Abstract CN04-02: Interrogating the heritable genome to achieve the goal of personalized cancer therapeutics.
Authors: Innocenti Federico 
Published: June 2012

Abstract
In oncology, response “phenotypes” result from the interaction of several factors related to the patient (“the host”), the tumor, and the environment. Although non-genetic factors such as diet, age, gender and concomitant medications can influence a patient's response to chemotherapeutic treatments, understanding the contribution of genetics may be the key to maximizing drug efficacy and minimizing adverse side effects. Germline DNA is more accessible than tumor DNA, and there is accumulating evidence that optimization of treatment strategies may require stratification based on germline genetic factors. Most studies have focused on single-nucleotide polymorphisms (SNPs), a DNA sequence variation occurring at a single base in the genome. Elucidation of the unique sets of genetic variables (both common SNPs and rare alleles) that contribute to the patient's at risk for severe toxicity (or non-response) will provide critical information required for developing personalized therapy for cancer patients.
Hypertension, coronary artery disease, asthma, and other chronic diseases have a significant heritable component. In cancer patients, the contribution of germline variants to patient survival is difficult to assess due to the presence of acquired tumor-related factors with prognostic impact. The tumor DNA is thought to be far more important in the assessment of genetic variables contributing to treatment response. However, recent epidemiologic studies suggest an inherited component for prognosis, and that even tumor grade and metastatic potential might be, in part, under germline control.
We currently have only a few pharmacogenetic markers to predict the best course of therapy for patients. For the most part, these genetic variants are within drug-metabolizing genes that have a large effect on the degree or rate at which a drug is converted to its metabolites. Examples include thiopurine methyltransferase (TPMT) variants that account for greater than 90% of cases with low or intermediate TPMT enzyme activity and lead to increased risk for severe myelosuppression after 6-mercaptopurine treatment; UDP-glucuronosyltransferase (UGT)1A1*28 associated with a decrease in UGT1A1 expression and increased risk of severe neutropenia when irinotecan is administered; and the still controversial lack of response to tamoxifen in CYP2D6 poor metabolizers. These studies generally used a candidate gene or pathway-centric approach; both make assumptions about which genes are most important in the drug's activity. The consequence has been that only the strongest signals (ie, the Mendelian markers with the strongest penetrance) were then replicated across multiple independent studies. Aside from these discoveries, the literature is saturated with underpowered studies demonstrating or refuting the relationship between single genetic variants within “candidate genes or pathways” resulting in some clinical outcome.
Drug response is a complex trait and its heritable component is likely to result from the contribution of multiple genes, each of them conferring a modest effect. Even for the single-gene toxicity traits described above, the predictive power of those genetic tests is not optimal. Over the past 10 years, technology has evolved allowing for changes in the focus of research from single genes to the entire genome, thereby replacing the term “pharmacogenetics” with “pharmacogenomics”. This expansion has been made possible by the advent of SNP platforms for interrogating the entire genome of patients through genome-wide association studies (GWAS). This unbiased approach is expected to provide novel candidate genes associated with outcome that might provide unexpected insights into the pathophysiology of the disease (cancer) or the pharmacology of the drugs (chemotherapy). GWAS can also be applied to preclinical studies for discovery of genetic variants associated with pharmacological phenotypes.
The GWAS catalog is a repository of GWAS data published since November 2008. The catalogued studies have used platforms with at least 100,000 SNPs and the results were significant at P &lt;10–5. These studies have been performed to identify the genetic basis of chronic diseases, drug response, risk of cancer, and other complex traits. Of the 618 GWAS conducted so far, only 30 studies have investigated drug response as an outcome measure. Of those, only two oncology studies are currently listed. These studies have used SNP platforms with at least 500,000 SNPs.
Relative to the experience in GWAS of cancer risk, the experience of GWAS regarding outcome of chemotherapy remains limited, but it provides interesting insights. Although immediate translation of these findings into clinical utility may not occur in the short term, they have led to the discovery of novel genes outside of those previously thought to be involved in the pathophysiology of the investigated traits. This is clearly a major contribution to the field, opening new venues to biological discoveries. At the present stage, the most benefit from GWAS derives from postulating new biological mechanisms and directing research on novel pathways and novel targets for therapeutic intervention. It is quite interesting to observe that the interleukin pathway emerges from a few GWAS studies, not only in cancer GWAS, but also in GWAS of response to interferon and ribavirin in chronic hepatitis C.
The opportunities for discoveries provided by GWAS are unprecedented. Repositories for public deposit of GWAS information allow comparisons across studies and meta-analyses, as many of these studies might have overlapping characteristics, i.e., tumor types, drugs, drugs classes, toxicities, and toxicities that are specific to a particular drug class. Such comparisons might clarify prognostic versus predictive effects, and even identify prognostic factors that are tumor-type-independent.
For analysis of heritable genetic factors, it is expected that full genome sequencing will soon replace genomic interrogation through genotyping. In reality, this is currently happening, as sequencing platforms have already generated refined and more accurate maps of population variation in non-phenotyped subjects. Large international projects launched to entirely sequence the tumors of thousands of subjects will provide the matching heritable information from each study participant. Integrated analyses of acquired alterations (from the tumor) and matching heritable information (from the host) will define genetic signatures of survival for each sequenced tumor type. They also will clarify the role of rare variants, improving the understanding of “missing heritability” of traits.
Even if markers are independently validated through a very lengthy and expensive process, is this information sufficient to apply them in the clinic? So far, the uptake of genomic information from the cancer risk and outcome studies in the clinic has been limited. Oncologists will be inclined to add genetic information to their decision-making process if the performance of the genetic test is robust enough to add meaningful information to the traditional methods based on staging, family history, and other characteristics. Existing cancer risk algorithms have uncertain clinical validity. Increased oversight by the US Food and Drug Administration and other regulatory agencies on genetic and genomic tests will increase the confidence of clinicians in genomic medicine.
As a clear indication of how the field of genomic medicine is changing rapidly, the American Society of Clinical Oncology already has twice revised its policy statements of genetic and genomic testing for cancer susceptibility since 1996.
Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2011 Nov 12-16; San Francisco, CA. Philadelphia (PA): AACR; Mol Cancer Ther 2011;10(11 Suppl):Abstract nr CN04-02.