Skip to main content
Diagnostics logoLink to Diagnostics
. 2023 Jun 16;13(12):2088. doi: 10.3390/diagnostics13122088

A Robust FISH Assay to Detect FGFR2 Translocations in Intrahepatic Cholangiocarcinoma Patients

Lei Zhang 1,*, Hao Zheng 1, Linyu Xu 1, Si You 1, Yuanyuan Shen 1, Yang Han 1, Steve Anderson 1,*
Editor: Gian Paolo Caviglia1
PMCID: PMC10296881  PMID: 37370984

Abstract

FGFR fusions retaining the FGFR kinase domain are active kinases that are either overexpressed or constitutively activated throughout diverse cancer types. The presence of FGFR translocations enhances tumor cell proliferation and contributes to significant sensitivity to FGFR kinase inhibitors. FGFR2 as an actionable target in intrahepatic cholangiocarcinoma (iCCA) has been tested in many clinical trials. FISH (fluorescence in situ hybridization) and NGS (next-generation sequence) are well-known tools to investigate the translocations of FGFR with multiple or unknown translocation partners. A rapid and robust FISH assay was developed and validated to detect FGFR2 translocations from FFPE specimens in iCCA. The analytical performance of the FISH assay was evaluated for probe localization, probe sensitivity and specificity, and assay precision. Twenty-five archival FFPE specimens from local iCCA patients were tested for FGFR2 translocations. FISH results were correlated with that of NGS on some samples. Biallelic translocations and a novel FGFR2 translocation involving the partner gene, SHROOM3, t(4;10) (q21;q26), were identified in a local iCCA patient.

Keywords: FGFR2 translocation, intrahepatic cholangiocarcinoma, break-apart FISH, biallelic translocation

1. Introduction

Intrahepatic Cholangiocarcinoma (iCCA) is the second most common primary hepatic malignancy after hepatocellular carcinoma and is associated with hepatitis virus infection. It accounts for 3% of the malignant tumors of the gastrointestinal system and 15% of primary hepatic malignancies, and has high incidence and mortality in East Asia. The prognosis of patients with cholangiocarcinoma is poor. Surgery is the only potentially curative therapeutic option. However, as most patients present with advanced disease, only approximately one-third of newly diagnosed patients qualify for surgery. For other patients with locally advanced or metastatic disease, the standard-of-care first-line systemic treatment is chemotherapy. The FGFR inhibitor targeting tumors with FGFR rearrangements is becoming a promising therapy and gradually changing the paradigm in the treatment of iCCA. FGFR inhibitors are already recommended for the treatment of patients with FGFR2 rearrangements when diseases have progressed after >1 prior line of systemic therapy, with Pemigatinib approved by both FDA and EMA, and Infigratinib and Futibatinib approved by FDA as well [1,2].

FGFR fusion proteins retaining the FGFR kinase domain are active kinases either overexpressed or constitutively activated throughout diverse cancer types. The presence of FGFR fusions not only enhances tumor cell proliferation but also leads to significant sensitivity to FGFR kinase inhibitors.

FGFR2 as an actionable target in intrahepatic cholangiocarcinoma (iCCA) has been studied in many clinical trials [3,4,5]. Pemigatinib is the first targeted therapeutic agent approved in the United States for cholangiocarcinoma with FGFR2 fusions or rearrangements [6]. After that, Infigratinib was granted accelerated approval by FDA for cholangiocarcinoma with an FGFR2 fusion or rearrangement. Several other approaches, including novel FGFR inhibitors, are being investigated to build upon the success of Pemigatinib and Infigratinib [7]. A rapid and accurate assay to detect FGFR2 translocations, a major type in FGFR2 gene alterations, is critical for patient selection in treatment with FGFR non-selective/selective kinase inhibitors.

Break-apart FISH and NGS are well-known effective tools for investigating translocations with multiple or unknown translocation partners. Ninety FGFR2 translocation partners have been discovered in iCCA thus far, involving inter- and intra-chromosome translocations or intra-chromosome arm translocations [8].

Here, we showed the establishment of a rapid and robust FGFR2 break-apart FISH assay in a CAP/ISO-accredited laboratory and reported a novel FGFR2::SHROOM3 translocation in iCCA patients in China accompanied by a snapshot of the literature-reported FGFR2 rearrangement in iCCA.

2. Methods and Materials

2.1. Samples

A variety of samples was used for these studies. Normal Metaphase CGH Target Slides (Abbott Molecular Inc., Des Plaines, IL, USA) were used in the probe localization, sensitivity, and specificity assay. Twenty-two FFPE samples from individuals who were known not to have iCCA were tested to establish an FGFR2 FISH database, including 4 normal gallbladder samples, 5 chronic cholecystitis samples, 5 chronic intrahepatic cholecystitis samples, and 8 normal tonsil samples (Fanpu Biotech Inc., Guilin, Guangxi, China). Twenty-five archival FFPE samples from different local patients with iCCA were included in this study (Fanpu Biotech Inc., Guilin, Guangxi, China). Samples used for this analytical validation were remnant tissues that were anonymized and their use for such studies was approved by an ethics board and was found to be acceptable for this use and consistent with ethical and medical standards for clinical research with human samples (Table 1).

Table 1.

The demographic information of 25 patients with cholangiocarcinoma.

Case No. Age Gender Location Histology Type Stage TNM_T TNM_N TNM_M
1 73 F Liver Cholangiocarcinoma II T2 N1 M0
2 38 F Liver Cholangiocarcinoma III T2 N0 M0
3 51 M Liver Cholangiocarcinoma II T2 N1 M0
4 42 M Liver Cholangiocarcinoma II T2 N1 M0
5 53 M Liver Cholangiocarcinoma II T2 N0 M0
6 64 F Liver Cholangiocarcinoma III T2 N0 M0
7 63 M Liver Cholangiocarcinoma II T2 N0 M0
8 64 F Liver Cholangiocarcinoma I T2 N0 M0
9 26 F Liver Cholangiocarcinoma II T2 N0 M0
10 66 M Liver Cholangiocarcinoma III T2 N0 M0
11 58 M Liver Cholangiocarcinoma II T2 N0 M0
12 60 M Liver Cholangiocarcinoma II T3 N1 M0
13 72 F Liver Cholangiocarcinoma I T3 N1 M0
14 56 F Liver Cholangiocarcinoma I~II T2 N1 M0
15 47 F Liver Cholangiocarcinoma II T2 N0 M0
16 47 M Liver Cholangiocarcinoma II T2 N0 M0
17 70 M Liver Cholangiocarcinoma II T2 N0 M0
18 47 M Liver Cholangiocarcinoma III T2 N0 M0
19 29 F Liver Cholangiocarcinoma II T2 N0 M0
20 57 F Liver Cholangiocarcinoma II T2 N1 M0
21 66 F Liver Cholangiocarcinoma III T2 N1 M0
22 64 F Liver Cholangiocarcinoma I T1 N0 M0
23 51 F Liver Cholangiocarcinoma II~III T2 N0 M0
24 61 F Liver Cholangiocarcinoma I~II T1 N0 M0
25 33 F Liver Cholangiocarcinoma I T2 N1 M0

2.2. FISH Hybridization

FISH was performed on formalin-fixed paraffin-embedded (FFPE) specimens using dual-color break-apart translocation probes specific to the FGFR2 gene (Abbott Molecular, 09N26-060, RUO). See Figure 1 for the probe configuration. The recommended protocol in the probe package insert was referred to. In brief, a Hematoxylin and Eosin (H & E) stained slide was examined by a pathologist to verify tumor content and encircle a representative tumor area for assessment. Then, the encircled tumor area on the H & E slide was used to target the same area on unstained slides by marking the glass slide on the opposite side using an etching tool and then dewaxed and air dried for the next steps. After that, the slides went through the pretreatment (80 ± 1 °C for 25 ± 15 min) and digestion (37 ± 1 °C for 20 ± 10 min) successively (Vysis IntelliFISH Universal FFPE Tissue Pretreatment and Wash Reagents, Abbott, 08N85-005 and Vysis IntelliFISH Protease, Abbott, 08N85-010). The exact time of pretreatment and digestion depended on the pre-analytical conditions of the tissue samples and can be adjusted in the range based on digestion conditions. Then, the denaturation (73 °C for 5 min) was carried out and followed by the hybridization (37 °C for 2 h) in the fast hybridization system (Vysis IntelliFISH Hybridization, Abbott, 08N87-001). After that, the stringent wash was conducted by gently agitating slides in the wash buffer maintained at 73 ± 1 °C for 1 to 3 s (Post-Hybridization Wash Buffer, 08N85-005). Finally, the slides were counterstained (DAPI I Counterstain, Abbott, 06J49-001) and kept at −20 °C until enumeration.

Figure 1.

Figure 1

Probe design and configurations. (A). The Vysis LSI FGFR2 (Cen) SpectrumOrange probe was positioned centromeric of the FGFR2 gene and was approximately 414 kb in size spanning chr10:122,841,555–123,255,766 on 10q26.12-q26.13 (February 2009, UCSC Genome Browser1). The Vysis LSI FGFR2 (Tel) SpectrumGreen probe was positioned telomeric of the FGFR2 gene and was approximately 491 kb in size spanning chr10:123,300,014–123,791,418 on 10q26.13. (B). The orange line indicates the location of Chr10: 123,255,766, the end of the orange probe (Tel), corresponding to the region of the 13th–14th exon of FGFR2. The green line indicates the location of Chr10: 123,300,014, the start of the green probe (Cen), corresponding to the region of the 6th–7th exon of FGFR2. Most FGFR2 breakpoints converge in the region from the last intron distal to the last exon (R). The pink line indicates the breakpoint for the positive case in this study, case 25. As a result of translocation, the green probe will move to the partner chromosomes or gene segments together with a small part of the orange probe and form a big green and a tiny orange break-apart signal (1G1g). Most of the orange probe remains at ch10q26.

2.3. FISH Scoring

Hybridized slides were stored at −20 °C, equilibrated at room temperature, and enumerated by trained readers within 3 weeks. Two readers, working independently and in a blinded fashion, selected different regions within the predetermined tumor area, and each scored 50 tumor cells. In total, 100 tumor cells were scored. A third reader was introduced under the condition that one reader had a positive count at or below the cutoff of 50 nuclei and the other has a count above this value or vice versa. The 3rd reader would count 50 cells and the two scores with the greatest agreement were combined to generate the count for 100 nuclei. According to the database setup, a cell is positive when at least one set of orange and green signals is split apart. Separate signals were defined as signals that do not touch or overlap and were therefore perceived as clearly distinct.

2.4. Probe Localization

Metaphase CGH Target Slides (Abbott Molecular Inc., USA) were probed using metaphase protocol. Five metaphase cells were examined through inverted DAPI banding to confirm that the probe correctly hybridized to the chromosomal region to which the probe’s target had been mapped and did not hybridize to other chromosomal regions.

2.5. Probe Sensitivity and Specificity

The same metaphase CGH slides were used. The autosomal targets in one hundred cells were examined. The sensitivity of the probes was calculated as the percentage of correct targets detected out of the total number of intended targets. The specificity of the probes was calculated as the percentage of correct targets detected out of the total number of targets detected.

2.6. Database, Reference Range

Twenty-two (22) FFPE samples from individuals known not to have cholangiocarcinoma included 8 normal tonsil samples, 4 normal gallbladder samples, and 10 chronic cholecystitis samples. The slides of these samples were analyzed as described above. Cells were scored for the number of different signal patterns. The total number of each signal pattern was tallied. The statistical method that was chosen to calculate the upper limit of the 95% confidence interval for abnormal FISH signal patterns was the mean and the inverse beta function (ACMG guideline recommendation). The mean and beta inverse statistic was performed using the Microsoft Excel program.

2.7. NGS

Next-generation sequencing experiments were performed by Precision Scientific (Beijing, China) Co., Ltd. The entire FFPE section was used to extract DNA. DNA libraries were prepared using the KAPA Hyper Prep Kit. Target capture and enrichment were performed by using Precision Scientific proprietary OncoComp enrichment reagents. The 17 intron of FGFR2 gene where most FGFR2 breakpoints converge was included in the capture probe panel. All libraries were sequenced on NextSeq550AR system. Raw sequencing data were mapped to the human reference genome (hg19/GRCh37) using the Burrows–Wheeler Aligner (BWA) version 0.7.17. Sorting, duplicate-read markup, and base-quality score recalibration were performed using the Genome Analysis Toolkit (GATK) version 4.0.12. Further detection of translocations was performed using Precision Scientific in-house software (v0.5.13). Four (4) FISH-negative samples randomly selected from 25 samples and one (1) FISH-positive sample, case 25 were included in the NGS analysis.

3. Results

3.1. FISH Method Optimization

FFPE tissue sections were used to verify the methods and conditions including pretreatment, digestion, denaturation, hybridization, and stringent wash recommended in the probe package insert. Two different hybridization systems were compared, Vysis LSI/WCP Hybridization Buffer (traditional hybridization system) and Vysis IntelliFISH Hybridization Buffer (fast hybridization system). The signal-to-noise ratio was evaluated. A comparable hybridization efficacy was achieved (Figure 2).

Figure 2.

Figure 2

Comparison of two hybridization systems. Two iCCA tissue samples, case 7 and 8, were used in the comparison. The two samples were subjected to FISH analysis (methods and materials) except for the hybridization step. Two hybridization methods were tested separately. Method I, IntelliFISH Hybridization system and hybridization for 2 h at 37 °C. Method II, LSI/WCP Hybridization system and hybridization for 24 h at 37 °C. The signal-to-noise ratio was evaluated. (A). Method I, case 7; (B). Method II, case 7; (C). Method I, case 8; (D). Method II, case 8. The shorter hybridization time provided comparable results. (Each of the four images fits one column).

3.2. Probe Localization, Probe Sensitivity, and Specificity

Five (5) cells with good metaphase chromosome spreads were examined under inverted DAPI banding mode. Probes were 100% hybridized to the correct site (the long arm of chromosome 10) and no other sites (Figure 3). The same slides were also used to determine the probe sensitivity and specificity. A total of 100 metaphase nuclei (equal to 400 target sites) was examined. Probe sensitivity and sensitivity of 10q26.12-q26.13 (R) were 98.5% and 99.5%, respectively. Probe sensitivity and sensitivity of 10q26.13 (G) were 98.5% and 99.5%, respectively (Table 2).

Figure 3.

Figure 3

Representative images of probe localization. (A). The inversed DAPI banding and chromosome recognition. The signal pattern observed in normal cells without disruption of FGFR2 gene in 10q26.1. Colocalized orange-green hybridization signals, representing the non-rearranged FGFR2 loci, are visible on both homologues of chromosome 10 by metaphase analysis. (Each chromosome has its unique anatomy in terms of chromosome size, location of centromere, arm size, and banding pattern.) (B). The same nucleus showed in combined fluorescent channels. The probe signals were mapped onto the intended chromosomal 10 long arms.

Table 2.

Probe sensitivity and specificity results.

Configurations Normal Other Normal Signal Patterns Break-Apart
Signal patterns 2F 1F 1R1G1F
100 target cells
(400 targets)
97 2 1
Probe sensitivity Probe specificity
10q26.12-q26.13 (R) * 98.5% (197/200) 99.5% (197/198)
10q26.13 (G) 98.5% (197/200) 99.5% (197/198)

*, the probe targeting the chromosome region 10q26.12-q26.13 and labeled with SpectrumOrange. ∥, the probe targeting the chromosome region 10q26.13 and labeled with SpectrumGreen.

3.3. Database, Reference Range

FISH database is a normal reference range and used for the interpretation of patient samples where acquired mosaic abnormalities exist. The database was constructed based on 22 “normal” FFPE samples. The definition of individual/separate signal was determined as single-color signals with no touch and overlap and perceived as clearly distinct signals. A cell is positive for FGFR2 translocation when at least one set of orange and green signals splits apart. The normal cutoffs were established as 11 for the total count of 50 cells and 19 for 100 cells (Table 3).

Table 3.

Cutoffs established for the break-apart FGFR2 FISH assay.

Signal Patterns BA * 1F 3F 4F 5F
Cutoffs (number of cells with the signal pattern in 100 cells enumerated) 7 25 11 10 5
Signal patterns BA 1F 3F 4F 5F
Cutoffs (number of cells with the signal pattern in 50 cells enumerated) 5 14 7 7 4

* BA represents the break-apart signal patterns. F represents fusion signal patterns.

3.4. FGFR2 Translocation Detection in Twenty-Five (25) iCCA Patients

3.4.1. FISH Results

Twenty-five (25) FFPE iCCA samples were tested using the established FISH method. One of twenty-five (1/25, 4%) samples was positive for FGFR2 translocation, while the remaining 24 samples were negative for FGFR2 translocation. For the positive sample, case 25, the break-apart signal was observed in 98 of 100 (98%) enumerated cells. The observed frequency of FGFR2 translocation in this sample cohort of iCCA was 4% (1/25) which is lower than literature reports (10–15%) [8,9,10], but similar to studies conducted in China iCCA patients (−5%) [11,12]. In addition, the separated signals were seen occurred on both homologous chromosomes (chr10) in many of enumerated nuclei (biallelic translocation), which took the “double split” signal pattern, two sets of split signals (two orange and two green or two orange/green and one green/orange) and zero fusion signals (Figure 4A,B).

Figure 4.

Figure 4

Representative images of the abnormal result (break-apart signals). (A,B). Different types of break-apart signals were observed in aberrant nuclei including “double split” (2R2G), biallelic translocation, and special 1R1G1r1G signals. R, represents the orange signal. G, represents the green signal. r, represents the small orange signal. (Both (A,B) fit 1.5 columns).

3.4.2. NGS Results

Five (5) iCCA samples were subjected to NGS and FISH FGFR2 fusion detection. Consistent results were found in three of five iCCA samples. Two samples detected as negative by FISH were indeterminate by NGS due to sample quality. It indicated that NGS might be more sensitive to FFPE sample pre-analytical conditions (Table 4).

Table 4.

FISH and NGS results.

Case No. FISH NGS
10 Negative Indeterminate
2 Negative Negative
1 Negative Negative
25 Positive Positive
24 Negative Indeterminate

3.5. FGFR2::SHROOM3 Translocation

The breakpoints identified in the positive sample by NGS are chr10:123,240,930 and chr4:76,645,542 at a frequency of 4.76%, based on which we constructed the fusion gene structure and breakpoint locations, for the positive sample case 25. The inter-chromosomal translocation between FGFR2 and SHROOM3 gene, t(4;10)(q21;q26), was identified (GRCh38 AR110) in local iCCA patients with a breakpoint within the intron 2 of SHROOM3, which we have not seen reported previously in the literature (Figure 5).

Figure 5.

Figure 5

The structure and breakpoints of the fusion genes identified in the study. The upper illustration shows the gene structure of FGFR2 and SHROOM3 genes. The lower illustration shows the fusion gene structure with breakpoint details (in the intron 17 of FGFR2 gene and intron 2 of SHROOM3 gene). (2 columns).

4. Discussion

4.1. Section Thickness

Fluorescence in situ hybridization, as a molecular cytogenetic method, is used routinely in both cytology and tissue samples. Although a commercial reagent kit provides a general protocol and advice on the range of conditions, a thorough investigation and extensive optimization of the pre-analytic and analytic conditions are critical to produce an acceptable or optimal result, which is especially true when used in FFPE tissue samples. Many factors will cause signal interference, including section thickness and polyploidy of hepatocytes and chromosome territory [13,14,15].

In the beginning of our database setup, we found that the proportion of one fusion (1F) signal pattern was extremely high, 32.60–40.40%. Other polyploidy signal patterns (3F–8F) were also observed. The high proportion of these background noises could skew the database setup and compromise its purpose of investigating the level of background noise of break-apart signal. It had been acknowledged that an age-related increase of polyploidization and nuclear volume is a development feature in hepatocytes in the adult human [14,16,17]. Contamination of hepatocytes in the database setup may cause abnormally high proportion of 1F signal due to nucleus truncation in FFPE slide preparation.

To address the issue of high 1F type signals in the database setup, we first re-enumerate the database samples, chronic intrahepatic cholecystitis samples with particular cautions not to count in hepatocytes to mitigate the interference of the polyploidy and truncated signals. Then, we investigated the potential impact of section thickness on high proportion of 1F signal pattern. Two sections of different thicknesses of the same sample were prepared and analyzed. The results showed that the 1F count of 100 cells in each sample significantly dropped with the increase in section thickness (Table 5). The 1F proportion decreased from the range of 32.60% to 40.40% in sections at 4 μm thickness to 7.69% to 15.09% in sections at 6μm thickness across different types of samples. It indicated that truncation may be the major reason for the observation of a high proportion of 1F. Therefore, the 6μm section thickness was strongly recommended, albeit 4–6 μm was suggested in the probe kit RUO package insert.

Table 5.

Impact of section thickness on the level of background noise 1F .

Tissue Type 1F Ratio at 4 μm, % 1F Ratio at 6 μm, %
Normal liver 40.00 9.23
Normal gallbladder 40.40 18.03
Hepatitis 32.60 7.69
Chronic cholecystitis 38.80 15.09
Chronic intrahepatic cholecystitis 37.20 13.24
Average 37.80 12.66

, F represents fusion signal patterns.

4.2. Break-Apart Signal Definitions and Its Diverse Signal Patterns

The definition of a separated signal is an important step in the setup of FISH assays employing break-apart probes. Based on it, the occurrence of chromosomal structure anomaly of interest is identified through the observation of signals separation (break-apart) in a nucleus. Three scenarios of signal distribution of break-apart signals can result from translocation events when observed under a fluorescent microscope: (A). where separate orange and green signals take the form of colocalization, touching, or partial overlap due to non-dividing cell nuclear geometry and chromosome steric arrangement. (B). where orange and green signals are determined as clearly distinct signals with no touch and overlap and located less than one signal diameter apart. (C). where orange and green signals are located one signal diameter or more apart. In FGFR2 break-apart FISH assay, a separate signal was defined as signals of different colors located one signal diameter or more apart (C) under a fluorescent microscope. The fusion signals were defined accordingly as colocalized green and orange signals (yellow) or signals of two colors that touch or partially overlap. Adoption of the definitions takes into account the fact that the two probes flanking the known breakpoint cluster region in FGFR2 gene sit only 44 kb apart, which is relatively closer compared to other break-apart probes, for example, ALK (695 kb) [18], ETV1 (530 kb) [19], and FGFR2 BAC probes (162 kb) [9]. This is consistent with what we observed in the database setup: most fusion signals were colocalized green and orange signals exhibiting yellow fusion signals or touching or partially overlapped green and orange signals (Figure 2).

A cell/nucleus was defined as positive when at least one set of orange and green signals split apart or when there is a single green/orange signal in addition to colocalized and/or break-apart signals. In our experiment, multiple types of broken-apart signal patterns were observed with study probes. They were signal patterns, 1R1G1F, 1G1F, 2R2G, 2R1G, and 1R2G (Figure 4A,B). Among them, 1R1G1F or 2R2G were orthodox break-apart signal patterns. Other break-apart signals, such as 1G1F, 2R1G, and 1R2G, may involve additional chromosomal aberrations such as deletions. In addition, we demonstrated the existence of the 1R1G1r1G signal in case 25, which was a break-apart signal pattern specific to the study probe design (Figure 1B and Figure 4B). Theoretically, every break-apart green signal should take the form in aberrant cancer cells where the translocation occurs. However, the 1R1G1r1G signal was only seen in rare cells in case 25. It might be because: 1. The parting-away section of the orange probe only accounts for 3% (12.5 kb/414 kb) of the orange probe. 2. The microscopic observation reduces the 3D nucleic geometry into 2D geometry and the presentation of 1R1G1r1G needs a specific spatial orientation of the chromosomal aberration.

4.3. Comparison between Break-Apart FISH and NGS

FISH and NGS are well-known effective tools for investigating translocations with multiple or unknown fusion partners. NGS has advantages in translocation detection by determining breakpoint locations, discovering de novo translocation, and revealing highly complicated rearrangements. However, in the clinical application when it comes to FFPE tissue samples, NGS may have some limitations. DNA-based, hybridization capture-based target enrichment on FFPE material highly depends on the degree of fragmentation. Assessing the integrity of DNA or RNA derived from FFPE material is thus critical to determine which samples are most likely to be successfully prepared for sequencing and producing quality data.

In this study, we adopted the Abbott IntelliFISH hybridization system. It shortened the FISH assay turnaround time by reducing hybridization time from overnight to 2 h. FISH method demonstrated some advantages over NGS in terms of a shorter turnaround time (3–5 d vs. 10–15 d), being less sensitive to FFPE sample pre-analytical conditions, and less consumption of FFPE materials (2–3 slides vs. 4 to 10 slides). Two of five samples subjected to NGS were reported QC failure due to low DNA quality (indeterminate). In the two samples unresolved by NGS, the initial sequencing data showed an average depth in the target region of ~586×. The repeats with doubled DNA input produced only ~783× depth in the target region, far below the target depth of 1000×. It indicated that the extent of DNA fragmentation in the FFPE samples made them unsuitable for NGS fusion analysis. The two samples, however, were analyzed successfully and detected as negative for FGFR2 translocations by the FISH.

Both methods identified the translocation in case 25 in the study. However, the frequencies at which the translocation was detected were markedly different between NGS and FISH with the former being 4.76% and the latter ≥96%. Not conducting a macrodissection of tumor FFPE sections in NGS procedures might be able to explain the much lower frequency of translocation.

FISH technology enables the detection in the context of the tissue and cellular microanatomy, thereby making it a good tool to spatially resolve gene structural or numeric alterations at a chromosomal level. In this study, FGFR2 break-apart FISH assay revealed the “double split” signal pattern (2R2G), FGFR2 biallelic translocation in case 25, which accounted for 60% of positive cells. The FGFR2 monoallelic translocation (1F1G1R or 1F1G) was found in only 4% of positive cells. The remaining positive cell (34%) showed the break-apart signals with a loss of either green or orange signal (1R2G, 2R1G, or 1R1G) (Figure 4A,B), while NGS technology needs first to pulverize specimens and extract nucleic acid before analysis, which makes the characterization a process to destroy and discard the spatial information. From this perspective, NGS is reduced to the detection of genetic variants with somatic mutations in a wild-type normal gene background, which is not intended for the detection of biallelic translocations. From the genetic level, NGS is not yet a phasing technology and cannot resolve haplotypic information.

Biallelic translocations identified by break-apart FISH have been reported in other malignancies. They usually coexist with corresponding monoallelic translocations where a fusion signal remains, indicating the intactness of the other homologous chromosome. A likely explanation for the coexistence of biallelic and monoallelic translocation might be that the aberrant malignant cell population consists of different clones [20,21]. Currently, no research data show if a variation in biological effects, such as gene dose effect, exists between these different translocations. The observed high ratio of non-standard break-apart signals (1R2G, 2R1G, or 1R1G) could be attributed to more complex chromosomal rearrangement events [22] in oncogenesis.

4.4. FGFR2::SHROOM3 Translocation

The FGFR2 fusion partner gene, SHROOM3, has been reported previously in iCCA [8,10,11]. In Silverman I et al., 3 of 74 FGFR2-rearranged iCCA cases were identified as translocations with SHROOM3 being the partner gene, of which the breakpoint genomic locations were not released [8]. In Maruki Y. et al., 1 of 21 FGFR2 fusion-positive iCCA cases was found to have SHROOM3 as the fusion partner gene. No breakpoint information was reported in the article [10]. In Zhu Z. et al., 1 of 14 FGFR2 translocation-positive iCCA cases was detected as translocation involving SHROOM2 [11]. The breakpoint was reported in exon 6 of SHROOM, which is different from what we found in the study. In our study, the breakpoint where the head gene segment containing the truncated FGFR2 joined the tail gene segment containing partial SHROOM3 was determined within the intron 2 of SHROOM3 (Figure 5).

Oncogenic mechanisms of FGFR2 rearrangements which give rise to phenotypes of overexpression or constitutive activation were identified as promotor switching or acquisition of oligomerization domains from partner genes, respectively [23]. An instance of the promotor switching is the case of SLC45A3::FGFR2 fusion with FGFR2 at 3′gene (tail gene) position. It contains most of the promoter region of SLC45A3 and only the non-coding region of exon 1 and results in overexpression of FGFR2 in some patients with prostate cancer. Data supporting the acquisition of oligomerization domain in FGFR2 fusions as one of the important mechanisms are accumulating. Different types of oligomerization domains were involved. The coiled coil domain was found in FGFR2::CCDC6 (breast cancer, iCCA), FGFR2::CIT (lung carcinoma), and FGFR2::KIAA1967 (lung squamous cell carcinoma) fusions, all of which exhibited constitutive dimerization [23]. The sterile alpha motif (SAM) domain is another oligomerization domain found in FGFR2::BICC1 fusion in metastatic cholangiocarcinoma. Therefore, FGFR2::BICC1 is another example of ligand-independent dimerization, most likely mediated by the presence of the SAM domain [23]. LIS1-homologous (LisH) domain, a very stable dimerization domain, was found in FGFR2::OFD1 fusion containing a LisH motif and five coiled-coil domains [24]. We interrogated LOGICOIL (http://coiledcoils.chm.bris.ac.uk/LOGICOIL/) (accessed on 4 February 2023) for oligomerization domains in SHROOM3. Both tetramer and antiparallel dimer were suggested. We assumed that the fusion protein encoded by FGFR2::SHROOM3 may acquire the oligomerization capability through SHROOM3, hence enabling malignant cells to come into possession of the oligomerization-induced and ligand-independent constitutive activation of FGFR2 receptor kinase in case 25. Furthermore, studies also showed that the constitutive activation of FGFR2 kinase resulting from FGFR2 rearrangement may be in part contributed by the truncation of FGFR2 in its C-terminal (a universal feature of FGFR2 rearrangements) in the process of forming various kinds of FGFR2 rearrangement aberrations, which leads to the “loss of molecular brake” or escape from microRNA regulation [8,25,26].

When searching previously published data to paint a full picture of SHROOM partner gene in FGFR2 rearrangement, we summarized FGFR2 rearrangement in iCCA in terms of the genomic involvement of its partners based on both the data reported in the literature and an interrogation of the public fusion databases (Table 6 and Figure 6) [8,9,10,11,24,25]. The fusion database, ChimerDB version 4.0, was queried in generating the summarized data, covering both publications (ChimerPub plus) and curated fusion sequences (ChimerSeq plus). To date, 90 partner genes have been identified and reported in FGFR2 rearrangement in iCCA. It contains inter-chromosomal, intra-chromosomal, and intra-chromosomal arm rearrangements and can be categorized into two types, gene-intragenic and gene-intergenic rearrangements. Some reported cases of the latter category were not included in the Circos (Figure 6) due to the lack of information on their genomic locations. Most FGFR2 gene rearrangements in iCCA have FGFR2 as 5′ head gene, with a breakpoint usually found in the region from the last intron distal to the last exon. As a result, the extracellular and transmembrane domains of the FGFR2 protein remain intact, as well as a significant portion of the kinase domain. Only a few of them were reported having FGFF2 as 3′ tail gene, with a breakpoint in the last exon or second last exon [26]. A wide distribution of its partner in genome was revealed, involving all chromosomes except for chr13, chr15, chr16, chr21, chrY, and chX (Figure 6). It is unclear why FGFR2 gene rearrangement occurs with such a profound partner diversity. Possibilities could be that the genetic locus bearing FGFR2 gene is more accessible across a multitude of chromatin conformations based on major ideas from the research of the mechanistic rationale for fusion formation [27,28] and widespread availability of oligomerization domains in human genome [29,30,31].

Table 6.

Summary of FGFR2 translocations partners in iCCA.

5′ HEAD Genes 3′ TAIL Partners FGFR2 Partner Chromosomal Locations
FGFR2 BICC1 chr10
KIAA1217 chr10
AHCYL1 chr1
CCDC6 chr10
TACC2 chr10
SHROOM3 chr4
AFF4 chr5
ARHGAP24 chr4
CCDC170 chr6
FILIP1 chr6
MACF1 chr1
NOL4 chr18
NRAP chr10
PAWR chr12
SLMAP chr3
TACC1 chr8
TRIM8 chr10
ACLY chr17
ARHGAP22 chr10
ATAD2 chr8
ATF2 chr2
BICD1 chr12
CCDC158 chr4
CDC42BPB chr14
CEP128 chr14
COL16A1 chr1
CTNNA3 chr10
DBP chr19
DNAJC12 chr10
EEA1 chr12
EIF4ENIF1 (intergenic) chr22
ERC1 chr12
GAB2 chr11
GOPC chr6
INSC chr11
KCTD1 chr18
SHTN1/KIAA1598 chr10
MATR3 chr5
MCU chr10
NEDD4L chr18
NRBF2 chr10
PAH chr12
POC1B chr12
PXN chr12
RABGAP1L chr1
RASSF4 chr10
ROBO2 chr3
RPAP3 chr12
SFI1 chr22
SOGA1 chr20
SPICE1 chr3
STRN4 chr19
TBC1D1 chr4
TCTN3 chr10
TFEC chr7
TTC28 chr22
TXLNB chr6
USH2A chr1
VCL (intergenic) chr10
WAC chr10
WDHD1 chr14
ZMYM4 chr1
ZNF521 chr18
MGEA5/OGA chr10
TACC3 chr4
CREB5 chr7
CCDC186/C10orf118 chr10
FRK chr6
PPFIA2 chr12
TLN1 chr9
BAIAP2L1 (intergenic) chr7
TNIP3 chr4
LINC00863 chr10
CBX5 (intergenic) chr12
CFAP57 chr1
PRDX3 chr10
GOLGB1 chr3
ETV6 chr12
TFCP2L1 chr2
TXLNA chr1
RBFOX2 chr22
BICC1 FGFR2 chr10
VCL chr10
CASC2 chr10
WAC chr10
ERICH2-DT chr2
KIAA1217 chr10
GOLGB1 chr3
GMCL1 chr2
SETD3 chr14

Notes: Gene name highlighted in bold and italic fonts were partners which only showed in translocations taking FGFR2 as 3′ tail gene.

Figure 6.

Figure 6

Visualization of FGFR2 partners distribution in genome by Circos ideogram. The link highlighted in red color indicated the translocation, FGFR2::SHROOM3, identified in this study. Highlighted strokes were placed to indicate the 5′ head gene positions in the radial direction immediately adjacent to the inner arc of chromosomal ideogram. For example, on chr10, six locations were highlighted as head gene positions, FGFR2 gene (chr10: 121,593,967–121,598,444), CASC2 gene (chr10: 118,045,862–118,216,096), BICC1 gene (chr10: 58,512,220–58,831,435), VCL gene (chr10: 73,995,193–74,121,363), WAC gene (chr10: 28,532,493–28,623,112), and KIAA1217 gene (chr10: 23,694,727–24,547,848). Other head gene positions were highlighted on chr2, chr3, and ch14, respectively. (2 columns).

5. Conclusions

FGFR2 as an actionable target in iCCA has been studied in many clinical trials. FGFR2 translocation is a common genetic lesion in iCCA and increasingly used as a biomarker to predict the sensitivity to FGFR kinase inhibitors in iCCA-targeted therapy. Here, a rapid and robust FGFR2 FISH assay was developed and validated for detecting FGFR2 translocations in iCCA from archival FFPE specimens. Compared to NGS, the FISH assay may have potential advantages in clinical applications such as a shorter turnaround time, less consumption of tissue samples, more tolerance to sample pre-analytical conditions, and spatial resolving of translocations. Additionally, a novel FGFR2 translocation involving the SHROOM3 gene (intron 2) was identified in local iCCA patients.

Acknowledgments

The authors wish to thank Precision Scientific (Beijing) Co., Ltd. for its interests in collaboration in the study and providing free NGS testing service for some of the study samples. The authors also thank Yiqing Guan, Xinxin Peng, and Chang Liu for coordinating NGS testing from the end of Precision Scientific (Beijing) Co., Ltd.

Author Contributions

L.Z. conceived and designed the paper, performed data analysis, and wrote the paper. H.Z. and L.X. collected data. L.X., S.Y. and Y.S. conducted the experiments. Y.H. participated in pathology review. S.A. reviewed and made final approval of the version to be published. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Samples used for this analytical validation were remnant tissues that were anonymized and their use for such studies was approved by an ethics board and was found to be acceptable for this use and consistent with ethical and medical standards for clinical research with human sample.

Informed Consent Statement

Not applicable.

Data Availability Statement

No new data were created or analyzed in this study. Data sharing is not applicable to this article.

Conflicts of Interest

After completing the research work of the manuscript, authors, Lei Zhang, Hao Zheng, and Yang Han, left the institution listed in the author affiliations where the research work had been conceptualized and executed. Authors, Linyu Xu, Si, You, Yuanyuan Shen, and Steve Anderson are still employees of the institution listed in author affiliations. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Funding Statement

This research received no specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Footnotes

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

References

  • 1.Bath N.M., Pawlik T.M. Narrative Review: Current Management and Novel Targeted Therapies in Intrahepatic Cholangiocarcinoma. Chin. Clin. Oncol. 2023;12:5. doi: 10.21037/cco-22-109. [DOI] [PubMed] [Google Scholar]
  • 2.Proskuriakova E., Khedr A. Current Targeted Therapy Options in the Treatment of Cholangiocarcinoma: A Literature Review. Cureus. 2022;14:e26233. doi: 10.7759/cureus.26233. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Mazzaferro V., El-Rayes B.F., Droz dit Busset M., Cotsoglou C., Harris W.P., Damjanov N., Masi G., Rimassa L., Personeni N., Braiteh F., et al. Derazantinib (ARQ 087) in Advanced or Inoperable FGFR2 Gene Fusion-Positive Intrahepatic Cholangiocarcinoma. Br. J. Cancer. 2019;120:165–171. doi: 10.1038/s41416-018-0334-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Sootome H., Fujita H., Ito K., Ochiiwa H., Fujioka Y., Ito K., Miura A., Sagara T., Ito S., Ohsawa H., et al. Futibatinib Is a Novel Irreversible FGFR 1-4 Inhibitor That Shows Selective Antitumor Activity against FGFR-Deregulated Tumors. Cancer Res. 2020;80:4986–4997. doi: 10.1158/0008-5472.CAN-19-2568. [DOI] [PubMed] [Google Scholar]
  • 5.Liu P.C., Koblish H., Wu L., Bowman K., Diamond S., DiMatteo D., Zhang Y., Hansbury M., Rupar M., Wen X., et al. INCB054828 (Pemigatinib), a Potent and Selective Inhibitor of Fibroblast Growth Factor Receptors 1, 2, and 3, Displays Activity against Genetically Defined Tumor Models. PLoS ONE. 2020;15:e0231877. doi: 10.1371/journal.pone.0231877. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Hoy S.M. Pemigatinib: First Approval. Drugs. 2020;80:923–929. doi: 10.1007/s40265-020-01330-y. [DOI] [PubMed] [Google Scholar]
  • 7.Chakrabarti S., Finnes H.D., Mahipal A. Fibroblast Growth Factor Receptor (FGFR) Inhibitors in Cholangiocarcinoma: Current Status, Insight on Resistance Mechanisms and Toxicity Management. Expert Opin. Drug Metab. Toxicol. 2022;18:85–98. doi: 10.1080/17425255.2022.2039118. [DOI] [PubMed] [Google Scholar]
  • 8.Silverman I.M., Hollebecque A., Friboulet L., Owens S., Newton R.C., Zhen H., Féliz L., Zecchetto C., Melisi D., Burn T.C. Clinicogenomic Analysis of FGFR2-Rearranged Cholangiocarcinoma Identifies Correlates of Response and Mechanisms of Resistance to Pemigatinib. Cancer Discov. 2021;11:326–339. doi: 10.1158/2159-8290.CD-20-0766. [DOI] [PubMed] [Google Scholar]
  • 9.Borad M.J., Gores G.J., Roberts L.R. Fibroblast Growth Factor Receptor 2 Fusions as a Target for Treating Cholangiocarcinoma. Curr. Opin. Gastroenterol. 2015;31:264–268. doi: 10.1097/MOG.0000000000000171. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Maruki Y., Morizane C., Arai Y., Ikeda M., Ueno M., Ioka T., Naganuma A., Furukawa M., Mizuno N., Uwagawa T., et al. Molecular Detection and Clinicopathological Characteristics of Advanced/Recurrent Biliary Tract Carcinomas Harboring the FGFR2 Rearrangements: A Prospective Observational Study (PRELUDE Study) J. Gastroenterol. 2021;56:250–260. doi: 10.1007/s00535-020-01735-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Zhu Z., Dong H., Wu J., Dong W., Guo X., Yu H., Fang J., Gao S., Chen X., Lu H., et al. Targeted Genomic Profiling Revealed a Unique Clinical Phenotype in Intrahepatic Cholangiocarcinoma with Fibroblast Growth Factor Receptor Rearrangement. Transl. Oncol. 2021;14:101168. doi: 10.1016/j.tranon.2021.101168. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Pu X., Ye Q., Cai J., Yang X., Fu Y., Fan X., Wu H., Chen J., Qiu Y., Yue S. Typing FGFR2 Translocation Determines the Response to Targeted Therapy of Intrahepatic Cholangiocarcinomas. Cell Death Dis. 2021;12:716. doi: 10.1038/s41419-021-03548-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Cremer T., Cremer M. Chromosome Territories. Cold Spring Harb. Perspect. Biol. 2010;2:a003889. doi: 10.1101/cshperspect.a003889. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Wang M.J., Chen F., Lau J.T.Y., Hu Y.P. Hepatocyte Polyploidization and Its Association with Pathophysiological Processes. Cell Death Dis. 2017;8:e2805. doi: 10.1038/cddis.2017.167. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Cremer T., Cremer M., Dietzel S., Müller S., Solovei I., Fakan S. Chromosome Territories—A Functional Nuclear Landscape. Curr. Opin. Cell Biol. 2006;18:307–316. doi: 10.1016/j.ceb.2006.04.007. [DOI] [PubMed] [Google Scholar]
  • 16.Donne R., Saroul-Aïnama M., Cordier P., Celton-Morizur S., Desdouets C. Polyploidy in Liver Development, Homeostasis and Disease. Nat. Rev. Gastroenterol. Hepatol. 2020;17:391–405. doi: 10.1038/s41575-020-0284-x. [DOI] [PubMed] [Google Scholar]
  • 17.Watanabe T., Tanaka Y. Age-Related Alterations in the Size of Human Hepatocytes. A Study of Mononuclear and Binucleate Cells. Virchows Arch. B. 1982;39:9–20. doi: 10.1007/BF02892832. [DOI] [PubMed] [Google Scholar]
  • 18.Soda M., Choi Y.L., Enomoto M., Takada S., Yamashita Y., Ishikawa S., Fujiwara S.I., Watanabe H., Kurashina K., Hatanaka H., et al. Identification of the Transforming EML4-ALK Fusion Gene in Non-Small-Cell Lung Cancer. Nature. 2007;448:561–566. doi: 10.1038/nature05945. [DOI] [PubMed] [Google Scholar]
  • 19.De Melo V., Vetter M., Mazzullo H., Howard J.D., Betts D.R., Nacheva E.P., Apperley J.F., Reid A.G. A Simple FISH Assay for the Detection of 3q26 Rearrangements in Myeloid Malignancy. Leukemia. 2008;22:434–437. doi: 10.1038/sj.leu.2404906. [DOI] [PubMed] [Google Scholar]
  • 20.Tirado C.A., Shabsovich D., Kim Y., Traum P., Pullarkat S., Kallen M., Rao N. A Case of B-Cell Acute Lymphoblastic Leukemia in a Child with Down Syndrome Bearing a t(2;12)(P12;P13) Involving ETV6 and Biallelic IGH@ Rearrangements. Biomark. Res. 2015;3:11. doi: 10.1186/s40364-015-0036-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Gao X., Sholl L.M., Nishino M., Heng J.C., Jänne P.A., Oxnard G.R. Clinical Implications of Variant ALK FISH Rearrangement Patterns. J. Thorac. Oncol. 2015;10:1648–1652. doi: 10.1097/JTO.0000000000000665. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Bunting S.F., Nussenzweig A. End-Joining, Translocations and Cancer. Nat. Rev. Cancer. 2013;13:443. doi: 10.1038/nrc3537. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Wu Y.M., Su F., Kalyana-Sundaram S., Khazanov N., Ateeq B., Cao X., Lonigro R.J., Vats P., Wang R., Lin S.F., et al. Identification of Targetable FGFR Gene Fusions in Diverse Cancers. Cancer Discov. 2013;3:636–647. doi: 10.1158/2159-8290.CD-13-0050. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Parker B.C., Engels M., Annala M., Zhang W. Emergence of FGFR Family Gene Fusions as Therapeutic Targets in a Wide Spectrum of Solid Tumours. J. Pathol. 2014;232:4–15. doi: 10.1002/path.4297. [DOI] [PubMed] [Google Scholar]
  • 25.Li F., Peiris M.N., Donoghue D.J. Functions of FGFR2 Corrupted by Translocations in Intrahepatic Cholangiocarcinoma. Cytokine Growth Factor Rev. 2020;52:56–67. doi: 10.1016/j.cytogfr.2019.12.005. [DOI] [PubMed] [Google Scholar]
  • 26.Yin L., Han Z., Feng M., Wang J., Xie Z., Yu W., Fu X., Shen N., Wang X., Duan A., et al. Chimeric Transcripts Observed in Non-Canonical FGFR2 Fusions with Partner Genes’ Breakpoint Located in Intergenic Region in Intrahepatic Cholangiocarcinoma. Cancer Genet. 2022;266–267:39–43. doi: 10.1016/j.cancergen.2022.06.004. [DOI] [PubMed] [Google Scholar]
  • 27.Zhang Y., McCord R.P., Ho Y.J., Lajoie B.R., Hildebrand D.G., Simon A.C., Becker M.S., Alt F.W., Dekker J. Spatial Organization of the Mouse Genome and Its Role in Recurrent Chromosomal Translocations. Cell. 2012;148:908–921. doi: 10.1016/j.cell.2012.02.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Lin C., Yang L., Tanasa B., Hutt K., Ju B.G., Ohgi K., Zhang J., Rose D.W., Fu X.D., Glass C.K., et al. Nuclear Receptor-Induced Chromosomal Proximity and DNA Breaks Underlie Specific Translocations in Cancer. Cell. 2009;139:1069–1083. doi: 10.1016/j.cell.2009.11.030. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Stapleton D., Balan I., Pawson T., Sicheri F. The Crystal Structure of an Eph Receptor SAM Domain Reveals a Mechanism for Modular Dimerization. Nat. Struct. Biol. 1999;6:44–49. doi: 10.1038/4917. [DOI] [PubMed] [Google Scholar]
  • 30.Gerlitz G., Darhin E., Giorgio G., Franco B., Reiner O. Novel Functional Features of the LIS-H Domain: Role in Protein Dimerization, Half-Life and Cellular Localization. Cell Cycle. 2005;4:1632–1640. doi: 10.4161/cc.4.11.2151. [DOI] [PubMed] [Google Scholar]
  • 31.Rackham O.J.L., Madera M., Armstrong C.T., Vincent T.L., Woolfson D.N., Gough J. The Evolution and Structure Prediction of Coiled Coils across All Genomes. J. Mol. Biol. 2010;403:480–493. doi: 10.1016/j.jmb.2010.08.032. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

No new data were created or analyzed in this study. Data sharing is not applicable to this article.


Articles from Diagnostics are provided here courtesy of Multidisciplinary Digital Publishing Institute (MDPI)

RESOURCES