Title |
Circulating tumor DNA dynamically predicts response and/or relapse in patients with hematological malignancies
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Published in |
International Journal of Hematology, June 2018
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DOI | 10.1007/s12185-018-2487-2 |
Pubmed ID | |
Authors |
Sousuke Nakamura, Kazuaki Yokoyama, Nozomi Yusa, Miho Ogawa, Tomomi Takei, Asako Kobayashi, Mika Ito, Eigo Shimizu, Rika Kasajima, Yuka Wada, Rui Yamaguchi, Seiya Imoto, Tokiko Nagamura-Inoue, Satoru Miyano, Arinobu Tojo |
Abstract |
A growing body of evidence suggests that tumor-derived fragmentary DNA, known as circulating tumor DNA (ctDNA), has the potential to serve as a non-invasive biomarker for disease monitoring. However, in the setting of hematological malignancy, few published studies support the utility of ctDNA. We retrospectively investigated ctDNA levels of 17 patients with various hematological malignancies who had achieved remission after first-line therapy. We identified somatic driver mutations by next-generation sequencing, and designed droplet digital PCR assays for each mutation to measure ctDNA. Variant allele frequencies of ctDNA changed in association with clinical response in all patients. Eight patients clinically relapsed after a median of 297 days post-first-line therapy (termed, "relapsed group"); the remaining nine patients remained disease-free for a median of 332 days (termed, "remission group"). Among patients in the relapsed group, ctDNA levels increased more than twofold at paired serial time points. In marked contrast, ctDNA levels of all patients in the remission group remained undetectable or stable during clinical remission. Notably, ctDNA-based molecular relapse demonstrated a median 30-day lead time over clinical relapse. In summary, ctDNA monitoring may help identify hematologic cancer patients at risk for relapse in advance of established clinical parameters. |
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Geographical breakdown
Country | Count | As % |
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Unknown | 1 | 100% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 1 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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Unknown | 37 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
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Student > Ph. D. Student | 8 | 22% |
Student > Doctoral Student | 5 | 14% |
Researcher | 5 | 14% |
Other | 3 | 8% |
Student > Master | 2 | 5% |
Other | 5 | 14% |
Unknown | 9 | 24% |
Readers by discipline | Count | As % |
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Medicine and Dentistry | 13 | 35% |
Biochemistry, Genetics and Molecular Biology | 5 | 14% |
Agricultural and Biological Sciences | 5 | 14% |
Chemistry | 2 | 5% |
Neuroscience | 1 | 3% |
Other | 1 | 3% |
Unknown | 10 | 27% |