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1.
J Transl Med ; 18(1): 25, 2020 Jan 14.
Artigo em Inglês | MEDLINE | ID: mdl-31937321

RESUMO

BACKGROUND: Immune checkpoint inhibitors are effective in some cases of lung adenocarcinoma (LUAD). Whole-exome sequencing has revealed that the tumour mutation burden (TMB) is associated with clinical benefits among patients from immune checkpoint inhibitors. Several commercial mutation panels have been developed for estimating the TMB regardless of the cancer type. However, different cancer types have different mutational landscapes; hence, this study aimed to develop a small cancer-type-specific mutation panel for high-accuracy estimation of the TMB of LUAD patients. METHODS: We developed a small cancer-type-specific mutation panel based on coding sequences (CDSs) rather than genes, for LUAD patients. Using somatic CDSs mutation data from 486 LUAD patients in The Cancer Genome Atlas (TCGA) database, we pre-selected a set of CDSs with mutation states significantly correlated with the TMB, from which we selected a CDS mutation panel with a panel-score most significantly correlated with the TMB, using a genetic algorithm. RESULTS: A mutation panel containing 106 CDSs of 100 genes with only 0.34 Mb was developed, whose length was much shorter than current commercial mutation panels of 0.80-0.92 Mb. The correlation of this panel with the TMB was validated in two independent LUAD datasets with progression-free survival data for patients treated with nivolumab plus ipilimumab and pembrolizumab immunotherapies, respectively. In both test datasets, survival analyses revealed that patients with a high TMB predicted via the 106-CDS mutation panel with a cut-point of 6.20 mutations per megabase, median panel score in the training dataset, had a significantly longer progression-free survival than those with a low predicted TMB (log-rank p = 0.0018, HR = 3.35, 95% CI 1.51-7.42; log-rank p = 0.0020, HR = 5.06, 95% CI 1.63-15.69). This small panel better predicted the efficacy of immunotherapy than current commercial mutation panels. CONCLUSIONS: The small-CDS mutation panel of only 0.34 Mb is superior to current commercial mutation panels and can better predict the efficacy of immunotherapy for LUAD patients, and its low cost and time-intensiveness make it more suitable for clinical applications.

2.
Cancer Gene Ther ; 2019 Dec 05.
Artigo em Inglês | MEDLINE | ID: mdl-31801988

RESUMO

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

3.
BMC Genomics ; 20(1): 881, 2019 Nov 21.
Artigo em Inglês | MEDLINE | ID: mdl-31752667

RESUMO

BACKGROUND: Targeted therapy for non-small cell lung cancer is histology dependent. However, histological classification by routine pathological assessment with hematoxylin-eosin staining and immunostaining for poorly differentiated tumors, particularly those from small biopsies, is still challenging. Additionally, the effectiveness of immunomarkers is limited by technical inconsistencies of immunostaining and lack of standardization for staining interpretation. RESULTS: Using gene expression profiles of pathologically-determined lung adenocarcinomas and squamous cell carcinomas, denoted as pADC and pSCC respectively, we developed a qualitative transcriptional signature, based on the within-sample relative gene expression orderings (REOs) of gene pairs, to distinguish ADC from SCC. The signature consists of two genes, KRT5 and AGR2, which has the stable REO pattern of KRT5 > AGR2 in pSCC and KRT5 < AGR2 in pADC. In the two test datasets with relative unambiguous NSCLC types, the apparent accuracy of the signature were 94.44 and 98.41%, respectively. In the other integrated dataset for frozen tissues, the signature reclassified 4.22% of the 805 pADC patients as SCC and 12% of the 125 pSCC patients as ADC. Similar results were observed in the clinical challenging cases, including FFPE specimens, mixed tumors, small biopsy specimens and poorly differentiated specimens. The survival analyses showed that the pADC patients reclassified as SCC had significantly shorter overall survival than the signature-confirmed pADC patients (log-rank p = 0.0123, HR = 1.89), consisting with the knowledge that SCC patients suffer poor prognoses than ADC patients. The proliferative activity, subtype-specific marker genes and consensus clustering analyses also supported the correctness of our signature. CONCLUSIONS: The non-subjective qualitative REOs signature could effectively distinguish ADC from SCC, which would be an auxiliary test for the pathological assessment of the ambiguous cases.

4.
Front Oncol ; 9: 1094, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-31681618

RESUMO

Resistance to platinum and taxane adjuvant chemotherapy (ACT) is the main cause of the recurrence and poor prognosis of high-grade serous ovarian cancer (HGS-OvCa) patients receiving platinum-taxane ACT after surgery. However, currently reported quantitative transcriptional signatures, which are commonly based on risk scores summarized from gene expression, are unsuitable for clinical application because of their high sensitivity to experimental batch effects and quality uncertainties of clinical samples. Using 226 samples of HGS-OvCa patients receiving platinum-taxane ACT in TCGA, we developed a qualitative transcriptional signature, consisting of four gene pairs whose within-samples relative expression orderings could robustly predict patient recurrence-free survival (RFS). In two independent test datasets, the predicted non-responders had significantly shorter RFS than the predicted responders (log-rank p < 0.05). In a test dataset containing data for patient pathological response state, the signature reclassified 12 out of 22 pathological complete response patients as non-responders and two out of 16 pathological non-complete response patients as responders. Notably, the 12 predicted non-responders in the pathological complete response group had significantly shorter RFS than the predicted responders (log-rank p = 0.0122). This qualitative transcriptional signature, which is insensitive to experimental batch effects and quality uncertainties of clinical samples, can individually identify HGS-OvCa patients who are more likely to benefit from platinum-taxane adjuvant chemotherapy.

5.
Cancer Gene Ther ; 2019 Oct 09.
Artigo em Inglês | MEDLINE | ID: mdl-31595030

RESUMO

Histological grading (HG) is an important prognostic factor of colorectal adenocarcinoma (CRAC): the high-grade CRAC patients have poorer prognosis after tumor resection. Especially, the high-grade stage II CRAC patients are recommended to receive adjuvant chemotherapy. Due to the subjective nature of HG assessment, it is difficult to achieve consistency among pathologists, which brings patients uncertain grading outcomes and inappropriate treatments. We developed a qualitative transcriptional signature based on the within-sample relative expression orderings (REOs) of gene pairs to discriminate high-grade and low-grade CRAC. Using the stage II-III CRAC samples, we detected gene pairs with stable REOs in the high-grade samples and reversal stable REOs in the low-grade samples, and retained the gene pairs whose specific REO patterns were significantly associated with the disease-free survival of patients by univariate Cox regression model. Then, we used a forward-backward searching procedure to extract gene pairs with the highest concordance index as the final grading signature. Finally, 9 gene pairs (9-GPS) were developed to divide CRAC patients into high-grade and low-grade groups. With the signature, there were more differential expression characteristics between reclassified high-grade and low-grade groups. Significant difference of prognosis between the classified two group patients could be seen in four independent datasets. Additionally, genomic analyses showed that the classified high-grade groups were characterized by hypermutation while classified low-grade groups were characterized by frequent copy number alternations. In conclusion, the 9-GPS can provide an objective and robust grading assessment for CRAC patients, which could assist clinical treatment decision.

6.
BMC Genomics ; 20(1): 769, 2019 Oct 23.
Artigo em Inglês | MEDLINE | ID: mdl-31646964

RESUMO

BACKGROUND: Microsatellite instability (MSI) accounts for about 15% of colorectal cancer and is associated with prognosis. Today, MSI is usually detected by polymerase chain reaction amplification of specific microsatellite markers. However, the instability is identified by comparing the length of microsatellite repeats in tumor and normal samples. In this work, we developed a qualitative transcriptional signature to individually predict MSI status for right-sided colon cancer (RCC) based on tumor samples. RESULTS: Using RCC samples, based on the relative expression orderings (REOs) of gene pairs, we extracted a signature consisting of 10 gene pairs (10-GPS) to predict MSI status for RCC through a feature selection process. A sample is predicted as MSI when the gene expression orderings of at least 7 gene pairs vote for MSI; otherwise the microsatellite stability (MSS). The classification performance reached the largest F-score in the training dataset. This signature was verified in four independent datasets of RCCs with the F-scores of 1, 0.9630, 0.9412 and 0.8798, respectively. Additionally, the hierarchical clustering analyses and molecular features also supported the correctness of the reclassifications of the MSI status by 10-GPS. CONCLUSIONS: The qualitative transcriptional signature can be used to classify MSI status of RCC samples at the individualized level.


Assuntos
Neoplasias do Colo/genética , Instabilidade de Microssatélites , Transcriptoma , Algoritmos , Análise por Conglomerados , Biologia Computacional , Humanos
7.
Mol Ther Nucleic Acids ; 17: 688-700, 2019 Sep 06.
Artigo em Inglês | MEDLINE | ID: mdl-31400611

RESUMO

Cancer cells generally harbor hundreds of alterations in the cancer genomes and act as crucial factors in the development and progression of cancer. Gene alterations in the cancer genome form genetic interactions, which affect the response of patients to drugs. We developed an algorithm that mines copy number alteration and whole-exome mutation profiles from The Cancer Genome Atlas (TCGA), as well as functional screen data generated to identify potential genetic interactions for specific cancer types. As a result, 4,529 synthetic viability (SV) interactions and 10,637 synthetic lethality (SL) interactions were detected. The pharmacogenomic datasets revealed that SV interactions induced drug resistance in cancer cells and that SL interactions mediated drug sensitivity in cancer cells. Deletions of HDAC1 and DVL1, both of which participate in the Notch signaling pathway, had an SV effect in cancer cells, and deletion of DVL1 induced resistance to HDAC1 inhibitors in cancer cells. In addition, patients with low expression of both HDAC1 and DVL1 had poor prognosis. Finally, by integrating current reported genetic interactions from other studies, the Cancer Genetic Interaction database (CGIdb) (http://www.medsysbio.org/CGIdb) was constructed, providing a convenient retrieval for genetic interactions in cancer.

8.
BMC Cancer ; 19(1): 67, 2019 Jan 14.
Artigo em Inglês | MEDLINE | ID: mdl-30642283

RESUMO

BACKGROUND: Precise diagnosis of the tissue origin for metastatic cancer of unknown primary (CUP) is essential for deciding the treatment scheme to improve patients' prognoses, since the treatment for the metastases is the same as their primary counterparts. The purpose of this study is to identify a robust gene signature that can predict the origin for CUPs. METHODS: The within-sample relative gene expression orderings (REOs) of gene pairs within individual samples, which are insensitive to experimental batch effects and data normalizations, were exploited for identifying the prediction signature. RESULTS: Using gene expression profiles of the lung-limited metastatic colorectal cancer (LmCRC), we firstly showed that the within-sample REOs in lung metastases of colorectal cancer (CRC) samples were concordant with the REOs in primary CRC samples rather than with the REOs in primary lung cancer. Based on this phenomenon, we selected five gene pairs with consistent REOs in 498 primary CRC and reversely consistent REOs in 509 lung cancer samples, which were used as a signature for predicting primary sites of metastatic CRC based on the majority voting rule. Applying the signature to 654 primary CRC and 204 primary lung cancer samples collected from multiple datasets, the prediction accuracy reached 99.36%. This signature was also applied to 24 LmCRC samples collected from three datasets produced by different laboratories and the accuracy reached 100%, suggesting that the within-sample REOs in the primary site could reveal the original tissue of metastatic cancers. CONCLUSIONS: The result demonstrated that the signature based on within-sample REOs of five gene pairs could exactly and robustly identify the primary sites of CUPs.


Assuntos
Regulação Neoplásica da Expressão Gênica , Neoplasias Pulmonares/diagnóstico , Neoplasias Pulmonares/genética , Neoplasias Primárias Desconhecidas/diagnóstico , Neoplasias Primárias Desconhecidas/genética , Transcriptoma , Antineoplásicos/farmacologia , Antineoplásicos/uso terapêutico , Biomarcadores Tumorais , Bases de Dados Genéticas , Perfilação da Expressão Gênica , Humanos , Neoplasias Pulmonares/tratamento farmacológico , Neoplasias Pulmonares/metabolismo , Metástase Neoplásica , Neoplasias Primárias Desconhecidas/tratamento farmacológico , Neoplasias Primárias Desconhecidas/metabolismo , Mapeamento de Interação de Proteínas/métodos , Mapas de Interação de Proteínas
9.
Brief Bioinform ; 20(2): 482-491, 2019 03 22.
Artigo em Inglês | MEDLINE | ID: mdl-29040359

RESUMO

To detect differentially expressed genes (DEGs) in small-scale cell line experiments, usually with only two or three technical replicates for each state, the commonly used statistical methods such as significance analysis of microarrays (SAM), limma and RankProd (RP) lack statistical power, while the fold change method lacks any statistical control. In this study, we demonstrated that the within-sample relative expression orderings (REOs) of gene pairs were highly stable among technical replicates of a cell line but often widely disrupted after certain treatments such like gene knockdown, gene transfection and drug treatment. Based on this finding, we customized the RankComp algorithm, previously designed for individualized differential expression analysis through REO comparison, to identify DEGs with certain statistical control for small-scale cell line data. In both simulated and real data, the new algorithm, named CellComp, exhibited high precision with much higher sensitivity than the original RankComp, SAM, limma and RP methods. Therefore, CellComp provides an efficient tool for analyzing small-scale cell line data.


Assuntos
Algoritmos , Perfilação da Expressão Gênica/métodos , Regulação Neoplásica da Expressão Gênica , Neoplasias/genética , Análise de Sequência com Séries de Oligonucleotídeos/métodos , Linhagem Celular Tumoral , Interpretação Estatística de Dados , Humanos
10.
Brief Bioinform ; 20(4): 1295-1307, 2019 07 19.
Artigo em Inglês | MEDLINE | ID: mdl-29300844

RESUMO

Synthetic lethal (SL) interactions occur when alterations in two genes lead to cell death but alteration in only one of them is not lethal. SL interactions provide a new strategy for molecular-targeted cancer therapy. Currently, there are few drugs targeting SL interactions that entered into clinical trials. Therefore, it is necessary to investigate the link between SL interactions and drug sensitivity of cancer cells systematically for drug development purpose. We identified SL interactions by integrating the high-throughput data from The Cancer Genome Atlas, small hairpin RNA data and genetic interactions of yeast. By integrating SL interactions from other studies, we tested whether the SL pairs that consist of drug target genes and the genes with genomic alterations are related with drug sensitivity of cancer cells. We found that only 6.26%∼34.61% of SL interactions showed the expected significant drug sensitivity using the pooled cancer cell line data from different tissues, but the proportion increased significantly to approximately 90% using the cancer cell line data for each specific tissue. From an independent pharmacogenomics data of 41 breast cancer cell lines, we found three SL interactions (ABL1-IFI16, ABL1-SLC50A1 and ABL1-SYT11) showed significantly better prognosis for the patients with both genes being altered than the patients with only one gene being altered, which partially supports the SL effect between the gene pairs. Our study not only provides a new way for unraveling the complex mechanisms of drug sensitivity but also suggests numerous potentially important drug targets for cancer therapy.

11.
Brief Bioinform ; 19(5): 793-802, 2018 09 28.
Artigo em Inglês | MEDLINE | ID: mdl-28334118

RESUMO

Identifying differentially expressed microRNAs (DE miRNAs) between cancer samples and normal controls is a common way to investigate carcinogenesis mechanisms. However, for a DE miRNA detected at the population-level, we do not know whether it is DE in a particular cancer sample. Here, based on the finding that the within-sample relative expression orderings of miRNA pairs are highly stable in a particular type of normal tissues but widely disrupted in the corresponding cancer tissues, we proposed a method, called RankMiRNA, to identify DE miRNAs in each cancer tissue compared with its own normal state. Evaluated with pair-matched miRNA expression profiles of cancer tissues and adjacent normal tissues for lung and liver cancers, RankMiRNA exhibited excellent performance. Finally, we exemplified an application of the individual-level differential expression analysis by finding miRNAs DE in at least 90% lung cancer tissues, defined as common DE miRNAs of lung cancer. After identifying DE miRNAs for each of 991 lung cancer samples from The Cancer Genome Atlas with RankMiRNA, we found that hsa-mir-210 was upregulated, while hsa-mir-490 and hsa-mir-486 were downregulated in > 90% of the 991 lung cancer samples. These common DE miRNAs were validated in independent pair-matched samples of cancer tissues and adjacent normal tissues measured with different platforms. In conclusion, RankMiRNA provides us a novel tool to find common and subtype-specific miRNAs for a type of cancer, allowing us to study cancer mechanisms in a novel way.


Assuntos
Neoplasias Pulmonares/genética , MicroRNAs/genética , Algoritmos , Biomarcadores Tumorais/genética , Biologia Computacional/métodos , Perfilação da Expressão Gênica/métodos , Perfilação da Expressão Gênica/estatística & dados numéricos , Regulação Neoplásica da Expressão Gênica , Humanos , Valores de Referência
12.
Brief Bioinform ; 19(4): 644-655, 2018 07 20.
Artigo em Inglês | MEDLINE | ID: mdl-28096076

RESUMO

Synthetic viability, which is defined as the combination of gene alterations that can rescue the lethal effects of a single gene alteration, may represent a mechanism by which cancer cells resist targeted drugs. Approaches to detect synthetic viable (SV) interactions in cancer genome to investigate drug resistance are still scarce. Here, we present a computational method to detect synthetic viability-induced drug resistance (SVDR) by integrating the multidimensional data sets, including copy number alteration, whole-exome mutation, expression profile and clinical data. SVDR comprehensively characterized the landscape of SV interactions across 8580 tumors in 32 cancer types by integrating The Cancer Genome Atlas data, small hairpin RNA-based functional experimental data and yeast genetic interaction data. We revealed that the SV interactions are favorable to cells and can predict clinical prognosis for cancer patients, which were robustly observed in an independent data set. By integrating the cancer pharmacogenomics data sets from Cancer Cell Line Encyclopedia (CCLE) and Broad Cancer Therapeutics Response Portal, we have demonstrated that SVDR enables drug resistance prediction and exhibits high reliability between two databases. To our knowledge, SVDR is the first genome-scale data-driven approach for the identification of SV interactions related to drug resistance in cancer cells. This data-driven approach lays the foundation for identifying the genomic markers to predict drug resistance and successfully infers the potential drug combination for anti-cancer therapy.


Assuntos
Biologia Computacional/métodos , Resistencia a Medicamentos Antineoplásicos/genética , Genes Letais , Mutação , Proteínas de Neoplasias/genética , Neoplasias/genética , Farmacogenética , Perfilação da Expressão Gênica , Humanos , Neoplasias/metabolismo
13.
Mol Oncol ; 11(11): 1630-1645, 2017 11.
Artigo em Inglês | MEDLINE | ID: mdl-28922552

RESUMO

Our laboratory previously reported an individual-level signature consisting of nine gene pairs, named 9-GPS. This signature was developed by training on microarray expression data and validated using three independent integrated microarray data sets, with samples of stage I non-small-cell lung cancer after complete surgical resection. In this study, we first validated the cross-platform robustness of 9-GPS by demonstrating that 9-GPS could significantly stratify the overall survival of 213 stage I lung adenocarcinoma (LUAD) patients detected with RNA-sequencing platform in The Cancer Genome Atlas (TCGA; log-rank P = 0.0318, C-index = 0.55). Applying 9-GPS to all the 423 stage I-IV LUAD samples in TCGA, the predicted high-risk samples were significantly enriched with clinically diagnosed metastatic samples (Fisher's exact test, P = 0.0015). We further modified the voting rule of 9-GPS and found that the modified 9-GPS had a better performance in predicting metastasis states (Fisher's exact test, P < 0.0001). With the aid of the modified 9-GPS for reclassifying the metastasis states of patients with LUAD, the reclassified metastatic samples presented clearer transcriptional and genomic characteristics compared to the reclassified nonmetastatic samples. Finally, regulator network analysis identified TP53 and IRF1 with frequent genomic aberrations in the reclassified metastatic samples, indicating their key roles in driving tumor metastasis. In conclusion, 9-GPS is a robust signature for identifying early-stage LUAD patients with potential occult metastasis. This occult metastasis prediction was associated with clear transcriptional and genomic characteristics as well as the clinical diagnoses.


Assuntos
Adenocarcinoma/genética , Perfilação da Expressão Gênica , Neoplasias Pulmonares/genética , Metástase Neoplásica/genética , Adenocarcinoma/diagnóstico , Adenocarcinoma/patologia , Adenocarcinoma de Pulmão , Idoso , Feminino , Regulação Neoplásica da Expressão Gênica , Redes Reguladoras de Genes , Humanos , Neoplasias Pulmonares/diagnóstico , Neoplasias Pulmonares/patologia , Masculino , Pessoa de Meia-Idade , Metástase Neoplásica/diagnóstico , Metástase Neoplásica/patologia , Prognóstico , Transcriptoma
14.
Mol Oncol ; 11(10): 1459-1474, 2017 10.
Artigo em Inglês | MEDLINE | ID: mdl-28719033

RESUMO

Results from numerous studies suggest an important role for somatic copy number alterations (SCNAs) in cancer progression. Our work aimed to identify the drivers (oncogenes or tumor suppressor genes) that reside in recurrently aberrant genomic regions, including a large number of genes or non-coding genes, which remain a challenge for decoding the SCNAs involved in carcinogenesis. Here, we propose a new approach to comprehensively identify drivers, using 8740 cancer samples involving 18 cancer types from The Cancer Genome Atlas (TCGA). On average, 84 drivers were revealed for each cancer type, including protein-coding genes, long non-coding RNAs (lncRNA) and microRNAs (miRNAs). We demonstrated that the drivers showed significant attributes of cancer genes, and significantly overlapped with known cancer genes, including MYC, CCND1 and ERBB2 in breast cancer, and the lncRNA PVT1 in multiple cancer types. Pan-cancer analyses of drivers revealed specificity and commonality across cancer types, and the non-coding drivers showed a higher cancer-type specificity than that of coding drivers. Some cancer types from different tissue origins were found to converge to a high similarity because of the significant overlap of drivers, such as head and neck squamous cell carcinoma (HNSC) and lung squamous cell carcinoma (LUSC). The lncRNA SOX2-OT, a common driver of HNSC and LUSC, showed significant expression correlation with the oncogene SOX2. In addition, because some drivers are common in multiple cancer types and have been targeted by known drugs, we found that some drugs could be successfully repositioned, as validated by the datasets of drug response assays in cell lines. Our work reported a new method to comprehensively identify drivers in SCNAs across diverse cancer types, providing a feasible strategy for cancer drug repositioning as well as novel findings regarding cancer-associated non-coding RNA discovery.


Assuntos
Variações do Número de Cópias de DNA , Reposicionamento de Medicamentos , Regulação Neoplásica da Expressão Gênica , Neoplasias/genética , Reposicionamento de Medicamentos/métodos , Genômica/métodos , Humanos , MicroRNAs/genética , Neoplasias/tratamento farmacológico , Oncogenes , RNA Longo não Codificante/genética
15.
Mol Cancer ; 16(1): 98, 2017 06 06.
Artigo em Inglês | MEDLINE | ID: mdl-28587642

RESUMO

BACKGROUND: Deregulations of long non-coding RNAs (lncRNAs) have been implicated in cancer initiation and progression. Current methods can only capture differential expression of lncRNAs at the population level and ignore the heterogeneous expression of lncRNAs in individual patients. METHODS: We propose a method (LncRIndiv) to identify differentially expressed (DE) lncRNAs in individual cancer patients by exploiting the disrupted ordering of expression levels of lncRNAs in each disease sample in comparison with stable normal ordering. LncRIndiv was applied to lncRNA expression profiles of lung adenocarcinoma (LUAD). Based on the expression profile of LUAD individual-level DE lncRNAs, we used a forward selection procedure to identify prognostic signature for stage I-II LUAD patients without adjuvant therapy. RESULTS: In both simulated data and real pair-wise cancer and normal sample data, LncRIndiv method showed good performance. Based on the individual-level DE lncRNAs, we developed a robust prognostic signature consisting of two lncRNA (C1orf132 and TMPO-AS1) for stage I-II LUAD patients without adjuvant therapy (P = 3.06 × 10-6, log-rank test), which was confirmed in two independent datasets of GSE50081 (P = 1.82 × 10-2, log-rank test) and GSE31210 (P = 7.43 × 10-4, log-rank test) after adjusting other clinical factors such as smoking status and stages. Pathway analysis showed that TMPO-AS1 and C1orf132 could affect the prognosis of LUAD patients through regulating cell cycle and cell adhesion. CONCLUSIONS: LncRIndiv can successfully detect DE lncRNAs in individuals and be applied to identify prognostic signature for LUAD patients.


Assuntos
Adenocarcinoma/genética , Adenocarcinoma/mortalidade , Regulação Neoplásica da Expressão Gênica , Neoplasias Pulmonares/genética , Neoplasias Pulmonares/mortalidade , RNA Longo não Codificante/genética , Adenocarcinoma/patologia , Adenocarcinoma/terapia , Adenocarcinoma de Pulmão , Biomarcadores Tumorais , Perfilação da Expressão Gênica , Redes Reguladoras de Genes , Sequenciamento de Nucleotídeos em Larga Escala , Humanos , Estimativa de Kaplan-Meier , Neoplasias Pulmonares/patologia , Neoplasias Pulmonares/terapia , Prognóstico , Reprodutibilidade dos Testes , Sensibilidade e Especificidade , Transcriptoma , Fluxo de Trabalho
16.
Oncotarget ; 8(29): 47356-47364, 2017 Jul 18.
Artigo em Inglês | MEDLINE | ID: mdl-28537885

RESUMO

A big challenge to clinical diagnosis and therapy of colorectal cancer (CRC) is its extreme heterogeneity, and thus it would be of special importance if we could find common biomarkers besides subtype-specific biomarkers for CRC. Here, with DNA methylation data produced by different laboratories, we firstly revealed that the relative methylation-level orderings (RMOs) of CpG sites within colorectal normal tissues are highly stable but widely disrupted in the CRC tissues. This finding provides the basis for using the RankComp algorithm to identify differentially methylated (DM) CpG sites in every individual CRC sample through comparing the RMOs within the individual sample with the stable RMOs predetermined in normal tissues. For 75 CRC samples, RankComp detected averagely 4,062 DM CpG sites per sample and reached an average precision of 91.34% in terms that the hypermethylation or hypomethylation states of the DM CpG sites detected for each cancer sample were consistent with the observed differences between this cancer sample and its paired adjacent normal sample. Finally, we applied RankComp to identify DM CpG sites for each of the 268 CRC samples from The Cancer Genome Atlas and found 26 and 143 genes whose promoter regions included CpG sites that were hypermethylated and hypomethylated, respectively, in more than 95% of the 268 CRC samples. Individualized pathway analysis identified six pathways that were significantly enriched with DM genes in more than 90% of the CRC tissues. These universal DNA methylation biomarkers could be important diagnostic makers and therapy targets for CRC.


Assuntos
Neoplasias Colorretais/genética , Ilhas de CpG , Metilação de DNA , Epigênese Genética , Regulação Neoplásica da Expressão Gênica , Algoritmos , Biomarcadores Tumorais , Neoplasias Colorretais/mortalidade , Neoplasias Colorretais/patologia , Biologia Computacional/métodos , Perfilação da Expressão Gênica , Humanos , Estimativa de Kaplan-Meier , Prognóstico , Reprodutibilidade dos Testes
17.
Br J Cancer ; 115(12): 1513-1519, 2016 Dec 06.
Artigo em Inglês | MEDLINE | ID: mdl-27855439

RESUMO

BACKGROUND: For lung adenocarcinoma (LUAD) patients receiving platinum-based adjuvant chemotherapy (ACT), predictive signatures extracted from survival data solely are not directly associated with platinum response. Another limitation of reported signatures, commonly based on risk scores summarised from gene expressions, is that they could not be applied directly to samples measured by different laboratories due to experimental batch effects. METHODS: Using 60 samples of LUAD patients receiving platinum-based ACT in TCGA, we pre-selected gene pairs whose within-samples relative expression orderings (REOs) were significantly associated with both pathological response and 5-year survival, from which we selected an optimal signature whose within-samples REOs could identify responders with improved 5-year survival rate. RESULTS: A predictive signature consisting of three gene pairs was developed. In an independent data set integrated from five small data sets, the predicted responders had a significantly higher 5-year survival rate than the predicted non-responders if and only if they received platinum-based ACT (log-rank P=0.0006). The predicted responders showed a 22% absolute benefit of platinum-based ACT in 5-year survival rate compared with untreated patients (log-rank P=0.0019). CONCLUSIONS: The REO-based signature can individually predict response to platinum-based ACT with concordant survival benefit directly for LUAD samples measured by different laboratories.


Assuntos
Adenocarcinoma/tratamento farmacológico , Antineoplásicos/uso terapêutico , Neoplasias Pulmonares/tratamento farmacológico , Compostos Organoplatínicos/uso terapêutico , Adenocarcinoma/genética , Perfilação da Expressão Gênica , Humanos , Neoplasias Pulmonares/genética , Taxa de Sobrevida
18.
Oncotarget ; 7(14): 19060-71, 2016 Apr 05.
Artigo em Inglês | MEDLINE | ID: mdl-26967049

RESUMO

The irreproducibility problem seriously hinders the studies on transcriptional signatures for predicting relapse risk of early stage colorectal cancer (CRC) patients. Through reviewing recently published 34 literatures for the development of CRC prognostic signatures based on gene expression profiles, we revealed a surprising phenomenon that 33 of these studies analyzed CRC samples with and without adjuvant chemotherapy together in the training and/or validation datasets. This data misuse problem could be partially attributed to the unclear and incomplete data annotation in public data sources. Furthermore, all the signatures proposed by these studies were based on risk scores summarized from gene expression levels, which are sensitive to experimental batch effects and risk compositions of the samples analyzed together. To avoid the above-mentioned problems, we carefully selected three qualified large datasets to develop and validate a signature consisting of three pairs of genes. The within-sample relative expression orderings of these gene pairs could robustly predict relapse risk of stage II CRC samples assessed in different laboratories. The transcriptional and functional analyses provided clear evidence that the high risk patients predicted by the proposed signature represent patients with micro-metastases.


Assuntos
Neoplasias Colorretais/genética , Neoplasias Colorretais/patologia , Bases de Dados Genéticas , Conjuntos de Dados como Assunto , Feminino , Perfilação da Expressão Gênica , Humanos , Masculino , Recidiva Local de Neoplasia/genética , Recidiva Local de Neoplasia/patologia , Estadiamento de Neoplasias , Prognóstico , Ativação Transcricional
19.
Mol Carcinog ; 55(3): 292-9, 2016 Mar.
Artigo em Inglês | MEDLINE | ID: mdl-25620657

RESUMO

Autophagy is a process that degrades intracellular constituents, such as long-lived or damaged proteins and organelles, to buffer metabolic stress under starvation conditions. Deregulation of autophagy is involved in the progression of cancer. However, the predictive value of autophagy for breast cancer prognosis remains unclear. First, based on gene expression profiling, we found that autophagy genes were implicated in breast cancer. Then, using the Cox proportional hazard regression model, we detected autophagy prognostic signature for breast cancer in a training dataset. We identified a set of eight autophagy genes (BCL2, BIRC5, EIF4EBP1, ERO1L, FOS, GAPDH, ITPR1 and VEGFA) that were significantly associated with overall survival in breast cancer. The eight autophagy genes were assigned as a autophagy-related prognostic signature for breast cancer. Based on the autophagy-related signature, the training dataset GSE21653 could be classified into high-risk and low-risk subgroups with significantly different survival times (HR = 2.72, 95% CI = (1.91, 3.87); P = 1.37 × 10(-5)). Inactivation of autophagy was associated with shortened survival of breast cancer patients. The prognostic value of the autophagy-related signature was confirmed in the testing dataset GSE3494 (HR = 2.12, 95% CI = (1.48, 3.03); P = 1.65 × 10(-3)) and GSE7390 (HR = 1.76, 95% CI = (1.22, 2.54); P = 9.95 × 10(-4)). Further analysis revealed that the prognostic value of the autophagy signature was independent of known clinical prognostic factors, including age, tumor size, grade, estrogen receptor status, progesterone receptor status, ERBB2 status, lymph node status and TP53 mutation status. Finally, we demonstrated that the autophagy signature could also predict distant metastasis-free survival for breast cancer.


Assuntos
Autofagia , Neoplasias da Mama/diagnóstico , Neoplasias da Mama/genética , Mama/patologia , Perfilação da Expressão Gênica , Biomarcadores Tumorais/genética , Mama/metabolismo , Intervalo Livre de Doença , Feminino , Humanos , Pessoa de Meia-Idade , Mutação , Prognóstico , Modelos de Riscos Proporcionais , Análise de Sobrevida , Proteína Supressora de Tumor p53/genética
20.
Brief Bioinform ; 17(2): 233-42, 2016 Mar.
Artigo em Inglês | MEDLINE | ID: mdl-26254430

RESUMO

Most of current gene expression signatures for cancer prognosis are based on risk scores, usually calculated as some summaries of expression levels of the signature genes, whose applications require presetting risk score thresholds and data normalization. In this study, we demonstrate the critical limitations of such type of signatures that the risk scores of samples will change greatly when they are normalized together with different samples, which would induce spurious risk classification and difficulty in clinical settings, and the risk scores of independent samples are incomparable if data normalization is not adopted. To overcome these limitations, we propose a rank-based method to extract a prognostic gene pair signature for overall survival of stage I non-small-cell lung cancer. The prognostic gene pair signature is verified in three integrated data sets detected by different laboratories with different microarray platforms. We conclude that, different from the type of signatures based on risk scores summarized from gene expression levels, the rank-based signatures could be robustly applied at the individualized level to independent clinical samples assessed in different laboratories.


Assuntos
Algoritmos , Biomarcadores Tumorais/genética , Carcinoma Pulmonar de Células não Pequenas/mortalidade , Carcinoma Pulmonar de Células não Pequenas/cirurgia , Neoplasias Pulmonares/patologia , Neoplasias Pulmonares/cirurgia , Carcinoma Pulmonar de Células não Pequenas/genética , Perfilação da Expressão Gênica/métodos , Predisposição Genética para Doença/epidemiologia , Predisposição Genética para Doença/genética , Humanos , Estadiamento de Neoplasias , Prevalência , Prognóstico , Reprodutibilidade dos Testes , Medição de Risco/métodos , Sensibilidade e Especificidade , Análise de Sobrevida , Resultado do Tratamento
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