Random Subspace Aggregation for Cancer Prediction with Gene Expression Profiles

Methods and approaches in the analysis of gene expression data

Gene selection and classification using Taguchi chaotic binary particle swarm optimization

Background - In the NeoTRIPaPDL1 phase III trial, triple-negative breast cancer (TNBC) patients were randomized to receive nab-paclitaxel/carboplatin for 8 cycles (CT arm) with or without atezolizumab (CT/A arm). We previously reported that the majority of patients (65%) with PD-L1- at baseline converted to PD-L1+ (the majority with IC2/IC3) after the first cycle of treatment in the atezolizumab arm. Here we studied treatment associated changes in the molecular tumour features and immune microenvironment by PD-L1 expression. Methods – A total of 158 (56.4%) of patients enrolled in the NeoTRIPaPDL1 trial (76 and 82 from CT/A and CT arm, respectively) were included in this analysis by satisfying the following criteria: i) availability of a paired core biopsy at baseline and at day 1 of the second treatment cycle (D1C2); ii) evaluation of stromal TILs (sTILs) and staining for PD-L1 (Ventana SP142); iii) successful RNA-seq gene expression profiling. PD-L1 groups were defined as IC0/IC1 (<5% PD-L1low) vs IC2/IC3 (>=5% PD-L1high). Presence of different immune cell populations was quantified by gene expression profile deconvolution using ConsensusTME R package. Cancer hallmark gene set collection and custom signature activation status were estimated in each sample using singscore R package. Differences in score distribution were evaluated by 2-sided t-test. Results At baseline, both sTILs and immune cell signatures were upregulated in PD-L1high compared to PD-L1low tumours (p<0.001). No significant differences were found between the two treatment arms in the PD-L1high subpopulation. In the PD-L1low cases, both sTILs and immune related signatures were slightly downregulated in the CT/A arm (p<0.05), suggesting a modest unbalance among treatment arms with CT arm being slightly more inflamed than CT/A arm.At D1C2, PD-L1high tumours in the CT arm systematically had high sTILs (median=70%, range=30-90%), while PD-L1low tumours receiving CT/A had low sTILs in a significant proportion of cases (median=30%, range=0-90%; p<0.001). Similarly, at D1C2 several gene expression-estimated immune cell populations and immune-related signatures were upregulated in the CT arm compared to CT/A arm in PD-L1high tumours, with the weakest association observed for M2 macrophages (p=0.058). No tumour-related signatures were differentially expressed among the two treatment arms within groups with PD-L1high, suggesting that different treatment. modulate PD-L1 by engaging a different immune mileau instead of modulating tumor related features.Considering PD-L1low groups at D1C2, in the CT arm 21 tumors had >30% sTILs (n=21/69, 30.4%), while in the CT/A arm only 2 had sTILs >30% (2/27, 7.4%) (p<0.001). Analysis of gene expression data identified IFN-related signatures as the most upregulated in the CT compared to CT/A arm in PD-L1low cases at D1C2 (p<0.05). Conclusions Integrated dynamic analysis of PD-L1 expression and gene expression data highlighted significant treatment-specific changes of the immune landscape according to PD-L1 expression, when this biomarker is assessed during treatment. This indicates that the immune milieu associated with PD-L1 status is strongly dependent on the time of assessment in relationship to treatment received. Such observation may explain why in the NeoTRIP trial, baseline PD-L1 but not on-treatment PD-L1 was predictive of pCR in CT/A arm. In addition, our findings could have implications related to the use of PD-L1 as a predictive biomarker in pre-treated patients, especially when assessed early on during treatment. Citation Format: Maurizio Callari, Chiun-Sheng Huang, Daniel Egle, Begoña Bermejo, Claudio Zamagni, Matteo Dugo, Marc Thill, Antonio Anton, Marco Barreca, Stefania Russo, Eva Maria Ciruelos, Richard Greil, Stefania Zambelli, Balázs Gyorffy, Chanel Smart, Olivia Biasi, Pinuccia Valagussa, Giuseppe Viale, Luca Gianni, Giampaolo Bianchini. Immune milieu associated with PD-L1 status in TNBC is dependent on time of biomarker assessment and treatment received: A secondary analysis of the NeoTRIPaPDL1 trial [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P1-04-02.

Gene expression profiling in colorectal cancer using microarray technologies: Results and perspectives

Gene Expression Analysis Uncovers Similarity and Differences Among Burkitt Lymphoma Subtypes

Integrated analysis of global gene and microRNA expression profiling associated with aplastic anaemia

151P - Gene expression profiling for a better understanding of gastric cancer: From the perspective of metabolic rearrangement

The problems of analyzing dose effects on gene expression are gaining attention in toxicological research. Determining how gene expression profiles change with toxicant dose will improve the utility of arrays in identifying biomarkers and elucidating their modes of toxic action. In the present study, we focused on determining the dose-dependent alterations of gene expression profiles with hexanal exposure and we identified the possible biomarkers of hexanal in A549 human alveolar cells. A549 cells were exposed to a 5% inhibitory concentration (IC5) and a 20% inhibitory concentration (IC20) of hexanal for 48 h. Through microarray analysis using an oligonucleotide chip, we identified that the gene expression patterns were differentially shown in the control group and the hexanalexposed groups. The hexanal-exposed groups are more sensitive to gene alteration than the control group, and gene expressions are more significantly altered in the IC20 exposure group than in the IC5 exposure group. With clustering analysis of gene expression profiles, we identified 2,929 IC5− and 3,678 IC20-specific genes, and 302 dose-dependently expressed genes. Gene ontology (GO) analysis with 246 annotated genes of the 302 dosedependent expressed genes showed correlation with the key biological processes involved in neurological system processes, immune system development, cell activation, and cell-cell signaling. In conclusion, current study describes alterations in gene expression profiles in response to exposure to different doses of hexanal and related toxic pathways induced by significantly expressed genes. Moreover, novel genes and pathways that could potentially play a role in the prevention of respiratory disease due to aldehydes are identified.

We present a method for individual and integrative analysis of high dimension, low sample size data that capitalizes on the recurring theme in multivariate analysis of projecting higher dimensional data onto a few meaningful directions that are solutions to a generalized eigenvalue problem. We propose a general framework, called SELP (Sparse Estimation with Linear Programming), with which one can obtain a sparse estimate for a solution vector of a generalized eigenvalue problem. We demonstrate the utility of SELP on canonical correlation analysis for an integrative analysis of methylation and gene expression profiles from a breast cancer study, and we identify some genes known to be associated with breast carcinogenesis, which indicates that the proposed method is capable of generating biologically meaningful insights. Simulation studies suggest that the proposed method performs competitive in comparison with some existing methods in identifying true signals in various underlying covariance structures.

Pluripotent embryonal carcinoma (EC) cells derived from germ cell tumors (GCTs) are an excellent model system to study the interface between malignancy, pluripotency, and lineage differentiation. We previously characterized the gene expression profile (GEP) of a panel of 135 germ cell tumor (GCT) biopsies comprising all described differentiation lineages and 6 normal testes as controls, using the Affymetrix U133A+B arrays (Korkola et al. Cancer Res., 66: 820-7, 2006 and J. Clin. Oncol., 27: 5240-7, 2009). We applied the ARACNE reverse engineering algorithm to this GEP to obtain the first de novo and context-specific transcriptional interactome (GCTNet) comprising 1,310 transcription factors (TFs). GCTnet recapitulated 830 regulatory targets together of POU5F1, NANOG, and SOX2, the core pluripotency TFs. ChIP-seq and ChIP-PCR analysis of NT2/D1 embryonal carcinoma and H9 ES cells provided significant validation of ARACNe-inferred targets, besides providing the first detailed mapping of DNA binding sites of these 3 genes in human cells. Functional validation of ARACNe-inferred targets by stable knockdown of the POU5F1, NANOG and SOX2 in NT2/D1 cells also showed a significant enrichment of ARACNe targets. Analysis of GEP in a time-course of SOX2 knockdown by principal component analysis (PCA) identified 3 orthogonal gene expression programs: (1) genes with monotonic change (PC1), (2) genes showing a strong and focal gene expression at day 5 (PC2), and (3) genes which show oscillatory pattern of expression (PC3). Ingenuity and GO annotation analysis of PCA provided new insights into the pathways regulated by this TF. Our results provide a novel, comprehensive, and accurate regulatory networks responsible for pluripotency and lineage differentiation in GCTs. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 3410. doi:10.1158/1538-7445.AM2011-3410

Analysis of gene expression profiles under water stress in tolerant and sensitive sugarcane plants

PurposeThis study aims to explore gene expression signatures and serum biomarkers to predict intrinsic chemoresistance in epithelial ovarian cancer (EOC).Patients and MethodsGene expression profiling data of 322 high-grade EOC cases between 2009 and 2010 in The Cancer Genome Atlas project (TCGA) were used to develop and validate gene expression signatures that could discriminate different responses to first-line platinum/paclitaxel-based treatments. A gene regulation network was then built to further identify hub genes responsible for differential gene expression between the complete response (CR) group and the progressive disease (PD) group. Further, to find more robust serum biomarkers for clinical application, we integrated our gene signatures and gene signatures reported previously to identify secretory protein-encoding genes by searching the DAVID database. In the end, gene-drug interaction network was constructed by searching Comparative Toxicogenomics Database (CTD) and literature.ResultsA 349-gene predictive model and an 18-gene model independent of key clinical features with high accuracy were developed for prediction of chemoresistance in EOC. Among them, ten important hub genes and six critical signaling pathways were identified to have important implications in chemotherapeutic response. Further, ten potential serum biomarkers were identified for predicting chemoresistance in EOC. Finally, we suggested some drugs for individualized treatment.ConclusionWe have developed the predictive models and serum biomarkers for platinum/paclitaxel response and established the new approach to discover potential serum biomarkers from gene expression profiles. The potential drugs that target hub genes are also suggested.

Single-Cell Profiling of CD8 Landscape in Treatment Naïve and Relapsed/Refractory AML Patients Reveals Distinct Effector Cellular States Predictive of Outcomes

BackgroundClustering methods are widely used on gene expression data to categorize genes with similar expression profiles. Finding an appropriate (dis)similarity measure is critical to the analysis. In our study, we developed a new measure for clustering the genes when the key factor is the shape of the profile, and when the expression magnitude should also be accounted for in determining the gene relationship. This is achieved by modeling the shape and magnitude parameters separately in a gene expression profile, and then using the estimated shape and magnitude parameters to define a measure in a new feature space.ResultsWe explored several different transformation schemes to construct the feature spaces that include a space whose features are determined by the mutual differences of the original expression components, a space derived from a parametric covariance matrix, and the principal component space in traditional PCA analysis. The former two are the newly proposed and the latter is explored for comparison purposes. The new measures we defined in these feature spaces were employed in a K-means clustering procedure to perform analyses. Applying these algorithms to a simulation dataset, a developing mouse retina SAGE dataset, a small yeast sporulation cDNA dataset, and a maize root affymetrix microarray dataset, we found from the results that the algorithm associated with the first feature space, named TransChisq, showed clear advantages over other methods.ConclusionThe proposed TransChisq is very promising in capturing meaningful gene expression clusters. This study also demonstrates the importance of data transformations in defining an efficient distance measure. Our method should provide new insights in analyzing gene expression data. The clustering algorithms are available upon request.

To compare fracture healing therapies, the gene expression profiles of rat fracture samples treated with nail and plate fixation were analyzed at 1 day, 3 days, 1 week, 2 weeks, 4 weeks, and 6 weeks after surgery. The gene expression profiles GSE1685, which include 19 samples, were downloaded from the Gene Expression Omnibus database. After preprocessing, the gene expression profiles were subjected to time series analysis using the Short Time-series Expression Miner software, and the significantly differentially expressed gene (DEG) sets were selected. Further, the distributions of those DEG sets on the corresponding chromosomes were identified using the functional classification tool. Finally, the DEGs were subjected to function and pathway enrichment analysis. DEG analysis indicated that the number of DEGs (854 genes) from nail fixation was significantly lower than that of DEGs (1029 genes) from plate fixation. The DEGs were mainly enriched in cell proliferation, cellular localization, and response to wounding functions. Several critical DEGs expressed during the fracture healing process were screened, and 2 common pathways were enriched for the DEGs in the nail fixation and plate fixation. These DEGs and pathways may be potential targets or predictive markers during fracture healing.