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Cited by 224Cancer Genomics and DiagnosticsGenomics and Rare DiseasesBRCA gene mutations in cancer

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Development and Validation of a Scalable Next-Generation Sequencing System for Assessing Relevant Somatic Variants in Solid Tumors

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Hovelson, Daniel H., McDaniel, Andrew S., Cani, Andi K., Johnson, Bryan, Rhodes, Kate, Williams, Paul D., Bandla, Santhoshi, Bien, Geoffrey, Choppa, Paul, Hyland, Fiona, Gottimukkala, Rajesh, Liu, Guoying, Manivannan, Manimozhi, Schageman, Jeoffrey, Ballesteros-Villagrana, Efren, Grasso, Catherine S., Quist, Michael J., Yadati, Venkata, Amin, Anmol, Siddiqui, Javed, Betz, Bryan L., Knudsen, Karen E., Cooney, Kathleen A., Feng, Felix Y., Roh, Michael H., Nelson, Peter S., Liu, Chia-Jen, Beer, David G., Wyngaard, Peter, Chinnaiyan, Arul M., Sadis, Seth, Rhodes, Daniel R., Tomlins, Scott A.·2015·Neoplasia
DOI 10.1016/j.neo.2015.03.004PMC PMC4415141
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Abstract

Next-generation sequencing (NGS) has enabled genome-wide personalized oncology efforts at centers and companies with the specialty expertise and infrastructure required to identify and prioritize actionable variants. Such approaches are not scalable, preventing widespread adoption. Likewise, most targeted NGS approaches fail to assess key relevant genomic alteration classes. To address these challenges, we predefined the catalog of relevant solid tumor somatic genome variants (gain-of-function or loss-of-function mutations, high-level copy number alterations, and gene fusions) through comprehensive bioinformatics analysis of >700,000 samples. To detect these variants, we developed the Oncomine Comprehensive Panel (OCP), an integrative NGS-based assay [compatible with <20 ng of DNA/RNA from formalin-fixed paraffin-embedded (FFPE) tissues], coupled with an informatics pipeline to specifically identify relevant predefined variants and created a knowledge base of related potential treatments, current practice guidelines, and open clinical trials. We validated OCP using molecular standards and more than 300 FFPE tumor samples, achieving >95% accuracy for KRAS, epidermal growth factor receptor, and BRAF mutation detection as well as for ALK and TMPRSS2:ERG gene fusions. Associating positive variants with potential targeted treatments demonstrated that 6% to 42% of profiled samples (depending on cancer type) harbored alterations beyond routine molecular testing that were associated with approved or guideline-referenced therapies. As a translational research tool, OCP identified adaptive CTNNB1 amplifications/mutations in treated prostate cancers. Through predefining somatic variants in solid tumors and compiling associated potential treatment strategies, OCP represents a simplified, broadly applicable targeted NGS system with the potential to advance precision oncology efforts.

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Figure 1 Pan-solid tumor cancer somatic alteration analysis to identify relevant variants. (A) Using the Oncomine database supplemented with data from COSMIC, more than 700,000 tumor samples (including > 8000 cancer exomes) were used to assess genes for overrepresentation of hotspot (GoF) and deleterious (LoF) mutations to identify oncogenes and tumor suppressors, respectively. Array-based copy number profiles from > 30,000 tumors were assessed by MCR analysis to identify targets of focal, high-level amplifications or deletions. Transcriptomes from > 7000 cancers were similarly assessed for driver gene fusions. Prioritized genes were further filtered to include only near-term relevant alterations for inclusion into the OCP. (B) Frequency of somatic alterations (type according to color in the legend) in OCP included genes across publically available TCGA data. For each gene per cancer type, alteration frequency (< 0.01 to > 0.20) is indicated by the size of the circle according to the legend. Selected genes of interest are highlighted. (C) The OCP was designed for compatibility with routine FFPE tissues, with co-isolation of DNA/RNA from FFPE tissues used in our validation. The OCP consists of multiplexed PCR (AmpliSeq) panels compatible with 20 ng of DNA and 15 ng of RNA, which can be combined after library generation for NGS on Ion Torrent benchtop sequencers. By predefining relevant somatic variants, identified variants can be linked to potential treatment strategies.
Figure 1 Pan-solid tumor cancer somatic alteration analysis to identify relevant variants. (A) Using the Oncomine database supplemented with data from COSMIC, more than 700,000 tumor samples (including > 8000 cancer exomes) were used to assess genes for overrepresentation of hotspot (GoF) and deleterious (LoF) mutations to identify oncogenes and tumor suppressors, respectively. Array-based copy number profiles from > 30,000 tumors were assessed by MCR analysis to identify targets of focal, high-level amplifications or deletions. Transcriptomes from > 7000 cancers were similarly assessed for driver gene fusions. Prioritized genes were further filtered to include only near-term relevant alterations for inclusion into the OCP. (B) Frequency of somatic alterations (type according to color in the legend) in OCP included genes across publically available TCGA data. For each gene per cancer type, alteration frequency (< 0.01 to > 0.20) is indicated by the size of the circle according to the legend. Selected genes of interest are highlighted. (C) The OCP was designed for compatibility with routine FFPE tissues, with co-isolation of DNA/RNA from FFPE tissues used in our validation. The OCP consists of multiplexed PCR (AmpliSeq) panels compatible with 20 ng of DNA and 15 ng of RNA, which can be combined after library generation for NGS on Ion Torrent benchtop sequencers. By predefining relevant somatic variants, identified variants can be linked to potential treatment strategies.CC BY-NC-ND · PubMed Central
Figure 2 Validation of the OCP using an oncology cohort undergoing molecular diagnostics testing. (A) We applied the OCP to a prospectively identified cohort of FFPE cancer samples undergoing molecular diagnostics testing for somatic mutations in BRAF, KRAS or EGFR, or ALK rearrangements (MO cohort). All OCP-defined relevant alterations from the RNA (in the header) and DNA components of the OCP for the 104 informative samples are shown in the heat map. Specific alteration types are indicated according to the legend (Nonsyn. SNV = nonsynonymous SNV; Fs and Fp indel = frame-shifting and frame-preserving indels, respectively). Slashed boxes indicate two alterations. Samples not sequenced in OCP RNA analysis are indicated as in the legend. Samples excluded from copy number analysis due to noisy profiles are shown in italics. Clinicopathologic information is given in the header according to the legend (LUAD, lung adenocarcinoma; COAD, colon adenocarcinoma; MEL, melanoma); 100% concordance with molecular testing was observed for mutations (see Table W10). Detailed OCP RNA-seq results, including 3′/5′ expression imbalance, for the ALK rearrangement–positive lung cancers are shown in Figure 3B. (B) Integrative OCP results from two cases, MO-17 and MO-25 (names bolded in A) harboring relevant gene fusions. Copy number plots show log2 copy number ratios (compared to a composite normal sample) per amplicon, with each individual amplicon represented by a single dot and individual genes indicated by different colors. Gene-level copy number estimates are shown as black bars. By OCP, MO-17 (top), a BRAF wild-type melanoma by clinical testing, harbored CDKN2A high-level copy number loss, TP53 R158L mutation, and a novel ERC1:BRAF gene fusion. OCP profiling of MO-35, a KRAS/BRAF wild-type colon adenocarcinoma by clinical testing, identified an FBXW7 L647fs mutation and a TPR:NTRK1 gene fusion. For mutations, variant allele containing reads/total reads and the variant allele frequency are shown. (C) Validation of OCP identified gene fusions using qRT-PCR for ERC1:BRAF [ERC1 exon 12 fused to BRAF exon 9 (E12B9, blue) or 10 (E12B10, cyan)] and TPR:NTRK1 [TPR exon 21 fused to NTRK1 exon 10 (T21N10, orange)]. qRT-PCR was performed on MO-17, MO-35, and five control MO samples without OCP-detected gene fusions. Mean log2 expression (normalized to the arithmetic mean of glyceraldehyde-3-phosphate dehydrogenase (GAPDH) + hydroxymethylbilane synthase (HMBS) calibrated to the mean of the MO control samples) + SD of triplicate qPCRs is plotted. No detectable expression of ERC1:BRAF or TPR:NTRK1 was present in any sample other than that identified by OCP.
Figure 2 Validation of the OCP using an oncology cohort undergoing molecular diagnostics testing. (A) We applied the OCP to a prospectively identified cohort of FFPE cancer samples undergoing molecular diagnostics testing for somatic mutations in BRAF, KRAS or EGFR, or ALK rearrangements (MO cohort). All OCP-defined relevant alterations from the RNA (in the header) and DNA components of the OCP for the 104 informative samples are shown in the heat map. Specific alteration types are indicated according to the legend (Nonsyn. SNV = nonsynonymous SNV; Fs and Fp indel = frame-shifting and frame-preserving indels, respectively). Slashed boxes indicate two alterations. Samples not sequenced in OCP RNA analysis are indicated as in the legend. Samples excluded from copy number analysis due to noisy profiles are shown in italics. Clinicopathologic information is given in the header according to the legend (LUAD, lung adenocarcinoma; COAD, colon adenocarcinoma; MEL, melanoma); 100% concordance with molecular testing was observed for mutations (see Table W10). Detailed OCP RNA-seq results, including 3′/5′ expression imbalance, for the ALK rearrangement–positive lung cancers are shown in Figure 3B. (B) Integrative OCP results from two cases, MO-17 and MO-25 (names bolded in A) harboring relevant gene fusions. Copy number plots show log2 copy number ratios (compared to a composite normal sample) per amplicon, with each individual amplicon represented by a single dot and individual genes indicated by different colors. Gene-level copy number estimates are shown as black bars. By OCP, MO-17 (top), a BRAF wild-type melanoma by clinical testing, harbored CDKN2A high-level copy number loss, TP53 R158L mutation, and a novel ERC1:BRAF gene fusion. OCP profiling of MO-35, a KRAS/BRAF wild-type colon adenocarcinoma by clinical testing, identified an FBXW7 L647fs mutation and a TPR:NTRK1 gene fusion. For mutations, variant allele containing reads/total reads and the variant allele frequency are shown. (C) Validation of OCP identified gene fusions using qRT-PCR for ERC1:BRAF [ERC1 exon 12 fused to BRAF exon 9 (E12B9, blue) or 10 (E12B10, cyan)] and TPR:NTRK1 [TPR exon 21 fused to NTRK1 exon 10 (T21N10, orange)]. qRT-PCR was performed on MO-17, MO-35, and five control MO samples without OCP-detected gene fusions. Mean log2 expression (normalized to the arithmetic mean of glyceraldehyde-3-phosphate dehydrogenase (GAPDH) + hydroxymethylbilane synthase (HMBS) calibrated to the mean of the MO control samples) + SD of triplicate qPCRs is plotted. No detectable expression of ERC1:BRAF or TPR:NTRK1 was present in any sample other than that identified by OCP.CC BY-NC-ND · PubMed Central
Figure 3 OCP identified relevant somatic alterations, including gene fusions, in a lung cancer cohort. (A) We applied the OCP to a retrospective cohort of FFPE lung tumors selected to represent diverse pathology (LU cohort). All OCP defined relevant alterations from the RNA (in header) and DNA components of the OCP for the 101 informative samples are shown in the heat map. Clinicopathologic information is given in the header according to the legend [Met, metastasis; LUSQ, squamous cell carcinoma; ADSQ, adenosquamous carcinoma; BAC, bronchioloalveolar carcinoma (adenocarcinoma in situ or well-differentiated lepidic predominant adenocarcinoma); SCC, small cell carcinoma; Resect., resection; Bx, biopsy]. All 101 informative lung samples were included in OCP RNA analysis. Samples excluded from copy number analysis due to noisy profiles are shown in italics. (B) In addition to primers for pan-cancer prioritized 5′ and 3′ gene fusion partners, OCP includes 5′ and 3′ amplicons for ALK, ROS1, and RET to identify 3′/5′ expression imbalance indicative of gene fusions. For all lung tumors (including those from the MO cohort), normalized OCP RNA-seq expression of gene fusions involving ALK (red) and ROS1 (green) is plotted. No fusions involving RET were detected. Corresponding normalized 3′/5′ expression imbalance for ALK (top panel) and ROS1 (middle panel) for each sample is plotted. ALK rearrangement positive (bolded red), negative (blue), or untested (gray) samples by molecular testing are indicated.
Figure 3 OCP identified relevant somatic alterations, including gene fusions, in a lung cancer cohort. (A) We applied the OCP to a retrospective cohort of FFPE lung tumors selected to represent diverse pathology (LU cohort). All OCP defined relevant alterations from the RNA (in header) and DNA components of the OCP for the 101 informative samples are shown in the heat map. Clinicopathologic information is given in the header according to the legend [Met, metastasis; LUSQ, squamous cell carcinoma; ADSQ, adenosquamous carcinoma; BAC, bronchioloalveolar carcinoma (adenocarcinoma in situ or well-differentiated lepidic predominant adenocarcinoma); SCC, small cell carcinoma; Resect., resection; Bx, biopsy]. All 101 informative lung samples were included in OCP RNA analysis. Samples excluded from copy number analysis due to noisy profiles are shown in italics. (B) In addition to primers for pan-cancer prioritized 5′ and 3′ gene fusion partners, OCP includes 5′ and 3′ amplicons for ALK, ROS1, and RET to identify 3′/5′ expression imbalance indicative of gene fusions. For all lung tumors (including those from the MO cohort), normalized OCP RNA-seq expression of gene fusions involving ALK (red) and ROS1 (green) is plotted. No fusions involving RET were detected. Corresponding normalized 3′/5′ expression imbalance for ALK (top panel) and ROS1 (middle panel) for each sample is plotted. ALK rearrangement positive (bolded red), negative (blue), or untested (gray) samples by molecular testing are indicated.CC BY-NC-ND · PubMed Central
Figure 4 Application of OCP to a prostate cancer cohort identifies variable alterations across histologic and treatment subtypes and confirms isoform-specific gene fusion detection. (A) We applied the OCP to a retrospective cohort of aggressive FFPE prostate cancers. All OCP-defined relevant alterations from the RNA (in the header) and DNA components of the OCP for the 116 informative samples are shown in the heat map. Clinicopathologic information is given in the header according to the legend (Met, metastasis; Pros., prostate; LN met, lymph node metastasis; PRAD, prostatic adenocarcinoma; SCC, small cell carcinoma; SQ, squamous differentiation; RRP, radical prostatectomy). For treatment subtype, ADT = prior androgen deprivation therapy, XRT = radiation therapy, ADT + = ADT plus XRT and/or chemotherapy, AR − = no (or reduced) AR signaling as indicated by no/focal prostate specific antigen (PSA) staining. Samples excluded from or not sequenced in OCP RNA analysis are indicated as in the legend. (B) The RNA component of the OCP contains forward primers in known 5′ fusion partners and reverse primers in known 3′ fusion partners for recurrent gene fusions in prostate cancer. Normalized log2 read counts for indicated gene fusion isoforms are indicated in each cell according to the color scale, with individual fusions indicated by the color blocks (right) and fusion isoforms named by the exon junctions of the involved genes (e.g., T2:ERG T1E4 indicates a fusion junction of TMPRSS2 exon 1 and ERG exon 4). qRT-PCR was previously performed on a subset of these cases, as indicated in qPCR type. T2:ERG T1E4 status (including low expression), and ERG outlier expression without T1E4 isoform detection (ERG+), ETV1 (ETV1+), ETV4 (ETV4+), or ETV5 (ETV5+) are indicated in the header. Samples without any of these alterations (Neg) or not tested (N/A) by qPCR are indicated.
Figure 4 Application of OCP to a prostate cancer cohort identifies variable alterations across histologic and treatment subtypes and confirms isoform-specific gene fusion detection. (A) We applied the OCP to a retrospective cohort of aggressive FFPE prostate cancers. All OCP-defined relevant alterations from the RNA (in the header) and DNA components of the OCP for the 116 informative samples are shown in the heat map. Clinicopathologic information is given in the header according to the legend (Met, metastasis; Pros., prostate; LN met, lymph node metastasis; PRAD, prostatic adenocarcinoma; SCC, small cell carcinoma; SQ, squamous differentiation; RRP, radical prostatectomy). For treatment subtype, ADT = prior androgen deprivation therapy, XRT = radiation therapy, ADT + = ADT plus XRT and/or chemotherapy, AR − = no (or reduced) AR signaling as indicated by no/focal prostate specific antigen (PSA) staining. Samples excluded from or not sequenced in OCP RNA analysis are indicated as in the legend. (B) The RNA component of the OCP contains forward primers in known 5′ fusion partners and reverse primers in known 3′ fusion partners for recurrent gene fusions in prostate cancer. Normalized log2 read counts for indicated gene fusion isoforms are indicated in each cell according to the color scale, with individual fusions indicated by the color blocks (right) and fusion isoforms named by the exon junctions of the involved genes (e.g., T2:ERG T1E4 indicates a fusion junction of TMPRSS2 exon 1 and ERG exon 4). qRT-PCR was previously performed on a subset of these cases, as indicated in qPCR type. T2:ERG T1E4 status (including low expression), and ERG outlier expression without T1E4 isoform detection (ERG+), ETV1 (ETV1+), ETV4 (ETV4+), or ETV5 (ETV5+) are indicated in the header. Samples without any of these alterations (Neg) or not tested (N/A) by qPCR are indicated.CC BY-NC-ND · PubMed Central
Figure 5 Automated treatment prioritization by OCP identifies relevant alterations beyond routine molecular testing. (A) For each OCP assessed cohort, the breakdown of the highest prioritized alteration per sample is shown, according to whether the alteration is associated with 1) FDA-approved therapies (red), 2) therapies within NCCN indications (orange), 3) therapies outside that specific cancer type’s NCCN indication (yellow), or 4) clinical trial entry requirements (blue). This assessment incorporates variants precluding treatment strategies based on other identified variants but does not prioritize variants that only exclude approved agents. Individual prioritized alterations are indicated as slices of each pie and are shown in the histogram. (B) Integrative OCP profiling prioritized high-level ERBB2 copy gains in two lung carcinomas. Integrative OCP results are shown as in Figure 2B (gene fusions were not identified in either sample). OCP profiling prioritized high-level ERBB2 copy number gains in MO-65 (top), an EGFR/ALK wild-type lung adenocarcinoma by diagnostic molecular testing, and LU-31 (bottom), a lung SCC with no previous molecular diagnostic testing. Morphology by hematoxylin and eosin staining is shown (inset of LU-31 shows typical small cell morphology). Diffuse 3 + ERBB2 protein expression was confirmed by IHC.
Figure 5 Automated treatment prioritization by OCP identifies relevant alterations beyond routine molecular testing. (A) For each OCP assessed cohort, the breakdown of the highest prioritized alteration per sample is shown, according to whether the alteration is associated with 1) FDA-approved therapies (red), 2) therapies within NCCN indications (orange), 3) therapies outside that specific cancer type’s NCCN indication (yellow), or 4) clinical trial entry requirements (blue). This assessment incorporates variants precluding treatment strategies based on other identified variants but does not prioritize variants that only exclude approved agents. Individual prioritized alterations are indicated as slices of each pie and are shown in the histogram. (B) Integrative OCP profiling prioritized high-level ERBB2 copy gains in two lung carcinomas. Integrative OCP results are shown as in Figure 2B (gene fusions were not identified in either sample). OCP profiling prioritized high-level ERBB2 copy number gains in MO-65 (top), an EGFR/ALK wild-type lung adenocarcinoma by diagnostic molecular testing, and LU-31 (bottom), a lung SCC with no previous molecular diagnostic testing. Morphology by hematoxylin and eosin staining is shown (inset of LU-31 shows typical small cell morphology). Diffuse 3 + ERBB2 protein expression was confirmed by IHC.CC BY-NC-ND · PubMed Central

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Authors
Hovelson, D. H., McDaniel, A. S., Cani, A. K., Johnson, B., Rhodes, K., Williams, P. D., Bandla, S., Bien, G., Choppa, P., Hyland, F., Gottimukkala, R., Liu, G., Manivannan, M., Schageman, J., Ballesteros-Villagrana, E., Grasso, C. S., Quist, M. J., Yadati, V., Amin, A., Siddiqui, J., Betz, B. L., Knudsen, K. E., Cooney, K. A., Feng, F. Y., Roh, M. H., Nelson, P. S., Liu, C.-J., Beer, D. G., Wyngaard, P., Chinnaiyan, A. M., Sadis, S., Rhodes, D. R., & Tomlins, S. A.
Affiliations
  • University of Michigan (US)
  • Thermo Fisher Scientific (United States) (US)
  • Oregon Health & Science University (US)
  • Thomas Jefferson University (US)
  • Fred Hutch Cancer Center (US)
  • Howard Hughes Medical Institute (US)
Corresponding author
Tomlins, Scott A.
ORCID
Venue
Neoplasia
Publisher
Elsevier BV
Published
2015-04
Volume
17
Issue
4
Pages
385-399
ISSN
1476-5586 (ISSN-L)
DOI
10.1016/j.neo.2015.03.004
PMC
PMC4415141
OpenAlex
W2141420424
Cited by
224
References
56
Citations by year
2026: 1 · 2025: 7 · 2024: 13 · 2023: 17 · 2022: 14 · 2021: 21 · 2020: 23 · 2019: 29 · 2018: 28 · 2017: 28 · 2016: 28 · 2015: 14
FWCI
10.80
Type
journal article
Language
en
Open access
Licence
Locations
  • Neoplasia (publishedVersion, cc-by-nc-nd)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • National Human Genome Research Institute (UM1HG006508)
  • National Cancer Institute (R01CA159945, R01CA183857, R01CA181605, R01CA154365, P50CA186786)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer Genomics and Diagnostics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › BRCA gene mutations in cancer
Keywords
targeted next-generation sequencing, solid tumors, somatic mutations, actionable mutations, gene fusion, copy number alterations, formalin-fixed paraffin-embedded tissue, precision oncology, KRAS, EGFR, BRAF, anaplastic lymphoma kinase, TMPRSS2-ERG fusion, CTNNB1, prostate cancer, targeted therapy
MeSH terms
Transcriptional Regulator ERG, Anaplastic Lymphoma Kinase, Aged, DNA Mutational Analysis, DNA, Neoplasm, Female, Humans, Male, Middle Aged, Mutation, Neoplasms, Proto-Oncogene Proteins, Retrospective Studies, Serine Endopeptidases, Trans-Activators, Proto-Oncogene Proteins p21(ras), ras Proteins, Computational Biology, Receptor Protein-Tyrosine Kinases, Proto-Oncogene Proteins B-raf, beta Catenin, High-Throughput Nucleotide Sequencing, ErbB Receptors

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