Analytical Validation of Multiplex Biomarker Assay to Stratify Colorectal Cancer into Molecular Subtypes.

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Authors

Ragulan, C
Eason, K
Fontana, E
Nyamundanda, G
Tarazona, N
Patil, Y
Poudel, P
Lawlor, RT
Del Rio, M
Koo, S-L
Tan, W-S
Sclafani, F
Begum, R
Teixeira Mendes, LS
Martineau, P
Scarpa, A
Cervantes, A
Tan, IB
Cunningham, D
Sadanandam, A

Document Type

Journal Article

Date

2019-05-21

Date Accepted

2019-04-23

Abstract

Previously, we classified colorectal cancers (CRCs) into five CRCAssigner (CRCA) subtypes with different prognoses and potential treatment responses, later consolidated into four consensus molecular subtypes (CMS). Here we demonstrate the analytical development and validation of a custom NanoString nCounter platform-based biomarker assay (NanoCRCA) to stratify CRCs into subtypes. To reduce costs, we switched from the standard nCounter protocol to a custom modified protocol. The assay included a reduced 38-gene panel that was selected using an in-house machine-learning pipeline. We applied NanoCRCA to 413 samples from 355 CRC patients. From the fresh frozen samples (n = 237), a subset had matched microarray/RNAseq profiles (n = 47) or formalin-fixed paraffin-embedded (FFPE) samples (n = 58). We also analyzed a further 118 FFPE samples. We compared the assay results with the CMS classifier, different platforms (microarrays/RNAseq) and gene-set classifiers (38 and the original 786 genes). The standard and modified protocols showed high correlation (> 0.88) for gene expression. Technical replicates were highly correlated (> 0.96). NanoCRCA classified fresh frozen and FFPE samples into all five CRCA subtypes with consistent classification of selected matched fresh frozen/FFPE samples. We demonstrate high and significant subtype concordance across protocols (100%), gene sets (95%), platforms (87%) and with CMS subtypes (75%) when evaluated across multiple datasets. Overall, our NanoCRCA assay with further validation may facilitate prospective validation of CRC subtypes in clinical trials and beyond.

Citation

Scientific reports, 2019, 9 (1), pp. 7665 - ?

Source Title

Publisher

NATURE PORTFOLIO

ISSN

2045-2322

eISSN

2045-2322

Research Team

Medicine (RMH Smith Cunningham)
Systems and Precision Cancer Medicine

Notes