Commentary: Cancer research quality and tumour classification

A New Hierarchy of Research Evidence for Tumor Pathology: A Delphi Study to Define Levels of Evidence in Tumor Pathology

The hierarchy of evidence is a fundamental concept in evidence-based medicine, but existing models can be challenging to apply in laboratory-based health care disciplines, such as pathology, where the types of evidence and contexts are significantly different from interventional medicine. This project aimed to define a comprehensive and complementary framework of new levels of evidence for evaluating research in tumor pathology-introducing a novel Hierarchy of Research Evidence for Tumor Pathology collaboratively designed by pathologists with help from epidemiologists, public health professionals, oncologists, and scientists, specifically tailored for use by pathologists-and to aid in the production of the World Health Organization Classification of Tumors (WCT) evidence gap maps. To achieve this, we adopted a modified Delphi approach, encompassing iterative online surveys, expert oversight, and external peer review, to establish the criteria for evidence in tumor pathology, determine the optimal structure for the new hierarchy, and ascertain the levels of confidence for each type of evidence. Over a span of 4 months and 3 survey rounds, we collected 1104 survey responses, culminating in a 3-day hybrid meeting in 2023, where a new hierarchy was unanimously agreed upon. The hierarchy is organized into 5 research theme groupings closely aligned with the subheadings of the WCT, and it consists of 5 levels of evidence-level P1 representing evidence types that merit the greatest level of confidence and level P5 reflecting the greatest risk of bias. For the first time, an international collaboration of pathology experts, supported by the International Agency for Research on Cancer, has successfully united to establish a standardized approach for evaluating evidence in tumor pathology. We intend to implement this novel Hierarchy of Research Evidence for Tumor Pathology to map the available evidence, thereby enriching and informing the WCT effectively.

Understanding the use of evidence in the WHO Classification of Tumours: a protocol for an evidence gap map of the classification of tumours of the lung

INTRODUCTION

There are gaps in the evidence base of tumour classification despite being essential for cancer diagnosis, treatment and patient care. The WHO in charge of the production of an updated international classification, the WHO Classification of Tumours (WCT), aims to adapt evidence gap map (EGM) methodology to inform future editions of the WCT, by providing a visual summary of the existing evidence.

METHODS AND ANALYSIS

Bibliographical references used in the WCT fifth edition of Tumours of the Lung (Thoracic Tumours volume) will be used as search results of a literature search. A descriptive analysis of the cited evidence for tumour types and descriptors will be drafted and plotted in EPPI-Reviewer to develop a visual evidence map. The resulting EGM will reflect the number of cited studies in the size of the spheres, and the level of evidence by applying a four-colour code (red=low level evidence, orange=moderate level, green=high level and blue=unclassifiable). Overview of the findings will be provided in narrative form and a report will discuss the overall stage of cited research in the WCT and will include analysis of gaps, under-researched categories of tumour descriptors and pockets of low-level evidence.

ETHICS AND DISSEMINATION

No ethics approval will be required as this is a study of previously published material. Findings of the EGM will be published and used to guide editors, stakeholders and researchers for future research planning and related decision-making, especially for the development of future editions of the WCT.

PROSPERO REGISTRATION NUMBER

CRD42022302327.

Diagnostic Value of MAML2 Rearrangements in Mucoepidermoid Carcinoma

Mucoepidermoid carcinoma (MEC) is often seen in salivary glands and can harbor MAML2 translocations (MAML2+). The translocation status has diagnostic utility as an objective confirmation of the MEC diagnosis, for example, when distinction from the more aggressive adenosquamous carcinoma (ASC) is not straightforward. To assess the diagnostic relevance of MAML2, we examined our 5-year experience in prospective testing of 8106 solid tumors using RNA-seq panel testing in combinations with a two-round Delphi-based scenario survey. The prevalence of MAML2+ across all tumors was 0.28% (n = 23/8106) and the majority of MAML2+ cases were found in head and neck tumors (78.3%), where the overall prevalence was 5.9% (n = 18/307). The sensitivity of MAML2 for MEC was 60% and most cases (80%) were submitted for diagnostic confirmation; in 24% of cases, the MAML2 results changed the working diagnosis. An independent survey of 15 experts showed relative importance indexes of 0.8 and 0.65 for "confirmatory MAML2 testing" in suspected MEC and ASC, respectively. Real-world evidence confirmed that the added value of MAML2 is a composite of an imperfect confirmation test for MEC and a highly specific exclusion tool for the diagnosis of ASC. Real-world evidence can help move a rare molecular-genetic biomarker from an emerging tool to the clinic.

Preliminary Exploration of Main Elements for Systematic Classification Development: Case Study of Patient Safety Incidents (Preprint)

Background

Currently, there is no holistic theoretical approach available for guiding classification development. On the basis of our recent classification development research in the area of patient safety in health information technology, this focus area would benefit from a more systematic approach. Although some valuable theoretical and methodological approaches have been presented, classification development literature typically is limited to methodological development in a specific domain or is practically oriented.

Objective

The main purposes of this study are to fill the methodological gap in classification development research by exploring possible elements of systematic development based on previous literature and to promote sustainable and well-grounded classification outcomes by identifying a set of recommended elements. Specifically, the aim is to answer the following question: what are the main elements for systematic classification development based on research evidence and our use case?

Methods

This study applied a qualitative research approach. On the basis of previous literature, preliminary elements for classification development were specified, as follows: defining a concept model, documenting the development process, incorporating multidisciplinary expertise, validating results, and maintaining the classification. The elements were compiled as guiding principles for the research process and tested in the case of patient safety incidents (n=501).

Results

The results illustrate classification development based on the chosen elements, with 4 examples of technology-induced errors. Examples from the use case regard usability, system downtime, clinical workflow, and medication section problems. The study results confirm and thus suggest that a more comprehensive and theory-based systematic approach promotes well-grounded classification work by enhancing transparency and possibilities for assessing the development process.

Conclusions

We recommend further testing the preliminary main elements presented in this study. The research presented herein could serve as a basis for future work. Our recently developed classification and the use case presented here serve as examples. Data retrieved from, for example, other type of electronic health records and use contexts could refine and validate the suggested methodological approach.

Diagnosis of digestive system tumours

The WHO Classification of Tumours provides the international standards for the classification and diagnosis of tumours. It enables direct comparisons to be made between different countries. In the new fifth edition, the series has gone digital with the launch of a website as well as a series of books, known widely as the WHO Blue Books. The first volume to be produced is on the classification of Digestive System tumours, replacing the successful 2010 version. It has been rewritten and updated accordingly. This article summarises the major diagnostic innovations that have occurred over the last decade and that have now been incorporated in the classification. As an example, it incorporates the recently proposed classification of neuroendocrine tumours, based on the recognition that neuroendocrine tumours and carcinomas differ substantially in the genetic abnormalities that drive their growth, findings relevant to treatment selection and outcome prediction. Several themes have emerged during the production process. One is the importance of the progression from hyperplasia to dysplasia to carcinoma in the evolution of the malignant process. Advances in imaging techniques and endoscopy have resulted in enhanced access to precancerous lesions in the gastrointestinal and biliary tract, necessitating both changes in classification schema and clinical practice. Diagnosis of tumours is no longer the sole purview of pathologists, and some patients now receive treatment before tissue is obtained, based on clinical, radiological and liquid biopsy results. This makes the classification relevant to many disciplines involved in the care of patients with tumours of the digestive system.

OncoTree: A Cancer Classification System for Precision Oncology

PURPOSE

Cancer classification is foundational for patient care and oncology research. Systems such as International Classification of Diseases for Oncology (ICD-O), Systematized Nomenclature of Medicine Clinical Terms (SNOMED-CT), and National Cancer Institute Thesaurus (NCIt) provide large sets of cancer classification terminologies but they lack a dynamic modernized cancer classification platform that addresses the fast-evolving needs in clinical reporting of genomic sequencing results and associated oncology research.

METHODS

To meet these needs, we have developed OncoTree, an open-source cancer classification system. It is maintained by a cross-institutional committee of oncologists, pathologists, scientists, and engineers, accessible via an open-source Web user interface and an application programming interface.

RESULTS

OncoTree currently includes 868 tumor types across 32 organ sites. OncoTree has been adopted as the tumor classification system for American Association for Cancer Research (AACR) Project Genomics Evidence Neoplasia Information Exchange (GENIE), a large genomic and clinical data-sharing consortium, and for clinical molecular testing efforts at Memorial Sloan Kettering Cancer Center and Dana-Farber Cancer Institute. It is also used by precision oncology tools such as OncoKB and cBioPortal for Cancer Genomics.

CONCLUSION

OncoTree is a dynamic and flexible community-driven cancer classification platform encompassing rare and common cancers that provides clinically relevant and appropriately granular cancer classification for clinical decision support systems and oncology research.