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Single-cell exome sequencing identifies mutations in KCP , LOC440040 , and LOC440563 as drivers in renal cell carcinoma stem cells

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We estimated the percentage of CSCs in the CD31CD45CD133 cell sample was more than seven fold higher than in the other two cell types (Supplementary information, Figure S1C), indicating that CD133cells isolated from RCC have stem-like properties... WES of the cancer tissue revealed 160 somatic SNVs and single-cell sequencing of the 20 tumor cells identified 297 somatic SNVs (Supplementary information, Figure S1E)... Commonly mutated genes in RCC, such as VHL, BAP1, TRA and CHD4, were found to carry variations in both RCC single cells and tissue samples, demonstrating the reliability of our WES analysis (Supplementary information, Table S1B)... Among them, three missense mutations, c.A241T>p.R81W in Kielin/chordin-like protein (KCP), c.G316A>p.G106S in LOC440563 and c.A406T>p.N136Y in LOC440040, have not been reported in RCC... While other mutations in KCP can be found in the RCC TCGA database (c.G2590A>p.A864T, c.C1250G>p.A417G, and c.A2680G>p.R894G), no mutations in LOC440563 and LOC440040 have been linked to RCC and other cancer before (Figure 1A, Supplementary information, Table S1C)... In addition, we found that KCP and LOC440040 mutations could be detected in both single cells and in the original cancer tissue (Figure 1A and Supplementary information, Table S1B)... Among the 29 mutated genes detected in CD133 RCC cells, 18 genes, including KCP (not LOC440563 or LOC440040), were listed in the TCGA RCC database as mutated genes, and the frequency of the 18 mutated genes in the 416 RCC cases in TCGA was less than 2% (Supplementary information, Table S1D)... Because multiple mutations may be required for the maintenance and development of CD133 RCC cells, we mutated each of the remaining 19 genes in combination with KPC... The LOC440040 mutation was most effective, which enhanced spherogenesis capabilities of the KPC mutation (Figure 1G, middle)... We also additionally mutated LOC440563 and found KPC-LOC440040-LOC440563 triple mutation could increase the sphere-formation abilities by more than 30% (Figure 1G, right)... To validate that KCP is indeed a prominent driver gene among the 20 candidates, we mutated the LOC440040 and one of the other 19 genes and found that LOC440040 plus KCP double mutations had significantly higher spherogenesis than other combination... Finally our study indicates KCP, LOC440040, LOC440563 mutations, which are present in at least 20% of patients in our survey, constitute robust and dangerous drivers promoting reprogramming of RCC cancer cells into CSCs.

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Identification of driver genes in renal cell carcinoma stem cells via single-cell exome sequencing. (A) Detection of somatic mutations in CD133+CD133− RCC cells and in cancer tissue. The main plot shows information for genes with mutations for 20 cells and original cancer tissue. The red color represents non-silent mutations and green color represents silent mutations. (B) Somatic mutation graph. Two substitutions (A/T>G/C and C/G>T/A) are clearly frequent. (C) Venn plots show the somatic mutations in CD133+ and CD133− RCC cells. (D) Principle component analysis (PCA) of the mutations in the CD133+ RCC cells (red), CD133− RCC cells (green) and normal cells (blue). Eigenvector is defined as the Covariance Matrix. (E) A neighbor-joining tree was constructed using the somatic mutation data set. The normal cells are labeled in green, CD133− RCC cells are labeled in blue, and CD133+ RCC cells are labeled in red. (F) The average mutation frequency of 29 genes with variations in at least 3 CD133+ RCC cells. The mutation frequency indicates the percentage of CD133+ RCC cells with the mutated gene. (G) Data points indicate the average number of spheres of RCC cells with distinct mutations in serum-free conditions. Each of the 20 mutations was tested alone (first column, 'single mutation'), in combination with a KCP mutation (second column) or in combination with KCP and LOC440040 mutations (third column). Other mutations were also tested in combination with KCP, LOC440040, and LOC440563 mutations (fourth column). Mutation combinations that enhanced the in vitro spherogenicity (blue) were selected for in vivo validation. CD133+ cells spheres served as the positive control (red). (H) Representative Sanger-sequencing data of KCP, LOC440040, and LOC440563 in wild-type (WT) and mutated (Mut) renal cancer cells are listed below. (I) Representative oncospheres in mutated (Mut) and vehicle renal cancer cells. (J) The 18-week tumor-free rate of NOD/SCID mice after subcutaneous injection at the indicated dilutions of 786-O WT, 786-O Mut, 769-P WT, and 769-P cells (left panel, n = 6 mice per group). The estimated percentage of CSCs in 786-O WT, 786-O Mut, 769-P WT, and 769-P Mut cells in xenografted mice using extreme limiting dilution analysis (n = 6 grafted tumors per dilution; right panel). (K) The CD31−CD45−CD133+ cells from 57 RCC patients were individually sorted and pooled together for the indicated targeted sequencing. The mutation rates of KCP, LOC440040, and LOC440563 are indicated. (L) The average tumor-free time of 57 renal cancer patients with or without KCP, LOC440040, and/or LOC440563 mutation(s) after primary tumor resection.
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fig1: Identification of driver genes in renal cell carcinoma stem cells via single-cell exome sequencing. (A) Detection of somatic mutations in CD133+CD133− RCC cells and in cancer tissue. The main plot shows information for genes with mutations for 20 cells and original cancer tissue. The red color represents non-silent mutations and green color represents silent mutations. (B) Somatic mutation graph. Two substitutions (A/T>G/C and C/G>T/A) are clearly frequent. (C) Venn plots show the somatic mutations in CD133+ and CD133− RCC cells. (D) Principle component analysis (PCA) of the mutations in the CD133+ RCC cells (red), CD133− RCC cells (green) and normal cells (blue). Eigenvector is defined as the Covariance Matrix. (E) A neighbor-joining tree was constructed using the somatic mutation data set. The normal cells are labeled in green, CD133− RCC cells are labeled in blue, and CD133+ RCC cells are labeled in red. (F) The average mutation frequency of 29 genes with variations in at least 3 CD133+ RCC cells. The mutation frequency indicates the percentage of CD133+ RCC cells with the mutated gene. (G) Data points indicate the average number of spheres of RCC cells with distinct mutations in serum-free conditions. Each of the 20 mutations was tested alone (first column, 'single mutation'), in combination with a KCP mutation (second column) or in combination with KCP and LOC440040 mutations (third column). Other mutations were also tested in combination with KCP, LOC440040, and LOC440563 mutations (fourth column). Mutation combinations that enhanced the in vitro spherogenicity (blue) were selected for in vivo validation. CD133+ cells spheres served as the positive control (red). (H) Representative Sanger-sequencing data of KCP, LOC440040, and LOC440563 in wild-type (WT) and mutated (Mut) renal cancer cells are listed below. (I) Representative oncospheres in mutated (Mut) and vehicle renal cancer cells. (J) The 18-week tumor-free rate of NOD/SCID mice after subcutaneous injection at the indicated dilutions of 786-O WT, 786-O Mut, 769-P WT, and 769-P cells (left panel, n = 6 mice per group). The estimated percentage of CSCs in 786-O WT, 786-O Mut, 769-P WT, and 769-P Mut cells in xenografted mice using extreme limiting dilution analysis (n = 6 grafted tumors per dilution; right panel). (K) The CD31−CD45−CD133+ cells from 57 RCC patients were individually sorted and pooled together for the indicated targeted sequencing. The mutation rates of KCP, LOC440040, and LOC440563 are indicated. (L) The average tumor-free time of 57 renal cancer patients with or without KCP, LOC440040, and/or LOC440563 mutation(s) after primary tumor resection.

Mentions: To eliminate the false-positive variations arising from WGA, we only selected single-nucleotide variants (SNVs) present in CD133+ or CD133− RCC cells but absent in normal cells. Similarly, for whole-tissue WES analysis, SNVs specifically present in the RCC tissue were chosen. WES of the cancer tissue revealed 160 somatic SNVs and single-cell sequencing of the 20 tumor cells identified 297 somatic SNVs (Supplementary information, Figure S1E). Commonly mutated genes in RCC, such as VHL, BAP1, TRA and CHD4, were found to carry variations in both RCC single cells and tissue samples, demonstrating the reliability of our WES analysis (Supplementary information, Table S1B). Among the SNVs in single cells, 141 were located in coding regions. These SNVs were more enriched in CD133+ cells than in CD133− cells (Figure 1A and 1C). More importantly, we discovered several coding region mutations that are unique to CD133+ RCC cells. Among them, three missense mutations, c.A241T>p.R81W in Kielin/chordin-like protein (KCP), c.G316A>p.G106S in LOC440563 and c.A406T>p.N136Y in LOC440040, have not been reported in RCC. While other mutations in KCP can be found in the RCC TCGA database (c.G2590A>p.A864T, c.C1250G>p.A417G, and c.A2680G>p.R894G), no mutations in LOC440563 and LOC440040 have been linked to RCC and other cancer before (Figure 1A, Supplementary information, Table S1C). In addition, we found that KCP and LOC440040 mutations could be detected in both single cells and in the original cancer tissue (Figure 1A and Supplementary information, Table S1B). Furthermore, we identified that C/G>T/A, A/T>C/G and A/T>G/C transitions were the most common mutations in RCC (Figure 1B). Notably, the A/T>T/A transitions were significantly more frequent in CD133+ cells than in CD133− cells (Figure 1B, P = 0.31 × 10−9).


Single-cell exome sequencing identifies mutations in KCP , LOC440040 , and LOC440563 as drivers in renal cell carcinoma stem cells
Identification of driver genes in renal cell carcinoma stem cells via single-cell exome sequencing. (A) Detection of somatic mutations in CD133+CD133− RCC cells and in cancer tissue. The main plot shows information for genes with mutations for 20 cells and original cancer tissue. The red color represents non-silent mutations and green color represents silent mutations. (B) Somatic mutation graph. Two substitutions (A/T>G/C and C/G>T/A) are clearly frequent. (C) Venn plots show the somatic mutations in CD133+ and CD133− RCC cells. (D) Principle component analysis (PCA) of the mutations in the CD133+ RCC cells (red), CD133− RCC cells (green) and normal cells (blue). Eigenvector is defined as the Covariance Matrix. (E) A neighbor-joining tree was constructed using the somatic mutation data set. The normal cells are labeled in green, CD133− RCC cells are labeled in blue, and CD133+ RCC cells are labeled in red. (F) The average mutation frequency of 29 genes with variations in at least 3 CD133+ RCC cells. The mutation frequency indicates the percentage of CD133+ RCC cells with the mutated gene. (G) Data points indicate the average number of spheres of RCC cells with distinct mutations in serum-free conditions. Each of the 20 mutations was tested alone (first column, 'single mutation'), in combination with a KCP mutation (second column) or in combination with KCP and LOC440040 mutations (third column). Other mutations were also tested in combination with KCP, LOC440040, and LOC440563 mutations (fourth column). Mutation combinations that enhanced the in vitro spherogenicity (blue) were selected for in vivo validation. CD133+ cells spheres served as the positive control (red). (H) Representative Sanger-sequencing data of KCP, LOC440040, and LOC440563 in wild-type (WT) and mutated (Mut) renal cancer cells are listed below. (I) Representative oncospheres in mutated (Mut) and vehicle renal cancer cells. (J) The 18-week tumor-free rate of NOD/SCID mice after subcutaneous injection at the indicated dilutions of 786-O WT, 786-O Mut, 769-P WT, and 769-P cells (left panel, n = 6 mice per group). The estimated percentage of CSCs in 786-O WT, 786-O Mut, 769-P WT, and 769-P Mut cells in xenografted mice using extreme limiting dilution analysis (n = 6 grafted tumors per dilution; right panel). (K) The CD31−CD45−CD133+ cells from 57 RCC patients were individually sorted and pooled together for the indicated targeted sequencing. The mutation rates of KCP, LOC440040, and LOC440563 are indicated. (L) The average tumor-free time of 57 renal cancer patients with or without KCP, LOC440040, and/or LOC440563 mutation(s) after primary tumor resection.
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Related In: Results  -  Collection

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fig1: Identification of driver genes in renal cell carcinoma stem cells via single-cell exome sequencing. (A) Detection of somatic mutations in CD133+CD133− RCC cells and in cancer tissue. The main plot shows information for genes with mutations for 20 cells and original cancer tissue. The red color represents non-silent mutations and green color represents silent mutations. (B) Somatic mutation graph. Two substitutions (A/T>G/C and C/G>T/A) are clearly frequent. (C) Venn plots show the somatic mutations in CD133+ and CD133− RCC cells. (D) Principle component analysis (PCA) of the mutations in the CD133+ RCC cells (red), CD133− RCC cells (green) and normal cells (blue). Eigenvector is defined as the Covariance Matrix. (E) A neighbor-joining tree was constructed using the somatic mutation data set. The normal cells are labeled in green, CD133− RCC cells are labeled in blue, and CD133+ RCC cells are labeled in red. (F) The average mutation frequency of 29 genes with variations in at least 3 CD133+ RCC cells. The mutation frequency indicates the percentage of CD133+ RCC cells with the mutated gene. (G) Data points indicate the average number of spheres of RCC cells with distinct mutations in serum-free conditions. Each of the 20 mutations was tested alone (first column, 'single mutation'), in combination with a KCP mutation (second column) or in combination with KCP and LOC440040 mutations (third column). Other mutations were also tested in combination with KCP, LOC440040, and LOC440563 mutations (fourth column). Mutation combinations that enhanced the in vitro spherogenicity (blue) were selected for in vivo validation. CD133+ cells spheres served as the positive control (red). (H) Representative Sanger-sequencing data of KCP, LOC440040, and LOC440563 in wild-type (WT) and mutated (Mut) renal cancer cells are listed below. (I) Representative oncospheres in mutated (Mut) and vehicle renal cancer cells. (J) The 18-week tumor-free rate of NOD/SCID mice after subcutaneous injection at the indicated dilutions of 786-O WT, 786-O Mut, 769-P WT, and 769-P cells (left panel, n = 6 mice per group). The estimated percentage of CSCs in 786-O WT, 786-O Mut, 769-P WT, and 769-P Mut cells in xenografted mice using extreme limiting dilution analysis (n = 6 grafted tumors per dilution; right panel). (K) The CD31−CD45−CD133+ cells from 57 RCC patients were individually sorted and pooled together for the indicated targeted sequencing. The mutation rates of KCP, LOC440040, and LOC440563 are indicated. (L) The average tumor-free time of 57 renal cancer patients with or without KCP, LOC440040, and/or LOC440563 mutation(s) after primary tumor resection.
Mentions: To eliminate the false-positive variations arising from WGA, we only selected single-nucleotide variants (SNVs) present in CD133+ or CD133− RCC cells but absent in normal cells. Similarly, for whole-tissue WES analysis, SNVs specifically present in the RCC tissue were chosen. WES of the cancer tissue revealed 160 somatic SNVs and single-cell sequencing of the 20 tumor cells identified 297 somatic SNVs (Supplementary information, Figure S1E). Commonly mutated genes in RCC, such as VHL, BAP1, TRA and CHD4, were found to carry variations in both RCC single cells and tissue samples, demonstrating the reliability of our WES analysis (Supplementary information, Table S1B). Among the SNVs in single cells, 141 were located in coding regions. These SNVs were more enriched in CD133+ cells than in CD133− cells (Figure 1A and 1C). More importantly, we discovered several coding region mutations that are unique to CD133+ RCC cells. Among them, three missense mutations, c.A241T>p.R81W in Kielin/chordin-like protein (KCP), c.G316A>p.G106S in LOC440563 and c.A406T>p.N136Y in LOC440040, have not been reported in RCC. While other mutations in KCP can be found in the RCC TCGA database (c.G2590A>p.A864T, c.C1250G>p.A417G, and c.A2680G>p.R894G), no mutations in LOC440563 and LOC440040 have been linked to RCC and other cancer before (Figure 1A, Supplementary information, Table S1C). In addition, we found that KCP and LOC440040 mutations could be detected in both single cells and in the original cancer tissue (Figure 1A and Supplementary information, Table S1B). Furthermore, we identified that C/G>T/A, A/T>C/G and A/T>G/C transitions were the most common mutations in RCC (Figure 1B). Notably, the A/T>T/A transitions were significantly more frequent in CD133+ cells than in CD133− cells (Figure 1B, P = 0.31 × 10−9).

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We estimated the percentage of CSCs in the CD31CD45CD133 cell sample was more than seven fold higher than in the other two cell types (Supplementary information, Figure S1C), indicating that CD133cells isolated from RCC have stem-like properties... WES of the cancer tissue revealed 160 somatic SNVs and single-cell sequencing of the 20 tumor cells identified 297 somatic SNVs (Supplementary information, Figure S1E)... Commonly mutated genes in RCC, such as VHL, BAP1, TRA and CHD4, were found to carry variations in both RCC single cells and tissue samples, demonstrating the reliability of our WES analysis (Supplementary information, Table S1B)... Among them, three missense mutations, c.A241T>p.R81W in Kielin/chordin-like protein (KCP), c.G316A>p.G106S in LOC440563 and c.A406T>p.N136Y in LOC440040, have not been reported in RCC... While other mutations in KCP can be found in the RCC TCGA database (c.G2590A>p.A864T, c.C1250G>p.A417G, and c.A2680G>p.R894G), no mutations in LOC440563 and LOC440040 have been linked to RCC and other cancer before (Figure 1A, Supplementary information, Table S1C)... In addition, we found that KCP and LOC440040 mutations could be detected in both single cells and in the original cancer tissue (Figure 1A and Supplementary information, Table S1B)... Among the 29 mutated genes detected in CD133 RCC cells, 18 genes, including KCP (not LOC440563 or LOC440040), were listed in the TCGA RCC database as mutated genes, and the frequency of the 18 mutated genes in the 416 RCC cases in TCGA was less than 2% (Supplementary information, Table S1D)... Because multiple mutations may be required for the maintenance and development of CD133 RCC cells, we mutated each of the remaining 19 genes in combination with KPC... The LOC440040 mutation was most effective, which enhanced spherogenesis capabilities of the KPC mutation (Figure 1G, middle)... We also additionally mutated LOC440563 and found KPC-LOC440040-LOC440563 triple mutation could increase the sphere-formation abilities by more than 30% (Figure 1G, right)... To validate that KCP is indeed a prominent driver gene among the 20 candidates, we mutated the LOC440040 and one of the other 19 genes and found that LOC440040 plus KCP double mutations had significantly higher spherogenesis than other combination... Finally our study indicates KCP, LOC440040, LOC440563 mutations, which are present in at least 20% of patients in our survey, constitute robust and dangerous drivers promoting reprogramming of RCC cancer cells into CSCs.

No MeSH data available.


Related in: MedlinePlus