Update on prognostic and predictive biomarkers of breast cancer

Biomarker profiling and integrating heterogeneous models for enhanced multi-grade breast cancer prognostication

BACKGROUND

Breast cancer remains a leading cause of female mortality worldwide, exacerbated by limited awareness, inadequate screening resources, and treatment options. Accurate and early diagnosis is crucial for improving survival rates and effective treatment.

OBJECTIVES

This study aims to develop an innovative artificial intelligence (AI) based model for predicting breast cancer and its various histopathological grades by integrating multiple biomarkers and subject age, thereby enhancing diagnostic accuracy and prognostication.

METHODS

A novel ensemble-based machine learning (ML) framework has been introduced that integrates three distinct biomarkers-beta-human chorionic gonadotropin (β-hCG), Programmed Cell Death Ligand 1 (PD-L1), and alpha-fetoprotein (AFP)-alongside subject age. Hyperparameter optimization was performed using the Particle Swarm Optimization (PSO) algorithm, and minority oversampling techniques were employed to mitigate overfitting. The model's performance was validated through rigorous five-fold cross-validation.

RESULTS

The proposed model demonstrated superior performance, achieving a 97.93% accuracy and a 98.06% F1-score on meticulously labeled test data across diverse age groups. Comparative analysis showed that the model outperforms state-of-the-art approaches, highlighting its robustness and generalizability.

CONCLUSION

By providing a comprehensive analysis of multiple biomarkers and effectively predicting tumor grades, this study offers a significant advancement in breast cancer screening, particularly in regions with limited medical resources. The proposed framework has the potential to reduce breast cancer mortality rates and improve early intervention and personalized treatment strategies.

Pharmacogenetics in Oncology: A useful tool for individualizing drug therapy

With the continuous development of genetics in healthcare, there has been a significant contribution to the development of precision medicine, which is ultimately aimed at improving the care of patients. Generally, drug treatments used in Oncology are characterized by a narrow therapeutic range and by their potential toxicity. Knowledge of pharmacogenomics and pharmacogenetics can be very useful in the area of Oncology, as they constitute additional tools that can help to individualize patients' treatment. This work includes a description of some genes that have been revealed to be useful in the field of Oncology, as they play a role in drug prescription and in the prediction of treatment response.

A Clinically Feasible Diagnostic Electrochemical Micronano Motors Biosensor Built on Miniature Swimmer for Multiplex Detection and Grading of Breast Cancer Biomarkers

Estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor-2 (HER2), and Ki67 are four crucial biomarkers used in the clinical diagnosis of breast cancer. Accurate detection of these biomarkers is essential for an effective diagnosis and treatment. MOF-based micronano motors (MOFtors) are promising for various applications, including environmental remediation, targeted nanosurgery, and biomarker detection. This paper presents a clinically feasible diagnostic electrochemical micronano motor biosensor, built on a miniature swimmer, for the multiplex detection and grading of breast cancer biomarkers. We designed a biosensor, named MOFtor-MSEM, incorporating aptamers and antibodies functionalized on SiO2@Co-Fe-MOF, which acts as a miniature swimmer in solution. The SiO2@Co-Fe-MOF serves as the body, while complementary double-chain-linked antibodies function as paddles. In a homogeneous solution, when a positive voltage is applied to the working electrode, the electrostatic interaction between the neutral SiO2@Co-Fe-MOF and the negatively charged complementary double-linked antibody causes the antibody to move toward the electrode and then regress due to water resistance. This back-and-forth motion propels the miniature swimmer, enabling it to move the target analyte through the solution. The sensor features an automatic "sample-amplifying signal-output" process, achieving simultaneous signal amplification and output of four electrochemical signals on a single nanomaterial, a significant challenge in electrochemical sensing. The biosensor boasts a short detection time of 40 min, compared to approximately 1 week for current clinical tissue testing. Additionally, the bioplatform selectively detects HER2, ER, Ki67, and PR in the range of 0-1500 pg/mL, with detection limits of 0.01420, 0.03201, 0.01430, and 0.01229 pg/mL, respectively.

A new insight into the early detection of HER2 protein in breast cancer patients with a focus on electrochemical biosensors approaches: A review

Breast cancer is one of the leading causes of death in women and is a prevalent kind of cancerous growth, representing a substantial risk to women's health. Early detection of breast cancer is essential for effective treatment and improved survival rates. Biomarkers, active substances that signal the existence and advancement of a tumor, play a significant role in the early detection of breast cancer. Hence, accurate identification of biomarkers for tumors is crucial for diagnosing and treating breast cancer. However, the primary diagnostic methods used for the detection of breast cancer require specific equipment, skilled professionals, and specialized analysis, leading to elevated detection expenses. Regarding this obstacle, recent studies emphasize electrochemical biosensors as more advanced and sensitive detection tools compared to traditional methods. Electrochemical biosensors are employed to identify biomarkers that act as unique indicators for the onset, recurrence, and monitoring of therapeutic interventions for breast cancer. This study aims to provide a summary of the electrochemical biosensors that have been employed for the detection of breast cancer at an early stage over the past decade. Initially, the text provides concise information about breast cancer and tumor biomarkers. Subsequently, an in-depth analysis is conducted to systematically review the progress of electrochemical biosensors developed for the stable, specific, and sensitive identification of biomarkers associated with breast cancer. Particular emphasis was given to crucial clinical biomarkers, specifically the human epidermal growth factor receptor-2 (HER2). The analysis then explores the limitations and challenges inherent in the design of effective biosensors for diagnosing and treating breast cancer. Ultimately, we provided an overview of future research directions and concluded by outlining the advantages of electrochemical biosensor approaches.

Aptamer as a targeted approach towards treatment of breast cancer

Aptamers, a novel type of targeted ligand used in drug delivery, have quickly gained popularity due to their high target specificity and affinity. Different aptamer-mediated drug delivery systems, such as aptamer-drug conjugate (ApDC), aptamer-siRNA, and aptamer-functionalised nanoparticle systems, are currently being developed for the successful treatment of cancer based on the excellent properties of aptamers. These systems can decrease potential toxicity and enhance therapeutic efficacy by targeting the drug moiety. In this review, we provide an overview of recent developments in aptamer-mediated delivery systems for cancer therapy, specifically for breast cancer, and talk about the potential applications and current issues of novel aptamer-based techniques. This study in aptamer technology for breast cancer therapy highlights key aptamers targeting well-established biomarkers such as HER2, oestrogen receptor, and progesterone receptor. Additionally, we explore the potential of aptamers in overcoming various challenges such as drug resistance and improving the delivery of therapeutic agents. This review aims to provide a deeper understanding of the present aptamer-based targeted delivery applications through in-depth analysis to increase efficacy and create new therapeutic approaches that may ultimately lead to better treatment outcomes for cancer patients.

Retrospective analysis of estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor-2 (HER2), Ki67 changes and their clinical significance between primary breast cancer and metastatic tumors

Objective

To explore the relationship between receptor heterogeneity and clinicopathological characteristics in 166 patients with invasive breast cancer during metastasis.

Methods

We conducted a retrospective analysis of 166 patients diagnosed with metastatic breast cancer through biopsy, who were admitted to our hospital from January 2018 to December 2022. Statistical analysis was employed to assess the heterogeneity of receptors in both primary and metastatic lesions, including estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor-2 (HER2), Ki67, as well as their association with clinicopathological features such as tumor size, lymph node metastasis, treatment regimen, and disease-free survival.

Results

The discordant expression rates of ER, PR, HER2, Ki-67 and Luminal classification between primary and metastatic lesions were 21.7%, 41.6%, 8.9%, 34.4% and 36.8%, respectively. There is a significant difference in disease-free survival between patients with consistent and inconsistent receptor status of primary and metastatic lesions, which is statistically significant. The median DFS for primary HER2(-) to metastatic HER2(+) was 84 months, which was relatively high. The Cox multivariate regression analysis revealed that the expression differences of ER, PR, HER2, and Ki67 were not influenced by endocrine therapy and chemotherapy. However, a statistically significant difference in HER2 expression was observed with targeted therapy. Tumor size was correlated with ER and Ki67 receptor status (P = 0.019, 0.016). Tumor size was not correlated with PR, and HER2 (P = 0.679, 0.440). Lymph node metastasis was not associated with changes in ER, PR, HER2, and Ki67. The discordant rates of ER, PR, HER2, and Ki-67 in patients with local recurrence were 22%, 23.7%, 5.1%, and 28.8% respectively, whereas those in patients with distant metastasis were 21.5%, 36.4%, 10.3%, and 31.8% respectively.

Conclusions

The expression levels of ER, PR, HER2, and Ki-67 in primary and metastatic breast cancer exhibit heterogeneity, which is closely associated with the prognosis and treatment outcomes of patients.

Correlation Between miR-497-5p Expression With Clinicopathological Characteristics and Prognosis in Patients With Breast Cancer

Breast cancer (BC) comprises multiple biological and histologic properties. MicroRNAs show key functions in cancer prognosis. This paper explored the relationship between miR-497-5p with clinicopathological characteristics and prognosis in BC. Cancer tissues and normal adjacent tissues (NATs) were collected from 140 included patients with BC. The clinical baseline data, including age, tumor size, pathologic grade, clinical stage, modified Scraff-Bloom-Richardson grade, and lymph node metastasis, were recorded. miR-497-5p expression in cancer tissues and NAT was determined by reverse transcription-quantitative polymerase chain reaction. Patients with BC were followed up for 5 years to record their survival. Patients were divided into the miR-497-5p low expression and high expression groups to assess the correlation between miR-497-5p expression with clinicopathological characteristics and overall survival of patients. The role of miR-497-5p as an independent risk factor for death was further analyzed by a multivariate Cox regression model. miR-497-5p was downregulated in BC tissues than NAT. Tumor size, clinical stage, and lymph node metastasis showed significant differences among patients with high and low miR-497-5p expression levels. Patients with BC with low miR-497-5p expression presented decreased survival. Lowly-expressed miR-497-5p was an independent risk factor for death in patients. Collectively, cancer tissue miR-497-5p low expression increases the risk of death and serves as an independent risk factor for death in patients with BC.

Efficacy and safety of minimally invasive laparoscopic surgery under general anesthesia for ovarian cancer

BACKGROUND

Ovarian cancer is one of the most common malignant tumors in female reproductive system in the world, and the choice of its treatment is very important for the survival rate and prognosis of patients. Traditional open surgery is the main treatment for ovarian cancer, but it has the disadvantages of big trauma and slow recovery. With the continuous development of minimally invasive technology, minimally invasive laparoscopic surgery under general anesthesia has been gradually applied to the treatment of ovarian cancer because of its advantages of less trauma and quick recovery. However, the efficacy and safety of minimally invasive laparoscopic surgery under general anesthesia in the treatment of ovarian cancer are still controversial.

AIM

To explore the efficacy and safety of general anesthesia minimally invasive surgery in the treatment of ovarian cancer.

METHODS

The clinical data of 90 patients with early ovarian cancer in our hospital were analyzed retrospectively. According to the different surgical treatment methods, patients were divided into study group and control group (45 cases in each group). The study group received minimally invasive laparoscopic surgery under general anesthesia for ovarian cancer, while the control group received traditional open surgery for ovarian cancer. The European Organization for Research and Treatment of Cancer Quality of Life Questionnaire (EORTC QLQ-C30), clinical efficacy and safety of the two groups were compared.

RESULTS

The intraoperative blood loss, length of hospital stay, postoperative gas evacuation time, and postoperative EORTC QLQ-C30 score of the study group were significantly better than those of the control group (P < 0.05). The incidence of postoperative complications in the study group was significantly lower than in the control group (P < 0.05). The two groups had no significant differences in the preoperative adrenocorticotropic hormone (ACTH), androstenedione (AD), cortisol (Cor), cluster of differentiation 3 positive (CD3+), and cluster of differentiation 4 positive (CD4+) indexes (P > 0.05). In contrast, postoperatively, the study group's ACTH, AD, and Cor indexes were lower, and the CD3+ and CD4+ indexes were higher than those in the control group (P < 0.05).

CONCLUSION

Minimally invasive laparoscopic surgery under general anesthesia in patients with early ovarian cancer can significantly improve the efficacy and safety, improve the short-term prognosis and quality of life of patients, and is worth popularizing.

Prognostic utility of biopsy-based PTEN and ERG status on biochemical progression and overall survival after SBRT for localized prostate cancer

Introduction/background

Phosphatase and tensin homolog (PTEN) genomic deletions and transmembrane protease, serine 2/v-ets avian erthyroblastosis virus E26 oncogene homolog (ERG) rearrangements are two of the most common genetic abnormalities associated with prostate cancer. Prior studies have demonstrated these alterations portend worse clinical outcomes. Our objective is to evaluate the impact of biopsy-determined PTEN losses and TMPRSS2-ERG fusion on biochemical progression-free survival (bPFS) and overall survival (OS) in patients who receive SBRT for localized prostate cancer.

Methods/materials

Patients received SBRT for localized prostate cancer on a prospective quality-of-life (QoL) and cancer outcomes study. For each patient, the single biopsy core with the highest grade/volume of cancer was evaluated for PTEN and ERG abnormalities. Differences in baseline patient and disease characteristics between groups were analyzed using ANOVA for age and χ2 for categorical groupings. bPFS and OS were calculated using the Kaplan Meier (KM) method with Log-Rank test comparison between groups. Predictors of bPFS and OS were identified using the Cox proportional hazards method. For all analyses, p <0.05 was considered statistically significant.

Results

Ninety-nine consecutive patients were included in the analysis with a median follow-up of 72 months. A statistically significant improvement in bPFS (p = 0.018) was observed for wild type ERG patients with an estimated 5-year bPFS of 94.1% vs. 72.4%. Regarding PTEN mutational status, significant improvements in were observed in both bPFS (p = 0.006) and OS (p < 0.001), with estimated 5-year bPFS rates of 91.0% vs. 67.9% and 5-year OS rates of 96.4% vs. 79.4%. When including both ERG and PTEN mutational status in the analysis, there were statistically significant differences in both bPFS (p = 0.011) and OS (p < 0.001). The estimated 5-year bPFS rates were 100%, 76.6%, 72.9%, and 63.8% for patients with ERG+/PTEN+, ERG-/PTEN+, ERG+/PTEN-, and ERG-/PTEN- phenotypes respectively. The estimated 5-year OS rates were 93.9%, 100%, 80.0%, and 78.7% for patients with ERG+/PTEN+, ERG-/PTEN+, ERG+/PTEN-, and ERG-/PTEN- phenotypes respectively.

Conclusion

ERG rearrangements and PTEN deletions detected on biopsy samples are associated with poorer oncologic outcomes in prostate cancer patients treated with SBRT and merit further study in a dedicated prospective trial.

PIK3CA testing in hormone receptor-positive/HER2-negative metastatic breast cancer: real-world data from Italian molecular pathology laboratories

Introduction: PIK3CA gene mutations occur in approximately 40% of hormone receptor-positive/HER2-negative (HR+/HER2-) metastatic breast cancers (MBCs), electing them to targeted therapy. Testing PIK3CA status is complex due to selection of biological specimen and testing method. Materials & methods: This work investigates real-life experience on PIK3CA testing in HR+/HER2- MBC. Clinical, technical and molecular data on PIK3CA testing were collected from two referral laboratories. Additionally, the results of a nationwide PIK3CA survey involving 116 institutions were assessed. Results: Overall, n = 35 MBCs were PIK3CA-mutated, with mutations mostly occurring in exons 9 (n = 19; 51.4%) and 20 (n = 15; 40.5%). The nationwide survey revealed significant variability across laboratories in terms of sampling methodology, technical assessment and clinical report signing healthcare figures for PIK3CA molecular testing in diagnostic routine practice. Conclusion: This study provides insights into the real-world routine of PIK3CA testing in HR+/HER2- MBC and highlights the need for standardization and networking in predictive pathology.

Prediction of neoadjuvant chemotherapy pathological complete response for breast cancer based on radiomics nomogram of intratumoral and derived tissue

BACKGROUND

Non-invasive identification of breast cancer (BCa) patients with pathological complete response (pCR) after neoadjuvant chemotherapy (NACT) is critical to determine appropriate surgical strategies and guide the resection range of tumor. This study aimed to examine the effectiveness of a nomogram created by combining radiomics signatures from both intratumoral and derived tissues with clinical characteristics for predicting pCR after NACT.

METHODS

The clinical data of 133 BCa patients were analyzed retrospectively and divided into training and validation sets. The radiomics features for Intratumoral, peritumoral, and background parenchymal enhancement (BPE) in the training set were dimensionalized. Logistic regression analysis was used to select the optimal feature set, and a radiomics signature was constructed using a decision tree. The signature was combined with clinical features to build joint models and generate nomograms. The area under curve (AUC) value of receiver operating characteristic (ROC) curve was then used to assess the performance of the nomogram and independent predictors.

RESULTS

Among single region, intratumoral had the best predictive value. The diagnostic performance of the intratumoral improved after adding the BPE features. The AUC values of the radiomics signature were 0.822 and 0.82 in the training and validation sets. Multivariate logistic regression analysis revealed that age, ER, PR, Ki-67, and radiomics signature were independent predictors of pCR in constructing a nomogram. The AUC of the nomogram in the training and validation sets were 0.947 and 0.933. The DeLong test showed that the nomogram had statistically significant differences compared to other independent predictors in both the training and validation sets (P < 0.05).

CONCLUSION

BPE has value in predicting the efficacy of neoadjuvant chemotherapy, thereby revealing the potential impact of tumor growth environment on the efficacy of neoadjuvant chemotherapy.

Exploring Biomarkers in Breast Cancer: Hallmarks of Diagnosis, Treatment, and Follow-Up in Clinical Practice

Breast cancer is a prevalent malignancy in the present day, particularly affecting women as one of the most common forms of cancer. A significant portion of patients initially present with localized disease, for which curative treatments are pursued. Conversely, another substantial segment is diagnosed with metastatic disease, which has a worse prognosis. Recent years have witnessed a profound transformation in the prognosis for this latter group, primarily due to the discovery of various biomarkers and the emergence of targeted therapies. These biomarkers, encompassing serological, histological, and genetic indicators, have demonstrated their value across multiple aspects of breast cancer management. They play crucial roles in initial diagnosis, aiding in the detection of relapses during follow-up, guiding the application of targeted treatments, and offering valuable insights for prognostic stratification, especially for highly aggressive tumor types. Molecular markers have now become the keystone of metastatic breast cancer diagnosis, given the diverse array of chemotherapy options and treatment modalities available. These markers signify a transformative shift in the arsenal of therapeutic options against breast cancer. Their diagnostic precision enables the categorization of tumors with elevated risks of recurrence, increased aggressiveness, and heightened mortality. Furthermore, the existence of therapies tailored to target specific molecular anomalies triggers a cascade of changes in tumor behavior. Therefore, the primary objective of this article is to offer a comprehensive review of the clinical, diagnostic, prognostic, and therapeutic utility of the principal biomarkers currently in use, as well as of their clinical impact on metastatic breast cancer. In doing so, our goal is to contribute to a more profound comprehension of this complex disease and, ultimately, to enhance patient outcomes through more precise and effective treatment strategies.

The expression of O-linked glycosyltransferase GALNT7 in breast cancer is dependent on estrogen-, progesterone-, and HER2-receptor status, with prognostic implications

GALNT7 is a glycosyltransferase enzyme transferring N-acetylgalactosamine to initiate O-linked glycosylation in the Golgi apparatus. Breast cancer is the most common cancer in women globally. Estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2; ERBB2) are important biomarkers in the prognosis and molecular subtyping of breast cancer. Here, we showed that ER-positive, PR-positive or HER2-positive breast tumors have higher expression of GALNT7 compared to ER-negative, PR-negative or HER2-negative breast tumors, respectively. We found that CpG-aggregated methylation of GALNT7 gene is decreased, and in parallel, its transcript levels are increased in breast cancer compared to healthy breast tissue. We observed that the difference in the expression of GALNT7 between negative and positive status of the receptors is the highest for HER2, followed by ER and PR, pointing that HER2 might be relatively more influential than ER and PR on the expression of GALNT7 in breast cancer. We reported that basal-like breast tumors have decreased expression of GALNT7 compared to non-basal-like tumors, and that high GALNT7 expression is associated with favorable relapse-free and distant metastasis-free survival in HER2 status-dependent manner in breast cancer patients. Moreover, we showed that GALNT7 expression in breast cancer is cell type- (epithelial vs stromal cells), tumor grade- and ethnicity-dependent. Combined, we propose that GALNT7 might contribute to different clinical outcomes depending on the receptor status in breast cancer, and that a better understanding of GALNT7 and its function in the context of breast cancer is needed.

Investigation of the Use of Circulating Long Non-coding RNA HOXA Transcript at the Distal Tip (LncRNA HOTTIP) as a Biomarker in Breast Cancer

The critical need for new diagnostic and prognostic methods is highlighted by the fact that breast cancer continues to be the top cause of cancer-related deaths globally. Due to the dysregulation of long non-coding RNAs (lncRNAs) in numerous malignancies, they have become potential biomarkers in cancer. Recent research has focused on the lncRNA HOTTIP (HOXA transcript at the distal tip), which has a function in breast cancer metastasis and carcinogenesis. Until recently, HOTTIP had only been measured in cancer tissues and specimens. The aim of this study is to assess the amounts of the lncRNA HOTTIP in the blood serum of 46 breast cancer patients using real-time PCR analysis and identify the relationships between HOTTIP expression and several known clinical and pathological factors, including tumor grade, stage, lymph node involvement, hormone receptor status, and cell proliferation. The results of the study confirmed a positive relation of HOTTIP expression and breast cancer aggressiveness and metastatic behavior. The analysis results showed elevated HOTTIP values in stage III and T3/T4 tumors with multifocal characteristics and in lymph node involvement. Our findings raise the possibility of HOTTIP serving as a future prognostic biomarker for breast cancer patients.

Trefoil Family Factor Peptide 1-A New Biomarker in Liquid Biopsies of Retinoblastoma under Therapy

Effective management of retinoblastoma (RB), the most prevalent childhood eye cancer, depends on reliable monitoring and diagnosis. A promising candidate in this context is the secreted trefoil family factor peptide 1 (TFF1), recently discovered as a promising new biomarker in patients with a more advanced subtype of retinoblastoma. The present study investigated TFF1 expression within aqueous humor (AH) of enucleated eyes and compared TFF1 levels in AH and corresponding blood serum samples from RB patients undergoing intravitreal chemotherapy (IVC). TFF1 was consistently detectable in AH, confirming its potential as a biomarker. Crucially, our data confirmed that TFF1-secreting cells within the tumor mass originate from RB tumor cells, not from surrounding stromal cells. IVC-therapy-responsive patients exhibited remarkably reduced TFF1 levels post-therapy. By contrast, RB patients' blood serum displayed low-to-undetectable levels of TFF1 even after sample concentration and no therapy-dependent changes were observed. Our findings suggest that compared with blood serum, AH represents the more reliable source of TFF1 if used for liquid biopsy RB marker analysis in RB patients. Thus, analysis of TFF1 in AH of RB patients potentially provides a minimally invasive tool for monitoring RB therapy efficacy, suggesting its importance for effective treatment regimens.

Survival analysis of recurrent breast cancer patients using mix Bayesian network

Introduction

Breast cancer (BC) is the most common cancer among women. Iranians have an 11% BC recurrence rate, which lowers their survival rates. Few studies have investigated cancer recurrence survival rates. This study's major purpose is to use a mixed Bayesian network (BN) to analyze recurrent patients' survival.

Material and methods

This study aimed to evaluate the pathobiological features, age, gender, final status, and survival time of the patients. Bayesian imputation was used for missing data. The performance of BN was optimized through the utilization of a blacklist and prior probability. After structural and parametric learning, posterior conditional probabilities and mean survival periods for the node arcs were predicted. The hold-out technique based on the posterior classification error was used to investigate the model's validation.

Results

The study included 220 cancer recurrence patients. These patients averaged 47 years old. The BN with a blacklist and prior probability has a higher network score than other networks. The hold-out technique verified structural learning. The Directed Acyclic Graph showed a statistically significant relationship between cancer biomarkers (ER, PR, and HER2 receptors), cancer stage, and tumor grade and patient survival duration. Patient death was also significantly associated with education, ER, PR, HER2, and tumor grade. The BN reports that HER2 negative, ER positive, and PR positive patients had a higher survival rate.

Conclusion

Survival and death of relapsed patients depend on biomarkers. Based on the findings, patient survival can be predicted with their features.

Development and testing of a random forest-based machine learning model for predicting events among breast cancer patients with a poor response to neoadjuvant chemotherapy

BACKGROUND

Breast cancer (BC) is the most common malignant tumor around the world. Timely detection of the tumor progression after treatment could improve the survival outcome of patients. This study aimed to develop machine learning models to predict events (defined as either (1) the first tumor relapse locally, regionally, or distantly; (2) a diagnosis of secondary malignant tumor; or (3) death because of any reason.) in BC patients post-treatment.

METHODS

The patients with the response of stable disease (SD) and progressive disease (PD) after neoadjuvant chemotherapy (NAC) were selected. The clinicopathological features and the survival data were recorded in 1 year and 5 years, respectively. Patients were randomly divided into the training set and test set in the ratio of 8:2. A random forest (RF) and a logistic regression were established in both of 1-year cohort and the 5-year cohort. The performance was compared between the two models. The models were validated using data from the Surveillance, Epidemiology, and End Results (SEER) database.

RESULTS

A total of 315 patients were included. In the 1-year cohort, 197 patients were divided into a training set while 87 were into a test set. The specificity, sensitivity, and AUC were 0.800, 0.833, and 0.810 in the RF model. And 0.520, 0.833, and 0.653 of the logistic regression. In the 5-year cohort, 132 patients were divided into the training set while 33 were into the test set. The specificity, sensitivity, and AUC were 0.882, 0.750, and 0.829 in the RF model. And 0.882, 0.688, and 0.752 of the logistic regression. In the external validation set, of the RF model, the specificity, sensitivity, and AUC were 0.765, 0.812, and 0.779. Of the logistics regression model, the specificity, sensitivity, and AUC were 0.833, 0.376, and 0.619.

CONCLUSION

The RF model has a good performance in predicting events among BC patients with SD and PD post-NAC. It may be beneficial to BC patients, assisting in detecting tumor recurrence.

Applications of Artificial Intelligence in Breast Pathology

CONTEXT.—

Increasing implementation of whole slide imaging together with digital workflow and advances in computing capacity enable the use of artificial intelligence (AI) in pathology, including breast pathology. Breast pathologists often face a significant workload, with diagnosis complexity, tedious repetitive tasks, and semiquantitative evaluation of biomarkers. Recent advances in developing AI algorithms have provided promising approaches to meet the demand in breast pathology.

OBJECTIVE.—

To provide an updated review of AI in breast pathology. We examined the success and challenges of current and potential AI applications in diagnosing and grading breast carcinomas and other pathologic changes, detecting lymph node metastasis, quantifying breast cancer biomarkers, predicting prognosis and therapy response, and predicting potential molecular changes.

DATA SOURCES.—

We obtained data and information by searching and reviewing literature on AI in breast pathology from PubMed and based our own experience.

CONCLUSIONS.—

With the increasing application in breast pathology, AI not only assists in pathology diagnosis to improve accuracy and reduce pathologists' workload, but also provides new information in predicting prognosis and therapy response.

A retrospective study in tumour characteristics and clinical outcomes of overweight and obese women with breast cancer

INTRODUCTION

Obesity and breast cancer are two major pathologies closely associated with increasing incidence and mortality rates, especially amongst women. The association between both diseases have been thoroughly discussed but much is still to uncover.

AIM

The aim of this study is to analyse tumour characteristics and clinical outcomes of overweight and obese women to disclosure potential associations and better understand the impact of obesity in breast cancer.

MATERIALS AND METHODS

Clinicopathological information of 2246 women were extracted from the institutional database of comprehensive cancer centre in Portugal diagnosed between 2012 and 2016. Women were stratified according to body mass index as normal, overweight, and obese. Patients' demographic information and tumour features (age, family history, topographic localization, laterality, histological type, and receptor status) were taken as independent variables and overall survival, tumour stage, differentiation grade and bilaterality were considered clinical outcomes.

RESULTS

The main results reveal that overweight and obesity are predominantly associated with worse outcomes in breast cancer patients. Obese patients present larger (p-value: 0.002; OR 1.422; 95% CI 1.134-1.783) and more poorly differentiated tumours (p-value: 0.002; OR 1.480; 95% CI 1.154-1.898) and tend to have lower overall survival although without statistical significance (p-value: 0.117; OR 1.309; 95% CI 0.934-1.833). Overweighted women are more likely to have bilateral breast cancer (p-value: 0.017; OR 3.076; 95% CI 1.225-7.722) than obese women. The results also reveal that overweight women present less distant metastasis (p-value: 0.024; OR 0.525; 95%CI 0.299-0.920). Topographic localization and laterality did not achieve statistical significance.

Immunogenic cell death-related classifications in breast cancer identify precise immunotherapy biomarkers and enable prognostic stratification

Background: Immunogenic cell death (ICD) remodels the tumor immune microenvironment, plays an inherent role in tumor cell apoptosis, and promotes durable protective antitumor immunity. Currently, appropriate biomarker-based ICD immunotherapy for breast cancer (BC) is under active exploration. Methods: To determine the potential link between ICD genes and the clinical risk of BC, TCGA-BC was used as the training set and GSE58812 was used as the validation set. Gene expression, consistent clustering, enrichment analysis, and mutation omics analyses were performed to analyze the potential biological pathways of ICD genes involved in BC. Furthermore, a risk and prognosis model of ICD was constructed to evaluate the correlation between risk grade and immune infiltration, clinical stage, and survival prognosis. Results: We identified two ICD-related subtypes by consistent clustering and found that the C2 subtype was associated with good survival prognosis, abundant immune cell infiltration, and high activity of immune biological processes. Based on this, we constructed and validated an ICD risk and prognosis model of BC, including ATG5, HSP90AA1, PIK3CA, EIF2AK3, MYD88, IL1R1, and CD8A. This model can effectively predict the survival rate of patients with BC and is negatively correlated with the immune microenvironment and clinical stage. Conclusion: This study provides new insights into the role of ICD in BC. The novel classification risk model based on ICD in BC established in this study can aid in estimating the potential prognosis of patients with BC and the clinical outcomes of immunotherapy and postulates targets that are more useful in comprehensive treatment strategies.