Integrating germline and somatic variation information using genomic data for the discovery of biomarkers in prostate cancer

Identifying proteomic risk factors for overall, aggressive, and early onset prostate cancer using Mendelian Randomisation and tumour spatial transcriptomics

BACKGROUND

Understanding the role of circulating proteins in prostate cancer risk can reveal key biological pathways and identify novel targets for cancer prevention.

METHODS

We investigated the association of 2002 genetically predicted circulating protein levels with risk of prostate cancer overall, and of aggressive and early onset disease, using cis-pQTL Mendelian randomisation (MR) and colocalisation. Findings for proteins with support from both MR, after correction for multiple-testing, and colocalisation were replicated using two independent cancer GWAS, one of European and one of African ancestry. Proteins with evidence of prostate-specific tissue expression were additionally investigated using spatial transcriptomic data in prostate tumour tissue to assess their role in tumour aggressiveness. Finally, we mapped risk proteins to drug and ongoing clinical trials targets.

FINDINGS

We identified 20 proteins genetically linked to prostate cancer risk (14 for overall [8 specific], 7 for aggressive [3 specific], and 8 for early onset disease [2 specific]), of which the majority replicated where data were available. Among these were proteins associated with aggressive disease, such as PPA2 [Odds Ratio (OR) per 1 SD increment = 2.13, 95% CI: 1.54-2.93], PYY [OR = 1.87, 95% CI: 1.43-2.44] and PRSS3 [OR = 0.80, 95% CI: 0.73-0.89], and those associated with early onset disease, including EHPB1 [OR = 2.89, 95% CI: 1.99-4.21], POGLUT3 [OR = 0.76, 95% CI: 0.67-0.86] and TPM3 [OR = 0.47, 95% CI: 0.34-0.64]. We confirmed an inverse association of MSMB with prostate cancer overall [OR = 0.81, 95% CI: 0.80-0.82], and also found an inverse association with both aggressive [OR = 0.84, 95% CI: 0.82-0.86] and early onset disease [OR = 0.71, 95% CI: 0.68-0.74]. Using spatial transcriptomics data, we identified MSMB as the genome-wide top-most predictive gene to distinguish benign regions from high grade cancer regions that comparatively had five-fold lower MSMB expression. Additionally, ten proteins that were associated with prostate cancer risk also mapped to existing therapeutic interventions.

INTERPRETATION

Our findings emphasise the importance of proteomics for improving our understanding of prostate cancer aetiology and of opportunities for novel therapeutic interventions. Additionally, we demonstrate the added benefit of in-depth functional analyses to triangulate the role of risk proteins in the clinical aggressiveness of prostate tumours. Using these integrated methods, we identify a subset of risk proteins associated with aggressive and early onset disease as priorities for investigation for the future prevention and treatment of prostate cancer.

FUNDING

This work was supported by Cancer Research UK (grant no. C8221/A29017).

Germline determinants of aberrant signaling pathways in cancer

Cancer is a complex disease influenced by a heterogeneous landscape of both germline genetic variants and somatic aberrations. While there is growing evidence suggesting an interplay between germline and somatic variants, and a substantial number of somatic aberrations in specific pathways are now recognized as hallmarks in many well-known forms of cancer, the interaction landscape between germline variants and the aberration of those pathways in cancer remains largely unexplored. Utilizing over 8500 human samples across 33 cancer types characterized by TCGA and considering binary traits defined using a large collection of somatic aberration profiles across ten well-known oncogenic signaling pathways, we conducted a series of GWAS and identified genome-wide and suggestive associations involving 276 SNPs. Among these, 94 SNPs revealed cis-eQTL links with cancer-related genes or with genes functionally correlated with the corresponding traits' oncogenic pathways. GWAS summary statistics for all tested traits were then used to construct a set of polygenic scores employing a customized computational strategy. Polygenic scores for 24 traits demonstrated significant performance and were validated using data from PCAWG and CCLE datasets. These scores showed prognostic value for clinical variables and exhibited significant effectiveness in classifying patients into specific cancer subtypes or stratifying patients with cancer-specific aggressive phenotypes. Overall, we demonstrate that germline genetics can describe patients' genetic liability to develop specific cancer molecular and clinical profiles.

Data-adaptive and pathway-based tests for association studies between somatic mutations and germline variations in human cancers

Cancer is a disease driven by a combination of inherited genetic variants and somatic mutations. Recently available large-scale sequencing data of cancer genomes have provided an unprecedented opportunity to study the interactions between them. However, previous studies on this topic have been limited by simple, low statistical power tests such as Fisher's exact test. In this paper, we design data-adaptive and pathway-based tests based on the score statistic for association studies between somatic mutations and germline variations. Previous research has shown that two single-nucleotide polymorphism (SNP)-set-based association tests, adaptive sum of powered score (aSPU) and data-adaptive pathway-based (aSPUpath) tests, increase the power in genome-wide association studies (GWASs) with a single disease trait in a case-control study. We extend aSPU and aSPUpath to multi-traits, that is, somatic mutations of multiple genes in a cohort study, allowing extensive information aggregation at both SNP and gene levels.p $p$ -values from different parameters assuming varying genetic architecture are combined to yield data-adaptive tests for somatic mutations and germline variations. Extensive simulations show that, in comparison with some commonly used methods, our data-adaptive somatic mutations/germline variations tests can be applied to multiple germline SNPs/genes/pathways, and generally have much higher statistical powers while maintaining the appropriate type I error. The proposed tests are applied to a large-scale real-world International Cancer Genome Consortium whole genome sequencing data set of 2583 subjects, detecting more significant and biologically relevant associations compared with the other existing methods on both gene and pathway levels. Our study has systematically identified the associations between various germline variations and somatic mutations across different cancer types, which potentially provides valuable utility for cancer risk prediction, prognosis, and therapeutics.

Identifying proteomic risk factors for overall, aggressive and early onset prostate cancer using Mendelian randomization and tumor spatial transcriptomics

Background

Understanding the role of circulating proteins in prostate cancer risk can reveal key biological pathways and identify novel targets for cancer prevention.

Methods

We investigated the association of 2,002 genetically predicted circulating protein levels with risk of prostate cancer overall, and of aggressive and early onset disease, using cis-pQTL Mendelian randomization (MR) and colocalization. Findings for proteins with support from both MR, after correction for multiple-testing, and colocalization were replicated using two independent cancer GWAS, one of European and one of African ancestry. Proteins with evidence of prostate-specific tissue expression were additionally investigated using spatial transcriptomic data in prostate tumor tissue to assess their role in tumor aggressiveness. Finally, we mapped risk proteins to drug and ongoing clinical trials targets.

Results

We identified 20 proteins genetically linked to prostate cancer risk (14 for overall [8 specific], 7 for aggressive [3 specific], and 8 for early onset disease [2 specific]), of which a majority were novel and replicated. Among these were proteins associated with aggressive disease, such as PPA2 [Odds Ratio (OR) per 1 SD increment = 2.13, 95% CI: 1.54-2.93], PYY [OR = 1.87, 95% CI: 1.43-2.44] and PRSS3 [OR = 0.80, 95% CI: 0.73-0.89], and those associated with early onset disease, including EHPB1 [OR = 2.89, 95% CI: 1.99-4.21], POGLUT3 [OR = 0.76, 95% CI: 0.67-0.86] and TPM3 [OR = 0.47, 95% CI: 0.34-0.64]. We confirm an inverse association of MSMB with prostate cancer overall [OR = 0.81, 95% CI: 0.80-0.82], and also find an inverse association with both aggressive [OR = 0.84, 95% CI: 0.82-0.86] and early onset disease [OR = 0.71, 95% CI: 0.68-0.74]. Using spatial transcriptomics data, we identified MSMB as the genome-wide top-most predictive gene to distinguish benign regions from high grade cancer regions that had five-fold lower MSMB expression. Additionally, ten proteins that were associated with prostate cancer risk mapped to existing therapeutic interventions.

Conclusion

Our findings emphasize the importance of proteomics for improving our understanding of prostate cancer etiology and of opportunities for novel therapeutic interventions. Additionally, we demonstrate the added benefit of in-depth functional analyses to triangulate the role of risk proteins in the clinical aggressiveness of prostate tumors. Using these integrated methods, we identify a subset of risk proteins associated with aggressive and early onset disease as priorities for investigation for the future prevention and treatment of prostate cancer.

A novel artificial intelligence approach to detect the breast cancer using KNNet technique with EPM gene profiling

Women's most frequent type of cancer is breast cancer, second only to lung cancer. This paper summarizes changes in genomics and epigenetics and incremental biological activities. A tumour develops through a series of phases involving a separate abnormal gene. Even though many diseases cause DNA mutations, most treatments are designed to relieve symptoms rather than change the DNA. Clustering short palindromic repeats (CRISPR) or Cas9 is the primary approach for discovering and confirming tumorigenic genomic targets. A Kohonen neural network with an expression programming model was developed for gene selection. The main problem in genetic selection is reducing the number of features chosen while maintaining accuracy. This purpose is accomplished systematically. In the end, the approach method performed better than the existing quantum squirrel-inspired algorithm and the recurrent neural network oppositional call search algorithm for genetic selection. The KNNet-EPM model used an expression programming approach to identify gene biomarkers for breast cancer. This method was achieved with RAE of 42%, sensitivity of 93%, f1 score of 88%, accuracy of 98%, kappa score of 83%, specificity of 92% and MAE of 30%.

Biomarkers: Promising and valuable tools towards diagnosis, prognosis and treatment of Covid-19 and other diseases

The use of biomarkers as early warning systems in the evaluation of disease risk has increased markedly in the last decade. Biomarkers are indicators of typical biological processes, pathogenic processes, or pharmacological reactions to therapy. The application and identification of biomarkers in the medical and clinical fields have an enormous impact on society. In this review, we discuss the history, various definitions, classifications, characteristics, and discovery of biomarkers. Furthermore, the potential application of biomarkers in the diagnosis, prognosis, and treatment of various diseases over the last decade are reviewed. The present review aims to inspire readers to explore new avenues in biomarker research and development.

Image-Based Deep Neural Network for Individualizing Radiotherapy Dose Is Transportable Across Health Systems

PURPOSE

We developed a deep neural network that queries the lung computed tomography-derived feature space to identify radiation sensitivity parameters that can predict treatment failures and hence guide the individualization of radiotherapy dose. In this article, we examine the transportability of this model across health systems.

METHODS

This multicenter cohort-based registry included 1,120 patients with cancer in the lung treated with stereotactic body radiotherapy. Pretherapy lung computed tomography images from the internal study cohort (n = 849) were input into a multitask deep neural network to generate an image fingerprint score that predicts time to local failure. Deep learning (DL) scores were input into a regression model to derive iGray, an individualized radiation dose estimate that projects a treatment failure probability of < 5% at 24 months. We validated our findings in an external, holdout cohort (n = 271).

RESULTS

There were substantive differences in the baseline patient characteristics of the two study populations, permitting an assessment of model transportability. In the external cohort, radiation treatments in patients with high DL scores failed at a significantly higher rate with 3-year cumulative incidences of local failure of 28.5% (95% CI, 19.8 to 37.8) versus 10.2% (95% CI, 5.9 to 16.2; hazard ratio, 3.3 [95% CI, 1.74 to 6.49]; P < .001). A model that included DL score alone predicted treatment failures with a concordance index of 0.68 (95% CI, 0.59 to 0.77), which had a similar performance to a nested model derived from within the internal cohort (0.70 [0.64 to 0.75]). External cohort patients with iGray values that exceeded the delivered doses had proportionately higher rates of local failure (P < .001).

CONCLUSION

Our results support the development and implementation of new DL-guided treatment guidance tools in the image-replete and highly standardized discipline of radiation oncology.

Genomic crossroads between non-Hodgkin's lymphoma and common variable immunodeficiency

Common variable immunodeficiency (CVID) represents the largest group of primary immunodeficiencies that may manifest with infections, inflammation, autoimmunity, and cancer, mainly B-cell non-Hodgkin's lymphoma (NHL). Indeed, NHL may result from chronic or recurrent infections and has, therefore, been recognized as a clinical phenotype of CVID, although rare. The more one delves into the mechanisms involved in CVID and cancer, the stronger the idea that both pathologies can be a reflection of the same primer events observed from different angles. The potential effects of germline variants on specific somatic modifications in malignancies suggest that it might be possible to anticipate critical events during tumor development. In the same way, a somatic alteration in NHL could be conditioning a similar response at the transcriptional level in the shared signaling pathways with genetic germline alterations in CVID. We aimed to explore the genomic substrate shared between these entities to better characterize the CVID phenotype immunodeficiency in NHL. By means of an in-silico approach, we interrogated the large, publicly available datasets contained in cBioPortal for the presence of genes associated with genetic pathogenic variants in a panel of 50 genes recurrently altered in CVID and previously described as causative or disease-modifying. We found that 323 (25%) of the 1,309 NHL samples available for analysis harbored variants of the CVID spectrum, with the most recurrent alteration presented in NHL occurring in PIK3CD (6%) and STAT3 (4%). Pathway analysis of common gene alterations showed enrichment in inflammatory, immune surveillance, and defective DNA repair mechanisms similar to those affected in CVID, with PIK3R1 appearing as a central node in the protein interaction network. The co-occurrence of gene alterations was a frequent phenomenon. This study represents an attempt to identify common genomic grounds between CVID and NHL. Further prospective studies are required to better know the role of genetic variants associated with CVID and their reflection on the somatic pathogenic variants responsible for cancer, as well as to characterize the CVID-like phenotype in NHL, with the potential to influence early CVID detection and therapeutic management.

The Genomic Landscape of Corticotroph Tumors: From Silent Adenomas to ACTH-Secreting Carcinomas

Corticotroph cells give rise to aggressive and rare pituitary neoplasms comprising ACTH-producing adenomas resulting in Cushing disease (CD), clinically silent ACTH adenomas (SCA), Crooke cell adenomas (CCA) and ACTH-producing carcinomas (CA). The molecular pathogenesis of these tumors is still poorly understood. To better understand the genomic landscape of all the lesions of the corticotroph lineage, we sequenced the whole exome of three SCA, one CCA, four ACTH-secreting PA causing CD, one corticotrophinoma occurring in a CD patient who developed Nelson syndrome after adrenalectomy and one patient with an ACTH-producing CA. The ACTH-producing CA was the lesion with the highest number of single nucleotide variants (SNV) in genes such as USP8, TP53, AURKA, EGFR, HSD3B1 and CDKN1A. The USP8 variant was found only in the ACTH-CA and in the corticotrophinoma occurring in a patient with Nelson syndrome. In CCA, SNV in TP53, EGFR, HSD3B1 and CDKN1A SNV were present. HSD3B1 and CDKN1A SNVs were present in all three SCA, whereas in two of these tumors SNV in TP53, AURKA and EGFR were found. None of the analyzed tumors showed SNV in USP48, BRAF, BRG1 or CABLES1. The amplification of 17q12 was found in all tumors, except for the ACTH-producing carcinoma. The four clinically functioning ACTH adenomas and the ACTH-CA shared the amplification of 10q11.22 and showed more copy-number variation (CNV) gains and single-nucleotide variations than the nonfunctioning tumors.

Germline variants in DNA repair genes are associated with young-onset head and neck cancer

The genetic predisposition to head and neck carcinomas (HNSCC) and how the known risk factors (papillomavirus infection, alcohol, and tobacco consumption) contribute to the early-onset disease are barely explored. Although HNSCC at early onset is rare, its frequency is increasing in recent years. Germline and somatic variants were assessed to build a comprehensive genetic influence pattern in HNSCC predisposition and patient outcome. Whole-exome sequencing was performed in 45 oral and oropharynx carcinomas paired with normal samples of young adults (≤49 years). We found FANCG, CDKN2A, and TPP germline variants previously associated with HNSCC risk. At least one germline variant in DNA repair pathway genes was detected in 67% of cases. Germline and somatic variants (including copy number variations) in FAT1 gene were identified in 9 patients (20%) and 12 tumors (30%), respectively. Somatic variants were found in HNSCC associated genes, such as TP53, CDKN2A, and PIK3CA. To date, 55 of 521 cases from the large cohort of TCGA presented < 49 years old. A comparison between the somatic alterations of TCGA-HNSCC at early onset and our dataset revealed strong similarities. Protein-protein interaction analysis between somatic and germline altered genes revealed a central role of TP53. Altogether, germline alterations in DNA repair genes potentially contribute to an increased risk of developing HNSCC at early-onset, while FAT1 could impact the prognosis.

Feature Selection for Breast Cancer Classification by Integrating Somatic Mutation and Gene Expression

Exploring the molecular mechanisms of breast cancer is essential for the early prediction, diagnosis, and treatment of cancer patients. The large scale of data obtained from the high-throughput sequencing technology makes it difficult to identify the driver mutations and a minimal optimal set of genes that are critical to the classification of cancer. In this study, we propose a novel method without any prior information to identify mutated genes associated with breast cancer. For the somatic mutation data, it is processed to a mutated matrix, from which the mutation frequency of each gene can be obtained. By setting a reasonable threshold for the mutation frequency, a mutated gene set is filtered from the mutated matrix. For the gene expression data, it is used to generate the gene expression matrix, while the mutated gene set is mapped onto the matrix to construct a co-expression profile. In the stage of feature selection, we propose a staged feature selection algorithm, using fold change, false discovery rate to select differentially expressed genes, mutual information to remove the irrelevant and redundant features, and the embedded method based on gradient boosting decision tree with Bayesian optimization to obtain an optimal model. In the stage of evaluation, we propose a weighted metric to modify the traditional accuracy to solve the sample imbalance problem. We apply the proposed method to The Cancer Genome Atlas breast cancer data and identify a mutated gene set, among which the implicated genes are oncogenes or tumor suppressors previously reported to be associated with carcinogenesis. As a comparison with the integrative network, we also perform the optimal model on the individual gene expression and the gold standard PMA50. The results show that the integrative network outperforms the gene expression and PMA50 in the average of most metrics, which indicate the effectiveness of our proposed method by integrating multiple data sources, and can discover the associated mutated genes in breast cancer.

Antitumour activity of TH1579, a novel MTH1 inhibitor, against castration-resistant prostate cancer

Castration-resistant prostate cancer (CRPC) treatment still remains difficult. The aim of the present study was to determine the antitumour efficacy of the MutT homolog 1 (MTH1) inhibitor, TH1579, against castration-resistant prostate cancer. PC-3 and DU-145 prostate cancer cells were treated with different concentrations of TH1579. C4-2 cells with or without androgen receptor (AR) were also treated with TH1579 to assess AR function. Cell survival, 8-oxo-dG levels and DNA damage were measured using cell viability assays, western blotting, immunofluorescence analysis and flow cytometry. TH1579 inhibited CRPC cell proliferation in a dose-dependent manner. The viabilities of PC-3 and DU-145 cells treated with 1 µM of TH1579 were 28.6 and 24.1%, respectively. The viabilities of C4-2 cells with and without AR treated with 1 µM TH1579 were 10.6 and 19.0%, respectively. Moreover, TH1579 treatment increased 8-oxo-dG levels, as well as the number of 53BP1 and γH2A.X foci, resulting in increased DNA double-strand breakage and apoptosis in PC-3 and DU-145 cells. The findings of the present study demonstrated that TH1579 exerted strong antitumour effects on CRPC cells, and may therefore be used as a potential therapeutic agent for the clinical treatment of CRPC.

Application Areas of Traditional Molecular Genetic Methods and NGS in relation to Hereditary Urological Cancer Diagnosis

Next generation sequencing (NGS) is widely used for diagnosing hereditary cancer syndromes. Often, exome sequencing and extended gene panel approaches are the only means that can be used to detect a pathogenic germline mutation in the case of multiple primary tumors, early onset, a family history of cancer, or a lack of specific signs associated with a particular syndrome. Certain germline mutations of oncogenes and tumor suppressor genes that determine specific clinical phenotypes may occur in mutation hot spots. Diagnosis of such cases, which involve hereditary cancer, does not require NGS, but may be made using PCR and Sanger sequencing. Diagnostic criteria and professional community guidelines developed for hereditary cancers of particular organs should be followed when ordering molecular diagnostic tests for a patient. This review focuses on urological oncology associated with germline mutations. Clinical signs and genetic diagnostic laboratory tests for hereditary forms of renal cell cancer, prostate cancer, and bladder cancer are summarized. While exome sequencing, or, conversely, traditional molecular genetic methods are the procedure of choice in some cases, in most situations, sequencing of multigene panels that are specifically aimed at detecting germline mutations in early onset renal cancer, prostate cancer, and bladder cancer seems to be the basic solution for molecular genetic diagnosis of hereditary cancers.

Mapping the Germline and Somatic Mutation Interaction Landscape in Indolent and Aggressive Prostate Cancers

BACKGROUND

A majority of prostate cancers (PCas) are indolent and cause no harm even without treatment. However, a significant proportion of patients with PCa have aggressive tumors that progress rapidly to metastatic disease and are often lethal. PCa develops through somatic mutagenesis, but emerging evidence suggests that germline genetic variation can markedly contribute to tumorigenesis. However, the causal association between genetic susceptibility and tumorigenesis has not been well characterized. The objective of this study was to map the germline and somatic mutation interaction landscape in indolent and aggressive tumors and to discover signatures of mutated genes associated with each type and distinguishing the two types of PCa.

MATERIALS AND METHODS

We integrated germline mutation information from genome-wide association studies (GWAS) with somatic mutation information from The Cancer Genome Atlas (TCGA) using gene expression data from TCGA on indolent and aggressive PCas as the intermediate phenotypes. Germline and somatic mutated genes associated with each type of PCa were functionally characterized using network and pathway analysis.

RESULTS

We discovered gene signatures containing germline and somatic mutations associated with each type and distinguishing the two types of PCa. We discovered multiple gene regulatory networks and signaling pathways enriched with germline and somatic mutations including axon guidance, RAR, WINT, MSP-RON, STAT3, PI3K, TR/RxR, and molecular mechanisms of cancer, NF-kB, prostate cancer, GP6, androgen, and VEGF signaling pathways for indolent PCa and MSP-RON, axon guidance, RAR, adipogenesis, and molecular mechanisms of cancer and NF-kB signaling pathways for aggressive PCa.

CONCLUSION

The investigation revealed germline and somatic mutated genes associated with indolent and aggressive PCas and distinguishing the two types of PCa. The study revealed multiple gene regulatory networks and signaling pathways dysregulated by germline and somatic alterations. Integrative analysis combining germline and somatic mutations is a powerful approach to mapping germline and somatic mutation interaction landscape.

Clinical outcomes associated with pathogenic genomic instability mutations in prostate cancer: a retrospective analysis of US pharmacy and medical claims data

Background: Prostate cancer (PC) is a clinically heterogenous disease, and genetic mutations may be useful for patient risk stratification. This retrospective cohort study compared clinical outcomes, pharmacy use, and outpatient resource use in PC patients with and without pathogenic genomic instability mutations, including DNA repair deficiency (DRD) mutations and those in TP53, PTEN, and RB1. Methods: Patients ≥18 years newly-diagnosed with PC between June 2011-March 2016 were identified in medical and prescription claims databases linked to a genomic dataset. All-cause and PC-specific pharmacy use and outpatient resource use (office visits, laboratory tests, radiology examinations, and radiation therapies) over 1, 2, and 3 years and time to evidence of disease progression after PC diagnosis, based on secondary cancer diagnosis codes and treatments received, were evaluated in mutation carriers with ≥1 of 24 gene mutations and in a sub-set of DRD gene mutation carriers, with each compared to non-mutation carriers. Results: Mutation carriers (n = 274) and non-mutation carriers (n = 74) had similar demographic and clinical features. Non-mutation carriers had lower risks of developing metastasis and castration-resistant PC than mutation carriers (hazard ratio [HR] = 0.7, 95% CI = 0.5-0.9; HR = 0.5, 95% CI = 0.3-0.9, respectively) and DRD mutation carriers (HR = 0.5, 95% CI = 0.3-0.7; HR = 0.4, 95% CI = 0.2-0.7, respectively). Compared to non-mutation carriers, mutation carriers had more all-cause pharmacy claims over 2 years of follow-up (74.4 vs 59.1, p = 0.04) and more PC-specific pharmacy claims over 2 years (11.1 vs 6.5, p = 0.01) and 3 years (17.9 vs 9.8, p = 0.01) of follow-up. No differences were observed in outpatient resource use during the follow-up period by mutation status. Conclusion: PC patients carrying ≥1 pathogenic DNA instability mutation, and DRD mutation carriers specifically, had higher clinical burden than non-mutation carriers. Targeted therapies for these patients are needed to reduce clinical burden and associated healthcare resource utilization.

A paradigm shift in medicine: A comprehensive review of network-based approaches

Network medicine is a rapidly evolving new field of medical research, which combines principles and approaches of systems biology and network science, holding the promise to uncovering the causes and to revolutionize the diagnosis and treatments of human diseases. This new paradigm reflects the fact that human diseases are not caused by single molecular defects, but driven by complex interactions among a variety of molecular mediators. The complexity of these interactions embraces different types of information: from the cellular-molecular level of protein-protein interactions to correlational studies of gene expression and regulation, to metabolic and disease pathways up to drug-disease relationships. The analysis of these complex networks can reveal new disease genes and/or disease pathways and identify possible targets for new drug development, as well as new uses for existing drugs. In this review, we offer a comprehensive overview of network types and algorithms used in the framework of network medicine. This article is part of a Special Issue entitled: Transcriptional Profiles and Regulatory Gene Networks edited by Dr. Dr. Federico Manuel Giorgi and Dr. Shaun Mahony.