Characterization of Human Cancer Cell Lines by Reverse-phase Protein Arrays

Y-box binding protein 1/cyclin A1 axis specifically promotes cell cycle progression at G2/M phase in ovarian cancer

Y-box binding protein 1 (YBX1) promotes oncogenic transformation and tumor growth. YBX1 plays a role in regulation of cell cycle promotion via upregulation of cell cycle-related genes. In ovarian cancer, YBX1 also promotes tumor growth, but the mechanisms of YBX1 in cell growth and cell cycle in ovarian cancer remain not to be fully understood. Here, we investigated whether YBX1-dependent cancer cell proliferation was specifically associated with expression of cell cycle related genes in ovarian cancer. Protein and mRNA expression levels of YBX1 and cell cycle-related genes in ovarian cancer cell lines and tissues were determined by western blot analysis, immunohistochemical analysis and reverse transcription-quantitative PCR. Cell cycle analysis was performed by flow cytometry. Luciferase assay and Chromatin immunoprecipitation assay were used to investigate a transcriptional function of YBX1. YBX1 silencing induced marked growth suppression in 4 cell lines (group A), moderate suppression in 5 cell lines (group B), and no suppression in 3 cell lines (group C) among 12 ovarian cancer cell lines in culture. The YBX1 silencing induced cell cycle arrest at G2/M phase and suppressed expression of cyclin A1 gene in group A and B cell lines, but not in group C cell lines. Cyclin A1 silencing specifically suppressed cell proliferation in group A cell lines and partially in group B cell lines, but not at all in group C cell lines. YBX1 mRNA levels were significantly correlated with cyclin A1 mRNA levels in patients with high-grade serous carcinoma. Augmented YBX1 expression plays a key role in tumor growth promotion in ovarian cancer in its close association with cyclin A1.

A protein expression atlas on tissue samples and cell lines from cancer patients provides insights into tumor heterogeneity and dependencies

The Cancer Genome Atlas (TCGA) and the Cancer Cell Line Encyclopedia (CCLE) are foundational resources in cancer research, providing extensive molecular and phenotypic data. However, large-scale proteomic data across various cancer types for these cohorts remain limited. Here, we expand upon our previous work to generate high-quality protein expression data for approximately 8,000 TCGA patient samples and around 900 CCLE cell line samples, covering 447 clinically relevant proteins, using reverse-phase protein arrays. These protein expression profiles offer profound insights into intertumor heterogeneity and cancer dependency and serve as sensitive functional readouts for somatic alterations. We develop a systematic protein-centered strategy for identifying synthetic lethality pairs and experimentally validate an interaction between protein kinase A subunit α and epidermal growth factor receptor. We also identify metastasis-related protein markers with clinical relevance. This dataset represents a valuable resource for advancing our understanding of cancer mechanisms, discovering protein biomarkers and developing innovative therapeutic strategies.

Pro-apoptotic gene BAX is a pan-cancer predictive biomarker for prognosis and immunotherapy efficacy

BACKGROUND

Apoptosis Regulator BCL2 Associated X (BAX) is a pro-apoptotic gene. Apoptosis is one of the important components of immune response and immune regulation. However, there is no systematic pan-cancer analysis of BAX.

METHODS

Original data of this study were downloaded from TCGA databases and GTEX databases. We conducted the gene expression analysis and survival analysis of BAX in 33 types of cancer via Gene Expression Profiling Interactive Analysis (GEPIA) database. Real-time PCR and immunohistochemistry (IHC) were further performed to examine the BAX expression in cancer cells and tissues. Moreover, the relationship between BAX and immune infiltration and gene alteration was studied by the Tumor Immune Estimation Resource (TIMER) and cBioPortal tools. Protein-protein interaction analysis was performed in the STRING database. Finally, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were utilized to evaluate the enrichment analysis.

RESULTS

BAX was highly expressed in most cancers and was associated with poor prognosis in nine cancer types. In addition, BAX showed significant clinical relevance, and the mRNA expression of BAX was also strongly associated with drug sensitivity of many drugs. Furthermore, BAX may participate in proliferation and metastasis of many cancers and was associated with methylation. Importantly, BAX expression was positively correlated with most immune infiltrating cells.

CONCLUSION

Our findings suggested that BAX can function as an oncogene and may be used as a potential predictive biomarker for prognosis and immunotherapy efficacy of human cancer, which could provide a new approach for cancer therapy.

PPAR-γ agonists reactivate the ALDOC-NR2F1 axis to enhance sensitivity to temozolomide and suppress glioblastoma progression

Glioblastoma (GBM) is a type of brain cancer categorized as a high-grade glioma. GBM is characterized by limited treatment options, low patient survival rates, and abnormal serotonin metabolism. Previous studies have investigated the tumor suppressor function of aldolase C (ALDOC), a glycolytic enzyme in GBM. However, it is unclear how ALDOC regulates production of serotonin and its associated receptors, HTRs. In this study, we analyzed ALDOC mRNA levels and methylation status using sequencing data and in silico datasets. Furthermore, we investigated pathways, phenotypes, and drug effects using cell and mouse models. Our results suggest that loss of ALDOC function in GBM promotes tumor cell invasion and migration. We observed that hypermethylation, which results in loss of ALDOC expression, is associated with serotonin hypersecretion and the inhibition of PPAR-γ signaling. Using several omics datasets, we present evidence that ALDOC regulates serotonin levels and safeguards PPAR-γ against serotonin metabolism mediated by 5-HT, which leads to a reduction in PPAR-γ expression. PPAR-γ activation inhibits serotonin release by HTR and diminishes GBM tumor growth in our cellular and animal models. Importantly, research has demonstrated that PPAR-γ agonists prolong animal survival rates and increase the efficacy of temozolomide in an orthotopic brain model of GBM. The relationship and function of the ALDOC-PPAR-γ axis could serve as a potential prognostic indicator. Furthermore, PPAR-γ agonists offer a new treatment alternative for glioblastoma multiforme (GBM).

Targeting of focal adhesion kinase enhances the immunogenic cell death of PEGylated liposome doxorubicin to optimize therapeutic responses of immune checkpoint blockade

BACKGROUNDS

Immune checkpoint blockade (ICB) is widely considered to exert long-term treatment benefits by activating antitumor immunity. However, many cancer patients show poor clinical responses to ICB due in part to the lack of an immunogenic niche. Focal adhesion kinase (FAK) is frequently amplified and acts as an immune modulator across cancer types. However, evidence illustrates that targeting FAK is most effective in combination therapy rather than in monotherapy.

METHODS

Here, we used drug screening, in vitro and in vivo assays to filter out that doxorubicin and its liposomal form pegylated liposome doxorubicin (PLD) showed synergistic anti-tumor effects in combination with FAK inhibitor IN10018. We hypothesized that anti-tumor immunity and immunogenic cell death (ICD) may be involved in the treatment outcomes through the data analysis of our clinical trial testing the combination of IN10018 and PLD. We then performed cell-based assays and animal studies to detect whether FAK inhibition by IN10018 can boost the ICD of PLD/doxorubicin and further established syngeneic models to test the antitumor effect of triplet combination of PLD, IN10018, and ICB.

RESULTS

We demonstrated that the combination of FAK inhibitor IN10018, and PLD/doxorubicin exerted effective antitumor activity. Notably, the doublet combination regimen exhibited response latency and long-lasting treatment effects clinically, outcomes frequently observed in immunotherapy. Our preclinical study confirmed that the 2-drug combination can maximize the ICD of cancer cells. This approach primed the tumor microenvironment, supplementing it with sufficient tumor-infiltrating lymphocytes (TILs) to activate antitumor immunity. Finally, different animal studies confirmed that the antitumor effects of ICB can be significantly enhanced by this doublet regimen.

CONCLUSIONS

We confirmed that targeting FAK by IN10018 can enhance the ICD of PLD/doxorubicin, further benefiting the anti-tumor effect of ICB. The animal tests of the triplet regimen warrant further discovery in the real world.

Preclinical liver cancer models in the context of immunoprecision therapy: Application and perspectives

Hepatocellular carcinoma (HCC), ranking as the third leading cause of cancer-related mortality globally, continues to pose challenges in achieving optimal treatment outcomes. The complex nature of HCC, characterized by high spatiotemporal heterogeneity, invasive potential, and drug resistance, presents difficulties in its research. Consequently, an in-depth understanding and accurate simulation of the immune microenvironment of HCC are of paramount importance. This article comprehensively explores the application of preclinical models in HCC research, encompassing cell line models, patient-derived xenograft mouse models, genetically engineered mouse models, chemically induced models, humanized mouse models, organoid models, and microfluidic chip-based patient derived organotypic spheroids models. Each model possesses its distinct advantages and limitations in replicating the biological behavior and immune microenvironment of HCC. By scrutinizing the limitations of existing models, this paper aims to propel the development of next-generation cancer models, enabling more precise emulation of HCC characteristics. This will, in turn, facilitate the optimization of treatment strategies, drug efficacy prediction, and safety assessments, ultimately contributing to the realization of personalized and precision therapies. Additionally, this article also provides insights into future trends and challenges in the fields of tumor biology and preclinical research.

PRMT blockade induces defective DNA replication stress response and synergizes with PARP inhibition

Multiple cancers exhibit aberrant protein arginine methylation by both type I arginine methyltransferases, predominately protein arginine methyltransferase 1 (PRMT1) and to a lesser extent PRMT4, and by type II PRMTs, predominately PRMT5. Here, we perform targeted proteomics following inhibition of PRMT1, PRMT4, and PRMT5 across 12 cancer cell lines. We find that inhibition of type I and II PRMTs suppresses phosphorylated and total ATR in cancer cells. Loss of ATR from PRMT inhibition results in defective DNA replication stress response activation, including from PARP inhibitors. Inhibition of type I and II PRMTs is synergistic with PARP inhibition regardless of homologous recombination function, but type I PRMT inhibition is more toxic to non-malignant cells. Finally, we demonstrate that the combination of PARP and PRMT5 inhibition improves survival in both BRCA-mutant and wild-type patient-derived xenografts without toxicity. Taken together, these results demonstrate that PRMT5 inhibition may be a well-tolerated approach to sensitize tumors to PARP inhibition.

Pan-Cancer Drug Response Prediction Using Integrative Principal Component Regression

The pursuit of precision oncology heavily relies on large-scale genomic and pharmacological data garnered from preclinical cancer model systems such as cell lines. While cell lines are instrumental in understanding the interplay between genomic programs and drug response, it well-established that they are not fully representative of patient tumors. Development of integrative methods that can systematically assess the commonalities between patient tumors and cell-lines can help bridge this gap. To this end, we introduce the Integrative Principal Component Regression (iPCR) model which uncovers both joint and model-specific structured variations in the genomic data of cell lines and patient tumors through matrix decompositions. The extracted joint variation is then used to predict patient drug responses based on the pharmacological data from preclinical models. Moreover, the interpretability of our model allows for the identification of key driver genes and pathways associated with the treatment-specific response in patients across multiple cancers. We demonstrate that the outputs of the iPCR model can assist in inferring both model-specific and shared co-expression networks between cell lines and patients. We show that iPCR performs favorably compared to competing approaches in predicting patient drug responses, in both simulation studies and real-world applications, in addition to identifying key genomic drivers of cancer drug responses.

β-catenin/CBP activation of mTORC1 signaling promotes partial epithelial-mesenchymal states in head and neck cancer

Head and neck cancers, which include oral squamous cell carcinoma (OSCC) as a major subsite, exhibit cellular plasticity that includes features of an epithelial-mesenchymal transition (EMT), referred to as partial-EMT (p-EMT). To identify molecular mechanisms contributing to OSCC plasticity, we performed a multiphase analysis of single cell RNA sequencing (scRNAseq) data from human OSCC. This included a multiresolution characterization of cancer cell subgroups to identify pathways and cell states that are heterogeneously represented, followed by casual inference analysis to elucidate activating and inhibitory relationships between these pathways and cell states. This approach revealed signaling networks associated with hierarchical cell state transitions, which notably included an association between β-catenin-driven CREB-binding protein (CBP) activity and mTORC1 signaling. This network was associated with subpopulations of cancer cells that were enriched for markers of the p-EMT state and poor patient survival. Functional analyses revealed that β-catenin/CBP induced mTORC1 activity in part through the transcriptional regulation of a raptor-interacting protein, chaperonin containing TCP1 subunit 5 (CCT5). Inhibition of β-catenin-CBP activity through the use of the orally active small molecule, E7386, reduced the expression of CCT5 and mTORC1 activity in vitro, and inhibited p-EMT-associated markers and tumor development in a murine model of OSCC. Our study highlights the use of multiresolution network analyses of scRNAseq data to identify targetable signals for therapeutic benefit, thus defining an underappreciated association between β-catenin/CBP and mTORC1 signaling in head and neck cancer plasticity.

Dual blockade of BRD4 and ATR/WEE1 pathways exploits ARID1A loss in clear cell ovarian cancer

ARID1A, an epigenetic tumor suppressor, is the most common gene mutation in clear-cell ovarian cancers (CCOCs). CCOCs are often resistant to standard chemotherapy and lack effective therapies. We hypothesized that ARID1A loss would increase CCOC cell dependency on chromatin remodeling and DNA repair pathways for survival. We demonstrate that combining BRD4 inhibitor (BRD4i) with DNA damage response inhibitors (ATR or WEE1 inhibitors; e.g. BRD4i-ATRi) was synergistic at low doses leading to decreased survival, and colony formation in CCOC in an ARID1A dependent manner. BRD4i-ATRi caused significant tumor regression and increased overall survival in ARID1AMUT but not ARID1AWT patient-derived xenografts. Combination BRD4i-ATRi significantly increased γH2AX, and decreased RAD51 foci and BRCA1 expression, suggesting decreased ability to repair DNA double-strand-breaks (DSBs) by homologous-recombination in ARID1AMUT cells, and these effects were greater than monotherapies. These studies demonstrate BRD4i-ATRi is an effective treatment strategy that capitalizes on synthetic lethality with ARID1A loss in CCOC.

Emerging roles of the cellular prion protein (PrPC) and 37/67 kDa laminin receptor (RPSA) interaction in cancer biology

The cellular prion protein (PrPC) is well-known for its involvement, under its pathogenic protease-resistant form (PrPSc), in a group of neurodegenerative diseases, known as prion diseases. PrPC is expressed in nervous system, as well as in other peripheral organs, and has been found overexpressed in several types of solid tumors. Notwithstanding, studies in recent years have disclosed an emerging role for PrPC in various cancer associated processes. PrPC has high binding affinity for 37/67 kDa laminin receptor (RPSA), a molecule that acts as a key player in tumorigenesis, affecting cell growth, adhesion, migration, invasion and cell death processes. Recently, we have characterized at cellular level, small molecules able to antagonize the direct PrPC binding to RPSA and their intracellular trafficking. These findings are very crucial considering that the main function of RPSA is to modulate key events in the metastasis cascade. Elucidation of the role played by PrPC/RPSA interaction in regulating tumor development, progression and response to treatment, represents a very promising challenge to gain pathogenetic information and discover novel specific biomarkers and/or therapeutic targets to be exploited in clinical settings. This review attempts to convey a detailed description of the complexity surrounding these multifaceted proteins from the perspective of cancer hallmarks, but with a specific focus on the role of their interaction in the control of proliferation, migration and invasion, genome instability and mutation, as well as resistance to cell death controlled by autophagic pathway.

Small molecule-mediated targeting of microRNAs for drug discovery: Experiments, computational techniques, and disease implications

Small molecules have been providing medical breakthroughs for human diseases for more than a century. Recently, identifying small molecule inhibitors that target microRNAs (miRNAs) has gained importance, despite the challenges posed by labour-intensive screening experiments and the significant efforts required for medicinal chemistry optimization. Numerous experimentally-verified cases have demonstrated the potential of miRNA-targeted small molecule inhibitors for disease treatment. This new approach is grounded in their posttranscriptional regulation of the expression of disease-associated genes. Reversing dysregulated gene expression using this mechanism may help control dysfunctional pathways. Furthermore, the ongoing improvement of algorithms has allowed for the integration of computational strategies built on top of laboratory-based data, facilitating a more precise and rational design and discovery of lead compounds. To complement the use of extensive pharmacogenomics data in prioritising potential drugs, our previous work introduced a computational approach based on only molecular sequences. Moreover, various computational tools for predicting molecular interactions in biological networks using similarity-based inference techniques have been accumulated in established studies. However, there are a limited number of comprehensive reviews covering both computational and experimental drug discovery processes. In this review, we outline a cohesive overview of both biological and computational applications in miRNA-targeted drug discovery, along with their disease implications and clinical significance. Finally, utilizing drug-target interaction (DTIs) data from DrugBank, we showcase the effectiveness of deep learning for obtaining the physicochemical characterization of DTIs.

Quantitative proteomics and phosphoproteomics of urinary extracellular vesicles define putative diagnostic biosignatures for Parkinson's disease

BACKGROUND

Mutations in the leucine-rich repeat kinase 2 (LRRK2) gene have been recognized as genetic risk factors for Parkinson's disease (PD). However, compared to cancer, fewer genetic mutations contribute to the cause of PD, propelling the search for protein biomarkers for early detection of the disease.

METHODS

Utilizing 138 urine samples from four groups, healthy individuals (control), healthy individuals with G2019S mutation in the LRRK2 gene (non-manifesting carrier/NMC), PD individuals without G2019S mutation (idiopathic PD/iPD), and PD individuals with G2019S mutation (LRRK2 PD), we applied a proteomics strategy to determine potential diagnostic biomarkers for PD from urinary extracellular vesicles (EVs).

RESULTS

After efficient isolation of urinary EVs through chemical affinity followed by mass spectrometric analyses of EV peptides and enriched phosphopeptides, we identify and quantify 4476 unique proteins and 2680 unique phosphoproteins. We detect multiple proteins and phosphoproteins elevated in PD EVs that are known to be involved in important PD pathways, in particular the autophagy pathway, as well as neuronal cell death, neuroinflammation, and formation of amyloid fibrils. We establish a panel of proteins and phosphoproteins as novel candidates for disease biomarkers and substantiate the biomarkers using machine learning, ROC, clinical correlation, and in-depth network analysis. Several putative disease biomarkers are further partially validated in patients with PD using parallel reaction monitoring (PRM) and immunoassay for targeted quantitation.

CONCLUSIONS

These findings demonstrate a general strategy of utilizing biofluid EV proteome/phosphoproteome as an outstanding and non-invasive source for a wide range of disease exploration.

A review of recent advances in the novel therapeutic targets and immunotherapy for lung cancer

Lung cancer is amongst the most pervasive malignancies having high mortality rates. It is broadly grouped into non-small-cell lung cancer (NSCLC) and small-cell lung cancer (SCLC). The concept of personalized medicine has overshadowed the conventional chemotherapy given to all patients with lung cancer. The targeted therapy is given to a particular population having specific mutations to help in the better management of lung cancer. The targeting pathways for NSCLC include the epidermal growth factor receptor, vascular endothelial growth factor receptor, MET (Mesenchymal epithelial transition factor) oncogene, Kirsten rat sarcoma viral oncogene (KRAS), and anaplastic lymphoma kinase (ALK). SCLC targeting pathway includes Poly (ADP-ribose) polymerases (PARP) inhibitors, checkpoint kinase 1 (CHK 1) pathway, WEE1 pathway, Ataxia Telangiectasia and Rad3-related (ATR)/Ataxia telangiectasia mutated (ATM), and Delta-like canonical Notch ligand 3 (DLL-Immune checkpoint inhibitors like programmed cell death protein 1 (PD-1)/ programmed death-ligand 1 (PD-L1) inhibitors and Cytotoxic T-lymphocyte-associated antigen-4 (CTLA4) blockade are also utilized in the management of lung cancer. Many of the targeted therapies are still under development and require clinical trials to establish their safety and efficacy. This review summarizes the mechanism of molecular targets and immune-mediated targets, recently approved drugs, and their clinical trials for lung cancer.

Buccal delivery of small molecules and biologics: Of mucoadhesive polymers, films, and nanoparticles - An update

Buccal delivery of small and large molecules is an attractive route of administration that has been studied extensively over the past few decades. This route bypasses first-pass metabolism and can be used to deliver therapeutics directly to systemic circulation. Moreover, buccal films are efficient dosage forms for drug delivery due to their simplicity, portability, and patient comfort. Films have traditionally been formulated using conventional techniques, including hot-melt extrusion and solvent casting. However, newer methods are now being exploited to improve the delivery of small molecules and biologics. This review discusses recent advances in buccal film manufacturing, using the latest technologies, such as 2D and 3D printing, electrospraying, and electrospinning. This review also focuses on the excipients used in the preparation of these films, with emphasis on mucoadhesive polymers and plasticizers. Along with advances in manufacturing technology, newer analytical tools have also been used for the assessment of permeation of the active agents across the buccal mucosa, the most critical biological barrier and limiting factor of this route. Additionally, preclinical and clinical trial challenges are discussed, and some small molecule products already on the market are explored.

Protein Identification of Seminal Plasma in Bali Bull (Bos javanicus)

The purpose of this study was to identify seminal plasma proteins in Bali bull and their potential as biomarkers of fertility. Semen was collected from 10 bulls aged 5-10 years using an artificial vagina. Fresh semen was then centrifuged (3000× g for 30 min). The supernatant was put into straws and stored in liquid nitrogen. The semen plasma protein concentration was determined using the Bradford method, and the protein was characterized using 1D-SDS-PAGE. Coomassie Brilliant Blue (CBB) was used to color the gel, and the molecular weight of the protein was determined using PM2700. A total of 94 proteins were identified in the seminal plasma of Bali bulls analyzed using LC-MS/MS (liquid chromatography-mass spectrometry). Proteins spermadhesin 1 (SPADH1), C-type natriuretic peptide (NPPC), clusterin (CLU), apoliprotein A-II (APOA2), inositol-3-phosphate synthase 1 (ISYNA1), and sulfhydryl oxidase 1 (QSOX1) were identified as important for fertility in Bos javanicus. These proteins may prove to be important biomarkers of fertility in Bali bulls. These proteins are important for reproductive function, which includes spermatozoa motility, capacitation, and acrosome reactions. This study provides new information about the protein content in seminal plasma in Bali bulls. The LC-MS/MS-based proteome approach that we applied in this study obtained 94 proteins. The identification of these seminal plasma proteins of Bali bulls and their potential as fertility biomarkers may have an impact on the success of future artificial insemination (AI).

The impact of violating the independence assumption in meta-analysis on biomarker discovery

With rapid advancements in high-throughput sequencing technologies, massive amounts of "-omics" data are now available in almost every biomedical field. Due to variance in biological models and analytic methods, findings from clinical and biological studies are often not generalizable when tested in independent cohorts. Meta-analysis, a set of statistical tools to integrate independent studies addressing similar research questions, has been proposed to improve the accuracy and robustness of new biological insights. However, it is common practice among biomarker discovery studies using preclinical pharmacogenomic data to borrow molecular profiles of cancer cell lines from one study to another, creating dependence across studies. The impact of violating the independence assumption in meta-analyses is largely unknown. In this study, we review and compare different meta-analyses to estimate variations across studies along with biomarker discoveries using preclinical pharmacogenomics data. We further evaluate the performance of conventional meta-analysis where the dependence of the effects was ignored via simulation studies. Results show that, as the number of non-independent effects increased, relative mean squared error and lower coverage probability increased. Additionally, we also assess potential bias in the estimation of effects for established meta-analysis approaches when data are duplicated and the assumption of independence is violated. Using pharmacogenomics biomarker discovery, we find that treating dependent studies as independent can substantially increase the bias of meta-analyses. Importantly, we show that violating the independence assumption decreases the generalizability of the biomarker discovery process and increases false positive results, a key challenge in precision oncology.

Integrated In Silico Analysis of Proteogenomic and Drug Targets for Pancreatic Cancer Survival

Pancreatic cancer remains a major health concern, being among the deadliest forms of cancer with over 80% of the patients presenting with metastatic disease. According to the American Cancer Society, for all stages of pancreatic cancer combined, the 5-year survival rate is less than 10%. Genetic research on pancreatic cancer has generally been focused on familial pancreatic cancer, which is only 10% of all pancreatic cancer patients. This study focuses on finding genes that impact the survival of pancreatic cancer patients which can be used as biomarkers and potential targets to develop personalized treatment options. We used cBioPortal platform using NCI-initiated The Cancer Genome Atlas (TCGA) dataset to find genes that were altered differently in different ethnic groups which can serve as potential biomarkers and analyzed the genes' impact on patient survival. MD Anderson Cell Lines Project (MCLP) and genecards.org were also utilized to identify potential drug candidates that can target the proteins encoded by the genes. The results showed that there are unique genes that are associated with each race category which may influence the survival outcomes of patients, and their potential drug candidates were identified.

Machine learning multi-omics analysis reveals cancer driver dysregulation in pan-cancer cell lines compared to primary tumors

Cancer cell lines have been widely used for decades to study biological processes driving cancer development, and to identify biomarkers of response to therapeutic agents. Advances in genomic sequencing have made possible large-scale genomic characterizations of collections of cancer cell lines and primary tumors, such as the Cancer Cell Line Encyclopedia (CCLE) and The Cancer Genome Atlas (TCGA). These studies allow for the first time a comprehensive evaluation of the comparability of cancer cell lines and primary tumors on the genomic and proteomic level. Here we employ bulk mRNA and micro-RNA sequencing data from thousands of samples in CCLE and TCGA, and proteomic data from partner studies in the MD Anderson Cell Line Project (MCLP) and The Cancer Proteome Atlas (TCPA), to characterize the extent to which cancer cell lines recapitulate tumors. We identify dysregulation of a long non-coding RNA and microRNA regulatory network in cancer cell lines, associated with differential expression between cell lines and primary tumors in four key cancer driver pathways: KRAS signaling, NFKB signaling, IL2/STAT5 signaling and TP53 signaling. Our results emphasize the necessity for careful interpretation of cancer cell line experiments, particularly with respect to therapeutic treatments targeting these important cancer pathways.

NetCellMatch: Multiscale Network-Based Matching of Cancer Cell Lines to Patients Using Graphical Wavelets

Cancer cell lines serve as model in vitro systems for investigating therapeutic interventions. Recent advances in high-throughput genomic profiling have enabled the systematic comparison between cell lines and patient tumor samples. The highly interconnected nature of biological data, however, presents a challenge when mapping patient tumors to cell lines. Standard clustering methods can be particularly susceptible to the high level of noise present in these datasets and only output clusters at one unknown scale of the data. In light of these challenges, we present NetCellMatch, a robust framework for network-based matching of cell lines to patient tumors. NetCellMatch first constructs a global network across all cell line-patient samples using their genomic similarity. Then, a multi-scale community detection algorithm integrates information across topologically meaningful (clustering) scales to obtain Network-Based Matching Scores (NBMS). NBMS are measures of cluster robustness which map patient tumors to cell lines. We use NBMS to determine representative "avatar" cell lines for subgroups of patients. We apply NetCellMatch to reverse-phase protein array data obtained from The Cancer Genome Atlas for patients and the MD Anderson Cell Line Project for cell lines. Along with avatar cell line identification, we evaluate connectivity patterns for breast, lung, and colon cancer and explore the proteomic profiles of avatars and their corresponding top matching patients. Our results demonstrate our framework's ability to identify both patient-cell line matches and potential proteomic drivers of similarity. Our methods are general and can be easily adapted to other'omic datasets.

Tumor-intrinsic SIRPA promotes sensitivity to checkpoint inhibition immunotherapy in melanoma

Checkpoint inhibition immunotherapy has revolutionized cancer treatment, but many patients show resistance. Here we perform integrative transcriptomic and proteomic analyses on emerging immuno-oncology targets across multiple clinical cohorts of melanoma under anti-PD-1 treatment, on both bulk and single-cell levels. We reveal a surprising role of tumor-intrinsic SIRPA in enhancing antitumor immunity, in contrast to its well-established role as a major inhibitory immune modulator in macrophages. The loss of SIRPA expression is a marker of melanoma dedifferentiation, a key phenotype linked to immunotherapy efficacy. Inhibition of SIRPA in melanoma cells abrogates tumor killing by activated CD8+ T cells in a co-culture system. Mice bearing SIRPA-deficient melanoma tumors show no response to anti-PD-L1 treatment, whereas melanoma-specific SIRPA overexpression significantly enhances immunotherapy response. Mechanistically, SIRPA is regulated by its pseudogene, SIRPAP1. Our results suggest a complicated role of SIRPA in the tumor ecosystem, highlighting cell-type-dependent antagonistic effects of the same target on immunotherapy.

Overexpression of FAM83A Is Associated with Poor Prognosis of Lung Adenocarcinoma

Family with sequence similarity 83, member A (FAM83A) plays an essential and fundamental role in the proliferation, progression, and apoptosis of many malignant tumors, including lung cancer. This study aimed to determine the expression pattern of FAM83A in lung adenocarcinoma (LUAD) and its correlation with the prognosis of cancer and the survival of the patients. Bioinformatics analysis, immunohistochemistry, and Western blotting were used to explore and detect the expression of FAM83A in LUAD cells. The mechanism of FAM83A in proliferation and migration was examined. The correlation between FAM83A expression and survival rate was assessed by the Kaplan-Meier and Cox regression. FAM83A expression was elevated in LUAD tissues and was related to shorter overall survival (P < 0.05). A significant increase in FAM83A protein was observed in the LUAD tissue (P < 0.05). Compared with patients with early-stage tumors (stage I-II), those with advanced stage tumors (stage III-IV) had significantly higher FAM83A expression levels (P < 0.05). Downregulation of FAM83A led to a reduction in cell proliferation, a decrease in migration ability, and diminished epithelial-mesenchymal transition (EMT) in the lung cancer cell lines. Overexpression of FAM83A was associated with early lymph node metastasis and poor overall survival among LUAD patients. The findings indicated that FAM83A may play a critical role in promoting the LUAD progression and thus might serve as a novel prognostic marker in LUAD.

A Subset of Secreted Proteins in Ascites Can Predict Platinum-Free Interval in Ovarian Cancer

The time between the last cycle of chemotherapy and recurrence, the platinum-free interval (PFI), predicts overall survival in high-grade serous ovarian cancer (HGSOC). To identify secreted proteins associated with a shorter PFI, we utilized machine learning to predict the PFI from ascites composition. Ascites from stage III/IV HGSOC patients treated with neoadjuvant chemotherapy (NACT) or primary debulking surgery (PDS) were screened for secreted proteins and Lasso regression models were built to predict the PFI. Through regularization techniques, the number of analytes used in each model was reduced; to minimize overfitting, we utilized an analysis of model robustness. This resulted in models with 26 analytes and a root-mean-square error (RMSE) of 19 days for the NACT cohort and 16 analytes and an RMSE of 7 days for the PDS cohort. High concentrations of MMP-2 and EMMPRIN correlated with a shorter PFI in the NACT patients, whereas high concentrations of uPA Urokinase and MMP-3 correlated with a shorter PFI in PDS patients. Our results suggest that the analysis of ascites may be useful for outcome prediction and identified factors in the tumor microenvironment that may lead to worse outcomes. Our approach to tuning for model stability, rather than only model accuracy, may be applicable to other biomarker discovery tasks.

Pan-cancer proteomic map of 949 human cell lines

The proteome provides unique insights into disease biology beyond the genome and transcriptome. A lack of large proteomic datasets has restricted the identification of new cancer biomarkers. Here, proteomes of 949 cancer cell lines across 28 tissue types are analyzed by mass spectrometry. Deploying a workflow to quantify 8,498 proteins, these data capture evidence of cell-type and post-transcriptional modifications. Integrating multi-omics, drug response, and CRISPR-Cas9 gene essentiality screens with a deep learning-based pipeline reveals thousands of protein biomarkers of cancer vulnerabilities that are not significant at the transcript level. The power of the proteome to predict drug response is very similar to that of the transcriptome. Further, random downsampling to only 1,500 proteins has limited impact on predictive power, consistent with protein networks being highly connected and co-regulated. This pan-cancer proteomic map (ProCan-DepMapSanger) is a comprehensive resource available at https://cellmodelpassports.sanger.ac.uk.

Na+/H+-exchanger 1 enhances antitumor activity of engineered NK-92 natural killer cells

Adoptive cell transfer (ACT) immunotherapy has remarkable efficacy against some hematological malignancies. However, its efficacy in solid tumors is limited by the adverse tumor microenvironment (TME) conditions, most notably that acidity inhibits T and natural killer (NK) cell mTOR complex 1 (mTORC1) activity and impairs cytotoxicity. In several reported studies, systemic buffering of tumor acidity enhanced the efficacy of immune checkpoint inhibitors. Paradoxically, we found in a c-Myc-driven hepatocellular carcinoma model that systemic buffering increased tumor mTORC1 activity, negating inhibition of tumor growth by anti-PD1 treatment. Therefore, in this proof-of-concept study, we tested the metabolic engineering of immune effector cells to mitigate the inhibitory effect of tumor acidity while avoiding side effects associated with systemic buffering. We first overexpressed an activated RHEB in the human NK cell line NK-92, thereby rescuing acid-blunted mTORC1 activity and enhancing cytolytic activity. Then, to directly mitigate the effect of acidity, we ectopically expressed acid extruder proteins. Whereas ectopic expression of carbonic anhydrase IX (CA9) moderately increased mTORC1 activity, it did not enhance effector function. In contrast, overexpressing a constitutively active Na⁺/H⁺-exchanger 1 (NHE1; SLC9A1) in NK-92 did not elevate mTORC1 but enhanced degranulation, target engagement, in vitro cytotoxicity, and in vivo antitumor activity. Our findings suggest the feasibility of overcoming the inhibitory effect of the TME by metabolically engineering immune effector cells, which can enhance ACT for better efficacy against solid tumors.

Subtype of Neuroblastoma Cells with High KIT Expression Are Dependent on KIT and Its Knockdown Induces Compensatory Activation of Pro-Survival Signaling

Neuroblastoma (NB) is a pediatric cancer with high clinical and molecular heterogeneity, and patients with high-risk tumors have limited treatment options. Receptor tyrosine kinase KIT has been identified as a potential marker of high-risk NB and a promising target for NB treatment. We investigated 19,145 tumor RNA expression and molecular pathway activation profiles for 20 cancer types and detected relatively high levels of KIT expression in NB. Increased KIT expression was associated with activation of cell survival pathways, downregulated apoptosis induction, and cell cycle checkpoint control pathways. KIT knockdown with shRNA encoded by lentiviral vectors in SH-SY5Y cells led to reduced cell proliferation and apoptosis induction up to 50%. Our data suggest that apoptosis induction was caused by mitotic catastrophe, and there was a 2-fold decrease in percentage of G2-M cell cycle phase after KIT knockdown. We found that KIT knockdown in NB cells leads to strong upregulation of other pro-survival growth factor signaling cascades such as EPO, NGF, IL-6, and IGF-1 pathways. NGF, IGF-1 and EPO were able to increase cell proliferation in KIT-depleted cells in an ERK1/2-dependent manner. Overall, we show that KIT is a promising therapeutic target in NB, although such therapy efficiency could be impeded by growth factor signaling activation.

RNF43 G659fs is an oncogenic colorectal cancer mutation and sensitizes tumor cells to PI3K/mTOR inhibition

The RNF43_p.G659fs mutation occurs frequently in colorectal cancer, but its function remains poorly understood and there are no specific therapies directed against this alteration. In this study, we find that RNF43_p.G659fs promotes cell growth independent of Wnt signaling. We perform a drug repurposing library screen and discover that cells with RNF43_p.G659 mutations are selectively killed by inhibition of PI3K signaling. PI3K/mTOR inhibitors yield promising antitumor activity in RNF43659mut isogenic cell lines and xenograft models, as well as in patient-derived organoids harboring RNF43_p.G659fs mutations. We find that RNF43659mut binds p85 leading to increased PI3K signaling through p85 ubiquitination and degradation. Additionally, RNA-sequencing of RNF43659mut isogenic cells reveals decreased interferon response gene expression, that is reversed by PI3K/mTOR inhibition, suggesting that RNF43659mut may alter tumor immunity. Our findings suggest a therapeutic application for PI3K/mTOR inhibitors in treating RNF43_p.G659fs mutant cancers.

PRMT inhibition induces a viral mimicry response in triple-negative breast cancer

Triple-negative breast cancer (TNBC) is the most aggressive breast cancer subtype with the worst prognosis and few effective therapies. Here we identified MS023, an inhibitor of type I protein arginine methyltransferases (PRMTs), which has antitumor growth activity in TNBC. Pathway analysis of TNBC cell lines indicates that the activation of interferon responses before and after MS023 treatment is a functional biomarker and determinant of response, and these observations extend to a panel of human-derived organoids. Inhibition of type I PRMT triggers an interferon response through the antiviral defense pathway with the induction of double-stranded RNA, which is derived, at least in part, from inverted repeat Alu elements. Together, our results represent a shift in understanding the antitumor mechanism of type I PRMT inhibitors and provide a rationale and biomarker approach for the clinical development of type I PRMT inhibitors.

Type I PRMT inhibition elicits potent antitumor activity associated with increased interferon response and intron-retained dsRNA accumulation, suggesting its potential combination with immune checkpoint inhibitors for cancer treatment.

Analysis of the metabolic proteome of lung adenocarcinomas by reverse-phase protein arrays (RPPA) emphasizes mitochondria as targets for therapy

Lung cancer is the leading cause of cancer-related death worldwide despite the success of therapies targeting oncogenic drivers and immune-checkpoint inhibitors. Although metabolic enzymes offer additional targets for therapy, the precise metabolic proteome of lung adenocarcinomas is unknown, hampering its clinical translation. Herein, we used Reverse Phase Protein Arrays to quantify the changes in enzymes of glycolysis, oxidation of pyruvate, fatty acid metabolism, oxidative phosphorylation, antioxidant response and protein oxidative damage in 128 tumors and paired non-tumor adjacent tissue of lung adenocarcinomas to profile the proteome of metabolism. Steady-state levels of mitochondrial proteins of fatty acid oxidation, oxidative phosphorylation and of the antioxidant response are independent predictors of survival and/or of disease recurrence in lung adenocarcinoma patients. Next, we addressed the mechanisms by which the overexpression of ATPase Inhibitory Factor 1, the physiological inhibitor of oxidative phosphorylation, which is an independent predictor of disease recurrence, prevents metastatic disease. We highlight that IF1 overexpression promotes a more vulnerable and less invasive phenotype in lung adenocarcinoma cells. Finally, and as proof of concept, the therapeutic potential of targeting fatty acid assimilation or oxidation in combination with an inhibitor of oxidative phosphorylation was studied in mice bearing lung adenocarcinomas. The results revealed that this therapeutic approach significantly extended the lifespan and provided better welfare to mice than cisplatin treatments, supporting mitochondrial activities as targets of therapy in lung adenocarcinoma patients.

Novel Ex Vivo Models of Epithelial Ovarian Cancer: The Future of Biomarker and Therapeutic Research

Epithelial ovarian cancer (EOC) is a heterogenous disease associated with variations in presentation, pathology and prognosis. Advanced EOC is typified by frequent relapse and a historical 5-year survival of less than 30% despite improvements in surgical and systemic treatment. The advent of next generation sequencing has led to notable advances in the field of personalised medicine for many cancer types. Success in achieving cure in advanced EOC has however been limited, although significant prolongation of survival has been demonstrated. Development of novel research platforms is therefore necessary to address the rapidly advancing field of early diagnostics and therapeutics, whilst also acknowledging the significant tumour heterogeneity associated with EOC. Within available tumour models, patient-derived organoids (PDO) and explant tumour slices have demonstrated particular promise as novel ex vivo systems to model different cancer types including ovarian cancer. PDOs are organ specific 3D tumour cultures that can accurately represent the histology and genomics of their native tumour, as well as offer the possibility as models for pharmaceutical drug testing platforms, offering timing advantages and potential use as prospective personalised models to guide clinical decision-making. Such applications could maximise the benefit of drug treatments to patients on an individual level whilst minimising use of less effective, yet toxic, therapies. PDOs are likely to play a greater role in both academic research and drug development in the future and have the potential to revolutionise future patient treatment and clinical trial pathways. Similarly, ex vivo tumour slices or explants have also shown recent renewed promise in their ability to provide a fast, specific, platform for drug testing that accurately represents in vivo tumour response. Tumour explants retain tissue architecture, and thus incorporate the majority of tumour microenvironment making them an attractive method to re-capitulate in vivo conditions, again with significant timing and personalisation of treatment advantages for patients. This review will discuss the current treatment landscape and research models for EOC, their development and new advances towards the discovery of novel biomarkers or combinational therapeutic strategies to increase treatment options for women with ovarian cancer.

Precision combination therapies based on recurrent oncogenic co-alterations

Cancer cells depend on multiple driver alterations whose oncogenic effects can be suppressed by drug combinations. Here, we provide a comprehensive resource of precision combination therapies tailored to oncogenic co-alterations that are recurrent across patient cohorts. To generate the resource, we developed Recurrent Features Leveraged for Combination Therapy (REFLECT), which integrates machine learning and cancer informatics algorithms. Using multi-omic data, the method maps recurrent co-alteration signatures in patient cohorts to combination therapies. We validated the REFLECT pipeline using data from patient-derived xenografts, in vitro drug screens, and a combination therapy clinical trial. These validations demonstrate that REFLECT-selected combination therapies have significantly improved efficacy, synergy, and survival outcomes. In patient cohorts with immunotherapy response markers, DNA repair aberrations, and HER2 activation, we have identified therapeutically actionable and recurrent co-alteration signatures. REFLECT provides a resource and framework to design combination therapies tailored to tumor cohorts in data-driven clinical trials and pre-clinical studies.

Regorafenib inhibits epithelial-mesenchymal transition and suppresses cholangiocarcinoma metastasis via YAP1-AREG axis

Cholangiocarcinoma (CCA) is a subtype of bile duct cancer usually diagnosed late with a low survival rate and no satisfactorily systemic treatment. Recently, regorafenib has been accepted as a second-line treatment for CCA patients. In this study, we investigated the potential signal transduction pathways mediated by regorafenib. We established a transcriptomic database for regorafenib-treated CCA cells using expression microarray chips. Our data indicate that regorafenib inhibits yes-associated protein 1 (YAP1) activity in various CCA cells. In addition, we demonstrated that YAP1 regulates epithelial-mesenchymal transition (EMT)-related genes, including E-cadherin and SNAI2. We further examined YAP1 activity, phosphorylation status, and expression levels of YAP1 downstream target genes in the regorafenib model. We found that regorafenib dramatically suppressed these events in CCA cells. Moreover, in vivo results revealed that regorafenib could significantly inhibit lung foci formation and tumorigenicity. Most importantly, regorafenib and amphiregulin (AREG) neutralize antibody exhibited synergistic effects against CCA cells. In a clinical setting, patients with high YAP1 and EMT expression had a worse survival rate than patients with low YAP1, and EMT expression did. In addition, we found that YAP1 upregulated the downstream target amphiregulin in CCA. Our findings suggest that AREG neutralizing antibody antibodies combined with regorafenib can reverse the CCA metastatic phenotype and EMT in vitro and in vivo. These findings provide novel therapeutic strategies to combat the metastasis of CCA.

Integrative multi-omics and drug response profiling of childhood acute lymphoblastic leukemia cell lines

Acute lymphoblastic leukemia (ALL) is the most common childhood cancer. Although standard-of-care chemotherapeutics are sufficient for most ALL cases, there are subsets of patients with poor response who relapse in disease. The biology underlying differences between subtypes and their response to therapy has only partially been explained by genetic and transcriptomic profiling. Here, we perform comprehensive multi-omic analyses of 49 readily available childhood ALL cell lines, using proteomics, transcriptomics, and pharmacoproteomic characterization. We connect the molecular phenotypes with drug responses to 528 oncology drugs, identifying drug correlations as well as lineage-dependent correlations. We also identify the diacylglycerol-analog bryostatin-1 as a therapeutic candidate in the MEF2D-HNRNPUL1 fusion high-risk subtype, for which this drug activates pro-apoptotic ERK signaling associated with molecular mediators of pre-B cell negative selection. Our data is the foundation for the interactive online Functional Omics Resource of ALL (FORALL) with navigable proteomics, transcriptomics, and drug sensitivity profiles at https://proteomics.se/forall.

Childhood acute lymphoblastic leukemia is characterised by a range of genetic aberrations. Here, the authors use multi-omics profiling of ALL cell lines to connect molecular phenotypes and drug responses to provide an interactive resource of drug sensitivity.

Proteomic analysis of spermatozoa reveals caseins play a pivotal role in preventing short-term periods of subfertility in stallions

Stallions experience transient fluctuations in fertility throughout the breeding season. Considering pregnancy diagnoses cannot be ascertained until ~14 days post-breeding, the timely detection of decreases in stallion fertility would enhance industry economic and welfare outcomes. Therefore, this study aimed to identify the proteomic signatures reflective of short-term fertility fluctuations, and to determine the biological mechanisms governing such differences. Using LC–MS/MS, we compared the proteomic profile of semen samples collected from commercially “fertile” stallions, during high- and low-fertility periods. A total of 1702 proteins were identified, of which, 38 showed a significant change in abundance (p ≤ 0.05). Assessment of intra- and inter-stallion variability revealed that caseins (namely κ-, α-S1-, and α-S2-casein), were significantly more abundant during “high-fertility” periods, while several epididymal, and seminal plasma proteins (chiefly, epididymal sperm binding protein 1 [ELSPbP1], horse seminal plasma protein 1 [HSP-1] and clusterin), were significantly more abundant during “low-fertility” periods. We hypothesised that an increased abundance of caseins offers greater protection from potentially harmful seminal plasma proteins, thereby preserving cell functionality and fertility. In vitro exposure of spermatozoa to casein resulted in decreased levels of lipid scrambling (Merocyanine 540), higher abundance of sperm-bound caseins (α-S1-, α-S2-, and κ-casein), and lower abundance of sperm-bound HSP-1 (p ≤ 0.05). This study demonstrates key pathways governing short-term fertility fluctuations in the stallion, thereby providing a platform to develop robust, fertility assessment strategies into the future.

Mini-batch optimization enables training of ODE models on large-scale datasets

Quantitative dynamic models are widely used to study cellular signal processing. A critical step in modelling is the estimation of unknown model parameters from experimental data. As model sizes and datasets are steadily growing, established parameter optimization approaches for mechanistic models become computationally extremely challenging. Mini-batch optimization methods, as employed in deep learning, have better scaling properties. In this work, we adapt, apply, and benchmark mini-batch optimization for ordinary differential equation (ODE) models, thereby establishing a direct link between dynamic modelling and machine learning. On our main application example, a large-scale model of cancer signaling, we benchmark mini-batch optimization against established methods, achieving better optimization results and reducing computation by more than an order of magnitude. We expect that our work will serve as a first step towards mini-batch optimization tailored to ODE models and enable modelling of even larger and more complex systems than what is currently possible.

Purification and Phosphoproteomic Analysis of Plasma-Derived Extracellular Vesicles

A successful phosphoproteomics analysis of extracellular vesicles (EVs) requires a unique approach, fine-tuned to address the challenges that have plagued plasma-based biomarker discovery. Here, I detail a procedure, which combines EVtrap-based high-recovery EV isolation, phase-transfer surfactant method for protein extraction, and PolyMAC-based enrichment of phosphopeptides. The combination of these methods provides a highly effective strategy for EV-based phosphoproteome analysis and leads to the discovery of novel phospho-markers previously undetectable.

A proof of concept for targeting the PrPC - Amyloid β peptide interaction in basal prostate cancer and mesenchymal colon cancer

The cellular prion protein PrP^C partners with caveolin-1 (CAV1) in neurodegenerative diseases but whether this interplay occurs in cancer has never been investigated. By leveraging patient and cell line datasets, we uncover a molecular link between PrP^C and CAV1 across cancer. Using cell-based assays, we show that PrP^C regulates the expression of and interacts with CAV1. PrP^C additionally controls the expression of the amyloid precursor protein APP and of the Aβ generating enzyme BACE1, and regulates the levels of Aβ, whose accumulation is a central event in Alzheimer's disease. We further identify DKK1 and DKK3, involved in both Alzheimer's disease and cancer progression, as targets of the PrP^C-dependent axis. Finally, we establish that antibody-mediated blocking of the Aβ-PrP^C interaction delays the growth of prostate cancer cell line-derived xenografts and prevents the development of metastases. Our data additionally support an enrichment of the Aβ-PrP^C-dependent pathway in the basal subtype of prostate cancer, associated with anti-hormonal therapy resistance, and in mesenchymal colon cancer, associated with poor prognosis. Thus, based on a parallel with neurodegenerative diseases, our results bring to light an Aβ-PrP^C axis and support the potential of targeting this pathway in patients with selected subtypes of prostate and colon cancer.

Oligonucleotide conjugated antibody strategies for cyclic immunostaining

A number of highly multiplexed immunostaining and imaging methods have advanced spatial proteomics of cancer for improved treatment strategies. While a variety of methods have been developed, the most widely used methods are limited by harmful signal removal techniques, difficulties with reagent production and antigen sensitivity. Multiplexed immunostaining employing oligonucleotide (oligos)-barcoded antibodies is an alternative approach that is growing in popularity. However, challenges remain in consistent conjugation of oligos to antibodies with maintained antigenicity as well as non-destructive, robust and cost-effective signal removal methods. Herein, a variety of oligo conjugation and signal removal methods were evaluated in the development of a robust oligo conjugated antibody cyclic immunofluorescence (Ab-oligo cyCIF) methodology. Both non- and site-specific conjugation strategies were assessed to label antibodies, where site-specific conjugation resulted in higher retained binding affinity and antigen-specific staining. A variety of fluorescence signal removal methods were also evaluated, where incorporation of a photocleavable link (PCL) resulted in full fluorescence signal removal with minimal tissue disruption. In summary, this work resulted in an optimized Ab-oligo cyCIF platform capable of generating high dimensional images to characterize the spatial proteomics of the hallmarks of cancer.

BTK inhibition sensitizes acute lymphoblastic leukemia to asparaginase by suppressing the amino acid response pathway

Asparaginase (ASNase) therapy has been a mainstay of acute lymphoblastic leukemia (ALL) protocols for decades and shows promise in the treatment of a variety of other cancers. To improve the efficacy of ASNase treatment, we used a CRISPR/Cas9-based screen to identify actionable signaling intermediates that improve the response to ASNase. Both genetic inactivation of Bruton's tyrosine kinase (BTK) and pharmacological inhibition by the BTK inhibitor ibrutinib strongly synergize with ASNase by inhibiting the amino acid response pathway, a mechanism involving c-Myc-mediated suppression of GCN2 activity. This synthetic lethal interaction was observed in 90% of patient-derived xenografts, regardless of the genomic subtype. Moreover, ibrutinib substantially improved ASNase treatment response in a murine PDX model. Hence, ibrutinib may be used to enhance the clinical efficacy of ASNase in ALL. This trial was registered at www.clinicaltrials.gov as # NCT02884453.

Integrative analysis reveals clinically relevant molecular fingerprints in pancreatic cancer

Pancreatic cancer is a highly aggressive cancer with an exceedingly low rate of response to treatments, which calls for comprehensive molecular characterization of pancreatic cancer cell lines (PCCLs). We screened multi-layer molecular data of 36 PCCLs, including gene mutation, gene expression, microRNA (miRNA) expression, and protein profiles. Our comparative analysis of genomic mutations found that PCCLs recapitulated genomic alterations of the primary tumor and suggested potential therapeutic strategies for clinical interventions. The panel of 36 PCCLs was classified into 2 subgroups based on transcriptomic mRNA expression, wherein the C1 subgroup was characterized with differentiation, whereas C2 cell lines were featured with immunity, angiogenesis, epidermis, and proliferation. Transcriptomic classification was further recapitulated by miRNA and protein expression. Additionally, the differential proteins between C1 and C2 subgroups were prominently involved in epidermal growth factor receptor (EGFR) signaling, phosphatidylinositol 3-kinase (PI3K) signaling, and mitogen-activated protein kinase (MAPK) signaling pathways. Tumor samples from different subgroups exhibited distinct infiltration of CD4 naive cells and monocytes. Remarkably, patients in subgroups C1 showed longer survival, whereas those in C2 had worse clinical outcome. Further integrative analysis revealed that temozolomide and NVP-TAE684 showed higher sensitivity in the C1 subgroup, whereas the C2 cell lines were more sensitive to SR1001 and SRT-1720. Our results also showed that PCCLs with mutations in CDKN2A, TP53, and SMAD4 were more sensitive to certain anti-cancer drugs. Our integrative analysis identified molecular features of pancreatic cancer that were associated with clinical significance and drug sensitivity, providing potentially effective strategies for precision treatments of patients with pancreatic cancer.

Identification of 2 Chinese Primary Pancreatic Ductal Adenocarcinoma Cancer Cell Lines and Their Phenotypes

OBJECTIVES

Pancreatic cancer (PC) is one of the most lethal cancers. Currently, the most commonly used PC cell lines were all derived from White individuals, and few models can be used to study in Asian populations. In China, the incidence of PC is increasing every year, highlighting the need to develop new Chinese PC cell lines for cancer research.

METHODS

A total of 203 patients diagnosed with PC, enrolled from January 2012 to December 2017 in our hospital (Department of Hepatobiliary and Pancreatic Surgery), had received surgery. Primary PC patient tumor samples were harvested sterilely from surgery, minced, and digested in collagenase. Cells were cultured in plates precoated with l-polylysine.

RESULTS

Two primary PC cell lines, Si-Liang 187 and Si-Liang 188, were established, both of which grew as adherent monolayers and demonstrated distinguished phenotypes. Si-Liang 187 shows the typical mesenchymal pattern, whereas Si-Liang 188 shows epithelial phenotype. Moreover, they carry different genetic backgrounds according to whole exome sequencing.

CONCLUSIONS

Two new Chinese PC cell lines were established, both of which were characterized and confirmed with high tumorigenicity. They may serve as useful tools for pathogenesis research when evaluating new treatment strategies in Asian patients.

Functional genomics approaches to improve pre-clinical drug screening and biomarker discovery

Advances in sequencing technology have enabled the genomic and transcriptomic characterization of human malignancies with unprecedented detail. However, this wealth of information has been slow to translate into clinically meaningful outcomes. Different models to study human cancers have been established and extensively characterized. Using these models, functional genomic screens and pre-clinical drug screening platforms have identified genetic dependencies that can be exploited with drug therapy. These genetic dependencies can also be used as biomarkers to predict response to treatment. For many cancers, the identification of such biomarkers remains elusive. In this review, we discuss the development and characterization of models used to study human cancers, RNA interference and CRISPR screens to identify genetic dependencies, large-scale pharmacogenomics studies and drug screening approaches to improve pre-clinical drug screening and biomarker discovery.

A highly multiplexed quantitative phosphosite assay for biology and preclinical studies

Reliable methods to quantify dynamic signaling changes across diverse pathways are needed to better understand the effects of disease and drug treatment in cells and tissues but are presently lacking. Here, we present SigPath, a targeted mass spectrometry (MS) assay that measures 284 phosphosites in 200 phosphoproteins of biological interest. SigPath probes a broad swath of signaling biology with high throughput and quantitative precision. We applied the assay to investigate changes in phospho-signaling in drug-treated cancer cell lines, breast cancer preclinical models, and human medulloblastoma tumors. In addition to validating previous findings, SigPath detected and quantified a large number of differentially regulated phosphosites newly associated with disease models and human tumors at baseline or with drug perturbation. Our results highlight the potential of SigPath to monitor phosphoproteomic signaling events and to nominate mechanistic hypotheses regarding oncogenesis, response, and resistance to therapy.

Predicting Cancer Cell Line Dependencies From the Protein Expression Data of Reverse-Phase Protein Arrays

PURPOSE

Predicting cancer dependencies from molecular data can help stratify patients and identify novel therapeutic targets. Recently available data on large-scale cancer cell line dependency allow a systematic assessment of the predictive power of diverse molecular features; however, the protein expression data have not been rigorously evaluated. By using the protein expression data generated by reverse-phase protein arrays, we aimed to assess their predictive power in identifying cancer dependencies and to develop a related analytic tool for community use.

MATERIALS AND METHODS

By using a machine learning schema, we conducted an analysis of feature importance based on cancer dependency and multiomic data from the DepMap and Cancer Cell Line Encyclopedia projects. We assessed the consistency of cancer dependency data between CRISPR/Cas9 and short hairpin RNA–mediated perturbation platforms. For a fair comparison, we focused on a set of genes with robust dependency data and four available expression-related features (copy number alteration, DNA methylation, messenger RNA expression, and protein expression) and performed the same-gene predictions of the cancer dependency using different molecular features.

RESULTS

For the genes surveyed, we observed that the protein expression data contained substantial predictive power for cancer dependencies, and they were the best predictive feature for the CRISPR/Cas9-based dependency data. We also developed a user-friendly protein-dependency analytic module and integrated it with The Cancer Proteome Atlas; this module allows researchers to explore and analyze our results intuitively.

CONCLUSION

This study provides a systematic assessment for predicting cancer dependencies of cell lines from different expression-related features of a gene. Our results suggest that protein expression data are a highly valuable information resource for understanding tumor vulnerabilities and identifying therapeutic opportunities.

Proteomic profiling dataset of chemical perturbations in multiple biological backgrounds

While gene expression profiling has traditionally been the method of choice for large-scale perturbational profiling studies, proteomics has emerged as an effective tool in this context for directly monitoring cellular responses to perturbations. We previously reported a pilot library containing 3400 profiles of multiple perturbations across diverse cellular backgrounds in the reduced-representation phosphoproteome (P100) and chromatin space (Global Chromatin Profiling, GCP). Here, we expand our original dataset to include profiles from a new set of cardiotoxic compounds and from astrocytes, an additional neural cell model, totaling 5300 proteomic signatures. We describe filtering criteria and quality control metrics used to assess and validate the technical quality and reproducibility of our data. To demonstrate the power of the library, we present two case studies where data is queried using the concept of "connectivity" to obtain biological insight. All data presented in this study have been deposited to the ProteomeXchange Consortium with identifiers PXD017458 (P100) and PXD017459 (GCP) and can be queried at https://clue.io/proteomics .

Bayesian Structure Learning in Multi-layered Genomic Networks

Integrative network modeling of data arising from multiple genomic platforms provides insight into the holistic picture of the interactive system, as well as the flow of information across many disease domains including cancer. The basic data structure consists of a sequence of hierarchically ordered datasets for each individual subject, which facilitates integration of diverse inputs, such as genomic, transcriptomic, and proteomic data. A primary analytical task in such contexts is to model the layered architecture of networks where the vertices can be naturally partitioned into ordered layers, dictated by multiple platforms, and exhibit both undirected and directed relationships. We propose a multi-layered Gaussian graphical model (mlGGM) to investigate conditional independence structures in such multi-level genomic networks in human cancers. We implement a Bayesian node-wise selection (BANS) approach based on variable selection techniques that coherently accounts for the multiple types of dependencies in mlGGM; this flexible strategy exploits edge-specific prior knowledge and selects sparse and interpretable models. Through simulated data generated under various scenarios, we demonstrate that BANS outperforms other existing multivariate regression-based methodologies. Our integrative genomic network analysis for key signaling pathways across multiple cancer types highlights commonalities and differences of p53 integrative networks and epigenetic effects of BRCA2 on p53 and its interaction with T68 phosphorylated CHK2, that may have translational utilities of finding biomarkers and therapeutic targets.

Proapoptotic and immunomodulatory effects of SYK inhibitor entospletinib in combination with obinutuzumab in patients with chronic lymphocytic leukaemia

Spleen tyrosine kinase (SYK) is indispensable in B-cell receptor signalling. SYK inhibitor entospletinib demonstrated clinical efficacy in patients with chronic lymphocytic leukaemia (CLL). However, pharmacodynamic effects of SYK inhibition in CLL cells and immunomodulatory effects of B-cell receptor-signalling inhibitors in patients with CLL are poorly understood. We conducted a phase 2 trial of entospletinib in combination with obinutuzumab, an anti-CD20 antibody, in 17 patients with relapsed/refractory CLL. Pharmacodynamic analysis demonstrated that treatment with entospletinib led to rapid downmodulation of pSTAT3 and the anti-apoptotic protein MCL1 in CLL cells. Meanwhile, 6 months of combination therapy was accompanied by a reduction in interferon-γ secretion in CD4⁺ T-cells and a reversal of exhausted phenotype, as evidenced by downregulation of PD-1. Thus, SYK inhibition downmodulates MCL-1 and partially restores T-cell immunity in CLL. Trial registration number NCT03010358.

Overcoming resistance to BRAFV600E inhibition in melanoma by deciphering and targeting personalized protein network alterations

BRAFV600E melanoma patients, despite initially responding to the clinically prescribed anti-BRAFV600E therapy, often relapse, and their tumors develop drug resistance. While it is widely accepted that these tumors are originally driven by the BRAFV600E mutation, they often eventually diverge and become supported by various signaling networks. Therefore, patient-specific altered signaling signatures should be deciphered and treated individually. In this study, we design individualized melanoma combination treatments based on personalized network alterations. Using an information-theoretic approach, we compute high-resolution patient-specific altered signaling signatures. These altered signaling signatures each consist of several co-expressed subnetworks, which should all be targeted to optimally inhibit the entire altered signaling flux. Based on these data, we design smart, personalized drug combinations, often consisting of FDA-approved drugs. We validate our approach in vitro and in vivo showing that individualized drug combinations that are rationally based on patient-specific altered signaling signatures are more efficient than the clinically used anti-BRAFV600E or BRAFV600E/MEK targeted therapy. Furthermore, these drug combinations are highly selective, as a drug combination efficient for one BRAFV600E tumor is significantly less efficient for another, and vice versa. The approach presented herein can be broadly applicable to aid clinicians to rationally design patient-specific anti-melanoma drug combinations.

Proteomics, Personalized Medicine and Cancer

As of 2020 the human genome and proteome are both at >90% completion based on high stringency analyses. This has been largely achieved by major technological advances over the last 20 years and has enlarged our understanding of human health and disease, including cancer, and is supporting the current trend towards personalized/precision medicine. This is due to improved screening, novel therapeutic approaches and an increased understanding of underlying cancer biology. However, cancer is a complex, heterogeneous disease modulated by genetic, molecular, cellular, tissue, population, environmental and socioeconomic factors, which evolve with time. In spite of recent advances in treatment that have resulted in improved patient outcomes, prognosis is still poor for many patients with certain cancers (e.g., mesothelioma, pancreatic and brain cancer) with a high death rate associated with late diagnosis. In this review we overview key hallmarks of cancer (e.g., autophagy, the role of redox signaling), current unmet clinical needs, the requirement for sensitive and specific biomarkers for early detection, surveillance, prognosis and drug monitoring, the role of the microbiome and the goals of personalized/precision medicine, discussing how emerging omics technologies can further inform on these areas. Exemplars from recent onco-proteogenomic-related publications will be given. Finally, we will address future perspectives, not only from the standpoint of perceived advances in treatment, but also from the hurdles that have to be overcome.