Summary: The RDAVIDWebService package provides a class-based interface from R programs/scripts to fully access/control the database for annotation, visualization and integrated discovery, without the need for human interaction on its Web site (http://david.abcc.ncifcrf.gov). The library enhances the database for annotation, visualization and integrated discovery capabilities for Gene Ontology analysis by means of GOstats-based direct acyclic graph conversion methods, in addition to the usual many-genes-to-many-terms visualization. Availability and implementation: RDAVIDWebService is available as an R package from the Bioconductor project (www.bioconductor.org) and on the authors’ Web site (www.bdmg.com.ar) under GPL-2 license, subjected to the terms of use of DAVID (http://david.abcc.ncifcrf.gov/content.jsp?file=WS.html). Contact:cfresno@bdmg.com.ar or efernandez@bdmg.com.ar

Over the last few years, RNA-seq has been used to study alterations in alternative splicing related to several diseases. Bioinformatics workflows used to perform these studies can be divided into two groups, those finding changes in the absolute isoform expression and those studying differential splicing. Many computational methods for transcriptomics analysis have been developed, evaluated and compared; however, there are not enough reports of systematic and objective assessment of processing pipelines as a whole. Moreover, comparative studies have been performed considering separately the changes in absolute or relative isoform expression levels. Consequently, no consensus exists about the best practices and appropriate workflows to analyse alternative and differential splicing. To assist the adequate pipeline choice, we present here a benchmarking of nine commonly used workflows to detect differential isoform expression and splicing. We evaluated the workflows performance over different experimental scenarios where changes in absolute and relative isoform expression occurred simultaneously. In addition, the effect of the number of isoforms per gene, and the magnitude of the expression change over pipeline performances were also evaluated. Our results suggest that workflow performance is influenced by the number of replicates per condition and the conditions heterogeneity. In general, workflows based on DESeq2, DEXSeq, Limma and NOISeq performed well over a wide range of transcriptomics experiments. In particular, we suggest the use of workflows based on Limma when high precision is required, and DESeq2 and DEXseq pipelines to prioritize sensitivity. When several replicates per condition are available, NOISeq and Limma pipelines are indicated.

Invasive micropapillary carcinoma of the breast (IMPC) is a histological tumor variant that occurs with low frequency characterized by an inside-out formation of tumor clusters with a pseudopapillary arrangement. IMPC is an aggressive tumor with poor clinical outcome. In addition, this histological subtype usually expresses human epidermal growth factor receptor 2 (HER2) which also correlates with a more aggressive tumor. In this work we studied the clinical significance of IMPC in HER2-positive breast cancer patients treated with adjuvant trastuzumab. We also analyzed mucin 4 (MUC4) expression as a novel biomarker to identify IMPC. We retrospectively studied 86 HER2-positive breast cancer patients treated with trastuzumab and chemotherapy in the adjuvant setting. We explored the association of the IMPC component with clinicopathological parameters at diagnosis and its prognostic value. We compared MUC4 expression in IMPC with respect to other histological breast cancer subtypes by immunohistochemistry. IMPC, either as a pure entity or associated with invasive ductal carcinoma (IDC), was present in 18.6% of HER2-positive cases. It was positively correlated with estrogen receptor expression and tumor size and inversely correlated with patient’s age. Disease-free survival was significantly lower in patients with IMPC (hazard ratio = 2.6; 95%, confidence interval 1.1–6.1, P = 0.0340). MUC4, a glycoprotein associated with metastasis, was strongly expressed in all IMPC cases tested. IMPC appeared as the histological breast cancer subtype with the highest MUC4 expression compared to IDC, lobular and mucinous carcinoma. In HER2-positive breast cancer, the presence of IMPC should be carefully examined. As it is often not informed, because it is relatively difficult to identify or altogether overlooked, we propose MUC4 expression as a useful biomarker to highlight IMPC presence. Patients with MUC4-positive tumors with IMPC component should be more frequently monitored and/or receive additional therapies.

Locally advanced rectal cancer (LARC) remains a medical challenge. Reliable biomarkers to predict which patients will significantly respond to neoadjuvant chemoradiotherapy (nCRT) have not been identified. We evaluated baseline genomic and transcriptomic features to detect differences that may help predict response to nCRT. Eligible LARC patients received nCRT (3D-LCRT 50.4 Gy plus capecitabine 825 mg/m${}^2$/bid), preceded by three cycles of CAPOX in high systemic-relapse risk tumors, and subsequent surgery. Frozen tumor biopsies at diagnosis were sequenced using a colorectal cancer panel. Transcriptomic data was used for pathway and cell deconvolution inferential algorithms, coupled with immunohistochemical validation. Clinical and molecular data were analyzed according to nCRT outcome. Pathways related to DNA repair and proliferation (p < 0.005), and co-occurrence of RAS and TP53 mutations (p = 0.001) were associated with poor response. Enrichment of expression signatures related to enhanced immune response, particularly B cells and interferon signaling (p < 0.005), was detected in good responders. Immunohistochemical analysis of CD20${}^{+}$ cells validated the association of good response with B cell infiltration (p = 0.047). Findings indicate that the presence of B cells is associated with successful tumor regression following nCRT in LARC. The prevalence of simultaneous RAS and TP53 mutations along with a proficient DNA repair system that may counteract chemoradio-induced DNA damage was associated with poor response.

Introduction: Identification of tumor specific neoantigen (TSN) immunogenicity is crucial to develop peptide/mRNA based anti-tumoral vaccines and/or adoptive T-cell immunotherapies; thus, accurate in-silico classification/prioritization proves critical for cost-effective clinical applications. Several methods were proposed as TSNs immunogenicity predictors; however, comprehensive performance comparison is still lacking due to the absence of well documented and adequate TSN databases.Methods: Here, by developing a new curated database having 199 TSNs with experimentally-validated MHC-I presentation and positive/negative immune response (ITSNdb), sixteen metrics were evaluated as immunogenicity predictors. In addition, by using a dataset emulating patient derived TSNs and immunotherapy cohorts containing predicted TSNs for tumor neoantigen burden (TNB) with outcome association, the metrics were evaluated as TSNs prioritizers and as immunotherapy response biomarkers.Results: Our results show high performance variability among methods, highlighting the need for substantial improvement. Deep learning predictors were top ranked on ITSNdb but show discrepancy on validation databases. In overall, current predicted TNB did not outperform existing biomarkers.Conclusion: Recommendations for their clinical application and the ITSNdb are presented to promote development and comparison of computational TSNs immunogenicity predictors.

The accurate quantification of tumor-infiltrating immune cells turns crucial to uncover their role in tumor immune escape, to determine patient prognosis and to predict response to immune checkpoint blockade. Current state-of-the-art methods that quantify immune cells from tumor biopsies using gene expression data apply computational deconvolution methods that present multicollinearity and estimation errors resulting in the overestimation or underestimation of the diversity of infiltrating immune cells and their quantity. To overcome such limitations, we developed MIXTURE, a new ν-support vector regression-based noise constrained recursive feature selection algorithm based on validated immune cell molecular signatures. MIXTURE provides increased robustness to cell type identification and proportion estimation, outperforms the current methods, and is available to the wider scientific community. We applied MIXTURE to transcriptomic data from tumor biopsies and found relevant novel associations between the components of the immune infiltrate and molecular subtypes, tumor driver biomarkers, tumor mutational burden, microsatellite instability, intratumor heterogeneity, cytolytic score, programmed cell death ligand 1 expression, patients’ survival and response to anti-cytotoxic T-lymphocyte-associated antigen 4 and anti-programmed cell death protein 1 immunotherapy.