Prediction of relapse-free survival according to adjuvant chemotherapy and regulator of chromosome condensation 2 (RCC2) expression in colorectal cancer

Background There is a need for improved selection of patients for adjuvant chemotherapy after resection of non-metastatic colorectal cancer (CRC). Regulator of chromosome condensation 2 (RCC2) is a potential prognostic biomarker. We report on the establishment of a robust protocol for RCC2 expression analysis and prognostic tumour biomarker evaluation in patients who did and did not receive adjuvant chemotherapy. Materials and methods RCC2 was analysed in 2916 primary CRCs from the QUASAR2 randomised trial and two single-hospital Norwegian series. A new protocol using fluorescent antibody staining and digital image analysis was optimised. Biomarker value for 5-year relapse-free survival was analysed in relation to tumour stage, adjuvant chemotherapy and the molecular markers microsatellite instability, KRAS/BRAF(V600E)/TP53 mutations and CDX2 expression. Results Low RCC2 expression was scored in 41% of 2696 evaluable samples. Among patients with stage I-III CRC who had not received adjuvant chemotherapy, low RCC2 expression was an independent marker of inferior 5-year relapse-free survival in multivariable Cox models including clinicopathological factors and molecular markers (HR 1.45, 95% CI 1.09 to 1.94, p=0.012, N=521). RCC2 was not prognostic in patients who had received adjuvant chemotherapy, neither in QUASAR2 nor the pooled Norwegian series. The interaction between RCC2 and adjuvant chemotherapy for prediction of patient outcome was significant in stage III, and strongest among patients with microsatellite stable tumours (p(interaction)=0.028). Conclusions Low expression of RCC2 is a biomarker for poor prognosis in patients with stage I-III CRC and seems to be a predictive biomarker for effect of adjuvant chemotherapy.Peer reviewe

Patient-Derived Organoids from Multiple Colorectal Cancer Liver Metastases Reveal Moderate Intra-patient Pharmacotranscriptomic Heterogeneity

Peer reviewe

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe