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Hava Izci,1 Gilles Macq,2 Tim Tambuyzer,2 Harlinde De Schutter,2 Hans Wildiers,1,3 Francois P Duhoux,4 Evandro de Azambuja,5 Donatienne Taylor,6 Gracienne Staelens,7 Guy Orye,8 Zuzana Hlavata,9 Helga Hellemans,10 Carine De Rop,11 Patrick Neven,1,3 Freija Verdoodt2 1KU Leuven - University of Leuven, Department of Oncology, Leuven, B-3000, Belgium; 2Belgian Cancer Registry, Research Department, Brussels, Belgium; 3University Hospitals Leuven, Multidisciplinary Breast Center, Leuven, B-3000, Belgium; 4Department of Medical Oncology, King Albert II Cancer Institute, Cliniques Universitaires Saint-Luc, Brussels, Belgium; 5Institut Jules Bordet and l’Université Libre de Bruxelles (U.L.B), Brussels, Belgium; 6CHU UCL Namur, Site Sainte-Elisabeth, Namur, Belgium; 7Multidisciplinary Breast Center, General Hospital Groeninge, Kortrijk, Belgium; 8Department of Obstetrics and Gynecology, Jessa Hospital, Hasselt, Belgium; 9Department of Medical Oncology, CHR Mons-Hainaut, Mons, Hainaut, Belgium; 10Department of Obstetrics and Gynaecology, AZ Delta, Roeselaere, Belgium; 11Department of Obstetrics and Gynaecology, Imelda Hospital, Bonheiden, BelgiumCorrespondence: Hava Izci, KU Leuven, Department of oncology, Herestraat 49 Box 7003-06, Leuven, 3000, Belgium, Email hava.izci@kuleuven.bePurpose: High-quality population-based cancer recurrence data are scarcely available, mainly due to complexity and cost of registration. For the first time in Belgium, we developed a tool to estimate distant recurrence after a breast cancer diagnosis at the population level, based on real-world cancer registration and administrative data.Methods: Data on distant cancer recurrence (including progression) from patients diagnosed with breast cancer between 2009– 2014 were collected from medical files at 9 Belgian centers to train, test and externally validate an algorithm (i.e., gold standard). Distant recurrence was defined as the occurrence of distant metastases between 120 days and within 10 years after the primary diagnosis, with follow-up until December 31, 2018. Data from the gold standard were linked to population-based data from the Belgian Cancer Registry (BCR) and administrative data sources. Potential features to detect recurrences in administrative data were defined based on expert opinion from breast oncologists, and subsequently selected using bootstrap aggregation. Based on the selected features, classification and regression tree (CART) analysis was performed to construct an algorithm for classifying patients as having a distant recurrence or not.Results: A total of 2507 patients were included of whom 216 had a distant recurrence in the clinical data set. The performance of the algorithm showed sensitivity of 79.5% (95% CI 68.8– 87.8%), positive predictive value (PPV) of 79.5% (95% CI 68.8– 87.8%), and accuracy of 96.7% (95% CI 95.4– 97.7%). The external validation resulted in a sensitivity of 84.1% (95% CI 74.4– 91.3%), PPV of 84.1% (95% CI 74.4– 91.3%), and an accuracy of 96.8% (95% CI 95.4– 97.9%).Conclusion: Our algorithm detected distant breast cancer recurrences with an overall good accuracy of 96.8% for patients with breast cancer, as observed in the first multi-centric external validation exercise.Keywords: machine learning, breast cancer, distant metastases, recurrences, algorithm, administrative data

The immune landscape of hormone receptor positive, HER2-negative metastatic breast cancer (HR+/HER2- mBC), the most common subtype of BC, remains understudied. This is mainly due to reduced sample acquisition opportunities from metastases as compared to primary tumors. In this study, we explored stromal tumor-infiltrating lymphocytes (sTIL) in metastatic samples collected through our post-mortem tissue donation program UPTIDER (NCT04531696). sTIL were scored as a continuous parameter according to international guidelines on 427 metastases and 38 primary untreated tumors acquired from 20 patients with HR+/HER2- mBC. ER-status was evaluated on 362 metastases with a cut-off for positivity set at 1% according to ASCO/CAP guidelines. Our analyses show that 54% and 15% of metastases had sTIL levels >1% and >5% respectively. sTIL levels tended to be lower in metastases as compared to their respective primary tumor (Estimate: -2.83, 95%CI: -5.77-0.11, p:0.07). sTIL levels were lower in metastases from invasive lobular carcinoma than in metastases from invasive breast carcinoma of no special type (Estimate: -1.67, 95%CI: -2.35--0.98, p:<0.001). A loss of ER expression was observed in 14% of all metastases, yet a negative ER-status was not significantly associated with increased sTIL levels. Finally, sTIL levels were significantly higher in lung and axillary lymph node metastases compared to all metastases. While these analyses were conducted on multiple metastases obtained at the end of life after several lines of treatment, the data provides novel and valuable insights into the state of immune infiltration in patients with metastatic HR+/HER2- BC., (Copyright © 2024. Published by Elsevier Inc.)

Primary tumors with a mixed invasive breast carcinoma of no-special type (IBC-NST) and invasive lobular cancer (ILC) histology are present in approximately five percent of all patients with breast cancer and are understudied at the metastatic level. Here, we characterized the histology of metastases from two patients with primary mixed IBC-NST/ILC from the postmortem tissue donation program UPTIDER (NCT04531696). The 14 and 43 metastatic lesions collected at autopsy had morphological features and E-cadherin staining patterns consistent with pure ILC. While our findings still require further validation, they may challenge current clinical practice and imaging modalities used in these patients., Competing Interests: Declaration of Competing interest All authors declare to have no conflict of interest, (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Limited data exist regarding the associations between TROP-2 protein expression, clinical-pathological characteristics, and outcome in triple-negative breast cancer (TNBC). TROP-2 expression was determined for patients diagnosed with TNBC between 2000 and 2017 by immunohistochemistry (IHC) (ab227689, Abcam) on whole slide tumor sections, and assessed as continuous and categorical variables (H-score high, 201-300, medium 100-200 and low < 100). We investigated the prognostic value of TROP-2 expression for relapse and survival, associations between TROP-2 expression and baseline patient and tumor characteristics, stromal tumor-infiltrating lymphocytes (sTILs), androgen receptor (AR), standardized mitotic index (SMI) and pathological complete response (pCR, in patients with neoadjuvant chemotherapy) were assessed. We included 685 patients with a median age at diagnosis of 54 years (range 22-90 years). After median follow-up of 9.6 years, 17.5% of patients experienced distant relapse. TROP-2 expression was high, medium and low in 97 (16.5%), 149 (25.3%) and 343 (58.2%) of patients, respectively. The presence of LVI, associated DCIS, nodal involvement, apocrine histology and AR expression were correlated with higher TROP-2 levels. There were no associations between TROP-2 expression and sTILs, time-to-event outcomes, or pCR rate after neoadjuvant chemotherapy. TROP-2 expression is not associated with sTILs level and has no prognostic value in our cohort of stage 1-3 TNBC. However, an association with histotype and AR expression was found, suggesting a histotype specific TROP-2 expression pattern with highest expression in apocrine subtype, warranting further research., (© 2022. The Author(s).)

Objectives: This study sought to investigate if contractile asymmetry between septum and left ventricular (LV) lateral wall drives heart failure development in patients with left bundle branch block (LBBB) and whether the presence of lateral wall dysfunction affects potential for recovery of LV function with cardiac resynchronization therapy (CRT)., Background: LBBB may induce or aggravate heart failure. Understanding the underlying mechanisms is important to optimize timing of CRT., Methods: In 76 nonischemic patients with LBBB and 11 controls, we measured strain using speckle-tracking echocardiography and regional work using pressure-strain analysis. Patients with LBBB were stratified according to LV ejection fraction (EF) ≥50% (EF preserved ), 36% to 49% (EF mid ), and ≤35% (EF low ). Sixty-four patients underwent CRT and were re-examined after 6 months., Results: Septal work was successively reduced from controls, through EF preserved , EF mid , and EF low (all p < 0.005), and showed a strong correlation to left ventricular ejection fraction (LVEF; r = 0.84; p < 0.005). In contrast, LV lateral wall work was numerically increased in EF preserved and EF mid versus controls, and did not significantly correlate with LVEF in these groups. In EF low, however, LV lateral wall work was substantially reduced (p < 0.005). There was a moderate overall correlation between LV lateral wall work and LVEF (r = 0.58; p < 0.005). In CRT recipients, LVEF was normalized (≥50%) in 54% of patients with preserved LV lateral wall work, but only in 13% of patients with reduced LV lateral wall work (p < 0.005)., Conclusions: In early stages, LBBB-induced heart failure is associated with impaired septal function but preserved lateral wall function. The advent of LV lateral wall dysfunction may be an optimal time-point for CRT., Competing Interests: Funding Support and Author Disclosures Drs Sletten and Aalen were supported by grants from the Norwegian Health Association. Dr Larsen was a recipient of a clinical research fellowships form the South-Eastern Norway Regional Health Authority. Prof Voigt holds a personal research mandate of the Flemish Research Council. Prof Voigt and Dr Duchenne were further supported by a grant of the University of Leuven. Dr Smiseth is co-inventor, but since May 24, 2017 has had no financial relations to the patent “Method for myocardial segment work analysis,” which was used to calculate the myocardial work. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose, (Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: Registration and coding of cause of death is prone to error since determining the exact underlying condition leading directly to death is challenging. In this study, causes of death from the death certificates were compared to patients' medical files interpreted by experts at University Hospitals Leuven (UHL), to assess concordance between sources and its impact on cancer survival assessment., Methods: Breast cancer patients treated at UHL (2009-2014) (follow-up until December 31st 2016) were included in this study. Cause of death was obtained from death certificates and expert-reviewed medical files at UHL. Agreement was calculated using Cohen's kappa coefficient. Cause-specific survival (CSS) was calculated using the Kaplan-Meier method and the relative survival probability (RS) using the Ederer II and Pohar Perme method., Results: A total of 2862 patients, of whom 354 died, were included. We found an agreement of 84.7% (kappa-value of 0.69 (95% C.I.: 0.62-0.77)) between death certificates and medical files. Death certificates had 10.7% false positive and 4.5% false negative rates. However, five-year CSS and RS measures were comparable for both sources., Conclusion: For breast cancer patients included in our study, fair agreement of cause of death was seen between death certificates and medical files with similar CSS and RS estimations.

Background: Exact numbers of breast cancer recurrences are currently unknown at the population level, because they are challenging to actively collect. Previously, real-world data such as administrative claims have been used within expert- or data-driven (machine learning) algorithms for estimating cancer recurrence. We present the first systematic review and meta-analysis, to our knowledge, of publications estimating breast cancer recurrence at the population level using algorithms based on administrative data., Methods: The systematic literature search followed Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. We evaluated and compared sensitivity, specificity, positive predictive value, negative predictive value, and overall accuracy of algorithms. A random-effects meta-analysis was performed using a generalized linear mixed model to obtain a pooled estimate of accuracy., Results: Seventeen articles met the inclusion criteria. Most articles used information from medical files as the gold standard, defined as any recurrence. Two studies included bone metastases only in the definition of recurrence. Fewer studies used a model-based approach (decision trees or logistic regression) (41.2%) compared with studies using detection rules without specified model (58.8%). The generalized linear mixed model for all recurrence types reported an accuracy of 92.2% (95% confidence interval = 88.4% to 94.8%)., Conclusions: Publications reporting algorithms for detecting breast cancer recurrence are limited in number and heterogeneous. A thorough analysis of the existing algorithms demonstrated the need for more standardization and validation. The meta-analysis reported a high accuracy overall, which indicates algorithms as promising tools to identify breast cancer recurrence at the population level. The rule-based approach combined with emerging machine learning algorithms could be interesting to explore in the future., (© The Author(s) 2020. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com.)