Clinical validation of a cell-free DNA fragmentome assay for augmentation of lung cancer early detection.

Lung cancer screening via annual low-dose computed tomography (LDCT) has poor adoption. We conducted a prospective case-control study among 958 individuals eligible for lung cancer screening to develop a blood-based lung cancer detection test that when positive is followed by an LDCT. Changes in genome-wide cell-free DNA (cfDNA) fragmentation profiles (fragmentomes) in peripheral blood reflected genomic and chromatin characteristics of lung cancer. We applied machine learning to fragmentome features to identify individuals who were more or less likely to have lung cancer. We trained the classifier using 576 cases and controls from study samples, and then validated it in a held-out group of 382 cases and controls. The validation demonstrated high sensitivity for lung cancer, and consistency across demographic groups and comorbid conditions. Applying test performance to the screening eligible population in a five-year model with modest utilization assumptions suggested the potential to prevent thousands of lung cancer deaths.

Cell-free DNA approaches for cancer early detection and interception.

Rapid advancements in the area of early cancer detection have brought us closer to achieving the goals of finding cancer early enough to treat or cure it, while avoiding harms of overdiagnosis. We evaluate progress in the development of early cancer detection tests in the context of the current principles for cancer screening. We review cell-free DNA (cfDNA)-based approaches using mutations, methylation, or fragmentomes for early cancer detection. Lastly, we discuss the challenges in demonstrating clinical utility of these tests before integration into routine clinical care.

Cell-Free DNA Fragmentomes in the Diagnostic Evaluation of Patients With Symptoms Suggestive of Lung Cancer.

BACKGROUND: The diagnostic workup of individuals suspected of having lung cancer can be complex and protracted because conventional symptoms of lung cancer have low specificity and sensitivity. RESEARCH QUESTION: Among individuals with symptoms of lung cancer, can a blood-based approach to analyze cell-free DNA (cfDNA) fragmentation (the DNA evaluation of fragments for early interception [DELFI] score) enhance evaluation for the possible presence of lung cancer? STUDY DESIGN AND METHODS: Adults were referred to Bispebjerg Hospital (Copenhagen, Denmark) for diagnostic evaluation of initial imaging anomalies and symptoms consistent with lung cancer. Numbers and types of symptoms were extracted from medical records. cfDNA from plasma samples obtained at the prediagnostic visit was isolated, sequenced, and analyzed for genome-wide cfDNA fragmentation patterns. The relationships among clinical presentation, cancer status, and DELFI score were examined. RESULTS: A total of 296 individuals were analyzed. Median DELFI scores were higher for those with lung cancer (n = 98) than those without cancer (n = 198; 0.94 vs 0.19; P < .001). In a multivariate model adjusted for age, smoking history, and presenting symptoms, the addition of the DELFI score improved the prediction of lung cancer for those who demonstrated symptoms (area under the receiver operating characteristic curve, 0.74-0.94). INTERPRETATION: The DELFI score distinguishes individuals with lung cancer from those without cancer better than suspicious symptoms do. These results represent proof-of-concept support that fragmentation-based biomarker approaches may facilitate diagnostic resolution for patients with concerning symptoms of lung cancer.

Detection and characterization of lung cancer using cell-free DNA fragmentomes.

Non-invasive approaches for cell-free DNA (cfDNA) assessment provide an opportunity for cancer detection and intervention. Here, we use a machine learning model for detecting tumor-derived cfDNA through genome-wide analyses of cfDNA fragmentation in a prospective study of 365 individuals at risk for lung cancer. We validate the cancer detection model using an independent cohort of 385 non-cancer individuals and 46 lung cancer patients. Combining fragmentation features, clinical risk factors, and CEA levels, followed by CT imaging, detected 94% of patients with cancer across stages and subtypes, including 91% of stage I/II and 96% of stage III/IV, at 80% specificity. Genome-wide fragmentation profiles across ~13,000 ASCL1 transcription factor binding sites distinguished individuals with small cell lung cancer from those with non-small cell lung cancer with high accuracy (AUC = 0.98). A higher fragmentation score represented an independent prognostic indicator of survival. This approach provides a facile avenue for non-invasive detection of lung cancer.

The Evolution of Oncology Companion Diagnostics from Signal Transduction to Immuno-Oncology.

Sixteen oncology drugs have been approved with a companion diagnostic (CDx) test by the FDA. These represent only 9.6% of the 167 oncology drug approvals since 1998, the year the first CDx test for Herceptin was approved. The great majority of CDx tests are for drugs that inhibit signal transduction pathways by either inhibiting the intracellular kinase activity with a small molecule or preventing ligand-induced receptor activation with a monoclonal antibody. In most of these cases, prospective patient selection for the biomarker-positive subpopulation was initiated in or before Phase II. The development of CDx tests for emerging immunotherapies will be more complicated because they are not dependent on driver mutations in the drug target, the mechanism of action is often pleiotropic, and will require both protein and cell-based assays to evaluate the interaction of the tumor with the immune system. Consequently, we will need to develop new biomarker strategies for the development of immunotherapies and to determine whether the optimum strategy is to release a prior checkpoint blockade in patients with a suppressed immune response, or to prime a new immune response to the tumor.

Genetic and gene expression studies implicate renin and endothelin-1 in edema caused by peroxisome proliferator-activated receptor gamma agonists.

OBJECTIVE: Peroxisome proliferator-activated receptor gamma (PPARgamma) agonists can cause peripheral edema in susceptible individuals. To investigate the mechanistic basis underlying this adverse event, we performed a candidate gene analysis of patients enrolled in clinical trials of muraglitazar, an investigational PPARalpha/gamma dual agonist, and developed a cell culture-based gene expression assay and nonhuman primate model of edema to study the edemagenic properties of PPARgamma agonists. METHODS: A total of 213 single nucleotide polymorphisms (SNPs) in 63 genes were genotyped in 730 participants. Chi-square and logistic regression analyses were used to test for association with edema. Transcriptional responses to PPARgamma agonists were evaluated in Calu-6 cells using quantitative real-time PCR. Male Cynomolgus monkeys were treated with PPAR agonists and were evaluated for edema using MRI. RESULTS: SNPs in renin (rs2368564) and endothelin-1 (rs5370) were associated with reduced risk of edema (P=0.003 and P=0.028, respectively) and an SNP in beta1 adrenergic receptor (rs1801253) was associated with increased susceptibility to edema (P=0.034). Gene expression studies revealed that renin and endothelin-1 were regulated by PPARgamma in Calu-6 cells. A survey of 10 PPARgamma agonists further revealed that a compound's in vitro potency was correlated with its edemagenic potential leading to the prediction that one of three previously uncharacterized PPARgamma agonists would cause less edema. This prediction was validated in a nonhuman primate model of PPARgamma agonist-induced edema. CONCLUSION: Our results implicate a key role for renin and endothelin-1 in the edema caused by PPARgamma agonists and demonstrate how knowledge gained from pharmacogenetic studies can be applied in drug discovery.

KRAS mutations predict response to EGFR inhibitors.

Five antiepidermal growth factor receptor therapies have been approved for the treatment of solid tumors. However, response rates are relatively low. Several biomarkers that enrich for patients with tumors most likely to respond to these therapeutic agents have been identified. Mutations in the intermediate signal transduction pathway member KRAS also selects patients with tumors depending on signaling through this pathway. However, because KRAS acts downstream of the EGF receptor, somatic changes in this gene can be used as a marker to exclude patients unlikely to benefit from anti-EGFR therapy. Recent clinical data have provided substantial evidence that KRAS mutational status should be utilized as a diagnostic marker for predicting that response to anti-EGFR therapies in colorectal and non-small cell lung cancer.

Oncogenomics 2005 meeting report: dissecting cancer through genome research.

The Oncogenomics Conference was held on February 2 to 5, 2005 at the Omni San Diego Hotel, San Diego, CA. The meeting chairpersons were Dr. Jeffrey Trent (Translational Genomics Research Institute, Phoenix, AZ) and Dr. Nicholas Dracopoli (Bristol-Myers Squibb Pharmaceutical Research Institute, Princeton, NJ). This fourth conference sponsored by the AACR brought together a diverse group of scientists working in the fields of genome research, cancer biology, and epidemiology, and explored how genomic research can be used most effectively to impact the clinical response to cancer. Advances in the clinical application of pharmacogenomics to predict response to oncology therapeutic modalities led to a dramatic increase in the number of presentations and posters with a clinical focus at this year's meeting. The keynote address was given by Dr. William Evans (St. Jude Children's Hospital, Memphis, TN) who discussed the pharmacogenomics of acute lymphoblastic leukemia as a cancer paradigm.

Evaluation of the paraoxonases as candidate genes for stroke: Gln192Arg polymorphism in the paraoxonase 1 gene is associated with increased risk of stroke.

BACKGROUND AND PURPOSE: The paraoxonases are involved in protecting low-density lipoprotein (LDL) from lipid oxidation. Paraoxonase 1 (PON1) was implicated in susceptibility to coronary artery disease and stroke in previous studies. We evaluated, in a comprehensive way, all 3 paraoxonase genes for association with stroke observed in the Cholesterol and Recurrent Events (CARE) trial. METHODS: Over 2500 subjects enrolled in the CARE trial were genotyped for 14 single nucleotide polymorphisms, including 7 newly identified in this study, in the 3 paraoxonase genes. RESULTS: A glutamine (Gln)/arginine (Arg) polymorphism at amino acid residue 192 in PON1 was significantly associated with stroke (P=0.003 in multivariate analysis, including age, sex, LDL, hypertension, diabetes, smoking, and pravastatin treatment as covariates). The odds ratios were 2.28 (95% CI, 1.38 to 3.79) for Gln/Arg heterozygotes and 2.47 (95% CI, 1.18 to 5.19) for Arg/Arg homozygotes compared with Gln/Gln homozygotes. These results are consistent with 2 of 3 other published studies. In combined analysis of all 4 studies, the association between Gln192Arg SNP and stroke was highly significant (chi2(8df)=45.58, P<0.000001). Sequence analysis of the PON1 gene from seventy stroke cases revealed a novel nonsense mutation at codon 32 in one stroke case, which was not detected in over 2500 unaffected individuals. Polymorphisms in the PON2 and PON3 genes were not associated with stroke. CONCLUSIONS: These results suggest that Gln192Arg genotype is an important risk factor for stroke.

Development of oncology drug response markers using transcription profiling.

Transcriptional profiling of a tumor's entire genomic complement has become a key tool in the analysis of human cancers and identification of novel markers to predict disease state, outcome, and response to therapy. At present, this technology provides the most comprehensive approach for the analysis of somatic changes altering critical pathways during transformation of stable diploid cells into unstable tumor cells. Such analyses are impacting the development of novel anti-cancer drugs through the early detection of cancer, development of targeted therapies, identification of optimal dose and regimens for new drugs, and segmentation of patients to enrich response rates and reduce risk of adverse events.

Pharmacogenomic applications in clinical drug development.

Pharmacogenomics is being increasingly applied to the development of novel oncology drugs. Single nucleotide polymorphism genotyping of known drug-metabolizing and transport genes is used to screen for pharmacokinetic differences in drug exposure, and genome-wide transcription profiling is being widely used to identify expression profiles that can be correlated to pharmacodynamic variables resulting from molecular changes within tumors. Given the relatively low efficacy of most cancer therapies, and the molecular heterogeneity within tumors, pharmacogenomics provides one of the best opportunities for improvements in drug efficacy through effective stratification of patients, and early identification of those individuals most likely to respond effectively to a specific therapeutic approach.