Early-stage Invasive Breast Cancer Research Articles

70-Gene signature-guided adjuvant systemic treatment adjustments in early-stage ER+ breast cancer patients: 7-year follow-up of a prospective multicenter cohort study.

A previous prospective multicenter study revealed the change of the oncologists' chemotherapy advice due to the 70-Gene signature (GS) test result in half of the estrogen receptor-positive (ER+) invasive early-stage breast cancer patients with disputable chemotherapy indication. This resulted in less patients receiving chemotherapy. This study aims to complement these results by the 7-year oncological outcomes according to the 70-GS test result and the oncologists' pre-test advice. Patients operated for early-stage ER+ breast cancer with disputable chemotherapy indication, had been prospectively included between 2013 and 2015. Oncologists were asked whether they intended to administer adjuvant chemotherapy before deployment of the 70-GS test. Information on adjuvant systemic treatment and oncological outcome was obtained through active follow-up by data managers of the Netherlands Cancer Registry. The primary endpoint of this study was distant metastasis-free survival (DMFS) according to the genomic risk. Exploratory analyses were done to evaluate DMFS in relation to the oncologists' pre-test advice. After a median follow-up of 7 years, distant metastases were diagnosed in 23 of the 606 patients (3.8%) and 36 (5.9%) patients had died. The DMFS rate for the 357 70-GS genomic low-risk patients was 94.2% (95% CI 91.2-96.2) and 89.1% for the 249 genomic high-risk patients (95% CI 84.3-92.4). Of the low-risk patients 3% had received chemotherapy compared to 80% of the high-risk patients. For the subgroups based on the pre-test oncologists' advice (no chemotherapy/chemotherapy/unsure) there were no clinically relevant differences in DMFS (89.8, 93.2 and 92.0%, respectively), while comparable proportions of patients had received chemotherapy. In patients with early-stage ER+ breast cancer with a disputable chemotherapy indication it is sensible to deploy the 70-GS to better select patients for adjuvant chemotherapy.

The Effect of Statins on Markers of Breast Cancer Proliferation and Apoptosis in Women with In Situ or Early-Stage Invasive Breast Cancer.

Statins, inhibitors of HMG-CoA reductase, have been shown to have potential anti-carcinogenic effects through the inhibition of the mevalonate pathway and their impact on Ras and RhoGTAases. Prior studies have demonstrated a reduction in breast tumor proliferation, as well as increased apoptosis, among women with early-stage breast cancer who received statins between the time of diagnosis and the time of surgery. The aim of this study was to evaluate the impact of short-term oral high-potency statin therapy on the expression of markers of breast tumor proliferation, apoptosis, and cell cycle arrest in a window-of-opportunity trial. This single-arm study enrolled 24 women with stage 0-II invasive breast cancer who were administered daily simvastatin (20 mg) for 2-4 weeks between diagnosis and surgical resection. Pre- and post-treatment tumor samples were analyzed for fold changes in Ki-67, cyclin D1, p27, and cleaved caspase-3 (CC3) expression. Out of 24 enrolled participants, 18 received statin treatment and 17 were evaluable for changes in marker expression. There was no significant change in Ki-67 expression (fold change = 1.4, p = 0.597). There were, however, significant increases in the expression of cyclin D1 (fold change = 2.8, p = 0.0003), p27 cytoplasmic (fold change = 3.2, p = 0.025), and CC3 (fold change = 2.1, p = 0.016). Statin treatment was well tolerated, with two reported grade-1 adverse events. These results align with previous window-of-opportunity studies suggesting a pro-apoptotic role of statins in breast cancer. The increased expression of markers of cell cycle arrest and apoptosis seen in this window-of-opportunity study supports further investigation into the anti-cancer properties of statins in larger-scale clinical trials.

U.S. payer budget impact of using an AI-augmented cancer risk discrimination digital histopathology platform to identify high-risk of recurrence in women with early-stage invasive breast cancer

Aims Use of gene expression signatures to predict adjuvant chemotherapy benefit in women with early-stage breast cancer is increasing. However, high cost, limited access, and eligibility for these tests results in the adoption of less precise assessment approaches. This study evaluates the cost impact of PreciseDx Breast (PDxBr), an AI-augmented histopathology platform that assesses the 6-year risk of recurrence in early-stage invasive breast cancer patients to help improve informed use of adjuvant chemotherapy. Materials and methods A decision-tree Markov model was developed to compare the costs of treatment guided by standard of care (SOC) risk assessment (i.e. clinical diagnostic workup with or without Oncotype DX) versus PDxBr with SOC in a hypothetical cohort of U.S. women with early-stage invasive breast cancer. A commercial payer perspective compares costs of testing, adjuvant therapy, recurrence, adverse events, surveillance, and end-of-life care. Results PDxBr use in prognostic evaluation resulted in savings of $4 million (M) in year one compared to current SOC in 1 M females members. Over 6-years, savings increased to $12.5 M. The per-treated patient costs in year one amounted to $19.5 thousand (K) for SOC and $16.9K for PDxBr. Limitations For simplicity, recurrence was not specified. We performed scenario analyses to account for variations in rates for local, regional, and distant recurrence. Second, a recurrent patient incurs the total cost of treated recurrence in the first year and goes back to remission or death. Third, CDK4/6i treatment is only incorporated in the recurrence costs but not in the first line of treatment for early-stage breast cancer due to limited data. Conclusions Sensitivity analyses demonstrated robust overall savings to changes in all variables in the model. The use of PDxBr to assess breast cancer recurrence risk has the potential to fill gaps in care and reduce costs when gene expression signatures are not available.

Select gene mutations associated with survival outcomes in ER-positive ERBB2-negative early-stage invasive breast cancer: A single-institutional tissue bank study.

The prognostic capability of targeted sequencing of primary tumors in patients with estrogen receptor-positive, human epidermal growth factor receptor-2-negative early-stage invasive breast cancer (EBC) in a real-world setting is uncertain. Therefore, we aimed to determine the correlation between a 22-gene mutational profile and long-term survival outcomes in patients with ER+/ERBB2- EBC. A total of 73 women diagnosed with ER+/ERBB2- EBC between January 10, 2004, and June 2, 2008, were followed up until December 31, 2022. Univariate and multivariate Cox models were constructed to plot the relapse-free survival (RFS) and overall survival (OS). The log-rank test derived p-value was obtained. For external validation, we performed a survival analysis of 1163 comparable patients retrieved from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) dataset. At follow-up, 16 (21.9%) patients had relapsed, while 21 (nearly 29%) harbored mutant genes. Thirty-three missense mutations were detected in 14 genes. The median ages were 51 and 46 years in patients with and without mutations, respectively. Patients with any mutation had a 1.85-fold higher risk of relapse (hazard ratio [HR]: 1.85, 95% confidence interval [CI]: 0.60-5.69) compared to those without any mutation. Patients who harbored any of the six genes (MAP2K4, FGFR3, APC, KIT, RB1, and PTEN) had a nearly 6-fold increase in the risk of relapse (HR: 5.82, 95% CI: 1.31-18.56; p = 0.0069). Multivariate Cox models revealed that the adjusted HR for RFS and OS were 6.67 (95% CI: 1.32-27.57) and 8.31 (p = 0.0443), respectively. METABRIC analysis also demonstrated a trend to significantly worse RFS (p = 0.0576) in the subcohort grouped by having a mutation in any of the six genes. Our single-institution tissue bank study of Taiwanese women with ER+/ERBB2- EBC suggests that a novel combination of six gene mutations might have prognostic capability for survival outcomes.

AI-enabled digital test to predict disease recurrence for patients with early-stage invasive breast cancer and performance in a MammaPrint low-risk cohort from the Netherlands with a median 6-year follow-up.

1569 Background: Traditional invasive breast cancer (IBC) grading, although useful, remains limited due to diagnostic subjectivity and absence of phenotypic diversity including the recently observed importance of tumor epithelial – stromal interactions and lymphocyte content-distribution. We developed and validated a clinical grade digital test (PreciseDx Breast, PDxBr) which combines image-derived Artificial Intelligent (AI)-grading features and clinical data (i.e. age, stage, tumor size, LN status) to predict recurrence in early-stage IBC and sought to understand performance in a MammaPrint cohort with outcome data. Methods: A MammaPrint cohort with median 6-year follow-up was identified at the Laboratory of Pathology, Dordrecht, the Netherlands (NTH). H&E stained images (digitized at Philips, Eindhoven, NTH) with clinical data (from the pathology and Dutch cancer registries) including demographics, pathology results, MammaPrint risk classification, treatment type and recurrence events were obtained. Performance of the PDxBr validated model (AI-grade + clinical) on the MammaPrint cohort was evaluated using the AUC/concordance index, along with NPV, PPV, Hazards ratio (HR), sensitivity, and specificity. Results: 250 patients, median age 57 years, majority stage 1-IIIa, 100% HR+ve, Her2-ve, 84% LN-ve, 67% grade 2 and 66% MammaPrint low risk. There were 15 events (6%: 7 deaths, 3 second primaries,4 metastases and 1 local regional). PDxBR model classified 134 patients as high risk and 116 as low with 10 of 15 (67%) events identified as high risk. The NPV for PDxBr was 96% with a HR 1.63, Se 70%, Sp 46% vs. MammaPrint with an NPV of 94%, HR of 0.96, Se 40%, Sp 66%; identifying only 5 of the 15 events (33%) as high risk while missing 10. Of note, the AI- grade/imaging model (without clinical features) yielded an NPV of 95% with a HR of 1.46, Se 70%, Sp 42% and correctly identified 10 of 15 events. Conclusions: The results from this observational study suggest that triaging patients with the PDxBr test could potentially be adjunctive to the management decision process of patients with early-stage IBC including the use and subsequent interpretation of genomic tests such as MammaPrint. Additional studies are underway to confirm these initial findings.

Characterizing the 70-gene MammaPrint risk stratification in Chinese luminal type breast cancer and its correlation with clinicopathological characteristics, immune and metabolic functions.

e12559 Background: The 70-gene MammaPrint is endorsed by the NCCN guidelines for predicting 5-year distant metastasis risk in HR+, HER2-, with 0-3 lymph nodes, early invasive breast cancer. Similarly, it is recommended by the CSCO breast cancer guidelines for prognostic assessment post breast cancer surgery. However, no prior studies have investigated its application in Chinese luminal-type breast patients or its correlation with immune and metabolic functions. Methods: This analysis encompasses 76 early-stage invasive Luminal breast cancer patients, registered at the First People's Hospital of Foshan between 2020 and 2023, with recorded clinical characteristics under informed consent. Patient-reported data include clinical features, pathology, and the 70-gene MammaPrint risk of recurrence. Additionally, RNA-seq analysis was conducted in 24 patients. Results: Among 76 enrolled patients,55.3% were categorized as MammaPrint low risk (MP-Low), and 44.7% as MammaPrint high risk (MP-High). Patients over 50 were more likely MP-Low (p=0.028, OR=2.860), while those with >20% Ki67-stained cells were prone to MP-High (p<0.001, OR=0.160). Comparing with MINDACT, there was 52.3% consistency, with 55.6% of MP-Low patients being C-Low (MINDACT risk Low) and 47.1% of MP-High patients being C-High (MINDACT risk High). Notably,21% of C-Low patients showed MP-High risk, and 27% of C-High patients exhibited MP-Low risk, of which 83.3% were ≥50 years old. Based on MINDACT and ISPY2 trial outcomes, patients were further categorized into four groups: MP-Ultralow, MP-Low but not ultralow (LNUL), MP-High but not ultrahigh (HNUH) and MP-Ultrahigh. The study identified an 18% MP-Ultralow population, suggesting that excessive adjuvant therapy might be avoided in these patients. Additionally, 41% of patients were identified as LNUL group, indicating potential benefit from adjuvant treatment or endocrine prolongation therapy. 4% were identified as MP-Ultrahigh group, suggesting potential benefits from neoadjuvant targeted therapy, albeit a poorer long-term benefit in the non-pCR portion. Further transcriptome analysis in 24 patients revealed significantly higher levels of Th2 cells in the MP-High group, with no significant difference observed in CD8 T cells between the two groups. Meanwhile, metabolic-related signatures also differed, with significantly higher levels of Dopamine, Norepinephrine, Heme, and Epinephrine biosynthesis in the MP-Low group. Conclusions: This study elucidates the intricate relationships between MammaPrint prediction results of 70 genes and clinical characteristics, immune and metabolic signatures in Chinese Luminal type breast cancer patients. The refined risk classification provides valuable insights for precision treatment, advancing personalized therapeutic strategies.

Tripartite Motif-Containing 2, a Glutamine Metabolism-Associated Protein, Predicts Poor Patient Outcome in Triple-Negative Breast Cancer Treated with Chemotherapy.

Breast cancer (BC) remains heterogeneous in terms of prognosis and response to treatment. Metabolic reprogramming is a critical part of oncogenesis and a potential therapeutic target. Glutaminase (GLS), which generates glutamate from glutamine, plays a role in triple-negative breast cancer (TNBC). However, targeting GLS directly may be difficult, as it is essential for normal cell function. This study aimed to determine potential targets in BC associated with glutamine metabolism and evaluate their prognostic value in BC. The iNET model was used to identify genes in BC that are associated with GLS using RNA-sequencing data. The prognostic significance of tripartite motif-containing 2 (TRIM2) mRNA was assessed in BC transcriptomic data (n = 16,575), and TRIM2 protein expression was evaluated using immunohistochemistry (n = 749) in patients with early-stage invasive breast cancer with long-term follow-up. The associations between TRIM2 expression and clinicopathological features and patient outcomes were evaluated. Pathway analysis identified TRIM2 expression as an important gene co-expressed with high GLS expression in BC. High TRIM2 mRNA and TRIM2 protein expression were associated with TNBC (p < 0.01). TRIM2 was a predictor of poor distant metastasis-free survival (DMFS) in TNBC (p < 0.01), and this was independent of established prognostic factors (p < 0.05), particularly in those who received chemotherapy (p < 0.05). In addition, TRIM2 was a predictor of shorter DMFS in TNBC treated with chemotherapy (p < 0.01). This study provides evidence of an association between TRIM2 and poor patient outcomes in TNBC, especially those treated with chemotherapy. The molecular mechanisms and functional behaviour of TRIM2 and the functional link with GLS in BC warrant further exploration using in vitro models.

Clinicopathological Significance of Cyclin-Dependent Kinase 2 (CDK2) in Ductal Carcinoma In Situ and Early-Stage Invasive Breast Cancers.

Cyclin-dependent kinase 2 (CDK2) is a key cell cycle regulator, with essential roles during G1/S transition. The clinicopathological significance of CDK2 in ductal carcinomas in situ (DCIS) and early-stage invasive breast cancers (BCs) remains largely unknown. Here, we evaluated CDK2's protein expression in 479 BC samples and 216 DCIS specimens. Analysis of CDK2 transcripts was completed in the METABRIC cohort (n = 1980) and TCGA cohort (n = 1090), respectively. A high nuclear CDK2 protein expression was significantly associated with aggressive phenotypes, including a high tumour grade, lymph vascular invasion, a poor Nottingham prognostic index (all p-values < 0.0001), and shorter survival (p = 0.006), especially in luminal BC (p = 0.009). In p53-mutant BC, high nuclear CDK2 remained linked with worse survival (p = 0.01). In DCIS, high nuclear/low cytoplasmic co-expression showed significant association with a high tumour grade (p = 0.043), triple-negative and HER2-enriched molecular subtypes (p = 0.01), Comedo necrosis (p = 0.024), negative ER status (p = 0.004), negative PR status (p < 0.0001), and a high proliferation index (p < 0.0001). Tumours with high CDK2 transcripts were more likely to have higher expressions of genes involved in the cell cycle, homologous recombination, and p53 signaling. We provide compelling evidence that high CDK2 is a feature of aggressive breast cancers. The clinical evaluation of CDK2 inhibitors in early-stage BC patients will have a clinical impact.

Abstract PO2-07-09: Early stage invasive lobular breast cancer is underestimated on conventional imaging including MRI

Abstract Background: Invasive lobular carcinoma (ILC), representing 15% of all invasive breast cancers, is characterized by loss or dysfunction of the adhesion molecule E-cadherin. This leads to an infiltrative growth pattern that is unlikely to disrupt the normal architecture of breast tissue. As a result, ILC is often non-mass forming and imaging is limited in correctly diagnosing stage I-III ILC. Mammography is believed to underestimate ILC, while MRI tends to overestimate the extent of ILC. The aim of this retrospective study was to correlate radiological with pathological findings. Methods: All patients diagnosed with stage I-III pure (i.e. not mixed) ILC between January 2000 and December 2020 who underwent primary surgery in University Hospitals Leuven, were included. Data on patient characteristics, preoperative imaging and pathology were collected from the patient files. Imaging and pathology measurements were compared by use of Pearson correlation coefficient, Whitney U test or Chi-square test. Weighted kappa statistics were used to assess agreement. Since the techniques of mammography and ultrasound have evolved in the past 20 years, different time periods were considered in sub-analyses (2000-2004, 2005-2009, 2010-2014, 2015-2020). Results: In total 1029 patients were included. Median age at diagnosis was 61.0 years (range 32.0 – 95.0 years). The breast density score determined on mammogram was Bi-RADS type A for 52 (5.1%) patients, B for 238 (23.1%) patients, C for 235 (22.8%) patients and D for 141 (13.7%) patients. Density score was missing from the reports of the remaining patients (n=363, 35.3%). Contrast enhanced breast magnetic resonance imaging (MRI) was performed in 709 (68.9%) patients. An increase in preoperative use of MRI was seen over the years. In comparison to tumor size reported by pathology, all imaging techniques underestimated the tumor size. The mean difference in largest tumor size compared to pathology was 14.64 ± 21.15 mm for mammography, 18.19 ± 21.66 mm for ultrasound and 9.93 ± 20.63 mm for MRI. A higher breast density level and a larger tumor size were significantly associated with a larger difference between diameter on pathology versus mammography (ρ=0.102, p-value 0.025 and ρ=0.530, p-value &amp;lt; 0.001 respectively). Changes over different time periods are shown in Table 1 for mammography and ultrasound. There was an agreement on unifocality versus multifocality between pathology and mammography for 81.2% of the patients. In 52.1% of the 236 cases where multifocality was reported on MRI, only 1 lesion was reported by the pathologist. Multifocality was seen on pathology in 12.0% of the 460 cases that were seen as unifocal lesions on MRI. Considering adenopathies, the false positive rate of ultrasound was 3.0%. However, the false negative rate was 68.3%. Conclusions: The local extent of ILC is underestimated by conventional imaging techniques. Unlike previous reports, our results suggest that tumor size of ILC is also underestimated by MRI. Ultrasound was inferior to mammography and MRI in estimating tumor size in our series. It was confirmed that MRI tends to overestimate the number of foci which might lead to unnecessary secondary ultrasounds and biopsies. The presurgical underestimation of lymph node involvement might increase the need of secondary surgeries. It is crucial to address these limitations in imaging of ILC and to prioritize the development of enhanced imaging techniques to improve diagnostic accuracy for these patients. Table 1: difference between imaging techniques and pathology by year of diagnosis Citation Format: Kaat Van Herck, Karen Van Baelen, Chantal Van Ongeval, Valerie Celis, Helen De Boodt, Machteld Keupers, Renate Prevos, Giuseppe Floris, Ines Nevelsteen, Ann Smeets, Thaïs Baert, Sileny Han, Hans Wildiers, Annouschka Laenen, Christine Desmedt, Patrick Neven. Early stage invasive lobular breast cancer is underestimated on conventional imaging including MRI [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-07-09.

Abstract PO1-07-01: Artificial intelligence-based prediction of Oncotype DX Score from whole slide images using human-interpretable features and breast biomarkers

Abstract Background The Oncotype DX Breast Recurrence Score assay (ODX) is a commonly used genomic test for patients with estrogen receptor (ER)-positive, HER2-negative, early-stage invasive breast cancer. While ODX predicts patients’ recurrence risk and benefit from chemotherapy, it is tissue and time-consuming, and expensive. Previous deep-learning or non-linear ODX prediction models achieved promising performance using whole-slide images (WSI) or with other covariates (e.g., ER, progesterone receptor (PR), HER-2, Ki-67 scores and tumor stage) but systematic quantification of the contribution of individual histological features to the ODX score remained challenging. Here, we extracted a rich set of human-interpretable features (HIFs) quantifying nuclear morphology and the distribution of cells and tissues in the tumor microenvironment. We used these HIFs, along with manually assessed ER, PR, HER-2 scores, and tumor stage, to predict ODX scores. We also explored the role of Ki-67 features in augmenting our model predictions Methods We developed machine learning models to extract cell, tissue, and nuclear HIFs from WSI stained with hematoxylin and eosin (H&amp;E) and immunohistochemistry (IHC) against Ki-67. These models were deployed on 353 H&amp;E WSI to extract 276 HIFs quantifying cell densities, tissue areas, relative cell counts, and nuclei size, shape and color. Hierarchical agglomerative clustering was applied to cluster these features into 11 groups. Univariate regression was performed to identify correlations between ODX scores and feature clusters. To predict ODX score using HIFs, a multivariable regression model was fitted to the data where regressors are representative features from each cluster, in addition to ER, PR, HER-2 scores and stage as covariates. The model was fitted on the training set (N=266 slides) and evaluated on the held-out test set (N=87 slides). To examine the utility of Ki-67 IHC features in ODX prediction, a Ki-67 model was deployed on a subset of 194 matched Ki-67 IHC WSI to extract count proportions and densities of Ki-67 positive cells in cancer epithelium and stroma. Results Significant positive correlations were identified between ODX scores and feature clusters corresponding to cancer cell density (p &amp;lt; 10-7), macrophage density (p &amp;lt; 10-4), immune cell density (p &amp;lt; 10-16), and variations in cancer nuclear size (p &amp;lt; 10-5) and color (p &amp;lt; 10-3). Significant negative correlations were observed between ODX scores and clusters related to fibroblast density (p &amp;lt; 10-3), and variations of non-cancer cell nuclear color (p = 0.02). Evaluation of our model’s ability to predict ODX scores revealed an association between predicted and observed scores (Pearson r = 0.74). The AUROC for model predictions of high/low classifications (with reference to a cutoff ODX score of 20) was 0.80. Ki-67 features were clustered together with H&amp;E cancer cell density features and showed a stronger correlation with ODX scores compared to the H&amp;E features alone. Combining Ki-67 cell density features with H&amp;E features led to increased performance of the multivariable regression model on the test set compared to H&amp;E features only (r = 0.62 vs. r = 0.59). However, a model combining Ki-67, H&amp;E and covariates performed similarly to a model with H&amp;E and covariates (r = 0.73), suggesting that Ki-67 features may not be needed for ODX prediction when H&amp;E and covariate information is available. Conclusions Our model predicts ODX recurrence scores with comparable performance to other black-box approaches using WSI and breast biomarker status and without the need for, Ki-67 features. Using readily available information, our model has the potential to be a convenient screening tool for patient stratification, which may lead to better patient care, lower costs, and faster treatment. Citation Format: Nhat Le, John Van Arnam, Christian Kirkup, Ylaine Gerardin, Michael Drage, Neha Khaitan, Anurag Sharma, Georgi Galev, Nicholas Indorf, Emily Sansevere, Ilan Wapinski, Stephanie Hennek, Gloria Zhang. Artificial intelligence-based prediction of Oncotype DX Score from whole slide images using human-interpretable features and breast biomarkers [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO1-07-01.

Abstract PO5-15-11: A Novel Biosignature to Predict Radiation Therapy Response in Early-Stage Invasive Breast Cancer Treated with Breast-Conserving Surgery

Abstract Introduction: Radiation therapy (RT) is a standard component of treatment for most patients with early-stage, hormone receptor-positive, invasive breast cancer (BC) following breast-conserving surgery (BCS). RT recommendations are primarily based on clinicopathologic (CP) factors, such as tumor size, nodal status, and histological grade. While these factors provide prognostic information, their ability to accurately identify patients at a higher risk of ipsilateral breast recurrence (IBR) or those who will significantly benefit from RT is unclear. Therefore, incorporation of additional predictive markers beyond CP factors is warranted and there remains a need to refine the selection criteria for patients who will derive the greatest benefit from RT. In this study, we aimed to explore the role of immune and metabolic signaling axes as potential predictive markers for RT response in hormone receptor-positive breast cancer patients. Methods: A cohort of 939 women from Sweden were diagnosed with BC between 1987 and 2004. A subset of 471 patients was diagnosed with T1-T2N0M0 BC and underwent BCS with negative margins. Patients were treated with BCS plus/minus RT (median dose 50Gy) based upon physician preference. Biomarkers were assayed in formalin-fixed paraffin-embedded tissue micro arrays using multiplex immunofluorescence and multi-spectral imaging in a CLIA lab (Laguna Hills, CA). Immune and metabolic axes were defined employing biomarkers and used to classify patients into three risk groups: 1) Low Risk (lower baseline risk and no RT benefit), 2) Elevated Risk (higher baseline risk and significant RT benefit), and 3) Residual Risk (higher baseline risk and suboptimal RT benefit). Risk groups were analyzed for IBR rate using Kaplan-Meier and Cox proportional hazards analyses. Risk group prognosis and RT prediction was assessed for RT-risk group interaction in multivariable analysis, adjusting for CP factors. Results: Among the patients with complete biomarker data, 261 were hormone receptor-positive, and had a median follow-up of 140 months. Median age was 62 years (28-88 years), and median tumor size was 1.1 cm (0.1-4 cm). Some patients (21%) received endocrine therapy (ET), and most (76%) received RT, with 79% of patients who received ET also receiving RT. In the overall population, RT following BCS was associated with a reduction in HR for recurrence (HR=0.3, 95%CI [0.1, 0.5], p&amp;lt; 0.001). Patients classified as Low Risk (41%) did not have a significant benefit from RT (HR=0.9, 95%CI [0.1, 8.3], p=0.95) in multivariable analysis adjusted for CP factors. For patients who were classified as Elevated Risk (31%), multivariable analysis showed a higher IBR rate without RT (HR=5.7, 95%CI [1.3, 25.1], p&amp;lt; 0.021) relative to the Low Risk group who had a significant benefit from RT (HR=0.1, 95%CI [0.02, 0.2], p&amp;lt; 0.001). Patients classified into the Residual Risk group (28%) had no significant reduction from RT (HR=0.7, 95%CI [0.2, 2.0], p=0.61). However, in multivariable analysis adjusted for CP factors, patients treated with RT in the Residual Risk group had higher IBR rates (HR=3.7, 95%CI [1.6, 8.7], p=0.0026) compared to RT-treated patients in other risk groups. The biosignature was predictive for RT benefit in multivariable analysis adjusted for CP factors (p-interaction &amp;lt; 0.001). Conclusions:The biosignature used in this study demonstrated the potential to identify patients who may derive limited benefit from RT, allowing for tailored therapeutic approaches. These findings underscore the importance of individualized treatment decisions based on risk stratification. Further clinical validation and refinement of risk stratification strategies are warranted to optimize outcomes in hormone receptor-positive, BC patients undergoing BCS. Citation Format: Troy Bremer, Karuna Mittal, Chirag Shah, Frank Vicini, Steven Shivers, Charlotta Wadsten. A Novel Biosignature to Predict Radiation Therapy Response in Early-Stage Invasive Breast Cancer Treated with Breast-Conserving Surgery [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-15-11.

Increase in Hypofractionated Radiation Therapy Among Patients with Invasive Breast Cancer or Ductal Carcinoma In Situ: Who is Left Behind?

We aimed to update the trend of hypofractionated whole-breast irradiation (HF-WBI) use over time in the US and examine factors associated with lack of HF-WBI adoption for patients with early-stage invasive breast cancer (IBC) or ductal carcinoma in situ (DCIS) undergoing a lumpectomy. Among patients who underwent a lumpectomy, we identified 928,034 patients with early-stage IBC and 330,964 patients with DCIS in the 2004 to 2020 National Cancer Database. We defined HF-WBI as 2.5-3.33 Gy/fraction to the breast and conventionally fractionated WBI as 1.8-2.0 Gy/fraction. We evaluated the trend of HF-WBI utilization using a generalized linear model with the log link and binomial distribution. Factors associated with HF-WBI utilization were assessed using multivariable logistic regression in patients diagnosed between 2018 and 2020. Among patients with IBC, HF-WBI use has significantly increased from 0.7% in 2004 to 63.9% in 2020. Similarly, HF-WBI usage among patients with DCIS has also increased significantly from 0.4% in 2004 to 56.6% in 2020. Black patients with IBC were less likely than White patients to receive HF-WBI (adjusted odds ratio [AOR] 0.81; 95% CI, 0.77-0.85). Community cancer programs were less likely to administer HF-WBI to patients with IBC (AOR, 0.80; 95% CI, 0.77-0.84) and to those with DCIS (AOR, 0.87; 95% CI, 0.79-0.96) than academic/research programs. Younger age, positive nodes, larger tumor size, low volume programs, and facility location were also associated with lack of HF-WBI adoption in both patient cohorts. HF-WBI utilization among postlumpectomy patients has significantly increased from 2004 to 2020 and can finally be considered standard of care in the US. We found substantial disparities in adoption within patient and facility subgroups. Reducing disparities in HF-WBI adoption has the potential to further alleviate health care costs while improving patients' quality of life.

Axillary ultrasonography for early-stage invasive breast cancer

BackgroundAmong women with early invasive breast cancer and 1–2 positive sentinel nodes, sentinel lymph node biopsy (SLNB) is non-inferior to axillary lymph node dissection (ALND).1-3 However, preoperative axillary ultrasonography (AxUS) may not be sensitive enough to discriminate burden of nodal metastasis in these patients, potentially leading to overtreatment.4-6 This study compares axillary operation rates in patients who did and did not receive preoperative AxUS, assessing its utility and risks for overtreatment. MethodsThis is a retrospective cohort study of patients with clinical T1/T2 breast tumors who were clinically node negative and underwent an axillary operation. ResultsPatients who had preoperative AxUS received more ALND compared to patients who did not (5.6% vs. 1.4%, p ​< ​0.001). There was no significant difference in the number of additional axillary operations following SLNB (2.1% vs. 2.3%, p ​= ​0.77). ConclusionEliminating preoperative AxUS is associated with fewer invasive ALND procedures, without increased rate of axillary reoperations.

Identification of MicroRNAs Associated with Histological Grade in Early-Stage Invasive Breast Cancer.

This study aimed to identify microRNAs associated with histological grade using comprehensive microRNA analysis data obtained by next-generation sequencing from early-stage invasive breast cancer. RNA-seq data from normal breast and breast cancer samples were compared to identify candidate microRNAs with differential expression using bioinformatics. A total of 108 microRNAs were significantly differentially expressed in normal breast and breast cancer tissues. Using clinicopathological information and microRNA sequencing data of 430 patients with breast cancer from The Cancer Genome Atlas (TCGA), the differences in candidate microRNAs between low- and high-grade tumors were identified. Comparing the expression of the 108 microRNAs between low- and high-grade cases, 25 and 18 microRNAs were significantly upregulated and downregulated, respectively, in high-grade cases. Clustering analysis of the TCGA cohort using these 43 microRNAs identified two groups strongly predictive of histological grade. miR-3677 is a microRNA upregulated in high-grade breast cancer. The outcome analysis revealed that patients with high miR-3677 expression had significantly worse prognosis than those with low miR-3677 expression. This study shows that microRNAs are associated with histological grade in early-stage invasive breast cancer. These findings contribute to the elucidation of a new mechanism of breast cancer growth regulated by specific microRNAs.

Non-invasive prediction model of axillary lymph node status in patients with early-stage breast cancer: a feasibility study based on dynamic contrast-enhanced-MRI radiomics.

Accurate axillary evaluation plays an important role in prognosis and treatment planning for breast cancer. This study aimed to develop and validate a dynamic contrast-enhanced (DCE)-MRI-based radiomics model for preoperative evaluation of axillary lymph node (ALN) status in early-stage breast cancer. A total of 410 patients with pathologically confirmed early-stage invasive breast cancer (training cohort, N = 286; validation cohort, N = 124) from June 2018 to August 2022 were retrospectively recruited. Radiomics features were derived from the second phase of DCE-MRI images for each patient. ALN status-related features were obtained, and a radiomics signature was constructed using SelectKBest and least absolute shrinkage and selection operator regression. Logistic regression was applied to build a combined model and corresponding nomogram incorporating the radiomics score (Rad-score) with clinical predictors. The predictive performance of the nomogram was evaluated using receiver operator characteristic (ROC) curve analysis and calibration curves. Fourteen radiomic features were selected to construct the radiomics signature. The Rad-score, MRI-reported ALN status, BI-RADS category, and tumour size were independent predictors of ALN status and were incorporated into the combined model. The nomogram showed good calibration and favourable performance for discriminating metastatic ALNs (N + (≥1)) from non-metastatic ALNs (N0) and metastatic ALNs with heavy burden (N + (≥3)) from low burden (N + (1-2)), with the area under the ROC curve values of 0.877 and 0.879 in the training cohort and 0.859 and 0.881 in the validation cohort, respectively. The DCE-MRI-based radiomics nomogram could serve as a potential non-invasive technique for accurate preoperative evaluation of ALN burden, thereby assisting physicians in the personalized axillary treatment for early-stage breast cancer patients. This study developed a potential surrogate of preoperative accurate evaluation of ALN status, which is non-invasive and easy-to-use.

Publisher's Note

Publisher's Note

Machine Learning-Based Breast Tumor Ultrasound Radiomics for Pre-operative Prediction of Axillary Sentinel Lymph Node Metastasis Burden in Early-Stage Invasive Breast Cancer

The aim of the work described here was to assess the application of ultrasound (US) radiomics with machine learning (ML) classifiers to the prediction of axillary sentinel lymph node metastasis (SLNM) burden in early-stage invasive breast cancer (IBC). In this study, 278 early-stage IBC patients with at least one SLNM (195 in the training set and 83 in the test set) were studied at our institution. Pathologic SLNM burden was used as the reference standard. The US radiomics features of breast tumors were extracted by using 3D-Slicer and PyRadiomics software. Four ML classifiers-linear discriminant analysis (LDA), support vector machine (SVM), random forest (RF) and decision tree (DT)-were used to construct radiomics models for the prediction of SLNM burden. The combined clinicopathologic-radiomics models were also assessed with respect to sensitivity, specificity, accuracy and areas under the curve (AUCs). Among the US radiomics models, the SVM classifier achieved better predictive performance with an AUC of 0.920 compared with RF (AUC = 0.874), LDA (AUC = 0.835) and DT (AUC = 0.800) in the test set. The clinicopathologic model had low efficacy, with AUCs of 0.678 and 0.710 in the training and test sets, respectively. The combined clinicopathologic (C) factors and SVM classifier (C + SVM) model improved the predictive ability with an AUC of 0.934, sensitivity of 86.7%, specificity of 89.9% and accuracy of 91.0% in the test set. ML-based US radiomics analysis, as a novel and promising predictive tool, is conducive to a precise clinical treatment strategy.

Analytical Validation of the PreciseDx Digital Prognostic Breast Cancer Test in Early-Stage Breast Cancer

BackgroundPreciseDx Breast (PDxBr) is a digital test that predicts early-stage breast cancer recurrence within 6-years of diagnosis. Materials and MethodsUsing hematoxylin and eosin-stained whole slide images of invasive breast cancer (IBC) and artificial intelligence-enabled morphology feature array, microanatomic features are generated. Morphometric attributes in combination with patient's age, tumor size, stage, and lymph node status predict disease free survival using a proprietary algorithm. Here, analytical validation of the automated annotation process and extracted histologic digital features of the PDxBr test, including impact of methodologic variability on the composite risk score is presented. Studies of precision, repeatability, reproducibility and interference were performed on morphology feature array-derived features. The final risk score was assessed over 20-days with 2-operators, 2-runs/day, and 2-replicates across 8-patients, allowing for calculation of within-run repeatability, between-run and within-laboratory reproducibility. ResultsAnalytical validation of features derived from whole slide images demonstrated a high degree of precision for tumor segmentation (0.98, 0.98), lymphocyte detection (0.91, 0.93), and mitotic figures (0.85, 0.84). Correlation of variation of the assay risk score for both reproducibility and repeatability were less than 2%, and interference from variation in hematoxylin and eosin staining or tumor thickness was not observed demonstrating assay robustness across standard histopathology preparations. ConclusionIn summary, the analytical validation of the digital IBC risk assessment test demonstrated a strong performance across all features in the model and complimented the clinical validation of the assay previously shown to accurately predict recurrence within 6-years in early-stage invasive breast cancer patients.

Deep (phospho)proteomics profiling of pre- treatment needle biopsies identifies signatures of treatment resistance in HER2+ breast cancer

Patients with early-stage HER2-overexpressing breast cancer struggle with treatment resistance in 20%-40% of cases. More information is needed to predict HER2 therapy response and resistance invivo. In this study, we perform (phospho)proteomics analysis of pre-treatment HER2+ needle biopsies of early-stage invasive breast cancer to identify molecular signatures predictive of treatment response to trastuzumab, pertuzumab, and chemotherapy. Our data show that accurate quantification of the estrogen receptor (ER) and HER2 biomarkers, combined with the assessment of associated biological features, has the potential to enable better treatment outcome prediction. In addition, we identify cellular mechanisms that potentially precondition tumors to resist therapy. We find proteins with expression changes that correlate with resistance and constitute to a strong predictive signature for treatment success in our patient cohort. Our results highlight the multifactorial nature of drug resistance invivo and demonstrate the necessity of deep tumor profiling.

A Biosignature Integrating Immune and Metabolic Signaling Axes to Assess Limited Radiation Therapy Response in Early-Stage Breast Cancer from a Low-Risk Cohort

Radiation therapy (RT) post breast conserving surgery (BCS) reduces in-breast recurrence (IBR) rate in early-stage invasive breast cancer (BC) patients. The RT treatment recommendation is often driven by clinicopathological (CP) factors; however, CP factors alone have limited ability to identify which women significantly benefit from RT, or those with higher IBR risk after BCS plus RT. Biologic factors driving unique phenotypes, in addition to CP, may improve prediction of RT response. In this study we evaluated the role of immune and metabolic signaling axes in predicting RT response in hormone receptor positive, HER2 negative, early-stage BC patients. Biomarkers from immune and metabolic signaling axes were studied in a cohort of 939 women from Sweden, at a CLIA certified lab (Laguna Hills, CA). Formalin fixed paraffin embedded tissues were assayed for protein expression using multiplex immunofluorescence and multi-spectral imaging. Immune and metabolic axes were assessed using biomarkers combined with a non-linear model, adjusting for patient age. RT prediction by the model was assessed, along and adjusted for CP factors and also among patients over 50-yrs. The model defined patient risk groups that were analyzed for IBR rate using Kaplan Meier analyses and Cox proportional hazards to test for RT-risk group interaction. Within the cohort, 440 patients had hormone receptor positive, HER2 negative BC treated with BCS (negative margins) and +/- RT without chemotherapy, where 296 patients had complete biomarker data. CP factors individually were not predictive for RT benefit, but grade was prognostic for IBR rate (p = 0.02) after BCS without RT. In multivariable analysis, adjusting for CP factors (grade, palpability, continuous size and age), the model was predictive for RT benefit (p-interaction = 0.046), identifying patients (n = 129) with worse RT benefit (HR = 7.8) compared to baseline RT benefit. The model was not prognostic for IBR rate in patients treated with BCS without RT (16% 10-yr IBR rate) but identified patients with increased IBR rates after BCS plus RT (HR = 3.9, p<0.001), where corresponding 10-yr IBR rates increased from 3% to 15%. The model was also predictive for RT benefit in women over 50-yrs (p-interaction = 0.05). The model identified 28% of women over 50-yrs who had increased IBR rates after BCS plus RT (HR = 4.0, p = 0.004), where corresponding 10-yr IBR rates increased from 3% to 12%. The model incorporating metabolic and immune signaling axes assessed in the study was predictive for RT benefit among women with early-stage hormone receptor positive, HER2 negative BC. While CP factors were not predictive of RT benefit, the inclusion of metabolic and immune signaling axes improved identification of patients with high residual risk after BCS plus RT and can potentially aid in personalized treatment of early-stage breast cancer based on individualized risk.