FANCD2, was over two times more common in the combined Finnish hereditary cohort compared to controls. A deletion (c.640_644del5) in RNF168, causative for recessive RIDDLE syndrome, had high prevalence in majority of the analyzed cohorts, but did not associate with breast cancer. In conclusion, truncating variants in TEX15 and FANCD2 are potential breast cancer risk factors, warranting further investigations in other populations. Furthermore, high frequency of RNF168 c.640_644del5 indicates the need for its testing in Finnish patients with RIDDLE syndrome symptoms.

As patient derived xenograft (PDX) models are increasingly used for preclinical drug development, strategies to account for the nonhuman component of PDX RNA expression data are critical to its interpretation. A bioinformatics pipeline to separate donor tumor and mouse stroma transcriptome profiles was devised and tested. To examine the molecular fidelity of PDX versus donor tumors, we compared mRNA differences between paired PDX-donor tumors from nine ovarian cancer patients. 1,935 differentially expressed genes were identified between PDX and donor tumors. Over 90% (n = 1767) of these genes were down-regulated in PDX models and enriched in stroma-specific functions. Several protein kinases were also differentially expressed in PDX tumors, e.g. PDGFRA, PDGFRB and CSF1R. Upon in silico removal of these PDX-donor tumor differentially expressed genes, a stronger transcriptional resemblance between PDX-donor tumor pairs was seen (average correlation coefficient increases from 0.91 to 0.95). We devised and validated an effective bioinformatics strategy to separate mouse stroma expression from human tumor expression for PDX RNAseq. In addition, we showed most of the PDX-donor differentially expressed genes were implicated in stromal components. The molecular similarities and differences between PDX and donor tumors have implications in future therapeutic trial designs and treatment response evaluations using PDX models.

Precision medicine approaches are required for more effective therapies for cancer. As small non-coding RNAs (sncRNAs) have recently been suggested as intriguing candidates for cancer biomarkers and have shown potential also as novel therapeutic targets, we aimed at profiling the non-miRNA sncRNAs in a large sample set to evaluate their role in invasive breast cancer (BC). We used small RNA sequencing and 195 fresh-frozen invasive BC and 22 benign breast tissue samples to identify significant associations of small nucleolar RNAs, small nuclear RNAs, and miscellaneous RNAs with the clinicopathological features and patient outcome of BC. Ninety-six and five sncRNAs significantly distinguished (Padj < 0.01) invasive local BC from benign breast tissue and metastasized BC from invasive local BC, respectively. Furthermore, 69 sncRNAs significantly associated (Padj < 0.01) with the tumor grade, hormone receptor status, subtype, and/or tumor histology. Additionally, 42 sncRNAs were observed as candidates for prognostic markers and 29 for predictive markers for radiotherapy and/or tamoxifen response (P < 0.05). We discovered the clinical relevance of sncRNAs from each studied RNA type. By introducing new sncRNA biomarker candidates for invasive BC and validating the potential of previously described ones, we have guided the way for further research that is warranted for providing novel insights into BC biology.

Identifying genetic cancer risk factors will lead to improved genetic counseling, cancer prevention and cancer care. Analyzing families with a strong history of breast cancer (BC) has been a successful method to identify genes that contribute to the disease. This has led to discoveries of high-risk genes like the BRCA-genes. Nevertheless, many BC incidences are of unknown causes. In this study, exome sequencing on 59 BC patients from 24 Swedish families with a strong history of BC was performed to identify variants in known and novel BC predisposing genes. First, we screened known BC genes and identified two pathogenic variants in the BRIP1 and PALB2 genes. Secondly, to identify novel BC genes, rare and high impact variants and segregating in families were analyzed to identify 544 variants in novel BC candidate genes. Of those, 22 variants were defined as high-risk variants. Several interesting genes, either previously linked with BC or in pathways that when flawed could contribute to BC, were among the detected genes. The strongest candidates identified are the FANCM gene, involved in DNA double-strand break repair, and the RAD54L gene, involved in DNA recombination. Our study shows identifying pathogenic variants is challenging despite a strong family history of BC. Several interesting candidates were observed here that need to be further studied.

Breast cancer metastasis accounts for most of the deaths from breast cancer. Identification of germline variants associated with survival in aggressive types of breast cancer may inform understanding of breast cancer progression and assist treatment. In this analysis, we studied the associations between germline variants and breast cancer survival for patients with distant metastases at primary breast cancer diagnosis. We used data from the Breast Cancer Association Consortium (BCAC) including 1062 women of European ancestry with metastatic breast cancer, 606 of whom died of breast cancer. We identified two germline variants on chromosome 1, rs138569520 and rs146023652, significantly associated with breast cancer-specific survival (P = 3.19 × 10-8 and 4.42 × 10-8). In silico analysis suggested a potential regulatory effect of the variants on the nearby target genes SDE2 and H3F3A. However, the variants showed no evidence of association in a smaller replication dataset. The validation dataset was obtained from the SNPs to Risk of Metastasis (StoRM) study and included 293 patients with metastatic primary breast cancer at diagnosis. Ultimately, larger replication studies are needed to confirm the identified associations.

In breast cancer, high levels of homeobox protein Hox-B13 (HOXB13) have been associated with disease progression of ER-positive breast cancer patients and resistance to tamoxifen treatment. Since HOXB13 p.G84E is a prostate cancer risk allele, we evaluated the association between HOXB13 germline mutations and breast cancer risk in a previous study consisting of 3,270 familial non-BRCA1/2 breast cancer cases and 2,327 controls from the Netherlands. Although both recurrent HOXB13 mutations p.G84E and p.R217C were not associated with breast cancer risk, the risk estimation for p.R217C was not very precise. To provide more conclusive evidence regarding the role of HOXB13 in breast cancer susceptibility, we here evaluated the association between HOXB13 mutations and increased breast cancer risk within 81 studies of the international Breast Cancer Association Consortium containing 68,521 invasive breast cancer patients and 54,865 controls. Both HOXB13 p.G84E and p.R217C did not associate with the development of breast cancer in European women, neither in the overall analysis (OR = 1.035, 95% CI = 0.859-1.246, P = 0.718 and OR = 0.798, 95% CI = 0.482-1.322, P = 0.381 respectively), nor in specific high-risk subgroups or breast cancer subtypes. Thus, although involved in breast cancer progression, HOXB13 is not a material breast cancer susceptibility gene.

NBS1, also known as NBN, plays an important role in maintaining genomic stability. Interestingly, rs2735383 G > C, located in a microRNA binding site in the 3'-untranslated region (UTR) of NBS1, was shown to be associated with increased susceptibility to lung and colorectal cancer. However, the relation between rs2735383 and susceptibility to breast cancer is not yet clear. Therefore, we genotyped rs2735383 in 1,170 familial non-BRCA1/2 breast cancer cases and 1,077 controls using PCR-based restriction fragment length polymorphism (RFLP-PCR) analysis, but found no association between rs2735383CC and breast cancer risk (OR = 1.214, 95% CI = 0.936-1.574, P = 0.144). Because we could not exclude a small effect size due to a limited sample size, we further analyzed imputed rs2735383 genotypes (r2 > 0.999) of 47,640 breast cancer cases and 46,656 controls from the Breast Cancer Association Consortium (BCAC). However, rs2735383CC was not associated with overall breast cancer risk in European (OR = 1.014, 95% CI = 0.969-1.060, P = 0.556) nor in Asian women (OR = 0.998, 95% CI = 0.905-1.100, P = 0.961). Subgroup analyses by age, age at menarche, age at menopause, menopausal status, number of pregnancies, breast feeding, family history and receptor status also did not reveal a significant association. This study therefore does not support the involvement of the genotype at NBS1 rs2735383 in breast cancer susceptibility.

E-cadherin (CDH1) is a putative tumor suppressor gene implicated in breast carcinogenesis. Yet, whether risk factors or survival differ by E-cadherin tumor expression is unclear. We evaluated E-cadherin tumor immunohistochemistry expression using tissue microarrays of 5,933 female invasive breast cancers from 12 studies from the Breast Cancer Consortium. H-scores were calculated and case-case odds ratios (OR) and 95% confidence intervals (CIs) were estimated using logistic regression. Survival analyses were performed using Cox regression models. All analyses were stratified by estrogen receptor (ER) status and histologic subtype. E-cadherin low cases (N = 1191, 20%) were more frequently of lobular histology, low grade, >2 cm, and HER2-negative. Loss of E-cadherin expression (score < 100) was associated with menopausal hormone use among ER-positive tumors (ever compared to never users, OR = 1.24, 95% CI = 0.97-1.59), which was stronger when we evaluated complete loss of E-cadherin (i.e. H-score = 0), OR = 1.57, 95% CI = 1.06-2.33. Breast cancer specific mortality was unrelated to E-cadherin expression in multivariable models. E-cadherin low expression is associated with lobular histology, tumor characteristics and menopausal hormone use, with no evidence of an association with breast cancer specific survival. These data support loss of E-cadherin expression as an important marker of tumor subtypes.