MinerVa: A high performance bioinformatic algorithm for the detection of minimal residual disease in solid tumors_Journal of Biomedical Engineering

Authors：

YANG Piao ¹ , ZHANG Yaxi ¹ , XIA Liang ² , MEI Jiandong ² , FAN Rui ¹ , HUANG Yu ¹ , LIU Lunxu ² ,  CHEN Weizhi ¹

1. Genecast Biotechnology Co., Ltd, Wuxi, Jiangsu 214000, P. R. China;
2. Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu 610041, P. R. China;

Corresponding author：

CHEN Weizhi, Email: chen.weizhi@genecast.com.cn

Keywords：

Circulating tumor DNA; Minimal residual disease; Multi-variant joint confidence analysis; Technical noise baseline; Single-base resolution

DOI：

10.7507/1001-5515.202303039

Video：

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Abstract Full text Figures/Tables Video References Cited by

How to improve the performance of circulating tumor DNA (ctDNA) signal acquisition and the accuracy to authenticate ultra low-frequency mutation are major challenges of minimal residual disease (MRD) detection in solid tumors. In this study, we developed a new MRD bioinformatics algorithm, namely multi-variant joint confidence analysis (MinerVa), and tested this algorithm both in contrived ctDNA standards and plasma DNA samples of patients with early non-small cell lung cancer (NSCLC). Our results showed that the specificity of multi-variant tracking of MinerVa algorithm ranged from 99.62% to 99.70%, and when tracking 30 variants, variant signals could be detected as low as 6.3 × 10⁻⁵ variant abundance. Furthermore, in a cohort of 27 NSCLC patients, the specificity of ctDNA-MRD for recurrence monitoring was 100%, and the sensitivity was 78.6%. These findings indicate that the MinerVa algorithm can efficiently capture ctDNA signals in blood samples and exhibit high accuracy in MRD detection.

Citation： YANG Piao, ZHANG Yaxi, XIA Liang, MEI Jiandong, FAN Rui, HUANG Yu, LIU Lunxu, CHEN Weizhi. MinerVa: A high performance bioinformatic algorithm for the detection of minimal residual disease in solid tumors. Journal of Biomedical Engineering, 2023, 40(2): 313-319. doi: 10.7507/1001-5515.202303039 Copy

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7.	Pang S, Li H, Xu S, et al. Circulating tumour cells at baseline and late phase of treatment provide prognostic value in breast cancer. Sci Rep, 2021, 11(1): 13441.
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9.	Moding E J, Nabet B Y, Alizadeh A A, et al. Detecting liquid remnants of solid tumors: circulating tumor dna minimal residual disease. Cancer Discov, 2021, 11(12): 2968-2986.
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11.	Zviran A, Schulman R C, Shah M, et al. Genome-wide cell-free DNA mutational integration enables ultra-sensitive cancer monitoring. Nat Med, 2020, 26(7): 1114-1124.
12.	Ma X, Shao Y, Tian L, et al. Analysis of error profiles in deep next-generation sequencing data. Genome Biol, 2019, 20(1): 50.
13.	Xia L, Mei J, Kang R, et al. Perioperative ctDNA-based molecular residual disease detection for non-small cell lung cancer: A prospective multicenter cohort study (LUNGCA-1). Clin Cancer Res, 2022, 28(15): 3308-3317.
14.	Wan J C M, Heider K, Gale D, et al. ctDNA monitoring using patient-specific sequencing and integration of variant reads. Sci Transl Med, 2020, 12(548): eaaz8084.
15.	Abelson S, Zeng A G X, Nofech-Mozes I, et al. Integration of intra-sample contextual error modeling for improved detection of somatic mutations from deep sequencing. Sci Adv, 2020, 6(50): eabe3722.
16.	Newman A M, Bratman S V, To J, et al. An ultrasensitive method for quantitating circulating tumor DNA with broad patient coverage. Nat Med, 2014, 20(5): 548-554.
17.	Newman A M, Lovejoy A F, Klass D M, et al. Integrated digital error suppression for improved detection of circulating tumor DNA. Nat Biotechnol, 2016, 34(5): 547-555.
18.	Qiu B, Guo W, Zhang F, et al. Dynamic recurrence risk and adjuvant chemotherapy benefit prediction by ctDNA in resected NSCLC. Nat Commun, 2021, 12(1): 6770.
19.	Taniguchi H, Nakamura Y, Kotani D, et al. CIRCULATE-Japan: Circulating tumor DNA-guided adaptive platform trials to refine adjuvant therapy for colorectal cancer. Cancer Sci, 2021, 112(7): 2915-2920.

1. Ulz P, Perakis S, Zhou Q, et al. Inference of transcription factor binding from cell-free DNA enables tumor subtype prediction and early detection. Nat Commun, 2019, 10(1): 4666.
2. Nguyen H T, Khoa Huynh L A, Nguyen T V, et al. Multimodal analysis of ctDNA methylation and fragmentomic profiles enhances detection of nonmetastatic colorectal cancer. Future Oncol, 2022, 18(35): 3895-3912.
3. Benson A B, Venook A P, Al-Hawary M M, et al. Colon Cancer, Version 2. 2021, NCCN Clinical Practice Guidelines in Oncology. J Natl Compr Canc Netw, 2021, 19(3): 329-359.
4. Kasi P M, Fehringer G, Taniguchi H, et al. Impact of circulating tumor DNA-based detection of molecular residual disease on the conduct and design of clinical trials for solid tumors. JCO Precis Oncol, 2022, 6: e2100181.
5. Tie J, Cohen J D, Wang Y, et al. Circulating tumor dna analyses as markers of recurrence risk and benefit of adjuvant therapy for stage III colon cancer. JAMA Oncol, 2019, 5(12): 1710-1717.
6. Christensen E, Birkenkamp-Demtroder K, Sethi H, et al. Early detection of metastatic relapse and monitoring of therapeutic efficacy by ultra-deep sequencing of plasma cell-free DNA in patients with urothelial bladder carcinoma. J Clin Oncol, 2019, 37(18): 1547-1557.
7. Pang S, Li H, Xu S, et al. Circulating tumour cells at baseline and late phase of treatment provide prognostic value in breast cancer. Sci Rep, 2021, 11(1): 13441.
8. Chaudhuri A A, Chabon J J, Lovejoy A F, et al. Early detection of molecular residual disease in localized lung cancer by circulating tumor DNA profiling. Cancer Discov, 2017, 7(12): 1394-1403.
9. Moding E J, Nabet B Y, Alizadeh A A, et al. Detecting liquid remnants of solid tumors: circulating tumor dna minimal residual disease. Cancer Discov, 2021, 11(12): 2968-2986.
10. Avanzini S, Kurtz D M, Chabon J J, et al. A mathematical model of ctDNA shedding predicts tumor detection size. Sci Adv, 2020, 6(50): eabc4308.
11. Zviran A, Schulman R C, Shah M, et al. Genome-wide cell-free DNA mutational integration enables ultra-sensitive cancer monitoring. Nat Med, 2020, 26(7): 1114-1124.
12. Ma X, Shao Y, Tian L, et al. Analysis of error profiles in deep next-generation sequencing data. Genome Biol, 2019, 20(1): 50.
13. Xia L, Mei J, Kang R, et al. Perioperative ctDNA-based molecular residual disease detection for non-small cell lung cancer: A prospective multicenter cohort study (LUNGCA-1). Clin Cancer Res, 2022, 28(15): 3308-3317.
14. Wan J C M, Heider K, Gale D, et al. ctDNA monitoring using patient-specific sequencing and integration of variant reads. Sci Transl Med, 2020, 12(548): eaaz8084.
15. Abelson S, Zeng A G X, Nofech-Mozes I, et al. Integration of intra-sample contextual error modeling for improved detection of somatic mutations from deep sequencing. Sci Adv, 2020, 6(50): eabe3722.
16. Newman A M, Bratman S V, To J, et al. An ultrasensitive method for quantitating circulating tumor DNA with broad patient coverage. Nat Med, 2014, 20(5): 548-554.
17. Newman A M, Lovejoy A F, Klass D M, et al. Integrated digital error suppression for improved detection of circulating tumor DNA. Nat Biotechnol, 2016, 34(5): 547-555.
18. Qiu B, Guo W, Zhang F, et al. Dynamic recurrence risk and adjuvant chemotherapy benefit prediction by ctDNA in resected NSCLC. Nat Commun, 2021, 12(1): 6770.
19. Taniguchi H, Nakamura Y, Kotani D, et al. CIRCULATE-Japan: Circulating tumor DNA-guided adaptive platform trials to refine adjuvant therapy for colorectal cancer. Cancer Sci, 2021, 112(7): 2915-2920.

Journal of Biomedical Engineering

MinerVa: A high performance bioinformatic algorithm for the detection of minimal residual disease in solid tumors

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