Stephanie S. Yee

Detection of therapeutically targetable driver and resistance mutations in lung cancer patients by next generation sequencing of cell-free circulating tumor DNA.

Purpose: The expanding number of targeted therapeutics for non–small cell lung cancer (NSCLC) necessitates real-time tumor genotyping, yet tissue biopsies are difficult to perform serially and often yield inadequate DNA for next-generation sequencing (NGS). We evaluated the feasibility of using cell-free circulating tumor DNA (ctDNA) NGS as a complement or alternative to tissue NGS. Experimental Design: A total of 112 plasma samples obtained from a consecutive study of 102 prospectively enrolled patients with advanced NSCLC were subjected to ultra-deep sequencing of up to 70 genes and matched with tissue samples, when possible. Results: We detected 275 alterations in 45 genes, and at least one alteration in the ctDNA for 86 of 102 patients (84%), with EGFR variants being most common. ctDNA NGS detected 50 driver and 12 resistance mutations, and mutations in 22 additional genes for which experimental therapies, including clinical trials, are available. Although ctDNA NGS was completed for 102 consecutive patients, tissue sequencing was only successful for 50 patients (49%). Actionable EGFR mutations were detected in 24 tissue and 19 ctDNA samples, yielding concordance of 79%, with a shorter time interval between tissue and blood collection associated with increased concordance (P = 0.038). ctDNA sequencing identified eight patients harboring a resistance mutation who developed progressive disease while on targeted therapy, and for whom tissue sequencing was not possible. Conclusions: Therapeutically targetable driver and resistance mutations can be detected by ctDNA NGS, even when tissue is unavailable, thus allowing more accurate diagnosis, improved patient management, and serial sampling to monitor disease progression and clonal evolution. Clin Cancer Res; 22(23); 5772–82. ©2016 AACR.

Comprehensive analysis of gene expression in human retina and supporting tissues

Understanding the influence of gene expression on the molecular mechanisms underpinning human phenotypic diversity is fundamental to being able to predict health outcomes and treat disease. We have carried out whole transcriptome expression analysis on a series of eight normal human postmortem eyes by RNA sequencing. Here we present data showing that ∼80% of the transcriptome is expressed in the posterior layers of the eye and that there is significant differential expression not only between the layers of the posterior part of the eye but also between locations of a tissue layer. These differences in expression also extend to alternative splicing and splicing factors. Differentially expressed genes are enriched for genes associated with psychiatric, immune and cardiovascular disorders. Enrichment categories for gene ontology included ion transport, synaptic transmission and visual and sensory perception. Lastly, allele-specific expression was found to be significant forCFH,C3 andCFB, which are known risk genes for age-related macular degeneration. These expression differences should be useful in determining the underlying biology of associations with common diseases of the human retina, retinal pigment epithelium and choroid and in guiding the analysis of the genomic regions involved in the control of normal gene expression.

Matrix metalloproteinases and educational attainment in refractive error: evidence of gene-environment interactions in the Age-Related Eye Disease Study.

PURPOSE A previous study of Old Order Amish families showed an association of ocular refraction with markers proximal to matrix metalloproteinase (MMP) genes MMP1 and MMP10 and intragenic to MMP2. A candidate gene replication study of association between refraction and single nucleotide polymorphisms (SNPs) within these genomic regions was conducted. DESIGN Candidate gene genetic association study. PARTICIPANTS Two thousand participants drawn from the Age-Related Eye Disease Study (AREDS) were chosen for genotyping. After quality-control filtering, 1912 individuals were available for analysis. METHODS Microarray genotyping was performed using the HumanOmni 2.5 bead array (Illumina, Inc., San Diego, CA). Single nucleotide polymorphisms originally typed in the previous Amish association study were extracted for analysis. In addition, haplotype tagging SNPs were genotyped using TaqMan assays. Quantitative trait association analyses of mean spherical equivalent refraction were performed on 30 markers using linear regression models and an additive genetic risk model while adjusting for age, sex, education, and population substructure. Post hoc analyses were performed after stratifying on a dichotomous education variable. Pointwise (P(emp)) and multiple-test study-wise (P(multi)) significance levels were calculated empirically through permutation. MAIN OUTCOME MEASURES Mean spherical equivalent refraction was used as a quantitative measure of ocular refraction. RESULTS The mean age and ocular refraction were 68 years (standard deviation [SD], 4.7 years) and +0.55 diopters (D; SD, 2.14 D), respectively. Pointwise statistical significance was obtained for rs1939008 (P(emp) = 0.0326). No SNP attained statistical significance after correcting for multiple testing. In stratified analyses, multiple SNPs reached pointwise significance in the lower-education group: 2 of these were statistically significant after multiple testing correction. The 2 highest-ranking SNPs in Amish families (rs1939008 and rs9928731) showed pointwise P(emp)<0.01 in the lower-education stratum of AREDS participants. CONCLUSIONS This study showed suggestive evidence of replication of an association signal for ocular refraction to a marker between MMP1 and MMP10. Evidence of a gene-environment interaction between previously reported markers and education on refractive error also was shown. Variants in MMP1 through MMP10 and MMP2 regions seem to affect population variation in ocular refraction in environmental conditions less favorable for myopia development.

Combining Machine Learning and Nanofluidic Technology To Diagnose Pancreatic Cancer Using Exosomes

Circulating exosomes contain a wealth of proteomic and genetic information, presenting an enormous opportunity in cancer diagnostics. While microfluidic approaches have been used to successfully isolate cells from complex samples, scaling these approaches for exosome isolation has been limited by the low throughput and susceptibility to clogging of nanofluidics. Moreover, the analysis of exosomal biomarkers is confounded by substantial heterogeneity between patients and within a tumor itself. To address these challenges, we developed a multichannel nanofluidic system to analyze crude clinical samples. Using this platform, we isolated exosomes from healthy and diseased murine and clinical cohorts, profiled the RNA cargo inside of these exosomes, and applied a machine learning algorithm to generate predictive panels that could identify samples derived from heterogeneous cancer-bearing individuals. Using this approach, we classified cancer and precancer mice from healthy controls, as well as pancreatic cancer patients from healthy controls, in blinded studies.

A novel approach for next-generation sequencing of circulating tumor cells.

Next‐generation sequencing (NGS) of surgically resected solid tumor samples has become integral to personalized medicine approaches for cancer treatment and monitoring. Liquid biopsies, or the enrichment and characterization of circulating tumor cells (CTCs) from blood, can provide noninvasive detection of evolving tumor mutations to improve cancer patient care. However, the application of solid tumor NGS approaches to circulating tumor samples has been hampered by the low‐input DNA available from rare CTCs. Moreover, whole genome amplification (WGA) approaches used to generate sufficient input DNA are often incompatible with blood collection tube preservatives used to facilitate clinical sample batching.

An integrated flow cytometry-based platform for isolation and molecular characterization of circulating tumor single cells and clusters

Comprehensive molecular analysis of rare circulating tumor cells (CTCs) and cell clusters is often hampered by low throughput and purity, as well as cell loss. To address this, we developed a fully integrated platform for flow cytometry-based isolation of CTCs and clusters from blood that can be combined with whole transcriptome analysis or targeted RNA transcript quantification. Downstream molecular signature can be linked to cell phenotype through index sorting. This newly developed platform utilizes in-line magnetic particle-based leukocyte depletion, and acoustic cell focusing and washing to achieve >98% reduction of blood cells and non-cellular debris, along with >1.5 log-fold enrichment of spiked tumor cells. We could also detect 1 spiked-in tumor cell in 1 million WBCs in 4/7 replicates. Importantly, the use of a large 200μm nozzle and low sheath pressure (3.5 psi) minimized shear forces, thereby maintaining cell viability and integrity while allowing for simultaneous recovery of single cells and clusters from blood. As proof of principle, we isolated and transcriptionally characterized 63 single CTCs from a genetically engineered pancreatic cancer mouse model (n = 12 mice) and, using index sorting, were able to identify distinct epithelial and mesenchymal sub-populations based on linked single cell protein and gene expression.

Feasibility of monitoring advanced melanoma patients using cell-free DNA from plasma

To determine the feasibility of liquid biopsy for monitoring of patients with advanced melanoma, cell‐free DNA was extracted from plasma for 25 Stage III/IV patients, most (84.0%) having received previous therapy. DNA concentrations ranged from 0.6 to 390.0 ng/ml (median = 7.8 ng/ml) and were positively correlated with tumor burden as measured by imaging (Spearman rho = 0.5435, p = .0363). Using ultra‐deep sequencing for a 61‐gene panel, one or more mutations were detected in 12 of 25 samples (48.0%), and this proportion did not vary significantly for patients on or off therapy at the time of blood draw (52.9% and 37.5% respectively; p = .673). Sixteen mutations were detected in eight different genes, with the most frequent mutations detected in BRAF, NRAS, and KIT. Allele fractions ranged from 1.1% to 63.2% (median = 29.1%). Among patients with tissue next‐generation sequencing, nine of 11 plasma mutations were also detected in matched tissue, for a concordance of 81.8%.

Clinical Implications of Plasma-Based Genotyping With the Delivery of Personalized Therapy in Metastatic Non–Small Cell Lung Cancer

Importance The clinical implications of adding plasma-based circulating tumor DNA next-generation sequencing (NGS) to tissue NGS for targetable mutation detection in non–small cell lung cancer (NSCLC) have not been formally assessed. Objective To determine whether plasma NGS testing was associated with improved mutation detection and enhanced delivery of personalized therapy in a real-world clinical setting. Design, Setting, and Participants This prospective cohort study enrolled 323 patients with metastatic NSCLC who had plasma testing ordered as part of routine clinical management. Plasma NGS was performed using a 73-gene commercial platform. Patients were enrolled at the Hospital of the University of Pennsylvania from April 1, 2016, through January 2, 2018. The database was locked for follow-up and analyses on January 2, 2018, with a median follow-up of 7 months (range, 1-21 months). Main Outcomes and Measures The number of patients with targetable alterations detected with plasma and tissue NGS; the association between the allele fractions (AFs) of mutations detected in tissue and plasma; and the association of response rate with the plasma AF of the targeted mutations. Results Among the 323 patients with NSCLC (60.1% female; median age, 65 years [range, 33-93 years]), therapeutically targetable mutations were detected in EGFR, ALK, MET, BRCA1, ROS1, RET, ERBB2, or BRAF for 113 (35.0%) overall. Ninety-four patients (29.1%) had plasma testing only at the discretion of the treating physician or patient preference. Among the 94 patients with plasma testing alone, 31 (33.0%) had a therapeutically targetable mutation detected, thus obviating the need for an invasive biopsy. Among the remaining 229 patients who had concurrent plasma and tissue NGS or were unable to have tissue NGS, a therapeutically targetable mutation was detected in tissue alone for 47 patients (20.5%), whereas the addition of plasma testing increased this number to 82 (35.8%). Thirty-six of 42 patients (85.7%) who received a targeted therapy based on the plasma result achieved a complete or a partial response or stable disease. The plasma-based targeted mutation AF had no correlation with depth of Response Evaluation Criteria in Solid Tumors response (r = −0.121; P = .45). Conclusions and Relevance Integration of plasma NGS testing into the routine management of stage IV NSCLC demonstrates a marked increase of the detection of therapeutically targetable mutations and improved delivery of molecularly guided therapy.

miRNA profiling of magnetic nanopore-isolated extracellular vesicles for the diagnosis of pancreatic cancer

Improved diagnostics for pancreatic ductal adenocarcinoma (PDAC) to detect the disease at earlier, curative stages and to guide treatments is crucial to progress against this disease. The development of a liquid biopsy for PDAC has proven challenging due to the sparsity and variable phenotypic expression of circulating biomarkers. Here we report methods we developed for isolating specific subsets of extracellular vesicles (EV) from plasma using a novel magnetic nanopore capture technique. In addition, we present a workflow for identifying EV miRNA biomarkers using RNA sequencing and machine-learning algorithms, which we used in combination to classify distinct cancer states. Applying this approach to a mouse model of PDAC, we identified a biomarker panel of 11 EV miRNAs that could distinguish mice with PDAC from either healthy mice or those with precancerous lesions in a training set of n = 27 mice and a user-blinded validation set of n = 57 mice (88% accuracy in a three-way classification). These results provide strong proof-of-concept support for the feasibility of using EV miRNA profiling and machine learning for liquid biopsy.Significance: These findings present a panel of extracellular vesicle miRNA blood-based biomarkers that can detect pancreatic cancer at a precancerous stage in a transgenic mouse model. Cancer Res; 78(13); 3688-97. ©2018 AACR.

Enumeration, Dielectrophoretic Capture, and Molecular Analysis of Circulating Tumor Cells

The identification of therapeutically targetable mutations in circulating tumor cells (CTCs) from cancer patient blood is increasingly used to personalize patient care. Here, we describe a novel approach for the enumeration, capture, and molecular analysis of CTCs from blood using an FDA-approved CTC enrichment and enumeration platform followed by dielectrophoretic capture and next-generation sequencing.