Purpose: We recently reported that the mRNA-based, 21-gene Genomic Health recurrence score (GHI-RS) provided additional prognostic information regarding distant recurrence beyond that obtained from classical clinicopathologic factors (age, nodal status, tumor size, grade, endocrine treatment) in women with early breast cancer, confirming earlier reports. The aim of this article is to determine how much of this information is contained in standard immunohistochemical (IHC) markers. Patients and Methods: The primary cohort comprised 1,125 estrogen receptor–positive (ER-positive) patients from the Arimidex, Tamoxifen, Alone or in Combination (ATAC) trial who did not receive adjuvant chemotherapy, had the GHI-RS computed, and had adequate tissue for the four IHC measurements: ER, progesterone receptor (PgR), human epidermal growth factor receptor 2 (HER2), and Ki-67. Distant recurrence was the primary end point, and proportional hazards models were used with sample splitting to control for overfitting. A prognostic model that used classical variables and the four IHC markers (IHC4 score) was created and assessed in a separate cohort of 786 patients. Results: All four IHC markers provided independent prognostic information in the presence of classical variables. In sample-splitting analyses, the information in the IHC4 score was found to be similar to that in the GHI-RS, and little additional prognostic value was seen in the combined use of both scores. The prognostic value of the IHC4 score was further validated in the second separate cohort. Conclusion: 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.

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.

Introduction: Gene 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. Methods: To 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. Results: Amplification 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. Conclusions: In 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 Registration: Current Controlled Trials ISRCTN18233230.

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. At present, it is already possible to generate different type of omics–high throughput–data in the same individuals. However, we lack methodology to adequately combine them. Many challenges arise while the amount of data increases and we need to find the way to identify and understand the complex relationships when integrating data. In this regard, new statistical approaches are needed, such as the ones we propose and apply here to integrate three types of omics data (genomics, epigenomics, and transcriptomics) generated using bladder cancer tumor samples. These innovative approaches (LASSO and ENET combined with a permutation-based MaxT method) allowed us to find 48 genes whose expression levels were significantly associated with genomics and epigenomics markers. The adequacy of this approach was confirmed by the use of an independent data set from The Cancer Genome Atlas Consortium: 75% of the genes were replicated. Previous sound biological evidences further support the results obtained.

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.

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.