This study suggests that the amount of prognostic information contained in four widely performed IHC assays is similar to that in the GHI-RS. Additional studies are needed to determine the general applicability of the IHC4 score.

IntroductionGene amplification of CCND1 is observed in a subgroup of breast cancers with poor prognosis, whereas overexpression of the protein cyclin D1 has been linked to both worse and better clinical outcome. CCND1 amplification and protein overexpression have also been associated with resistance to treatment with tamoxifen or even to a potentially detrimental effect of tamoxifen.MethodsTo clarify these challenging and partly contrasting treatment predictive and prognostic links for cyclin D1 we analysed a large cohort of postmenopausal breast cancer patients randomised to receive either adjuvant anastrozole or tamoxifen, as part of the Arimidex, Tamoxifen, Alone or in Combination (ATAC) trial. The CCND1 amplification status and protein expression of cyclin D1 were assessed by chromogenic in situ hybridisation and immunohistochemistry, respectively, in 1,155 postmenopausal, oestrogen-receptor-positive breast cancer patients included in the TransATAC substudy.ResultsAmplification of CCND1 was observed in 8.7% of the tumours and was associated with increased risk of disease recurrence (hazard ratio = 1.61; 95% confidence interval, 1.08 to 2.41) after adjustment for other clinicopathological parameters. In contrast, nuclear expression of cyclin D1 protein was associated with decreased recurrence rate (hazard ratio = 0.6; 95% confidence interval, 0.39 to 0.92). The intensity of nuclear or cytoplasmic expression was not of prognostic value. There was no significant interaction between cyclin D1 status and treatment efficacy, ruling out any major detrimental effect of tamoxifen in CCND1-amplified postmenopausal breast cancer.ConclusionsIn summary, CCND1 amplification and low nuclear expression of cyclin D1 predicted poor clinical outcome in postmenopausal breast cancer patients treated with either anastrozole or tamoxifen.Trial RegistrationCurrent Controlled Trials ISRCTN18233230.

Omics data integration is already a reality. However, few omics-based algorithms show enough predictive ability to be implemented into clinics or public health domains. Clinical/epidemiological data tend to explain most of the variation of health-related traits, and its joint modeling with omics data is crucial to increase the algorithm’s predictive ability. Only a small number of published studies performed a “real” integration of omics and non-omics (OnO) data, mainly to predict cancer outcomes. Challenges in OnO data integration regard the nature and heterogeneity of non-omics data, the possibility of integrating large-scale non-omics data with high-throughput omics data, the relationship between OnO data (i.e., ascertainment bias), the presence of interactions, the fairness of the models, and the presence of subphenotypes. These challenges demand the development and application of new analysis strategies to integrate OnO data. In this contribution we discuss different attempts of OnO data integration in clinical and epidemiological studies. Most of the reviewed papers considered only one type of omics data set, mainly RNA expression data. All selected papers incorporated non-omics data in a low-dimensionality fashion. The integrative strategies used in the identified papers adopted three modeling methods: Independent, conditional, and joint modeling. This review presents, discusses, and proposes integrative analytical strategies towards OnO data integration.

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Transplant rejection is the critical clinical end-point limiting indefinite survival after histocompatibility antigen (HLA) mismatched organ transplantation. The predominant cause of late graft loss is antibody-mediated rejection (AMR), a process whereby injury to the organ is caused by donor-specific antibodies, which bind to HLA and non-HLA (nHLA) antigens. AMR is incompletely diagnosed as donor/recipient (D/R) matching is only limited to the HLA locus and critical nHLA immunogenic antigens remain to be identified. We have developed an integrative computational approach leveraging D/R exome sequencing and gene expression to predict clinical post-transplant outcome. We performed a rigorous statistical analysis of 28 highly annotated D/R kidney transplant pairs with biopsy-confirmed clinical outcomes of rejection [either AMR or T-cell-mediated rejection (CMR)] and no-rejection (NoRej), identifying a significantly higher number of mismatched nHLA variants in AMR (ANOVA—p-value = 0.02). Using Fisher’s exact test, we identified 123 variants associated mainly with risk of AMR (p-value < 0.001). In addition, we applied a machine-learning technique to circumvent the issue of statistical power and we found a subset of 65 variants using random forest, that are predictive of post-tx AMR showing a very low error rate. These variants are functionally relevant to the rejection process in the kidney and AMR as they relate to genes and/or expression quantitative trait loci (eQTLs) that are enriched in genes expressed in kidney and vascular endothelium and underlie the immunobiology of graft rejection. In addition to current D/R HLA mismatch evaluation, additional mismatch nHLA D/R variants will enhance the stratification of post-tx AMR risk even before engraftment of the organ. This innovative study design is applicable in all solid organ transplants, where the impact of mitigating AMR on graft survival may be greater, with considerable benefits on improving human morbidity and mortality and opens the door to precision immunosuppression and extended tx survival.

Background. A number of studies have provided widely heterogeneous results for and against the hypothesis that genetic variants in drug metabolizing enzymes can influence patient response to tamoxifen. Most of these studies are confounded by relatively small numbers of patients, lack of comprehensive genotype information, lack of detailed clinical outcome data, and patient selection biases. The ATAC trial was a prospective randomized double-blind placebo-controlled trial designed to compare the adjuvant use of anastrozole versus tamoxifen for 5 years. The trial's detailed efficacy and safety data, long term (10-year) follow-up and high number of events make it an ideal setting for pharmacogenetic analyses. Here we report our initial findings testing for correlations with SNPs in CYP2D6 and UGT2B7, the primary enzymes responsible for the presumed activation and inactivation of tamoxifen, respectively, with clinical outcomes including any disease recurrence, distant recurrence, and overall survival. Methods: CYP2D6 genotype data for the 7 most common alleles were used to assign each patient a ‘score’ based on predicted allele activities from 0 (no activity) to 2 (high activity). Patients were also tested for the UGT2B7*2 variant, which has lower enzymatic activity and has been shown to correlate with higher serum endoxifen concentrations. All genotype determinations were made without knowledge of the patient's treatment assignment or outcomes. Results: Comprehensive CYP2D6 genotype data were obtained on 588 and 615 patients randomized to receive tamoxifen, and anastrozole, respectively. After median 10 years of follow-up, there were 115 recurrences in the tamoxifen cohort. We found no associations between any of the CYP2D6 scores and rates of recurrence in tamoxifen treated patients. A test for trend across CYP2D6 scores was not significant. In the anastrozole cohort, there were 92 recurrences, and there was no evidence of an association between CYP2D6 score with rates of recurrence. Likewise, there was no detectable association in either the tamoxifen or anastrozole group with UGT2B7 genotype alone or in combination with CYP2D6 score. Concomitant SSRI usage was recorded in these patients and is being assessed for possible impact on these results. Conclusion: These data from a large prospective clinical trial, with detailed outcome data and long-term follow-up do not support the hypothesis that patient with decreased CYP2D6 enzymatic activity receive less benefit from tamoxifen therapy compared to wild-type CYP2D6 patients. Variants in drug targets and estrogen signaling pathways may be the genetic basis for patient variability in anti-estrogen risk versus benefit profiles. Citation Information: Cancer Res 2010;70(24 Suppl):Abstract nr S1-7.

Studying immune repertoire in the context of organ transplant provides important information on how adaptive immunity may contribute and modulate graft rejection. Here we characterize the peripheral blood immune repertoire of individuals before and after kidney transplant using B cell receptor sequencing in a longitudinal clinical study. Individuals who develop rejection after transplantation have a more diverse immune repertoire before transplant, suggesting a predisposition for post-transplant rejection risk. Additionally, over 2 years of follow-up, patients who develop rejection demonstrate a specific set of expanded clones that persist after the rejection. While there is an overall reduction of peripheral B cell diversity, likely due to increased general immunosuppression exposure in this cohort, the detection of specific IGHV gene usage across all rejecting patients supports that a common pool of immunogenic antigens may drive post-transplant rejection. Our findings may have clinical implications for the prediction and clinical management of kidney transplant rejection.

Omics data integration is becoming necessary to investigate the genomic mechanisms involved in complex diseases. During the integration process, many challenges arise such as data heterogeneity, the smaller number of individuals in comparison to the number of parameters, multicollinearity, and interpretation and validation of results due to their complexity and lack of knowledge about biological processes. To overcome some of these issues, innovative statistical approaches are being developed. In this work, we propose a permutation-based method to concomitantly assess significance and correct by multiple testing with the MaxT algorithm. This was applied with penalized regression methods (LASSO and ENET) when exploring relationships between common genetic variants, DNA methylation and gene expression measured in bladder tumor samples. The overall analysis flow consisted of three steps: (1) SNPs/CpGs were selected per each gene probe within 1Mb window upstream and downstream the gene; (2) LASSO and ENET were applied to assess the association between each expression probe and the selected SNPs/CpGs in three multivariable models (SNP, CPG, and Global models, the latter integrating SNPs and CPGs); and (3) the significance of each model was assessed using the permutation-based MaxT method. We identified 48 genes whose expression levels were significantly associated with both SNPs and CPGs. Importantly, 36 (75%) of them were replicated in an independent data set (TCGA) and the performance of the proposed method was checked with a simulation study. We further support our results with a biological interpretation based on an enrichment analysis. The approach we propose allows reducing computational time and is flexible and easy to implement when analyzing several types of omics data. Our results highlight the importance of integrating omics data by applying appropriate statistical strategies to discover new insights into the complex genetic mechanisms involved in disease conditions.