Predictors of pathologic complete response after standard neoadjuvant chemotherapy in triple-negative breast carcinoma

Impact of Neoadjuvant Therapy on PD-L1 Expression in Triple-Negative Breast Cancer and Correlation with Clinicopathological Factors

BACKGROUND

This study aims to deliver more insights on the impact of neoadjuvant treatment on Pd-L1 expression and to evaluate its correlation with clinicopathological factors.

METHODS

We reviewed 88 TNBC cases for the period 2021-2023. Data on age, tumor size, stage, and treatment were collected. Histological slides were assessed for subtype, grade, and TILs. A total of 48 received neoadjuvant treatment. HER2 and Ki67 were evaluated via immunohistochemistry. PD-L1 expression was tested on primary and residual tumors. Statistical analysis was performed using IBM SPSS (p < 0.05).

RESULTS

In this study, PD-L1 positive expression was found in 44.3% of primary tumors, with 52.9% of initially positive cases losing expression post-treatment. TILs were significantly higher in PD-L1-positive tumors (mean 41.79% vs. 27.55%, p = 0.001). A notable correlation was found between PD-L1 expression and Ki-67 proliferation index, with PD-L1-positive tumors having a median Ki-67 of 64.49 compared to 52.86 in negative cases (p = 0.015). Neoadjuvant immunotherapy led to a lower mean residual cancer burden (0.95 vs. 2.55, p = 0.002) compared to chemotherapy alone. Higher Ki-67 levels (≥50%) were associated with better treatment outcomes, showing a mean RCB score of 1.60 versus 3.16 for lower levels (p = 0.022). HER2-negative cases had a higher prevalence of favorable pathological response (54.5%) compared to HER2-low tumors (25%, p = 0.048), because of the strong correlation to high proliferative index.

CONCLUSIONS

In conclusion, PD-L1 expression in TNBC shows significant discordance post-treatment, highlighting the need for routine testing and further research on predictive biomarkers.

HER2-low and tumor infiltrating lymphocytes in triple-negative breast cancer: Are they connected?

Most patients with triple-negative breast cancer (TNBC) are not candidates for targeted therapy, leaving chemotherapy as the primary treatment option. Recently, immunotherapy has demonstrated promising results in TNBC, due to its immunogenicity. In addition, a novel antibody-drug conjugate, namely, trastuzumab-deruxtecan, has shown effectiveness in TNBC patients with low-HER2 expression (HER2-low). These novel treatment options raise the question about the potential association between the density of stromal tumor-infiltrating lymphocytes (sTILs) and the level of HER2 expression. We aimed to evaluate the association between the level of HER2 expression (HER2-low versus HER2-0) and density of sTILs in TNBC patients, and how they impact the response to neoadjuvant chemotherapy (NAC). This was a retrospective multicenter study including all TNBC patients diagnosed between 2018 and 2022. Central pathology review included sTILs percentages and level of HER2 expression. Tumors were reclassified as either HER2-0 (HER2 IHC 0) or HER2-low (IHC 1 + or 2 + with negative reflex test). Various clinicopathologic characteristics, including sTILs density, and response to NAC were compared between HER2-0 and HER2-low cases. In total, 753 TNBC patients were included in this study, of which 292 patients received NAC. Interobserver agreement between the original pathology report and central review was moderate (77% had the same IHC status after reclassification in either HER2-0 or HER2-low; k = 0.45). HER2-low TNBC represented about one third (36%) of the tumors. No significant difference in sTILs density or complete pathologic response rate was found between HER2-0 and HER2-low cases (p = 0.476 and p = 0.339, respectively). The density of sTILs (≥ 10% sTILs vs. < 10%) was independently associated with achieving a pCR (p = 0.011). In conclusion, no significant association was found between HER2-low status and density of sTILs nor response to NAC. Nonetheless, sTILs could be an independent biomarker for predicting NAC response in TNBC patients.

Emerging Prognostic and Predictive Significance of Stress Keratin 17 in HPV-Associated and Non HPV-Associated Human Cancers: A Scoping Review

A growing body of literature suggests that the expression of cytokeratin 17 (K17) correlates with inferior clinical outcomes across various cancer types. In this scoping review, we aimed to review and map the available clinical evidence of the prognostic and predictive value of K17 in human cancers. PubMed, Web of Science, Embase (via Scopus), Cochrane Central Register of Controlled Trials, and Google Scholar were searched for studies of K17 expression in human cancers. Eligible studies were peer-reviewed, published in English, presented original data, and directly evaluated the association between K17 and clinical outcomes in human cancers. Of the 1705 studies identified in our search, 58 studies met criteria for inclusion. Studies assessed the prognostic significance (n = 54), predictive significance (n = 2), or both the prognostic and predictive significance (n = 2). Altogether, 11 studies (19.0%) investigated the clinical relevance of K17 in cancers with a known etiologic association to HPV; of those, 8 (13.8%) were focused on head and neck squamous cell carcinoma (HNSCC), and 3 (5.1%) were focused on cervical squamous cell carcinoma (SCC). To date, HNSCC, as well as triple-negative breast cancer (TNBC) and pancreatic cancer, were the most frequently studied cancer types. K17 had prognostic significance in 16/17 investigated cancer types and 43/56 studies. Our analysis suggests that K17 is a negative prognostic factor in the majority of studied cancer types, including HPV-associated types such as HNSCC and cervical cancer (13/17), and a positive prognostic factor in 2/17 studied cancer types (urothelial carcinoma of the upper urinary tract and breast cancer). In three out of four predictive studies, K17 was a negative predictive factor for chemotherapy and immune checkpoint blockade therapy response.

Predicting Response of Triple-Negative Breast Cancer to Neoadjuvant Chemotherapy Using a Deep Convolutional Neural Network-Based Artificial Intelligence Tool

PURPOSE

Achieving a pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) is associated with improved patient outcomes in triple-negative breast cancer (TNBC). Currently, there are no validated predictive biomarkers for the response to NAC in TNBC. We developed and validated a deep convolutional neural network-based artificial intelligence (AI) model to predict the response of TNBC to NAC.

MATERIALS AND METHODS

Whole-slide images (WSIs) of hematoxylin and eosin-stained core biopsies from 165 (pCR in 60 and non-pCR in 105) and 78 (pCR in 31 and non-pCR in 47) patients with TNBC were used to train and validate the model. The model extracts morphometric features from WSIs in an unsupervised manner, thereby generating clusters of morphologically similar patterns. Downstream ranking of clusters provided regions of interest and morphometric scores; a low score close to zero and a high score close to one represented a high or low probability of response to NAC.

RESULTS

The predictive ability of AI score for the entire cohort of 78 patients with TNBC ascertained by receiver operating characteristic analysis demonstrated an area under the curve (AUC) of 0.75. The AUC for stages I, II, and III disease were 0.88, 0.73, and 0.74, respectively. Using a cutoff value of 0.35, the positive predictive value of the AI score for pCR was 73.7%, and the negative predictive value was 76.2% for non-pCR patients.

CONCLUSION

To our knowledge, this study is the first to demonstrate the use of an AI tool on digitized hematoxylin and eosin-stained tissue images to predict the response to NAC in patients with TNBC with high accuracy. If validated in subsequent studies, these results may serve as an ancillary aid for individualized therapeutic decisions in patients with TNBC.

Triple-Negative Breast Cancer and Predictive Markers of Response to Neoadjuvant Chemotherapy: A Systematic Review

Around 40-50% of all triple-negative breast cancer (TNBC) patients achieve a pathological complete response (pCR) after treatment with neoadjuvant chemotherapy (NAC). The identification of biomarkers predicting the response to NAC could be helpful for personalized treatment. This systematic review provides an overview of putative biomarkers at baseline that are predictive for a pCR following NAC. Embase, Medline and Web of Science were searched for articles published between January 2010 and August 2022. The articles had to meet the following criteria: patients with primary invasive TNBC without distant metastases and patients must have received NAC. In total, 2045 articles were screened by two reviewers resulting in the inclusion of 92 articles. Overall, the most frequently reported biomarkers associated with a pCR were a high expression of Ki-67, an expression of PD-L1 and the abundance of tumor-infiltrating lymphocytes, particularly CD8+ T cells, and corresponding immune gene signatures. In addition, our review reveals proteomic, genomic and transcriptomic markers that relate to cancer cells, the tumor microenvironment and the peripheral blood, which also affect chemo-sensitivity. We conclude that a prediction model based on a combination of tumor and immune markers is likely to better stratify TNBC patients with respect to NAC response.

Analysis of tumor nuclear features using artificial intelligence to predict response to neoadjuvant chemotherapy in high-risk breast cancer patients

PURPOSE

Neoadjuvant chemotherapy (NAC) is used to treat patients with high-risk breast cancer. The tumor response to NAC can be classified as either a pathological partial response (pPR) or pathological complete response (pCR), defined as complete eradication of invasive tumor cells, with a pCR conferring a significantly lower risk of recurrence. Predicting the response to NAC, however, remains a significant clinical challenge. The objective of this study was to determine if analysis of nuclear features on core biopsies using artificial intelligence (AI) can predict response to NAC.

METHODS

Fifty-eight HER2-positive or triple-negative breast cancer patients were included in this study (pCR n = 37, pPR n = 21). Multiple deep convolutional neural networks were developed to automate tumor detection and nuclear segmentation. Nuclear count, area, and circularity, as well as image-based first- and second-order features including mean pixel intensity and correlation of the gray-level co-occurrence matrix (GLCM-COR) were determined.

RESULTS

In univariate analysis, the pCR group had fewer multifocal/multicentric tumors, higher nuclear intensity, and lower GLCM-COR compared to the pPR group. In multivariate binary logistic regression, tumor multifocality/multicentricity (OR = 0.14, p = 0.012), nuclear intensity (OR = 1.23, p = 0.018), and GLCM-COR (OR = 0.96, p = 0.043) were each independently associated with likelihood of achieving a pCR, and the model was able to successful classify 79% of cases (62% for pPR and 89% for pCR).

CONCLUSION

Analysis of tumor nuclear features using digital pathology/AI can significantly improve models to predict pathological response to NAC.

Analysis of the radio-pathological factors of triple negative breast cancer and determination of risk profiles

OBJECTIVE

Triple-negative tumors are the most aggressive type of breast cancer. We aimed to analyze the main radiologic and histopathologic factors of these tumors to create a risk profile.

MATERIALS AND METHODS

We analyzed data from 140 patients diagnosed with triple-negative breast cancer between January 2007 and December 2016, with follow-up through April 2018. We analyzed the following variables in the breast MRI done for staging: size, necrosis, associated findings, adenopathies, and perfusion and diffusion parameters. We analyzed the following variables in histopathologic studies of biopsy specimens: histological type, Scarf-Bloom, Ki67, and p53 in the infiltrating component as well as in the in situ component. We analyzed the following variables in histopathologic studies of positive lymph nodes and surgical specimens: size, lymphovascular/perineural invasion, and microglandular adenosis. We analyzed the relation between the radiologic and histopathologic factors and recurrence and disease-free survival.

RESULTS

MRI tumor size>25mm, non-nodular enhancement, breast edema, areola-nipple complex retraction, and lymph-node involvement were associated with recurrence and lower disease-free survival. Invasive lobular carcinoma, postsurgical size>20mm, and p53<15% were also associated with recurrence and lower disease-free survival. Histologically positive lymph nodes were associated with a greater percentage of recurrence and lymphovascular invasion and with lower disease-free survival. The multivariate analysis found that the variables MRI size>25mm, non-nodular enhancement, adenopathies on MRI, and p53 expression <15% were independent predictors of lower disease-free survival.

CONCLUSIONS

In triple-negative breast tumors, factors associated with lower disease-free survival are non-nodular enhancement, size>25mm, and adenopathies on MRI, and p53 expression <15% on histopathologic study.

Patient Selection for Clinical Trials Eliminating Surgery for HER2-Positive Breast Cancer Treated with Neoadjuvant Systemic Therapy

BACKGROUND

Patients with epidermal growth factor receptor 2-positive (HER2+) breast cancer and pathologic complete response (pCR) after neoadjuvant systemic therapy (NST) may be candidates for nonoperative clinical trials if residual invasive and in situ disease are eradicated.

METHODS

This study analyzed 280 patients with clinical T1-2N0-1 HER2+ breast cancer who underwent NST followed by surgical resection to determine key characteristics of patients with pCR in the breast and lymph nodes compared with those with residual disease.

RESULTS

Of the 280 patients, 102 (36.4%) had pCR in the breast and lymph nodes after NST, and 50 patients (17.9%) had residual ductal carcinoma in situ (DCIS) in the breast only. For 129 patients (46.1%), DCIS was present on the pretreatment biopsy, and NST failed to eradicate the DCIS component in 64.3%. Patients with residual disease were more likely to have hormone receptor-positive (HR+) tumors than those with negative tumors (73.4% vs. 50.8%; p < 0.0001). Radiologic response (odds ratio [OR], 5.62; p = 0.002) and HR+ status (OR, 2.56; p < 0.0001) were predictive of residual disease. Combined imaging methods after NST had a sensitivity of 97.1% and a negative predictive value of 70.6% for detection of residual disease. Patients with invasive disease and DCIS shown on the pretreatment core biopsy were less likely than those without DCIS to achieve pCR in the breast (31% vs. 43%; p  = 0.038).

CONCLUSION

The study results delineate and identify unique characteristics associated with HER2+ breast cancers that are important in selecting patients for inclusion in clinical trials assessing nonoperative management after NST, and the low negative predictive value of imaging mandates image-guided biopsy for selection.

Baseline factors predicting a response to neoadjuvant chemotherapy with implications for non-surgical management of triple-negative breast cancer

BACKGROUND

Patients with triple-negative breast cancer (TNBC) and a pathological complete response (pCR) after neoadjuvant chemotherapy may be suitable for non-surgical management. The goal of this study was to identify baseline clinicopathological variables that are associated with residual disease, and to evaluate the effect of neoadjuvant chemotherapy on both the invasive and ductal carcinoma in situ (DCIS) components in TNBC.

METHODS

Patients with TNBC treated with neoadjuvant chemotherapy followed by surgical resection were identified. Patients with a pCR were compared with those who had residual disease in the breast and/or lymph nodes. Clinicopathological variables were analysed to determine their association with residual disease.

RESULTS

Of the 328 patients, 36·9 per cent had no residual disease and 9·1 per cent had residual DCIS only. Patients with residual disease were more likely to have malignant microcalcifications (P = 0·023) and DCIS on the initial core needle biopsy (CNB) (P = 0·030). Variables independently associated with residual disease included: DCIS on CNB (odds ratio (OR) 2·46; P = 0·022), T2 disease (OR 2·40; P = 0·029), N1 status (OR 2·03; P = 0·030) and low Ki-67 (OR 2·41; P = 0·083). Imaging after neoadjuvant chemotherapy had an accuracy of 71·7 (95 per cent c.i. 66·3 to 76·6) per cent and a negative predictive value of 76·9 (60·7 to 88·9) per cent for identifying residual disease in the breast and lymph nodes. Neoadjuvant chemotherapy did not eradicate the DCIS component in 55 per cent of patients.

CONCLUSION

The presence of microcalcifications on imaging and DCIS on initial CNB are associated with residual disease after neoadjuvant chemotherapy in TNBC. These variables can aid in identifying patients with TNBC suitable for inclusion in trials evaluating non-surgical management after neoadjuvant chemotherapy.

Association of T-cadherin levels with the response to neoadjuvant chemotherapy in locally advanced breast cancer

Purpose

To examine the association of T-cadherin with pathologic complete response (pCR) after neoadjuvant chemotherapy for locally advanced breast cancer.

Results

T-cadherin expression before and after neoadjuvant chemotherapy was similar (P = 0.162). The multivariable analysis indicated that negative T-cadherin expression was independently associated with pCR after neoadjuvant TAC chemotherapy (P = 0.001).

Materials and Methods

A total of 136 patients with locally advanced breast cancer received four cycles of neoadjuvant TAC chemotherapy (docetaxel + epirubicin + cyclophosphamide), followed by surgery. T-cadherin, estrogen receptor (ER), progesterone receptor (PR), HER-2, and Ki-67 were analyzed by immunohistochemistry. The association between T-cadherin expression and pCR after neoadjuvant chemotherapy was analyzed using multivariable logistic analysis.

Conclusions

Negative T-cadherin expression before and after neoadjuvant chemotherapy for locally advanced breast cancer was similar. T-cadherin could be considered an independent factor associated with the efficacy of such therapy.

The predictive value of Ki-67 before neoadjuvant chemotherapy for breast cancer: a systematic review and meta-analysis

AIM

To review the predictive values of Ki-67 before neoadjuvant chemotherapy (NAC) for breast cancer patients.

METHODS

PubMed and EMBASE were searched. Random-effect model meta-analysis was conducted using Revman software.

RESULTS

High Ki-67 was associated with more pathological complete responses (pCRs) events (odds ratio: 3.10; 95% CI: 2.52-3.81; 53 studies, 10,848 patients) regardless of HR⁺, HER2⁺ and triple-negative breast cancer types, the definitions of pCR and cut-off points for Ki-67. Ki-67 could predict pCR in those who received anthracyclines plus taxanes, and anthracyclines only, and those from Asia and Europe.

CONCLUSION

High Ki-67 before NAC was a predictor for pCR in neoadjuvant setting for breast cancer patients.

Clinical relevance of host immunity in breast cancer: from TILs to the clinic

The clinical relevance of the host immune system in breast cancer has long been unexplored. Studies developed over the past decade have highlighted the biological heterogeneity of breast cancer, prompting researchers to investigate whether the role of the immune system in this malignancy is similar across different molecular subtypes of the disease. The presence of high levels of lymphocytic infiltration has been consistently associated with a more-favourable prognosis in patients with early stage triple-negative and HER2-positive breast cancer. These infiltrates seem to reflect favourable host antitumour immune responses, suggesting that immune activation is important for improving survival outcomes. In this Review, we discuss the composition of the immune infiltrates observed in breast cancers, as well as data supporting the clinical relevance of host antitumour immunity, as represented by lymphocytic infiltration, and how this biomarker could be used in the clinical setting. We also discuss the rationale for enhancing immunity in breast cancer, including early data on the efficacy of T-cell checkpoint inhibition in this setting.

Triple-negative breast cancer: bridging the gap from cancer genomics to predictive biomarkers

Triple-negative breast cancer (TNBC) represents a challenge clinically due to a lack of response to hormonal and HER2-targeted agents coupled with an aggressive disease course. As the biology of this breast cancer subtype is better understood, it is clear that TNBC is a heterogeneous disease and one targeted therapy is unlikely to be active in all patients. Biomarkers predictive of response to treatment are thus of great importance in TNBC. This review outlines studies evaluating biomarkers predictive of response to neoadjuvant chemotherapy and to targeted therapies in the advanced setting. The development of validated biomarkers in conjunction with novel targeted therapies represents an opportunity to improve patient outcomes in TNBC.