A Survey on Data Reproducibility in Cancer Research Provides Insights into Our Limited Ability to Translate Findings from the Laboratory to the Clinic

TLDR

The pharmaceutical and biotechnology industries rely on academic findings to develop cancer diagnostics and therapeutics, but growing concerns over poor reproducibility and rising retraction rates threaten the validity of these programs. The study seeks to determine the prevalence and causes of non‑reproducible data in academic cancer research and to emphasize the need for processes that reduce this problem. The authors surveyed MD Anderson Cancer Center faculty and trainees with an anonymous computerized questionnaire to assess data reproducibility. Approximately half of respondents reported at least one reproducibility failure, and attempts to resolve these issues often failed or were uncooperative, highlighting the real problem of data reproducibility.

Abstract

The pharmaceutical and biotechnology industries depend on findings from academic investigators prior to initiating programs to develop new diagnostic and therapeutic agents to benefit cancer patients. The success of these programs depends on the validity of published findings. This validity, represented by the reproducibility of published findings, has come into question recently as investigators from companies have raised the issue of poor reproducibility of published results from academic laboratories. Furthermore, retraction rates in high impact journals are climbing.To examine a microcosm of the academic experience with data reproducibility, we surveyed the faculty and trainees at MD Anderson Cancer Center using an anonymous computerized questionnaire; we sought to ascertain the frequency and potential causes of non-reproducible data. We found that ∼50% of respondents had experienced at least one episode of the inability to reproduce published data; many who pursued this issue with the original authors were never able to identify the reason for the lack of reproducibility; some were even met with a less than "collegial" interaction.These results suggest that the problem of data reproducibility is real. Biomedical science needs to establish processes to decrease the problem and adjudicate discrepancies in findings when they are discovered.