Biology subjects

Cerami, E.

Publications and source records attributed to Cerami, E..

5 recordsLinked to original sources

bioRxiv2025

Pan-cancer spatial characterization of key immune biomarkers in the tumor microenvironment

Deciphering the composition and spatial organization of the tumor immune microenvironment (TiME) is key to uncovering the mechanisms driving cancer progression and treatment response. Spatial biology techniques like multiplex immunofluorescence (mIF) offer detailed insights into the TiME but are often limited to retrospective research studies of individual cancer types. Conversely, bulk omics techniques have been studied in pan-cancer settings but fail to capture single-cell spatial information. Here, we provide a pan-cancer spatial characterization of key biomarkers (CD8, FOXP3, PD-1, PD-L1) of the TiME using data from a mIF assay performed prospectively in a clinical setting on 2,019 tumors across 14 major cancer types. By integrating interpretable compositional and spatial metrics, we identified patterns of TiME variation that are conserved across cancer types and stages. We assess associations between these TiME spatial factors and tumor, genomic, and clinical features, where the results both extend prior findings and uncover new links. Altogether, our findings offer pan-cancer insights of the TiME to further the fields of spatial biology and cancer immunology.

cancer biology↗

bioRxiv2024

Sharing Data from the Human Tumor Atlas Network through Standards, Infrastructure, and Community Engagement

The Data Coordinating Center (DCC) of the Human Tumor Atlas Network (HTAN) has played a crucial role in enabling the broad sharing and effective utilization of HTAN data within the scienti[fi]c community. Data from the [fi]rst phase of HTAN are now available publicly. We describe the diverse datasets and modalities shared, multiple access routes to HTAN assay data and metadata, data standards, technical infrastructure and governance approaches, as well as our approach to sustained community engagement. HTAN data can be accessed via the HTAN Portal, explored in visualization tools--including CellxGene, Minerva, and cBioPortal--and analyzed in the cloud through the NCI Cancer Research Data Commons nodes. We have developed a streamlined infrastructure to ingest and disseminate data by leveraging the Synapse platform. Taken together, the HTAN DCCs approach demonstrates a successful model for coordinating, standardizing, and disseminating complex cancer research data via multiple resources in the cancer data ecosystem, offering valuable insights for similar consortia, and researchers looking to leverage HTAN data.

cancer biology↗

bioRxiv2019

Pathway Commons: 2019 Update

Pathway Commons (https://www.pathwaycommons.org) is an integrated resource of publicly available information about biological pathways including biochemical reactions, assembly of biomolecular complexes, transport and catalysis events and physical interactions involving proteins, DNA, RNA, and small molecules (e.g., metabolites and drug compounds). Data is collected from multiple providers in standard formats, including the Biological Pathway Exchange (BioPAX) language and the Proteomics Standards Initiative Molecular Interactions format, and then integrated. Pathway Commons provides biologists with (1) tools to search this comprehensive resource, (2) a download site offering integrated bulk sets of pathway data (e.g., tables of interactions and gene sets), (3) reusable software libraries for working with pathway information in several programming languages (Java, R, Python, and Javascript), and (4) a web service for programmatically querying the entire dataset. Visualization of pathways is supported using the Systems Biological Graphical Notation (SBGN). Pathway Commons currently contains data from 22 databases with 4,794 detailed human biochemical processes (i.e., pathways) and [~]2.3 million interactions. To enhance the usability of this large resource for end-users, we develop and maintain interactive web applications and training materials that enable pathway exploration and advanced analysis.

bioinformatics↗

bioRxiv2018

A harmonized meta-knowledgebase of clinical interpretations of cancer genomic variants

Precision oncology relies on the accurate discovery and interpretation of genomic variants to enable individualized diagnosis, prognosis, and therapy selection. We found that knowledgebases containing clinical interpretations of somatic cancer variants are highly disparate in interpretation content, structure, and supporting primary literature, impeding consensus when evaluating variants and their relevance in a clinical setting. With the cooperation of experts of the Global Alliance for Genomics and Health (GA4GH) and six prominent cancer variant knowledgebases, we developed a framework for aggregating and harmonizing variant interpretations to produce a meta-knowledgebase of 12,856 aggregate interpretations covering 3,437 unique variants in 415 genes, 357 diseases, and 791 drugs. We demonstrated large gains in overlap between resources across variants, diseases, and drugs as a result of this harmonization. We subsequently demonstrated improved matching between a patient cohort and harmonized interpretations of potential clinical significance, observing an increase from an average of 33% per individual knowledgebase to 56% in aggregate. Our analyses illuminate the need for open, interoperable sharing of variant interpretation data. We also provide an open and freely available web interface (search.cancervariants.org) for exploring the harmonized interpretations from these six knowledgebases.

bioinformatics↗

bioRxiv2017

MatchMiner: An open source computational platform for real-time matching of cancer patients to precision medicine clinical trials using genomic and clinical criteria

BackgroundMolecular profiling of cancers is now routine at many cancer centers, and the number of precision cancer medicine clinical trials, which are informed by profiling, is steadily rising. Additionally, these trials are becoming increasingly complex, often having multiple arms and many genomic eligibility criteria. Currently, it is a challenging for physicians to match patients to relevant clinical trials using the patients genomic profile, which can lead to missed opportunities. Automated matching against uniformly structured and encoded genomic eligibility criteria is essential to keep pace with the complex landscape of precision medicine clinical trials.\n\nResultsTo meet these needs, we built and deployed an automated clinical trial matching platform called MatchMiner at the Dana-Farber Cancer Institute (DFCI). The platform has been integrated with Profile, DFCIs enterprise genomic profiling project, which contains tumor profile data for >20,000 patients, and has been made available to physicians across the Institute. As no current standard exists for encoding clinical trial eligibility criteria, a new language called Clinical Trial Markup Language (CTML) was developed, and over 178 genomically-driven clinical trials were encoded using this language. The platform is open source and freely available for adoption by other institutions.\n\nConclusionMatchMiner is the first open platform developed to enable computational matching of patient-specific genomic profiles to precision cancer medicine clinical trials. Creating MatchMiner required developing clinical trial eligibility standards to support genome-driven matching and developing intuitive interfaces to support practical use-cases. Given the complexity of tumor profiling and the rapidly changing multi-site nature of genome-driven clinical trials, open source software is the most efficient, scalable, and economical option for matching cancer patients to clinical trials.

bioinformatics↗