Using microarrays to study the microenvironment in tumor biology: The crucial role of statistics

Baker, Stuart G.; Kramer, Barnett S.

doi:10.1016/j.semcancer.2008.03.001

Cited by 10 publications

(4 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…One could argue that this is an inefficient strategy because use of convenience samples fosters discovery of false leads that go to validation thereby wasting precious resources. At the same time small convenience sets miss potentially useful markers (11,16). An alternative strategy would use well designed PRoBE sets for discovery research and convenience sets, if necessary, for verification purposes before embarking on a fully fledged validation phase of research.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Improving the Quality of Biomarker Discovery Research: The Right Samples and Enough of Them

Pepe

Feng

2015

Cancer Epidemiology, Biomarkers &Amp; Prevention

View full text Add to dashboard Cite

Background Biomarker discovery research has yielded few biomarkers that validate for clinical use. A contributing factor may be poor study designs. Methods The goal in discovery research is to identify a subset of potentially useful markers from a large set of candidates assayed on case and control samples. We recommend the PRoBE design for selecting samples. We propose sample size calculations that require specifying: (i) a definition for biomarker performance; (ii) the proportion of useful markers the study should identify (Discovery Power); and (iii) the tolerable number of useless markers amongst those identified (False Leads Expected, FLE). Results We apply the methodology to a study of 9,000 candidate biomarkers for risk of colon cancer recurrence where a useful biomarker has positive predictive value ≥30%. We find that 40 patients with recurrence and 160 without recurrence suffice to filter out 98% of useless markers (2% FLE) while identifying 95% of useful biomarkers (95% Discovery Power). Alternative methods for sample size calculation required more assumptions. Conclusions Biomarker discovery research should utilize quality biospecimen repositories and include sample sizes that enable markers meeting prespecified performance characteristics for well-defined clinical applications to be identified. Impact The scientific rigor of discovery research should be improved.

show abstract

Section: Discussionmentioning

confidence: 99%

“…We contrast our approach with other approaches that are both more complex and less robust (4-8). Finally, we emphasize some of the most important points already made by others (1, 3, 5, 9-11) about tying the design of the discovery study to the clinical application of interest.…”

Section: Introductionmentioning

confidence: 86%

Improving the Quality of Biomarker Discovery Research: The Right Samples and Enough of Them

Pepe

Feng

2015

Cancer Epidemiology, Biomarkers &Amp; Prevention

View full text Add to dashboard Cite

show abstract

“…Having a first-degree relative with PC increases the risk of developing the disease by 2.5-fold, and the risk increases additionally if the relative has early-onset of PC or if multiple first-degree relatives are diagnosed with PC [4]. Family history and twin studies support the notion that heredity plays an important role in PC, and observations of clustering in certain families gave rise to the definition of hereditary prostate cancer (HPC) [5][6][7][8] HPC does not seem to differ clinico-pathologically from the sporadic form except that disease presentation has been reported to be 6 -7 years earlier [5,9,10].…”

Section: Introductionmentioning

confidence: 98%

Clinico-Pathological Characterization of Hereditary, Familial and Sporadic Prostate Cancer

Bentzon¹,

Lynnerup²,

Borre³

2012

OJU

View full text Add to dashboard Cite

Aim: To characterize familial prostate cancer including hereditary prostate cancer and assess the disease-free survival following radical prostatectomy. Methods: A self-administered written questionnaire was forwarded to 709 prostatectomized patients from the Aarhus Prostate Cancer Study containing questions about cases of prostate cancer (PC), age at diagnosis, vital status, and age at death for all first-degree relatives. Patients were then divided into groups according to their family history: hereditary prostate cancer (HPC), familial prostate cancer (FPC), and sporadic prostate cancer (SPC) groups. The information from a subset of both FPC (n = 17) and SPC (n = 17) groups were validated in the Danish Cancer Register and the Civil Registration System. Between groups, we described the association of age, prostatespecific antigen (PSA), postoperative Gleason score and T Stage. A Kaplan-Meier curve demonstrated postoperative disease-free survival in each group. Results: The response rate was 81% (574/709). About 21% of the patients were categorized in the FPC group, of which 7% were identified as having HPC. The median follow-up time was 63 months for HPC, 65 months for FPC and 88 months for SPC. Overall, there was no significant difference between groups in clinical features and disease-free survival except that patients with HPC were significantly associated with younger age than sporadic cases (p = 0.02). The proportion of self-reported PC diagnoses confirmed in the cancer register was 27.8%. The index persons with SPC reported no PC in first-degree relatives and none was found the cancer register. Conclusion: Overall, we found no difference in clinical characteristics and survival, following radical prostatectomy except that patients with HPC were younger at diagnosis. These results are in line with previously reported data.

show abstract

“…Moreover, the experiments are often affected by numerous factors that can lead to unwanted, random or systematic (non-biological), variation (Chua, 2006). To obtain reliable results the collected data have to be normalized and analyzed using proper statistical methods (Yang et al, 2001;Quackenbush, 2002;Simon et al, 2007;Ness, 2007;Baker, 2008). Due to the wider and wider application of microarray-based techniques, a broad spectrum of programs devoted to microarray data analysis is available.…”

Section: Introductionmentioning

confidence: 99%

Impact of DNA microarray data transformation on gene expression analysis - comparison of two normalization methods.

et al. 2011

View full text Add to dashboard Cite

Two-color DNA microarrays are commonly used for the analysis of global gene expression. They provide information on relative abundance of thousands of mRNAs. However, the generated data need to be normalized to minimize systematic variations so that biologically significant differences can be more easily identified. A large number of normalization procedures have been proposed and many softwares for microarray data analysis are available. Here, we have applied two normalization methods (median and loess) from two packages of microarray data analysis softwares. They were examined using a sample data set. We found that the number of genes identified as differentially expressed varied significantly depending on the method applied. The obtained results, i.e. lists of differentially expressed genes, were consistent only when we used median normalization methods. Loess normalization implemented in the two software packages provided less coherent and for some probes even contradictory results. In general, our results provide an additional piece of evidence that the normalization method can profoundly influence final results of DNA microarray-based analysis. The impact of the normalization method depends greatly on the algorithm employed. Consequently, the normalization procedure must be carefully considered and optimized for each individual data set.

show abstract

Using microarrays to study the microenvironment in tumor biology: The crucial role of statistics

Cited by 10 publications

References 24 publications

Improving the Quality of Biomarker Discovery Research: The Right Samples and Enough of Them

Improving the Quality of Biomarker Discovery Research: The Right Samples and Enough of Them

Clinico-Pathological Characterization of Hereditary, Familial and Sporadic Prostate Cancer

Impact of DNA microarray data transformation on gene expression analysis - comparison of two normalization methods.

Contact Info

Product

Resources

About