Breast cancer immunohistochemical patterns in Morocco: changes suggested by comparison with contemporary data

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In low- and middle-income countries, immunohistochemistry (IHC) remains the cornerstone of tumor biological characterization due to limited access to genomic profiling. Understanding real-world IHC patterns is essential to contextualize prognosis and guide clinical decision-making in such settings. Methods : In this retrospective monocentric study, we described the immunohistochemical profile and surrogate molecular subtype distribution of breast cancer in a Moroccan cohort diagnosed in 2014 at a tertiary referral center in Casablanca. Results : Among 655 patients, IHC data were available for 456 cases and included estrogen receptor (ER), progesterone receptor (PR), HER2 status, and Ki-67 when reported. Estrogen receptor positivity was observed in 74.1% of cases with available data, and progesterone receptor positivity in 65.5%. HER2 positivity was present in 22.1% of tumors. Luminal A tumors accounted for 38.8% of cases, followed by luminal B (14.5%), HER2-positive (20.6%) and triple-negative tumors (13.9%), with a proportion of tumors unclassified due to incomplete data. Comparison with previously published Moroccan series suggests a temporal evolution in molecular subtype distributions, highlighting the influence of evolving IHC practices. Conclusions : These findings provide updated population-specific data on breast cancer immunohistochemical profiles and emphasize the continued relevance of IHC as a pragmatic tool in resource-constrained settings. Breast cancer Immunohistochemistry Molecular subtypes HER2 Low- and middle-income countries INTRODUCTION Breast cancer remains the most frequently diagnosed malignancy among women worldwide and represents a major public health challenge across all regions, including low- and middle-income countries (LMICs) [ 1 ]. While advances in early detection and systemic therapies have led to substantial improvements in outcomes in high-income settings, important disparities persist globally, particularly in regions where access to comprehensive diagnostic and therapeutic resources remains limited. Beyond traditional clinicopathological parameters, tumor biological characteristics play a central role in breast cancer classification and prognostication. Molecular profiling has identified biologically distinct subtypes associated with different clinical behaviors and therapeutic sensitivities. However, genomic assays remain costly and are not routinely available in many LMICs. In this context, immunohistochemistry (IHC) continues to serve as the cornerstone of tumor biological characterization in routine clinical practice, providing essential information through widely accessible biomarkers such as estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), and the proliferation marker Ki-67 [ 2 ]. Based on IHC assessment, breast cancers can be classified into surrogate molecular subtypes, including luminal A, luminal B, HER2-enriched, and triple-negative tumors. These subtypes have been shown to correlate with distinct epidemiological patterns, therapeutic strategies, and clinical outcomes across multiple populations [ 3 ]. Large population-based studies have consistently demonstrated substantial variability in the distribution of these subtypes according to geographic region, ethnicity, and healthcare context, underscoring the importance of generating region-specific data [ 4 ]. Among IHC biomarkers, Ki-67 reflects tumor proliferative activity and has been widely investigated as a marker of tumor aggressiveness. Despite ongoing debate regarding optimal cut-off values and inter-laboratory variability, Ki-67 remains an integral component of surrogate molecular subtype classification and risk stratification in routine practice, particularly in settings where more advanced molecular tools are not available [ 5 , 6 ]. International expert consensus meetings, including the St Gallen conferences, have highlighted both the clinical relevance and the practical limitations of Ki-67 assessment in daily oncology care [ 6 , 7 ]. In Morocco, breast cancer is the most common cancer among women and is frequently diagnosed at a younger age compared with Western populations. Previous Moroccan studies have primarily focused on the distribution of immunohistochemical subtypes and their clinicopathological associations, often in relatively small cohorts or without long-term contextualization [ 8 ]. Moreover, data allowing comparison of local biological profiles with more contemporary international series remain scarce, particularly from large real-world cohorts treated in routine clinical settings. Understanding how the immunohistochemical profile of breast cancer in Morocco compares with contemporary published data from other regions may provide valuable insights into potential regional particularities and evolving diagnostic patterns. Although direct temporal trend analyses require longitudinal population-based data, descriptive comparison with recent literature can help contextualize local findings and identify differences that may warrant further investigation. The present study aimed to describe the immunohistochemical profile and surrogate molecular subtype distribution in a large real-world cohort of breast cancer patients treated at a tertiary referral center in Casablanca, Morocco, and to compare these findings with contemporary published data. By focusing on routinely assessed biomarkers, this work seeks to provide pragmatic population-specific insights and contribute to a better understanding of breast cancer biology in a North African context. MATERIALS AND METHODS Study design and setting This was a retrospective, monocentric observational study conducted at the Mohammed VI Cancer Treatment Center, Ibn Rochd University Hospital, Casablanca, Morocco. The aim of the study was to describe the immunohistochemical profile of breast cancer in a large real-world Moroccan cohort and to contextualize these findings through comparison with contemporary published data. Study population All consecutive patients with a histologically confirmed diagnosis of invasive breast carcinoma between January 1 and December 31, 2014, were eligible for inclusion. Patients with non-invasive breast lesions, metastatic tumors from non-breast primary sites, or recurrent breast cancer at diagnosis were excluded. A total of 655 patients met the inclusion criteria and constituted the final study cohort. Data collection Clinicopathological data were extracted retrospectively from institutional medical records and archived pathology reports. Collected variables included age at diagnosis, estrogen receptor (ER) status, progesterone receptor (PR) status, human epidermal growth factor receptor 2 (HER2) status, and Ki-67 proliferation index when available. Only routinely assessed immunohistochemical biomarkers were considered in order to reflect real-world diagnostic practice. Missing data were recorded and reported for each variable. Immunohistochemical assessment and biomarker definitions Immunohistochemical data were available for 456 cases and were analyzed for estrogen receptor (ER), progesterone receptor (PR), HER2, and Ki-67 when reported. Hormone receptor status was defined as positive when ER and/or PR expression exceeded 10%. Molecular phenotypes were classified into five categories: luminal A (ER + and/or PR+, HER2−, Ki-67 < 14% when available), luminal B (ER + and/or PR+, HER2 − with Ki-67 ≥ 14% or ER + and/or PR + with HER2+), HER2-positive (ER−, PR−, HER2+), triple-negative (ER−, PR−, HER2−), and unclassified in cases of missing or insufficient immunohistochemical data [ 6 , 8 , 9 ]. Ki-67 proliferation index was reported as the percentage of positively stained tumor cell nuclei and categorized using a 14% cut-off, in line with international expert consensus recommendations during the study period [ 7 ]. Comparison with contemporary published data To contextualize the immunohistochemical profile observed in the Moroccan cohort, a narrative comparison was performed with contemporary published studies reporting breast cancer biomarker distributions. Relevant studies published between approximately 2018 and 2024 were identified through review of the scientific literature, with a focus on Moroccan, North African, and international cohorts. Comparisons were descriptive and qualitative, without formal statistical testing, given heterogeneity in study design, population characteristics, and biomarker assessment methods across published reports. This approach was chosen to explore potential similarities and differences in immunohistochemical patterns while avoiding overinterpretation of indirect comparisons. Statistical analysis Descriptive statistics were used to summarize patient characteristics and biomarker distributions. Categorical variables were reported as frequencies and percentages, and continuous variables were summarized using means and ranges. No inferential statistical comparisons were performed between the study cohort and external published data. All analyses were conducted using standard statistical software. Ethical considerations The study was conducted in accordance with the principles of the Declaration of Helsinki. Patient data were anonymized prior to analysis. Given the retrospective nature of the study and the use of routinely collected clinical data, the requirement for informed consent was waived according to institutional regulations. Approval was obtained from the local ethics committee. RESULTS Patient characteristics A total of 655 patients diagnosed with invasive breast carcinoma in 2014 were included. Immunohistochemical data for at least one biomarker were available for 456 patients, who constituted the analyzable population. The mean age at diagnosis was 51 years (range: 17–91 years). Immunohistochemical profile Hormone receptor–positive tumors predominated in the analyzable cohort. HER2 overexpression and high proliferative index were observed in a substantial subset of cases, reflecting biological heterogeneity in routine clinical practice (Table 1 ). Surrogate molecular subtypes Based on available immunohistochemical data, luminal A and luminal B tumors were the most frequent surrogate molecular subtypes, followed by HER2-enriched and triple-negative breast cancer. An unclassified category was retained to reflect missing immunohistochemical data in routine practice (Table 1 ). Table 1 Immunohistochemical characteristics of breast cancer patients in the 2014 Moroccan cohort Variable n / N % Total cohort 655 — Patients with ≥ 1 IHC marker available 456 — Hormone receptor (HR) status (ER and/or PR) HR positive 342 75.2 HR negative 113 24.8 ER positive — 74.1 PR positive — 65.5 HER2 status HER2 positive 92 22.1 HER2 negative 324 77.9 Ki-67 proliferation index < 14% 248 87.9 ≥ 14% 34 12.1 Surrogate molecular subtype Luminal A 254 38.8 Luminal B 95 14.5 HER2-enriched 135 20.6 Triple-negative 91 13.9 Unclassified 80 12.2 This table summarizes the distribution of routinely assessed immunohistochemical biomarkers and surrogate molecular subtypes in patients diagnosed with invasive breast carcinoma in 2014 at a tertiary referral center in Casablanca, Morocco. Percentages for estrogen receptor (ER), progesterone receptor (PR), hormone receptor status (HR: ER and/or PR), HER2 status, and Ki-67 proliferation index are calculated using the number of cases with available data for each biomarker. Missing data reflect incomplete immunohistochemical assessment in routine clinical practice. Surrogate molecular subtypes were defined according to immunohistochemical criteria based on ER, PR, HER2 status, and Ki-67 proliferation index, with a pragmatic approach adopted in cases of missing Ki-67 data. An unclassified category was retained to reflect cases in which surrogate molecular subtype assignment was not possible due to incomplete biomarker information. Evolution of molecular phenotypes in Moroccan breast cancer over time Table 2 summarizes the immunohistochemical profiles and molecular phenotype distributions reported across Moroccan breast cancer series published before and after 2014, allowing providing a descriptive overview of variations over time. Table 2 Immunohistochemical biomarker profiles in Moroccan breast cancer cohorts published between 2005 and 2021 Study / Period n ER+ (%) PR+ (%) RH+ (%) HER2+ (%) Ki-67 (%) Eastern Morocco [ 8 ] 2005–2012 2260 64.2 66.5 73.0 28.6 NR Cut-off : <14% / ≥14% Present study 2014 574 74.1 65.5 75.2 22.1 < 14% = 87.9 ≥ 14% = 12.1 Rabat ; 2016 [ 9 ] 2010–2014 88 NR NR 77.3 38.6 < 20% = 43.9% ≥ 20% = 56.1 Marrakech [ 10 ] 2015–2019 350 NR NR 64.8 NR NR Cut-off : < 20% / ≥20% Casablanca [ 11 ] 2020–2021 1558 74.3 66.6 74.8 21.0 < 20% = 18.5 ≥ 20% = 81.5 This table compares the distribution of key immunohistochemical biomarkers reported in the present 2014 Moroccan cohort with selected published Moroccan breast cancer series spanning different time periods and geographic regions. For each study, percentages are reported as published by the original authors and correspond to the number of cases with available immunohistochemical data for each biomarker. Differences in reported sample sizes, biomarker availability, and laboratory assessment methods across studies should be taken into account when interpreting comparisons. Ki-67 proliferation index cut-offs vary between studies and are indicated accordingly. NR indicates data not reported in the original publication. No formal statistical comparisons were performed due to methodological heterogeneity among studies. Table 3 summarizes the distribution of surrogate molecular subtypes reported in the 2014 Moroccan cohort and in selected Moroccan breast cancer studies published before and after this period. Table 3 Distribution of surrogate molecular subtypes in the 2014 Moroccan cohort compared with selected Moroccan breast cancer studies Study / Period n Luminal A (%) Luminal B (%) HER2 (%) TNBC (%) Unclassified (%) Eastern Morocco [ 8 ] 2005–2012 2260 60.1 16.1 8.6 14.2 NR Present study 2014 655 38.8 14.5 20.6 13.9 12.2 Rabat ; 2016 [ 9 ] 2010–2014 88 23.9 53.4 15.2 6.8 NR Marrakech [ 10 ] 2015–2019 350 23.0 42.1 20.0 14.5 NR Casablanca [ 11 ] 2020–2021 1558 10.8 52.2 17.4 19.6 NR Molecular phenotypes were defined according to the St Gallen 2015 consensus, with pragmatic adaptation in cases of missing Ki-67 data. In the present study, an unclassified category was retained to reflect real-world immunohistochemical practice in 2014, whereas some published series did not report this category. Differences observed across studies should primarily be interpreted in light of temporal evolution and methodological heterogeneity in immunohistochemical assessment. The Marrakech series [ 10 ] included only patients younger than 40 years, which should be considered when interpreting molecular subtype distributions. Data are presented as number (percentage). Percentages are reported as published by the original authors and are based on the number of cases with available data for subtype classification. NR indicates data not reported. Direct statistical comparisons between studies were not performed due to heterogeneity in study design, populations, and laboratory methods. DISCUSSION In this retrospective Moroccan cohort diagnosed in 2014, hormone receptor–positive tumors predominated, and luminal subtypes represented the majority of surrogate molecular phenotypes. Overall, the distribution of luminal, HER2-enriched, and triple-negative breast cancer subtypes was consistent with previously published Moroccan series from a similar period, supporting the representativeness of the present cohort within the national context. When examined in a temporal perspective, Moroccan studies reveal a clear evolution in surrogate molecular subtype distribution. Earlier series, particularly the large Eastern Morocco cohort conducted between 2005 and 2012, reported a strong predominance of luminal A tumors, accounting for more than half of cases, with lower proportions of luminal B tumors [ 8 ]. Similar patterns were observed in other cohorts from the early 2010s, where luminal A tumors remained the dominant subtype and luminal B tumors were less frequently identified [ 9 ]. In contrast, the present 2014 cohort already shows a relative reduction in luminal A tumors and a modest proportion of luminal B tumors, suggesting a transitional phase in molecular classification practices. More recent Moroccan series demonstrate a marked shift toward luminal B predominance. In the Marrakech cohort (2015–2019), luminal B tumors accounted for over 40% of cases, while luminal A tumors represented less than one quarter of the cohort [ 10 ]. This trend is even more pronounced in the Casablanca 2020–2021 series, in which luminal B tumors constituted more than half of all cases and luminal A tumors represented approximately 11% [ 11 ]. Taken together, these findings strongly suggest a progressive reclassification of hormone receptor–positive tumors over time rather than a true underlying biological shift in breast cancer epidemiology. Several methodological factors likely explain this evolution. In the present study, Ki-67 was not systematically reported, reflecting routine pathological practice in 2014. Consequently, surrogate molecular classification relied primarily on ER, PR, and HER2 status, with Ki-67 incorporated only when available. This approach resulted in the retention of an unclassified category due to missing data, which explains why subtype proportions do not sum to 100%. In contrast, more recent studies have implemented systematic Ki-67 assessment, often using higher cut-offs in accordance with updated St Gallen recommendations, leading to a greater identification of luminal B tumors at the expense of luminal A tumors [ 10 , 11 ]. Importantly, the proportion of HER2-enriched tumors appears relatively stable across Moroccan series, ranging from approximately 17% to 22% over time, suggesting that HER2 overexpression is less influenced by changes in immunohistochemical practices. Similarly, the proportion of triple-negative breast cancer shows limited variation across cohorts, remaining broadly consistent with international data. These observations support the notion that methodological evolution predominantly affects the subclassification of hormone receptor–positive tumors rather than HER2-positive or triple-negative disease. Finally, it should be noted that population characteristics also contribute to inter-study variability. The Marrakech series included exclusively women younger than 40 years, a group known to present more aggressive tumor biology, which may partially explain the higher proportion of luminal B and other aggressive subtypes observed in that cohort [ 10 ]. Overall, these findings highlight the importance of interpreting surrogate molecular subtype distributions within their methodological and temporal context. Differences observed across Moroccan studies primarily reflect evolving immunohistochemical practices and improved tumor characterization rather than major epidemiological shifts. This is particularly relevant in low- and middle-income settings, where access to genomic profiling remains limited and immunohistochemistry continues to play a central role in guiding breast cancer management and therapeutic decision-making. CONCLUSION This study provides a detailed snapshot of breast cancer immunohistochemical profiles in Morocco during a pivotal period in 2014. In this cohort, hormone receptor–positive tumors predominated, with luminal A remaining the most frequent molecular phenotype, alongside substantial proportions of HER2-positive and triple-negative breast cancers. These findings are consistent with earlier Moroccan series and confirm the predominance of luminal tumors in the local population. Comparison with Moroccan studies published before and after 2014 highlights a clear temporal evolution in molecular subtype distribution, characterized by a progressive decrease in luminal A tumors and a marked increase in luminal B tumors in more recent cohorts. This shift appears to be driven primarily by improvements and standardization in immunohistochemical assessment, particularly the increasing and more systematic use of Ki-67, rather than by abrupt biological changes in tumor behavior. By situating the present cohort within a national temporal framework, this work underscores the importance of methodological context when interpreting molecular subtype distributions, especially in low- and middle-income settings. Continued efforts toward standardization of immunohistochemical practices are essential to ensure reliable epidemiological comparisons and to optimize breast cancer classification and management in Morocco. Declarations Ethics approval and consent to participate The study was approved by the local ethics committee of Ibn Rochd University Hospital, Casablanca. Given the retrospective nature of the study and the use of anonymized data, informed consent was waived. Consent for publication Not applicable. Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding The authors received no specific funding for this study. Authors’ contributions RIA contributed to the study conception and design, data collection, interpretation of the data, and drafting of the manuscript. EMR and NA contributed to data collection and validation of clinical and pathological data. AFZ, OA, REA contributed to data interpretation and critical revision of the manuscript. BN and BA contributed to critical revision of the manuscript for important intellectual content, supervised the study, contributed to study design and interpretation of the data, and critically revised the manuscript. All authors read and approved the final manuscript and agree to be accountable for all aspects of the work. Acknowledgements The authors thank the medical and pathology staff of the Mohammed VI Cancer Treatment Center for their support. References Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71(3):209–49. 10.3322/caac.21660 . Colomer R, Mondejar R, Romero-Laorden N, Alfranca A, Sanchez-Muñoz A, Quintela-Fandino M, et al. Biomarkers in breast cancer 2024: an updated consensus. Clin Transl Oncol. 2024;26(12):2935–51. 10.1007/s12094-024-03541-1 . Parise CA, Caggiano V. Breast Cancer Survival Defined by the ER/PR/HER2 Subtypes and a Surrogate Classification according to Tumor Grade and Immunohistochemical Biomarkers. J Cancer Epidemiol. 2014;2014:469251. 10.1155/2014/469251 . Epub 2014 May 26. Howlader N, Altekruse SF, Li CI, Chen VW, Clarke CA, Ries LA, Cronin KA. 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While advances in early detection and systemic therapies have led to substantial improvements in outcomes in high-income settings, important disparities persist globally, particularly in regions where access to comprehensive diagnostic and therapeutic resources remains limited.\u003c/p\u003e \u003cp\u003eBeyond traditional clinicopathological parameters, tumor biological characteristics play a central role in breast cancer classification and prognostication. Molecular profiling has identified biologically distinct subtypes associated with different clinical behaviors and therapeutic sensitivities. However, genomic assays remain costly and are not routinely available in many LMICs. In this context, immunohistochemistry (IHC) continues to serve as the cornerstone of tumor biological characterization in routine clinical practice, providing essential information through widely accessible biomarkers such as estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), and the proliferation marker Ki-67 [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBased on IHC assessment, breast cancers can be classified into surrogate molecular subtypes, including luminal A, luminal B, HER2-enriched, and triple-negative tumors. These subtypes have been shown to correlate with distinct epidemiological patterns, therapeutic strategies, and clinical outcomes across multiple populations [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Large population-based studies have consistently demonstrated substantial variability in the distribution of these subtypes according to geographic region, ethnicity, and healthcare context, underscoring the importance of generating region-specific data [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAmong IHC biomarkers, Ki-67 reflects tumor proliferative activity and has been widely investigated as a marker of tumor aggressiveness. Despite ongoing debate regarding optimal cut-off values and inter-laboratory variability, Ki-67 remains an integral component of surrogate molecular subtype classification and risk stratification in routine practice, particularly in settings where more advanced molecular tools are not available [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. International expert consensus meetings, including the St Gallen conferences, have highlighted both the clinical relevance and the practical limitations of Ki-67 assessment in daily oncology care [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn Morocco, breast cancer is the most common cancer among women and is frequently diagnosed at a younger age compared with Western populations. Previous Moroccan studies have primarily focused on the distribution of immunohistochemical subtypes and their clinicopathological associations, often in relatively small cohorts or without long-term contextualization [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Moreover, data allowing comparison of local biological profiles with more contemporary international series remain scarce, particularly from large real-world cohorts treated in routine clinical settings.\u003c/p\u003e \u003cp\u003eUnderstanding how the immunohistochemical profile of breast cancer in Morocco compares with contemporary published data from other regions may provide valuable insights into potential regional particularities and evolving diagnostic patterns. Although direct temporal trend analyses require longitudinal population-based data, descriptive comparison with recent literature can help contextualize local findings and identify differences that may warrant further investigation.\u003c/p\u003e \u003cp\u003eThe present study aimed to describe the immunohistochemical profile and surrogate molecular subtype distribution in a large real-world cohort of breast cancer patients treated at a tertiary referral center in Casablanca, Morocco, and to compare these findings with contemporary published data. By focusing on routinely assessed biomarkers, this work seeks to provide pragmatic population-specific insights and contribute to a better understanding of breast cancer biology in a North African context.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and setting\u003c/h2\u003e \u003cp\u003eThis was a retrospective, monocentric observational study conducted at the Mohammed VI Cancer Treatment Center, Ibn Rochd University Hospital, Casablanca, Morocco. The aim of the study was to describe the immunohistochemical profile of breast cancer in a large real-world Moroccan cohort and to contextualize these findings through comparison with contemporary published data.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy population\u003c/h3\u003e\n\u003cp\u003eAll consecutive patients with a histologically confirmed diagnosis of invasive breast carcinoma between January 1 and December 31, 2014, were eligible for inclusion. Patients with non-invasive breast lesions, metastatic tumors from non-breast primary sites, or recurrent breast cancer at diagnosis were excluded. A total of 655 patients met the inclusion criteria and constituted the final study cohort.\u003c/p\u003e\n\u003ch3\u003eData collection\u003c/h3\u003e\n\u003cp\u003eClinicopathological data were extracted retrospectively from institutional medical records and archived pathology reports. Collected variables included age at diagnosis, estrogen receptor (ER) status, progesterone receptor (PR) status, human epidermal growth factor receptor 2 (HER2) status, and Ki-67 proliferation index when available. Only routinely assessed immunohistochemical biomarkers were considered in order to reflect real-world diagnostic practice. Missing data were recorded and reported for each variable.\u003c/p\u003e\n\u003ch3\u003eImmunohistochemical assessment and biomarker definitions\u003c/h3\u003e\n\u003cp\u003eImmunohistochemical data were available for 456 cases and were analyzed for estrogen receptor (ER), progesterone receptor (PR), HER2, and Ki-67 when reported. Hormone receptor status was defined as positive when ER and/or PR expression exceeded 10%. Molecular phenotypes were classified into five categories: luminal A (ER\u0026thinsp;+\u0026thinsp;and/or PR+, HER2\u0026minus;, Ki-67\u0026thinsp;\u0026lt;\u0026thinsp;14% when available), luminal B (ER\u0026thinsp;+\u0026thinsp;and/or PR+, HER2\u0026thinsp;\u0026minus;\u0026thinsp;with Ki-67\u0026thinsp;\u0026ge;\u0026thinsp;14% or ER\u0026thinsp;+\u0026thinsp;and/or PR\u0026thinsp;+\u0026thinsp;with HER2+), HER2-positive (ER\u0026minus;, PR\u0026minus;, HER2+), triple-negative (ER\u0026minus;, PR\u0026minus;, HER2\u0026minus;), and unclassified in cases of missing or insufficient immunohistochemical data [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eKi-67 proliferation index was reported as the percentage of positively stained tumor cell nuclei and categorized using a 14% cut-off, in line with international expert consensus recommendations during the study period [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eComparison with contemporary published data\u003c/h3\u003e\n\u003cp\u003eTo contextualize the immunohistochemical profile observed in the Moroccan cohort, a narrative comparison was performed with contemporary published studies reporting breast cancer biomarker distributions. Relevant studies published between approximately 2018 and 2024 were identified through review of the scientific literature, with a focus on Moroccan, North African, and international cohorts.\u003c/p\u003e \u003cp\u003eComparisons were descriptive and qualitative, without formal statistical testing, given heterogeneity in study design, population characteristics, and biomarker assessment methods across published reports. This approach was chosen to explore potential similarities and differences in immunohistochemical patterns while avoiding overinterpretation of indirect comparisons.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eDescriptive statistics were used to summarize patient characteristics and biomarker distributions. Categorical variables were reported as frequencies and percentages, and continuous variables were summarized using means and ranges. No inferential statistical comparisons were performed between the study cohort and external published data. All analyses were conducted using standard statistical software.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEthical considerations\u003c/h3\u003e\n\u003cp\u003e The study was conducted in accordance with the principles of the Declaration of Helsinki. Patient data were anonymized prior to analysis. Given the retrospective nature of the study and the use of routinely collected clinical data, the requirement for informed consent was waived according to institutional regulations. Approval was obtained from the local ethics committee.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003ePatient characteristics\u003c/h2\u003e \u003cp\u003eA total of 655 patients diagnosed with invasive breast carcinoma in 2014 were included. Immunohistochemical data for at least one biomarker were available for 456 patients, who constituted the analyzable population. The mean age at diagnosis was 51 years (range: 17\u0026ndash;91 years).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eImmunohistochemical profile\u003c/h2\u003e \u003cp\u003eHormone receptor\u0026ndash;positive tumors predominated in the analyzable cohort. HER2 overexpression and high proliferative index were observed in a substantial subset of cases, reflecting biological heterogeneity in routine clinical practice (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eSurrogate molecular subtypes\u003c/h2\u003e \u003cp\u003eBased on available immunohistochemical data, luminal A and luminal B tumors were the most frequent surrogate molecular subtypes, followed by HER2-enriched and triple-negative breast cancer. An unclassified category was retained to reflect missing immunohistochemical data in routine practice (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eImmunohistochemical characteristics of breast cancer patients in the 2014 Moroccan cohort\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en / N\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal cohort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e655\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePatients with \u0026ge;\u0026thinsp;1 IHC marker available\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eHormone receptor (HR) status (ER and/or PR)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHR positive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e342\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHR negative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eER positive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePR positive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eHER2 status\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHER2 positive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHER2 negative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eKi-67 proliferation index\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;14%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e87.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;14%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eSurrogate molecular subtype\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLuminal A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLuminal B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHER2-enriched\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriple-negative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnclassified\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThis table summarizes the distribution of routinely assessed immunohistochemical biomarkers and surrogate molecular subtypes in patients diagnosed with invasive breast carcinoma in 2014 at a tertiary referral center in Casablanca, Morocco.\u003c/p\u003e \u003cp\u003ePercentages for estrogen receptor (ER), progesterone receptor (PR), hormone receptor status (HR: ER and/or PR), HER2 status, and Ki-67 proliferation index are calculated using the number of cases with available data for each biomarker. Missing data reflect incomplete immunohistochemical assessment in routine clinical practice.\u003c/p\u003e \u003cp\u003eSurrogate molecular subtypes were defined according to immunohistochemical criteria based on ER, PR, HER2 status, and Ki-67 proliferation index, with a pragmatic approach adopted in cases of missing Ki-67 data. An unclassified category was retained to reflect cases in which surrogate molecular subtype assignment was not possible due to incomplete biomarker information.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eEvolution of molecular phenotypes in Moroccan breast cancer over time\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e summarizes the immunohistochemical profiles and molecular phenotype distributions reported across Moroccan breast cancer series published before and after 2014, allowing providing a descriptive overview of variations over time.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eImmunohistochemical biomarker profiles in Moroccan breast cancer cohorts published between 2005 and 2021\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudy /\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePeriod\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eER+ (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePR+ (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRH+\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHER2+ (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eKi-67\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEastern Morocco [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2005\u0026ndash;2012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2260\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e64.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e66.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e73.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e28.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003cp\u003eCut-off\u0026nbsp;:\u003c/p\u003e \u003cp\u003e\u0026lt;14% / \u0026ge;14%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePresent study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e574\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e74.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e65.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e75.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e22.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;14% = 87.9\u003c/p\u003e \u003cp\u003e\u0026ge;\u0026thinsp;14% = 12.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRabat\u0026nbsp;;\u003c/p\u003e \u003cp\u003e2016 [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2010\u0026ndash;2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e77.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e38.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;20% = 43.9%\u003c/p\u003e \u003cp\u003e\u0026ge;\u0026thinsp;20% = 56.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarrakech\u003c/p\u003e \u003cp\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2015\u0026ndash;2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e64.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003cp\u003eCut-off\u0026nbsp;:\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;20% / \u0026ge;20%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCasablanca\u003c/p\u003e \u003cp\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2020\u0026ndash;2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1558\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e74.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e66.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e74.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e21.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;20% = 18.5\u003c/p\u003e \u003cp\u003e\u0026ge;\u0026thinsp;20% = 81.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThis table compares the distribution of key immunohistochemical biomarkers reported in the present 2014 Moroccan cohort with selected published Moroccan breast cancer series spanning different time periods and geographic regions.\u003c/p\u003e \u003cp\u003eFor each study, percentages are reported as published by the original authors and correspond to the number of cases with available immunohistochemical data for each biomarker. Differences in reported sample sizes, biomarker availability, and laboratory assessment methods across studies should be taken into account when interpreting comparisons.\u003c/p\u003e \u003cp\u003eKi-67 proliferation index cut-offs vary between studies and are indicated accordingly. \u003cem\u003eNR\u003c/em\u003e indicates data not reported in the original publication. No formal statistical comparisons were performed due to methodological heterogeneity among studies.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e summarizes the distribution of surrogate molecular subtypes reported in the 2014 Moroccan cohort and in selected Moroccan breast cancer studies published before and after this period.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDistribution of surrogate molecular subtypes in the 2014 Moroccan cohort compared with selected Moroccan breast cancer studies\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudy /\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePeriod\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLuminal A (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLuminal\u003c/p\u003e \u003cp\u003eB (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHER2\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTNBC (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eUnclassified\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEastern Morocco [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2005\u0026ndash;2012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2260\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e14.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePresent study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e655\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e20.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e13.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e12.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRabat\u0026nbsp;;\u003c/p\u003e \u003cp\u003e2016 [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2010\u0026ndash;2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e53.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e15.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarrakech\u003c/p\u003e \u003cp\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2015\u0026ndash;2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e42.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e20.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e14.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCasablanca\u003c/p\u003e \u003cp\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2020\u0026ndash;2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1558\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e52.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e17.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e19.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eMolecular phenotypes were defined according to the St Gallen 2015 consensus, with pragmatic adaptation in cases of missing Ki-67 data. In the present study, an unclassified category was retained to reflect real-world immunohistochemical practice in 2014, whereas some published series did not report this category. Differences observed across studies should primarily be interpreted in light of temporal evolution and methodological heterogeneity in immunohistochemical assessment. The Marrakech series [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] included only patients younger than 40 years, which should be considered when interpreting molecular subtype distributions.\u003c/p\u003e \u003cp\u003eData are presented as number (percentage). Percentages are reported as published by the original authors and are based on the number of cases with available data for subtype classification. \u003cem\u003eNR\u003c/em\u003e indicates data not reported. Direct statistical comparisons between studies were not performed due to heterogeneity in study design, populations, and laboratory methods.\u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eIn this retrospective Moroccan cohort diagnosed in 2014, hormone receptor\u0026ndash;positive tumors predominated, and luminal subtypes represented the majority of surrogate molecular phenotypes. Overall, the distribution of luminal, HER2-enriched, and triple-negative breast cancer subtypes was consistent with previously published Moroccan series from a similar period, supporting the representativeness of the present cohort within the national context.\u003c/p\u003e \u003cp\u003eWhen examined in a temporal perspective, Moroccan studies reveal a clear evolution in surrogate molecular subtype distribution. Earlier series, particularly the large Eastern Morocco cohort conducted between 2005 and 2012, reported a strong predominance of luminal A tumors, accounting for more than half of cases, with lower proportions of luminal B tumors [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Similar patterns were observed in other cohorts from the early 2010s, where luminal A tumors remained the dominant subtype and luminal B tumors were less frequently identified [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. In contrast, the present 2014 cohort already shows a relative reduction in luminal A tumors and a modest proportion of luminal B tumors, suggesting a transitional phase in molecular classification practices.\u003c/p\u003e \u003cp\u003eMore recent Moroccan series demonstrate a marked shift toward luminal B predominance. In the Marrakech cohort (2015\u0026ndash;2019), luminal B tumors accounted for over 40% of cases, while luminal A tumors represented less than one quarter of the cohort [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. This trend is even more pronounced in the Casablanca 2020\u0026ndash;2021 series, in which luminal B tumors constituted more than half of all cases and luminal A tumors represented approximately 11% [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Taken together, these findings strongly suggest a progressive reclassification of hormone receptor\u0026ndash;positive tumors over time rather than a true underlying biological shift in breast cancer epidemiology.\u003c/p\u003e \u003cp\u003eSeveral methodological factors likely explain this evolution. In the present study, Ki-67 was not systematically reported, reflecting routine pathological practice in 2014. Consequently, surrogate molecular classification relied primarily on ER, PR, and HER2 status, with Ki-67 incorporated only when available. This approach resulted in the retention of an unclassified category due to missing data, which explains why subtype proportions do not sum to 100%. In contrast, more recent studies have implemented systematic Ki-67 assessment, often using higher cut-offs in accordance with updated St Gallen recommendations, leading to a greater identification of luminal B tumors at the expense of luminal A tumors [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eImportantly, the proportion of HER2-enriched tumors appears relatively stable across Moroccan series, ranging from approximately 17% to 22% over time, suggesting that HER2 overexpression is less influenced by changes in immunohistochemical practices. Similarly, the proportion of triple-negative breast cancer shows limited variation across cohorts, remaining broadly consistent with international data. These observations support the notion that methodological evolution predominantly affects the subclassification of hormone receptor\u0026ndash;positive tumors rather than HER2-positive or triple-negative disease.\u003c/p\u003e \u003cp\u003eFinally, it should be noted that population characteristics also contribute to inter-study variability. The Marrakech series included exclusively women younger than 40 years, a group known to present more aggressive tumor biology, which may partially explain the higher proportion of luminal B and other aggressive subtypes observed in that cohort [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOverall, these findings highlight the importance of interpreting surrogate molecular subtype distributions within their methodological and temporal context. Differences observed across Moroccan studies primarily reflect evolving immunohistochemical practices and improved tumor characterization rather than major epidemiological shifts. This is particularly relevant in low- and middle-income settings, where access to genomic profiling remains limited and immunohistochemistry continues to play a central role in guiding breast cancer management and therapeutic decision-making.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThis study provides a detailed snapshot of breast cancer immunohistochemical profiles in Morocco during a pivotal period in 2014. In this cohort, hormone receptor\u0026ndash;positive tumors predominated, with luminal A remaining the most frequent molecular phenotype, alongside substantial proportions of HER2-positive and triple-negative breast cancers. These findings are consistent with earlier Moroccan series and confirm the predominance of luminal tumors in the local population.\u003c/p\u003e \u003cp\u003eComparison with Moroccan studies published before and after 2014 highlights a clear temporal evolution in molecular subtype distribution, characterized by a progressive decrease in luminal A tumors and a marked increase in luminal B tumors in more recent cohorts. This shift appears to be driven primarily by improvements and standardization in immunohistochemical assessment, particularly the increasing and more systematic use of Ki-67, rather than by abrupt biological changes in tumor behavior.\u003c/p\u003e \u003cp\u003eBy situating the present cohort within a national temporal framework, this work underscores the importance of methodological context when interpreting molecular subtype distributions, especially in low- and middle-income settings. Continued efforts toward standardization of immunohistochemical practices are essential to ensure reliable epidemiological comparisons and to optimize breast cancer classification and management in Morocco.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the local ethics committee of Ibn Rochd University Hospital, Casablanca. Given the retrospective nature of the study and the use of anonymized data, informed consent was waived.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors received no specific funding for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRIA contributed to the study conception and design, data collection, interpretation of the data, and drafting of the manuscript.\u003cbr\u003e\u0026nbsp;EMR and NA contributed to data collection and validation of clinical and pathological data.\u003cbr\u003e\u0026nbsp;AFZ, OA, REA contributed to data interpretation and critical revision of the manuscript.\u003cbr\u003e\u0026nbsp;BN and BA contributed to critical revision of the manuscript for important intellectual content, supervised the study, contributed to study design and interpretation of the data, and critically revised the manuscript.\u003c/p\u003e\n\u003cp\u003eAll authors read and approved the final manuscript and agree to be accountable for all aspects of the work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank the medical and pathology staff of the Mohammed VI Cancer Treatment Center for their support.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. 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Pan Afr Med J. 2025;50:105. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.11604/pamj.2025.50.105.43868\u003c/span\u003e\u003cspan address=\"10.11604/pamj.2025.50.105.43868\" 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":false,"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":"bmc-womens-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmwh","sideBox":"Learn more about [BMC Women's Health](http://bmcwomenshealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmwh/default.aspx","title":"BMC Women's Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Breast cancer, Immunohistochemistry, Molecular subtypes, HER2, Low- and middle-income countries","lastPublishedDoi":"10.21203/rs.3.rs-8386239/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8386239/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground :\u003c/h2\u003e \u003cp\u003eBreast cancer remains a major public health challenge worldwide, with significant variability in tumor biology and outcomes. In low- and middle-income countries, immunohistochemistry (IHC) remains the cornerstone of tumor biological characterization due to limited access to genomic profiling. Understanding real-world IHC patterns is essential to contextualize prognosis and guide clinical decision-making in such settings.\u003c/p\u003e\u003ch2\u003eMethods :\u003c/h2\u003e \u003cp\u003eIn this retrospective monocentric study, we described the immunohistochemical profile and surrogate molecular subtype distribution of breast cancer in a Moroccan cohort diagnosed in 2014 at a tertiary referral center in Casablanca.\u003c/p\u003e\u003ch2\u003eResults :\u003c/h2\u003e \u003cp\u003eAmong 655 patients, IHC data were available for 456 cases and included estrogen receptor (ER), progesterone receptor (PR), HER2 status, and Ki-67 when reported. Estrogen receptor positivity was observed in 74.1% of cases with available data, and progesterone receptor positivity in 65.5%. HER2 positivity was present in 22.1% of tumors. Luminal A tumors accounted for 38.8% of cases, followed by luminal B (14.5%), HER2-positive (20.6%) and triple-negative tumors (13.9%), with a proportion of tumors unclassified due to incomplete data. Comparison with previously published Moroccan series suggests a temporal evolution in molecular subtype distributions, highlighting the influence of evolving IHC practices.\u003c/p\u003e\u003ch2\u003eConclusions :\u003c/h2\u003e \u003cp\u003eThese findings provide updated population-specific data on breast cancer immunohistochemical profiles and emphasize the continued relevance of IHC as a pragmatic tool in resource-constrained settings.\u003c/p\u003e","manuscriptTitle":"Breast cancer immunohistochemical patterns in Morocco: changes suggested by comparison with contemporary data","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-09 18:23:59","doi":"10.21203/rs.3.rs-8386239/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-01-07T18:27:34+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-12-18T14:41:09+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-17T23:54:44+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-17T23:53:45+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Women's Health","date":"2025-12-17T13:18:46+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-womens-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmwh","sideBox":"Learn more about [BMC Women's Health](http://bmcwomenshealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmwh/default.aspx","title":"BMC Women's Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"21564446-e17b-4d42-97ce-958810883087","owner":[],"postedDate":"January 9th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-01-09T18:23:59+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-09 18:23:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8386239","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8386239","identity":"rs-8386239","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}