571 Background: The recurrence risk (RS) based on transcriptomic profiles or Ki-67 level of HR+ EBC has been used for adjuvant treatment decisions and may also aid selecting patients who could benefit from novel treatments including CDK4/6 inhibitor or oral selective estrogen receptor degraders (SERDs). Artificial intelligence-powered H&E whole slide image (WSI) models can be a practical approach for this biomarker without the need of additional tissue samples. We developed ML-based models of histopathologic features from an H&E WSI to approximate RS of 21-gene assay (OncotypeDx) using various algorithms of different complexities. Methods: The development dataset was composed of 1,009 cases from TCGA-BRCA with gene expression profiles and 483 EBC cases from Samsung Medical Center with RS results. The prediction model was developed in two-stages. First, Lunit SCOPE, an AI-powered H&E WSI analyzer, detects various cell types and segments tissue areas resulting in various cell densities in tissue regions of interest. Next, four traditional ML models were applied on top of the first stage results. In addition, a deep learning model based on multiple instance learning (DL-MIL) was developed using three features: a supervised convolutional neural network, a supervised cell detection model, and an AI-based pathology profiling analyzer, for extracting semantic contents. The model performance was validated using two independent EBC test sets: 248 cases for comparison with the RS results (set 1), and 708 cases with DFS (set 2). Results: The cross-validation performance of the four traditional ML algorithms had area under the curve of receiver operating characteristics curve (AUROC) ranging from 0.728 to 0.779, where mitotic cell density, tumor cell density, and fibroblast density in cancer area were the variables with the highest importance for all four models. The DL-MIL model had cross validation performance of AUROC 0.831. In test set 1, DL-MIL model showed the highest discrimination with AUROC of 0.828 compared to traditional ML models where logistic regression showed the highest discrimination with AUROC of 0.786 (Table). The DL-MIL model showed the highest performance among the models to predict DFS with hazard ratio (HR) of 2.48 (1.47-4.18). Conclusions: Histopathomic models can accurately predict the RS of high risk as well as poor DFS from only H&E WSIs. These AI models can be a practical tool for treatment selection including emerging drugs such as SERDs. [Table: see text]

571 Background: The recurrence risk (RS) based on transcriptomic profiles or Ki-67 level of HR+ EBC has been used for adjuvant treatment decisions and may also aid selecting patients who could benefit from novel treatments including CDK4/6 inhibitor or oral selective estrogen receptor degraders (SERDs). Artificial intelligence-powered H&E whole slide image (WSI) models can be a practical approach for this biomarker without the need of additional tissue samples. We developed ML-based models of histopathologic features from an H&E WSI to approximate RS of 21-gene assay (OncotypeDx) using various algorithms of different complexities. Methods: The development dataset was composed of 1,009 cases from TCGA-BRCA with gene expression profiles and 483 EBC cases from Samsung Medical Center with RS results. The prediction model was developed in two-stages. First, Lunit SCOPE, an AI-powered H&E WSI analyzer, detects various cell types and segments tissue areas resulting in various cell densities in tissue regions of interest. Next, four traditional ML models were applied on top of the first stage results. In addition, a deep learning model based on multiple instance learning (DL-MIL) was developed using three features: a supervised convolutional neural network, a supervised cell detection model, and an AI-based pathology profiling analyzer, for extracting semantic contents. The model performance was validated using two independent EBC test sets: 248 cases for comparison with the RS results (set 1), and 708 cases with DFS (set 2). Results: The cross-validation performance of the four traditional ML algorithms had area under the curve of receiver operating characteristics curve (AUROC) ranging from 0.728 to 0.779, where mitotic cell density, tumor cell density, and fibroblast density in cancer area were the variables with the highest importance for all four models. The DL-MIL model had cross validation performance of AUROC 0.831. In test set 1, DL-MIL model showed the highest discrimination with AUROC of 0.828 compared to traditional ML models where logistic regression showed the highest discrimination with AUROC of 0.786 (Table). The DL-MIL model showed the highest performance among the models to predict DFS with hazard ratio (HR) of 2.48 (1.47-4.18). Conclusions: Histopathomic models can accurately predict the RS of high risk as well as poor DFS from only H&E WSIs. These AI models can be a practical tool for treatment selection including emerging drugs such as SERDs. [Table: see text]

3135 Background: Quantifying tumor-associated antigen (TAA) expression levels on tumor cells (TC) from immunohistochemistry (IHC) stained slides has been widely used to predict response to novel anti-tumor agents. However, obtaining the precise quantification of TAA expression levels from various cancer types is still challenging. To quantify five targets of interest, we applied an artificial intelligence (AI)-powered analyzer to 16 cancer types. Methods: AI-powered whole slide image (WSI) analyzers were developed using IHC positive or negative tumor cells (TC) from PD-L1 22C3 pharmDx and HER2 4B5 datasets, annotated by pathologists. The universal model was trained by IHC-positive or negative TCs from all datasets, while the second model was trained by the intensity of positive (from 1+ to 3+) or negative TCs from the HER2 dataset. Both AI models were applied to the pan-cancer tissue microarray dataset (n = 1,370) with MUC16, CLDN18.2, MET, HER3, and HER2 IHC staining, respectively. The expression levels of those TAAs were calculated with tumor proportion score (TPS) or ASCO/CAP scoring criteria for HER2 in 16 cancer types including biliary tract, breast, colorectum, esophagus, head and neck, kidney, liver, lung, ovary, bladder, pancreas, prostate, stomach, thyroid, cervical, and endometrial cancer. Results: Based on the TPS of 1% or more, the positivity of MUC16 and CLDN18.2 were most frequently observed in ovary (51.6%) and kidney (24.2%), respectively, and MET and HER3 in colorectum (63.8%; 40.5%). The five most highly expressed tumors and their frequency of TPS≥1% are shown. HER2 3+ tumor cells were most highly expressed in the breast, followed by the bladder. Conclusions: This study describes the TAA expression levels in various tumors using an AI-powered IHC analyzer. This method could be useful for exploring target cancer types and predicting response to novel TAA targeted agents. [Table: see text]

Our study highlights that HD patients with PJP may encounter delays in diagnosis and treatment compared to non-HD PJP patients.

Soybean is an extensively utilized oilseed crop, and improved cultivars and cultivation efficiency of soybean have contributed to the increased use of soybean in edible oil applications. The food industry necessitates the development of soybean oil with an optimized balance of polyunsaturated and saturated fatty acids to meet both nutritional requirements and industrial applications. This study aimed to elucidate the protein structure and functional characterization of a novel allele of KASII-A derived from an EMS-induced mutant line and assess its potential as a genetic resource for developing soybean cultivars with elevated saturated fatty acid composition. Sequence variation in the KASII-A gene was evaluated for PE1544 (~ 16.1% palmitic acid composition), an EMS-induced mutant with high-palmitic acid. A single-nucleotide polymorphism was identified in the KASII-A gene of PE1544, resulting in an amino acid substitution from Gly309 to Asp309. Comparative analysis of three-dimensional protein structures revealed that Gly309 plays a critical role in stabilizing the catalytic residue in the KASII-A active site. Co-segregation analysis revealed that the novel allele was recessive to KASII-A and was associated with high-palmitic acid composition. Furthermore, we analyzed the F2 population derived from the cross between the high-stearic acid line with homozygous recessive sacpd-c allele and PE1544. The F2 progeny with both mutations exhibited a lower stearic acid composition compared to the single sacpd-c mutant. Notably, the F2 progeny with both mutations exhibited a similar ratio of polyunsaturated to saturated fatty acids (P/S index) compared to the single sacpd-c mutant. These findings suggest that KASII-A regulates the palmitic acid and stearic acid composition regardless of the total composition of saturated fatty acids in the single sacpd-c mutant. Comprehensively, the regulation of KASII-A in the single sacpd-c mutant is effective for the development of soybean oil with an ideal P/S index by regulating the content of palmitic and stearic acid while maintaining high-saturated fatty acids. These results suggest that the conversion of palmitic acid to stearic acid is impaired due to the loss-of-function of KASII-A, indicating that the novel allele of KASII-A plays a crucial role in this biochemical conversion in soybean.

COPS5 depletion or inhibition using a small molecule COPS5 inhibitor CSN5i-3 sensitizes high-grade serous carcinoma to platinum chemotherapy through downregulation of nucleotide excision repair and interstrand crosslink repair.

Pinellia Tuber is defined as Pinellia ternata Breitenbach (Family Araceae), according to the Korean Pharmacopoeia (KP). Recent studies have reported the mass production of Pinellia Tuber using tissue culture technology. However, somatic variations during tissue culture have been reported to produce tuber forms resembling those of P. pedatisecta. Additionally, the potential for closely related species, such as P. tripartita and Typhonium flagelliforme, to be distributed as Pinellia Tuber should be monitored carefully for species identification. In this study, we identified indel (insertion/deletion) regions through a comparative analysis of chloroplast genomes and developed specific markers to distinguish P. ternata (182 bp) from P. pedatisecta (352 bp) and P. tripartita (388 bp). Using the developed marker (CP_2F/2R), we established a genetic analysis method that enables the effective and rapid identification of Pinellia Tuber and its potential substitutes with a single PCR reaction. This method can also detect the presence of P. pedatisecta contamination in mixed samples. The proposed approach is expected to contribute to the quality control of Pinellia Tuber, a frequently prescribed herbal medicine, and to prevent misidentification or adulteration.

Abstract The circadian clock generates autonomous molecular rhythms that regulate sleep cycles, chronotypes, daily physiology, and expression patterns of almost the entire genome. Temporal phosphorylation of PERIOD (PER) proteins creates a phosphotimer that is critical for clock function. Although previous studies show that PER stably binds the casein kinase CK1, cis-acting phosphorylation of PER by CK1 is not compatible with slow PER phosphorylation extended over 24 hrs. So how can PER phosphorylation be programmed in a slow and controlled manner? Here, we show that the timing cues are encoded by PER-PER dimerization-mediated trans-phosphorylation, which enables the necessary time delay in phosphorylation and slow phosphorylation. When PER dimerization is disrupted, PER phosphorylation and circadian rhythms are severely compromised. In mouse models with point mutations in the PER dimerization domain, circadian period is shortened to ∼20 hrs, and the phase of wake/sleep cycles is dramatically advanced, switching a nocturnal animal to a half diurnal animal.