Feasibility of Phosphoproteomics on Leftover Samples After RNA Extraction With Guanidinium Thiocyanate

Inferring kinase activity from phosphoproteomic data: Tool comparison and recent applications

Aberrant cellular signaling pathways are a hallmark of cancer and other diseases. One of the most important signaling mechanisms involves protein phosphorylation/dephosphorylation. Protein phosphorylation is catalyzed by protein kinases, and over 530 protein kinases have been identified in the human genome. Aberrant kinase activity is one of the drivers of tumorigenesis and cancer progression and results in altered phosphorylation abundance of downstream substrates. Upstream kinase activity can be inferred from the global collection of phosphorylated substrates. Mass spectrometry-based phosphoproteomic experiments nowadays routinely allow identification and quantitation of >10k phosphosites per biological sample. This substrate phosphorylation footprint can be used to infer upstream kinase activities using tools like Kinase Substrate Enrichment Analysis (KSEA), Posttranslational Modification Substrate Enrichment Analysis (PTM-SEA), and Integrative Inferred Kinase Activity Analysis (INKA). Since the topic of kinase activity inference is very active with many new approaches reported in the past 3 years, we would like to give an overview of the field. In this review, an inventory of kinase activity inference tools, their underlying algorithms, statistical frameworks, kinase-substrate databases, and user-friendliness is presented. The most widely-used tools are compared in-depth. Subsequently, recent applications of the tools are described focusing on clinical tissues and hematological samples. Two main application areas for kinase activity inference tools can be discerned. (1) Maximal biological insights can be obtained from large data sets with group comparisons using multiple complementary tools (e.g., PTM-SEA and KSEA or INKA). (2) In the oncology context where personalized treatment requires analysis of single samples, INKA for example, has emerged as tool that can prioritize actionable kinases for targeted inhibition.

Rummagene: massive mining of gene sets from supporting materials of biomedical research publications

Many biomedical research publications contain gene sets in their supporting tables, and these sets are currently not available for search and reuse. By crawling PubMed Central, the Rummagene server provides access to hundreds of thousands of such mammalian gene sets. So far, we scanned 5,448,589 articles to find 121,237 articles that contain 642,389 gene sets. These sets are served for enrichment analysis, free text, and table title search. Investigating statistical patterns within the Rummagene database, we demonstrate that Rummagene can be used for transcription factor and kinase enrichment analyses, and for gene function predictions. By combining gene set similarity with abstract similarity, Rummagene can find surprising relationships between biological processes, concepts, and named entities. Overall, Rummagene brings to surface the ability to search a massive collection of published biomedical datasets that are currently buried and inaccessible. The Rummagene web application is available at https://rummagene.com .

Deep Learning Prediction Boosts Phosphoproteomics-Based Discoveries Through Improved Phosphopeptide Identification

Shotgun phosphoproteomics enables high-throughput analysis of phosphopeptides in biological samples. One of the primary challenges associated with this technology is the relatively low rate of phosphopeptide identification during data analysis. This limitation hampers the full realization of the potential offered by shotgun phosphoproteomics. Here we present DeepRescore2, a computational workflow that leverages deep learning-based retention time and fragment ion intensity predictions to improve phosphopeptide identification and phosphosite localization. Using a state-of-the-art computational workflow as a benchmark, DeepRescore2 increases the number of correctly identified peptide-spectrum matches by 17% in a synthetic dataset and identifies 19% to 46% more phosphopeptides in biological datasets. In a liver cancer dataset, 30% of the significantly altered phosphosites between tumor and normal tissues and 60% of the prognosis-associated phosphosites identified from DeepRescore2-processed data could not be identified based on the state-of-the-art workflow. Notably, DeepRescore2-processed data uniquely identifies EGFR hyperactivation as a new target in poor-prognosis liver cancer, which is validated experimentally. Integration of deep learning prediction in DeepRescore2 improves phosphopeptide identification and facilitates biological discoveries.

Phosphoproteomics of patient-derived xenografts identifies targets and markers associated with sensitivity and resistance to EGFR blockade in colorectal cancer

Epidermal growth factor receptor (EGFR) is a well-exploited therapeutic target in metastatic colorectal cancer (mCRC). Unfortunately, not all patients benefit from current EGFR inhibitors. Mass spectrometry-based proteomics and phosphoproteomics were performed on 30 genomically and pharmacologically characterized mCRC patient-derived xenografts (PDXs) to investigate the molecular basis of response to EGFR blockade and identify alternative drug targets to overcome resistance. Both the tyrosine and global phosphoproteome as well as the proteome harbored distinctive response signatures. We found that increased pathway activity related to mitogen-activated protein kinase (MAPK) inhibition and abundant tyrosine phosphorylation of cell junction proteins, such as CXADR and CLDN1/3, in sensitive tumors, whereas epithelial-mesenchymal transition and increased MAPK and AKT signaling were more prevalent in resistant tumors. Furthermore, the ranking of kinase activities in single samples confirmed the driver activity of ERBB2, EGFR, and MET in cetuximab-resistant tumors. This analysis also revealed high kinase activity of several members of the Src and ephrin kinase family in 2 CRC PDX models with genomically unexplained resistance. Inhibition of these hyperactive kinases, alone or in combination with cetuximab, resulted in growth inhibition of ex vivo PDX-derived organoids and in vivo PDXs. Together, these findings highlight the potential value of phosphoproteomics to improve our understanding of anti-EGFR treatment and response prediction in mCRC and bring to the forefront alternative drug targets in cetuximab-resistant tumors.

The 2022 Report on the Human Proteome from the HUPO Human Proteome Project

The 2022 Metrics of the Human Proteome from the HUPO Human Proteome Project (HPP) show that protein expression has now been credibly detected (neXtProt PE1 level) for 18 407 (93.2%) of the 19 750 predicted proteins coded in the human genome, a net gain of 50 since 2021 from data sets generated around the world and reanalyzed by the HPP. Conversely, the number of neXtProt PE2, PE3, and PE4 missing proteins has been reduced by 78 from 1421 to 1343. This represents continuing experimental progress on the human proteome parts list across all the chromosomes, as well as significant reclassifications. Meanwhile, applying proteomics in a vast array of biological and clinical studies continues to yield significant findings and growing integration with other omics platforms. We present highlights from the Chromosome-Centric HPP, Biology and Disease-driven HPP, and HPP Resource Pillars, compare features of mass spectrometry and Olink and Somalogic platforms, note the emergence of translation products from ribosome profiling of small open reading frames, and discuss the launch of the initial HPP Grand Challenge Project, "A Function for Each Protein".

Deep learning prediction boosts phosphoproteomics-based discoveries through improved phosphopeptide identification

Shotgun phosphoproteomics enables high-throughput analysis of phosphopeptides in biological samples, but low phosphopeptide identification rate in data analysis limits the potential of this technology. Here we present DeepRescore2, a computational workflow that leverages deep learning-based retention time and fragment ion intensity predictions to improve phosphopeptide identification and phosphosite localization. Using a state-of-the-art computational workflow as a benchmark, DeepRescore2 increases the number of correctly identified peptide-spectrum matches by 17% in a synthetic dataset and identifies 19%-46% more phosphopeptides in biological datasets. In a liver cancer dataset, 30% of the significantly altered phosphosites between tumor and normal tissues and 60% of the prognosis-associated phosphosites identified from DeepRescore2-processed data could not be identified based on the state-of-the-art workflow. Notably, DeepRescore2-processed data uniquely identifies EGFR hyperactivation as a new target in poor-prognosis liver cancer, which is validated experimentally. Integration of deep learning prediction in DeepRescore2 improves phosphopeptide identification and facilitates biological discoveries.

Phosphoproteomics After Guanidinium Thiocyanate Extraction of Tissue Biopsies

Proteogenomic analysis is emerging as an advantageous tool to assist personalized therapy decisions in clinical health care and integrates complementary information from the genome, transcriptome, and (phospho)proteome. A prerequisite for such analysis is a workflow for the simultaneous isolation of DNA, RNA, and protein from a single sample that does not compromise the different biological molecules and their examination. Focusing on the phosphoproteomic aspect of this workflow, we here provide detailed information on our protocol, which is based on commonly used acid guanidinium thiocyanate-phenol-chloroform (AGPC) extraction with RNA-Bee. We describe the necessary steps for biopsy collection, cryoprocessing, and protein extraction. We further share our practice on protein digestion and cleanup of small samples (200 μg protein) and describe settings for automated IMAC-based phosphopeptide enrichment with the AssayMAP Bravo platform.

Truncated FGFR2 is a clinically actionable oncogene in multiple cancers

Somatic hotspot mutations and structural amplifications and fusions that affect fibroblast growth factor receptor 2 (encoded by FGFR2) occur in multiple types of cancer¹. However, clinical responses to FGFR inhibitors have remained variable^1–9, emphasizing the need to better understand which FGFR2 alterations are oncogenic and therapeutically targetable. Here we apply transposon-based screening^10,11 and tumour modelling in mice^12,13, and find that the truncation of exon 18 (E18) of Fgfr2 is a potent driver mutation. Human oncogenomic datasets revealed a diverse set of FGFR2 alterations, including rearrangements, E1–E17 partial amplifications, and E18 nonsense and frameshift mutations, each causing the transcription of E18-truncated FGFR2 (FGFR2^ΔE18). Functional in vitro and in vivo examination of a compendium of FGFR2^ΔE18 and full-length variants pinpointed FGFR2-E18 truncation as single-driver alteration in cancer. By contrast, the oncogenic competence of FGFR2 full-length amplifications depended on a distinct landscape of cooperating driver genes. This suggests that genomic alterations that generate stable FGFR2^ΔE18 variants are actionable therapeutic targets, which we confirmed in preclinical mouse and human tumour models, and in a clinical trial. We propose that cancers containing any FGFR2 variant with a truncated E18 should be considered for FGFR-targeted therapies.

Truncation of exon 18 of FGFR2 (FGFR2^ΔE18) is a potent driver mutation in mice and humans, and FGFR-targeted therapy should be considered for patients with cancer expressing stable FGFR2^ΔE18 variants.

Relationship between miRNA-433 and SPP1 in the presence of fracture and traumatic brain injury

Limb fracture combined with traumatic brain injury (TBI) is one of the most common multiple injuries and patients often suffer from severe craniocerebral injury combined with long bone fracture of the limbs. The present study examined the expression of osteopontin (SPP1) in the tibial fracture callus and heterotopic ossification tissues in craniocerebral injury and investigated its relationship with miR-433. A total of 26 patients with tibial fracture combined with brain injury were included in the TBI group, and 26 patients with simple tibial fracture were included in the control group. The patients received immobilization treatment and callus was collected during the operation. At the time of steel plate removal tissue ossification samples from patients with heterotopic ossification were collected. Peripheral blood was collected from all patients on the morning of the operation day. Expression of miR-433 and SPP1 mRNA was determined by reverse transcription-quantitative PCR and SPP1 protein expression was measured by western blotting. Dual luciferase reporter assay was used to identify the direct interaction between miR-433 and SPP1 mRNA. The human osteoblast line hFOB1.19 was transfected with agomiR-433 to overexpress miR-433 and expression of SPP1 was also examined. TBI enhanced the incidence of callus formation and heterotopic ossification in patients with fracture but did not alter fracture healing time. SPP1 mRNA and protein expression was elevated in patients who had tibial fracture in combination with craniocerebral injury in comparison with controls By contrast, expression of miR-433 was decreased in patients who had tibial fracture in combination with craniocerebral injury in comparison with controls. miR-433 regulated the expression of SPP1 mRNA and protein by directly binding to the 3'-untranslated region of SPP1 mRNA. The present study suggests that SPP1 mRNA and protein levels are increased in the callus, heterotopic ossification tissues and plasma from patients with tibial fracture combined with brain injury in comparison with controls. This elevation may be due to the reduced expression of miR-433.