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Measuring intratumor heterogeneity by network entropy using RNA-seq data

View Article: PubMed Central - PubMed

ABSTRACT

Intratumor heterogeneity (ITH) is observed at different stages of tumor progression, metastasis and reouccurence, which can be important for clinical applications. We used RNA-sequencing data from tumor samples, and measured the level of ITH in terms of biological network states. To model complex relationships among genes, we used a protein interaction network to consider gene-gene dependency. ITH was measured by using an entropy-based distance metric between two networks, nJSD, with Jensen-Shannon Divergence (JSD). With nJSD, we defined transcriptome-based ITH (tITH). The effectiveness of tITH was extensively tested for the issues related with ITH using real biological data sets. Human cancer cell line data and single-cell sequencing data were investigated to verify our approach. Then, we analyzed TCGA pan-cancer 6,320 patients. Our result was in agreement with widely used genome-based ITH inference methods, while showed better performance at survival analysis. Analysis of mouse clonal evolution data further confirmed that our transcriptome-based ITH was consistent with genetic heterogeneity at different clonal evolution stages. Additionally, we found that cell cycle related pathways have significant contribution to increasing heterogeneity on the network during clonal evolution. We believe that the proposed transcriptome-based ITH is useful to characterize heterogeneity of a tumor sample at RNA level.

No MeSH data available.


Related in: MedlinePlus

Overproduction during clonal evolution made ambiguous network status.(A) The consequence of clonal evolution is single tumor with heterogeneous population of cancer clones. The red shade which has smallest area on darwin’s tree would be early cancer and it’s sequencing result is represented as red circled ‘T’. Orange one has larger area than red one, of course, orange one has more diverse population. Lime one has the most diverse population. We set a maximal state of ambiguous like lime one, most diverse population of cancer clones, and measured tITH. (B) Network represents tumor with diver population. Distance between each state measured with nJSD, described in Method.
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f2: Overproduction during clonal evolution made ambiguous network status.(A) The consequence of clonal evolution is single tumor with heterogeneous population of cancer clones. The red shade which has smallest area on darwin’s tree would be early cancer and it’s sequencing result is represented as red circled ‘T’. Orange one has larger area than red one, of course, orange one has more diverse population. Lime one has the most diverse population. We set a maximal state of ambiguous like lime one, most diverse population of cancer clones, and measured tITH. (B) Network represents tumor with diver population. Distance between each state measured with nJSD, described in Method.

Mentions: To define transcriptome-based ITH (tITH), we set a maximally ambiguous network where whole gene-expression values were equal. nJSD was applied as a distance measure between two network states. Here, we defined tITH with two distance values, distance from normal data to cancer data (NT) and distance from cancer data to maximally ambiguous network (TA) (Fig. 2). This distance based approach was inspired by recent study about cancer evolution that described embryonic stem cell as cancer evolutionary destination53. Combining NT and TA into a single metric, we defined the transcriptome-based ITH and we named it as tITH in comparison with genomic ITH (gITH).


Measuring intratumor heterogeneity by network entropy using RNA-seq data
Overproduction during clonal evolution made ambiguous network status.(A) The consequence of clonal evolution is single tumor with heterogeneous population of cancer clones. The red shade which has smallest area on darwin’s tree would be early cancer and it’s sequencing result is represented as red circled ‘T’. Orange one has larger area than red one, of course, orange one has more diverse population. Lime one has the most diverse population. We set a maximal state of ambiguous like lime one, most diverse population of cancer clones, and measured tITH. (B) Network represents tumor with diver population. Distance between each state measured with nJSD, described in Method.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5121893&req=5

f2: Overproduction during clonal evolution made ambiguous network status.(A) The consequence of clonal evolution is single tumor with heterogeneous population of cancer clones. The red shade which has smallest area on darwin’s tree would be early cancer and it’s sequencing result is represented as red circled ‘T’. Orange one has larger area than red one, of course, orange one has more diverse population. Lime one has the most diverse population. We set a maximal state of ambiguous like lime one, most diverse population of cancer clones, and measured tITH. (B) Network represents tumor with diver population. Distance between each state measured with nJSD, described in Method.
Mentions: To define transcriptome-based ITH (tITH), we set a maximally ambiguous network where whole gene-expression values were equal. nJSD was applied as a distance measure between two network states. Here, we defined tITH with two distance values, distance from normal data to cancer data (NT) and distance from cancer data to maximally ambiguous network (TA) (Fig. 2). This distance based approach was inspired by recent study about cancer evolution that described embryonic stem cell as cancer evolutionary destination53. Combining NT and TA into a single metric, we defined the transcriptome-based ITH and we named it as tITH in comparison with genomic ITH (gITH).

View Article: PubMed Central - PubMed

ABSTRACT

Intratumor heterogeneity (ITH) is observed at different stages of tumor progression, metastasis and reouccurence, which can be important for clinical applications. We used RNA-sequencing data from tumor samples, and measured the level of ITH in terms of biological network states. To model complex relationships among genes, we used a protein interaction network to consider gene-gene dependency. ITH was measured by using an entropy-based distance metric between two networks, nJSD, with Jensen-Shannon Divergence (JSD). With nJSD, we defined transcriptome-based ITH (tITH). The effectiveness of tITH was extensively tested for the issues related with ITH using real biological data sets. Human cancer cell line data and single-cell sequencing data were investigated to verify our approach. Then, we analyzed TCGA pan-cancer 6,320 patients. Our result was in agreement with widely used genome-based ITH inference methods, while showed better performance at survival analysis. Analysis of mouse clonal evolution data further confirmed that our transcriptome-based ITH was consistent with genetic heterogeneity at different clonal evolution stages. Additionally, we found that cell cycle related pathways have significant contribution to increasing heterogeneity on the network during clonal evolution. We believe that the proposed transcriptome-based ITH is useful to characterize heterogeneity of a tumor sample at RNA level.

No MeSH data available.


Related in: MedlinePlus