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TumorTracer: a method to identify the tissue of origin from the somatic mutations of a tumor specimen.

Marquard AM, Birkbak NJ, Thomas CE, Favero F, Krzystanek M, Lefebvre C, Ferté C, Jamal-Hanjani M, Wilson GA, Shafi S, Swanton C, André F, Szallasi Z, Eklund AC - BMC Med Genomics (2015)

Bottom Line: On the left-out COSMIC data not used for training, we achieved a classification accuracy of 85 % across 6 primary sites (with copy numbers), and 69 % across 10 primary sites (without copy numbers).Accuracy in the independent data sets was 46 %, 53 % and 89 % respectively, similar to the accuracy expected from the training data.Identification of primary site from point mutation and/or copy number data may be accurate enough to aid clinical diagnosis of cancers of unknown primary origin.

View Article: PubMed Central - PubMed

Affiliation: Center for Biological Sequence Analysis, Department of Systems Biology, Technical University of Denmark, Kemitorvet 8, DK-2800, Lyngby, Denmark. marquard@cbs.dtu.dk.

ABSTRACT

Background: A substantial proportion of cancer cases present with a metastatic tumor and require further testing to determine the primary site; many of these are never fully diagnosed and remain cancer of unknown primary origin (CUP). It has been previously demonstrated that the somatic point mutations detected in a tumor can be used to identify its site of origin with limited accuracy. We hypothesized that higher accuracy could be achieved by a classification algorithm based on the following feature sets: 1) the number of nonsynonymous point mutations in a set of 232 specific cancer-associated genes, 2) frequencies of the 96 classes of single-nucleotide substitution determined by the flanking bases, and 3) copy number profiles, if available.

Methods: We used publicly available somatic mutation data from the COSMIC database to train random forest classifiers to distinguish among those tissues of origin for which sufficient data was available. We selected feature sets using cross-validation and then derived two final classifiers (with or without copy number profiles) using 80 % of the available tumors. We evaluated the accuracy using the remaining 20 %. For further validation, we assessed accuracy of the without-copy-number classifier on three independent data sets: 1669 newly available public tumors of various types, a cohort of 91 breast metastases, and a set of 24 specimens from 9 lung cancer patients subjected to multiregion sequencing.

Results: The cross-validation accuracy was highest when all three types of information were used. On the left-out COSMIC data not used for training, we achieved a classification accuracy of 85 % across 6 primary sites (with copy numbers), and 69 % across 10 primary sites (without copy numbers). Importantly, a derived confidence score could distinguish tumors that could be identified with 95 % accuracy (32 %/75 % of tumors with/without copy numbers) from those that were less certain. Accuracy in the independent data sets was 46 %, 53 % and 89 % respectively, similar to the accuracy expected from the training data.

Conclusions: Identification of primary site from point mutation and/or copy number data may be accurate enough to aid clinical diagnosis of cancers of unknown primary origin.

No MeSH data available.


Related in: MedlinePlus

Consistency of the PM classifier on data from multiple samples from the same tumor. The classifier was applied to 24 specimens from 9 NSCLC patients, including primary regions (R) and lymph node metastases (L). The proposed primary site is indicated by color along with the confidence score
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Fig6: Consistency of the PM classifier on data from multiple samples from the same tumor. The classifier was applied to 24 specimens from 9 NSCLC patients, including primary regions (R) and lymph node metastases (L). The proposed primary site is indicated by color along with the confidence score

Mentions: Finally, we applied the PM classifier to point mutation calls from whole exome sequencing of 24 specimens from 9 non-small cell lung cancer (NSCLC) patients in a cohort study in which multiple regions from the same lesion were sequenced to study intratumor heterogeneity. In addition, lymph node metastases had been analysed in some cases. When pooling the mutations called in all specimens of a lung tumor, our method correctly proposed lung as the primary site in eight out of nine tumors (Fig. 5c). When the 24 specimens were analysed individually, we found that the majority of the subregions and metastases were proposed to be of the same origin as the pooled specimens (Fig. 6).Fig. 6


TumorTracer: a method to identify the tissue of origin from the somatic mutations of a tumor specimen.

Marquard AM, Birkbak NJ, Thomas CE, Favero F, Krzystanek M, Lefebvre C, Ferté C, Jamal-Hanjani M, Wilson GA, Shafi S, Swanton C, André F, Szallasi Z, Eklund AC - BMC Med Genomics (2015)

Consistency of the PM classifier on data from multiple samples from the same tumor. The classifier was applied to 24 specimens from 9 NSCLC patients, including primary regions (R) and lymph node metastases (L). The proposed primary site is indicated by color along with the confidence score
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4590711&req=5

Fig6: Consistency of the PM classifier on data from multiple samples from the same tumor. The classifier was applied to 24 specimens from 9 NSCLC patients, including primary regions (R) and lymph node metastases (L). The proposed primary site is indicated by color along with the confidence score
Mentions: Finally, we applied the PM classifier to point mutation calls from whole exome sequencing of 24 specimens from 9 non-small cell lung cancer (NSCLC) patients in a cohort study in which multiple regions from the same lesion were sequenced to study intratumor heterogeneity. In addition, lymph node metastases had been analysed in some cases. When pooling the mutations called in all specimens of a lung tumor, our method correctly proposed lung as the primary site in eight out of nine tumors (Fig. 5c). When the 24 specimens were analysed individually, we found that the majority of the subregions and metastases were proposed to be of the same origin as the pooled specimens (Fig. 6).Fig. 6

Bottom Line: On the left-out COSMIC data not used for training, we achieved a classification accuracy of 85 % across 6 primary sites (with copy numbers), and 69 % across 10 primary sites (without copy numbers).Accuracy in the independent data sets was 46 %, 53 % and 89 % respectively, similar to the accuracy expected from the training data.Identification of primary site from point mutation and/or copy number data may be accurate enough to aid clinical diagnosis of cancers of unknown primary origin.

View Article: PubMed Central - PubMed

Affiliation: Center for Biological Sequence Analysis, Department of Systems Biology, Technical University of Denmark, Kemitorvet 8, DK-2800, Lyngby, Denmark. marquard@cbs.dtu.dk.

ABSTRACT

Background: A substantial proportion of cancer cases present with a metastatic tumor and require further testing to determine the primary site; many of these are never fully diagnosed and remain cancer of unknown primary origin (CUP). It has been previously demonstrated that the somatic point mutations detected in a tumor can be used to identify its site of origin with limited accuracy. We hypothesized that higher accuracy could be achieved by a classification algorithm based on the following feature sets: 1) the number of nonsynonymous point mutations in a set of 232 specific cancer-associated genes, 2) frequencies of the 96 classes of single-nucleotide substitution determined by the flanking bases, and 3) copy number profiles, if available.

Methods: We used publicly available somatic mutation data from the COSMIC database to train random forest classifiers to distinguish among those tissues of origin for which sufficient data was available. We selected feature sets using cross-validation and then derived two final classifiers (with or without copy number profiles) using 80 % of the available tumors. We evaluated the accuracy using the remaining 20 %. For further validation, we assessed accuracy of the without-copy-number classifier on three independent data sets: 1669 newly available public tumors of various types, a cohort of 91 breast metastases, and a set of 24 specimens from 9 lung cancer patients subjected to multiregion sequencing.

Results: The cross-validation accuracy was highest when all three types of information were used. On the left-out COSMIC data not used for training, we achieved a classification accuracy of 85 % across 6 primary sites (with copy numbers), and 69 % across 10 primary sites (without copy numbers). Importantly, a derived confidence score could distinguish tumors that could be identified with 95 % accuracy (32 %/75 % of tumors with/without copy numbers) from those that were less certain. Accuracy in the independent data sets was 46 %, 53 % and 89 % respectively, similar to the accuracy expected from the training data.

Conclusions: Identification of primary site from point mutation and/or copy number data may be accurate enough to aid clinical diagnosis of cancers of unknown primary origin.

No MeSH data available.


Related in: MedlinePlus