Squamous cell carcinoma (SCC) and adenocarcinoma (AC) are two main histological subtypes of solid cancer; however, SCCs are derived from different organs with similar morphologies, and it is challenging to distinguish the origin of metastatic SCCs. Here we report a deep proteomic analysis of 333 SCCs of 17 organs and 69 ACs of 7 organs. Proteomic comparison between SCCs and ACs identifies distinguishable pivotal pathways and molecules in those pathways play consistent adverse or opposite prognostic roles in ACs and SCCs. A comparison between common and rare SCCs highlights lipid metabolism may reinforce the malignancy of rare SCCs. Proteomic clusters reveal anatomical features, and kinase-transcription factor networks indicate differential SCC characteristics, while immune subtyping reveals diverse tumor microenvironments across and within diagnoses and identified potential druggable targets. Furthermore, tumor-specific proteins provide candidates with differentially diagnostic values. This proteomics architecture represents a public resource for researchers seeking a better understanding of SCCs and ACs.

To directly and quantitatively identify the transcriptional protein complexes assembled on accessible chromatin, we develop an assay for transposase-accessible chromatin using mass spectrum (ATAC-MS) based on direct transposition of biotinylated adaptors into open chromatin. Coupling with activated gene sequence information by ATAC-seq, ATAC-MS can profile the accessible chromatin-protein machinery. ATAC-MS, combined with fractionation strategies (fATAC-MS), can provide a high-resolution chromatin-transcriptional machinery atlas. ATAC-MS with a novel Tn5-dCas9 fusion protein [dCas9-targeted ATAC-MS (ctATAC-MS)] further facilitates systematic pinpointing of the transcriptional machinery at specific open chromatin regions. We used ATAC-MS and ATAC-seq to investigate transcriptional regulation during C2C12 cell differentiation and demonstrated the role of RFX1 in regulating the proliferation and differentiation of C2C12 cells. Our strategy provides a universal toolbox including ATAC-MS, fATAC-MS, and ctATAC-MS, which enables us to portray the transcriptional regulation machinery atlas in genome scale and investigate the protein-DNA complex at a specific genomic locus.

The presence of lymph node metastasis (LNM) affects treatment strategy decisions in T1NxM0 colorectal cancer (CRC), but the currently used clinicopathological-based risk stratification cannot predict LNM accurately. In this study, we detected proteins in formalin-fixed paraffin-embedded (FFPE) tumor samples from 143 LNM-negative and 78 LNM-positive patients with T1 CRC and revealed changes in molecular and biological pathways by label-free liquid chromatography tandem mass spectrometry (LC-MS/MS) and established classifiers for predicting LNM in T1 CRC. An effective 55-proteins prediction model was built by machine learning and validated in a training cohort (N=132) and two validation cohorts (VC1, N=42; VC2, N=47), achieved an impressive AUC of 1.00 in the training cohort, 0.96 in VC1 and 0.93 in VC2, respectively. We further built a simplified classifier with 9 proteins, and achieved an AUC of 0.824. The simplified classifier was performed excellent in two external validation cohorts. The expression patterns of 13 proteins were confirmed by immunohistochemistry, and the IHC score of 5 proteins were used to build a IHC predict model with an AUC of 0.825. RHOT2 silence significantly enhanced migration and invasion of colon cancer cells. Our study explored the mechanism of metastasis in T1 CRC and can be used to facilitate the individualized prediction of LNM in patients with T1 CRC, which may provide a guidance for clinical practice in T1 CRC.