Micro-costing diagnostics in oncology: from single-gene testing to whole- genome sequencing

ABSTRACT

Purpose: Predictive diagnostics play an increasingly important role in personalized medicine for cancer treatment. Whole-genome sequencing (WGS)-based treatment selection is expected to rapidly increase worldwide. This study aimed to calculate and compare the total cost of currently used diagnostic techniques and of WGS in treatment of non-small cell lung carcinoma (NSCLC), melanoma, colorectal cancer (CRC), and gastrointestinal stromal tumor (GIST) in the Netherlands.

Methods: The activity-based costing (ABC) method was conducted to calculate total cost of included diagnostic techniques based on data provided by Dutch pathology laboratories and the Dutch-centralized cancer WGS facility. Costs were allocated to four categories: capital costs, maintenance costs, software costs, and operational costs.

Results: The total cost per cancer patient per technique varied from € 58 (Sanger sequencing, three amplicons) to € 2925 (paired tumor-normal WGS). The operational costs accounted for the vast majority (over 90%) of the total per cancer patient technique costs.

Conclusion: This study outlined in detail all costing aspects and cost prices of current and new diagnostic modalities used in treatment of NSCLC, melanoma, CRC, and GIST in the Netherlands. Detailed cost differences and value comparisons between these diagnostic techniques enable future economic evaluations to support decision-making.

KEYWORDS:

• Micro-costing
• whole genome sequencing
• standard diagnostic techniques
• oncology
• personalized medicine

Acknowledgments

The authors would like to thank Wim van Harten, Manuela Joore, Martijn Simons, Erik Koffijberg, Maarten IJzerman, Michiel van de Ven, and Inge Eekhout from the Technology Assessment of Next-Generation Sequencing in Personalized Oncology (TANGO) consortium, and Astrid Eijkelenboom, Arja ter Elst, Robert van der Geize, Winand Dinjens, Carel van Noesel, Clemens Prinses, Ernst-Jan Speel from the Predictive Analysis for Therapy (PATH) consortium. Furthermore, they would like to express gratitude to the HMF facility and the Dutch pathology laboratories who participated in this study.

Declaration of interest

The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.

Reviewers disclosure

Peer reviewers on this manuscript have no relevant financial relationships or otherwise to disclose.

Author contributions

Each of the included authors has contributed significantly to this manuscript and has approved the most recent submitted version. They also agreed to be personally accountable for the author’s own contributions.

Ethics statement

Ethical approval was not needed.

Additional information

Funding

This work is part of the research program Personalized Medicine, which is financed by the Netherlands Organisation for Health Research and Development [ZonMw, project numbers 846001001 and 846001002]. Other grant providers are the HMF, the Dutch Cancer Society (KWF Kankerbestrijding) and the Dutch health-care insurance company Zilveren Kruis.