Assessment of the predictive role of pretreatment Ki-67 and Ki-67 changes in breast cancer patients receiving neoadjuvant chemotherapy according to the molecular classification: a retrospective study of 1010 patients

Activity and Efficacy of Neoadjuvant Chemotherapy in Luminal-HER2 Negative Early Breast Cancer According to HER2 Score (Low vs. Score 0): A Retrospective Study

BACKGROUND

The predictive and prognostic role of HER2 status in patients with luminal-HER2 negative early breast cancer (BC) undergoing neoadjuvant chemotherapy is unclear. A retrospective analysis evaluating the correlation between HER2 status (low vs. score 0) and pCR/IDFS was conducted.

METHODS

Patients with BC undergoing neoadjuvant chemotherapy and surgery were included. HER2 low BC was defined as IHC 1+ or 2+ with negative FISH. Logistic regression model and Cox proportional hazard model were adopted to investigate the independent role of HER2 status and outcomes of interest (pCR, CPS-EG and IDFS).

RESULTS

About 566 patients were included: 60% were HER2 low and 40% were HER2 0. pCR was achieved in 13.2% of HER2 low versus 17.7% of HER2 0 (P = .15). There was no correlation between baseline HER2 status and CPS-EG score (P = .18). A trend toward improved IDFS for HER2 low BC was observed (P = .07). The relapse rate of the HER2 0 cohort peaked at 12 months after surgery, similar to the HER2 low cohort, which showed an additional peak at 36 months after surgery.

CONCLUSIONS

Among Luminal-HER2 negative early BCs, our results do not support a clear predictive and prognostic effect of HER2 status, although a trend of worse pCR and better survival for HER2 low BCs cannot be ruled out.

Nomogram based on dual-energy computed tomography to predict the response to induction chemotherapy in patients with nasopharyngeal carcinoma: a two-center study

BACKGROUND

Previous studies utilizing dual-energy CT (DECT) for evaluating treatment efficacy in nasopharyngeal cancinoma (NPC) are limited. This study aimed to investigate whether the parameters from DECT can predict the response to induction chemotherapy in NPC patients in two centers.

METHODS

This two-center retrospective study included patients diagnosed with NPC who underwent contrast-enhanced DECT between March 2019 and November 2023. The clinical and DECT-derived parameters of tumor lesions were calculated to predict the response. We employed univariate and multivariate analysis to identify significant factors. Subsequently, the clinical, DECT, and clinical-DECT nomogram models were developed using independent predictors in the training cohort and validated in the test cohort. Receiver operating characteristic analysis was performed to evaluate the models' performance.

RESULTS

A total of 321 patients were included in the study, predominantly male [247 (76.9%)] with an average age of 52.04 ± 10.87 years. The training cohort (Center 1) comprised 252 patients, while the test cohort (Center 2) comprised 69 patients. Of these, 233 out of 321 patients (72.6%) were responders to induction chemotherapy. The clinical-DECT nomogram showed an AUC of 0.805 (95% CI, 0.688-0.906), outperforming both the DECT model (Extracellular volume fraction [ECVf]) (AUC, 0.706 [95% CI, 0.571-0.825]) and the clinical model (Ki67) (AUC, 0.693 [95% CI, 0.580-0.806]) in the test cohort.

CONCLUSIONS

Ki67 and ECVf emerged as independent predictive factors for response to induction chemotherapy in NPC patients. The proposed nomogram, incorporating ECVf, demonstrated accurate prediction of treatment response.

Predictive analysis of breast cancer response to neoadjuvant chemotherapy through plasma metabolomics

PURPOSE

Preoperative chemotherapy is a critical component of breast cancer management, yet its effectiveness is not uniform. Moreover, the adverse effects associated with chemotherapy necessitate the identification of a patient subgroup that would derive the maximum benefit from this treatment. This study aimed to establish a method for predicting the response to neoadjuvant chemotherapy in breast cancer patients utilizing a metabolomic approach.

METHODS

Plasma samples were obtained from 87 breast cancer patients undergoing neoadjuvant chemotherapy at our facility, collected both before the commencement of the treatment and before the second treatment cycle. Metabolite analysis was conducted using capillary electrophoresis-mass spectrometry (CE-MS) and liquid chromatography-mass spectrometry (LC-MS). We performed comparative profiling of metabolite concentrations by assessing the metabolite profiles of patients who achieved a pathological complete response (pCR) against those who did not, both in initial and subsequent treatment cycles.

RESULTS

Significant variances were observed in the metabolite profiles between pCR and non-pCR cases, both at the onset of preoperative chemotherapy and before the second cycle. Noteworthy distinctions were also evident between the metabolite profiles from the initial and the second neoadjuvant chemotherapy courses. Furthermore, metabolite profiles exhibited variations associated with intrinsic subtypes at all assessed time points.

CONCLUSION

The application of plasma metabolomics, utilizing CE-MS and LC-MS, may serve as a tool for predicting the efficacy of neoadjuvant chemotherapy in breast cancer in the future after all necessary validations have been completed.

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.

Assessment of the Predictive Role of Ki-67 in Breast Cancer Patients' Responses to Neoadjuvant Chemotherapy

OBJECTIVE

Neoadjuvant chemotherapy (NAC) in breast cancer (BC) is being considered for a broader range of cases, including locally advanced tumors and situations where downstaging could reduce extensive surgery. Several trials have explored predictive markers of pathological complete response (pCR). The role of Ki-67 as a predictor of pCR has been demonstrated in studies. However, the cut-off remains vague, given the lack of standardization of measurement methods. The aim of our study was to evaluate the predictive value of Ki-67 in response to NAC and to identify the cut-off values that exhibit the strongest correlation with best response.

MATERIALS AND METHODS

This retrospective study included 187 patients who had undergone surgery following NAC for BC at the CHU Souss Massa of Agadir between January 2020 and January 2023. Logistic regression was used to assess the correlation between Ki-67 and patients' characteristics. Optimal Ki-67 cutoff was identified by receiver operating characteristic curve. Kaplan-Meier curves were used to assess disease-free survival (DFS), and survival comparisons were assessed with the log-rank test.

RESULTS

The median age was 51.8±10.7 years and 51.4% of tumors were smaller than 5 cm. Node invasion was found in 55.4%. Luminal B subtype was found in 49.7%, followed by human epidermal growth factor receptor-2 (HER-2)-positive in 27.4%, triple-negative in 14.3% and Luminal A in 8.6%. pCR occurred in 40% of patients overall. Subgroup analysis revealed a significant association between pCR and tumor size (p<0.001), lymph node involvement (p<0.001), grade 2 (p<0.001), vascular invasion (p<0.001), and positive HER-2 status (p = 0.022). In statistical analysis, pathological responses were improved in patients with Ki-67 >35% (p<0.001). DFS was 98.8% at 12 months. No statistical difference was found in DFS according to Ki-67 values and pCR status.

CONCLUSION

Our results indicate that Ki-67 is a predictive marker for response in the neoadjuvant setting in BC patients. Our study showed that a Ki-67 cut-off >35% predicts a better pCR rate in response to NAC. However, this cutoff value remains controversial due to the absence of a standard method of measurement, with inter- and intra-observer variability. It would be necessary to validate this cutoff in other studies.

Retrospective evaluation of the contribution of radiotherapy to survival in breast cancer treatment with propensity score based on stage and subgroup

BACKGROUND

Breast cancer has been a disease in which treatment strategy has changed over time under the influence of different hypotheses and evidence for more than a century. We analyzed the contribution of radiotherapy to disease-free survival and overall survival by classifying according to stage, 1-3 lymph node involvement, and molecular subgroups.

METHODS

Following the approval of the Institutional Review Board, records of patients with breast cancer who were admitted to University School of Medicine Departments of Radiation Oncology and Medical Oncology between July 1999 and December 2020 were reviewed. Using data propensity score matching was performed between the groups that did and did not receive radiotherapy using an optimal matching algorithm (optimum, 1:1). Disease-free survival and overall survival after propensity score matching were calculated using the Kaplan-Meier method. Univariate and multivariate Cox regression analysis was used to estimate hazard ratios.

RESULTS

In the radiotherapy and non-radiotherapy groups, disease-free survival was 257.42 ± 5.46 (246.72- 268.13), 208,96 ± 8,15 (192,97-224,94) months respectively, (p = < 0.001), overall survival was 272,46 ± 8,68 (255,43-289,49), 219,05 ± 7,32 (204,70-233,41) months respectively (p = .002). We compared the 19 N1 patient groups who received radiotherapy with the 19 patients who did not receive radiotherapy and calculated the disease-free survival times was 202,21 ± 10,50 (181,62-222,79) and 148,82 ± 24,91 (99,99-197,65) months respectively (p = .011) and overall survival times was 200,85 ± 12,79 (175,77-225,92) and 166,90 ± 20,39 (126,93-206,82) months respectively (p = .055). We examined disease-free survival and overall survival times in both groups according to Luminal A, Luminal B, TNBC, and HER2-enriched subgroups. In the Luminal B subgroup, the disease-free survival duration in the groups receiving radiotherapy and not receiving radiotherapy was 264.83 ± 4.95 (255.13-274.54) and 187.09 ± 11.06 (165.41-208.78) months (p < .001), and overall survival times were 252.29 ± 10.54 (231.62-272.97) and 197.74 ± 9.72 (178.69-216.80) months (p = .001) respectively.

CONCLUSIONS

Thanks to studies proving that RT increases long-term survival rates in breast cancer as a result of reducing locoregional recurrence and systemic metastasis rates, it has been understood that the spectrum hypothesis is the hypothesis that most accurately describes breast cancer to date. We found that patients with Luminal B invasive breast cancer benefited significantly more from RT compared to other subgroups.

Exploring the effectiveness of molecular subtypes, biomarkers, and genetic variations as first-line treatment predictors in Asian breast cancer patients: a systematic review and meta-analysis

BACKGROUND

Breast cancer incidence has been on the rise significantly in the Asian population, occurring at an earlier age and a later stage. The potential predictive value of molecular subtypes, biomarkers, and genetic variations has not been deeply explored in the Asian population. This study evaluated the effect of molecular subtype classification and the presence or absence of biomarkers and genetic variations on pathological complete response (pCR) after neoadjuvant treatment in Asian breast cancer patients.

METHODS

A systematic search was conducted in MEDLINE (PubMed), Science Direct, Scopus, and Cochrane Library databases. Studies were selected if they included Asian breast cancer patients treated with neoadjuvant chemotherapy and contained data for qualitative or quantitative analyses. The quality of the included studies was assessed using the Newcastle Ottawa Scale. Following the random effects model, pooled odds ratios or hazard ratios with 95% confidence intervals for pCR were analysed using Review Manager Software. Heterogeneity between studies was assessed using Cochran's Q-test and I2 test statistics.

RESULTS

In total, 19,708 Asian breast cancer patients were pooled from 101 studies. In the neoadjuvant setting, taxane-anthracycline (TA) chemotherapy showed better pCR outcomes in triple-negative breast cancer (TNBC) (p<0.0001) and human epidermal growth factor receptor 2 enriched (HER2E) (p<0.0001) than luminal breast cancer patients. Similarly, taxane-platinum (TP) chemotherapy also showed better pCR outcomes in TNBC (p<0.0001) and HER2E (p<0.0001). Oestrogen receptor (ER)-negative, progesterone receptor (PR)-negative, HER2-positive and high Ki-67 were significantly associated with better pCR outcomes when treated with either TA or TP. Asian breast cancer patients harbouring wildtype PIK3CA were significantly associated with better pCR outcomes when treated with TA in the neoadjuvant setting (p=0.001).

CONCLUSIONS

In the neoadjuvant setting, molecular subtypes (HER2E and TNBC), biomarkers (ER, PR, HER2, HR, Ki-67, nm23-H1, CK5/6, and Tau), and gene (PIK3CA) are associated with increased pCR rates in Asian breast cancer patients. Hence, they could be further explored for their possible role in first-line treatment response, which can be utilised to treat breast cancer more efficiently in the Asian population. However, it needs to be further validated with additional powered studies.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO CRD42021246295.

Neoadjuvant Chemotherapy in Patients with HER2-Negative Breast Cancer: A Report from Clinical Breast Cancer Registry of Iran

BACKGROUND

Neoadjuvant chemotherapy (NCT) has become an increasingly popular approach in management of breast cancer (BC). This study was conducted to evaluate the pathologic response and 36-month recurrence and survival rates of patients with human epidermal growth factor receptor 2 (HER2)-negative BC treated with different NCT regimens.

METHODS

A total of 163 female patients with HER2-negative BC who received NCT during 2017-2020 were identified from the Clinical Breast Cancer Registry of Iran and entered the study. The prescribed NCT regimens included 4 cycles of doxorubicin plus cyclophosphamide, 4 cycles of doxorubicin plus cyclophosphamide followed by 4 cycles of paclitaxel, 4 cycles of doxorubicin plus cyclophosphamide followed by 4 cycles of docetaxel or 6 cycles of doxorubicin plus cyclophosphamide plus docetaxel (TAC).

RESULTS

Thirty-two patients (19.6%) experienced pathologic complete response (pCR). TAC regimen, triple negative-BC and ki67>10% were significantly associated with increased pCR. The recurrence, overall survival (OS) and disease-free survival (DFS) rate at 36 months for all patients were 16.6%, 84.7% and 79.8%, respectively. Type of neoadjuvant regimen as well as age, hormone receptor status, Ki67, grade, clinical stage, type of surgery and pathologic response to chemotherapy did not significantly influence the survival and recurrence; however, TAC results in improved recurrence, OS and DFS rates.

CONCLUSION

This study provides further evidence that NCT is a viable treatment option for patients with HER2-negative BC. The TAC regimen resulted in a significantly higher pCR rate compared to other regimens, but did not result in a significant improvement in recurrence, OS and DFS and rates.

Potential biomarkers in breast cancer drug development: application of the biomarker qualification evidentiary framework

Breast cancer treatments have evolved rapidly, and clinically meaningful biomarkers have been used to guide therapy. These biomarkers hold utility within the drug development process to increase the efficiency and effectiveness. To this purpose, the US FDA developed an evidentiary framework. Literature searches conducted of literature published between 2016 and 2022 identified biomarkers in breast cancer. These biomarkers were reviewed for drug development utility through the biomarker qualification evidentiary framework. In the breast cancer setting, several promising biomarkers (ctDNA, Ki-67 and PIK3CA) were identified. There is a need for increased transparency regarding the requirements for qualification of specific biomarkers and increased awareness of the processes involved in biomarker qualification.

Construction and validation of a prognostic nutritional index-based nomogram for predicting pathological complete response in breast cancer: a two-center study of 1,170 patients

Background

Pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) is associated with favorable outcomes in breast cancer patients. Identifying reliable predictors for pCR can assist in selecting patients who will derive the most benefit from NAC. The prognostic nutritional index (PNI) serves as an indicator of nutritional status and systemic immune competence. It has emerged as a prognostic biomarker in several malignancies; however, its predictive value for pCR in breast cancer remains uncertain. The objective of this study is to assess the predictive value of pretreatment PNI for pCR in breast cancer patients.

Methods

A total of 1170 patients who received NAC in two centers were retrospectively analyzed. The patients were divided into three cohorts: a training cohort (n=545), an internal validation cohort (n=233), and an external validation cohort (n=392). Univariate and multivariate analyses were performed to assess the predictive value of PNI and other clinicopathological factors. A stepwise logistic regression model for pCR based on the smallest Akaike information criterion was utilized to develop a nomogram. The C-index, calibration plots and decision curve analysis (DCA) were used to evaluate the discrimination, calibration and clinical value of the model.

Results

Patients with a high PNI (≥53) had a significantly increased pCR rate (OR 2.217, 95% CI 1.215-4.043, p=0.009). Tumor size, clinical nodal status, histological grade, ER, Ki67 and PNI were identified as independent predictors and included in the final model. A nomogram was developed as a graphical representation of the model, which incorporated the PNI and five other factors (AIC=356.13). The nomogram demonstrated satisfactory calibration and discrimination in the training cohort (C-index: 0.816, 95% CI 0.765-0.866), the internal validation cohort (C-index: 0.780, 95% CI 0.697-0.864) and external validation cohort (C-index: 0.714, 95% CI 0.660-0.769). Furthermore, DCA indicated a clinical net benefit from the nomogram.

Conclusion

The pretreatment PNI is a reliable predictor for pCR in breast cancer patients. The PNI-based nomogram is a low-cost, noninvasive tool with favorable predictive accuracy for pCR, which can assist in determining individualized treatment strategies for breast cancer patients.

Predicting Neoadjuvant Treatment Response in Triple-Negative Breast Cancer Using Machine Learning

BACKGROUND

Neoadjuvant chemotherapy (NAC) is the standard treatment for early-stage triple negative breast cancer (TNBC). The primary endpoint of NAC is a pathological complete response (pCR). NAC results in pCR in only 30-40% of TNBC patients. Tumor-infiltrating lymphocytes (TILs), Ki67 and phosphohistone H3 (pH3) are a few known biomarkers to predict NAC response. Currently, systematic evaluation of the combined value of these biomarkers in predicting NAC response is lacking. In this study, the predictive value of markers derived from H&E and IHC stained biopsy tissue was comprehensively evaluated using a supervised machine learning (ML)-based approach. Identifying predictive biomarkers could help guide therapeutic decisions by enabling precise stratification of TNBC patients into responders and partial or non-responders.

METHODS

Serial sections from core needle biopsies (n = 76) were stained with H&E and immunohistochemically for the Ki67 and pH3 markers, followed by whole-slide image (WSI) generation. The serial section stains in H&E stain, Ki67 and pH3 markers formed WSI triplets for each patient. The resulting WSI triplets were co-registered with H&E WSIs serving as the reference. Separate mask region-based CNN (MRCNN) models were trained with annotated H&E, Ki67 and pH3 images for detecting tumor cells, stromal and intratumoral TILs (sTILs and tTILs), Ki67+, and pH3+ cells. Top image patches with a high density of cells of interest were identified as hotspots. Best classifiers for NAC response prediction were identified by training multiple ML models and evaluating their performance by accuracy, area under curve, and confusion matrix analyses.

RESULTS

Highest prediction accuracy was achieved when hotspot regions were identified by tTIL counts and each hotspot was represented by measures of tTILs, sTILs, tumor cells, Ki67+, and pH3+ features. Regardless of the hotspot selection metric, a complementary use of multiple histological features (tTILs, sTILs) and molecular biomarkers (Ki67 and pH3) resulted in top ranked performance at the patient level.

CONCLUSIONS

Overall, our results emphasize that prediction models for NAC response should be based on biomarkers in combination rather than in isolation. Our study provides compelling evidence to support the use of ML-based models to predict NAC response in patients with TNBC.

A nomogram incorporating Ki67 to predict survival of acral melanoma

BACKGROUND

The proliferation marker Ki67 is associated with the progression and prognosis of melanoma. However, its prognostic impact on acral melanoma (AM) remains unclear.

METHODS

A total of 314 AM patients were enrolled from a cohort of 5758 patients with melanoma at Peking University Cancer Hospital between 2006 and 2018. The patients were divided into Ki67 high- and low-expressing groups using a cut-off value of 30%. The associations between Ki67 and clinicopathologic characteristics as well as survival were analyzed. Cox proportional regression analysis was used to establish a nomogram to predict the survival probabilities of AM.

RESULTS

Among 314 patients, the Ki67-high group (Ki67 ≥ 30%) included 49.4% of patients at diagnosis. Patients in the Ki67-high group had lower median melanoma-specific survival (MSS) than those in the Ki67-low group (60.7 months vs. not reached, p < 0.001). In multivariate analyses, Ki67, lymph node metastasis and primary site were independent prognostic factors for MSS. The nomogram showed that Ki67 had the fourth greatest impact on survival, following Breslow thickness, lymph node metastasis and primary site. The C-index of the nomogram was 0.765 and 0.758 in the training and validation cohort, respectively. Area under the curve values were both near 0.8 in the training and validation cohorts. Net reclassification improvement and integrated discrimination improvement demonstrated that the predictive nomogram performed better than the traditional AJCC staging system.

CONCLUSION

Ki67 expression is an independent prognostic factor for MSS in AM. A predictive model incorporating Ki67 and clinical factors was constructed to predict the prognosis of AM.

Can Ki-67 predict radiotherapy response in neuroendocrine tumors? Retrospective analysis of a monocentric series of patients

BACKGROUND

The impact of radiotherapy (RT) in neuroendocrine neoplasms is still unknown, and outcomes could be improved by a better insight in RT response predictors. This retrospective analysis investigates the potential correlation between Ki-67 and RT response to evaluate its role as biological marker of radiosensitivity.

MATERIAL AND METHODS

Data from patients treated at an Italian NET-referral center between 2015 and 2020 were retrieved. Inclusion criteria included: histologically-proven diagnosis of NEN, Ki-67 status, indication (symptomatic and/or ablative) and at least one post-RT radiological assessment.

RESULTS

Forty-two patients and 63 different treatment lines were included. Primary tumors presented Ki-67 values < 3% in 21% of cases, between 3 and 20% in 45% and >20% in the remaining 33%. Almost all patients were metastatic at the time of RT, which was performed with symptomatic purpose in 43% of cases. At a median time of three months, a complete response on the target lesion was observed in nine cases (14%), a partial response in 17 (27%), stability in 23 (37%) and local progression in 14 (22%). With median FU of 22.8 months, OS does not show statistically significant differences among three Ki-67 groups. Considering all lines of therapy, the relationship between ORR and Ki-67, did not show statistically significant differences, even following adjustments for drug types and delivered RT doses.

CONCLUSION

No association between Ki67 and local tumor response to RT could be observed in the present cohort, regardless of whether the evaluation was performed on a categorical or continuous scale.

The impact of PIK3CA mutations and PTEN expression on the effect of neoadjuvant therapy for postmenopausal luminal breast cancer patients

BACKGROUND

There is pressing needs to find the biomarker in the selection of neoadjuvant therapy in postmenopausal luminal breast cancer patients. We examined the hypothesis that PIK3CA mutations and low phosphatase and tensin homolog (PTEN) expression affect the response to neoadjuvant therapy and prognosis in postmenopausal luminal breast cancer patients.

METHODS

Postmenopausal patients with estrogen receptor-positive, human epidermal growth factor receptor 2-negative breast cancer, up to stage II, who underwent neoadjuvant chemotherapy (NAC; n = 60) or neoadjuvant endocrine therapy (NAE; n = 55) were selected. PIK3CA exon 9 and exon 20 mutations were screened by high resolution melting analysis and confirmed by Sanger sequence. PTEN expression was evaluated by immunohistochemistry. The relationships among PIK3CA mutations, PTEN expression, clinicopathological features, the pathological effect of neoadjuvant therapy, recurrence-free survival (RFS) and overall survival were analyzed.

RESULTS

Among 115 patients, PIK3CA mutations and low PTEN expression before treatment were detected in 35 patients (30.4%) and in 28 patients (24.3%), respectively. In the NAC group, tumor with PIK3CA mutations showed significantly poorer response than tumor with PIK3CA wild-type (p = 0.03). On the other hand, in the NAE group, there was no significant difference in pathological therapeutic effect between tumor with PIK3CA mutations and tumor with PIK3CA wild-type (p = 0.54). In the NAC group, the log-rank test showed no difference in RFS between patients with PIK3CA mutations and PIK3CA wild-type (p = 0.43), but patients with low PTEN expression showed significantly worse RFS compared to patients with high PTEN expression (5 year RFS 0.64 vs. 0.87, p = 0.01). In the Cox proportional hazards model for RFS, PTEN expression, progesterone receptor, and pathological therapeutic effect were predictive factors for time to recurrence (All p < 0.05).

CONCLUSIONS

PIK3CA mutations are associated with resistance to NAC but do not affect the response to NAE. Low PTEN expression does not affect response to either NAC or NAE but correlates with shorter RFS in patients who received NAC. These biomarkers will be further evaluated for clinical use to treat postmenopausal luminal breast cancer patients.

Predicting neoadjuvant treatment response in triple-negative breast cancer using machine learning

Background

Neoadjuvant chemotherapy (NAC) is the standard treatment for early-stage triple negative breast cancer (TNBC). The primary endpoint of NAC is a pathological complete response (pCR). NAC results in pCR in only 30%â€"40% of TNBC patients. Tumor-infiltrating lymphocytes (TILs), Ki67 and phosphohistone H3 (pH3) are a few known biomarkers to predict NAC response. Currently, systematic evaluation of the combined value of these biomarkers in predicting NAC response is lacking. In this study, the predictive value of markers derived from H&E and IHC stained biopsy tissue was comprehensively evaluated using a supervised machine learning (ML)-based approach. Identifying predictive biomarkers could help guide therapeutic decisions by enabling precise stratification of TNBC patients into responders and partial or non-responders.

Methods

Serial sections from core needle biopsies (n=76) were stained with H&E, and immunohistochemically for the Ki67 and pH3 markers, followed by whole slide image (WSI) generation. The resulting WSI triplets were co-registered with H&E WSIs serving as the reference. Separate mask region-based CNN (MRCNN) models were trained with annotated H&E, Ki67 and pH3 images for detecting tumor cells, stromal and intratumoral TILs (sTILs and tTILs), Ki67 + , and pH3 + cells. Top image patches with a high density of cells of interest were identified as hotspots. Best classifiers for NAC response prediction were identified by training multiple ML models, and evaluating their performance by accuracy, area under curve, and confusion matrix analyses.

Results

Highest prediction accuracy was achieved when hotspot regions were identified by tTIL counts and each hotspot was represented by measures of tTILs, sTILs, tumor cells, Ki67 + , and pH3 + features. Regardless of the hotspot selection metric, a complementary use of multiple histological features (tTILs, sTILs) and molecular biomarkers (Ki67 and pH3) resulted in top ranked performance at the patient level.

Conclusions

Overall, our results emphasize that prediction models for NAC response should be based on biomarkers in combination rather than in isolation. Our study provides compelling evidence to support the use of ML-based models to predict NAC response in patients with TNBC.

Comprehensive Profiling of Cancer-Associated Cells in the Blood of Breast Cancer Patients Undergoing Neoadjuvant Chemotherapy to Predict Pathological Complete Response

Neoadjuvant chemotherapy (NAC) can affect pathological complete response (pCR) in breast cancers; the resection that follows identifies patients with residual disease who are then offered second-line therapies. Circulating tumor cells (CTCs) and cancer-associated macrophage-like cells (CAMLs) in the blood can be used as potential biomarkers for predicting pCR before resection. CTCs are of epithelial origin that undergo epithelial-to-mesenchymal transition to become more motile and invasive, thereby leading to invasive mesenchymal cells that seed in distant organs, causing metastasis. Additionally, CAMLs in the blood of cancer patients are reported to either engulf or aid the transport of cancer cells to distant organs. To study these rare cancer-associated cells, we conducted a preliminary study where we collected blood from patients treated with NAC after obtaining their written and informed consent. Blood was collected before, during, and after NAC, and Labyrinth microfluidic technology was used to isolate CTCs and CAMLs. Demographic, tumor marker, and treatment response data were collected. Non-parametric tests were used to compare pCR and non-pCR groups. Univariate and multivariate models were used where CTCs and CAMLs were analyzed for predicting pCR. Sixty-three samples from 21 patients were analyzed. The median(IQR) pre-NAC total and mesenchymal CTC count/5 mL was lower in the pCR vs. non-pCR group [1(3.5) vs. 5(5.75); p = 0.096], [0 vs. 2.5(7.5); p = 0.084], respectively. The median(IQR) post-NAC CAML count/5 mL was higher in the pCR vs. non-pCR group [15(6) vs. 6(4.5); p = 0.004]. The pCR group was more likely to have >10 CAMLs post-NAC vs. non-pCR group [7(100%) vs. 3(21.4%); p = 0.001]. In a multivariate logistic regression model predicting pCR, CAML count was positively associated with the log-odds of pCR [OR = 1.49(1.01, 2.18); p = 0.041], while CTCs showed a negative trend [Odds Ratio (OR) = 0.44(0.18, 1.06); p = 0.068]. In conclusion, increased CAMLs in circulation after treatment combined with lowered CTCs was associated with pCR.

Use of Ultrasound and Ki-67 Proliferation Index to Predict Breast Cancer Tumor Response to Neoadjuvant Endocrine Therapy

BACKGROUND

Prediction of tumor shrinkage and pattern of treatment response following neoadjuvant endocrine therapy (NET) for estrogen receptor positive (ER+), Her2 negative (Her2-) breast cancers have had limited assessment. We examined if ultrasound (US) and Ki-67 could predict the pathologic response to treatment with NET and how the pattern of response may impact surgical planning.

METHODS

A total of 103 postmenopausal women with ER+, HER2- breast cancer enrolled on the FELINE trial had Ki-67 obtained at baseline, day 14, and surgical pathology. A total of 70 patients had an US at baseline and at the end of treatment (EOT). A total of 48 patients had residual tumor bed cellularity (RTBC) assessed. The US response was defined as complete response (CR), partial response (PR), stable disease (SD), and progressive disease (PD). CR or PR on imaging and ≤70% residual tumor bed cellularity (RTBC) defined a contracted response pattern.

RESULTS

A decrease in Ki-67 at day 14 was not predictive of EOT US response or RTBC. A contracted response pattern was identified in one patient with CR and in sixteen patients (33%) with PR on US. Although 26 patients (54%) had SD on imaging, 22 (85%) had RTBC ≤70%, suggesting a non-contracted response pattern of the tumor bed. The remaining four (15%) with SD and five with PD had no response.

CONCLUSION

Ki-67 does not predict a change in tumor size or RTBC. NET does not uniformly result in a contracted response pattern of the tumor bed. Caution should be taken when using NET for the purpose of downstaging tumor size or converting borderline mastectomy/lumpectomy patients.

The Evolution of Ki-67 and Breast Carcinoma: Past Observations, Present Directions, and Future Considerations

The 1983 discovery of a mouse monoclonal antibody-the Ki-67 antibody-that recognized a nuclear antigen present only in proliferating cells represented a seminal discovery for the pathologic assessment of cellular proliferation in breast cancer and other solid tumors. Cellular proliferation is a central determinant of prognosis and response to cytotoxic chemotherapy in patients with breast cancer, and since the discovery of the Ki-67 antibody, Ki-67 has evolved as an important biomarker with both prognostic and predictive potential in breast cancer. Although there is universal recognition among the international guideline recommendations of the value of Ki-67 in breast cancer, recommendations for the actual use of Ki-67 assays in the prognostic and predictive evaluation of breast cancer remain mixed, primarily due to the lack of assay standardization and inconsistent inter-observer and inter-laboratory reproducibility. The treatment of high-risk ER-positive/human epidermal growth factor receptor-2 (HER2) negative breast cancer with the recently FDA-approved drug abemaciclib relies on a quantitative assessment of Ki-67 expression in the treatment decision algorithm. This further reinforces the urgent need for standardization of Ki-67 antibody selection and staining interpretation, which will hopefully lead to multidisciplinary consensus on the use of Ki-67 as a prognostic and predictive marker in breast cancer. The goals of this review are to highlight the historical evolution of Ki-67 in breast cancer, summarize the present literature on Ki-67 in breast cancer, and discuss the evolving literature on the use of Ki-67 as a companion diagnostic biomarker in breast cancer, with consideration for the necessary changes required across pathology practices to help increase the reliability and widespread adoption of Ki-67 as a prognostic and predictive marker for breast cancer in clinical practice.

Neoadjuvant docetaxel and capecitabine (TX) versus docetaxel and epirubicin (TE) for locally advanced or early her2-negative breast cancer: an open-label, randomized, multi-center, phase II Trial

PURPOSE

The combination of taxanes and anthracyclines is still the mainstay of chemotherapy for early breast cancer. Capecitabine is an active drug with a favorable toxicity profile, showing strong anti-tumor activity against metastatic breast cancer. This trial assessed the efficacy and safety of the TX regimen (docetaxel and capecitabine) and compared it with the TE (docetaxel and epirubicin) regimen in locally advanced or high risk early HER2-negative breast cancer.

PATIENTS AND METHODS

This randomized clinical trial was conducted at five academic centers in China. Eligible female patients were randomly assigned (1:1) to the TX (docetaxel 75 mg/m² d1 plus capecitabine 1000 mg/m² twice d1-14, q3w) or TE (docetaxel 75 mg/m² d1 plus epirubicin 75 mg/m² d1, q3w) groups for four cycles. The primary endpoint was a pathological complete response in the breast (pCR). Secondary endpoints included pCR in the breast and axilla, invasive disease-free survival (iDFS), overall survival (OS), and safety.

RESULTS

Between September 1, 2012, and December 31, 2018, 113 HER2-negative patients were randomly assigned to the study groups (TX: n = 54; TE: n = 59). In the primary endpoint analysis, 14 patients in the TX group achieved a pCR, and nine patients in the TE group achieved a pCR (25.9% vs. 15.3%), with a not significant difference of 10.6% (95% CI -6.0-27.3%; P = 0.241). In a subgroup with high Ki-67 score, TX increased the pCR rate by 24.2% (95% CI 2.2-46.1%; P = 0.029). At the end of the 69-month median follow-up period, both groups had equivalent iDFS and OS rates. TX was associated with a higher incidence of hand-foot syndrome and less alopecia, with a manageable toxicity profile.

CONCLUSION

The anthracycline-free TX regimen yielded comparable pCR and long-term survival rates to the TE regimen. Thus, this anthracycline-free regimen could be considered in selected patients.

TRIAL REGISTRATION

ACTRN12613000206729 on 21/02/2013, retrospectively registered.

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.

Proliferation Marker Ki67 as a Stratification Index of Adjuvant Chemotherapy for Resectable Mucosal Melanoma

Background

Adjuvant chemotherapy has been shown to produce a favorable prognosis for patients with resectable mucosal melanoma (MM), resulting in the need for stratification to optimally select patients to benefit from adjuvant therapy. This study analyzed Ki67 as a potential stratification index for adjuvant chemotherapy in resectable MM.

Methods

Patients with resected MM who received subsequent adjuvant therapy in Beijing Cancer Hospital between 2010 and 2018 were retrospectively enrolled and analyzed. Relapse-free survival (RFS) and melanoma-specific survival (MSS) curves were used to perform the survival comparisons across different subgroups.

Results

From Jan 2010 to Dec 2018, 1106 MM patients were screened from a database of 4706 patients and 175 of these patients were finally enrolled. A total of 100 patients received temozolomide (TMZ)-based adjuvant chemotherapy and 75 patients received high-dose interferon-α2b (HDI) adjuvant therapy. Compared with HDI, patients who received TMZ-based adjuvant chemotherapy had significantly superior RFS (21.0 vs. 9.6 months, P = 0.002). For patients with low Ki67 expression (&lt;30%), the two regimens showed no significant difference for impact on RFS (33.9 vs. 22.7 months, P = 0.329). However, for patients with high Ki67 expression (≥30%), TMZ-based adjuvant chemotherapy achieved favorable RFS compared with HDI (18.0 vs. 6.7 months, P &lt; 0.001) and tended to improve MSS compared to HDI (41.4 vs. 25.1 months, P = 0.067).

Conclusion

Compared with HDI, adjuvant chemotherapy may be more relevant for patients with Ki67 ≥ 30%. Ki67 may serve as a potential index to distinguish populations benefiting from adjuvant chemotherapy in resectable MM, and may provide a basis for stratification in the selection of adjuvant regimens.

Developing a Prediction Model for Pathologic Complete Response Following Neoadjuvant Chemotherapy in Breast Cancer: A Comparison of Model Building Approaches

The optimal characteristics among patients with breast cancer to recommend neoadjuvant chemotherapy is an active area of clinical research. We developed and compared several approaches to developing prediction models for pathologic complete response (pCR) among patients with breast cancer in Alberta.

Quantification of Ki67 Change as a Valid Prognostic Indicator of Luminal B Type Breast Cancer After Neoadjuvant Therapy

Introduction: Ki67 value and its variation before and after neoadjuvant chemotherapy are commonly tested in relation to breast cancer patient prognosis. This study aims to quantify the extent of changes in Ki67 proliferation pre- and post-neoadjuvant chemotherapy, confirm an optimal cut-off point, and evaluate its potential value for predicting survival outcomes in patients with different molecular subtypes of breast cancer.

Methods: This retrospective real-world study recruited 828 patients at the Department of Breast Surgery of the First Affiliated Hospital of China Medical University and the Cancer Hospital of China Medical University from Jan 2014 to Nov 2020. Patient demographic features and disease pathology characteristics were recorded, and biomarkers were verified through immunohistochemistry. Various statistical methods were used to validate the relationships between different characteristics and survival outcomes irrespective of disease-free and overall survival.

Results: Among 828 patients, statistically significant effects between pathological complete response and survival outcome were found in both HER2-enriched and triple-negative breast cancer (p < 0.05) but not in Luminal breast cancer (p > 0.05). Evident decrease of Ki67 was confirmed after neoadjuvant chemotherapy. To quantify the extent of Ki67 changes between pre- and post-NAC timepoints, we adopted a computational equation termed ΔKi67% for research. We found the optimal cut-off value to be “ΔKi67% = −63%” via the operating characteristic curve, defining ΔKi67% ≤ −63% as positive status and ΔKi67% > −63% as negative status. Patients with positive ΔKi67% status were 37.1% of the entire cohort. Additionally, 4.7, 39.9, 34.5 and 39.6% of patients with Luminal A, Luminal B, HER2-enriched and triple negative breast cancer were also validated with positive ΔKi67% status. The statistically significant differences between ΔKi67% status and prognostic outcomes were confirmed by univariate and multivariate analysis in Luminal B (univariate and multivariate analysis: p < 0.05) and triple negative breast cancer (univariate and multivariate analysis: p < 0.05). We proved ΔKi67% as a statistically significant independent prognostic factor irrespective of disease-free or overall survival among patients with Luminal B and triple-negative breast cancer.

Conclusions:ΔKi67% can aid in predicting patient prognostic outcome, provide a measurement of NAC efficacy, and assist in further clinical decisions, especially for patients with Luminal B breast cancer.

Pathological responses and survival outcomes in patients with locally advanced breast cancer after neoadjuvant chemotherapy: a single-institute experience

BACKGROUND

Pathological complete response (pCR) is a surrogate for the efficacy of neoadjuvant chemotherapy (NCT) in locally advanced breast cancer (LABC). We analyzed the predictive clinical factors for pathological responses and survival outcomes in a cohort of Egyptian patients.

METHODS

We evaluated the medical records of patients with breast cancer who received NCT in our academic institute. Survival curves were estimated with the Kaplan-Meier method. Cox proportional models were used for multiple regression analysis.

RESULTS

Our cohort included 368 patients with a median age of 48 years (range 21-70). The median follow-up time was 3 years. The clinical tumor stage (T3-4) represented 58%, with 80% having positive axillary nodes. The luminal subgroup prevailed by 68%. The objective response rate (ORR) reached 78%, and 16% of patients achieved pCR. The clinical node stage and optimal chemotherapy were associated with higher ORR (p = 0.035 and p = 0.001, respectively). Predictors of pCR were clinical T-stage (p = 0.026), high Ki-67 index > 20 (p = 0.05), and receiving optimal chemotherapy (p = 0.014). The estimated 3-year disease free-survival (DFS) was 53%. Receptor status, achieving ORR, and pCR were associated with better DFS with hazard ratios of 0.56, p = 0.008; 0.38, p = 0.04; and 0.28, p = 0.007, respectively.

CONCLUSIONS

Luminal tumors still draw benefit from neoadjuvant chemotherapy in terms of clinical response and breast conservative surgery. Treatment escalation to those who did not achieve pCR requires more investigation, given a higher recurrence rate in real-world experience.

Construction and Validation of a Serum Albumin-to-Alkaline Phosphatase Ratio-Based Nomogram for Predicting Pathological Complete Response in Breast Cancer

Background

Breast cancer patients who achieve pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) have favorable outcomes. Reliable predictors for pCR help to identify patients who will benefit most from NAC. The pretreatment serum albumin-to-alkaline phosphatase ratio (AAPR) has been shown to be a prognostic predictor in several malignancies, but its predictive value for pCR in breast cancer is still unknown. This study aims to investigate the predictive role of AAPR in breast cancer patients and develop an AAPR-based nomogram for pCR rate prediction.

Methods

A total of 780 patients who received anthracycline and taxane-based NAC from January 2012 to March 2018 were retrospectively analyzed. Univariate and multivariate analyses were performed to assess the predictive value of AAPR and other clinicopathological factors. A nomogram was developed and calibrated based on multivariate logistic regression. A validation cohort of 234 patients was utilized to further validate the predictive performance of the model. The C-index, calibration plots and decision curve analysis (DCA) were used to evaluate the discrimination, calibration and clinical value of the model.

Results

Patients with a lower AAPR (<0.583) had a significantly reduced pCR rate (OR 2.228, 95% CI 1.246-3.986, p=0.007). Tumor size, clinical nodal status, histological grade, PR, Ki67 and AAPR were identified as independent predictors and included in the final model. The nomogram was used as a graphical representation of the model. The nomogram had satisfactory calibration and discrimination in both the training cohort and validation cohort (the C-index was 0.792 in the training cohort and 0.790 in the validation cohort). Furthermore, DCA indicated a clinical net benefit from the nomogram.

Conclusions

Pretreatment serum AAPR is a potentially valuable predictor for pCR in breast cancer patients who receive NAC. The AAPR-based nomogram is a noninvasive tool with favorable predictive accuracy for pCR, which helps to make individualized treatment strategy decisions.

CDKN1C as a prognostic biomarker correlated with immune infiltrates and therapeutic responses in breast cancer patients

Breast cancer (BC) prognosis and therapeutic sensitivity could not be predicted efficiently. Previous evidence have shown the vital roles of CDKN1C in BC. Therefore, we aimed to construct a CDKN1C‐based model to accurately predicting overall survival (OS) and treatment responses in BC patients. In this study, 995 BC patients from The Cancer Genome Atlas database were selected. Kaplan‐Meier curve, Gene set enrichment and immune infiltrates analyses were executed. We developed a novel CDKN1C‐based nomogram to predict the OS, verified by the time‐dependent receiver operating characteristic curve, calibration curve and decision curve. Therapeutic response prediction was followed based on the low‐ and high‐nomogram score groups. Our results indicated that low‐CDKN1C expression was associated with shorter OS and lower proportion of naïve B cells, CD8 T cells, activated NK cells. The predictive accuracy of the nomogram for 5‐year OS was superior to the tumour‐node‐metastasis stage (area under the curve: 0.746 vs. 0.634, p < 0.001). The nomogram exhibited excellent predictive performance, calibration ability and clinical utility. Moreover, low‐risk patients were identified with stronger sensitivity to therapeutic agents. This tool can improve BC prognosis and therapeutic responses prediction, thus guiding individualized treatment decisions.

The Impact of Preoperative Radiomics Signature on the Survival of Breast Cancer Patients With Residual Tumors After NAC

Background

Residual cancer cells remaining after chemotherapy may have more aggressive behavior that promotes recurrence or metastasis, and which patients would benefit from subsequent additional treatment is controversial. The purpose of our study was to evaluate the prognostic value of the preoperative radiomics features of computed tomography (CT) imaging in breast cancer (BC) patients with residual tumors after neoadjuvant chemotherapy (NAC).

Methods

Post-NAC CT images were reviewed from 114 patients who had received breast surgery and had residual breast tumors. The association of the 110 radiomics features derived from CT images with 5-year disease-free survival (DFS) was assessed by log-rank test in the training cohort, resulting in 13 prognostic radiomics features.

Results

We constructed a radiomics signature consisting of four selected features by using least absolute shrinkage and selection operator (LASSO) Cox regression analysis, which performed well in the discrimination with an area under the curve (AUC) of 0.78 (95% CI, 0.67–0.89) and 0.73 (95% CI, 0.59–0.87) in the training and validation cohorts, respectively. Radiomics nomogram, incorporating the radiomics signature with the conventional clinical variables, also performed well in the two cohorts (training cohort: AUC, 0.84; validation cohort: AUC, 0.82). Moreover, we found that the high-risk patients determined by our radiomics nomogram could benefit from postoperative adjuvant chemotherapy, while the low-risk and total patient groups could not.

Conclusions

Our novel radiomics nomogram is a promising and favorable prognostic biomarker for preoperatively predicting survival outcomes and may aid in clinical decision-making in BC patients with residual tumors after NAC.

The Role of Ki67 in Evaluating Neoadjuvant Endocrine Therapy of Hormone Receptor-Positive Breast Cancer

Ki67 is a proliferation marker. It has been proposed as a useful clinical marker for breast cancer subtype classification, prognosis, and prediction of therapeutic response. But the questionable analytical validity of Ki67 prevents its widespread adoption of these measures for treatment decisions in breast cancer. Currently, Ki67 has been tested as a predictive marker for chemotherapy using clinical and pathological response as endpoints in neoadjuvant endocrine therapy. Ki67 can be used as a predictor to evaluate the recurrence-free survival rate of patients, or its change can be used to predict the preoperative "window of opportunity" in neoadjuvant endocrine therapy. In this review, we will elaborate on the role of Ki67 in neoadjuvant endocrine therapy in breast cancer.

Development and Validation of a Nomogram for Individually Predicting Pathologic Complete Remission After Preoperative Chemotherapy in Chinese Breast Cancer: A Population-Based Study

PURPOSE

To explore the independent predictors of pathologic complete remission response (pCR) for Chinese patients with breast cancer (BC) after preoperative chemotherapy and to develop an individualized nomogram for predicting the probability of pCR.

PATIENTS AND METHODS

The clinicopathologic data of clinical stage I-III BC patients who received preoperative chemotherapy in Xijing Hospital were retrospectively analyzed. A total of 689 BC patients diagnosed in 2015-2017 were included in the training set to develop a nomogram. A separate cohort of 357 patients in the same center was regarded as a validation set for externally examining the performance of the model. The area under the receiver operating characteristic curve and calibration curve were used to verify the predictive performance of the nomogram.

RESULTS

Multivariate logistic regression analysis showed that independent predictors of pCR were menopause status at diagnosis, family history of BC, initial tumor size, estrogen receptor status, HER2/neu (human epidermal growth factor receptor 2) status, and Ki-67 expression. On the basis of these factors, a nomogram was developed using R software. Our nomogram had good discrimination in the training and validation set (area under the receiver operating characteristic curve, 0.762 and 0.768, respectively). The calibration curves further confirmed that the model performs well.

CONCLUSION

Menopause status and family history of BC were independent predictors of pCR after preoperative chemotherapy for the first time. The nomogram can accurately predict pCR rate in BC, which may provide some guidelines for breast surgery options and patient counseling.

Impact on survival of estrogen receptor, progesterone receptor and Ki-67 expression discordance pre- and post-neoadjuvant chemotherapy in breast cancer

PURPOSE

To investigate whether estrogen receptor (ER), progesterone receptor (PR) and Ki-67 expression discordance before and after neoadjuvant chemotherapy (NAC) correlates with prognosis and treatment of breast cancer patients.

METHODS

The study cohort included 482 breast cancer patients at the Zhejiang Cancer Hospital from January 1, 2008, to December 31, 2018. Core needle biopsies and excised tissue biopsies pre- and post-NAC were obtained. Immunohistochemistry was used to determine ER, PR and Ki-67 status. The relationship between biomarker discordance before and after NAC and clinicopathological features was compared retrospectively.

RESULTS

ER (n = 482), PR (n = 482) and Ki-67 (n = 448) expression was assessed in the same lesion pre- and post-NAC. Discordance in the three markers pre- and post-NAC was observed in 50 (10.4%), 82 (17.0%) and 373 (77.4%) cases, respectively. Positive-to-negative PR expression changes were the most common type of discordance observed. The risk of death in patients with a PR positive-to-negative conversion was 6.58 times greater than for patients with stable PR expression. The risk of death in patients with increased Ki-67 expression following NAC treatment was 2.05 times greater than for patients with stable Ki-67 expression.

CONCLUSION

Breast cancer patients showed changes in ER, PR and/or Ki-67 status throughout NAC, and these changes possibly influenced disease-free survival and overall survival. A switch to negative hormone receptor expression with increased Ki-67 expression following NAC could be indicators of a worse prognosis. Biomarker expression investigations following NAC may potentially improve patient management and survival.

18F-FDG PET/CT radiomic predictors of pathologic complete response (pCR) to neoadjuvant chemotherapy in breast cancer patients

PURPOSE

Pathologic complete response (pCR) to neoadjuvant chemotherapy (NAC) is commonly accepted as the gold standard to assess outcome after NAC in breast cancer patients. ¹⁸F-Fluorodeoxyglucose positron emission tomography/computed tomography (PET/CT) has unique value in tumor staging, predicting prognosis, and evaluating treatment response. Our aim was to determine if we could identify radiomic predictors from PET/CT in breast cancer patient therapeutic efficacy prior to NAC.

METHODS

This retrospective study included 100 breast cancer patients who received NAC; there were 2210 PET/CT radiomic features extracted. Unsupervised and supervised machine learning models were used to identify the prognostic radiomic predictors through the following: (1) selection of the significant (p < 0.05) imaging features from consensus clustering and the Wilcoxon signed-rank test; (2) selection of the most discriminative features via univariate random forest (Uni-RF) and the Pearson correlation matrix (PCM); and (3) determination of the most predictive features from a traversal feature selection (TFS) based on a multivariate random forest (RF). The prediction model was constructed with RF and then validated with 10-fold cross-validation for 30 times and then independently validated. The performance of the radiomic predictors was measured in terms of area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).

RESULTS

The PET/CT radiomic predictors achieved a prediction accuracy of 0.857 (AUC = 0.844) on the training split set and 0.767 (AUC = 0.722) on the independent validation set. When age was incorporated, the accuracy for the split set increased to 0.857 (AUC = 0.958) and 0.8 (AUC = 0.73) for the independent validation set and both outperformed the clinical prediction model. We also found a close association between the radiomic features, receptor expression, and tumor T stage.

CONCLUSION

Radiomic predictors from pre-treatment PET/CT scans when combined with patient age were able to predict pCR after NAC. We suggest that these data will be valuable for patient management.

Predictive and Prognostic Roles of Pathological Indicators for Patients with Breast Cancer on Neoadjuvant Chemotherapy

Currently, neoadjuvant chemotherapy is a standard therapeutic strategy for breast cancer, as it can provide timely and individualized chemo-sensitivity information and is beneficial for custom-designing subsequent treatment strategies. To accurately select candidates for neoadjuvant chemotherapy, the association between various immunohistochemical biomarkers of primary disease and tumor response to neoadjuvant chemotherapy has been investigated, and results have shown that certain pathological indicators evaluated after neoadjuvant chemotherapy are associated with long-term prognosis. The Food and Drug Administration (FDA) has recommended that complete pathological response can be used as a surrogate endpoint for neoadjuvant chemotherapy, which is related to better prognosis. Considering that residual tumor persists in the majority of patients after neoadjuvant chemotherapy, the value of various pathological indicators of residual disease in predicting the long-term outcomes is being extensively investigated. This review summarizes and compares various predictive and prognostic indicators for patients who have received neoadjuvant chemotherapy, and analyzes their efficacy in different breast cancer subtypes.

Ki-67 (30-9) scoring and differentiation of Luminal A- and Luminal B-like breast cancer subtypes

INTRODUCTION

Ki-67 labeling index assessed by immunohistochemical assays has been shown useful in assessing the risk of recurrence for estrogen receptor (ER)-positive HER2-negative breast cancers (BC) and distinguishing Luminal A-like from Luminal B-like tumors. We aimed to assess the performance of the Ventana CONFIRM anti-Ki-67 (30-9) Rabbit Monoclonal Primary Antibody.

METHODS

We constructed a case-cohort design based on a random sample (n = 679) of all patients operated on for a first primary, non-metastatic, ER-positive, HER2-negative BC at the European Institute of Oncology (IEO) Milan, Italy during 1998-2002 and all additional patients (n = 303) operated during the same period, who developed an event (metastasis in distant organs or death due to BC as primary event) and were not included in the previous subset. Multivariable Cox proportional hazards regression with inverse subcohort sampling probability weighting was used to evaluate the risk of event according to Ki-67 (30-9) and derived intrinsic molecular subtype, using previously defined cutoff values, i.e., respectively 14% and 20%.

RESULTS

Ki-67 was < 14% in 318 patients (32.4%), comprised between 14 and 19% in 245 patients (24.9%) and ≥ 20 in 419 patients (42.7%). At multivariable analysis, the risk of developing distant disease was 1.88 (95% CI 1.20-2.93; P = 0.006) for those with Ki-67 comprised between 14 and 19%, and 3.06 (95% CI 1.93-4.84; P < 0.0001) for those with Ki-67 ≥ 20% compared to those with Ki-67 < 14%. Patients with Luminal B-like BC had an approximate twofold risk of developing distant disease (HR = 1.91; 95% CI 1.35-2.71; P = 0.0003) than patients with Luminal A-like BC defined using Ki-67 (30-9).

CONCLUSIONS

Ki-67 evaluation using the 30-9 rabbit monoclonal primary antibody was able to stratify patients with ER-positive HER2-negative BC into prognostically distinct groups. Ki-67 assessment, with strict adherence to the international recommendations, should be included among the clinically useful biological parameters for the best treatment of patients with BC.

Evaluation of Breast and Axillary Lymph Node Specimens in Breast Cancer Patients Treated With Neoadjuvant Systemic Therapy

Breast and axillary lymph node specimens from breast cancer patients treated with neoadjuvant systemic therapy are being encountered by pathologists with increasing frequency. Evaluation of these specimens presents challenges that differ from those encountered during the examination of other types of breast specimens. This article reviews the key issues regarding the gross and microscopic evaluation of post-neoadjuvant systemic therapy breast and lymph node specimens, and emphasizes the importance of accurate specimen evaluation in assessing treatment response.