Huang, Z.-C. | bioRxiv Science

bioRxiv2024

Allelic DNA synthesis followed by template switching underlies BRCA1-linked tandem duplication

Microhomology-mediated short tandem duplication (TD) is among specific mutational signatures associated with BRCA1-deficient tumors. Several mechanisms have been proposed for its generation, but may not be applicable in repeat-less regions of the human genome. We thus developed a repeat-less TD reporter and a PCR-based site-specific TD assay to analyze short TDs induced by one-ended DNA double strand breaks (DSBs) converted from DNA nicks in Brca1-deficient cells. We found that short TDs induced by DNA nicks are significantly stimulated in Brca1-deficient cells. Analysis of TD products revealed that the TD formation is partly mediated by template switching of displaced nascent strand after allelic DNA synthesis. This suggests either allelic DNA synthesis or the strand annealing step of allelic break-induced replication might be more easily aborted in Brca1-deficient cells, thus promoting TD. Neither depletion of Rad51 or Brca2 nor inactivation of the Brca1 coiled-coil domain stimulated nick-induced TD, indicating that RAD51 loading by BRCA1 is dispensable for BRCA1-mediated TD suppression. These results together provide novel insights into the mechanisms underlying BRCA1-linked TD formation in cancer.

cancer biology↗

bioRxiv2020

Accurate Diagnosis of Colorectal Cancer Based on Histopathology Images Using Artificial Intelligence

BackgroundAccurate and robust pathological image analysis for colorectal cancer (CRC) diagnosis is time-consuming and knowledge-intensive, but is essential for CRC patients treatment. The current heavy workload of pathologists in clinics/hospitals may easily lead to unconscious misdiagnosis of CRC based on their daily image analyses. MethodsBased on a state-of-the-art transfer-learned deep convolutional neural network in artificial intelligence (AI), we proposed a novel patch aggregation strategy for clinic CRC prediction/diagnosis using weakly labeled pathological whole slide image (WSI) patches. This approach was trained and validated using an unprecedented and enormously large number of 170,099 patches, >14,680 WSIs, from >9,631 subjects that covered diverse and representative clinical cases from multi-independent-sources across China, U.S., and Germany. ResultsOur innovative AI tool was consistently nearly perfectly agreed with (average Kappa-statistic 0.896) and even often better than most of the experienced expert pathologists when tested in diagnosing CRC WSIs from multi-centers. The average area under the receiver operating characteristics curve (AUC) of AI was greater than that of the pathologists (0.981 vs 0.970) and achieved the best performance among the application of other AI methods to CRC diagnosis. Our AI-generated heatmap highlights the image regions of cancer tissue/cells. ConclusionsThis first-ever generalizable AI system can handle large amounts of WSIs consistently and robustly without potential bias due to fatigue commonly experienced by clinical pathologists. Hence, it will drastically alleviate the heavy clinical burden of daily pathology diagnosis, and improve the treatment for CRC patients. This tool is generalizable to other cancer diagnosis based on image recognition.

pathology↗