Current Pharmacogenomics and Personalized Medicine - Volume 8, Issue 1, 2010

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Full text available

Premature loss of telomere repeats underlies the pathologies of inherited bone marrow failure syndromes. Over the past decade, researchers have mapped genetic lesions responsible for the accelerated loss of telomere repeats. Haploinsufficiencies in the catalytic core components of the telomere maintenance enzyme telomerase, as well as genetic defects in telomerase holoenzyme components responsible for enzyme stability, have been linked to hematopoietic failure pathologies. Frequencies of these disease-associated alleles in human populations are low. Accordingly, the diseases themselves are rare. On the other hand, single nucleotide polymorphisms of telomerase enzyme components are found with much higher frequencies, with several non-synonymous SNP alleles observed in 2-4% of the general population. Importantly, recent advents of molecular diagnostic techniques have uncovered links between telomere length maintenance deficiencies and an increasing number of pathologies unrelated to the hematopoietic system. In these cases, short telomere length correlates to tissue renewal capacities and predicts clinical progression and disease severity. To the authors of this review, these new discoveries imply that even minor genetic defects in telomere maintenance can culminate in the premature failure of tissue compartments with high renewal rates. In this review, we discuss the biology and molecules of telomere maintenance, and the pathologies associated with an accelerated loss of telomeres, along with their etiologies. We also discuss single nucleotide polymorphisms of key telomerase components and their association with tissue renewal deficiency syndromes and other pathologies. We suggest that inter-individual variability in telomere maintenance capacity could play a significant role in chronic inflammatory diseases, and that this is not yet fully appreciated in the translational research of pharmacogenomics and personalized medicine.

The euphoria of stem cell therapy has diminished, allowing scientists, clinicians and the general public to seriously re-examine how and what types of stem cells would effectively repair damaged tissue, prevent further tissue damage and/or replace lost cells. Importantly, there is a growing recognition that there are substantial person-to-person differences in the outcome of stem cell therapy. Even though the small molecule pharmaceuticals have long remained a primary focus of the personalized medicine research, individualized or targeted use of stem cells to suit a particular individual could help forecast potential failures of the therapy or identify, early on, the individuals who might benefit from stem cell interventions. This would however demand collaboration among several specialties such as pharmacology, immunology, genomics and transplantation medicine. Such transdisciplinary work could also inform how best to achieve efficient and predictable stem cell migration to sites of tissue damage, thereby facilitating tissue repair. This paper discusses the possibility of polarizing immune responses to rationalize and individualize therapy with stem cell interventions, since generalized “one-size-fits-all” therapy is difficult to achieve in the face of the diverse complexities posed by stem cell biology. We also present the challenges to stem cell delivery in the context of the host related factors. Although we focus on the mesenchymal stem cells in this paper, the overarching rationale can be extrapolated to other types of stem cells as well. Hence, the broader purpose of this paper is to initiate a dialogue within the personalized medicine community by expanding the scope of inquiry in the field from pharmaceuticals to stem cells and related cell-based health interventions.

As high-throughput genotyping studies are increasingly being complemented with global gene expression/ transcriptomics analyses, tissue sampling and biopsy methods are becoming a prominent bottleneck in personalized medicine. Surprisingly, this topic has seldom been studied in detail. Quantitative molecular biology and high quality tissue sampling methods need to be developed in parallel, in order to understand disease pathophysiology, determine optimal individualized care, and identify molecular targets for novel therapies. This also calls for a critical reexamination of the subject of tissue acquisition and biopsy instruments in pharmacogenomics and personalized medicine. A perfect biopsy should be patient and operator friendly, of high quality, in adequate volume amenable to multiplexed molecular analyses, and affordable. Surgery, although still the gold standard, is seldom chosen because of the costs, risks and constraints of general anesthesia. Tru-cut microbiopsies are largely inappropriate as the samples are too small. Vacuum-assisted large core devices are expensive and principally restricted to the breast tissue. These traditional sampling approaches tend to take more ad random tissue resulting in substantial contamination with blood, and adjacent normal and non-target tissues. Contamination means low sample quality when it comes to quantitative analysis of gene products. Test results most probably become unreliable. The tissue acquisition problem, i.e., “not enough high quality tissue in sufficient quantity”, is addressed by the newer Direct and Frontal (D&F) harvesting tools which this paper aims to discuss. Recent literature provides evidence that these harvesting tools give tissue samples between 150 and 300 mg of highly specified parts of the diseased area in a way very similar to open surgery. In addition, they open new avenues for future bio-banking, pharmacogenomics and personalized medicine. We suggest that further comparative research and clinical applications of the different tissue sampling and biopsy methods for quantitative molecular biology are timely and essential in the field of personalized medicine.

Glycomics is an emerging field of research that examines the structure and function of glycans attached to biomolecules. Glycans are chains of sugars that often form complex branched structures on proteins or lipids. Study of glycans represents a previously “untapped” and overlooked field of inquiry in personalized medicine. In the past, technological approaches in personalized medicine heavily relied on high-throughput genotyping or gene expression analyses with relatively little exploration of the downstream biological processes such as glycans and associated variability among individuals and populations. Glycans are important modulators of numerous biological processes that determine person-to-person differences in susceptibility to common complex diseases and response to drug treatment. The heterogeneity and diversity of glycans in part reflect the genetic, population ancestry and age of each patient, as well as stages of disease and nutritional/environmental exposures. However, little effort has been made thus far to exploit the glycosylation state of a patient in relation to prevention of future disease risks or to determine the appropriate treatment for an individual patient. On the other hand, the vast heterogeneity and complexity of glycans limit their comprehensive study. Recent developments in glycan arrays now offer new possibilities in the horizon to achieve a high-throughput profiling of glycan diversity to discover novel molecular targets that are druggable, and better understand and predict variability in drug treatment outcomes. Glycan arrays present carbohydrate ligands in a manner that physiologically mimics interactions at cell-to-cell interfaces through multivalent binding. Glycomics can significantly complement the genomics and proteomics tools that are already being evaluated for developing personalized medicines. This paper aims to give an overview of the current status of glycan research and applications in diagnostic medicine. Moreover, we propose that glycomics and nanobiotechnology form a powerful and synergistic technological combination on the critical path to personalized medicine.

Cancer is a heterogeneous disease, originating in different tissues and involving a number of distinct molecular pathways. With the growing availability of high-throughput biological data, including but not limited to gene expression microarrays, integrative computational approaches are increasingly being applied to associate malignancies with specific underlying molecular mechanism(s). The goal of these approaches is to design more accurate diagnostics or “personalized” therapeutic regimens, and to generate a much more fine-grained knowledgebase of the progression of each cancer subtype. Many bioinformatic methods have shown limited success at identifying diagnostic and prognostic signatures for specific malignancies, such as breast or prostate cancer. In a similar context, these analyses have also been used to characterize pharmacological interventions. However, a key drawback thus far has been a focus on genes or proteins in relative isolation, without fully accounting for how their activity is mediated by an underlying network of interactions with other molecules in the cell. Furthermore, it is often difficult to separate the phenotypic cause versus its downstream effects, a problem referred to as the “driver” and “passenger” question. In this article, we present a critical synthesis of an emerging class of methods that use systems biology, or networks of gene interactions inside the cell, to help characterize cancer progression. We describe this emerging field, the types of high-throughput data used, and the various approaches investigators have taken. Lastly, we provide a discussion of the fields' ability to more thoroughly capture the complex disease mechanisms at work and in doing so, work towards the promise of personalized medicine.

Despite recent advances in diagnosis and treatment of certain cancers such as breast, colon and prostate cancers, pancreatic cancer remains a deadly malignancy with a mortality rate almost equal to its incidence. The survival benefit after all possible medical interventions is still disappointing for the majority of patients with pancreatic cancer. Improving the quality of life and increasing survival remain as the key objectives of pharmacogenomics and clinical investigation in pancreatic cancer. There are several notable genetic loci (e.g., RRM1, CDA, DPYD, UGT1A1) known to predict the toxicity of drugs used in pancreatic cancer such as gemcitabine, capecitabine, platinum agents and irinotecan. Other genes are associated with efficacy of these drugs (e.g., HuR, DCK, TS, ERCC1), although prospective validation of their clinical utility is still required. This paper presents the latest research advances in pharmacogenomics of pancreatic cancer with a view to molecular guidance for clinical diagnosis and individualized patient care.