JCI | First Genome-Wide Proteogenomic Landscape of Cervical Cancer

JCI | First Genome-Wide Proteogenomic Landscape of Cervical Cancer

Multi-Omics Sequencing Identifies Targets; Shutong Completes In Vitro and In Vivo CRISPR Validation

Journal: Journal of Clinical Investigation   |   2026

📋   Three Key Highlights

① Multi-Omics Mapping: 139 tumor-normal pairs; WGS + transcriptome + proteome + phosphoproteome integration; 10,037 proteins and 32,767 phosphorylation sites identified — the first genome-wide proteogenomic map of cervical cancer.

② Shutong CRISPR Screen Identifies Lethal Targets: Integrating DepMap CRISPR dependency scores with proteomic and prognostic data, Shutong pinpointed MFAP1 and SF3B5 as the essential "lethal vulnerabilities" of the C2 proliferative subtype.

③ Shutong Completes CRISPR/Cas9 Knockout Validation: End-to-end validation of three targets (MFAP1, SF3B5, KRT16) from sgRNA design to nude mouse xenograft models, published with co-author affiliation.

01   Background: Cervical Cancer Urgently Needs Systematic Proteomic Characterization

Cervical cancer (CC) is the fourth leading cause of cancer death in women globally, with >300,000 deaths annually (~200,000 in Asia alone). For metastatic or recurrent patients, five-year survival is only 15-20%. Genomic research has advanced understanding of CC mutations, but whether genetic changes translate to protein-level functional alterations — which determines true target druggability — remained unclear.

Proteogenomics — integrating genomics, transcriptomics, and proteomics — bridges this gap to identify truly functional tumor-driving targets. This study is the first such systematic effort in cervical cancer.

02   Four-Omics Integration: A Molecular Landscape of 139 Cervical Cancer Cases

2.1   Study Design and Sample Scale

The team prospectively collected pre-treatment cervical cancer specimens (2014-2019) from Wuhan Central Hospital and Tongji Hospital, building the largest CC multi-omics dataset to date:

◆   130 tumor/matched blood: WGS (2x150 bp, Illumina HiSeq X)

◆   101 tumors: RNA-seq (DNBSEQ-T7 platform)

◆   139 tumor/NAT pairs: Proteomics + phosphoproteomics (LC-MS/MS, Spectronaut v18.0)

▲ Figure 1 (original Fig. 1): Study framework and genome/proteome/phosphoproteome overview

2.2   Genomic Level: High-Frequency Mutations and Genomic Instability

WGS analysis identified frequently mutated genes: PIK3CA (25%), KMT2C (11%), SYNE2 (7%), KMT2D (6%). Structurally, 16% had whole-genome doubling (WGD), 27% showed chromothripsis, and 82% harbored ≥2 clonal subpopulations — revealing high genomic instability and intra-tumor heterogeneity.

Novel CNA driver genes: Beyond known hotspots, novel recurrent copy number gains were discovered at 1p34.2 (MYCL), 9p23 (NFIB), 21p12 (ICOSLG), and recurrent loss at 6p22.1 (HLA-A). RABIF copy number gain promotes glucose uptake via RAB10 regulation, drives tumor growth, and independently correlates with poor prognosis (OS: HR=2.125, P=0.04).

2.3   Proteomic Level: Tumor Immune Suppression and RNA Metabolism Activation

Proteomics identified 10,037 proteins. Compared to adjacent normal tissue:

◆   1,041 downregulated proteins enriched in complement activation and humoral immunity — revealing systemic immune suppression in the CC tumor microenvironment

◆   1,706 upregulated proteins enriched in RNA metabolism, ribonucleoprotein biogenesis, translation, and RNA splicing

◆   23 proteins are highly upregulated (>1.5 Log2 FC) in ≥90% of tumors; TACSTD2, TFRC, and TYMP have FDA-approved targeted drugs — direct clinical translation potential

2.4   Phosphoproteomic Level: Widespread Phosphorylation Amplification

Phosphoproteomics identified 32,767 phosphorylation sites across 6,364 phosphoproteins (site localization confidence >0.75). Tumors showed significantly more phosphorylation sites than paired normal tissue (P=4.53x10⁻¹⁴), primarily enriched in RNA processing. Among 13 sites with independent prognostic value, elevated NUCKS S181 phosphorylation most significantly correlated with shorter overall survival (P=0.003) — without corresponding protein abundance association, highlighting the unique value of phosphoproteomics for biomarker discovery.

2.5   Four Molecular Subtypes: Products of Multi-Omics Clustering

Using SNF-CC unsupervised clustering of transcriptomic and proteomic data, 101 CC patients were stratified into four proteogenomic subtypes:

◆   C1 (EMT): Enriched for ECM remodeling and wound healing; fibroblast- and endothelial-rich; highly invasive

◆   C2 (Proliferative): Highly upregulated RNA processing; highest proliferation score; worst prognosis (OS and PFS significantly inferior)

◆   C3 (Immune-Active): Highest immune score, abundant CD8+ T cell infiltration, high checkpoint expression — 90% dual PD-1/CTLA-4 blockade response rate (vs. 18% for non-C3, P=0.002)

◆   C4 (Epithelial): High keratin expression; highest lymphovascular invasion (LVSI) frequency

03   Shutong CRISPR Screen: Pinpointing Functionally Lethal Targets from Multi-Omics Candidates

⭐ The CRISPR functional screening analysis in this study was performed by the Shutong team.

While multi-omics analysis identifies many "differentially expressed" candidates, determining which genes are truly essential for tumor cell survival requires functional screening data. Shutong integrated DepMap gene dependency scores to build a triple-layer prioritization strategy targeting the worst-prognosis C2 subtype:

Filter 1: High protein expression in C2 vs. the other three subtypes — 435 candidate proteins

Filter 2: DepMap CRISPR dependency score < -0.7 (knockout causes significant cell death)

Filter 3: High expression independently associated with shorter overall survival (prognostic value)

After triple filtering, two core targets emerged:

◆   MFAP1 (dependency score: -1.66) — core spliceosome component involved in pre-mRNA splicing

◆   SF3B5 (dependency score: -3.21) — extremely low score indicating high dependency

The same strategy identified KRT16 (Keratin 16) as a candidate target in the C4 epithelial subtype.

▲ Figure 2 (original Fig. 4): Gene dependency score distributions by subtype and MFAP1/SF3B5 prognostic associations in C2. Lower scores indicate stronger lethal effects in DepMap CRISPR screens.

💡 Shutong's Contribution: By integrating DepMap data, differential protein expression, and patient prognosis, Shutong elevated candidates from "statistically overexpressed" to "functionally essential for survival," providing the scientific basis for subsequent in vitro and in vivo validation.

04   Shutong CRISPR/Cas9 Knockout Validation: Full Pipeline from Cells to Animals

⭐ CRISPR screening analysis and CRISPR/Cas9 validation were performed by Shutong as co-author, published in JCI.

Shutong completed end-to-end experimental validation of three targets (MFAP1, SF3B5, KRT16) from sgRNA design to in vivo tumor models:

4.1   sgRNA Design and Knockout Efficiency Validation

Two independent sgRNAs per target were designed and delivered via lentivirus to establish stable knockout lines in C2-representative SiHa cells (MFAP1/SF3B5) and C4-representative ME-180 cells (KRT16). Western blot confirmed efficient protein-level knockout for all three targets; scramble sequences served as controls.

4.2   Colony Formation: Loss of In Vitro Proliferative Capacity

Colony formation assays in stable knockout lines showed significantly reduced colony numbers after MFAP1/SF3B5 knockout in SiHa and KRT16 knockout in ME-180 (all statistically significant vs. scramble controls, n=3, one-way ANOVA), directly demonstrating that all three targets are essential for CC cell proliferation.

4.3   Nude Mouse Xenograft: Effective In Vivo Tumor Growth Suppression

Knockout cells were subcutaneously implanted in nude mice (n=5 per group) with regular tumor volume measurements:

◆   MFAP1 KO: Significantly reduced tumor volume and growth rate vs. control (two-way ANOVA)

◆   SF3B5 KO: Even more pronounced tumor inhibition, consistent with its extreme dependency score (-3.21)

◆   KRT16 KO: Significantly suppressed ME-180-derived xenograft growth

In vivo validation elevated functional necessity from "cellular level" to "whole animal level," strongly supporting clinical translation potential.

4.4   Mechanistic Insight: MFAP1 Drives Tumor via the RBM4-mTORC1 Axis

RNA-seq and splicing analysis (rMATS) of MFAP1 knockout cells showed increased exon 3 inclusion in RBM4, reduced short isoform RBM4-S, and subsequent mTORC1 suppression (decreased S6K phosphorylation). This reveals a novel spliceosome-dependent mechanism by which MFAP1 drives oncogenic mTORC1 signaling, providing a basis for combined targeting strategies.

▲ Figure 3 (original Fig. 5; CRISPR/Cas9 experiments performed by Shutong). H/K/Q: Western blot verifying MFAP1, SF3B5, KRT16 knockout efficiency; I/L/R: Colony formation — all three KOs significantly suppressed in vitro proliferation; J/M/S: Xenograft — all three KOs significantly suppressed in vivo tumor growth.

Shutong's Complete Contributions to This Project:[CRISPR Screen Analysis] Integrated DepMap dependency scores + proteomic differential expression + patient prognosis to build a triple-filter prioritization strategy, identifying MFAP1, SF3B5, and KRT16. [CRISPR/Cas9 Knockout Validation] sgRNA design & synthesis → lentiviral packaging & cell transduction → stable KO cell line construction → Western blot KO efficiency validation → colony formation assay → nude mouse xenograft → data organization & figure preparation → published as co-author (Generulor Company Bio-X Lab) in JCI.

05   Conclusions: Multi-Omics Mapping + CRISPR Validation — A Complete Closed Loop for Precision Targeting

This study demonstrates a complete translational pipeline from "multi-omics big data" to "functionally validated targets":

◆   WGS + Transcriptome + Proteome + Phosphoproteome → CC molecular landscape

◆   Differential protein expression + DepMap CRISPR scores + prognosis → target prioritization (by Shutong)

◆   CRISPR/Cas9 KO (in vitro + in vivo) → target necessity validated (by Shutong)

◆   Splicing analysis + pathway profiling → mechanism elucidated

Ultimately, MFAP1 and SF3B5 were established as functionally essential genes in the C2 proliferative subtype, KRT16 as a validated target in C4, and a four-protein classifier (CDH13/TP53BP1/NNMT/HSPB1) provides a practical tool for immunotherapy patient selection.

Shutong: Empowering Your Target Research — From Multi-Omics Analysis to CRISPR Functional Validation

Zhuhai Shutong Medical Technology Co., Ltd. is a biopharmaceutical enterprise with gene editing at its core, headquartered in the Hengqin New Area (Guangdong-Macao In-Depth Cooperation Zone), with subsidiaries including Gusheng (Wuhan), Guangzhou Shutong, and Hangzhou Shutong. A national high-tech enterprise with a postdoctoral research station, 40+ patent applications, and multiple national key R&D projects. As demonstrated in this study, Shutong is fully capable of independently delivering publication-ready experimental data.

🔬   CRISPR Library Screening Services

As demonstrated in this study, we offer genome-wide CRISPR KO libraries (GeCKO, Brunello, etc.) and customized targeted libraries covering kinases, RNA-binding proteins, transcription factors, and more. Supports CRISPRi, CRISPRa, and Perturb-seq (single-cell CRISPR) — applicable to 2D cell lines, 3D organoids, and in vivo models.

✂️   CRISPR/Cas9 Knockout Services (Including In Vitro and In Vivo Functional Validation)

As demonstrated in this study, we provide a complete integrated service: sgRNA design → lentiviral packaging → stable KO cell line construction → Western blot validation → colony formation/proliferation assays → nude mouse xenograft validation. Single and multi-gene KO available; editing efficiency ≥95%; supports 2D cell lines, 3D organoids, and primary cells. Data publication-ready.

📊   Multi-Omics Sequencing and Bioinformatics Analysis

Supports analysis of RNA-seq, proteomics (DIA/DDA), phosphoproteomics, and CRISPR screen NGS data, including DepMap dependency score integration, KSEA, protein interaction networks, survival analysis, and multi-omics target prioritization — one-stop delivery from raw data to publication-ready figures.

We welcome inquiries and collaborations to advance precision oncology research!

Zhuhai Shutong Medical Technology Co., Ltd.   |   Generulor Company Bio-X Lab, Zhuhai, Guangdong

References

Tian X, Li M, Wang Z, et al. Proteogenomic characterization of cervical cancer identifies molecular subtypes predictive of clinical outcomes and subtype-specific targets. J Clin Invest. 2026. https://doi.org/10.1172/JCI199497