Closing the gap: prognostic and predictive biomarker validation for personalized care in a Latin American hormone-dependent breast cancer cohort.

BACKGROUND: Several guidelines recommend the use of different classifiers to determine the risk of recurrence (ROR) and treatment decisions in patients with HR+HER2- breast cancer. However, data are still lacking for their usefulness in Latin American (LA) patients. Our aim was to evaluate the comparative prognostic and predictive performance of different ROR classifiers in a real-world LA cohort. METHODS: The Molecular Profile of Breast Cancer Study (MPBCS) is an LA case-cohort study with 5-year follow-up. Stages I and II, clinically node-negative HR+HER2- patients (n = 340) who received adjuvant hormone therapy and/or chemotherapy, were analyzed. Time-dependent receiver-operator characteristic-area under the curve, univariate and multivariate Cox proportional hazards regression (CPHR) models were used to compare the prognostic performance of several risk biomarkers. Multivariate CPHR with interaction models tested the predictive ability of selected risk classifiers. RESULTS: Within this cohort, transcriptomic-based classifiers such as the recurrence score (RS), EndoPredict (EP risk and EPClin), and PAM50-risk of recurrence scores (ROR-S and ROR-PC) presented better prognostic performances for node-negative patients (univariate C-index 0.61-0.68, adjusted C-index 0.77-0.80, adjusted hazard ratios [HR] between high and low risk: 4.06-9.97) than the traditional classifiers Ki67 and Nottingham Prognostic Index (univariate C-index 0.53-0.59, adjusted C-index 0.72-0.75, and adjusted HR 1.85-2.54). RS (and to some extent, EndoPredict) also showed predictive capacity for chemotherapy benefit in node-negative patients (interaction P = .0200 and .0510, respectively). CONCLUSION: In summary, we could prove the clinical validity of most transcriptomic-based risk classifiers and their superiority over clinical and immunohistochemical-based methods in the heterogenous, real-world node-negative HR+HER2- MPBCS cohort.

Volatilomic signatures of different strains of Helicobacter pylori.

BACKGROUND: Helicobacter pylori (H. pylori) infection is the most extensively studied risk factor for gastric cancer. As with any bacteria, H. pylori will release distinctive odors that result from an emission of volatile metabolic byproducts in unique combinations and proportions. Effectively capturing and identifying these volatiles can pave the way for the development of innovative and non-invasive diagnostic methods for determining infection. Here we characterize the H. pylori volatilomic signature, pinpoint potential biomarkers of its presence, and evaluate the variability of volatilomic signatures between different H. pylori isolates. MATERIALS AND METHODS: Using needle trap extraction, volatiles in the headspace above H. pylori cultures were collected and, following thermal desorption at 290°C in a splitless mode, were analyzed using gas chromatography-mass spectrometry. The resulting volatilomic signatures of H. pylori cultures were compared to those obtained from an analysis of the volatiles in the headspace above the cultivating medium only. RESULTS: Amongst the volatiles detected, 21 showed consistent differences between the bacteria cultures and the cultivation medium, with 11 compounds being elevated and 10 showing decreased levels in the culture's headspace. The 11 elevated volatiles are four ketones (2-pentanone, 5-methyl-3-heptanone, 2-heptanone, and 2-nonanone), three alcohols (2-methyl-1-propanol, 3-methyl-1-butanol, and 1 butanol), one aromatic (styrene), one aldehyde (2-ethyl-hexanal), one hydrocarbon (n-octane), and one sulfur compound (dimethyl disulfide). The 10 volatiles with lower levels in the headspace of the cultures are four aldehydes (2-methylpropanal, benzaldehyde, 3-methylbutanal, and butanal), two heterocyclic compounds (2-ethylfuran and 2-pentylfuran), one ketone (2-butanone), one aromatic (benzene), one alcohol (2-butanol) and bromodichloromethane. Of the volatile species showing increased levels, the highest emissions are found to be for 3-methyl-1-butanol, 1-butanol and dimethyl disulfide. Qualitative variations in their emissions from the different isolates was observed. CONCLUSIONS: The volatiles emitted by H. pylori provide a characteristic volatilome signature that has the potential of being developed as a tool for monitoring infections caused by this pathogen. Furthermore, using the volatilome signature, we are able to differentiate different isolates of H. pylori. However, the volatiles also represent potential confounders for the recognition of gastric cancer volatile markers.

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.

Genome-wide identification of MITE-derived microRNAs and their targets in bread wheat.

BACKGROUND: Plant miRNAs are a class of small non-coding RNAs that can repress gene expression at the post-transcriptional level by targeting RNA degradation or promoting translational repression. There is increasing evidence that some miRNAs can derive from a group of non-autonomous class II transposable elements called Miniature Inverted-repeat Transposable Elements (MITEs). RESULTS: We used public small RNA and degradome libraries from Triticum aestivum to screen for microRNAs production and predict their cleavage target sites. In parallel, we also created a comprehensive wheat MITE database by identifying novel elements and compiling known ones. When comparing both data sets, we found high homology between MITEs and 14% of all the miRNAs production sites detected. Furthermore, we show that MITE-derived miRNAs have preference for targeting degradation sites with MITE insertions in the 3' UTR regions of the transcripts. CONCLUSIONS: Our results revealed that MITE-derived miRNAs can underlay the origin of some miRNAs and potentially shape a regulatory gene network. Since MITEs are found in millions of insertions in the wheat genome and are closely linked to genic regions, this kind of regulatory network could have a significant impact on the post-transcriptional control of gene expression.

High IRF8 expression correlates with CD8 T cell infiltration and is a predictive biomarker of therapy response in ER-negative breast cancer.

BACKGROUND: Characterization of breast cancer (BC) through the determination of conventional markers such as ER, PR, HER2, and Ki67 has been useful as a predictive and therapeutic tool. Also, assessment of tumor-infiltrating lymphocytes has been proposed as an important prognostic aspect to be considered in certain BC subtypes. However, there is still a need to identify additional biomarkers that could add precision in distinguishing therapeutic response of individual patients. To this end, we focused in the expression of interferon regulatory factor 8 (IRF8) in BC cells. IRF8 is a transcription factor which plays a well-determined role in myeloid cells and that seems to have multiple antitumoral roles: it has tumor suppressor functions; it acts downstream IFN/STAT1, required for the success of some therapeutic regimes, and its expression in neoplastic cells seems to depend on a cross talk between the immune contexture and the tumor cells. The goal of the present study was to examine the relationship between IRF8 with the therapeutic response and the immune contexture in BC, since its clinical significance in this type of cancer has not been thoroughly addressed. METHODS: We identified the relationship between IRF8 expression and the clinical outcome of BC patients and validated IRF8 as predictive biomarker by using public databases and then performed in silico analysis. To correlate the expression of IRF8 with the immune infiltrate in BC samples, we performed quantitative multiplex immunohistochemistry. RESULTS: IRF8 expression can precisely predict the complete pathological response to monoclonal antibody therapy or to select combinations of chemotherapy such as FAC (fluorouracil, adriamycin, and cytoxan) in ER-negative BC subtypes. Analysis of immune cell infiltration indicates there is a strong correlation between activated and effector CD8+ T cell infiltration and tumoral IRF8 expression. CONCLUSIONS: We propose IRF8 expression as a potent biomarker not only for prognosis, but also for predicting therapy response in ER-negative BC phenotypes. Its expression in neoplastic cells also correlates with CD8+ T cell activation and infiltration. Therefore, our results justify new efforts towards understanding mechanisms regulating IRF8 expression and how they can be therapeutically manipulated.

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.

Differential splicing analysis based on isoforms expression with NBSplice.

Alternative splicing alterations have been widely related to several human diseases revealing the importance of their study for the success of translational medicine. Differential splicing (DS) occurrence has been mainly analyzed through exon-based approaches over RNA-seq data. Although these strategies allow identifying differentially spliced genes, they ignore the identity of the affected gene isoforms which is crucial to understand the underlying pathological processes behind alternative splicing changes. Moreover, despite several isoform quantification tools for RNA-seq data have been recently developed, DS tools have not taken advantage of them. Here, the NBSplice R package for differential splicing analysis by means of isoform expression data is presented. It estimates differences on relative expressions of gene transcripts between experimental conditions to infer changes in gene alternative splicing patterns. The developed tool was evaluated using a synthetic RNA-seq dataset with controlled differential splicing. NBSplice accurately predicted DS occurrence, outperforming current methods in terms of accuracy, sensitivity, F-score, and false discovery rate control. The usefulness of our development was demonstrated by the analysis of a real cancer dataset, revealing new differentially spliced genes that could be studied pursuing new colorectal cancer biomarkers discovery.

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.

A novel non-parametric method for uncertainty evaluation of correlation-based molecular signatures: its application on PAM50 algorithm.

Motivation: The PAM50 classifier is used to assign patients to the highest correlated breast cancer subtype irrespectively of the obtained value. Nonetheless, all subtype correlations are required to build the risk of recurrence (ROR) score, currently used in therapeutic decisions. Present subtype uncertainty estimations are not accurate, seldom considered or require a population-based approach for this context. Results: Here we present a novel single-subject non-parametric uncertainty estimation based on PAM50's gene label permutations. Simulations results ( n = 5228) showed that only 61% subjects can be reliably 'Assigned' to the PAM50 subtype, whereas 33% should be 'Not Assigned' (NA), leaving the rest to tight 'Ambiguous' correlations between subtypes. The NA subjects exclusion from the analysis improved survival subtype curves discrimination yielding a higher proportion of low and high ROR values. Conversely, all NA subjects showed similar survival behaviour regardless of the original PAM50 assignment. We propose to incorporate our PAM50 uncertainty estimation to support therapeutic decisions. Availability and Implementation: Source code can be found in 'pbcmc' R package at Bioconductor. Contacts: cristobalfresno@gmail.com or efernandez@bdmg.com.ar. Supplementary information: Supplementary data are available at Bioinformatics online.

Early Detection of Chronic Asymptomatic Chagas Infection.

Chagas disease, also known as American trypanosomiasis, is a chronic and systemic parasitic infection which has become a serious epidemiological problem not only in endemic regions (Latin America), but also in non-endemic ones like North America, Europe, and Oceania. Subjects with the indeterminate chagasic form (ICF), a chronic asymptomatic disease stage, are the main sources of non-vectorial dissemination through blood transfusion, organ transplantation, and congenital transmission. It has been suggested that 94% of urban infections can be explained by these subjects. Under this scenario, the availability of simple and effective screening methods for ICF detection becomes crucial for both prevention of disease propagation and detection of clinical stages. Recently, a new non-invasive method has been proposed for ICF detection. It is based on surface high-resolution ECG and it could be easily adopted and included in modern ECG devices, overcoming the limitations of serological-based tests. The proposed method shows accuracy for early ICF screening, thus improving prognosis by defining the clinical stages and allowing appropriate and effective treatment.

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 polymyxin B-induced transcriptomic response of a clinical, multidrug-resistant Klebsiella pneumoniae involves multiple regulatory elements and intracellular targets.

BACKGROUND: The emergence of multidrug-resistant Klebsiella pneumoniae is a major public health concern. Many K. pneumoniae infections can only be treated when resorting to last-line drugs such as polymyxin B (PB). However, resistance to this antibiotic is also observed, although insufficient information is described on its mode of action as well as the mechanisms used by resistant bacteria to evade its effects. We aimed to study PB resistance and the influence of abiotic stresses in a clinical K. pneumoniae strain using whole transcriptome profiling. RESULTS: We sequenced 12 cDNA libraries of K. pneumoniae Kp13 bacteria, from two biological replicates of the original strain Kp13 (Kp13) and five derivative strains: induced high-level PB resistance in acidic pH (Kp13pH), magnesium deprivation (Kp13Mg), high concentrations of calcium (Kp13Ca) and iron (Kp13Fe), and a control condition with PB (Kp13PolB). Our results show the involvement of multiple regulatory loci that differentially respond to each condition as well as a shared gene expression response elicited by PB treatment, and indicate the participation of two-regulatory components such as ArcA-ArcB, which could be involved in re-routing the K. pneumoniae metabolism following PB treatment. Modules of co-expressed genes could be determined, which correlated to growth in acid stress and PB exposure. We hypothesize that polymyxin B induces metabolic shifts in K. pneumoniae that could relate to surviving against the action of this antibiotic. CONCLUSIONS: We obtained whole transcriptome data for K. pneumoniae under different environmental conditions and PB treatment. Our results supports the notion that the K. pneumoniae response to PB exposure goes beyond damaged membrane reconstruction and involves recruitment of multiple gene modules and intracellular targets.

Comparison of algorithms to infer genetic population structure from unlinked molecular markers.

Identifying population genetic structure (PGS) is crucial for breeding and conservation. Several clustering algorithms are available to identify the underlying PGS to be used with genetic data of maize genotypes. In this work, six methods to identify PGS from unlinked molecular marker data were compared using simulated and experimental data consisting of multilocus-biallelic genotypes. Datasets were delineated under different biological scenarios characterized by three levels of genetic divergence among populations (low, medium, and high FST) and two numbers of sub-populations (K=3 and K=5). The relative performance of hierarchical and non-hierarchical clustering, as well as model-based clustering (STRUCTURE) and clustering from neural networks (SOM-RP-Q). We use the clustering error rate of genotypes into discrete sub-populations as comparison criterion. In scenarios with great level of divergence among genotype groups all methods performed well. With moderate level of genetic divergence (FST=0.2), the algorithms SOM-RP-Q and STRUCTURE performed better than hierarchical and non-hierarchical clustering. In all simulated scenarios with low genetic divergence and in the experimental SNP maize panel (largely unlinked), SOM-RP-Q achieved the lowest clustering error rate. The SOM algorithm used here is more effective than other evaluated methods for sparse unlinked genetic data.

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.