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Stradella A, Johnson M, Goel S, Chandana S, Galsky M, Calvo E, Moreno V, Park H, Arkenau T, Cervantes A, Fariñas-Madrid L, Mileshkin L, Fu S, Plummer R, Evans J, Horvath L, Prawira A, Qu K, Pelham R, Barve M. 530MO Clinical benefit in biomarker-positive patients (pts) with locally advanced or metastatic solid tumours treated with the PARP1/2 inhibitor pamiparib in combination with low-dose (LD) temozolomide (TMZ). Ann Oncol 2020. [DOI: 10.1016/j.annonc.2020.08.644] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
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Jamal-Hanjani M, Wilson GA, Horswell S, Mitter R, Sakarya O, Constantin T, Salari R, Kirkizlar E, Sigurjonsson S, Pelham R, Kareht S, Zimmermann B, Swanton C. Detection of ubiquitous and heterogeneous mutations in cell-free DNA from patients with early-stage non-small-cell lung cancer. Ann Oncol 2016; 27:862-7. [PMID: 26823523 DOI: 10.1093/annonc/mdw037] [Citation(s) in RCA: 123] [Impact Index Per Article: 15.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2015] [Accepted: 01/19/2016] [Indexed: 01/14/2023] Open
Abstract
BACKGROUND The aim of this pilot study was to assess whether both ubiquitous and heterogeneous somatic mutations could be detected in cell-free DNA (cfDNA) from patients with early-stage non-small-cell lung cancer (NSCLC). PATIENTS AND METHODS Three stage I and one stage II primary NSCLC tumors were subjected to multiregion whole-exome sequencing (WES) and validated with AmpliSeq. A subset of ubiquitous and heterogeneous single-nucleotide variants (SNVs) were chosen. Multiplexed PCR using custom-designed primers, coupled with next-generation sequencing (mPCR-NGS), was used to detect these SNVs in both tumor DNA and cfDNA isolated from plasma obtained before surgical resection of the tumors. The limit of detection for each assay was determined using cfDNA from 48 presumed-normal healthy volunteers. RESULTS Tumor DNA and plasma-derived cfDNA was successfully amplified and sequenced for 37/50 (74%) SNVs using the mPCR-NGS method. Twenty-five (68%) were ubiquitous and 12 (32%) were heterogeneous SNVs. Variant detection by mPCR-NGS and WES-AmpliSeq in tumor tissue was well correlated (R(2) = 0.8722, P < 0.0001). Sixteen (43%) out of 37 SNVs were detected in cfDNA. Twelve of these were ubiquitous SNVs with a variant allele frequency (VAF) range of 0.15-23.25%, and four of these were heterogeneous SNVs with a VAF range of 0.28-1.71%. There was a statistically significant linear relationship between the VAFs for tumor and cfDNA (R(2) = 0.5144; P = 0.0018). For all four patients, at least two variants were detected in plasma. The estimated number of copies of variant DNA present in each sample ranged from 5 to 524. The average number of variant copies required for detection (VCRD) was 3.16 (range: 0.2-7.6 copies). CONCLUSIONS The mPCR-NGS method revealed intratumor heterogeneity in early-stage NSCLC tumors, and was able to detect both ubiquitous and heterogeneous SNVs in cfDNA. Further validation of mPCR-NGS in cfDNA is required to define its potential use in clinical practice.
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Affiliation(s)
- M Jamal-Hanjani
- Translational Cancer Therapeutics Laboratory, Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London
| | - G A Wilson
- Translational Cancer Therapeutics Laboratory, Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London Translational Cancer Therapeutics Laboratory, The Francis Crick Institute, London
| | - S Horswell
- Department of Bioinformatics and Biostatistics, The Francis Crick Institute, London, UK
| | - R Mitter
- Department of Bioinformatics and Biostatistics, The Francis Crick Institute, London, UK
| | - O Sakarya
- Department of Bioinformatics and Biostatistics, The Francis Crick Institute, London, UK
| | | | | | | | | | | | | | | | - C Swanton
- Translational Cancer Therapeutics Laboratory, Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London Translational Cancer Therapeutics Laboratory, The Francis Crick Institute, London
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Baker J, Liu ML, Crager M, Stephans J, Pho M, Jeong J, Scott A, Ambannavar R, Morlan J, Pelham R, Qu K, Mena RR, Esteban J, Collin F, Sinicropi D. PD03-09: Breast Cancer Recurrence Risk Probed by Whole Transcriptome Next Generation Sequencing in 136 Patients. Cancer Res 2011. [DOI: 10.1158/0008-5472.sabcs11-pd03-09] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Abstract
Background: RNA biomarkers discovered by RT-PCR-based gene expression profiling of archival formalin-fixed paraffin-embedded (FFPE) tissue are the basis for very precise and sensitive clinical diagnostic tests, such as the 21 gene Oncotype DX® breast cancer assay. Both inherent limits of technical scalability and the small amounts of patient FFPE RNA available place practical constraints on the number of transcripts that can be interrogated by RT-PCR. We developed new methods for RNA profiling through massively parallel “next generation” sequencing (RNA-Seq) of archival FFPE specimens. We report here the technical performance of this methodology and compare the results to RT-PCR results obtained in one of the studies that were carried out to develop the 21 gene assay.
Methods: RNA was extracted in 2002 from 136 invasive breast tumors that were formalin-fixed and paraffin-embedded between 1990 and 1997. RNA-Seq was carried out using minor modifications to methods we have reported previously (Sinicropi et al., Advances in Genome Biology and Technology Conference, p. 170, 2010 and p. 198, 2011). Briefly, 0.1 mg of total RNA was selectively depleted of ribosomal RNA and sequencing libraries were prepared using a modification of the ScriptSeq™ kit from Epicentre. The libraries were sequenced on an Illumina HiSeq 2000 instrument with multiplexing of two libraries per lane for 50 cycles in one direction. The resulting FASTQ sequences were mapped to version hg19 of the human genome using the Illumina CASAVA pipeline. The total number of sequences (reads) that uniquely mapped to all exons of each RefSeq entry was used for quantification of expression levels.
Results: On average, there were 43 million reads per sample (range 31 - 58 million; SD=4.6 million) of which 69% uniquely mapped to the human genome. Ribosomal RNA was effectively removed and accounted for <0.3% of total counts. Significant coverage of a high proportion of the human genome was obtained, with 40% of RefSeq transcripts represented by a median of more than 100 reads. Using Cox proportional hazards analysis to evaluate the association of quantitative gene expression with breast cancer recurrence, the standardized hazard ratios and p-values for the 21 Oncotype DX genes determined by RNA-Seq were comparable to those originally obtained using RT-PCR. Moreover, whole transcriptome RNA-Seq identified more than 1800 new coding, intronic, and intergenic transcripts that strongly associated with breast cancer recurrence risk (at a false discovery rate <10%) and revealed heretofore unappreciated co-expressed gene networks. Summary: New methodology has been developed for application of next generation sequencing-based whole transcriptome profiling to small amounts of archival FFPE tissue. This technology has sensitivity and selectivity comparable to RT-PCR, can provide a vast increase in the number of interrogated transcripts, can reveal new biological relationships, and has excellent performance suitable for the discovery of RNA biomarkers.
Citation Information: Cancer Res 2011;71(24 Suppl):Abstract nr PD03-09.
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Affiliation(s)
- J Baker
- 1Genomic Health Inc., Redwood City, CA; Providence St. Joseph Medical Center, Burbank, CA
| | - M-L Liu
- 1Genomic Health Inc., Redwood City, CA; Providence St. Joseph Medical Center, Burbank, CA
| | - M Crager
- 1Genomic Health Inc., Redwood City, CA; Providence St. Joseph Medical Center, Burbank, CA
| | - J Stephans
- 1Genomic Health Inc., Redwood City, CA; Providence St. Joseph Medical Center, Burbank, CA
| | - M Pho
- 1Genomic Health Inc., Redwood City, CA; Providence St. Joseph Medical Center, Burbank, CA
| | - J Jeong
- 1Genomic Health Inc., Redwood City, CA; Providence St. Joseph Medical Center, Burbank, CA
| | - A Scott
- 1Genomic Health Inc., Redwood City, CA; Providence St. Joseph Medical Center, Burbank, CA
| | - R Ambannavar
- 1Genomic Health Inc., Redwood City, CA; Providence St. Joseph Medical Center, Burbank, CA
| | - J Morlan
- 1Genomic Health Inc., Redwood City, CA; Providence St. Joseph Medical Center, Burbank, CA
| | - R Pelham
- 1Genomic Health Inc., Redwood City, CA; Providence St. Joseph Medical Center, Burbank, CA
| | - K Qu
- 1Genomic Health Inc., Redwood City, CA; Providence St. Joseph Medical Center, Burbank, CA
| | - RR Mena
- 1Genomic Health Inc., Redwood City, CA; Providence St. Joseph Medical Center, Burbank, CA
| | - J Esteban
- 1Genomic Health Inc., Redwood City, CA; Providence St. Joseph Medical Center, Burbank, CA
| | - F Collin
- 1Genomic Health Inc., Redwood City, CA; Providence St. Joseph Medical Center, Burbank, CA
| | - D Sinicropi
- 1Genomic Health Inc., Redwood City, CA; Providence St. Joseph Medical Center, Burbank, CA
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