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An integrated tool for determining the primary origin site of metastatic tumours
  1. Marcos Tadeu dos Santos1,2,
  2. Bruno Feres de Souza3,
  3. Flavio Mavignier Cárcano4,
  4. Ramon de Oliveira Vidal2,5,
  5. Cristovam Scapulatempo-Neto5,
  6. Cristiano Ribeiro Viana5,
  7. Andre Lopes Carvalho5
  1. 1 ONKOS Molecular Diagnostics, Ribeirão Preto, São Paulo, Brazil
  2. 2 Department of Research and Development (R&D), Fleury Group, Sao Paulo, Brazil
  3. 3 Federal University of Maranhão, UFMA, Sao Luis, Maranhão, Brazil
  4. 4 Department of Medical Oncology, Barretos Cancer Hospital, Barretos, Brazil
  5. 5 Molecular Oncology Research Center, Barretos Cancer Hospital, Barretos, Brazil
  1. Correspondence to Dr Marcos Tadeu dos Santos, ONKOS Molecular Diagnostics, Department of Research and Development (R&D), Ribeirão Preto, SP 14056-680, Brazil; marcos{at}onkos.com.br

Abstract

Aims Cancers of unknown primary sites account for 3%–5% of all malignant neoplasms. Current diagnostic workflows based on immunohistochemistry and imaging tests have low accuracy and are highly subjective. We aim to develop and validate a gene-expression classifier to identify potential primary sites for metastatic cancers more accurately.

Methods We built the largest Reference Database (RefDB) reported to date, composed of microarray data from 4429 known tumour samples obtained from 100 different sources and divided into 25 cancer superclasses formed by 58 cancer subclass. Based on specific profiles generated by 95 genes, we developed a gene-expression classifier which was first trained and tested by a cross-validation. Then, we performed a double-blinded retrospective validation study using a real-time PCR-based assay on a set of 105 metastatic formalin-fixed, paraffin-embedded (FFPE) samples. A histopathological review performed by two independent pathologists served as a reference diagnosis.

Results The gene-expression classifier correctly identified, by a cross-validation, 86.6% of the expected cancer superclasses of 4429 samples from the RefDB, with a specificity of 99.43%. Next, the performance of the algorithm for classifying the validation set of metastatic FFPE samples was 83.81%, with 99.04% specificity. The overall reproducibility of our gene-expression-classifier system was 97.22% of precision, with a coefficient of variation for inter-assays and intra-assays and intra-lots <4.1%.

Conclusion We developed a complete integrated workflow for the classification of metastatic tumour samples which may help on tumour primary site definition.

  • cancer of unknown primary site
  • molecular pathology
  • metastasis

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Footnotes

  • Handling editor Runjan Chetty.

  • Contributors MTdS and ALC designed and supervised the study. MTdS performed all the experiments and drafted the manuscript. FMC selected all metastatic FFPE samples. BFdS and RdOV contributed with bioinformatics. CS-N and CRV contributed as pathologists. FMC and ALC contributed as clinical oncologists. All authors read and approved the final version.

  • Funding This study was supported by FINEP (02.12.0223.00).

  • Competing interests MTdS holds equity at ONKOS Molecular Diagnostics but does not serve as a consultant or holds equity or equity options at Fleury Group. All other authors have no competing interests to declare.

  • Ethics approval The study was approved by the Fleury Group and Barretos Cancer Hospital institution-specific investigational ethics committees’ boards and assigned as CAAE 10670112.0.0000.5437 and conducted according to the Declaration of Helsinki.

  • Provenance and peer review Not commissioned; internally peer reviewed.

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