Expectations, concerns, and attitudes regarding whole-genome sequencing studies: a survey of cancer patients, families, and the public in Japan

Nationwide survey of the secondary findings in cancer genomic profiling: survey including liquid biopsy

We surveyed the status of the secondary finding (SF) disclosure in comprehensive genome profiling (CGP) in 2020. The situation has changed: increase in the number of hospitals that provide CGP, an update to the Comprehensive Tumor Genomic Profiling: Materials for Review of Secondary Findings (CTGPMRSF), and the addition of a liquid biopsy test, FoundationOne® Liquid CDx (F1L). Moreover, the actual situation was unclear because the 2020 survey did not include all designated and cooperative hospitals. Herein, we conducted a questionnaire survey of all designated-core, designated, and cooperative hospitals to identify the current status and challenges concerning SF in the CGP in 2022. A total of 82.1% of the hospitals responded and 77.7% of the response was from cooperative hospitals. Approximately 80% of the hospitals used CTGPMRSF. SF disclosure, confirmatory test implementation, and SF confirmation rates were 12.4%, 31.6%, and 46.6% for FoundationOne® CDx (F1CDx), respectively, and 6.8%, 31.8%, and 70.7% for F1L, respectively. The implementation rate of the confirmatory test was substantially higher in hospitals with genetic experts and in hospitals that could conduct confirmatory tests on the same day. Our survey provides insight into how SF is handled in Japan. The percentage of cases leading to confirmatory tests has gradually increased, although challenges such as insurance coverage limitations and varied understanding of SF among patients and healthcare providers persist. With the increasing use of whole-genome analysis, our findings will provide valuable insights into establishing an effective SF disclosure system.

Deep-Learning Model for Tumor-Type Prediction Using Targeted Clinical Genomic Sequencing Data

Tumor type guides clinical treatment decisions in cancer, but histology-based diagnosis remains challenging. Genomic alterations are highly diagnostic of tumor type, and tumor-type classifiers trained on genomic features have been explored, but the most accurate methods are not clinically feasible, relying on features derived from whole-genome sequencing (WGS), or predicting across limited cancer types. We use genomic features from a data set of 39,787 solid tumors sequenced using a clinically targeted cancer gene panel to develop Genome-Derived-Diagnosis Ensemble (GDD-ENS): a hyperparameter ensemble for classifying tumor type using deep neural networks. GDD-ENS achieves 93% accuracy for high-confidence predictions across 38 cancer types, rivaling the performance of WGS-based methods. GDD-ENS can also guide diagnoses of rare type and cancers of unknown primary and incorporate patient-specific clinical information for improved predictions. Overall, integrating GDD-ENS into prospective clinical sequencing workflows could provide clinically relevant tumor-type predictions to guide treatment decisions in real time.

SIGNIFICANCE

We describe a highly accurate tumor-type prediction model, designed specifically for clinical implementation. Our model relies only on widely used cancer gene panel sequencing data, predicts across 38 distinct cancer types, and supports integration of patient-specific nongenomic information for enhanced decision support in challenging diagnostic situations. See related commentary by Garg, p. 906. This article is featured in Selected Articles from This Issue, p. 897.

A conceptual analysis of public opinion regarding genome research in Japan

In the 20 years since the completion of the Human Genome Project, the gap between scientific development and public understanding of genome research has been widening. While genome research has been increasingly utilized for social and clinical purposes in a multifaceted manner, this has resulted in an increase in the potential risks associated with genomic data. In this context, our study aims to consider the nature of public perceptions of genome research, primarily by using as a case study the results of previous public surveys relevant to donations for social benefits in Japan. We explored certain types of awareness, attitude, and intention (A-A-I) in such surveys and discussed the resultant key findings through the cultural transmission framework. Reframing the public's response toward genome research based on A-A-I analysis and behavioral science may contribute to developing more systematic communication approaches with the public. With a view to establishing such approaches, our perspective suggests some new insights to discuss the science-society gap in genome research internationally.

Deep Learning Model for Tumor Type Prediction using Targeted Clinical Genomic Sequencing Data

Tumor type guides clinical treatment decisions in cancer, but histology-based diagnosis remains challenging. Genomic alterations are highly diagnostic of tumor type, and tumor type classifiers trained on genomic features have been explored, but the most accurate methods are not clinically feasible, relying on features derived from whole genome sequencing (WGS), or predicting across limited cancer types. We use genomic features from a dataset of 39,787 solid tumors sequenced using a clinical targeted cancer gene panel to develop Genome-Derived-Diagnosis Ensemble (GDD-ENS): a hyperparameter ensemble for classifying tumor type using deep neural networks. GDD-ENS achieves 93% accuracy for high-confidence predictions across 38 cancer types, rivalling performance of WGS-based methods. GDD-ENS can also guide diagnoses on rare type and cancers of unknown primary, and incorporate patient-specific clinical information for improved predictions. Overall, integrating GDD-ENS into prospective clinical sequencing workflows has enabled clinically-relevant tumor type predictions to guide treatment decisions in real time.

Is legislation to prevent genetic discrimination necessary in Japan? An overview of the current policies and public attitudes

Genetic discrimination (GD) has not been discussed in East Asia as extensively as in Europe and North America. Influenced by UNESCO's universal declaration in 1997, the Japanese government took a stringent approach toward GD by releasing the Basic Principles on Human Genome Research in 2000. However, Japanese society has mostly been ignoring the prevention of GD for decades, and the principle of prohibiting GD was never adhered to in any of the Japanese laws. We conducted anonymous surveys among the general adult population in 2017 and 2022 to explore their experiences of GD and attitudes toward laws carrying penalties to prevent GD in Japan. In both years, approximately 3% of the respondents had experienced some unfavorable treatment regarding their genetic information. They showed higher recognition of the benefits of using genetic information and lower recognition of concerns about using genetic information and GD in 2022 than in 2017. However, the awareness regarding the need for legislation with penalties on GD had increased over the five-year period. In 2022, the framework of a bill to promote genomic medicine and prevent GD without any relevant penalties was released by the Bipartisan Diet Members Caucus. Considering that the absence of regulations may be a barrier to obtaining genomic medicine, as the initial step toward making the prohibition of GD more effective, legislation that no form of GD will be tolerated may stimulate education and awareness regarding respect for the human genome and its diversity.