Sandra Fitzgerald

Genomics‐informed cancer care is central to precision oncology; however, integrating genomic competencies into routine practice remains challenging. Despite the development of competency frameworks, limited clarity exists regarding how these competencies are operationalised within oncology settings. Aim: To synthesise and critically examine how genomic competencies are conceptualised across the literature, identify areas of convergence and gaps and examine implications for clinical implementation and cancer nursing workforce development. Design: Scoping review. Methods: The review followed Joanna Briggs Institute (JBI) methodology and was reported in accordance with PRISMA‐ScR guidelines. Data Sources: CINAHL, Embase, PubMed, Google Scholar and grey literature sources were searched (2010–2026). Evidence describing genomic competencies for healthcare professionals was included. Data were extracted and analysed using inductive thematic analysis. Results: Forty‐one sources were included. One overarching theme (holistic patient‐centred care), two major themes (genomic knowledge and genomic literacy) and seven interconnected competency domains were identified: social, ethical and legal considerations, communication, assessment, care planning, collaboration, professional practice and systems and process. Although these domains were consistently described across the literature, competencies were frequently articulated at a high level with limited specificity regarding their application within genomics‐informed cancer care and oncology workflows. Conclusion: Genomic competencies are well described across healthcare frameworks; however, a significant gap exists between their conceptualisation and translation into cancer care practice. While educational initiatives support genomic knowledge development, integration requires sustained workforce and implementation approaches that enable competencies to be enacted through clinically embedded oncology roles, pathways and service models. Implications for Practice: Existing genomic competencies provide a foundation for education and practice; however, their application within cancer care requires contextualisation within oncology workflows and roles. Aligning competencies with cancer nursing practice may support development of clinically relevant pathways and workforce models for genomics‐informed care. Impact: This review highlights the need for workforce and service‐level approaches, including clinically embedded nursing roles, to support the integration of genomics into cancer care. What Problem Did the Study Address?: Global healthcare systems are working to integrate genomic testing into routine cancer care; however, there is limited clarity on how existing genomic competency frameworks translate into clinical practice. This review addresses the gap between the conceptualisation of genomic competencies and their operationalisation within oncology settings, particularly in relation to cancer nursing roles. What Were the Main Findings?: Genomic competencies are consistently described across the literature but are often articulated at a high level and lack specificity for application in cancer care. While these competencies align with established nursing capabilities, education alone is insufficient to support their implementation, highlighting a gap between competency frameworks and clinical practice. Where and on Whom Will the Research Have an Impact?: This review demonstrates that the challenge is no longer defining genomic competencies, but creating the clinical, educational and organisational conditions that enable genomic competencies to be enacted within routine cancer care. Reporting Method: Inductive thematic analysis with findings descriptively summarised and narratively reported according to JBI methodology and PRISMA‐ScR guidelines. Patient or Public Contribution: This study did not include patient or public involvement in its design, conduct or reporting.

Background: Endometrial cancer (EC) is the most common gynaecological malignancy globally, with rising incidence and notable disparities in outcomes. In New Zealand, EC rates have increased significantly, particularly among Māori and Pacific women, who face higher risks of advanced disease and poorer outcomes. Microbial dysbiosis has been implicated in EC pathogenesis, but characterising the uterine microbiome is challenging due to low microbial biomass and high contamination risk. Aims: This study aimed to pilot a protocol that could inform the preparation of a larger cohort trial. Short‐read Illumina MiSeq and long‐read Oxford Nanopore Technologies (ONT) 16S rRNA gene sequencing were investigated to profile the uterine microbiome in people with EC. Methods and Results: Uterine and vaginal swabs were analysed to assess platform performance in terms of DNA recovery, sequencing success, diversity metrics, and taxonomic resolution. The impact of sample freezing or immediate lysis prior to DNA extraction was also evaluated. ONT sequencing provided enhanced species‐level resolution and improved detection of low‐abundance taxa but showed variable performance in low‐yield samples. Freezing prior to cell DNA extraction modestly increased bacterial 16S copy numbers and improved community consistency. Contamination was a problem across both platforms, particularly in low‐biomass samples, but can be minimised during data analysis. Conclusion: This study provides practical guidance for sequencing platform selection and sample handling in uterine microbiome research. Our findings support future efforts to elucidate microbial contributions to EC pathogenesis and highlight the importance of rigorous contamination control. Importantly, this is the first presentation of a New Zealand cohort and contributes valuable data from an underrepresented population and informs future research in diverse clinical settings.

Supplementary Figure S5: Inference of putative sequences of genomic changes consistent with the data using the LICHeE and REVOLVER methods LICHeE and Revolver were used to further speculate the order of genomic changes in this patient's dataset. ClonEvol was also trialed, using PyClone putative clonal clusters as input, however the results did not converge. A, LICHeE was run according to the tool creator's instructions, using VAFs as input, to generate clonal relationships of tumor samples. Numbers inside colored circles represent the number of mutations defining a clone, and the squares represent the clonal structures of individual samples. Shaded regions indicate the proportion of cells belonging to that clone, where the white regions represent normal cells. Four dominant tumor groups were apparent. Where two or more colors are present, LICHeE has predicted the sample to contain a mixture of clones (most apparent in Pa1 and Lu1 but also present in In2, Pa6 and others). This feature is also visible in the VAF plot (Figure 5a) but not represented on the DNA phylogram (Figure 5b). Technical reasons prevented the inclusion of some samples, e.g. Lu8 and Pa5. B, Clonal relationships between tumor samples as predicted by REVOLVER, using PyClone putative clonal clusters as input (from VAF and chromosomal copy number), and default tool parameters. Clones are labeled according to one variant defining that clone (e.g., MASTL), and the circle size is proportional to the number of variants defining the clone. Number refers to clone number. REVOLVER highlighted the same pattern of progression seen in other analyses: from normal cells, common variants accumulate (labeled as MASTL in Figure 5b), and a further set of variants shared by most tumors was identified (labeled GPAM) before a split into two dominant tumor groups, each characterized by their own set of variants (labeled SLIT1 and PAQR6 respectively). REVOLVER highlights the progression of variant accumulation across all samples, rather than indicating individual samples. All coloring consistent with Figure 5. Overall, LICHeE and REVOLVER explore the clonal and subclonal structure of individual tumor samples and suggest the same sequence of genomic progression evident in the DNA phylogram (Figure 5b). None of these cancer-specific methods attempts to time the evolutionary divergence events.

Supplementary Figure S3: Mutational signatures across all samples collected at autopsy A, Overall mutational burden does not reveal significant mutational signatures (MuSiCa). The signature contribution is indicated by the degree of shading (key to right). The signature with the highest contribution was Signature 3, however it was not statistically significant in most samples according to tool Signal. B, The scarHRD method for quantifying homologous recombination deficiency did not identify any tumors to be HR-deficient; with all having an HRD-sum score < 20 (known HR-deficient tumors usually have HRD-sums of > 40)28.

Supplementary Figure S3: Mutational signatures across all samples collected at autopsy A, Overall mutational burden does not reveal significant mutational signatures (MuSiCa). The signature contribution is indicated by the degree of shading (key to right). The signature with the highest contribution was Signature 3, however it was not statistically significant in most samples according to tool Signal. B, The scarHRD method for quantifying homologous recombination deficiency did not identify any tumors to be HR-deficient; with all having an HRD-sum score < 20 (known HR-deficient tumors usually have HRD-sums of > 40)28.

Supplementary Figure S4: Absence of ARID1A variant in targeted panel sequencing of Biopsy 1 despite adequate depth Top panel shows Biopsy 1 targeted panel sequencing. Bottom panel shows representative tumor sampled at autopsy (Sc6) with heterozygous deletion in ARID1A, clearly absent from Biopsy 1 sequencing despite adequate depth (7240 unique molecules). The probability of not sampling the ARID1A variant in Biopsy 1 due to chance alone was calculated assuming tumor cellularity of 80% and the heterozygous ARID1A variant being present in 50% of tumor reads. 7240 unique molecules covered this genomic position. The binomial distribution in R was used to calculate the probability of not sampling this variant: dbinom(7240, size=7240, prob=0.6).

Supplementary Figure S4: Absence of ARID1A variant in targeted panel sequencing of Biopsy 1 despite adequate depth Top panel shows Biopsy 1 targeted panel sequencing. Bottom panel shows representative tumor sampled at autopsy (Sc6) with heterozygous deletion in ARID1A, clearly absent from Biopsy 1 sequencing despite adequate depth (7240 unique molecules). The probability of not sampling the ARID1A variant in Biopsy 1 due to chance alone was calculated assuming tumor cellularity of 80% and the heterozygous ARID1A variant being present in 50% of tumor reads. 7240 unique molecules covered this genomic position. The binomial distribution in R was used to calculate the probability of not sampling this variant: dbinom(7240, size=7240, prob=0.6).

Supplementary Table S1 includes a description of the tumour samples analysed.Supplementary Table S2 includes DNA WES summary statistics.Supplementary Table S3 includes RNA-Seq summary statistics.Supplementary Table S4 includes WGS linked-read summary statistics.Supplementary Table S5 includes regions covered in the custom ThermoFisher Scientific Ampliseq HD panel. Supplementary Table S6 includes targeted DNA sequencing custom panel summary statistics. Supplementary Table S7 includes low-coverage DNA WES summary statistics.Supplementary Table S8 includes gene set enrichment of genes lost from Chr 11.Supplementary Table S9 includes variants identified in two or more tumour sites.Supplementary Table S10 includes the consequence of somatic variants in Biopsy 1.

Supplementary Figure S6: ctDNA detection of private variants While only four private variants were reported by the sequencing platform’s IonReporter software (indicated in orange, key top right), there were sequencing reads to support other private variants. Private variants unique to samples La1 (3 variants) and Pa6 (1 variant) were supported by 2 to 3 variant molecules, and there was a single variant molecule detected to support private variant detection in the following samples: Br2, Cr1, In2, Ki2, La1, Lh1, Lh2, Lu6, Lu7, Lu9, Pa2, Pa3, Pa6, Sc7, Th1, Th2, Ut1, Ve2, Ve3.

Supplementary Figure S2: Breakpoints in 10x Loupe software linked-reads view.Each bar represents a sequencing read, and those joined with a horizontal line share the same barcode.Reads are grouped by haplotype (green and purple), with unphased reads in grey. Vertical orange lines indicate putative breakpoints defined by the software. Given that the maximum region of the chromosome phased was significantly shorter than the length of Chr 11 (as indicated in the ‘phase block view’ plot), some breakpoints are assigned to different haplotypes, therefore haplotypes should not be compared between regions (the breakpoint appears in purple in some regions and in green in others).The copy number patterns are consistent with all breakpoints occurring on the same copy of the chromosome.