PAIP 2020: Microsatellite instability prediction in colorectal cancer.

Microsatellite instability (MSI) refers to alterations in the length of simple repetitive genomic sequences. MSI status serves as a prognostic and predictive factor in colorectal cancer. The MSI-high status is a good prognostic factor in stage II/III cancer, and predicts a lack of benefit to adjuvant fluorouracil chemotherapy in stage II cancer but a good response to immunotherapy in stage IV cancer. Therefore, determining MSI status in patients with colorectal cancer is important for identifying the appropriate treatment protocol. In the Pathology Artificial Intelligence Platform (PAIP) 2020 challenge, artificial intelligence researchers were invited to predict MSI status based on colorectal cancer slide images. Participants were required to perform two tasks. The primary task was to classify a given slide image as belonging to either the MSI-high or the microsatellite-stable group. The second task was tumor area segmentation to avoid ties with the main task. A total of 210 of the 495 participants enrolled in the challenge downloaded the images, and 23 teams submitted their final results. Seven teams from the top 10 participants agreed to disclose their algorithms, most of which were convolutional neural network-based deep learning models, such as EfficientNet and UNet. The top-ranked system achieved the highest F1 score (0.9231). This paper summarizes the various methods used in the PAIP 2020 challenge. This paper supports the effectiveness of digital pathology for identifying the relationship between colorectal cancer and the MSI characteristics.

Characterisation and validation of an in vitro transactivation assay based on the 22Rv1/MMTV_GR-KO cell line to detect human androgen receptor agonists and antagonists.

We describe the characterisation and validation of an androgen receptor (AR) transactivation assay for detection of AR agonists and antagonists using a stably transfected human prostate cancer cell line. This 22Rv1/mouse mammary tumour virus glucocorticoid knock-out cell line based AR transactivation assay was validated by criteria in Organisation for Economic Cooperation and Development Guidance Document 34 to determine if the assay performed equally well to the AR EcoScreen Assay included in Test Guideline for AR Transactivation (OECD TG 458). There was no Glucocorticoid Receptor (GR) crosstalk, and no changes in the AR DNA sequence in cells after the successful knock out of GR. Subsequently, the concordance of classifications of the 22 test chemicals was 100% in all laboratories. The AR agonistic and antagonistic inter-laboratory coefficients of variation based on log[10% effect for 10 nM DHT, PC10] and log[inhibitory response of 800 pM DHT by at 30%, IC30] from comprehensive tests were 2.75% and 2.44%, respectively. The AR agonist/antagonist test chemical classifications were consistent across AR EcoScreen ARTA assay data for 82/89%, and the balanced accuracy, sensitivity, and specificity were 83/90%, 88/100% and 78/80%, respectively. This assay was successfully validated and was approved for inclusion in TG 458 in 2020.