Tumor-Infiltrating Lymphocytes as Prognostic Markers in a Brazilian Population with Neoadjuvant Chemotherapy-Treated Breast Cancer: A survival study.

Tumor-Infiltrating Lymphocytes as Prognostic Markers in a Brazilian Population with Neoadjuvant Chemotherapy-Treated Breast Cancer: A survival study. | 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 Article Tumor-Infiltrating Lymphocytes as Prognostic Markers in a Brazilian Population with Neoadjuvant Chemotherapy-Treated Breast Cancer: A survival study. RENATA MONTARROYOS LEITE, HUGO LEITE DE FARIAS BRITO, ERIKA DE ABREU COSTA BRITO, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3878961/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 Breast cancer is recognized as a heterogeneous disease, displaying distinct responses to treatments in different populations. In recent decades, the role of Tumor-Infiltrating Lymphocytes (TILs) has been acknowledged as prognostic and predictive of response to neoadjuvant chemotherapy. Our study aimed to assess the association between 5-year overall survival and the percentage of TILs in a state of Brazil. It is a uni and multivariate analyses using a retrospective cohort of breast carcinoma patients who underwent neoadjuvant chemotherapy followed by surgical treatment. Core biopsy slides were reanalyzed to evaluate the quantity of TILs. Survival curves were estimated using the Kaplan-Meier method, and adjusted through Cox regression modeling. There was no significant difference between patients exhibiting complete pathological response and those who did not, concerning the quantity of TILs and its influence on overall survival in this study. However, a distinction in 5-year survival was observed, with 79.3% in patients with TILs below 5% compared to 49.9% in those with TILs above 5%. This trend persisted across all subtypes, but reached statistical significance in Her 2-positive and triple-negative subtypes, indicating that TILs might serve as a determinant factor in the prognosis and survival of these patient subgroups. Biological sciences/Cancer Biological sciences/Immunology Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Breast cancer stands as the most prevalent and fatal malignancy among women globally, and has a considerable impact on health systems in emerging countries, such as Brazil 1 . In 2020, witnessing a 20% surge, it became the most incident cancer worldwide, surpassing lung cancer 2 . Projections for Brazil anticipate 73,000 new cases for the triennium of 2023–2025. In 2021, the mortality rate due to this disease, adjusted for the world population, was 11.71 deaths/100,000 women 3 . Neoadjuvant treatment provides an in vivo validation model capable of testing drug effectiveness based on the observed pathological response during surgery. The complete absence of tumor in the surgical bed, or pathologic complete response (pCR), stands as a strong predictive factor for recurrence risk and positive clinical outcomes 4 . Breast neoplasia is a heterogeneous disease, influenced by genetic and environmental factors, exhibiting varying degrees of aggressiveness and diverse responses to treatments 5 . Mounting evidence suggests that the tumor microenvironment plays a crucial role in tumor formation, growth, invasion, and metastasis 6 . Consequently, an individualized patient approach has become increasingly imperative, leading to the study of different drug classes 7 . In recent decades, extensive research has delved into tumor immunological mechanisms, with the concept of "immunoediting," based on the principle of tumor elimination through T cell recognition, intriguing researchers in the quest for novel treatment modalities 8 . This reasoning has helped recognize the predictive and prognostic effect of infiltrating lymphocytes in tumors, linking them to both pCR 9–10 and overall survival in certain tumor subtypes of patients undergoing neoadjuvant chemotherapy, suggesting their crucial relevance in daily clinical practice 11 . Our study aimed to evaluate the association between 5-year overall survival and the percentage of TILs in core needle biopsy samples from breast cancer patients subjected to neoadjuvant chemotherapy followed by surgery at a university hospital in Sergipe state, between 2011 and 2017. As a secondary objective, we analyzed the relationship between TILs and intraoperative pCR in these patients, stratifying the influences of TILs in these variables among different molecular subtypes. Methods This is a survival study involving uni- and multivariate analyses, utilizing a retrospective cohort of patients diagnosed with breast carcinoma, who underwent neoadjuvant chemotherapy followed by surgical treatment, with core biopsy slides reanalyzed to evaluate the quantity of Tumor-Infiltrating Lymphocytes. We included female patients over 18 years old diagnosed with invasive breast carcinoma, treated surgically at the University Hospital of the Federal University of Sergipe, clinically staged from I to III by the 7th American Joint Committee on Cancer (AJCC), and receiving a minimum of three cycles of neoadjuvant chemotherapy. Data were collected from the medical records of patients who underwent neoadjuvant chemotherapy followed by surgery between 2011 and 2017. Variables analyzed included patient age, histological type, nuclear and histological tumor grades, initial clinical staging, and described immunohistochemical profile. Furthermore, a review of pathology reports from surgical specimens was conducted, assessing the presence or absence of microscopic residual neoplasia and its characteristics when present in the surgical specimen from both the breast and axillary regions. Pathological complete response (pCR or RCB-0) was defined as no viable residual tumor in the breast or axilla (ypT0N0)¹ 2 . Retrospectively selected core biopsy slides in histological sections with a thickness of 4 to 7 µm were utilized, stained using the routine H&E technique. These slides were reviewed by a senior pathologist, assessing tumor characteristics such as histological type, histological and nuclear grades. Additionally, evaluation of tumor-infiltrating lymphocytes (TILs) was conducted on all slides following standardized criteria proposed by the International TILs Working Group¹ 3 , with permissible adaptations. The quantity was described as percentages ranging from 0-100%.TILs assessment in literature has been semi-quantitative, considering a tumor "rich in lymphocytes" when TILs exceed 60%¹ 3 . In our assessment, the percentile of TILs that maximized the long-rank test statistic was 5%. Categorical variables were described using absolute and relative frequencies, while continuous variables were summarized with mean, median, standard deviation, and interquartile range. Independence between categorical variables was tested using Pearson's Chi-square test and Fisher's Exact test. The normality of continuous distributions was assessed using the Shapiro-Wilk test. As normality was not confirmed, tests for equality of position measures were conducted using Mann-Whitney (for 2 independent groups) or Kruskal-Wallis (for 3 or more groups) tests. Spearman's correlation was used to assess correlations between continuous variables¹ 4 . Survival curves were estimated via the Kaplan-Meier method, and the equality of survival curves was tested using LogRank tests. For survival analysis, the time between diagnosis (core biopsy date) and the date of death or the last recorded date in the medical record of living patients was considered. Crude and adjusted hazard ratios (HR) were estimated via Cox regression, and the proportional hazards assumption for each model was tested using the Schoenfeld residuals test. The statistical software used for all analyses was R Core Team 2023 (Version 4.2.3), and a significance level of 5% was adopted. This study received approval from the Research Ethics Committee involving Human Subjects at the Federal University of Sergipe (UFS) following the guidelines of Resolution CNS 466/12, under CAAE number 629670322.8.0000.5546, as per approval number 5.925.948. It was conducted by analyzing medical records, previously issued reports, and slides that had already been prepared and analyzed. Therefore, informed consent requirement was waived by the such committee. Data confidentiality and patient anonymity were ensured. Results A total of 46 cases were analyzed. Table 1 presents a dataset related to breast cancer, providing relevant information on histological, clinical, and molecular characteristics. The mean age among participants was 49.5 years, with an interquartile range (IQR) ranging from 42 to 60 years. The most prevalent histological type was ductal, representing 87% of cases. Lobular, micropapillary, and mucinous types showed lower proportions at 2.2%, 2.2%, and 8.7%, respectively. Concerning pre-neoadjuvant treatment histological grade, grade 2 was most common, prevalent in 66.7% of cases. Grades 1 and 3 accounted for 15.6% and 17.7% of cases, respectively. Regarding pre-neoadjuvant treatment nuclear grade, grades 2 and 3 were the most predominant, each representing 47.8% of cases. Grade 1 was observed in 4.3%. The percentage of tumoral infiltrating lymphocytes (TILs %) with a median of 5 and an IQR of 1 to 15, demonstrated that 37% of cases had values above 5, while 63% had values equal to or below 5. Survival, expressed in months, had a median of 60 months with an IQR of 40 to 60. Pathological complete response (pCR) was observed in 17.4% of cases. Concerning post-neoadjuvant treatment nuclear grade, the distribution was as follows: 15.2% in grade 0, 39.1% in grade 1, 30.4% in grade 2, and 15.2% in grade 3. Receptor results indicated that 65.2% of cases tested positive for estrogen receptor (ER) status, while 34.8% tested negative. For progesterone receptor (PR), 56.5% tested positive, and 43.5% tested negative. HER2 status revealed that 23.2% tested positive and 76.1% tested negative. Ki67, reflecting the cell proliferation rate, had a median of 0.4 and an IQR of 0.2 to 0.6. Finally, molecular subtypes were classified as follows: 10.9% HER2-enriched subtype, 50% luminal A subtype, 26.1% triple-negative subtype, and 13% triple-positive subtype. Analyzing the relationship between patient outcomes and other variables revealed significant results. In the group of surviving patients (n = 32, representing 69.6% of the total), the median survival was 60 months with an interquartile range (IQR) between 59 and 60 months. Conversely, in the deceased group (n = 14, 30.4% of the total), the median survival was 31 months with an IQR of 18 to 40 months. The difference in survival between the two groups was statistically significant, with a p-value less than 0.001. Regarding post-neoadjuvant treatment nuclear grade, among surviving patients, the distribution was as follows: 18.8% in grade 0, 43.8% in grade 1, 34.4% in grade 2, and 3.1% in grade 3. In deceased patients, the distribution was 7.1% in grade 0, 28.6% in grade 1, 21.4% in grade 2, and 42.9% in grade 3. This difference was also statistically significant, with a p-value of 0.007. Analysis of hormonal receptors revealed that among surviving patients, 78.1% tested positive for estrogen receptor (ER) status compared to 35.7% in deceased patients. This difference was statistically significant, with a p-value of 0.008. Regarding progesterone receptor (PR), 68.8% of surviving patients had positive status compared to 28.6% in deceased patients. This difference was also statistically significant, with a p-value of 0.022. The cell proliferation rate measured by KI67 showed a significant difference between the groups. Among surviving patients, the median KI67 was 0.3 with an IQR of 0.1 to 0.4, while in deceased patients, the median was 0.7 with an IQR of 0.5 to 0.8. The difference between the medians was statistically significant, with a p-value of 0.013. Table 1 Clinicopathological characteristics of patients undergoing neoadjuvant chemotherapy Total (n = 46) Alive (n = 32/69,6%) Death (n = 14/30,4%) p-value Age (Years) , Median [IIQ] 49,5 [42–60] 51,5 [42–60] 47,5 [42–56] 0,747 M Histological Type , n (%) Ductal 40 (87) 28 (87,5) 12 (85,7) 0,661 Q Lobular 1 (2,2) 0 (0) 1 (7,1) Micropapillary 1 (2,2) 1 (3,1) 0 (0) Mucinous 4 (8,7) 3 (9,4) 1 (7,1) Histological Grade Neoadjuvant pretreatment , n (%) 1 7 (15,6) 6 (19,4) 1 (7,1) 0,085 Q 2 30 (66,7) 22 (71) 8 (57,1) 3 8 (17,7) 3 (9,7) 5 (35,7) Nuclear Grade Neoadjuvant pretreatment 1 2 (4,3) 1 (3,1) 1 (7,1) 0,058 Q 2 22 (47,8) 19 (59,4) 3 (21,4) 3 22 (47,8) 12 (37,5) 10 (71,4) Mitotic Index Neoadjuvant pretreatment , n (%) 1 36 (80) 27 (87,1) 9 (64,3) 0,085 F 2 8 (17,8) 3 (9,7) 5 (35,7) 3 1 (2,2) 1 (3,2) 0 (0) TILs % , Median [IIQ] 5 [1–15] 5 [1–12,5] 10 [2–15] 0,229 M TILs % , n (%) > 5 17 (37) 9 (28,1) 8 (57,1) 0,097 F <=5 29 (63) 23 (71,9) 6 (42,9) Survival (months) , Median[IIQ] 60 [40–60] 60 [59–60] 31 [18–40] < 0,001 M pCR , n (%) Yes 8 (17,4) 6 (18,8) 2 (14,3) 1,000 F No 38 (82,6) 26 (81,3) 12 (85,7) Histological Type Post Neoadjuvant treatment , n (%) Ductal 43 (93,5) 29 (90,6) 14 (100) 0,543 F Mucinous 3 (6,5) 3 (9,4) 0 (0) Nuclear Grade Post Neoadjuvant treatment , n (%) 0 7 (15,2) 6 (18,8) 1 (7,1) 0,007 Q 1 18 (39,1) 14 (43,8) 4 (28,6) 2 14 (30,4) 11 (34,4) 3 (21,4) 3 7 (15,2) 1 (3,1) 6 (42,9) ER , n (%) Positive 30 (65,2) 25 (78,1) 5 (35,7) 0,008 F Negative 16 (34,8) 7 (21,9) 9 (64,3) PR , n (%) Positive 26 (56,5) 22 (68,8) 4 (28,6) 0,022 F Negative 20 (43,5) 10 (31,3) 10 (71,4) Her 2 , n (%) Positive 11 (23,9) 7 (21,9) 4 (28,6) 0,713 F Negative 35 (76,1) 25 (78,1) 10 (71,4) Ki67 , Median [IIQ] 0,4 [0,2 − 0,6] 0,3 [0,1 − 0,4] 0,7 [0,5 − 0,8] 0,013 M MOLECULAR SUBTYPE , n (%) HER2 5 (10,9) 2 (6,3) 3 (21,4) 0,076 Q Luminal 23 (50) 19 (59,4) 4 (28,6) Triple Negative 12 (26,1) 6 (18,8) 6 (42,9) Triple Positive 6 (13) 5 (15,6) 1 (7,1) Legend: n – absolute frequency. % – percentage relative frequency. IIQ – Interquartile Range. F – Fisher's Exact Test. Q – Pearson's Chi-square test. M – Mann-Whitney test TILs – Tumor-Infiltrating Lymphocytes. pCR – complete pathological response. ER – estrogen receptor. RP – progesterone receptor. Her 2 - Human Epidermal growth factor receptor 2. Ki-67 – Ki-67 antigen (Kihel 67) Statistical analysis using the Kruskal-Wallis test revealed a p-value of 0.1, indicating no significant differences among subtypes (Fig. 1 ). Employing the Mann-Whitney test for statistical analysis revealed a p-value of 0.50, indicating no significant difference between patients who exhibited complete pathological response and those who did not concerning the quantity of TILs, as both groups had medians of 5% (Fig. 2 ). No statistical significance was observed for the influence of TILs on overall survival in our study. The Spearman correlation calculated was − 0.26, with a p-value of 0.08 (Fig. 3 ). Regarding 5-year survival (Fig. 4 ), patients with TILs above 5% showed survival stabilization only after 53 months, with a survival rate of 49.4%. Conversely, patients with TILs below 5% reached survival stabilization at 43 months, with a survival rate of 79.3%. Analyzing the total person-years, a total of 229.17 person-years were observed, with 72.49 person-years for patients with TILs above 5% and 156.68 person-years for patients with TILs below 5%. To assess the difference between survival curves, the Log-Rank test was applied, revealing statistically significant differences between them with a p-value of 0.04. This indicates that based on the statistical analysis performed, there is sufficient evidence to assert a significant difference in survival between the two groups based on the TILs criterion. When stratifying survival assessment for luminal subtypes (ER + PR + and Her2 negative) and Triple Positives (or hybrid luminals), here referred to as only luminals, it was observed that patients with TILs below 5% showed survival stabilization only after 18 months, with a survival rate of 90.9% (Fig. 5 ). Conversely, patients with TILs above 5% reached survival stabilization at 37 months, with a survival rate of 80%. Analyzing the total person-years, a total of 96.77 person-years were observed, with 31.70 person-years for patients with TILs above 5% and 65.07 person-years for patients with TILs below 5%. To evaluate the difference between survival curves, the Log-Rank test was applied, revealing no statistically significant differences between them with a p-value of 0.59. This indicates that based on the statistical analysis performed, there is insufficient evidence to assert a significant difference in survival between the two groups based on the TILs criterion among luminal patients. The comparison of the 5-year survival curve estimated by Kaplan-Meier in Her2 and Triple Negative patients (referred to as Non-Luminals) (Fig. 6 ) demonstrates that patients with TILs above 5% showed survival stabilization only after 53 months, with a survival rate of 36.7%, whereas patients with TILs below 5% achieved survival stabilization at 43 months, with a survival rate of 72.2%. Analyzing the total person-years, a total of 132.41 person-years were observed, with 40.80 person-years for patients with TILs above 5% and 91.61 person-years for patients with TILs below 5%. By applying the Log-Rank test, statistically significant differences were observed between them, with a p-value of 0.05, indicating sufficient evidence to assert a difference in survival based on the TILs criterion among non-luminal patients. In a crude analysis, a patient with TILs greater than 5 had 2.89 (95% CI: 1.00-8.34; p = 0.05) times higher risk of death over the observed 5 years. However, when analyzing TILs adjusted for other prognostic variables such as molecular subtype, pre-neoadjuvant treatment nuclear grade, post-neoadjuvant treatment nuclear grade, and progesterone receptor, this risk increased to 4.22 (95% CI: 1.05–17.03; p = 0.043) times higher risk of death over the observed 5 years. Regarding the assumption of proportional risks, the models presented p-values above 0.05, indicating that this assumption was not violated (Table 2 ). Table 2 Survival analysis adjusted for variables HR (IC95%) p-value Schoenfeld p-value Crude (TILs > 5) 2,89 [1,00–8,34] 0,050 0,790 Adjusted for molecular subtype 4,06 [1,18 − 13,88] 0,026 0,700 Adjusted for molecular subtype and pre-neoadjuvant treatment nuclear grade 3,25 [0,86 − 12,28] 0,082 0,350 Adjusted for molecular subtype, pre-neoadjuvant treatment nuclear grade, and post-neoadjuvant treatment nuclear grade 3,58 [0,88 − 14,59] 0,075 0,064 Adjusted for molecular subtype, pre-neoadjuvant treatment nuclear grade, post-neoadjuvant treatment nuclear grade, and progesterone receptor 4,22 [1,05–17,03] 0,043 0,111 Legend: HR – Hazard Ratio. 95%CI – 95% confidence interval. TILs – infiltrating intratumoral lymphocytes. Discussion In this survival study, we demonstrated the relationship between the quantity of TILs in core biopsy samples of patients undergoing neoadjuvant chemotherapy (NAC) and the its intersection with other clinicopathological prognostic variables regarding the outcome of 5-year survival and the pathological response rate of these patients tumors. No statistically significant differences were found in the quantity of TILs among various molecular subtypes. Similarly, no differences were found in terms of age and histological type between surviving patients and those who deceased. However, factors such as histological tumor grade, absence of hormonal receptors, and higher Ki67 quantification were more prevalent in patients who deceased during the follow-up. We didn´t find significant association pCR and TILs in the analyzed patients. Additionally, our sample demonstrated higher survival rates in patients with TILs below 5%. This finding remained consistent regardless of the evaluated molecular subtype. Based on statistical evaluation, although no significance was found in the luminal subgroup, sufficient evidence was found to assert a significant difference in survival between the two non-luminal patient groups (Her2 and triple-negative) based on TILs criteria. Patients were included in the study between 2011 and 2017, a period when the precision therapeutic arsenal currently available in our country, including strategies such as dense dose regimens and specific checkpoint inhibitors, was not yet accessible 15 . Although not statistically demonstrated in this study, it is known that delays in chemotherapy and/or surgery after neoadjuvant chemotherapy can negatively impact treatment outcomes 16 , independently of TILs quantity in these tumors. When conducting an analysis adjusted for other well-known clinicopathological criteria related to better or worse prognosis, the presence of TILs contributes to these criteria, influencing survival outcomes. Regarding hormonal receptors, for instance, both independently and in conjunction with TILs, this subgroup showed the highest 5-year survival, as previously anticipated, in agreement with literature data 17 . In our sample, the majority of patients were luminal (pure or hybrid), which may have influenced the association results between TILs and survival outcomes. Robust evidence in the literature demonstrates higher survival associated with TILs in Her 2 and triple-negative subtypes 18 . However, for luminal subtypes, this remains highly controversial, even with a tendency toward worsened prognosis for these patients 19 . Additionally, other clinicopathological variables may have influenced the results. In our study, patients who deceased showed statistically significantly worse histological grades after neoadjuvant chemotherapy, lower expression of hormonal receptors, higher Ki67, and a lower observed complete pathological response at surgery. The prevalence of luminal tumors in our study aligns with global findings 20 , but subtype frequencies may vary among races 21 . Interestingly, no significant difference was observed in the quantity of TILs among different subtypes. In the triple-negative subgroup, an opposite association relationship of TILs with survival was found (TILs < 5% showed a better clinical outcome). This subgroup had a low representation in our sample. Identifying subgroups of patients with a worse prognosis from the moment of diagnosis, including markers allowing this assessment, is an auxiliary tool in tailoring treatment. Studies with a larger number of women and providing more statistical robustness in Brazilian populations are necessary to determine candidates for specific target therapies in this scenario, especially in this subgroup, which has been the subject of extensive studies with immunotherapy due to the absence of target receptors for specific drugs 22 . The strength of this study lies in the detailed analysis of the correlation between the quantity of TILs and its consequent impact on survival, including the differentiation of results among various molecular subtypes, without using robust and costly analysis techniques but rather an evaluation commonly used in pathology laboratories - hematoxylin-eosin staining - in a scarcely studied Brazilian population. However, the major limitation is its retrospective nature, potentially involving biases of confounding, including characteristics unique to the studied patients, whose results might be influenced by their geographical homogeneity. Conclusion This study demonstrated that the quantity of TILs is associated with breast cancer survival, especially in TN and Her2 subtypes, and may be a determinant factor in the prognosis and survival of this patient subgroup. The assessment of this type of marker should be further explored in future studies, especially in the northeastern population of Brazil. Studies with a larger sample size are still necessary to clarify the utility of these markers in different molecular subtypes and to gather more evidence for the development of specific target drugs. Declarations Competing interests: Authors declare no competing interests. Author Contribution Conception and design: R.M.L, I.D.C.B and C.A.L.; Analysis and interpretation of data: R.M.L, H.L.F.B and C.A.L.; Drafing and creation (figures; tables): R.M.L, H.L.F.B., C.A.L and I.D.C.B.; Statistical design, analysis of the data and critical evaluation of statistical methods employed: I.D.C.B; Review and evaluation of biopsy slides: H.L.F.B and E.A.C.B;Critical revision of the article: C.A.L., H.L.F.B.; final approval of the article: all authors approved the manuscript Acknowledgements The authors are grateful to the personnel of the Cancer Registry for their great work in collecting data and preparing the database for this research work: José Erinaldo Lobo de Oliveira and Cecília Ferreira. We also would like to express our sincere gratitude to the individuals who played instrumental roles in facilitating access to the patient biopsy slides in the laboratories, enabling the execution of this research: Monica de Araujo, Sonia Lima Marcena, Ricardo Fakhouri, Ivison Xavier Duarte, Maria do Carmo Correia, Leandro Domingues Duran References Sierra, M. S. et al . Cancer patterns and trends in Central and South America. Cancer Epidemiol. 44(Suppl 1), S23–S42 (2016). WHO. International agency for research on cancer - Cancer today. Available at: https://gco.iarc.fr/today/home . Accessed 9th Oct. 2023. INCA - Instituto Nacional de Câncer José Alencar Gomes da Silva. Atlas da mortalidade. Rio de Janeiro: INCA, 2023. Available at: https://www.inca.gov.br/app/mortalidade . Accessed 12th Oct. 2023. Cortazar, P. et al . Pathological complete response and long-term clinical benefit in breast cancer: the CTNeoBC pooled analysis. The Lancet, vol. 384, no. 9938, July 2014. 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New England Journal of Medicine 382.9 810–821,2020. Additional Declarations No competing interests reported. Supplementary Files Dataavailability.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-3878961","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":271571725,"identity":"c882d60d-1759-4208-bb13-50741b3f1bd0","order_by":0,"name":"RENATA MONTARROYOS LEITE","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3klEQVRIie3PsQrCMBCA4YNAugS7XtGHqBS0LvZVWrIGZ0VRQdDFBxB8j84WQZdI14qLmlnQzUmMzqWtm0P+6Yb7uATAZPrXzoAA1vI7s1r5PgEIARGYBNhoQisSfQbFl0ApaVtpcg/7/th2bol6iG6DArlcswLSWXKCoUR01j3uJjHXD6OeJwqIu+GA0RzRPYkWJjHRhNF6IUkVeUYvxOAoP2RSgWScYjTVV5B9yLYKUS0/3KGzksJzD/GeUVL2lzRS2X00tu2FbJ4H8TCwrdlFFZGcyG/rJpPJZMrpDZDqQ0JRF3GkAAAAAElFTkSuQmCC","orcid":"","institution":"Universidade Federal de Sergipe","correspondingAuthor":true,"prefix":"","firstName":"RENATA","middleName":"MONTARROYOS","lastName":"LEITE","suffix":""},{"id":271571726,"identity":"67133461-f1e0-4f16-a790-79fa68e9a339","order_by":1,"name":"HUGO LEITE DE FARIAS BRITO","email":"","orcid":"","institution":"Universidade Federal de Sergipe","correspondingAuthor":false,"prefix":"","firstName":"HUGO","middleName":"LEITE DE FARIAS","lastName":"BRITO","suffix":""},{"id":271571727,"identity":"fcbcb0c4-e0e6-4fb4-a5b3-3370ce78d3d3","order_by":2,"name":"ERIKA DE ABREU COSTA BRITO","email":"","orcid":"","institution":"Universidade Federal de Sergipe","correspondingAuthor":false,"prefix":"","firstName":"ERIKA","middleName":"DE ABREU COSTA","lastName":"BRITO","suffix":""},{"id":271571728,"identity":"a82212d4-9b7e-4197-b06a-8b2525699519","order_by":3,"name":"CARLOS ANSELMO LIMA","email":"","orcid":"","institution":"Federal University of Sergipe","correspondingAuthor":false,"prefix":"","firstName":"CARLOS","middleName":"ANSELMO","lastName":"LIMA","suffix":""},{"id":271571729,"identity":"76266295-6af3-49ae-ae40-bf048fdd5471","order_by":4,"name":"IKARO DANIEL DE CARVALHO BARRETO","email":"","orcid":"","institution":"Brazilian Research Center for Evaluation and Selection of Event Promotion-CEBRASPE","correspondingAuthor":false,"prefix":"","firstName":"IKARO","middleName":"DANIEL DE CARVALHO","lastName":"BARRETO","suffix":""}],"badges":[],"createdAt":"2024-01-19 14:14:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3878961/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3878961/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":50925554,"identity":"3595cbf0-5493-4453-8b59-25d944108f11","added_by":"auto","created_at":"2024-02-09 17:07:44","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":219106,"visible":true,"origin":"","legend":"\u003cp\u003eQuantity of TILs according to various molecular subtypes\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3878961/v1/9c0b8d2008ef794cff87e8da.jpeg"},{"id":50925556,"identity":"477d38c4-1c19-4a7a-b85b-9033e8bfda0d","added_by":"auto","created_at":"2024-02-09 17:07:44","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":172828,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between TILs and pCR.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3878961/v1/c92de2097f3de8b3d43cc930.jpeg"},{"id":50925557,"identity":"18b61dd1-fd8c-4553-876c-3c6cc8b57fc2","added_by":"auto","created_at":"2024-02-09 17:07:44","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":13841,"visible":true,"origin":"","legend":"\u003cp\u003eScatter plot correlating TILs and overall survival (OS).\u003c/p\u003e","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-3878961/v1/124785ef6295f35c7b9146c8.png"},{"id":50925558,"identity":"fe303ce3-3cf8-4baf-a118-8b84abcae117","added_by":"auto","created_at":"2024-02-09 17:07:44","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":195703,"visible":true,"origin":"","legend":"\u003cp\u003e5-year survival according to TILs\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3878961/v1/e19cb0cff6b57e94fc19ca27.jpeg"},{"id":50925561,"identity":"ad8d2587-1c5d-4e17-a132-2dbb2da41227","added_by":"auto","created_at":"2024-02-09 17:07:44","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":14800,"visible":true,"origin":"","legend":"\u003cp\u003e5-year survival according to TILs in luminal patients (pure and hybrid).\u003c/p\u003e","description":"","filename":"Onlinefloatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-3878961/v1/e89ecd0bb1ca6746bae8ea1f.png"},{"id":50925560,"identity":"86426783-eaac-4c44-9948-5512c0096adb","added_by":"auto","created_at":"2024-02-09 17:07:44","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":14297,"visible":true,"origin":"","legend":"\u003cp\u003e5-year survival according to TILs in non-luminal patients (Her-2 and TN)\u003c/p\u003e","description":"","filename":"Onlinefloatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-3878961/v1/bf3dfba6f035fa18d724f4e3.png"},{"id":56236864,"identity":"40c6ba68-dd9e-44fc-b70f-b650208e3583","added_by":"auto","created_at":"2024-05-10 08:59:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":809470,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3878961/v1/54eaf791-2da8-43a4-ad64-56298fc5eff7.pdf"},{"id":50925555,"identity":"62c7f224-b2a7-4b95-94bd-c36094a35871","added_by":"auto","created_at":"2024-02-09 17:07:44","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":18574,"visible":true,"origin":"","legend":"","description":"","filename":"Dataavailability.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3878961/v1/23860cb98576542fc1917280.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Tumor-Infiltrating Lymphocytes as Prognostic Markers in a Brazilian Population with Neoadjuvant Chemotherapy-Treated Breast Cancer: A survival study.","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBreast cancer stands as the most prevalent and fatal malignancy among women globally, and has a considerable impact on health systems in emerging countries, such as Brazil\u003csup\u003e1\u003c/sup\u003e. In 2020, witnessing a 20% surge, it became the most incident cancer worldwide, surpassing lung cancer\u003csup\u003e2\u003c/sup\u003e. Projections for Brazil anticipate 73,000 new cases for the triennium of 2023\u0026ndash;2025. In 2021, the mortality rate due to this disease, adjusted for the world population, was 11.71 deaths/100,000 women\u003csup\u003e3\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eNeoadjuvant treatment provides an in vivo validation model capable of testing drug effectiveness based on the observed pathological response during surgery. The complete absence of tumor in the surgical bed, or pathologic complete response (pCR), stands as a strong predictive factor for recurrence risk and positive clinical outcomes\u003csup\u003e4\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eBreast neoplasia is a heterogeneous disease, influenced by genetic and environmental factors, exhibiting varying degrees of aggressiveness and diverse responses to treatments\u003csup\u003e5\u003c/sup\u003e. Mounting evidence suggests that the tumor microenvironment plays a crucial role in tumor formation, growth, invasion, and metastasis\u003csup\u003e6\u003c/sup\u003e. Consequently, an individualized patient approach has become increasingly imperative, leading to the study of different drug classes\u003csup\u003e7\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn recent decades, extensive research has delved into tumor immunological mechanisms, with the concept of \"immunoediting,\" based on the principle of tumor elimination through T cell recognition, intriguing researchers in the quest for novel treatment modalities\u003csup\u003e8\u003c/sup\u003e. This reasoning has helped recognize the predictive and prognostic effect of infiltrating lymphocytes in tumors, linking them to both pCR\u003csup\u003e9\u0026ndash;10\u003c/sup\u003e and overall survival in certain tumor subtypes of patients undergoing neoadjuvant chemotherapy, suggesting their crucial relevance in daily clinical practice\u003csup\u003e11\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOur study aimed to evaluate the association between 5-year overall survival and the percentage of TILs in core needle biopsy samples from breast cancer patients subjected to neoadjuvant chemotherapy followed by surgery at a university hospital in Sergipe state, between 2011 and 2017. As a secondary objective, we analyzed the relationship between TILs and intraoperative pCR in these patients, stratifying the influences of TILs in these variables among different molecular subtypes.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThis is a survival study involving uni- and multivariate analyses, utilizing a retrospective cohort of patients diagnosed with breast carcinoma, who underwent neoadjuvant chemotherapy followed by surgical treatment, with core biopsy slides reanalyzed to evaluate the quantity of Tumor-Infiltrating Lymphocytes.\u003c/p\u003e \u003cp\u003e We included female patients over 18 years old diagnosed with invasive breast carcinoma, treated surgically at the University Hospital of the Federal University of Sergipe, clinically staged from I to III by the 7th American Joint Committee on Cancer (AJCC), and receiving a minimum of three cycles of neoadjuvant chemotherapy.\u003c/p\u003e \u003cp\u003eData were collected from the medical records of patients who underwent neoadjuvant chemotherapy followed by surgery between 2011 and 2017. Variables analyzed included patient age, histological type, nuclear and histological tumor grades, initial clinical staging, and described immunohistochemical profile. Furthermore, a review of pathology reports from surgical specimens was conducted, assessing the presence or absence of microscopic residual neoplasia and its characteristics when present in the surgical specimen from both the breast and axillary regions. Pathological complete response (pCR or RCB-0) was defined as no viable residual tumor in the breast or axilla (ypT0N0)\u0026sup1;\u003csup\u003e2\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eRetrospectively selected core biopsy slides in histological sections with a thickness of 4 to 7 \u0026micro;m were utilized, stained using the routine H\u0026amp;E technique. These slides were reviewed by a senior pathologist, assessing tumor characteristics such as histological type, histological and nuclear grades. Additionally, evaluation of tumor-infiltrating lymphocytes (TILs) was conducted on all slides following standardized criteria proposed by the International TILs Working Group\u0026sup1;\u003csup\u003e3\u003c/sup\u003e, with permissible adaptations. The quantity was described as percentages ranging from 0-100%.TILs assessment in literature has been semi-quantitative, considering a tumor \"rich in lymphocytes\" when TILs exceed 60%\u0026sup1;\u003csup\u003e3\u003c/sup\u003e. In our assessment, the percentile of TILs that maximized the long-rank test statistic was 5%.\u003c/p\u003e \u003cp\u003eCategorical variables were described using absolute and relative frequencies, while continuous variables were summarized with mean, median, standard deviation, and interquartile range. Independence between categorical variables was tested using Pearson's Chi-square test and Fisher's Exact test. The normality of continuous distributions was assessed using the Shapiro-Wilk test. As normality was not confirmed, tests for equality of position measures were conducted using Mann-Whitney (for 2 independent groups) or Kruskal-Wallis (for 3 or more groups) tests. Spearman's correlation was used to assess correlations between continuous variables\u0026sup1;\u003csup\u003e4\u003c/sup\u003e. Survival curves were estimated via the Kaplan-Meier method, and the equality of survival curves was tested using LogRank tests. For survival analysis, the time between diagnosis (core biopsy date) and the date of death or the last recorded date in the medical record of living patients was considered. Crude and adjusted hazard ratios (HR) were estimated via Cox regression, and the proportional hazards assumption for each model was tested using the Schoenfeld residuals test. The statistical software used for all analyses was R Core Team 2023 (Version 4.2.3), and a significance level of 5% was adopted.\u003c/p\u003e \u003cp\u003e This study received approval from the Research Ethics Committee involving Human Subjects at the Federal University of Sergipe (UFS) following the guidelines of Resolution CNS 466/12, under CAAE number 629670322.8.0000.5546, as per approval number 5.925.948. It was conducted by analyzing medical records, previously issued reports, and slides that had already been prepared and analyzed. Therefore, informed consent requirement was waived by the such committee. Data confidentiality and patient anonymity were ensured.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 46 cases were analyzed. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents a dataset related to breast cancer, providing relevant information on histological, clinical, and molecular characteristics.\u003c/p\u003e \u003cp\u003eThe mean age among participants was 49.5 years, with an interquartile range (IQR) ranging from 42 to 60 years. The most prevalent histological type was ductal, representing 87% of cases. Lobular, micropapillary, and mucinous types showed lower proportions at 2.2%, 2.2%, and 8.7%, respectively. Concerning pre-neoadjuvant treatment histological grade, grade 2 was most common, prevalent in 66.7% of cases. Grades 1 and 3 accounted for 15.6% and 17.7% of cases, respectively. Regarding pre-neoadjuvant treatment nuclear grade, grades 2 and 3 were the most predominant, each representing 47.8% of cases. Grade 1 was observed in 4.3%. The percentage of tumoral infiltrating lymphocytes (TILs %) with a median of 5 and an IQR of 1 to 15, demonstrated that 37% of cases had values above 5, while 63% had values equal to or below 5. Survival, expressed in months, had a median of 60 months with an IQR of 40 to 60. Pathological complete response (pCR) was observed in 17.4% of cases. Concerning post-neoadjuvant treatment nuclear grade, the distribution was as follows: 15.2% in grade 0, 39.1% in grade 1, 30.4% in grade 2, and 15.2% in grade 3. Receptor results indicated that 65.2% of cases tested positive for estrogen receptor (ER) status, while 34.8% tested negative. For progesterone receptor (PR), 56.5% tested positive, and 43.5% tested negative. HER2 status revealed that 23.2% tested positive and 76.1% tested negative. Ki67, reflecting the cell proliferation rate, had a median of 0.4 and an IQR of 0.2 to 0.6. Finally, molecular subtypes were classified as follows: 10.9% HER2-enriched subtype, 50% luminal A subtype, 26.1% triple-negative subtype, and 13% triple-positive subtype.\u003c/p\u003e \u003cp\u003eAnalyzing the relationship between patient outcomes and other variables revealed significant results. In the group of surviving patients (n\u0026thinsp;=\u0026thinsp;32, representing 69.6% of the total), the median survival was 60 months with an interquartile range (IQR) between 59 and 60 months. Conversely, in the deceased group (n\u0026thinsp;=\u0026thinsp;14, 30.4% of the total), the median survival was 31 months with an IQR of 18 to 40 months. The difference in survival between the two groups was statistically significant, with a p-value less than 0.001. Regarding post-neoadjuvant treatment nuclear grade, among surviving patients, the distribution was as follows: 18.8% in grade 0, 43.8% in grade 1, 34.4% in grade 2, and 3.1% in grade 3. In deceased patients, the distribution was 7.1% in grade 0, 28.6% in grade 1, 21.4% in grade 2, and 42.9% in grade 3. This difference was also statistically significant, with a p-value of 0.007. Analysis of hormonal receptors revealed that among surviving patients, 78.1% tested positive for estrogen receptor (ER) status compared to 35.7% in deceased patients. This difference was statistically significant, with a p-value of 0.008. Regarding progesterone receptor (PR), 68.8% of surviving patients had positive status compared to 28.6% in deceased patients. This difference was also statistically significant, with a p-value of 0.022. The cell proliferation rate measured by KI67 showed a significant difference between the groups. Among surviving patients, the median KI67 was 0.3 with an IQR of 0.1 to 0.4, while in deceased patients, the median was 0.7 with an IQR of 0.5 to 0.8. The difference between the medians was statistically significant, with a p-value of 0.013.\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\u003eClinicopathological characteristics of patients undergoing neoadjuvant chemotherapy\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;46)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAlive\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;32/69,6%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDeath\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;14/30,4%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (Years)\u003c/b\u003e, \u003cem\u003eMedian [IIQ]\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49,5 [42\u0026ndash;60]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51,5 [42\u0026ndash;60]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47,5 [42\u0026ndash;56]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,747 \u003csup\u003eM\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHistological Type\u003c/b\u003e, \u003cem\u003en (%)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuctal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40 (87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28 (87,5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 (85,7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,661 \u003csup\u003eQ\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLobular\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (2,2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (7,1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMicropapillary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (2,2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (3,1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMucinous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (8,7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (9,4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (7,1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHistological Grade\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eNeoadjuvant pretreatment\u003c/b\u003e, \u003cem\u003en (%)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (15,6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (19,4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (7,1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,085 \u003csup\u003eQ\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30 (66,7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22 (71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (57,1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (17,7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (9,7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (35,7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNuclear Grade\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eNeoadjuvant pretreatment\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (4,3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (3,1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (7,1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,058 \u003csup\u003eQ\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22 (47,8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (59,4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (21,4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22 (47,8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (37,5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 (71,4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMitotic Index\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eNeoadjuvant pretreatment\u003c/b\u003e, \u003cem\u003en (%)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36 (80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27 (87,1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (64,3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,085 \u003csup\u003eF\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (17,8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (9,7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (35,7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (2,2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (3,2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTILs %\u003c/b\u003e, \u003cem\u003eMedian [IIQ]\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 [1\u0026ndash;15]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 [1\u0026ndash;12,5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 [2\u0026ndash;15]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,229 \u003csup\u003eM\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTILs %\u003c/b\u003e, \u003cem\u003en (%)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17 (37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (28,1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (57,1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,097 \u003csup\u003eF\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;=5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29 (63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23 (71,9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (42,9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSurvival (months)\u003c/b\u003e, \u003cem\u003eMedian[IIQ]\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60 [40\u0026ndash;60]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60 [59\u0026ndash;60]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31 [18\u0026ndash;40]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0,001 \u003csup\u003eM\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003epCR\u003c/b\u003e, \u003cem\u003en (%)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (17,4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (18,8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (14,3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,000 \u003csup\u003eF\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38 (82,6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26 (81,3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 (85,7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHistological Type\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003ePost Neoadjuvant treatment\u003c/b\u003e, \u003cem\u003en (%)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuctal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43 (93,5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29 (90,6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,543 \u003csup\u003eF\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMucinous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (6,5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (9,4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNuclear Grade\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003ePost Neoadjuvant treatment\u003c/b\u003e, \u003cem\u003en (%)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (15,2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (18,8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (7,1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,007 \u003csup\u003eQ\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18 (39,1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (43,8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (28,6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (30,4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (34,4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (21,4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (15,2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (3,1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (42,9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eER\u003c/b\u003e, \u003cem\u003en (%)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30 (65,2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 (78,1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (35,7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,008 \u003csup\u003eF\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16 (34,8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (21,9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (64,3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePR\u003c/b\u003e, \u003cem\u003en (%)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26 (56,5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22 (68,8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (28,6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,022 \u003csup\u003eF\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 (43,5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (31,3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 (71,4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHer 2\u003c/b\u003e, \u003cem\u003en (%)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (23,9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (21,9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (28,6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,713 \u003csup\u003eF\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35 (76,1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 (78,1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 (71,4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eKi67\u003c/b\u003e, \u003cem\u003eMedian [IIQ]\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,4 [0,2\u0026thinsp;\u0026minus;\u0026thinsp;0,6]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,3 [0,1\u0026thinsp;\u0026minus;\u0026thinsp;0,4]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,7 [0,5\u0026thinsp;\u0026minus;\u0026thinsp;0,8]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,013 \u003csup\u003eM\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMOLECULAR SUBTYPE\u003c/b\u003e, \u003cem\u003en (%)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHER2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (10,9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (6,3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (21,4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,076 \u003csup\u003eQ\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLuminal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23 (50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (59,4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (28,6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\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\u003e12 (26,1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (18,8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (42,9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriple Positive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (15,6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (7,1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eLegend: n \u0026ndash; absolute frequency. % \u0026ndash; percentage relative frequency. IIQ \u0026ndash; Interquartile Range. F \u0026ndash; Fisher's Exact Test. Q \u0026ndash; Pearson's Chi-square test. M \u0026ndash; Mann-Whitney test TILs \u0026ndash; Tumor-Infiltrating Lymphocytes. pCR \u0026ndash; complete pathological response. ER \u0026ndash; estrogen receptor. RP \u0026ndash; progesterone receptor. Her 2 - Human Epidermal growth factor receptor 2. Ki-67 \u0026ndash; Ki-67 antigen (Kihel 67)\u003c/p\u003e \u003cp\u003eStatistical analysis using the Kruskal-Wallis test revealed a p-value of 0.1, indicating no significant differences among subtypes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Employing the Mann-Whitney test for statistical analysis revealed a p-value of 0.50, indicating no significant difference between patients who exhibited complete pathological response and those who did not concerning the quantity of TILs, as both groups had medians of 5% (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eNo statistical significance was observed for the influence of TILs on overall survival in our study. The Spearman correlation calculated was \u0026minus;\u0026thinsp;0.26, with a p-value of 0.08 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Regarding 5-year survival (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), patients with TILs above 5% showed survival stabilization only after 53 months, with a survival rate of 49.4%. Conversely, patients with TILs below 5% reached survival stabilization at 43 months, with a survival rate of 79.3%. Analyzing the total person-years, a total of 229.17 person-years were observed, with 72.49 person-years for patients with TILs above 5% and 156.68 person-years for patients with TILs below 5%. To assess the difference between survival curves, the Log-Rank test was applied, revealing statistically significant differences between them with a p-value of 0.04. This indicates that based on the statistical analysis performed, there is sufficient evidence to assert a significant difference in survival between the two groups based on the TILs criterion.\u003c/p\u003e \u003cp\u003eWhen stratifying survival assessment for luminal subtypes (ER\u0026thinsp;+\u0026thinsp;PR\u0026thinsp;+\u0026thinsp;and Her2 negative) and Triple Positives (or hybrid luminals), here referred to as only luminals, it was observed that patients with TILs below 5% showed survival stabilization only after 18 months, with a survival rate of 90.9% (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Conversely, patients with TILs above 5% reached survival stabilization at 37 months, with a survival rate of 80%. Analyzing the total person-years, a total of 96.77 person-years were observed, with 31.70 person-years for patients with TILs above 5% and 65.07 person-years for patients with TILs below 5%. To evaluate the difference between survival curves, the Log-Rank test was applied, revealing no statistically significant differences between them with a p-value of 0.59. This indicates that based on the statistical analysis performed, there is insufficient evidence to assert a significant difference in survival between the two groups based on the TILs criterion among luminal patients.\u003c/p\u003e \u003cp\u003eThe comparison of the 5-year survival curve estimated by Kaplan-Meier in Her2 and Triple Negative patients (referred to as Non-Luminals) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e) demonstrates that patients with TILs above 5% showed survival stabilization only after 53 months, with a survival rate of 36.7%, whereas patients with TILs below 5% achieved survival stabilization at 43 months, with a survival rate of 72.2%. Analyzing the total person-years, a total of 132.41 person-years were observed, with 40.80 person-years for patients with TILs above 5% and 91.61 person-years for patients with TILs below 5%. By applying the Log-Rank test, statistically significant differences were observed between them, with a p-value of 0.05, indicating sufficient evidence to assert a difference in survival based on the TILs criterion among non-luminal patients.\u003c/p\u003e \u003cp\u003eIn a crude analysis, a patient with TILs greater than 5 had 2.89 (95% CI: 1.00-8.34; p\u0026thinsp;=\u0026thinsp;0.05) times higher risk of death over the observed 5 years. However, when analyzing TILs adjusted for other prognostic variables such as molecular subtype, pre-neoadjuvant treatment nuclear grade, post-neoadjuvant treatment nuclear grade, and progesterone receptor, this risk increased to 4.22 (95% CI: 1.05\u0026ndash;17.03; p\u0026thinsp;=\u0026thinsp;0.043) times higher risk of death over the observed 5 years. Regarding the assumption of proportional risks, the models presented p-values above 0.05, indicating that this assumption was not violated (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\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\u003eSurvival analysis adjusted for variables\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR (IC95%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSchoenfeld\u003c/p\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrude (TILs\u0026thinsp;\u0026gt;\u0026thinsp;5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,89 [1,00\u0026ndash;8,34]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0,050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,790\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdjusted for molecular subtype\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,06 [1,18\u0026thinsp;\u0026minus;\u0026thinsp;13,88]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0,026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,700\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdjusted for molecular subtype and pre-neoadjuvant treatment nuclear grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,25 [0,86\u0026thinsp;\u0026minus;\u0026thinsp;12,28]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0,082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,350\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdjusted for molecular subtype, pre-neoadjuvant treatment nuclear grade, and post-neoadjuvant treatment nuclear grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,58 [0,88\u0026thinsp;\u0026minus;\u0026thinsp;14,59]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0,075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,064\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdjusted for molecular subtype, pre-neoadjuvant treatment nuclear grade, post-neoadjuvant treatment nuclear grade, and progesterone receptor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,22 [1,05\u0026ndash;17,03]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0,043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,111\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\u003eLegend: HR \u0026ndash; Hazard Ratio. 95%CI \u0026ndash; 95% confidence interval. TILs \u0026ndash; infiltrating intratumoral lymphocytes.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this survival study, we demonstrated the relationship between the quantity of TILs in core biopsy samples of patients undergoing neoadjuvant chemotherapy (NAC) and the its intersection with other clinicopathological prognostic variables regarding the outcome of 5-year survival and the pathological response rate of these patients tumors.\u003c/p\u003e \u003cp\u003eNo statistically significant differences were found in the quantity of TILs among various molecular subtypes. Similarly, no differences were found in terms of age and histological type between surviving patients and those who deceased. However, factors such as histological tumor grade, absence of hormonal receptors, and higher Ki67 quantification were more prevalent in patients who deceased during the follow-up.\u003c/p\u003e \u003cp\u003eWe didn\u0026acute;t find significant association pCR and TILs in the analyzed patients. Additionally, our sample demonstrated higher survival rates in patients with TILs below 5%. This finding remained consistent regardless of the evaluated molecular subtype. Based on statistical evaluation, although no significance was found in the luminal subgroup, sufficient evidence was found to assert a significant difference in survival between the two non-luminal patient groups (Her2 and triple-negative) based on TILs criteria. Patients were included in the study between 2011 and 2017, a period when the precision therapeutic arsenal currently available in our country, including strategies such as dense dose regimens and specific checkpoint inhibitors, was not yet accessible\u003csup\u003e15\u003c/sup\u003e. Although not statistically demonstrated in this study, it is known that delays in chemotherapy and/or surgery after neoadjuvant chemotherapy can negatively impact treatment outcomes\u003csup\u003e16\u003c/sup\u003e, independently of TILs quantity in these tumors. When conducting an analysis adjusted for other well-known clinicopathological criteria related to better or worse prognosis, the presence of TILs contributes to these criteria, influencing survival outcomes. Regarding hormonal receptors, for instance, both independently and in conjunction with TILs, this subgroup showed the highest 5-year survival, as previously anticipated, in agreement with literature data\u003csup\u003e17\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn our sample, the majority of patients were luminal (pure or hybrid), which may have influenced the association results between TILs and survival outcomes. Robust evidence in the literature demonstrates higher survival associated with TILs in Her 2 and triple-negative subtypes\u003csup\u003e18\u003c/sup\u003e. However, for luminal subtypes, this remains highly controversial, even with a tendency toward worsened prognosis for these patients\u003csup\u003e19\u003c/sup\u003e. Additionally, other clinicopathological variables may have influenced the results. In our study, patients who deceased showed statistically significantly worse histological grades after neoadjuvant chemotherapy, lower expression of hormonal receptors, higher Ki67, and a lower observed complete pathological response at surgery. The prevalence of luminal tumors in our study aligns with global findings\u003csup\u003e20\u003c/sup\u003e, but subtype frequencies may vary among races\u003csup\u003e21\u003c/sup\u003e. Interestingly, no significant difference was observed in the quantity of TILs among different subtypes.\u003c/p\u003e \u003cp\u003eIn the triple-negative subgroup, an opposite association relationship of TILs with survival was found (TILs\u0026thinsp;\u0026lt;\u0026thinsp;5% showed a better clinical outcome). This subgroup had a low representation in our sample. Identifying subgroups of patients with a worse prognosis from the moment of diagnosis, including markers allowing this assessment, is an auxiliary tool in tailoring treatment. Studies with a larger number of women and providing more statistical robustness in Brazilian populations are necessary to determine candidates for specific target therapies in this scenario, especially in this subgroup, which has been the subject of extensive studies with immunotherapy due to the absence of target receptors for specific drugs\u003csup\u003e22\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe strength of this study lies in the detailed analysis of the correlation between the quantity of TILs and its consequent impact on survival, including the differentiation of results among various molecular subtypes, without using robust and costly analysis techniques but rather an evaluation commonly used in pathology laboratories - hematoxylin-eosin staining - in a scarcely studied Brazilian population.\u003c/p\u003e \u003cp\u003eHowever, the major limitation is its retrospective nature, potentially involving biases of confounding, including characteristics unique to the studied patients, whose results might be influenced by their geographical homogeneity.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study demonstrated that the quantity of TILs is associated with breast cancer survival, especially in TN and Her2 subtypes, and may be a determinant factor in the prognosis and survival of this patient subgroup.\u003c/p\u003e \u003cp\u003eThe assessment of this type of marker should be further explored in future studies, especially in the northeastern population of Brazil.\u003c/p\u003e \u003cp\u003eStudies with a larger sample size are still necessary to clarify the utility of these markers in different molecular subtypes and to gather more evidence for the development of specific target drugs.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting interests:\u003c/h2\u003e \u003cp\u003eAuthors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConception and design: R.M.L, I.D.C.B and C.A.L.; Analysis and interpretation of data: R.M.L, H.L.F.B and C.A.L.; Drafing and creation (figures; tables): R.M.L, H.L.F.B., C.A.L and I.D.C.B.; Statistical design, analysis of the data and critical evaluation of statistical methods employed: I.D.C.B; Review and evaluation of biopsy slides: H.L.F.B and E.A.C.B;Critical revision of the article: C.A.L., H.L.F.B.; final approval of the article: all authors approved the manuscript\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eThe authors are grateful to the personnel of the Cancer Registry for their great work in collecting data and preparing the database for this research work: Jos\u0026eacute; Erinaldo Lobo de Oliveira and Cec\u0026iacute;lia Ferreira. We also would like to express our sincere gratitude to the individuals who played instrumental roles in facilitating access to the patient biopsy slides in the laboratories, enabling the execution of this research: Monica de Araujo, Sonia Lima Marcena, Ricardo Fakhouri, Ivison Xavier Duarte, Maria do Carmo Correia, Leandro Domingues Duran\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSierra, M. S. \u003cem\u003eet al\u003c/em\u003e. Cancer patterns and trends in Central and South America. Cancer Epidemiol. 44(Suppl 1), S23\u0026ndash;S42 (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWHO. International agency for research on cancer - Cancer today. Available at: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gco.iarc.fr/today/home\u003c/span\u003e\u003cspan address=\"https://gco.iarc.fr/today/home\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 9th Oct. 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eINCA - Instituto Nacional de C\u0026acirc;ncer Jos\u0026eacute; Alencar Gomes da Silva. Atlas da mortalidade. Rio de Janeiro: INCA, 2023. Available at: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.inca.gov.br/app/mortalidade\u003c/span\u003e\u003cspan address=\"https://www.inca.gov.br/app/mortalidade\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 12th Oct. 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCortazar, P. \u003cem\u003eet al\u003c/em\u003e. Pathological complete response and long-term clinical benefit in breast cancer: the CTNeoBC pooled analysis. The Lancet, vol. 384, no. 9938, July 2014.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarzaman, K. \u003cem\u003eet al\u003c/em\u003e. Breast cancer: Biology, biomarkers, and treatments. International Immunopharmacology, v. 84, jul. 2020.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcAllister, S. S., \u0026amp; Weinberg, R. A. (2010). Tumor-host interactions: a far-reaching relationship. Journal of clinical oncology, 28(26), 4022\u0026ndash;4028.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYeo, S. K., \u0026amp; Guan, J. L. Breast cancer: multiple subtypes within a tumor?. Trends in cancer, 3(11), 753\u0026ndash;760. (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDunn, G. P., Old, L. J., \u0026amp; Schreiber, R. D. The three Es of cancer immunoediting. Annu. Rev. Immunol., 22, 329\u0026ndash;360. (2004).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDenkert, C., \u003cem\u003eet al\u003c/em\u003e. Tumor-associated lymphocytes as an independent predictor of response to neoadjuvant chemotherapy in breast cancer. J Clin Oncol, 28.1: 105\u0026ndash;113, 2010.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e.______. Tumor-Infiltrating Lymphocytes and Response to Neoadjuvant Chemotherapy With or Without Carboplatin in Human Epidermal Growth Factor Receptor 2\u0026ndash;Positive and Triple-Negative Primary Breast Cancers. Journal of Clinical Oncology, v. 33, n. 9, 20 mar. 2015.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDushyanthen, S. \u003cem\u003eet al.\u003c/em\u003e Relevance of tumor-infiltrating lymphocytes in breast cancer. BMC medicine, 13: 1\u0026ndash;13, 2015.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNahleh, Z., Sivasubramaniam, D., Dhaliwal, S., Sundarajan, V., \u0026amp; Komrokji, R. Residual cancer burden in locally advanced breast cancer: a superior tool. Current Oncology, 15(6), 271\u0026ndash;278. (2008).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSalgado, R. \u003cem\u003eet al\u003c/em\u003e. The evaluation of tumor-infiltrating lymphocytes (TILs) in breast cancer: recommendations by an International TILs Working Group 2014. Annals of oncology, 2015, 26.2: 259\u0026ndash;271.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDancey C, Reidy J. Statistics without math for psychology: Prentice Hall. 2004.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShin D. \u003cem\u003eet al.\u003c/em\u003e Delay to curative surgery greater than 12 weeks is associated with increased mortality in patients with colorectal and breast cancer but not lung or thyroid cancer. Annals Surgical Oncology Aug;20(8):2468\u0026ndash;76, 2013\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eColzani, E. \u003cem\u003eet al\u003c/em\u003e. Prognosis of patients with breast cancer: causes of death and effects of time since diagnosis, age, and tumor characteristics. Journal of Clinical Oncology, 29.30: 4014\u0026ndash;4021, 2011,.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLoi, S. \u003cem\u003eet al.\u003c/em\u003e Prognostic and predictive value of tumor-infiltrating lymphocytes in a phase III randomized adjuvant breast cancer trial in node-positive breast cancer comparing the addition of docetaxel to doxorubicin with doxorubicin-based chemotherapy: BIG 02\u0026ndash;98. J. Clin. Oncol. 31, 860\u0026ndash;867 (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao-hua, Gao \u003cem\u003eet al.\u003c/em\u003e Predictive and prognostic role of tumour infiltrating lymphocytes in breast cancer patients with different molecular subtypes: a meta-analysis. BMC Cancer, v.20:1150, 2020\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eParise C. \u003cem\u003eet al\u003c/em\u003e. Breast cancer subtypes as defined by the estrogen receptor (ER), progesterone receptor (PR), and the human epidermal growth factor receptor 2 (HER2) among women with invasive breast cancer in California, 1999\u0026ndash;2004. \u003cem\u003eBreast Journal\u003c/em\u003e, 15:593,2020.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eO'BRIEN K. \u003cem\u003eet al\u003c/em\u003e. Intrinsic breast tumor subtypes, race, and long-term survival in the Carolina Breast Cancer Study. \u003cem\u003eClin Cancer Res\u003c/em\u003e 16:6100, 2010.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchmid, P. \u003cem\u003eet al.\u003c/em\u003e \"Pembrolizumab for early triple-negative breast cancer.\" New England Journal of Medicine 382.9 810\u0026ndash;821,2020.\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":"","lastPublishedDoi":"10.21203/rs.3.rs-3878961/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3878961/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Breast cancer is recognized as a heterogeneous disease, displaying distinct responses to treatments in different populations. In recent decades, the role of Tumor-Infiltrating Lymphocytes (TILs) has been acknowledged as prognostic and predictive of response to neoadjuvant chemotherapy. Our study aimed to assess the association between 5-year overall survival and the percentage of TILs in a state of Brazil. It is a uni and multivariate analyses using a retrospective cohort of breast carcinoma patients who underwent neoadjuvant chemotherapy followed by surgical treatment. Core biopsy slides were reanalyzed to evaluate the quantity of TILs. Survival curves were estimated using the Kaplan-Meier method, and adjusted through Cox regression modeling. There was no significant difference between patients exhibiting complete pathological response and those who did not, concerning the quantity of TILs and its influence on overall survival in this study. However, a distinction in 5-year survival was observed, with 79.3% in patients with TILs below 5% compared to 49.9% in those with TILs above 5%. This trend persisted across all subtypes, but reached statistical significance in Her 2-positive and triple-negative subtypes, indicating that TILs might serve as a determinant factor in the prognosis and survival of these patient subgroups.","manuscriptTitle":"Tumor-Infiltrating Lymphocytes as Prognostic Markers in a Brazilian Population with Neoadjuvant Chemotherapy-Treated Breast Cancer: A survival study.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-09 17:07:39","doi":"10.21203/rs.3.rs-3878961/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":"0abb0a3f-20c2-4364-8e6c-64f813a8e3db","owner":[],"postedDate":"February 9th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":28632113,"name":"Biological sciences/Cancer"},{"id":28632114,"name":"Biological sciences/Immunology"}],"tags":[],"updatedAt":"2024-05-10T08:51:16+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-09 17:07:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3878961","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3878961","identity":"rs-3878961","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}