Regulated Phosphosignaling Associated with Breast Cancer Subtypes and Druggability

Coordinated inflammation and immune response transcriptional regulation in breast cancer molecular subtypes

Breast cancer, characterized by its complexity and diversity, presents significant challenges in understanding its underlying biology. In this study, we employed gene co-expression network analysis to investigate the gene composition and functional patterns in breast cancer subtypes and normal breast tissue. Our objective was to elucidate the detailed immunological features distinguishing these tumors at the transcriptional level and to explore their implications for diagnosis and treatment. The analysis identified nine distinct gene module clusters, each representing unique transcriptional signatures within breast cancer subtypes and normal tissue. Interestingly, while some clusters exhibited high similarity in gene composition between normal tissue and certain subtypes, others showed lower similarity and shared traits. These clusters provided insights into the immune responses within breast cancer subtypes, revealing diverse immunological functions, including innate and adaptive immune responses. Our findings contribute to a deeper understanding of the molecular mechanisms underlying breast cancer subtypes and highlight their unique characteristics. The immunological signatures identified in this study hold potential implications for diagnostic and therapeutic strategies. Additionally, the network-based approach introduced herein presents a valuable framework for understanding the complexities of other diseases and elucidating their underlying biology.

Structurally-informed human interactome reveals proteome-wide perturbations by disease mutations

Human genome sequencing studies have identified numerous loci associated with complex diseases. However, translating human genetic and genomic findings to disease pathobiology and therapeutic discovery remains a major challenge at multiscale interactome network levels. Here, we present a deep-learning-based ensemble framework, termed PIONEER (Protein-protein InteractiOn iNtErfacE pRediction), that accurately predicts protein binding partner-specific interfaces for all known protein interactions in humans and seven other common model organisms, generating comprehensive structurally-informed protein interactomes. We demonstrate that PIONEER outperforms existing state-of-the-art methods. We further systematically validated PIONEER predictions experimentally through generating 2,395 mutations and testing their impact on 6,754 mutation-interaction pairs, confirming the high quality and validity of PIONEER predictions. We show that disease-associated mutations are enriched in PIONEER-predicted protein-protein interfaces after mapping mutations from ~60,000 germline exomes and ~36,000 somatic genomes. We identify 586 significant protein-protein interactions (PPIs) enriched with PIONEER-predicted interface somatic mutations (termed oncoPPIs) from pan-cancer analysis of ~11,000 tumor whole-exomes across 33 cancer types. We show that PIONEER-predicted oncoPPIs are significantly associated with patient survival and drug responses from both cancer cell lines and patient-derived xenograft mouse models. We identify a landscape of PPI-perturbing tumor alleles upon ubiquitination by E3 ligases, and we experimentally validate the tumorigenic KEAP1-NRF2 interface mutation p.Thr80Lys in non-small cell lung cancer. We show that PIONEER-predicted PPI-perturbing alleles alter protein abundance and correlates with drug responses and patient survival in colon and uterine cancers as demonstrated by proteogenomic data from the National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium. PIONEER, implemented as both a web server platform and a software package, identifies functional consequences of disease-associated alleles and offers a deep learning tool for precision medicine at multiscale interactome network levels.

PhosphoDisco: A Toolkit for Co-regulated Phosphorylation Module Discovery in Phosphoproteomic Data

Kinases are key players in cancer-relevant pathways and are the targets of many successful precision cancer therapies. Phosphoproteomics is a powerful approach to study kinase activity and has been used increasingly for the characterization of tumor samples leading to the identification of novel chemotherapeutic targets and biomarkers. Finding co-regulated phosphorylation sites which represent potential kinase-substrate sets or members of the same signaling pathway allows us to harness these data to identify clinically relevant and targetable alterations in signaling cascades. Unfortunately, studies have found that databases of co-regulated phosphorylation sites are only experimentally supported in a small number of substrate sets. To address the inherent challenge of defining co-regulated phosphorylation modules relevant to a given dataset, we developed PhosphoDisco, a toolkit for determining co-regulated phosphorylation modules. We applied this approach to tandem mass spectrometry based phosphoproteomic data for breast and non-small cell lung cancer and identified canonical as well as putative new phosphorylation site modules. Our analysis identified several interesting modules in each cohort. Among these was a new cell cycle checkpoint module enriched in basal breast cancer samples and a module of PRKC isozymes putatively co-regulated by CDK12 in lung cancer. We demonstrate that modules defined by PhosphoDisco can be used to further personalized cancer treatment strategies by establishing active signaling pathways in a given patient tumor or set of tumors, and in providing new ways to classify tumors based on signaling activity.

An optogenetic-phosphoproteomic study reveals dynamic Akt1 signaling profiles in endothelial cells

The serine/threonine kinase AKT is a central node in cell signaling. While aberrant AKT activation underlies the development of a variety of human diseases, how different patterns of AKT-dependent phosphorylation dictate downstream signaling and phenotypic outcomes remains largely enigmatic. Herein, we perform a systems-level analysis that integrates methodological advances in optogenetics, mass spectrometry-based phosphoproteomics, and bioinformatics to elucidate how different intensity, duration, and pattern of Akt1 stimulation lead to distinct temporal phosphorylation profiles in vascular endothelial cells. Through the analysis of ~35,000 phosphorylation sites across multiple conditions precisely controlled by light stimulation, we identify a series of signaling circuits activated downstream of Akt1 and interrogate how Akt1 signaling integrates with growth factor signaling in endothelial cells. Furthermore, our results categorize kinase substrates that are preferably activated by oscillating, transient, and sustained Akt1 signals. We validate a list of phosphorylation sites that covaried with Akt1 phosphorylation across experimental conditions as potential Akt1 substrates. Our resulting dataset provides a rich resource for future studies on AKT signaling and dynamics.

Proteins in Tumor-Derived Plasma Extracellular Vesicles Indicate Tumor Origin

Cancer-derived extracellular vesicles (EVs) promote tumorigenesis, premetastatic niche formation, and metastasis via their protein cargo. However, the proteins packaged by patient tumors into EVs cannot be determined in vivo because of the presence of EVs derived from other tissues. We therefore developed a cross-species proteomic method to quantify the human tumor-derived proteome of plasma EVs produced by patient-derived xenografts of four cancer types. Proteomic profiling revealed individualized packaging of novel protein cargo, and machine learning accurately classified the type of the underlying tumor.

Proteomics-Based Identification of Dysregulated Proteins in Breast Cancer

Immunohistochemistry (IHC) is still widely used as a morphology-based assay for in situ analysis of target proteins as specific tumor antigens. However, as a very heterogeneous collection of neoplastic diseases, breast cancer (BC) requires an accurate identification and characterization of larger panels of candidate biomarkers, beyond ER, PR, and HER2 proteins, for diagnosis and personalized treatment, without the limited availability of antibodies that are required to identify specific proteins. Top-down, middle-down, and bottom-up mass spectrometry (MS)-based proteomics approaches complement traditional histopathological tissue analysis to examine expression, modification, and interaction of hundreds to thousands of proteins simultaneously. In this review, we discuss the proteomics-based identification of dysregulated proteins in BC that are essential for the following issues: discovery and validation of new biomarkers by analysis of solid and liquid/non-invasive biopsies, cell lines, organoids and xenograft models; identification of panels of biomarkers for early detection and accurate discrimination between cancer, benign and normal tissues; identification of subtype-specific and stage-specific protein expression profiles in BC grading and measurement of disease progression; characterization of new subtypes of BC; characterization and quantitation of post-translational modifications (PTMs) and aberrant protein-protein interactions (PPI) involved in tumor development; characterization of the global remodeling of BC tissue homeostasis, diagnosis and prognostic information; and deciphering of molecular functions, biological processes and mechanisms through which the dysregulated proteins cause tumor initiation, invasion, and treatment resistance.

Inhibition of PFKFB3 in HER2-positive gastric cancer improves sensitivity to trastuzumab by inducing tumour vessel normalisation

BACKGROUND

Multiple mechanisms have been proposed that lead to reduced effectiveness of trastuzumab in HER2-positive gastric cancer (GC), yet resistance to trastuzumab remains a challenge in clinics.

METHODS

We established trastuzumab-resistant cells and patient-derived xenografts models to measure metabolic levels and vascular density and shape. The HER2-positive GC patient samples were used to determine clinical significance. We also measured protein expression and phosphorylation modifications to determine those alterations related to resistance. In vivo studies combining inhibitor of PFKFB3 with trastuzumab corroborated the in vitro findings.

RESULTS

The 6-phosphofructo-2-kinase (PFKFB3)-mediated trastuzumab resistance pathways in HER2-positive GC by activating the glycolytic pathway. We also found vessels are chaotic and destabilised in the tumour during the trastuzumab resistance process. Inhibition of PFKFB3 significantly diminished tumour proliferation and promoted vessel normalisation in the patient-derived xenograft model. Mechanistically, PFKFB3 promoted the secretion of CXCL8 into the tumour microenvironment, and phosphorylated Ser1151 of ERBB2, enhancing the transcription of CXCL8 by activating the PI3K/AKT/NFκB p65 pathway.

CONCLUSIONS

Our current findings discover that PFKFB3 inhibitors might be effective tools to overcome adjuvant therapy resistance in HER2-positive GC and reshaping the microenvironment by normalising tumour vessels is a novel strategy to overcome trastuzumab resistance.

Proteomic Analyses Identify Therapeutic Targets in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is the fourth cause of cancer-related mortality worldwide. While many targeted therapies have been developed, the majority of HCC tumors do not harbor clinically actionable mutations. Protein-level aberrations, especially those not evident at the genomic level, present therapeutic opportunities but have rarely been systematically characterized in HCC. In this study, we performed proteogenomic analyses of 260 primary tumors from two HBV-related HCC patient cohorts with global mass-spectrometry (MS) proteomics data. Combining tumor-normal and inter-tumor analyses, we identified overexpressed targets including PDGFRB, FGFR4, ERBB2/3, CDK6 kinases and MFAP5, HMCN1, and Hsp proteins in HCC, many of which showed low frequencies of genomic and/or transcriptomic aberrations. Protein expression of FGFR4 kinase and Hsp proteins were significantly associated with response to their corresponding inhibitors. Our results provide a catalog of protein targets in HCC and demonstrate the potential of proteomics approaches in advancing precision medicine in cancer types lacking druggable mutations.

WNK1 Enhances Migration and Invasion in Breast Cancer Models

Metastasis is the major cause of mortality in patients with breast cancer. Many signaling pathways have been linked to cancer invasiveness, but blockade of few protein components has succeeded in reducing metastasis. Thus, identification of proteins contributing to invasion that are manipulable by small molecules may be valuable in inhibiting spread of the disease. The protein kinase with no lysine (K) 1 (WNK1) has been suggested to induce migration of cells representing a range of cancer types. Analyses of mouse models and patient data have implicated WNK1 as one of a handful of genes uniquely linked to invasive breast cancer. Here, we present evidence that inhibition of WNK1 slows breast cancer metastasis. We show that depletion or inhibition of WNK1 reduces migration of several breast cancer cell lines in wound healing assays and decreases invasion in collagen matrices. Furthermore, WNK1 depletion suppresses expression of AXL, a tyrosine kinase implicated in metastasis. Finally, we demonstrate that WNK inhibition in mice attenuates tumor progression and metastatic burden. These data showing reduced migration, invasion, and metastasis upon WNK1 depletion in multiple breast cancer models suggest that WNK1 contributes to the metastatic phenotype, and that WNK1 inhibition may offer a therapeutic avenue for attenuating progression of invasive breast cancers.

Spatially interacting phosphorylation sites and mutations in cancer

Advances in mass-spectrometry have generated increasingly large-scale proteomics datasets containing tens of thousands of phosphorylation sites (phosphosites) that require prioritization. We develop a bioinformatics tool called HotPho and systematically discover 3D co-clustering of phosphosites and cancer mutations on protein structures. HotPho identifies 474 such hybrid clusters containing 1255 co-clustering phosphosites, including RET p.S904/Y928, the conserved HRAS/KRAS p.Y96, and IDH1 p.Y139/IDH2 p.Y179 that are adjacent to recurrent mutations on protein structures not found by linear proximity approaches. Hybrid clusters, enriched in histone and kinase domains, frequently include expression-associated mutations experimentally shown as activating and conferring genetic dependency. Approximately 300 co-clustering phosphosites are verified in patient samples of 5 cancer types or previously implicated in cancer, including CTNNB1 p.S29/Y30, EGFR p.S720, MAPK1 p.S142, and PTPN12 p.S275. In summary, systematic 3D clustering analysis highlights nearly 3,000 likely functional mutations and over 1000 cancer phosphosites for downstream investigation and evaluation of potential clinical relevance.

THE ROLE OF PHOSPHORYLATION IN ATRIAL FIBRILLATION: A FOCUS ON MASS SPECTROMETRY APPROACHES

Atrial fibrillation (AF) is the most common arrhythmia worldwide. It is associated with significant increases in morbidity in the form of stroke and heart failure, and a doubling in all-cause mortality. The pathophysiology of AF is incompletely understood, and this has contributed to a lack of effective treatments and disease-modifying therapies. An important cellular process that may explain how risk factors give rise to AF includes post-translational modification (PTM) of proteins. As the most commonly occurring PTM, protein phosphorylation is especially relevant. Although many methods exist for studying protein phosphorylation, a common and highly resolute technique is mass spectrometry (MS). This review will discuss recent evidence surrounding the role of protein phosphorylation in the pathogenesis of AF. MS-based technology to study phosphorylation and uses of MS in other areas of medicine such as oncology will also be presented. Based on these data, future goals and experiments will be outlined that utilize MS technology to better understand the role of phosphorylation in AF and elucidate its role in AF pathophysiology. This may ultimately allow for the development of more effective AF therapies.

Adipocytes Under Obese-Like Conditions Change Cell Cycle Distribution and Phosphorylation Profiles of Breast Cancer Cells: The Adipokine Receptor CAP1 Matters

BACKGROUND

Obesity and associated metabolic conditions impact adipocyte functionality with potential consequences for breast cancer risk and prognosis, but contributing mechanisms remain to be understood. The adipokine receptor adenylyl cyclase-associated protein-1 (CAP1) has been implicated in the progression of breast cancer, but results are conflicting and the underlying molecular mechanisms are still unknown. In this study, molecular and cellular effects in breast cancer cells by stimulation of adipocytes under normal or obese-like conditions, and potential involvement of CAP1, were assessed.

MATERIAL AND METHODS

Estrogen receptor (ER)-positive T47D and ER-negative MDA-MB-231 breast cancer cells were exposed to adipocyte-secretome from adipocytes placed under pressures mimicking normal and obese-like metabolic conditions. Changes in phosphorylated kinase proteins and related biological pathways were assessed by phospho-antibody array and PANTHER analysis, cell proliferation were investigated through sulforhodamine B, cell cycle distribution by flow cytometry. Functional effects of CAP1 were subsequently examined following small interfering (si)RNA-mediated knockdown.

RESULTS

Protein phosphorylations involved in important biological processes were enriched in T47D breast cancer cells in response to adipocyte secretome from obese-like compared with normal conditions. The obesity-associated adipocyte secretome further stimulated cell proliferation and a shift from cell cycle G1-phase to S- and G2/M-phase was observed. Silencing of CAP1 decreased cell proliferation in both T47D and MDA-MB-231 cells, and reduced the obesity-associated secretome-induction of phosphoproteins involved in cell proliferation pathways.

CONCLUSIONS

These results indicate that the adipocyte secretome and CAP1 are mechanistically important for the proliferation of both ER-positive and ER-negative breast cancer cells, and potential signaling mediators were identified. These studies provide biological insight into how obesity-associated factors could affect breast cancer.