2020
DOI: 10.1001/jamaoncol.2019.3985
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Development of Genome-Derived Tumor Type Prediction to Inform Clinical Cancer Care

Abstract: Diagnosing the site of origin for cancer is a pillar of disease classification that has directed clinical care for more than a century. Even in an era of precision oncologic practice, in which treatment is increasingly informed by the presence or absence of mutant genes responsible for cancer growth and progression, tumor origin remains a critical factor in tumor biologic characteristics and therapeutic sensitivity. OBJECTIVE To evaluate whether data derived from routine clinical DNA sequencing of tumors could… Show more

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Cited by 87 publications
(72 citation statements)
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References 31 publications
(66 reference statements)
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“…The accuracy of MI GPSai compares favorably to recent data on the use of DNA NGS panels for tissue of origin identification or guidance of utilization of targeted- and immunotherapies [10] , [28] . However, overall accuracy of these approaches is limited.…”
Section: Discussionmentioning
confidence: 75%
See 1 more Smart Citation
“…The accuracy of MI GPSai compares favorably to recent data on the use of DNA NGS panels for tissue of origin identification or guidance of utilization of targeted- and immunotherapies [10] , [28] . However, overall accuracy of these approaches is limited.…”
Section: Discussionmentioning
confidence: 75%
“…However, overall accuracy of these approaches is limited. For example, predictions made by a Random Forrest Classifier using results from a 468-gene NGS panel as input, resulted in an overall accuracy of 74.1% [10] . Analysis of circulating tumor DNA data from a commercial 70-gene NGS panel revealed potentially targetable mutations.…”
Section: Discussionmentioning
confidence: 99%
“…The path to improving the tumour type classification accuracy may be to consider including other potential features such as somatic point mutations [79] and histopathology images [80] in the model. Mutational profiling of tumours is steadily being incorporated into mainstream work-up of cancer patients and recently several tissue of origin classification methods have been developed based on DNA features alone either from panel [81] whole-exome, and whole-genome sequencing (WGS) [82] . Interestingly, the reported accuracy of these methods especially when using WGS passenger mutational profiles for the tissue of origin classification is similar to using gene-expression profiling and DNA methylation classification.…”
Section: Discussionmentioning
confidence: 99%
“…In their report, Penson et al [ 184 ] applied a machine-learning approach for tumor type prediction using target DNA sequencing data. The algorithm was trained on a cohort of 7791 patients of advanced tumors belonging to 22 different classes, and later tested on an independent set of 11,644 cases.…”
Section: Driver Mutations and Precision Medicinementioning
confidence: 99%