Low Tumor Content Research Articles

Rapid and highly sensitive approach for multiplexed somatic fusion detection

AbstractSomatic gene translocations are key to making an accurate diagnosis in many cancers including many pediatric sarcomas. Currently available molecular diagnostic approaches to identifying somatic pathognomonic translocations have limitations such as minimal multiplexing, high cost, complex computational requirements, or slow turnaround times. We sought to develop a new fusion-detection assay optimized to mitigate these challenges. To accomplish this goal, we developed a highly sensitive multiplexed digital PCR-based approach that can identify the gene partners of multiple somatic fusion transcripts. This assay was validated for specificity with cell lines and synthetized DNA fragments. Assay sensitivity was optimized using a tiered amplification approach for fusion detection from low input and/or degraded RNA. The assay was then tested for the potential application of fusion detection from FFPE tissue and liquid biopsy samples. We found that this multiplexed PCR approach was able to accurately identify the presence of seven different targeted fusion transcripts with a turnaround time of 1 to 2 days. The addition of a tiered amplification step allowed the detection of targeted fusions from as little as 1 pg of RNA input. We also identified fusions from as little as two unstained slides of FFPE tumor biopsy tissue, from circulating tumor cells collected from tumor-bearing mice, and from liquid biopsy samples from patients with known fusion-positive cancers. We also demonstrated that the assay could be easily adapted for additional fusion targets. In summary, this novel assay detects multiple somatic fusion partners in biologic samples with low tumor content and low-quality RNA in less than two days. The assay is inexpensive and could be applied to surgical and liquid biopsies, particularly in places with inadequate resources for more expensive and expertise-dependent assays such as next-generation sequencing.

Abstract 5066: Accurate detection of tumor sequence at low tumor content for MRD

Abstract Background Accurate detection of tumor sequence when the overall tumor content (TC) of the sample is low (<0.01%) is critical for tumor-informed minimal residual disease (MRD) monitoring. Current methodology generally relies on detection of multiple low-VAF (variant allele fraction) co-mutations. At low TC and low DNA input (<30ng, 9,000 genome equivalents), many variant sites are not expected to have any tumor-derived fragments or will be only marginally above background noise. A method that does not rely on the positive identification of mutations would be useful for detecting early disease recurrence. Methods We developed a maximum likelihood (ML) method for inferring the TC of MRD samples, rather than VAF at individual sites. This method considers the frequency of non-reference reads at each variant site along with estimated error rates. To evaluate our method at low TC, we carried out spike-in simulations of MRD samples across a range of sample TC from 0 to 0.1%. For each simulation, we randomly selected a single normal from a pool of sequenced normal samples to serve as background. Using this background, we randomly selected sites and mutations to represent somatic variants, sampled the existing noise at each site, and the number of ctDNA fragments and reads given the tumor content. Intrinsic error rates were modeled as either the mean error of the sample, the per-reference base error, or the mutation error. An additional randomly selected normal sample was used for extrinsic, per-site error estimates. We created contrived tumor-informed MRD samples by serial two-fold dilution of tumor DNA in a normal background (0.2%-0.0125% sample TC), followed by amplicon sequencing and characterization by our ML method. Results In our spike-in simulations, using 32 variants sites, we achieved 99% sensitivity in detecting tumor sequence at a TC of 0.01% (average ~1 tumor fragment per site), 73% sensitivity at 0.005% (average ~0.5 tumor fragments per site), and 99.9% specificity when using extrinsic error from a normal sample. At 16 sites we achieved 95% and 68% sensitivity respectively, and 99.6% specificity. Without the extrinsic error, specificity was greatly reduced to 71% (16 sites) and 77% (32 sites) at .005% TC. This highlights the advantage of extrinsic error in estimating low TC. In our contrived samples, we found that TC estimates from our ML approach correlated well with the expected values (r = 0.99). Conclusions With our approach, we were able to accurately quantify TC in both simulated and contrived samples. In simulated samples, we can detect TC well below 0.01% with high specificity and using only 16 sites. Importantly, the TC estimation does not rely on identifying individual positive sites. While modeling the intrinsic sequencing error rates in a sample does increase our ability to characterize TC, extrinsic, per-site error rates are needed to control false positives introduced by constitutively noisy sites. Citation Format: Andrew Conley, Huazhang Li, Alex V. Kotlar. Accurate detection of tumor sequence at low tumor content for MRD [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5066.

A computational framework to unify orthogonal information in DNA methylation and copy number aberrations in cell-free DNA for early cancer detection.

Cell-free DNA (cfDNA) provides a convenient diagnosis avenue for noninvasive cancer detection. The current methods are focused on identifying circulating tumor DNA (ctDNA)s genomic aberrations, e.g. mutations, copy number aberrations (CNAs) or methylation changes. In this study, we report a new computational method that unifies two orthogonal pieces of information, namely methylation and CNAs, derived from whole-genome bisulfite sequencing (WGBS) data to quantify low tumor content in cfDNA. It implements a Bayes model to enrich ctDNA from WGBS data based on hypomethylation haplotypes, and subsequently, models CNAs for cancer detection. We generated WGBS data in a total of 262 samples, including high-depth (>20×, deduped high mapping quality reads) data in 76 samples with matched triplets (tumor, adjacent normal and cfDNA) and low-depth (~2.5×, deduped high mapping quality reads) data in 186 samples. We identified a total of 54Mb regions of hypomethylation haplotypes for model building, a vast majority of which are not covered in the HumanMethylation450 arrays. We showed that our model is able to substantially enrich ctDNA reads (tens of folds), with clearly elevated CNAs that faithfully match the CNAs in the paired tumor samples. In the 19 hepatocellular carcinoma cfDNA samples, the estimated enrichment is as high as 16 fold, and in the simulation data, it can achieve over 30-fold enrichment for a ctDNA level of 0.5% with a sequencing depth of 600×. We also found that these hypomethylation regions are also shared among many cancer types, thus demonstrating the potential of our framework for pancancer early detection.

Copy number features as a novel biomarker of homologous recombination (HR) status in high-grade serous ovarian cancer (HGSOC).

5547 Background: Copy number alterations (CNA) arise as a result of somatic changes to chromosome structure, resulting in gain or loss of genomic regions. A likely source of copy number variation is an incorrect repair of DNA damage that causes chromosome instability (CI). Hence, many tumors with a high degree of CNAs suffer CI. This is the case of high grade serous ovarian cancer (HGSOC), where deficiency in homologous recombination (HRD) is highly prevalent, and so are CI and CNAs. Clinically, HR status in HGSOC is a biomarker of iPARP response and required for proper patient management. Available HRD tests measure genomic loss of heterozygosity (LOH) features, closely related to CI. Such features are complex to acquire in samples with low tumor content; overall, 15% of HGSOC specimens fail HR testing. CNA profiling is technically more amenable to lower tumor cellularity and may be captured by a wider range of techniques and applications (gene panels, low pass WGS...). We present here the analysis and identification of CNA features in HGSOC as a novel biomarker of HR status. Methods: A cohort of 123 primary HGSOC tumors were analysed with a custom hybrid capture-based NGS panel (VHIO-300) that provides, along mutations in 450 genes, genome-wide CNA profiles. B-allele fractions, obtained from single nucleotide polymorphisms also in the panel, allowed HRD-LOH score calculation (Marquard et al., 2015). 41 CNA segments correlated to HRD-LOH scores (proportions test, FDR < 0.001) and selected as features to calculate a CNA-HGSOC score (range 0-100). Among them, 10 segments appeared altered almost exclusively in BRCA1/2 pathogenic/likely pathogenic mutant tumors. Results: The density plot of the CNA-HGSOC score showed a bimodal distribution with modes at 7 and 65. A cut-off value of 34 was selected based on the lowest density CNA-score value between the modes. Hence, high CNA-HGSOC was defined as tumors with scores ≥ 34. Our CNA-HGSOC score was strongly correlated as a continuous variable to HRD-LOH (Pearson correlation = 0.72; p < 0.0001) and ROC analysis (AUC = 0.84; CI 95% 0.77-0.91; p < 0.0001) demonstrated high predictability to classify tumors as if using an HRD-LOH test (HRD-LOH score; “very high”: 100-75, “high”: 74-50,”mid”: 49-25 and “low”: 24-0). BRCA mutation status was also accurately predicted using a subset of CNA features (AUC = 0.71; CI 95% 0.59-0.82; p < 0.0001). Conclusions: CNAs may provide a new powerful genomic resource to HRD determination. We identified 41 CNA features in tumors to inform HR status and a subset of 10 revealed mutation status of BRCA1/2 in HGSOC. Upon further validation, a CNA-HCSOC score would be easily implemented in routine analysis pipelines in clinical labs, allowing HR testing or even broaden its application to emerging fields, such as liquid biopsy.

Tumor cell enrichment by tissue suspension enables detection of mutations with low variant allele frequency and estimation of germline mutations

Targeted sequencing offers an opportunity to select specific drugs for cancer patients based on alterations in their genome. However, accurate sequencing cannot be performed in cancers harboring diffuse tumor cells because of low tumor content. We performed tumor cell enrichment using tissue suspension of formalin-fixed, paraffin-embedded (FFPE) tissue sections with low tumor cell content. The enriched fractions were used to efficiently identify mutations by sequencing a target panel of cancer-related genes. Tumor-enriched and residual fractions were isolated from FFPE tissue sections of intestinal and diffuse gastric cancers harboring diffuse tumor cells and DNA of suitable quality was isolated for next-generation sequencing. Sequencing of a target panel of cancer-related genes using the tumor-enriched fraction increased the number of detectable mutations and variant allele frequency. Furthermore, mutation analysis of DNA isolated from tumor-enriched and residual fractions allowed us to estimate germline mutations without a blood reference. This approach of tumor cell enrichment will not only enhance the success rate of target panel sequencing, but can also improve the accuracy of detection of somatic mutations in archived specimens.

Accurate Prediction of Chromosome-Level CNVs from Targeted NGS

Background: Aneuploidy and large-scale Copy Number Variations (CNVs) are prominent features of cancer cells. While Fluorescence in situ hybridization (FISH) and conventional cytogenetics (CC) are the gold standard for detecting aneuploidy and CNVs, NGS-based assays are currently used for high-resolution detection of copy number alterations assessing the whole genome. However, although an increasing number of NGS-based tools have been developed for detecting aneuploidy or CNVs from whole genome or exome sequencing data, only a limited number of options are available for targeted gene panels. Despite mechanisms provided to establish normal profiles for a specific panel, the accuracy of these tools at the chromosome level suffer when only a small number of regions are targeted on each chromosome. Here we leveraged on a custom amplicon based NGS assay designed to detect somatic alterations (SNVs and indels) in 297 hematological cancer relevant genes, previously validated in our clinical laboratory. We introduce a simple approach to accurately predict chromosome-level CNVs such as monosomy and trisomy for a targeted gene panel, commonly used in a clinical setting.Methods: Mutation profiles, including SNVs, INDELs, and structural changes, were interrogated with an in-house bioinformatics pipeline that utilized PureCN and CNVkit algorithms to detect structural changes. The first step consists of finding optimal panel-specific decision thresholds for gains and losses at the gene level. This step was performed using an independent set of 1,314 clinical samples sequenced with the NeoType® Heme assay developed by NeoGenomics Laboratories, Inc. for which at least one FISH test was performed in addition to the sequencing. Three genes (ATM, TP53, and NF1) were used to find optimal decision thresholds based on the FISH result for these markers. These thresholds are used afterward to predict a gain or a loss for any other gene in the panel. The second step consists of predicting the chromosome-level gain or loss based on the individual predictions at the gene level by simply observing the frequency of targeted genes on the corresponding chromosome predicted as either gained or lost by the first step approach. The 19, 7, and 18 targeted genes in the NGS panel (Table 1) were respectively used to predict monosomy 7, trisomy 8, and trisomy 12 in a second set of over 7,000 clinical samples with known ploidy for chromosomes with clinically relevant ploidy abnormalities in hematological malignancies.Results: Evaluation of the first stage gene-level CNV prediction on 1,314 clinical samples shows a concordance rate of 97.95% between NGS and FISH results on ATM, TP53, and NF1. When we evaluated the second stage chromosome-level CNV prediction in clinical samples sequenced using the same targeted panel and assessed by FISH for chromosome-level variation on chromosomes 7, 8 and 12 (Table 1), a heatmap of the predicted Log 2 ratios for each sample and targeted gene from the first step shows a clear distinctive signal between aneuploidy and diploid samples (Figure 1). At the chromosome level, the concordance rate between the final prediction and the FISH results is consistently observed above 93% (Table 2). Roughly 50% of the 12, 78, and 40 discordant calls for monosomy 7, trisomy 8, and trisomy 12, respectively captured by FISH but not by NGS can be explained by low tumor content (less than 20%) in the tested samples. The concordance rate between NGS and FISH is consistently observed above 96% when leaving these samples aside. Note that results in Table 2 are obtained using all samples to decide the optimal decision threshold for the chromosome-level prediction, but are found identical when using a leave-one-out evaluation procedure, and nearly identical when using a repeated cross-validation procedure.Conclusion: This study demonstrates that chromosome-level CNVs can be accurately predicted in hematologic malignancies even when the number of targeted genes on a given chromosome is low. Despite the simplicity of the approach, the two stages bioinformatics pipeline based on an ensemble method allowed us to gain between 8% and 46% accuracy compared to relying only on the prediction of a single tool like PureCN. Samples with low tumor content remain, however, a difficult case to tackle with bulk NGS as it is difficult to distinguish a CNV from the natural variability of the sequencing coverage. [Display omitted] DisclosuresNam: NeoGenomics Laboratories, Inc.: Current Employment. Magnan: NeoGenomics Laboratories, Inc.: Current Employment. Lopez-Diaz: NeoGenomics Laboratories, Inc.: Current Employment. Bender: NeoGenomics Laboratories, Inc.: Current Employment. Agersborg: NeoGenomics Laboratories, Inc.: Current Employment. Jung: NeoGenomics Laboratories, Inc.: Current Employment. Funari: NeoGenomics Laboratories, Inc.: Current Employment.

Rapid non-destructive volumetric tumor yield assessment in fresh lung core needle biopsies using polarization sensitive optical coherence tomography.

Adequate tumor yield in core-needle biopsy (CNB) specimens is essential in lung cancer for accurate histological diagnosis, molecular testing for therapeutic decision-making, and tumor biobanking for research. Insufficient tumor sampling in CNB is common, primarily due to inadvertent sampling of tumor-associated fibrosis or atelectatic lung, leading to repeat procedures and delayed diagnosis. Currently, there is no method for rapid, non-destructive intraprocedural assessment of CNBs. Polarization-sensitive optical coherence tomography (PS-OCT) is a high-resolution, volumetric imaging technique that has the potential to meet this clinical need. PS-OCT detects endogenous tissue properties, including birefringence from collagen, and degree of polarization uniformity (DOPU) indicative of tissue depolarization. Here, PS-OCT birefringence and DOPU measurements were used to quantify the amount of tumor, fibrosis, and normal lung parenchyma in 42 fresh, intact lung CNB specimens. PS-OCT results were compared to and validated against matched histology in a blinded assessment. Linear regression analysis showed strong correlations between PS-OCT and matched histology for quantification of tumors, fibrosis, and normal lung parenchyma in CNBs. PS-OCT distinguished CNBs with low tumor content from those with higher tumor content with high sensitivity and specificity. This study demonstrates the potential of PS-OCT as a method for rapid, non-destructive, label-free intra-procedural tumor yield assessment.

Sensitive detection of tumor mutations from blood and its application to immunotherapy prognosis

Cell-free DNA (cfDNA) is attractive for many applications, including detecting cancer, identifying the tissue of origin, and monitoring. A fundamental task underlying these applications is SNV calling from cfDNA, which is hindered by the very low tumor content. Thus sensitive and accurate detection of low-frequency mutations (<5%) remains challenging for existing SNV callers. Here we present cfSNV, a method incorporating multi-layer error suppression and hierarchical mutation calling, to address this challenge. Furthermore, by leveraging cfDNA’s comprehensive coverage of tumor clonal landscape, cfSNV can profile mutations in subclones. In both simulated and real patient data, cfSNV outperforms existing tools in sensitivity while maintaining high precision. cfSNV enhances the clinical utilities of cfDNA by improving mutation detection performance in medium-depth sequencing data, therefore making Whole-Exome Sequencing a viable option. As an example, we demonstrate that the tumor mutation profile from cfDNA WES data can provide an effective biomarker to predict immunotherapy outcomes.

Abstract 567: cMonitor: Comprehensive and sensitive monitoring of diverse cancer treatment outcomes by exome-wide mutation analysis in plasma cfDNA

Abstract Monitoring cancer patients for the early detection of minimal residual disease (MRD), cancer recurrence, and cancer progression is essential for assessing treatment response and predicting early relapse during/after treatment. Plasma cell-free DNA (cfDNA) provides unique opportunities for cancer monitoring given its non-invasive and comprehensive sampling of heterogeneous tumor clones. However, the low tumor content in cfDNA poses a major challenge for detecting tumor signals. Previous methods mostly rely on deep sequencing of small panels to capture the tumor signal. These methods usually require the labor-intensive design of customized gene panel, yet fail to identify the evolving tumor, which is essential for detecting second primary diseases or emerging subclones. To address these limitations, here we present OncoMonitor, a cancer monitoring method that comprehensively analyzes tumor mutations in cfDNA whole-exome sequencing data. Taking advantage of the availability of mutation information across the whole exome, our method (1) integrates all clonal tumor mutations identified from pre-treatment cfDNA samples (or tumor samples) to compensate for low tumor fraction in cfDNA, (2) suppresses sequencing errors at read level with an accurate random forest classifier to model the observed features from cfDNA fragments and further enhance the tumor signal, (3) builds sample-specific background noise distributions to predict recurrence and MRD to avoid interference from inter-individual variations and inter-experimental biases, and (4) detects tumor changes, especially second primary diseases, by identifying newly emerging tumor mutations de novo. Combining these techniques, for the first time, we show that OncoMonitor can sensitively and specifically detect both cancer recurrence/MRD and secondary primary cancers from plasma samples with low tumor fraction. Using simulated cfDNA sequence data with artificial mutation spike-ins, our method can detect recurrence at 0.025% tumor fraction with &amp;gt; 95% sensitivity and 95% specificity, and the second primary disease at 0.1% tumor fraction with around 75% sensitivity and 100% specificity. In a cohort of 9 non-small-cell lung cancer patients, we show that OncoMonitor can provide comprehensive tumor changes for treatment response prediction and capture emerging tumor clones; this cannot be achieved by previous methods that are based only on mutations in the pre-treatment surgery samples. In summary, with broad genomic sequencing coverage and comprehensive mutation analysis, our method can identify patients suffering from MRD or cancer recurrence/progression, provide a thorough view of their tumor status, and enable early intervention and personalized treatment. Citation Format: Shuo Li, Weihua Zeng, Xiaohui Ni, Mary L. Stackpole, Yonggang Zhou, Zorawar Noor, Zuyang Yuan, Edward B. Garon, Steven M. Dubinett, Wenyuan Li, Xianghong Zhou. cMonitor: Comprehensive and sensitive monitoring of diverse cancer treatment outcomes by exome-wide mutation analysis in plasma cfDNA [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 567.

Modeling clonal structure over narrow time frames via circulating tumor DNA in metastatic breast cancer

BackgroundCirculating tumor DNA (ctDNA) offers minimally invasive means to repeatedly interrogate tumor genomes, providing opportunities to monitor clonal dynamics induced by metastasis and therapeutic selective pressures. In metastatic cancers, ctDNA profiling allows for simultaneous analysis of both local and distant sites of recurrence. Despite the promise of ctDNA sampling, its utility in real-time genetic monitoring remains largely unexplored.MethodsIn this exploratory analysis, we characterize high-frequency ctDNA sample series collected over narrow time frames from seven patients with metastatic triple-negative breast cancer, each undergoing treatment with Cabozantinib, a multi-tyrosine kinase inhibitor (NCT01738438, https://clinicaltrials.gov/ct2/show/NCT01738438). Applying orthogonal whole exome sequencing, ultra-low pass whole genome sequencing, and 396-gene targeted panel sequencing, we analyzed 42 plasma-derived ctDNA libraries, representing 4–8 samples per patient with 6–42 days between samples. Integrating tumor fraction, copy number, and somatic variant information, we model tumor clonal dynamics, predict neoantigens, and evaluate consistency of genomic information from orthogonal assays.ResultsWe measured considerable variation in ctDNA tumor faction in each patient, often conflicting with RECIST imaging response metrics. In orthogonal sequencing, we found high concordance between targeted panel and whole exome sequencing in both variant detection and variant allele frequency estimation (specificity = 95.5%, VAF correlation, r = 0.949), Copy number remained generally stable, despite resolution limitations posed by low tumor fraction. Through modeling, we inferred and tracked distinct clonal populations specific to each patient and built phylogenetic trees revealing alterations in hallmark breast cancer drivers, including TP53, PIK3CA, CDK4, and PTEN. Our modeling revealed varied responses to therapy, with some individuals displaying stable clonal profiles, while others showed signs of substantial expansion or reduction in prevalence, with characteristic alterations of varied literature annotation in relation to the study drug. Finally, we predicted and tracked neoantigen-producing alterations across time, exposing translationally relevant detection patterns.ConclusionsDespite technical challenges arising from low tumor content, metastatic ctDNA monitoring can aid our understanding of response and progression, while minimizing patient risk and discomfort. In this study, we demonstrate the potential for high-frequency monitoring of evolving genomic features, providing an important step toward scalable, translational genomics for clinical decision making.

Clinical utility of whole genome sequencing in hematological neoplasms.

7028 Background: Hematological neoplasms are often characterized by acute onset and rapid disease progression. Cytogenetics, FISH, SNP arrays, targeted DNA and RNA sequencing are performed to inform diagnosis, risk stratification and guide treatment decisions. Whole genome sequencing (WGS) offers the opportunity to comprehensively characterize all putative biomarkers in a single assay. However, a limitation in current WGS implementation is the requirement for a germline sample, as sources of control tissue are frequently contaminated with leukemic cells resulting in false negative calls. Methods: To evaluate the clinical utility and feasibility of WGS in the diagnostic work up of leukemias, we analyzed 57 B-cell acute lymphoblastic leukemia (B-ALL) from the UKALL14 trial (NCT01085617) with no informative biomarkers at diagnosis. WGS analysis was performed on the leukemic sample and a matching control sample (with minimal residual disease level of &lt;1%). Using this dataset, we trained the development of an unmatched (uWGS) analytical workflow (Isabl) for a tumor only WGS study. This workflow was validated across 20 hematologic neoplasms (12 B-ALL, 6 AML and 2 T-ALL). Results: Among the 57 cases, 5 failed QC owing to low tumor content (&lt;25%). Of the remaining 52, putative biomarkers of clinical relevance were identified by WGS in 69% (36/52). These included delineation of aberrant karyotypes where conventional chromosome banding failed (4/52), the detection of newly described fusion genes (such as IGH-DUX4 and EP300-ZNF384 in 21/52) and recurrent gene mutations (i.e. PAX5 P80R, ZEB2 H1038R in 11/52). uWGS workflow in our training dataset captured 86% of biomarkers identified in the matched analysis (3/3 ploidy, 21/22 fusion and 7/11 coding). Concordance between the matched and uWGS workflow for arm-level and focal copy number alterations (CNAs), structural variants (SVs) and annotated hotspot mutations were 94%, 84%, 83% and 100% respectively. Independent validation of the uWGS workflow across 20 myeloid and lymphoid neoplasms, recapitulated all clinically reported biomarkers (14/15 CNAs, 16/16 SVs) as well as captured two novel findings not previously detected in two B-ALL patients, to include a focal deletion in BTG1 and the fusion gene P2RY8-CRLF2, as well as a NOTCH1 translocation in T-ALL. Conclusions: Our findings demonstrate that comprehensive WGS allows for the detection of the same biomarkers as a range of clinical assays using a single test, as well as the opportunity to discover novel clinical and research findings to support future correlative research and biomarker development. Additionally, we developed and validated an uWGS workflow that allows WGS analysis of hematopoietic neoplasms at diagnosis, enabling detection and reporting of clinically relevant biomarkers.

Influence of low tumor content on tumor mutational burden estimation by whole-exome sequencing and targeted panel sequencing.

BackgroundTumor mutational burden (TMB) is a promising biomarker for stratifying patient subpopulation who would benefit from immune checkpoint blockade (ICB) therapies. Although great efforts have been made for standardizing TMB measurement, mutation calling and TMB quantification can be challenging in samples with low tumor content including liquid biopsies. The effect of varying tumor content on TMB estimation by different assay methods has never been systematically investigated.MethodWe established a series of reference standard DNA samples derived from 11 pairs of tumor–normal matched human cell lines across different cancer types. Each tumor cell line was mixed with its matched normal at 0% (control), 1%, 2%, 5%, and 10% mass‐to‐mass ratio to mimic the clinical samples with low tumor content. TMB of these reference standards was evaluated by both ∼1000× whole‐exome sequencing (wesTMB) and targeted panel sequencing (psTMB) at four different vendors. Both regression and classification analyses of TMB were performed for theoretical investigation and clinical practice purposes.ResultsLinear regression model was established that demonstrated in silico psTMB determined by regions of interest (ROI) as a great representative of wesTMB based on TCGA dataset. It was also true in our reference standard samples as the predicted psTMB interval based on the observed wesTMB captured the intended 90% of the in silico psTMB values. Although ∼1000× deep WES was applied, reference standard samples with less than 5% of tumor proportions are below the assay limit of detection (LoD) of wesTMB quantification. However, predicted wesTMB based on observed psTMB accurately classify (>0.97 AUC) for TMB high and low patient stratification even in samples with 2% of tumor content, which is more clinically relevant, as TMB determination should be a qualitative assay for TMB high and low patient classification. One targeted panel sequencing vendor using an optimized blood psTMB pipeline can further classify TMB status accurately (>0.82 AUC) in samples with only 1% of tumor content.ConclusionsWe developed a linear model to establish the quantitative correlation between wesTMB and psTMB. A set of DNA reference standards was produced in aid to standardize TMB measurements in samples with low tumor content across different targeted sequencing panels. This study is a significant contribution aiming to harmonize TMB estimation and extend its future application in clinical samples with low tumor content including liquid biopsy.

High sensitivity sanger sequencing detection of BRAF mutations in metastatic melanoma FFPE tissue specimens

Mutations in the BRAF gene at or near the p. V600 locus are informative for therapy selection, but current methods for analyzing FFPE tissue DNA generally have a limit of detection of 5% variant allele frequency (VAF), or are limited to the single variant (V600E). These can result in false negatives for samples with low VAFs due to low tumor content or subclonal heterogeneity, or harbor non-V600 mutations. Here, we show that Sanger sequencing using the NuProbe VarTrace BRAF assay, based on the Blocker Displacement Amplification (BDA) technology, is capable of detecting BRAF V600 mutations down to 0.20% VAF from FFPE lymph node tissue samples. Comparison experiments on adjacent tissue sections using BDA Sanger, immunohistochemistry (IHC), digital droplet PCR (ddPCR), and NGS showed 100% concordance among all 4 methods for samples with BRAF mutations at ≥ 1% VAF, though ddPCR did not distinguish the V600K mutation from the V600E mutation. BDA Sanger, ddPCR, and NGS (with orthogonal confirmation) were also pairwise concordant for lower VAF mutations down to 0.26% VAF, but IHC produced a false negative. Thus, we have shown that Sanger sequencing can be effective for rapid detection and quantitation of multiple low VAF BRAF mutations from FFPE samples. BDA Sanger method also enabled detection and quantitation of less frequent, potentially actionable non-V600 mutations as demonstrated by synthetic samples.

Automated Dissection Protocol for Tumor Enrichment in Low Tumor Content Tissues.

Tumor enrichment in low tumor content tissues, those below 20% tumor content depending on the method, is required to generate quality data reproducibly with many downstream assays such as next generation sequencing. Automated tissue dissection is a new methodology that automates and improves tumor enrichment in these common, low tumor content tissues by decreasing the user-dependent imprecision of traditional macro-dissection and time, cost, and expertise limitations of laser capture microdissection by using digital image annotation overlay onto unstained slides. Here, digital hematoxylin and eosin (H&E) annotations are used to target small tumor areas using a blade that is 250 µm2 in diameter in unstained formalin fixed paraffin embedded (FFPE) or fresh frozen sections up to 20 µm in thickness for automated tumor enrichment prior to nucleic acid extraction and whole exome sequencing (WES). Automated dissection can harvest annotated regions in low tumor content tissues from single or multiple sections for nucleic acid extraction. It also allows for capture of extensive pre- and post-harvest collection metrics while improving accuracy, reproducibility, and increasing throughput with utilization of fewer slides. The described protocol enables digital annotation with automated dissection on animal and/or human FFPE or fresh frozen tissues with low tumor content and could also be used for any region of interest enrichment to boost adequacy for downstream sequencing applications in clinical or research workflows.

Automated Dissection Protocol for Tumor Enrichment in Low Tumor Content Tissues

Digital annotation with automated tissue dissection provides an innovative approach to enriching tumor in low tumor content cases and is adaptable to both paraffin and frozen tissue types. The described workflow improves accuracy, reproducibility and throughput and could be applied to both research and clinical settings.

SNP-based detection of allelic imbalance: A novel approach for identifying KIAA1549-BRAF fusion in pilocytic astrocytoma using DNA sequencing

Pilocytic astrocytoma (PA) is the most common glioma subtype found in children, and it is a non-malignant tumor type. The majority of PAs is caused by an approximately 2 Mb tandem duplication within 7q34 which creates an in-frame KIAA1549-BRAF fusion gene. The kinase domain of BRAF is fused to the N-terminal of KIAA1549, whereby BRAF is constitutively activated. We here present a novel approach for identifying KIAA1549-BRAF fusion based on single nucleotide polymorphism (SNP) analysis and next generation sequencing (NGS). Highly polymorphic SNPs in the duplicated area and in adjacent areas were selected and a custom targeted amplicon based NGS panel was designed. The panel was tested on DNA extracted from formalin fixed and paraffin embedded tissue from a retrospective cohort, consisting of biopsies from patients with PA, anaplastic astrocytoma, oligodendroglioma and glioblastoma as well as two non-tumor biopsies. The panel could distinguish chromosome 7 gain from BRAF fusion and correctly identified 8/9 PA samples with KIAA1549-BRAF fusion confirmed by RNA sequencing. The one biopsy where no fusion was detected was fresh frozen and from the RNA sequencing expected to have very low tumor content. No allelic imbalance was detected in either oligodendroglioma or in the non-tumor biopsies.

Innovative Tumor Tissue Dissection Tool for Molecular Oncology Diagnostics

Formalin-fixed, paraffin-embedded (FFPE) tissue is the most commonly used material for tumor molecular profiling, therapy selection, and prognostication. Tumor tissue enrichment by tissue dissection is highly recommended to generate quality data reproducibly for use in downstream assays, such as real-time PCR and next-generation sequencing. The aim of this study was to evaluate the performance of the automated tissue dissection tool AVENIO Millisect System compared with a manual dissection method using 18 FFPE tissue specimens. The study assessed performance of these two methods with paraffinized and deparaffinized sections at 5- and 10-μm thickness as well as at low (5% to 10%) and high (>50%) tumor content. In addition, compatibility with various nucleic acid and protein extraction methods was assessed. Overall, dissection by Millisect resulted in statistically significantly higher yields of nucleic acids and protein compared with manual dissection (P=0.00524). In downstream analysis on a statistically nonpowered sample set, EGFR mutation testing by PCR led to highly concordant results, and next-generation sequencing testing yielded significantly higher allelic frequencies when tissue was dissected by Millisect compared with manual scraping, demonstrating noninferiority of the automated method. In summary, the AVENIO Millisect System may replace manual labor and support automation of FFPE tumor tissue workflows in clinical molecular laboratories with high testing volumes with adequate validation.

Comparison of adequacy between transbronchial lung cryobiopsy samples and endobronchial ultrasound-guided transbronchial needle aspiration samples for next-generation sequencing analysis.

BackgroundMost lung cancer patients present with lesions in both lung fields and lymphadenopathy. Thus, transbronchial lung cryobiopsy (TBLC) and endobronchial ultrasound‐guided transbronchial needle aspiration (EBUS‐TBNA) are commonly performed for diagnosing lung cancer. However, the adequacy of these samples for next‐generation sequencing (NGS) analysis remains unclear. This study aimed to compare the adequacy between TBLC and EBUS‐TBNA samples for NGS analysis.MethodsThis retrospective cohort study included patients whose lung samples were collected via TBLC or EBUS‐TBNA and analyzed using NGS. Out of 46 genes, the number of genes in TBNA and TBLC samples that could not be assessed via NGS analysis was mainly evaluated.ResultsA total of 37 patients were included and classified into two groups (TBLC group, n = 18 and TBNA group, n = 19). The mean number of genes that could not be evaluated via NGS analysis was significantly lower in the TBLC group than in the TBNA group (0.9 vs. 10.3, P = 0.024). The median total area of tumor cells in TBLC samples was significantly greater than that in TBNA samples (6.3 [1.6–4.2] vs. 2.6 [0.2–17.3] mm2, P < 0.01). In the TBNA group, there were two fully inadequate samples for NGS analysis with a high degree of cell crush or low tumor content, while there was no fully inadequate sample in the TBLC group.ConclusionsTBLC is more effective in obtaining adequate samples for NGS analysis than EBUS‐TBNA. TBLC should be performed to obtain adequate samples for NGS analysis in lung cancer patients with target lesions in lung fields, even if they have lymphadenopathy.Key points Significant findings of the study The mean number of genes that could not be evaluated was significantly lower in TBLC samples than in EBUS‐TBNA samples (0.9 vs. 10.3, P = 0.024). TBLC could obtain adequate samples with a high concentration of uncrushed tumor cells for NGS. What this study adds To obtain samples for NGS analysis, the use of TBLC should be aggressively considered in lung‐cancer patients with target lesions located in lung fields, even if they have lymphadenopathy.

A Highly Sensitive and Specific Gene Fusion Algorithm Based on Multiple Fusion Callers and an Ensemble Machine Learning Approach

Background: Gene Fusion events are common occurrences in malignancies, and are frequently drivers of malignancy. FISH and qPCR are two methods often used for identifying highly prevalent gene fusions/translocations. However, these are single target assays, requiring a lot of effort and sample if multiple assays are needed for multiple targets like sarcoma. High-throughput parallel (NextGen) DNA and RNA sequencing are also in current use to detect and characterize gene fusions. RNA sequencing (RNAseq) has the advantage that multiple markers can be targeted at one time and RNA fusions are readily identified from their product transcripts. While many fusion calling algorithms exist for use on RNAseq data, sensitive fusion callers, needed for samples of low tumor content, often present high false positive rates. Further, there currently is no single variable or element in NGS data that can be used to filter out false positive calls by extant callers. Individual sensitive fusion callers may be considered weak predictors of gene fusions. Combining their results into a single fusion call involves evaluating many elements, which can be a time consuming and difficult manual task. In order to achieve higher accuracy in fusion calls than can be achieved using individual fusion callers, we have combined the results of multiple fusion callers by use of an ensemble learning approach based on random forest models. Our method selects the best group of callers from among several callers, and provides an algorithmic means of combining their results, presenting a metric that can be immediately interpreted as the probability that a called fusion is a true fusion call. Methods: Random forest models were generated with the randomForest package in R, and then tuned using the R caret package. Training data sets consisted of fusion calls deemed true by review and by orthogonal methods including PCR/Sanger sequencing and the commercial Archer™ fusion calling system. We present the results of training on calls made by five fusion callers Arriba, STAR-Fusion, FusionCatcher, deFuse, and Kallisto/pizzly. Logistic training variables (seen vs not seen by the fusion caller) were used for the five callers. Variables also included metrics for the magnitude and balance of coverage on either side of candidate fusion breakpoints reported by Arriba and STAR Fusion ("coverage balance") and a single metric consisting of the number of sequencing reads that cross the candidate breakpoint. The model was validated by 10-fold cross-validation on 598 fusion calls by the five callers. Results: The resulting model is superior to the simple strategy of requiring agreement by n of five callers, particularly with regard to specificity (Table 1). Also, "importance of variables," reported by randomForest, gauges the relative contribution of variables in the model. Here it shows that one caller, Kallisto\pizzly, does not contribute to the model (Table 2). Conclusion: Random Forest modeling provides a viable means of combining gene fusion call data from multiple callers into a single fusion calling tool with improved performance over simple combinations of fusion calls. An additional benefit is seen in that building and evaluating such models can guide the selection of fusion callers, thereby eliminating non-contributory calling methods and ensuring optimal utilization of computational resources. Disclosures Thomas: NeoGenomics,Inc.: Current Employment. Mou:NeoGenomics: Current Employment. Keeler:NeoGenomics: Current Employment. Magnan:NeoGenomics: Current Employment. Funari:NeoGenomics: Current Employment. Weiss:Merck: Other: Speaker; Bayer: Other: speaker; Genentech: Other: Speaker; NeoGenomics: Current Employment. Brown:NeoGenomics,Inc.: Current Employment. Agersborg:NeoGenomics: Current Employment.

Abstract 802: Implementation of optimized research biopsy analyses for clinical pharmacodynamic (PD) studies

Abstract Research biopsies collected for evaluation of pharmacodynamic (PD) biomarkers present requirements beyond those obtained for diagnostic purposes. Despite establishing stringent post-collection handling and processing procedures, we have found that many biopsy pairs collected from the early phase trial patient population demonstrate significant tissue heterogeneity and viable tumor content too low to adequately assess biomarker response in the tumor cell population. A retrospective analysis of samples collected from NCI's Developmental Therapeutics Clinic (DTC) from 1/2010-9/2016 revealed an initial success rate for analysis of paired biopsies of only 53%; this was primarily due to a high incidence of biopsies with no or very low tumor content. Based on this initial high failure rate, several clinical and laboratory initiatives were undertaken to identify and mitigate factors which contribute to biopsy unsuitability. After consultation with other translational oncology teams to review best practices, we have published and implemented critical recommendations to improve research biopsy quality at the clinical project team level (PMID: 30285529). In addition to important updates to the clinical trial strategy, several actionable improvements have been identified and implemented at the laboratory level to significantly increase the success rate of research biopsy analyses. Improvements in the biopsy processing approach include use of multiple 18-gauge biopsy cores per timepoint as a standard approach for all PD trials, implementation of methods to prevent biopsy curling during fixation and processing to ensure full longitudinal facing of the tissue during sectioning, and deeper sectioning of the blocks with H&amp;E evaluation of flanking slides by an anatomic pathologist with subsequent release of nonadjacent slides from the most optimal slide range for the primary PD analyses. Development of advanced image analysis methods including the use of β-catenin as a tissue segmentation marker for multiplex immunofluorescence assays has allowed for successful analysis of biopsies with significant heterogeneity and low tumor content. Additionally, defining the minimum cell number required to reach significance for PD marker measurements and capturing additional image fields in low tumor content biopsies to reach the required threshold has led to the implementation of highly robust methods across multiple clinical trials and laboratories. An analysis of biopsies collected from 10/2016-9/2019, after which several clinical and laboratory improvements had been implemented, revealed a significantly improved success rate for paired biopsies of 83%. Funded by NCI Contract No HHSN261200800001E. Citation Format: Katherine V. Ferry-Galow, Deborah Wilsker, Robert J. Kinders, Hala R. Makhlouf, Angie Dull, Tony Navas, Rachel E. Andrews, Geraldine O'Sullivan Coyne, Alice P. Chen, James H. Doroshow, Ralph E. Parchment. Implementation of optimized research biopsy analyses for clinical pharmacodynamic (PD) studies [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 802.