American Association for Cancer Research Project Genomics Evidence Neoplasia Information Exchange: From Inception to First Data Release and Beyond-Lessons Learned and Member Institutions' Perspectives

Generating and using real-world data: A worthwhile uphill battle

The precision oncology paradigm challenges the feasibility and data generalizability of traditional clinical trials. Consequently, an unmet need exists for practical approaches to test many subgroups, evaluate real-world drug value, and gather comprehensive, accessible datasets to validate novel biomarkers. Real-world data (RWD) are increasingly recognized to have the potential to fill this gap in research methodology. Established applications of RWD include informing disease epidemiology, pharmacovigilance, and healthcare quality assessment. Currently, concerns regarding RWD quality and comprehensiveness, privacy, and biases hamper their broader application. Nonetheless, RWD may play a pivotal role in supplementing clinical trials, enabling conditional reimbursement and accelerated drug access, and innovating trial conduct. Moreover, purpose-built RWD repositories may support the extension or refinement of drug indications and facilitate the discovery and validation of new biomarkers. This perspective explores the potential of leveraging RWD to advance oncology, highlights its benefits and challenges, and suggests a path forward in this evolving field.

Widespread alteration of protein autoinhibition in human cancers

Autoinhibition is a prevalent allosteric regulatory mechanism in signaling proteins. Reduced autoinhibition underlies the tumorigenic effect of some known cancer drivers, but whether autoinhibition is altered generally in cancer remains elusive. Here, we demonstrate that cancer-associated missense mutations, in-frame insertions/deletions, and fusion breakpoints are enriched within inhibitory allosteric switches (IASs) across all cancer types. Selection for IASs that are recurrently mutated in cancers identifies established and unknown cancer drivers. Recurrent missense mutations in IASs of these drivers are associated with distinct, cancer-specific changes in molecular signaling. For the specific case of PPP3CA, the catalytic subunit of calcineurin, we provide insights into the molecular mechanisms of altered autoinhibition by cancer mutations using biomolecular simulations, and demonstrate that such mutations are associated with transcriptome changes consistent with increased calcineurin signaling. Our integrative study shows that autoinhibition-modulating genetic alterations are positively selected for by cancer cells.

Pragmatic guide to the macroscopic evaluation of breast specimens

The pathological assessment of a breast surgical specimen starts with macroscopic evaluation, arguably one of the most critical steps, as only a small percentage of the tissue is examined microscopically. To properly evaluate and select tissue sections from breast specimens, it is essential to correlate radiological findings, prior biopsies, procedures and treatment with the gross findings. Owing to its fatty nature, breast tissue requires special attention for proper fixation to ensure appropriate microscopic evaluation and performance of ancillary studies. In addition, knowledge of the information necessary for patient management will ensure that these data are collected during the macroscopic evaluation, and appropriate sections are taken to obtain the information needed from the microscopic evaluation. Herein, we present a review of the macroscopic evaluation of different breast specimen types, including processing requirements, challenges and recommendations.

Mapping inherited genetic variation with opposite effects on autoimmune disease and cancer identifies candidate drug targets associated with the anti-tumor immune response

Background

Germline alleles near genes that encode certain immune checkpoints (CTLA4, CD200) are associated with autoimmune/autoinflammatory disease and cancer but in opposite directions. This motivates a systematic search for additional germline alleles which demonstrate this pattern with the aim of identifying potential cancer immunotherapeutic targets using human genetic evidence.

Methods

Pairwise fixed effect cross-disorder meta-analyses combining genome-wide association studies (GWAS) for breast, prostate, ovarian and endometrial cancers (240,540 cases/317,000 controls) and seven autoimmune/autoinflammatory diseases (112,631 cases/895,386 controls) coupled with in silico follow-up. To ensure detection of alleles with opposite effects on cancer and autoimmune/autoinflammatory disease, the signs on the beta coefficients in the autoimmune/autoinflammatory GWAS were reversed prior to meta-analyses.

Results

Meta-analyses followed by linkage disequilibrium clumping identified 312 unique, independent lead variants with Pmeta<5x10-8 associated with at least one of the cancer types at Pcancer<10-3 and one of the autoimmune/autoinflammatory diseases at Pauto<10-3. At each lead variant, the allele that conferred autoimmune/autoinflammatory disease risk was protective for cancer. Mapping each lead variant to its nearest gene as its putative functional target and focusing on genes with established immunological effects implicated 32 of the nearest genes. Tumor bulk RNA-Seq data highlighted that the tumor expression of 5/32 genes (IRF1, IKZF1, SPI1, SH2B3, LAT) were each strongly correlated (Spearman's ρ>0.5) with at least one intra-tumor T/myeloid cell infiltration marker (CD4, CD8A, CD11B, CD45) in every one of the cancer types. Tumor single-cell RNA-Seq data from all cancer types showed that the five genes were more likely to be expressed in intra-tumor immune versus malignant cells. The five lead SNPs corresponding to these genes were linked to them via expression quantitative trait locus mechanisms and at least one additional line of functional evidence. Proteins encoded by the genes were predicted to be druggable.

Conclusion

We provide population-scale germline genetic and functional genomic evidence to support further evaluation of the proteins encoded by IRF1, IKZF1, SPI1, SH2B3, and LAT as possible targets for cancer immunotherapy.

Should I take aspirin? A qualitative study on the implementation of a decision aid on taking aspirin for bowel cancer prevention

OBJECTIVES

Australian guidelines recommend 50-70 years consider taking aspirin to reduce their bowel cancer risk. We trialled a decision aid in general practice to facilitate the implementation of these guidelines into clinical practice. This publication reports on the qualitative results from the process evaluation of the trial. We aimed to explore general practitioners' (GPs) and their patients' approach to shared decision-making (SDM) about taking aspirin to prevent bowel cancer and how the decision aids were used in practice.

METHODS

Semistructured interviews were conducted with 17 participants who received the decision aid and 12 GPs who participated in the trial between June and November 2021. The interviews were coded inductively, and emerging themes were mapped onto the Revised Programme Theory for SDM.

RESULTS

The study highlighted the dynamics of SDM for taking aspirin to prevent bowel cancer. Some participants discussed the decision aid with their GPs as advised prior to taking aspirin, others either took aspirin or dismissed it outright without discussing it with their GPs. Notably, participants' trust in their GPs, and participants' diverse worldviews played pivotal roles in their decisions. Although the decision aid supported SDM for some, it was not always prioritised in a consultation. This was likely impacted during the trial period as the COVID-19 pandemic was the focus for general practice.

CONCLUSION

In summary, this study illustrated the complexities of SDM through using a decision aid in general practice to implement the guidelines for low-dose aspirin to prevent bowel cancer. While the decision aid prompted some participants to speak to their GPs, they were also heavily influenced by their unwavering trust in the GPs and their different worldviews. In the face of the COVID-19 pandemic, SDM was not highly prioritised. This study provides insights into the implementation of guidelines into clinical practice and highlights the need for ongoing support and prioritisation of cancer prevention in general practice consultations.

TRIAL REGISTRATION NUMBER

ACTRN12620001003965.

Neuroblastoma: an ongoing cold front for cancer immunotherapy

Neuroblastoma is the most frequent extracranial childhood tumour but effective treatment with current immunotherapies is challenging due to its immunosuppressive microenvironment. Efforts to date have focused on using immunotherapy to increase tumour immunogenicity and enhance anticancer immune responses, including anti-GD2 antibodies; immune checkpoint inhibitors; drugs which enhance macrophage and natural killer T (NKT) cell function; modulation of the cyclic GMP-AMP synthase-stimulator of interferon genes pathway; and engineering neuroblastoma-targeting chimeric-antigen receptor-T cells. Some of these strategies have strong preclinical foundation and are being tested clinically, although none have demonstrated notable success in treating paediatric neuroblastoma to date. Recently, approaches to overcome heterogeneity of neuroblastoma tumours and treatment resistance are being explored. These include rational combination strategies with the aim of achieving synergy, such as dual targeting of GD2 and tumour-associated macrophages or natural killer cells; GD2 and the B7-H3 immune checkpoint; GD2 and enhancer of zeste-2 methyltransferase inhibitors. Such combination strategies provide opportunities to overcome primary resistance to and maximize the benefits of immunotherapy in neuroblastoma.

Therapeutic landscape and future direction of metastatic colorectal cancer

In the era of targeted therapy based on genomic alterations, the treatment strategy for metastatic colorectal cancer (mCRC) has been changing. Before systemic treatment initiation, determination of tumour genomic status for KRAS and NRAS, BRAF^V600E mutations, ERBB2, and microsatellite instability and/or mismatch repair (MMR) status is recommended. In patients with deficient MMR and BRAF^V600E mCRC, randomized phase III trials have established the efficacy of pembrolizumab as first-line therapy and the combination of encorafenib and cetuximab as second-line or third-line therapy. In addition, new agents have been actively developed in other rare molecular fractions such as ERBB2 alterations and KRAS^G12C mutations. In March 2022, the combination of pertuzumab and trastuzumab for ERBB2-positive mCRC was approved in Japan, thereby combining real-world evidence from the SCRUM-Japan Registry. As the populations are highly fragmented owing to rare genomic alterations, various strategies in clinical development are expected. Clinical development of a tumour-agnostic approach, such as NTRK fusion and tumour mutational burden, has successfully introduced corresponding drugs to clinical practice. Considering the difficulty of randomized trials owing to cost-benefit and rarity, a promising solution could be real-world evidence utilized as an external control from the molecular-based disease registry.

A data processing pipeline for the AACR project GENIE biopharma collaborative data with the {genieBPC} R package

MOTIVATION

Data from the American Association for Cancer Research Project Genomics Evidence Neoplasia Information Exchange Biopharma Collaborative (GENIE BPC) represent comprehensive clinical data linked to high-throughput sequencing data, providing a multi-institution, pan-cancer, publicly available data repository. GENIE BPC data provide detailed demographic, clinical, treatment, genomic and outcome data for patients with cancer. These data result in a unique observational database of molecularly characterized tumors with comprehensive clinical annotation that can be used for health outcomes and precision medicine research in oncology. Due to the inherently complex structure of the multiple phenomic and genomic datasets, the use of these data requires a robust process for data integration and preparation in order to build analytic models.

RESULTS

We present the {genieBPC} package, a user-friendly data processing pipeline to facilitate the creation of analytic cohorts from the GENIE BPC data that are ready for clinico-genomic modeling and analyses.

AVAILABILITY AND IMPLEMENTATION

{genieBPC} is available on CRAN and GitHub.

Synthetic lethal gene pairs: Experimental approaches and predictive models

Synthetic lethality (SL) refers to a genetic interaction in which the simultaneous perturbation of two genes leads to cell or organism death, whereas viability is maintained when only one of the pair is altered. The experimental exploration of these pairs and predictive modeling in computational biology contribute to our understanding of cancer biology and the development of cancer therapies. We extensively reviewed experimental technologies, public data sources, and predictive models in the study of synthetic lethal gene pairs and herein detail biological assumptions, experimental data, statistical models, and computational schemes of various predictive models, speculate regarding their influence on individual sample- and population-based synthetic lethal interactions, discuss the pros and cons of existing SL data and models, and highlight potential research directions in SL discovery.

A network medicine approach for identifying diagnostic and prognostic biomarkers and exploring drug repurposing in human cancer

Cancer is a heterogeneous disease mainly driven by abnormal gene perturbations in regulatory networks. Therefore, it is appealing to identify the common and specific perturbed genes from multiple cancer networks. We developed an integrative network medicine approach to identify novel biomarkers and investigate drug repurposing across cancer types. We used a network-based method to prioritize genes in cancer-specific networks reconstructed using human transcriptome and interactome data. The prioritized genes show extensive perturbation and strong regulatory interaction with other highly perturbed genes, suggesting their vital contribution to tumorigenesis and tumor progression, and are therefore regarded as cancer genes. The cancer genes detected show remarkable performances in discriminating tumors from normal tissues and predicting survival times of cancer patients. Finally, we developed a network proximity approach to systematically screen drugs and identified dozens of candidates with repurposable potential in several cancer types. Taken together, we demonstrated the power of the network medicine approach to identify novel biomarkers and repurposable drugs in multiple cancer types. We have also made the data and code freely accessible to ensure reproducibility and reusability of the developed computational workflow.

Comprehensive cancer predisposition testing within the prospective MASTER trial identifies hereditary cancer patients and supports treatment decisions for rare cancers

BACKGROUND

Germline variant evaluation in precision oncology opens new paths towards the identification of patients with genetic tumor risk syndromes and the exploration of therapeutic relevance. Here, we present the results of germline variant analysis and their clinical implications in a precision oncology study for patients with predominantly rare cancers.

PATIENTS AND METHODS

Matched tumor and control genome/exome and RNA sequencing was performed for 1,485 patients with rare cancers (79%) and/or young adults (77% younger than 51 years) in the NCT/DKTK MASTER trial, a German multicenter, prospective observational precision oncology study. Clinical and therapeutic relevance of prospective pathogenic germline variant (PGV) evaluation was analyzed and compared to other precision oncology studies.

RESULTS

Ten percent of patients (n=157) harbored PGVs in 35 genes associated with autosomal dominant cancer predisposition, whereof up to 75% were unknown before study participation. Another five percent of patients (n=75) were heterozygous carriers for recessive genetic tumor risk syndromes. Particularly high PGV yields were found in patients with gastrointestinal stromal tumors (GISTs) (28%, 11/40), and more specific in wild-type GISTS (50%, n=10/20), leiomyosarcomas (21%, n=19/89), and hepatopancreaticobiliary cancers (16%, n=16/97). Forty-five percent of PGVs (n=100/221) supported treatment recommendations, and its implementation led to a clinical benefit in 40% of patients (n=10/25). A comparison of different precision oncology studies revealed variable PGV yields and considerable differences in germline variant analysis workflows. We therefore propose a detailed workflow for germline variant evaluation.

CONCLUSIONS

Genetic germline testing in patients with rare cancers can identify the very first patient in a hereditary cancer family and can lead to clinical benefit in a broad range of entities. Its routine implementation in precision oncology accompanied by the harmonization of germline variant evaluation workflows will increase clinical benefit and boost research.

Artificial Intelligence, Healthcare, Clinical Genomics, and Pharmacogenomics Approaches in Precision Medicine

Precision medicine has greatly aided in improving health outcomes using earlier diagnosis and better prognosis for chronic diseases. It makes use of clinical data associated with the patient as well as their multi-omics/genomic data to reach a conclusion regarding how a physician should proceed with a specific treatment. Compared to the symptom-driven approach in medicine, precision medicine considers the critical fact that all patients do not react to the same treatment or medication in the same way. When considering the intersection of traditionally distinct arenas of medicine, that is, artificial intelligence, healthcare, clinical genomics, and pharmacogenomics—what ties them together is their impact on the development of precision medicine as a field and how they each contribute to patient-specific, rather than symptom-specific patient outcomes. This study discusses the impact and integration of these different fields in the scope of precision medicine and how they can be used in preventing and predicting acute or chronic diseases. Additionally, this study also discusses the advantages as well as the current challenges associated with artificial intelligence, healthcare, clinical genomics, and pharmacogenomics.

Computational methods and translational applications for targeted next-generation sequencing platforms

During the past decade, next-generation sequencing (NGS) technologies have become widely adopted in cancer research and clinical care. Common applications within the clinical setting include patient stratification into relevant molecular subtypes, identification of biomarkers of response and resistance to targeted and systemic therapies, assessment of heritable cancer risk based on known pathogenic variants, and longitudinal monitoring of treatment response. The need for efficient downstream processing and reliable interpretation of sequencing data has led to the development of novel algorithms and computational pipelines, as well as structured knowledge bases that link genomic alterations to currently available drugs and ongoing clinical trials. Cancer centers around the world use different types of targeted solid-tissue and blood based NGS assays to analyze the genomic and transcriptomic profile of patients as part of their routine clinical care. Recently, cross-institutional collaborations have led to the creation of large pooled datasets that can offer valuable insights into the genomics of rare cancers.

CancerVar: An artificial intelligence-empowered platform for clinical interpretation of somatic mutations in cancer

Several knowledgebases are manually curated to support clinical interpretations of thousands of hotspot somatic mutations in cancer. However, discrepancies or even conflicting interpretations are observed among these databases. Furthermore, many previously undocumented mutations may have clinical or functional impacts on cancer but are not systematically interpreted by existing knowledgebases. To address these challenges, we developed CancerVar to facilitate automated and standardized interpretations for 13 million somatic mutations based on the AMP/ASCO/CAP 2017 guidelines. We further introduced a deep learning framework to predict oncogenicity for these variants using both functional and clinical features. CancerVar achieved satisfactory performance when compared to several independent knowledgebases and, using clinically curated datasets, demonstrated practical utility in classifying somatic variants. In summary, by integrating clinical guidelines with a deep learning framework, CancerVar facilitates clinical interpretation of somatic variants, reduces manual work, improves consistency in variant classification, and promotes implementation of the guidelines.

A Scalable Quality Assurance Process for Curating Oncology Electronic Health Records: The Project GENIE Biopharma Collaborative Approach

The American Association for Cancer Research Project Genomics Evidence Neoplasia Information Exchange Biopharma Collaborative is a multi-institution effort to build a pan-cancer repository of genomic and clinical data curated from the electronic health record. For the research community to be confident that data extracted from electronic health record text are reliable, transparency of the approach used to ensure data quality is essential.

Transparent QA processes for GENIE BPC ensure that the data can be used to support advances in precision oncology OR @jessicalavs of @MSKBiostats & coauthors discuss @AACR Project GENIE BPC, a multi-institution effort to aggregate clinical plus genomic data for patients with cancer. Transparent QA processes for GENIE BPC ensure that the data can be used to support advances in precision oncology.

Fluvastatin suppresses the proliferation, invasion, and migration and promotes the apoptosis of endometrial cancer cells by upregulating Sirtuin 6 (SIRT6)

Fluvastatin, the first fully synthesized 3-Hydroxy-3-methylglutaryl coenzyme A (HMG-CoA) reductase (HMGCR) inhibitor, has been reported to inhibit the development and metastasis of multiple cancers. The present study aimed to explore the effects of fluvastatin on endometrial cancer (EC) as well as reveal its potential mechanism. After exposure to fluvastatin, the cell viability, proliferation, migration, and invasion of EC cells were measured by Cell Counting Kit-8 (CCK-8), 5-ethynyl-2ʹ-deoxyuridine (EDU), wound healing, and invasion assays, respectively. The apoptosis and its related proteins of fluvastatin-treated EC cells were detected by TUNEL and Western blot, separately. In order to figure out the effects of SIRT6 silence on EC cells, a series of cellular activities were performed again. Fluvastatin suppressed the proliferation, migration, and invasion of EC cells, but induced the apoptosis. The expression of SIRT6 was elevated in EC cells upon fluvastatin exposure. After silencing SIRT6 in fluvastatin-treated EC cells, the proliferation, migration, and invasion were promoted whereas the apoptosis was decreased. To sum up, this study firstly evidenced that fluvastatin suppresses the proliferation, invasion, and migration and promotes the apoptosis of endometrial cancer cells by regulating SIRT6 expression.

Molecularly profiled trials: toward a framework of actions for the "nil actionables"

The sequencing of tumour or blood samples is increasingly used to stratify patients into clinical trials of molecularly targeted agents, and this approach has frequently demonstrated clinical benefit for those who are deemed eligible. But what of those who have no clear and evident molecular driver? What of those deemed to have "nil actionable" mutations? How might we deliver better therapeutic opportunities for those left behind in the clamour toward stratified therapeutics? And what significant learnings lie hidden in the data we amass but do not interrogate and understand? This Perspective article suggests a holistic approach to the future treatment of such patients, and sets a framework through which significant additional patient benefit might be achieved. In order to deliver upon this framework, it encourages and invites the clinical community to engage more enthusiastically and share learnings with colleagues in the early drug discovery community, in order to deliver a step change in patient care.

Genomic alterations and possible druggable mutations in carcinoma of unknown primary (CUP)

Carcinoma of Unknown Primary (CUP) is a heterogeneous and metastatic disease where the primary site of origin is undetectable. Currently, chemotherapy is the only state-of-art treatment option for CUP patients. The molecular profiling of the tumour, particularly mutation detection, offers a new treatment approach for CUP in a personalized fashion using targeted agents. We analyzed the mutation and copy number alterations profile of 1709 CUP samples deposited in the AACR Project Genomics Evidence Neoplasia Information Exchange (GENIE) cohort and explored potentially druggable mutations. We identified 52 significant mutated genes (SMGs) among CUP samples, in which 13 (25%) of SMGs were potentially targetable with either drugs are approved for the know primary tumour or undergoing clinical trials. The most variants detected were TP53 (43%), KRAS (19.90%), KMT2D (12.60%), and CDKN2A (10.30%). Additionally, using pan-cancer analysis, we found similar variants of TERT promoter in CUP and NSCLC samples, suggesting that these mutations may serve as a diagnostic marker for identifying the primary tumour in CUP. Taken together, the mutation profiling analysis of the CUP tumours may open a new way of identifying druggable targets and consequently administrating appropriate treatment in a personalized manner.

OSIRIS: A Minimum Data Set for Data Sharing and Interoperability in Oncology

PURPOSE

Many institutions throughout the world have launched precision medicine initiatives in oncology, and a large amount of clinical and genomic data is being produced. Although there have been attempts at data sharing with the community, initiatives are still limited. In this context, a French task force composed of Integrated Cancer Research Sites (SIRICs), comprehensive cancer centers from the Unicancer network (one of Europe's largest cancer research organization), and university hospitals launched an initiative to improve and accelerate retrospective and prospective clinical and genomic data sharing in oncology.

MATERIALS AND METHODS

For 5 years, the OSIRIS group has worked on structuring data and identifying technical solutions for collecting and sharing them. The group used a multidisciplinary approach that included weekly scientific and technical meetings over several months to foster a national consensus on a minimal data set.

RESULTS

The resulting OSIRIS set and event-based data model, which is able to capture the disease course, was built with 67 clinical and 65 omics items. The group made it compatible with the HL7 Fast Healthcare Interoperability Resources (FHIR) format to maximize interoperability. The OSIRIS set was reviewed, approved by a National Plan Strategic Committee, and freely released to the community. A proof-of-concept study was carried out to put the OSIRIS set and Common Data Model into practice using a cohort of 300 patients.

CONCLUSION

Using a national and bottom-up approach, the OSIRIS group has defined a model including a minimal set of clinical and genomic data that can be used to accelerate data sharing produced in oncology. The model relies on clear and formally defined terminologies and, as such, may also benefit the larger international community.

Pan-cancer identification of clinically relevant genomic subtypes using outcome-weighted integrative clustering

BACKGROUND

Comprehensive molecular profiling has revealed somatic variations in cancer at genomic, epigenomic, transcriptomic, and proteomic levels. The accumulating data has shown clearly that molecular phenotypes of cancer are complex and influenced by a multitude of factors. Conventional unsupervised clustering applied to a large patient population is inevitably driven by the dominant variation from major factors such as cell-of-origin or histology. Translation of these data into clinical relevance requires more effective extraction of information directly associated with patient outcome.

METHODS

Drawing from ideas in supervised text classification, we developed survClust, an outcome-weighted clustering algorithm for integrative molecular stratification focusing on patient survival. survClust was performed on 18 cancer types across multiple data modalities including somatic mutation, DNA copy number, DNA methylation, and mRNA, miRNA, and protein expression from the Cancer Genome Atlas study to identify novel prognostic subtypes.

RESULTS

Our analysis identified the prognostic role of high tumor mutation burden with concurrently high CD8 T cell immune marker expression and the aggressive clinical behavior associated with CDKN2A deletion across cancer types. Visualization of somatic alterations, at a genome-wide scale (total mutation burden, mutational signature, fraction genome altered) and at the individual gene level, using circomap further revealed indolent versus aggressive subgroups in a pan-cancer setting.

CONCLUSIONS

Our analysis has revealed prognostic molecular subtypes not previously identified by unsupervised clustering. The algorithm and tools we developed have direct utility toward patient stratification based on tumor genomics to inform clinical decision-making. The survClust software tool is available at https://github.com/arorarshi/survClust .

Comprehensive elaboration of database resources utilized in next-generation sequencing-based tumor somatic mutation detection

The rapid evolution of next-generation sequencing (NGS)-based tumor genomic profile detection and the emergence of molecularly targeted therapies have enabled precision oncology. In NGS-based analysis, various types of databases have been developed to perform different functions. However, many problems still exist when using these public databases. Therefore, it is important to better understand the characteristics and limitations of each database and have them complement each other to provide useful clinical evidence for NGS testing. In this review, we elaborate on the important role of databases and their concrete applications in NGS-based somatic mutation detection. We introduce the typically used databases for sequence alignment, variant filtration, and variant interpretation, and compare the differences between the databases with similar functions. Subsequently, we determine the limitations of each database and provide the corresponding solutions. Furthermore, we present an overview diagram to clearly illustrate the database used in the entire NGS-based somatic mutation detection pipeline.