Evaluation of the Predictive and Prognostic Values of Stromal Tumor-Infiltrating Lymphocytes in HER2-Positive Breast Cancers treated with neoadjuvant chemotherapy

Predictive significance of HIF-1α, Snail, and PD-L1 expression in breast cancer

Currently, the prediction of breast cancer (BC) effectiveness to drug treatment is based on determining the expression level of steroid hormone receptors and human epidermal growth factor receptor type 2 (HER2). However, significant differences in individual response to drug treatment require the search for new predictive markers. Here, by comprehensively examining HIF-1α, Snail, and PD-L1 expression in BC tumor tissue, we demonstrate that high levels of these markers correlate with unfavorable factors of BC prognosis: the presence of regional and distant metastases and lymphovascular and perineural invasion. Analyzing the predictive significance of markers, we show that the most significant predictors of chemoresistant HER2-negative BC are a high PD-L1 level and a low Snail level, while in HER2-positive BC, only a high PD-L1 level is an independent predictor of chemoresistant BC. Our results suggest that using immune checkpoint inhibitors in these groups of patients may improve drug therapy effectiveness.

Construction and validation of a novel prognostic model for thyroid cancer based on N7-methylguanosine modification-related lncRNAs

BACKGROUND

To construct and verify a novel prognostic model for thyroid cancer (THCA) based on N7-methylguanosine modification-related lncRNAs (m7G-lncRNAs) and their association with immune cell infiltration.

METHODS

In this study, we identified m7G-lncRNAs using co-expression analysis and performed differential expression analysis of m7G-lncRNAs between groups. We then constructed a THCA prognostic model, performed survival analysis and risk assessment for the THCA prognostic model, and performed independent prognostic analysis and receiver operating characteristic curve analyses to evaluate and validate the prognostic value of the model. Furthermore, analysis of the regulatory relationship between prognostic differentially expressed m7G-related lncRNAs (PDEm7G-lncRNAs) and mRNAs and correlation analysis of immune cells and risk scores in THCA patients were carried out.

RESULTS

We identified 29 N7-methylguanosine modification-related mRNAs and 116 differentially expressed m7G-related lncRNAs, including 87 downregulated and 29 upregulated lncRNAs. Next, we obtained 8 PDEm7G-lncRNAs. A final optimized model was constructed consisting of 5 PDEm7G-lncRNAs (DOCK9-DT, DPP4-DT, TMEM105, SMG7-AS1 and HMGA2-AS1). Six PDEm7G-lncRNAs (DOCK9-DT, DPP4-DT, HMGA2-AS1, LINC01976, MID1IP1-AS1, and SMG7-AS1) had positive regulatory relationships with 10 PDEm7G-mRNAs, while 2 PDEm7G-lncRNAs (LINC02026 and TMEM105) had negative regulatory relationships with 2 PDEm7G-mRNAs. Survival curves and risk assessment predicted the prognostic risk in both groups of patients with THCA. Forest maps and receiver operating characteristic curves were used to evaluate and validate the prognostic value of the model. Finally, we demonstrated a correlation between different immune cells and risk scores.

CONCLUSION

Our results will help identify high-risk or low-risk patients with THCA and facilitate early prediction and clinical intervention in patients with high risk and poor prognosis.

Deep learning with biopsy whole slide images for pretreatment prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer：A multicenter study

INTRODUCTION

Predicting pathological complete response (pCR) for patients receiving neoadjuvant chemotherapy (NAC) is crucial in establishing individualized treatment. Whole-slide images (WSIs) of tumor tissues reflect the histopathologic information of the tumor, which is important for therapeutic response effectiveness. In this study, we aimed to investigate whether predictive information for pCR could be detected from WSIs.

MATERIALS AND METHODS

We retrospectively collected data from four cohorts of 874 patients diagnosed with biopsy-proven breast cancer. A deep learning pathological model (DLPM) was constructed to predict pCR using biopsy WSIs in the primary cohort, and it was then validated in three external cohorts. The DLPM could generate a deep learning pathological score (DLPs) for each patient; stromal tumor-infiltrating lymphocytes (TILs) were selected for comparison with DLPs.

RESULTS

The WSI feature-based DLPM showed good predictive performance with the highest area under the curve (AUC) of 0.72 among the cohorts. Alternatively, the combination of the DLPM and clinical characteristics offered a better prediction performance (AUC >0.70) in all cohorts. We also evaluated the performance of DLPM in three different breast subtypes with the best prediction for the triple-negative breast cancer (TNBC) subtype (AUC: 0.73). Moreover, DLPM combined with clinical characteristics and stromal TILs achieved the highest AUC in the primary cohort (AUC: 0.82) and validation cohort 1 (AUC: 0.80).

CONCLUSION

Our study suggested that WSIs integrated with deep learning could potentially predict pCR to NAC in breast cancer. The predictive performance will be improved by combining clinical characteristics. DLPs from DLPM can provide more information compared to stromal TILs for pCR prediction.

Predictive and prognostic values of tumor infiltrating lymphocytes in breast cancers treated with neoadjuvant chemotherapy: A meta-analysis

BACKGROUND

This meta-analysis assessed the predictive and prognostic value of tumor infiltrating lymphocytes (TILs) in neoadjuvant chemotherapy (NACT) treated breast cancer and an optimal threshold for predicting pathologic complete response (pCR).

METHODS

A systematic search of PubMed, EMBASE and Web of Science electronic databases was conducted to identify eligible studies published before April 2022. Either a fixed or random effects model was applied to estimate the pooled hazard ratio (HR) and odds ratio (OR) for prognosis and predictive values of TILs in breast cancer patients treated with NACT. The study is registered with PROSPERO (CRD42020221521).

RESULTS

A total of 29 published studies were eligible. Increased levels of TILs predicted response to NACT in HER2 positive breast cancer (OR = 2.54 95%CI, 1.50-4.29) and triple negative breast cancer (TNBC) (OR = 3.67, 95%CI, 1.93-6.97), but not for hormone receptor (HR) positive breast cancer (OR = 1.68, 95 %CI, 0.67-4.25). A threshold of 20% of H & E-stained TILs was associated with prediction of pCR in both HER2 positive breast cancer (P = 0.035) and TNBC (P = 0.001). Moreover, increased levels of TILs (either iTILs or sTILs) were associated with survival benefit in HER2-positive breast cancer and TNBC. However, an increased level of TILs was not a prognostic factor for survival in HR positive breast cancer (pooled HR = 0.64, 95%CI: 0.03-14.1, P = 0.78).

CONCLUSIONS

Increased levels of TILs were associated with increased rates of response to NACT and improved prognosis for the molecular subtypes of TNBC and HER2-positive breast cancer, but not for patients with HR positive breast cancer. A threshold of 20% TILs was the most powerful outcome prognosticator of pCR.

The Leptin Axis and Its Association With the Adaptive Immune System in Breast Cancer

Adipose tissue secretes various peptides, including leptin. This hormone acts through the leptin receptor (Ob-R), which is expressed ubiquitously on the surface of various cells, including breast cancer cells and immune cells. Increasing evidence points to an interaction between the tumor microenvironment, tumor cells, and the immune system. Leptin plays an important role in breast cancer tumorigenesis and may be implicated in activation of the immune system. While breast cancer cannot be considered an immunogenic cancer, the triple-negative subtype is an exception. Specific immune cells - tumor infiltrating lymphocytes - are involved in the immune response and act as predictive and prognostic factors in certain breast cancer subtypes. The aim of this article is to review the interaction between adipose tissue, through the expression of leptin and its receptor, and the adaptive immune system in breast cancer.

The role of infiltrating lymphocytes in the neo-adjuvant treatment of women with HER2-positive breast cancer

Background

Pre-treatment tumour-associated lymphocytes (TILs) and stromal lymphocytes (SLs) are independent predictive markers of future pathological complete response (pCR) in HER2-positive breast cancer. Whilst studies have correlated baseline lymphocyte levels with subsequent pCR, few have studied the impact of neoadjuvant therapy on the immune environment.

Methods

We performed TIL analysis and T-cell analysis by IHC on the pretreatment and ‘On-treatment’ samples from patients recruited on the Phase-II TCHL (NCT01485926) clinical trial. Data were analysed using the Wilcoxon signed-rank test and the Spearman rank correlation.

Results

In our sample cohort (n = 66), patients who achieved a pCR at surgery, post-chemotherapy, had significantly higher counts of TILs (p = 0.05) but not SLs (p = 0.08) in their pre-treatment tumour samples. Patients who achieved a subsequent pCR after completing neo-adjuvant chemotherapy had significantly higher SLs (p = 9.09 × 10^–3) but not TILs (p = 0.1) in their ‘On-treatment’ tumour biopsies. In a small cohort of samples (n = 16), infiltrating lymphocyte counts increased after 1 cycle of neo-adjuvant chemotherapy only in those tumours of patients who did not achieve a subsequent pCR. Finally, reduced CD3 + (p = 0.04, rho = 0.60) and CD4 + (p = 0.01, rho = 0.72) T-cell counts in 'On-treatment' biopsies were associated with decreased residual tumour content post-1 cycle of treatment; the latter being significantly associated with increased likelihood of subsequent pCR (p < 0.01).

Conclusions

The immune system may be ‘primed’ prior to neoadjuvant treatment in those patients who subsequently achieve a pCR. In those patients who achieve a pCR, their immune response may return to baseline after only 1 cycle of treatment. However, in those who did not achieve a pCR, neo-adjuvant treatment may stimulate lymphocyte influx into the tumour.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10549-021-06244-1.

Predictive and prognostic role of tumour-infiltrating lymphocytes in breast cancer patients with different molecular subtypes: a meta-analysis

BACKGROUND

Whether tumour-infiltrating lymphocytes (TILs) play different roles in different molecular subtypes of breast cancer remains unknown. Additionally, their prognostic and predictive value in different molecular subtypes of breast cancer is still controversial. The aim of our meta-analysis was to assess the prognostic and predictive value of TILs in different molecular subtypes of breast cancer by summarizing all relevant studies performing multivariate analysis.

METHODS

PubMed, Embase, EBSCO, ScienceDirect, the Cochrane Database and Web of Science were comprehensively searched (until March 2020). Hazard ratios (HRs), odds ratios (ORs) and their 95% confidence intervals (CIs) were used as effect measures to perform our meta-analysis. A random effect model was used. Stata software, version 15 (2017) (StataCorp, College Station, TX, USA) was used to perform the statistical analysis.

RESULTS

Thirty-three studies including 18,170 eligible breast cancer patients were analysed. The meta-analysis showed that high TIL expression was significantly associated with increased pathological complete response (pCR) rates after neoadjuvant chemotherapy in patients with the HER2-enriched molecular subtype (OR = 1.137, 95% CI [1.061 ~ 1.218], p < 0.001) and triple-negative breast cancer (TNBC) subtype (OR = 1.120, 95% CI [1.061 ~ 1.182], p < 0.001). However, high TIL expression was not significantly associated with high pCR rates after neoadjuvant chemotherapy in patients with the luminal molecular subtype of breast cancer (OR = 1.154, 95% CI [0.789 ~ 1.690], p = 0.460). We carried out a meta-analysis on the HRs of overall survival (OS) and disease-free survival (DFS) to assess the prognostic value of TILs in breast cancer with different molecular subtypes more deeply. Our meta-analysis confirmed that high TILs were associated with significantly improved DFS in patients with the HER2-enriched molecular subtype [HR = 0.940, 95% CI (0.903 ~ 0.979), p = 0.003] and TNBC molecular subtype [HR = 0.907, 95% CI (0.862 ~ 0.954), p < 0.001]. However, high TILs were not associated with significantly better DFS in patients with the luminal molecular subtype of breast cancer [HR = 0.998, 95% CI (0.977 ~ 1.019), p = 0.840]. Furthermore, the results confirmed that high TILs were significantly related to better OS in patients with the HER2-enriched molecular subtype [HR = 0.910, 95% CI (0.866 ~ 0.957), p < 0.001] and TNBC molecular subtype [HR = 0.869, 95% CI (0.836 ~ 0.904), p < 0.001]. Conversely, the summarized results indicated that high TILs were significantly associated with poor OS in patients with the luminal molecular subtype of breast cancer [HR = 1.077, 95% CI (1.016 ~ 1.141), p = 0.012].

CONCLUSIONS

Our meta-analysis confirms that high TILs are associated with favourable survival and predicts pCR in breast cancer patients with the TNBC and HER2-enriched molecular subtypes.

Predictors of Neoadjuvant Chemotherapy Response in Breast Cancer: A Review

Neoadjuvant chemotherapy (NAC) largely increases operative chances and improves prognosis of the local advanced breast cancer patients. However, no specific means have been invented to predict the therapy responses of patients receiving NAC. Therefore, we focus on the alterations of tumor tissue-related microenvironments such as stromal tumor-infiltrating lymphocytes status, cyclin-dependent kinase expression, non-coding RNA transcription or other small molecular changes, in order to detect potentially predicted biomarkers which reflect the therapeutic efficacy of NAC in different subtypes of breast cancer. Further, possible mechanisms are also discussed to discover feasible treatment targets. Thus, these findings will be helpful to promote the prognosis of breast cancer patients who received NAC and summarized in this review.

Quantitative digital image analysis of tumor-infiltrating lymphocytes in HER2-positive breast cancer

As visual quantification of the density of tumor-infiltrating lymphocytes (TILs) lacks in precision, digital image analysis (DIA) approach has been applied in order to improve. In several studies, TIL density has been examined on hematoxylin and eosin (HE)-stained sections using DIA. The aim of the present study was to quantify TIL density on HE sections of core needle biopsies using DIA and investigate its association with clinicopathological parameters and pathological response to neoadjuvant chemotherapy in human epidermal growth factor receptor 2 (HER2)-positive breast cancer. The study cohort comprised of patients with HER2-positive breast cancer, all treated with neoadjuvant anti-HER2 therapy. DIA software applying machine learning-based classification of epithelial and stromal elements was used to count TILs. TIL density was determined as the number of TILs per square millimeter of stromal tissue. Median TIL density was 1287/mm² (range, 123-8101/mm²). A high TIL density was associated with higher histological grade (P = 0.02), estrogen receptor negativity (P = 0.036), and pathological complete response (pCR) (P < 0.0001). In analyses using receiver operating characteristic curves, a threshold TIL density of 2420/mm² best discriminated pCR from non-pCR. In multivariate analysis, high TIL density (> 2420/mm²) was significantly associated with pCR (P < 0.0001). Our results indicate that DIA can assess TIL density quantitatively, machine learning-based classification algorithm allowing determination of TIL density as the number of TILs per unit area, and TIL density established by this method appears to be an independent predictor of pCR in HER2-positive breast cancer.

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.

Can Tumor-Infiltrating Lymphocytes (TILs) Be a Predictive Factor for Lymph Nodes Status in Both Early Stage and Locally Advanced Breast Cancer?

The status of axillary lymph nodes is an important prognostic factor in the outcome of breast cancer tumors. New trials changed the attitude towards axillary clearance. In the era of development of new immune therapies for breast cancer, it is important to identify a biomarker that can predict lymph node status. Tumor-infiltrating lymphocytes (TILs) are a valuable indicator of the immune microenvironment that plays the central role in new anticancer drugs. Although the correlation between TILs and response to chemotherapy was established by previous studies, our retrospective study investigated the correlation between TILs and lymph node status. We analyzed data on 172 patients. According to stage, patients were divided in two groups: patients who underwent primary surgical treatment (breast-conserving or mastectomy and sentinel lymph node (SLN) biopsy +/− axillary clearance in conformity with lymph node status) and patients who received chemotherapy prior to surgical treatment (breast-conserving or mastectomy + axillary clearance). We showed a good inverse correlation between TILs and lymph nodes status for both early stage and locally advanced breast cancers. Moreover, TILs are a predictor for positive lymph nodes in the axilla in patients undergoing axillary clearance after SLN biopsy, with no statistical difference between the intrinsic or histological subtype of breast cancers. We also obtained a significant correlation between TILs and response to chemotherapy with no significative difference according to histological subtype. Although further data have still to be gathered before meeting the criteria for clinical utility, this study demonstrates that TILs are one of the most accredited forthcoming biomarkers for breast cancer (BC) patients.