Unveiling the immune infiltrate modulation in cancer and response to immunotherapy by MIXTURE-an enhanced deconvolution method.

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.

Novel evaluation approach for molecular signature-based deconvolution methods.

The tumoral immune microenvironment (TIME) plays a key role in prognosis, therapeutic approach and pathophysiological understanding over oncological processes. Several computational immune cell-type deconvolution methods (DM), supported by diverse molecular signatures (MS), have been developed to uncover such TIME interplay from RNA-seq tumor biopsies. MS-DM pairs were benchmarked against each other by means of different metrics, such as Pearson's correlation, R2 and RMSE, but these only evaluate the linear association of the estimated proportion related to the expected one, missing the analysis of prediction-dependent bias trends and cell identification accuracy. We present a novel protocol composed of four tests allowing appropriate evaluation of the cell type identification performance and proportion prediction accuracy of molecular signature-deconvolution method pair by means of certainty and confidence cell-type identification scores (F1-score, distance to the optimal point and error rates) as well the Bland-Altman method for error-trend analysis. Our protocol was used to benchmark six state-of-the-art DMs (CIBERSORTx, DCQ, DeconRNASeq, EPIC, MIXTURE and quanTIseq) paired to five murine tissue-specific MSs, revealing a systematic overestimation of the number of different cell types across almost all methods.

Comprehensive characterization of Cysteine-rich protein-coding genes of Giardia lamblia and their role during antigenic variation.

Giardia lamblia encodes several families of cysteine-rich proteins, including the Variant-specific Surface Proteins (VSPs) involved in the process of antigenic variation. Their characteristics, definition and relationships are still controversial. An exhaustive analysis of the Cys-rich families including organization, features, evolution and levels of expression was performed, by combining pattern searches and predictions with massive sequencing techniques. Thus, a new classification for Cys-rich proteins, genes and pseudogenes that better describes their involvement in Giardia's biology is presented. Moreover, three novel characteristics exclusive to the VSP genes, comprising an Initiator element/Kozak-like sequence, an extended polyadenylation signal and a unique pattern of mutually exclusive transcript accumulation are presented, as well as the finding that High Cysteine Membrane Proteins, upregulated under stress, may protect the parasite during VSP switching. These results allow better interpretation of previous reports providing the basis for further studies of the biology of this early-branching eukaryote.

A benchmarking of workflows for detecting differential splicing and differential expression at isoform level in human RNA-seq studies.

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.

Massive integrative gene set analysis enables functional characterization of breast cancer subtypes.

The availability of large-scale repositories and integrated cancer genome efforts have created unprecedented opportunities to study and describe cancer biology. In this sense, the aim of translational researchers is the integration of multiple omics data to achieve a better identification of homogeneous subgroups of patients in order to develop adequate diagnostic and treatment strategies from the personalized medicine perspective. So far, existing integrative methods have grouped together omics data information, leaving out individual omics data phenotypic interpretation. Here, we present the Massive and Integrative Gene Set Analysis (MIGSA) R package. This tool can analyze several high throughput experiments in a comprehensive way through a functional analysis strategy, relating a phenotype to its biological function counterpart defined by means of gene sets. By simultaneously querying different multiple omics data from the same or different groups of patients, common and specific functional patterns for each studied phenotype can be obtained. The usefulness of MIGSA was demonstrated by applying the package to functionally characterize the intrinsic breast cancer PAM50 subtypes. For each subtype, specific functional transcriptomic profiles and gene sets enriched by transcriptomic and proteomic data were identified. To achieve this, transcriptomic and proteomic data from 28 datasets were analyzed using MIGSA. As a result, enriched gene sets and important genes were consistently found as related to a specific subtype across experiments or data types and thus can be used as molecular signature biomarkers.

TarSeqQC: Quality control on targeted sequencing experiments in R.

Targeted sequencing (TS) is growing as a screening methodology used in research and medical genetics to identify genomic alterations causing human diseases. In general, a list of possible genomic variants is derived from mapped reads through a variant calling step. This processing step is usually based on variant coverage, although it may be affected by several factors. Therefore, undercovered relevant clinical variants may not be reported, affecting pathology diagnosis or treatment. Thus, a prior quality control of the experiment is critical to determine variant detection accuracy and to avoid erroneous medical conclusions. There are several quality control tools, but they are focused on issues related to whole-genome sequencing. However, in TS, quality control should assess experiment, gene, and genomic region performances based on achieved coverages. Here, we propose TarSeqQC R package for quality control in TS experiments. The tool is freely available at Bioconductor repository. TarSeqQC was used to analyze two datasets; low-performance primer pools and features were detected, enhancing the quality of experiment results. Read count profiles were also explored, showing TarSeqQC's effectiveness as an exploration tool. Our proposal may be a valuable bioinformatic tool for routinely TS experiments in both research and medical genetics.

Invasive micropapillary carcinoma of the breast overexpresses MUC4 and is associated with poor outcome to adjuvant trastuzumab in HER2-positive breast cancer.

BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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.

Biosensors: Enhancing the Natural Ability to Sense and Their Dependence on Bioinformatics.

Single cells, as part of their evolution, acquired the ability to sense their internal and external environment, move to or away from a particular environment, the latter depending on the appropriate integration of the sensory input with motor ability. Clearly, the ability to sense stimuli must be a rapid process and one that has been selected upon for survival over long periods of time in concert with environmental challenges. Interestingly, various differing sensory inputs have their own receptors to respond to a specific stimulus.

The low-abundance transcriptome reveals novel biomarkers, specific intracellular pathways and targetable genes associated with advanced gastric cancer.

Studies on the low-abundance transcriptome are of paramount importance for identifying the intimate mechanisms of tumor progression that can lead to novel therapies. The aim of the present study was to identify novel markers and targetable genes and pathways in advanced human gastric cancer through analyses of the low-abundance transcriptome. The procedure involved an initial subtractive hybridization step, followed by global gene expression analysis using microarrays. We observed profound differences, both at the single gene and gene ontology levels, between the low-abundance transcriptome and the whole transcriptome. Analysis of the low-abundance transcriptome led to the identification and validation by tissue microarrays of novel biomarkers, such as LAMA3 and TTN; moreover, we identified cancer type-specific intracellular pathways and targetable genes, such as IRS2, IL17, IFNγ, VEGF-C, WISP1, FZD5 and CTBP1 that were not detectable by whole transcriptome analyses. We also demonstrated that knocking down the expression of CTBP1 sensitized gastric cancer cells to mainstay chemotherapeutic drugs. We conclude that the analysis of the low-abundance transcriptome provides useful insights into the molecular basis and treatment of cancer.

RDAVIDWebService: a versatile R interface to DAVID.

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.

Unraveling tumor specific neoantigen immunogenicity prediction: a comprehensive analysis.

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.

Cyclooxygenase-2 Blockade Is Crucial to Restore Natural Killer Cell Activity before Anti-CTLA-4 Therapy against High-Grade Serous Ovarian Cancer.

Chronic inflammation influences the tumor immune microenvironment (TIME) in high-grade serous ovarian cancer (HGSOC). Specifically, cyclooxygenase-2 (COX-2) overexpression promotes cytotoxic T-lymphocyte-associated protein-4 (CTLA-4) expression. Notably, elevated COX-2 levels in the TIME have been associated with reduced response to anti-CTLA-4 immunotherapy. However, the precise impact of COX-2, encoded by PTGS2, on the immune profile remains unknown. To address this, we performed an integrated bioinformatics analysis using data from the HGSOC cohorts (TCGA-OV, n = 368; Australian cohort AOCS, n = 80; GSE26193, n = 62; and GSE30161, n = 45). Employing Gene Set Variation Analysis (GSVA), MIXTURE and Ecotyper cell deconvolution algorithms, we concluded that COX-2 was linked to immune cell ecosystems associated with shorter survival, cell dysfunction and lower NK cell effector cytotoxicity capacity. Next, we validated these results by characterizing circulating NK cells from HGSOC patients through flow cytometry and cytotoxic assays while undergoing COX-2 and CTLA-4 blockade. The blockade of COX-2 improved the cytotoxic capacity of NK cells against HGSOC cell lines. Our findings underscore the relevance of COX-2 in shaping the TIME and suggest its potential as a prognostic indicator and therapeutic target. Increased COX-2 expression may hamper the effectivity of immunotherapies that require NK cell effector function. These results provide a foundation for experimental validation and clinical trials investigating combined therapies targeting COX-2 and CTLA-4 in HGSOC.

Blocking soluble TNFα sensitizes HER2-positive breast cancer to trastuzumab through MUC4 downregulation and subverts immunosuppression.

BACKGROUND: The success of HER2-positive (HER2+) breast cancer treatment with trastuzumab, an antibody that targets HER2, relies on immune response. We demonstrated that TNFα induces mucin 4 (MUC4) expression, which shields the trastuzumab epitope on the HER2 molecule decreasing its therapeutic effect. Here, we used mouse models and samples from HER2+ breast cancer patients to unravel MUC4 participation in hindering trastuzumab effect by fostering immune evasion. METHODS: We used a dominant negative TNFα inhibitor (DN) selective for soluble TNFα (sTNFα) together with trastuzumab. Preclinical experiments were performed using two models of conditionally MUC4-silenced tumors to characterize the immune cell infiltration. A cohort of 91 patients treated with trastuzumab was used to correlate tumor MUC4 with tumor-infiltrating lymphocytes. RESULTS: In mice bearing de novo trastuzumab-resistant HER2+ breast tumors, neutralizing sTNFα with DN induced MUC4 downregulation. Using the conditionally MUC4-silenced tumor models, the antitumor effect of trastuzumab was reinstated and the addition of TNFα-blocking agents did not further decrease tumor burden. DN administration with trastuzumab modifies the immunosuppressive tumor milieu through M1-like phenotype macrophage polarization and NK cells degranulation. Depletion experiments revealed a cross-talk between macrophages and NK cells necessary for trastuzumab antitumor effect. In addition, tumor cells treated with DN are more susceptible to trastuzumab-dependent cellular phagocytosis. Finally, MUC4 expression in HER2+ breast cancer is associated with immune desert tumors. CONCLUSIONS: These findings provide rationale to pursue sTNFα blockade combined with trastuzumab or trastuzumab drug conjugates for MUC4+ and HER2+ breast cancer patients to overcome trastuzumab resistance.

Antibodies to variable surface antigens induce antigenic variation in the intestinal parasite Giardia lamblia.

The genomes of most protozoa encode families of variant surface antigens. In some parasitic microorganisms, it has been demonstrated that mutually exclusive changes in the expression of these antigens allow parasites to evade the host's immune response. It is widely assumed that antigenic variation in protozoan parasites is accomplished by the spontaneous appearance within the population of cells expressing antigenic variants that escape antibody-mediated cytotoxicity. Here we show, both in vitro and in animal infections, that antibodies to Variant-specific Surface Proteins (VSPs) of the intestinal parasite Giardia lamblia are not cytotoxic, inducing instead VSP clustering into liquid-ordered phase membrane microdomains that trigger a massive release of microvesicles carrying the original VSP and switch in expression to different VSPs by a calcium-dependent mechanism. This novel mechanism of surface antigen clearance throughout its release into microvesicles coupled to the stochastic induction of new phenotypic variants not only changes current paradigms of antigenic switching but also provides a new framework for understanding the course of protozoan infections as a host/parasite adaptive process.

The Transcriptomic Portrait of Locally Advanced Breast Cancer and Its Prognostic Value in a Multi-Country Cohort of Latin American Patients.

Purposes: Most molecular-based published studies on breast cancer do not adequately represent the unique and diverse genetic admixture of the Latin American population. Searching for similarities and differences in molecular pathways associated with these tumors and evaluating its impact on prognosis may help to select better therapeutic approaches. Patients and Methods: We collected clinical, pathological, and transcriptomic data of a multi-country Latin American cohort of 1,071 stage II-III breast cancer patients of the Molecular Profile of Breast Cancer Study (MPBCS) cohort. The 5-year prognostic ability of intrinsic (transcriptomic-based) PAM50 and immunohistochemical classifications, both at the cancer-specific (OSC) and disease-free survival (DFS) stages, was compared. Pathway analyses (GSEA, GSVA and MetaCore) were performed to explore differences among intrinsic subtypes. Results: PAM50 classification of the MPBCS cohort defined 42·6% of tumors as LumA, 21·3% as LumB, 13·3% as HER2E and 16·6% as Basal. Both OSC and DFS for LumA tumors were significantly better than for other subtypes, while Basal tumors had the worst prognosis. While the prognostic power of traditional subtypes calculated with hormone receptors (HR), HER2 and Ki67 determinations showed an acceptable performance, PAM50-derived risk of recurrence best discriminated low, intermediate and high-risk groups. Transcriptomic pathway analysis showed high proliferation (i.e. cell cycle control and DNA damage repair) associated with LumB, HER2E and Basal tumors, and a strong dependency on the estrogen pathway for LumA. Terms related to both innate and adaptive immune responses were seen predominantly upregulated in Basal tumors, and, to a lesser extent, in HER2E, with respect to LumA and B tumors. Conclusions: This is the first study that assesses molecular features at the transcriptomic level in a multicountry Latin American breast cancer patient cohort. Hormone-related and proliferation pathways that predominate in PAM50 and other breast cancer molecular classifications are also the main tumor-driving mechanisms in this cohort and have prognostic power. The immune-related features seen in the most aggressive subtypes may pave the way for therapeutic approaches not yet disseminated in Latin America. Clinical Trial Registration: ClinicalTrials.gov (Identifier: NCT02326857).

Pan-Cancer Molecular Patterns and Biological Implications Associated with a Tumor-Specific Molecular Signature.

Studying tissue-independent components of cancer and defining pan-cancer subtypes could be addressed using tissue-specific molecular signatures if classification errors are controlled. Since PAM50 is a well-known, United States Food and Drug Administration (FDA)-approved and commercially available breast cancer signature, we applied it with uncertainty assessment to classify tumor samples from over 33 cancer types, discarded unassigned samples, and studied the emerging tumor-agnostic molecular patterns. The percentage of unassigned samples ranged between 55.5% and 86.9% in non-breast tissues, and gene set analysis suggested that the remaining samples could be grouped into two classes (named C1 and C2) regardless of the tissue. The C2 class was more dedifferentiated, more proliferative, with higher centrosome amplification, and potentially more TP53 and RB1 mutations. We identified 28 gene sets and 95 genes mainly associated with cell-cycle progression, cell-cycle checkpoints, and DNA damage that were consistently exacerbated in the C2 class. In some cancer types, the C1/C2 classification was associated with survival and drug sensitivity, and modulated the prognostic meaning of the immune infiltrate. Our results suggest that PAM50 could be repurposed for a pan-cancer context when paired with uncertainty assessment, resulting in two classes with molecular, biological, and clinical implications.

Blockade of Stat3 oncogene addiction induces cellular senescence and reveals a cell-nonautonomous activity suitable for cancer immunotherapy.

Stat3 is constitutively activated in several tumor types and plays an essential role in maintaining their malignant phenotype and immunosupression. To take advantage of the promising antitumor activity of Stat3 targeting, it is vital to understand the mechanism by which Stat3 regulates both cell autonomous and non-autonomous processes. Here, we demonstrated that turning off Stat3 constitutive activation in different cancer cell types induces senescence, thus revealing their Stat3 addiction. Taking advantage of the senescence-associated secretory phenotype (SASP) induced by Stat3 silencing (SASP-siStat3), we designed an immunotherapy. The administration of SASP-siStat3 immunotherapy induced a strong inhibition of triple-negative breast cancer and melanoma growth associated with activation of CD4 + T and NK cells. Combining this immunotherapy with anti-PD-1 antibody resulted in survival improvement in mice bearing melanoma. The characterization of the SASP components revealed that type I IFN-related mediators, triggered by the activation of the cyclic GMP-AMP synthase DNA sensing pathway, are important for its immunosurveillance activity. Overall, our findings provided evidence that administration of SASP-siStat3 or low dose of Stat3-blocking agents would benefit patients with Stat3-addicted tumors to unleash an antitumor immune response and to improve the effectiveness of immune checkpoint inhibitors.

Improving 2D-DIGE protein expression analysis by two-stage linear mixed models: assessing experimental effects in a melanoma cell study.

MOTIVATION: Difference in-gel electrophoresis (DIGE)-based protein expression analysis allows assessing the relative expression of proteins in two biological samples differently labeled (Cy5, Cy3 CyDyes). In the same gel, a reference sample is also used (Cy2 CyDye) for spot matching during image analysis and volume normalization. The standard statistical techniques to identify differentially expressed (DE) proteins are the calculation of fold-changes and the comparison of treatment means by the t-test. The analyses rarely accounts for other experimental effects, such as CyDye and gel effects, which could be important sources of noise while detecting treatment effects. RESULTS: We propose to identify DIGE DE proteins using a two-stage linear mixed model. The proposal consists of splitting the overall model for the measured intensity into two interconnected models. First, we fit a normalization model that accounts for the general experimental effects, such as gel and CyDye effects as well as for the features of the associated random term distributions. Second, we fit a model that uses the residuals from the first step to account for differences between treatments in protein-by-protein basis. The modeling strategy was evaluated using data from a melanoma cell study. We found that a heteroskedastic model in the first stage, which also account for CyDye and gel effects, best normalized the data, while allowing for an efficient estimation of the treatment effects. The Cy2 reference channel was used as a covariate in the normalization model to avoid skewness of the residual distribution. Its inclusion improved the detection of DE proteins in the second stage.

Efficient oral vaccination by bioengineering virus-like particles with protozoan surface proteins.

Intestinal and free-living protozoa, such as Giardia lamblia, express a dense coat of variant-specific surface proteins (VSPs) on trophozoites that protects the parasite inside the host's intestine. Here we show that VSPs not only are resistant to proteolytic digestion and extreme pH and temperatures but also stimulate host innate immune responses in a TLR-4 dependent manner. We show that these properties can be exploited to both protect and adjuvant vaccine antigens for oral administration. Chimeric Virus-like Particles (VLPs) decorated with VSPs and expressing model surface antigens, such as influenza virus hemagglutinin (HA) and neuraminidase (NA), are protected from degradation and activate antigen presenting cells in vitro. Orally administered VSP-pseudotyped VLPs, but not plain VLPs, generate robust immune responses that protect mice from influenza infection and HA-expressing tumors. This versatile vaccine platform has the attributes to meet the ultimate challenge of generating safe, stable and efficient oral vaccines.

Inverse expression of survivin and reprimo correlates with poor patient prognosis in gastric cancer.

BACKGROUND: The objective of the study was to determine the relationship between Survivin and Reprimo transcript/protein expression levels, and gastric cancer outcome. METHODS: In silico correlations between an agnostic set of twelve p53-dependent apoptosis and cell-cycle genes were explored in the gastric adenocarcinoma TCGA database, using cBioPortal. Findings were validated by regression analysis of RNAseq data. Separate regression analyses were performed to assess the impact of p53 status on Survivin and Reprimo. Quantitative reverse-transcription PCR (RT-qPCR) and immunohistochemistry confirmed in silico findings on fresh-frozen and paraffin-embedded gastric cancer tissues, respectively. Wild-type (AGS, SNU-1) and mutated p53 (NCI-N87) cell lines transfected with pEGFP-Survivin or pCMV6-Reprimo were evaluated by RT-qPCR and Western blotting. Kaplan-Meier method and Long-Rank test were used to assess differences in patient outcome. RESULTS: cBioPortal analysis revealed an inverse correlation between Survivin and Reprimo expression (Pearson's r= -0.3, Spearman's ρ= -0.55). RNAseq analyses confirmed these findings (Spearman's ρ= -0.37, p<4.2e-09) and revealed p53 dependence in linear regression models (p<0.05). mRNA and protein levels validated these observations in clinical samples (p<0.001). In vitro analysis in cell lines demonstrated that increasing Survivin reduced Reprimo, while increasing Reprimo reduced Survivin expression, but only did so in p53 wild-type gastric cells (p<0.05). Survivin-positive but Reprimo-negative patients displayed shorter overall survival rates (p=0.047, Long Rank Test) (HR=0.32; 95%IC: 0.11-0.97; p=0.044). CONCLUSIONS: TCGA RNAseq data analysis, evaluation of clinical samples and studies in cell lines identified an inverse relationship between Survivin and Reprimo. Elevated Survivin and reduced Reprimo protein expression correlated with poor patient prognosis in gastric cancer.