The SNP rs6500843 in 16p13.3 is associated with survival specifically among chemotherapy-treated breast cancer patients

ViLoN-a multi-layer network approach to data integration demonstrated for patient stratification

With more and more data being collected, modern network representations exploit the complementary nature of different data sources as well as similarities across patients. We here introduce the Variation of information fused Layers of Networks algorithm (ViLoN), a novel network-based approach for the integration of multiple molecular profiles. As a key innovation, it directly incorporates prior functional knowledge (KEGG, GO). In the constructed network of patients, patients are represented by networks of pathways, comprising genes that are linked by common functions and joint regulation in the disease. Patient stratification remains a key challenge both in the clinic and for research on disease mechanisms and treatments. We thus validated ViLoN for patient stratification on multiple data type combinations (gene expression, methylation, copy number), showing substantial improvements and consistently competitive performance for all. Notably, the incorporation of prior functional knowledge was critical for good results in the smaller cohorts (rectum adenocarcinoma: 90, esophageal carcinoma: 180), where alternative methods failed.

Association of germline genetic variants with breast cancer-specific survival in patient subgroups defined by clinic-pathological variables related to tumor biology and type of systemic treatment

BACKGROUND

Given the high heterogeneity among breast tumors, associations between common germline genetic variants and survival that may exist within specific subgroups could go undetected in an unstratified set of breast cancer patients.

METHODS

We performed genome-wide association analyses within 15 subgroups of breast cancer patients based on prognostic factors, including hormone receptors, tumor grade, age, and type of systemic treatment. Analyses were based on 91,686 female patients of European ancestry from the Breast Cancer Association Consortium, including 7531 breast cancer-specific deaths over a median follow-up of 8.1 years. Cox regression was used to assess associations of common germline variants with 15-year and 5-year breast cancer-specific survival. We assessed the probability of these associations being true positives via the Bayesian false discovery probability (BFDP < 0.15).

RESULTS

Evidence of associations with breast cancer-specific survival was observed in three patient subgroups, with variant rs5934618 in patients with grade 3 tumors (15-year-hazard ratio (HR) [95% confidence interval (CI)] 1.32 [1.20, 1.45], P = 1.4E-08, BFDP = 0.01, per G allele); variant rs4679741 in patients with ER-positive tumors treated with endocrine therapy (15-year-HR [95% CI] 1.18 [1.11, 1.26], P = 1.6E-07, BFDP = 0.09, per G allele); variants rs1106333 (15-year-HR [95% CI] 1.68 [1.39,2.03], P = 5.6E-08, BFDP = 0.12, per A allele) and rs78754389 (5-year-HR [95% CI] 1.79 [1.46,2.20], P = 1.7E-08, BFDP = 0.07, per A allele), in patients with ER-negative tumors treated with chemotherapy.

CONCLUSIONS

We found evidence of four loci associated with breast cancer-specific survival within three patient subgroups. There was limited evidence for the existence of associations in other patient subgroups. However, the power for many subgroups is limited due to the low number of events. Even so, our results suggest that the impact of common germline genetic variants on breast cancer-specific survival might be limited.

Association of germline variation with the survival of women with BRCA1/2 pathogenic variants and breast cancer

Germline genetic variation has been suggested to influence the survival of breast cancer patients independently of tumor pathology. We have studied survival associations of genetic variants in two etiologically unique groups of breast cancer patients, the carriers of germline pathogenic variants in BRCA1 or BRCA2 genes. We found that rs57025206 was significantly associated with the overall survival, predicting higher mortality of BRCA1 carrier patients with estrogen receptor-negative breast cancer, with a hazard ratio 4.37 (95% confidence interval 3.03-6.30, P = 3.1 × 10-9). Multivariable analysis adjusted for tumor characteristics suggested that rs57025206 was an independent survival marker. In addition, our exploratory analyses suggest that the associations between genetic variants and breast cancer patient survival may depend on tumor biological subgroup and clinical patient characteristics.

Polymorphisms of rs4787050 and rs8045980 are associated with lung cancer risk in northeast Chinese female nonsmokers

Aim: We studied the association between two single-nucleotide polymorphisms (SNPs: rs4787050 and rs8045980) in RBFOX1 and lung cancer risk, and explored the interaction between the two SNPs and exposure to cooking oil fume on lung cancer risk in northeast Chinese female nonsmokers. Methods: Northeast Chinese female nonsmokers were enrolled into the study (people with lung cancer, 647; people without lung cancer, 675). All statistical analyses were performed using SPSS software. Results: The SNPs rs4787050 and rs8045980 showed a significant association with susceptibility to lung cancer. Moreover, cooking oil fume exposure was found to increase the risk of lung cancer. However, no gene-environment interactions were discovered. Conclusion: The present study revealed that rs4787050 and rs8045980 in RBFOX1 may be meaningful as a novel biomarker for lung cancer susceptibility.

Report of the AAPM TG-256 on the relative biological effectiveness of proton beams in radiation therapy

The biological effectiveness of proton beams relative to photon beams in radiation therapy has been taken to be 1.1 throughout the history of proton therapy. While potentially appropriate as an average value, actual relative biological effectiveness (RBE) values may differ. This Task Group report outlines the basic concepts of RBE as well as the biophysical interpretation and mathematical concepts. The current knowledge on RBE variations is reviewed and discussed in the context of the current clinical use of RBE and the clinical relevance of RBE variations (with respect to physical as well as biological parameters). The following task group aims were designed to guide the current clinical practice: Assess whether the current clinical practice of using a constant RBE for protons should be revised or maintained. Identifying sites and treatment strategies where variable RBE might be utilized for a clinical benefit. Assess the potential clinical consequences of delivering biologically weighted proton doses based on variable RBE and/or LET models implemented in treatment planning systems. Recommend experiments needed to improve our current understanding of the relationships among in vitro, in vivo, and clinical RBE, and the research required to develop models. Develop recommendations to minimize the effects of uncertainties associated with proton RBE for well-defined tumor types and critical structures.

Association between breast cancer risk factors and molecular type in postmenopausal patients with hormone receptor-positive early breast cancer

PURPOSE

Evidence shows that genetic and non-genetic risk factors for breast cancer (BC) differ relative to the molecular subtype. This analysis aimed to investigate associations between epidemiological risk factors and immunohistochemical subtypes in a cohort of postmenopausal, hormone receptor-positive BC patients.

METHODS

The prospective, single-arm, multicenter phase IV PreFace study (Evaluation of Predictive Factors Regarding the Effectivity of Aromatase Inhibitor Therapy) included 3529 postmenopausal patients with hormone receptor-positive early BC. Data on their epidemiological risk factors were obtained from patients' diaries and their medical histories. Data on estrogen receptor, progesterone receptor, and HER2 receptor status were obtained from pathology reports. Patients with incomplete information were excluded. Data were analyzed using conditional inference regression analysis, analysis of variance, and the chi-squared test.

RESULTS

In a cohort of 3392 patients, the strongest association with the molecular subtypes of BC was found for hormone replacement therapy (HRT) before diagnosis of early BC. The analysis showed that patients who took HRT at diagnosis had luminal A-like BC more often (83.7%) than those who had never taken HRT or had stopped taking it (75.5%). Luminal B-like BC and HER2-positive BC were diagnosed more often in women who had never taken HRT or had stopped taking it (13.3% and 11.2%, respectively) than in women who were taking HRT at diagnosis of BC (8.3% and 8.0%, respectively).

CONCLUSIONS

This analysis shows an association between HRT and the distribution of molecular subtypes of BC. However, no associations between other factors (e.g., age at diagnosis, body mass index, smoking status, age at menopause, number of deliveries, age at first delivery, breastfeeding history, or family history) were noted.

Deep sequencing reveals the mitochondrial DNA variation landscapes of breast-to-brain metastasis blood samples

Breast-to-brain metastasis (BBM) often represents a terminal event, due to the inability of many systemic treatments to cross the blood-brain barrier (BBB), rendering the brain a sanctuary site for tumour cells. Identifying genetic variations that can predict the patients who will develop BBM would allow targeting of adjuvant treatments to reduce risk while disease bulk is minimal. Germ-line genetic variations may contribute to whether a BBM forms by influencing the primary tumour subtype that presents, or by influencing the host response to the tumour or treatment regimen, or by facilitating transition of tumour cells across the BBB and establish a viable brain metastasis. The role of mitochondrial DNA (mtDNA) variants specifically in BBM is underexplored. Consequently, using a sensitive deep sequencing approach, we characterized the mtDNA variation landscapes of blood samples derived from 13 females who were diagnosed with early-onset breast cancer and later went on to develop BBM. We also predicted the potential pathogenic significance of variations identified in all mtDNA-encoded oxidative phosphorylation (OXPHOS) proteins using 3D protein structural mapping and analysis, to identify variations worthy of follow-up. From the 70 variations found in protein coding regions, we reveal novel links between three specific mtDNA variations and altered OXPHOS structure and function in 23% of the BBM samples. Further studies are required to confirm the origin of mtDNA variations, and whether they correlate with (1) the predicted alterations in mitochondrial function and (2) increased risk of developing breast-to-brain metastasis using a much larger cohort of samples.

Risk, Prediction and Prevention of Hereditary Breast Cancer - Large-Scale Genomic Studies in Times of Big and Smart Data

Over the last two decades genetic testing for mutations in BRCA1 and BRCA2 has become standard of care for women and men who are at familial risk for breast or ovarian cancer. Currently, genetic testing more often also includes so-called panel genes, which are assumed to be moderate-risk genes for breast cancer. Recently, new large-scale studies provided more information about the risk estimation of those genes. The utilization of information on panel genes with regard to their association with the individual breast cancer risk might become part of future clinical practice. Furthermore, large efforts have been made to understand the influence of common genetic variants with a low impact on breast cancer risk. For this purpose, almost 450 000 individuals have been genotyped for almost 500 000 genetic variants in the OncoArray project. Based on first results it can be assumed that – together with previously identified common variants – more than 170 breast cancer risk single nucleotide polymorphisms can explain up to 18% of familial breast cancer risk. The knowledge about genetic and non-genetic risk factors and its implementation in clinical practice could especially be of use for individualized prevention. This includes an individualized risk prediction as well as the individualized selection of screening methods regarding imaging and possible lifestyle interventions. The aim of this review is to summarize the most recent developments in this area and to provide an overview on breast cancer risk genes, risk prediction models and their utilization for the individual patient.

Genome-wide association study identifies the GLDC/IL33 locus associated with survival of osteosarcoma patients

Survival rates for osteosarcoma, the most common primary bone cancer, have changed little over the past three decades and are particularly low for patients with metastatic disease. We conducted a multi-institutional genome-wide association study (GWAS) to identify germline genetic variants associated with overall survival in 632 patients with osteosarcoma, including 523 patients of European ancestry and 109 from Brazil. We conducted a time-to-event analysis and estimated hazard ratios (HR) and 95% confidence intervals (CI) using Cox proportional hazards models, with and without adjustment for metastatic disease. The results were combined across the European and Brazilian case sets using a random-effects meta-analysis. The strongest association after meta-analysis was for rs3765555 at 9p24.1, which was inversely associated with overall survival (HR = 1.76; 95% CI 1.41-2.18, p = 4.84 × 10-7 ). After imputation across this region, the combined analysis identified two SNPs that reached genome-wide significance. The strongest single association was with rs55933544 (HR = 1.9; 95% CI 1.5-2.4; p = 1.3 × 10-8 ), which localizes to the GLDC gene, adjacent to the IL33 gene and was consistent across both the European and Brazilian case sets. Using publicly available data, the risk allele was associated with lower expression of IL33 and low expression of IL33 was associated with poor survival in an independent set of patients with osteosarcoma. In conclusion, we have identified the GLDC/IL33 locus on chromosome 9p24.1 as associated with overall survival in patients with osteosarcoma. Further studies are needed to confirm this association and shed light on the biological underpinnings of this susceptibility locus.

Update Breast Cancer 2018 (Part 1) - Primary Breast Cancer and Biomarkers

This summary provides an overview of how new therapies or new aspects of established therapies relate to the latest findings. Neoadjuvant therapy, local therapy, new aspects of systemic therapy, and prognostic and predictive factors are presented. In the neoadjuvant setting, the association between pathological complete response (pCR) and prognosis is still of interest as is the identification of new molecular predictors for new therapies such as CDK4/6 inhibitors. As regards surgical treatment, the target is still to reduce the aggressiveness of surgery. To achieve this, a better understanding particularly of ductal carcinoma in situ is required. With regard to systemic therapy, more data on the best combinations and therapy sequences for existing therapies is available. Finally, the use of prognostic and predictive factors may help to avoid overtreatment and ensure that patients only receive therapies which have been shown to be effective for their specific condition and have fewer side effects.

Meta-analysis of three genome-wide association studies identifies two loci that predict survival and treatment outcome in breast cancer

The majority of breast cancers are driven by the female hormone oestrogen via oestrogen receptor (ER) alpha. ER-positive patients are commonly treated with adjuvant endocrine therapy, however, resistance is a common occurrence and aside from ER-status, no unequivocal predictive biomarkers are currently in clinical use. In this study, we aimed to identify constitutional genetic variants influencing breast cancer survival among ER-positive patients and specifically, among endocrine-treated patients. We conducted a meta-analysis of three genome-wide association studies comprising in total 3,136 patients with ER-positive breast cancer of which 2,751 had received adjuvant endocrine therapy. We identified a novel locus (rs992531 at 8p21.2) associated with reduced survival among the patients with ER-positive breast cancer (P = 3.77 × 10-8). Another locus (rs7701292 at 5q21.3) was associated with reduced survival among the endocrine-treated patients (P = 2.13 × 10-8). Interaction analysis indicated that the survival association of rs7701292 is treatment-specific and independent of conventional prognostic markers. In silico functional studies suggest plausible biological mechanisms for the observed survival associations and a functional link between the putative target genes of the rs992531 and rs7701292 (RHOBTB2 and RAB9P1, respectively). We further explored the genetic interaction between rs992531 and rs7701292 and found a significant, treatment-specific interactive effect on survival among ER-positive, endocrine-treated patients (hazard ratio = 6.97; 95% confidence interval, 1.79-27.08, Pinteraction= 0.036). This is the first study to identify a genetic interaction that specifically predicts treatment outcome. These findings may provide predictive biomarkers based on germ line genotype informing more personalized treatment selection.

Parametric Linkage Analysis Identifies Five Novel Genome-Wide Significant Loci for Familial Lung Cancer

OBJECTIVE

One of four American cancer patients dies of lung cancer. Environmental factors such as tobacco smoking are known to affect lung cancer risk. However, there is a genetic factor to lung cancer risk as well. Here, we perform parametric linkage analysis on family-based genotype data in an effort to find genetic loci linked to the disease.

METHODS

197 individuals from families with a high-risk history of lung cancer were recruited and genotyped using an Illumina array. Parametric linkage analyses were performed using an affected-only phenotype model with an autosomal dominant inheritance using a disease allele frequency of 0.01. Three types of analyses were performed: single variant two-point, collapsed haplotype pattern variant two-point, and multipoint analysis.

RESULTS

Five novel genome-wide significant loci were identified at 18p11.23, 2p22.2, 14q13.1, 16p13, and 20q13.11. The families most informative for linkage were also determined.

CONCLUSIONS

The 5 novel signals are good candidate regions, containing genes that have been implicated as having somatic changes in lung cancer or other cancers (though not in germ line cells). Targeted sequencing on the significant loci is planned to determine the causal variants at these loci.

Detecting Susceptibility to Breast Cancer with SNP-SNP Interaction Using BPSOHS and Emotional Neural Networks

Studies for the association between diseases and informative single nucleotide polymorphisms (SNPs) have received great attention. However, most of them just use the whole set of useful SNPs and fail to consider the SNP-SNP interactions, while these interactions have already been proven in biology experiments. In this paper, we use a binary particle swarm optimization with hierarchical structure (BPSOHS) algorithm to improve the effective of PSO for the identification of the SNP-SNP interactions. Furthermore, in order to use these SNP interactions in the susceptibility analysis, we propose an emotional neural network (ENN) to treat SNP interactions as emotional tendency. Different from the normal architecture, just as the emotional brain, this architecture provides a specific path to treat the emotional value, by which the SNP interactions can be considered more quickly and directly. The ENN helps us use the prior knowledge about the SNP interactions and other influence factors together. Finally, the experimental results prove that the proposed BPSOHS_ENN algorithm can detect the informative SNP-SNP interaction and predict the breast cancer risk with a much higher accuracy than existing methods.

A genome-wide association study identifies WT1 variant with better response to 5-fluorouracil, pirarubicin and cyclophosphamide neoadjuvant chemotherapy in breast cancer patients

Breast cancer is believed to result from the interplay of genetic and non-genetic risk factors, and individual genetic variation may influence the efficacy of chemotherapy. Here we conducted a genome-wide association study to identify single nucleotide polymorphisms (SNPs) associated with response to anthracycline- and taxane-based neoadjuvant chemotherapy in breast cancer patients. In the discovery stage, we divided 92 patients who received anthracycline-based neoadjuvant chemotherapy into 2 groups according to pathologic response and performed a genome-wide study using Affymetrix SNP6.0 genechip. Of 389,795 SNPs associated with pathologic complete response (pCR), we identified 2 SNPs, rs6044100 and rs1799937, that were significantly associated with pCR after neoadjuvant chemotherapy. In the validation stage, genotype analysis of samples from an independent cohort of 401 patients who received anthracycline-based neoadjuvant regimens and 467 patients who received taxane-based regimens was performed using sequencing analysis. We found that only SNP rs1799937, located in the WT1 gene, was associated with pCR after anthracycline-based neoadjuvant therapy (AA vs GG; odds ratio [OR], 2.81; 95% confidence interval [CI], 1.13-6.98; P < 0.05) but not after taxane-based neoadjuvant therapy (AA vs GG; OR, 0.85; 95% CI, 0.36-2.04; P = 0.72). These results suggest that WT1 may be a potential target of anthracycline-based neoadjuvant therapy for breast cancer.