Molecular Oncology Almanac (MOAlmanac) is a clinical interpretation algorithm paired with an underlying knowledge base for precision oncology. The primary objective of MOAlmanac is to identify and associate molecular alterations with therapeutic sensitivity and resistance as well as disease prognosis. This is done for “first-order” genomic alterations -- individual events such as somatic variants, copy number alterations, fusions, and germline -- as well as “second-order” events -- those that are not associated with one single mutation, and may be descriptive of global processes in the tumor such as tumor mutational burden, microsatellite instability, mutational signatures, and whole-genome doubling.

The underlying database of this method is dependent on expert curation of the current body of knowledge on how molecular alterations affect clinical actionability. As the field of precision oncology grows, the quantity of research on how specific alterations affect therapeutic response and patient prognosis expands at an increasing rate. Curating the latest literature and presenting it in an accessible manner increases the abilities of clinicians and researchers alike to rapidly assess the importance of a molecular feature.

This webpage is specifically for the browsing and accessing the underlying knowledge base and there are several other resources within the Molecular Oncology Almanac ecosystem:

• Molecular Oncology Almanac, a clinical interpretation algorithm for precision cancer medicine [GitHub].
• Molecular Oncology Almanac Browser, a website to browse our underlying knowledge base [GitHub].
• Molecular Oncology Almanac Connector, a Google Chrome extension to quickly suggest literature for cataloging [GitHub].
• Molecular Oncology Almanac Database, the content and release notes of our underlying knowledge base [GitHub].
• Molecular Oncology Almanac Portal, a website to launch this method on the Broad Institute's Google Cloud platform (Terra) [GitHub].

This method is also available on Docker and Terra.

If you use Molecular Oncology Almanac, please cite our publication:
Reardon, B., Moore, N.D., Moore, N.S. et al. Integrating molecular profiles into clinical frameworks through the Molecular Oncology Almanac to prospectively guide precision oncology. Nat Cancer (2021). https://doi.org/10.1038/s43018-021-00243-3

Entries catalogued within Molecular Oncology Almanac cite research articles, review articles, FDA approvals, and clinical guidelines. Please view our database content release notes and standard operating procedure on Github for more information and consider suggesting relationships through our web form or Google Chrome extension.

Curated assertions are categorized by their associated evidence. Categories include:

Evidence	Description
FDA-Approved	The Food and Drug Association (FDA) recognizes an association between the alteration and recommended clinical action.
Guideline	This relationship is catalogued as a guideline for standard of care treatment.
Clinical trial	The alteration is or has been used as an eligibility criterion for a clinical trial.
Clinical evidence	The relationship is reported in a clinical study that did not directly involve a clinical trial.
Preclinical evidence	This relationship is reported in a study involving mice, cell lines, or patient derived models.
Inferential evidence	The relationship is inferred as a result of mathematical modeling or an association between molecular features.

The contents associated with the most current release of Molecular Oncology Almanac may be downloaded directly from this webpage. Alternatively, the current and prior releases along with associated release notes can be downloaded from Github. In both cases, each feature type represented in the knowledge base (e.g., "somatic variant", "knockdown", etc.) is represented in a separate tab-separated value (TSV) file. The collection of TSV files representing the entire database is provided in a single ZIP file.

The knowledge base can also be programatically interfaced with through our API endpoints.