Lu Liu

Adenoid cystic carcinoma (ACC) is a rare malignancy with 3 components (tubular, cribriform, and solid), and these components often coexist in the same patient. In the past few decades, great strides have been made in the pathological classification of ACC. However, due to the complexity of the pathological morphology, establishing a unified grading/assessment standard for clinical implementation process remains challenging. This study aimed to develop improved prognostic biomarkers to assist clinical pathological assessment at the molecular level. We used laser capture microdissection to isolate tumor microsamples with specific pathological features (solid, cribriform, and tubular component) and paracancerous normal glands from 100 patients, then performed DNA sequencing ( n = 955) and RNA sequencing ( n = 723) to obtain the genome and transcriptome profile, including copy number variation (CNV) type, gene fusion, and transcriptional expression. We found that CNV and gene fusion patterns differed among the 3 pathological subtypes and correlated with clinical outcome. We also found a mutually exclusive relationship between MYBL1::NFIB fusion and 6q-loss, and they displayed distinct different transcriptional profiles. We discovered that molecular markers 6q-loss and 14q-loss were strongly associated with poor prognosis in ACC. Patients with 6q-loss or 14q-loss had a higher risk of tumor metastasis and recurrence, whereas MYB::NFIB and MYBL1::NFIB fusions showed no adverse prognostic impact. Based on these findings, we propose a new CNV-based molecular classification that stratifies patients into the subgroups of 6q-loss, 14q-loss, and others. The molecular classification method increased the prognostic accuracy from 74.49% (the pathological method) to now 82.65%, especially in cribriform patients (increased from 64% to 80%). Fluorescence in situ hybridization–based detection of 6q-loss and 14q-loss by probe ESR1-6q25 and FOS-14q24 provided a rapid and clinically feasible validation in ACC prognostic assessment. Further multicohort and multifactor analysis demonstrated the robustness and independence of the prognostic value in overall survival and metastasis-free survival of the molecular classification method.

The interactome networks at the DNA, RNA, and protein levels are crucial for cellular functions, and the diverse variations of these networks are heavily involved in the establishment of different cell states. We have developed a diffusion-based method, Hi-C to geometry (C_TG), to obtain reliable geometric information on the chromatin from Hi-C data. C_TG produces a consistent and reproducible framework for the 3D genomic structure and provides a reliable and quantitative understanding of the alterations of genomic structures under different cellular conditions. The genomic structure yielded by C_TG serves as an architectural blueprint of the dynamic gene regulatory network, based on which cell-specific correspondence between gene–gene and corresponding protein–protein physical interactions, as well as transcription correlation, is revealed. We also find that gene fusion events are significantly enriched between genes of short C_TG distances and are thus close in 3D space. These findings indicate that 3D chromatin structure is at least partially correlated with downstream processes such as transcription, gene regulation, and even regulatory networking through affecting protein–protein interactions.

High-throughput chromosome conformation capture (Hi-C) technique profiles the genomic structure in a genome-wide fashion. The reproducibility and consistency of Hi-C data are essential in characterizing dynamics of genomic structures. We developed a diffusion-based method, C _T G (Hi-C To Geometry), to deal with the technical bias induced by insufficient sampling in sequencing and obtain reliable gemeotric information of the chromatin. C _T G properly quantifies dubiously weak or even undetected interactions and produces a consistent and reproducible framework for the 3D genomic structure. C _T G allows for a reliable genome-wide insight on the alteration of genomic structures under different cellular conditions and reveals correlations between genomic-proximal genes at both transcriptional and translational levels. Cell-specific correspondence between gene-gene and corresponding protein-protein physical interactions, as well as that with the transcription correlation reveals the coordinated inter-molecular structural and regulatory information passage in the central dogma.

Single‐cell RNA sequencing (scRNA‐seq) has emerged as a powerful tool for profiling gene expression of distinct cell populations at the single‐cell level. However, the information of the positions of cells within the multicellular samples is missing in scRNA‐seq datasets. To overcome this limitation, we herein develop OpTAG (optical cell tagging) as a new chemical platform for attaching functional tags onto cell surfaces in a spatially resolved manner. With OpTAG, we establish OpTAG‐seq, which enables spatially resolved scRNA‐seq. We apply OpTAG‐seq to investigate the spatially defined transcriptional program in migrating cancer cells and identified a list of genes that are potential regulators for cancer cell migration and invasion. OpTAG‐seq provides a convenient method for mapping cellular heterogeneity with spatial information within multicellular biological systems.

Single‐cell RNA sequencing (scRNA‐seq) has emerged as a powerful tool for profiling gene expression of distinct cell populations at the single‐cell level. However, the information of the positions of cells within the multicellular samples is missing in scRNA‐seq datasets. To overcome this limitation, we herein develop OpTAG (optical cell tagging) as a new chemical platform for attaching functional tags onto cell surfaces in a spatially resolved manner. With OpTAG, we establish OpTAG‐seq, which enables spatially resolved scRNA‐seq. We apply OpTAG‐seq to investigate the spatially defined transcriptional program in migrating cancer cells and identified a list of genes that are potential regulators for cancer cell migration and invasion. OpTAG‐seq provides a convenient method for mapping cellular heterogeneity with spatial information within multicellular biological systems.