Copy Number–Based Chromosomal Instability Ex-Score Predicts Breast Cancer Malignancy

Copy Number–Based Chromosomal Instability Ex-Score Predicts Breast Cancer Malignancy | 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 Copy Number–Based Chromosomal Instability Ex-Score Predicts Breast Cancer Malignancy Chise Matsui, Seiichi Imanishi, Chikage Kato, Akira Watanabe, and 11 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8925909/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 Histological grade (HG) is an essential pathological factor of malignancy in breast cancer, but its evaluation remains partly subjective and affected by interobserver variability. Because genomic instability reflects intrinsic tumor aggressiveness, we hypothesized that genome-wide copy number variation (CNV) profiles in primary tumors may serve as an objective indicator of malignancy. We therefore developed the Copy Number–based Chromosomal Instability Ex-Score (CIS) and evaluated its ability to predict HG. Methods Two independent cohorts of estrogen receptor–positive/human epidermal growth factor receptor 2–negative (ER-positive/HER2-negative) breast cancers were analyzed: The Cancer Genome Atlas (TCGA, n = 175) and an independent Asian validation cohort (n = 31). Genome-wide CNVs were extracted using ASCAT (version 2.5), and the numbers of copy gains, losses, and loss of heterozygosity (LOH) were integrated via logistic regression to generate CIS. An ASCAT-derived aberrant cell fraction (ACF)–based quality-control step was applied to enhance CNV reliability. The predictive ability of CIS for HG was assessed using receiver operating characteristic analysis. Results CIS showed a strong association with HG, achieving an AUC of 0.852 in the TCGA cohort, which improved to 0.878 after applying the ACF threshold (ACF < 0.72). Its reproducibility was confirmed in the Asian cohort, supporting cross-ethnic generalizability. CIS showed predictive performance comparable to or exceeding that of the homologous recombination deficiency (HRD) score. Conclusions CIS, reflecting CNV-driven chromosomal instability, provides an objective predictor of histological grade in breast cancer and may enhance the accuracy and objectivity of pathological assessment in clinical practice. Breast cancer Histological grade Copy number variation Chromosomal instability Homologous recombination deficiency Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Breast cancer remains one of the most prevalent malignancies among women worldwide, representing a major cause of cancer-related morbidity and mortality. Among pathological parameters, histological grade (HG) together with stage and molecular subtypes play a key role in determining prognosis [ 1 – 4 ]. Accurate assessment of tumor aggressiveness is essential for determining prognosis and optimizing therapeutic strategies. Histological grade (HG), which integrates tumor differentiation, nuclear atypia, and mitotic activity,serves as a fundamental prognostic indicator. However, despite its clinical importance, HG evaluation is still partly dependent on subjective interpretation, leading to considerable interobserver variability and inconsistent classification, particularly in intermediate-grade tumors [ 5 , 6 ]. These limitations highlight the need for more objective, reproducible markers that can characterize the biological aggressiveness of breast cancer. Copy number variation (CNV), a form of structural genomic alteration encompassing copy number gains, losses, and loss of heterozygosity (LOH), represents one of the major structural manifestations of chromosomal instability. Genomic instability is a hallmark of cancer and plays a pivotal role in tumor initiation, clonal evolution, and disease progression [ 7 – 9 ]. In breast cancer, CNVs have been linked to molecular subtypes, therapeutic resistance, and clinical outcomes [ 10 – 12 ]. For example, amplification of ERBB2 is well established as a predictive and therapeutic biomarker and is routinely assessed in clinical practice as a companion diagnostic. Moreover, next-generation sequencing–based genomic tests, such as FoundationOne® CDx, enable the detection of ERBB2 copy number alterations [ 13 ]. Despite these advances, the biological and clinical significance of CNVs in other genomic regions—and their direct relationship with tumor malignancy—remains poorly characterized. The concept that genomic architecture itself may encode the degree of tumor aggressiveness has been long hypothesized but insufficiently tested in primary human breast cancers. Sotiriou and colleagues introduced the Genomic Grade Index (GGI), which utilized gene expression profiling to provide an objective molecular correlate of HG and demonstrated that transcriptional signatures could reproduce pathological grading [ 14 ]. This landmark work established a conceptual framework for quantifying tumor grade at the molecular level. However, most subsequent efforts have focused on transcriptomic or epigenetic data, which reflect downstream consequences of genomic instability rather than its structural origin. Direct prediction of histological grade from CNVs—representing the most proximal readout of chromosomal instability—has remained largely unexplored. In this context, we hypothesized that the degree of CNV across the genome of a primary breast tumor reflects its intrinsic genomic instability and, consequently, its malignant potential. To test this hypothesis, we developed the Chromosomal Instability Ex-Score (CIS), a novel quantitative index derived from genome-wide CNV profiles. Using the Allele-Specific Copy Number Analysis of Tumors (ASCAT) algorithm, we comprehensively analyzed copy number gains, losses, and LOH across all chromosomal regions of primary breast cancers. These variables were integrated through logistic regression to calculate the CIS. To improve the reliability of CNV profiles, we further incorporated an ASCAT-derived ACF-based quality-control step. By applying this framework to two independent cohorts of ER-positive/HER2-negative breast cancer, we demonstrate that CNV patterns within the primary tumor directly correlate with histological grade, revealing that genomic-level structural alterations can objectively mirror the pathological assessment of malignancy. This study provides, to our knowledge, the first direct evidence that copy number–derived chromosomal instability serves as a quantifiable determinant of breast cancer aggressiveness, offering a novel, objective approach to refine histological grading and enhance clinical evaluation. 2. Materials and Methods 2.1. Study Cohorts To elucidate the relationship between chromosomal copy number variation (CNV) and histological grade (HG) in primary breast cancer, two independent cohorts from distinct ethnic backgrounds—Caucasian and Asian—were analyzed. The Caucasian cohort was used to construct the Chromosomal Instability Ex-Score (CIS), serving as the discovery dataset. Clinical and genomic data were obtained from The Cancer Genome Atlas (TCGA) [dbGaP Project ID: 13643]. A total of 175 ER-positive (IHC > 1%) and HER2-negative (evaluated by both IHC and FISH) cases were included. HG information was extracted from TCGA pathology reports and categorized as HG1, HG2, or HG3 according to the Nottingham system. To validate the findings in a genetically distinct population, an independent Asian cohort was retrospectively collected at Osaka University Hospital between 2004 and 2016. This validation cohort comprised 31 ER-positive, HER2-negative breast cancer cases who underwent surgery after neoadjuvant chemotherapy (NAC). Formalin-fixed paraffin-embedded (FFPE) tumor tissues were obtained from biopsy specimens before NAC, and histological grading was assessed by experienced pathologists using the Nottingham criteria. 2.2. Genome-wide Copy Number Variation (CNV) Analysis To comprehensively characterize the chromosomal alterations underlying tumor aggressiveness, allele-specific CNV profiles were analyzed using the Allele-Specific Copy Number Analysis of Tumors (ASCAT, version 2.5) algorithm [ 15 ]. ASCAT provides allele-specific copy numbers (nA, nB) and estimates the aberrant cell fraction (ACF) in each tumor sample [ 15 , 16 ]. The total copy number (nA + nB) represents the degree of chromosomal imbalance, allowing identification of gain (copy number > 2), loss (copy number < 2), and loss of heterozygosity (LOH) regions. In this study, we focused on ASCAT-derived genomic segments exceeding a predefined length threshold, which were classified as gain, loss, or LOH. The number of these segments was quantified as structural alteration scores. This approach enabled us to characterize the global landscape of CNVs in the primary tumor and evaluate their association with histological grade—thereby directly testing whether CNV burden reflects tumor malignancy. ACF was used as a quality-control metric to exclude samples with less reliable CNV profiles. 2.3. Assessment of CNV-Based Prediction of Histological Grade 2.3.1. Copy Number Scoring and Optimization of Genomic Length Thresholds To explore whether the quantity and size of CNV segments predict tumor grade, copy number data from the Caucasian cohort were subjected to receiver operating characteristic (ROC) analyses. For each case, segments were categorized as gain, loss, or LOH. Segment counts (scores) were calculated across variable genomic length thresholds (100–2,500 kbp), and the threshold that produced the maximum area under the curve (AUC_max) was selected for each aberration type. Gain, loss, and LOH scores at this optimal threshold were defined as genomic structural alteration scores, representing distinct aspects of chromosomal instability. 2.3.2. Construction of the Chromosomal Instability Ex-Score (CIS) To integrate the predictive information derived from multiple types of chromosomal aberrations, a logistic regression model was developed. Regression coefficients were assigned to each alteration score, and their weighted sum defined the Chromosomal Instability Ex-Score (CIS): CIS = constant + (coefficient₁ × Gain score) + (coefficient₂ × Loss score) + (coefficient₃ × LOH score) CIS thus represents a quantitative index of CNV-driven chromosomal instability. This model was designed to test the hypothesis that structural genomic instability in the primary tumor reflects its histological malignancy. 2.3.3. Evaluation of the Accuracy of CIS for Predicting Histological Grade The predictive performance of CIS for HG (discriminating HG3 from HG1) was evaluated using ROC analysis. For comparison, homologous recombination deficiency (HRD)–related indices were also assessed. Following Marquard et al. [ 17 – 20 ], telomeric allelic imbalance (TAI), large-scale state transitions (LST), and HRD-LOH scores were summed to yield the HRD score, and its AUC for HG prediction was computed. 2.3.4. ACF-based quality-control filtering ASCAT reports an aberrant cell fraction (ACF). ACF is an ASCAT-derived parameter that reflects the inferred fraction of aberrant cells and may be influenced by purity/ploidy estimation uncertainty. In this study, we used ACF empirically as a quality-control index for CNV profile reliability. To examine the effect of ACF filtering, we lowered the maximum allowable ACF stepwise and evaluated how the predictive accuracy of CIS changed when only samples below each ACF threshold were retained. The upper limit of ACF was gradually reduced from 1.00 to 0.30 in increments of 0.01, and cases meeting each threshold were reanalyzed. By comparing AUC values and the number of eligible cases across these thresholds, we identified an ACF cutoff that provided an optimal balance between predictive performance and sample size. This quality-control approach is consistent with previous reports showing that low tumor cellularity can reduce the reliability of SNP array–based CNV calling (e.g., OncoScan™) [ 32 ]. 2.3.5. Validation in the Asian Cohort To verify the robustness and ethnic generalizability of our findings, CIS performance for HG prediction was tested in the independent Asian cohort (n = 31). The association between CIS and HG was examined using the Mann–Whitney U test. This external validation confirmed that CNV-derived chromosomal instability in the primary tumor is a reproducible predictor of histological malignancy across populations. 2.4. Statistical Analysis All statistical analyses were performed using R software (version 4.0.5). Associations between categorical variables were assessed using Fisher’s exact test. Comparisons of CIS and HRD scores between high-grade (HG3) and low-grade (HG1) tumors were conducted using the Mann–Whitney U test. All tests were two-sided, and p < 0.05 was considered statistically significant. 3. Results 3.1. Genome-wide allele-specific copy number profiles generated by ASCAT To investigate the relationship between chromosomal instability and histological grade, allele-specific copy number profiles were examined in representative cases from the Asian cohort. In the HG1 case, a largely preserved diploid pattern was observed, with only minimal copy number alterations. In contrast, the HG3 case exhibited widespread copy number gains and losses across multiple chromosomal regions, indicating pronounced chromosomal instability (Fig. 1 ). These observations highlight a clear association between CNV burden and tumor histological malignancy. 3.2. Determination of optimal base pair thresholds in the Caucasian cohort To define optimal segment length thresholds for evaluating structural genomic alterations, segment length cutoffs ranging from 100 kbp to 2,500 kbp were sequentially tested. For each threshold, receiver operating characteristic (ROC) curves were generated, and the area under the curve (AUC) was calculated for discrimination between tumors with HG1 and those with HG3. The cutoff yielding the maximum AUC was defined as the optimal segment length threshold for each alteration type—copy number gain, copy number loss, and loss of heterozygosity (LOH). Using this approach, the maximum AUCs were achieved at 200 kbp for gain (0.732), 300 kbp for loss (0.732), and 1,700 kbp for LOH (0.756) (Fig. 2 a–c). For each case, the number of gain, loss, and LOH segments exceeding the selected segment length cutoff was counted. These counts were defined as the final Gain, Loss, and LOH scores and used for subsequent analyses. 3.3. Calculation of CIS and evaluation of its predictive accuracy for HG To improve HG prediction, logistic regression was applied to integrate Gain, Loss, and LOH scores into the Chromosomal Instability Ex-Score (CIS): CIS = − 1.611 + 0.005 × Gain score + 0.056 × Loss score + 0.052 × LOH score All three scores contributed positively to CIS, with the Loss and LOH scores showing stronger effects. CIS values provided a quantitative measure of chromosomal instability. Boxplot analysis demonstrated that CIS values were significantly higher in HG3 compared with HG1 (Mann–Whitney U test: p = 2.17 × 10⁻¹⁴) (Fig. 3 a). ROC analysis demonstrated high predictive accuracy of CIS for histological grade (AUC = 0.852) (Fig. 3 b). The Youden index identified an optimal CIS cut-off of 0.532, yielding a sensitivity of 71.3% and specificity of 85.0%. Based on this cutoff, cases were classified into CIS-Low (≤ 0.532) and CIS-High (> 0.532) groups. Contingency analysis showed that 82 of 91 cases (90.1%) in CIS-High were HG3, whereas 51 of 84 cases (60.7%) in CIS-Low were HG1 (Fisher’s exact test: p = 4.87 × 10⁻¹³), demonstrating a highly significant association. Comparative evaluation with HRD score For reference, the HRD score—sum of LOH, TAI, and LST—was also evaluated in the cohort (n = 164, excluding 11 cases with unavailable HRD scores). Boxplot analysis indicated significantly higher HRD scores in HG3 than HG1 (Mann–Whitney U test: p = 1.15 × 10⁻¹³) (Fig. 3 c). ROC analysis yielded an AUC of 0.852, with an optimal cut-off of 23.5 (sensitivity 74.8%, specificity 86.0%) (Fig. 3 d). Contingency analysis indicated 80 of 88 HRD-High cases (90.9%) were HG3, and 49/76 HRD-Low cases (64.5%) were HG1 (Fisher’s exact test: p = 3.77 × 10⁻¹⁴). These results indicate that CIS performs comparably to the HRD score in predicting histological grade. 3.4. Optimal ACF threshold and improved predictive accuracy Based on the ACF-based quality-control filtering described in the Methods, we evaluated the impact of excluding samples with less reliable CNV profiles on the predictive performance of CIS. The upper bound of ACF was sequentially reduced from 1.00 to 0.30 in 0.01 increments, and ROC analyses were performed at each level to identify the threshold that maximized predictive accuracy while preserving an adequate sample size. More stringent ACF filtering improved AUC but reduced sample size, indicating a trade-off between predictive performance and statistical power. An ACF threshold of < 0.72 was selected (n = 127), providing optimal predictive performance. This analysis identified an ACF cutoff of 0.72 as the optimal balance between predictive accuracy and sample size. ROC analysis at this threshold yielded AUC = 0.878 (Fig. 4 a, b). Because CIS was re-estimated in the ACF-restricted subset, the optimal Youden-derived cutoff shifted accordingly. Using a cut-off of − 0.041, cases were stratified into CIS-High ( > − 0.041) and CIS-Low ( ≤ − 0.041). Contingency analysis showed 75/86 CIS-High cases (87.2%) were HG3, and 31/41 CIS-Low cases (75.6%) were HG1 (Fisher’s exact test: p = 3.77 × 10⁻¹²). Without ACF restriction, HG1 enrichment in CIS-Low was weaker (60.7%, 51/84), highlighting the importance of ACF adjustment. Boxplot analysis confirmed significant differences in CIS values between HG1 (n = 42) and HG3 (n = 85) under ACF < 0.72 (Mann–Whitney U test: p = 4.83 × 10⁻¹²) (Fig. 4 c). 3.5. Validation of CIS in the Asian cohort CIS values for the independent Asian cohort were calculated using logistic regression coefficients derived from the TCGA data. Boxplot analysis showed significantly higher CIS in HG3 compared to HG1 (median 0.141 [IQR − 1.554 to 3.470] vs. −1.211 [IQR − 1.549 to − 0.414]; Mann–Whitney U test: p = 6.15 × 10⁻³) (Fig. 5 a). Applying the TCGA-derived cut-off (− 0.041), cases were stratified into CIS-Low (n = 22) and CIS-High (n = 9). All HG1 cases (15/15) fell into the CIS-Low, whereas 9 of 16 HG3 cases (56.3%) were CIS-High (Fisher’s exact test: p = 8.10 × 10⁻⁴). Restricting to ACF < 0.72 (n = 18) further improved prediction. Under this condition, CIS values remained significantly higher in HG3 (Mann–Whitney U test: p = 4.11 × 10⁻⁵) (Fig. 5 b). Notably, when nine HG1 and nine HG3 cases were classified by cut-off, all HG1 cases were CIS-Low and all HG3 cases were CIS-High, demonstrating complete separation under this condition (Fisher’s exact test: p = 4.11 × 10⁻⁵). 3.6. Comparison of patient characteristics between HG1 and HG3 Clinical and pathological features were compared between HG1 (n = 42) and HG3 (n = 85) in ER-positive/HER2-negative TCGA cases under the ACF < 0.72 condition (Table 1 ) (Fig. 4 c). Table 1 Comparison of clinical and pathological characteristics between HG1 and HG3 cases in the TCGA cohort Variable Classification HG1(n=42) HG3(n=85) p value cT 1 23 14 2.00e-04 2 15 53 3,4 4 18 cN Negative 21 26 0.050 Positive 21 59 ER < 10% 0 1 1.000 ≧ 10% 13 41 NA 29 43 PR Negative 3 15 0.175 Positive 39 69 NA 0 1 HER2 Negative 42 85 1.000 Positive 0 0 Clinical parameters, including tumor size (cT classification), nodal status (cN classification), progesterone receptor (PR) status, and HER2 status, were compared between HG1 (n = 42) and HG3 (n = 85) cases of ER-positive, HER2-negative breast cancer in the TCGA cohort restricted to aberrant cell fraction (ACF) < 0.72 (Fig. 4c). Tumor size was significantly larger in HG3 than in HG1 (p = 2.00 × 10⁻⁴). Nodal positivity was more frequent in HG3 (69%) than in HG1 (50%), showing borderline significance (p = 0.050). No significant differences were observed in PR status (p = 0.175) or HER2 expression (p = 1.000). Tumor size (cT) differed significantly: cT1 predominated in HG1 (55%, 23/42), whereas most HG3 tumors were larger (cT2: 53 cases; cT3/4: 18 cases; p = 2.00 × 10⁻⁴). Nodal involvement (cN) was more frequent in HG3 than HG1 (69% compared with 50%), with borderline significance (p = 0.050). PR negativity was slightly higher in HG3 (15/85) compared with HG1 (3/42), but not statistically significant (p = 0.175). HER2 status was uniformly negative in both groups (p = 1.000). 4. Discussion In this study, we developed the Chromosomal Instability Ex-Score (CIS) based on CNV analysis in breast cancer and evaluated its predictive performance for histological grade (HG). CIS showed a strong correlation with HG, and incorporating the aberrant cell fraction (ACF) adjustment further improved its clinical predictive accuracy. Additionally, the reproducibility of CIS was validated in an independent cohort with a distinct ethnic background, suggesting its potential universal applicability. The implications and significance of these findings are discussed below. 4.1. A novel integrative indicator of chromosomal instability: CIS CIS developed in this study is a weighted composite score integrating copy number gains, losses, and LOH through logistic regression. This score captures genome-wide chromosomal alterations, with coefficients calibrated according to their relationship with histological grade. In this way, CIS provides a quantitative and objective measure of chromosomal instability that conventional single-parameter methods cannot fully capture. 4.2. Contribution and significance of CIS components In the logistic regression model, the relative importance of copy number gains was small (coefficient: 0.005), while losses and LOH had greater influence (coefficients: 0.056 and 0.052, respectively). Loss and LOH are associated with tumor suppressor gene inactivation and progression of genomic instability, representing key mechanisms driving tumor aggressiveness [ 21 – 23 ]. In contrast, copy number gains reflect the activation of driver genes such as ERBB2 and CCND1 , which are important therapeutic and prognostic markers [ 13 , 24 ]. The novelty of this study lies in quantitatively demonstrating that loss and LOH play a more significant role in predicting HG, thereby highlighting the previously underappreciated significance of deletion-type alterations. 4.3. Comparison with HRD scores Recently, homologous recombination deficiency (HRD)–related biomarkers, including HRD scores and BRCA mutations, have been studied as predictors of sensitivity to PARP inhibitors and platinum-based chemotherapy [ 16 , 25 – 28 ]. Tumors with high HRD scores are reported to be more sensitive to platinum agents [ 25 ]. Emerging predictive models based on mutational signatures, such as HRDetect, are still in early clinical validation and their utility has not yet been fully established [ 27 ]. The relationship between HRD status and platinum sensitivity remains inconsistent and controversial across clinical trials [ 29 – 31 ]. While HRD scores have provided important insights into drug sensitivity, they were originally developed to capture homologous recombination defects in BRCA1/2 -mutated tumors rather than to predict HG. In our analysis, both CIS and HRD scores achieved high predictive accuracy for HG (AUC = 0.852), suggesting HRD scores may also relate to grade prediction. Importantly, CIS captures a broader spectrum of genomic structural alterations beyond the HR pathway, potentially offering a clinically meaningful tool for more refined prediction of histological grade in breast cancer. 4.4. Improvement of accuracy by ACF adjustment ACF is an ASCAT-derived estimate that can affect the stability of CNV-based metrics in our dataset. Restricting the analysis to cases with ACF < 0.72 increased the AUC of CIS from 0.852 to 0.878, supporting the use of an ACF-based quality-control step to reduce noise from less reliable CNV profiles [ 15 , 32 ]. This refinement supports the clinical implementation of CIS. 4.5. Reproducibility in the Asian cohort When CIS developed in the Caucasian cohort was applied to an independent Asian cohort, CIS values effectively stratified HG: high CIS tumors corresponded to HG3, and low CIS tumors to HG1. Complete separation was achieved when the analysis was limited to cases with ACF < 0.72. These results support the applicability of CIS across ethnicities and its clinical promise. 4.6. Clinical implications and future perspectives CIS can be calculated from data generated by existing genomic platforms such as OncoScan or SNP arrays, highlighting its clinical feasibility. Future studies should assess its independence from other clinicopathological factors—including tumor size, lymph node status, and molecular subtypes through multivariate analyses and investigate its association with clinically relevant outcomes such as recurrence, survival, and treatment response. Comparison with existing biomarkers is also necessary. While Ki-67 is simple and widely used, reproducibility concerns remain. Oncotype DX provides useful prognostic information but is costly and not universally applicable. In contrast, CIS can be derived from routinely available genomic data, offering both objectivity and practicality, and may thus represent a more advantageous tool for clinical application. 4.7. Limitations This study has several limitations. First, it was a retrospective analysis, and the number of cases in the Asian cohort was relatively small. In addition, the study population was restricted to ER-positive, HER2-negative cancers. Future research should include other subtypes, such as triple-negative and HER2-positive tumors, and involve large-scale, prospective external validation. Abbreviations ACF aberrant cell fraction ASCAT Allele-Specific Copy Number Analysis of Tumors AUC area under the curve bp base pair CIS Copy Number–Based Chromosomal Instability Ex-Score cN clinical nodal status cT clinical tumor size CNV copy number variation ER estrogen receptor FFPE formalin-fixed paraffin-embedded FISH fluorescence in situ hybridization HG histological grade HER2 human epidermal growth factor receptor 2 HRD homologous recombination deficiency IHC immunohistochemistry kbp kilobase pair LOH loss of heterozygosity LST large-scale state transitions NAC neoadjuvant chemotherapy ROC receiver operating characteristic TAI telomeric allelic imbalance TCGA The Cancer Genome Atlas. Declarations Acknowledgements The authors are grateful to all members of the Department of Endocrine and Breast Surgery, Kyoto Prefectural University of Medicine, for their support. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or non-profit sectors. Author Contributions S.I. designed the study and took the lead in this experiment. C.M. performed data collection and wrote the first draft of the manuscript. C.M., S.I., and C.K. carried out data analysis. A.W. and S.K. provided technical guidance and assistance with data analysis, including R programming support. S.I. also critically reviewed and revised the manuscript. Y.N. provided overall supervision and critical guidance throughout the study. All authors read and approved the final manuscript. Data availability Publicly available TCGA data were used in this study. Additional datasets are not publicly available due to ethical restrictions but can be obtained from the corresponding author upon reasonable request. Conflict of Interest Yasuto Naoihas received research funding from Eisai, Shimazu, Murata, ONO, Daiichi-Sankyo and AstraZeneca, as well as honoraria from Eisai, AstraZeneca, Pfizer, Eli Lilly, Daiichi-Sankyo and Chugai outside the submitted work; he holds joint patents with Sysmex including Curebest™ 95GC Breast (JP.5725274.B2). The other authors declare no conflicts of interest. Ethics approval This study complies with the current relevant laws and guidelines for Japan. The study protocol for the Asian cohort was approved by the Ethical Review Board of Osaka University Hospital. Analyses of TCGA data were performed using publicly available datasets. Informed consent Informed consent was obtained from each patient before tumor biopsy. References Elston CW, Ellis IO. Pathological prognostic factors in breast cancer. I. The value of histological grade in breast cancer: experience from a large study with long-term follow-up. 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Ryland GL, Doyle MA, Goode D, Boyle SE, Choong DY, Rowley SM, et al. Loss of heterozygosity: what is it good for? BMC Med Genomics. 2015;8:45. 10.1186/s12920-015-0123-9 . Ormandy CJ, Musgrove EA, Hui R, Daly RJ, Sutherland RL. Cyclin D1, EMS1 and 11q13 amplification in breast cancer. Breast Cancer Res Treat. 2003;78(3):323–35. 10.1023/A:1023034932468 . Telli ML, Timms KM, Reid J, Hennessy B, Mills GB, Jensen KC, et al. Homologous recombination deficiency (HRD) score predicts response to platinum-containing neoadjuvant chemotherapy in patients with triple-negative breast cancer. Clin Cancer Res. 2016;22(15):3764–73. 10.1158/1078-0432.CCR-15-2477 . Telli ML, Hellyer J, Audeh W, Jensen KC, Bose S, Timms KM, et al. Homologous recombination deficiency (HRD) status predicts response to standard neoadjuvant chemotherapy in patients with triple-negative or BRCA1/2 mutation-associated breast cancer. Breast Cancer Res Treat. 2018;168(3):625–30. 10.1007/s10549-017-4633-9 . Davies H, Glodzik D, Morganella S, Yates LR, Staaf J, Zou X, et al. HRDetect is a predictor of BRCA1 and BRCA2 deficiency based on mutational signatures. Nat Med. 2017;23(4):517–25. 10.1038/nm.4292 . Robson M, Im SA, Senkus E, Xu B, Domchek SM, Masuda N, et al. Olaparib for metastatic breast cancer in patients with a germline BRCA mutation. N Engl J Med. 2017;377(17):1700–9. 10.1056/NEJMoa1706450 . Loibl S, Weber KE, Timms KM, Elkin EP, Hahnen E, Fasching PA, et al. Survival analysis of carboplatin added to an anthracycline/taxane-based neoadjuvant chemotherapy and HRD score as predictor of response: final results from GeparSixto. Ann Oncol. 2018;29(12):2341–7. 10.1093/annonc/mdy460 . Loibl S, O'Shaughnessy J, Untch M, Sikov WM, Rugo HS, McKee MD, et al. Addition of the PARP inhibitor veliparib plus carboplatin or carboplatin alone to standard neoadjuvant chemotherapy in triple-negative breast cancer (BrighTNess): a randomised, phase 3 trial. Lancet Oncol. 2018;19(4):497–509. 10.1016/S1470-2045(18)30111-6 . Tutt A, Tovey H, Cheang MCU, Kernaghan S, Kilburn L, Gazinska P, et al. Carboplatin in BRCA1/2-mutated and triple-negative breast cancer BRCAness subgroups: the TNT trial. Nat Med. 2018;24(5):628–37. 10.1038/s41591-018-0009-7 . Akazawa K, Kagara N, Sota Y, Motooka D, Nakamura S, Miyake T, et al. Comparison of the multigene panel test and OncoScan for the determination of HER2 amplification in breast cancer. Oncol Rep. 2021;46(4):213. 10.3892/or.2021.8161 . 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. 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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-8925909","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":595923121,"identity":"b07561c0-6370-4815-9ed8-2ff688825c3d","order_by":0,"name":"Chise Matsui","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+klEQVRIiWNgGAWjYDACCcYGIJkgJwHmHYCLsxHUYizBwEy0FjCZkDgDTQtuwD+7ufnDB4a09Jnt/QcfMJy5k9ggdoDxww8Gvjycltw52CY5gyEndzbPYWYDhhvPEhukE5glexjYinFpMZBIbGPm/VeRO08imU2C4cPhxP23ExikgX5JbMCtpfnzH4aKdDmYFpAtvwloaQCamZMgDdZyA6yFDa8tEjcS24AuTzOc2XPY2CDhzDPjBunENsseA9x+4Z+R/hgYPsnyEscbHz74cOyObIN08uEbPyqO4QwxVJAAjhhQ5BocSyBOC1Jc1hCtZRSMglEwCoY9AAAm6FWkhp8CggAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0009-0007-1469-1331","institution":"Kyoto Prefectural University of Medicine: Kyoto Furitsu Ika Daigaku","correspondingAuthor":true,"prefix":"","firstName":"Chise","middleName":"","lastName":"Matsui","suffix":""},{"id":595923122,"identity":"5907b019-96aa-4048-9176-0312b5f6ec6f","order_by":1,"name":"Seiichi Imanishi","email":"","orcid":"https://orcid.org/0000-0003-4335-4514","institution":"Osaka Rosai Hospital: Osaka Rosai Byoin","correspondingAuthor":false,"prefix":"","firstName":"Seiichi","middleName":"","lastName":"Imanishi","suffix":""},{"id":595923123,"identity":"8828d9d9-77c3-4c2e-be74-ddff9dc4609d","order_by":2,"name":"Chikage Kato","email":"","orcid":"","institution":"Kyoto Prefectural University of Medicine: Kyoto Furitsu Ika Daigaku","correspondingAuthor":false,"prefix":"","firstName":"Chikage","middleName":"","lastName":"Kato","suffix":""},{"id":595923124,"identity":"094379a7-890f-4b8c-8f7c-b23488627c10","order_by":3,"name":"Akira Watanabe","email":"","orcid":"","institution":"Kyoto Prefectural University of Medicine: Kyoto Furitsu Ika Daigaku","correspondingAuthor":false,"prefix":"","firstName":"Akira","middleName":"","lastName":"Watanabe","suffix":""},{"id":595923125,"identity":"c1880571-6684-4c3e-a1b3-1c5a93dae5ca","order_by":4,"name":"Sae Kitano","email":"","orcid":"","institution":"Kyoto Prefectural University: Kyoto Furitsu Daigaku","correspondingAuthor":false,"prefix":"","firstName":"Sae","middleName":"","lastName":"Kitano","suffix":""},{"id":595923126,"identity":"bdd4aa9e-6ead-40af-bbc0-0f934f175cab","order_by":5,"name":"Saya Matsumoto","email":"","orcid":"","institution":"Kyoto Prefectural University: Kyoto Furitsu Daigaku","correspondingAuthor":false,"prefix":"","firstName":"Saya","middleName":"","lastName":"Matsumoto","suffix":""},{"id":595923127,"identity":"1a378c93-0927-4fa2-be8f-839642336c8b","order_by":6,"name":"Nagisa Hirotani","email":"","orcid":"","institution":"Kyoto Prefectural University of Medicine: Kyoto Furitsu Ika Daigaku","correspondingAuthor":false,"prefix":"","firstName":"Nagisa","middleName":"","lastName":"Hirotani","suffix":""},{"id":595923128,"identity":"d047c2bb-193a-463c-a2f6-e71f12874ede","order_by":7,"name":"Maiko Nishida","email":"","orcid":"","institution":"Kyoto Prefectural University of Medicine: Kyoto Furitsu Ika Daigaku","correspondingAuthor":false,"prefix":"","firstName":"Maiko","middleName":"","lastName":"Nishida","suffix":""},{"id":595923129,"identity":"16140498-eb95-4571-af26-83b79ef3cbe0","order_by":8,"name":"Yuka Okuyama","email":"","orcid":"","institution":"Kyoto Prefectural University of Medicine: Kyoto Furitsu Ika Daigaku","correspondingAuthor":false,"prefix":"","firstName":"Yuka","middleName":"","lastName":"Okuyama","suffix":""},{"id":595923130,"identity":"8e49188c-5ad0-4618-947c-88d272945f92","order_by":9,"name":"Erika Iguchi","email":"","orcid":"","institution":"Kyoto Prefectural University of Medicine: Kyoto Furitsu Ika Daigaku","correspondingAuthor":false,"prefix":"","firstName":"Erika","middleName":"","lastName":"Iguchi","suffix":""},{"id":595923131,"identity":"a33eea89-0054-47fe-831b-dd156682254f","order_by":10,"name":"Mahiro IIZUKA Ohashi","email":"","orcid":"","institution":"Kyoto Prefectural University of Medicine: Kyoto Furitsu Ika Daigaku","correspondingAuthor":false,"prefix":"","firstName":"Mahiro","middleName":"IIZUKA","lastName":"Ohashi","suffix":""},{"id":595923132,"identity":"2a7f56c1-bc3e-4a51-bcc1-9b54ba1f0f88","order_by":11,"name":"Midori Morita","email":"","orcid":"","institution":"Kyoto Prefectural University of Medicine: Kyoto Furitsu Ika Daigaku","correspondingAuthor":false,"prefix":"","firstName":"Midori","middleName":"","lastName":"Morita","suffix":""},{"id":595923133,"identity":"623f80fe-fab9-4bd7-85d8-bc632a0657b1","order_by":12,"name":"Koichi Sakaguchi","email":"","orcid":"","institution":"Kyoto Prefectural University of Medicine: Kyoto Furitsu Ika Daigaku","correspondingAuthor":false,"prefix":"","firstName":"Koichi","middleName":"","lastName":"Sakaguchi","suffix":""},{"id":595923134,"identity":"f99c8f26-29b9-4ef2-821d-5d6975d6c674","order_by":13,"name":"Kenzo Shimazu","email":"","orcid":"","institution":"Osaka University: Osaka Daigaku","correspondingAuthor":false,"prefix":"","firstName":"Kenzo","middleName":"","lastName":"Shimazu","suffix":""},{"id":595923135,"identity":"88cc53b1-13c6-4ea0-8d60-88fde63ae90a","order_by":14,"name":"Yasuto Naoi","email":"","orcid":"","institution":"Kyoto Prefectural University of Medicine: Kyoto Furitsu Ika Daigaku","correspondingAuthor":false,"prefix":"","firstName":"Yasuto","middleName":"","lastName":"Naoi","suffix":""}],"badges":[],"createdAt":"2026-02-20 12:25:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8925909/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8925909/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103494527,"identity":"37eb5f88-1e3d-4c46-86b1-3ff15f00a4c4","added_by":"auto","created_at":"2026-02-26 10:45:16","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":83037,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRepresentative allele-specific chromosomal copy number profiles in the Asian cohort\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAllele-specific copy number profiles generated by ASCAT are shown for a low-grade (HG1, upper panel) and a high-grade (HG3, lower panel) tumor. The x-axis indicates chromosomal positions (chromosomes 1–22, X, and Y), and the y-axis denotes allele-specific copy number (0–5). Red bars represent the major allele, and blue bars represent the minor allele; when one red and one blue bar are of equal height (red = 1, blue = 1), this corresponds to a normal diploid state. The HG1 case retained a largely normal diploid pattern with minimal alterations, whereas the HG3 case showed widespread copy number gains and losses, indicative of complex chromosomal instability.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8925909/v1/ec4ff558d697408153a7784a.png"},{"id":103508135,"identity":"a8118a3e-6633-4fbf-85fa-11ccf789a111","added_by":"auto","created_at":"2026-02-26 13:47:20","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":285241,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOptimization of segment length thresholds in the Caucasian cohort\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eROC curve analyses were performed using segment length thresholds ranging from 100 kbp to 2,500 kbp. For each alteration type, the threshold yielding the maximum area under the curve (AUC_max) was defined as the optimal segment length and is indicated by the vertical dashed line. The optimal thresholds were 200 kbp for copy number gain (a, AUC = 0.732), 300 kbp for copy number loss (b, AUC = 0.732), and 1,700 kbp for loss of heterozygosity (LOH) (c, AUC = 0.756).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8925909/v1/ab706bbebde93ed99881de1c.png"},{"id":103507802,"identity":"cd65e927-67c4-4d39-924e-80782795fc1d","added_by":"auto","created_at":"2026-02-26 13:45:27","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":279083,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociation of the Chromosomal Instability Ex-Score (CIS) and HRD score with histological grade and their predictive performance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnalyses were performed in the TCGA cohort. For CIS analyses, all available cases were included (n = 175), whereas HRD score analyses were restricted to cases with available HRD data (n = 164).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(a) \u003c/strong\u003eBoxplot analysis of CIS values according to histological grade (HG1 and HG3). The x-axis shows histological grades, and the y-axis shows CIS values. Each dot represents an individual case, with the median, interquartile range, and outliers indicated. CIS values were significantly higher in HG3 than in HG1 (Mann–Whitney U test: p = 2.169 × 10⁻¹⁴).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(b)\u003c/strong\u003eReceiver operating characteristic (ROC) curve analysis evaluating the predictive accuracy of CIS for discriminating between HG1 and HG3. The x-axis represents 1 – specificity, and the y-axis represents sensitivity. CIS achieved an AUC of 0.852. The optimal cut-off value determined by the Youden index was 0.532, which corresponded to a sensitivity of 71.3% and a specificity of 85.0%.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(c)\u003c/strong\u003eBoxplot analysis of HRD score according to histological grade (HG1 and HG3). The x-axis shows histological grades, and the y-axis shows HRD score values. Each dot represents an individual case, with the median, interquartile range, and outliers displayed. HRD scores were significantly higher in HG3 than in HG1 (Mann–Whitney U test: p = 1.15 × 10⁻¹³).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(d)\u003c/strong\u003eReceiver operating characteristic (ROC) curve analysis of HRD score for histological grade prediction. The x-axis represents 1 – specificity, and the y-axis represents sensitivity. The AUC was 0.852. The optimal cut-off value was 23.5, yielding a sensitivity of 74.8% and a specificity of 86.0%.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8925909/v1/c7d7a83748369d77306c52d2.png"},{"id":103508081,"identity":"d6cf69c9-b083-4592-b27b-b48c704aa4d8","added_by":"auto","created_at":"2026-02-26 13:47:08","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":215228,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eImprovement of histological grade prediction using the Chromosomal Instability Ex-Score (CIS) after ACF adjustment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(a)\u003c/strong\u003eOptimization of the ACF upper bound in the TCGA training cohort.\u003c/p\u003e\n\u003cp\u003eLeft panel: For each candidate upper bound of the ASCAT-derived aberrant cell fraction (ACF; used here as a quality-control metric; x-axis, 0.30–1.00 in 0.01 increments), cases with ACF below the bound were retained, and the ROC AUC of CIS for discriminating between HG1 and HG3 was recalculated (y-axis).\u003c/p\u003e\n\u003cp\u003eThe best performance while maintaining an adequate sample size was observed at ACF \u0026lt; 0.72 (AUC = 0.878), as indicated by the vertical dashed line.\u003cbr\u003e\nRight panel: The number of eligible cases (y-axis) at each ACF bound (x-axis); ACF \u0026lt; 0.72 retained n = 127 cases. Together, these two panels illustrate the trade-off between predictive performance and sample size and provide the rationale for adopting ACF \u0026lt; 0.72 as a quality-control criterion for subsequent analyses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(b) \u003c/strong\u003eComparison of ROC curves with and without ACF restriction. Under the ACF \u0026lt; 0.72 condition, CIS demonstrated improved discrimination of high-grade tumors, with an AUC of 0.878. Using the optimal cut-off value (−0.041), the majority of CIS-High cases (\u0026gt; −0.041) were classified as HG3 (87.2%, 75/86), indicating a strong enrichment for high malignancy, whereas CIS-Low cases (≤ −0.041) were predominantly HG1 (75.6%, 31/41) (Fisher’s exact test: p = 3.77 × 10⁻¹²). Without ACF restriction, enrichment for HG3 was attenuated (60.7% of CIS-Low cases were HG1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(c) \u003c/strong\u003eBoxplot analysis of CIS values in HG1 (n = 42) and HG3 (n = 85) cases under the ACF \u0026lt; 0.72 condition, showing a significant difference between the two groups (Mann–Whitney U test: p = 4.83 × 10⁻¹²).\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8925909/v1/bf7724895caf52a4b63b58e3.png"},{"id":103507889,"identity":"53b0cb39-7b02-4f0d-9656-8a3b87e990c4","added_by":"auto","created_at":"2026-02-26 13:46:15","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":127264,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eValidation of CIS for predicting histological grade in the Asian cohort\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(a)\u003c/strong\u003e Distribution of CIS values in HG1 (n = 15) and HG3 (n = 16) cases. CIS values were significantly higher in HG3 than in HG1 (median 0.141, IQR −1.554 to 3.470 vs. median −1.211, IQR −1.549 to −0.414; Mann–Whitney U test: p = 6.15 × 10⁻³). Using the cut-off value (−0.041) determined in the Caucasian cohort, cases were classified into CIS-Low (n = 22) and CIS-High (n = 9). All HG1 cases (15/15) were classified as CIS-Low, whereas 56.3% of HG3 cases (9/16) were classified as CIS-High, demonstrating enrichment of high-grade tumors in the CIS-High group (Fisher’s exact test: p = 8.10 × 10⁻⁴).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(b)\u003c/strong\u003eAnalysis restricted to cases with ACF \u0026lt; 0.72 (n = 18). CIS values remained significantly higher in HG3 than in HG1 (Mann–Whitney U test: p = 4.11 × 10⁻⁵). In the subset of nine HG1 and nine HG3 cases, all HG1 cases were classified as CIS-Low and all HG3 cases as CIS-High, indicating perfect separation in this subset.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8925909/v1/c37f052ab2fe06b5b9c00626.png"},{"id":108753833,"identity":"a8d00b7d-ed7c-44d3-aca1-c9ec74882a93","added_by":"auto","created_at":"2026-05-08 04:27:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1230826,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8925909/v1/70ed807b-8771-419e-bcb8-359aff4708d6.pdf"}],"financialInterests":"","formattedTitle":"Copy Number–Based Chromosomal Instability Ex-Score Predicts Breast Cancer Malignancy","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eBreast cancer remains one of the most prevalent malignancies among women worldwide, representing a major cause of cancer-related morbidity and mortality. Among pathological parameters, histological grade (HG) together with stage and molecular subtypes play a key role in determining prognosis [\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Accurate assessment of tumor aggressiveness is essential for determining prognosis and optimizing therapeutic strategies. Histological grade (HG), which integrates tumor differentiation, nuclear atypia, and mitotic activity,serves as a fundamental prognostic indicator. However, despite its clinical importance, HG evaluation is still partly dependent on subjective interpretation, leading to considerable interobserver variability and inconsistent classification, particularly in intermediate-grade tumors [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. These limitations highlight the need for more objective, reproducible markers that can characterize the biological aggressiveness of breast cancer.\u003c/p\u003e \u003cp\u003eCopy number variation (CNV), a form of structural genomic alteration encompassing copy number gains, losses, and loss of heterozygosity (LOH), represents one of the major structural manifestations of chromosomal instability. Genomic instability is a hallmark of cancer and plays a pivotal role in tumor initiation, clonal evolution, and disease progression [\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. In breast cancer, CNVs have been linked to molecular subtypes, therapeutic resistance, and clinical outcomes [\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. For example, amplification of \u003cem\u003eERBB2\u003c/em\u003e is well established as a predictive and therapeutic biomarker and is routinely assessed in clinical practice as a companion diagnostic. Moreover, next-generation sequencing\u0026ndash;based genomic tests, such as FoundationOne\u0026reg; CDx, enable the detection of \u003cem\u003eERBB2\u003c/em\u003e copy number alterations [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Despite these advances, the biological and clinical significance of CNVs in other genomic regions\u0026mdash;and their direct relationship with tumor malignancy\u0026mdash;remains poorly characterized.\u003c/p\u003e \u003cp\u003eThe concept that genomic architecture itself may encode the degree of tumor aggressiveness has been long hypothesized but insufficiently tested in primary human breast cancers. Sotiriou and colleagues introduced the Genomic Grade Index (GGI), which utilized gene expression profiling to provide an objective molecular correlate of HG and demonstrated that transcriptional signatures could reproduce pathological grading [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. This landmark work established a conceptual framework for quantifying tumor grade at the molecular level. However, most subsequent efforts have focused on transcriptomic or epigenetic data, which reflect downstream consequences of genomic instability rather than its structural origin. Direct prediction of histological grade from CNVs\u0026mdash;representing the most proximal readout of chromosomal instability\u0026mdash;has remained largely unexplored.\u003c/p\u003e \u003cp\u003eIn this context, we hypothesized that the degree of CNV across the genome of a primary breast tumor reflects its intrinsic genomic instability and, consequently, its malignant potential. To test this hypothesis, we developed the Chromosomal Instability Ex-Score (CIS), a novel quantitative index derived from genome-wide CNV profiles. Using the Allele-Specific Copy Number Analysis of Tumors (ASCAT) algorithm, we comprehensively analyzed copy number gains, losses, and LOH across all chromosomal regions of primary breast cancers. These variables were integrated through logistic regression to calculate the CIS. To improve the reliability of CNV profiles, we further incorporated an ASCAT-derived ACF-based quality-control step.\u003c/p\u003e \u003cp\u003eBy applying this framework to two independent cohorts of ER-positive/HER2-negative breast cancer, we demonstrate that CNV patterns within the primary tumor directly correlate with histological grade, revealing that genomic-level structural alterations can objectively mirror the pathological assessment of malignancy. This study provides, to our knowledge, the first direct evidence that copy number\u0026ndash;derived chromosomal instability serves as a quantifiable determinant of breast cancer aggressiveness, offering a novel, objective approach to refine histological grading and enhance clinical evaluation.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Study Cohorts\u003c/h2\u003e \u003cp\u003eTo elucidate the relationship between chromosomal copy number variation (CNV) and histological grade (HG) in primary breast cancer, two independent cohorts from distinct ethnic backgrounds\u0026mdash;Caucasian and Asian\u0026mdash;were analyzed.\u003c/p\u003e \u003cp\u003eThe Caucasian cohort was used to construct the Chromosomal Instability Ex-Score (CIS), serving as the discovery dataset. Clinical and genomic data were obtained from The Cancer Genome Atlas (TCGA) [dbGaP Project ID: 13643]. A total of 175 ER-positive (IHC\u0026thinsp;\u0026gt;\u0026thinsp;1%) and HER2-negative (evaluated by both IHC and FISH) cases were included. HG information was extracted from TCGA pathology reports and categorized as HG1, HG2, or HG3 according to the Nottingham system.\u003c/p\u003e \u003cp\u003eTo validate the findings in a genetically distinct population, an independent Asian cohort was retrospectively collected at Osaka University Hospital between 2004 and 2016. This validation cohort comprised 31 ER-positive, HER2-negative breast cancer cases who underwent surgery after neoadjuvant chemotherapy (NAC). Formalin-fixed paraffin-embedded (FFPE) tumor tissues were obtained from biopsy specimens before NAC, and histological grading was assessed by experienced pathologists using the Nottingham criteria.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Genome-wide Copy Number Variation (CNV) Analysis\u003c/h2\u003e \u003cp\u003eTo comprehensively characterize the chromosomal alterations underlying tumor aggressiveness, allele-specific CNV profiles were analyzed using the Allele-Specific Copy Number Analysis of Tumors (ASCAT, version 2.5) algorithm [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eASCAT provides allele-specific copy numbers (nA, nB) and estimates the aberrant cell fraction (ACF) in each tumor sample [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The total copy number (nA\u0026thinsp;+\u0026thinsp;nB) represents the degree of chromosomal imbalance, allowing identification of gain (copy number\u0026thinsp;\u0026gt;\u0026thinsp;2), loss (copy number\u0026thinsp;\u0026lt;\u0026thinsp;2), and loss of heterozygosity (LOH) regions.\u003c/p\u003e \u003cp\u003eIn this study, we focused on ASCAT-derived genomic segments exceeding a predefined length threshold, which were classified as gain, loss, or LOH. The number of these segments was quantified as structural alteration scores. This approach enabled us to characterize the global landscape of CNVs in the primary tumor and evaluate their association with histological grade\u0026mdash;thereby directly testing whether CNV burden reflects tumor malignancy. ACF was used as a quality-control metric to exclude samples with less reliable CNV profiles.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Assessment of CNV-Based Prediction of Histological Grade\u003c/h2\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1. Copy Number Scoring and Optimization of Genomic Length Thresholds\u003c/h2\u003e \u003cp\u003eTo explore whether the quantity and size of CNV segments predict tumor grade, copy number data from the Caucasian cohort were subjected to receiver operating characteristic (ROC) analyses. For each case, segments were categorized as gain, loss, or LOH.\u003c/p\u003e \u003cp\u003eSegment counts (scores) were calculated across variable genomic length thresholds (100\u0026ndash;2,500 kbp), and the threshold that produced the maximum area under the curve (AUC_max) was selected for each aberration type.\u003c/p\u003e \u003cp\u003eGain, loss, and LOH scores at this optimal threshold were defined as genomic structural alteration scores, representing distinct aspects of chromosomal instability.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.3.2. Construction of the Chromosomal Instability Ex-Score (CIS)\u003c/h2\u003e \u003cp\u003eTo integrate the predictive information derived from multiple types of chromosomal aberrations, a logistic regression model was developed. Regression coefficients were assigned to each alteration score, and their weighted sum defined the Chromosomal Instability Ex-Score (CIS):\u003c/p\u003e \u003cp\u003eCIS\u0026thinsp;=\u0026thinsp;constant + (coefficient₁ \u0026times; Gain score) + (coefficient₂ \u0026times; Loss score) + (coefficient₃ \u0026times; LOH score)\u003c/p\u003e \u003cp\u003eCIS thus represents a quantitative index of CNV-driven chromosomal instability. This model was designed to test the hypothesis that structural genomic instability in the primary tumor reflects its histological malignancy.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.3.3. Evaluation of the Accuracy of CIS for Predicting Histological Grade\u003c/h2\u003e \u003cp\u003eThe predictive performance of CIS for HG (discriminating HG3 from HG1) was evaluated using ROC analysis. For comparison, homologous recombination deficiency (HRD)\u0026ndash;related indices were also assessed. Following Marquard et al. [\u003cspan additionalcitationids=\"CR18 CR19\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], telomeric allelic imbalance (TAI), large-scale state transitions (LST), and HRD-LOH scores were summed to yield the HRD score, and its AUC for HG prediction was computed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.3.4. ACF-based quality-control filtering\u003c/h2\u003e \u003cp\u003eASCAT reports an aberrant cell fraction (ACF). ACF is an ASCAT-derived parameter that reflects the inferred fraction of aberrant cells and may be influenced by purity/ploidy estimation uncertainty. In this study, we used ACF empirically as a quality-control index for CNV profile reliability. To examine the effect of ACF filtering, we lowered the maximum allowable ACF stepwise and evaluated how the predictive accuracy of CIS changed when only samples below each ACF threshold were retained.\u003c/p\u003e \u003cp\u003eThe upper limit of ACF was gradually reduced from 1.00 to 0.30 in increments of 0.01, and cases meeting each threshold were reanalyzed. By comparing AUC values and the number of eligible cases across these thresholds, we identified an ACF cutoff that provided an optimal balance between predictive performance and sample size. This quality-control approach is consistent with previous reports showing that low tumor cellularity can reduce the reliability of SNP array\u0026ndash;based CNV calling (e.g., OncoScan\u0026trade;) [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.3.5. Validation in the Asian Cohort\u003c/h2\u003e \u003cp\u003eTo verify the robustness and ethnic generalizability of our findings, CIS performance for HG prediction was tested in the independent Asian cohort (n\u0026thinsp;=\u0026thinsp;31). The association between CIS and HG was examined using the Mann\u0026ndash;Whitney U test. This external validation confirmed that CNV-derived chromosomal instability in the primary tumor is a reproducible predictor of histological malignancy across populations.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Statistical Analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses were performed using R software (version 4.0.5). Associations between categorical variables were assessed using Fisher\u0026rsquo;s exact test.\u003c/p\u003e \u003cp\u003eComparisons of CIS and HRD scores between high-grade (HG3) and low-grade (HG1) tumors were conducted using the Mann\u0026ndash;Whitney U test.\u003c/p\u003e \u003cp\u003eAll tests were two-sided, and p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n\u003ch2\u003e3.1. Genome-wide allele-specific copy number profiles generated by ASCAT\u003c/h2\u003e\n\u003cp\u003eTo investigate the relationship between chromosomal instability and histological grade, allele-specific copy number profiles were examined in representative cases from the Asian cohort.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eIn the HG1 case, a largely preserved diploid pattern was observed, with only minimal copy number alterations.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eIn contrast, the HG3 case exhibited widespread copy number gains and losses across multiple chromosomal regions, indicating pronounced chromosomal instability (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThese observations highlight a clear association between CNV burden and tumor histological malignancy.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n\u003ch2\u003e3.2. Determination of optimal base pair thresholds in the Caucasian cohort\u003c/h2\u003e\n\u003cp\u003eTo define optimal segment length thresholds for evaluating structural genomic alterations, segment length cutoffs ranging from 100 kbp to 2,500 kbp were sequentially tested. For each threshold, receiver operating characteristic (ROC) curves were generated, and the area under the curve (AUC) was calculated for discrimination between tumors with HG1 and those with HG3. The cutoff yielding the maximum AUC was defined as the optimal segment length threshold for each alteration type\u0026mdash;copy number gain, copy number loss, and loss of heterozygosity (LOH).\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eUsing this approach, the maximum AUCs were achieved at 200 kbp for gain (0.732), 300 kbp for loss (0.732), and 1,700 kbp for LOH (0.756) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ea\u0026ndash;c).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eFor each case, the number of gain, loss, and LOH segments exceeding the selected segment length cutoff was counted. These counts were defined as the final Gain, Loss, and LOH scores and used for subsequent analyses.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n\u003ch2\u003e3.3. Calculation of CIS and evaluation of its predictive accuracy for HG\u003c/h2\u003e\n\u003cp\u003eTo improve HG prediction, logistic regression was applied to integrate Gain, Loss, and LOH scores into the Chromosomal Instability Ex-Score (CIS):\u003c/p\u003e\n\u003cp\u003eCIS\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;1.611\u0026thinsp;+\u0026thinsp;0.005 \u0026times; Gain score\u0026thinsp;+\u0026thinsp;0.056 \u0026times; Loss score\u0026thinsp;+\u0026thinsp;0.052 \u0026times; LOH score\u003c/p\u003e\n\u003cp\u003eAll three scores contributed positively to CIS, with the Loss and LOH scores showing stronger effects. CIS values provided a quantitative measure of chromosomal instability.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eBoxplot analysis demonstrated that CIS values were significantly higher in HG3 compared with HG1 (Mann\u0026ndash;Whitney U test: p\u0026thinsp;=\u0026thinsp;2.17 \u0026times; 10⁻\u0026sup1;⁴) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ea).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eROC analysis demonstrated high predictive accuracy of CIS for histological grade (AUC\u0026thinsp;=\u0026thinsp;0.852) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eb).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThe Youden index identified an optimal CIS cut-off of 0.532, yielding a sensitivity of 71.3% and specificity of 85.0%.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eBased on this cutoff, cases were classified into CIS-Low (\u0026le;\u0026thinsp;0.532) and CIS-High (\u0026gt;\u0026thinsp;0.532) groups. Contingency analysis showed that 82 of 91 cases (90.1%) in CIS-High were HG3, whereas 51 of 84 cases (60.7%) in CIS-Low were HG1 (Fisher\u0026rsquo;s exact test: p\u0026thinsp;=\u0026thinsp;4.87 \u0026times; 10⁻\u0026sup1;\u0026sup3;), demonstrating a highly significant association.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eComparative evaluation with HRD score\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor reference, the HRD score\u0026mdash;sum of LOH, TAI, and LST\u0026mdash;was also evaluated in the cohort (n\u0026thinsp;=\u0026thinsp;164, excluding 11 cases with unavailable HRD scores).\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eBoxplot analysis indicated significantly higher HRD scores in HG3 than HG1 (Mann\u0026ndash;Whitney U test: p\u0026thinsp;=\u0026thinsp;1.15 \u0026times; 10⁻\u0026sup1;\u0026sup3;) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ec).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eROC analysis yielded an AUC of 0.852, with an optimal cut-off of 23.5 (sensitivity 74.8%, specificity 86.0%) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ed).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eContingency analysis indicated 80 of 88 HRD-High cases (90.9%) were HG3, and 49/76 HRD-Low cases (64.5%) were HG1 (Fisher\u0026rsquo;s exact test: p\u0026thinsp;=\u0026thinsp;3.77 \u0026times; 10⁻\u0026sup1;⁴).\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThese results indicate that CIS performs comparably to the HRD score in predicting histological grade.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n\u003ch2\u003e3.4. Optimal ACF threshold and improved predictive accuracy\u003c/h2\u003e\n\u003cp\u003eBased on the ACF-based quality-control filtering described in the Methods, we evaluated the impact of excluding samples with less reliable CNV profiles on the predictive performance of CIS. The upper bound of ACF was sequentially reduced from 1.00 to 0.30 in 0.01 increments, and ROC analyses were performed at each level to identify the threshold that maximized predictive accuracy while preserving an adequate sample size.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eMore stringent ACF filtering improved AUC but reduced sample size, indicating a trade-off between predictive performance and statistical power.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eAn ACF threshold of \u0026lt;\u0026thinsp;0.72 was selected (n\u0026thinsp;=\u0026thinsp;127), providing optimal predictive performance. This analysis identified an ACF cutoff of 0.72 as the optimal balance between predictive accuracy and sample size.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eROC analysis at this threshold yielded AUC\u0026thinsp;=\u0026thinsp;0.878 (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ea, b). Because CIS was re-estimated in the ACF-restricted subset, the optimal Youden-derived cutoff shifted accordingly.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eUsing a cut-off of \u0026minus;\u0026thinsp;0.041, cases were stratified into CIS-High (\u0026thinsp;\u0026gt;\u0026thinsp;\u0026minus;\u0026thinsp;0.041) and CIS-Low (\u0026thinsp;\u0026le;\u0026thinsp;\u0026minus;\u0026thinsp;0.041). Contingency analysis showed 75/86 CIS-High cases (87.2%) were HG3, and 31/41 CIS-Low cases (75.6%) were HG1 (Fisher\u0026rsquo;s exact test: p\u0026thinsp;=\u0026thinsp;3.77 \u0026times; 10⁻\u0026sup1;\u0026sup2;).\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eWithout ACF restriction, HG1 enrichment in CIS-Low was weaker (60.7%, 51/84), highlighting the importance of ACF adjustment. Boxplot analysis confirmed significant differences in CIS values between HG1 (n\u0026thinsp;=\u0026thinsp;42) and HG3 (n\u0026thinsp;=\u0026thinsp;85) under ACF\u0026thinsp;\u0026lt;\u0026thinsp;0.72 (Mann\u0026ndash;Whitney U test: p\u0026thinsp;=\u0026thinsp;4.83 \u0026times; 10⁻\u0026sup1;\u0026sup2;) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ec).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n\u003ch2\u003e3.5. Validation of CIS in the Asian cohort\u003c/h2\u003e\n\u003cp\u003eCIS values for the independent Asian cohort were calculated using logistic regression coefficients derived from the TCGA data.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eBoxplot analysis showed significantly higher CIS in HG3 compared to HG1 (median 0.141 [IQR\u0026thinsp;\u0026minus;\u0026thinsp;1.554 to 3.470] vs. \u0026minus;1.211 [IQR\u0026thinsp;\u0026minus;\u0026thinsp;1.549 to \u0026minus;\u0026thinsp;0.414]; Mann\u0026ndash;Whitney U test: p\u0026thinsp;=\u0026thinsp;6.15 \u0026times; 10⁻\u0026sup3;) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ea).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eApplying the TCGA-derived cut-off (\u0026minus;\u0026thinsp;0.041), cases were stratified into CIS-Low (n\u0026thinsp;=\u0026thinsp;22) and CIS-High (n\u0026thinsp;=\u0026thinsp;9). All HG1 cases (15/15) fell into the CIS-Low, whereas 9 of 16 HG3 cases (56.3%) were CIS-High (Fisher\u0026rsquo;s exact test: p\u0026thinsp;=\u0026thinsp;8.10 \u0026times; 10⁻⁴).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eRestricting to ACF\u0026thinsp;\u0026lt;\u0026thinsp;0.72 (n\u0026thinsp;=\u0026thinsp;18) further improved prediction. Under this condition, CIS values remained significantly higher in HG3 (Mann\u0026ndash;Whitney U test: p\u0026thinsp;=\u0026thinsp;4.11 \u0026times; 10⁻⁵) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eb).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eNotably, when nine HG1 and nine HG3 cases were classified by cut-off, all HG1 cases were CIS-Low and all HG3 cases were CIS-High, demonstrating complete separation under this condition (Fisher\u0026rsquo;s exact test: p\u0026thinsp;=\u0026thinsp;4.11 \u0026times; 10⁻⁵).\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n\u003ch2\u003e3.6. Comparison of patient characteristics between HG1 and HG3\u003c/h2\u003e\n\u003cp\u003eClinical and pathological features were compared between HG1 (n\u0026thinsp;=\u0026thinsp;42) and HG3 (n\u0026thinsp;=\u0026thinsp;85) in ER-positive/HER2-negative TCGA cases under the ACF\u0026thinsp;\u0026lt;\u0026thinsp;0.72 condition (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ec).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"char\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eComparison of clinical and pathological characteristics between HG1 and HG3 cases in the TCGA cohort\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eVariable\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eClassification\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eHG1(n=42)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eHG3(n=85)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003ecT\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e23\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e14\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e2.00e-04\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e15\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e53\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e3,4\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e18\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ecN\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eNegative\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e21\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e26\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.050\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePositive\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e21\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e59\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eER\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;10%\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e1.000\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e≧\u0026thinsp;10%\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e13\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e41\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eNA\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e29\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e43\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePR\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eNegative\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e15\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.175\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePositive\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e39\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e69\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eNA\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eHER2\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eNegative\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e42\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e85\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e1.000\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePositive\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eClinical parameters, including tumor size (cT classification), nodal status (cN classification), progesterone receptor (PR) status, and HER2 status, were compared between HG1 (n = 42) and HG3 (n = 85) cases of ER-positive, HER2-negative breast cancer in the TCGA cohort restricted to aberrant cell fraction (ACF) \u0026lt; 0.72 (Fig. 4c). Tumor size was significantly larger in HG3 than in HG1 (p = 2.00 \u0026times; 10⁻⁴). Nodal positivity was more frequent in HG3 (69%) than in HG1 (50%), showing borderline significance (p = 0.050). No significant differences were observed in PR status (p = 0.175) or HER2 expression (p = 1.000).\u003c/p\u003e\n\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eTumor size (cT) differed significantly: cT1 predominated in HG1 (55%, 23/42), whereas most HG3 tumors were larger (cT2: 53 cases; cT3/4: 18 cases; p\u0026thinsp;=\u0026thinsp;2.00 \u0026times; 10⁻⁴).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eNodal involvement (cN) was more frequent in HG3 than HG1 (69% compared with 50%), with borderline significance (p\u0026thinsp;=\u0026thinsp;0.050).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ePR negativity was slightly higher in HG3 (15/85) compared with HG1 (3/42), but not statistically significant (p\u0026thinsp;=\u0026thinsp;0.175).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eHER2 status was uniformly negative in both groups (p\u0026thinsp;=\u0026thinsp;1.000).\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn this study, we developed the Chromosomal Instability Ex-Score (CIS) based on CNV analysis in breast cancer and evaluated its predictive performance for histological grade (HG). CIS showed a strong correlation with HG, and incorporating the aberrant cell fraction (ACF) adjustment further improved its clinical predictive accuracy. Additionally, the reproducibility of CIS was validated in an independent cohort with a distinct ethnic background, suggesting its potential universal applicability. The implications and significance of these findings are discussed below.\u003c/p\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e4.1. A novel integrative indicator of chromosomal instability: CIS\u003c/h2\u003e \u003cp\u003eCIS developed in this study is a weighted composite score integrating copy number gains, losses, and LOH through logistic regression. This score captures genome-wide chromosomal alterations, with coefficients calibrated according to their relationship with histological grade. In this way, CIS provides a quantitative and objective measure of chromosomal instability that conventional single-parameter methods cannot fully capture.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Contribution and significance of CIS components\u003c/h2\u003e \u003cp\u003eIn the logistic regression model, the relative importance of copy number gains was small (coefficient: 0.005), while losses and LOH had greater influence (coefficients: 0.056 and 0.052, respectively). Loss and LOH are associated with tumor suppressor gene inactivation and progression of genomic instability, representing key mechanisms driving tumor aggressiveness [\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In contrast, copy number gains reflect the activation of driver genes such as \u003cem\u003eERBB2\u003c/em\u003e and \u003cem\u003eCCND1\u003c/em\u003e, which are important therapeutic and prognostic markers [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe novelty of this study lies in quantitatively demonstrating that loss and LOH play a more significant role in predicting HG, thereby highlighting the previously underappreciated significance of deletion-type alterations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e4.3. Comparison with HRD scores\u003c/h2\u003e \u003cp\u003eRecently, homologous recombination deficiency (HRD)\u0026ndash;related biomarkers, including HRD scores and BRCA mutations, have been studied as predictors of sensitivity to PARP inhibitors and platinum-based chemotherapy [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan additionalcitationids=\"CR26 CR27\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Tumors with high HRD scores are reported to be more sensitive to platinum agents [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Emerging predictive models based on mutational signatures, such as HRDetect, are still in early clinical validation and their utility has not yet been fully established [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe relationship between HRD status and platinum sensitivity remains inconsistent and controversial across clinical trials [\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. While HRD scores have provided important insights into drug sensitivity, they were originally developed to capture homologous recombination defects in \u003cem\u003eBRCA1/2\u003c/em\u003e-mutated tumors rather than to predict HG. In our analysis, both CIS and HRD scores achieved high predictive accuracy for HG (AUC\u0026thinsp;=\u0026thinsp;0.852), suggesting HRD scores may also relate to grade prediction. Importantly, CIS captures a broader spectrum of genomic structural alterations beyond the HR pathway, potentially offering a clinically meaningful tool for more refined prediction of histological grade in breast cancer.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e4.4. Improvement of accuracy by ACF adjustment\u003c/h2\u003e \u003cp\u003eACF is an ASCAT-derived estimate that can affect the stability of CNV-based metrics in our dataset. Restricting the analysis to cases with ACF\u0026thinsp;\u0026lt;\u0026thinsp;0.72 increased the AUC of CIS from 0.852 to 0.878, supporting the use of an ACF-based quality-control step to reduce noise from less reliable CNV profiles [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. This refinement supports the clinical implementation of CIS.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e4.5. Reproducibility in the Asian cohort\u003c/h2\u003e \u003cp\u003eWhen CIS developed in the Caucasian cohort was applied to an independent Asian cohort, CIS values effectively stratified HG: high CIS tumors corresponded to HG3, and low CIS tumors to HG1. Complete separation was achieved when the analysis was limited to cases with ACF\u0026thinsp;\u0026lt;\u0026thinsp;0.72. These results support the applicability of CIS across ethnicities and its clinical promise.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e4.6. Clinical implications and future perspectives\u003c/h2\u003e \u003cp\u003eCIS can be calculated from data generated by existing genomic platforms such as OncoScan or SNP arrays, highlighting its clinical feasibility. Future studies should assess its independence from other clinicopathological factors\u0026mdash;including tumor size, lymph node status, and molecular subtypes through multivariate analyses and investigate its association with clinically relevant outcomes such as recurrence, survival, and treatment response.\u003c/p\u003e \u003cp\u003eComparison with existing biomarkers is also necessary. While Ki-67 is simple and widely used, reproducibility concerns remain. Oncotype DX provides useful prognostic information but is costly and not universally applicable. In contrast, CIS can be derived from routinely available genomic data, offering both objectivity and practicality, and may thus represent a more advantageous tool for clinical application.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e4.7. Limitations\u003c/h2\u003e \u003cp\u003eThis study has several limitations. First, it was a retrospective analysis, and the number of cases in the Asian cohort was relatively small. In addition, the study population was restricted to ER-positive, HER2-negative cancers. Future research should include other subtypes, such as triple-negative and HER2-positive tumors, and involve large-scale, prospective external validation.\u003c/p\u003e \u003c/div\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eACF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eaberrant cell fraction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eASCAT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAllele-Specific Copy Number Analysis of Tumors\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAUC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003earea under the curve\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ebp\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ebase pair\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCIS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCopy Number\u0026ndash;Based Chromosomal Instability Ex-Score\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ecN\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eclinical nodal status\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ecT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eclinical tumor size\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCNV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecopy number variation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eER\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eestrogen receptor\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFFPE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eformalin-fixed paraffin-embedded\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFISH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003efluorescence in situ hybridization\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ehistological grade\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHER2\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ehuman epidermal growth factor receptor 2\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHRD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ehomologous recombination deficiency\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIHC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eimmunohistochemistry\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ekbp\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ekilobase pair\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLOH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eloss of heterozygosity\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLST\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003elarge-scale state transitions\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNAC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eneoadjuvant chemotherapy\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eROC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ereceiver operating characteristic\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTAI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003etelomeric allelic imbalance\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTCGA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eThe Cancer Genome Atlas.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors are grateful to all members of the Department of Endocrine and Breast Surgery, Kyoto Prefectural University of Medicine, for their support.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or non-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eS.I. designed the study and took the lead in this experiment. C.M. performed data collection and wrote the first draft of the manuscript. C.M., S.I., and C.K. carried out data analysis. A.W. and S.K. provided technical guidance and assistance with data analysis, including R programming support. S.I. also critically reviewed and revised the manuscript. Y.N. provided overall supervision and critical guidance throughout the study. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePublicly available TCGA data were used in this study. Additional datasets are not publicly available due to ethical restrictions but can be obtained from the corresponding author upon reasonable request.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYasuto Naoihas received research funding from Eisai, Shimazu, Murata, ONO, Daiichi-Sankyo and AstraZeneca, as well as honoraria from Eisai, AstraZeneca, Pfizer, Eli Lilly, Daiichi-Sankyo and Chugai outside the submitted work; he holds joint patents with Sysmex including Curebest™ 95GC Breast (JP.5725274.B2). The other authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study complies with the current relevant laws and guidelines for Japan. The study protocol for the Asian cohort was approved by the Ethical Review Board of Osaka University Hospital. Analyses of TCGA data were performed using publicly available datasets.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed consent\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from each patient before tumor biopsy.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eElston CW, Ellis IO. Pathological prognostic factors in breast cancer. I. The value of histological grade in breast cancer: experience from a large study with long-term follow-up. Histopathology. 1991;19(5):403\u0026ndash;10. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/j.1365-2559.1991.tb00229.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1365-2559.1991.tb00229.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRakha EA, El-Sayed ME, Lee AH, Elston CW, Grainge MJ, Hodi Z, et al. 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Oncol Rep. 2021;46(4):213. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3892/or.2021.8161\u003c/span\u003e\u003cspan address=\"10.3892/or.2021.8161\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\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":"Breast cancer, Histological grade, Copy number variation, Chromosomal instability, Homologous recombination deficiency","lastPublishedDoi":"10.21203/rs.3.rs-8925909/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8925909/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eHistological grade (HG) is an essential pathological factor of malignancy in breast cancer, but its evaluation remains partly subjective and affected by interobserver variability. Because genomic instability reflects intrinsic tumor aggressiveness, we hypothesized that genome-wide copy number variation (CNV) profiles in primary tumors may serve as an objective indicator of malignancy. We therefore developed the Copy Number\u0026ndash;based Chromosomal Instability Ex-Score (CIS) and evaluated its ability to predict HG.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eTwo independent cohorts of estrogen receptor\u0026ndash;positive/human epidermal growth factor receptor 2\u0026ndash;negative (ER-positive/HER2-negative) breast cancers were analyzed: The Cancer Genome Atlas (TCGA, n\u0026thinsp;=\u0026thinsp;175) and an independent Asian validation cohort (n\u0026thinsp;=\u0026thinsp;31). Genome-wide CNVs were extracted using ASCAT (version 2.5), and the numbers of copy gains, losses, and loss of heterozygosity (LOH) were integrated via logistic regression to generate CIS. An ASCAT-derived aberrant cell fraction (ACF)\u0026ndash;based quality-control step was applied to enhance CNV reliability. The predictive ability of CIS for HG was assessed using receiver operating characteristic analysis.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eCIS showed a strong association with HG, achieving an AUC of 0.852 in the TCGA cohort, which improved to 0.878 after applying the ACF threshold (ACF\u0026thinsp;\u0026lt;\u0026thinsp;0.72). Its reproducibility was confirmed in the Asian cohort, supporting cross-ethnic generalizability. CIS showed predictive performance comparable to or exceeding that of the homologous recombination deficiency (HRD) score.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eCIS, reflecting CNV-driven chromosomal instability, provides an objective predictor of histological grade in breast cancer and may enhance the accuracy and objectivity of pathological assessment in clinical practice.\u003c/p\u003e","manuscriptTitle":"Copy Number–Based Chromosomal Instability Ex-Score Predicts Breast Cancer Malignancy","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-26 10:45:09","doi":"10.21203/rs.3.rs-8925909/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":"c0f10a2f-0a88-41ec-96d0-58efa377e8ae","owner":[],"postedDate":"February 26th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Reject","date":"2026-05-08T00:26:18+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-08T04:27:10+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-26 10:45:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8925909","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8925909","identity":"rs-8925909","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}