Significance
This report is an in-depth genetic profiling of pulmonary sclerosing hemangioma (PSH). We have discovered that PSH harbor recurrent AKT1 mutations (45.6%), most of which were AKT1 p.E17K mutations. This mutation may be the single-most common driver alteration to develop PSHs. In contrast to lung adenocarcinoma, PSH genomes harbor only a single driver mutation (AKT1 or β-catenin), which may provide clues to understanding the benign biology of PSH and for differential genomic diagnosis of lung tumors.
Keywords: pulmonary sclerosing hemangioma, whole-exome sequencing, AKT1 mutation, copy number alteration
Abstract
Pulmonary sclerosing hemangioma (PSH) is a benign tumor with two cell populations (epithelial and stromal cells), for which genomic profiles remain unknown. We conducted exome sequencing of 44 PSHs and identified recurrent somatic mutations of AKT1 (43.2%) and β-catenin (4.5%). We used a second subset of 24 PSHs to confirm the high frequency of AKT1 mutations (overall 31/68, 45.6%; p.E17K, 33.8%) and recurrent β-catenin mutations (overall 3 of 68, 4.4%). Of the PSHs without AKT1 mutations, two exhibited AKT1 copy gain. AKT1 mutations existed in both epithelial and stromal cells. In two separate PSHs from one patient, we observed two different AKT1 mutations, indicating they were not disseminated but independent arising tumors. Because the AKT1 mutations were not found to co-occur with β-catenin mutations (or any other known driver alterations) in any of the PSHs studied, we speculate that this may be the single-most common driver alteration to develop PSHs. Our study revealed genomic differences between PSHs and lung adenocarcinomas, including a high rate of AKT1 mutation in PSHs. These genomic features of PSH identified in the present study provide clues to understanding the biology of PSH and for differential genomic diagnosis of lung tumors.
Pulmonary sclerosing hemangioma (PSH) is a benign tumor that usually presents as a solitary, well-defined mass in the lung (1). PSH predominantly affects females (1:5) with a higher incidence in the Far East (2). As the name indicates, PSH is often hemorrhagic and sclerotic. Histologically, the tumor cells in PSH consist of two cell types (cuboidal epithelial and polygonal stromal cells) (3). Immunohistochemical and ultrastructural studies have identified that both cells are derived from undifferentiated respiratory epithelium that is the histologic origin of lung adenocarcinoma as well. Previous studies have shown that PSH and adenocarcinoma in the lung share some immunohistochemical and genetic features. For example, expression of TTF-1, which plays a crucial role in normal lung function and morphogenesis, is common to PSH and lung adenocarcinoma (3). In addition, allelic imbalance and CpG island methylation in some loci have been reported in these two tumors (4, 5). However, whereas many driver genes for lung adenocarcinomas have been identified, for somatic mutations, there have not been any candidate driver mutations identified in PSHs, except for low-frequency mutations in β-catenin and TP53 (6, 7). Frequent somatic mutations identified in lung adenocarcinomas, such as KRAS and EGFR, have not been detected in PSH, suggesting that genomic alterations of these two lung tumors might be different from each other. Furthermore, there is no evidence of PSH progression to lung cancers. These earlier data indicate that despite the common cellular origin of PSH and lung adenocarcinoma, genetic mechanisms for their development may be different.
Somatic genetic alteration is a driving force for the development of tumor. Even a benign tumor contains somatic mutations, albeit less common than in a malignant tumor. For example, uterine leiomyoma and breast fibroadenoma, common benign tumors, harbor recurrent mutations in MED12 (8, 9). Based on the established concept that PSH is a true tumor, we hypothesize that it may harbor somatic mutations. For a comprehensive elucidation of genetic alterations in cancers, genomes of many tumors have been studied by using whole-exome (WES) or whole-genome sequencing analysis (10–12). However, to date, such high-throughput sequencing data on PSH is lacking. In this study we analyzed genomes of the PSH by WES.
Results
Whole-Exome Sequencing.
We conducted a comprehensive examination of genetic alterations (somatic mutations and copy number alterations, CNAs) in 44 cases of PSH: 8 fresh-frozen and 36 formalin-fixed paraffin-embedded (FFPE), from 43 patients (two separate PSHs were from one patient), with matched normal tissues using WES. Most of the patients were female (91%) and the median age was 52 y (range 12–74 y) (Table S1). Coverages of the sequencing depth were mean of 156× for PSHs and 152× for matched normal, with an average of 95% of bases covered by at least 20 reads in each sample, respectively (Table S2).
Table S1.
Patient characteristics of the discovery and validation sets
| Sample ID | Sets | Gender | Age at diagnosis (y) | Tumor size (diameter, cm) | Sample source |
| SH01 | Discovery | Female | 56 | 2.5 | Frozen |
| SH02 | Discovery | Male | 42 | 1.5 | Frozen |
| SH03 | Discovery | Female | 30 | 1.7 | FFPE |
| SH04 | Discovery | Female | 55 | 1.5 | FFPE |
| SH05 | Discovery | Female | 34 | 3.0 | Frozen |
| SH06T1 | Discovery | Female | 46 | 0.6 | FFPE |
| SH06T2 | Discovery | Female | 46 | 1.4 | FFPE |
| SH07 | Discovery | Female | 62 | 1.5 | Frozen |
| SH08 | Discovery | Female | 48 | 1.8 | Frozen |
| SH09 | Discovery | Female | 55 | 1.5 | FFPE |
| SH10 | Discovery | Female | 74 | 3.5 | FFPE |
| SH11 | Discovery | Female | 50 | 2.2 | FFPE |
| SH18 | Discovery | Female | 29 | 1.5 | FFPE |
| SH20 | Discovery | Female | 61 | 2.2 | FFPE |
| SH23 | Discovery | Female | 28 | 2.4 | FFPE |
| SH25 | Discovery | Female | 73 | 1.3 | FFPE |
| SH34 | Discovery | Female | 64 | 1.5 | FFPE |
| SH36 | Discovery | Female | 43 | 1 × 1 | FFPE |
| SH37 | Discovery | Male | 27 | 6 | FFPE |
| SH40 | Discovery | Female | 54 | 4.5 | FFPE |
| SH41 | Discovery | Female | 70 | 2 | FFPE |
| SH42 | Discovery | Female | 54 | 2 | FFPE |
| SH43 | Discovery | Female | 49 | 3.5 | FFPE |
| SH46 | Discovery | Female | 42 | 2.0 | FFPE |
| SH47 | Discovery | Female | 52 | 2.5 | FFPE |
| SH48 | Discovery | Female | 68 | 3.5 | FFPE |
| SH49 | Discovery | Female | 51 | 2.8 | FFPE |
| SH50 | Discovery | Male | 12 | 2.8 | FFPE |
| SH51 | Discovery | Female | 22 | 4.5 | FFPE |
| SH52 | Discovery | Female | 67 | 2.0 | FFPE |
| SH53 | Discovery | Male | 48 | 2.3 | FFPE |
| SH54 | Discovery | Female | 49 | 2.2 | FFPE |
| SH55 | Discovery | Female | 61 | 3.5 | FFPE |
| SH56 | Discovery | Female | 36 | 1.8 | FFPE |
| SH57 | Discovery | Female | 64 | 2.1 | FFPE |
| SH58 | Discovery | Female | 49 | 2.0 | FFPE |
| SH59 | Discovery | Female | 63 | 2.3 | FFPE |
| SH60 | Discovery | Female | 63 | 1.0 | FFPE |
| SH61 | Discovery | Female | 17 | 1.5 | FFPE |
| SH62 | Discovery | Female | 58 | 1.0 | FFPE |
| SH63 | Discovery | Female | 53 | 3.5 | FFPE |
| SH64 | Discovery | Female | 61 | 4.5 | Frozen |
| SH66 | Discovery | Female | 40 | 1.8 | Frozen |
| SH67 | Discovery | Female | 52 | 2.0 | Frozen |
| SH12 | Validation | Female | 30 | 1.2 | FFPE |
| SH13 | Validation | Female | 60 | 2.0 | FFPE |
| SH14 | Validation | Female | 51 | 1.7 | FFPE |
| SH15 | Validation | Female | 55 | 1.5 | FFPE |
| SH16 | Validation | Male | 65 | 0.5 | FFPE |
| SH17 | Validation | Male | 30 | 2.0 | FFPE |
| SH19 | Validation | Female | 66 | 1.5 | FFPE |
| SH21 | Validation | Female | 53 | 1.7 | FFPE |
| SH22 | Validation | Female | 55 | 3.0 | FFPE |
| SH24 | Validation | Female | 48 | 3.0 | FFPE |
| SH26 | Validation | Female | 54 | 1.2 | FFPE |
| SH27 | Validation | Female | 48 | 2.0 | FFPE |
| SH28 | Validation | Female | 55 | 2.0 | FFPE |
| SH29 | Validation | Female | 61 | 1.9 | FFPE |
| SH30 | Validation | Female | 49 | 5.0 | FFPE |
| SH31 | Validation | Female | 28 | 5.0 | FFPE |
| SH32 | Validation | Female | 32 | 3.5 | FFPE |
| SH33 | Validation | Female | 57 | 2.0 | FFPE |
| SH35 | Validation | Female | 42 | 2.5 | FFPE |
| SH38 | Validation | Male | 17 | 2.5 | FFPE |
| SH39 | Validation | Male | 38 | 2.5 | FFPE |
| SH44 | Validation | Male | 58 | 2.5 | FFPE |
| SH45 | Validation | Female | 55 | 2.5 | FFPE |
| SH65 | Validation | Female | 55 | 3.0 | Frozen |
Table S2.
The description of whole-exome sequencing data
| Sample ID* | Sequencing reads | Mapped reads (%) | Mapped reads in exon (%) | Coverage (mean)† | Percent of bases (≥ 20 reads)† |
| SH01N | 118,197,262 | 108,879,483 (92.1) | 85,500,955 (72.3) | 144.1 | 91.5 |
| SH01T | 132,337,323 | 130,797,497 (98.8) | 98,995,812 (74.8) | 160.6 | 91.3 |
| SH02N | 140,640,591 | 138,969,521 (98.8) | 105,375,731 (74.9) | 170.9 | 92.0 |
| SH02T | 137,049,720 | 135,327,809 (98.7) | 102,447,257 (74.8) | 166.2 | 91.9 |
| SH03N | 114,313,694 | 106,844,646 (93.5) | 83,339,445 (72.9) | 140.6 | 92.1 |
| SH03T | 110,777,788 | 102,084,391 (92.2) | 79,862,041 (72.1) | 135.8 | 94.4 |
| SH04N | 150,751,493 | 118,523,596 (78.6) | 89,610,363 (59.4) | 149.4 | 90.9 |
| SH04T | 136,372,096 | 118,007,518 (86.5) | 90,664,838 (66.5) | 152.0 | 94.0 |
| SH05N | 124,952,345 | 122,723,818 (98.2) | 95,145,874 (76.2) | 155.2 | 91.2 |
| SH05T | 119,526,275 | 117,738,851 (98.5) | 91,443,701 (76.5) | 149.1 | 91.1 |
| SH06N | 157,568,019 | 117,318,182 (74.5) | 89,172,393 (56.6) | 149.4 | 91.1 |
| SH06T1 | 126,569,663 | 109,392,999 (86.4) | 85,479,819 (67.5) | 144.9 | 93.2 |
| SH06T2 | 175,568,457 | 151,033,814 (86.0) | 118,488,533 (67.5) | 209.1 | 98.5 |
| SH07N | 128,818,752 | 126,787,029 (98.4) | 98,534,473 (76.5) | 160.5 | 91.2 |
| SH07T | 136,319,429 | 134,117,448 (98.4) | 103,376,647 (75.8) | 168.7 | 92.0 |
| SH08N | 119,529,810 | 117,625,696 (98.4) | 92,132,837 (77.1) | 150.3 | 90.5 |
| SH08T | 125,915,096 | 124,153,408 (98.6) | 96,643,803 (76.8) | 157.7 | 91.2 |
| SH09N | 131,003,302 | 107,128,096 (81.8) | 79,116,007 (60.4) | 131.9 | 90.3 |
| SH09T | 117,101,034 | 107,342,807 (91.7) | 84,463,728 (72.1) | 141.8 | 93.8 |
| SH10N | 175,228,607 | 108,181,567 (61.7) | 83,647,239 (47.7) | 141.9 | 91.2 |
| SH10T | 152,523,855 | 105,434,450 (69.1) | 81,423,260 (53.4) | 137.9 | 93.5 |
| SH11N | 122,820,217 | 100,337,622 (81.7) | 78,532,100 (63.9) | 134.0 | 92.4 |
| SH11T | 112,382,717 | 89,922,844 (80.0) | 69,735,021 (62.1) | 119.3 | 94.2 |
| SH18N | 116,732,588 | 101,458,107 (86.9) | 78,567,017 (67.3) | 131.0 | 89.1 |
| SH18T | 124,536,062 | 105,694,862 (84.9) | 81,013,572 (65.1) | 136.7 | 90.6 |
| SH20N | 132,753,120 | 106,365,793 (80.1) | 76,391,752 (57.5) | 127.0 | 89.2 |
| SH20T | 135,387,229 | 106,936,962 (79.0) | 80,198,879 (59.2) | 135.0 | 90.2 |
| SH23N | 130,043,054 | 106,614,510 (82.0) | 79,473,894 (61.1) | 131.4 | 90.0 |
| SH23T | 135,166,104 | 103,163,691 (76.3) | 76,516,158 (56.6) | 125.7 | 88.5 |
| SH25N | 127,078,498 | 102,184,670 (80.4) | 78,437,273 (61.7) | 131.5 | 89.9 |
| SH25T | 124,186,239 | 110,542,064 (89.0) | 88,827,247 (71.5) | 149.5 | 90.0 |
| SH34N | 124,973,391 | 108,950,892 (87.2) | 83,095,723 (66.5) | 137.3 | 90.4 |
| SH34T | 103,537,061 | 93,226,848 (90.0) | 70,780,434 (68.4) | 117.8 | 91.4 |
| SH36N | 163,872,197 | 89,147,617 (54.4) | 65,042,895 (39.7) | 114.6 | 97.0 |
| SH36T | 140,366,854 | 81,719,297 (58.2) | 59,154,273 (42.1) | 104.1 | 97.2 |
| SH37N | 142,985,090 | 105,154,566 (73.5) | 77,234,318 (54.0) | 134.3 | 97.5 |
| SH37T | 133,475,817 | 73,305,138 (54.9) | 52,805,549 (39.6) | 92.3 | 94.9 |
| SH40N | 160,504,261 | 145,416,934 (90.6) | 111,372,236 (69.4) | 194.1 | 98.6 |
| SH40T | 140,261,603 | 98,583,945 (70.3) | 78,287,285 (55.8) | 138.4 | 96.9 |
| SH41N | 145,064,883 | 124,950,335 (86.1) | 84,615,275 (58.3) | 144.3 | 98.3 |
| SH41T | 188,747,902 | 172,127,790 (91.2) | 120,456,820 (63.8) | 207.8 | 98.9 |
| SH42N | 137,224,204 | 120,501,439 (87.8) | 92,099,191 (67.1) | 160.1 | 97.7 |
| SH42T | 138,967,416 | 121,023,015 (87.1) | 89,481,085 (64.4) | 155.2 | 97.4 |
| SH43N | 137,223,277 | 123,882,313 (90.3) | 91,977,036 (67.0) | 158.5 | 98.0 |
| SH43T | 136,061,555 | 116,221,260 (85.4) | 81,271,036 (59.7\) | 139.0 | 97.4 |
| SH46N | 145,984,632 | 113,670,486 (77.9) | 85,169,792 (58.3) | 148.7 | 97.7 |
| SH46T | 136,066,310 | 97,784,225 (71.9) | 74,352,808 (54.6) | 131.0 | 96.8 |
| SH47N | 112,702,783 | 45,464,195 (40.3) | 31,150,064 (27.6) | 54.4 | 93.6 |
| SH47T | 140,632,773 | 90,996,850 (64.7) | 68,922,067 (49.0) | 121.8 | 96.6 |
| SH48N | 132,620,143 | 83,943,164 (63.3) | 60,714,739 (45.8) | 106.7 | 96.3 |
| SH48T | 130,261,444 | 83,203,421 (63.9) | 56,450,165 (43.3) | 97.5 | 94.3 |
| SH49N | 150,088,974 | 124,607,378 (83.0) | 92,205,060 (61.4) | 160.6 | 97.5 |
| SH49T | 154,486,652 | 107,733,911 (69.7) | 81,895,817 (53.0) | 144.3 | 97.5 |
| SH50N | 156,684,408 | 116,450,452 (74.3) | 89,703,913 (57.3) | 157.8 | 97.9 |
| SH50T | 148,467,320 | 121,555,412 (81.9) | 90,435,071 (60.9) | 156.2 | 98.2 |
| SH51N | 133,692,879 | 99,903,393 (74.7) | 75,958,803 (56.8) | 132.3 | 97.2 |
| SH51T | 141,749,106 | 109,951,940 (77.6) | 81,990,349 (57.8) | 143.4 | 97.1 |
| SH52N | 121,310,408 | 104,169,224 (85.9) | 77,096,639 (63.6) | 134.2 | 97.5 |
| SH52T | 145,366,322 | 116,649,306 (80.3) | 82,772,777 (56.9) | 142.8 | 97.1 |
| SH53N | 137,496,941 | 109,009,039 (79.3) | 85,140,313 (61.9) | 149.8 | 97.6 |
| SH53T | 135,892,527 | 105,840,036 (77.9) | 80,076,893 (58.9) | 140.2 | 97.6 |
| SH54N | 153,828,328 | 120,765,196 (78.5) | 90,628,736 (58.9) | 156.9 | 97.7 |
| SH54T | 167,380,457 | 128,427,177 (76.7) | 98,636,057 (58.9) | 172.1 | 98.0 |
| SH55N | 160,101,576 | 137,028,025 (85.6) | 104,740,233 (65.4) | 182.4 | 98.3 |
| SH55T | 151,215,434 | 132,918,226 (87.9) | 98,304,888 (65.0) | 169.4 | 98.0 |
| SH56N | 170,826,963 | 152,859,159 (89.5) | 116,900,850 (68.4) | 201.7 | 98.6 |
| SH56T | 150,291,217 | 135,292,900 (90.0) | 102,087,386 (67.9) | 176.6 | 98.1 |
| SH57N | 164,562,624 | 140,950,814 (85.7) | 104,274,204 (63.4) | 180.2 | 97.7 |
| SH57T | 166,910,588 | 142,483,940 (85.4) | 106,590,622 (63.6) | 184.4 | 98.5 |
| SH58N | 158,765,599 | 136,063,847 (85.7) | 103,308,734 (65.1) | 179.7 | 98.4 |
| SH58T | 256,146,286 | 226,360,879 (88.4) | 168,681,694 (65.9) | 293.1 | 99.2 |
| SH59N | 131,869,090 | 107,582,973 (81.6) | 80,198,460 (60.8) | 140.1 | 97.9 |
| SH59T | 141,531,702 | 113,100,510 (79.9) | 84,029,778 (59.4) | 145.5 | 97.8 |
| SH60N | 233,204,919 | 209,703,150 (89.9) | 150,638,813 (64.6) | 260.3 | 99.1 |
| SH60T | 164,913,390 | 150,459,918 (91.2) | 109,937,873 (66.7) | 189.9 | 98.8 |
| SH61N | 159,817,195 | 140,643,778 (88.0) | 106,019,548 (66.3) | 184.4 | 98.2 |
| SH61T | 163,215,050 | 141,695,828 (86.8) | 107,223,877 (65.7) | 187.4 | 98.5 |
| SH62N | 158,222,605 | 150,463,576 (95.1) | 111,403,676 (70.4) | 192.2 | 98.7 |
| SH62T | 174,369,192 | 167,645,515 (96.1) | 122,531,374 (70.3) | 212.4 | 99.0 |
| SH63N | 172,873,073 | 160,521,206 (92.9) | 116,714,622 (67.5) | 200.2 | 99.1 |
| SH63T | 210,210,880 | 194,797,210 (92.7) | 143,211,592 (68.1) | 246.0 | 99.2 |
| SH64N | 105,638,112 | 103,775,831 (98.2) | 80,371,219 (76.1) | 131.3 | 90.2 |
| SH64T | 107,562,508 | 105,508,055 (98.1) | 81,160,444 (75.5) | 132.2 | 89.9 |
| SH66N | 127,938,350 | 126,054,904 (98.5) | 98,196,686 (76.8) | 160.2 | 91.1 |
| SH66T | 138,341,322 | 136,278,324 (98.5) | 104,747,058 (75.7) | 170.7 | 91.7 |
| SH67N | 116,586,515 | 114,970,060 (98.6) | 89,608,070 (76.9) | 146.1 | 90.5 |
| SH67T | 121,895,590 | 120,181,499 (98.6) | 95,463,406 (78.3) | 155.6 | 90.4 |
The neoplasia and matched normal genomes are discriminated with the use of “T” and “N,” respectively.
The mean coverage and the percent of bases (≥20 reads) were calculated onto the targeted regions (Agilent SureSelect 50 megabase exon).
A total of 672 nonsilent mutations were identified in the 44 PSH genomes (12 mutations per genome, range 0–76) (Fig. 1A and Dataset S1), corresponding to a mean rate of 0.3 somatic mutations per megabase. This finding is similar to the rates of other benign tumors [e.g., leiomyoma (13) (0.24 per megabase) and fibroadenoma (9) (0.11 per megabase)] but much lower than those observed in lung adenocarcinoma (10) (8.9 per megabase), lung squamous carcinoma (11) (8.1 per megabase), and other cancers (14) (Fig. 1B and Fig. S1). C > T substitutions are the most common mutation type (49.3%; 27.8% at CpG and 21.5% at non-CpG context) in PSHs (Fig. 1A). In contrast, C > A substitutions, the most common in lung cancer (26.8%), is less common in the PSHs (14.1%), suggesting that tumorigenic events in PSH may be different from those in lung cancer.
Fig. 1.
Landscape of somatic alterations in the PSH genomes. (A) The numbers of somatic nonsilent mutations (Upper) and types of base pair substitutions (Lower) are shown. (B) Mutation frequency in PSHs compared with those in other tumor types. Black bars represent the median values. Lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) data are from The Cancer Genome Atlas (TCGA) by Lawrence at al. (14). Fibroadenoma and leiomyoma data are from reports by Lim et al. (9) and Mehine et al. (13), respectively.
Fig. S1.
Mutation frequency in PSHs compared with those in other tumor types. Black bars represent the median values. Fibroadenoma and leiomyoma data are from reports by Lim et al. (9) and Mehine et al. (13), respectively. The other tumor data are from TCGA by Lawrence at al. (14). AML, acute myeloid leukemia; CLL, chronic lymphocytic leukemia; DLBCL, diffuse large B-cell lymphoma; ESAD, esophageal adenocarcinoma; GBM, glioblastoma multiforme; LUAD, lung adenocarcinoma; LUSC, lung squamous cell carcinoma.
AKT1 and β-Catenin Mutations in PSH Genomes.
In the PSHs, nonsilent, recurrent mutations were found in AKT1 (19 of 44 PSHs: 43.2%), CTNNB1 (β-catenin) (2 of 44), and ARID1B (2 of 44) (Fig. 2A). We also identified nonsilent mutations in an additional eight genes, which overlapped with both the Cancer Gene Census (15) and Cancer Drivers Database (16): APC, BLM, PLCG1, FAS, ATRX, EP300, KMT2D, and ATM (Fig. 2A), but no TP53 mutations were observed. Among these mutations, three truncating mutations were identified in tumor-suppressor genes (ARID1B, KMT2D, and ATM). To address whether these mutations could be causally implicated in the PSH development, we performed MutSigCV (14) and OncodriveFM (17) analyses and found that only AKT1 was significantly mutated in PSH genomes (q-value < 0.01). Key mutations were validated using Sanger sequencing or digital PCR (Fig. S2 and Table S3). Clinical and histopathological parameters could not distinguish AKT1 mutation (+) and (−) cases of PSHs (P > 0.05).
Fig. 2.
Cancer-related mutations in the PSH genomes, and a schematic diagram of AKT1 mutations. (A) Cancer-related mutations according to WES in the 44 PSH cases (discovery set). (B) Schematic diagram of the AKT1 mutations in PSHs (Upper). The x axis represents amino acid positions. The y axis represents the number of mutations. Distribution of AKT1 mutations in the discovery (44 cases) and validation (24 cases) sets are indicated.
Fig. S2.
Validation of somatic mutations. (A). Sanger sequencings of 18 genes (AKT1, ATM, ATRX, BLM, CTNNB1, EP300, FMR1, GLI1, KDM4B, KMT2D, NEDD4L, PABPC3, PIK3AP1, SETDB1, TJP1, ZMYM2, MYT1, and SPHK2) in PSH with matched normal. All of the mutations are identified in tumors, but not normal and interpreted as somatic. (B). Digital PCRs of six genes (APC, FAS, TCF7L1, FOXO1, MYT1, and MTOR) in tumor with matched normal. Red (VIC) and blue (6FAM) dots represent the reference allele and mutant allele, respectively. All of the mutations are identified in tumors, but not normal and interpreted as somatic.
Table S3.
Validation of somatic mutations
| Whole-exome sequencing | Validation | ||||||||||
| Gene | Genomic position | Ref | Alt | Exonic function | AA change | Sample ID [tumor cell content (%)] | WES VAF, % (adjusted VAF, %)* | Method | Status† | VAF, % | CI, % |
| AKT1 | chr14:105246551 | C | T | Missense | p.E17K | SH01 (50) | 18.6 (37) | Sanger sequencing | Somatic | — | — |
| AKT1 | chr14:105246443 | T | G | Missense | p.N53H | SH04 (80) | 37.0 (46) | Sanger sequencing | Somatic | — | — |
| AKT1 | chr14:105246455 | C | T | Missense | p.E49K | SH04 (80) | 37.1 (46) | Sanger sequencing | Somatic | — | — |
| AKT1 | chr14:105246436 | A | T | Missense | p.F55Y | SH06T1 (80) | 21.5 (27) | Sanger sequencing | Somatic | — | — |
| AKT1 | chr14:105246445 | A | C | Missense | p.L52R | SH06T1 (80) | 21.3 (27) | Sanger sequencing | Somatic | — | — |
| AKT1 | chr14:105246551 | C | T | Missense | p.E17K | SH06T2 (50) | 22.5 (45) | Sanger sequencing | Somatic | — | — |
| AKT1 | chr14:105246551 | C | T | Missense | p.E17K | SH20 (20) | 7.1 (36) | Sanger sequencing | Somatic | — | — |
| AKT1 | chr14:105246551 | C | T | Missense | p.E17K | SH23 (70) | 34.8 (50) | Sanger sequencing | Somatic | — | — |
| AKT1 | chr14:105246551 | C | T | Missense | p.E17K | SH25 (80) | 38.1 (48) | Sanger sequencing | Somatic | — | — |
| AKT1 | chr14:105246551 | C | T | Missense | p.E17K | SH34 (60) | 29.6 (49) | Sanger sequencing | Somatic | — | — |
| AKT1 | chr14:105246551 | C | T | Missense | p.E17K | SH36 (70) | 17.5 (25) | Sanger sequencing | Somatic | — | — |
| AKT1 | chr14:105243045 | A | T | Missense | p.W80R | SH37 (60) | 18.4 (31) | Sanger sequencing | Somatic | — | — |
| AKT1 | chr14:105243048 | G | T | Missense | p.Q79K | SH37 (60) | 18.4 (31) | Sanger sequencing | Somatic | — | — |
| AKT1 | chr14:105246551 | C | T | Missense | p.E17K | SH40 (80) | 39.5 (49) | Sanger sequencing | Somatic | — | — |
| AKT1 | chr14:105246545 | T | G | Missense | p.I19L | SH42 (70) | 30.9 (44) | Sanger sequencing | Somatic | — | — |
| AKT1 | chr14:105246551 | C | T | Missense | p.E17K | SH42 (70) | 29.4 (44) | Sanger sequencing | Somatic | — | — |
| AKT1 | chr14:105246551 | C | T | Missense | p.E17K | SH48 (80) | 77.8 (97) | Sanger sequencing | Somatic | — | — |
| AKT1 | chr14:105246551 | C | T | Missense | p.E17K | SH52 (70) | 33.3 (48) | Sanger sequencing | Somatic | — | — |
| AKT1 | chr14:105246551 | C | T | Missense | p.E17K | SH55 (60) | 52.9 (88) | Sanger sequencing | Somatic | — | — |
| AKT1 | chr14:105243045 | A | T | Missense | p.W80R | SH56 (70) | 31.9 (46) | Sanger sequencing | Somatic | — | — |
| AKT1 | chr14:105243048 | G | T | Missense | p.Q79K | SH56 (70) | 30.5 (44) | Sanger sequencing | Somatic | — | — |
| AKT1 | chr14:105243045 | A | T | Missense | p.W80R | SH60 (40) | 14.5 (36) | Sanger sequencing | Somatic | — | — |
| AKT1 | chr14:105243048 | G | T | Missense | p.Q79K | SH60 (40) | 15.3 (38) | Sanger sequencing | Somatic | — | — |
| AKT1 | chr14:105243045 | A | C | Missense | p.W80G | SH61 (70) | 27.2 (39) | Sanger sequencing | Somatic | — | — |
| AKT1 | chr14:105243048 | G | T | Missense | p.Q79K | SH61 (70) | 27.7 (39) | Sanger sequencing | Somatic | — | — |
| AKT1 | chr14:105243045 | A | T | Missense | p.W80R | SH62 (50) | 19.2 (38) | Sanger sequencing | Somatic | — | — |
| AKT1 | chr14:105243048 | G | T | Missense | p.Q79K | SH62 (50) | 19.2 (38) | Sanger sequencing | Somatic | — | — |
| APC | chr5:112176515 | C | T | Missense | p.R1724C | SH47 (70) | 5.8 (8) | Digital PCR | Somatic | 1.39 | 0.90–2.16 |
| ATM | chr11:108180895 | C | A | Nonsense | p.S1924X | SH59 (60) | 10.8 (18) | Sanger sequencing | Somatic | — | — |
| ATRX | chrX:76776914 | C | A | Missense | p.R2308S | SH23 (70) | 7.1 (10) | Sanger sequencing | Somatic | — | — |
| BLM | chr15:91292707 | A | G | Missense | p.D70G | SH51 (70) | 27.8 (40) | Sanger sequencing | Somatic | — | — |
| CTNNB1 | chr3:41266113 | C | T | Missense | p.S37F | SH66 (60) | 21 (35) | Sanger sequencing | Somatic | — | — |
| CTNNB1 | chr3:41266113 | C | T | Missense | p.S37F | SH18 (85) | 42 (49) | Sanger sequencing | Somatic | — | — |
| EP300 | chr22:41523647 | G | A | Missense | p.E355K | SH18 (85) | 33.1 (39) | Sanger sequencing | Somatic | — | — |
| FAS | chr10:90774110 | G | C | Missense | p.C283S | SH60 (40) | 3.7 (9) | Digital PCR | Somatic | 1.77 | 1.32–2.36 |
| FMR1 | chrX:147024825 | C | T | Missense | p.R463C | SH10 (80) | 36.8 (46) | Sanger sequencing | Somatic | — | — |
| GLI1 | chr12:57861886 | G | A | Missense | p.R268Q | SH25 (80) | 28.0 (35) | Sanger sequencing | Somatic | — | — |
| KDM4B | chr19:5082513 | C | T | Nonsense | p.Q306X | SH06T1 (80) | 25.9 (32) | Sanger sequencing | Somatic | — | — |
| KMT2D | chr12:49420876 | G | C | Nonsense | p.S4958X | SH18 (85) | 30.9 (36) | Sanger sequencing | Somatic | — | — |
| MTOR | chr1:11303294 | C | T | Missense | p.R430H | SH49 (70) | 4.4 (6) | Digital PCR | Somatic | 1.12 | 0.72–1.73 |
| NEDD4L | chr18:56018239 | G | C | Missense | p.R416T | SH18 (85) | 23.0 (27) | Sanger sequencing | Somatic | — | — |
| PABPC3 | chr13:25670833 | G | A | Missense | p.R166H | SH41 (70) | 26.2 (37) | Sanger sequencing | Somatic | — | — |
| PIK3AP1 | chr10:98376435 | G | A | Nonsense | p.R659X | SH66 (60) | 14.9 (25) | Sanger sequencing | Somatic | — | — |
| SETDB1 | chr1:150902552 | G | C | Missense | p.E124Q | SH18 (85) | 41.0 (48) | Sanger sequencing | Somatic | — | — |
| TCF7L1 | chr2:85536268 | G | A | Missense | p.A484T | SH67 (70) | 6.7 (10) | Digital PCR | Somatic | 6.68 | 5.97–7.46 |
| TJP1 | chr15:30012156 | G | C | Missense | p.S943C | SH18 (85) | 21.2 (25) | Sanger sequencing | Somatic | — | — |
| ZMYM2 | chr13:20657914 | A | C | Missense | p.K1313N | SH25 (80) | 7.0 (9) | Sanger sequencing | Somatic | — | — |
| FOXO1 | chr13:41133744 | C | G | Missense | p.L628F | SH02 (70) | 5.1 (7) | Digital PCR | Somatic | 4.02 | 3.45–4.67 |
| MYT1 | chr20:62839489 | C | G | Missense | p.Q314E | SH64 (70) | 4.2 (6) | Digital PCR | Somatic | 2.36 | 1.96–2.83 |
| MYT1 | chr20:62839121 | G | A | Missense | p.G191D | SH10 (80) | 38.9 (49) | Sanger sequencing | Somatic | — | — |
| SPHK2 | chr19:49131018 | G | C | Missense | p.G158A | SH66 (60) | 15.7 (26) | Sanger sequencing | Somatic | — | — |
AA, amino acid; Alt, altered allele; CI, 95% confidence interval; Ref, reference allele.
Adjusted VAF: 100/%tumor content × WES VAF.
All validations were performed with their paired normal sample.
AKT1 is a serine/threonine kinase that stimulates many cancer-related processes, including cell proliferation, survival, and growth (18). All 19 AKT1 mutations identified (11 p.E17K, 1 p.E17K/p.I19L, 4 p.Q79K/p.W80R, 1 p.Q79K/p.W80G, 1 p.E49K/p.N53H, and 1 p.L52R/p.F55Y) were localized to the pleckstrin homology domain (Fig. 2B), which is crucial for membrane localization and downstream activation of AKT1 (19). Not only AKT1 p.E17K, but also p.N53-, p.F55-, p.Q79-, and p.W80-altering mutations are known to promote growth factor-independent cell proliferation compared with wild-type AKT1 (20). To further assess the recurrence of AKT1 mutations in PSHs, we performed Sanger sequencing of AKT1 in an independent validation set (24 FFPE PSHs), which confirmed the high frequency of AKT1 mutations in PSHs [12 of 24 (50%): 11 p.E17K and 1 p.Q79K/p.W80R] (Table 1 and Fig. 2B). Among PSHs with p.E17K mutations, all but one harbored a single AKT1 mutation. Those PSHs with non-p.E17K mutations harbored additional AKT1 mutations (“double” AKT1 mutations). All double AKT1 mutations were detected in the identical sequencing reads with similar variant allele frequency (VAF) (Fig. S3), suggesting that the double AKT1 mutations occurred in cis configuration in the same cells. The overall AKT1 mutation prevalence in PSHs was 45.6% (31 of 68), most of which were p.E17K (23 of 68, 33.8%). Recurrent AKT1 mutations, especially p.E17K, have been detected in breast cancers (4–8%) (20, 21) but rarely in the other cancers, including lung cancers (0–0.9%) (10–12).
Table 1.
Somatic mutations in AKT1 and CTNNB1
| Gene | Discovery set (n = 44) | Validation set (n = 24) | Combined set (n = 68) | ||||||
| Mut | WT | Mut rate (%) | Mut | WT | Mut rate (%) | Mut | WT | Mut rate (%) | |
| AKT1 | 19 | 25 | 43.2 | 12 | 12 | 50.0 | 31 | 37 | 45.6 |
| p.E17K | 11 | 33 | 25.0 | 11 | 13 | 45.8 | 22 | 46 | 32.4 |
| p.E17K and p.I19L | 1 | 43 | 2.3 | 0 | 24 | 0 | 1 | 67 | 1.5 |
| p.E49K and p.N53H | 1 | 43 | 2.3 | 0 | 24 | 0 | 1 | 67 | 1.5 |
| p.L52R and p.F55Y | 1 | 43 | 2.3 | 0 | 24 | 0 | 1 | 67 | 1.5 |
| p.Q79K and p.W80R | 4 | 40 | 9.1 | 1 | 23 | 4.2 | 5 | 63 | 7.4 |
| p.Q79K and p.W80G | 1 | 43 | 2.3 | 0 | 24 | 0 | 1 | 67 | 1.5 |
| CTNNB1 | 2 | 42 | 4.5 | 1 | 23 | 4.2 | 3 | 65 | 4.4 |
| p.S37F | 2 | 42 | 4.5 | 0 | 24 | 0 | 2 | 66 | 2.9 |
| p.G38C | 0 | 44 | 0 | 1 | 23 | 4.2 | 1 | 67 | 1.5 |
Mut, mutation; WT, wild type.
Fig. S3.
Double AKT1 mutations identified in WES. (A) WES snapshots of double AKT1 mutations. (B) VAF of AKT1 mutations. All double AKT1 mutations in each PSH were detected in the identical sequencing reads with similar VAFs, suggesting that the double AKT1 mutations occurred in cis configuration.
As for β-catenin mutations, p.S37F was identified in two different PSHs in the discovery set and p.G38C in a PSH in the validation set (3 of 68, 4.4%). β-catenin p.S37F is a hotspot mutation reported in many tumors (22, 23) and β-catenin mutations have been previously reported in PSHs (1 of 37, 2.7%, p.S33C) (6). AKT1 and β-catenin mutations did not co-occur in any of the PSHs we studied here.
Mutation Profiles in Multiple PSHs in One Patient.
Although multiple PSHs is a rare entity (24), one patient (SH06) in our study presented with two separate masses in the upper (tumor 1, T1) and lower (tumor 2, T2) lobes. To investigate their clonal origins, we compared the tumors’ germ-line and somatic variants. Almost all of the germ-line variants in the two PSH masses were shared with their matched normal (96.1% for T1 and 93.0% for T2), whereas somatic variants identified in the two PSH masses were mutually exclusive, indicating distinct clonal origins (i.e., double primary tumors) (Fig. 3). Interestingly, ATK1 mutations were identified in both lesions, but the variants were different from each other (p.L52R/p.F55Y in T1 and p.E17K in T2).
Fig. 3.
Mutation profiles of two separate PSHs from one patient. (Left) Correlation heat map of germ-line variants in the two PSHs with the matched normal. (Right) Somatic mutations identified in the two separate PSHs (SH06-T1 and SH06-T2) with the three functional categories.
AKT1 Mutations in PSHs from Histologically Different Cells.
Evidence has shown, by analyzing microsatellite patterns, that both cuboidal epithelial and polygonal stromal cells in PSHs could be monoclonal (25), but this hypothesis needs more evidence. In addition, there are still doubts about the true neoplastic nature of the components (entrapped normal epithelial cells and nonneoplastic stromal cells). We performed microdissections to procure two cell types in three PSHs and analyzed somatic and germ-line variant status, including the AKT1 mutation in the two types of cells. We found AKT1 p.E17K mutations in both cell types in the PSHs (Fig. 4), suggesting their uniclonal origin. To further validate this finding, we performed Sanger sequencing of six somatic and two germ-line variants in both epithelial and stromal cells. Regardless of somatic and germ-line event, all of the variants were detected in both cell types (Fig. S4).
Fig. 4.
Presence of AKT1 E17K mutation in both epithelial and stromal cells in represented PSH cases. (A) A sclerosing hemangioma shows both epithelial (arrows) and stromal (arrowheads) components before the microdissection. (B) The microdissection procures the epithelial cells and leaves holes behind. (C) Separately microdissected epithelial and stromal cells in three PSHs show identical mutations.
Fig. S4.
Presence of somatic (SETDB1, TJP1, KMT2D, NEDD4L, SLC25A30, and PROM2) and germ-line (BRCA2) variants in both epithelial and stromal cells in PSH cases. Separately microdissected epithelial and stromal cells in SH18 (A) and SH34 (B) show identical mutations.
CNA and Loss of Heterozygosity.
To characterize PSH genomic alterations at a copy number level, we analyzed the same 44 PSHs using the WES data (Table S4). Arm-level somatic CNAs were detected on 1q, 5p, 5q, 8p, 8q, 14q, 19p, and 19q (gains) and 13q, 15q, 16q, and 19p (losses) (7 of 44, 15.9%) (Fig. 5A), which is far fewer than that in lung cancers (10, 11), but only 1q and 14q gains were recurrent in the PSHs. The most common CNA was 14q gains (3 of 44, 6.8%), where AKT1 resides (Fig. 5B). One of the three PSHs with 14q gains also harbored AKT1 mutations, whereas the other two did not. In total, 33 PSHs (48.5%) harbored either an AKT1 mutation or 14q copy gain.
Table S4.
Copy number alterations identified across 44 PSHs by WES data
| Sample ID | Chr | Start | End | Event | Length | Cytoband | Cancer Gene Census* |
| SH66 | 1 | 144,009,907 | 249,212,668 | CN Gain | 105,202,762 | q21.1-q44 | PDE4DIP, BCL9, ARNT, TPM3, MUC1, PRCC, NTRK1, SDHC, FCGR2B, PBX1, ABL2, TPR, MDM4, ELK4, SLC45A3, H3F3A, FH |
| SH18 | 1 | 144,009,907 | 249,250,621 | CN Gain | 105,240,715 | q21.1-q44 | PDE4DIP, BCL9, ARNT, TPM3, MUC1, PRCC, NTRK1, SDHC, FCGR2B, PBX1, ABL2, TPR, MDM4, ELK4, SLC45A3, H3F3A, FH |
| SH51 | 5 | 1 | 48,400,000 | CN Gain | 48,400,001 | p15.33-q11.1 | IL7R, LIFR |
| SH51 | 5 | 49,411,098 | 180,915,260 | CN Gain | 131,504,163 | q11.1-q35.3 | IL6ST, PIK3R1, APC, PDGFRB, CD74, ITK, EBF1, RANBP17, TLX3, NPM1, NSD1 |
| SH51 | 8 | 1 | 45,600,000 | CN Gain | 45,600,001 | p23.3-q11.1 | PCM1, WRN, WHSC1L1, FGFR1, HOOK3 |
| SH51 | 8 | 46,842,549 | 146,364,022 | CN Gain | 99,521,474 | q11.1-q24.3 | TCEA1, PLAG1, CHCHD7, NCOA2, HEY1, COX6C, EXT1, MYC, NDRG1, RECQL4 |
| SH36 | 13 | 30,524,423 | 77,870,582 | CN Loss | 47,346,160 | q12.3-q22.3 | BRCA2, LHFP, LCP1, RB1 |
| SH42 | 14 | 19,000,023 | 107,349,540 | CN Gain | 88,349,518 | q11.1-q32.33 | CCNB1IP1, NKX2-1, NIN, KTN1, GPHN, TSHR, TRIP11, GOLGA5, DICER1, TCL6, TCL1A, BCL11B, AKT1 |
| SH51 | 14 | 19,000,232 | 107,349,540 | CN Gain | 88,349,309 | q11.1-q32.33 | CCNB1IP1, NKX2-1, NIN, KTN1, GPHN, TSHR, TRIP11, GOLGA5, DICER1, TCL6, TCL1A, BCL11B, AKT1 |
| SH53 | 14 | 19,000,265 | 107,349,540 | CN Gain | 88,349,276 | q11.1-q32.33 | CCNB1IP1, NKX2-1, NIN, KTN1, GPHN, TSHR, TRIP11, GOLGA5, DICER1, TCL6, TCL1A, BCL11B, AKT1 |
| SH60 | 15 | 20,000,050 | 102,531,392 | CN Loss | 82,531,343 | q11.1-q26.3 | C15orf55, BUB1B, FLJ27352, TCF12, PML, NTRK3, IDH2, CRTC3, BLM |
| SH18 | 16 | 46,500,741 | 88,125,293 | CN Loss | 41,624,553 | q11.2-q24.2 | CYLD, HERPUD1, CDH11, CBFB, CDH1, MAF |
| SH66 | 19 | 1 | 14,064,025 | CN Loss | 14,064,025 | p13.3-p13.12 | FSTL3, STK11, TCF3, GNA11, SH3GL1, MLLT1, DNM2, SMARCA4, LYL1 |
| SH18 | 19 | 1 | 19,664,859 | CN Gain | 19,664,859 | p13.3-p13.11 | FSTL3, STK11, TCF3, GNA11, SH3GL1, MLLT1, DNM2, SMARCA4, LYL1, BRD4, TPM4, JAK3, ELL |
| SH18 | 19 | 35,471,245 | 56,507,071 | CN Gain | 21,035,827 | q13.11-q13.43 | AKT2, CD79A, CIC, BCL3, CBLC, ERCC2, KLK2, PPP2R1A, ZNF331, TFPT |
Cancer Gene Census (cancer.sanger.ac.uk/census).
Fig. 5.
CNAs identified across 44 PSH genomes. (A) The heatmap summarizes chromosomal copy gains (red) and copy losses (blue) in the tumors; each row represents PSH cases. The red boxes denote the arm-level copy gains and the blue boxes denote the arm-level copy losses. Copy number gains on 1q (SH18 and SH66) and 14q (SH42, SH51, and SH53) are identified recurrently. (B) Genome-wide log2 ratio plots from three PSHs with copy number gains on 14q. The red arrows represent the 14q copy gains. The x axis represents individual chromosomes and the y axis represents the relative depth ratio (tumor/matched normal) in log2 scale.
We also assessed the B-allele profiles and identified three copy-neutral loss-of-heterozygosity (LOH) events (Fig. 6). Two of these events (SH48 and SH55) harbored LOH on 14q and co-occurred with AKT1 p.E17K mutation. Mean VAF of AKT1 mutations in the two PSHs with 14q LOH was 2.5 times higher than that without 14p LOH (65.4% vs. 25.6%, respectively) (Fig. 6B), suggesting that the 14q LOH might have occurred after AKT1 mutation events in these cases. Wide distribution of AKT1 VAFs may be a result of subclonal nature of AKT1 mutations in some samples or nonneoplastic cell contamination. When looking at the histology of PSHs, we observed that nonneoplastic stromal components (for example, inflammatory cells) besides the neoplastic polygonal stromal cells were embedded in the analyzed tissues for WES at various degrees (Fig. S5 and Table S3). Adjusted VAFs considering tumor cell contents in AKT1-mutated cases were 25–49% in non-LOH cases and 88–97% in LOH cases (Table S3), suggesting that the AKT1 mutations in PSH may be clonal. The other one (SH18) harbored 59-megabase–sized LOH on chromosome 10 (10q22.2-q26.3), which has been observed in PSHs previously (4). Tumor-suppressor genes PTEN and TCF7L2 reside in this area.
Fig. 6.
Copy-neutral LOH events identified across 44 PSH genomes. (A) List of copy-neutral LOH events identified in this study. Three PSHs with copy-neutral LOH either on 14q or 10q. (B) VAF of AKT1 mutations. The red circle represents PSHs with 14q LOH. Mean VAF of AKT1 mutations in the PSHs with 14q LOH is 2.5 times higher than those without 14p LOH (65.4% vs. 25.6%, respectively). (C) Genome-wide B allele frequency (BAF) plot of the three PSHs with copy-neutral LOH. Red boxes represent copy-neutral LOH events. The x axis represents individual chromosomes and the y axis represents the BAF.
Fig. S5.
Histology of sclerosing hemangiomas. Sclerosing hemangiomas with AKT1 mutations (SH01, -04, -6T1, -6T2, -18, -20, -25, -34, -36, -37, -40, -42, -48, -52, -55, -56, -60, -61, and -62) and with other mutations (SH18, -23, -47, -51, -59, and -66). Nonneoplastic stromal components (for example, inflammatory cells) besides the neoplastic polygonal stromal cells were embedded in the analyzed tissues for WES at various degrees. Case SH20 shows massive infiltration of lymphoid cells in the tumor (marked in circle). Case SH20 showed WES VAF of AKT1-mutation was 7.1% (Table S3), but it was adjusted to VAF 36% when considering the huge infiltration of inflammatory cells (20% of tumor cell content).
Pathway Analysis.
To further gain insights into the role of genomic alterations underlying PSH development, we analyzed the genome data through pathway analysis using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Several mutated genes in PSHs were significantly enriched for cell survival, proliferation, growth, and angiogenesis activities (Table S5). Of note, AKT1 activation is closely connected to these pathways (Fig. 7A). We identified mutations in multiple components of the AKT/GSK3/β-catenin signalings, including CTNNB1, APC, and TCF7L1. In addition, a copy loss of STK11 and mutations in mammalian target of rapamycin (MTOR) and EIF4E2 in PI3K/AKT/mTOR signaling pathways, as well as mutations in MYT1 and FOXO1 responsible for cell cycle regulation and cell survival, were detected in the PSH genomes (Fig. 7B). Interestingly, several gene alterations are related to the VEGF signaling pathway that activates angiogenesis, possibly suggesting their roles in the vascular phenotype (“hemangioma”) of PSH. AKT1 has been engaged as a target in ongoing clinical trials (26), suggesting that AKT1 inhibitors may have potential therapeutic value in PSH.
Table S5.
Pathway analysis of mutations using IntOGen v2.4.1 software
| KEGG identifier | Description | FM-bias | Samples | Recurrence | Mutation frequency (%) |
| hsa05215 | Prostate cancer | 8.4 × 10−18 | 44 | 25 | 56.8 |
| hsa04150 | Mammalian target of rapamycin (mTOR) signaling pathway | 3.2 × 10−17 | 44 | 22 | 50.0 |
| hsa04910 | Insulin signaling pathway | 3.2 × 10−17 | 44 | 25 | 56.8 |
| hsa05218 | Melanoma | 4.8 × 10−17 | 44 | 20 | 45.5 |
| hsa05222 | Small cell lung cancer | 7.8 × 10−16 | 44 | 19 | 43.2 |
| hsa04510 | Focal adhesion | 9.9 × 10−16 | 44 | 26 | 59.1 |
| hsa04725 | Cholinergic synapse | 9.9 × 10−16 | 44 | 21 | 47.7 |
| hsa05166 | HTLV-I infection | 1.6 × 10−15 | 44 | 25 | 56.8 |
| hsa04920 | Adipocytokine signaling pathway | 1.7 × 10−15 | 44 | 21 | 47.7 |
| hsa05212 | Pancreatic cancer | 6.4 × 10−15 | 44 | 20 | 45.5 |
| hsa05220 | Chronic myeloid leukemia | 6.4 × 10−15 | 44 | 19 | 43.2 |
| hsa04630 | Jak-STAT signaling pathway | 8.1 × 10−15 | 44 | 22 | 50.0 |
| hsa05210 | Colorectal cancer | 1.3 × 10−14 | 44 | 23 | 52.3 |
| hsa04210 | Apoptosis | 1.3 × 10−14 | 44 | 23 | 52.3 |
| hsa05221 | Acute myeloid leukemia | 1.3 × 10−14 | 44 | 22 | 50.0 |
| hsa04973 | Carbohydrate digestion and absorption | 2.5 × 10−14 | 44 | 20 | 45.5 |
| hsa04728 | Dopaminergic synapse | 3.4 × 10−14 | 44 | 23 | 52.3 |
| hsa05200 | Pathways in cancer | 5.6 × 10−14 | 44 | 27 | 61.4 |
| hsa05161 | Hepatitis B | 7.1 × 10−14 | 44 | 21 | 47.7 |
| hsa05214 | Glioma | 9.6 × 10−14 | 44 | 21 | 47.7 |
| hsa04914 | Progesterone-mediated oocyte maturation | 1.1 × 10−13 | 44 | 22 | 50.0 |
| hsa05211 | Renal cell carcinoma | 1.1 × 10−13 | 44 | 20 | 45.5 |
| hsa05152 | Tuberculosis | 3.9 × 10−13 | 44 | 25 | 56.8 |
| hsa04066 | HIF-1 signaling pathway | 3.9 × 10−13 | 44 | 22 | 50.0 |
| hsa04620 | Toll-like receptor signaling pathway | 4.5 × 10−13 | 44 | 22 | 50.0 |
| hsa05162 | Measles | 6.0 × 10−13 | 44 | 21 | 47.7 |
| hsa04010 | MAPK signaling pathway | 9.1 × 10−13 | 44 | 23 | 52.3 |
| hsa05142 | Chagas disease (American trypanosomiasis) | 2.3 × 10−12 | 44 | 22 | 50.0 |
| hsa04151 | PI3K–Akt signaling pathway | 2.9 × 10−12 | 44 | 29 | 65.9 |
| hsa04660 | T-cell receptor signaling pathway | 2.9 × 10−12 | 44 | 21 | 47.7 |
| hsa04662 | B-cell receptor signaling pathway | 2.0 × 10−11 | 44 | 23 | 52.3 |
| hsa04370 | VEGF signaling pathway | 3.2 × 10−11 | 44 | 21 | 47.7 |
| hsa05160 | Hepatitis C | 3.7 × 10−11 | 44 | 23 | 52.3 |
| hsa05145 | Toxoplasmosis | 3.7 × 10−11 | 44 | 22 | 50.0 |
| hsa04012 | ErbB signaling pathway | 1.3 × 10−10 | 44 | 22 | 50.0 |
| hsa04666 | Fcγ R-mediated phagocytosis | 1.5 × 10−10 | 44 | 22 | 50.0 |
| hsa05213 | Endometrial cancer | 4.5 × 10−10 | 44 | 25 | 56.8 |
| hsa05164 | Influenza A | 1.7 × 10−09 | 44 | 25 | 56.8 |
| hsa04062 | Chemokine signaling pathway | 1.3 × 10−08 | 44 | 23 | 52.3 |
| hsa04664 | Fcε RI signaling pathway | 1.5 × 10−08 | 44 | 21 | 47.7 |
| hsa04380 | Osteoclast differentiation | 1.5 × 10−08 | 44 | 20 | 45.5 |
| hsa04530 | Tight junction | 4.9 × 10−08 | 44 | 30 | 68.2 |
| hsa05223 | Nonsmall cell lung cancer | 1.7 × 10−07 | 44 | 21 | 47.7 |
| hsa05169 | Epstein–Barr virus infection | 6.5 × 10−07 | 44 | 21 | 47.7 |
| hsa04722 | Neurotrophin signaling pathway | 1.1 × 10−05 | 44 | 24 | 54.5 |
| hsa04540 | Gap junction | 2.2 × 10−04 | 44 | 5 | 11.4 |
| hsa04310 | Wnt signaling pathway | 0.002 | 44 | 9 | 20.5 |
| hsa04730 | Long-term depression | 0.003 | 44 | 3 | 6.8 |
| hsa04916 | Melanogenesis | 0.004 | 44 | 7 | 15.9 |
| hsa04060 | Cytokine-cytokine receptor interaction | 0.006 | 44 | 10 | 22.7 |
| hsa00310 | Lysine degradation | 0.009 | 44 | 5 | 11.4 |
| hsa04810 | Regulation of actin cytoskeleton | 0.013 | 44 | 11 | 25.0 |
| hsa04520 | Adherens junction | 0.014 | 44 | 7 | 15.9 |
| hsa04720 | Long-term potentiation | 0.014 | 44 | 7 | 15.9 |
| hsa05143 | African trypanosomiasis | 0.016 | 44 | 4 | 9.1 |
| hsa04972 | Pancreatic secretion | 0.017 | 44 | 4 | 9.1 |
| hsa04726 | Serotonergic synapse | 0.023 | 44 | 9 | 20.5 |
| hsa04120 | Ubiquitin mediated proteolysis | 0.027 | 44 | 9 | 20.5 |
| hsa04110 | Cell cycle | 0.029 | 44 | 7 | 15.9 |
| hsa05217 | Basal cell carcinoma | 0.055 | 44 | 6 | 13.6 |
| hsa04144 | Endocytosis | 0.056 | 44 | 12 | 27.3 |
| hsa04723 | Retrograde endocannabinoid signaling | 0.080 | 44 | 8 | 18.2 |
| hsa03013 | RNA transport | 0.091 | 44 | 12 | 27.3 |
| hsa04270 | Vascular smooth muscle contraction | 0.096 | 44 | 8 | 18.2 |
| hsa05202 | Transcriptional misregulation in cancer | 0.146 | 44 | 8 | 18.2 |
| hsa02010 | ABC transporters | 0.162 | 44 | 5 | 11.4 |
| hsa00600 | Sphingolipid metabolism | 0.175 | 44 | 3 | 6.8 |
| hsa04713 | Circadian entrainment | 0.201 | 44 | 6 | 13.6 |
| hsa04912 | GnRH signaling pathway | 0.201 | 44 | 6 | 13.6 |
| hsa04142 | Lysosome | 0.304 | 44 | 11 | 25.0 |
| hsa05032 | Morphine addiction | 0.309 | 44 | 7 | 15.9 |
| hsa04114 | Oocyte meiosis | 0.309 | 44 | 5 | 11.4 |
| hsa01100 | Metabolic pathways | 0.342 | 44 | 23 | 52.3 |
| hsa05203 | Viral carcinogenesis | 0.373 | 44 | 6 | 13.6 |
| hsa04020 | Calcium signaling pathway | 0.376 | 44 | 11 | 25.0 |
| hsa05412 | Arrhythmogenic right ventricular cardiomyopathy (ARVC) | 0.433 | 44 | 11 | 25.0 |
| hsa03440 | Homologous recombination | 0.477 | 44 | 4 | 9.1 |
| hsa04064 | NF-κB signaling pathway | 0.492 | 44 | 5 | 11.4 |
| hsa00230 | Purine metabolism | 0.597 | 44 | 9 | 20.5 |
| hsa03015 | mRNA surveillance pathway | 0.681 | 44 | 6 | 13.6 |
| hsa04070 | Phosphatidylinositol signaling system | 0.717 | 44 | 6 | 13.6 |
| hsa05016 | Huntington's disease | 0.717 | 44 | 5 | 11.4 |
| hsa04360 | Axon guidance | 0.756 | 44 | 3 | 6.8 |
| hsa03040 | Spliceosome | 0.846 | 44 | 7 | 15.9 |
| hsa04670 | Leukocyte transendothelial migration | 0.899 | 44 | 11 | 25.0 |
| hsa05414 | Dilated cardiomyopathy | 0.899 | 44 | 8 | 18.2 |
| hsa05010 | Alzheimer's disease | 0.899 | 44 | 7 | 15.9 |
| hsa05410 | Hypertrophic cardiomyopathy (HCM) | 0.899 | 44 | 7 | 15.9 |
| hsa05110 | Vibrio cholerae infection | 0.949 | 44 | 10 | 22.7 |
| hsa00562 | Inositol phosphate metabolism | 0.949 | 44 | 5 | 11.4 |
| hsa04650 | Natural killer cell mediated cytotoxicity | 0.963 | 44 | 7 | 15.9 |
| hsa05168 | Herpes simplex infection | 0.963 | 44 | 7 | 15.9 |
| hsa04512 | ECM-receptor interaction | 0.969 | 44 | 4 | 9.1 |
| hsa05100 | Bacterial invasion of epithelial cells | 0.994 | 44 | 7 | 15.9 |
| hsa05130 | Pathogenic Escherichia coli infection | 0.996 | 44 | 9 | 20.5 |
| hsa04330 | Notch signaling pathway | 1.000 | 44 | 9 | 20.5 |
| hsa05131 | Shigellosis | 1.000 | 44 | 5 | 11.4 |
| hsa05120 | Epithelial cell signaling in Helicobacter pylori infection | 1.000 | 44 | 5 | 11.4 |
Fig. 7.
Somatically altered pathways in PSH. (A) Schematic representation of possible genetic mechanisms for PSH development. Predicted pathways from mutation data are depicted. Genes are shown along with the percentage of somatic mutations and CNAs. A potential druggable gene is highlighted in green lightning symbols. (B) Somatic mutations and CNAs in the discovery (44 cases) and validation (24 cases) sets of PSHs.
Discussion
In this study, we attempted to identify the genetic alterations of PSH genomes by WES. First, we observed highly frequent AKT1 gene alterations (somatic mutations and copy gains) in the PSH genomes. Based on the current knowledge of AKT1 mutations (oncogenic activities and recurrent hotspot mutations) (20, 27, 28), our data suggest that AKT1 gene alterations are the most common genetic driver that might contribute to the PSH development. Recurrent AKT1 mutation was first reported in breast cancers and subsequently in other tumors (29). In these tumors, however, the frequency of AKT1 mutations was much lower (∼8%) than that of the PSHs in our study. When combining AKT1 mutations with AKT1 copy gain, AKT1 genetic alterations in PSH reached 48.5%, which is the highest incidence of AKT1 alterations in human tumors (the COSMIC database). We also observed β-catenin mutation in 4.4% of the PSHs that was mutually exclusive with AKT1 mutation in PSHs. In addition, several tumor-related genes, including ARID1B, APC, BLM, PLCG1, FAS, ATRX, EP300, KMT2D, and ATM were detected in the PSHs at low incidences (2.3–4.5%). As for CNAs, 15.9% of the PSHs harbored alterations, whereas most PSHs (84.1%) did not exhibit any noticeable CNAs. Such a high incidence of oncogenic AKT1 alterations and a low incidence of other mutations, as well as few CNAs in the PSHs, suggest that AKT1 alterations are likely key players in PSH development. Other mutations and CNAs might possibly contribute to PSH pathogenesis, but their causal roles or the roles in conjunction with AKT1 signaling remain to be clarified.
Of note, either AKT1 or β-catenin mutation in malignant tumors usually co-occurs with other driver genomic alterations (22, 30, 31). However, AKT1 mutations in benign tumors, such as meningiomas and PSHs, and β-catenin mutations in pilomatricoma do not usually co-occur with each other or other known driver mutations (32–34). It is possible that AKT1 or β-catenin mutation alone is able to produce a benign tumor but not a malignant tumor. AKT1 mutation itself has a weak transforming activity (35), which might make the cells proliferate and change morphology but not acquire the capability to further progress to malignancy.
The histogenesis of the dual cells present in PSHs has long puzzled scientists. One theory suggests that both cuboidal epithelial and polygonal stromal cells represent a neoplastic origin by identifying monoclonality of these two cells (3). The other theory suggests that the polygonal cell population may represent neoplastic cells, whereas the cuboidal cells represent entrapped epithelial cells from immunohistochemical analysis (25). Our study identified that both of the cell components in PSHs, cuboidal epithelial and polygonal stromal, harbored the same somatic and germ-line variants, supporting the former theory. Our data suggest that both cells are a true neoplastic component and that they may be histologically different but not genetically. Some nonneoplastic respiratory epithelial cells may be entrapped in the PSHs, but many of the cuboidal epithelial cells, especially deep inside of the PSHs, may represent neoplastic cells. It could be possible that the microdissection was not specific enough and that both components were present in the preparations. However, this possibility, if any, with a minor contamination of stromal cells into epithelial cells, may be low because the height of mutant peaks in the sequencing (Fig. 4 and Fig. S4) are similar (even higher in epithelial cells in some cases) between the two cells. Furthermore, we discovered that multinodular PSHs in a patient harbored two different AKT1 mutation variants, indicating that the multinodularity in PSH may not result from metastasis.
Tumor-suppressor genes constitute the largest group of hereditary cancer genes, whereas only a few oncogenes—including RET, MET, KIT, and AKT1—are associated with hereditary tumor development (36). For example, Proteus syndrome, a congenital disease involving overgrowth of skin, muscles, fatty tissues, and blood vessels, as well as pulmonary cysts and venous dilatation, is caused by somatic mosaic mutations of AKT1 p.E17K (37). Afflicted individuals are at increased risk for developing benign tumors of the ovary, meninx, and the parotid gland. However, the risk of malignant tumor is not increased (38). Congenital AKT1 p.E17K mutations associated with benign tumors and vascular lesions might explain the role of somatic AKT1 mutation in the PSH phenotype.
Despite the predominant prevalence of AKT1 mutations, ∼40% of PSHs exhibited neither driver mutation nor CNA, suggesting epigenetic or not yet identified genetic origin of this disease. Further genetic or epigenetic studies with AKT1 mutation-negative PSHs will be required to discover other driver alterations of PSH.
In conclusion, our study has revealed genomic differences between PSHs and lung adenocarcinomas, including a high rate of AKT1 mutation and few CNAs in PSH. These genomic features of PSH identified in the present study provide clues to understanding the benign biology of PSH and for differential genomic diagnosis of lung tumors.
Materials and Methods
Sclerosing Hemangioma Tissues.
Approval for this study was obtained from the Institutional Review Board at the Catholic University of Korea, College of Medicine. Clinicopathologic features of the patients are summarized in Table S1. The PSH patients in our study did not show any congenital abnormalities of Proteus syndrome. All of the patients were Korean. Fresh-frozen tissues from eight patients with PSH were obtained from the Tissue Banks of Seoul St. Mary Hospital of Catholic University (Seoul, Korea), Guro Hospital of Korea University (Seoul, Korea), and Pusan National University Hospital (Pusan, Korea). Initially, frozen tissues from the tissue banks were cut, stained with H&E, and examined under a microscope by a pathologist, who identified areas rich in PSH tumor cells. To procure matched normal tissues from each patient, we used lung tissues that were confirmed to be free of tumor cells based on examination under the microscope. The purities of PSH tumor cells were ∼20–80%. In addition, 36 FFPE PSHs from 35 patients (two PSHs in one patient) were used as the discovery set for WES. Another 24 FFPE PSHs were used for validation of important alterations. The FFPE tissues were archival tissues collected from Seoul St. Mary Hospital (Seoul, Korea), Incheon St. Mary Hospital (Incheon, Korea), Ujeongbu St. Mary Hospital (Ujeongbu, Korea), St. Vincent Hospital (Suwon, Korea), St. Paul Hospital (Seoul, Korea), Bucheon St. Mary Hospital (Bucheon, Korea), Yeouido St. Mary Hospital (Seoul, Korea), and Kyungpook National University Hospital (Daegu, Korea) under the permission of the Institutional Review Board at the Catholic University of Korea, College of Medicine (MC14SISI0002). The tumor cells and normal cells were separately procured from H&E-stained slides. For genomic DNA extraction, we used the DNeasy Blood & Tissue Kit (Qiagen).
WES.
WES was performed for the genomic DNA obtained from tumor and matched normal specimen using the Agilent SureSelect Human All Exome 50Mb kit (Agilent Technologies) and Illumina HiSeq2000 platform, according to the manufacturer’s instructions. Acquisition and processing of the sequencing data were performed as previously described (39). A Burrows–Wheeler aligner was used to align the sequencing reads onto the human reference genome (UCSC hg19). The aligned sequencing reads were evaluated using Qualimap (40), and the sequences were deposited in the Sequence Read Archive database (Project ID: PRJNA297066).
Identification of Somatic Variants.
Somatic variants were identified using MuTect (41) and SomaticIndelDetector (42) for point mutations and indels, respectively. The ANNOVAR package (43) was used to select somatic variants located in the exonic sequences and to predict their functional consequences. To obtain reliable and robust mutation calling, the following somatic variants were eliminated: (i) read depth fewer than 20 in either the tumor or matched normal; (ii) polymorphisms referenced in either 1000 Genomes Project, Exome Aggregation Consortium, or Exome Sequencing Project with a minor allele frequency greater than 2%; and (iii) variants with VAF between 45% and 55% in a copy-neutral region. Finally, validated variants according to Sanger sequencing or digital PCR were manually curated.
Cancer-Related Mutations and Validation.
We performed MutSigCV (14) and OncodriveFM (17) analyses to identify and validate cancer-related mutations. Significantly mutated genes with q-value < 0.01 were considered driver mutations. Cancer-related genes are also defined as those identified in both the Cancer Gene Census (15) and Cancer Drivers Database (16). We validated 51 mutations of 23 genes using either digital-PCR or Sanger sequencing. Details are summarized in Table S3. Digital-PCR was performed using the TaqMan Genotyping assay and the QuantStudio 3D digital PCR system (Life Technologies) as described elsewhere (44). The digital PCR data were analyzed using the Relative Quantification module of the QuantStudio 3D AnalysisSuite Cloud Software. The confidence level was set to 95%, and the desired precision value was 10%. In addition, to further assess the recurrent mutations in PSHs, we performed Sanger sequencing of AKT1 and CTNNB1 in the independent validation set (24 FFPE PSHs).
DNA Copy Number and LOH Analysis.
DNA copy number and LOH were estimated using WES data. The ngCGH module and RankSegmentation statistical algorithm in NEXUS software v7.5 (Biodiscovery) were used to define CNAs of each sample (44). We inferred the LOH events using Sequenza (45). All of the identified CNAs and LOH events were manually curated in terms of depth ratio and B allele frequency.
Supplementary Material
Acknowledgments
This study was supported by National Research Foundation of Korea (Grant 2012R1A5A2047939), the Korean Health Industry Development Institute of the Ministry of Health and Welfare of Korea (Grant HI13C2148), and the National Cancer Institute of the NIH (Grant P30CA034196). C.L. is a distinguished Ewha Womans University Professor supported in part by the Ewha Womans University Research grant of 2015.
Footnotes
The authors declare no conflict of interest.
This article is a PNAS Direct Submission. J.D.M. is a Guest Editor invited by the Editorial Board.
Data deposition: The sequences have been deposited in the NCBI Sequence Read Archive database, www.ncbi.nlm.nih.gov/sra (Project ID: PRJNA297066).
This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1606946113/-/DCSupplemental.
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