Integrative landscape of dysregulated signaling pathways of clinically distinct pancreatic cancer subtypes

Machine learning and bioinformatic analyses link the cell surface receptor transcript levels to the drug response of breast cancer cells and drug off-target effects

Breast cancer responds variably to anticancer therapies, often leading to significant off-target effects. This study proposes that the variability in tumour responses and drug-induced adverse events is linked to the transcriptional profiles of cell surface receptors (CSRs) in breast tumours and normal tissues. We analysed multiple datasets to compare CSR expression in breast tumours with that in non-cancerous human tissues. Our findings correlate the drug responses of breast cancer cell lines with the expression levels of their targeted CSRs. Notably, we identified distinct differences in CSR expression between primary breast tumour subtypes and corresponding cell lines, which may influence drug response predictions. Additionally, we used clinical trial data to uncover associations between CSR gene expression in healthy tissues and the incidence of adverse drug reactions. This integrative approach facilitates the selection of optimal CSR targets for therapy, leveraging cell line dose-responses, CSR expression in normal tissues, and patient adverse event profiles.

Mutational landscape of cancer-driver genes across human cancers

The genetic mutations that contribute to the transformation of healthy cells into cancerous cells have been the subject of extensive research. The molecular aberrations that lead to cancer development are often characterised by gain-of-function or loss-of-function mutations in a variety of oncogenes and tumour suppressor genes. In this study, we investigate the genomic sequences of 20,331 primary tumours representing 41 distinct human cancer types to identify and catalogue the driver mutations present in 727 known cancer genes. Our findings reveal significant variations in the frequency of cancer gene mutations across different cancer types and highlight the frequent involvement of tumour suppressor genes (94%), oncogenes (93%), transcription factors (72%), kinases (64%), cell surface receptors (63%), and phosphatases (22%), in cancer. Additionally, our analysis reveals that cancer gene mutations are predominantly co-occurring rather than exclusive in all types of cancer. Notably, we discover that patients with tumours displaying different combinations of gene mutation patterns tend to exhibit variable survival outcomes. These findings provide new insights into the genetic landscape of cancer and bring us closer to a comprehensive understanding of the underlying mechanisms driving the development of various forms of cancer.

Advancing disease genomics beyond COVID-19 and reducing health disparities: what does the future hold for Africa?

The COVID-19 pandemic has ushered in high-throughput sequencing technology as an essential public health tool. Scaling up and operationalizing genomics in Africa is crucial as enhanced capacity for genome sequencing could address key health problems relevant to African populations. High-quality genomics research can be leveraged to improve diagnosis, understand the aetiology of unexplained illnesses, improve surveillance of infectious diseases and inform efficient control and therapeutic methods of known, rare and emerging infectious diseases. Achieving these within Africa requires strong commitment from stakeholders. A roadmap is needed to guide training of scientists, infrastructural development, research funding, international collaboration as well as promote public–private partnerships. Although the COVID-19 pandemic has significantly boosted genomics capacity in Africa, the continent still lags other regions. Here, we highlighted key initiatives in genomics research and efforts to address health challenges facing the diverse and fast-growing populations on the continent. We explore the scalability of genomic tools and techniques to tackle a broader range of infectious diseases in Africa, a continent that desperately requires a boost from genomic science.

Prognostic biomarkers of pancreatic cancer identified based on a competing endogenous RNA regulatory network

BACKGROUND

Pancreatic cancer is an insidious and heterogeneous malignancy with poor prognosis that is often locally unresectable. Therefore, determining the underlying mechanisms and effective prognostic indicators of pancreatic cancer may help optimize clinical management. This study was conducted to develop a prognostic model for pancreatic cancer based on a competing endogenous RNA (ceRNA) network.

METHODS

We obtained transcriptomic data and corresponding clinicopathological information of pancreatic cancer samples from The Cancer Genome Atlas (TCGA) database (training set). Based on the ceRNA interaction network, we screened candidate genes to build prediction models. Univariate Cox regression analysis was performed to screen for genes associated with prognosis, and least absolute shrinkage and selection operator (LASSO) regression analysis was conducted to construct a predictive model. A receiver operating characteristic (ROC) curve was drawn, and the C-index was calculated to evaluate the accuracy of the prediction model. Furthermore, we downloaded transcriptomic data and related clinical information of pancreatic cancer samples from the Gene Expression Omnibus database (validation set) to evaluate the robustness of our prediction model.

RESULTS

Eight genes (ANLN, FHDC1, LY6D, SMAD6, ACKR4, RAB27B, AUNIP, and GPRIN3) were used to construct the prediction model, which was confirmed as an independent predictor for evaluating the prognosis of patients with pancreatic cancer through univariate and multivariate Cox regression analysis. By plotting the decision curve, we found that the risk score model is an independent predictor has the greatest impact on survival compared to pathological stage and targeted molecular therapy.

CONCLUSIONS

An eight-gene prediction model was constructed for effectively and independently predicting the prognosis of patients with pancreatic cancer. These eight genes identified show potential as diagnostic and therapeutic targets.

Proteome and secretome analysis of pancreatic cancer cells

Pancreatic cancer is a lethal malignancy and no screening biomarker or targeted therapy is currently available. Here, we performed a shotgun proteomic label-free quantification (LFQ) to define protein changes in the cellular proteome and secretome of four pancreatic cancer cell lines (PANC1, Paca44, Paca2, and BXPC3) versus normal human pancreatic ductal epithelial cells (HPDE). In the cellular proteome and secretome, 149 and 43 proteins were dysregulated in the most cancer cell lines, respectively. Using Ingenuity Pathway Analysis (IPA), the most dysregulated signaling pathways in pancreatic cancer cells included the activation of epidermal growth factor receptor (EGFR), phosphoinositide 3-kinase (PI3K), protein kinase B (AKT), extracellular regulated kinase (ERK), and the deactivation of type-I interferon (IFN) pathways, which could promote cancer cell progression and decrease antitumor immunity. Parallel reaction monitoring (PRM) mass spectrometry was used to confirm the changes of seven regulated proteins quantified by LFQ: EGFR, growth/differentiation factor 15 (GDF15), protein-glutamine gamma-glutamyltransferase 2 (TGM2), leukemia inhibitory factor (LIF), interferon-induced GTP-binding protein Mx1 (MX1), signal transducer and activator of transcription 1 (STAT1), and serpin B5 (SERPINB5). Together, this proteomic analysis highlights protein changes associated with pancreatic cancer cells that should be further investigated as potential biomarkers or therapeutic targets.

Proteogenomic analysis of pancreatic cancer subtypes

Pancreatic cancer remains a significant public health problem with an ever-rising incidence of disease. Cancers of the pancreas are characterised by various molecular aberrations, including changes in the proteomics and genomics landscape of the tumour cells. Therefore, there is a need to identify the proteomic landscape of pancreatic cancer and the specific genomic and molecular alterations associated with disease subtypes. Here, we carry out an integrative bioinformatics analysis of The Cancer Genome Atlas dataset, including proteomics and whole-exome sequencing data collected from pancreatic cancer patients. We apply unsupervised clustering on the proteomics dataset to reveal the two distinct subtypes of pancreatic cancer. Using functional and pathway analysis based on the proteomics data, we demonstrate the different molecular processes and signalling aberrations of the pancreatic cancer subtypes. In addition, we explore the clinical characteristics of these subtypes to show differences in disease outcome. Using datasets of mutations and copy number alterations, we show that various signalling pathways previously associated with pancreatic cancer are altered among both subtypes of pancreatic tumours, including the Wnt pathway, Notch pathway and PI3K-mTOR pathways. Altogether, we reveal the proteogenomic landscape of pancreatic cancer subtypes and the altered molecular processes that can be leveraged to devise more effective treatments.

Identification of Novel Metabolism-Associated Subtypes for Pancreatic Cancer to Establish an Eighteen-Gene Risk Prediction Model

Pancreatic cancer (PanC) is an intractable malignancy with a high mortality. Metabolic processes contribute to cancer progression and therapeutic responses, and histopathological subtypes are insufficient for determining prognosis and treatment strategies. In this study, PanC subtypes based on metabolism-related genes were identified and further utilized to construct a prognostic model. Using a cohort of 171 patients from The Cancer Genome Atlas (TCGA) database, transcriptome data, simple nucleotide variants (SNV), and clinical information were analyzed. We divided patients with PanC into metabolic gene-enriched and metabolic gene-desert subtypes. The metabolic gene-enriched subgroup is a high-risk subtype with worse outcomes and a higher frequency of SNVs, especially in KRAS. After further characterizing the subtypes, we constructed a risk score algorithm involving multiple genes (i.e., NEU2, GMPS, PRIM2, PNPT1, LDHA, INPP4B, DPYD, PYGL, CA12, DHRS9, SULT1E1, ENPP2, PDE1C, TPH1, CHST12, POLR3GL, DNMT3A, and PGS1). We verified the reproducibility and reliability of the risk score using three validation cohorts (i.e., independent datasets from TCGA, Gene Expression Omnibus, and Ensemble databases). Finally, drug prediction was completed using a ridge regression model, yielding nine candidate drugs for high-risk patients. These findings support the classification of PanC into two metabolic subtypes and further suggest that the metabolic gene-enriched subgroup is associated with worse outcomes. The newly established risk model for prognosis and therapeutic responses may improve outcomes in patients with PanC.

Integrated molecular characterisation of the MAPK pathways in human cancers reveals pharmacologically vulnerable mutations and gene dependencies

The mitogen-activated protein kinase (MAPK) pathways are crucial regulators of the cellular processes that fuel the malignant transformation of normal cells. The molecular aberrations which lead to cancer involve mutations in, and transcription variations of, various MAPK pathway genes. Here, we examine the genome sequences of 40,848 patient-derived tumours representing 101 distinct human cancers to identify cancer-associated mutations in MAPK signalling pathway genes. We show that patients with tumours that have mutations within genes of the ERK-1/2 pathway, the p38 pathways, or multiple MAPK pathway modules, tend to have worse disease outcomes than patients with tumours that have no mutations within the MAPK pathways genes. Furthermore, by integrating information extracted from various large-scale molecular datasets, we expose the relationship between the fitness of cancer cells after CRISPR mediated gene knockout of MAPK pathway genes, and their dose-responses to MAPK pathway inhibitors. Besides providing new insights into MAPK pathways, we unearth vulnerabilities in specific pathway genes that are reflected in the re sponses of cancer cells to MAPK targeting drugs: a revelation with great potential for guiding the development of innovative therapies.

Targeting AKT/PKB to improve treatment outcomes for solid tumors

The serine/threonine kinase AKT, also known as protein kinase B (PKB), is the major substrate to phosphoinositide 3-kinase (PI3K) and consists of three paralogs: AKT1 (PKBα), AKT2 (PKBβ) and AKT3 (PKBγ). The PI3K/AKT pathway is normally activated by binding of ligands to membrane-bound receptor tyrosine kinases (RTKs) as well as downstream to G-protein coupled receptors and integrin-linked kinase. Through multiple downstream substrates, activated AKT controls a wide variety of cellular functions including cell proliferation, survival, metabolism, and angiogenesis in both normal and malignant cells. In human cancers, the PI3K/AKT pathway is most frequently hyperactivated due to mutations and/or overexpression of upstream components. Aberrant expression of RTKs, gain of function mutations in PIK3CA, RAS, PDPK1, and AKT itself, as well as loss of function mutation in AKT phosphatases are genetic lesions that confer hyperactivation of AKT. Activated AKT stimulates DNA repair, e.g. double strand break repair after radiotherapy. Likewise, AKT attenuates chemotherapy-induced apoptosis. These observations suggest that a crucial link exists between AKT and DNA damage. Thus, AKT could be a major predictive marker of conventional cancer therapy, molecularly targeted therapy, and immunotherapy for solid tumors. In this review, we summarize the current understanding by which activated AKT mediates resistance to cancer treatment modalities, i.e. radiotherapy, chemotherapy, and RTK targeted therapy. Next, the effect of AKT on response of tumor cells to RTK targeted strategies will be discussed. Finally, we will provide a brief summary on the clinical trials of AKT inhibitors in combination with radiochemotherapy, RTK targeted therapy, and immunotherapy.

Machine Learning and Network Analyses Reveal Disease Subtypes of Pancreatic Cancer and their Molecular Characteristics

Given that the biological processes governing the oncogenesis of pancreatic cancers could present useful therapeutic targets, there is a pressing need to molecularly distinguish between different clinically relevant pancreatic cancer subtypes. To address this challenge, we used targeted proteomics and other molecular data compiled by The Cancer Genome Atlas to reveal that pancreatic tumours can be broadly segregated into two distinct subtypes. Besides being associated with substantially different clinical outcomes, tumours belonging to each of these subtypes also display notable differences in diverse signalling pathways and biological processes. At the proteome level, we show that tumours belonging to the less severe subtype are characterised by aberrant mTOR signalling, whereas those belonging to the more severe subtype are characterised by disruptions in SMAD and cell cycle-related processes. We use machine learning algorithms to define sets of proteins, mRNAs, miRNAs and DNA methylation patterns that could serve as biomarkers to accurately differentiate between the two pancreatic cancer subtypes. Lastly, we confirm the biological relevance of the identified biomarkers by showing that these can be used together with pattern-recognition algorithms to accurately infer the drug sensitivity of pancreatic cancer cell lines. Our study shows that integrative profiling of multiple data types enables a biological and clinical representation of pancreatic cancer that is comprehensive enough to provide a foundation for future therapeutic strategies.

Metabolic gene alterations impact the clinical aggressiveness and drug responses of 32 human cancers

Malignant cells reconfigure their metabolism to support oncogenic processes such as accelerated growth and proliferation. The mechanisms by which this occurs likely involve alterations to genes that encode metabolic enzymes. Here, using genomics data for 10,528 tumours of 32 different cancer types, we characterise the alterations of genes involved in various metabolic pathways. We find that mutations and copy number variations of metabolic genes are pervasive across all human cancers. Based on the frequencies of metabolic gene alterations, we further find that there are two distinct cancer supertypes that tend to be associated with different clinical outcomes. By utilising the known dose-response profiles of 825 cancer cell lines, we infer that cancers belonging to these supertypes are likely to respond differently to various anticancer drugs. Collectively our analyses define the foundational metabolic features of different cancer supertypes and subtypes upon which discriminatory strategies for treating particular tumours could be constructed.

Systems Biology Approach to Identify Novel Genomic Determinants for Pancreatic Cancer Pathogenesis

Pancreatic ductal adenocarcinoma (PDAC) is a lethal malignancy with a 5-year survival rate of <8%. Its dismal prognosis stems from inefficient therapeutic modalities owing to the lack of understanding about pancreatic cancer pathogenesis. Considering the molecular complexity and heterogeneity of PDAC, identification of novel molecular contributors involved in PDAC onset and progression using global "omics" analysis will pave the way to improved strategies for disease prevention and therapeutic targeting. Meta-analysis of multiple miRNA microarray datasets containing healthy controls (HC), chronic pancreatitis (CP) and PDAC cases, identified 13 miRNAs involved in the progression of PDAC. These miRNAs showed dysregulation in both tissue as well as blood samples, along with progressive decrease in expression from HC to CP to PDAC. Gene-miRNA interaction analysis further elucidated 5 miRNAs (29a/b, 27a, 130b and 148a) that are significantly downregulated in conjunction with concomitant upregulation of their target genes throughout PDAC progression. Among these, miRNA-29a/b targeted genes were found to be most significantly altered in comparative profiling of HC, CP and PDAC, indicating its involvement in malignant evolution. Further, pathway analysis suggested direct involvement of miRNA-29a/b in downregulating the key pathways associated with PDAC development and metastasis including focal adhesion signaling and extracellular matrix organization. Our systems biology data analysis, in combination with real-time PCR validation indicates direct functional involvement of miRNA-29a in PDAC progression and is a potential prognostic marker and therapeutic candidate for patients with progressive disease.