Pathological complete response in neoadjuvant treatment of breast cancer

QL1209 (pertuzumab biosimilar) versus reference pertuzumab plus trastuzumab and docetaxel in neoadjuvant treatment for HER2-positive, ER/PR-negative, early or locally advanced breast cancer: A multicenter, randomized, double-blinded, parallel-controlled, phase III equivalence trial

BACKGROUND

This randomized, parallel-controlled, double-blinded, phase III equivalence study evaluated the equivalence of a proposed pertuzumab biosimilar QL1209 to the pertuzumab (Perjeta®) each with trastuzumab and docetaxel in neoadjuvant treatment of early or locally advanced breast cancer patients with HER2-positive, ER/PR-negative.

METHODS

Eligible patients were randomly (1:1) assigned to receive 4 cycles of neoadjuvant QL1209 or pertuzumab each with trastuzumab and docetaxel, and adjuvant treatment. The primary endpoint was total pathologic complete response (tpCR), with equivalence margins of 0.76 to 1.32.

RESULTS

Among the 585 patients enrolled, 257 and 259 patients were assigned to the QL1209 and pertuzumab groups, respectively. The tpCR rates were comparable in the QL1209 (109/255, 42.75%; 90% CI 37.65 to 47.84) and pertuzumab (117/259, 45.17%; 90% CI 40.09 to 50.26) groups. The tpCR risk ratio was 0.95 (90% CI, 0.80 to 1.11), and the 90% CI fell within the predefined equivalence margin. The most common grade ≥3 treatment-related adverse event was decreased neutrophil count (10. 9% vs. 12.7%) in the QL1209 and pertuzumab groups.

CONCLUSIONS

QL1209 demonstrated equivalent efficacy and comparable safety profile to the reference pertuzumab in neoadjuvant treatment of HER2-positive, ER/PR-negative, early, or locally advanced breast cancer.

TRIAL REGISTRATION

Chinadrugtrials.org CTR20201073; ClinicalTrials.gov NCT04629846.

Real-world experience with pertuzumab and trastuzumab combined with chemotherapy in neoadjuvant treatment for patients with early-stage HER2-positive breast cancer: the NEOPERSUR study

PURPOSE

HER2-targeted therapies have dramatically improved outcomes of patients with HER2-positive breast cancer (BC), as demonstrated in neoadjuvant trials. This study aims to provide real-world evidence on the use and effectiveness of combined pertuzumab, trastuzumab and chemotherapy (CT) in early-stage HER2-positive BC.

METHODS

A retrospective, multicentre study was conducted on patients diagnosed with HER2-positive early BC treated with neoadjuvant pertuzumab and trastuzumab plus CT at 13 Spanish sites. The primary endpoint was pathological complete response (pCR).

RESULTS

A total of 310 patients were included. Pertuzumab and trastuzumab were combined with anthracyclines and taxanes, carboplatin and docetaxel, and taxane-based CT in 77.1%, 16.5%, and 6.5% of patients, respectively. Overall, the pCR rate was 62.2%. The pCR was higher amongst patients with hormone receptor-negative tumours and with tumours expressing higher levels of Ki-67 (> 20%). After postoperative adjuvant treatment, 13.9% of patients relapsed. Those patients who did not achieve pCR, with tumours at advanced stages (III), and with node-positive disease were more likely to experience distant relapse. Median overall survival (OS) and distant disease-free survival (D-DFS) were not reached at the study end. The estimated mean OS and D-DFS times were 7.5 (95% CI 7.3-7.7) and 7.3 (95% CI 7.1-7.5) years, respectively (both were significantly longer amongst patients who achieved pCR). Grade 3-4 anti-HER2 related toxicities were reported in six (1.9%) patients.

CONCLUSION

Neoadjuvant pertuzumab and trastuzumab plus CT achieve high pCR rates in real-life patients with HER2-positive early BC, showing an acceptable safety profile. Innovative adjuvant strategies are essential in patients at high risk of distant disease recurrence.

Prediction of breast cancer and axillary positive-node response to neoadjuvant chemotherapy based on multi-parametric magnetic resonance imaging radiomics models

PURPOSE

Accurate identification of primary breast cancer and axillary positive-node response to neoadjuvant chemotherapy (NAC) is important for determining appropriate surgery strategies. We aimed to develop combining models based on breast multi-parametric magnetic resonance imaging and clinicopathologic characteristics for predicting therapeutic response of primary tumor and axillary positive-node prior to treatment.

MATERIALS AND METHODS

A total of 268 breast cancer patients who completed NAC and underwent surgery were enrolled. Radiomics features and clinicopathologic characteristics were analyzed through the analysis of variance and the least absolute shrinkage and selection operator algorithm. Finally, 24 and 28 optimal features were selected to construct machine learning models based on 6 algorithms for predicting each clinical outcome, respectively. The diagnostic performances of models were evaluated in the testing set by the area under the curve (AUC), sensitivity, specificity, and accuracy.

RESULTS

Of the 268 patients, 94 (35.1 %) achieved breast cancer pathological complete response (bpCR) and of the 240 patients with clinical positive-node, 120 (50.0 %) achieved axillary lymph node pathological complete response (apCR). The multi-layer perception (MLP) algorithm yielded the best diagnostic performances in predicting apCR with an AUC of 0.825 (95 % CI, 0.764-0.886) and an accuracy of 77.1 %. And MLP also outperformed other models in predicting bpCR with an AUC of 0.852 (95 % CI, 0.798-0.906) and an accuracy of 81.3 %.

CONCLUSIONS

Our study established non-invasive combining models to predict the therapeutic response of primary breast cancer and axillary positive-node prior to NAC, which may help to modify preoperative treatment and determine post-NAC surgery strategy.

Association of residual ductal carcinoma in situ with breast cancer treatment outcomes after neoadjuvant chemotherapy according to hormone receptor status

PURPOSE

This research aimed to clarify the impact of residual ductal carcinoma in situ(DCIS) in surgical specimens obtained after neoadjuvant chemotherapy(NAC) for breast cancer on the associated prognosis outcomes.

METHODS

This retrospective study was performed on a cohort of 1,009 patients who achieved pCR following NAC for breast cancer and underwent subsequent breast surgery at a single institution between January 2008 and December 2019. Overall survival, local recurrence-free survival, distant metastasis-free survival, and disease-free survival of the residual and non-residual DCIS groups were the outcomes compared, with further subgroup analysis performed according to hormone receptor status.

RESULTS

260 individuals (25.8%) presented with residual DCIS. Based on a median follow-up of 54.0 months, no significant differences in outcomes were observed between the two groups. Patients with residual DCIS and hormone receptor-negative (HR-) breast cancer demonstrated a significant decrease in distant metastasis-free survival (p = 0.030) compared to those without residual DCIS. In the HR + cohort, no significant difference was observed between the two groups. Multivariate analysis of the HR- cohort demonstrated a significant association between residual DCIS and an elevated risk for distant recurrence (hazard ratio = 2.3, 95% confidence interval = 1.01-5.20, p = 0.047).

CONCLUSIONS

Residual DCIS following NAC may impact breast cancer outcomes, particularly with respect to the occurrence of distant metastasis in HR- patients. Therefore, clinicians must vigilantly monitor patients with residual DCIS after NAC, and further research is needed to expand our understanding of the clinical implications of residual DCIS.

Promoter profiles in plasma CfDNA exhibits a potential utility of predicting the efficacy of neoadjuvant chemotherapy in breast cancer patients

BACKGROUND

Gene expression profiles in breast tissue biopsies contain information related to chemotherapy efficacy. The promoter profiles in cell-free DNA (cfDNA) carrying gene expression information of the original tissues may be used to predict the response to neoadjuvant chemotherapy in breast cancer as a non-invasive biomarker. In this study, the feasibility of the promoter profiles in plasma cfDNA was evaluated as a novel clinical model for noninvasively predicting the efficacy of neoadjuvant chemotherapy in breast cancer.

METHOD

First of all, global chromatin (5 Mb windows), sub-compartments and promoter profiles in plasma cfDNA samples from 94 patients with breast cancer before neoadjuvant chemotherapy (pCR = 31 vs. non-pCR = 63) were analyzed, and then classifiers were developed for predicting the efficacy of neoadjuvant chemotherapy in breast cancer. Further, the promoter profile changes in sequential cfDNA samples from 30 patients (pCR = 8 vs. non-pCR = 22) during neoadjuvant chemotherapy were analyzed to explore the potential benefits of cfDNA promoter profile changes as a novel potential biomarker for predicting the treatment efficacy.

RESULTS

The results showed significantly distinct promoter profile in plasma cfDNA of pCR patients compared with non-pCR patients before neoadjuvant chemotherapy. The classifier based on promoter profiles in a Random Forest model produced the largest area under the curve of 0.980 (95% CI: 0.978-0.983). After neoadjuvant chemotherapy, 332 genes with significantly differential promoter profile changes in sequential cfDNA samples of pCR patients was observed, compared with non-pCR patients, and their functions were closely related to treatment response.

CONCLUSION

These results suggest that promoter profiles in plasma cfDNA may be a powerful, non-invasive tool for predicting the efficacy of neoadjuvant chemotherapy breast cancer patients before treatment, and the on-treatment cfDNA promoter profiles have potential benefits for predicting the treatment efficacy.

Serum miRNA-1 may serve as a promising noninvasive biomarker for predicting treatment response in breast cancer patients receiving neoadjuvant chemotherapy

BACKGROUND

MicroRNA-1 (miR-1) is a tumour suppressor that can inhibit cell proliferation and invasion in several cancer types. In addition, miR-1 was found to be associated with drug sensitivity. Circulating miRNAs have been proven to be potential biomarkers with predictive and prognostic value. However, studies of miR-1 expression in the serum of breast cancer (BC) patients are relatively scarce, especially in patients receiving neoadjuvant chemotherapy (NAC).

METHODS

Serum samples from 80 patients were collected before chemotherapy, and RT-PCR was performed to detect the serum expression of miR-1. The correlation between miR-1 expression in serum and clinicopathological factors, including pathological complete response (pCR), was analyzed by the chi-squared test and logistic regression. KEGG and GSEA analysis were also performed to determine the biological processes and signalling pathways involved.

RESULTS

The miR-1 high group included more patients who achieved a pCR than did the miR-1 low group (p < 0.001). Higher serum miR-1 levels showed a strong correlation with decreased ER (R = 0.368, p < 0.001) and PR (R = 0.238, p = 0.033) levels. The univariate model of miR-1 for predicting pCR achieved an AUC of 0.705 according to the ROC curve. According to the interaction analysis, miR-1 interacted with Ki67 to predict the NAC response. According to the Kaplan-Meier plot, a high serum miR-1 level was related to better disease-free survival (DFS) in the NAC cohort. KEGG analysis and GSEA results indicated that miR-1 may be related to the PPAR signalling pathway and glycolysis.

CONCLUSIONS

In summary, our data suggested that miR-1 could be a potential biomarker for pCR and survival outcomes in patients with BC treated with NAC.

Breast Cancer Histopathology in the Age of Molecular Oncology

For more than a century, microscopic histology has been the cornerstone for cancer diagnosis, and breast carcinoma is no exception. In recent years, clinical biomarkers, gene expression profiles, and other molecular tests have shown increasing utility for identifying the key biological features that guide prognosis and treatment of breast cancer. Indeed, the most common histologic pattern-invasive ductal carcinoma of no special type-provides relatively little guidance to management beyond triggering grading, biomarker testing, and clinical staging. However, many less common histologic patterns can be recognized by trained pathologists, which in many cases can be linked to characteristic biomarker and gene expression patterns, underlying mutations, prognosis, and therapy. Herein we describe more than a dozen such histomorphologic subtypes (including lobular, metaplastic, salivary analog, and several good prognosis special types of breast cancer) in the context of their molecular and clinical features.

Tumor-Infiltrating Lymphocytes Refine Outcomes in Triple-Negative Breast Cancer Treated with Anthracycline-Free Neoadjuvant Chemotherapy

PURPOSE

Stromal tumor-infiltrating lymphocytes (sTIL) are associated with pathologic complete response (pCR) and long-term outcomes for triple-negative breast cancer (TNBC) in the setting of anthracycline-based chemotherapy. The impact of sTILs on refining outcomes beyond prognostic information provided by pCR in anthracycline-free neoadjuvant chemotherapy (NAC) is not known.

EXPERIMENTAL DESIGN

This is a pooled analysis of two studies where patients with stage I (T>1 cm)-III TNBC received carboplatin (AUC 6) plus docetaxel (75 mg/m2; CbD) NAC. sTILs were evaluated centrally on pre-treatment hematoxylin and eosin slides using standard criteria. Cox regression analysis was used to examine the effect of variables on event-free survival (EFS) and overall survival (OS).

RESULTS

Among 474 patients, 44% had node-positive disease. Median sTILs were 5% (range, 1%-95%), and 32% of patients had ≥30% sTILs. pCR rate was 51%. On multivariable analysis, T stage (OR, 2.08; P = 0.007), nodal status (OR, 1.64; P = 0.035), and sTILs (OR, 1.10; P = 0.011) were associated with pCR. On multivariate analysis, nodal status (HR, 0.46; P = 0.008), pCR (HR, 0.20; P < 0.001), and sTILs (HR, 0.95; P = 0.049) were associated with OS. At 30% cut-point, sTILs stratified outcomes in stage III disease, with 5-year OS 86% versus 57% in ≥30% versus <30% sTILs (HR, 0.29; P = 0.014), and numeric trend in stage II, with 5-year OS 93% versus 89% in ≥30% versus <30% sTILs (HR, 0.55; P = 0.179). Among stage II-III patients with pCR, EFS was better in those with ≥30% sTILs (HR, 0.16; P, 0.047).

CONCLUSIONS

sTILs density was an independent predictor of OS beyond clinicopathologic features and pathologic response in patients with TNBC treated with anthracycline-free CbD chemotherapy. Notably, sTILs density stratified outcomes beyond tumor-node-metastasis (TNM) stage and pathologic response. These findings highlight the role of sTILs in patient selection and stratification for neo/adjuvant escalation and de-escalation strategies.

Association between pathologic complete response and biochemical indicators after neoadjuvant therapy for HER2-positive breast cancer

PURPOSE

This study investigated the changes in the fasting blood glucose (FBG), fasting triglyceride (FTG), and fasting total cholesterol (FTC) levels during neoadjuvant therapy (NAT) for human epidermal growth factor receptor 2 (HER2)-positive breast cancer (BC) and the association with pathologic complete response (pCR).

METHODS

Relevant data from Sichuan Cancer Hospital from June 2019 to June 2022 were collected and analyzed, and FBG, FTG, and FTC were divided into baseline, change, and process groups, which were grouped to analyze the changes after receiving NAT and the association with pCR.

RESULTS

In the estrogen receptor (ER)-negative subgroup, patients with low levels of FTG in the process group were more likely to achieve pCR compared to high levels, and in the progesterone receptor (PR)-negative subgroup, patients with lower FTG compared to higher FTG after receiving NAT was more likely to achieve pCR.

CONCLUSIONS

Patients with HER2-positive BC undergoing NAT develop varying degrees of abnormalities (elevated or decreased) in FBG, FTG, and FTC; moreover, the status of FTG levels during NAT may predict pCR in ER-negative or PR-negative HER2-positive BC.Early monitoring and timely intervention for FTG abnormalities may enable this subset of patients to increase the likelihood of obtaining a pCR along with management of abnormal markers.

Long-term outcomes of a randomized, open-label, phase II study comparing cabazitaxel versus paclitaxel as neoadjuvant treatment in patients with triple-negative or luminal B/HER2-negative breast cancer (GENEVIEVE)

BACKGROUND

The GENEVIEVE study, comparing neoadjuvant cabazitaxel versus paclitaxel in triple-negative breast cancer (TNBC) and luminal B/human epidermal growth factor receptor 2 (HER2)-negative breast cancer (BC), previously reported significant differences in pathological complete response (pCR) rates. Effects on long-term outcome are unknown.

PATIENTS AND METHODS

GENEVIEVE randomized patients with cT2-3, any cN or cT1, cN+/pNSLN+, centrally confirmed TNBC or luminal B/HER2-negative BC (latter defined as estrogen/progesterone receptor-positive and >14% Ki-67-stained cells) to receive either cabazitaxel 25 mg/m2 q3w for four cycles or paclitaxel 80 mg/m2 weekly for 12 weeks. Anthracycline-containing chemotherapy was allowed in case of histologically proven invasive residuals as neoadjuvant treatment or after surgery as adjuvant treatment. Here we report the secondary endpoints invasive disease-free survival (iDFS), distant disease-free survival (DDFS), and overall survival (OS).

RESULTS

Of the 333 patients randomized, 74.7% and 83.2% completed treatment in the cabazitaxel and paclitaxel arms, respectively. After a median follow-up of 89.3 months (interquartile range 68.8-97.3 months), 80 iDFS events (43 after cabazitaxel and 37 after paclitaxel) and 47 deaths (23 after cabazitaxel and 24 after paclitaxel) were reported. IDFS rates were not significantly different between the cabazitaxel and paclitaxel arms after a 3-year (83.6% versus 85.0%) and 5-year follow-up (76.2% versus 78.3%) [hazard ratio (HR) = 1.27, 95% confidence interval 0.82-1.96, P = 0.294], respectively. DDFS rates at 3 years (88.6% versus 87.8%) and 5 years (82.1% versus 82.8%) for cabazitaxel and paclitaxel were comparable (HR = 1.15, P = 0.573). Similarly, OS rates at 3 years (91.6% versus 91.8%) and 5 years (89.2% versus 86.8%) showed no significant differences (HR = 1.05, P = 0.872). Subgroup analysis for TNBC and luminal B/HER2-negative BCs indicated no significant variations in 3- or 5-year iDFS, DDFS, or OS.

CONCLUSIONS

The significant differences in pCR rates observed in both treatment arms did not significantly impact long-term outcomes for patients treated with cabazitaxel versus paclitaxel in the GENEVIEVE trial.

Improving drug response prediction via integrating gene relationships with deep learning

Predicting the drug response of cancer cell lines is crucial for advancing personalized cancer treatment, yet remains challenging due to tumor heterogeneity and individual diversity. In this study, we present a deep learning-based framework named Deep neural network Integrating Prior Knowledge (DIPK) (DIPK), which adopts self-supervised techniques to integrate multiple valuable information, including gene interaction relationships, gene expression profiles and molecular topologies, to enhance prediction accuracy and robustness. We demonstrated the superior performance of DIPK compared to existing methods on both known and novel cells and drugs, underscoring the importance of gene interaction relationships in drug response prediction. In addition, DIPK extends its applicability to single-cell RNA sequencing data, showcasing its capability for single-cell-level response prediction and cell identification. Further, we assess the applicability of DIPK on clinical data. DIPK accurately predicted a higher response to paclitaxel in the pathological complete response (pCR) group compared to the residual disease group, affirming the better response of the pCR group to the chemotherapy compound. We believe that the integration of DIPK into clinical decision-making processes has the potential to enhance individualized treatment strategies for cancer patients.

Comprehensive evaluation of the relationship between biomarker profiles and neoadjuvant chemotherapy outcomes for breast cancer patients

BACKGROUND

Accurately predicting the response to neoadjuvant chemotherapy (NAC) in breast cancer patients is crucial for guiding treatment strategies and enhancing clinical outcomes. Current studies have primarily focused on a limited set of biomarkers. More importantly, the results of many studies are in conflict. To address this, we conducted a comprehensive evaluation of the predictive value of a diverse range of clinically available molecular biomarkers in breast cancer, including HER2, ER, PR, TOPO II, EGFR, Ki67, CK5/6, AR, and p53. Additionally, we assessed changes in these biomarkers after NAC administration.

METHODS

Our study involved 189 patients with invasive breast cancer who underwent NAC at our institute. We examined biomarker profiles in core-needle biopsies taken before NAC and in surgical specimens obtained after NAC. We examined the association between these biomarkers and NAC outcomes, focusing on two main aspects: the rate of pathological complete response (pCR) and the reduction in tumor size. We used Chi-square and Mann-Whitney U tests to compare biomarker status changes between pCR and non-pCR patients. Linear regression analysis was employed to evaluate the relationship between biomarker status and tumor shrinkage rate. Additionally, we compared the expression status of these biomarkers before and after NAC using Chi-square and Wilcoxon signed-rank tests.

RESULTS AND CONCLUSIONS

Our results demonstrated significant differences in the expression levels of HER2, ER, PR, TOPO II, EGFR, and Ki67 between pCR and non-pCR patients, underscoring their potential as predictive markers for NAC outcomes. Importantly, our results have shed light on the contentious issue surrounding TOPO II in NAC outcome prediction. We have provided evidence that establishes a significantly positive association between TOPO II expression level and the pCR rate. Notably, tumor size was identified as a relevant predictive factor for achieving pCR. Regarding biomarker profiles, only Ki67 levels and TOPO II status exhibited changes following NAC, resolving previous controversies. While the ER and PR status remained unchanged, their expression values exhibited a slight but significant decrease post-NAC. Our results provide clarity and insights into the value and potential of using these biomarkers to predict NAC responses and prognosis in breast cancer patients.

Multiparametric MRI-based radiomics combined with pathomics features for prediction of the efficacy of neoadjuvant chemotherapy in breast cancer

Purpose

The aim of this study is to investigate a new method that combines radiological and pathological breast cancer information to predict discrepancies in pathological responses for individualized treatment planning. We used baseline multiparametric magnetic resonance imaging and hematoxylin and eosin-stained biopsy slides to extract quantitative feature information and predict the pathological response to neoadjuvant chemotherapy in breast cancer patients.

Methods

We retrospectively collected data from breast cancer patients who received neoadjuvant chemotherapy in our hospital from August 2016 to January 2018; multiparametric magnetic resonance imaging (contrast-enhanced T1-weighted imaging and diffusion-weighted imaging) and whole slide image of hematoxylin and eosin-stained biopsy sections were collected. Quantitative imaging features were extracted from the multiparametric magnetic resonance imaging and the whole slide image were used to construct a radiopathomics signature model powered by machine learning methods. Models based on multiparametric magnetic resonance imaging or whole slide image alone were also constructed for comparison and referred to as the radiomics signature and pathomics signature models, respectively. Four modeling methods were used to establish prediction models. Model performances were evaluated using receiver operating characteristic curve analysis and the area under the curve, accuracy, sensitivity, specificity, positive predictive value, and negative predictive value.

Results

The radiopathomics signature model had favourable performance for the prediction of pathological complete response in the training set (the best value: area under the curve 0.83, accuracy 0.84, and sensitivity 0.87), and in the test set (the best value: area under the curve 0.91, accuracy 0.90, and sensitivity 0.88). In the test set, the radiopathomics signature model also significantly outperformed the radiomics signature (the best value: area under the curve 0.83, accuracy 0.64, and sensitivity 0.62), pathomics signature (the best value: area under the curve 0.60, accuracy 0.74, and sensitivity 0.62) (p > 0.05). Decision curve analysis and calibration curves confirmed the excellent performance of these prediction models in discrimination, calibration, and clinical usefulness.

Conclusions

The results of this study suggest that radiopathomics, the combination of both radiological information regarding the whole tumor and pathological information at the cellular level, could potentially predict discrepancies in pathological response and provide evidence for rational treatment plans.

Strain ultrasonic elastography imaging features of locally advanced breast cancer: association with response to neoadjuvant chemotherapy and recurrence-free survival

BACKGROUND

Due to the highly heterogeneity of the breast cancer, it would be desirable to obtain a non-invasive method to early predict the treatment response and survival outcome of the locally advanced breast cancer (LABC) patients undergoing neoadjuvant chemotherapy (NAC). This study aimed at investigating whether strain elastography (SE) can early predict the pathologic complete response (pCR) and recurrence-free survival (RFS) in LABC patients receiving NAC.

METHODS

In this single-center retrospective study, 122 consecutive women with LABC who underwent SE examination pre-NAC and after one and two cycles of NAC enrolled in the SHPD001(NCT02199418) and SHPD002 (NCT02221999) trials between January 2014 and August 2017 were included. The SE parameters (Elasticity score, ES; Strain ratio, SR; Hardness percentage, HP, and Area ratio, AR) before and during NAC were assessed. The relative changes in SE parameters after one and two cycles of NAC were describe as ΔA1 and ΔA2, respectively. Logistic regression analysis and Cox proportional hazards model were used to identify independent variables associated with pCR and RFS.

RESULTS

Forty-nine (40.2%) of the 122 patients experienced pCR. After 2 cycles of NAC, SR2 (odds ratio [OR], 1.502; P = 0.003) and ΔSR2 (OR, 0.013; P = 0.015) were independently associated with pCR, and the area under the receiver operating characteristic curve for the combination of them to predict pCR was 0.855 (95%CI: 0.779, 0.912). Eighteen (14.8%) recurrences developed at a median follow-up of 60.7 months. A higher clinical T stage (hazard ratio [HR] = 4.165; P = 0.005.), a higher SR (HR = 1.114; P = 0.002.) and AR (HR = 1.064; P <  0.001.) values at pre-NAC SE imaging were independently associated with poorer RFS.

CONCLUSION

SE imaging features have the potential to early predict pCR and RFS in LABC patients undergoing NAC, and then may offer valuable predictive information to guide personalized treatment.

Multimodal prediction of neoadjuvant treatment outcome by serial FDG PET and MRI in women with locally advanced breast cancer

PURPOSE

To investigate combined MRI and 18F-FDG PET for assessing breast tumor metabolism/perfusion mismatch and predicting pathological response and recurrence-free survival (RFS) in women treated for breast cancer.

METHODS

Patients undergoing neoadjuvant chemotherapy (NAC) for locally-advanced breast cancer were imaged at three timepoints (pre, mid, and post-NAC), prior to surgery. Imaging included diffusion-weighted and dynamic contrast-enhanced (DCE-) MRI and quantitative 18F-FDG PET. Tumor imaging measures included apparent diffusion coefficient, peak percent enhancement (PE), peak signal enhancement ratio (SER), functional tumor volume, and washout volume on MRI and standardized uptake value (SUVmax), glucose delivery (K1) and FDG metabolic rate (MRFDG) on PET, with percentage changes from baseline calculated at mid- and post-NAC. Associations of imaging measures with pathological response (residual cancer burden [RCB] 0/I vs. II/III) and RFS were evaluated.

RESULTS

Thirty-five patients with stage II/III invasive breast cancer were enrolled in the prospective study (median age: 43, range: 31-66 years, RCB 0/I: N = 11/35, 31%). Baseline imaging metrics were not significantly associated with pathologic response or RFS (p > 0.05). Greater mid-treatment decreases in peak PE, along with greater post-treatment decreases in several DCE-MRI and 18F-FDG PET measures were associated with RCB 0/I after NAC (p < 0.05). Additionally, greater mid- and post-treatment decreases in DCE-MRI (peak SER, washout volume) and 18F-FDG PET (K1) were predictive of prolonged RFS. Mid-treatment decreases in metabolism/perfusion ratios (MRFDG/peak PE, MRFDG/peak SER) were associated with improved RFS.

CONCLUSION

Mid-treatment changes in both PET and MRI measures were predictive of RCB status and RFS following NAC. Specifically, our results indicate a complementary relationship between DCE-MRI and 18F-FDG PET metrics and potential value of metabolism/perfusion mismatch as a marker of patient outcome.

Distinct Prognosis of Minimal Residual Disease According to Breast Cancer Subtype in Patients with Breast or Nodal Pathologic Complete Response After Neoadjuvant Chemotherapy

PURPOSE

Few studies have reported on patient prognosis according to residual cancer burden after neoadjuvant chemotherapy (NAC). Herein, we evaluated the survival of patients based on residual disease after NAC to identify subpopulations with distinct prognoses.

METHODS

We retrospectively reviewed 728 patients treated with NAC from 2010 to 2017. Patients were divided into four subgroups depending on post-surgical residual disease according to the staging system: pathological complete response (pCR) (ypT0/TisN0), minimal residual disease (MRD) (ypT1mi/T1aN0 or ypT0/Tis ypN0i+/N1mic), node-only pCR (≥ ypT1b ypN0), and breast-only pCR (ypT0/Tis ≥ ypN1a). Clinicopathological characteristics and survival outcomes were analyzed by adjusting for factors affecting survival.

RESULTS

Overall, 50.4% (n = 367) of patients achieved pCR, with the MRD group accounting for 16.5% (n = 120). Although age and clinical stage were not different among the study groups, histologic grade, subtypes, chemotherapy response, and local treatment showed differences. Event-free survival (EFS) and overall survival (OS) demonstrated no significant difference between the pCR and MRD groups. In the multivariate analysis, pCR status was the only significant factor in EFS, and no statistical difference was noted between the pCR and MRD groups. However, clinical stage, pCR status, and subtype significantly affected the OS. MRD showed favorable outcomes in terms of both EFS and OS in all subtypes, except for those with triple-negative breast cancer (TNBC).

CONCLUSION

Patients with MRD showed outcomes comparable to those of patients who achieved pCR and may be candidates for de-escalation of post-NAC treatment, except for those with a TNBC subtype.

Recent advancements in artificial intelligence for breast cancer: Image augmentation, segmentation, diagnosis, and prognosis approaches

Breast cancer is a significant global health burden, with increasing morbidity and mortality worldwide. Early screening and accurate diagnosis are crucial for improving prognosis. Radiographic imaging modalities such as digital mammography (DM), digital breast tomosynthesis (DBT), magnetic resonance imaging (MRI), ultrasound (US), and nuclear medicine techniques, are commonly used for breast cancer assessment. And histopathology (HP) serves as the gold standard for confirming malignancy. Artificial intelligence (AI) technologies show great potential for quantitative representation of medical images to effectively assist in segmentation, diagnosis, and prognosis of breast cancer. In this review, we overview the recent advancements of AI technologies for breast cancer, including 1) improving image quality by data augmentation, 2) fast detection and segmentation of breast lesions and diagnosis of malignancy, 3) biological characterization of the cancer such as staging and subtyping by AI-based classification technologies, 4) prediction of clinical outcomes such as metastasis, treatment response, and survival by integrating multi-omics data. Then, we then summarize large-scale databases available to help train robust, generalizable, and reproducible deep learning models. Furthermore, we conclude the challenges faced by AI in real-world applications, including data curating, model interpretability, and practice regulations. Besides, we expect that clinical implementation of AI will provide important guidance for the patient-tailored management.

Neoadjuvant systemic treatment in breast cancer surgery: is it always worth it?

BACKGROUND

Surgeons perspective of breast cancer (BC) treatment has deeply changed in recent time. We investigated survival outcomes of BC patients who underwent Neoadjuvant systemic treatment (NAT) before surgery and to assess the role of NAT in determining possible prognosis.

METHODS

We retrospectively analyzed a total of 2372 BC patients consecutively enrolled in our prospective institutional database. Seventy-eight patients over 2372 reached the inclusion criteria and underwent surgery after NAT.

RESULTS

After NAT, the 50% of luminal-B-HER2+ and the 53% of HER2+ had a pathological complete response (pCR) and only 18.5% of the TNs had a pCR. NAT significantly changed lymph node status (P=0.05). All women with pCR are still alive (No-pCR 0.732 CI: 0.589-0.832; yes-pCR 1.000 CI: 1.00-1.00; P=0.02). The molecular biology of the tumor, after NAT, is strictly related to survival both for 3- and 5-years OS. A triple negative BC have the worst prognosis (HER2+ 0.796 CI: 0.614-1; Luminal-A: 1 CI:1-1; LuminalB-HER2 -: 0.801 CI: 0.659-0975; LuminalB-HER2+: 1 CI:1-1; TN 0.542 CI: 0.372-0789, P=0.002).

CONCLUSIONS

We can state that, based on our experience, we can consider safe and effective conservative interventions following neoadjuvant therapy. An adequate selection of patients is crucial. It is also clear how the planning of the therapeutic path plays a key role in an interdisciplinary context. NAT is a source of hope for the future both for the identification of new predictors of prognosis and in the field of research, for the development of new drugs.

Correlation between synthetic MRI relaxometry and apparent diffusion coefficient in breast cancer subtypes with different neoadjuvant therapy response

BACKGROUND

To evaluate the correlation between synthetic MRI (syMRI) relaxometry and apparent diffusion coefficient (ADC) maps in different breast cancer subtypes and treatment response subgroups.

METHODS

Two hundred sixty-three neoadjuvant therapy (NAT)-treated breast cancer patients with baseline MRI were enrolled. Tumor annotations were obtained by drawing regions of interest (ROIs) along the lesion on T1/T2/PD and ADC maps respectively. Histogram features from T1/T2/PD and ADC maps were respectively calculated, and the correlation between each pair of identical features was analyzed. Meanwhile, features between different NAT treatment response groups were compared, and their discriminatory power was evaluated.

RESULTS

Among all patients, 20 out of 27 pairs of features weakly correlated (r = - 0.13-0.30). For triple-negative breast cancer (TNBC), features from PD map in the pathological complete response (pCR) group (r = 0.60-0.86) showed higher correlation with ADC than that of the non-pCR group (r = 0.30-0.43), and the mean from the ADC and PD maps in the pCR group strongly correlated (r = 0.86). For HER2-positive, few correlations were found both in the pCR and non-pCR groups. For luminal HER2-negative, T2 map correlated more with ADC than T1 and PD maps. Significant differences were seen in T2 low percentiles and median in the luminal-HER2 negative subtype, yielding moderate AUCs (0.68/0.72/0.71).

CONCLUSIONS

The relationship between ADC and PD maps in TNBC may indicate different NAT responses. The no-to-weak correlation between the ADC and syMRI suggests their complementary roles in tumor microenvironment evaluation.

CRITICAL RELEVANCE STATEMENT

The relationship between ADC and PD maps in TNBC may indicate different NAT responses, and the no-to-weak correlation between the ADC and syMRI suggests their complementary roles in tumor microenvironment evaluation.

KEY POINTS

• The relationship between ADC and PD in TNBC indicates different NAT responses. • The no-to-weak correlations between ADC and syMRI complementarily evaluate tumor microenvironment. • T2 low percentiles and median predict NAT response in luminal-HER2-negative subtype.

Imaging findings for response evaluation of ductal carcinoma in situ in breast cancer patients treated with neoadjuvant systemic therapy: a systematic review and meta-analysis

OBJECTIVES

In approximately 45% of invasive breast cancer (IBC) patients treated with neoadjuvant systemic therapy (NST), ductal carcinoma in situ (DCIS) is present. Recent studies suggest response of DCIS to NST. The aim of this systematic review and meta-analysis was to summarise and examine the current literature on imaging findings for different imaging modalities evaluating DCIS response to NST. More specifically, imaging findings of DCIS pre- and post-NST, and the effect of different pathological complete response (pCR) definitions, will be evaluated on mammography, breast MRI, and contrast-enhanced mammography (CEM).

METHODS

PubMed and Embase databases were searched for studies investigating NST response of IBC, including information on DCIS. Imaging findings and response evaluation of DCIS were assessed for mammography, breast MRI, and CEM. A meta-analysis was conducted per imaging modality to calculate pooled sensitivity and specificity for detecting residual disease between pCR definition no residual invasive disease (ypT0/is) and no residual invasive or in situ disease (ypT0).

RESULTS

Thirty-one studies were included. Calcifications on mammography are related to DCIS, but can persist despite complete response of DCIS. In 20 breast MRI studies, an average of 57% of residual DCIS showed enhancement. A meta-analysis of 17 breast MRI studies confirmed higher pooled sensitivity (0.86 versus 0.82) and lower pooled specificity (0.61 versus 0.68) for detection of residual disease when DCIS is considered pCR (ypT0/is). Three CEM studies suggest the potential benefit of simultaneous evaluation of calcifications and enhancement.

CONCLUSIONS AND CLINICAL RELEVANCE

Calcifications on mammography can remain despite complete response of DCIS, and residual DCIS does not always show enhancement on breast MRI and CEM. Moreover, pCR definition effects diagnostic performance of breast MRI. Given the lack of evidence on imaging findings of response of the DCIS component to NST, further research is demanded.

KEY POINTS

• Ductal carcinoma in situ has shown to be responsive to neoadjuvant systemic therapy, but imaging studies mainly focus on response of the invasive tumour. • The 31 included studies demonstrate that after neoadjuvant systemic therapy, calcifications on mammography can remain despite complete response of DCIS and residual DCIS does not always show enhancement on MRI and contrast-enhanced mammography. • The definition of pCR has impact on the diagnostic performance of MRI in detecting residual disease, and when DCIS is considered pCR, pooled sensitivity was slightly higher and pooled specificity slightly lower.

Predicting pathological complete response to neoadjuvant systemic therapy for triple-negative breast cancers using deep learning on multiparametric MRIs

We trained and validated a deep learning model that can predict the treatment response to neoadjuvant systemic therapy (NAST) for patients with triple negative breast cancer (TNBC). Dynamic contrast enhanced (DCE) MRI and diffusion-weighted imaging (DWI) of the pre-treatment (baseline) and after four cycles (C4) of doxorubicin/cyclophosphamide treatment were used as inputs to the model for prediction of pathologic complete response (pCR). Based on the standard pCR definition that includes disease status in either breast or axilla, the model achieved areas under the receiver operating characteristic curves (AUCs) of 0.96 ± 0.05, 0.78 ± 0.09, 0.88 ± 0.02, and 0.76 ± 0.03, for the training, validation, testing, and prospective testing groups, respectively. For the pCR status of breast only, the retrained model achieved prediction AUCs of 0.97 ± 0.04, 0.82 ± 0.10, 0.86 ± 0.03, and 0.83 ± 0.02, for the training, validation, testing, and prospective testing groups, respectively. Thus, the developed deep learning model is highly promising for predicting the treatment response to NAST of TNBC.Clinical Relevance- Deep learning based on serial and multiparametric MRIs can potentially distinguish TNBC patients with pCR from non-pCR at the early stage of neoadjuvant systemic therapy, potentially enabling more personalized treatment of TNBC patients.

Pre- and Post-Neoadjuvant Clinicopathological Parameters Can Help in the Prognosis and the Prediction of Response in HER2+ and Triple Negative Breast Cancer

Neoadjuvant treatment (NAT) is one of the most widely used options for HER2+ and triple negative (TN) early breast cancer (BC). Since around half of the patients treated with NAT do not achieve a pathologically complete response (pCR), biomarkers to predict resistance are urgently needed. The correlation of clinicopathological factors with pCR was studied in 150 patients (HER2 = 81; TN = 69) and pre- and post-NAT differences in tumour biomarkers were compared. Low estrogen receptor (ER) expression, high tumour-infiltrating lymphocytes (TILs) and low cT-stage were associated with pCR in HER2+ tumours (p = 0.022; p = 0.032 and p = 0.005, respectively). Furthermore, ER expression was also associated with residual cancer burden (RCB; p = 0.046) in the HER2+ subtype. Similarly, pre-NAT, low progesterone receptor expression (PR; 1-10%) was associated with higher RCB (p < 0.001) in TN tumours. Only clinical and pathological T-stage (cpT-stage) had prognostic capacity in HER2+ tumours, whereas pre-NAT cpT-stage and post-NAT TILs had this capacity for the prognosis of TN tumours. We conclude that ER and PR expression may help predict response to NAT in HER2 and TN BC and should be taken into account in residual tumours. Also, changes observed in the phenotype after NAT suggest the need to reevaluate biomarkers in surviving residual tumour cells.

Evaluation of Multiparametric MRI Radiomics-Based Nomogram in Prediction of Response to Neoadjuvant Chemotherapy in Breast Cancer: A Two-Center study

INTRODUCTION

This study evaluated the performance of primary foci of breast cancer on multiparametric magnetic resonance imaging (MRI) contributing to establish and validate radiomics-based nomograms for predicting the different pathological outcome of breast cancer patients after neoadjuvant chemotherapy (NAC).

MATERIALS AND METHODS

Retrospectively collected 387 patients with locally advanced breast cancer, all treated with NAC and received breast dynamic contrast-enhanced MRI (DCE-MRI) before NAC. Radiomics signatures were extracted from region of interest (ROI) on multiparametric MRI to build rad score. Clinical-pathologic data and radiological features established the clinical model. The comprehensive model featured rad-score, predictive clinical-pathologic data and radiological features, which was ultimately displayed as a nomogram. Patients were grouped in 2 different ways in accordance with the Miller-Payne (MP) grading of surgical specimens. The first grouping method: 181 patients with pathological reaction grades Ⅳ∼Ⅴ were included in the significant remission group, while 206 patients with pathological reaction grades Ⅰ∼Ⅲ were included in the nonsignificant remission group. The second grouping method: 117 patients with pathological complete response (pCR) were assigned to the pCR group, and 270 patients who failed to meet pCR were assigned to in the non-pCR group. Two combined nomograms are created from 2 grouped data for predicting different pathological responses to NAC. The area under the curves (AUC) of the receiver operating characteristic curves (ROC) were used to evaluate the performance of each model. While decision curve analysis (DCA) and calibration curves were used for estimating the clinical application value of the nomogram.

RESULTS

Two combined nomograms embodying rad score and clinical-pathologic data outperformed, showing good calibrations for predicting response to NAC. The combined nomogram predicting pCR showed the best performance with the AUC values of 0.97, 0.90 and 0.86 in the training, testing, and external validation cohorts respectively. The AUC values of another combined nomogram predicting significant remission: 0.98, 0.88 0.80 in the training, testing and external validation cohorts. DCA showed the comprehensive model nomogram obtained the most clinical benefit.

CONCLUSIONS

The combined nomogram could preoperatively predict significant remission or even pCR to NAC in breast cancer based on multiparametric MRI and clinical-pathologic data.

Personalizing neoadjuvant immune-checkpoint inhibition in patients with melanoma

Neoadjuvant immune-checkpoint inhibition is a promising emerging treatment approach for patients with surgically resectable macroscopic stage III melanoma. The neoadjuvant setting provides an ideal platform for personalized therapy owing to the very homogeneous nature of the patient population and the opportunity for pathological response assessments within several weeks of starting treatment, thereby facilitating the efficient identification of novel biomarkers. A pathological response to immune-checkpoint inhibitors has been shown to be a strong surrogate marker of both recurrence-free survival and overall survival, enabling timely analyses of the efficacy of novel therapies in patients with early stage disease. Patients with a major pathological response (defined as the presence of ≤10% viable tumour cells) have a very low risk of recurrence, which offers an opportunity to adjust the extent of surgery and any subsequent adjuvant therapy and follow-up monitoring. Conversely, patients who have only a partial pathological response or who do not respond to neoadjuvant therapy still might benefit from therapy escalation and/or class switch during adjuvant therapy. In this Review, we outline the concept of a fully personalized neoadjuvant treatment approach exemplified by the current developments in neoadjuvant therapy for patients with resectable melanoma, which could provide a template for the development of similar approaches for patients with other immune-responsive cancers in the near future.

The impact of race and age on response to neoadjuvant therapy and long-term outcomes in Black and White women with early-stage breast cancer

PURPOSE

There are a paucity of data and a pressing need to evaluate response to neoadjuvant chemotherapy (NACT) and determine long-term outcomes in young Black women with early-stage breast cancer (EBC).

METHODS

We analyzed data from 2196 Black and White women with EBC treated at the University of Chicago over the last 2 decades. Patients were divided into groups based on race and age at diagnosis: Black women [Formula: see text] 40 years, White women [Formula: see text] 40 years, Black women [Formula: see text] 55 years, and White women [Formula: see text] 55 years. Pathological complete response rate (pCR) was analyzed using logistic regression. Overall survival (OS) and disease-free survival (DFS) were analyzed using Cox proportional hazard and piecewise Cox models.

RESULTS

Young Black women had the highest risk of recurrence, which was 22% higher than young White women (p = 0.434) and 76% higher than older Black women (p = 0.008). These age/racial differences in recurrence rates were not statistically significant after adjusting for subtype, stage, and grade. In terms of OS, older Black women had the worst outcome. In the 397 women receiving NACT, 47.5% of young White women achieved pCR, compared to 26.8% of young Black women (p = 0.012).

CONCLUSIONS

Black women with EBC had significantly worse outcomes compared to White women in our cohort study. There is an urgent need to understand the disparities in outcomes between Black and White breast cancer patients, particularly in young women where the disparity in outcome is the greatest.

Assessing the Correlation of Rate of Pathological Complete Response and Outcome in Post Neoadjuvant Chemotherapy Setting and Molecular Subtypes of Breast Cancer

Background Neoadjuvant chemotherapy (NAC) is being widely used in treating breast cancer (BC). This study aimed to analyze the correlation between clinicopathological features, immunohistochemistry (IHC)-based molecular subtypes, and the pathological response to NAC and its relationship with disease-free survival (DFS) and overall survival (OS). Materials and methods A retrospective analysis of 211 breast cancer patients who received NAC between 2008 and 2018 was performed. Tumors were classified by IHC into luminal A, luminal B, human epidermal growth factor receptor 2 (HER2)-enriched, and triple-negative subtypes. The chi-square test was used to assess the association between pathological response and clinicopathological parameters. Cox regression analysis was used to assess factors related to DFS and OS. Results Post NAC, 19.4% of patients achieved a pathologic complete response (pCR). Estrogen receptor (ER), progesterone receptor (PR), HER2 (p<0.001, 0.005, and 0.02), Ki67 (p=0.03), molecular subtypes (p<0.001), T stage (p=0.04), and N stage (p=0.01) were significantly associated with pathological response. The rate of pCR was highest among HER2-enriched and triple-negative tumors (45.2% and 28%, respectively) with OR=0.13 and p<0.001 for the HER2-enriched subtype. Patients with pCR were 61% less likely to develop metastasis (adjusted hazard ratio [aHR]=0.39, p=0.06, 95% CI=0.14-1.06) and were significantly associated with better OS (aHR=0.07, p=0.02, 95% CI=0.01-0.61). Patients who were ≤40 years old (aHR=2.1, p=0.01), with T4 (aHR=3.4, p=0.02), grade 3 (aHR=2.5, p=0.01), and node-positive disease (HR=2.24, p=0.02) were at an increased risk of developing metastasis. High Ki67 was found to be significantly associated with better DFS (p=0.006). Conclusion HER2-enriched and triple-negative BC were associated with a higher rate of pCR. Patients with pCR had significantly better DFS and OS. Younger age, advanced stage, higher grade, and lymph node involvement were risk factors for metastasis.

A New Method of Identifying Pathologic Complete Response After Neoadjuvant Chemotherapy for Breast Cancer Patients Using a Population-Based Electronic Medical Record System

BACKGROUND

Accurate identification of pathologic complete response (pCR) from population-based electronic narrative data in a timely and cost-efficient manner is critical. This study aimed to derive and validate a set of natural language processing (NLP)-based machine-learning algorithms to capture pCR from surgical pathology reports of breast cancer patients who underwent neoadjuvant chemotherapy (NAC).

METHODS

This retrospective cohort study included all invasive breast cancer patients who underwent NAC and subsequent curative-intent surgery during their admission at all four tertiary acute care hospitals in Calgary, Alberta, Canada, between 1 January 2010 and 31 December 2017. Surgical pathology reports were extracted and processed with NLP. Decision tree classifiers were constructed and validated against chart review results. Machine-learning algorithms were evaluated with a performance matrix including sensitivity, specificity, positive predictive value (PPV), negative predictive value [NPV], accuracy, area under the receiver operating characteristic curve [AUC], and F1 score.

RESULTS

The study included 351 female patients. Of these patients, 102 (29%) achieved pCR after NAC. The high-sensitivity model achieved a sensitivity of 90.5% (95% confidence interval [CI], 69.6-98.9%), a PPV of 76% (95% CI, 59.6-87.2), an accuracy of 88.6% (95% CI, 78.7-94.9%), an AUC of 0.891 (95% CI, 0.795-0.987), and an F1 score of 82.61. The high-PPV algorithm reached a sensitivity of 85.7% (95% CI, 63.7-97%), a PPV of 81.8% (95% CI, 63.4-92.1%), an accuracy of 90% (95% CI, 80.5-95.9%), an AUC of 0.888 (95% CI, 0.790-0.985), and an F1 score of 83.72. The high-F1 score algorithm obtained a performance equivalent to that of the high-PPV algorithm.

CONCLUSION

The developed algorithms demonstrated excellent accuracy in identifying pCR from surgical pathology reports of breast cancer patients who received NAC treatment.

Predictive value of neutrophil-to-lymphocyte-ratio in neoadjuvant-treated patients with breast cancer

PURPOSE

Breast cancer (BC) is the most common malignancy among women and prognosis is strongly influenced by tumor subtype. Neoadjuvant chemotherapy (NAC) is the standard treatment for both locally advanced- and early-stage triple-negative and Her2-positive BC. Pathologic complete response (pCR) to NAC is an important predictor of patient outcomes. Neutrophil-to-lymphocyte-ratio (NLR) in peripheral blood is associated with prognosis in various malignancies. Here, we investigated the value of the pretreatment NLR as a response predictor in neoadjuvant-treated patients with BC.

METHODS

A retrospective chart analysis of 862 patients with invasive BC treated with NAC at the Heidelberg University Hospital during 2003-2015 was conducted. NLR was calculated as the ratio of the absolute neutrophil and lymphocyte counts in peripheral blood, and pCR was defined as absence of invasive or in situ carcinoma in breast and axillary lymph nodes.

RESULTS

A total of 151 patients with invasive BC who underwent NAC were included in this study. NLR tended to be higher in the pCR group than the non-pCR group (p < 0.1). Analyses of BC subtypes demonstrated that NLR was significantly higher in the pCR- compared with the non-pCR group (3.304 vs. 2.379, respectively; p = 0.048) in patients with luminal B/Her2-negative tumors. Further, we found a significant difference in NLR according to remission status in postmenopausal patients (2.861 vs. 2.313, respectively; p = 0.043).

CONCLUSION

NLR was significantly higher only for patients achieving pCR in the Luminal B/Her2-negative and postmenopausal subgroups. Hence, NLR is a candidate additional predictive factor in patients with Luminal B/Her2-negative BC.

Aspirin use is associated with improvement in distant metastases outcome in patients with residual disease after neoadjuvant chemotherapy

PURPOSE

Aspirin (ASA) use has been correlated with improved outcomes in high-risk patients at risk for distant metastases. Breast cancer (BC) patients with residual disease, particularly nodal disease (ypN +) after neoadjuvant chemotherapy (NAC), are high-risk patients portending worse outcomes. We hypothesized that ASA use can reduce distant metastases and improve outcomes in these patients.

METHODS

Patients at our institutions from 2005 to 2018, with BC who did not achieve complete response (pCR) after NAC were reviewed (IRB protocol STU- 052012-019). Data, including evidence of ASA use, and clinico-pathologic parameters were analyzed. Survival outcomes were obtained (Kaplan Meier analysis) and univariate (UVA) and multivariable (MVA) Cox proportional hazards regression analyses were performed.

RESULTS

637 did not achieve pCR (ypN+ = 422). 138 were ASA users. Median follow-up for the control and ASA group were 3.8 (IQR 2.2-6.3) and 3.8 (IQR 2.5-6.4) years, respectively. Majority were stage II/III. 387 were hormone receptor positive, 191 HER2 +, and 157 triple negative. On UVA, ASA use, PR status, pathologic and clinical stage showed significance for DMFS, and disease-free survival (DFS). On MVA, ASA use associated with improved 5-year DFS (p = .01, 87.0% vs 79.6%, adjusted HR = 0.48) and improved 5-year DMFS (p = .04, 92.8% vs 89.2%, adjusted HR = 0.57). In the ypN + patients, ASA use associated with improved 5-year DMFS (p = .008, 85.7% vs 70.7%, adjusted HR = 0.43) and DFS (p = .02, 86.8% vs 74.3%, adjusted HR = 0.48).

CONCLUSION

For non-responders, particularly ypN + patients, ASA use associated with improved outcome. These hypotheses-generating results suggest for development of prospective clinical trials of augmented ASA use in selected very high-risk BC patients.

Combination of Anatomical and Biological Factors to Predict Disease-Free Survival in Breast Cancer

PURPOSE

The combination of anatomical and biological factors of breast cancer in a new staging system has a prognostic role. This study investigates the prognostic value of the Bioscore among patients with breast cancer with respect to disease-free survival (DFS).

MATERIAL AND METHODS

This study included 317 patients with breast cancer who were identified between January 2015 and December 2018 at Clinical Oncology Department of Assiut University Hospital. Their cancer baseline characteristics were recorded: pathologic stage (PS), T stage (T), nodal stage (N), grade (G), estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor (HER2) status. Univariate and two multivariate analyses were performed to identify which of these variables are associated with DFS. Model performance was quantified using Harrell's concordance index (C-index), and the Akaike information criterion (AIC) was used to compare model fits.

RESULTS

The significant factors in the univariate analysis were PS3, T2, T3, T4, N3, G2, G3, ER-negative, PR-negative, and HER2-negative. In the first multivariate analysis, PS3, G3, and ER-negative were the significant factors, and in the second multivariate analysis, T2, T4, N3, G3, and ER-negative were the significant factors. Two sets of models were built to determine the utility of combining variables. Models incorporating G and ER status had the highest C-index (0.72) for T + N + G + ER in comparison with (0.69) PS + G + ER and the lowest AIC (953.01) for T + N + G + ER and (966.9) for PS + G + ER.

CONCLUSION

Using the Bioscore in breast cancer staging helps to identify patients at increased risk of recurrence. It provides more optimistic prognostic stratification than the anatomical staging alone for DFS.

Prognostic analysis of three forms of Ki-67 in patients with breast cancer with non-pathological complete response before and after neoadjuvant systemic treatment

BACKGROUND

Patients who do not achieve a pathological complete response (pCR) after neoadjuvant systemic treatment (NST) have a significantly worse prognosis. A reliable predictor of prognosis is required to further subdivide non-pCR patients. To date, the prognostic role in terms of disease-free survival (DFS) between the terminal index of Ki-67 after surgery (Ki-67_T ) and the combination of the baseline Ki-67 at biopsy before NST (Ki-67_B ) and the percentage change in Ki-67 before and after NST (Ki-67_C ) has not been compared.

AIM

This study aimed to explore the most useful form or combination of Ki-67 that can provide prognostic information to non-pCR patients.

PATIENTS AND METHODS

We retrospectively reviewed 499 patients who were diagnosed with inoperable breast cancer between August 2013 and December 2020 and received NST with anthracycline plus taxane.

RESULTS

Among all the patients, 335 did not achieve pCR (with a follow-up period of ≥1 year). The median follow-up duration was 36 months. The optimal cutoff value of Ki-67_C to predict a DFS was 30%. A significantly worse DFS was observed in patients with a low Ki-67_C (p < 0.001). In addition, the exploratory subgroup analysis showed relatively good internal consistency. Ki-67_C and Ki-67_T were considered as independent risk factors for DFS (both p < 0.001). The forecasting model combining Ki-67_B and Ki-67_C showed a significantly higher area under the curve at years 3 and 5 than Ki-67_T (p = 0.029 and p = 0.022, respectively).

CONCLUSIONS

Ki-67_C and Ki-67_T were good independent predictors of DFS, whereas Ki-67_B was a slightly inferior predictor. The combination of Ki-67_B and Ki-67_C is superior to Ki-67_T for predicting DFS, especially at longer follow-ups. Regarding clinical application, this combination could be used as a novel indicator for predicting DFS to more clearly identify high-risk patients.

Using the HER2/CEP17 FISH Ratio to Predict Pathologic Complete Response Following Neoadjuvant Anti-HER2 Doublet Therapy in HER2+ Breast Cancer

BACKGROUND

Clinical trials of HER2-directed therapy that omit neoadjuvant conventional chemotherapy for HER+ breast cancer demonstrate that a subset of patients still obtains a pCR. Identifying tumor characteristics which predict pCR may help select patients for de-escalated neoadjuvant dual HER2-targeted treatment without chemotherapy. This is the first study evaluating the HER2/CEP17 ratio by FISH as a biomarker to predict pCR among patients who received neoadjuvant anti-HER2 regimens without chemotherapy.

PATIENTS AND METHODS

Data from patients with locally advanced HER2+ breast cancer who received neoadjuvant dual HER2-targeted therapy without conventional chemotherapy from a single center was retrospectively reviewed. All patients were enrolled in one of 3 clinical trials evaluating chemotherapy de-escalation. Logistic regression modeling assessed for a relationship between the HER2/CEP17 FISH ratio obtained from baseline tissue biopsy and pCR based on pathology at the time of definitive breast surgery following neoadjuvant treatment.

RESULTS

Following neoadjuvant treatment with dual HER2-targeted therapies in 56 patients, the probability of pCR was 73% among patients with a HER2 ratio of 13.1 compared to a probability of 38% among patients with HER2 ratio of 5.5 (OR 4.14, 95% CI 1.44-11.89; P = .012). This positive association persisted after controlling for different treatment regimens administered (OR 2.87, 95% CI 0.9-9.18, P = .020).

CONCLUSIONS

These data found a positive association between the HER2/CEP17 FISH ratio and pCR following neoadjuvant dual HER2-targeted therapy without chemotherapy. Larger prospective studies are needed to validate the HER2 ratio as a biomarker to select patients for neoadjuvant dual anti-HER2 therapy without chemotherapy.

RNA disruption is a widespread phenomenon associated with stress-induced cell death in tumour cells

We have previously shown that neoadjuvant chemotherapy can induce the degradation of tumour ribosomal RNA (rRNA) in patients with advanced breast cancer, a phenomenon we termed "RNA disruption". Extensive tumour RNA disruption during chemotherapy was associated with a post-treatment pathological complete response and improved disease-free survival. The RNA disruption assay (RDA), which quantifies this phenomenon, is now being evaluated for its clinical utility in a large multinational clinical trial. However, it remains unclear if RNA disruption (i) is manifested across many tumour and non-tumour cell types, (ii) can occur in response to cell stress, and (iii) is associated with tumour cell death. In this study, we show that RNA disruption is induced by several mechanistically distinct chemotherapy agents and report that this phenomenon is observed in response to oxidative stress, endoplasmic reticulum (ER) stress, protein translation inhibition and nutrient/growth factor limitation. We further show that RNA disruption is dose- and time-dependent, and occurs in both tumourigenic and non-tumourigenic cell types. Northern blotting experiments suggest that the rRNA fragments generated during RNA disruption stem (at least in part) from the 28S rRNA. Moreover, we demonstrate that RNA disruption is reproducibly associated with three robust biomarkers of cell death: strongly reduced cell numbers, lost cell replicative capacity, and the generation of cells with a subG1 DNA content. Thus, our findings indicate that RNA disruption is a widespread phenomenon exhibited in mammalian cells under stress, and that high RNA disruption is associated with the onset of cell death.

Prediction of pathologic complete response to neoadjuvant systemic therapy in triple negative breast cancer using deep learning on multiparametric MRI

Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer. Neoadjuvant systemic therapy (NAST) followed by surgery are currently standard of care for TNBC with 50-60% of patients achieving pathologic complete response (pCR). We investigated ability of deep learning (DL) on dynamic contrast enhanced (DCE) MRI and diffusion weighted imaging acquired early during NAST to predict TNBC patients' pCR status in the breast. During the development phase using the images of 130 TNBC patients, the DL model achieved areas under the receiver operating characteristic curves (AUCs) of 0.97 ± 0.04 and 0.82 ± 0.10 for the training and the validation, respectively. The model achieved an AUC of 0.86 ± 0.03 when evaluated in the independent testing group of 32 patients. In an additional prospective blinded testing group of 48 patients, the model achieved an AUC of 0.83 ± 0.02. These results demonstrated that DL based on multiparametric MRI can potentially differentiate TNBC patients with pCR or non-pCR in the breast early during NAST.

Incorporating weekly carboplatin in anthracycline and paclitaxel-containing neoadjuvant chemotherapy for triple-negative breast cancer: propensity-score matching analysis and TIL evaluation

BACKGROUND

The generation of data capturing the risk-benefit ratio of incorporating carboplatin (Cb) to neoadjuvant chemotherapy (NACT) for triple-negative breast cancer (TNBC) in a clinical practice setting is urgently needed. Tumour-infiltrating lymphocytes (TILs) have an established role in TNBC receiving NACT, however, the role of TIL dynamics under NACT exposure in patients receiving the current standard of care is largely uncharted.

METHODS

Consecutive TNBC patients receiving anthracycline-taxane [A-T] +/- Cb NACT at three Institutions were enrolled. Stromal-TILs were evaluated on pre-NACT and residual disease (RD) specimens. In the clinical cohort, propensity-score-matching was used to control selection bias.

RESULTS

In total, 247 patients were included (A-T = 40.5%, A-TCb = 59.5%). After propensity-score-matching, pCR was significantly higher for A-TCb vs A-T (51.9% vs 34.2%, multivariate: OR = 2.40, P = 0.01). No differences in grade ≥3 haematological toxicities were observed. TILs increased from baseline to RD in the overall population and across A-T/A-TCb subgroups. TIL increase from baseline to RD was positively and independently associated with distant disease-free survival (multivariate: HR = 0.43, P = 0.05).

CONCLUSIONS

We confirmed in a clinical practice setting of TNBC patients receiving A-T NACT that the incorporation of weekly Cb significantly improved pCR. In addition, A-T +/- Cb enhanced immune infiltration from baseline to RD. Finally, we reported a positive independent prognostic role of TIL increase after NACT exposure.

A phase II study of palbociclib plus letrozole plus trastuzumab as neoadjuvant treatment for clinical stages II and III ER+ HER2+ breast cancer (PALTAN)

Patients with ER+/HER2+ breast cancer (BC) are less likely to achieve pathological complete response (pCR) after chemotherapy with dual HER2 blockade than ER-/HER2+ BC. Endocrine therapy plus trastuzumab is effective in advanced ER+/HER2+ BC. Inhibition of CDK4/6 and HER2 results in synergistic cell proliferation reduction. We combined palbociclib, letrozole, and trastuzumab (PLT) as a chemotherapy-sparing regimen. We evaluated neoadjuvant PLT in early ER+/HER2+ BC. Primary endpoint was pCR after 16 weeks. Research biopsies were performed for whole exome and RNA sequencing, PAM50 subtyping, and Ki67 assessment for complete cell cycle arrest (CCCA: Ki67 ≤ 2.7%). After 26 patients, accrual stopped due to futility. pCR (residual cancer burden-RCB 0) was 7.7%, RCB 0/I was 38.5%. Grade (G) 3/4 treatment-emergent adverse events occurred in 19. Among these, G3/4 neutropenia was 50%, hypertension 26.9%, and leucopenia 7.7%. Analysis indicated CCCA in 85% at C1 day 15 (C1D15), compared to 27% at surgery after palbociclib was discontinued. Baseline PAM50 subtyping identified 31.2% HER2-E, 43.8% Luminal B, and 25% Luminal A. 161 genes were differentially expressed comparing C1D15 to baseline. MKI67, TK1, CCNB1, AURKB, and PLK1 were among the genes downregulated, consistent with CCCA at C1D15. Molecular Signatures Database gene-sets analyses demonstrated downregulated processes involved in proliferation, ER and mTORC1 signaling, and DNA damage repair at C1D15, consistent with the study drug's mechanisms of action. Neoadjuvant PLT showed a pCR of 7.7% and an RCB 0/I rate of 38.5%. RNA sequencing and Ki67 data indicated potent anti-proliferative effects of study treatments. ClinicalTrials.gov- NCT02907918.

The diagnostic, prognostic role and molecular mechanism of miR-328 in human cancer

MicroRNA are non-coding small RNAs that bind to their target mRNA and cause mRNA degradation or translation inhibition. MiRNA dysregulation is linked to a variety of human cancers and has a role in the genesis and development of cancer pathology. MiR-328 has been reported to be involved in various human cancers. And miR-328 is considered a key regulator in human cancer. It participates in biological processes such as proliferation, apoptosis, invasion, migration, and EMT. The present review will combine the basic and clinical studies to find that miR-328 promotes tumorigenesis and metastasis in human cancer. And we will describe the diagnostic, prognostic, and therapeutic value of miR-328 in various human cancers.

Machine learning with textural analysis of longitudinal multiparametric MRI and molecular subtypes accurately predicts pathologic complete response in patients with invasive breast cancer

PURPOSE

To predict pathological complete response (pCR) after neoadjuvant chemotherapy using extreme gradient boosting (XGBoost) with MRI and non-imaging data at multiple treatment timepoints.

MATERIAL AND METHODS

This retrospective study included breast cancer patients (n = 117) who underwent neoadjuvant chemotherapy. Data types used included tumor ADC values, diffusion-weighted and dynamic-contrast-enhanced MRI at three treatment timepoints, and patient demographics and tumor data. GLCM textural analysis was performed on MRI data. An extreme gradient boosting machine learning algorithm was used to predict pCR. Prediction performance was evaluated using the area under the curve (AUC) of the receiver operating curve along with precision and recall.

RESULTS

Prediction using texture features of DWI and DCE images at multiple treatment time points (AUC = 0.871; 95% CI: (0.768, 0.974; p<0.001) and (AUC = 0.903 95% CI: 0.854, 0.952; p<0.001) respectively), outperformed that using mean tumor ADC (AUC = 0.850 (95% CI: 0.764, 0.936; p<0.001)). The AUC using all MRI data was 0.933 (95% CI: 0.836, 1.03; p<0.001). The AUC using non-MRI data was 0.919 (95% CI: 0.848, 0.99; p<0.001). The highest AUC of 0.951 (95% CI: 0.909, 0.993; p<0.001) was achieved with all MRI and all non-MRI data at all time points as inputs.

CONCLUSION

Using XGBoost on extracted GLCM features and non-imaging data accurately predicts pCR. This early prediction of response can minimize exposure to toxic chemotherapy, allowing regimen modification mid-treatment and ultimately achieving better outcomes.

Deep learning prediction of pathological complete response, residual cancer burden, and progression-free survival in breast cancer patients

The goal of this study was to employ novel deep-learning convolutional-neural-network (CNN) to predict pathological complete response (PCR), residual cancer burden (RCB), and progression-free survival (PFS) in breast cancer patients treated with neoadjuvant chemotherapy using longitudinal multiparametric MRI, demographics, and molecular subtypes as inputs. In the I-SPY-1 TRIAL, 155 patients with stage 2 or 3 breast cancer with breast tumors underwent neoadjuvant chemotherapy met the inclusion/exclusion criteria. The inputs were dynamic-contrast-enhanced (DCE) MRI, and T2- weighted MRI as three-dimensional whole-images without the tumor segmentation, as well as molecular subtypes and demographics. The outcomes were PCR, RCB, and PFS. Three ("Integrated", "Stack" and "Concatenation") CNN were evaluated using receiver-operating characteristics and mean absolute errors. The Integrated approach outperformed the "Stack" or "Concatenation" CNN. Inclusion of both MRI and non-MRI data outperformed either alone. The combined pre- and post-neoadjuvant chemotherapy data outperformed either alone. Using the best model and data combination, PCR prediction yielded an accuracy of 0.81±0.03 and AUC of 0.83±0.03; RCB prediction yielded an accuracy of 0.80±0.02 and Cohen's κ of 0.73±0.03; PFS prediction yielded a mean absolute error of 24.6±0.7 months (survival ranged from 6.6 to 127.5 months). Deep learning using longitudinal multiparametric MRI, demographics, and molecular subtypes accurately predicts PCR, RCB, and PFS in breast cancer patients. This approach may prove useful for treatment selection, planning, execution, and mid-treatment adjustment.

Four-Dimensional Machine Learning Radiomics for the Pretreatment Assessment of Breast Cancer Pathologic Complete Response to Neoadjuvant Chemotherapy in Dynamic Contrast-Enhanced MRI

BACKGROUND

Breast cancer response to neoadjuvant chemotherapy (NAC) is typically evaluated through the assessment of tumor size reduction after a few cycles of NAC. In case of treatment ineffectiveness, this results in the patient suffering potentially severe secondary effects without achieving any actual benefit.

PURPOSE

To identify patients achieving pathologic complete response (pCR) after NAC by spatio-temporal radiomic analysis of dynamic contrast-enhanced (DCE) MRI images acquired before treatment.

STUDY TYPE

Single-center, retrospective.

POPULATION

A total of 251 DCE-MRI pretreatment images of breast cancer patients.

FIELD STRENGTH/SEQUENCE

1.5 T/3 T, T1-weighted DCE-MRI.

ASSESSMENT

Tumor and peritumoral regions were segmented, and 348 radiomic features that quantify texture temporal variation, enhancement kinetics heterogeneity, and morphology were extracted. Based on subsets of features identified through forward selection, machine learning (ML) logistic regression models were trained separately with all images and stratifying on cancer molecular subtype and validated with leave-one-out cross-validation.

STATISTICAL TESTS

Feature significance was assessed using the Mann-Whitney U-test. Significance of the area under the receiver operating characteristics (ROC) curve (AUC) of the ML models was assessed using the associated 95% confidence interval (CI). Significance threshold was set to 0.05, adjusted with Bonferroni correction.

RESULTS

Nine features related to texture temporal variation and enhancement kinetics heterogeneity were significant in the discrimination of cases achieving pCR vs. non-pCR. The ML models achieved significant AUC of 0.707 (all cancers, n = 251, 59 pCR), 0.824 (luminal A, n = 107, 14 pCR), 0.823 (luminal B, n = 47, 15 pCR), 0.844 (HER2 enriched, n = 25, 11 pCR), 0.803 (triple negative, n = 72, 19 pCR).

DATA CONCLUSIONS

Differences in imaging phenotypes were found between complete and noncomplete responders. Furthermore, ML models trained per cancer subtype achieved high performance in classifying pCR vs. non-pCR cases. They may, therefore, have potential to help stratify patients according to the level of response predicted before treatment, pending further validation with larger prospective cohorts.

EVIDENCE LEVEL

4 TECHNICAL EFFICACY: Stage 4.

Neoadjuvant Trastuzumab and Pertuzumab in Combination with Standard Chemotherapy for HER2-Positive Early Breast Cancer: Real-World Practice in Cuba

PURPOSE

Dual HER2 blockade chemotherapy is the standard of care for localized HER2+ breast cancer (BC). However, despite the efficacy of neoadjuvant therapy, relapses occurring in around 10% of patients highlight the need to improve its clinical approach. Therefore, this study aimed to evaluate the effectiveness/safety of neoadjuvant therapy with subcutaneous (SC) trastuzumab- pertuzumab chemotherapy (real world) to extend the evidence, which comes mainly from clinical trials (selected population; intravenous [IV] trastuzumab).

MATERIALS AND METHODS

A prospective, longitudinal, observational study in a Cuban hospital.

POPULATION

women aged ≥18 years with histologically confirmed HER2+ early-stage BC (2017-2021) eligible for neoadjuvant treatment (IV pertuzumab, SC trastuzumab, taxane-based chemotherapy). The aim was to determine the pathological complete response (pCR) rate to this scheme, its safety, and the impact of patient's characteristics on the outcomes.

RESULTS

Eighty-seven women were included: n=29 (DPT [docetaxel-IV pertuzumab- SC trastuzumab 600 mg; 4 cycles]); n=58 (ddAC-DPT [dose-dense anthracycline-based scheme+DPT]; 8 cycles). The median age was 57 years (range 30-83), ECOG 0: 97%. Time from diagnosis to treatment (median) was 28 days. The overall pCR rate was 62.1% (55.2%, DPT; 66.5%, ddAC-DPT; p =0.351); HR+, 47.7% vs. HR-, 76.7% (p=0.006). There were no statistically significant differences based on nodal status, stage, or Ki-67 levels. Overall, 94.2% of patients experienced ≥1 adverse event related to treatment, all of them grade 1-3 and more common with ddAC-DPT. The main cause of treatment delays (n=19; ddAC-DPT, 16; DPT, 3) was treatment-related toxicities.

CONCLUSION

Neoadjuvant trastuzumab (SC) and pertuzumab plus chemotherapy for HER2+ early-stage BC showed benefits in a real-life setting, with an acceptable safety profile.

Evaluation of pretreatment ADC values as predictors of treatment response to neoadjuvant chemotherapy in patients with breast cancer - a multicenter study

BACKGROUND

Magnetic resonance imaging (MRI) can be used to diagnose breast cancer. Diffusion weighted imaging (DWI) and the apparent diffusion coefficient (ADC) can reflect tumor microstructure in a non-invasive manner. The correct prediction of response of neoadjuvant chemotherapy (NAC) is crucial for clinical routine. Our aim was to compare ADC values between patients with pathological complete response (pCR) and non-responders based upon a multi-center design to improve the correct patient selection, which patient would more benefit from NAC and which patient would not.

METHODS

For this study, data from 4 centers (from Japan, Brazil, Spain and United Kingdom) were retrospectively acquired. The time period was overall 2003-2019. The patient sample comprises 250 patients (all female; median age, 50.5). In every case, pretreatment breast MRI with DWI was performed. pCR was assessed by experienced pathologists in every center using the surgical specimen in the clinical routine work up. pCR was defined as no residual invasive disease in either breast or axillary lymph nodes after NAC. ADC values between the group with pCR and those with no pCR were compared using the Mann-Whitney U test (two-group comparisons). Univariable and multivariabe logistic regression analysis was performed to predict pCR status.

RESULTS

Overall, 83 patients (33.2%) achieved pCR. The ADC values of the patient group with pCR were lower compared with patients without pCR (0.98 ± 0.23 × 10^- 3 mm²/s versus 1.07 ± 0.24 × 10^- 3 mm²/s, p = 0.02). The ADC value achieved an odds ratio of 4.65 (95% CI 1.40-15.49) in univariable analysis and of 3.0 (95% CI 0.85-10.63) in multivariable analysis (overall sample) to be associated with pCR status. The odds ratios differed in the subgroup analyses in accordance with the molecular subtype.

CONCLUSIONS

The pretreatment ADC-value is associated with pathological complete response after NAC in breast cancer patients. This could aid in clinical routine to reduce treatment toxicity for patients, who would not benefit from NAC. However, this must be tested in further studies, as the overlap of the ADC values in both groups is too high for clinical prediction.

Time to Optimize Deescalation Strategies in Triple-Negative Breast Cancer?

Pathologic response serves as a tool to optimize escalation and deescalation of adjuvant treatment. Neoadjuvant carboplatin-taxane combination is a promising chemotherapy deescalation strategy for triple-negative breast cancer. Many key points, such as trial design/patient selection, response biomarkers, role of immunotherapy, and patient advocate input, will have to be carefully considered in order to advance neoadjuvant chemotherapy deescalation investigations. See related article by Gluz et al., p. 4995.

Clinical N3 is an independent risk factor of recurrence for breast cancer patients achieving pathological complete response and near-pathological complete response after neoadjuvant chemotherapy

Background

Although achieving pathological complete response (pCR) and near-pathological complete response (near-pCR) after neoadjuvant chemotherapy (NAC) in breast cancer predicts a better outcome, some patients still experience recurrence. The aim of our study was to investigate the predictive factors of recurrence in the pCR and near-pCR population.

Methods

We reviewed 1,209 breast cancer patients treated with NAC between January 2010 and April 2021 in the Cancer Hospital, Chinese Academy of Medical Sciences (CHCAMS). A total of 292 patients achieving pCR and near-pCR were included in our analysis. pCR was defined as ypT0N0/ypTisN0. Near-pCR was defined as ypT1mi/1a/1bN0 or ypT0/isN1mi. Clinical features and follow-up information were collected. Survival and predictive factors of recurrence were analyzed.

Results

Of the 292 patients, 173 were pCR and 119 were near-pCR. The median age was 46 years (range, 23–75 years). The predominant tumor subtypes were human epidermal growth factor receptor type 2 (HER2)-positive breast cancer (49.0%) and triple-negative breast cancer (TNBC) (30.8%). The median duration of follow-up was 53 months (range, 9–138 months). A total of 25 (8.6%) patients developed recurrence, with 9 (5.2%) in the pCR group and 16 (13.4%) in the near-pCR group. The vast majority of recurrence occurred within 36 months from onset of NAC. The 5-year recurrence-free survival (RFS) rate of patients achieving pCR was significantly higher than that of patients achieving near-pCR (94.6% vs. 85.6%, p = 0.008). However, the 5-year overall survival (OS) rate between the two cohorts had no statistical difference (94.3% vs. 89.6%, p = 0.304). Clinical N3 (cN3) before NAC was an independent risk factor of recurrence in patients who achieved pCR (p = 0.003) and near-pCR (p = 0.036). Tumor size before NAC, subtypes of breast cancer, and chemotherapy regimens showed no significant association with RFS both for patients who achieved pCR and for those who achieved near-pCR (p > 0.05).

Conclusions

cN3 before NAC was an independent risk factor of recurrence in patients who achieved pCR and near-pCR. It is worthwhile to closely monitor patients with cN3, especially in the first 3 years.

Updated Neoadjuvant Treatment Landscape for Early Triple Negative Breast Cancer: Immunotherapy, Potential Predictive Biomarkers, and Novel Agents

Simple Summary

In recent years, several agents have been tested in randomized clinical trials in addition to anthracycline and taxane-based neoadjuvant chemotherapy (NACT) in early-stage triple-negative breast cancer (TNBC) to improve pathological complete response rate and, ultimately, survival outcome. Platinum agents, immune checkpoint inhibitors (ICIs), and PARP-inhibitors are the most extensively studied, while established predictors of their efficacy are lacking. Based on the biological features of TNBC, the purpose of this review is to provide an overview of the role of platinum agents, immunotherapy, and novel target therapies in the neoadjuvant setting. Moreover, based on safety issues and financial costs, we provide an overview of potential biomarkers associated with increased likelihood of benefit from the addition of platinum, ICIs, and novel target therapies to NACT.

Abstract

Triple-negative breast cancer (TNBC) is characterized by the absence of hormone receptor and HER2 expression, and therefore a lack of therapeutic targets. Anthracyclines and taxane-based neoadjuvant chemotherapy have historically been the cornerstone of treatment of early TNBC. However, genomic and transcriptomic analyses have suggested that TNBCs include various subtypes, characterized by peculiar genomic drivers and potential therapeutic targets. Therefore, several efforts have been made to expand the therapeutic landscape of early TNBC, leading to the introduction of platinum and immunomodulatory agents into the neoadjuvant setting. This review provides a comprehensive overview of the currently available evidence regarding platinum agents and immune-checkpoint-inhibitors for the neoadjuvant treatment of TNBC, as well as the novel target therapies that are currently being evaluated in this setting. Taking into account the economic issues and the side effects of the expanding therapeutic options, we focus on the potential efficacy biomarkers of the emerging therapies, in order to select the best therapeutic strategy for each specific patient.

Predictive Biomarkers of Response to Neoadjuvant Chemotherapy in Breast Cancer: Current and Future Perspectives for Precision Medicine

Pathological complete response (pCR) after neoadjuvant chemotherapy in patients with early breast cancer is correlated with better survival. Meanwhile, an expanding arsenal of post-neoadjuvant treatment strategies have proven beneficial in the absence of pCR, leading to an increased use of neoadjuvant systemic therapy in patients with early breast cancer and the search for predictive biomarkers of response. The better prediction of response to neoadjuvant chemotherapy could enable the escalation or de-escalation of neoadjuvant treatment strategies, with the ultimate goal of improving the clinical management of early breast cancer. Clinico-pathological prognostic factors are currently used to estimate the potential benefit of neoadjuvant systemic treatment but are not accurate enough to allow for personalized response prediction. Other factors have recently been proposed but are not yet implementable in daily clinical practice or remain of limited utility due to the intertumoral heterogeneity of breast cancer. In this review, we describe the current knowledge about predictive factors for response to neoadjuvant chemotherapy in breast cancer patients and highlight the future perspectives that could lead to the better prediction of response, focusing on the current biomarkers used for clinical decision making and the different gene signatures that have recently been proposed for patient stratification and the prediction of response to therapies. We also discuss the intratumoral phenotypic heterogeneity in breast cancers as well as the emerging techniques and relevant pre-clinical models that could integrate this biological factor currently limiting the reliable prediction of response to neoadjuvant systemic therapy.

Biomarker Dynamics and Long-Term Treatment Outcomes in Breast Cancer Patients with Residual Cancer Burden after Neoadjuvant Therapy

A residual cancer burden after neoadjuvant therapy (NAT) for breast cancer (BC) is associated with worse treatment outcomes compared to patients who achieved pathologic complete remission. This single-institutional retrospective study of 767 consecutive patients, including 468 patients with assessable residual cancer burden (aRCB) after NAT, with a median follow-up of 36 months, evaluated the biomarkers assessed before NAT from a biopsy and after NAT from a surgical specimen, their dynamics, and effect on long-term outcomes in specific breast cancer subtypes. The leading focus was on proliferation index Ki-67, which was significantly altered by NAT in all BC subtypes (p < 0.001 for HER2 positive and luminal A/B HER2 negative and p = 0.001 for TNBC). Multivariable analysis showed pre-NAT and post-NAT Ki-67 as independent predictors of survival outcomes for luminal A/B HER2 negative subtype. For TNBC, post-NAT Ki-67 was significant alone, and, for HER2 positive, the only borderline association of pre-NAT Ki-67 was observed in relation to the overall survival. Steroid and HER2 receptors were re-assessed just in a portion of the patients with aRCB. The concordance of both assessments was 92.9% for ER status, 80.1% for PR, and 92.2% for HER2. In conclusion, these real-world data of a consecutive cohort confirmed the importance of biomarkers assessment in patients with aRCB, and the need to consider specific BC subtypes when interpreting their influence on prognosis.

Clinically Interpretable Radiomics-Based Prediction of Histopathologic Response to Neoadjuvant Chemotherapy in High-Grade Serous Ovarian Carcinoma

Background

Pathological response to neoadjuvant treatment for patients with high-grade serous ovarian carcinoma (HGSOC) is assessed using the chemotherapy response score (CRS) for omental tumor deposits. The main limitation of CRS is that it requires surgical sampling after initial neoadjuvant chemotherapy (NACT) treatment. Earlier and non-invasive response predictors could improve patient stratification. We developed computed tomography (CT) radiomic measures to predict neoadjuvant response before NACT using CRS as a gold standard.

Methods

Omental CT-based radiomics models, yielding a simplified fully interpretable radiomic signature, were developed using Elastic Net logistic regression and compared to predictions based on omental tumor volume alone. Models were developed on a single institution cohort of neoadjuvant-treated HGSOC (n = 61; 41% complete response to NCT) and tested on an external test cohort (n = 48; 21% complete response).

Results

The performance of the comprehensive radiomics models and the fully interpretable radiomics model was significantly higher than volume-based predictions of response in both the discovery and external test sets when assessed using G-mean (geometric mean of sensitivity and specificity) and NPV, indicating high generalizability and reliability in identifying non-responders when using radiomics. The performance of a fully interpretable model was similar to that of comprehensive radiomics models.

Conclusions

CT-based radiomics allows for predicting response to NACT in a timely manner and without the need for abdominal surgery. Adding pre-NACT radiomics to volumetry improved model performance for predictions of response to NACT in HGSOC and was robust to external testing. A radiomic signature based on five robust predictive features provides improved clinical interpretability and may thus facilitate clinical acceptance and application.

Improved Prediction of Survival Outcomes Using Residual Cancer Burden in Combination With Ki-67 in Breast Cancer Patients Underwent Neoadjuvant Chemotherapy

We developed a model for improving the prediction of survival outcome using postoperative Ki-67 value in combination with residual cancer burden (RCB) in patients with breast cancer (BC) who underwent neoadjuvant chemotherapy (NAC). We analyzed the data from BC patients who underwent NAC between 2010 and 2019 at Samsung Medical Center and developed our residual proliferative cancer burden (RPCB) model using semi-quantitative Ki-67 value and RCB class. The Cox proportional hazard model was used to develop our RPCB model according to disease free survival (DFS) and overall survival (OS). In total, 1,959 patients were included in this analysis. Of 1,959 patients, 905 patients were excluded due to RCB class 0, and 32 were due to a lack of Ki-67 data. Finally, an RPCB model was developed using data from 1,022 patients. The RPCB score was calculated for DFS and OS outcomes, respectively (RPCB-DFS and RPCB-OS). For further survival analysis, we divided the population into 3 classes according to the RPCB score. In the prediction of DFS, C-indices were 0.751 vs 0.670 and time-dependent areas under the receiver operating characteristic curves (AUCs) at 3-year were 0.740 vs 0.669 for RPCB-DFS and RCB models, respectively. In the prediction of OS, C-indices were 0.819 vs 0.720 and time-dependent AUCs at 3-year were 0.875 vs 0.747 for RPCB-OS and RCB models, respectively. The RPCB model developed using RCB class and semi-quantitative Ki-67 had superior predictive value for DFS and OS compared with that of RCB class. This prediction model could provide the basis to decide risk-stratified treatment plan for BC patients who had residual disease after NAC.

Latissimus dorsi myocutaneous flap repair is effective after neoadjuvant chemotherapy for locally advanced breast cancer

OBJECTIVE

To describe the clinical outcome and physical condition of patients with locally advanced breast cancer (LABC) who received neoadjuvant chemotherapy followed by mastectomy and latissimus dorsi myocutaneous flap repair.

METHODS

A retrospective review of 142 patients with locally advanced breast cancer was selected from 1156 breast cancer patients in the South and North areas of The Affiliated Calmette Hospital of Kunming Medical University between May 2008 and December 2018.

RESULTS

All participants (n = 142) were women aged 40-55 years (average age 47.35 ± 0.43 years) who received neoadjuvant chemotherapy followed by mastectomy and latissimus dorsi flap repair. The median follow-up period was 16 months (range 12-24 months). For stage of disease, there were 19 cases (13%) in stage IIB, 31 cases (22%) in stage IIIA, 39 cases (28%) in stage IIIB, and 53 cases (37%) in stage IIIC, which were statistically significant with the physical condition of patients (≤ 0.001). Neoadjuvant chemotherapy was administered to shrink the tumors, and an average tumor size decrease from 10.05 ± 1.59 cm × (8.07 ± 1.54) cm to 6.11 ± 1.72 cm × (3.91 ± 1.52) cm (P < 0.001) was considered statistically significant. A t test was used for the ECOG score statistics, and the results showed that the scores were statistically significant (≤ 0.001) before and after neoadjuvant chemotherapy and after surgery.

CONCLUSIONS

Neoadjuvant chemotherapy is an accepted treatment option for patients with locally advanced breast cancer, and the use of a latissimus dorsi musculocutaneous flap for post-mastectomy reconstruction may improve the patients' physical condition. Our results indicated that this strategy was safe and feasible.