Background Conventional blood and stool tests are normally used for early screening of colorectal cancer (CRC) but the accuracy and efficiency remain to be improved. Recent findings suggest Fusobacterium nucleatum to be a biomarker for CRC. This study evaluated the role of F. nucleatum and developed CRC diagnostic models by combining F. nucleatum with fecal occult blood (FOB), transferrin (TRF), carcinoembryonic antigen (CEA), carbohydrate antigen 19‐9 (CA19‐9), gender, and age. Materials and Methods Candidates including 71 healthy individuals and 59 CRC patients were recruited. Abundance of F. nucleatum in stool or tissue samples was measured by quantitative real‐time PCR. CEA, CA19‐9, TRF, and FOB were measured in parallel. These biomarkers together with genders and ages were the seven parameters used to develop CRC diagnostic models. Ten different machine learning algorithms were tested to achieve the best performance. Results Fecal F. nucleatum abundance was found significantly higher in CRC group compared to healthy group (p = 0.0005). Among the CRC patients, F. nucleatum abundance in tumor tissue was significantly higher than that in paracancerous tissue (p = 0.0087). CRC diagnostic models using different parameters were generated based on Logistic Regression algorithm, which showed good performance. The area under the curve (AUC) score of fecal F. nucleatum as the single diagnostic biomarker was 0.68 while the accuracy was 0.56. The diagnostic performance was obviously improved with the highest AUC (0.93) and accuracy (0.87) achieved when using all the 7 clinical parameters. The combination F. nucleatum + FOB + gender + age had the second highest AUC (0.92) and accuracy (0.85). A more utilitarian model using F. nucleatum + FOB showed relatively high AUC at 0.86 and accuracy at 0.81. Conclusions F. nucleatum is valuable for CRC diagnosis. Combination of different clinical parameters could significantly improve CRC diagnostic performance. The combination F. nucleatum + FOB + gender + age may be an effective and noninvasive method for clinical application.

e17561 Background: HRD test from Myriad Inc. was approved by FDA as a biomarker for niraparib in ovarian cancer. Its threshold (as 42) was initially established by capturing 95% of tumors with BRCA1/2 mutation and BRCA1 promoter methylation in the training cohort. Here, we explored the stochasticity of such threshold, when the test noises themselves were concerned. Methods: We simulated HRD scores from figure 1 of Telli et al., 2016 by assigning the median from each histogram bin to the associated samples. The threshold was calculated, as described in the original paper, and the standard error (SE) and confidence interval (CI) for such threshold were estimated from bootstrapping the cohort. Furthermore, the entire cohort was down-sampled to determine the effect of sample size on the threshold. Results: The threshold of 42.5 was calculated from our simulated dataset, close to the original one, with SE of 1.62 and 95% CI of 40.75 - 47.5 for the bootstrap. As expected, SE steadily increased to 2.21, 2.84, 2.97 when the sample sizes decreased to 250, 230, and 210 respectively. When the dataset was further down-sampled to include only 200 BRCA deficient samples, SE increased to 3.76, and drastically, CI ranged between 36.5 and 51.2. Conclusions: We have showed there exists manifest stochasticity in the original threshold of 42 for the Myriad HRD test, which is likely close to platform noise in magnitude. The minimum size of BRCA deficient patients to obtain a stable threshold is 200, as shown in our study.

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e20516 Background: Approval Selpercatinib of FDA for lung and thyroid cancer patients harboured RET mutations or fusions attracts the investigation RET fusion partners and their ability for RET‐based targeted therapy. Here, we presented a genomic characteristics of RET fusion in six cancer type among 24,087 Chinese cancer patients. Methods: Tumor samples (including tissues, formalin-fixed paraffin-embedded or plasma) obtained from patients between March 2021 and February 2023 were used for RET fusion detection. Customed probes were designed to covered all exonic and intronic regions of 6, 10 and 11 for RET. GeneFuse and Delly softwares were used for analyzing RET fusion. To ensure both sensitivity and accuracy of fusion calling, RET fusions were only reported to fuse with a set of 600 cancer-related genes and require at least two individual supporting reads. Results: A total of 233 cases with RET fusion was identified among 24,087 patients with the given six cancer types including lung cancer, thyroid cancer, coloretal cancer, pancreatic cancer, oesophageal cancer and cancer of unknown primary site. A total of 6 fusion patterns were identified in the study cohort with KIF5B, CCDC6, NCOA4 and ERC1 being the most common RET fusion partners. Notably, two novel fusion partners (i.e. GOPC and VCL) were first reported in this study. RET variants frequencies were the highest in thyroid carcinoma (13/210, 6.19%), lung cancer (212/20,402; 1.04%), esophageal cancer (1/156, 0.64%), cancer of unknown primary site (2/374, 0.53%) , pancreatic cancer (1/337, 0.29%) and colorectal cancer (5/2,608, 0.19%). The results of the analysis revealed that different tumor types tend to have preferred fusion partners. For instance, KIF5B is highly specific in lung cancer, account for 72% of cases. Meanwhile, CCDC6 and NCOA4 are both enriched in thyroid cancer (both 46%). NCOA4 also present frequently in colorectal cancer, with 80% of cases displaying this partner. Conclusions: Detection of co‐occurrence of partner and RET fusions provide a guide to clinicians in deciding tumors as candidates for RET targeted therapies.

Whole genome sequencing (WGS) is championed by the UK National Health Service (NHS) to identify genetic variants that cause particular diseases. The full potential of WGS has yet to be realised as early data analytic steps prioritise protein-coding genes, and effectively ignore the less well annotated non-coding genome which is rich in transcribed and critical regulatory regions. To address, we developed a filter, which we call GROFFFY, and validated in WGS data from hereditary haemorrhagic telangiectasia patients within the 100,000 Genomes Project. Before filter application, the mean number of DNA variants compared to human reference sequence GRCh38 was 4,867,167 (range 4,786,039-5,070,340), and one-third lay within intergenic areas. GROFFFY removed a mean of 2,812,015 variants per DNA. In combination with allele frequency and other filters, GROFFFY enabled a 99.56% reduction in variant number. The proportion of intergenic variants was maintained, and no pathogenic variants in disease genes were lost. We conclude that the filter applied to NHS diagnostic samples in the 100,000 Genomes pipeline offers an efficient method to prioritise intergenic, intronic and coding gDNA variants. Reducing the overwhelming number of variants while retaining functional genome variation of importance to patients, enhances the near-term value of WGS in clinical diagnostics.