Abstract PGDx elio® tissue complete (ETC) is an FDA-cleared kitted IVD class II tumor profiling assay that detects somatic cancer-associated genomic alterations in formalin-fixed paraffin embedded (FFPE) tissue from all solid tumors. ETC is a hybrid capture 505-gene next-generation sequencing (NGS) test that reports single nucleotide variants (SNVs), insertions and deletions (indels), copy number amplifications, translocations, microsatellite instability (MSI), and tumor mutation burden (TMB). Tumor-specific variants are reported based on AMP/ASCO/CAP guideline-supported clinical significance. The size and design of the gene panel enable reporting of recognized variants with evidence of clinical significance across 16 tumor types (bladder, breast, cholangiocarcinoma, CNS, colon, gastric, GIST, IMT, melanoma, NSCLC, ovarian, pancreatic, prostate, rectal, thyroid, uterine), including variants that are indicated for all solid tumors, empowering comprehensive clinical utility. These variant classes include the following: SNVs and/or indels in 36 genes (AKT1, ATM, ATR, BARD1, BRAF, BRCA1, BRCA2, BRIP1, CDK12, CHEK1, CHEK2, EGFR, ERBB2, ESR1, FANCA, FANCL, FGFR3, IDH1, IDH2, KRAS, KIT, MET, MLH1, MRE11A, NBN, NRAS, NTRK3, PALB2, PDGFRA, PIK3CA, PTEN, RAD51B, RAD51C, RAD51D, RAD54L, and RET), translocations in 4 genes (ALK, RET, NTRK2, and NTRK3), amplifications in ERBB2, and 2 genomic signatures (MSI and TMB). In addition, resistance mutations in ALK, EGFR, BRAF, KIT, MET, and PTCH1 that impact treatment decisions with targeted therapies are reported. Analytical validation studies assessed the specificity for each variant and the sensitivity, accuracy, and reproducibility for many of them, yielding competitive analytical performance. Clinical validation has been performed for BRAF V600E/K in melanoma, demonstrating comparable performance relative to other on-market FDA-approved companion diagnostic tests. Analysis of real-world evidence for approximately 20,000 cases reveals that ETC detects clinically significant variants at expected rates when compared to available cancer variant databases. ETC, due to its large gene panel, provides a more comprehensive view of patients’ mutational profile, enabling physicians to make treatment decisions that are more precisely tailored to individual needs. Overall, these results demonstrate the exceptional performance of ETC in FFPE tissue and the power of comprehensive genomic profiling over single gene tests in standard testing workflows and discovery studies. Citation Format: Kenneth C. Valkenburg, Jesse Fox, Jennifer Jackson, Robert Auber, Ann L. Carr, Eric Severson, Taylor Jensen, Shakti Ramkissoon, Marcia Eisenberg, Brian Caveney, Christopher Coldren. Clinically significant cancer variants detected by comprehensive genomic profiling test PGDx elio tissue complete [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 7284.

PDF file - 57K, Distribution of histological responders and non-responders in iloprost and in placebo arm Legend :"Worst histology score criterion" = response is defined as a decrease of 1 or more in worst histology score of all biopsies from the same patients after treatment as compared to before, as used in the iloprost chemoprevention trial (11); "paired sample based criteria" = response is defined by a decrease of one histological grade within matched paired biopsies taken at the same site before and after treatment.

CCR Translation for This Article from Development of an Integrated Genomic Classifier for a Novel Agent in Colorectal Cancer: Approach to Individualized Therapy in Early Development

PDF - 240K, Overall segmentation results across the entire autosomal chromosomes in the genomic DNA titration series.

XLS - 100K, The list of SCAs detected by delta-theta in clinical specimens.

PDF file - 36K, Distribution of histologies at BL and at FU in pairs of samples with best or worse histology at baseline. Legend : N=number, BL=baseline, FU=follow-up

Supplementary Table S2 from Baseline Gene Expression Predicts Sensitivity to Gefitinib in Non–Small Cell Lung Cancer Cell Lines

PDF file - 73K, Association of miRNA expression at BL with response. Legend: This table reports the three miRNAs (miR-34c, miR-224, miR-375) that are significantly differentially expressed at BL in responders as compared to non-responders in at least one comparison The table shows up- or down-regulation in responders compared to non-responders. The significant p-values from Welch's t-test are reported. "FDR" indicates that the p-value is significant after adjustment for False Discovery Rate (FDR). Odds ratios (OR) were calculated by logistic regression model and reported with their 95% confidence interval and p-value. When either the t-test or the OR was significant, both are reported.

Supplementary Table S1 from Baseline Gene Expression Predicts Sensitivity to Gefitinib in Non–Small Cell Lung Cancer Cell Lines

PDF - 138K, The comparisons of representative delta-θ signals between 2 different methods of DNA isolation from brushing.

PDF - 79K, Legends for Supplementary Figures 1 through 5

Supplemental table S1 documents comparability between final groupings of baseline biopsies used for gene expression comparisons between persistent and regressive sites demonstrating comparability in all key histologic and clinical variables with the exception of higher inflammation in the regressive group. Supplemental table S2 describes the number of differentially expressed genes that distinguish the four initial groups of bronchial biopsies including persistent bronchial dysplasia (BD), regressive BD, progressive non-dysplasia (ND) and stable ND. The number of genes differentially expressed between the groups highlight the finding that differences are greater in respect to the outcome of the lesions at a given site versus the degree of dysplasia in the baseline biopsy. Most notably the difference between regressive BD and stable ND, two groups with histologically distinct baseline biopsies show fewer differentially expressed genes than all but one other group comparison including the histologically indistinguishable persistent and regressive BD groups. These findings are consistent with persistent and regressive sites representing biologically distinct lesions. Supplemental table S3 shows airway locations, temporal histologic features of full airways and clinical characteristics associated with each specimen used for gene expression analyses. The table is arranged by persistent versus regressive status and presents specimens in descending order of baseline histologic score within each group. The specimen numbers correspond to those notated in figure 1B of the manuscript. Supplemental figure S1 shows a heat map using differentially expressed genes to demonstrate the rationale for combining groups in the final comparison. The heatmap demonstrates that persistent BD and progressive ND sites, which are combined to represent persistent sites, show similar gene expression profiles, with only cases Group3_051, Group3_086, Group3_113 and Group4_070 showing profiles that demonstrate similarity to the regressive BD/stable ND group. The regressive BD and stable ND, which are combined to represent regressive sites, also show similar gene expression profiles that are opposite to those seen in the persistent site group with only cases Group2_037, Group2_085 and Group2_092 showing profiles that demonstrate similarity to the persistent BD/progressive ND group. Supplemental tables S4 - S6 present full data genelists for genes associated with pathways showing significantly altered activity in comparisons of persistent and regressive BD (supplemental table S3), genes associated with pathways that are altered in relation to increasing histologic score (supplemental table S4) and upstream regulators related to persistence and histologic score (supplemental table S5). Supplemental table S7 shows documents comparability between final groupings of baseline biopsies used for analyses of the composition of inflammatory infiltrates in comparisons between a validational set of persistent and regressive sites. Comparability is demonstrated in all key histologic and clinical variables including mean inflammation score and percent of high inflammation score biopsies between the two groups. A higher mean baseline histologic score was seen in the persistent group. Supplemental figure S2 shows subset analyses of the validational set of persistent and regressive sites with comparison of these groups by tissue compartment (epithelial and stromal) and overall degree of inflammation (supplemental figures S2A and S2B). Supplemental figure S2C shows the changes in expression of genes from the original gene expression array analysis data for those genes associated with polarization of subsets of inflammatory cells. The genes with significant or near significant change provide evidence that the activity of certain subsets of macrophages and T-lymphocytes correlate with persistence or regression of BD. These subsets are further studied in figure 5 of the main text.

PDF - 85K, Different concepts of delta-theta and modified BAF.

PDF - 494K, Detection and validation of SCAs in a heterogeneous cancer case by FISH assays.

Introduction: Detection of gene translocations is a key component of clinical diagnostics to enable precision medicine in oncology. Several methods such as fluorescence in situ hybridization or RT-PCR have historically been employed, however, next-generation sequencing (NGS)-based comprehensive genomic profiling (CGP) including DNA and RNA sequencing approaches have been validated for this purpose. Here we explore the complimentary nature of these methods to enable detection of clinically relevant translocations to guide patient care. Methods: We utilized Endeavor, a 505 gene DNA-based CGP assay developed by Personal Genome Diagnostics, to assess single nucleotide variants, insertion/deletions, amplifications, translocations, microsatellite instability, and tumor mutation burden as well as a 53 gene RNA-based Invitae NGS FusionPlex Solid Tumor v1 assay. 151 patients with advanced or metastatic solid tumors, including lung, colorectal, esophageal, breast, brain, bladder, and prostate cancers, were consecutively enrolled and only overlapping target regions were evaluated to compare performance. Results: Of the 151 cases selected for the study, failure rates were 2.0% and 9.9% for Endeavor and FusionPlex, respectively. Translocations were detected in 21/151 (13.9%) cases by Endeavor and 17/105 (11.3%) cases by FusionPlex. For the 133 cases where data was available from both assays, 12 (9.0%) concordant translocation positive cases were detected involving ALK, RET, NTRK1, NTRK3, MET exon 14 skipping, EGFRvIII, and EWSR1 with 109 (82.0%) cases translocation negative by both assays achieving a concordance rate of 91%. For 7 (5.3%) cases, Endeavor detected translocations events in FGFR1, FGFR2, ETV4, ETV6, MYC, and NTRK3 that were not detected by FusionPlex. Conversely, FusionPlex identified 5 (3.8%) cases with translocations in ROS1, NTRK2, and EGFRvIII that were not detected by Endeavor (Table 1). Discrepancies in translocation detection were attributed to variability in assay failure rates, panel design, and underlying biological differences in detectability associated with DNA- and RNA-based methods. Conclusions: In this study, comparison of translocation detection using DNA and RNA-based NGS approaches revealed a high concordance between the two assays and were equally valuable for identifying actionable targets. These findings provide confirmatory support for the complimentary use of DNA- and RNA-based NGS approaches to most accurately identify clinically relevant translocations thereby providing more comprehensive results to help guide cancer treatment strategies. Citation Format: Rongqin Ren, Jennifer Jackson, Jacob Kames, David Riles, Christopher Coldren, Scott Wheeler, Pranil Chandra, Michelle Shiller. Complimentary use of DNA- and RNA-based NGS assays optimizes detection of clinically relevant translocations for comprehensive genomic profiling. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5452.

PDF file - 37K, Distribution of histologies at BL and FU between the treatment arms and stratified by smoking status Legend: N=number, pts=patients