By Kevin E. Noonan —

A large part of the debate on patenting genetic diagnostic method and isolated genes has revolved around the effects of such patents on what is loosely termed "personalized medicine."  Personalized medicine can be summarized as a dream/holy grail/GATTACA future of universal genetic information — every infant having her genomic DNA sequence determined at birth and contained on a medical identity card, to determine what diseases she may get, what drugs she should not and even who she should or should not marry (or at least mate with).  It is the most recent of the promises of the biotechnology or genetic revolution, made possible (in theory) by the fruits of the Human Genome Project; advances in genomic sequencing technology (as described in The $1,000 Genome and elsewhere) have quickened the expectations surrounding the technology.  But many have begun to wonder why this genetic fruit is taking so long to ripen (see, for example, "How Bright Promise of Genetic Testing Fell Apart"), and a recent study published in Science Translational Medicine may provide some clues.

The article, entitled "The Predictive Capacity of Personal Genomic Sequencing," was published on April 2, 2012 by Nicholas Roberts, Kenneth Kinzler, Bert Vogelstein, and Victor Velculescu from the Howard Hughes Medical Institute at Johns Hopkins University Kimmel Cancer Center; Joshua Vogelstein from the Department of Neuroscience, Johns Hopkins University; and Giovanni Parmigiani from the Department of Biostatistics and Computational Biology, Dana Farber Cancer Center and the Department of Biostatistics, Harvard School of Public Health.  The paper presents the results of a bioinformatics study on monozygotic twin pairs, using whole genome sequencing (WGS) that was interrogated for 24 different diseases.  For 23 of the 24 diseases tested, the results were negative, i.e., uninformative results for 19 of the 24, with risk of 50-80% for disease as compared with the general population.  However, 90% of the tested individuals were alerted to "a clinically significant predisposition" to at least one disease thus reflecting a silver lining if not a rainbow from the data.

The paper notes that there is an estimate of three million sequence variants per person (K. A. Frazer et al.,  2009, "Human genetic variation and its contribution to complex traits," Nat. Rev. Genet. 10: 241-51); of these, thousands of genetic variants have been associated with human disease, including Mendelian traits (e.g., sickle cell anemia), SNPs (e.g., Huntington's s disease), or by genome-wide association studies (GWAS) (examples include familial pancreatic cancer and Miller syndrome).

The paper addresses the question of what the benefit of such information would be, defining "benefit" as "receiving information indicating that the risk of disease is increased or decreased to a degree that would alter an individual's lifestyle or medical management."  The authors recognize that it is impossible to assess these benefits generally, but monozygotic twins present the possibility to make this assessment:  as they have in many other instances, the identity of genetic information make it possible that diseases and other traits with a large genetic component should be experienced in common between twins:  "If one twin of the pair has a disease, then the probability of the other twin developing that disease is dependent on the genome whenever that disease has some genetic component."  However, the paper also notes that "[t]he general public does not appear to be aware that, despite their very similar height and appearance, monozygotic twins in general do not always develop or die from the same
maladies," citing Wong et al., 2005, "Phenotypic differences in genetically identical organisms: The epigenetic perspective," Hum. Mol. Genet. 14 Spec No 1, R11-R18, and "Identical Twins Not As Identical As Believed," ScienceDaily, reflecting a limitation in the predictive power of genetic disease assessment even between individuals having almost identical genetic complements.  (Interestingly, even monozygotic twins are not necessarily identical genetic copies of one another, there being copy number variants between them; Bruder et al., 2008, "Phenotypically concordant and discordant monozygotic twins display different DNA copy-number variation profiles," Am. J. Hum. Genet. 82: 763-71.)  The twin populations were selected from "the Swedish Twin Registry, Danish Twin Registry, Finnish Twin Cohort, Norwegian National Birth Registry and the National Academy of Science – National Research World War II Veteran Twins Registry."

The authors also define "heritability" as the difference between the incidence of disease in monozygotic twins compared with dizygotic twins, "reflect[ing] the average genetic contribution to disease" in the population of twins studied.  Averages not as informative as distributions in this regard, since a given average incidence of disease could reflect either "a small fraction of twin-pairs with genometypes [(i.e., a complete genomic DNA sequence from an individual)] conferring high genetic risk or a larger fraction of twin-pairs with genometypes conferring a moderate genetic risk."  This challenge is illustrated by an example:

Suppose a woman receives a whole-genome test result indicating that she has a 90% lifetime risk (the total risk over her entire life) of developing breast cancer.  She may decide to have a prophylactic double mastectomy to prevent this outcome.  Similarly, if the test indicated an 80% or even a 50% lifetime risk of developing breast cancer, she may consider mastectomy.  On the other hand, if the test indicated only a 14% risk of developing breast cancer, then mastectomies would be considered by very few women, given that most women today do not opt for prophylactic mastectomies even though the lifetime risk of developing breast cancer in the general population is 12%.

The authors adopt the threshold of a "positive predictive value" of 10%, meaning that 10% of patients with a "positive" test result are expected to develop a disease, according to Clarke-Pearson, 2009, "Clinical practice. Screening for ovarian cancer," N. Engl. J. Med. 361: 170-77.  However, for several diseases, including chronic fatigue syndrome, gastro-esophageal reflux disorder, coronary heart disease-related death and general dystocia, this threshold is inappropriate due in part to the prevalence of these diseases in the population; for these diseases the threshold is a two-fold higher risk of disease compared with the general population.  In addition to these diseases, the study assessed the risk for coronary artery disease, stroke, cancer (bladder, breast, colorectal, lung, leukemia, ovarian, pancreatic, prostate and stomach), thyroid autoimmunity, diabetes (types 1 and 2), Alzheimer's disease, dementia, Parkinson's disease, irritable bowel syndrome, pelvic organ prolapse, and stress urinary incontinence.

The bulk of paper set forth a mathematical treatment of these biostatistics that is beyond the scope of the discussion here; the results, on the other hand, are informative.  These include:

•  "The fraction of patients that would receive a positive test varies widely from disease to disease."
•  "The majority of patients (>50%) who would ultimately develop 13 of the 27 disease categories would not test positive, even in the best-case scenario."
•  "There were four disease categories — thyroid autoimmunity, type I diabetes, Alzheimer's disease, and coronary heart disease-related deaths in males — for which genetic tests might identify more than 75% of the patients who ultimately develop the disease."
•  "The fraction of individuals in the population who would receive positive test results for each disease is small."
•  For 22 of the 27 disease categories studied, "a negative test would not indicate a risk that is less than half that in the general population, even in the best-case scenario" (which is probably not sufficient to warrant changes in behavior, lifestyle, or preventative medical practices).
•  An exception is Alzheimer's disease, where a negative test "could indicate as little as a ~12% relative risk of disease compared to the entire twin cohort . . . .  Knowledge of such a reduced risk might be comforting and relieve anxiety, particularly to those with a family history of Alzheimer's disease."
•  ">95% of men and >90% of women could receive at least one positive test result."
•  Many of these results represent the best-case scenarios and thus the true benefits of genetic disease testing may be overestimated.

The authors' conclusions are a dose of cold water on the hopes and expectations of many in the field (and even more laypersons outside the field, including policymakers, judges, and even Supreme Court justices):

[O]ur results suggest that genetic testing, at its best, will not be the dominant determinant of patient care and will not be a substitute for preventative medicine strategies incorporating routine checkups and risk management based on the history, physical status and life style of the patient.

The authors best state the significance of an accurate assessment of the likelihood of predictive genetic testing:  "Recognition of these merits and limits [for genetic diagnostic testing] can be useful to consumers, researchers, and industry, as they can minimize unrealistic expectations and foster fruitful investigations."  These are words policymakers should no doubt heed when considering changes to established patent and other policies based on perhaps unrealistic prospects for a brighter genetic future.