Breast Cancer Treatment Response Research Articles

Cancer-associated fibroblast-derived gene signature discriminates distinct prognoses by integrated single-cell and bulk RNA-seq analyses in breast cancer.

Cancer-associated fibroblasts (CAFs) are one of the most predominant cellular subpopulations in the tumor stroma and play an integral role in cancer occurrence and progression. However, the prognostic role of CAFs in breast cancer remains poorly understood. We identified a number of CAF-related biomarkers in breast cancer by combining single-cell and bulk RNA-seq analyses. Based on univariate Cox regression as well as Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis, a novel CAF-associated prognostic model was developed. Breast cancer patients were grouped according to the median risk score and further analyzed for outcome, clinical characteristic, pathway activity, genomic feature, immune landscape, and drug sensitivity. A total of 341 CAF-related biomarkers were identified from single-cell and bulk RNA-seq analyses. We eventually screened eight candidate prognostic genes, including CERCAM, EMP1, SDC1, PRKG1, XG, TNN, WLS, and PDLIM4, and constructed the novel CAF-related prognostic model. Grouped by the median risk score, high-risk patients showed a significantly worse prognosis and exhibited distinct pathway activities such as uncontrolled cell cycle progression, angiogenesis, and activation of glycolysis. In addition, the combined risk score and tumor mutation burden significantly improved the ability to predict patient prognosis. Importantly, patients in the high-risk group had a higher infiltration of M2 macrophages and a lower infiltration of CD8+ T cells and activated NK cells. Finally, we calculated the IC50 for a range of anticancer drugs and personalized the treatment regimen for each patient. Integrating single-cell and bulk RNA-seq analyses, we identified a list of compositive CAF-associated biomarkers and developed a novel CAF-related prognostic model for breast cancer. This robust CAF-derived gene signature acts as an excellent predictor of patient outcomes and treatment responses in breast cancer.

Abstract PO5-24-09: Optimizing the Therapeutic Treatment of Breast Cancer Through a Dual Data-driven Theoretical Model

Abstract Despite rapid advancements in treatments and technology, breast cancer remains the second leading cause of cancer related morbidity in women in the US. As a heterogeneous disease, the unique genetic and phenotypic profile of each tumor complicates the prediction of treatment responses and optimized treatment strategies. One primary manifestation of phenotypic plasticity and concomitant intra-tumoral heterogeneity is the Epithelial-Mesenchymal Transition (EMT). The acquisition of migratory and invasive properties through EMT facilitates tumor metastasis and results in worse patient outcomes. As a result, the EMT and its reverse process (MET) often influence treatment response. However, the different mechanisms through which these therapeutic drugs affect the EMT-related phenotypic switching rates as well as each phenotype’s replication rates are unclear, and further investigation is required for better understanding their precise effect for directing optimal treatment strategies and improved patient outcomes. Here, we compile an atlas of publicly available metastatic and non-metastatic RNA sequencing data of breast cancer cell lines treated with therapeutic and chemotherapeutic drugs and propose a dual data-driven theoretical framework for understanding the effects of each drug on breast cancer treatment response. We infer the cell fractions, EMT phenotypic transition rates and phenotypic replication rates of each case through an extension of our previously developed hybrid data-driven and theoretical model, COMET. We infer single-cell RNA sequencing-derived EMT-related trajectories and apply a continuous time Markov chain model to infer inter-state transition rates. By analyzing the atlas of RNA sequencing data and integration with our framework, we gain novel insights into the context-specific mechanisms by which therapeutic drugs affect the phenotypic transition rates. This comprehensive understanding will contribute to optimizing breast cancer therapy and ultimately improving patient outcomes. Citation Format: A. Najafi. Optimizing the Therapeutic Treatment of Breast Cancer Through a Dual Data-driven Theoretical Model [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-24-09.

Differential whole-genome doubling based signatures for improvement on clinical outcomes and drug response in patients with breast cancer

Whole genome doublings (WGD), a hallmark of human cancer, is pervasive in breast cancer patients. However, the molecular mechanism of the complete impact of WGD on survival and treatment response in breast cancer remains unclear. To address this, we performed a comprehensive and systematic analysis of WGD, aiming to identify distinct genetic alterations linked to WGD and highlight its improvement on clinical outcomes and treatment response for breast cancer. A linear regression model along with weighted gene co-expression network analysis (WGCNA) was applied on The Cancer Genome Atlas (TCGA) dataset to identify critical genes related to WGD. Further Cox regression models with random selection were used to optimize the most useful prognostic markers in the TCGA dataset. The clinical implication of the risk model was further assessed through prognostic impact evaluation, tumor stratification, functional analysis, genomic feature difference analysis, drug response analysis, and multiple independent datasets for validation. Our findings revealed a high aneuploidy burden, chromosomal instability (CIN), copy number variation (CNV), and mutation burden in breast tumors exhibiting WGD events. Moreover, 247 key genes associated with WGD were identified from the distinct genomic patterns in the TCGA dataset. A risk model consisting of 22 genes was optimized from the key genes. High-risk breast cancer patients were more prone to WGD and exhibited greater genomic diversity compared to low-risk patients. Some oncogenic signaling pathways were enriched in the high-risk group, while primary immune deficiency pathways were enriched in the low-risk group. We also identified a risk gene, ANLN (anillin), which displayed a strong positive correlation with two crucial WGD genes, KIF18A and CCNE2. Tumors with high expression of ANLN were more prone to WGD events and displayed worse clinical survival outcomes. Furthermore, the expression levels of these risk genes were significantly associated with the sensitivities of BRCA cell lines to multiple drugs, providing valuable insights for targeted therapies. These findings will be helpful for further improvement on clinical outcomes and contribution to drug development in breast cancer.

Exploration of a noninvasive radiomics classifier for breast cancer tumor microenvironment categorization and prognostic outcome prediction

Rationale and Objectives: Breast cancer progression and treatment response are significantly influenced by the tumor microenvironment (TME). Traditional methods for assessing TME are invasive, posing a challenge for patient care. This study introduces a non-invasive approach to TME classification by integrating radiomics and machine learning, aiming to predict the TME status using imaging data, thereby aiding in prognostic outcome prediction. Materials and MethodsUtilizing multi-omics data from The Cancer Genome Atlas (TCGA) and The Cancer Imaging Archive (TCIA), this study employed CIBERSORT and MCP-counter algorithms analyze immune infiltration in breast cancer. A radiomics classifier was developed using a random forest algorithm, leveraging quantitative features extracted from intratumoral and peritumoral regions of Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) scans. The classifer’s ability to predict diverse TME states were and their prognostic implications were evaluated using Kaplan-Meier survival curves. ResultsThree distinct TME states were identified using RNA-Seq data, each displaying unique prognostic and biological characteristics. Notably, patients with increased immune cell infiltration showed significantly improved prognoses (P < 0.05). The classifier, comprising 24 radiomic features, demonstrated high predictive accuracy (AUC of training set = 0.960, 95 % CI: 0.922, 0.997; AUC of testing set = 0.853, 95 % CI: 0.687, 1.000) in differentiating these TME states. Predictions from the classifier also correlated significantly with overall patient survival (P < 0.05). ConclusionThis study offers a detailed analysis of the complex TME states in breast cancer and presents a reliable, noninvasive radiomics classifier for TME assessment. The classifer’s accurate prediction of TME status and its correlation with prognosis highlight its potential as a tool in personalized breast cancer treatment, paving the way for more individualized and less invasive therapeutic strategies.

Abstract 4227: Human-derived hydrogels to support phenotypic changes in ER+ breast cancer cell line

Abstract Adipose tissue plays a critical role in breast cancer incidence, progression, and response to therapy. The development of human derived 3D culture systems can accelerate the understanding of the role of environmental factors in the progression of Breast cancer as well as a model for preclinical studies during drug development. MCF-7 spheroids were cultured in Obatala Sciences’ Obavate and human-derived hydrogels ObaGel® and ObaGel®-ECM and control media. Organoid structure and phenotypic changes were analyzed via live cell imaging on the Incucyte S3. Migration studies were performed and gene expression analysis of EMT markers. Finally, adipocyte-breast cancer cell crosstalk was evaluated using pooled adipocytes co-cultured with MCF-7 spheroids. The results demonstrated that ObaGel® and ObaGel®-ECM 3D cultures support the growth and proliferation of MCF-7 tumorspheres during an extended culture period. In addition, MCF-7 cells exhibit the characteristic morphology of tumorsphere-forming cells when cultured in Obavate, with morphological changes in the ObaGel® and ObaGel®-ECM 3D. ObaGel® and ObaGel®-ECM differentially supports MCF-7 migratory behavior and phenotypic changes characteristic of EMT. Conclusions: Human derived hydrogels are developed to support 3D culture of breast cancer cells and to recapitulate the phenotypic changes associated with their metastatic potential. Furthermore, the use of a human derived 3D coculture system provides a platform for the understanding of adipose tissue-breast cancer cell interactions as it relates to breast cancer initiation, progression, and treatment response. Defining these interactions will support the development of a tool for future use in patient stratification and precision medicine in identifying underlying causes of breast cancer progression and response to treatments. Citation Format: Cecilia G. Sanchez, Katie Hamel, Emma Rogers, Jordan Robinson, Haley Lassiter, Trivia Frazier, Christopher Williams. Human-derived hydrogels to support phenotypic changes in ER+ breast cancer cell line [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4227.

Diagnostic, prognostic and treatment response of perilipin1 gene in breast cancer

BackgroundGenetic alterations in the perilipin (PLIN) family genes (PLIN1 to PLIN5) were infrequent in breast cancer (BC) where enhanced levels of PLIN1, PLIN3-5 were observed in the luminal A and luminal B subgroups, whereas increased PLIN2 expression was observed in the HER2-enriched and basal-like subgroups. However, the predictive value of PLIN1 for BC patient outcomes remains uncertain. In the present study, we aim to investigate the diagnostic, prognostic and treatment response roles of the PLIN1 gene expression in BC. MethodsWe obtained microarray BC trancriptomic data of 320 tumor (T) and 62 normal (N) breast samples from five GEO data-series; GSE7904 (38 T:7N), GSE42568 (101 T: 15 N), GSE26910 (6 T:6N), GSE45827 (144 T:7N), and GSE10810 (31 T:27 N). The Welch t test was used to analyze the significant differences in gene expression including PLIN1 with fold change > ±2 and p-value < 0.05. The expression of PLIN1 was confirmed by RTqPCR using clinical specimen samples from BC patients. The Kaplan-Meier Plotter was used to assess survival on large independent dataset (31 dataset for relapse-free survival and 14 datasets for overall survival) and significance was determined by calculating hazard ratios (>1) and log-rank p-values < 0.05. We also assessed the treatment outcomes of endocrine therapy (tamoxifen and aromatase-inhibitors), anti-HER2 therapy (trastuzumab and lapatinib), and chemotherapy (taxane, anthracycline, and ixabepilone) using robust statistical methods and correlated with PLIN1 gene expression. ResultsWe identified significantly reduced expression of PLIN1 (FC = −30.76, p value = 2.183e−24) in BC samples compared with normal controls. Our qPCR result confirmed the microarray expression pattern of PLIN1 in BC. Survival analysis revealed PLIN1 to be a moderately important prognostic biomarker. Our findings highlight the effectiveness of trastuzumab and anthracycline in classifying treatment responses, supported by Mann-Whitney tests indicating statistical significance in gene expression differences between responders and non-responders. ConclusionIn conclusion, our findings indicate that PLIN1 is one of the most down-regulated genes and a moderately important biomarker in BC for prognostic purposes. PLIN1 was a good indicator of trastuzumab and anthracycline treatment responses in BC.

272 (PB-088) Poster - Association between prognosis, carboplatin treatment response and homologous recombination deficiency status in early triple-negative breast cancer

272 (PB-088) Poster - Association between prognosis, carboplatin treatment response and homologous recombination deficiency status in early triple-negative breast cancer

Molecular subtype identification and prognosis stratification based on lysosome-related genes in breast cancer

BackgroundLysosomes are known to have a significant impact on the development and recurrence of breast cancer. However, the association between lysosome-related genes (LRGs) and breast cancer remains unclear. This study aims to explore the potential role of LRGs in predicting the prognosis and treatment response of breast cancer. MethodsBreast cancer gene expression profile data and clinical information were downloaded from TCGA and GEO databases, and prognosis-related LRGs were screened for consensus clustering analysis. Lasso Cox regression analysis was used to construct risk features derived from LRGs, and immune cell infiltration, immune therapy response, drug sensitivity, and clinical pathological feature differences were evaluated for different molecular subtypes and risk groups. A nomogram based on risk features derived from LRGs was constructed and evaluated. ResultsOur study identified 176 differentially expressed LRGs that are associated with breast cancer prognosis. Based on these genes, we divided breast cancer into two molecular subtypes with significant prognostic differences. We also found significant differences in immune cell infiltration between these subtypes. Furthermore, we constructed a prognostic risk model consisting of 7 LRGs, which effectively divides breast cancer patients into high-risk and low-risk groups. Patients in the low-risk group have better prognostic characteristics, respond better to immunotherapy, and have lower sensitivity to chemotherapy drugs, indicating that the low-risk group is more likely to benefit from immunotherapy and chemotherapy. Additionally, the risk score based on LRGs is significantly correlated with immune cell infiltration, including CD8 T cells and macrophages. This risk score model, along with age, chemotherapy, clinical stage, and N stage, is an independent prognostic factor for breast cancer. Finally, the nomogram composed of these factors has excellent performance in predicting overall survival of breast cancer. ConclusionsIn conclusion, this study has constructed a novel LRG-derived breast cancer risk feature, which performs well in prognostic prediction when combined with clinical pathological features.

Diffusion Tensor Imaging for Characterizing Changes in Triple-Negative Breast Cancer During Neoadjuvant Systemic Therapy.

Assessment of treatment response in triple-negative breast cancer (TNBC) may guide individualized care for improved patient outcomes. Diffusion tensor imaging (DTI) measures tissue anisotropy and could be useful for characterizing changes in the tumors and adjacent fibroglandular tissue (FGT) of TNBC patients undergoing neoadjuvant systemic treatment (NAST). To evaluate the potential of DTI parameters for prediction of treatment response in TNBC patients undergoing NAST. Prospective. Eighty-six women (average age: 51 ± 11 years) with biopsy-proven clinical stage I-III TNBC who underwent NAST followed by definitive surgery. 47% of patients (40/86) had pathologic complete response (pCR). 3.0 T/reduced field of view single-shot echo-planar DTI sequence. Three MRI scans were acquired longitudinally (pre-treatment, after 2 cycles of NAST, and after 4 cycles of NAST). Eleven histogram features were extracted from DTI parameter maps of tumors, a peritumoral region (PTR), and FGT in the ipsilateral breast. DTI parameters included apparent diffusion coefficients and relative diffusion anisotropies. pCR status was determined at surgery. Longitudinal changes of DTI features were tested for discrimination of pCR using Mann-Whitney U test and area under the receiver operating characteristic curve (AUC). A P value <0.05 was considered statistically significant. 47% of patients (40/86) had pCR. DTI parameters assessed after 2 and 4 cycles of NAST were significantly different between pCR and non-pCR patients when compared between tumors, PTRs, and FGTs. The median surface/average anisotropy of the PTR, measured after 2 and 4 cycles of NAST, increased in pCR patients and decreased in non-pCR patients (AUC: 0.78; 0.027 ± 0.043 vs. -0.017 ± 0.042 mm2 /s). Quantitative DTI features from breast tumors and the peritumoral tissue may be useful for predicting the response to NAST in TNBC. 1 TECHNICAL EFFICACY: Stage 4.

Automated three-dimensional image registration for longitudinal photoacoustic imaging.

Photoacoustic tomography (PAT) has great potential in monitoring disease progression and treatment response in breast cancer. However, due to variations in breast repositioning, there is a chance of geometric misalignment between images. Further, poor repositioning can affect light fluence distribution and imaging field-of-view, making images different from one another. The net effect is that it becomes challenging to distinguish between image changes due to repositioning effects and those due to true biological variations. The aim is to develop a three-dimensional image registration framework for geometrically aligning repeated PAT volumetric images, which are potentially affected by repositioning effects such as misalignment, changed radiant exposure conditions, and different fields-of-view. The proposed framework involves the use of a coordinate-based neural network to represent the displacement field between pairs of PAT volumetric images. A loss function based on normalized cross correlation and Frangi vesselness feature extraction at multiple scales was implemented. We refer to our image registration framework as MUVINN-reg, which stands for multiscale vesselness-based image registration using neural networks. The approach was tested on a longitudinal dataset of healthy volunteer breast PAT images acquired with the hybrid photoacoustic-ultrasound Photoacoustic Mammoscope 3 imaging system. The registration performance was also tested under unfavorable repositioning conditions such as intentional mispositioning, and variation in breast-supporting cup size between measurements. A total of 13 pairs of repeated PAT scans were included in this study. MUVINN-reg showed excellent performance in co-registering each pair of images. The proposed framework was shown to be robust to image intensity shifts and field-of-view changes. Furthermore, MUVINN-reg could align vessels at imaging depths greater than 4cm. The proposed framework will enable the use of PAT for quantitative and reproducible monitoring of disease progression and treatment response.

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

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. 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. 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. 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.

Innovations in Positron Emission Tomography and State of the Art in the Evaluation of Breast Cancer Treatment Response.

The advent of hybrid Positron Emission Tomography/Computed Tomography (PET/CT) and PET/Magnetic Resonance Imaging (MRI) scanners resulted in an increased clinical relevance of nuclear medicine in oncology. The use of [18F]-Fluorodeoxyglucose ([18F]FDG) has also made it possible to study tumors (including breast cancer) from not only a dimensional perspective but also from a metabolic point of view. In particular, the use of [18F]FDG PET allowed early confirmation of the efficacy or failure of therapy. The purpose of this review was to assess the literature concerning the response to various therapies for different subtypes of breast cancer through PET. We start by summarizing studies that investigate the validation of PET/CT for the assessment of the response to therapy in breast cancer; then, we present studies that compare PET imaging (including PET devices dedicated to the breast) with CT and MRI, focusing on the identification of the most useful parameters obtainable from PET/CT. We also focus on novel non-FDG radiotracers, as they allow for the acquisition of information on specific aspects of the new therapies.

Beyond Blood Counts: Hematocrit Dynamics in Breast Cancer Monitoring

This review delves into the expanding role of hematocrit dynamics as a critical parameter in the monitoring and management of breast cancer. Moving beyond conventional reliance on blood counts, the analysis explores the intricate relationship between hematocrit fluctuations and various facets of breast cancer progression, treatment response, and overall patient outcomes. The review aims to elucidate the potential of hematocrit as a dynamic biomarker, offering insights into the underlying physiological changes associated with the disease. By examining the correlation between hematocrit levels and different stages of breast cancer, treatment modalities, and patient-specific factors, this review seeks to enhance the current understanding of hematocrit's prognostic and predictive value. Additionally, it explores the implications of hematocrit dynamics on therapeutic strategies, emphasizing the need for personalized approaches in breast cancer care. The synthesis of available evidence in this review underscores the significance of considering hematocrit as a valuable and dynamic parameter in breast cancer monitoring. The integration of hematocrit dynamics into clinical practice has the potential to refine diagnostic and prognostic frameworks, contributing to a more comprehensive and personalized approach to breast cancer management. In conclusion, this review provides a critical examination of the evolving landscape beyond blood counts, highlighting the emerging importance of hematocrit dynamics in enhancing our understanding and management of breast cancer.

Pathologic complete response prediction in breast cancer lesion segmentation and neoadjuvant therapy.

Predicting whether axillary lymph nodes could achieve pathologic Complete Response (pCR) after breast cancer patients receive neoadjuvant chemotherapy helps make a quick follow-up treatment plan. This paper presents a novel method to achieve this prediction with the most effective medical imaging method, Dynamic Contrast-enhanced Magnetic Resonance Imaging (DCE-MRI). In order to get an accurate prediction, we first proposed a two-step lesion segmentation method to extract the breast cancer lesion region from DCE-MRI images. With the segmented breast cancer lesion region, we then used a multi-modal fusion model to predict the probability of axillary lymph nodes achieving pCR. We collected 361 breast cancer samples from two hospitals to train and test the proposed segmentation model and the multi-modal fusion model. Both segmentation and prediction models obtained high accuracy. The results show that our method is effective in both the segmentation task and the pCR prediction task. It suggests that the presented methods, especially the multi-modal fusion model, can be used for the prediction of treatment response in breast cancer, given data from noninvasive methods only.

Abstract C174: MerTK as a putative biomarker for treatment response in triple negative breast cancer

Abstract Triple Negative Breast Cancer (TNBC) is characterized by the lack of estrogen, progesterone, and HER2 receptors. TNBC is a highly invasive subtype of breast cancer, with limited targeted therapies. Current therapeutic options for TNBC patients include surgery, chemotherapy, and radiation but disease recurrence is common. Anti-PD-1 and anti-PD-L1 therapeutic antibodies have been approved for the treatment of TNBC in combination with chemotherapy, but biomarkers for response and new therapeutic targets are still needed. The receptor tyrosine kinase MerTK has been associated with many types of cancer and is highly expressed in TNBC. To investigate the role of MerTK in TNBC, we created a syngeneic cell line (4T1) overexpressing MerTK to evaluate tumor growth, the tumor immune microenvironment (TIME), and response to immune checkpoint inhibitors (ICIs) in vivo. First, we tested the effect of MerTK on tumor growth in vivo by inoculating the 4T1 cell line, which has very low MerTK expression, and two 4T1 MerTK-overexpressing clones into eight Balb/c mice per group subcutaneously on the flank. We measured tumor growth over 27 days and found that MerTK-overexpressing tumors exhibited a statistically significant delay in tumor growth when compared to the parental 4T1 tumors. This indicates that MerTK may be inducing a hotter TIME in MerTK overexpressing tumors, as this slowed growth was not observed in athymic nude mice. To test this hypothesis, we evaluated the TIME of these tumors by flow cytometry, which revealed robust expansion of macrophage, natural killer cell and T cell compartments, and reduction in tumor-infiltrating myeloid derived suppressor cells. These results suggest that MerTK is playing a yet unidentified role  in promoting infiltration of anti-tumor immune cells into the TIME and excluding pro-tumor immune cells. Further, we saw robust changes in genes associated with cytokine signaling pathways including CD38, CCL2 and CCL5 by Nanostring nCounter analysis in MerTK-overexpressing 4T1 cells compared to parental 4T1 cells. Finally, there is evidence that hotter tumors respond well to ICI therapy, and we hypothesized that MerTK-overexpressing tumors would exhibit a significant response to ICI when compared to parental 4T1 tumors. Indeed, our data showed a robust and durable response marked by a significant reduction in tumor growth in the MerTK-overexpressing tumors compared to parental 4T1 tumors in Balb/c mice when treated with either anti-PD-L1 or anti-CTLA-4 antibodies. Together, these results suggest that MerTK overexpression could serve as a predictive biomarker for response to immune therapy in patients with TNBC. Citation Format: Bridget E Mehall, Mari Iida, Kourtney L Kostecki, Jillian M Adams, Deric L Wheeler. MerTK as a putative biomarker for treatment response in triple negative breast cancer [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2023 Oct 11-15; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2023;22(12 Suppl):Abstract nr C174.

Targeting the Tumor Microenvironment in Breast Cancer: Prognostic and Predictive Significance and Therapeutic Opportunities

Breast cancer is one of the most prevalent tumors among women. Its prognosis and treatment outcomes depend on factors related to tumor cell biology. However, recent studies have revealed the critical role of the tumor microenvironment (TME) in the development, progression, and treatment response of breast cancer. In this review, we explore the different components of the TME and their relevance as prognostic and predictive biomarkers in breast cancer. In addition, techniques for assessing the tumor microenvironment, such as immunohistochemistry or gene expression profiling, and their clinical utility in therapeutic decision-making are examined. Finally, therapeutic strategies targeting the TME are reviewed, highlighting their potential clinical benefits. Overall, this review emphasizes the importance of the TME in breast cancer and its potential as a clinical tool for better patient stratification and the design of personalized therapies.

MiRNA Expression Profiling in Human Breast Cancer Diagnostics and Therapy.

Breast cancer is one of the most commonly diagnosed cancer types worldwide. Regarding molecular characteristics and classification, it is a heterogeneous disease, which makes it more challenging to diagnose. As is commonly known, early detection plays a pivotal role in decreasing mortality and providing a better prognosis for all patients. Different treatment strategies can be adjusted based on tumor progression and molecular characteristics, including personalized therapies. However, dealing with resistance to drugs and recurrence is a challenge. The therapeutic options are limited and can still lead to poor clinical outcomes. This review aims to shed light on the current perspective on the role of miRNAs in breast cancer diagnostics, characteristics, and prognosis. We discuss the potential role of selected non-coding RNAs most commonly associated with breast cancer. These include miR-21, miR-106a, miR-155, miR-141, let-7c, miR-335, miR-126, miR-199a, miR-101, and miR-9, which are perceived as potential biomarkers in breast cancer prognosis, diagnostics, and treatment response monitoring. As miRNAs differ in expression levels in different types of cancer, they may provide novel cancer therapy strategies. However, some limitations regarding dynamic alterations, tissue-specific profiles, and detection methods must also be raised.

Breast cancer and its therapeutic targets: A comprehensive review.

Breast cancer is a common and deadly disease, so there is a constant need for research to find efficient targets and therapeutic approaches. Breast cancer can be classified on a molecular and histological base. Breast cancer can be divided into ER (estrogen receptor)-positive and ER-negative, HER2 (human epidermal growth factor receptor2)-positive and HER2-negative subtypes based on the presence of specific biomarkers. Targeting hormone receptors, such as the HER2, progesterone receptor (PR), and ER, is very significant and plays a vital role in the onset and progression of breast cancer. Endocrine treatments and HER2-targeted drugs are examples of targeted therapies now being used against these receptors. Emerging immune-based medicines with promising outcomes in the treatment of breast cancer include immune checkpoint inhibitors, cancer vaccines, and adoptive T-cell therapy. It is also explored how immune cells and the tumor microenvironment affect breast cancer development and treatment response. The major biochemical pathways, signaling cascades, and DNA repair mechanisms that are involved in the development and progression of breast cancer, include the PI3K/AKT/mTOR system, the MAPK pathway, and others. These pathways are intended to be inhibited by a variety of targeted drugs, which are then delivered with the goal of restoring normal cellular function. This review aims to shed light on types of breast cancer with the summarization of different therapeutic approaches which can target different pathways for tailored medicines and better patient outcomes.

Evaluation of Breast Cancer Tumor-Infiltrating Lymphocytes on Ultrasound Images Based on a Novel Multi-Cascade Residual U-Shaped Network.

Breast cancer has become the leading cancer of the 21st century. Tumor-infiltrating lymphocytes (TILs) have emerged as effective biomarkers for predicting treatment response and prognosis in breast cancer. The work described here was aimed at designing a novel deep learning network to assess the levels of TILs in breast ultrasound images. We propose the Multi-Cascade Residual U-Shaped Network (MCRUNet), which incorporates a gray feature enhancement (GFE) module for image reconstruction and normalization to achieve data synergy. Additionally, multiple residual U-shaped (RSU) modules are cascaded as the backbone network to maximize the fusion of global and local features, with a focus on the tumor's location and surrounding regions. The development of MCRUNet is based on data from two hospitals and uses a publicly available ultrasound data set for transfer learning. MCRUNet exhibits excellent performance in assessing TILs levels, achieving an area under the receiver operating characteristic curve of 0.8931, an accuracy of 85.71%, a sensitivity of 83.33%, a specificity of 88.64% and an F1 score of 86.54% in the test group. It outperforms six state-of-the-art networks in terms of performance. The MCRUNet network based on breast ultrasound images of breast cancer patients holds promise for non-invasively predicting TILs levels and aiding personalized treatment decisions.

Automated pipeline for breast cancer diagnosis using US assisted diffuse optical tomography.

Ultrasound (US)-guided diffuse optical tomography (DOT) is a portable and non-invasive imaging modality for breast cancer diagnosis and treatment response monitoring. However, DOT data pre-processing and imaging reconstruction often require labor intensive manual processing which hampers real-time diagnosis. In this study, we aim at providing an automated US-assisted DOT pre-processing, imaging and diagnosis pipeline to achieve near real-time diagnosis. We have developed an automated DOT pre-processing method including motion detection, mismatch classification using deep-learning approach, and outlier removal. US-lesion information needed for DOT reconstruction was extracted by a semi-automated lesion segmentation approach combined with a US reading algorithm. A deep learning model was used to evaluate the quality of the reconstructed DOT images and a two-step deep-learning model developed earlier is implemented to provide final diagnosis based on US imaging features and DOT measurements and imaging results. The presented US-assisted DOT pipeline accurately processed the DOT measurements and reconstruction and reduced the procedure time to 2 to 3 minutes while maintained a comparable classification result with manually processed dataset.