Use of Genomic Databases in Medicine: Comparison
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Please note this is a comparison between Version 2 by Sirius Huang and Version 1 by Magdalena Mroczek.

Attention has been increasingly focused on the non-coding sequences that encompass 98% of the genome and may play an important regulatory function. The first WGS-based datasets have already been released including underrepresented populations. Although many databases contain pooled data from several cohorts, the importance of local databases has been highlighted. Genomic databases are not only collecting data but may also contribute to better diagnostics and therapies. They may find applications in population studies, rare diseases, oncology, pharmacogenetics, and infectious and inflammatory diseases. Further data may be analysed with Al technologies and in the context of other omics data. 

  • database
  • genomic databases
  • whole-genome sequencing
  • WGS

1. Genomic Databases in Oncology

Thanks to genomics, our knowledge of cancer biology has expanded considerably in the past few years. The rapid development of genomic research in this field would be impossible without the joint efforts of the scientific community to generate public databases, which have been extensively used as a tool for further studies on various aspects of oncology.
By the end of 2005, the US National Institutes of Health launched The Cancer Genome Atlas (TCGA) project. In 12 years, TCGA characterised more than 20,000 samples from 33 different cancer types, generating over 2.5 petabytes of genomic, epigenomic, transcriptomic and proteomic data [52][1]. As it soon turned out that characterising a higher number of tumour samples from different cancer types would require international cooperation, the International Cancer Genome Consortium (ICGC) was initiated in 2008 to coordinate large-scale cancer genome sequencing studies in 50 different tumour types “that are of clinical and societal importance across the globe” [53][2]. It is the most significant cancer genome sequencing project to date. Over 80 million somatic mutations have been identified in this dataset. Both TCGA and ICGC were mainly focused on the exome. However, several studies have shown the important role of non-coding and regulatory regions in carcinogenesis. That is why the Pan-Cancer Analysis of Whole Genomes (PCAWG) initiative within the ICGC was established to identify common patterns of mutation in more than 2600 cancer whole genomes. According to the flagship paper of the TCGA/ICGC PCAWG consortium published in 2020, the majority of cancer genomes contain a few driver mutations in both coding and non-coding regions but in about 5% of them, no known mutation was identified, which leaves room for speculation. Is the catalogue of cancerogenic mutations incomplete or do other processes have more impact in these cases? The new phase of the project Accelerating Research in Genomic Oncology (ARGO) started in 2019. Its main goal is to improve the outcome of cancer patients. It will analyse 100,000 samples in comparison with clinical data to find out how to best use genomic knowledge in the prevention, detection and treatment of cancers [52,54][1][3].
Projects, as described above, have enabled the creation of such data collections as the Catalogue of Somatic Mutations in Cancer (COSMIC), the world’s largest and most detailed resource for exploring the effect of somatic mutations in human cancer, and the Cancer Gene Census (CGC) [55,56][4][5]. COSMIC covers all the known genetic mechanisms by which somatic mutations promote cancer such as coding and non-coding mutations, gene fusions, copy-number variants, and drug-resistance mutations, whereas CGC is an expert-curated catalogue of the genes driving human cancer that is used as a standard in cancer genetics across basic research, medical reporting and pharmaceutical development. It also includes functional descriptions of how each gene contributes to disease generation [55,56][4][5].
Another large database widely used in oncological research, as well as in other domains, is the Genome Aggregation Database (gnomAD), originally launched in 2014 as the Exome Aggregation Consortium (ExAC) [57][6]. It contains over 125,000 exome and 15,000 whole genome sequences from European, Latino African and African American, South Asian, East Asian, Ashkenazi Jewish and other populations (https://gnomad.broadinstitute.org (accessed on 10 December 2022)). All the data were contributed to the project from independent large-scale human sequencing studies led by more than 100 investigators, then processed into summary high-quality variant data and made available for the wider scientific community. The gnomAD papers report 241 million small genetic variants and over 335 thousand structural variants [57][6]. Even though this database is widely used in oncology, it remains a valuable and broad population database with many significant applications outside medicine too.
In addition to the already listed, numerous smaller, more specific databases have been created. Some of the interesting examples include a database of extrachromosomal circular DNA (eccDNAdb), which seems to play a crucial role in oncogene amplification and tumour progression [58][7]; single nucleotide polymorphisms (SNPs) databases, (SNPs can influence methylation and participate in signalling pathway degeneracy in cancer) [59][8] and upstream open reading frames (uORFs) databases. Genetic defects in the last ones have been linked to the development of various diseases, including cancer [60][9].
All these resources are used in cancer-related analyses. They allow detection of viral sequences in cancer tissues, e.g., herpesvirus family or HPV in bladder cancers [61][10]; finding new genetic markers to diagnose and treat diseases with relatively poor prognosis such as liver and oesophageal cancer [62,63][11][12]; examining the tumour microenvironment, which is thought to be essential, e.g., for breast cancer progression and metastasis [64][13].
Beyond questions, the role of non-coding variants in cancer genome is significant and should be incorporated into diagnostic and treatment procedures, which in fact is being preceded by several guidelines-producing bodies including ACMG (for example [65,66,67][14][15][16]). WGS of cancer genome allows to characterise the whole profile of genetic variants and assign them to a proper cancer signature or specific feature. Each of the more than a hundred signatures identified up to date across human cancers indicates a specific mechanism of cancer development [68][17]. Most of the signatures can be associated with a defective DNA maintenance process and a precisely pinpointed disrupted pathway, which brings us to the point where specific treatment may be administered on the basis of this information, such as PARP inhibitors.
PARP inhibitors (poly-(ADP-ribose)-polymerase inhibitors) are ground-breaking agents, effective in treating several cancer types including breast, ovarian, prostate and even pancreatic cancer [69,70,71,72,73,74][18][19][20][21][22][23]. Multiple randomised clinical trials have demonstrated their efficacy and the PARPi drug family constantly expands, comprising such agents as Olaparib, Niraparib, Rucaparib and Talazoparib, with many more under clinical trials around the world [75,76,77][24][25][26]. However, it remains worrisome that only a subset of cancer patients treated with PARPi actually benefit from the therapy [78,79][27][28].
The biomarker currently used for PARPi administration is far from being perfect: the BRCA1 and BRCA2 gene mutations [80,81][29][30]. Even though they are an excellent indicator of Homologous Recombination Deficiency (HRD), they are not the only hallmarks of HRD disruption [82][31]. However, clinical trials have clearly demonstrated that patients without BRCA1 nor BRCA2 mutations can also benefit from PARPi therapy [83][32]. For example, the PRIMA trial (PRIMA/ENGOT-OV26/GOG-3012 trial results presented at the European Society for Medical Oncology (ESMO) Congress in 2019) showed that assessing HRD status with the aid of computer algorithms may allow more cancer patients with no BRCA1 and BRCA2 mutations to undergo a successful PARPi treatment [83][32]. Thus, many more patients without BRCA1 and BRCA2 do respond to PARP inhibitors and therefore may benefit from the treatment.
In fact, the most advanced clinical application originating from cancer signatures, not only mutated genes, relates to Homologous Recombination Deficiency and PARPi [84,85][33][34]. WGS is being used in a couple of commercially available cancer diagnostics; for example, Illumina Comprehensive Genomic Profiling considers Tumour Mutation Burden (TMB) or Microsatellite Instability (MSI) or MyChoiceCDx created by Myriad Genetics Inc. The diagnosis and treatment based on advanced machine learning algorithms, such as HRDetect or myChoice already show promising results: several clinical trials of the drugs based on PARPi (such as Niraparib, most recently) were effective in reducing the risk of ovarian cancer progression by 38% [85,86,87][34][35][36]. AI-based computer algorithms are created to screen WGS data for rare and common variants potentially significant in pharmacogenomics, leading to new applications of the drugs already existing in the market, but also identification of novel regulatory variants located in non-coding parts of the genome and their function, patient stratification and, in some cases, even the mechanistic prediction of drug targets, response and their interactions [88,89][37][38]. Some cancer databases are designed to find patient target genes and potential treating molecules [90][39]. Although datasets contain various omics datasets, such as mRNA and epigenomics, WGS data are still the core of such databases. As a result, a hit containing a list of potential drugs targeting a particular genetic sequence is returned.
Regional databases also play an important role in cancer research. Numerous studies are focused on specific populations, such as 237 patients from a reported population-based south Swedish triple-negative breast cancer cohort profiled by RNA sequencing and whole-genome sequencing included in “Molecular analyses of triple-negative breast cancer in the young and elderly” or a population-based Estonian biobank (over 150,000) and breast cancer-affected cases from Latvia chosen to assess the spectrum and frequency of CHEK2 variants in the breast cancer-affected and general population in the Baltic states region [91,92][40][41].

2. Genomic Databases in Infectious Diseases

The same is true for many other human threats including infectious diseases. It has been long known that not only can we track pathogens’ routes of transmission or evolutionary development, as it has been done for MRSA strains [93,94][42][43] or cholera outbreaks in Haiti [95[44][45],96], but also genomic regions in human DNA connected with susceptibility or resistance to a certain pathogen, such as norovirus infections [97,98,99][46][47][48]. More recently, this phenomenon was beautifully depicted by the global cooperation established at the very early days of the COVID-19 pandemic, namely the COVID-19 Host Genetics Initiative (HGI) and the COVID Human Genetic Effort (HGE). These global initiatives aimed at understanding the disease enabled worldwide genomic sample collection, used further by us and others, and resulted in enormous datasets suitable for AI- and ML-based algorithms (exemplified by the HGI and HGE consortia findings described in [100,101][49][50]. Such great databases provide evidence that, as a scientific community worldwide, we are already very good at collecting data, but the time has come to share these datasets more eagerly. Especially in case of the genomic datasets, it may not be feasible nor technically doable for a single team to analyse and interpret properly whole genome sequences of such a huge and expanding collection.
It is worth emphasizing that all the genomic data collected during the COVID-19 pandemic can be used not only in the infectious context. The project “Search for Genomic Markers Predicting the Severity of the Response to COVID-19” may be taken as an example. Between April 2020 and April 2021, the researchers collected samples from 1222 Poles to study their genetic susceptibility to COVID-19 infections. Researchers analysed the whole genomes to identify and genotype a wide spectrum of genomic variation, such as small and structural variants, runs of homozygosity, mitochondrial haplogroups and de novo variants. This study is the biggest whole-genome screening of the Slavic and Central Europe populations done to date. The allele frequencies, calculated for 1076 unrelated individuals, were released as a publicly available resource, the Thousand Polish Genomes database. The Polish population, highly homogenous and sedentary by its nature, is unique and can serve as a genetic reference for the Slavic nations that account for over 4.5% of world inhabitants. The Thousand Polish Genomes database contributes to the worldwide genomic resources accessible to researchers and clinicians. It lays the foundation for further studies in the population history and epidemiology of diseases caused by mutations in the autosomal-recessive genes, as well as creates opportunities for tailoring NGS-based genetic screening tests and guidelines for clinical geneticists in Poland [48][51].
Genomic databases in infectious diseases can play multiple roles not only in relation to COVID [102][52]. They may help in identification of resistance biomarkers and treatment targets. This seems to be particularly crucial in Africa, especially for the detection and surveillance of malaria, HIV and drug-resistant tuberculosis [103][53].
As communicable diseases quite often have a localised character, creating small, local databases might be particularly useful in their case. During the 2019–2020 dengue fever epidemic in the Dominican Republic, a study on 488 children with a confirmed disease was conducted to find the genetic factors of its severity in this group [104][54]. On the African continent, there is a need to investigate tropical arboviruses with described zoonotic potential. The whole-genome sequencing using novel technological approaches allows a better understanding of their genetic diversity and distribution that may help to reduce the threat they pose to human and animal health [105][55]. The large international databases are also frequently used in this domain. In Asia, 10 Pasteurella canis and 16 Pasteurella multocida whole-genome sequences from National Center of Biotechnology database were selected to perform a comparative analysis of virulence factors (VFs) between two species that both cause zoonotic infections [106][56]. The collections such as the Comprehensive Antibiotic Resistance Database (CARD) or the Virulence Factor Database (VFDB) are used to identify the genes responsible for drug-resistance or virulence and characterise local pathogens. It was done recently in the case of multidrug-resistant Staphylococcus hominis isolated in Malaysia [107][57].

3. Genomic Databases in Rare Diseases

Rare diseases were one of the areas that profited from the WGS technology at first. Moreover, in terms of standardisation and guidelines, WGS in rare diseases is well established [108][58]. One of the most known and pioneering initiative is 100,000 Human Genomes, a project targeted at sequencing NHS patients affected with rare diseases [109][59]. The preliminary results gave a diagnostic yield of 35% for likely monogenic disorders and 11% for likely complex disorders [109][59]. In the US, Centers for Mendelian Genomics are pioneering institutions that use WGS in rare disease cohorts [110][60]. In Canada a centralized WGS database for rare diseases has been introduced to facilitate cooperation and new gene discovery [111][61]. On the European level several EU-founded projects, such as Solve-RD and ERN, implemented WGS as part of their workups [112][62]. In the recent years also regional initiatives, such as the Brasilian Rare Genomes Programme [113][63] and the Initiative on Rare and Undiagnosed Diseases in Japan [114][64], have successfully been implemented.

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