Microsatellite instability (MSI) has been approved as a pan-cancer biomarker for immune checkpoint blockade (ICB) therapy. However, current MSI identification methods are not available for all patients. We proposed an ensemble multiple instance deep learning model to predict microsatellite status based on histopathology images, and interpreted the pathomics-based model with multi-omics correlation. Methods: Two cohorts of patients were collected, including 429 from The Cancer Genome Atlas (TCGA-COAD) and 785 from an Asian colorectal cancer (CRC) cohort (Asian-CRC). We established the pathomics model, named Ensembled Patch Likelihood Aggregation (EPLA), based on two consecutive stages: patch-level prediction and WSI-level prediction. The initial model was developed and validated in TCGA-COAD, and then generalized in Asian-CRC through transfer learning. The pathological signatures extracted from the model were analyzed with genomic and transcriptomic profiles for model interpretation. Results: The EPLA model achieved an area-under-the-curve (AUC) of 0.8848 (95% CI: 0.8185-0.9512) in the TCGA-COAD test set and an AUC of 0.8504 (95% CI: 0.7591-0.9323) in the external validation set Asian-CRC after transfer learning. Notably, EPLA captured the relationship between pathological phenotype of poor differentiation and MSI ( P < 0.001). Furthermore, the five pathological imaging signatures identified from the EPLA model were associated with mutation burden and DNA damage repair related genotype in the genomic profiles, and antitumor immunity activated pathway in the transcriptomic profiles. Conclusions: Our pathomics-based deep learning model can effectively predict MSI from histopathology images and is transferable to a new patient cohort. The interpretability of our model by association with pathological, genomic and transcriptomic phenotypes lays the foundation for prospective clinical trials of the application of this artificial intelligence (AI) platform in ICB therapy.

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Triploids are rare in nature because of difficulties in meiotic and gametogenic processes, especially in vertebrates. The Carassius complex of cyprinid teleosts contains sexual tetraploid crucian carp/goldfish (C. auratus) and unisexual hexaploid gibel carp/Prussian carp (C. gibelio) lineages, providing a valuable model for studying the evolution and maintenance mechanism of unisexual polyploids in vertebrates. Here we sequence the genomes of the two species and assemble their haplotypes, which contain two subgenomes (A and B), to the chromosome level. Sequencing coverage analysis reveals that C. gibelio is an amphitriploid (AAABBB) with two triploid sets of chromosomes; each set is derived from a different ancestor. Resequencing data from different strains of C. gibelio show that unisexual reproduction has been maintained for over 0.82 million years. Comparative genomics show intensive expansion and alterations of meiotic cell cycle-related genes and an oocyte-specific histone variant. Cytological assays indicate that C. gibelio produces unreduced oocytes by an alternative ameiotic pathway; however, sporadic homologous recombination and a high rate of gene conversion also exist in C. gibelio. These genomic changes might have facilitated purging deleterious mutations and maintaining genome stability in this unisexual amphitriploid fish. Overall, the current results provide novel insights into the evolutionary mechanisms of the reproductive success in unisexual polyploid vertebrates.

The first examples of type B polycyclic polyprenylated acylphloroglucinols with a bicyclo[5.3.1]­hendecane core, hyperberins A (1) and B (2), were isolated from Hypericum beanii, together with three biosynthetic congeners. Their structures were established by a combination of NMR, electric circular dichroism (ECD), and X-ray diffraction analyses. These isolates indicated divergent cationic cyclization as key steps in the biosynthesis of PPAPs with diverse architectures. Compounds 1 and 2 were moderately cytotoxic and exhibited significant anti-inflammatory activities.