Browse
Submit Data
Databases
API
Help

Dataset Information

42 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Prognostic Cancer Gene Expression Signatures: Current Status and Challenges.

ABSTRACT: Current staging systems of cancer are mainly based on the anatomical extent of disease. They need refinement by biological parameters to improve stratification of patients for tumor therapy or surveillance strategies. Thanks to developments in genomic, transcriptomic, and big-data technologies, we are now able to explore molecular characteristics of tumors in detail and determine their clinical relevance. This has led to numerous prognostic and predictive gene expression signatures that have the potential to establish a classification of tumor subgroups by biological determinants. However, only a few gene signatures have reached the stage of clinical implementation so far. In this review article, we summarize the current status, and present and future challenges of prognostic gene signatures in three relevant cancer entities: breast cancer, colorectal cancer, and hepatocellular carcinoma.

SUBMITTER: Qian Y

PROVIDER: S-EPMC8000474 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Prognostic Cancer Gene Expression Signatures: Current Status and Challenges.

Qian Yuquan Y Daza Jimmy J Itzel Timo T Betge Johannes J Zhan Tianzuo T Marmé Frederik F Teufel Andreas A

Cells 20210315 3

Current staging systems of cancer are mainly based on the anatomical extent of disease. They need refinement by biological parameters to improve stratification of patients for tumor therapy or surveillance strategies. Thanks to developments in genomic, transcriptomic, and big-data technologies, we are now able to explore molecular characteristics of tumors in detail and determine their clinical relevance. This has led to numerous prognostic and predictive gene expression signatures that have the ...[more]

PMID: 33804045

Similar Datasets

Comparison of prognostic gene expression signatures for breast cancer.

Project description:BACKGROUND: During the last years, several groups have identified prognostic gene expression signatures with apparently similar performances. However, signatures were never compared on an independent population of untreated breast cancer patients, where risk assessment was computed using the original algorithms and microarray platforms. RESULTS: We compared three gene expression signatures, the 70-gene, the 76-gene and the Gene expression Grade Index (GGI) signatures, in terms of predicting distant metastasis free survival (DMFS) for the individual patient. To this end, we used the previously published TRANSBIG independent validation series of node-negative untreated primary breast cancer patients. We observed agreement in prediction for 135 of 198 patients (68%) when considering the three signatures. When comparing the signatures two by two, the agreement in prediction was 71% for the 70- and 76-gene signatures, 76% for the 76-gene signature and the GGI, and 88% for the 70-gene signature and the GGI. The three signatures had similar capabilities of predicting DMFS and added significant prognostic information to that provided by the classical parameters. CONCLUSION: Despite the difference in development of these signatures and the limited overlap in gene identity, they showed similar prognostic performance, adding to the growing evidence that these prognostic signatures are of clinical relevance.

| S-EPMC2533026 | biostudies-literature

Gene expression signatures for colorectal cancer microsatellite status and HNPCC.

Project description:The majority of microsatellite instable (MSI) colorectal cancers are sporadic, but a subset belongs to the syndrome hereditary non-polyposis colorectal cancer (HNPCC). Microsatellite instability is caused by dysfunction of the mismatch repair (MMR) system that leads to a mutator phenotype, and MSI is correlated to prognosis and response to chemotherapy. Gene expression signatures as predictive markers are being developed for many cancers, and the identification of a signature for MMR deficiency would be of interest both clinically and biologically. To address this issue, we profiled the gene expression of 101 stage II and III colorectal cancers (34 MSI, 67 microsatellite stable (MSS)) using high-density oligonucleotide microarrays. From these data, we constructed a nine-gene signature capable of separating the mismatch repair proficient and deficient tumours. Subsequently, we demonstrated the robustness of the signature by transferring it to a real-time RT-PCR platform. Using this platform, the signature was validated on an independent test set consisting of 47 tumours (10 MSI, 37 MSS), of which 45 were correctly classified. In a second step, we constructed a signature capable of separating MMR-deficient tumours into sporadic MSI and HNPCC cases, and validated this by a mathematical cross-validation approach. The demonstration that this two-step classification approach can identify MSI as well as HNPCC cases merits further gene expression studies to identify prognostic signatures.

| S-EPMC2361815 | biostudies-literature

Oligometastatic Prostate Cancer: Current Status and Future Challenges.

Project description:In accordance with the spectrum theory of metastatic disease, an oligometastatic clinical state has been proposed as an intermediary step along the natural history of cancer with few (typically 1-3) metastatic lesions identifiable on imaging that may be amenable to metastasis-directed therapy. Effective therapy of oligometastatic disease is anticipated to impact cancer evolution by delaying progression and improving patient outcome at a minimal or acceptable cost of toxicity. There has been increasing recognition of oligometastatic disease in prostate cancer with the advent of new-generation imaging agents, most notably the recently approved PET radiotracers based on targeting prostate-specific membrane antigen. Early clinical trials with metastasis-directed therapy of oligometastases have provided evidence for delaying the employment of systematic therapy and improving outcome in selected patients. Despite these encouraging results, much needs to be investigated and learned about the underlying biology of the oligometastatic state along the evolutionary clinical course of prostate cancer, the identification of relevant imaging and nonimaging predictive and prognostic biomarkers, and the development of treatment strategies to optimize short-term and long-term patient outcome. We provide a review of the current status and the lingering challenges of this rapidly evolving clinical space in prostate cancer.

| S-EPMC9635685 | biostudies-literature

Gene expression-based prognostic signatures in lung cancer: ready for clinical use?

Project description:A substantial number of studies have reported the development of gene expression-based prognostic signatures for lung cancer. The ultimate aim of such studies should be the development of well-validated clinically useful prognostic signatures that improve therapeutic decision making beyond current practice standards. We critically reviewed published studies reporting the development of gene expression-based prognostic signatures for non-small cell lung cancer to assess the progress made toward this objective. Studies published between January 1, 2002, and February 28, 2009, were identified through a PubMed search. Following hand-screening of abstracts of the identified articles, 16 were selected as relevant. Those publications were evaluated in detail for appropriateness of the study design, statistical validation of the prognostic signature on independent datasets, presentation of results in an unbiased manner, and demonstration of medical utility for the new signature beyond that obtained using existing treatment guidelines. Based on this review, we found little evidence that any of the reported gene expression signatures are ready for clinical application. We also found serious problems in the design and analysis of many of the studies. We suggest a set of guidelines to aid the design, analysis, and evaluation of prognostic signature studies. These guidelines emphasize the importance of focused study planning to address specific medically important questions and the use of unbiased analysis methods to evaluate whether the resulting signatures provide evidence of medical utility beyond standard of care-based prognostic factors.

| S-EPMC2902824 | biostudies-literature

Building Prognostic Models for Breast Cancer Patients Using Clinical Variables and Gene Expression Signatures

Project description:Our findings indicate that the integration of expression signatures and clinicopathological factors can better determine the individual risk of recurrence for newly diagnosed patients with lymph-node negative ER-positive breast cancer. Models incorporating other variables yet to be discovered will be needed to obtain robust prognostic models for ER-negative and HER2-positive breast cancer patients. A large data set was created by combining five different publicly available microarray datasets of node-negative breast cancer patients treated with local therapy only. The microarray gene expression data was combined using the batch effect adjustment by the Distance Weighted Discrimination method.

| S-ECPF-GEOD-15393 | biostudies-other

Prognostic gene expression signatures of breast cancer are lacking a sensible biological meaning.

Project description:The identification of prognostic biomarkers for predicting cancer progression is an important problem for two reasons. First, such biomarkers find practical application in a clinical context for the treatment of patients. Second, interrogation of the biomarkers themselves is assumed to lead to novel insights of disease mechanisms and the underlying molecular processes that cause the pathological behavior. For breast cancer, many signatures based on gene expression values have been reported to be associated with overall survival. Consequently, such signatures have been used for suggesting biological explanations of breast cancer and drug mechanisms. In this paper, we demonstrate for a large number of breast cancer signatures that such an implication is not justified. Our approach eliminates systematically all traces of biological meaning of signature genes and shows that among the remaining genes, surrogate gene sets can be formed with indistinguishable prognostic prediction capabilities and opposite biological meaning. Hence, our results demonstrate that none of the studied signatures has a sensible biological interpretation or meaning with respect to disease etiology. Overall, this shows that prognostic signatures are black-box models with sensible predictions of breast cancer outcome but no value for revealing causal connections. Furthermore, we show that the number of such surrogate gene sets is not small but very large.

| S-EPMC7794581 | biostudies-literature

Prognostic stromal gene signatures in breast cancer.

Project description:Global gene expression analysis of tumor samples has been a valuable tool to subgroup tumors and has the potential to be of prognostic and predictive value. However, tumors are heterogeneous, and homogenates will consist of several different cell types. This study was designed to obtain more refined expression data representing different compartments of the tumor.Formalin-fixed paraffin-embedded stroma-rich triple-negative breast cancer tumors were laser-microdissected, and RNA was extracted and processed to enable microarray hybridization. Genes enriched in stroma were identified and used to generate signatures by identifying correlating genes in publicly available data sets. The prognostic implications of the signature were analyzed.Comparison of the expression pattern from stromal and cancer cell compartments from three tumors revealed a number of genes that were essentially specifically expressed in the respective compartments. The stroma-specific genes indicated contribution from fibroblasts, endothelial cells, and immune/inflammatory cells. The gene set was expanded by identifying correlating mRNAs using breast cancer mRNA expression data from The Cancer Genome Atlas. By iterative analyses, 16 gene signatures of highly correlating genes were characterized. Based on the gene composition, they seem to represent different cell types. In multivariate Cox proportional hazard models, two immune/inflammatory signatures had opposing hazard ratios for breast cancer recurrence also after adjusting for clinicopathological variables and molecular subgroup. The signature associated with poor prognosis consisted mainly of C1Q genes and the one associated with good prognosis contained HLA genes. This association with prognosis was seen for other cancers as well as in other breast cancer data sets.Our data indicate that the molecular composition of the immune response in a tumor may be a powerful predictor of cancer prognosis.

| S-EPMC4360948 | biostudies-literature

Evaluation of frozen tissue-derived prognostic gene expression signatures in FFPE colorectal cancer samples.

Project description:Defining molecular features that can predict the recurrence of colorectal cancer (CRC) for stage II-III patients remains challenging in cancer research. Most available clinical samples are Formalin-Fixed, Paraffin-Embedded (FFPE). NanoString nCounter® and Affymetrix GeneChip® Human Transcriptome Array 2.0 (HTA) are the two platforms marketed for high-throughput gene expression profiling for FFPE samples. In this study, to evaluate the gene expression of frozen tissue-derived prognostic signatures in FFPE CRC samples, we evaluated the expression of 516 genes from published frozen tissue-derived prognostic signatures in 42 FFPE CRC samples measured by both platforms. Based on HTA platform-derived data, we identified both gene (99 individual genes, FDR < 0.05) and gene set (four of the six reported multi-gene signatures with sufficient information for evaluation, P < 0.05) expression differences associated with survival outcomes. Using nCounter platform-derived data, one of the six multi-gene signatures (P < 0.05) but no individual gene was associated with survival outcomes. Our study indicated that sufficiently high quality RNA could be obtained from FFPE tumor tissues to detect frozen tissue-derived prognostic gene expression signatures for CRC patients.

| S-EPMC5021945 | biostudies-literature

Maternal vaccination-current status, challenges, and opportunities.

Project description:AimMaternal vaccination is a promising strategy for protecting pregnant women and newborns against severe infections. This review aims to describe the current status and challenges associated with maternal vaccination against seasonal influenza, tetanus-diphtheria-pertussis (Tdap/DTaP), and novel coronavirus disease of 2019 (COVID-19) in Japan and other countries, mainly the United States and the United Kingdom.MethodsA literature search was conducted in PubMed and other public websites (e.g., Centers for Disease Control and Prevention) to obtain information on maternal vaccination.ResultsInactivated vaccines are recommended for pregnant women by gynecologic societies in Japan, the United States, and the United Kingdom. Among pregnant Japanese women, the influenza and COVID-19 (two doses) vaccine coverage rates were 27.0%-53.5% (six studies) and 73.6% (one study), respectively; there are no studies on maternal vaccination with DTaP. Concerns regarding vaccine safety are a major barrier to maternal vaccination across countries. Maternal vaccination is effective in preventing severe disease in pregnant women and protecting infants aged <6 months, is generally safe, and does not increase the risk of adverse maternal and fetal outcomes. Providing accurate information regarding vaccination through healthcare providers and the government and government funding for vaccines may help improve maternal vaccination rates in Japan.ConclusionCurrent coverage for maternal vaccination is still low globally mainly because of vaccine hesitancy among pregnant women. The government, drug-regulatory authorities, and healthcare professionals must educate pregnant women about the effectiveness and safety of maternal vaccines and encourage vaccination when the benefits outweigh the risks.

| S-EPMC10100318 | biostudies-literature

The eukaryotic ribosome: current status and challenges.

Project description:Despite having been identified first, their greater degree of complexity has resulted in our understanding of eukaryotic ribosomes lagging behind that of their bacterial and archaeal counterparts. A much more complicated biogenesis program results in ribosomes that are structurally, biochemically, and functionally more complex. However, recent advances in molecular genetics and structural biology are helping to reveal the intricacies of the eukaryotic ribosome and to address many longstanding questions regarding its many roles in the regulation of gene expression.

| S-EPMC2673243 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data