SJPedPanel: A Pan-Cancer Gene Panel for Childhood Malignancies to Enhance Cancer Monitoring and Early Detection Article Swipe
Pandurang Kolekar
,
Vidya Balagopal
,
Li Dong
,
Y. Liu
,
Scott G. Foy
,
Quang Tran
,
Heather L. Mulder
,
Anna L. W. Huskey
,
Emily M. Plyler
,
Zhikai Liang
,
Jingqun Ma
,
Joy Nakitandwe
,
Jiali Gu
,
Maria Namwanje
,
Jamie L. Maciaszek
,
Debbie Payne-Turner
,
Saradhi Mallampati
,
Lu Wang
,
John Easton
,
Jeffery M. Klco
,
Xiaotu Ma
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1158/1078-0432.ccr-24-1063
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1158/1078-0432.ccr-24-1063
Purpose: The purpose of the study was to design a pan-cancer gene panel for childhood malignancies and validate it using clinically characterized patient samples. Experimental Design: In addition to 5,275 coding exons, SJPedPanel also covers 297 introns for fusions/structural variations and 7,590 polymorphic sites for copy-number alterations. Capture uniformity and limit of detection are determined by targeted sequencing of cell lines using dilution experiment. We validate its coverage by in silico analysis of an established real-time clinical genomics (RTCG) cohort of 253 patients. We further validate its performance by targeted resequencing of 113 patient samples from the RTCG cohort. We demonstrate its power in analyzing low tumor burden specimens using morphologic remission and monitoring samples. Results: Among the 485 pathogenic variants reported in RTCG cohort, SJPedPanel covered 86% of variants, including 82% of 90 rearrangements responsible for fusion oncoproteins. In our targeted resequencing cohort, 91% of 389 pathogenic variants are detected. The gene panel enabled us to detect ∼95% of variants at allele fraction (AF) 0.5%, whereas the detection rate is ∼80% at AF 0.2%. The panel detected low-frequency driver alterations from morphologic leukemia remission samples and relapse-enriched alterations from monitoring samples, demonstrating its power for cancer monitoring and early detection. Conclusions: SJPedPanel enables the cost-effective detection of clinically relevant genetic alterations including rearrangements responsible for subtype-defining fusions by targeted sequencing of ∼0.15% of human genome for childhood malignancies. It will enhance the analysis of specimens with low tumor burdens for cancer monitoring and early detection.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1158/1078-0432.ccr-24-1063
- OA Status
- hybrid
- Cited By
- 10
- References
- 106
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4400944132
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4400944132Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1158/1078-0432.ccr-24-1063Digital Object Identifier
- Title
-
SJPedPanel: A Pan-Cancer Gene Panel for Childhood Malignancies to Enhance Cancer Monitoring and Early DetectionWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-07-24Full publication date if available
- Authors
-
Pandurang Kolekar, Vidya Balagopal, Li Dong, Y. Liu, Scott G. Foy, Quang Tran, Heather L. Mulder, Anna L. W. Huskey, Emily M. Plyler, Zhikai Liang, Jingqun Ma, Joy Nakitandwe, Jiali Gu, Maria Namwanje, Jamie L. Maciaszek, Debbie Payne-Turner, Saradhi Mallampati, Lu Wang, John Easton, Jeffery M. Klco, Xiaotu MaList of authors in order
- Landing page
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https://doi.org/10.1158/1078-0432.ccr-24-1063Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1158/1078-0432.ccr-24-1063Direct OA link when available
- Concepts
-
Cancer, Childhood cancer, Cancer detection, Medicine, Oncology, Internal medicineTop concepts (fields/topics) attached by OpenAlex
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10Total citation count in OpenAlex
- Citations by year (recent)
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2025: 7, 2024: 3Per-year citation counts (last 5 years)
- References (count)
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106Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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