We are pleased to announce the availability of a new release for COSMIC, the Catalogue of Somatic Mutations in Cancer. COSMIC Actionability v8 adds new actionability data to the world’s largest, expert-curated somatic mutation database. The release includes 22 new fully curated somatic genes, 387 new clinical trials, and 166 new oncology drugs.
In this article, we provide a summary of the COSMIC Actionability v8 release highlights.
COSMIC, the Catalogue of Somatic Mutations in Cancer, is an expert-curated database encompassing the wide variety of somatic mutation mechanisms causing human cancer. Owned and maintained by the Wellcome Sanger Institute, COSMIC is exclusively licensed through QIAGEN.
COSMIC’s team of variant scientists manually curates key cancer genes to provide in-depth information on mutation distributions and effects. The team relies on a semi-automated curation process of cancer genomes to provide broad somatic annotations toward target discovery and identification of patterns and signatures. To date, COSMIC contains over 23 million somatic mutations associated with human cancers.
COSMIC Actionability is a standalone product within the COSMIC database that focuses on providing information on the availability and development of drugs targeting somatic mutations in cancer. COSMIC’s certified curation team integrates data from case studies, clinical trials, and regulatory bodies to represent a full picture of the current precision oncology pipeline (from drug development, through safety and clinical phases, to market and repurposing).
Actionability contains information on three core units: mutations, diseases and drugs. By capturing relations between these units, COSMIC’s team identifies existing and upcoming drugs that target specific genetic variants in different cancer types. COSMIC Actionability is a cutting-edge, ‘living-tool’ that provides the most up-to-date data for precision oncology applications.
→ View the full Actionability v8 release notes here.
Learn more about COSMIC and how the industry-leading database can help you identify biomarkers, annotate variants, and explore the etiology of human cancers here.
See first-hand how COSMIC can be used in your lab by downloading sample data here.
We are pleased to announce the release of the latest version of the Human Somatic Mutation Database, a new somatic database developed by QIAGEN that contains extensive genomic content relevant to solid tumors and hematological malignancies. Expanding on the database’s current content and capabilities, HSMD 2.0 now contains data on structural variants, improved data visualization, and tools to enable seamless integration of the database into in-house workflows.
HSMD 2.0 release highlights
View the full list of new content updates here.
About HSMD
Combining over 2 decades of expert curation and data from real-world clinical oncology cases, HSMD is a new somatic database from QIAGEN that serves as a single, trusted data source for clinical labs to validate, assess, and better understand the clinical significance of detected variants.
HSMD aggregates manually curated content from the QIAGEN Knowledge Base, the industry’s largest collection of biological and clinical findings, with data from over 419,000 real-world clinical oncology cases that have been analyzed and interpreted by QIAGEN’s professional clinical interpretation service, to eliminate the need to manually collect inform-ation across knowledge bases and provide deep genomic insight into the molecular characterizations of your patient’s tumor.
Easy to search with new content added weekly, HSMD enables users to explore key genes or mutations with driving properties or clinical relevance, and lets users search for associated treatment options, off-label therapies, resistance markers, and regional and/or disease-specific clinical trials.
Learn more about HSMD here.
As NGS is increasingly used in precision oncology, there is an industry-wide issue of standardization: a high degree of variability in variant interpretation currently exists across laboratories. On September 30, in a live panel discussion, experts in NGS testing and clinical informatics explore the issues surrounding standardization and how to overcome them with real-world applications.
⇒ Learn more and register here.
While the world showed its support for affected people during Breast Cancer Awareness Month, COSMIC's expert curators were working hard behind the scenes to get information on rare breast cancers ready for the upcoming release later that month.
Breast cancer is still a big global problem
Breast cancer is the most prevalent cancer type worldwide. At the end of 2020, there were 7.8 million women alive who’d been diagnosed with breast cancer in the previous five years. And despite leaps and bounds in detection and treatment, there were still 685,000 deaths from breast cancer in 2020 alone.
Nevertheless, the numbers are still fairly optimistic. Breast cancer mortality in high-income countries has dropped 40% between the 1980s and 2020 - and it’s continuing to drop by around 2-4% in these countries each year. Innovations in treatment and targeted drug therapies, such as trastuzumab to treat HER-2 positive cancers, are showing outstanding results. And let’s be clear, early detection and effective treatment is really the only option. Unlike many common cancers, even if all the risk factors for breast cancer development were controlled, this would only reduce the risk by 30%. A multi-pronged approach is needed- optimize care pathways, facilitate early diagnosis, and provide the right treatment at the right time.
And for treatment-resistant breast cancers? We need to understand what’s driving these cancers at a genetic level and identify targets for the next generation of drugs.
Cue COSMIC as the navigational tool that hosts the data to springboard scientific innovation. COSMIC’s latest release v95 (November 2021) has a special focus on rare female cancers, including the rarer breast cancer subtype with notorious poor outcomes, metaplastic breast cancer (MpBC).
The lowdown on metaplastic breast cancer
Metaplastic breast cancer is a rare and aggressive malignancy. Although the incidence is low (it only accounts for 0.2–5% of all breast cancers), MpBC accounts for a significant proportion of breast-cancer deaths.
MpBCs are usually (but not always) triple-negative - meaning they lack expression of estrogen receptor (ER), progesterone receptor (PR), and HER2. So, some of the revolutions in current treatments are ineffective. Like other triple-negative breast cancers, they’re more likely to present with distant metastasis, but they generally have a worse prognosis than their grade-matched counterparts.
Compared to other cancers, relatively little is known about MpBC, but more studies are happening all the time. Speaking of, time to look at some of the new datasets being incorporated into V95 of COSMIC.
Classification: Phenotypic and molecular dissection of metaplastic breast cancer and the prognostic implications
When cancer is rare, it can be harder to create clear definitions, classifications, and guidance. This is especially true of MpBC, a cancer literally named for its heterogeneity in cell types. Metaplasia derives from Ancient Greek and means ‘change in form’. In this context, it’s the transformation of one differentiated cell type to another differentiated cell type. The World Health Organisation (WHO) guidelines currently rely on the structure, shape, and organization of the cells to create sub-categories - without any information on molecular causes or clinical guidance.
Caption: Table showing the various WHO classifications of metaplastic breast cancer [1]
But Lakhani et al. decided to look under the hood. Using strength in numbers, they formed the Asia-Pacific MpBC consortium to collect and study 347 MpBCs in depth.
What did the increased numbers provide? Let’s start with genetics. Exome sequencing was performed on 30 of the MpBC cases and found TP53, PTEN, and PIK3CA mutations co-occurring. This countered a long-held concept that TP53/PTEN and PI3CA/PTEN mutations were mutually exclusive.
Moving on to prognosis - the top question that patients have when diagnosed: ‘How bad is it’?’ This study shed a little light. The significant indicators of poor prognosis were large tumor size, loss of cytokeratin expression, EGFR overexpression, and the presence of more than three distinct morphological entities. On the flip side, favorable indicators included fewer morphological components and EGFR negativity.
With these findings, Lakhani et al. suggest minor changes to the WHO classification. The paper ends with the comment that, ‘thousands of samples would need to be reviewed centrally in order to tease out the subtleties required to make any bolder changes to the guidelines’. As more similar data is incorporated into COSMIC datasets, it will enable this scale of analysis.
New targets: Molecular Profiling of the Metaplastic Spindle Cell Carcinoma of the Breast Reveals Potentially Targetable Biomarkers
Having discussed the WHO guidelines more broadly, time to zone in on one of the even rarer subtypes of MbBC, WHO_5: Spindle Cell Carcinoma. This subtype displays the previously mentioned triple-negative characteristic, as well as having an association with resistance to conventional chemotherapy.
Evidently, new tactics are needed. Precision oncology and immuno-oncology could provide some strategies for targeted therapies, but first we need to know if the right mutations and immune markers are present. Gatalica et al. studied this with some promising results.
They used 23 MpBC spindle cell carcinomas and thoroughly analyzed them using immunohistochemistry and DNA/RNA sequencing. And some familiar faces emerge here - PIK3CA, TP53, HRAS, NF1, and PTEN pathogenic gene mutations were identified in the majority (21) of cases. As highlighted by the authors, PIK3CA mutations are particularly relevant because they’ve been classified as strong predictors of response to PIK3CA inhibitors, and several drugs have either been recently approved, or are in the pipeline, for targeting the PI3K pathway, including Piqray (alpelisib).
Finally, onto the immuno-oncology markers. PD1 is a molecular ‘gun’ found on the surface of immune cells - when they spot a threat they attach and fire. Unless the cell in question puts out a defence - PD-L1 - which puts the safety on and prevents firing. Tumors can use PD-L1 as a means of escaping destruction by the immune system, so new drugs have been developed to block PDL1 and PD1 from binding. But there’s an added layer of complexity, if another molecule - PTEN - is present, then it’s indicated that drugs designed to target PD-L1/PD1 will be less effective.
PDL1 and PD1 molecules interacting on surface of cells. PD1 is purple and PDL-1 is yellow.
Back to the research. The study found evidence of PD-L1 expression, but only above the threshold for treatments in two cases. And two cases also had concurrent PTEN expression. So, for biomarkers, there’s promise that targeted therapies could work in some cases, but a thorough analysis would need to be done on a case by case basis.
Power in numbers
It’s promising to see this volume of new studies and big data being gathered for the rarer cancers. And it’s evident that the route forward is in numbers, collaborations, and collating evidence from multiple sources. Seeing the mounting evidence for PIK3CA expression involvement in MpBC is just one example of this. But without a centralized database, curated from thousands of papers that have been read and analyzed by trusted experts (with a passion for their work) it would be easy to miss key cases.
Reach out to the QIAGEN team to get access to this exciting data. Test it out for yourself – the first 100 lines of COSMIC are made available for free download.
[1] McCart Reed AE, Kalaw E, Nones K, Bettington M, Lim M, Bennett J, Johnstone K, Kutasovic JR, Saunus JM, Kazakoff S, Xu Q, Wood S, Holmes O, Leonard C, Reid LE, Black D, Niland C, Ferguson K, Gresshoff I, Raghavendra A, Harvey K, Cooper C, Liu C, Kalinowski L, Reid AS, Davidson M, Pearson JV, Pathmanathan N, Tse G, Papadimos D, Pathmanathan R, Harris G, Yamaguchi R, Tan PH, Fox SB, O'Toole SA, Simpson PT, Waddell N, Lakhani SR. Phenotypic and molecular dissection of metaplastic breast cancer and the prognostic implications. J Pathol. 2019 Feb;247(2):214-227. doi: 10.1002/path.5184. Epub 2018 Dec 20. PMID: 30350370.
Before delving into details, it’s vital to understand that automation and manual curation might work with the same data, but they play in completely different tournaments. It’s almost like comparing Formula 1 and NASCAR – they’re both about cars, but their rules, quality, audience, and specifications are entirely different.
When scientists, bioinformaticians, and clinicians look for a somatic mutation resource to support their next-generation sequencing (NGS) data analysis and precision oncology activities, the priorities are accuracy, transparency, and flexibility. Only one database ticks all of these boxes, and that's COSMIC, the Catalogue of Somatic Mutations in Cancer.
On close inspection, it’s clear that COSMIC is the only major player with the scope and breadth to deliver its offerings. Our analysis provides four key reasons why manual curation is the ‘gold standard’ and will maintain that place on the podium for a while yet.
COSMIC deploys high-precision data curation methods. It comprises information from almost 1.5 million cancer samples, manually curated from more than 28,000 peer-reviewed papers by PhD-level experts with decades of experience. These experts perform exhaustive literature searches to select papers from which they reorganize, interpret, standardize, and catalog mutation data, phenotype information, and clinical details. To date, manual curation remains the gold standard for associating genotypic and phenotypic data, as it is most precise and delivers higher quality data.
On the other hand, there are advanced machine learning tools developed to accelerate the process of retrieving variant evidence from scientific literature. They, however, sometimes leave much to be desired in the quality and accuracy of genomic data extracted. For instance, significant error rates when associating variants with the correct genes are observed with the use of crowdsourcing and artificial intelligence (AI) applications. Significant amounts of undetected disease-associated mutations and false-positive article associations are also issues with text-mining approaches.
In precision oncology, quality is far more important than quantity, which is why we choose manual curation over other options.
Natural language processing (NLP) and machine learning (ML) approaches facilitate “seeding” relationships from articles to describe genotypic and phenotypic relationships. While these approaches let AI-driven databases scale the indexing of PubMed articles, they do not provide the necessary precision needed to curate deep, unstructured biological, phenotypic, and complex clinical data, including graphics, full text, and supplementary material.
However, deep, manual curation does. Consequently, human judgment remains critical to analyzing and capturing complex relationships, interactions, and contradictory evidence. The high-touch, human review process COSMIC employs ensures high accuracy, high specificity, relevance, context, and consistency in data.
In COSMIC, every data point is traceable to the source, and data processing is documented. The data sources that feed into COSMIC to characterize cancer samples and mutations include peer-reviewed papers, targeted gene-screening panels, genome-wide screen data, and cancer cell line omics data — all of which users have full access to and can use as preferred.
COSMIC’s cancer histology/phenotype classification system is unique and is the world’s most comprehensive cancer phenotype classification linked to somatic mutations. Asides from loosely following the World Health Organization’s (WHO) classification system, COSMIC goes into more detail and precedes/anticipates approvals to WHO.
COSMIC presents the cancer site and cancer histology separately: e.g. lung/left lower lobe/ns/ns, carcinoma/adenocarcinoma/ns/ns each in 4 levels.
In clinical genomics, data quality standards are high as outputs are only as reliable as the evidence used to obtain them.
Text-mining tools are useful and helpful in identifying relevant information, but to rely solely on them is to leave the door open to misleading or missing critical evidence.
COSMIC shuts, locks, and seals that door. A team of expert variant scientists updates COSMIC three times a year. They constantly manually review biomedical literature to classify variants and harmonize differences in nomenclature across gene transcripts. COSMIC’s experts focus on continuous curation and variant reclassification— never relying on updates from external entities. It is a database suited to deep, accurate, and thorough explorations of human genetics.
All of this is why COSMIC is a vital addition to any precision oncology toolbox.
With over 71 million somatic mutations, COSMIC is the world’s largest expert-curated somatic mutation database trusted by over 20,000 users. Learn more about the industry-leading database here, where you can explore features, watch videos, and request a complimentary demonstration.
Around two decades ago, the amount of data about cancer genomes began to increase rapidly. Managing, viewing, and searching for data from disparate locations, publications and databases became a significant challenge for researchers, bioinformaticians, and clinicians. Simple spreadsheets were no longer adequate enough to capture and house the data either.
To solve these issues, scientists at the Wellcome Sanger Institute developed the Catalogue of Somatic Mutations in Cancer (COSMIC) in 2004. COSMIC is the world’s largest and most comprehensive database of somatic mutations in human cancers. Through manually curation by experts for over 16 years, COSMIC holds all previously siloed high-quality somatic mutation information in one platform. The most recent release details more than 37 million coding mutations across almost 1.5 million tumor samples covering 1460 forms of human cancers.
COSMIC has gained several valuable features and functionalities over the years. Most of the early enhancements were focused on standardizing the genotypic and phenotypic terms and data formats used— making it possible to analyze data across different studies and cancer types.
Some of the more recent additions to COSMIC are:
And now, joining the COSMIC suite is the new ‘Mutation Actionability in Precision Oncology” product.
Actionability provides up-to-date information on drugs that target specific somatic mutations at all stages of development— from early case studies to clinical development all the way through to market. With Actionability, you can stay informed on the currently available drugs for a specific somatic mutation, the reasons behind clinical trial withdrawals or terminations, and the mutations being investigated outside of clinical trials. It curates this information from multiple sources, including clinical trials, journal articles, conference abstracts, corporate websites, and FDA submissions.
The first release of Actionability features manually curated data on 805 drugs, 1657 drug combinations, 181 genes, and a total of 2798 trials. The data is available in three forms: as a table organized per gene, as a complete download file, and as a sample file.
Since its inception, the COSMIC database has achieved its foundational goal—to be the source of all cancer genomic knowledge. It is the most comprehensive somatic mutation database available in the world. Now, COSMIC drives future breakthroughs and advances in cancer research and treatment.
Want to find out more about COSMIC and how it can support your investigations of cancer-causing mutations? Then watch our on-demand webinar on COSMIC.
