EDGCㅣONCOCATCH

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What is the
ONCOCATCH™ ㆍS
test?

ONCOCATCHtmㆍS uses multi-omics data to test the liquid biopsy method for cancer screening. Liquid biopsy can be used for early diagnosis (screening), recurrence of cancer (monitoring), and anticancer drug effect (companion diagnostics) using Circulating Tumor Cell* (CTC), a cfDNA derived from cancer cells. Cancer cells from cancer tissue continue dividing and apoptosis. During Apoptosis, the various constituent including proteins and DNA from the cancer cells flow out and circulate around in blood via blood vessel. It is a state-of-the-art personalized precision medical service that analyzes cfDNA derived from cancer cells through NGS technology and bioinformatics to check the presence or absence of cancer cells.

Early Cancer
detection
Blood collection
Easy to check
Reliable result

Why ONCOCATCH™ ㆍS is different - Strengths

Recently, the field of multi-omics that can reveal more clearly the biological mechanisms related to cancer by integrating omics analysis information from independent layers into a multi-layer is emerging. ONCOCATCH-S is based on three types of data, and multi-omics data analysis helps understand cancer mechanisms at various molecular levels. Multi-omics data integrating tumor markers, cfDNA concentration, and CNV data were analyzed for cancer diagnosis performance based on a machine learning model. It showed higher performance when using multi-omics data than when using single data.

02-1

process

Figure 1 The workflow of ONCOCATCH-S pipeline using multi-omics data.

02-2

Multi-omics data

(1) Proteome Marker

(2) cfDNA amount

cfDNA is released from normal and cancer cells and ctDNA can be used as a novel biomarker because it contains genetic changes useful for detecting cancer in general.1 Previous quantitative studies have shown that the cfDNA concentration in the blood of healthy subjects ranges from 0 to 100 ng/mL, with an average of 30 ng/mL, while the cfDNA concentration in the blood of cancer patients ranges from 0 to 1000 ng/mL, with an average of 180 ng/mL.24 The average concentration of cfDNA in blood was found to be about 6 times higher in cancer patients than in healthy controls; so the cfDNA concentration was used as multi-omics data.

(3) Copy Number Variation (CNV)

Non-cancer sample

Cancer sample (T4N3M1a)

Stair-Matrix: Various size of windows are sliding and overlapped to detect CNV. This method can check each chromosomes whole region deeply.

ONCOCATCH™ ㆍS test performance

ONCOCATCHtmㆍS has verified the accuracy of the results using various multi-omics data and provides reliable results.

*For multi-omics data combinations, we adopted an MLP machine learning model and performed nested cross-validation.

Table 1 Machine Learning model performance on multi-omics data combinations.

Figure 2 Comparison of performance of single-omics and multi-omics data

Figure 3 Score distribution by cancer stage according to 90% specificity

References

1. Fiala, C. & Diamandis, E. P. New approaches for detecting cancer with circulating cell-free DNA. BMC Med. 17, 159 (2019).

2. Leon, S. A., Shapiro, B., Sklaroff, D. M. & Yaros, M. J. Free DNA in the serum of cancer patients and the effect of therapy. Cancer Res. 37, 646-650 (1977).

3. Esposito, A., Criscitiello, C., Trapani, D. & Curigliano, G. The Emerging Role of 'Liquid Biopsies, Circulating Tumor Cells, and Circulating Cell-Free Tumor DNA in Lung Cancer Diagnosis and Identification of Resistance Mutations. Curr. Oncol. Rep. 19, 1 (2017).

4. Phallen, J. et al. Direct detection of early-stage cancers using circulating tumor DNA. Sci. Transl. Med. 9, eaan2415 (2017).

5. Pamies, R. J. & Crawford, D. R. Tumor markers. An update. Med. Clin. North Am. 80, 185- 199 (1996).

6. Chen, F. et al. Diagnostic value of CYFRA 21-1 and CEA for predicting lymph node metastasis in operable lung cancer.
Int. J. Clin. Exp. Med. 8, 9820-9824 (2015).

7. Tsavaris, N. et al. CEA and CA-19.9 serum tumor markers as prognostic factors in patients with locally advanced (unresectable) or metastatic pancreatic adenocarcinoma: a retrospective analysis. J. Chemother. Florence Italy 21, 673-680 (2009).

8. Tan, O., Shrestha, R., Cunich, M. & Schofield, D. J. Application of next-generation sequencing to improve cancer management: A review of the clinical effectiveness and cost- effectiveness. Clin. Genet. 93, 533-544 (2018).

9. Lui, Y. Y. N. et al. Predominant hematopoietic origin of cell-free DNA in plasma and serum after sex-mismatched bone marrow transplantation. Clin. Chem. 48, 421-427 (2002).

10. Lo, Y. M. et al. Presence of donor-specific DNA in plasma of kidney and liver-transplant recipients. Lancet Lond. Engl. 351, 1329-1330 (1998).

11. Siravegna, G., Marsoni, S., Siena, S. & Bardelli, A. Integrating liquid biopsies into the management of cancer. Nat. Rev. Clin. Oncol. 14, 531-548 (2017).

12. Haber, D. A. & Velculescu, V. E. Blood-based analyses of cancer: circulating tumor cells and circulating tumor DNA. Cancer Discov. 4, 650-661 (2014).

13. Lynch, C. M. et al. Prediction of lung cancer patient survival via supervised machine learning classification techniques. Int. J. Med. Inf. 108, 1-8 (2017).

14. Chakraborty, S., Hosen, M. I., Ahmed, M. & Shekhar, H. U. Onco-Multi-OMICS Approach: A New Frontier in Cancer Research. BioMed Res. Int. 2018, 9836256 (2018).

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