**3.3 Sample handling and logistics – Barcoding and annotation**

Clinical studies need the support of large numbers of samples to confirm the efficacy and safety of a drug. With the expanded usage of biomarkers in clinical trials, even more samples and patients may be needed to fully discover the population that will best be served by a given therapy. One clinical collection set can consist of as little as one sample or up to potentially 100 samples from a single patient in one day. The number of samples needed to generate statistically significant data will number in the tens of thousands across the different stages of a clinical trial. Clinical trial involvement necessitates scrupulous tracking of many details about each sample. Historically, this was all done on paper, but with increasing computing power and usage, tracking of the samples can be more effectively done by utilizing well built database systems. Effective use of computers also increases the option of analyzing samples across multiple trials, including the option of comparing biomarkers for a more customized treatment approach. To accomplish this, companies are relying on electronic data capture such as LIMS (Laboratory Information Management system), EMR (Electronic Medical Records) or CTMS (Clinical Trial Management System) and barcodes on individual samples (Burczynski et al., 2005; B. Choi et al., 2005; Niland & Rouse, 2010).

There is more than one approach towards connecting the annotation about a sample and an identifier on the sample container. Some systems rely on human readable text on the labels to tell the person handling the container what should be in it. There is the potential for error when depending on a human to read or type (Turner et al., 2003). Sometimes these labels with text also have a barcode on them. This type of barcoding system is referred to as an intelligent barcode system, only because there is specific sample information, other than the barcode, on the label. Other systems make full use of contemporary technology to track samples (naïve barcodes). With the use of the naïve barcode system, the sample collector needs to be able to associate the sample with a related database. This can be done by the collector writing on a piece of paper, which is then entered into the database at a later time by a data entry clerk. Alternatively, technology may be fully leveraged by supplying the collection sites with barcode readers, and access to the appropriate database, to associate the barcode on the container, with the given patient ID.

There are pros and cons for each of these barcoding methods. Having an intelligent barcode (pre-association of barcode with patient ID/time point) means that the person doing the collection needs only to find the correct label for the given sample, as the time point information should already be tracked in a database. If the labels are printed in a sequential fashion, then this may be simple. The con to this system is that if for some reason the correct label cannot be found, there is not usually a means to associate a new label with the sample.

site to a separate processing facility. FedEx pioneered the idea of hub shipments and overnight travel, but others have adopted and emulated their practices. Some couriers will replenish dry ice on shipments traveling more than 24 hours (World Courier). Coupled with this is the need for the initial shipper to pack the samples in such a fashion that they will be held at the correct temperature for at least 24 hours. Written or web based guidance should be given to all collection sites with explicit details as to size of shipping containers and

Clinical studies need the support of large numbers of samples to confirm the efficacy and safety of a drug. With the expanded usage of biomarkers in clinical trials, even more samples and patients may be needed to fully discover the population that will best be served by a given therapy. One clinical collection set can consist of as little as one sample or up to potentially 100 samples from a single patient in one day. The number of samples needed to generate statistically significant data will number in the tens of thousands across the different stages of a clinical trial. Clinical trial involvement necessitates scrupulous tracking of many details about each sample. Historically, this was all done on paper, but with increasing computing power and usage, tracking of the samples can be more effectively done by utilizing well built database systems. Effective use of computers also increases the option of analyzing samples across multiple trials, including the option of comparing biomarkers for a more customized treatment approach. To accomplish this, companies are relying on electronic data capture such as LIMS (Laboratory Information Management system), EMR (Electronic Medical Records) or CTMS (Clinical Trial Management System) and barcodes on individual samples (Burczynski et al., 2005; B. Choi

There is more than one approach towards connecting the annotation about a sample and an identifier on the sample container. Some systems rely on human readable text on the labels to tell the person handling the container what should be in it. There is the potential for error when depending on a human to read or type (Turner et al., 2003). Sometimes these labels with text also have a barcode on them. This type of barcoding system is referred to as an intelligent barcode system, only because there is specific sample information, other than the barcode, on the label. Other systems make full use of contemporary technology to track samples (naïve barcodes). With the use of the naïve barcode system, the sample collector needs to be able to associate the sample with a related database. This can be done by the collector writing on a piece of paper, which is then entered into the database at a later time by a data entry clerk. Alternatively, technology may be fully leveraged by supplying the collection sites with barcode readers, and access to the appropriate database, to associate the

There are pros and cons for each of these barcoding methods. Having an intelligent barcode (pre-association of barcode with patient ID/time point) means that the person doing the collection needs only to find the correct label for the given sample, as the time point information should already be tracked in a database. If the labels are printed in a sequential fashion, then this may be simple. The con to this system is that if for some reason the correct label cannot be found, there is not usually a means to associate a new label with the sample.

amount of dry ice to use to ensure safe passage of the samples.

et al., 2005; Niland & Rouse, 2010).

barcode on the container, with the given patient ID.

**3.3 Sample handling and logistics – Barcoding and annotation** 

Generally, projects that use this kind of labeling do not have any computer connection from the collection sites to the database storing the sample information. Before the advent of ubiquitous computers and hand held devices, associating the sample label information to a matching piece of paper seemed an effective way to track samples.

The major drawback with the naïve barcode system (barcoded tubes that are associated at the point of collection with the sample) is that if the association of sample to barcode is not made by the collection site, then the container is just a tube of tissue, useless for further study. To effectively use the naïve barcode, sites benefit from having access to the database while collecting samples. This can be as simple as barcode scanners that allow some amount of data entry. In some instances, double barcode labels can be supplied to the sites, one is affixed to the form and one is placed on the tube, with the association in to the database to be made later.

One method of association, which is a compromise between the intelligent barcode method and the naïve barcode method, is done by associating barcoded containers into a kit at a central laboratory assembly site. Then the kits are shipped to various collection sites. As the kit leaves the facility, the internal containers are still a naïve barcoded container, however at this point, they are associated with a tube type and a destination, all of this information is tracked at a the central laboratory, not on the containers. At the collection site, the kit is associated to a patient. This reduces the amount of data entry needed. The practice of associating the kit barcode at the site of collection to the patient ID allows some flexibility, while still allowing tracking of the tubes within the kits to be organized. This method ensures the highest quality association between a given sample and the donor.

In addition, given the current increase of hand held scanners with WiFi access, immediate computer access is no longer a large barrier. Car rental agencies and store inventory systems have been using portable scanners to track inventory for decades; similarly, it isn't too difficult to adopt similar technology for use in clinical trial data collection. The New York subway system integrates data from barcoded tickets, generated from identified machines, all with customer anonymity, to track where passenger flow is most active. There are some groups who have started to study the benefits of this type of live data association in studies involving human donors or patients (Avilés et al., 2008). While it is not essential for the sites to have computer access, as the paper trail of requisition forms is still common,, instant computer contact by the collection site does make the tracking easier. Handwriting barcodes and manual association outside of the database defeats the efficiency of the naïve barcode system, although downstream sample processing can make use of the barcoding system if there is a barcode and the association is made to the patient identifier.

In addition, there is an added benefit of naïve barcodes for double blind studies. Double blind studies mask the sample identity, including patient and treatment information. This is to prevent bias in the study and to protect the identity of the study patients. In the past a double tier system of identification numbers would cryptically hide the patient information from those involved in the collection or the analysis of the study. Only a select few would have access to source information about both the patient and drug information. Unique barcodes on the container, without any study information on the label, can provide a double blind labeling system, as long as the sample is always tracked in the LIMS system.

Novel Tissue Types for the Development of Genomic Biomarkers 285

Baraniskin, A., Kuhnhenn, J., Schlegel, U., Chan, A., Deckert, M., Gold, R., Maghnouj, A.,

Bosma, A. J., Weigelt, B., Lambrechts, A. C., Verhagen, O. J. H. M., Rodenhuis, S., & Veer, L.

Broemeling, D. J., Pel, J., Gunn, D. C., Mai, L., Thompson, J. D., Poon, H., & Marziali, A.

Burczynski, M. E., Oestreicher, J. L., Cahilly, M. J., Mounts, D. P., Whitley, M. Z., Speicher, L.

Caldas, C., Hahn, S. A., Hruban, R. H., Hahn, A., Redston, M. S., Yeo, J., & Kern, S. E. (1994).

Adenocarcinoma and Pancreatic Ductal Hyperplasia. *Cancer*, 3568-3573. Camidge, D. R., Pemberton, M. N., Growcott, J. W., Johnstone, D., Laud, P. J., Foster, J. R.,

biomarkers in drug development. *British Journal of Cancer*, *93*(2), 208-15. Campbell, K. L., & Rockett, J. C. (2006). Biomarkers of ovulation, endometrial receptivity,

Chai, V., Vassilakos, A., Lee, Y., Wright, J. A., & Young, A. H. (2005). Optimization of the

Chapkin, R. S., Zhao, C., Ivanov, I., Davidson, L. A., Goldsby, J. S., Lupton, J.R., Mathai, R.

Chen, X., Ba, Y., Ma, L., Cai, X., Yin, Y., Wang, K., Guo, J., Zhang, Y., Chen, J., Guo, X., Li, Q.,

samples. *Journal of Clinical Laboratory Analysis*, *19*(5), 182–188.

Expression of Marker Genes. *Clinical Cancer Research*, 1871-1877.

*Biomarkers & Prevention, 19*(3), 794-8.

*13*(1), 40-48.

*Epidemiology*, *20 Suppl 1*, 13-25.

102.

A., & Rubio, J. P. (2010). Saliva-derived DNA performs well in large-scale, highdensity single-nucleotide polymorphism microarray studies. *Cancer Epidemiology,* 

Zöllner, H., Reinacher-Schick, A., Schmiegel, W., Hahn, S.A., Schroers, R. (2011). Identification of microRNAs in the cerebrospinal fluid as marker for primary diffuse large B-cell lymphoma of the central nervous system. *Blood*, *117*(11), 3140-6.

J. V. (2002). Detection of Circulating Breast Tumor Cells by Differential Expression of Marker Genes Detection of Circulating Breast Tumor Cells by Differential

(2008). An instrument for automated purification of nucleic acids from contaminated forensic samples. *Journal of the Association for Laboratory Automation*,

A., & Trepicchio, W. L. (2005). Clinical pharmacogenomics and transcriptional profiling in early phase oncology clinical trials. *Current Molecular Medicine*, *5*(1), 83-

Detection of K-ras Mutations in the Stool of Patients with Pancreatic

Randall, K. J., et al. (2005). Assessing proliferation, cell-cycle arrest and apoptotic end points in human buccal punch biopsies for use as pharmacodynamic

fertilisation, implantation and early pregnancy progression. *Paediatric and Perinatal* 

PAXgeneTM blood RNA extraction system for gene expression analysis of clinical

A., et al. (2010). Noninvasive stool-based detection of infant gastrointestinal development using gene expression profiles from exfoliated epithelial cells. *American Journal of Physiology. Gastrointestinal and Liver Physiology*, *298*(5), G582-9. Chen, P. C., Tsai, M. H., Yip, S., Jou, Y. C., Ng, C. F., Chen, Y., Wang, X., Huang, W., Tung,

C. L., Chen, G. C., Huang, M. M., Tong, J. H., Song, E. J., Chang, D. C., Hsu, C. D., To, K. F., Shen, C. H., & Chan, M. W. (2011). Distinct DNA methylation epigenotypes in bladder cancer from different Chinese sub-populations and its implication in cancer detection using voided urine. *BMC Medical Genomics*, *4*(1), 45.

Li, X., Wang, W., Zhang, Y., Wang, J., Jiang, X., Xiang, Y., Xu, C., Zheng, P., Zhang, J., Li, R., Zhang, H., Shang, X., Gong, T., Ning, G., Wang, J., Zen, K., Zhang, J., & Zhang, C. Y. (2008). Characterization of microRNAs in serum: a novel class of
