SALSA attempts to capture the current body of knowledge on how genetic alterations affect clinical actionability. As the field of precision medicine grows, the quantity of research on how specific alterations affect therapeutic response and patient prognosis expands at an increasing rate. SALSA seeks to curate this information from the literature, increasing the abilities of clinicians and researchers alike to rapidly assess the importance of an alteration.
SALSA draws primarily from primary research articles, review articles, and the NCCN guidelines. Data is curated through both expert review and using a Natural Language Processing (NLP) approach to select potentially informative articles from PubMed.
SALSA is co-developed with the PHIAL (Precision Heuristics for Interpreting the Alteration Landscape) algorithm, which draws from SALSA and other alterations databases to classify patient tumor data.
SALSA classifies each curated assertion with a "predictive implication level" that describes the strength of the relationship between the given alteration and clinical action. The levels used are:
|FDA-Approved||There is a validated association between the alteration and the recommended clinical action.|
|Level A||The alteration is or has been used as an eligibility criterion for a clinical trial.|
|Level B||There is limited early clinical evidence in support of this alteration/action relationship.|
|Level C||There is limited early clinical evidence in support of this alteration/action relationship, but it is observed in a different tumor type.
Used in PHIAL, but not applicable to SALSA.
|Level D||There is preclinical evidence in support of this alteration/action relationship.|
|Level E||There is inferential evidence in support of this alteration/action relationship.|
SALSA is made available under the Open Database License (ODbL). A "plain-language" summary of the ODbL is available here.
You are free to share and modify the contents of SALSA under the following restrictions:
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