Thoughts on the Future of Dx

Workflow, procedures and organization of diagnostic laboratories have changed little since the end of the 19th century. Technology improved quality and safety, lead to higher throughput and allowed private electronic access to patients' lab test results - at least partially, but other than that not much has changed.

The introduction of first generation transcriptome technologies in the mid 1990s (Schena et al., 1995; DeRisi et al., 1996) has led to a phenomenal ability to simultaneously measure thousands of genes, create molecular profiles of cancers, all other diseases and conditions.

Proteomics - coined a couple of years later (James, 1997) - was accepted as an even more promising technique for effective diagnostics of diseases. Figure on the left, however, demonstrates that it too faces many challenges from discovery of biomarkers to their verification and approval. After more than a decade (Oliver et al., 1998), metabolomics has been accepted as - at least - an equally promising technique, but it still lagging behin genomics and proteomics.

All these techniques will have significant impact on the business model of diagnostics. Multiplexing (measuring multiple biomarkers at once) is obviously much more cost-effective. Diagnostics industry is historically very resistant to disruptive technological change, but potential cost advantages should outweigh this, leading to novel business models in health management.

A change will also come from the growing near patient testing (NPT) sector. NPT is already finding a role in wellness monitoring. Existing self tests - such as cholesterol kits - may not be very accurate, but with the advent of inexpensive multiplexing assays this will be overcome. Even when blood testing is done by trained professionals in a lab, there can be significant variability in test results. Same applies to blood pressure measurements - you may need to do three measurements per day for five days in order to get a decent baseline. This only justifies the need to have inexpensive tests that can be done more often.

But lets go back to metabolomics and its potential to provide noninvasive inexpensive diagnostics. Are there any clinical trials attempting to translate it into clinical practice?
Here are our favorite ones:

CANCER
NCT00757952: Diagnosing ovarian cancer in exhaled breath. (Pine Street Foundation & University of Maine)
NCT00898209: Diagnosing Lung cancer in exhaled breath. (Vanderbilt-Ingram Cancer Center)
NCT00898209: Exhaled breath analyzed for lung cancer. (Vanderbilt-Ingram Cancer Center)
NCT00639067: Breath test for early detection of lung cancer (Menssana Research)
NCT00873366: Breath tests to access effectiveness of breast cancer treatment (Mayo Clinic and National Cancer Institute (NCI))

OTHER
NCT00330603: Metabolomic breath analysis to predict treatment for chronic cough (University of Virginia)
NCT00632307: Breath analysis to diagnose COPD; lung cancer; airway infection; interstitial lung disease, sleep apnea; pulmonary disorders with pleural infusions; sarcoidosis (Lung Clinic Hemer, Germany)
NCT00294489: Breath analysis to diagnose Hepatitis C (Hadassah Medical Organization, Jerusalem, Israel)


References
Schena M, Shalon D, Davis RW, Brown PO 1995 Quantitative monitoring of gene expression patterns with complementary DNA microarray. Science 270 : 467 –470[Abstract/Free Full Text]

DeRisi J, Penland L, Brown PO, Bittner ML, Meltzer PS, Ray M, Chen Y, Su YA 1996 Use of a cDNA microarray to analyze gene expression patterns in human cancer. Nat Genet 14 : 457 –460[CrossRef][Medline]

James P 1997 Protein identification in the post-genome era: the rapid rise of proteomics.". Quarterly reviews of biophysics 30 (4): 279–331. doi:10.1017/S0033583597003399. PMID 9634650.

Oliver SG, Winson MK, Kell DB, Baganz F. 1998. Systematic functional analysis of the yeast genome. Trends in Biotechnology 16: 373-378.

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