TeloView ® technology predicts genomic instability in chronic lymphocytic leukemia

TeloView ® technology predicts genomic instability in chronic lymphocytic leukemia | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article TeloView ® technology predicts genomic instability in chronic lymphocytic leukemia Fábio Morato de Oliveira, Bruno Machado Rezende Ferreira, Cristina Mores Junta, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6939271/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Chronic lymphocytic leukemia (CLL) is a heterogeneous B-cell malignancy characterized by recurrent chromosomal abnormalities and variable clinical outcomes. Genomic instability plays a central role in disease progression, with telomere dysfunction emerging as a critical contributor. While the three-dimensional (3D) nuclear organization of telomeres has been linked to genomic integrity, its prognostic significance in CLL remains underexplored. Methods: In this study, we applied TeloView® technology to evaluate the 3D telomere architecture in peripheral blood samples from 28 CLL patients. Cytogenetic analysis identified key abnormalities including del(13q14), trisomy 12, del(17p13), and del(11q22). Quantitative parameters—such as telomere number, length (signal intensity), aggregate formation, nuclear volume, and a/c ratio—were assessed and compared across cytogenetic subgroups. Results: Patients with del(17p13) and del(11q22), associated with high-risk disease, showed increased telomere aggregation and shorter telomere lengths, reflecting higher genomic instability. Conversely, patients with del(13q14) exhibited more intact telomere profiles, consistent with a favorable prognosis. Trisomy 12 cases displayed intermediate features. Statistical analyses revealed significant differences in telomere architecture between cytogenetic groups. Conclusion: 3D telomere profiling using TeloView® provides insights into the genomic instability landscape of CLL and aligns with established cytogenetic risk profiles. Although not novel, these findings reinforce the relevance of telomere dynamics as a potential biomarker for disease aggressiveness and risk stratification in CLL. CLL Telomere Chromosomal abnormalities Genomic Instability Figures Figure 1 Figure 2 Figure 3 1. Introduction Chronic lymphocytic leukemia (CLL) is the most common form of leukemia in adults in Western countries. It is characterized by the clonal proliferation and accumulation of mature appearing, but functionally incompetent, B-lymphocytes in the blood, bone marrow, and lymphoid tissues [ 1 , 2 ]. The clinical course of CLL is highly variable, ranging from indolent cases that remain asymptomatic for years to aggressive forms requiring early intervention [ 1 , 2 ]. This heterogeneity is closely associated with underlying genetic and molecular alterations that drive disease behavior [ 3 ]. Cytogenetically, CLL is marked by recurrent chromosomal abnormalities that influence both prognosis and therapeutic decisions. Common alterations include del(13q14), trisomy 12, del(11q22–23), and del(17p13), typically identified through fluorescence in situ hybridization (FISH) and other molecular diagnostic methods [ 4 ]. Among these, del(13q14) is the most frequent and is generally linked to favorable prognosis, while del(17p13), involving the TP53 gene, is associated with poor outcomes and resistance to standard chemoimmunotherapy [ 5 , 6 ]. Genomic instability, a hallmark of many cancers, plays a critical role in tumor initiation, progression, and treatment resistance [ 7 , 8 ]. It is characterized by a high frequency of genetic alterations such as mutations, chromosomal rearrangements, and aneuploidy, which promote cellular heterogeneity and clonal evolution [ 9 ]. In hematological malignancies like CLL, genomic instability is a key driver of disease progression and therapeutic failure [ 10 ]. Telomeres, the protective caps at the ends of linear chromosomes, are essential for maintaining genomic stability [ 11 ]. Beyond their length, the three-dimensional (3D) organization of telomeres within the nucleus has emerged as an important marker of genomic integrity [ 12 , 13 ]. Telomeres are non-randomly positioned in the nuclear architecture and interact with other genomic regions to form a dynamic, structured 3D network [ 14 ]. Disruption of this organization, frequently seen in cancer cells, is associated with increased genomic instability and altered gene expression [ 15 ]. Changes in telomere clustering and spatial arrangement may promote chromosomal rearrangements and contribute to more aggressive disease phenotypes [ 12 – 16 ]. Advanced imaging technologies, such as 3D fluorescence in situ hybridization (3D-FISH), can identify specific telomere signatures linked to genetic abnormalities, disease stage, and treatment response. Quantification of this 3D telomere architecture is performed using TeloView® software [ 17 , 18 ]. In this study, we applied TeloView® analysis to CLL samples to assess genomic instability in relation to chromosomal abnormalities. 2. Material and Methods a. Patients A total of 28 peripheral blood samples from CLL patients with either normal or abnormal karyotypes were included in this study (20 male, 8 female; median age: 58.46 years; age range: 47–74 years). The diagnosis of chronic lymphocytic leukemia (CLL) was made according to the International Workshop on CLL (iwCLL) criteria [19], based on peripheral blood lymphocytosis (≥5×10⁹/L), characteristic lymphocyte morphology, and immunophenotyping confirming B-cell clonality with expression of CD5, CD19, CD20, and CD23. None of the patients had a prior diagnosis of other hematological disorders. Written informed consent was obtained from all participants in accordance with the Declaration of Helsinki. The study was approved by the Research Ethics Board for human studies (protocol no. 1243/2017). b. Metaphase induction (G-banding analysis) Metaphase induction was performed using 10⁶ peripheral blood mononuclear cells. The cells were cultured in RPMI 1640 medium (Thermo Fisher Scientific, Waltham, MA, USA) supplemented with 10% fetal calf serum, along with the immunostimulatory CpG-oligonucleotide DSP30 (TIB MolBiol, Berlin, Germany) and interleukin-2 (IL-2) (Peprotech, Colonia Narvarte, Mexico). After 72 hours of incubation, colcemid (Sigma, Munich, Germany) was added to arrest cells in metaphase prior to chromosome preparation. Chromosomes were prepared following standard cytogenetic procedures, and the analysis and interpretation were conducted according to International System for Human Cytogenomic Nomenclature guidelines (ISCN 2020) [20]. Image analysis was carried out using an AxioImager M1 microscope (Carl Zeiss, Jena, Germany) equipped with appropriate filters and image capture software. Chromosome identification was based on an average resolution of 450 bands per haploid set. A minimum of 20 metaphases were analyzed per sample. c. Interphase Fluorescence in Situ Hybridization (iFISH) Interphase fluorescence in situ hybridization (iFISH) was performed on peripheral blood samples to detect recurrent chromosomal abnormalities associated with CLL. The analysis was conducted on unstimulated cells, in accordance with standard cytogenetic practices for CLL diagnostics. The CLL FISH panel included probes specific for the following regions: 13q14 (D13S319), trisomy 12 (centromere 12, D12Z1), 11q22 (ATM), and 17p13 (TP53). Interphase nuclei were prepared from fresh, unstimulated peripheral blood collected in heparinized tubes. Following hypotonic treatment with 0.075 M KCl, cells were fixed in methanol:acetic acid (3:1) and dropped onto clean glass slides. FISH was performed using commercially available, locus-specific probes provided by Kreatech Diagnostics ( now part of Leica Biosystems ). Hybridization and post-hybridization procedures were carried out according to the manufacturer’s protocol. Hybridization was performed overnight at 37 °C in a humidified chamber. Slides were washed under stringent conditions to ensure specificity, and nuclei were counterstained with DAPI (4′,6-diamidino-2-phenylindole). Signal evaluation was conducted using a AxioImager M1 microscope (Carl Zeiss, Jena, Germany) fluorescence microscope equipped with appropriate filter sets. For each probe, at least 100 interphase nuclei were analyzed per sample. FISH results were interpreted following the ISCN 2020 [20]. Abnormalities were recorded as present when the number of nuclei with an abnormal signal pattern exceeded established laboratory cut-off values, validated through internal controls and existing literature. d. Quantitative Fluorescent in situ Hybridization (Q-FISH). For Q-FISH analysis, slides containing fixed stimulated cells were incubated in 3.7% formaldehyde/1xPBS solution for 10 minutes, and after the slides were soaked in 20% glycerol/1xPBS solution for 45min. The cells were treated by four repeated cycles of freeze-thaw in glycerol. After, the slides were incubated in 0.1 HCL solution and fixation in 70% formamide/2xSCC for 1 hour. For hybridization, slides were covered with 8μL of PNA telomeric probe (Agilent Dako, Santa Clara, California, USA), sealed with coverslip and rubber cement. For denaturation, the slides were placed on a hot plate, protected from direct light, for 3 minutes, at 82°C. The hybridization was carried out for 2 hours, at 30°C. The slides were then washed three times in 70% formamide/10mM Tris (pH 7.4) solution, for 15 minutes followed by washing in 1xPBS at room temperature for 2 minutes, while shaking and in 0.1xSSC at 55°C for 5 minutes while shaking. Finally, the slides were washed in 2xSSC/ 0.05% Tween 20 solution for three times, for 5 minutes, at room temperature while shaking. After the final, all cycles of washing, the nuclei were counter-stained with 4’,6-diamino-2-phenylindole (DAPI) (0.1μg/mL) and antifade reagent (Thermo Fisher Scientific, Waltham - Massachusetts, EUA), and covered with coverslips for image acquisition. e. 3D image acquisition and analysis using TeloView ® system. Thirty interphase nuclei were analyzed, for each sample, by using an AxioImager M1 microscope (Carl Zeiss, Jena, Germany), coupled to an AxioCam HRm charge-coupled device (Carl Zeiss, Jena, Germany) and a 63-x oil objective lens (Carl Zeiss, Jena, Germany). The acquisition time was 500 milliseconds (ms) for Cy3 (telomeres) and 5 ms for DAPI (nuclei). Sixty z-stacks were acquired at a sampling distance of x,y: 102 nm and z: 200 nm for each slice of the stack. AxioVision 4.8 software (Carl Zeiss, Jena, Germany) was used for 3D image acquisition. Deconvolved images were converted into TIFF files and exported for 3D-analysis using the TeloView ® software (Telo Genomics Corp., Toronto, ON, Canada) [17,18]. f. Data image analysis – 3D telomere architecture. The evaluation of the telomeric architecture of CLL cells was performed by TeloView ® software [17,18], proprietary to Telo Genomics, Toronto, Canada. It measures six distinct parameters for each sample: (1) telomere length based on signal intensity, (2) the number of telomere signals per nucleus, (3) the number of telomeric aggregates (clusters of telomeres that cannot be resolved further at an optical resolution limit of 200 nm), (4) nuclear volume, (5) a/c ratio (a spatial feature assessing cell cycle progression and proliferation), and (6) the spatial distribution of telomeres within the nuclear space, which reflects gene expression. For the distinct subgroups of CLL cells, based on cytogenetic profile, a graphical representation was obtained showing the distribution of the intensity of the acquired telomere fluorescent signals, the distribution of the frequency of telomere aggregates per cell and the acquired signals per cell. g. Statistical Analysis for telomere architecture Based on cytogenetic analysis of CLL cells, four distinct subgroups were defined. The telomeric parameters (number, length, telomere aggregates, nuclear volumes, and a/c ratio) were compared between these subgroups using analysis of variance (ANOVA). All telomere parameters in CLL subgroups were compared using chi-square analysis. Additionally, cell parameter averages were analyzed using nested factorial analysis of variance. All statistical analyses were performed using GraphPad Prism version 8.0 (GraphPad Software, San Diego, CA, USA) [21]. The significance level was set at 0.05. 3. Results Among the CLL cases, age distribution was categorized as follows: 6 patients were under 50 years old, 10 patients were between 50 and 60 years, and 12 patients were older than 60 years. Of the total 28 patients, 20 (71.4%) were male and 8 (28.6%) were female. Cytogenetic profiles varied, reflecting a range of karyotypic abnormalities with differing prognostic implications (Table 1 ). Six patients (21.4%) presented a normal karyotype (46,XX or 46,XY), and no chromosomal abnormalities were detected by iFISH in unstimulated cells. These cases may indicate a more indolent disease course, though additional molecular data would be required for further prognostic clarification. Deletion of 13q14 was identified in seven patients (25%), predominantly among males aged 51–74 years. While del(13)(q14) is generally associated with a favorable prognosis in CLL, its prognostic significance is influenced by several factors. Notably, the size of the clone harboring the deletion plays a critical role; larger clones with a higher percentage of nuclei exhibiting the deletion are associated with a shorter time to first treatment and overall survival. Additionally, the nature of the deletion—whether monoallelic or biallelic—can impact disease progression, with biallelic deletions linked to more aggressive disease​. The commonly deleted region includes the DLEU2 and miR-15a/16 − 1 genes, which are involved in regulating apoptosis and cell cycle control. Loss of these genes contribute to leukemogenesis and may affect disease behavior [ 22 , 23 ]. Trisomy 12 was identified in 7 patients (25%), either as an isolated finding or co-occurring with other abnormalities such as TP53 deletion, a pattern associated with intermediate risk and more aggressive clinical features (Fig. 1 ). Deletion of 17p13, involving the TP53 locus, was detected in 6 patients (21%) and is strongly linked to poor prognosis. Furthermore, deletion of 11q22 was observed in 5 patients (18%) and is typically associated with extensive lymphadenopathy and adverse disease outcomes. These findings underscore the importance of comprehensive cytogenetic evaluation in CLL for accurate risk stratification and individualized therapeutic strategies. Table 1 Characteristics of chronic lymphocytic leukemia patients based on age, sex and cytogenetic information. Patients Age Sex G-Banding Karyotype (stimulated cells) FISH (unstimulated cells) CLL001 49 F 46,XX[ 20 ] ish normal signal pattern CLL002 50 F 46,XX[ 20 ] ish normal signal pattern CLL003 51 M 46,XY[ 20 ] ish normal signal pattern CLL004 55 M 46,XY,del(13)(q14.3)[ 20 ] ish del(13q14)(D13S319×1)[55/100] CLL005 71 F 46,XX,del(13)(q14.2)[ 20 ] ish del(13q14)(D13S319×1)[64/100] CLL006 74 M 46,XY,del(13)(q14.3)[ 14 ]/46,XY[ 6 ] ish del(13q14)(D13S319×1)[61/100] CLL007 55 M 46,XY,del(13)(q14.1q14.3)[ 20 ] ish del(13q14)(D13S319×1)[58/100] CLL008 49 M 46,XY[ 20 ] ish normal signal pattern CLL009 57 F 46,XX[ 20 ] ish normal signal pattern CLL010 55 F 46,XX,del(13)(q14.2)[ 12 ]/46,XX[ 8 ] ish del(13q14)(D13S319×1)[67/100] CLL011 67 M 47,XY,+12[ 20 ] ish + 12(D12Z1×3)[32/100] CLL012 68 M 47,XY,+12[ 20 ] ish + 12(D12Z1×3)[41/100] CLL013 71 F 47,XX,+12[ 20 ] ish + 12(D12Z1×3)[36/100] CLL014 70 M 47,XY,+12,del(13)(q14.2q14.3)[ 20 ] ish + 12(D12Z1×3)[42/100], ish del(13q14)(D13S319×1)[32/100] CLL015 47 M 46,XY[ 20 ] ish normal signal pattern CLL016 48 F 47,XX,+12[ 20 ] ish + 12(D12Z1×3)[29/100] CLL017 49 M 46,XY,del(17)(p11.1),del(6)(q21)[ 20 ] ish del(17p13)(TP53×1)[54/100] CLL018 55 M 46,XY,del(17)(p11.2),add(1)(p36.1)[ 20 ] ish del(17p13)(TP53×1)[36/100] CLL019 52 M 46,XY,del(17)(p13.1)[ 20 ] ish del(17p13)(TP53×1)[41/100] CLL020 58 F 46,XX,del(17)(p11.1)[ 12 ]/46,XX[ 8 ] ish del(17p13)(TP53×1)[48/100] CLL021 62 M 47,XY,+12,del(17)(p11.2)[ 20 ] ish + 12(D12Z1×3)[41/100] CLL022 68 M 46,XY,del(13)(q14.2q21.1)[ 20 ] ish del(13q14)(D13S319×1)[66/100] CLL023 67 M 46,XY,del(11)(q22.3)[ 20 ] ish del(11q22)(ATM×1)[26/100] CLL024 66 M 46,XY,del(17)(p11.2)[ 14 ]/46,XY[ 6 ] ish del(17p13)(TP53×1)[38/100] CLL025 62 M 47,XY,del(11)(q23.1),+12[ 20 ] ish del(11q22)(ATM×1)[24/100], + 12(D12Z1×3)[39/100] CLL026 58 M 46,XY,del(11)(q22.3)[ 20 ] ish del(11q22)(ATM×1)[31/100] CLL027 52 M 46,XY,del(11)(q23.1),del(17)(p11.2)[ 20 ] ish del(11q22)(ATM×1)[28/100], del(17p13)(TP53×1)[43/100] CLL028 51 M 46,XY,del(13)(q14.2)[ 10 ]/46,XY[ 10 ] ish del(13q14)(D13S319×1)[57/100] For the 3D telomere investigation, we assessed the overall signal intensity of telomeres, which indicates their length, and found notable variations over specific chromosomal abnormalities in CLL patients. 3D telomere architecture, by TeloView® analysis (Telo Genomics Corp.) [ 17 , 18 ] showed that telomeres became shorter as the correlation between chromosomal changes and prognosis for CLL take place (Fig. 2 ). This shortening was reflected in a greater proportion of telomeres with low signal intensities. In Fig. 2 , telomere length (depicted by signal intensity on the x-axis) was plotted against the number of telomeres (y-axis) for each analyzed cell across all time points. Signals were categorized by intensity levels, highlighting telomere distribution within each sample or time point. Cancer cells often exhibit altered telomere counts per cell and shorter telomere lengths compared to normal cells [ 13 ]. In this study, we observed variations in the total detectable telomere signals across CLL samples with different cytogenetic alterations (Table 2 ). TeloView® analysis (Telo Genomics Corp.) [ 17 , 18 ] of a cohort of 28 CLL patients revealed that samples with del(17p13) and del(11q22)—markers of aggressive disease—exhibited the highest numbers of telomeric signals, aggregates, and signal intensities. This increase is likely attributed to telomere clustering, where closely spaced or fused telomeres are detected as brighter and sometimes more numerous signals. This pattern was consistently observed in all patients with del(17p13) and del(11q22) (Fig. 3 ). In contrast, patients with del(13q14) displayed fewer aggregates and more spatially separated telomeres in interphase nuclei, indicative of more stable nuclear architecture. The TeloView® analysis software distinguishes between single telomeres and aggregates based on size and intensity. While aggregates may appear as fewer discrete telomeres under higher resolution, they often contribute to increased total signal counts and intensities due to overlapping fluorescence. Therefore, rather than decreasing signal detection, aggregate formation can lead to an apparent increase in these metrics. Comparison of telomere intensities and distributions across different CLL cytogenetic profiles revealed significant differences, as indicated by the p-values shown in Fig. 2 . The 3D telomere architecture varied notably with cytogenetic status, showing increased nuclear volume and altered a/c ratios. A higher a/c ratio, which reflects a more disk-like nuclear shape, is typically associated with later stages of the cell cycle and increased proliferative activity. Telomere shortening may promote the formation of aggregates, linking these structural changes to genomic instability and the development of chromosomal abnormalities in CLL. Table 2 Statistical analysis of telomere parameters for CLL samples based on interphase nuclei information. CLL patients Total number of signals (Mean ± SD) Total number of aggregates (Mean ± SD) Total intensity (Mean ± SD) Average intensity of all signals (Mean ± SD) a/c Ratio (Mean ± SD) Nuclear Volume (Mean ± SD) Normal karyotype (a) 29,56732 ± 2,87 1,77 ± 0,89 387342,566 12023,897 ± 432 6,65 ± 2,21 334543 ± 14324 del(13q14) (b) 33,67832 ± 2,43 3,66 ± 1,12 403234,763 13287,432 ± 443 5,44 ± 1,12 389652 ± 15432 Trisomy 12 (c) 37,88341 ± 2,89 4,89 ± 1,15 603562,762 14432,432 ± 467 5,34 ± 1,54 443245 ± 16983 del(17p13) (d) 43,67343 ± 2,77 6,77 ± 0,94 656231,887 15893,341 ± 433 4,12 ± 0,99 489432 ± 18932 del(11q22) (e) 41,78322 ± 3,12 5,43 ± 1,21 778327,982 15584,453 ± 476 3,55 ± 1,66 456654 ± 13243 p value p < 0,0001 p < 0,0001 p < 0,0001 p < 0,0001 p < 0,0001 p < 0,0001 (a) vs (b)/(a) vs (c)/(a) vs (d)/ (a) vs (e) Discussion In this study, we used TeloView® technology [ 17 , 18 ] to analyze the 3D telomere architecture in CLL patients, revealing patterns associated with specific chromosomal abnormalities. These findings build on and extend previous research that highlights the relationship between 3D telomere dynamics, cytogenetic alterations, and tumor classification or evolution. The presence of 3D telomere aggregates, particularly in samples with del(17p13) and del(11q22), aligns with existing literature that links chromosomal abnormalities to altered telomere organization [ 12 – 16 ]. Jebaraj et al. (2021) [ 11 ] emphasized the pivotal role of telomere dysfunction in driving genomic instability and its strong association with high-risk cytogenetic profiles in CLL. Similarly, Lin et al. (2014) [ 24 ] demonstrated that telomere dysfunction is a reliable predictor of clinical outcomes in CLL, offering valuable prognostic information even in early-stage disease. The association between short telomeres and deletions of 17p and 11q has been consistently reported since at least 2008, using various methodologies including Q-FISH, TRF analysis, and flow-FISH. Notably, studies by Roos et al. (2008) [ 25 ], Ricca et al. (2012) [ 26 ] and Scarfo et al. (2019) [ 27 ] demonstrated that patients harboring 17p or 11q deletions often exhibit severe telomere attrition, correlating with genomic complexity, clonal evolution, and poor prognosis. Our findings corroborate these earlier observations, and while not novel, they reinforce the utility of 3D telomere profiling as a biomarker of genomic instability in CLL. These foundational studies should not be overlooked when interpreting our results. Consistent with these reports, our results demonstrate, using TeloView® technology [ 17 , 18 ], that telomere dysfunction is associated with higher aggregate formation, suggesting enhanced chromosomal rearrangement potential and clonal evolution in aggressive disease subgroups of CLL Interestingly, patients with del(13q14) exhibited relatively preserved telomeres and fewer aggregates, consistent with their favorable prognosis. These findings suggest that 3D telomere profiling may play a key role in understanding the development of chromosomal abnormalities during CLL progression. Specifically, increased telomere aggregation and shorter average telomere length were associated with del(17p13) and del(11q22), both of which are linked to aggressive CLL phenotypes and poor clinical outcomes. The 3D telomere dynamics revealed distinct nuclear telomere distribution patterns across different cytogenetic subgroups, aligning with findings from Gadji et al. (2012) [ 13 ] and Rangel-Pozzo et al. (2021) [ 14 ] in myelodysplastic syndromes and acute myeloid leukemia, as well as Kumar et al. (2024) [ 15 ] in multiple myeloma. These studies emphasized the prognostic value of 3D telomere profiling in stratifying patients by risk. Similarly, we observed increased telomere aggregation in del(17p13) and del(11q22) cases, supporting the view that disrupted telomere organization is a hallmark of genomic instability in more aggressive disease. In Hodgkin’s lymphoma, Knecht et al. (2024) [ 12 ] demonstrated that telomere aggregates could predict treatment response. Although our study did not assess treatment outcomes, the increased aggregate formation in high-risk CLL subgroups suggests a potential link between 3D telomere architecture and therapeutic resistance, meriting further investigation. Additionally, the altered a/c ratios observed in our study—reflecting changes in nuclear shape and cell cycle progression—are consistent with findings by Li et al. (2021) [ 7 ], who associated genomic instability with changes in nuclear architecture and cellular metabolism in cancer. Our findings on the cytogenetic landscape of CLL—including del(13q14), trisomy 12, del(11q22), and del(17p13)—are consistent with the recommendations by Baliakas et al. (2022) [ 2 ], who highlighted the importance of incorporating cytogenetic data into CLL risk stratification. Trisomy 12, associated with intermediate prognosis, showed moderate telomere aggregate formation, supporting the observations of Trivedi et al. (2023) [ 5 ] regarding its role in genomic heterogeneity. Additionally, we demonstrated improved detection of chromosomal abnormalities in CLL, aligning with previous studies that reported enhanced identification of cytogenetic alterations using CpG-based stimulation [ 28 ]. The combination of DSP30 and IL-2 increased the overall abnormality detection rate to 55%, compared to just 12% without mitogen stimulation, and significantly enhanced the identification of CLL subclones. Despite advances in interphase FISH (iFISH), conventional chromosome analysis remains a valuable diagnostic method. It provides a broader genomic overview than iFISH, allowing for the assessment of genomic complexity and the identification of abnormalities not targeted by standard FISH panels—an important consideration for accurate prognostic evaluation. These findings highlight telomere aggregation as a potential mechanism contributing to chromosomal instability. This concept is supported by Vermolen et al. (2005) [ 17 ], who showed that telomere clustering can promote chromosomal rearrangements by bringing distant genomic loci into proximity. The increased aggregate formation observed in high-risk subgroups, such as those with del(17p13), may represent a cellular adaptation to tolerate—or even leverage—genomic instability for clonal evolution. Studies like those by Salmaninejad et al. (2021) [ 8 ] have investigated the molecular basis of genomic instability in cancer. Our results contribute to this body of research by demonstrating that 3D telomere architecture, particularly when analyzed using TeloView® technology [ 17 , 18 ], offers a measurable indicator of genomic instability and a potential target for therapeutic intervention. The alignment of our findings with previous studies reinforces the prognostic value of 3D telomere analysis in CLL. Building on this foundation, integrating data from TeloView® technology [ 17 , 18 ] with genomic and transcriptomic profiles—as proposed by Condoluci and Rossi (2020) [ 10 ]—may provide a more comprehensive understanding of CLL biology and improve clinical decision-making. Additionally, longitudinal studies such as those suggested by Muñoz-Novas et al. (2024) [ 4 ] could shed light on the temporal dynamics of telomere alterations and their role in the clonal evolution of CLL. Despite the relevance of our findings, several limitations must be acknowledged. First, the relatively small sample size (n = 28) may limit the statistical power and the generalizability of the results to broader CLL populations. Second, the lack of clinical outcome data—such as treatment history, response rates, progression-free survival, or overall survival—prevents direct correlation between telomere architecture and clinical endpoints. Third, although our data supports and expands on previously published work, the absence of IGHV mutation status and CLL staging limits the ability to fully stratify patients by risk. Therefore, future studies involving larger, well-characterized patient cohorts with longitudinal follow-up and integrated clinical, molecular, and telomere architecture data are necessary to validate and expand upon these findings. Declarations Authors´ contribution: FMO: conceptualized the study, coordinated patient recruitment, supervised experimental procedures, and contributed to data analysis and manuscript writing. BMRF: assisted in cytogenetic and FISH analyses, provided clinical data interpretation, and participated in manuscript revisions. CMJ: contributed to the methodology development, sample processing, and critical review of the manuscript. SM: provided the TeloView® platform and technical support for 3D telomere analysis, contributed to image analysis interpretation, and supervised the final drafting and critical revision of the manuscript. All authors read and approved the final version of the manuscript. Acknowledgements: We thank Telo Genomics Corp. for the use of TeloView® software platform. The authors also thank the Genomic Centre for Cancer Research and Diagnosis (GCCRD) for imaging. The GCCRD is funded by the Canada Foundation for Innovation and supported by CancerCare Manitoba Foundation, the University of Manitoba and the Canada Research Chair Tier 1 (S.M.). The GCCRD is a member of the Canadian National Scientific Platforms (CNSP) and of Canada BioImaging. Research group in Molecular Epidemiology (EPIMOL), CNPq, Brazil. Association for Health Education & Research, Brazil. Genomic Medicine Study Group (GMEG). Ethics Statement: The study was approved by the Ethics Committee, which is affiliated with the Federal University of Jataí (98331018.7.0000.8155). Consent: Written informed consent was obtained from all patients. Data Availability Statement: The raw data generated and/or analyzed during the current study are not publicly available due to ethical and confidentiality restrictions imposed by the research ethics committee. The data includes sensitive and potentially identifiable information from participants, and even with anonymization, there is a risk of compromising their privacy. However, the datasets may be made available from the corresponding author upon reasonable request and pending approval by the ethics committee. Conflict of Interest Statement The authors declare no commercial or financial relationships that could be construed as a potential conflict of interest. Although one of the authors (S.M.) is affiliated with Telo Genomics Corp., which provided the TeloView® platform used in this study, all image acquisition, telomere measurements, data analysis, and interpretation were independently performed by the research team at the Federal University of Jataí. The involvement of Telo Genomics Corp. was limited to providing software access, and the company had no influence on study design, data analysis, or the decision to publish the results. References Wainman LM, Khan WA, Kaur P. Chronic Lymphocytic Leukemia: Current Knowledge and Future Advances in Cytogenomic Testing. In: Sergi CM, editor. Advancements in Cancer Research [Internet]. Brisbane (AU): Exon Publications; 2023 Aug 17. Chapter 6. PMID: 37756426. Baliakas P, Espinet B, Mellink C, Jarosova M, Athanasiadou A, Ghia P, Kater AP, Oscier D, Haferlach C, Stamatopoulos K. Cytogenetics in Chronic Lymphocytic Leukemia: ERIC Perspectives and Recommendations. Hemasphere. 2022 Mar 25;6(4):e707. Nadeu F, Diaz-Navarro A, Delgado J, Puente XS, Campo E. Genomic and Epigenomic Alterations in Chronic Lymphocytic Leukemia. Annu Rev Pathol. 2020 Jan 24;15:149-177. Muñoz-Novas C, González-Gascón-Y-Marín I, Figueroa I, Sánchez-Paz L, Pérez-Carretero C, Quijada-Álamo M, Rodríguez-Vicente AE, Infante MS, Foncillas MÁ, Landete E, Churruca J, Marín K, Ramos V, Sánchez Salto A, Hernández-Rivas JÁ. Association of Cytogenetics Aberrations and IGHV Mutations with Outcome in Chronic Lymphocytic Leukemia Patients in a Real-World Clinical Setting. Glob Med Genet. 2024 Feb 12;11(1):59-68. Trivedi PJ, Patel DM, Kazi M, Varma P. Cytogenetic Heterogeneity in Chronic Lymphocytic Leukemia. J Assoc Genet Technol. 2023;49(1):4-9. Ondroušková E, Bohúnová M, Závacká K, Čech P, Šmuhařová P, Boudný M, Oršulová M, Panovská A, Radová L, Doubek M, Plevová K, Jarošová M. Duplication of 8q24 in Chronic Lymphocytic Leukemia: Cytogenetic and Molecular Biologic Analysis of MYC Aberrations. Front Oncol. 2022 Jun 24;12:859618. Li H, Zimmerman SE, Weyemi U. Genomic instability and metabolism in cancer. Int Rev Cell Mol Biol. 2021;364:241-265. Salmaninejad A, Ilkhani K, Marzban H, Navashenaq JG, Rahimirad S, Radnia F, Yousefi M, Bahmanpour Z, Azhdari S, Sahebkar A. Genomic Instability in Cancer: Molecular Mechanisms and Therapeutic Potentials. Curr Pharm Des. 2021;27(28):3161-3169. Guo S, Zhu X, Huang Z, Wei C, Yu J, Zhang L, Feng J, Li M, Li Z. Genomic instability drives tumorigenesis and metastasis and its implications for cancer therapy. Biomed Pharmacother. 2023 Jan;157:114036. Condoluci A, Rossi D. Genomic Instability and Clonal Evolution in Chronic Lymphocytic Leukemia: Clinical Relevance. J Natl Compr Canc Netw. 2020 Dec 31;19(2):227-233. Jebaraj BMC, Stilgenbauer S. Telomere Dysfunction in Chronic Lymphocytic Leukemia. Front Oncol. 2021 Jan 15;10:612665. Knecht H, Johnson N, Bienz MN, Brousset P, Memeo L, Shifrin Y, Alikhah A, Louis SF, Mai S. Analysis by TeloView ® Technology Predicts the Response of Hodgkin's Lymphoma to First-Line ABVD Therapy. Cancers (Basel). 2024 Aug 10;16(16):2816. Gadji M, Adebayo Awe J, Rodrigues P, Kumar R, Houston DS, Klewes L, Dièye TN, Rego EM, Passetto RF, de Oliveira FM, Mai S. Profiling three-dimensional nuclear telomeric architecture of myelodysplastic syndromes and acute myeloid leukemia defines patient subgroups. Clin Cancer Res. 2012 Jun 15;18(12):3293-304. Rangel-Pozzo A, Corrêa de Souza D, Schmid-Braz AT, de Azambuja AP, Ferraz-Aguiar T, Borgonovo T, Mai S. 3D Telomere Structure Analysis to DetectGenomic Instability and Cytogenetic Evolutionin Myelodysplastic Syndromes. Cells. 2019 Apr 2;8(4):304. Kumar S, Rajkumar SV, Jevremovic D, Kyle RA, Shifrin Y, Nguyen M, Husain Z, Alikhah A, Jafari A, Mai S, Anderson K, Louis S. Three-dimensional telomere profiling predicts risk of progression in smoldering multiple myeloma. Am J Hematol. 2024 Aug;99(8):1532-1539. Oliveira FM, Jamur VR, Merfort LW, Pozzo AR, Mai S. Three-dimensional nuclear telomere architecture and differential expression of aurora kinase genes in chronic myeloid leukemia to measure cell transformation. BMC Cancer. 2022 Sep 29;22(1):1024. Vermolen B., Garini Y., Mai S., Mougey V., Fest T., Chuang T.C., Chuang A.Y., Wark L., Young I.T. Characterizing the three-dimensional organization of telomeres. Cytom. Part A J. Int. Soc. Anal. Cytol. 2005;67:144–150. Chuang T.C.Y., Moshir S., Garini Y., Chuang A.Y.-C., Young I.T., Vermolen B., Doel R.v.D., Mougey V., Perrin M., Braun M., et al. The three-dimensional organization of telomeres in the nucleus of mammalian cells. BMC Biol. 2004;2:12. doi: 10.1186/1741-7007-2-12. Hallek M, Cheson BD, Catovsky D, Caligaris-Cappio F, Dighiero G, Döhner H, et al. iwCLL guidelines for diagnosis, indications for treatment, response assessment, and supportive management of CLL. Blood. 2018;131(25):2745–2760. doi: 10.1182/blood-2017-09-806398. McGowan-Jordan, J., Hastings, R. J., & Moore, S. (Eds.). (2020). ISCN 2020: An International System for Human Cytogenomic Nomenclature (2020) . Karger. ISBN: 978-3-318-06706-4. GraphPad Software. GraphPad Prism version 8.0 for Windows . San Diego, California, USA: GraphPad Software; 2018. Van Dyke DL, Shanafelt TD, Call TG, et al. A comprehensive evaluation of the prognostic significance of 13q deletions in patients with B-chronic lymphocytic leukemia. Br J Haematol . 2010;148(4):544–550. Klein U, Lia M, Crespo M, et al. The DLEU2/miR-15a/16-1 cluster controls B cell proliferation and its deletion leads to chronic lymphocytic leukemia. Cancer Cell . 2010;17(1):28–40. Lin TT, Norris K, Heppel NH, Pratt G, Allan JM, Allsup DJ, Bailey J, Cawkwell L, Hills R, Grimstead JW, Jones RE, Britt-Compton B, Fegan C, Baird DM, Pepper C. Telomere dysfunction accurately predicts clinical outcome in chronic lymphocytic leukaemia, even in patients with early-stage disease. Br J Haematol. 2014 Oct;167(2):214-23. Roos G, Kröber A, Grabowski P, Kienle D, Bühler A, Döhner H, et al. Short telomeres are associated with genetic complexity, high-risk genomic aberrations, and short survival in chronic lymphocytic leukemia. Blood . 2008;111(4):2246–52. doi:10.1182/blood-2007-05-089219. Ricca I, Rocca B, Baldazzi C, Ciavarella S, Cavazzini F, Martinelli S, et al. Telomere length and telomerase expression are associated with genomic complexity in chronic lymphocytic leukemia. Haematologica . 2012;97(1):56–63. doi:10.3324/haematol.2011.047738. Scarfò L, Torelli GF, Oldani E, Zibellini S, Tedeschi A, Gianelli U, et al. Short telomeres correlate with clonal evolution and disease progression in chronic lymphocytic leukemia. Leukemia . 2019;33(1):163–70. doi:10.1038/s41375-018-0207. Holmes PJ, Peiper SC, Uppal GK, Gong JZ, Wang ZX, Bajaj R. Efficacy of DSP30-IL2/TPA for detection of cytogenetic abnormalities in chronic lymphocytic leukaemia/small lymphocytic lymphoma. Int J Lab Hematol. 2016 Oct;38(5):483-9. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6939271","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":483570870,"identity":"e5ea60ad-682f-4dea-b834-865bdd84196f","order_by":0,"name":"Fábio Morato de Oliveira","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7ElEQVRIiWNgGAWjYBAC9gYehgNQNuMDBhs2IJ3AwIxPC88BmBY2BmYDhjQitTBAtbBJMKQxEKGF/ezBAx9zbPL55zcfq+ZJ4GPgZ88xYC7cg0cLT17CwZnb0ixnHGNLu82TwMYg2fPGgHnGM9xa7BlyDA7zbjtswHCMx+w27w82BoMbQFt4DuCxhf8NRIs8UEsxyBZ7glokoLYYALUwg7QYSBDU8g7sFwPDY2nJknMS2HgkzjwrODwDr8NyD3/4uM3GQO7w4YMf3iQck+NvT974uACPFnRwDBxNJGhgYKghRfEoGAWjYBSMEAAAwMFM4mdLxj0AAAAASUVORK5CYII=","orcid":"","institution":"Federal University of Jataí","correspondingAuthor":true,"prefix":"","firstName":"Fábio","middleName":"Morato","lastName":"de Oliveira","suffix":""},{"id":483570871,"identity":"aa83f7e1-0b92-4e5b-9af9-851d5a3a6569","order_by":1,"name":"Bruno Machado Rezende Ferreira","email":"","orcid":"","institution":"Oncology Service","correspondingAuthor":false,"prefix":"","firstName":"Bruno","middleName":"Machado Rezende","lastName":"Ferreira","suffix":""},{"id":483570872,"identity":"fdb34bdc-3326-4a31-9e3a-5f6a40d06ca0","order_by":2,"name":"Cristina Mores Junta","email":"","orcid":"","institution":"Faculty of Santa Casa de Belo Horizonte","correspondingAuthor":false,"prefix":"","firstName":"Cristina","middleName":"Mores","lastName":"Junta","suffix":""},{"id":483570873,"identity":"2b5554a4-d55d-493d-b411-e7ce3bf55b2d","order_by":3,"name":"Sabine Mai","email":"","orcid":"","institution":"University of Manitoba","correspondingAuthor":false,"prefix":"","firstName":"Sabine","middleName":"","lastName":"Mai","suffix":""}],"badges":[],"createdAt":"2025-06-20 13:23:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6939271/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6939271/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":86660374,"identity":"66126d22-bb15-4035-99ba-10e1c45f669b","added_by":"auto","created_at":"2025-07-14 10:35:03","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":91662,"visible":true,"origin":"","legend":"\u003cp\u003eChronic lymphocytic leukemia karyotype immuno-stimulated by the combination of DSP30 and IL-2. The red arrows show the co-existence of trisomy 12 and del(17p11.2), 47,XY,+12,del(17)(p11.2)[20].\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6939271/v1/481d87dc72a162906d1aeb7c.png"},{"id":86660377,"identity":"af9ca7e4-25e0-4a5c-b004-1f3f1ae7d5f8","added_by":"auto","created_at":"2025-07-14 10:35:03","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":68438,"visible":true,"origin":"","legend":"\u003cp\u003eGraph distribution of number of telomeres according to their intensity (length of telomeres) for Chronic Lymphocytic Leukemia patients [del(13q14), Trisomy 12, del(17p13) and del(11q22)]. The image represents the 3D telomere distribution of the 3D telomeric profile.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6939271/v1/7ddd4bf72b5b539d0a1e792c.png"},{"id":86662336,"identity":"13b81362-7164-4c68-b024-412adfdbd304","added_by":"auto","created_at":"2025-07-14 10:43:03","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":111292,"visible":true,"origin":"","legend":"\u003cp\u003e3D telomere architecture in CLL cells. (A) Representative 3D nuclear telomere distribution (red) within the counterstained nucleus (blue) in a CLL sample harboring del(13q14). (B) 3D Telomere distribution in CLL sample with karyotype: 47,XY,+12,del(17)(p11.2)[20]. The orange arrows indicate the presence of telomere aggregates.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6939271/v1/9cae833b4508c7cd333d5954.png"},{"id":91349006,"identity":"dc18b1fe-57d0-4df9-be2b-19429b6530ee","added_by":"auto","created_at":"2025-09-15 14:18:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":977386,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6939271/v1/ce68b195-9670-44b2-a44a-091a8855ff12.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"TeloView ® technology predicts genomic instability in chronic lymphocytic leukemia","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eChronic lymphocytic leukemia (CLL) is the most common form of leukemia in adults in Western countries. It is characterized by the clonal proliferation and accumulation of mature appearing, but functionally incompetent, B-lymphocytes in the blood, bone marrow, and lymphoid tissues [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The clinical course of CLL is highly variable, ranging from indolent cases that remain asymptomatic for years to aggressive forms requiring early intervention [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. This heterogeneity is closely associated with underlying genetic and molecular alterations that drive disease behavior [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eCytogenetically, CLL is marked by recurrent chromosomal abnormalities that influence both prognosis and therapeutic decisions. Common alterations include del(13q14), trisomy 12, del(11q22\u0026ndash;23), and del(17p13), typically identified through fluorescence \u003cem\u003ein situ\u003c/em\u003e hybridization (FISH) and other molecular diagnostic methods [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Among these, del(13q14) is the most frequent and is generally linked to favorable prognosis, while del(17p13), involving the \u003cem\u003eTP53\u003c/em\u003e gene, is associated with poor outcomes and resistance to standard chemoimmunotherapy [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eGenomic instability, a hallmark of many cancers, plays a critical role in tumor initiation, progression, and treatment resistance [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. It is characterized by a high frequency of genetic alterations such as mutations, chromosomal rearrangements, and aneuploidy, which promote cellular heterogeneity and clonal evolution [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. In hematological malignancies like CLL, genomic instability is a key driver of disease progression and therapeutic failure [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Telomeres, the protective caps at the ends of linear chromosomes, are essential for maintaining genomic stability [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eBeyond their length, the three-dimensional (3D) organization of telomeres within the nucleus has emerged as an important marker of genomic integrity [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Telomeres are non-randomly positioned in the nuclear architecture and interact with other genomic regions to form a dynamic, structured 3D network [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Disruption of this organization, frequently seen in cancer cells, is associated with increased genomic instability and altered gene expression [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Changes in telomere clustering and spatial arrangement may promote chromosomal rearrangements and contribute to more aggressive disease phenotypes [\u003cspan additionalcitationids=\"CR13 CR14 CR15\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAdvanced imaging technologies, such as 3D fluorescence \u003cem\u003ein situ\u003c/em\u003e hybridization (3D-FISH), can identify specific telomere signatures linked to genetic abnormalities, disease stage, and treatment response. Quantification of this 3D telomere architecture is performed using TeloView\u0026reg; software [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. In this study, we applied TeloView\u0026reg; analysis to CLL samples to assess genomic instability in relation to chromosomal abnormalities.\u003c/p\u003e"},{"header":"2. Material and Methods","content":"\u003cp\u003e\u003cem\u003ea. \u003c/em\u003e\u003cem\u003ePatients\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eA total of 28 peripheral blood samples from CLL patients with either normal or abnormal karyotypes were included in this study (20 male, 8 female; median age: 58.46 years; age range: 47\u0026ndash;74 years). The diagnosis of chronic lymphocytic leukemia (CLL) was made according to the International Workshop on CLL (iwCLL) criteria [19], based on peripheral blood lymphocytosis (\u0026ge;5\u0026times;10⁹/L), characteristic lymphocyte morphology, and immunophenotyping confirming B-cell clonality with expression of CD5, CD19, CD20, and CD23. None of the patients had a prior diagnosis of other hematological disorders. Written informed consent was obtained from all participants in accordance with the Declaration of Helsinki. The study was approved by the Research Ethics Board for human studies (protocol no. 1243/2017).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eb. \u003c/em\u003e\u003cem\u003eMetaphase induction (G-banding analysis)\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eMetaphase induction was performed using 10⁶ peripheral blood mononuclear cells. The cells were cultured in RPMI 1640 medium (Thermo Fisher Scientific, Waltham, MA, USA) supplemented with 10% fetal calf serum, along with the immunostimulatory CpG-oligonucleotide DSP30 (TIB MolBiol, Berlin, Germany) and interleukin-2 (IL-2) (Peprotech, Colonia Narvarte, Mexico). After 72 hours of incubation, colcemid (Sigma, Munich, Germany) was added to arrest cells in metaphase prior to chromosome preparation. Chromosomes were prepared following standard cytogenetic procedures, and the analysis and interpretation were conducted according to International System for Human Cytogenomic Nomenclature guidelines (ISCN 2020) [20]. Image analysis was carried out using an AxioImager M1 microscope (Carl Zeiss, Jena, Germany) equipped with appropriate filters and image capture software. Chromosome identification was based on an average resolution of 450 bands per haploid set. A minimum of 20 metaphases were analyzed per sample.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ec. \u003c/em\u003e\u003cem\u003eInterphase Fluorescence in Situ Hybridization (iFISH)\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eInterphase fluorescence \u003cem\u003ein situ\u003c/em\u003e hybridization (iFISH) was performed on peripheral blood samples to detect recurrent chromosomal abnormalities associated with CLL. The analysis was conducted on unstimulated cells, in accordance with standard cytogenetic practices for CLL diagnostics. The CLL FISH panel included probes specific for the following regions: 13q14 (D13S319), trisomy 12 (centromere 12, D12Z1), 11q22 (ATM), and 17p13 (TP53). Interphase nuclei were prepared from fresh, unstimulated peripheral blood collected in heparinized tubes. Following hypotonic treatment with 0.075 M KCl, cells were fixed in methanol:acetic acid (3:1) and dropped onto clean glass slides. FISH was performed using commercially available, locus-specific probes provided by Kreatech Diagnostics (\u003cem\u003enow part of Leica Biosystems\u003c/em\u003e). Hybridization and post-hybridization procedures were carried out according to the manufacturer\u0026rsquo;s protocol. Hybridization was performed overnight at 37 \u0026deg;C in a humidified chamber. Slides were washed under stringent conditions to ensure specificity, and nuclei were counterstained with DAPI (4\u0026prime;,6-diamidino-2-phenylindole). Signal evaluation was conducted using a AxioImager M1 microscope (Carl Zeiss, Jena, Germany) fluorescence microscope equipped with appropriate filter sets. For each probe, at least 100 interphase nuclei were analyzed per sample. FISH results were interpreted following the ISCN 2020 [20]. Abnormalities were recorded as present when the number of nuclei with an abnormal signal pattern exceeded established laboratory cut-off values, validated through internal controls and existing literature.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ed. \u003c/em\u003e\u003cem\u003eQuantitative Fluorescent in situ Hybridization (Q-FISH).\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFor Q-FISH analysis, slides containing fixed stimulated cells were incubated in 3.7% formaldehyde/1xPBS solution for 10 minutes, and after the slides were soaked in 20% glycerol/1xPBS solution for 45min. The cells were treated by four repeated cycles of freeze-thaw in glycerol. After, the slides were incubated in 0.1 HCL solution and fixation in 70% formamide/2xSCC for 1 hour. For hybridization, slides were covered with 8\u0026mu;L of PNA telomeric probe (Agilent Dako, Santa Clara, California, USA), sealed with coverslip and rubber cement. For denaturation, the slides were placed on a hot plate, protected from direct light, for 3 minutes, at 82\u0026deg;C. The hybridization was carried out for 2 hours, at 30\u0026deg;C. The slides were then washed three times in 70% formamide/10mM Tris (pH 7.4) solution, for 15 minutes followed by washing in 1xPBS at room temperature for 2 minutes, while shaking and in 0.1xSSC at 55\u0026deg;C for 5 minutes while shaking. Finally, the slides were washed in 2xSSC/ 0.05% Tween 20 solution for three times, for 5 minutes, at room temperature while shaking. After the final, all cycles of washing, the nuclei were counter-stained with 4\u0026rsquo;,6-diamino-2-phenylindole (DAPI) (0.1\u0026mu;g/mL) and antifade reagent (Thermo Fisher Scientific, Waltham - Massachusetts, EUA), and covered with coverslips for image acquisition.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ee. \u003c/em\u003e\u003cem\u003e3D image acquisition and analysis using TeloView\u003c/em\u003e\u003csup\u003e\u0026reg;\u003c/sup\u003e\u003cem\u003e system.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThirty interphase nuclei were analyzed, for each sample, by using an AxioImager M1 microscope (Carl Zeiss, Jena, Germany), coupled to an AxioCam HRm charge-coupled device (Carl Zeiss, Jena, Germany) and a 63-x oil objective lens (Carl Zeiss, Jena, Germany). The acquisition time was 500 milliseconds (ms) for Cy3 (telomeres) and 5 ms for DAPI (nuclei). Sixty z-stacks were acquired at a sampling distance of x,y: 102 nm and z: 200 nm for each slice of the stack. AxioVision 4.8 software (Carl Zeiss, Jena, Germany) was used for 3D image acquisition. Deconvolved images were converted into TIFF files and exported for 3D-analysis using the TeloView\u003csup\u003e\u0026reg;\u003c/sup\u003e software (Telo Genomics Corp., Toronto, ON, Canada) [17,18].\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ef. \u003c/em\u003e\u003cem\u003eData image analysis \u0026ndash; 3D telomere architecture.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe evaluation of the telomeric architecture of CLL cells was performed by TeloView\u003csup\u003e\u0026reg;\u003c/sup\u003e software [17,18], proprietary to Telo Genomics, Toronto, Canada. It measures six distinct parameters for each sample: (1) telomere length based on signal intensity, (2) the number of telomere signals per nucleus, (3) the number of telomeric aggregates (clusters of telomeres that cannot be resolved further at an optical resolution limit of 200 nm), (4) nuclear volume, (5) a/c ratio (a spatial feature assessing cell cycle progression and proliferation), and (6) the spatial distribution of telomeres within the nuclear space, which reflects gene expression. For the distinct subgroups of CLL cells, based on cytogenetic profile, a graphical representation was obtained showing the distribution of the intensity of the acquired telomere fluorescent signals, the distribution of the frequency of telomere aggregates per cell and the acquired signals per cell.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eg. \u003c/em\u003e\u003cem\u003eStatistical Analysis for telomere architecture\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eBased on cytogenetic analysis of CLL cells, four distinct subgroups were defined. The telomeric parameters (number, length, telomere aggregates, nuclear volumes, and a/c ratio) were compared between these subgroups using analysis of variance (ANOVA). All telomere parameters in CLL subgroups were compared using chi-square analysis. Additionally, cell parameter averages were analyzed using nested factorial analysis of variance. All statistical analyses were performed using GraphPad Prism version 8.0 (GraphPad Software, San Diego, CA, USA) [21]. The significance level was set at 0.05.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003eAmong the CLL cases, age distribution was categorized as follows: 6 patients were under 50 years old, 10 patients were between 50 and 60 years, and 12 patients were older than 60 years. Of the total 28 patients, 20 (71.4%) were male and 8 (28.6%) were female. Cytogenetic profiles varied, reflecting a range of karyotypic abnormalities with differing prognostic implications (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Six patients (21.4%) presented a normal karyotype (46,XX or 46,XY), and no chromosomal abnormalities were detected by iFISH in unstimulated cells. These cases may indicate a more indolent disease course, though additional molecular data would be required for further prognostic clarification.\u003c/p\u003e\u003cp\u003eDeletion of 13q14 was identified in seven patients (25%), predominantly among males aged 51\u0026ndash;74 years. While del(13)(q14) is generally associated with a favorable prognosis in CLL, its prognostic significance is influenced by several factors. Notably, the size of the clone harboring the deletion plays a critical role; larger clones with a higher percentage of nuclei exhibiting the deletion are associated with a shorter time to first treatment and overall survival. Additionally, the nature of the deletion\u0026mdash;whether monoallelic or biallelic\u0026mdash;can impact disease progression, with biallelic deletions linked to more aggressive disease​. The commonly deleted region includes the \u003cem\u003eDLEU2\u003c/em\u003e and miR-15a/16\u0026thinsp;\u0026minus;\u0026thinsp;1 genes, which are involved in regulating apoptosis and cell cycle control. Loss of these genes contribute to leukemogenesis and may affect disease behavior [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTrisomy 12 was identified in 7 patients (25%), either as an isolated finding or co-occurring with other abnormalities such as \u003cem\u003eTP53\u003c/em\u003e deletion, a pattern associated with intermediate risk and more aggressive clinical features (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Deletion of 17p13, involving the \u003cem\u003eTP53\u003c/em\u003e locus, was detected in 6 patients (21%) and is strongly linked to poor prognosis. Furthermore, deletion of 11q22 was observed in 5 patients (18%) and is typically associated with extensive lymphadenopathy and adverse disease outcomes. These findings underscore the importance of comprehensive cytogenetic evaluation in CLL for accurate risk stratification and individualized therapeutic strategies.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCharacteristics of chronic lymphocytic leukemia patients based on age, sex and cytogenetic information.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePatients\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSex\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eG-Banding Karyotype (stimulated cells)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eFISH (unstimulated cells)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCLL001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e46,XX[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eish normal signal pattern\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCLL002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e46,XX[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eish normal signal pattern\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCLL003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e46,XY[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eish normal signal pattern\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCLL004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e46,XY,del(13)(q14.3)[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eish del(13q14)(D13S319\u0026times;1)[55/100]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCLL005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e46,XX,del(13)(q14.2)[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eish del(13q14)(D13S319\u0026times;1)[64/100]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCLL006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e46,XY,del(13)(q14.3)[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]/46,XY[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eish del(13q14)(D13S319\u0026times;1)[61/100]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCLL007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e46,XY,del(13)(q14.1q14.3)[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eish del(13q14)(D13S319\u0026times;1)[58/100]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCLL008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e46,XY[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eish normal signal pattern\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCLL009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e46,XX[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eish normal signal pattern\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCLL010\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e46,XX,del(13)(q14.2)[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]/46,XX[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eish del(13q14)(D13S319\u0026times;1)[67/100]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCLL011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e47,XY,+12[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eish\u0026thinsp;+\u0026thinsp;12(D12Z1\u0026times;3)[32/100]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCLL012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e47,XY,+12[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eish\u0026thinsp;+\u0026thinsp;12(D12Z1\u0026times;3)[41/100]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCLL013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e47,XX,+12[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eish\u0026thinsp;+\u0026thinsp;12(D12Z1\u0026times;3)[36/100]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCLL014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e47,XY,+12,del(13)(q14.2q14.3)[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eish\u0026thinsp;+\u0026thinsp;12(D12Z1\u0026times;3)[42/100], ish del(13q14)(D13S319\u0026times;1)[32/100]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCLL015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e46,XY[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eish normal signal pattern\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCLL016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e47,XX,+12[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eish\u0026thinsp;+\u0026thinsp;12(D12Z1\u0026times;3)[29/100]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCLL017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e46,XY,del(17)(p11.1),del(6)(q21)[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eish del(17p13)(TP53\u0026times;1)[54/100]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCLL018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e46,XY,del(17)(p11.2),add(1)(p36.1)[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eish del(17p13)(TP53\u0026times;1)[36/100]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCLL019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e46,XY,del(17)(p13.1)[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eish del(17p13)(TP53\u0026times;1)[41/100]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCLL020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e46,XX,del(17)(p11.1)[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]/46,XX[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eish del(17p13)(TP53\u0026times;1)[48/100]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCLL021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e47,XY,+12,del(17)(p11.2)[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eish\u0026thinsp;+\u0026thinsp;12(D12Z1\u0026times;3)[41/100]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCLL022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e46,XY,del(13)(q14.2q21.1)[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eish del(13q14)(D13S319\u0026times;1)[66/100]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCLL023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e46,XY,del(11)(q22.3)[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eish del(11q22)(ATM\u0026times;1)[26/100]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCLL024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e46,XY,del(17)(p11.2)[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]/46,XY[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eish del(17p13)(TP53\u0026times;1)[38/100]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCLL025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e47,XY,del(11)(q23.1),+12[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eish del(11q22)(ATM\u0026times;1)[24/100], +\u0026thinsp;12(D12Z1\u0026times;3)[39/100]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCLL026\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e46,XY,del(11)(q22.3)[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eish del(11q22)(ATM\u0026times;1)[31/100]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCLL027\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e46,XY,del(11)(q23.1),del(17)(p11.2)[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eish del(11q22)(ATM\u0026times;1)[28/100], del(17p13)(TP53\u0026times;1)[43/100]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCLL028\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e46,XY,del(13)(q14.2)[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]/46,XY[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eish del(13q14)(D13S319\u0026times;1)[57/100]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFor the 3D telomere investigation, we assessed the overall signal intensity of telomeres, which indicates their length, and found notable variations over specific chromosomal abnormalities in CLL patients. 3D telomere architecture, by TeloView\u0026reg; analysis (Telo Genomics Corp.) [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] showed that telomeres became shorter as the correlation between chromosomal changes and prognosis for CLL take place (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This shortening was reflected in a greater proportion of telomeres with low signal intensities. In Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, telomere length (depicted by signal intensity on the x-axis) was plotted against the number of telomeres (y-axis) for each analyzed cell across all time points. Signals were categorized by intensity levels, highlighting telomere distribution within each sample or time point. Cancer cells often exhibit altered telomere counts per cell and shorter telomere lengths compared to normal cells [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn this study, we observed variations in the total detectable telomere signals across CLL samples with different cytogenetic alterations (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). TeloView\u0026reg; analysis (Telo Genomics Corp.) [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] of a cohort of 28 CLL patients revealed that samples with del(17p13) and del(11q22)\u0026mdash;markers of aggressive disease\u0026mdash;exhibited the highest numbers of telomeric signals, aggregates, and signal intensities. This increase is likely attributed to telomere clustering, where closely spaced or fused telomeres are detected as brighter and sometimes more numerous signals. This pattern was consistently observed in all patients with del(17p13) and del(11q22) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In contrast, patients with del(13q14) displayed fewer aggregates and more spatially separated telomeres in interphase nuclei, indicative of more stable nuclear architecture. The TeloView\u0026reg; analysis software distinguishes between single telomeres and aggregates based on size and intensity. While aggregates may appear as fewer discrete telomeres under higher resolution, they often contribute to increased total signal counts and intensities due to overlapping fluorescence. Therefore, rather than decreasing signal detection, aggregate formation can lead to an apparent increase in these metrics.\u003c/p\u003e\u003cp\u003eComparison of telomere intensities and distributions across different CLL cytogenetic profiles revealed significant differences, as indicated by the p-values shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The 3D telomere architecture varied notably with cytogenetic status, showing increased nuclear volume and altered a/c ratios. A higher a/c ratio, which reflects a more disk-like nuclear shape, is typically associated with later stages of the cell cycle and increased proliferative activity. Telomere shortening may promote the formation of aggregates, linking these structural changes to genomic instability and the development of chromosomal abnormalities in CLL.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eStatistical analysis of telomere parameters for CLL samples based on interphase nuclei information.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCLL patients\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal number of signals (Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTotal number of aggregates (Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTotal intensity (Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAverage intensity of all signals (Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003ea/c\u003c/em\u003e Ratio (Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNuclear Volume (Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNormal karyotype\u003csup\u003e(a)\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e29,56732\u0026thinsp;\u0026plusmn;\u0026thinsp;2,87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,77\u0026thinsp;\u0026plusmn;\u0026thinsp;0,89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e387342,566\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12023,897\u0026thinsp;\u0026plusmn;\u0026thinsp;432\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6,65\u0026thinsp;\u0026plusmn;\u0026thinsp;2,21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e334543\u0026thinsp;\u0026plusmn;\u0026thinsp;14324\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003edel(13q14)\u003csup\u003e(b)\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e33,67832\u0026thinsp;\u0026plusmn;\u0026thinsp;2,43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3,66\u0026thinsp;\u0026plusmn;\u0026thinsp;1,12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e403234,763\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e13287,432\u0026thinsp;\u0026plusmn;\u0026thinsp;443\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5,44\u0026thinsp;\u0026plusmn;\u0026thinsp;1,12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e389652\u0026thinsp;\u0026plusmn;\u0026thinsp;15432\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTrisomy 12\u003csup\u003e(c)\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e37,88341\u0026thinsp;\u0026plusmn;\u0026thinsp;2,89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4,89\u0026thinsp;\u0026plusmn;\u0026thinsp;1,15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e603562,762\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e14432,432\u0026thinsp;\u0026plusmn;\u0026thinsp;467\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5,34\u0026thinsp;\u0026plusmn;\u0026thinsp;1,54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e443245\u0026thinsp;\u0026plusmn;\u0026thinsp;16983\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003edel(17p13)\u003csup\u003e(d)\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e43,67343\u0026thinsp;\u0026plusmn;\u0026thinsp;2,77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6,77\u0026thinsp;\u0026plusmn;\u0026thinsp;0,94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e656231,887\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e15893,341\u0026thinsp;\u0026plusmn;\u0026thinsp;433\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4,12\u0026thinsp;\u0026plusmn;\u0026thinsp;0,99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e489432\u0026thinsp;\u0026plusmn;\u0026thinsp;18932\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003edel(11q22)\u003csup\u003e(e)\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e41,78322\u0026thinsp;\u0026plusmn;\u0026thinsp;3,12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5,43\u0026thinsp;\u0026plusmn;\u0026thinsp;1,21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e778327,982\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e15584,453\u0026thinsp;\u0026plusmn;\u0026thinsp;476\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3,55\u0026thinsp;\u0026plusmn;\u0026thinsp;1,66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e456654\u0026thinsp;\u0026plusmn;\u0026thinsp;13243\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ep value\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0,0001\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0,0001\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0,0001\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0,0001\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0,0001\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0,0001\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e(a) vs (b)/(a) vs (c)/(a) vs (d)/\u003c/p\u003e\u003cp\u003e(a) vs (e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we used TeloView\u0026reg; technology [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] to analyze the 3D telomere architecture in CLL patients, revealing patterns associated with specific chromosomal abnormalities. These findings build on and extend previous research that highlights the relationship between 3D telomere dynamics, cytogenetic alterations, and tumor classification or evolution.\u003c/p\u003e\u003cp\u003eThe presence of 3D telomere aggregates, particularly in samples with del(17p13) and del(11q22), aligns with existing literature that links chromosomal abnormalities to altered telomere organization [\u003cspan additionalcitationids=\"CR13 CR14 CR15\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Jebaraj \u003cem\u003eet al.\u003c/em\u003e (2021) [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] emphasized the pivotal role of telomere dysfunction in driving genomic instability and its strong association with high-risk cytogenetic profiles in CLL. Similarly, Lin \u003cem\u003eet al.\u003c/em\u003e (2014) [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] demonstrated that telomere dysfunction is a reliable predictor of clinical outcomes in CLL, offering valuable prognostic information even in early-stage disease. The association between short telomeres and deletions of 17p and 11q has been consistently reported since at least 2008, using various methodologies including Q-FISH, TRF analysis, and flow-FISH. Notably, studies by Roos \u003cem\u003eet al.\u003c/em\u003e (2008) [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], Ricca \u003cem\u003eet al.\u003c/em\u003e (2012) [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] and Scarfo \u003cem\u003eet al.\u003c/em\u003e (2019) [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] demonstrated that patients harboring 17p or 11q deletions often exhibit severe telomere attrition, correlating with genomic complexity, clonal evolution, and poor prognosis. Our findings corroborate these earlier observations, and while not novel, they reinforce the utility of 3D telomere profiling as a biomarker of genomic instability in CLL. These foundational studies should not be overlooked when interpreting our results. Consistent with these reports, our results demonstrate, using TeloView\u0026reg; technology [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], that telomere dysfunction is associated with higher aggregate formation, suggesting enhanced chromosomal rearrangement potential and clonal evolution in aggressive disease subgroups of CLL\u003c/p\u003e\u003cp\u003eInterestingly, patients with del(13q14) exhibited relatively preserved telomeres and fewer aggregates, consistent with their favorable prognosis. These findings suggest that 3D telomere profiling may play a key role in understanding the development of chromosomal abnormalities during CLL progression. Specifically, increased telomere aggregation and shorter average telomere length were associated with del(17p13) and del(11q22), both of which are linked to aggressive CLL phenotypes and poor clinical outcomes.\u003c/p\u003e\u003cp\u003eThe 3D telomere dynamics revealed distinct nuclear telomere distribution patterns across different cytogenetic subgroups, aligning with findings from Gadji \u003cem\u003eet al.\u003c/em\u003e (2012) [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] and Rangel-Pozzo \u003cem\u003eet al.\u003c/em\u003e (2021) [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] in myelodysplastic syndromes and acute myeloid leukemia, as well as Kumar et al. (2024) [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] in multiple myeloma. These studies emphasized the prognostic value of 3D telomere profiling in stratifying patients by risk. Similarly, we observed increased telomere aggregation in del(17p13) and del(11q22) cases, supporting the view that disrupted telomere organization is a hallmark of genomic instability in more aggressive disease.\u003c/p\u003e\u003cp\u003eIn Hodgkin\u0026rsquo;s lymphoma, Knecht \u003cem\u003eet al.\u003c/em\u003e (2024) [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] demonstrated that telomere aggregates could predict treatment response. Although our study did not assess treatment outcomes, the increased aggregate formation in high-risk CLL subgroups suggests a potential link between 3D telomere architecture and therapeutic resistance, meriting further investigation. Additionally, the altered a/c ratios observed in our study\u0026mdash;reflecting changes in nuclear shape and cell cycle progression\u0026mdash;are consistent with findings by Li \u003cem\u003eet al.\u003c/em\u003e (2021) [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], who associated genomic instability with changes in nuclear architecture and cellular metabolism in cancer.\u003c/p\u003e\u003cp\u003eOur findings on the cytogenetic landscape of CLL\u0026mdash;including del(13q14), trisomy 12, del(11q22), and del(17p13)\u0026mdash;are consistent with the recommendations by Baliakas \u003cem\u003eet al.\u003c/em\u003e (2022) [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], who highlighted the importance of incorporating cytogenetic data into CLL risk stratification. Trisomy 12, associated with intermediate prognosis, showed moderate telomere aggregate formation, supporting the observations of Trivedi \u003cem\u003eet al.\u003c/em\u003e (2023) [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] regarding its role in genomic heterogeneity.\u003c/p\u003e\u003cp\u003eAdditionally, we demonstrated improved detection of chromosomal abnormalities in CLL, aligning with previous studies that reported enhanced identification of cytogenetic alterations using CpG-based stimulation [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The combination of DSP30 and IL-2 increased the overall abnormality detection rate to 55%, compared to just 12% without mitogen stimulation, and significantly enhanced the identification of CLL subclones. Despite advances in interphase FISH (iFISH), conventional chromosome analysis remains a valuable diagnostic method. It provides a broader genomic overview than iFISH, allowing for the assessment of genomic complexity and the identification of abnormalities not targeted by standard FISH panels\u0026mdash;an important consideration for accurate prognostic evaluation.\u003c/p\u003e\u003cp\u003eThese findings highlight telomere aggregation as a potential mechanism contributing to chromosomal instability. This concept is supported by Vermolen \u003cem\u003eet al.\u003c/em\u003e (2005) [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], who showed that telomere clustering can promote chromosomal rearrangements by bringing distant genomic loci into proximity. The increased aggregate formation observed in high-risk subgroups, such as those with del(17p13), may represent a cellular adaptation to tolerate\u0026mdash;or even leverage\u0026mdash;genomic instability for clonal evolution. Studies like those by Salmaninejad \u003cem\u003eet al.\u003c/em\u003e (2021) [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] have investigated the molecular basis of genomic instability in cancer. Our results contribute to this body of research by demonstrating that 3D telomere architecture, particularly when analyzed using TeloView\u0026reg; technology [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], offers a measurable indicator of genomic instability and a potential target for therapeutic intervention.\u003c/p\u003e\u003cp\u003eThe alignment of our findings with previous studies reinforces the prognostic value of 3D telomere analysis in CLL. Building on this foundation, integrating data from TeloView\u0026reg; technology [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] with genomic and transcriptomic profiles\u0026mdash;as proposed by Condoluci and Rossi (2020) [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u0026mdash;may provide a more comprehensive understanding of CLL biology and improve clinical decision-making. Additionally, longitudinal studies such as those suggested by Mu\u0026ntilde;oz-Novas \u003cem\u003eet al.\u003c/em\u003e (2024) [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] could shed light on the temporal dynamics of telomere alterations and their role in the clonal evolution of CLL.\u003c/p\u003e\u003cp\u003eDespite the relevance of our findings, several limitations must be acknowledged. First, the relatively small sample size (n\u0026thinsp;=\u0026thinsp;28) may limit the statistical power and the generalizability of the results to broader CLL populations. Second, the lack of clinical outcome data\u0026mdash;such as treatment history, response rates, progression-free survival, or overall survival\u0026mdash;prevents direct correlation between telomere architecture and clinical endpoints. Third, although our data supports and expands on previously published work, the absence of IGHV mutation status and CLL staging limits the ability to fully stratify patients by risk. Therefore, future studies involving larger, well-characterized patient cohorts with longitudinal follow-up and integrated clinical, molecular, and telomere architecture data are necessary to validate and expand upon these findings.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthors´ contribution:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFMO: conceptualized the study, coordinated patient recruitment, supervised experimental procedures, and contributed to data analysis and manuscript writing. BMRF: assisted in cytogenetic and FISH analyses, provided clinical data interpretation, and participated in manuscript revisions. CMJ: contributed to the methodology development, sample processing, and critical review of the manuscript. SM: provided the TeloView® platform and technical support for 3D telomere analysis, contributed to image analysis interpretation, and supervised the final drafting and critical revision of the manuscript. All authors read and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank Telo Genomics Corp. for the use of TeloView® software platform. The authors also thank the Genomic Centre for Cancer Research and Diagnosis (GCCRD) for imaging. The GCCRD is funded by the Canada Foundation for Innovation and supported by CancerCare Manitoba Foundation, the University of Manitoba and the Canada Research Chair Tier 1 (S.M.). The GCCRD is a member of the Canadian National Scientific Platforms (CNSP) and of Canada BioImaging.\u003c/p\u003e\n\u003cp\u003eResearch group in Molecular Epidemiology (EPIMOL), CNPq, Brazil.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAssociation for Health Education \u0026amp; Research, Brazil.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGenomic Medicine Study Group (GMEG).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Statement:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the Ethics Committee, which is affiliated with the Federal University of Jataí (98331018.7.0000.8155).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWritten informed consent was obtained from all patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe raw data generated and/or analyzed during the current study are not publicly available due to ethical and confidentiality restrictions imposed by the research ethics committee. The data includes sensitive and potentially identifiable information from participants, and even with anonymization, there is a risk of compromising their privacy. However, the datasets may be made available from the corresponding author upon reasonable request and pending approval by the ethics committee.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no commercial or financial relationships that could be construed as a potential conflict of interest. Although one of the authors (S.M.) is affiliated with Telo Genomics Corp., which provided the TeloView® platform used in this study, all image acquisition, telomere measurements, data analysis, and interpretation were independently performed by the research team at the Federal University of Jataí. The involvement of Telo Genomics Corp. was limited to providing software access, and the company had no influence on study design, data analysis, or the decision to publish the results.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWainman LM, Khan WA, Kaur P. Chronic Lymphocytic Leukemia: Current Knowledge and Future Advances in Cytogenomic Testing. In: Sergi CM, editor. Advancements in Cancer Research [Internet]. Brisbane (AU): Exon Publications; 2023 Aug 17. Chapter 6. PMID: 37756426.\u003c/li\u003e\n\u003cli\u003eBaliakas P, Espinet B, Mellink C, Jarosova M, Athanasiadou A, Ghia P, Kater AP, Oscier D, Haferlach C, Stamatopoulos K. Cytogenetics in Chronic Lymphocytic Leukemia: ERIC Perspectives and Recommendations. Hemasphere. 2022 Mar 25;6(4):e707.\u003c/li\u003e\n\u003cli\u003eNadeu F, Diaz-Navarro A, Delgado J, Puente XS, Campo E. Genomic and Epigenomic Alterations in Chronic Lymphocytic Leukemia. 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Int J Lab Hematol. 2016 Oct;38(5):483-9.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"CLL, Telomere, Chromosomal abnormalities, Genomic Instability","lastPublishedDoi":"10.21203/rs.3.rs-6939271/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6939271/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground: Chronic lymphocytic leukemia (CLL) is a heterogeneous B-cell malignancy characterized by recurrent chromosomal abnormalities and variable clinical outcomes. Genomic instability plays a central role in disease progression, with telomere dysfunction emerging as a critical contributor. While the three-dimensional (3D) nuclear organization of telomeres has been linked to genomic integrity, its prognostic significance in CLL remains underexplored.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMethods: In this study, we applied TeloView® technology to evaluate the 3D telomere architecture in peripheral blood samples from 28 CLL patients. Cytogenetic analysis identified key abnormalities including del(13q14), trisomy 12, del(17p13), and del(11q22). Quantitative parameters—such as telomere number, length (signal intensity), aggregate formation, nuclear volume, and a/c ratio—were assessed and compared across cytogenetic subgroups.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eResults: Patients with del(17p13) and del(11q22), associated with high-risk disease, showed increased telomere aggregation and shorter telomere lengths, reflecting higher genomic instability. Conversely, patients with del(13q14) exhibited more intact telomere profiles, consistent with a favorable prognosis. Trisomy 12 cases displayed intermediate features. Statistical analyses revealed significant differences in telomere architecture between cytogenetic groups.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConclusion: 3D telomere profiling using TeloView® provides insights into the genomic instability landscape of CLL and aligns with established cytogenetic risk profiles. Although not novel, these findings reinforce the relevance of telomere dynamics as a potential biomarker for disease aggressiveness and risk stratification in CLL.\u003c/p\u003e","manuscriptTitle":"TeloView ® technology predicts genomic instability in chronic lymphocytic leukemia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-14 10:34:58","doi":"10.21203/rs.3.rs-6939271/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"37586791-e33f-4e3b-8d1d-c081e6c488c5","owner":[],"postedDate":"July 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-15T14:10:06+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-14 10:34:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6939271","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6939271","identity":"rs-6939271","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}