Clinical oncology research; Review on contemporary methodology standards

https://doi.org/10.1016/j.currproblcancer.2021.100725Get rights and content

Abstract

Evaluation of novel treatments through clinical trials remains the backbone of oncological clinical research, but only a minor portion have been tested in Phase III trials. The continued publication of underpowered trials provides an ongoing need for meta-analyses to detect clinically significant outcomes. Although tumor relapse and survival are important issues and easily measured outcomes in trials, they are often not the most relevant indicators for treatment success. As diagnostic technologies and treatments continue to advance, methodologies defining high quality studies have been established, but still enthusiasm to adopt novel technologies that leads to studies holding well-described bias that do not aid the rational use of the studied test. Global awareness of such bias and standard research methodology is the clue toward iconic studies giving rational supporting novel cancer treatments and patients’ support.

Introduction

Optimizing treatment for cancer patients, clinical oncologists need to critically evaluate data from clinical studies and appropriately interpreting them. Clinicians should be proficient in the application of diagnostic tests, risk assessment and appreciation of disease prognosis. In addition, translational research should be purified of pitfalls in related clinical studies. This review aimed at providing a critical highlight on methodology used in clinical oncology research accompanied with scientific critic of related aspects.

Section snippets

Purpose and types of clinical oncology trials

Clinical trials are utilized to assess the effects of specific interventions on individuals' health. Possible interventions include treatment with chemotherapy, hormones, radiation, or surgery; modification of diet, behavior, or environment; and surveillance with physical examination, blood tests, or imaging tests. This review focuses on design and commitment of clinical oncology trials on disease management. Clinical trials may be divide conceptually into explanatory trials, designed to

Conclusions

Research investigating novel treatments in the field of oncology is suffering a major problem being only a minor portion have been tested in Phase III trials. The continued publication of underpowered trials provides an ongoing need for meta-analyses to detect clinically significant outcomes. As diagnostic technologies and treatments continue to advance, methodological principles that define high-quality studies have been established, but still enthusiasm to adopt such new technologies often

Authors' contributions

All the three authors whose names appear on the submission have made equal contributions to all of the following:

  • (1)

    The conception and design of the study, acquisition of data and interpretation of data; AND

  • (2)

    Drafting the article and revising it critically for important intellectual content; AND

  • (3)

    Final approval of the submitted research version; AND

  • (4)

    Agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are

References (41)

  • J. Peppercorn et al.

    Comparison of outcomes in cancer patients treated within and outside clinical trials: Conceptual framework and structured review

    Lancet

    (2004)
  • D. Schwartz et al.

    Clinical Trials

    (1980)
  • D. David et al.

    The CONSORT statement: Revised recommendations for improving the quality of reports of parallel group randomized trials

    BMC Med Res Methodol

    (2011)
  • A. Califano et al.

    The recurrent architecture of tumor initiation, progression and drug sensitivity

    Nature Rev Cancer

    (2016)
  • M. Levine et al.

    When is a prognostic factor useful? A guide for the perplexed

    J Clin Oncol

    (1991)
  • W. Hua et al.

    Clinical trial information as a measure of quality cancer care

    J Oncol Pract

    (2010)
  • E. Mardis

    The challenges of big data

    Dis Model Mech

    (2016)
  • Lehmann B., Yan Ding Y., Viox D., et al: Evaluation of public cancer datasets and signatures identifies TP53 mutant...
  • J. Weeks

    Performance status upstaged?

    J Clin Oncol. 199. Dec.

    (1992)
  • A. Viganò et al.

    Survival prediction in terminal cancer patients: A systematic review of the medical literature

    Palliat Med

    (2000)
  • D. Schrag et al.

    American Society of Clinical Technology Assessment: chemotherapy sensitivity and resistance assays

    J Clin Oncol

    (2004)
  • M. Tsao et al.

    Erlotinib in lung cancer— molecular and clinical predictors of outcome

    N Engl J Med

    (2005)
  • A. Lièvre et al.

    KRAS mutation status is predictive of response to cetuximab in colorectal cancer

    Cancer Res

    (2006)
  • B. Tran et al.

    Cancer genomics: technology, discovery, and translation

    J Clin Oncol

    (2012)
  • P. Nygren et al.

    Predictive tests for individualization of pharmacological cancer treatment

    Expert Opin Med Diagn

    (2008)
  • E. Jensen et al.

    Estrogen receptors and breast cancer response to adrenalectomy

    Natl Cancer Inst Monogr

    (1971)
  • D. Toft et al.

    A receptor molecule for estrogens: studies using a cell-free system

    Proc Natl Acad Sci USA.

    (1967)
  • J. Baselga et al.

    Phase II study of weekly intravenous recombinant humanized anti-p185HER2 monoclonal antibody in patients with HER2/neu-overexpressing metastatic breast cancer

    J Clin Oncol

    (1996)
  • R. Hudziak et al.

    P185HER2 monoclonal antibody has antiproliferative effects in vitro and sensitizes human breast tumor cells to tumor necrosis factor

    Mol Cell Biol

    (1989)
  • H. Joensuu et al.

    Effect of the tyrosine kinase inhibitor STI571 in a patient with a metastatic gastrointestinal stromal tumor

    N Engl J Med

    (2001)
  • Conflicts of interest: All authors declare nothing to disclose regarding the followings: corporate/commercial relationships including all consultantships, honoraria, stock ownership, gifts, free or reimbursed travel/vacations, equity interests, arrangements regarding patents or other vested interests, that might pose a conflict of interest.

    Funding: All authors declare nothing to disclose related to any aspect of funding.

    Availability of data and material (data transparency): Data sharing and data citation aspects are transparently achieved.

    View full text