Biomarkers Changes after Neoadjuvant Chemotherapy in Breast Cancer: A Seven-Year Single Institution Experience

The adoption of neoadjuvant chemotherapy (NACT) for breast cancer (BC) is increasing. The need to repeat the biomarkers on a residual tumor after NACT is still a matter of debate. We verified estrogen receptors (ER), progesterone receptors (PR), Ki67 and human epidermal growth factor receptor 2 (HER2) status changes impact in a retrospective monocentric series of 265 BCs undergoing NACT. All biomarkers changed with an overall tendency toward a reduced expression. Changes in PR and Ki67 were statistically significant (p = 0.001). Ki67 changed in 114/265 (43.0%) cases, PR in 44/265 (16.6%), ER in 31/265 (11.7%) and HER2 in 26/265 (9.8%). Overall, intrinsic subtype changed in 72/265 (27.2%) cases after NACT, and 10/265 (3.8%) cases switched to a different adjuvant therapy accordingly. Luminal subtypes changed most frequently (66/175; 31.7%) but with less impact on therapy (5/175; 2.8%). Only 3 of 58 triple-negative BCs (5.2%) changed their intrinsic subtype, but all of them switched treatment. No correlation was found between intrinsic subtype changes and clinicopathological features. To conclude, biomarkers changes with prognostic implications occurred in all BC intrinsic subtypes, albeit they impacted therapy mostly in HER2 negative and/or hormone receptors negative BCs. Biomarkers retesting after NACT is important to improve both tailored adjuvant therapies and prognostication of patients.

Proteomics-derived basal biomarker DNA-PKcs is associated with intrinsic subtype and long-term clinical outcomes in breast cancer

Precise biomarkers are needed to guide better diagnostics and therapeutics for basal-like breast cancer, for which DNA-dependent protein kinase catalytic subunit (DNA-PKcs) has been recently reported by the Clinical Proteomic Tumor Analysis Consortium as the most specific biomarker. We evaluated DNA-PKcs expression in clinically-annotated breast cancer tissue microarrays and correlated results with immune biomarkers (training set: n = 300; validation set: n = 2401). Following a pre-specified study design per REMARK criteria, we found that high expression of DNA-PKcs was significantly associated with stromal and CD8 + tumor infiltrating lymphocytes. Within the basal-like subtype, tumors with low DNA-PKcs and high tumor-infiltrating lymphocytes displayed the most favourable survival. DNA-PKcs expression by immunohistochemistry identified estrogen receptor-positive cases with a basal-like gene expression subtype. Non-silent mutations in PRKDC were significantly associated with poor outcomes. Integrating DNA-PKcs expression with validated immune biomarkers could guide patient selection for DNA-PKcs targeting strategies, DNA-damaging agents, and their combination with an immune-checkpoint blockade.

Genomic characteristics of breast cancer to predict response of neoadjuvant chemotherapy and long-term prognosis.

557 Background: To precisely predict neoadjuvant chemotherapy (NAC) response and long-term prognosis, we developed prediction model with clinical and genomic characteristics of breast cancer (BC). Methods: We included early and locally advanced BC that would be scheduled to receive standard NAC (four cycles of anthracycline and cyclophosphamide and four cycles of docetaxel or docetaxel plus trastuzumab for HER2+ BC) followed by curative surgery. For each patient, tumor tissue and matched blood were prospectively collected three times: at diagnosis (T1), three weeks after the first cycle of chemotherapy (T2), and curative surgery (T3). Whole exome sequencing (WES) was performed to detect somatic mutation, mutational signature and tumor mutational burden (TMB) while RNASeq with PAM50 prediction was to classify intrinsic subtype. In terms of clinical variables, clinical stage and IHC subtype at diagnosis, residual cancer burden (RCB) class and distant recurrence free survival (DRFS) were used. Logistic regression was used for predicting RCB class with clinical and genomic variables at T1. Univariate and multivariate Cox regression were performed to identify prognostic factors for DRFS. Results: In total, 210 patients were enrolled and treated with NAC as scheduled. We successfully conducted WES in 231 BC tissues (T1:117, T2:101 and T3:13) from 117 patients. In NAC response, 13 patients were in RCB class 3, 39 in class 2, 14 in class 1 and 46 in class 0. Median follow up duration was 44months and distant recurrence was observed in 13 patients. TP53 mutation (68%) was the most commonly detected genetic alteration. ARID1A, CDH1, CSMD3, LRP1B, PIK3CA, RUNX1 and TP53 were significantly mutated genes in driver gene analysis. Median TMB was 87 (range, 14-570) and signature 3 was most frequently observed. Among genetic characteristics, high TMB was significantly associated with better NAC response compared with low TMB (hazard ratio[HR] for RCB class III: 0.11, 95% confident interval[CI]: 0.01, 0.74, p = 0.05). In prediction model, combination of seven variables: intrinsic subtype, TMB, LRRK1, OPLAH, and PIK3CA hotspot mutation, ERBB2 amplification, and clinical stage had 0.83 in area under curve (AUC) and 0.75 in accuracy. High clinical stage, PTEN and PIK3CA hotspot mutation negatively affected to DRFS while high TMB had protective effect (all ps < 0.05). Prediction model made with five variables: intrinsic subtype, TMB, PTEN mutation, PIK3CA hotspot mutation and clinical stage had 0.88 in c-index (95% CI: 0.81, 0.95). Conclusions: TMB, PIK3CA hotspot mutation and clinical stage showed predictive roles on NAC response and distant recurrence of BC in NAC setting. In prediction model, intrinsic subtype, TMB, LRRK1, OPLAH, and PIK3CA hotspot mutation, ERBB2 amplification, and clinical stage affected to RCB class while intrinsic subtype, TMB, PTEN, PIK3CA hotspot mutation and clinical stage did to DRFS. Clinical trial information: NCT02591966.

Pregnancy-associated breast cancer: nationwide Dutch study confirms a discriminatory aggressive histopathologic profile

Purpose Breast cancer is the most common type of malignancy in pregnant women, occurring approximately once in every 3000 pregnancies. Pregnancy-associated breast cancer (PABC) is commonly defined as breast cancer diagnosed during or within one year after pregnancy, and it accounts for up to 6.9% of all breast cancers in women younger than 45 years old. Whether these cancers arise before or during pregnancy, and whether they are stimulated by the high hormonal environment of pregnancy, is currently unknown. This study assesses the histopathological profile of PABC in a large Dutch population-based cohort. Methods We identified 744 patients with PABC (in this cohort defined as breast cancer diagnosed during or within 6 months after pregnancy) diagnosed between 1988 and 2019, in the nationwide Dutch Pathology Registry (PALGA). An age-matched PALGA cohort of unselected breast cancer patients (≤ 45 years), diagnosed between 2013 and 2016, was used as a control. Histopathologic features of both cohorts were compared. Results The median age of PABC patients was 34.3 years old (range 19–45 years) and most breast cancers were diagnosed during pregnancy (74.2%). As compared to age-matched controls, PABC patients had tumors of higher Bloom–Richardson grade (grade I: 1.5% vs. 12.4%, grade II: 16.9% vs. 31.3%, grade III: 80.3% vs. 39.5%, p < 0.0001). Furthermore, estrogen (ER)- and progesterone (PR)-receptor expression was less frequently reported positive (ER: 38.9% vs. 68.2% and PR: 33.9% vs. 59.0%, p < 0.0001), while a higher percentage of PABC tumors overexpressed HER2 (20.0% vs. 10.0%, p < 0.0001). The most observed intrinsic subtype in PABC was triple-negative breast cancer (38.3% vs. 22.0%, p < 0.0001), whereas hormone-driven cancers were significantly less diagnosed (37.9% vs. 67.3%, p < 0.0001). Conclusion This study, based on a large population-based cohort of 744 PABC Dutch patients, underlines the more aggressive histopathologic profile compared to age-matched breast cancer patients ≤ 45 years. Further in-depth genetic analysis will be performed to unravel the origin of this discriminating phenotype. It definitely calls for timely detection and optimal treatment of this small but delicate subgroup of breast cancer patients.

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.