Metastases of primary mixed no-special type and lobular breast cancer display an exclusive lobular histology.

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.

Machine Learning Algorithm to Estimate Distant Breast Cancer Recurrence at the Population Level with Administrative Data.

Purpose: 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.

Correlation of TROP-2 expression with clinical-pathological characteristics and outcome in triple-negative breast cancer.

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.

Lateral Wall Dysfunction Signals Onset of Progressive Heart Failure in Left Bundle Branch Block.

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% (EFpreserved), 36% to 49% (EFmid), and ≤35% (EFlow). Sixty-four patients underwent CRT and were re-examined after 6 months. RESULTS: Septal work was successively reduced from controls, through EFpreserved, EFmid, and EFlow (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 EFpreserved and EFmid versus controls, and did not significantly correlate with LVEF in these groups. In EFlow, 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.

Cause of death for patients with breast cancer: discordance between death certificates and medical files, and impact on survival estimates.

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.

A Systematic Review of Estimating Breast Cancer Recurrence at the Population Level With Administrative Data.

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.