Malignancy Assessment Using Gene Identification in Captured Cells Algorithm for the Prediction of Malignancy in Women With a Pelvic Mass

Richard George Moore, Negar Khazan, Madeline Ann Coulter, Rakesh Singh, Michael Craig Miller, Umayal Sivagnanalingam, Brent DuBeshter, Cynthia Angel, Cici Liu, Kelly Seto, David Englert, Philip Meachem, Kyu Kwang Kim, Richard George Moore, Negar Khazan, Madeline Ann Coulter, Rakesh Singh, Michael Craig Miller, Umayal Sivagnanalingam, Brent DuBeshter, Cynthia Angel, Cici Liu, Kelly Seto, David Englert, Philip Meachem, Kyu Kwang Kim

Abstract

Objective: To evaluate the detection of malignancy in women with a pelvic mass by using multiplexed gene expression analysis of cells captured from peripheral blood.

Methods: This was an IRB-approved, prospective clinical study. Eligible patients had a pelvic mass and were scheduled for surgery or biopsy. Rare cells were captured from peripheral blood obtained preoperatively by using a microfluidic cell capture device. Isolated mRNA from the captured cells was analyzed for expression of 72 different gene transcripts. Serum levels for several commonly assayed biomarkers were measured. All patients had a tissue diagnosis. Univariate and multivariate logistic regression analyses for the prediction of malignancy using gene expression and serum biomarker levels were performed, and receiver operating characteristic curves were constructed and compared.

Results: A total of 183 evaluable patients were enrolled (average age 56 years, range 19-91 years). There were 104 benign tumors, 17 low malignant potential tumors, and 62 malignant tumors. Comparison of the area under the receiver operating characteristic curve for individual genes and various combinations of genes with or without serum biomarkers to differentiate between benign conditions (excluding low malignant potential tumors) and malignant tumors showed that a multivariate model combining the expression levels of eight genes and four serum biomarkers achieved the highest area under the curve (AUC) (95.1%, 95% CI 92.0-98.2%). The MAGIC (Malignancy Assessment using Gene Identification in Captured Cells) algorithm significantly outperformed all individual genes (AUC 50.2-65.2%; all P <.001) and a multivariate model combining 14 different genes (AUC 88.0%, 95% CI 82.9-93.0%; P =.005). Further, the MAGIC algorithm achieved an AUC of 89.5% (95% CI 81.3-97.8%) for stage I-II and 98.9% (95% CI 96.7-100%) for stage III-IV patients with epithelial ovarian cancer.

Conclusion: Multiplexed gene expression evaluation of cells captured from blood, with or without serum biomarker levels, accurately detects malignancy in women with a pelvic mass.

Clinical trial registration: ClinicalTrials.gov, NCT02781272.

Funding source: This study was funded by ANGLE Europe Limited (Surrey Research Park, Guildford, Surrey, United Kingdom).

Conflict of interest statement

Financial Disclosure Richard G. Moore disclosed receiving consulting payments from Fujirebio Diagnostic Inc. and Abcodia Inc. He receives research funding from ANGLE Europe Limited. Michael C. Miller is a full-time employee of ANGLE North America, while Kelly Seto and David Englert are full-time employees of ANGLE Biosciences, Inc. Brent DuBeshter disclosed that money was paid to his institution from Angle PLC. The other authors did not report any potential conflicts of interest.

Copyright © 2022 The Author(s). Published by Wolters Kluwer Health, Inc.

Figures

Fig. 1.. Comparison of areas under receiver…
Fig. 1.. Comparison of areas under receiver operating characteristic curves for the genes-only algorithm (green), the serum biomarkers–only algorithm (red), and the MAGIC (Malignancy Assessment Using Gene Identification in Captured Cells) algorithm (genes and serum biomarkers) (orange). Benign (n=104) vs all cancers (n=62).

References

    1. Curtin JP. Management of the adnexal mass. Gynecol Oncol 1994;55:S42–6. doi: 10.1006/gyno.1994.1340
    1. Bast RC, Jr, Skates S, Lokshin A, Moore RG. Differential diagnosis of a pelvic mass: improved algorithms and novel biomarkers. Int J Gynecol Cancer 2012;22(suppl 1):S5–8. doi: 10.1097/IGC.0b013e318251c97d
    1. Bristow RE, Tomacruz RS, Armstrong DK, Trimble EL, Montz FJ. Survival effect of maximal cytoreductive surgery for advanced ovarian carcinoma during the platinum era: a meta-analysis. J Clin Oncol 2002;20:1248–59. doi: 10.1200/JCO.2002.20.5.1248
    1. Eisenkop SM, Spirtos NM, Montag TW, Nalick RH, Wang HJ. The impact of subspecialty training on the management of advanced ovarian cancer. Gynecol Oncol 1992;47:203–9. doi: 10.1016/0090-8258(92)90107-t
    1. Tingulstad S, Skjeldestad FE, Hagen B. The effect of centralization of primary surgery on survival in ovarian cancer patients. Obstet Gynecol 2003;102:499–505. doi: 10.1016/s0029-7844(03)00579-9
    1. Evaluation and management of adnexal masses. Practice Bulletin No. 174. American College of Obstetricians and Gynecologists. Obstet Gynecol 2016;128:e210–26. doi: 10.1097/AOG.0000000000001768
    1. Carney ME, Lancaster JM, Ford C, Tsodikov A, Wiggins CL. A population-based study of patterns of care for ovarian cancer: who is seen by a gynecologic oncologist and who is not? Gynecol Oncol 2002;84:36–42. doi: 10.1006/gyno.2001.6460
    1. Gershenson DM. Why American women are not receiving state-of-the-art gynecologic cancer care. Cancer J 2001;7:450–7.
    1. Bristow RE, Smith A, Zhang Z, Chan DW, Crutcher G, Fung ET, et al. Ovarian malignancy risk stratification of the adnexal mass using a multivariate index assay. Gynecol Oncol 2013;128:252–9. doi: 10.1016/j.ygyno.2012.11.022
    1. Longoria TC, Ueland FR, Zhang Z, Chan DW, Smith A, Fung ET, et al. Clinical performance of a multivariate index assay for detecting early-stage ovarian cancer. Am J Obstet Gynecol 2014;210:78.e1–9. doi: 10.1016/j.ajog.2013.09.017
    1. Moore RG, McMeekin DS, Brown AK, Disilvestro P, Miller MC, Allard WJ, et al. A novel multiple marker bioassay utilizing HE4 and CA125 for the prediction of ovarian cancer in patients with a pelvic mass. Gynecol Oncol 2009;112:40–6. doi: 10.1016/j.ygyno.2008.08.031
    1. Moore RG, Miller MC, Disilvestro P, Landrum LM, Gajewski W, Ball JJ, et al. . Evaluation of the diagnostic accuracy of the risk of ovarian malignancy algorithm in women with a pelvic mass. Obstet Gynecol 2011;118:280–8. doi: 10.1097/AOG.0b013e318224fce2
    1. Lianidou E, Pantel K. Liquid biopsies. Genes Chromosomes Cancer 2019;58:219–32. doi: 10.1002/gcc.22695
    1. Miller MC, Robinson PS, Wagner C, O'Shannessy DJ. The ParsortixTM Cell Separation System - a versatile liquid biopsy platform. Cytometry A 2018;93:1234–9. doi: 10.1002/cyto.a.23571
    1. NIH consensus conference. Ovarian cancer. Screening, treatment, and follow-up. NIH Consensus Development Panel on Ovarian Cancer. JAMA 1995;273:491–7.
    1. Vernooij F, Witteveen PO, Verweij E, van der Graaf Y, Heintz AP. The impact of hospital type on the efficacy of chemotherapy treatment in ovarian cancer patients. Gynecol Oncol 2009;115:343–8. doi: 10.1016/j.ygyno.2009.08.018
    1. Goff BA, Matthews BJ, Larson EH, Andrilla CH, Wynn M, Lishner DM, et al. . Predictors of comprehensive surgical treatment in patients with ovarian cancer. Cancer 2007;109:2031–42. doi: 10.1002/cncr.22604
    1. The role of the obstetrician-gynecologist in the early detection of epithelial ovarian cancer in women at average risk. Committee Opinion No. 716. American College of Obstetricians and Gynecologists. Obstet Gynecol 2017;130:e146–9. doi: 10.1097/AOG.0000000000002299
    1. Engelen MJ, Kos HE, Willemse PH, Aalders JG, de Vries EG, Schaapveld M, et al. Surgery by consultant gynecologic oncologists improves survival in patients with ovarian carcinoma. Cancer 2006;106:589–98. doi: 10.1002/cncr.21616
    1. Earle CC, Schrag D, Neville BA, Yabroff KR, Topor M, Fahey A, et al. Effect of surgeon specialty on processes of care and outcomes for ovarian cancer patients. J Natl Cancer Inst 2006;98:172–80. doi: 10.1093/jnci/djj019
    1. Chan JK, Kapp DS, Shin JY, Husain A, Teng NN, Berek JS, et al. Influence of the gynecologic oncologist on the survival of ovarian cancer patients. Obstet Gynecol 2007;109:1342–50. doi: 10.1097/01.AOG.0000265207.27755.28
    1. Chao X, Wang X, Xiao Y, Ji M, Wang S, Shi H, et al. Clinical analysis of high risk factors for pelvic malignant tumors after hysterectomy for benign diseases. Medicine (Baltimore) 2019;98:e17540. doi: 10.1097/MD.0000000000017540
    1. Rim SH, Hirsch S, Thomas CC, Brewster WR, Cooney D, Thompson TD, et al. . Gynecologic oncologists involvement on ovarian cancer standard of care receipt and survival. World J Obstet Gynecol 2016;5:187–96. doi: 10.5317/wjog.v5.i2.187
    1. Montagnana M, Danese E, Ruzzenente O, Bresciani V, Nuzzo T, Gelati M, et al. The ROMA (Risk of Ovarian Malignancy Algorithm) for estimating the risk of epithelial ovarian cancer in women presenting with pelvic mass: is it really useful? Clin Chem Lab Med 2011. 49:521–5. doi: 10.1515/CCLM.2011.075
    1. Moore RG, Blackman A, Miller MC, Robison K, DiSilvestro PA, Eklund EE, et al. Multiple biomarker algorithms to predict epithelial ovarian cancer in women with a pelvic mass: can additional makers improve performance? Gynecol Oncol 2019;154:150–5. doi: 10.1016/j.ygyno.2019.04.006
    1. Moore RG, Brown AK, Miller MC, Skates S, Allard WJ, Verch T, et al. The use of multiple novel tumor biomarkers for the detection of ovarian carcinoma in patients with a pelvic mass. Gynecol Oncol 2008;108:402–8. doi: 10.1016/j.ygyno.2007.10.017
    1. Grenache DG, Heichman KA, Werner TL, Vucetic Z. Clinical performance of two multi-marker blood tests for predicting malignancy in women with an adnexal mass. Clin Chim Acta 2015;438:358–63. doi: 10.1016/j.cca.2014.09.028
    1. Leung F, Bernardini MQ, Brown MD, Zheng Y, Molina R, Bast RC, Jr, et al. Validation of a novel biomarker panel for the detection of ovarian cancer. Cancer Epidemiol Biomarkers Prev 2016;25:1333–40. doi: 10.1158/1055-9965.Epi-15-1299
    1. Nolen B, Velikokhatnaya L, Marrangoni A, De GK, Lomakin A, Bast RC, Jr, et al. . Serum biomarker panels for the discrimination of benign from malignant cases in patients with an adnexal mass. Gynecol Oncol 2010;117:440–5. doi: 10.1016/j.ygyno.2010.02.005
    1. Shaw R, Lokshin AE, Miller MC, Messerlian-Lambert G, Moore RG. Stacking machine learning algorithms for biomarker-based preoperative diagnosis of a pelvic mass. Cancers (Basel) 2022;14:1291. doi: 10.3390/cancers14051291
    1. Nelson NJ. Circulating tumor cells: will they be clinically useful? J Natl Cancer Inst 2010;102:146–8. doi: 10.1093/jnci/djq016
    1. Yu M, Stott S, Toner M, Maheswaran S, Haber DA. Circulating tumor cells: approaches to isolation and characterization. J Cell Biol 2011;192:373–82. doi: 10.1083/jcb.201010021
    1. Millner LM, Linder MW, Valdes R, Jr. Circulating tumor cells: a review of present methods and the need to identify heterogeneous phenotypes. Ann Clin Lab Sci 2013;43:295–304.
    1. Chemi F, Mohan S, Guevara T, Clipson A, Rothwell DG, Dive C. Early dissemination of circulating tumor cells: biological and clinical insights. Front Oncol 2021;11:672195. doi: 10.3389/fonc.2021.672195
    1. Ried K, Eng P, Sali A. Screening for circulating tumour cells allows early detection of cancer and monitoring of treatment effectiveness: an observational study. Asian Pac J Cancer Prev 2017;18:2275–85. doi: 10.22034/APJCP.2017.18.8.2275
    1. Ma X, Xiao Z, Li X, Wang F, Zhang J, Zhou R, et al. Prognostic role of circulating tumor cells and disseminated tumor cells in patients with prostate cancer: a systematic review and meta-analysis. Tumour Biol 2014;35:5551–60. doi: 10.1007/s13277-014-1731-5
    1. Wang J, Wang K, Xu J, Huang J, Zhang T. Prognostic significance of circulating tumor cells in non-small-cell lung cancer patients: a meta-analysis. PLoS One 2013;8:e78070. doi: 10.1371/journal.pone.0078070
    1. Zhang L, Riethdorf S, Wu G, Wang T, Yang K, Peng G, et al. Meta-analysis of the prognostic value of circulating tumor cells in breast cancer. Clin Cancer Res 2012;18:5701–10. doi: 10.1158/1078-0432.CCR-12-1587
    1. Sgroi DC, Carney E, Zarrella E, Steffel L, Binns SN, Finkelstein DM, et al. Prediction of late disease recurrence and extended adjuvant letrozole benefit by the HOXB13/IL17BR biomarker. J Natl Cancer Inst 2013;105:1036–42. doi: 10.1093/jnci/djt146
    1. Reme T, Hugnot JP, Bieche I, Rigau V, Burel-Vandenbos F, Prevot V, et al. A molecular predictor reassesses classification of human grade II/III gliomas. PLoS One 2013;8:e66574. doi: 10.1371/journal.pone.0066574
    1. Vallon-Christersson J, Häkkinen J, Hegardt C, Saal LH, Larsson C, Ehinger A, et al. Cross comparison and prognostic assessment of breast cancer multigene signatures in a large population-based contemporary clinical series. Sci Rep 2019;9:12184. doi: 10.1038/s41598-019-48570-x
    1. van Laar R, Flinchum R, Brown N, Ramsey J, Riccitelli S, Heuck C, et al. Translating a gene expression signature for multiple myeloma prognosis into a robust high-throughput assay for clinical use. BMC Med Genomics 2014;7:25. doi: 10.1186/1755-8794-7-25
    1. Xu L, Mao X, Imrali A, Syed F, Mutsvangwa K, Berney D, et al. Optimization and evaluation of a novel size based circulating tumor cell isolation system. PLoS One 2015;10:e0138032. doi: 10.1371/journal.pone.0138032
    1. Motohara T, Masuda K, Morotti M, Zheng Y, El-Sahhar S, Chong KY, et al. An evolving story of the metastatic voyage of ovarian cancer cells: cellular and molecular orchestration of the adipose-rich metastatic microenvironment. Oncogene 2019;38:2885–98. doi: 10.1038/s41388-018-0637-x
    1. Pradeep S, Kim SW, Wu SY, Nishimura M, Chaluvally-Raghavan P, Miyake T, et al. Hematogenous metastasis of ovarian cancer: rethinking mode of spread. Cancer Cell 2014;26:77–91. doi: 10.1016/j.ccr.2014.05.002
    1. Kolostova K, Pinkas M, Jakabova A, Pospisilova E, Svobodova P, Spicka J, et al. Molecular characterization of circulating tumor cells in ovarian cancer. Am J Cancer Res 2016;6:973–80.
    1. Zhao C, Annamalai L, Guo C, Kothandaraman N, Koh SC, Zhang H, et al. Circulating haptoglobin is an independent prognostic factor in the sera of patients with epithelial ovarian cancer. Neoplasia 2007;9:1–7. doi: 10.1593/neo.06619
    1. Rowswell-Turner RB, Singh RK, Urh A, Yano N, Kim KK, Khazan N, et al. HE4 overexpression by ovarian cancer promotes a suppressive tumor immune microenvironment and enhanced tumor and macrophage PD-L1 expression. J Immunol 2021;206:2478–88. doi: 10.4049/jimmunol.2000281
    1. Bailey J, Tailor A, Naik R, Lopes A, Godfrey K, Hatem HM, et al. . Risk of malignancy index for referral of ovarian cancer cases to a tertiary center: does it identify the correct cases? Int J Gynecol Cancer 2006;16(suppl 1):30–4. doi: 10.1111/j.1525-1438.2006.00468.x
    1. Jacobs I, Oram D, Fairbanks J, Turner J, Frost C, Grudzinskas JG. A risk of malignancy index incorporating CA 125, ultrasound and menopausal status for the accurate preoperative diagnosis of ovarian cancer. Br J Obstet Gynaecol 1990;97:922–9. doi: 10.1111/j.1471-0528.1990.tb02448.x
    1. Manjunath AP, Pratapkumar Sujatha K, Vani R. Comparison of three risk of malignancy indices in evaluation of pelvic masses. Gynecol Oncol 2001;81:225–9. doi: 10.1006/gyno.2001.6122

Source: PubMed

Sponsors and Collaborators

Medical Conditions

Drug Interventions

3
Subscribe