Biology subjects

Takano, A.

Publications and source records attributed to Takano, A..

4 recordsLinked to original sources

bioRxiv2023

Comparing virtual staining artificial intelligence model performance on same-tissue versus serial-tissue sections based on CD3+ T-cell ground truth

Immunophenotyping via multi-marker assays significantly contributes to patient selection, therapeutic monitoring, biomarker discovery, and personalized treatments. Despite its potential, the multiplex immunofluorescence (mIF) technique faces adoption challenges due to technical and financial constraints. Alternatively, hematoxylin and eosin (H&E)-based prediction models of cell phenotypes can provide crucial insights into tumor-immune cell interactions and advance immunotherapy. Current methods mostly rely on manually annotated cell label ground truths, with limitations including high variability and substantial labor costs. To mitigate these issues, researchers are increasingly turning to digitized cell-level data for accurate in-situ cell type prediction. Typically, immunohistochemical (IHC) staining is applied to a tissue section serial to one stained with H&E. However, this method may introduce distortions and tissue section shifts, challenging the assumption of consistent cellular locations. Conversely, mIF overcomes these limitations by allowing for mIF and H&E staining on the same tissue section. Importantly, the multiplexing capability of mIF allows for a thorough analysis of the tumor microenvironment by quantifying multiple cell markers within the same tissue section. In this study, we introduce a Pix2Pix generative adversarial network (P2P-GAN)-based virtual staining model, using CD3+ T-cells in lung cancer as a proof-of-concept. Using an independent CD3 IHC-stained lung cohort, we demonstrate that the model trained with cell label ground-truth from the same tissue section as H&E staining performed significantly better in both CD3+ and CD3- T-cell prediction. Moreover, the model also displayed prognostic significance on a public lung cohort, demonstrating its potential clinical utility. Notably, our proposed P2P-GAN virtual staining model facilitates image-to-image translation, enabling further spatial analysis of the predicted immune cells, deepening our understanding of tumor-immune interactions, and propelling advancements in personalized immunotherapy. This concept holds potential for the prediction of other cell phenotypes, including CD4+, CD8+, and CD20+ cells.

immunology↗

bioRxiv2023

Dearth of smoking-induced mutations in oncogene-driven non-small-cell lung cancer despite smoking exposure

Non-small cell lung cancers (NSCLCs) in non-smokers are mostly driven by mutations in the oncogenes EGFR, ERBB2, and MET, and fusions involving ALK and RET. We term these "non-smoking-related oncogenes" (NSROs). In addition to occurring in non-smokers, NSRO-driven tumors also occur in smokers, and the clonal architecture and genomic landscape of these tumors remain unknown. We investigated genomic and transcriptomic alterations in 173 tumor sectors from 48 patients with NSRO-driven or typical-smoking NSCLCs. NSRO-driven NSCLCs in smokers and non-smokers have similar genomic landscapes. Surprisingly, even in patients with prominent smoking histories, the mutational signature caused by tobacco smoking was essentially absent in NSRO-driven NSCLCs. However, NSRO-driven NSCLCs in smokers had higher transcriptomic activities related to regulation of the cell cycle, suggesting that smoking still affects tumor phenotype independently of genomic alterations. Statement of significanceThis study highlights the lack of genomic scars caused by smoking in NSCLCs driven by non-smoking-related oncogenes regardless of smoking history. The impact of smoking on these tumors is mainly non-genomic. The transcriptomic features of NSCLCs associated with smoking may help in the development of therapeutic approaches.

cancer biology↗

bioRxiv2021

Obtaining Spatially Resolved Tumor Purity Maps Using Deep Multiple Instance Learning In A Pan-cancer Study

Tumor purity is the proportion of cancer cells in the tumor tissue. An accurate tumor purity estimation is crucial for accurate pathologic evaluation and for sample selection to minimize normal cell contamination in high throughput genomic analysis. We developed a novel deep multiple instance learning model predicting tumor purity from H&E stained digital histopathology slides. Our model successfully predicted tumor purity from slides of fresh-frozen sections in eight different TCGA cohorts and formalin-fixed paraffin-embedded sections in a local Singapore cohort. The predictions were highly consistent with genomic tumor purity values, which were inferred from genomic data and accepted as the golden standard. Besides, we obtained spatially resolved tumor purity maps and showed that tumor purity varies spatially within a sample. Our analyses on tumor purity maps also suggested that pathologists might have chosen high tumor content regions inside the slides during tumor purity estimation in the TCGA cohorts, which resulted in higher values than genomic tumor purity values. In short, our model can be utilized for high throughput sample selection for genomic analysis, which will help reduce pathologists workload and decrease inter-observer variability. Moreover, spatial tumor purity maps can help better understand the tumor microenvironment as a key determinant in tumor formation and therapeutic response.

cancer biology↗

bioRxiv2020

CHLOROPLAST UNUSUAL POSITIONING 1 is a new type of actin nucleation factor in plants

Plants have evolved unique responses to fluctuating light conditions in their environment. One such response, chloroplast photorelocation movement, optimizes photosynthesis under weak light and prevents photodamage under strong light. CHLOROPLAST UNUSUAL POSITIONING 1 (CHUP1) plays a pivotal role in the light-responsive chloroplast movements, which are driven by dynamic reorganization of chloroplast actin (cp-actin) filaments. In this study, we demonstrated that fluorescently tagged CHUP1 colocalized and was coordinately reorganized with cp-actin filaments during chloroplast movement in Arabidopsis thaliana. The resulting asymmetric distribution of CHUP1 was reversibly regulated by the blue light receptor phototropin. X-ray crystallography indicated that the CHUP1 C-terminal domain shares structural similarity with the formin homology 2 (FH2) domain, although there is no sequence similarity between the two domains. The CHUP1 C-terminal domain stimulated actin polymerization in the presence of profilin. We conclude that CHUP1 is a novel, plant-specific actin nucleator that functions in cp-actin-based chloroplast movement. HighlightsO_LIBlue light changes the distribution pattern of CHUP1 C_LIO_LIFormin FH2 and CHUP1 C-terminal domains are structurally similar but not homologous C_LIO_LICHUP1 nucleates and severs actin filaments in vitro C_LIO_LICHUP1 is a novel, plant-specific actin nucleator for chloroplast movement C_LI

plant biology↗