Article

ISSN 0006-2979, Biochemistry (Moscow), 2024, Vol. 89, No. 4, pp. 637-652 © Pleiades Publishing, Ltd., 2024.

637

Modification of the Hi-C Technology

for Molecular Genetic Analysis of Formalin-Fixed

Paraffin-Embedded Sections of Tumor Tissues

Maria M. Gridina

1,2,a

*, Yana K. Stepanchuk

1,2

, Miroslav A. Nurridinov

1,2

Timofey A. Lagunov

1,2

, Nikita Yu. Torgunakov

1,2

, Artem A. Shadsky

1,2

Anastasia I. Ryabova

, Nikolay V. Vasiliev

, Sergey V. Vtorushin

3,5

Tatyana S. Gerashchenko

, Evgeny V. Denisov

, Mikhail A. Travin

Maxim A. Korolev

, and Veniamin S. Fishman

1,2

Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences,

630090 Novosibirsk, Russia

Novosibirsk State University, 630090 Novosibirsk, Russia

Research Institute of Oncology, Tomsk National Research Medical Center, Russian Academy of Sciences,

634009 Tomsk, Russia

Research Institute of Clinical and Experimental Lymphology, Institute of Cytology and Genetics,

Siberian Branch of the Russian Academy of Sciences, 630117 Novosibirsk, Russia

Siberian State Medical University, Ministry of Health of Russia, 634050 Tomsk, Russia

e-mail: gridinam@gmail.com

Received September 5, 2023

Revised October 31, 2023

Accepted October 31, 2023

Abstract— Molecular genetic analysis of tumor tissues is the most important step towards understanding the

mechanisms of cancer development; it is also necessary for the choice of targeted therapy. The Hi-C (high-through-

put chromatin conformation capture) technology can be used to detect various types of genomic variants, includ-

ing balanced chromosomal rearrangements, such as inversions and translocations. We propose a modification

of the Hi-C method for the analysis of chromatin contacts in formalin-fixed paraffin-embedded (FFPE) sections

oftumor tissues. The developed protocol allows to generate high-quality Hi-C data and detect all types of chro-

mosomal rearrangements. We have analyzed various databases to compile a comprehensive list of translocations

that hold clinical importance for the targeted therapy selection. The practical value of molecular genetic testing is

its ability to influence the treatment strategies and to provide prognostic insights. Detecting specific chromosomal

rearrangements can guide the choice of the targeted therapies, which is a critical aspect of personalized medicine

in oncology.

DOI: 10.1134/S0006297924040047

Keywords: chromosomal rearrangements, three-dimensional nuclear organization, oncology, FFPE sections

* To whom correspondence should be addressed.

INTRODUCTION

Together with single nucleotide variants (SNVs),

chromosomal rearrangements, including balanced trans-

locations and inversions, play a key role in the patho-

genesis of various cancers. Current genomic diagnostic

approaches enable genome-wide detection of SNVs and

copy number variations, offering significant insights

into oncogenic processes. However, efficient detection

of balanced chromosomal rearrangements remains

elusive. At the same time, these chromosomal rear-

rangements have been found in almost all types of can-

cer. Moreover, for some tumors, detection of balanced

chromosomal rearrangements is critical for the diag-

nosis, clarification of prognosis, and choice of therapy.

GRIDINA et al.638

BIOCHEMISTRY (Moscow) Vol. 89 No. 4 2024

In many tumors, chromosomal rearrangements

not only accompany the process of tumor develop-

ment, but act as the main cause (driver) of cell onco-

logical transformation. One of the examples is recipro-

cal translocations observed in Burkitt’s lymphoma, in

which the translocation of the MYC gene from chromo-

some 8 to chromosome 14 under the influence of the

immunoglobulin heavy chain enhancer results in dys-

regulation of its expression [1]. If breakpoints occur

within the genes, this can lead to the gene fusions re-

sulting in the formation of chimeric proteins. Such fu-

sion proteins often involve transcription factors (ERG,

MYB) or protein kinases (ABL1, ALK, BRAF, EGFR, JAK2,

RET) that play a pivotal role in the oncogenic process.

The TMPRSS2-ERG gene fusion, which is prevalent in

a majority of prostate adenocarcinomas and approxi-

mately 20% of high-grade prostate intraepithelial neo-

plasias, illustrates this mechanism. TMPRSS2 is a serine

protease regulated by the androgen-dependent promot-

er and its fusion with the ERG oncogene results in the

ERG overexpression, a key event in the prostate cancer

pathogenesis [2]. Similar mechanisms involving ERG

fusion with other partners, such as NDRG1, EWS, and

FUS, have been implicated in other cancer types[3-5].

Gene fusion, a hallmark of various cancers, can

dysregulate gene expression and alter the function of

the encoded protein. Thus, if gene fusion results in the

truncation of one of the fusion partners, this can lead

to its overexpression due to the loss of negative reg-

ulatory elements (e.g., binding sites for microRNA) or

domains determining the protein lifespan. A notable

example is the MYB-NFIB gene fusion in adenoid cys-

tic carcinoma, resulting from the t(6;9) translocation

[6]. In this fusion, the chimeric transcript partially or

completely loses a region encoding the C-terminal reg-

ulatory domain of MYB containing the sites for protein

post-translational modification, as well as a non-cod-

ing sequence essential for the binding of microRNAs.

Consequently, the absence of these regulatory ele-

ments in the MYB portion of the fusion protein leads

to the upregulation of MYB expression and prolonged

protein lifespan [7].

Gene fusions can also lead to the production of

chimeric proteins with significantly altered functional

domains. In the norm, the FGFR3 receptor tyrosine ki-

nase is activated through the homo/heterodimerization

in the presence of fibroblast growth factor (FGF) as a

ligand [8]. The translocation between chromosomes

4 and 7 results in the FGFR3 fusion with BAIAP2L1.

Theresulting chimeric protein possesses the ability for

constitutive, ligand-independent homodimerization.

This aberrant dimerization is facilitated by the BAR

domains of BAIAP2L1, resulting in the FGFR3 kinase

activation and potent oncogenic activity [9].

For the diagnostic purposes and long-term stor-

age, tumor samples are preserved as formalin-fixed

paraffin-embedded (FFPE) tissue blocks through for-

malin fixation and subsequent embedding in paraffin.

FFPE blocks have many advantages, including stability

at room temperature, extended shelf life, and compat-

ibility with immunohistochemical analysis. However,

such fixation and storage of samples can lead to the

degradation of nucleic acids and appearance of arti-

facts, which requires optimization of molecular anal-

ysis methods [10]. Furthermore, the degradation and

modification of nucleic acids in FFPE samples compli-

cate the use of RNA sequencing for the detection of

biomarkers [11].

Routine methods for identification of chromosom-

al rearrangements in tumor tissues include FISH (flu-

orescence in situ hybridization), immunohistochemical

analysis, and RT-PCR. These approaches have obvious

limitations in the detection of novel or complex chro-

mosome rearrangements. Recent advances in high-

throughput sequencing have revolutionized clinical

genetics. Whole-genome sequencing (WGS) and whole-

exome sequencing (WES) using the short-read technol-

ogy have excelled in identifying SNVs and unbalanced

chromosomal rearrangements, but their accuracy in

repetitive genome regions is limited. Detection of bal-

anced rearrangements using WGS and WES depends

on the presence of chimeric reads encompassing the

rearrangement breakpoints and therefore requires a

high sequencing depth. Long-read sequencing methods

(PacBio and Oxford Nanopore) are effective for detect-

ing balanced chromosomal rearrangements, but their

efficiency diminishes when analyzing FFPE samples

due to the DNA degradation. Balanced chromosomal

rearrangements often trigger carcinogenesis through

two mechanisms: gene fusion and disruption of gene

expression resulting from alterations in the gene reg-

ulatory environment. Consequently, RNA sequencing

has emerged as an important tool for analyzing tumor

samples [12-14]. However, this technique demands a

high RNA quality, which is challenging when RNA is

isolated from FFPE samples [11-16]. Degraded RNA

fragments may lack crucial information on the fusion

sites. Moreover, RNA-seq technology faces sensitivity

issues in the case of low expression of fusion tran-

scripts [13] and fusions with non-coding regions [17]

and requires significant sequencing depth (20-30 mil-

lion paired-end reads) or targeted gene enrichment

[11]. The Hi-C (high-throughput chromatin conforma-

tion capture) method has been increasingly used in

recent years as an alternative approach for detect-

ing various types of chromosomal rearrangements.

Theadvantage of the method is its ability to detect bal-

anced rearrangements at a lower sequencing depth.

This efficiency is partly due to the fact that Hi-C does

not rely solely on the reads containing the breakpoint.

Instead, it analyzes changes in the chromatin contact

frequency within broad genomic regions and, therefore,

HI-C ANALYSIS OF FFPE TUMOR SECTIONS 639

BIOCHEMISTRY (Moscow) Vol. 89 No. 4 2024

requires less sequencing depth for the detection of re-

arrangements [18-27].

Here, we propose a new Hi-C protocol for analyz-

ing material from FFPE tumor sections. We introduced

significant modifications to the existing protocols

[28, 29], resulting in a highly reproducible technique

capable of generating high-quality Hi-C data and de-

tecting all types of chromosomal rearrangements. A key

innovation in our approach that distinguishes it from

traditional 3C methods, is the use of a sequence-ag-

nostic nuclease, which not only facilitates detection

of chromosomal rearrangements but also expands the

application of this method to identification of SNVs in

clinically significant loci. We have analyzed various

databases to compile a comprehensive list of translo-

cations that hold clinical importance for selection of a

targeted therapy. The results of modeling conducted in

our study demonstrate that our method has a substan-

tial promise for clinical application.

MATERIALS AND METHODS

Analyzed samples. FFPE sections were obtained

from patients treated at several medical centers of the

Russian Federation. Eight patients were from the On-

cology Research Institute of the Tomsk National Med-

ical Research Center. Three patients (age, 43.6 ± 8.62

years) had morphologically verified grade 4 (G4) brain

tumors (glioblastoma, giant cell glioblastoma, and

diffuse astrocytoma). Five patients (age range, 28-65

years; average age, 50.4 ± 12.9 years) had morpholog-

ically confirmed chondrosarcomas of different lo-

calization (humerus, femur, tibia, pelvic bones, and

sternum); the tumor grades ranged from G2 to G3. Six

patients were treated at the Kemerovo Regional Clin-

ical Hospital; three of them had chronic lymphocytic

leukemia (CLL) and three patients had large cell lym-

phoma (LCL). The diagnoses were established based

on pathomorphological studies of excisional lymph

node biopsies and immunohistochemical verification

using a specialized antibody panel.

Tumor tissue samples were collected during surgi-

cal procedures. The samples were fixed in 10% neutral

buffered formalin for 24 h and embedded in paraffin

using standard techniques. For each tumor specimen,

10μm thick sections were prepared.

FFPE Hi-C. The developed protocol was based on

our previously proposed S1 Hi-C method [30] and in-

cluded the following steps:

1. Deparaffinization:

1.1. An FFPE section was placed in a 1.5-ml tube and

1 ml of lysis buffer Y (150mM Tris pH8.0; 140mM

NaCl, 0.5% Igepal, 1% Triton X-100) was added.

1.2. The FFPE section was incubated at 80°C

for3min.

1.3. Centrifugation was performed at 2500g for 5min.

1.4. The paraffin layer was removed from the solu-

tion surface.

1.5. Steps 1.2.-1.4. were repeated (i.e., the total num-

ber of incubations was two).

2. Lysis:

2.1. After the second centrifugation, the superna-

tant was removed and the pellet was resuspended

in 1ml of lysis buffer H (10mM Tris pH8.0, 10mM

NaCl, 1% Triton X-100, 0.1% SDC, 20% EtOH).

2.2. The sample was incubated at 45°C overnight.

2.3. Centrifugation was performed at 2500g for

5min.

2.4. The precipitate was washed once with 1ml of

lysis buffer Y.

2.5. The sample was incubated in 1ml of lysis buf-

fer Y for 1 h at room temperature on an orbital

shaker.

2.6. Centrifugation was performed at 2500g for

5min.

2.7. The supernatant was removed, and the pel-

let was resuspended in 500 μl of lysis buffer D

(50mM Tris pH 7.5, 0.5mM CaCl

, 0.3% SDS).

2.8. The sample was incubated at 37°C for 1h.

2.9. SDS was quenched by adding 91μl of 10% Tri-

ton X-100 for 10min at room temperature.

2.10. Centrifugation was performed at 2500g for

5min.

2.11. The pellet was washed once with 500 μl of

1× S1 nuclease buffer (Thermo Scientific) contain-

ing 1% Triton X-100.

3. Chromatin fragmentation:

3.1. The pellet was resuspended in 80 μl of 1× S1

nuclease buffer.

3.2. 200 U of S1 nuclease (Thermo Scientific) was

added and the mixture was incubated at 37°C

for1h.

3.3. The reaction was stopped by adding 5 μl

of 500 mM EDTA and purified with 1 volume of

AMPure magnetic beads according to the manu-

facturer’s recommendations.

3.4. Chromatin associated with magnetic beads

was resuspended in 100μl of H

O (chromatin re-

mained bound to the beads until the DNA isola-

tion stage).

4. Further steps, including biotin labeling, ligation,

DNA isolation, ligation fragment enrichment, and

preparation of NGS libraries, were performed ac-

cording to the protocol described by Gridina etal.

[31]. DNA quantity was determined with a Qubit

dsDNA HS Assay Kit.

The prepared libraries were sequenced using the

BGI sequencing platform with 150-bp paired-end reads.

The sequencing depth was 10-100 thousand reads per

sample for shallow sequencing and ~80 million paired-

end reads for deep sequencing.

GRIDINA et al.640

BIOCHEMISTRY (Moscow) Vol. 89 No. 4 2024

Modeling of chromosomal rearrangements was

performed with the Charm software (https://github.com/

genomech/Charm/) using the published results of ge-

nome-wide Hi-C studies in IMR90 cells [32] (identifiers

SRR1658675, SRR1658676, SRR1658679) to create a da-

tabase of reference contacts. Each chromosomal rear-

rangement was modeled as heterozygous, with a total

number of ~30 million Hi-C contacts. The coordinates

for the boundaries of modeled chromosomal rear-

rangements (Online Resource 1) were rounded to the

nearest 5kb. Rearrangements smaller than 25kb were

scaled up to this threshold.

Construction of contact maps and analysis of

quality metrics of Hi-C libraries. Analysis of Hi-C

data and construction of Hi-C heatmaps were conduct-

ed as outlined in [31]. A modified version of the Juicer

software version 1.6 (available on GitHub: https://

github.com/genomech/juicer1.6_compact) was used to

calculate the quality scores.

RESULTS

Development of Hi-C protocol for FFPE samples

using S1 nuclease. The majority of published Hi-C

protocols have been designed for living cells or fresh

tissues [19, 31-33]; few of them were adapted for frozen

samples [34]. While these protocols have been well-es-

tablished for the respective sample types, with known

details and critical points [35-37], their applicability

to FFPE tumor sections is limited. Currently, there are

only two Hi-C protocols for the analysis of FFPE sec-

tions [28, 29]. We compared these two existing proto-

cols (Table1) and identified significant methodological

differences, particularly, at the deparaffinization and

sample lysis stages. For deparaffinization, Troll etal. [28]

recommended xylene treatment followed by alcohol

washing, while Allahyar et al. [29] suggested a 3-min

incubation at 80°C, centrifugation, and removal of

the paraffin layer. After deparaffinization, in order

to ensure the availability of chromatin for restriction

enzymes, Troll et al. treated the samples with protein-

ase K (0.5 mg/ml for 1 h at 37°C), whereas Allahyar etal.

used sonication and subsequent incubation for 2 h

at 80°C. Both proteinase treatment and prolonged in-

cubation at 80°C can lead to the destruction of cross-

links formed by formaldehyde [38] and DNA release

from the chromatin. Finally, both studies suggested us-

ing restriction endonucleases with four-nucleotide rec-

ognition sites for chromatin fragmentation. However,

this approach can result in a low coverage of genom-

ic regions distant from the enzyme recognition sites,

which might limit identification of certain genomic

variants, such as SNVs in the oncogene exons located

far from the restriction sites.

We have developed a modified Hi-C protocol spe-

cifically designed for preparing libraries from FFPE

sections (Table 1). The lysis conditions were adjusted

to be less harsh, and we used S1 nuclease instead of

restriction endonucleases (see Materials and methods)

to provide uniform genome coverage [30].

During the preparation of the Hi-C libraries from

living cells or tissues, the quality of DNA fragments is

tested after the following key steps:

1. Post-lysis, pre-fragmentation;

2. Post-fragmentation, pre-ligation;

3. Post-ligation.

These control checkpoints are crucial for eval-

uating the quality of prepared libraries (Fig. 1, a, b).

Before fragmentation, a band corresponding to the

high-molecular-weight DNA should be detected, which

disappears after fragmentation with the formation

of many low-molecular fragments of various lengths.

After ligation, the distribution of fragment lengths

shifts to a higher molecular weight region.

We found that the standard quality controls typ-

ically employed in the Hi-C library preparation from

living tissues were not applicable or representative

Table 1. Comparison of FFPE Hi-C protocols

Steps

Conditions

Troll et al. [28] Allahyar et al. [29] Our protocol

Deparaffinization xylene

3 min at 80°C,

centrifugation

3 min at 80°C,

centrifugation

Lysis proteinase K, 1h at 37°C

0.6% SDS, sonication,

incubation at 80°C for 2 h

lysis in the presence of ionic

and nonionic detergents

Chromatin fragmentation MboI, 1h at 37°C NlaIII, 1 h at 37°C S1 nuclease, 1h at 37°C

Biotin labeling + – +

Ligation 2 h, room temperature 1 h, 16°C overnight, 16°C

Ligation product enrichment + – +

HI-C ANALYSIS OF FFPE TUMOR SECTIONS 641

BIOCHEMISTRY (Moscow) Vol. 89 No. 4 2024

Fig. 1. Results of chromatin digestion and ligation in Hi-C experiments in living cells and tissues using DpnII(a) and S1 nucle-

ase(b) and in FFPE sections using our protocol(c). Lanes: 1,pre-fragmentation, 2,post fragmentation, 3,post-ligation; M,100-bp

ladder (SibEnzyme). d)DNA quantification(ng) in Hi-C libraries obtained from cells and FFPE sections of different tumor types.

The number of analyzed libraries from peripheral blood mononuclear cells (PBMCs) was 16, from FFPE sections: LCL – 13,

CLL– 9, gliomas– 4, sarcomas– 5.

inthe case of FFPE samples. Long-term fixation of tu-

mor tissues in formaldehyde and subsequent embed-

ding in a paraffin block lead to a significant DNA deg-

radation [39, 40]. Consequently, DNA extracted from

FFPE blocks is already in a highly fragmented state.

According to our data, further nuclease treatment and

ligation do not result in contrasting changes in the

fragment length (Fig.1c). However, our analysis of the

sequencing data quality and visual examination of the

resulting FFPE Hi-C maps indicated successful com-

pletion of the key Hi-C protocol stages. For instance,

the Hi-C libraries represented in Fig.1, as well as ad-

ditional libraries detailed in Online Resource 2 (sam-

ples s11-s15), demonstrated acceptable quality metrics.

Hence, we believe that in the case of FFPE Hi-C, the de-

scribed controls are not necessary. Instead, we recom-

mend assessing the library quality based on the results

of shallow sequencing.

Unlike the FFPE Hi-C method, Hi-C analysis of live

cells using restriction endonuclease DpnII or S1 nu-

clease allows to easily vary the amount of the starting

material, while in the case of FFPE sections, accurate

estimation of the number of cells in each section can

be a challenge. We observed a significant variability in

the amount of DNA isolated from FFPE samples of dif-

ferent tumors, as well as in the samples of the same tu-

mor type (Fig.1d). Thus, FFPE sections from sarcomas

consistently yielded the lowest amount of DNA, which

forced us to utilize three FFPE sections from a single

block for analysis. Therefore, we recommend to deter-

mine the required number of sections for each tumor

type to obtain sufficient yield of DNA libraries.

GRIDINA et al.642

BIOCHEMISTRY (Moscow) Vol. 89 No. 4 2024

Fig. 2. Quality metrics of FFPE Hi-C datasets showing proportion of unmapped reads(a), proportion of PCR duplicates(b), pro-

portion of DEs(c), proportion of cis contacts among all Hi-C contacts(d). Each dot represents an independent Hi-C library prepa-

ration. The number of analyzed libraries from PBMCs was 16 and from FFPE sections: LCL– 13, CLL– 9, gliomas– 4, sarcomas– 2.

Using the newly developed protocol, we prepared

FFPE Hi-C libraries from CLL, LCL, gliomas, and sar-

coma samples. The CLL sections were also used to

construct the libraries according to the protocols sug-

gested by Troll etal. [28] and Allahyar etal. [29]. After

sequencing, the libraries were assessed for their quali-

ty. We observed a low number of unmapped reads and

PCR duplicates across all the libraries (Fig.2, a and b,

respectively). Another key quality metric for Hi-C li-

braries is a proportion of dangling ends (DEs), unin-

formative fragments that are not ligation products.

The DE content in the Hi-C libraries prepared from

peripheral blood mononuclear cells (PBMCs) using S1

nuclease averaged 40%. However, in the FFPE Hi-C li-

braries prepared according to the developed protocol,

this proportion was higher and varied depending on

the tumor type. The highest DE content was observed

in the libraries prepared following the protocols of

HI-C ANALYSIS OF FFPE TUMOR SECTIONS 643

BIOCHEMISTRY (Moscow) Vol. 89 No. 4 2024

Fig. 3. Representative Hi-C data obtained for FFPE sections using the developed protocol. a)Chromosome-scale Hi-C heatmap for

sample s26 (above the diagonal) and the map obtained for blood lymphocytes (control; below the diagonal) [30]. b)Examples

of contact patterns in Hi-C maps and their schematic representation: reciprocal translocations(b), inversions(c), heterozygous

deletions(d), heterozygous deletions and duplications(e). In panels(b-e), above the diagonal, contact map for sample s25; below

the diagonal, contact map for s26 (control).

Troll et al. [28] and Allahyar et al. (Fig. 2c). We have

previously shown [31] that the most important metric

of the library quality is a proportion of cis contacts [cis/

all ratio (FF and RR orient)], which reflects the propor-

tion of Hi-C reads aligned to the same chromosome.

Thus, a low proportion of cis contacts may result from

the reversal of cross-links and subsequent random li-

gation of released DNA. For all the libraries prepared

using our protocol, the proportion of cis contacts was

above 70%, while for the libraries prepared according

to Troll et al. and Allahyar et al., it was 20 and 15%,

respectively (Fig.2d).

However, it was not the aim of our study to com-

pare between our FFPE Hi-C protocol and protocols

proposed by Troll et al. [28] and Allahyar et al. [29],

as the latter ones utilize enzymes with specific recog-

nition sites, which limits their capacity to identify ge-

nomic variants with the efficacy that can be achieved

with our protocol, which uses S1 nuclease [31, 41].

Therefore, we have not used the protocols by Troll

et al. [28] and Allahyar et al. [29] for a larger set of

samples or different tumor types. Although the ob-

tained data did not allow us to draw a statistical com-

parison of the quality of Hi-C data obtained by differ-

ent protocols, we can still state a higher quality of Hi-C

libraries obtained using our protocol.

Two libraries were subjected to deep sequencing

and the results obtained were used to construct heat-

maps of the Hi-C contacts, which revealed distinct

patterns indicating various types of chromosomal re-

arrangements (Fig. 3, a-e). We were able to detect all

types of chromosomal structural rearrangements, such

as deletions, duplications, inversions, and transloca-

tions. Based on these findings, we can conclude that

the developed method for FFPE Hi-C analysis using S1

nuclease was effective in generating maps of three-

GRIDINA et al.644

BIOCHEMISTRY (Moscow) Vol. 89 No. 4 2024

dimensional chromatin contacts and identifying chro-

mosomal rearrangements based on the obtained data.

Prospects of FFPE Hi-C analysis in the selection

of targeted therapy in oncological diseases. The de-

veloped FFPE Hi-C protocol has a significant potential

for future clinical applications, as it can detect chromo-

somal rearrangements (translocations and inversions),

including balanced ones. These rearrangements are

often challenging to identify using standard sequenc-

ing methods. The practical value of molecular genetic

testing lies in its ability to influence patient treatment

strategies and provide prognostic insights. Detection of

specific chromosomal rearrangements can guide the

choice of targeted therapy, which is a critical aspect

of personalized medicine and oncology. To determine

the scope of applications for our method, we analyzed

the databases of the National Cancer Institute (https://

www.cancer.gov/about-cancer/treatment/drugs/

cancer-type), CIViC (https://civicdb.org/welcome), and

MyCancerGenome (https://www.mycancergenome.org/).

We identified balanced chromosomal rearrangements

leading to the gene fusions resulting in the generation

of chimeric products that have been already targeted

in existing or developed therapies (Table2). Our analy-

sis revealed that the highest number of gene fusions

for which the targeted therapy had been developed has

been described for hematological oncological diseases

and, in the case of solid tumors, for various types of

sarcomas and lung cancer. These diseases also exhibit

the greatest diversity of fusion partners. Thus, several

fusion variants have been identified in melanoma and

cholangiocarcinoma; however, in each of the fusions,

one of the partners remained the same: BRAF in mela-

noma and FGFR2 in cholangiocarcinoma.

In addition to the translocations listed in Table2,

there are other rearrangements that are important for

establishing a diagnosis and/or prognosis, but do not

currently have approved targeted drugs. For instance,

KMT2A/MLL gene fusions frequently observed in pa-

tients with acute leukemia, are known to be associ-

ated with a poor prognosis [42]. The list of clinically

relevant gene fusions will expand with the discovery

of new gene fusions and approval of novel therapeutic

drugs.

In order to assess the possibility of detecting chro-

mosomal rearrangements described in Table 2, we

simulated the results of the FFPE Hi-C experiment us-

ing the Charm tool (https://github.com/NuriddinovMA/

Charm). The simulated Hi-C experiments focused on 5

inversions and 7 translocations from Table 2 (Online

Resource 1). The models corresponded to the FFPE

Hi-C libraries sequenced with a depth of about 30

million paired-end reads. For each translocation, we

generated two models: one showed a reciprocal trans-

location and the other showed a translocation of the

minimal-size fragment sufficient to form a chimeric

protein, i.e., a translocation of a region from the 3′-end

of the fused gene to the breakpoint (Fig.4a). The size

of the simulated inversions ranged from 2.3 to 79.9

million bp, while the translocations varied in size from

7.9 to 109 kb. The Hi-C contact heatmaps of clearly

displayed all simulated chromosomal rearrangements

(Fig. 4,b-e; Online Resource 3). This result suggests that

the Hi-C analysis of FFPE samples has a high potential

in detecting known clinically significant chromosomal

rearrangements.

DISCUSSION

In this work, we proposed a protocol for the Hi-C

analysis of FFPE tumor sections using S1 nuclease and

demonstrated that this method can be applied for de-

tecting various types of chromosomal rearrangements.

To fully assess the power of the method, it is crucial

to compare it against other high-throughput diagnos-

tic techniques in terms of several key factors, such as

time, complexity, cost, and accuracy of analysis. Our

protocol can be implemented using standard laborato-

ry equipment commonly used for the preparation of

whole-genome libraries. The process of sample prepa-

ration took 2 more days compared to WGS; however,

the main time-consuming procedure was developing

the logistics for sample pooling and sequencing rather

than sample preparation itself. According to our esti-

mates, the cost of FFPE Hi-C exceeds the cost of WGS

by no more than 20%. A future direction for our re-

search will be understanding the characteristics and

limitations of the FFPE Hi-C method. We still have to

evaluate the sensitivity and specificity of this method,

its ability to determine the proportion of tumor cells in

a sample, and the amount of input material required

for different types of tumors. Then, it will be possible

to fully compare the proposed FFPE Hi-C protocol with

the existing high-throughput diagnostic methods.

The high-throughput sequencing technology has

opened a new era of personalized therapy for oncolo-

gy patients. It has dramatically enhanced our ability to

understand various oncological diseases at a molecular

level. However, it still remains unclear which sequenc-

ing methods and when should be used in daily clinical

practice. In 2020, the European Society of Medical On-

cology recommended routine use of high-throughput

sequencing methods for the diagnostics of non-squa-

mous non-small cell lung cancer, prostate adenocar-

cinoma, ovarian carcinoma, and cholangiocarcinoma

[43]. Despite this limited list, the use of high-through-

put sequencing methods may be worthwhile for other

types of cancer, as it can clarify the diagnosis and even

change the treatment regimen.

A compelling example of the practical applica-

tion of NGS methods for detection of chromosomal

HI-C ANALYSIS OF FFPE TUMOR SECTIONS 645

BIOCHEMISTRY (Moscow) Vol. 89 No. 4 2024

Fig. 4. Simulation of chromosomal rearrangements. a)Schematic representation of two variants of translocation leading to

the gene fusion. bandc)Simulated FFPE Hi-C heatmaps for translocations. d-f)Simulated FFPE Hi-C heatmaps for inversions.

Above the diagonal, simulated heatmap; below diagonal, control (simulated FFPE Hi-C map with no structural variations and

with the same number of contacts as the simulated map with the rearrangement).

GRIDINA et al.646

BIOCHEMISTRY (Moscow) Vol. 89 No. 4 2024

Table 2. List of balanced chromosomal rearrangements and gene fusions with developed targeted therapy

Rearrangement

Fusion partners

Targeted therapy drugs Diagnosis

t(9;22)(q34;q11)

ABL1

BCR

Dasatinib, Imatinib,

Nilotinib, Bosutinib,

Bafetinib, Asciminib

acute lymphocytic leukemia, acute

myeloid leukemia, chronic myelogenous

leukemia

t(9;12)(q34;p13) ETV6 Imatinib, Dasatinib, Nilotinib B-lymphoblastic leukemia/lymphoma

t(3;9)(p13;q34) FOXP1 Dasatinib B-lymphoblastic leukemia/lymphoma

t(2;9)(q12;q34) RANBP2 Dasatinib B-lymphoblastic leukemia/lymphoma

t(1;9)(q24;q34) RCSD1

Dasatinib, Imatinib,

Ponatinib

B-lymphoblastic leukemia/lymphoma

t(1;9)(p34;q34) SFPQ Dasatinib, Imatinib

acute lymphocytic leukemia, B-lympho-

blastic leukemia/lymphoma

t(5;9)(q23;q34) SNX2 Dasatinib, Imatinib B-lymphoblastic leukemia/lymphoma

inv(1)(q24q25)

ABL2

RCSD1 Imatinib B-lymphoblastic leukemia/lymphoma

t(1;7)(q25;q34) ZC3HAV1 Imatinib B-lymphoblastic leukemia/lymphoma

ALK

different

ALK signaling pathway

inhibitors, HSP90 inhibitors,

EGFR tyrosine kinase

inhibitors, Crizotinib,

Brigatinib, Alectinib,

Ceritinib, Entrectinib,

Pemetrexed, 17-AAG*

breast cancer, colorectal adenocarcino-

ma, epithelioid inflammation, myofibro-

blastic sarcoma, inflammatory myofi-

broblastic tumor, lung adenocarcinoma,

non-small cell lung cancer, thyroid

carcinoma, vaginal sarcoma, anaplastic

large cell lymphoma, diffuse large B-cell

lymphoma

inv(2)(p23;p23) CAD Entrectinib colorectal cancer

t(2;17)(p23;q23) CLTC Crizotinib diffuse large B-cell lymphoma

inv(2)(p23p21) EML4

17-AAG*, Alectinib

(CH5424802), AUY922*,

Ceritinib, Crizotinib,

Brigatinib, IPI-504*,

Erlotinib, Lorlatinib

acinar adenocarcinoma of the lung,

non-small cell lung cancer, renal cell

carcinoma

t(2;7)(p23;q11) HIP1 Crizotinib, Alectinib non-small cell lung cancer

t(2;5)(p23;q35) NPM Crizotinib anaplastic large cell lymphoma

inv(2)(p23q13) RANBP2 Crizotinib inflammatory myofibroblastic tumor

inv(2)(q21;q22) ASNS KMT2E Asparaginase T-cell lymphoblastic leukemia

t(X;17)(p11;q25) TFE3 ASPL Cabozantinib, Dasatinib alveolar soft tissue sarcoma

BRAF

different Cobimetinib, Trametinib melanoma, ovarian cancer

inv(7)(q34;q34) AGK Sorafenib, Vemurafenib melanoma

inv(7)(q21q34) AKAP9 MEK inhibitors papillary thyroid cancer

HI-C ANALYSIS OF FFPE TUMOR SECTIONS 647

BIOCHEMISTRY (Moscow) Vol. 89 No. 4 2024

Table 2 (cont.)

Rearrangement

Fusion partners

Targeted therapy drugs Diagnosis

inv(7)(q34;q36)

BRAF

CUL1 MEK inhibitors serous ovarian carcinoma

inv(7)(q22;q34) CUX1 Vemurafenib pancreas cancer

t(1;7)(p31;q34) MIGA1 BRAF Inhibitor, HL-085 Langerhans cell histiocytosis

inv(7)(q32;q34) NRF1 Trametinib transitional cell carcinoma

inv(7)(q33;q34) TRIM24 Vemurafenib melanoma

inv(7)(q22;q34) ZKSCAN1 Trametinib melanoma

t(7;11)(q34;p15) PPFIBP2 Trametinib melanoma

t(4;7)(q25;q34) PAPSS1 Trametinib, Vemurafenib melanoma

t(15;19)(q14;p13) BRD4 NUTM1 Birabresib NUT midline carcinoma

t(X;14)(p22;q32) CRLF2 IGH Ruxolitinib

pediatric B-cell acute lymphoblastic leu-

kemia, adult B-cell acute lymphoblastic

leukemia

t(1;5)(q22;q32) CSF1R MEF2D Imatinib acute lymphocytic leukemia

t(X;6)(q28;p22) DEK AFF2 Pembrolizumab head and neck cancer

t(7;15)(p11;q15) EGFR RAD51

Erlotinib, Osimertinib, Ico-

tinib, Afatinib

lung adenocarcinoma

EWSR1

different TK216* soft tissue sarcoma

t(12;22)(q13;12) ATF1 Crizotinib clear cell sarcoma

t(11;22)(q24;q12) FLI1 genotoxic chemotherapies Ewing sarcoma

t(9;22)(q22;q12) NR4A3 Pazopanib, Sunitinib extraskeletal myxoid chondrosarcoma

inv(8)(p11;p11)

FGFR1

BAG4 AZD4547* carcinoma

t(8;12)(p11;p11) FGFR1OP2 Ponatinib acute myeloid leukemia

t(8;13)(p11;q11) ZNF198 Midostaurin chronic myeloproliferative disease

FGFR2

different

BGJ-398 (Infigratinib),

AZD4575*, JNJ-42756493

(Balversa), Debio1347*, Futi-

batinib, Erdafitinib, Infigrati-

nib, Phosphate, Pemigatinib

cholangiocarcinoma, bladder cancer

t(1;10)(p13;q26) AHCYL1 BGJ398 cholangiocarcinoma

inv(10)(q21;q26) BICC1 BGJ398, JNJ-42756493

cholangiocarcinoma, endometrial can-

cer, urothelial carcinoma

t(4;10)(p16;q26) TACC3 Pazopanib, Ponatinib cholangiocarcinoma

GRIDINA et al.648

BIOCHEMISTRY (Moscow) Vol. 89 No. 4 2024

Table 2 (cont.)

Rearrangement

Fusion partners

Targeted therapy drugs Diagnosis

FGFR3 different

FGFR signaling blockers,

TORC1/2 inhibitors, AZD4547*,

JNJ-42756493, BGJ398,

Debio1347*, Erdafitinib

bladder cancer

FLT3 different

FLT3 signaling blockers,

Sorafenib, all-trans

retinoic acid, Anthracycline*,

Daunorubicin*, Lestaurtinib,

Selumetinib (AZD6244)

acute myeloid leukemia

t(2;13)(q36;q14) FOXO1 PAX3 Thapsigargin alveolar rhabdomyosarcoma

t(9;22)(p24;q11)

JAK2

BCR Ruxolitinib myeloid neoplasm

t(9;14)(p24;q32) GOLGA5 Ruxolitinib b-lymphoblastic leukemia/lymphoma

t(8;9)(p22;p24) PCM1 Ruxolitinib leukemia

t(5;9)(q14;p24) SSBP2 Ruxolitinib B-lymphoblastic leukemia/lymphoma

KIT different Imatinib malignant melanoma of the rectum

inv(7)(q22;q31)

MET

ATXN7L1 Crizotinib non-small cell lung cancer

t(7;10)(q31;p11) KIF5B Crizotinib Non-small cell lung cancer

t(7;8)(q31;p12) RBPMS Cabozantinib congenital fibrosarcoma

t(8;21)(q24,p12)

NRG1

APP Afatinib –

t(1;8)(q24;p12) ATP1B1 Afatinib

cholangiocarcinoma, pancreatic ductal

adenocarcinoma

t(5;8)(q33;p12) CD74 Afatinib mucinous adenocarcinoma

t(8;11)(p12;q12) SLC3A2

Afatinib, Erlotinib,

Lumretuzumab

lung adenocarcinoma, mucinous adeno-

carcinoma

t(8;20)(p12q13) SDC4 Afatinib lung adenocarcinoma

NTRK1

different

ARRY-470, Entrectinib,

Larotrectinib (LOXO-101),

LESTAURTINIB*, Crizotinib

colorectal cancer, sarcoma, all solid

tumors, lung adenocarcinoma

t(1;12)(q23;p13) ETV6 Entrectinib all types

t(1;5)(q23;q35) SQSTM1 Entrectinib non-small cell lung cancer

inv(1)(q21q23) TPM3 Larotrectinib, Entrectinib Sarcoma, papillary thyroid cancer

inv(1)(q23q31) TPR Larotrectinib all solid tumors

NTRK2 different Larotrectinib, Entrectinib all solid tumors

HI-C ANALYSIS OF FFPE TUMOR SECTIONS 649

BIOCHEMISTRY (Moscow) Vol. 89 No. 4 2024

Table 2 (cont.)

Rearrangement

Fusion partners

Targeted therapy drugs Diagnosis

NTRK3

different

Entrectinib, Larotrectinib,

blockers of PI3K, SRC, and

IGF1 signaling pathways

all solid tumors

t(12;15)(p13;q25) ETV6

Entrectinib, Etoposide,

Larotrectinib, Crizotinib

salivary gland carcinoma, breast cancer,

congenital mesoblastic nephroma,

B-lymphoblastic leukemia/lymphoma

PDGFRA

different Imatinib

myelodysplasia, myeloproliferative

neoplasms

t(4;22)(q12;q11) BCR Imatinib B-cell acute lymphoblastic leukemia

PDGFRB

different Imatinib

chronic myeloproliferative disease,

dermatofibrosarcoma, myelodysplasia,

myeloproliferative neoplasm, acute lym-

phocytic leukemia

t(12;17)(p13;q21) ATF7IP

Dasatinib, Imatinib, Nilo-

tinib, Ponatinib, Bafetinib,

Rebastinib

pediatric B-cell acute lymphoblastic

leukemia

t(17;22)(q21;q13) COL1A1 Sunitinib dermatofibrosarcoma

t(15;17)(q22;q21) PML RARA

Tretinoin, arsenic trioxide,

all-trans-retinoic acid

acute promyelocytic leukemia

RET

different

Cabozantinib, Vandetanib,

Pralsetinib, Selpercatinib

non-small cell lung cancer, thyroid

cancer

inv(10)(q11q21) CCDC6

Nintedanib, Agerafenib,

Pralsetinib

non-small cell lung cancer, colorectal

cancer, solid tumors

inv(10)(p11q11) KIF5B

Vandetanib, Selpercatinib,

Everolimus, Pralsetinib

lung adenocarcinoma, solid tumors

ROS1

different

Ceritinib, Entrectinib, Erlo-

tinib, Gefitinib

lung adenocarcinoma, bronchioloalve-

olar adenocarcinoma, colorectal adeno-

carcinoma, non-small cell lung cancer

t(5;6)(q33;q22) CD74

Cabozantinib, Crizotinib, Bri-

gatinib, Ceritinib, Foretinib,

Lorlatinib,

non-small cell lung carcinoma, lung

adenocarcinoma

t(3;6)(q12;q22) TFG Crizotinib inflammatory myofibroblastic tumor

RSPO2 different

Wnt signaling inhibitors,

CGX1321

digestive system cancer

Note. We call the fusion partner “A” the gene, the changes in the activity of which lead to the disease development.

* At the clinical trial stage.

rearrangements in clinical oncology was reported by

Hehir-Kwa etal. [44]. Based on histological analysis of

brain tumor, a one-year-old child was diagnosed with

glioblastoma. In accordance with the diagnosis, the

treatment was started using the HGG HIT protocol for

infants. After 6 weeks of active tumor growth, the pro-

tocol was changed to BBSFOP HGG. DNA methylation

analysis allowed the tumor to be classified as a glioma,

GRIDINA et al.650

BIOCHEMISTRY (Moscow) Vol. 89 No. 4 2024

and RNA sequencing revealed a fusion of the ZCCHC8

and ROS1 genes resulting from the translocation of

chromosomes 6 and 12. After fifteen months of treat-

ment, the tumor continued to grow and metastases

appeared. After consultation and obtaining a consent

from the child’s parents, treatment with the ROS1 ty-

rosine kinase inhibitor Entrectinib was initiated. Re-

markably, within a month from starting this targeted

therapy, the tumor size significantly decreased [44].

While this case represents a notable success, it

remains an exception in the current landscape of tar-

geted cancer therapy. Presently, targeted therapies are

predominantly developed for diseases characterized

by point mutations or gene fusions resulting in chi-

meric proteins. This is mostly due to the limitations

of existing detection methods, which are less adept at

identifying balanced chromosomal rearrangements,

particularly those occurring in non-coding regions.

Analysis of FFPE samples using Hi-C is capable of de-

tecting not only chromosomal rearrangements leading

to gene fusions, but also those that occur in non-cod-

ing regions and can disrupt regulatory element, which

might alter oncogene expression [45]. The application

of the Hi-C technique developed by us offers an exten-

sion of already existing high-throughput sequencing

methods. By enabling detection of a broader range of

genomic alterations, it allows researchers to uncover

clinically relevant information that might otherwise

remain elusive. This comprehensive genomic insight is

instrumental not only in promoting our understanding

of tumor biology, but also in identifying novel thera-

peutic targets.

Supplementary information. The online version

contains supplementary material available at https://

doi.org/10.1134/S0006297924040047.

Acknowledgments. We express our gratitude to

the Ministry of Science and Higher Education of the

Russian Federation (state project FWNR-2022-0019)

for providing access to computing power. Access to the

public resources and datasets was provided by Novo-

sibirsk State University with the support of the Minis

Ministry of Science and Higher Education of the Rus-

sian Federation (grant no.2019-0546; FSUS-2024-0018).

Contributions. M.M.G. and V.S.F. developed the

concept and managed the study; M.M.G., Ya.K.S., M.A.N.,

T.A.L., N.Yu.T., A.A.S., A.I.R., N.V.V. , S.V.V., T.S.G., E.V.D.,

and M.A.T. conducted experiments; M.M.G., V.S.F., E.V.D.,

and M.A.K. discussed the results; M.M.G. and Ya.K.S.

wrote the manuscript; V.S.F. edited the text of the

article.

Funding. The research was supported by the Rus-

sian Science Foundation (project no.22-24-00190).

Ethics declarations. This study was performed in

line with the principles of the Declaration of Helsinki.

Approval was granted by the Research Ethics Com-

mittee of the Research Institute of Medical Genetics,

Tomsk National Research Medical Center (27.07.2017/

no. 106). All study participants provided informed con-

sent. The authors of this work declare that they have

no conflicts of interest.

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