Proteomic Subtypes Research Articles

Integrated proteomics and scRNA-seq analyses of ovarian cancer reveal molecular subtype-associated cell landscapes and immunotherapy targets.

Epithelial ovarian cancer (EOC) represents the most lethal gynaecological malignancy, yet understanding the connections between its molecular subtypes and their therapeutic implications remains incomplete. We conducted mass spectrometry-based proteomics analyses of 154 EOC tumour samples and 29 normal fallopian tubes, and single-cell RNA sequencing (scRNA-seq) analyses of an additional eight EOC tumours to classify proteomic subtypes and assess their cellular ecosystems and clinical significance. The efficacy of identified therapeutic targets was evaluated in patient-derived xenograft (PDX) and orthotopic mouse models. We identified four proteomic subtypes with distinct clinical relevance: malignant proliferative (C1), immune infiltrating (C2), Fallopian-like (C3) and differentiated (C4) subtypes. C2 subtype was characterized by lymphocyte infiltration, notably an increased presence of GZMK CD8+ T cells and phagocytosis-like MRC+ macrophages. Additionally, we identified CD40 as a specific prognostic factor for C2 subtype. The interaction between CD40+ phagocytosis-like macrophages and CD40RL+ IL17R CD4+ T cells was correlated with a favourable prognosis. Finally, we established a druggable landscape for non-immune EOC patients and verified a TYMP inhibitor as a promising therapeutic strategy. Our study refines the current immune subtype for EOC, highlighting CD40 agonists as promising therapies for C2 subtype patients and targeting TYMP for non-immune patients.

Proteomic and phosphoproteomic landscape of localized prostate cancer unveils distinct molecular subtypes and insights into precision therapeutics

Building upon our previous investigation of genomic, epigenomic, and transcriptomic profiles of prostate cancer in China, we conducted a comprehensive analysis of proteomic and phosphoproteomic profiles of 82 tumor tissues and matched adjacent normal tissues from 41 Chinese patients with localized prostate cancer. We identified three distinct proteomic subtypes with significant difference in both molecular features and clinical prognosis. Notably, these proteomic subtypes exhibited a parallel degree of heterogeneity in the phosphoproteome, featuring unique metabolism, proliferation, and immune infiltration characteristics. We further demonstrated that a combination of proteins and phosphosites serves as the most effective biomarkers in prostate cancer to predict biochemical recurrence. Through an integrated multiomics analysis, we revealed mechanistic differences underlying different proteomic subtypes and highlighted the potential significance of Serine/arginine-rich splicing factor 1 (SRSF1) phosphorylation in promoting the malignant characteristics of prostate cancer cells. Our multiomics data provide valuable resources for understanding the molecular mechanisms of prostate cancer within the Chinese population, which have the potential to inform the development of personalized treatment strategies and enhance prognostic analyses for prostate cancer patients.

Proteomic Stratification of Prognosis and Treatment Options for Small Cell Lung Cancer.

Small cell lung cancer (SCLC) is a highly malignant and heterogeneous cancer with limited therapeutic options and prognosis prediction models. Here, we analyzed formalin-fixed, paraffin-embedded (FFPE) samples of surgical resections by proteomic profiling, and stratified SCLC into three proteomic subtypes (S-I, S-II, and S-III) with distinct clinical outcomes and chemotherapy responses. The proteomic subtyping was an independent prognostic factor and performed better than current tumor-node-metastasis or Veterans Administration Lung Study Group staging methods. The subtyping results could be further validated using FFPE biopsy samples from an independent cohort, extending the analysis to both surgical and biopsy samples. The signatures of the S-II subtype in particular suggested potential benefits from immunotherapy. Differentially overexpressed proteins in S-III, the worst prognostic subtype, allowed us to nominate potential therapeutic targets, indicating that patient selection may bring new hope for previously failed clinical trials. Finally, analysis of an independent cohort of SCLC patients who had received immunotherapy validated the prediction that the S-II patients had better progression-free survival and overall survival after first-line immunotherapy. Collectively, our study provides the rationale for future clinical investigations to validate the current findings for more accurate prognosis prediction and precise treatments.

Proteomic analysis of the urothelial cancer landscape.

Urothelial bladder cancer (UC) has a wide tumor biological spectrum with challenging prognostic stratification and relevant therapy-associated morbidity. Most molecular classifications relate only indirectly to the therapeutically relevant protein level. We improve the pre-analytics of clinical samples for proteome analyses and characterize a cohort of 434 samples with 242 tumors and 192 paired normal mucosae covering the full range of UC. We evaluate sample-wise tumor specificity and rank biomarkers by target relevance. We identify robust proteomic subtypes with prognostic information independent from histopathological groups. In silico drug prediction suggests efficacy of several compounds hitherto not in clinical use. Both in silico and in vitro data indicate predictive value of the proteomic clusters for these drugs. We underline that proteomics is relevant for personalized oncology and provide abundance and tumor specificity data for a large part of the UC proteome ( www.cancerproteins.org ).

Abstract 3495: Multi-omic landscape of squamous cell lung cancer

Abstract Patients with lung squamous cell carcinoma (LSCC) require new drug targets and improved biomarkers due to a lack of targetable genomic drivers and low response rates to immune checkpoint blockade. In a previous study, we analyzed a cohort of 108 LSCC patients by integrating DNA copy number variation, somatic mutations, RNA-sequencing, and expression proteomics. The principal discovery was the identification of three proteomic subtypes, with the majority (87%) of tumors comprising two of these subtypes. The "Inflamed" subtype showed enrichment for B-cell-rich tertiary lymphoid structures, while the "Redox" subtype exhibited enrichment for redox pathways and NFE2L2/KEAP1 alterations but had notably lower immune infiltration. We hypothesized that these subtypes would result in distinct metabolic signatures. Using ultra-high-performance liquid chromatographic separation on a HILIC column, followed by analysis on a Q Exactive HF high resolution mass spectrometer, we performed untargeted metabolomics on 87 tumors from the same LSCC proteogenomics cohort. A total of 7,392 features were obtained from this analysis, leading to the identification of 446 metabolites through m/z and retention time matching against an internal reference library. To understand if metabolomics could recapitulate our proteomic subtypes, we applied consensus clustering and non-negative matrix factorization (NMF) and assessed the resulting clusters using a Random Forest (RF) supervised classifier. Area Under the Curve (AUC) values for consensus clustering (5 clusters, AUC = 0.72), NMF (4 clusters, AUC = 0.73), and proteomics subtypes (Stewart et al. Nature communications. 2019:10:3578) (3 clusters, AUC = 0.74) suggest that metabolite abundances do indeed recapitulate the proteomic subtypes. Differential expression between Redox and Inflamed yielded 29 differentially expressed metabolites (p-value < 0.05 and 1.5 fold change). Glutathione, a key redox metabolite, was modestly elevated in the Redox proteomic subtype (0.58 log2 ratio, p = 1.14E-05). Notably, we identified glutathione metabolism (p = 1.29-5) and arginine biosynthesis (p = 5.22-4) as among the most significant pathways among differentially expressed metabolites. Glutathione, a key redox metabolite, was modestly elevated in the Redox proteomic subtype (0.58 log2 ratio, p = 1.14E-05). In conclusion, metabolomics recapitulates the proteomic subtypes, and there are distinct differences between these subtypes at the metabolite level. Ongoing work is developing a novel, network-based analysis framework to integrate these data quantitatively. Citation Format: Isis Y. Narvaez-Bandera, Ashley Lui, Eric Welsh, Dalia Ercan, Vanessa Rubio, Hayley Ackerman, Guohui Li, Lancia Darville, Min Liu, Bin Fang, Steven Eschrich, Brooke Fridley, John Koomen, Eric Haura, Gina M. DeNicola, Elsa Flores, Paul Stewart. Multi-omic landscape of squamous cell lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3495.

Abstract 5138: Proteomic analyses of the urothelial cancer landscape reveal highly distinct prognostic and predictive subtypes

Abstract Introduction: Urothelial cancer (UC) is a challenging disease with a wide tumor-biological spectrum. Most molecular classifications cover only muscle-invasive bladder cancer and are transcriptome-based, relating only indirectly to the therapeutically relevant and more stable protein level. Also, the recent introduction of antibody-drug conjugates (ADCs) requires quantitative data about the tumor specificity of their target proteins. We turned to the proteome to address these questions. Methods: We performed deep proteomic profiling of a comprehensive cohort by optimized tandem mass tag-labelled liquid chromatography-coupled tandem mass spectrometry. Data acquisition was validated internally with immunoblotting and externally by bioinformatic reclassification within existing, filtered transcriptomic data. After bioinformatics, the top cluster defining proteins were quantified immunohistochemically under real-world conditions. Protein profiles were individualized and separately evaluated with drug repurposing libraries. Cell viability assays were performed for a panel of twelve UC cell lines to validate these predictions in vitro. Results: We analyzed 434 samples with 242 tumors and 192 paired normal mucosae, covering all stages of UC. 9542 proteins were quantified and revealed five distinct proteomic subtypes. These were validated internally and externally, showing relevant survival stratification also in the TCGA dataset. The proteomic subtypes were independent from pathological groups with relevant stratification of progression- free and overall survival (low vs. high-risk: median 103 vs. 27 months). Tumor specificity of all proteins was highly heterogeneous across stages and subtypes. As an example, the ADC target NECTIN4 was generally overexpressed mainly in non-muscle-invasive UC. Drug repurposing revealed several new candidate drugs, each specific to different proteomic subtypes. In vitro data showed increased sensitivity by subtype in line with four out of seven representative predictions. Conclusions: Proteomic subtypes add independent prognostic information and carry predictive value for several newly identified adjuvant drug candidates. The actual tumor specificity of biomarkers and ADC targets is highly dependent on stage and subtype and calls for individualized patient-specific predictive testing. Citation Format: Franz Friedrich Dressler, Falk Diedrichs, Deema Sabtan, Sofie Hinrichs, Paulina Mackedanz, Mareile Schlotfeldt, Christoph Krisp, Martin Hennig, Hartmut Schlueter, Ulrich Wetterauer, Roman Zubarev, David Horst, Sven Perner, Philipp Wolf, Ákos Végvári. Proteomic analyses of the urothelial cancer landscape reveal highly distinct prognostic and predictive subtypes [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5138.

Evolutionary proteogenomic landscape from pre-invasive to invasive lung adenocarcinoma

Lung adenocarcinoma follows a stepwise progression from pre-invasive to invasive. However, there remains a knowledge gap regarding molecular events from pre-invasive to invasive. Here, we conduct a comprehensive proteogenomic analysis comprising whole-exon sequencing, RNA sequencing, and proteomic and phosphoproteomic profiling on 98 pre-invasive and 99 invasive lung adenocarcinomas. The deletion of chr4q12 contributes to the progression from pre-invasive to invasive adenocarcinoma by downregulating SPATA18, thus suppressing mitophagy and promoting cell invasion. Proteomics reveals diverse enriched pathways in normal lung tissues and pre-invasive and invasive adenocarcinoma. Proteomic analyses identify three proteomic subtypes, which represent different stages of tumor progression. We also illustrate the molecular characterization of four immune clusters, including endothelial cells, B cells, DCs, and immune depression subtype. In conclusion, this comprehensive proteogenomic study characterizes the molecular architecture and hallmarks from pre-invasive to invasive lung adenocarcinoma, guiding the way to a deeper understanding of the tumorigenesis and progression of this disease.

Integrated omics landscape of hepatocellular carcinoma suggests proteomic subtypes for precision therapy

Patients with hepatocellular carcinoma (HCC) at the same clinical stage can have extremely different prognoses, and molecular subtyping provides an opportunity for individualized precision treatment. In this study, genomic, transcriptomic, proteomic, and phosphoproteomic profiling of primary tumor tissues and paired para-tumor tissues from HCC patients (N= 160) are integrated. Proteomic profiling identifies three HCC subtypes with different clinical prognosis, which are validated in three publicly available external validation sets. A simplified panel of nine proteins associated with metabolic reprogramming is further identified as a potential subtype-specific biomarker for clinical application. Multi-omics analysis further reveals that three proteomic subtypes have significant differences in genetic alterations, microenvironment dysregulation, kinase-substrate regulatory networks, and therapeutic responses. Patient-derived cell-based drug tests (N= 26) show personalized responses for sorafenib in three proteomic subtypes, which can be predicted by a machine-learning response prediction model. Overall, this study provides a valuable resource for better understanding of HCC subtypes for precision clinical therapy.

Proteomics Identifies Circulating TIMP-1 as a Prognostic Biomarker for Diffuse Large B-Cell Lymphoma

Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous disease, although disease stratification using in-depth plasma proteomics has not been performed to date. By measuring more than 1000 proteins in the plasma of 147 DLBCL patients using data-independent acquisition mass spectrometry and antibody array, DLBCL patients were classified into four proteomic subtypes (PS-I-IV). Patients with the PS-IV subtype and worst prognosis had increased levels of proteins involved in inflammation, including a high expression of metalloproteinase inhibitor-1 (TIMP-1) that was associated with poor survival across two validation cohorts (n = 180). Notably, the combination of TIMP-1 with the international prognostic index (IPI) identified 64.00% to 88.24% of relapsed and 65.00% to 80.49% of deceased patients in the discovery and two validation cohorts, which represents a 24.00% to 41.67% and 20.00% to 31.70% improvement compared to the IPI score alone, respectively. Taken together, we demonstrate that DLBCL heterogeneity is reflected in the plasma proteome and that TIMP-1, together with the IPI, could improve the prognostic stratification of patients.

The proteomic landscape of soft tissue sarcomas

Soft tissue sarcomas (STS) are rare and diverse mesenchymal cancers with limited treatment options. Here we undertake comprehensive proteomic profiling of tumour specimens from 321 STS patients representing 11 histological subtypes. Within leiomyosarcomas, we identify three proteomic subtypes with distinct myogenesis and immune features, anatomical site distribution and survival outcomes. Characterisation of undifferentiated pleomorphic sarcomas and dedifferentiated liposarcomas with low infiltrating CD3 + T-lymphocyte levels nominates the complement cascade as a candidate immunotherapeutic target. Comparative analysis of proteomic and transcriptomic profiles highlights the proteomic-specific features for optimal risk stratification in angiosarcomas. Finally, we define functional signatures termed Sarcoma Proteomic Modules which transcend histological subtype classification and show that a vesicle transport protein signature is an independent prognostic factor for distant metastasis. Our study highlights the utility of proteomics for identifying molecular subgroups with implications for risk stratification and therapy selection and provides a rich resource for future sarcoma research.

Abstract A007: Proteogenomic landscape of KRASG12C lung adenocarcinomas reveals new subtypes and potential combination therapies

Abstract Despite the presence of the KRASG12C oncogene, not all patients respond to KRASG12C inhibitors (e.g., sotorasib, adagrasib) and duration of response has been variable. We hypothesized that subtypes of KRASG12C lung cancers could be responsible for such differential effects. To define subtypes, we performed targeted exon sequencing, transcriptomics, proteomics, and phosphoproteomics on 46 frozen surgically resected KRASG12C lung adenocarcinomas. We employed unbiased consensus clustering methods, pathway analysis, and PTM-SEA to identify kinase signatures from phosphoproteomics data. Transcriptomics data revealed 4 clusters. Pathway enrichment showed that gene cluster (GC) 1 was enriched for immune pathways including interferon gamma response. Epithelial-mesenchymal transition (EMT), assessed by a TGF-β gene signature, was also enriched in GC1. 100% of GC1 overlapped the Proximal-Inflammatory subtype (Wilkerson et al.) and 53% with the mesenchymal subtype (Daemen et al.). GC2 was enriched for inflammatory response and TNF-alpha signaling. GC3 represented a metabolic phenotype with enriched xenobiotic metabolism and other metabolism pathways. 83% of GC3 overlapped with the Terminal Respiratory Unit subtype (Wilkerson et al.) and 56% with the proliferative subtype (Daemen et al.). GC4 only had 5 samples and did not have any significant enrichment. We next identified four proteomic clusters with distinct biology. Protein Cluster 1 (PC1) was enriched for immune pathways, PC2 was enriched for EMT, and PC3 was enriched for TNF/NF-κβ signaling. PC4 was enriched for xenobiotic and fatty acid metabolism and had significantly more KEAP1 mutations. We observed concordance of protein based clusters with mRNA based clusters, as PC1 largely overlapped with GC1 (70%) and PC4 largely overlapped with GC3 (86%). Cell type inference using xCell largely recapitulated the immune subtypes identified by transcriptomics and proteomics. GC1/PC1 had significantly more CD8+ effector memory T-cells, CD4+ Th2 T-cells, and M1 macrophages, while GC3/PC4 had significantly less of these same cell types. Collectively, GC1/PC1 represents 43% of the cohort and was defined by enrichment of immune pathways, elevated immune populations, and significantly higher PD-L1. Finally, we leveraged the phosphoproteomics data to determine whether signaling activity differs across subtypes. Using PTM-SEA, we identified phosphoproteomic signatures of activity for 50 kinases and pathways. We identified 26% of samples (N=12) with high EGFR activity, which is known to affect sensitivity of cells to KRASG12C inhibitors. The EGFR-high samples were observed across all genomic, transcriptomic, and proteomic subtypes. Our results suggest concordance of some transcriptomic and proteomic subtypes, including an immune subtype, while a metabolic protein subtype and EGFR high signaling subtype may provide additional subclasses with potential relevance for treatment responses. Additional phosphoproteomics signatures such as PLK1 and AKT1 may identify subtypes amenable to combination therapy approaches. Citation Format: Paul A. Stewart, Hitendra Solanki, Eric Welsh, Yaakov Stern, Denis Imbody, Yonghong Zhang, Bruna Pellini, Bin Fang, Sean Yoder, Steven Eschrich, Jamie Teer, John Koomen, Eric B. Haura. Proteogenomic landscape of KRASG12C lung adenocarcinomas reveals new subtypes and potential combination therapies [abstract]. In: Proceedings of the AACR Special Conference: Targeting RAS; 2023 Mar 5-8; Philadelphia, PA. Philadelphia (PA): AACR; Mol Cancer Res 2023;21(5_Suppl):Abstract nr A007.

Abstract 6029: Multi-omic landscape of squamous cell lung cancer

Abstract Patients with squamous cell lung cancer (SCC) have high unmet medical need. Knowledge of these tumors is limited, and a lack of targetable genomic drivers means patients have few treatment options. To provide a detailed analysis on the influence of genomic alterations to proteome-level changes in SCC, we previously integrated DNA copy number, somatic mutations, RNA-sequencing, and expression proteomics in a cohort of 108 SCC patients. A major finding was identification of three proteomic subtypes, two of which made up the majority (87%) of tumors: the “Inflamed” subtype was enriched for B-cell rich tertiary lymphoid structures (TLS), and the “Redox” subtype was enriched for redox pathways and NFE2L2/KEAP1 alterations but had significantly less immune infiltration. We hypothesized these proteomic subtypes would give rise to distinct metabolic signatures. Therefore, we performed untargeted metabolomics on 87 tumors from the same cohort using chromatographic separation on a HILIC column, followed by analysis on a Q Exactive HF mass spectrometer. This analysis yielded 7,344 features corresponding to 7,072 unannotated metabolites and 272 identified metabolites. Glutathione, a key redox metabolite, was anticorrelated with immune score (R = -0.44, padj = 0.004) calculated from our transcriptomic data with the ESTIMATE algorithm, and glutathione was elevated in the Redox proteomic subtype (0.58 log2 ratio, padj = 9.87E-04). Consensus clustering was next used to identify novel metabolomic subtypes of SCC. Surprisingly, none of the five metabolomic subtypes we identified corresponded to proteomic subtype or NFE2L2/KEAP1 alteration (Fisher’s Exact test p-values > 0.05). The fifth subtype had 332 metabolites (26 identified) differentially expressed (> 1.5 fold-change, padj < 0.05) with ascorbate and aldarate metabolism as the top enriched pathway (padj = 3.36E-04). Interestingly, this fifth metabolomic subtype had significantly higher DNp63-alpha (p = 2.40E-05), a primary transcript of delta-N p63 that is known to promote non-small cell lung cancer. Ongoing integrative analyses across omic types will determine how p53, p63, and p73 transcripts influence these metabolomic subtypes, how these transcripts relate to the poor immune infiltration in some SCC tumors, and if these transcripts relate to novel metabolic vulnerabilities in SCC. Citation Format: Paul Stewart, Ashley Lui, Eric Welsh, Dalia Ercan, Vanessa Rubio, Hayley Ackerman, Guohui Li, Bin Fang, Steven Eschrich, John Koomen, Elsa Flores, Eric Haura, Gina DeNicola. Multi-omic landscape of squamous cell lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6029.

295 Identification of Proteomic Biomarkers in Puerto Ricans with Pancreatic Cancer

OBJECTIVES/GOALS: Our objective is to establish a proteomic protein labeling method from tumor tissue and blood samples obtained from patients undergoing surgery for pancreatic cancer in Puerto Rico. Our goal is to discover potential biomarkers in the patient tumor/blood samples that are not expressed in normal control samples obtained from potential organ donors. METHODS/STUDY POPULATION: A pilot study with ten patients undergoing surgery for pancreatic cancer will obtain tumor tissue and blood samples. Protein extracts isolated from tissue/cells will be reduced, alkylated, and digested overnight. Samples will be labeled with TMT reagents and mixed before fractionation and cleanup. Labeled samples will be analyzed with a high-resolution Orbitrap LC-MS/MS before data analysis to identify peptides and quantify the reporter ions. The altered proteins will be analyzed by ELISA to confirm their presence. The protein arrangements will be compared with results from proteomic profile banks to assess their prevalence. As controls, parallel protein analyses will be performed on normal tissue/blood samples from organ donors, facilitated by our local organ procurement organization. RESULTS/ANTICIPATED RESULTS: We anticipate finding proteogenomic material defining PC and new proteomic subtypes not previously described in this population. In addition, studying protein overexpression and underexpression can identify relevant genes and potential biomarkers. We hypothesize that PC in the Hispanic population will show slight variations in tumor protein expression than in other populations, which could lead to the discovery of a new Hispanic-specific biomarker. DISCUSSION/SIGNIFICANCE: We expect to provide essential information that will influence the next steps in developing future screening tests. Identifying specific proteins with the potential to become a preventive test should eventually lead to a reduction in morbidity and mortality of PC. The results of this work should lay the foundation that can guide future research.

Integrative proteomic characterization of adenocarcinoma of esophagogastric junction

The incidence of adenocarcinoma of the esophagogastric junction (AEG) has been rapidly increasing in recent decades, but its molecular alterations and subtypes are still obscure. Here, we conduct proteomics and phosphoproteomics profiling of 103 AEG tumors with paired normal adjacent tissues (NATs), whole exome sequencing of 94 tumor-NAT pairs, and RNA sequencing in 83 tumor-NAT pairs. Our analysis reveals an extensively altered proteome and 252 potential druggable proteins in AEG tumors. We identify three proteomic subtypes with significant clinical and molecular differences. The S-II subtype signature protein, FBXO44, is demonstrated to promote tumor progression and metastasis in vitro and in vivo. Our comparative analyses reveal distinct genomic features in AEG subtypes. We find a specific decrease of fibroblasts in the S-III subtype. Further phosphoproteomic comparisons reveal different kinase-phosphosubstrate regulatory networks among AEG subtypes. Our proteogenomics dataset provides valuable resources for understanding molecular mechanisms and developing precision treatment strategies of AEG.

Proteomics separates adult-type diffuse high-grade gliomas in metabolic subgroups independent of 1p/19q codeletion and across IDH mutational status

High-grade adult-type diffuse gliomas are malignant neuroepithelial tumors with poor survival rates in combined chemoradiotherapy. The current WHO classification is based on IDH1/2 mutational and 1p/19q codeletion status. Glioma proteome alterations remain undercharacterized despite their promise for a better molecular patient stratification and therapeutic target identification. Here, we use mass spectrometry to characterize 42 formalin-fixed, paraffin-embedded (FFPE) samples from IDH-wild-type (IDHwt) gliomas, IDH-mutant (IDHmut) gliomas with and without 1p/19q codeletion, and non-neoplastic controls. Based on more than 5,500 quantified proteins and 5,000 phosphosites, gliomas separate by IDH1/2 mutational status but not by 1p/19q status. Instead, IDHmut gliomas split into two proteomic subtypes with widespread perturbations, including aerobic/anaerobic energy metabolism. Validations with three independent glioma proteome datasets confirm these subgroups and link the IDHmut subtypes to the established proneural and classic/mesenchymal subtypes in IDHwt glioma. This demonstrates common phenotypic subtypes across the IDH status with potential therapeutic implications for patients with IDHmut gliomas.

Subgroup-Enriched Pathways and Kinase Signatures in Medulloblastoma Patient-Derived Xenografts.

Medulloblastoma (MB) is the most common malignant pediatric brain tumor. MB is classified into four primary molecular subgroups: wingless (WNT), sonic hedgehog (SHH), Group 3 (G3), and Group 4 (G4), and further genomic and proteomic subtypes have been reported. Subgroup heterogeneity and few actionable mutations have hindered the development of targeted therapies, especially for G3 MB, which has a particularly poor prognosis. To identify novel therapeutic targets for MB, we performed mass spectrometry-based deep expression proteomics and phosphoproteomics in 20 orthotopic patient-derived xenograft (PDX) models of MB comprising SHH, G3, and G4 subgroups. We found that the proteomic profiles of MB PDX tumors are closely aligned with those of primary human MB tumors illustrating the utility of PDX models. SHH PDXs were enriched for NFκB and p38 MAPK signaling, while G3 PDXs were characterized by MYC activity. Additionally, we found a significant association between actinomycin D sensitivity and increased abundance of MYC and MYC target genes. Our results highlight several candidate pathways that may serve as targets for new MB therapies. Mass spectrometry data are available via ProteomeXchange with identifier PXD035070.

Abstract 3911: Proteomic and transcriptomic subtypes of KRASG12C mutant lung cancer

Abstract Sotorasib (AMG510) is the first KRASG12C covalent inhibitor (KRASG12Ci) approved by the FDA in lung cancers harboring oncogenic KRASG12C mutation. However, it remains unclear why some patients show upfront resistance to the KRASG12Ci, and the mechanisms of adaptive resistance. To enhance the efficacy of the KRASG12Ci, several combination regimens are in clinical trials which includes Sotorasib combination with MEK, PD1, SHP2, pan-HER, PD-L1, and EGFR inhibitors. However, it remains unclear how to precisely identify an appropriate cohort for analysis and predict tumor-centric combination strategies that would be effective in the clinic. We hypothesized that subsets of KRASG12C lung cancer could be identified using transcriptomics and proteomics. To test our hypothesis, we performed hierarchical clustering of the microarray dataset on 87 NSCLC KRASG12C tumors and identified three novel transcriptomic subtypes - Subtype 1 had higher expression of extracellular matrix (ECM) genes, which are related to cellular adhesion, and lower expression of EMT genes. Subtype 2 had low ECM expression, high expression in EMT and cell cycle pathway genes suggesting that this is a more invasive subtype. Subtype 2 had lowest expression of small molecule metabolism, suggesting it may be more responsive to KRASG12Ci. Subtype 3 had the lowest expression in interferon gamma/cytotoxic T-cell, T-cell, and B-cell pathways. Subtype 3 had the highest expression of small molecule metabolism, suggesting that it could be more resistant to KRASG12Ci. Having observed these unique clusters, we next hypothesized that mass spectrometry-based expression and phosphoproteomic analysis (pY and pS/T/Y) will further refine these clusters and help identifying new subsets. Our previous work by Stewart et al., demonstrated the importance of proteomics integrated with genomic and transcriptomic analyses to define molecular subtypes in a cohort of 108 squamous cell lung cancer patients. We believe that a similar integrated proteogenomic study is important in context of KRASG12C tumors to understand the signaling diversity and phenotypically categorized them to optimize patient enrichment schemes for effective combination therapy. For that purpose, we performed an integrative proteogenomic analysis of 75 KRASG12C tumors (55 lung, 12 large bowel adenocarcinomas and 8 PDXs) with full clinical follow up and pathology data. We are presently classifying the 75 KRASG12C tumors into phosphoproteomic subtypes. We also carried out targeted exome sequencing for cancer-specific genes. Next, we plan to leverage existing gene expression microarray data and add exome sequencing proteomics, and phosphoproteomics for a comprehensive, protein-centric characterization on these tumors. Importantly, the inclusion of 8 PDXs being co-processed and co-analyzed along with the tumors will enable rapid follow-up experiments to validate the findings into preclinical therapy trials. Citation Format: Hitendra Singh Solanki, Paul A. Stewart, Eric A. Welsh, Victoria Izumi, Bin Fang, Sean J. Yoder, John M. Koomen, Eric B. Haura. Proteomic and transcriptomic subtypes of KRASG12C mutant lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3911.

OTHR-42. Missing data tolerant integration of proteomic datasets enables the identification and characterization of brain cancer subtypes

Investigating the proteome can add a significant layer of information to manifold existing methylation, mutation, and transcriptome data on brain tumors as proteins represent the pharmacologically addressable phenotype of a disease. Small cohorts limit the usability and validity of statistical methods, and variable technical setups and high numbers of missing values make data integration from public sources challenging. Using a newly developed framework being able to reduce batch effects without the need for data reduction or missing value imputation, we show –based on in-house and publicly available datasets- successful integration of proteomic data across different tissue types, quantification platforms, and technical setups. Exemplarily, data of a Sonic hedgehog (Shh) medulloblastoma mouse model were analyzed, showing efficient data integration independent of tissue preservation strategy or batch. We further integrated batches of publicly available data of human brain tumors, confirming proposed proteomic cancer subtypes correlating with clinical features. We show that, missing value tolerant reduction of technical variances may be helpful to identify biomarkers, proteomic signatures, and altered pathways characteristic for molecular brain cancer subtypes.

Deep Learning-Based Protein Features Predict Overall Survival and Chemotherapy Benefit in Gastric Cancer.

Gastric cancer (GC) is one of the most common malignant tumors with a high mortality rate worldwide and lacks effective methods for prognosis prediction. Postoperative adjuvant chemotherapy is the first-line treatment for advanced gastric cancer, but only a subgroup of patients benefits from it. Here, we used 833 formalin-fixed, paraffin-embedded resected tumor samples from patients with TNM stage II/III GC and established a proteomic subtyping workflow using 100 deep-learned features. Two proteomic subtypes (S-I and S-II) with overall survival differences were identified. S-I has a better survival rate and is sensitive to chemotherapy. Patients in the S-I who received adjuvant chemotherapy had a significant improvement in the 5-year overall survival rate compared with patients who received surgery alone (65.3% vs 52.6%; log-rank P = 0.014), but no improvement was observed in the S-II (54% vs 51%; log-rank P = 0.96). These results were verified in an independent validation set. Furthermore, we also evaluated the superiority and scalability of the deep learning-based workflow in cancer molecular subtyping, exhibiting its great utility and potential in prognosis prediction and therapeutic decision-making.

Identification of Carcinogenesis and Tumor Progression Processes in Pancreatic Ductal Adenocarcinoma Using High-Throughput Proteomics.

Simple SummaryPancreatic cancer is an aggressive disease with a high mortality rate. The study of the biological processes involved in carcinogenesis (tumor formation) and tumor progression (development of metastases) is still necessary. In this work, we established three subtypes of pancreatic tumors according to their protein profiles: one adhesion subtype, a metabolic subtype, and a nucleoplasm subtype. In addition, the identified mechanisms involved in carcinogenesis and in tumor progression differ between subtypes. These differences may need to be considered when designing new treatments.Pancreatic ductal adenocarcinoma (PDAC) is an aggressive disease with an overall 5-year survival rate of just 5%. A better understanding of the carcinogenesis processes and the mechanisms of the progression of PDAC is mandatory. Fifty-two PDAC patients treated with surgery and adjuvant therapy, with available primary tumors, normal tissue, preneoplastic lesions (PanIN), and/or lymph node metastases, were selected for the study. Proteins were extracted from small punches and analyzed by LC-MS/MS using data-independent acquisition. Proteomics data were analyzed using probabilistic graphical models, allowing functional characterization. Comparisons between groups were made using linear mixed models. Three proteomic tumor subtypes were defined. T1 (32% of patients) was related to adhesion, T2 (34%) had metabolic features, and T3 (34%) presented high splicing and nucleoplasm activity. These proteomics subtypes were validated in the PDAC TCGA cohort. Relevant biological processes related to carcinogenesis and tumor progression were studied in each subtype. Carcinogenesis in the T1 subtype seems to be related to an increase of adhesion and complement activation node activity, whereas tumor progression seems to be related to nucleoplasm and translation nodes. Regarding the T2 subtype, it seems that metabolism and, especially, mitochondria act as the motor of cancer development. T3 analyses point out that nucleoplasm, mitochondria and metabolism, and extracellular matrix nodes could be involved in T3 tumor carcinogenesis. The identified processes were different among proteomics subtypes, suggesting that the molecular motor of the disease is different in each subtype. These differences can have implications for the development of future tailored therapeutic approaches for each PDAC proteomics subtype.