Biomarker dynamics and prognosis in breast cancer after neoadjuvant chemotherapy

Breast cancer is a biologically diverse disease with treatment modalities selected based on tumor stage and tumor biology. Distinct intrinsic subtypes and surrogate biomarker profiles play a major role for therapeutic decisions. Response rates to systemic and local treatments as well as the interaction with epidemiological risk factors have been validated in clinical trials and translational studies. This retrospective study addresses the question how biomarker profiles and treatment modalities in the neoadjuvant chemotherapy setting have changed during the past 15 years and what prognostic impact these changes implicate. 342 female breast cancer stage I-IV patients receiving neoadjuvant chemotherapy between 2003 and 2017 were analyzed. Overall survival (OS) was correlated with preoperative clinical stage, postoperative pathological stage, treatment modalities and tumor biology before and after chemotherapy. Two subgroups were separated using an arbitrary cut-off year at 2009/2010, due to 2010 when platinum containing regimens were first administered. Median follow-up was 54 months. 57 (17%) patients died; recurrences occurred in 103 of 342 (30%) patients. Nodal stage and intrinsic subtypes (pre- and postoperative) significantly correlated with OS (p < 0.001). Preoperative histological grading lacked prognostic power. When comparing the patient characteristics of the subgroups, we found significant difference in the following characteristics: cT, ypT, ypN, pCR and chemotherapy regimens (p < 0.001). There was no difference in OS when comparing the two subgroups. Pathological complete response (pCR) rates had a significant impact on OS and disease-free survival (DFS) in HER2+ and triple negative subtypes (p = 0.03). In multivariate analysis, high proliferation index (> 30%), clinical metastatic stage and pathological tumor stage had prognostic impact on OS (p < 0.001, p = 0.0001, p = 0.002). Clinico-pathological factors and distinct therapy regiments especially in triple negative and HER2+ subtypes have prognostic impact on pCR, OS and DFS after neoadjuvant chemotherapy.

Predicting Breast Cancer Gene Expression Signature by Applying Deep Convolutional Neural Networks From Unannotated Pathological Images

We proposed a highly versatile two-step transfer learning pipeline for predicting the gene signature defining the intrinsic breast cancer subtypes using unannotated pathological images. Deciphering breast cancer molecular subtypes by deep learning approaches could provide a convenient and efficient method for the diagnosis of breast cancer patients. It could reduce costs associated with transcriptional profiling and subtyping discrepancy between IHC assays and mRNA expression. Four pretrained models such as VGG16, ResNet50, ResNet101, and Xception were trained with our in-house pathological images from breast cancer patient with recurrent status in the first transfer learning step and TCGA-BRCA dataset for the second transfer learning step. Furthermore, we also trained ResNet101 model with weight from ImageNet for comparison to the aforementioned models. The two-step deep learning models showed promising classification results of the four breast cancer intrinsic subtypes with accuracy ranging from 0.68 (ResNet50) to 0.78 (ResNet101) in both validation and testing sets. Additionally, the overall accuracy of slide-wise prediction showed even higher average accuracy of 0.913 with ResNet101 model. The micro- and macro-average area under the curve (AUC) for these models ranged from 0.88 (ResNet50) to 0.94 (ResNet101), whereas ResNet101_imgnet weighted with ImageNet archived an AUC of 0.92. We also show the deep learning model prediction performance is significantly improved relatively to the common Genefu tool for breast cancer classification. Our study demonstrated the capability of deep learning models to classify breast cancer intrinsic subtypes without the region of interest annotation, which will facilitate the clinical applicability of the proposed models.

Post San Antonio update—my top three abstracts!

SummaryRecent findings support the role of alpelisib in advanced HR-positive breast cancer harboring a PIK3CA mutation. A retrospective biomarker analysis on the intrinsic subtypes of HR-positive breast cancer reveals a subgroup that will not benefit under the addition of a CDK 4/6 inhibitor treatment. The detection of circulating tumor cells before start and during tumor treatment is associated with worse outcome in metastatic breast cancer patients.

HER2-Low Breast Cancer: Molecular Characteristics and Prognosis

Background: We aimed to determine the distribution of intrinsic subtypes within HER2-low breast cancer (BC), and to describe the prognostic impact of HER2-low status on survival outcomes. Methods: This is a retrospective, observational study of primary BC extracted from The Cancer Genome Atlas dataset. We described the distribution of PAM50 intrinsic subtypes within HER2-low BC subtype according to hormonal receptor status (positive (HR+) and negative (HR−)). Secondly, we assessed the impact of HER2-low on survival outcomes (progression-free interval (PFI), disease-free interval (DFI), and overall survival (OS)). Results: We analyzed 804 primary BCs, including 410 (51%) HER2-low BCs (336 HR+ and 74 HR−). The proportion of HER2-enriched tumors was higher in the HER2-low/HR− group compared to HER2-low/HR+ (13.7% versus 1.2%, respectively). HER2-enriched tumors were more frequent in HER2-low/HR− and HER2-low/HR+ subtypes, compared to HER2-negative/HR− and HER2-negative/HR+ subtypes, respectively (13.7% versus 1.6% and 1.2% versus 0.5%, respectively). We observed no significant differences in PFI, DFI, and OS between HER2-low subtypes and each non-HER2-low subtype paired by HR status. Conclusions: Our characterization of PAM50 intrinsic subtypes within HER2-low breast cancer may explain the different clinical behaviors and responses to treatment, and ultimately support further investigation of new treatment strategies in the HER2-low category. Moreover, it highlights the importance of considering HR status in the HER2-low category.

Overexpression of NF-kB as a predictor of neoadjuvant chemotherapy response in breast cancer

BACKGROUND: Cancer cells can defend themselves against apoptosis by activating NF-κB. Nuclear factor-kappa B (NF-κB) activity has also been associated with chemotherapy resistance. OBJECTIVE: We was aimed to investigate the relationship between NF-κB expression and intrinsic subtypes and anthracycline-based neoadjuvant chemotherapy responses in patients with locally advanced breast cancer. METHODS: This prospective cohort study examined NF-κB expression and intrinsic subtypes of breast cancer tissue using immunohistochemistry (IHC). We conducted descriptive statistical analyses as well as survival analyses. RESULTS: The study sample was 63 patients, of which 21 cases (33.33%) were responsive to neoadjuvant chemotherapy, and 42 cases (66.7%) were non-responsive. There is a significant relationship between negative ER, negative PR, grading, and high Ki67 expression with NF-κB overexpression (p < 0.05). No significant relationship was found between intrinsic subtypes and HER2 with NF-κB expression (p > 0.05). A significant relationship was found between NF-κB expression and responsive chemotherapy results (p < 0.01). CONCLUSION: In locally advanced breast cancer, there is a correlation between NF-B expression and response to anthracycline-based neoadjuvant chemotherapy. Patients who express NF-KB have a better response to chemotherapy than those who overexpress NF-kB.

Correlative Biomarker Analysis of Intrinsic Subtypes and Efficacy Across the MONALEESA Phase III Studies

PURPOSE The prognostic and predictive value of intrinsic subtypes in hormone receptor–positive, human epidermal growth factor receptor 2–negative advanced breast cancer treated with endocrine therapy and ribociclib (RIB) is currently unknown. We evaluated the association of intrinsic subtypes with progression-free survival (PFS) in the MONALEESA trials. METHODS A retrospective and exploratory PAM50-based analysis of tumor samples from the phase III MONALEESA-2, MONALEESA-3, and MONALEESA-7 trials was undertaken. The prognostic relationship of PAM50-based subtypes with PFS and risk of disease progression by subtype and treatment were evaluated using a multivariable Cox proportional hazards model, adjusting for age, prior chemotherapy, performance status, visceral disease, bone-only metastases, histological grade, number of metastatic sites, prior endocrine therapy, and de novo metastatic disease. RESULTS Overall, 1,160 tumors from the RIB (n = 672) and placebo (n = 488) cohorts were robustly profiled. Subtype distribution was luminal A (LumA), 46.7%; luminal B (LumB), 24.0%; normal-like, 14.0%; HER2-enriched (HER2E), 12.7%; and basal-like, 2.6% and was generally consistent across treatment arms and trials. The associations between subtypes and PFS were statistically significant in both arms ( P < .001). The risks of disease progression for LumB, HER2E, and basal-like subtypes were 1.44, 2.31, and 3.96 times higher compared with those for LumA, respectively. All subtypes except basal-like demonstrated significant PFS benefit with RIB. HER2E (hazard ratio [HR], 0.39; P < .0001), LumB (HR, 0.52; P < .0001), LumA (HR, 0.63; P = .0007), and normal-like (HR, 0.47; P = .0005) subtypes derived benefit from RIB. Patients with basal-like subtype (n = 30) did not derive benefit from RIB (HR, 1.15; P = .77). CONCLUSION In this retrospective exploratory analysis of hormone receptor–positive and human epidermal growth factor receptor 2–negative advanced breast cancer, each intrinsic subtype exhibited a consistent PFS benefit with RIB, except for basal-like.

Diagnostic Accuracy of Pre-Operative Breast Magnetic Resonance Imaging (MRI) In Predicting Axillary Lymph Node Metastasis: Variations In Intrinsic Subtypes, And Potential to Omit Sentinel Lymph Node Biopsy In Some Low Risk Groups of Patients With Invasive Breast Cancer.

Purpose: The value and utility of axillary lymph node(ALN) evaluation with MRI in breast cancer were not clear for various intrinsic subtypes. The potential of breast MRI to identify low risk groups for which is feasible to omit sentinel lymph node biopsy (SLNB) was evaluated according to the ACOSOG Z0011 trial.Methods: Patients with primary operable breast cancer with pre-operative breast MRI and post-operative pathologic reports were retrospectively collected. The concordance of MRI and pathology of ALN status was analyzed in different intrinsic subtypes. Furthermore, our rationale to omit SLNB in the low risks group was validated in a cohort of patients from the Taiwan Cancer Registry (TCR) by comparing locoregional events, distant metastasis, and survival outcomes.Results: A total of 1560 patients were enrolled. The overall accuracy and negative predictive value (NPV) of breast MRI to predict the ALN metastasis are 69.6% and 80.6%. Accuracy, sensitivity, specificity, NPV, and PPV of MRI in detecting metastatic ALN are all significantly different between intrinsic subtypes (p<0.05). Multivariate analysis identified tumor size and histologic type as independent predictive factors of ALN metastases. Patients with a pre-operative breast MRI node-negative status along with a tumor size ≤ 2 cm and intrinsic subtype of Luminal A, Luminal B1, or TNBC were potential candidates to omit SLNB. In a cohort of 19,620 patients with clinical node-negative status and tumor size ≤ 2cm(cT1N0M0) from the TCR database, there were not significant differences between those with or without ALN surgery in terms of locoregional recurrence, distant metastasis free survival, and overall survival after BCS and radiotherapy.Conclusion: The diagnostic accuracy of MRI to predict ALN metastasis varied according to intrinsic subtype. Combined pre-operative clinicopathologic and MRI findings might identify some low risk groups of invasive breast cancer patients who are potentially suitable to omit SLNB.