Evaluation of PD-L1, tumor-infiltrating lymphocytes, and CD8+ and FOXP3+ immune cells in HER2-positive breast cancer treated with neoadjuvant therapies

Oncogenes and tumor suppressor genes: functions and roles in cancers

Cancer, being the most formidable ailment, has had a profound impact on the human health. The disease is primarily associated with genetic mutations that impact oncogenes and tumor suppressor genes (TSGs). Recently, growing evidence have shown that X-linked TSGs have specific role in cancer progression and metastasis as well. Interestingly, our genome harbors around substantial portion of genes that function as tumor suppressors, and the X chromosome alone harbors a considerable number of TSGs. The scenario becomes even more compelling as X-linked TSGs are adaptive to key epigenetic processes such as X chromosome inactivation. Therefore, delineating the new paradigm related to X-linked TSGs, for instance, their crosstalk with autosome and involvement in cancer initiation, progression, and metastasis becomes utmost importance. Considering this, herein, we present a comprehensive discussion of X-linked TSG dysregulation in various cancers as a consequence of genetic variations and epigenetic alterations. In addition, the dynamic role of X-linked TSGs in sex chromosome-autosome crosstalk in cancer genome remodeling is being explored thoroughly. Besides, the functional roles of ncRNAs, role of X-linked TSG in immunomodulation and in gender-based cancer disparities has also been highlighted. Overall, the focal idea of the present article is to recapitulate the findings on X-linked TSG regulation in the cancer landscape and to redefine their role toward improving cancer treatment strategies.

High tumor infiltrating lymphocytes are significantly associated with pathological complete response in triple negative breast cancer treated with neoadjuvant KEYNOTE-522 chemoimmunotherapy

INTRODUCTION

For patients with locally advanced triple negative breast cancer (TNBC), the standard of care is to administer the KEYNOTE-522 (K522) regimen, including chemotherapy and immunotherapy (pembrolizumab) given in the neoadjuvant setting. Pathological complete response (pCR) is more likely in patients who receive the K522 regimen than in patients who receive standard chemotherapy. Studies have shown that pCR is a strong predictor of long-term disease-free survival. However, factors predicting pCR to K522 are not well understood and require further study in real-world populations.

METHODS

We evaluated 76 patients who were treated with the K522 regimen at our institution. Twenty-nine pre-treatment biopsy slides were available for pathology review. Nuclear grade, Nottingham histologic grade, Ki-67, lymphovascular invasion, and tumor infiltrating lymphocytes (TIL) were evaluated in these 29 cases. For the cases that did not have available slides for review from pre-treatment biopsies, these variables were retrieved from available pathology reports. In addition, clinical staging, race, and BMI at the time of biopsy were retrieved from all 76 patients' charts. Binary logistic regression models were used to correlate these variables with pCR.

RESULTS

At the current time, 64 of 76 patients have undergone surgery at our institution following completion of K522 and 31 (48.4%) of these achieved pCR. In univariate analysis, only TIL was significantly associated with pCR (p = 0.014) and this finding was also confirmed in multivariate analysis, whereas other variables including age, race, nuclear grade, Nottingham grade, Ki-67, lymphovascular invasion, BMI, pre-treatment tumor size, and lymph node status were not associated with pCR (p > 0.1).

CONCLUSION

Our real-world data demonstrates high TIL is significantly associated with pCR rate in the K522 regimen and may potentially serve as a biomarker to select optimal treatment. The pCR rate of 48.4% in our study is lower than that reported in K522, potentially due to the smaller size of our study; however, this may also indicate differences between real-world data and clinical trial results. Larger studies are warranted to further investigate the role of immune cells in TNBC response to K522 and other treatment regimens.

The association of PD-L1 expression and CD8-positive T cell infiltration rate with the pathological complete response after neoadjuvant treatment in HER2-positive breast cancer

PURPOSE

Achieving a pathological complete response (pCR) after neoadjuvant therapy in HER2-positive breast cancer patients is the most significant prognostic indicator, suggesting a low risk of recurrence and a survival advantage. This study aims to investigate clinicopathological parameters that can predict the response to neoadjuvant treatment in HER2 + breast cancers and to explore the roles of tumour-infiltrating lymphocytes (TILs), CD8 + T lymphocytes and PD-L1 expression.

METHODS

This single-centre retrospective study was conducted with 85 HER2-positive breast cancer patients who underwent surgery after receiving neoadjuvant therapy between January 2017 and January 2020. Paraffin blocks from these patients were selected for immunohistochemical studies.

RESULTS

A complete pathological response to neoadjuvant treatment was determined in 39 (45.9%) patients. High Ki-67 index (> 30%), moderate to high TIL infiltration, PD-L1 positivity and high CD8 cell count (≥ 25) were significantly associated with pCR in univariate analyses (p: 0.023, 0.025, 0.017 and 0.003, respectively). Multivariate regression analysis identified high Ki-67 index (> 30%) and CD8 cell infiltration as independent predictors for pCR in HER2-positive breast cancer.

CONCLUSIONS

High Ki-67 index, and high CD8 cell count are strong predictors for pCR in HER2-positive breast cancer. Tumours with high Ki-67 index, high TILs and CD8 infiltration may represent a subgroup where standard therapies are adequate. Conversely, those with low TILs and CD8 infiltration may identify a subgroup where use of novel strategies, including those that increase CD8 infiltration could be applied.

GPR81-mediated reprogramming of glucose metabolism contributes to the immune landscape in breast cancer

BACKGROUND

Local tumor microenvironment (TME) plays a crucial role in immunotherapy for breast cancer (BC). Whereas, the molecular mechanism responsible for the crosstalk between BC cells and surrounding immune cells remains unclear. The present study aimed to determine the interplay between GPR81-mediated glucometabolic reprogramming of BC and the immune landscape in TME.

MATERIALS AND METHODS

Immunohistochemistry (IHC) assay was first performed to evaluate the association between GPR81 and the immune landscape. Then, several stable BC cell lines with down-regulated GPR81 expression were established to directly identify the role of GPR81 in glucometabolic reprogramming, and western blotting assay was used to detect the underlying molecular mechanism. Finally, a transwell co-culture system confirmed the crosstalk between glucometabolic regulation mediated by GPR81 in BC and induced immune attenuation.

RESULTS

IHC analysis demonstrated that the representation of infiltrating CD8⁺ T cells and FOXP3⁺ T cells were dramatically higher in BC with a triple negative (TN) subtype in comparison with that with a non-TN subtype (P < 0.001). Additionally, the ratio of infiltrating CD8⁺ to FOXP3⁺ T cells was significantly negatively associated with GPR81 expression in BC with a TN subtype (P < 0.001). Furthermore, GPR81 was found to be substantially correlated with the glycolytic capability (P < 0.001) of BC cells depending on a Hippo-YAP signaling pathway (P < 0.001). In the transwell co-culture system, GPR81-mediated reprogramming of glucose metabolism in BC significantly contributed to a decreased proportion of CD8⁺ T (P < 0.001) and an increased percentage of FOXP3⁺ T (P < 0.001) in the co-cultured lymphocytes.

CONCLUSION

Glucometabolic reprogramming through a GPR81-mediated Hippo-YAP signaling pathway was responsible for the distinct immune landscape in BC. GPR81 was a potential biomarker to stratify patients before immunotherapy to improve BC's clinical prospect.

Deep learning in digital pathology for personalized treatment plans of cancer patients

Over the past decade, many new cancer treatments have been developed and made available to patients. However, in most cases, these treatments only benefit a specific subgroup of patients, making the selection of treatment for a specific patient an essential but challenging task for oncologists. Although some biomarkers were found to associate with treatment response, manual assessment is time-consuming and subjective. With the rapid developments and expanded implementation of artificial intelligence (AI) in digital pathology, many biomarkers can be quantified automatically from histopathology images. This approach allows for a more efficient and objective assessment of biomarkers, aiding oncologists in formulating personalized treatment plans for cancer patients. This review presents an overview and summary of the recent studies on biomarker quantification and treatment response prediction using hematoxylin-eosin (H&E) stained pathology images. These studies have shown that an AI-based digital pathology approach can be practical and will become increasingly important in improving the selection of cancer treatments for patients.

Deep Learning-Based Pathology Image Analysis Enhances Magee Feature Correlation With Oncotype DX Breast Recurrence Score

Background

Oncotype DX Recurrence Score (RS) has been widely used to predict chemotherapy benefits in patients with estrogen receptor-positive breast cancer. Studies showed that the features used in Magee equations correlate with RS. We aimed to examine whether deep learning (DL)-based histology image analyses can enhance such correlations.

Methods

We retrieved 382 cases with RS diagnosed between 2011 and 2015 from the Emory University and the Ohio State University. All patients received surgery. DL models were developed to detect nuclei of tumor cells and tumor-infiltrating lymphocytes (TILs) and segment tumor cell nuclei in hematoxylin and eosin (H&E) stained histopathology whole slide images (WSIs). Based on the DL-based analysis, we derived image features from WSIs, such as tumor cell number, TIL number variance, and nuclear grades. The entire patient cohorts were divided into one training set (125 cases) and two validation sets (82 and 175 cases) based on the data sources and WSI resolutions. The training set was used to train the linear regression models to predict RS. For prediction performance comparison, we used independent variables from Magee features alone or the combination of WSI-derived image and Magee features.

Results

The Pearson's correlation coefficients between the actual RS and predicted RS by DL-based analysis were 0.7058 (p-value = 1.32 × 10-13) and 0.5041 (p-value = 1.15 × 10-12) for the validation sets 1 and 2, respectively. The adjusted R 2 values using Magee features alone are 0.3442 and 0.2167 in the two validation sets, respectively. In contrast, the adjusted R 2 values were enhanced to 0.4431 and 0.2182 when WSI-derived imaging features were jointly used with Magee features.

Conclusion

Our results suggest that DL-based digital pathological features can enhance Magee feature correlation with RS.

Relationship Between Programmed Death Ligand 1 Expression and Other Clinicopathological Features in a Large Cohort of Gastric Cancer Patients

Background

Antibodies against programmed death 1 (PD-1) and its ligand, programmed death-ligand 1 (PD-L1) have recently shown promising results in gastric cancer (GC). However, clinicians still lack predictive biomarkers for the efficacy of anti-PD-1 therapy; thus, we investigated the expression of PD-L1 in GC and further assessed its clinical relevance with other clinicopathological features.

Methods

We retrospectively collected clinical data on 968 consecutive GC cases from Nanfang Hospital between November 2018 and August 2021. Discrepancy in the combined positive score (CPS) of PD-L1 protein expression between gastric mucosa biopsy and postoperative pathology were investigated. Correlations between CPS and clinicopathological parameters were determined using chi-squared test, multiple logistic aggression analysis, and linear regression analysis.

Results

Among the 968 consecutive GC patients, 199 who did not receive preoperative chemotherapy or immunotherapy were tested for CPS both in gastric mucosa biopsy and postoperative pathology, and the results showed that the CPS of gastric mucosa biopsy was significantly lower than that of postoperative pathology [mean ± SD: 5.5 ± 9.4 vs. 13.3 ± 17.4; M(IQR): 2(5) vs. 5(12), p<0.001)]. 62.3% of patients (579/930) had CPS≥ 1, 49.2% of patients (458/930) had CPS≥5, and 33.3% of patients (310/930) had CPS≥10. Mismatch repair deficiency (dMMR) status was seen in 6.1% of patients (56 of 919). Positive Epstein–Barr virus (EBV) status was detected in 4.4% of patients (38 of 854). The patients with CPS≥1/CPS≥5/CPS≥10 were significantly independently correlated with age, Lauren classification, Ki-67 index, and EBV status. According to linear regression analysis, PD-L1 expression was correlated with age (p<0.001), Ki-67 index (p<0.001), EBV (p<0.001), and Lauren classification (p=0.002).

Conclusions

Our results confirmed that PD-L1 expression has Intratumoral heterogeneity in GC. Furthermore, the variables of age, Ki-67 index, and Lauren classification, which are common and accessible in most hospitals, are worth exploring as potential biomarkers for anti-PD-1 therapy in GC.

Clinical trial data and emerging strategies: HER2-positive breast cancer

A deeper insight into tumor biology and HER2 signaling has led to the development of novel anti-HER2 drugs that have significantly improved the prognosis of patients with HER2-positive breast cancer. The breast cancer immune microenvironment has emerged as a potential prognostic factor. Moreover, the host immune system not only seems to play a critical role in the prognosis of HER2-positive breast cancer, but also seems to modulate treatment response to some HER2-targeted agents. Here, we review the latest evidence of the role of immunotherapy in HER2-positive breast cancer and present emerging strategies.

Tumor Microenvironment Characterization in Breast Cancer Identifies Prognostic and Neoadjuvant Chemotherapy Relevant Signatures

Immune response which involves distinct immune cells is associated with prognosis of breast cancer. Nonetheless, less study have determined the associations of different types of immune cells with patient survival and treatment response. In this study, A total of 1,502 estrogen receptor(ER)-negative breast cancers from public databases were used to infer the proportions of 22 subsets of immune cells. Another 320 ER-negative breast cancer patients from Guangdong Provincial People's Hospital were also included and divided into the testing and validation cohorts. CD8⁺ T cells, CD4⁺ T cells, B cells, and M1 macrophages were associated with favourable outcome (all p <0.01), whereas Treg cells were strongly associated with poor outcome (p = 0.005). Using the LASSO model, we classified patients into the stromal immunotype A and B subgroups according to immunoscores. The 10 years OS and DFS rates were significantly higher in the immunotype A subgroup than immunotype B subgroup. Stromal immunotype was identified as an independent prognostic indicator in multivariate analysis in all cohorts and was also related to pathological complete response(pCR) after neoadjuvant chemotherapy. The nomogram that integrated the immunotype and clinicopathologic features showed good predictive accuracy for pCR and discriminatory power. The stromal immunotype A subgroup had higher expression levels of immune checkpoint molecules (PD-L1, PD-1, and CTLA-4) and cytokines (IL-2, INF-γ, and TGF-β). In addition, patients with immunotype A and B diseases had distinct mutation signatures. Therefore, The stromal immunotypes could predict survival and responses of ER-negative breast cancer patients to neoadjuvant chemotherapy.

Molecular Classification of Triple Negative Breast Cancer and the Emergence of Targeted Therapies

Triple negative breast cancer (TNBC) represents 15% to 20% of all primary breast cancers and is the most aggressive subtype of breast cancer. There has been rapid progress in targeted therapy and biomarker development to identify the optimal treatments for TNBC. To update recent developments, this article comprehensively reviews molecular classification and biomarkers of TNBC and targeted therapy developments in immunotherapy, PARP and AKT pathway inhibitors, antibody-drug conjugates and androgen receptor blockade. The treatment of TNBC has dramatically evolved beyond basic cytotoxic chemotherapy into an expanding domain of targeted therapies tailored to the heterogeneity of this complex and aggressive disease. Progress will continue through the sustained and devoted efforts of our investigators and the patients who dedicatedly enroll in clinical trials. Through a daring persistence to challenge the status quo we now have the opportunity to offer our patients with TNBC a new sense of hope.