Predictive biomarkers for personalized medicine in breast cancer

Strategic advancements in targeting the PI3K/AKT/mTOR pathway for Breast cancer therapy

Breast cancer (BC) is a complex disease that affects millions of women worldwide. Its growing impact calls for advanced treatment strategies to improve patient outcomes. The PI3K/AKT/mTOR pathway is a key focus in BC therapy because it plays a major role in important processes like tumor growth, survival, and resistance to treatment. Targeting this pathway could lead to better treatment options and outcomes. The present review explores how the PI3K/AKT/mTOR pathway becomes dysregulated in BC, focusing on the genetic changes like PIK3CA mutations and PTEN loss that leads to its aggravation. Current treatment options include the use of inhibitors targeting PI3K, AKT, and mTOR with combination therapies showing promise in overcoming drug resistance and improving effectiveness. Looking ahead, next-generation inhibitors and personalized treatment plans guided by biomarker analysis may provide more accurate and effective options for patients. Integrating these pathway inhibitors with immunotherapy offers an exciting opportunity to boost anti-tumor responses and improve survival rates. This review offers a comprehensive summary of the current progress in targeting the PI3K/AKT/mTOR pathway in BC. It highlights future research directions and therapeutic strategies aimed at enhancing patient outcomes and quality of life.

Efficient discovery of robust prognostic biomarkers and signatures in solid tumors

Recent advancements in multi-omics and big-data technologies have facilitated the discovery of numerous cancer prognostic biomarkers and gene signatures. However, their clinical application remains limited due to poor reproducibility and insufficient independent validation. Despite the availability of high-quality datasets, achieving reliable biomarker identification across multiple cohorts continues to be a significant challenge. To address these issues, we developed a comprehensive platform, SurvivalML, designed to support the discovery and validation of prognostic biomarkers and gene signatures using large-scale and harmonized data from 21 cancer types. Through SurvivalML, we identified DCLRE1B as a novel prognostic biomarker for hepatocellular carcinoma, with experimental confirmation of its role in promoting tumor progression. Additionally, we developed the Chinese glioblastoma prognostic signature (CGPS) and its simplified version, SCGPS, a three-gene model. Both demonstrated superior predictive performance compared to other glioblastoma signatures in our in-house cohort and five independent Chinese datasets. The SCGPS model was further validated in 109 clinical samples using multiplex immunofluorescence, showing strong consistency with the original CGPS model. Overall, SurvivalML provides a robust platform for the identification and validation of prognostic biomarkers and gene signatures, offering a valuable resource for advancing cancer research and clinical application.

Development and validation of a 14-CpG DNA methylation signature and drug targets for prognostic prediction in breast cancer

Background

Breast cancer (BC) is the most prevalent cancer among women and a leading cause of cancer-related deaths worldwide. Emerging evidence suggests that DNA methylation, a well-studied epigenetic modification, regulates various cellular processes critical for cancer development and progression and holds promise as a biomarker for cancer diagnosis and prognosis, potentially enhancing the efficacy of precision therapies.

Methods

We developed a robust prognostic model for BC based on DNA methylation and clinical data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). We analyzed the association of the model with clinicopathological features, survival outcomes, and chemotherapy drug sensitivity.

Results

A set of 216 differentially methylated CpGs was identified by intersecting three datasets (TCGA, GSE22249, and GSE66695). Using univariate Cox proportional hazard and LASSO Cox regression analyses, we constructed a 14-CpG model significantly associated with progression-free interval (PFI), disease-specific survival (DSS), and overall survival (OS) in BC patients. Kaplan-Meier (KM) survival analysis, receiver operating characteristic (ROC) analysis, and nomogram validation confirmed the clinical value of the signature. The Cox analysis showed a significant association between the signature and PFI and DSS in BC patients. KM analysis effectively distinguished high-risk from low-risk patients, while ROC analysis demonstrated high sensitivity and specificity in predicting BC prognosis. A nomogram based on the signature effectively predicted 5- and 10-year PFI and DSS. Additionally, combining our model with clinical risk factors suggested that patients in the I-II & M+ subgroup could benefit from adjuvant chemotherapy regarding PFI, DSS, and OS. Gene Ontology (GO) functional enrichment and KEGG pathway analyses indicated that the top 3,000 differentially expressed genes (DEGs) were enriched in pathways related to DNA replication and repair and cell cycle regulation. Patients in the high-risk group might benefit from drugs targeting DNA replication and repair processes in tumor cells.

Conclusion

The 14-CpG model serves as a useful biomarker for predicting prognosis in BC patients. When combined with TNM staging, it offers a potential strategy for individualized clinical decision-making, guiding personalized therapeutic regimen selection for clinicians.

Precision Medicine in High-Grade Serous Ovarian Cancer: Targeted Therapies and the Challenge of Chemoresistance

The poor prognosis for high-grade serous ovarian cancer (HGSOC), the dominant subtype of ovarian cancer, reflects its aggressive nature, late diagnosis, and the highest mortality rate among all gynaecologic cancers. Apart from late diagnosis, the main reason for the poor prognosis and its unsuccessful treatment is primarily the emergence of chemoresistance to carboplatin. Although there is a good response to primary treatment, the disease recurs in 80% of cases, at which point it is largely resistant to carboplatin. The introduction of novel targeted therapies in the second decade of the 21st century has begun to transform the treatment of HGSOC, although their impact on overall survival remains unsatisfactory. Targeting the specific pathways known to be abnormally activated in HGSOC is especially difficult due to the molecular diversity of its subtypes. Moreover, a range of molecular changes are associated with acquired chemoresistance, e.g., reversion of BRCA1 and BRCA2 germline alleles. In this review, we examine the advantages and disadvantages of approved targeted therapies, including bevacizumab, PARP inhibitors (PARPis), and treatments targeting cells with neurotrophic tyrosine receptor kinase (NTRK), B-rapidly accelerated fibrosarcoma (BRAF), and rearranged during transfection (RET) gene alterations, as well as antibody-drug conjugates. Additionally, we explore promising new targets under investigation in ongoing clinical trials, such as immune checkpoint inhibitors, anti-angiogenic agents, phosphatidylinositol-3-kinase (PI3K) inhibitors, Wee1 kinase inhibitors, and ataxia telangiectasia and Rad3-related protein (ATR) inhibitors for platinum-resistant disease. Despite the development of new targeted therapies, carboplatin remains the fundamental medicine in HGSOC therapy. The correct choice of treatment strategy for better survival of patients with advanced HGSOC should therefore include a prediction of patients' risks of developing chemoresistance to platinum-based chemotherapy. Moreover, effective targeted therapy requires the selection of patients who are likely to derive clinical benefit while minimizing potential adverse effects, underscoring the essence of precision medicine.

A Multi-Omics-Based Exploration of the Predictive Role of MSMB in Prostate Cancer Recurrence: A Study Using Bayesian Inverse Convolution and 10 Machine Learning Combinations

Background: Prostate cancer (PCa) is a prevalent malignancy among elderly men. Biochemical recurrence (BCR), which typically occurs after radical treatments such as radical prostatectomy or radiation therapy, serves as a critical indicator of potential disease progression. However, reliable and effective methods for predicting BCR in PCa patients remain limited. Methods: In this study, we used Bayesian deconvolution combined with 10 machine learning algorithms to build a five-gene model for predicting PCa progression. The model and the five selected genes were externally validated. Various analyses such as prognosis, clinical subgroups, tumor microenvironment, immunity, genetic variants, and drug sensitivity were performed on MSMB/Epithelial_cells subgroups. Results: Our model outperformed 102 previously published prognostic features. Notably, PCa patients with a high proportion of MSMB/epithelial cells were characterized by a greater progression-free Interval (PFI), a higher proportion of early-stage tumors, a lower stromal component, and a reduced presence of tumor-associated fibroblasts (CAF). The high proportion of MSMB/epithelial cells was also associated with higher frequencies of SPOP and TP53 mutations. Drug sensitivity analysis revealed that patients with a poorer prognosis and lower MSMB/epithelial cell ratio showed increased sensitivity to cyclophosphamide, cisplatin, and dasatinib. Conclusions: The model developed in this study provides a robust and accurate tool for predicting PCa progression. It offers significant potential for enhancing risk stratification and informing personalized treatment strategies for PCa patients.

Lymph node metastasis in cancer: Clearing the clouds to see the dawn

Lymph node metastasis (LNM) is often regarded as an indicator of poor prognosis in various cancers. Despite over three centuries of exploration since its discovery, the molecular mechanisms underlying LNM remain inconclusive. This review summarizes the molecular mechanisms of LNM, using the "PUMP+" principle for clarification. Pathological examination remains the gold standard for LNM diagnosis, yet there is a need to explore early diagnostic strategies that can effectively improve patient outcomes. With the advent of immunotherapy, discussions on the fate of lymph nodes (LN) have emerged, emphasizing the importance of preserving LN integrity prior to immunotherapy. This, in turn, poses higher demands for diagnostic accuracy and precision treatment of LNM. This review comprehensively discusses the molecular mechanisms, diagnostic methods, and treatment strategies for cancer lymph node metastasis, along with current bottlenecks and future directions in this field.

Deciphering the TCF19/miR-199a-5p/SP1/LOXL2 pathway: Implications for breast cancer metastasis and epithelial-mesenchymal transition

Globally, breast cancer (BC) is the predominant malignancy with a significant death rate due to metastasis. The epithelial-mesenchymal transition (EMT) is a fundamental initiator for metastatic progression. Through advanced computational strategies, TCF19 was identified as a critical EMT-associated gene with diagnostic and prognostic significance in BC, based on a novel EMT score. Molecular details and the pro-EMT impact of the TCF19/miR-199a-5p/SP1/LOXL2 axis were explored in BC cell lines through in vitro validations, and the oncogenic and metastatic potential of TCF19 and LOXL2 were investigated using subcutaneous and tail-vein models. Additionally, BC-specific enrichment of TCF19 and LOXL2 was measured using a distribution landscape driven by diverse genomic analysis techniques. Molecular pathways revealed that TCF19-induced LOXL2 amplification facilitated migratory, invasive, and EMT activities of BC cells in vitro, and promoted the growth and metastatic establishment of xenografts in vivo. TCF19 decreases the expression of miR-199a-5p and alters the nuclear dynamics of SP1, modulating SP1's affinity for the LOXL2 promoter, leading to increased LOXL2 expression and more malignant characteristics in BC cells. These findings unveil a novel EMT-inducing pathway, the TCF19/miR-199a-5P/SP1/LOXL2 axis, highlighting the pivotal role of TCF19 and suggesting potential for novel therapeutic approaches for more focused BC interventions.

Investigating the effects of artificial intelligence on the personalization of breast cancer management: a systematic study

BACKGROUND

Providing appropriate specialized treatment to the right patient at the right time is considered necessary in cancer management. Targeted therapy tailored to the genetic changes of each breast cancer patient is a desirable feature of precision oncology, which can not only reduce disease progression but also potentially increase patient survival. The use of artificial intelligence alongside precision oncology can help physicians by identifying and selecting more effective treatment factors for patients.

METHOD

A systematic review was conducted using the PubMed, Embase, Scopus, and Web of Science databases in September 2023. We performed the search strategy with keywords, namely: Breast Cancer, Artificial intelligence, and precision Oncology along with their synonyms in the article titles. Descriptive, qualitative, review, and non-English studies were excluded. The quality assessment of the articles and evaluation of bias were determined based on the SJR journal and JBI indices, as well as the PRISMA2020 guideline.

RESULTS

Forty-six studies were selected that focused on personalized breast cancer management using artificial intelligence models. Seventeen studies using various deep learning methods achieved a satisfactory outcome in predicting treatment response and prognosis, contributing to personalized breast cancer management. Two studies utilizing neural networks and clustering provided acceptable indicators for predicting patient survival and categorizing breast tumors. One study employed transfer learning to predict treatment response. Twenty-six studies utilizing machine-learning methods demonstrated that these techniques can improve breast cancer classification, screening, diagnosis, and prognosis. The most frequent modeling techniques used were NB, SVM, RF, XGBoost, and Reinforcement Learning. The average area under the curve (AUC) for the models was 0.91. Moreover, the average values for accuracy, sensitivity, specificity, and precision were reported to be in the range of 90-96% for the models.

CONCLUSION

Artificial intelligence has proven to be effective in assisting physicians and researchers in managing breast cancer treatment by uncovering hidden patterns in complex omics and genetic data. Intelligent processing of omics data through protein and gene pattern classification and the utilization of deep neural patterns has the potential to significantly transform the field of complex disease management.

Evolving Management of Breast Cancer in the Era of Predictive Biomarkers and Precision Medicine

Breast cancer is the most common cancer among women in the world as well as in the United States. Molecular and histological differentiation have helped clinicians optimize treatments with various therapeutics, including hormonal therapy, chemotherapy, immunotherapy, and radiation therapy. Recently, immunotherapy has become the standard of care in locally advanced triple-negative breast cancer and an option across molecular subtypes for tumors with a high tumor mutation burden. Despite the advancements in personalized medicine directing the management of localized and advanced breast cancers, the emergence of resistance to these therapies is the leading cause of death among breast cancer patients. Therefore, there is a critical need to identify and validate predictive biomarkers to direct treatment selection, identify potential responders, and detect emerging resistance to standard therapies. Areas of active scientific and clinical research include novel personalized and predictive biomarkers incorporating tumor microenvironment, tumor immune profiling, molecular characterization, and histopathological differentiation to predict response and the potential emergence of resistance.

The somatic mutation profile of estrogen receptor-positive HER2-negative metastatic breast cancer in Brazilian patients

Background

Breast cancer is the leading cause of cancer death among women worldwide. Studies about the genomic landscape of metastatic breast cancer (MBC) have predominantly originated from developed nations. There are still limited data on the molecular epidemiology of MBC in low- and middle-income countries. This study aims to evaluate the prevalence of mutations in the PI3K-AKT pathway and other actionable drivers in estrogen receptor (ER)+/HER2- MBC among Brazilian patients treated at a large institution representative of the nation's demographic diversity.

Methods

We conducted a retrospective observational study using laboratory data (OC Precision Medicine). Our study included tumor samples from patients with ER+/HER2- MBC who underwent routine tumor testing from 2020 to 2023 and originated from several Brazilian centers within the Oncoclinicas network. Two distinct next-generation sequencing (NGS) assays were used: GS Focus (23 genes, covering PIK3CA, AKT1, ESR1, ERBB2, BRCA1, BRCA2, PALB2, TP53, but not PTEN) or GS 180 (180 genes, including PTEN, tumor mutation burden [TMB] and microsatellite instability [MSI]).

Results

Evaluation of tumor samples from 328 patients was undertaken, mostly (75.6%) with GS Focus. Of these, 69% were primary tumors, while 31% were metastatic lesions. The prevalence of mutations in the PI3K-AKT pathway was 39.3% (95% confidence interval, 33% to 43%), distributed as 37.5% in PIK3CA and 1.8% in AKT1. Stratification by age revealed a higher incidence of mutations in this pathway among patients over 50 (44.5% vs 29.1%, p=0.01). Among the PIK3CA mutations, 78% were canonical (included in the alpelisib companion diagnostic non-NGS test), while the remaining 22% were characterized as non-canonical mutations (identifiable only by NGS test). ESR1 mutations were detected in 6.1%, exhibiting a higher frequency in metastatic samples (15.1% vs 1.3%, p=0.003). Additionally, mutations in BRCA1, BRCA2, or PALB2 were identified in 3.9% of cases, while mutations in ERBB2 were found in 2.1%. No PTEN mutations were detected, nor were TMB high or MSI cases.

Conclusion

We describe the genomic landscape of Brazilian patients with ER+/HER2- MBC, in which the somatic mutation profile is comparable to what is described in the literature globally. These data are important for developing precision medicine strategies in this scenario, as well as for health systems management and research initiatives.

Deciphering the Puzzle: Literature Insights on Chlamydia trachomatis-Mediated Tumorigenesis, Paving the Way for Future Research

Some infectious agents have the potential to cause specific modifications in the cellular microenvironment that could be propitious to the carcinogenesis process. Currently, there are specific viruses and bacteria, such as human papillomavirus (HPV) and Helicobacter pylori, that are well established as risk factors for neoplasia. Chlamydia trachomatis (CT) infections are one of the most common bacterial sexually transmitted infections worldwide, and recent European data confirmed a continuous rise across Europe. The infection is often asymptomatic in both sexes, requiring a screening program for early detection. Notwithstanding, not all countries in Europe have it. Chlamydia trachomatis can cause chronic and persistent infections, resulting in inflammation, and there are plausible biological mechanisms that link the genital infection with tumorigenesis. Herein, we aimed to understand the epidemiological and biological plausibility of CT genital infections causing endometrial, ovarian, and cervical tumors. Also, we covered some of the best suitable in vitro techniques that could be used to study this potential association. In addition, we defend the point of view of a personalized medicine strategy to treat those patients through the discovery of some biomarkers that could allow it. This review supports the need for the development of further fundamental studies in this area, in order to investigate and establish the role of chlamydial genital infections in oncogenesis.

Identification of a 9-gene signature to enhance biochemical recurrence prediction in primary prostate cancer: A benchmarking study using ten machine learning methods and twelve patient cohorts

Prostate cancer (PCa) is a prevalent malignancy among men worldwide, and biochemical recurrence (BCR) after radical prostatectomy (RP) is a critical turning point commonly used to guide the development of treatment strategies for primary PCa. However, the clinical parameters currently in use are inadequate for precise risk stratification and informing treatment choice. To address this issue, we conducted a study that collected transcriptomic data and clinical information from 1662 primary PCa patients across 12 multicenter cohorts globally. We leveraged 101 algorithm combinations that consisted of 10 machine learning methods to develop and validate a 9-gene signature, named BCR SCR, for predicting the risk of BCR after RP. Our results demonstrated that BCR SCR generally outperformed 102 published prognostic signatures. We further established the clinical significance of these nine genes in PCa progression at the protein level through immunohistochemistry on Tissue Microarray (TMA). Moreover, our data showed that patients with higher BCR SCR tended to have higher rates of BCR and distant metastasis after radical radiotherapy. Through drug target prediction analysis, we identified nine potential therapeutic agents for patients with high BCR SCR. In conclusion, the newly developed BCR SCR has significant translational potential in accurately stratifying the risk of patients who undergo RP, monitoring treatment courses, and developing new therapies for the disease.

SOX13 is a novel prognostic biomarker and associates with immune infiltration in breast cancer

Background

The transcription factor, SOX13 is part of the SOX family. SOX proteins are crucial in the progression of many cancers, and some correlate with carcinogenesis. Nonetheless, the biological and clinical implications of SOX13 in human breast cancer (BC) remain rarely known.

Methods

We evaluated the survival and expression data of SOX13 in BC patients via the UNLCAL, GEPIA, TIMER, and Kaplan-Meier plotter databases. Immunohistochemistry (IHC) was used to verify clinical specimens. The gene alteration rates of SOX13 were acquired on the online web cBioportal. With the aid of the TCGA data, the association between SOX13 mRNA expression and copy number alterations (CNA) and methylation was determined. LinkedOmics was used to identify the genes that co-expressed with SOX13 and the regulators. Immune infiltration and tumor microenvironment evaluations were assessed by ImmuCellAI and TIMER2.0 databases. SOX13 correlated drug resistance analysis was performed using the GDSC2 database.

Results

Higher SOX13 expression was discovered in BC tissues in comparison to normal tissues. Moreover, increased gene mutation and amplification of SOX13 were found in BC. Patients with increased SOX13 expression levels showed worse overall survival (OS). Cox analysis showed that SOX13 independently served as a prognostic indicator for poor survival in BC. Further, the expression of SOX13 was also confirmed to be correlated with tumor microenvironment and diverse infiltration of immune cells. In terms of drug sensitivity analysis, we found higher expression level of SOX13 predicts a high IC50 value for most of 198 drugs which predicts drug resistance.

Conclusion

The present findings demonstrated that high expression of SOX13 negatively relates to prognosis and SOX13 plays an important role in cancer immunity. Therefore, SOX13 may potentially be adopted as a biomarker for predicting BC prognosis and infiltration of immune cells.

Cost-effective prognostic evaluation of breast cancer: using a STAR nomogram model based on routine blood tests

Background

Breast cancer (BC) is the most common and prominent deadly disease among women. Predicting BC survival mainly relies on TNM staging, molecular profiling and imaging, hampered by subjectivity and expenses. This study aimed to establish an economical and reliable model using the most common preoperative routine blood tests (RT) data for survival and surveillance strategy management.

Methods

We examined 2863 BC patients, dividing them into training and validation cohorts (7:3). We collected demographic features, pathomics characteristics and preoperative 24-item RT data. BC risk factors were identified through Cox regression, and a predictive nomogram was established. Its performance was assessed using C-index, area under curves (AUC), calibration curve and decision curve analysis. Kaplan-Meier curves stratified patients into different risk groups. We further compared the STAR model (utilizing HE and RT methodologies) with alternative nomograms grounded in molecular profiling (employing second-generation short-read sequencing methodologies) and imaging (utilizing PET-CT methodologies).

Results

The STAR nomogram, incorporating subtype, TNM stage, age and preoperative RT data (LYM, LYM%, EOSO%, RDW-SD, P-LCR), achieved a C-index of 0.828 in the training cohort and impressive AUCs (0.847, 0.823 and 0.780) for 3-, 5- and 7-year OS rates, outperforming other nomograms. The validation cohort showed similar impressive results. The nomogram calculates a patient's total score by assigning values to each risk factor, higher scores indicating a poor prognosis. STAR promises potential cost savings by enabling less intensive surveillance in around 90% of BC patients. Compared to nomograms based on molecular profiling and imaging, STAR presents a more cost-effective, with potential savings of approximately $700-800 per breast cancer patient.

Conclusion

Combining appropriate RT parameters, STAR nomogram could help in the detection of patient anemia, coagulation function, inflammation and immune status. Practical implementation of the STAR nomogram in a clinical setting is feasible, and its potential clinical impact lies in its ability to provide an early, economical and reliable tool for survival prediction and surveillance strategy management. However, our model still has limitations and requires external data validation. In subsequent studies, we plan to mitigate the potential impact on model robustness by further updating and adjusting the data and model.

The Revolution in Breast Cancer Diagnostics: From Visual Inspection of Histopathology Slides to Using Desktop Tissue Analysers for Automated Nanomechanical Profiling of Tumours

We aim to develop new portable desktop tissue analysers (DTAs) to provide fast, low-cost, and precise test results for fast nanomechanical profiling of tumours. This paper will explain the reasoning for choosing indentation-type atomic force microscopy (IT-AFM) to reveal the functional details of cancer. Determining the subtype, cancer stage, and prognosis will be possible, which aids in choosing the best treatment. DTAs are based on fast IT-AFM at the size of a small box that can be made for a low budget compared to other clinical imaging tools. The DTAs can work in remote areas and all parts of the world. There are a number of direct benefits: First, it is no longer needed to wait a week for the pathology report as the test will only take 10 min. Second, it avoids the complicated steps of making histopathology slides and saves costs of labour. Third, computers and robots are more consistent, more reliable, and more economical than human workers which may result in fewer diagnostic errors. Fourth, the IT-AFM analysis is capable of distinguishing between various cancer subtypes. Fifth, the IT-AFM analysis could reveal new insights about why immunotherapy fails. Sixth, IT-AFM may provide new insights into the neoadjuvant treatment response. Seventh, the healthcare system saves money by reducing diagnostic backlogs. Eighth, the results are stored on a central server and can be accessed to develop strategies to prevent cancer. To bring the IT-AFM technology from the bench to the operation theatre, a fast IT-AFM sensor needs to be developed and integrated into the DTAs.

Predictive, preventive, and personalized medicine in breast cancer: targeting the PI3K pathway

Breast cancer (BC) is a multifaceted disease characterized by distinct molecular subtypes and varying responses to treatment. In BC, the phosphatidylinositol 3-kinase (PI3K) pathway has emerged as a crucial contributor to the development, advancement, and resistance to treatment. This review article explores the implications of the PI3K pathway in predictive, preventive, and personalized medicine for BC. It emphasizes the identification of predictive biomarkers, such as PIK3CA mutations, and the utility of molecular profiling in guiding treatment decisions. The review also discusses the potential of targeting the PI3K pathway for preventive strategies and the customization of therapy based on tumor stage, molecular subtypes, and genetic alterations. Overcoming resistance to PI3K inhibitors and exploring combination therapies are addressed as important considerations. While this field holds promise in improving patient outcomes, further research and clinical trials are needed to validate these approaches and translate them into clinical practice.

Construction of a risk stratification model integrating ctDNA to predict response and survival in neoadjuvant-treated breast cancer

BACKGROUND

The pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) of breast cancer is closely related to a better prognosis. However, there are no reliable indicators to accurately identify which patients will achieve pCR before surgery, and a model for predicting pCR to NAC is required.

METHODS

A total of 269 breast cancer patients in Shandong Cancer Hospital and Liaocheng People's Hospital receiving anthracycline and taxane-based NAC were prospectively enrolled. Expression profiling using a 457 cancer-related gene sequencing panel (DNA sequencing) covering genes recurrently mutated in breast cancer was carried out on 243 formalin-fixed paraffin-embedded tumor biopsies samples before NAC from 243 patients. The unique personalized panel of nine individual somatic mutation genes from the constructed model was used to detect and analyze ctDNA on 216 blood samples. Blood samples were collected at indicated time points including before chemotherapy initiation, after the 1st NAC and before the 2nd NAC cycle, during intermediate evaluation, and prior to surgery. In this study, we characterized the value of gene profile mutation and circulating tumor DNA (ctDNA) in combination with clinical characteristics in the prediction of pCR before surgery and investigated the prognostic prediction. The median follow-up time for survival analysis was 898 days.

RESULTS

Firstly, we constructed a predictive NAC response model including five single nucleotide variant (SNV) mutations (TP53, SETBP1, PIK3CA, NOTCH4 and MSH2) and four copy number variation (CNV) mutations (FOXP1-gain, EGFR-gain, IL7R-gain, and NFKB1A-gain) in the breast tumor, combined with three clinical factors (luminal A, Her2 and Ki67 status). The tumor prediction model showed good discrimination of chemotherapy sensitivity for pCR and non-pCR with an AUC of 0.871 (95% CI, 0.797-0.927) in the training set, 0.771 (95% CI, 0.649-0.883) in the test set, and 0.726 (95% CI, 0.556-0.865) in an extra test set. This tumor prediction model can also effectively predict the prognosis of disease-free survival (DFS) with an AUC of 0.749 at 1 year and 0.830 at 3 years. We further screened the genes from the tumor prediction model to establish a unique personalized panel consisting of 9 individual somatic mutation genes to detect and analyze ctDNA. It was found that ctDNA positivity decreased with the passage of time during NAC, and ctDNA status can predict NAC response and metastasis recurrence. Finally, we constructed the chemotherapy prediction model combined with the tumor prediction model and pretreatment ctDNA levels, which has a better prediction effect of pCR with the AUC value of 0.961.

CONCLUSIONS

In this study, we established a chemotherapy predictive model with a non-invasive tool that is built based on genomic features, ctDNA status, as well as clinical characteristics for predicting pCR to recognize the responders and non-responders to NAC, and also predicting prognosis for DFS in breast cancer. Adding pretreatment ctDNA levels to a model containing gene profile mutation and clinical characteristics significantly improves stratification over the clinical variables alone.

Profiling regulatory T lymphocytes within the tumor microenvironment of breast cancer via radiomics

OBJECTIVE

To generate an image-driven biomarker (Rad_score) to predict tumor-infiltrating regulatory T lymphocytes (Treg) in breast cancer (BC).

METHODS

Overall, 928 BC patients were enrolled from the Cancer Genome Atlas (TCGA) for survival analysis; MRI (n = 71 and n = 30 in the training and validation sets, respectively) from the Cancer Imaging Archive (TCIA) were retrieved and subjected to repeat least absolute shrinkage and selection operator for feature reduction. The radiomic scores (rad_score) for Treg infiltration estimation were calculated via support vector machine (SVM) and logistic regression (LR) algorithms, and validated on the remaining patients.

RESULTS

Landmark analysis indicated Treg infiltration was a risk factor for BC patients in the first 5 years and after 10 years of diagnosis (p = 0.007 and 0.018, respectively). Altogether, 108 radiomic features were extracted from MRI images, 4 of which remained for model construction. Areas under curves (AUCs) of the SVM model were 0.744 (95% CI 0.622-0.867) and 0.733 (95% CI 0.535-0.931) for training and validation sets, respectively, while for the LR model, AUCs were 0.771 (95% CI 0.657-0.885) and 0.724 (95% CI 0.522-0.926). The calibration curves indicated good agreement between prediction and true value (p > 0.05), and DCA shows the high clinical utility of the radiomic model. Rad_score was significantly correlated with immune inhibitory genes like CTLA4 and PDCD1.

CONCLUSIONS

High Treg infiltration is a risk factor for patients with BC. The Rad_score formulated on radiomic features is a novel tool to predict Treg abundance in the tumor microenvironment.

An Innovative Non-Linear Prediction Model for Clinical Benefit in Women with Newly Diagnosed Breast Cancer Using Baseline FDG-PET/CT and Clinical Data

Objectives: We aimed to develop a novel non-linear statistical model integrating primary tumor features on baseline [18F]-fluorodeoxyglucose positron emission tomography/computed tomography (FDG-PET/CT), molecular subtype, and clinical data for treatment benefit prediction in women with newly diagnosed breast cancer using innovative statistical techniques, as opposed to conventional methodological approaches. Methods: In this single-center retrospective study, we conducted a comprehensive assessment of women newly diagnosed with breast cancer who had undergone a FDG-PET/CT scan for staging prior to treatment. Primary tumor (PT) volume, maximum and mean standardized uptake value (SUVmax and SUVmean), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) were measured on PET/CT. Clinical data including clinical staging (TNM) but also PT anatomical site, histology, receptor status, proliferation index, and molecular subtype were obtained from the medical records. Overall survival (OS), progression-free survival (PFS), and clinical benefit (CB) were assessed as endpoints. A logistic generalized additive model was chosen as the statistical approach to assess the impact of all listed variables on CB. Results: 70 women with newly diagnosed breast cancer (mean age 63.3 ± 15.4 years) were included. The most common location of breast cancer was the upper outer quadrant (40.0%) in the left breast (52.9%). An invasive ductal adenocarcinoma (88.6%) with a high tumor proliferation index (mean ki-67 expression 35.1 ± 24.5%) and molecular subtype B (51.4%) was by far the most detected breast tumor. Most PTs displayed on hybrid imaging a greater volume (12.8 ± 30.4 cm3) with hypermetabolism (mean ± SD of PT maximum SUVmax, SUVmean, MTV, and TLG, respectively: 8.1 ± 7.2, 4.9 ± 4.4, 12.7 ± 30.4, and 47.4 ± 80.2). Higher PT volume (p < 0.01), SUVmax (p = 0.04), SUVmean (p = 0.03), and MTV (<0.01) significantly compromised CB. A considerable majority of patients survived throughout this period (92.8%), while five women died (7.2%). In fact, the OS was 31.7 ± 14.2 months and PFS was 30.2 ± 14.1 months. A multivariate prediction model for CB with excellent accuracy could be developed using age, body mass index (BMI), T, M, PT TLG, and PT volume as predictive parameters. PT volume and PT TLG demonstrated a significant influence on CB in lower ranges; however, beyond a specific cutoff value (respectively, 29.52 cm3 for PT volume and 161.95 cm3 for PT TLG), their impact on CB only reached negligible levels. Ultimately, the absence of distant metastasis M displayed a strong positive impact on CB far ahead of the tumor size T (standardized average estimate 0.88 vs. 0.4). Conclusions: Our results emphasized the pivotal role played by FDG-PET/CT prior to treatment in forecasting treatment outcomes in women newly diagnosed with breast cancer. Nevertheless, careful consideration is required when selecting the methodological approach, as our innovative statistical techniques unveiled non-linear influences of predictive biomarkers on treatment benefit, highlighting also the importance of early breast cancer diagnosis.

Single-cell RNA-seq reveals characteristics in tumor microenvironment of PDAC with MSI-H following neoadjuvant chemotherapy with anti-PD-1 therapy

Accumulating evidence suggests the minority of patients with advanced pancreatic ductal adenocarcinoma (PDAC) that have microsatellite instability high (MSI-H) can benefit from immune checkpoint inhibitors (ICIs). However, the effects of ICIs on the tumor microenvironment (TME) of PDAC remain elusive. We conducted single-cell RNA-seq (scRNA-seq) analysis on a residual lesion from a MSI-H PDAC patient who received a radical operation after eight cycles of neoadjuvant treatment (nab-paclitaxel/gemcitabine plus pembrolizumab). Multiple tumor subclusters were identified in residual lesion after neoadjuvant treatment, one of which was mainly composed of cells in the S and G2M phases. This subcluster also had enriched expression of MKI67 and PCNA and cell cycle-related signatures and was thus defined as a proliferating tumor subcluster. This subcluster had higher S_score, Fatty acid_score, UPR_score, and Glycolysis_score than others. We also identified characteristics of the TME after neoadjuvant treatment by comparing the excised primary tumors form nontreated PDAC and the residual lesion. The residual lesion was characterized with activated pancreatic stellate cells (PSCs) and exhausted T cells (Tex). We compared the receptor-ligand interactions between the two groups, and found that no checkpoint receptor-ligand pairs between T cells and tumor cells were identified in the residual lesion, while there were many checkpoint receptor-ligand pairs in the nontreated primary PDAC. In conclusion, our findings revealed the characteristics of residual lesion of advanced PDAC with MSI-H upon combination treatment of chemotherapy and immunotherapy, which might provide some valuable clues for solving the puzzle of ICI in PDAC.

A three-stage eccDNA based molecular profiling significantly improves the identification, prognosis assessment and recurrence prediction accuracy in patients with glioma

Glioblastoma (GBM) progression is influenced by intratumoral heterogeneity. Emerging evidence has emphasized the pivotal role of extrachromosomal circular DNA (eccDNA) in accelerating tumor heterogeneity, particularly in GBM. However, the eccDNA landscape of GBM has not yet been elucidated. In this study, we first identified the eccDNA profiles in GBM and adjacent tissues using circle- and RNA-sequencing data from the same samples. A three-stage model was established based on eccDNA-carried genes that exhibited consistent upregulation and downregulation trends at the mRNA level. Combinations of machine learning algorithms and stacked ensemble models were used to improve the performance and robustness of the three-stage model. In stage 1, a total of 113 combinations of machine learning algorithms were constructed and validated in multiple external cohorts to accurately distinguish between low-grade glioma (LGG) and GBM in patients with glioma. The model with the highest area under the curve (AUC) across all cohorts was selected for interpretability analysis. In stage 2, a total of 101 combinations of machine learning algorithms were established and validated for prognostic prediction in patients with glioma. This prognostic model performed well in multiple glioma cohorts. Recurrent GBM is invariably associated with aggressive and refractory disease. Therefore, accurate prediction of recurrence risk is crucial for developing individualized treatment strategies, monitoring patient status, and improving clinical management. In stage 3, a large-scale GBM cohort (including primary and recurrent GBM samples) was used to fit the GBM recurrence prediction model. Multiple machine learning and stacked ensemble models were fitted to select the model with the best performance. Finally, a web tool was developed to facilitate the clinical application of the three-stage model.

A Network of 17 Microtubule-Related Genes Highlights Functional Deregulations in Breast Cancer

A wide panel of microtubule-associated proteins and kinases is involved in coordinated regulation of the microtubule cytoskeleton and may thus represent valuable molecular markers contributing to major cellular pathways deregulated in cancer. We previously identified a panel of 17 microtubule-related (MT-Rel) genes that are differentially expressed in breast tumors showing resistance to taxane-based chemotherapy. In the present study, we evaluated the expression, prognostic value and functional impact of these genes in breast cancer. We show that 14 MT-Rel genes (KIF4A, ASPM, KIF20A, KIF14, TPX2, KIF18B, KIFC1, AURKB, KIF2C, GTSE1, KIF15, KIF11, RACGAP1, STMN1) are up-regulated in breast tumors compared with adjacent normal tissue. Six of them (KIF4A, ASPM, KIF20A, KIF14, TPX2, KIF18B) are overexpressed by more than 10-fold in tumor samples and four of them (KIF11, AURKB, TPX2 and KIFC1) are essential for cell survival. Overexpression of all 14 genes, and underexpression of 3 other MT-Rel genes (MAST4, MAPT and MTUS1) are associated with poor breast cancer patient survival. A Systems Biology approach highlighted three major functional networks connecting the 17 MT-Rel genes and their partners, which are centered on spindle assembly, chromosome segregation and cytokinesis. Our studies identified mitotic Aurora kinases and their substrates as major targets for therapeutic approaches against breast cancer.

Advances in novel strategies for isolation, characterization, and analysis of CTCs and ctDNA

Over the past decade, the detection and analysis of liquid biopsy biomarkers such as circulating tumor cells (CTCs) and circulating tumor DNA (ctDNA) have advanced significantly. They have received recognition for their clinical usefulness in detecting cancer at an early stage, monitoring disease, and evaluating treatment response. The emergence of liquid biopsy has been a helpful development, as it offers a minimally invasive, rapid, real-time monitoring, and possible alternative to traditional tissue biopsies. In resource-limited settings, the ideal platform for liquid biopsy should not only extract more CTCs or ctDNA from a minimal sample volume but also accurately represent the molecular heterogeneity of the patient's disease. This review covers novel strategies and advancements in CTC and ctDNA-based liquid biopsy platforms, including microfluidic applications and comprehensive analysis of molecular complexity. We discuss these systems' operational principles and performance efficiencies, as well as future opportunities and challenges for their implementation in clinical settings. In addition, we emphasize the importance of integrated platforms that incorporate machine learning and artificial intelligence in accurate liquid biopsy detection systems, which can greatly improve cancer management and enable precision diagnostics.

One-pot wet-chemical fabrication of 3D urchin-like core-shell Au@PdCu nanocrystals for electrochemical breast cancer immunoassay

Carbohydrate antigen 15-3 (CA15-3) is an important biomarker for early diagnosis of breast cancer. Herein, a label-free electrochemical immunosensor was built based on three-dimensional (3D) urchin-like core-shell Au@PdCu nanocrystals (labeled Au@PdCu NCs) for highly sensitive detection of CA15-3, where K3[Fe(CN)6] behaved as an electroactive probe. The Au@PdCu NCs were synthesized by a simple one-pot wet-chemical approach and the morphology, structures, and electrocatalytic property were investigated by several techniques. The Au@PdCu NCs prepared worked as electrode material to anchor more antibodies and as signal magnification material by virtue of its exceptional catalytic property. The developed biosensor exhibited a wide linear detection range from 0.1 to 300 U mL-1 and a low limit of detection (0.011 U mL-1, S/N = 3) for determination of CA15-3 under the optimal conditions. The established biosensing platform exhibits some insights for detecting other tumor biomarkers in clinical assays and early diagnosis.

Deep Learning in Phosphoproteomics: Methods and Application in Cancer Drug Discovery

Protein phosphorylation is a key post-translational modification (PTM) that is a central regulatory mechanism of many cellular signaling pathways. Several protein kinases and phosphatases precisely control this biochemical process. Defects in the functions of these proteins have been implicated in many diseases, including cancer. Mass spectrometry (MS)-based analysis of biological samples provides in-depth coverage of phosphoproteome. A large amount of MS data available in public repositories has unveiled big data in the field of phosphoproteomics. To address the challenges associated with handling large data and expanding confidence in phosphorylation site prediction, the development of many computational algorithms and machine learning-based approaches have gained momentum in recent years. Together, the emergence of experimental methods with high resolution and sensitivity and data mining algorithms has provided robust analytical platforms for quantitative proteomics. In this review, we compile a comprehensive collection of bioinformatic resources used for the prediction of phosphorylation sites, and their potential therapeutic applications in the context of cancer.

The expression and clinical significance of STAMBP in breast cancer

BACKGROUND

Breast cancer is the leading cause of death from cancer in women worldwide. STAMBP functions as a JAMM family deubiquitinating enzyme that modulates the stability of substrate proteins in cells by cleaving ubiquitin moieties. The expression of STAMBP and its clinical significance in breast cancer remain unclear.

METHODS AND RESULTS

The level of the STAMBP protein in noncancerous and tumor tissues of breast cancer patients was examined by immunohistochemical staining. The expression of STAMBP mRNA in tissues based on healthy individual and breast cancer patient data in the TCGA database was evaluated. The association between the expression of STAMBP mRNA and clinical features and prognosis was evaluated using TCGA database. Cell growth was assessed by Cell Counting Kit-8 (CCK-8) assay, and cell migration and invasion were assessed by wound healing and Transwell assays. Activation of the ERK signaling was detected by Western blotting. The expression of STAMBP was markedly upregulated in the cytoplasm of tumor cells from breast cancer patients. The level of STAMBP was closely associated with the tumor subtype and size and the TNM stage of the breast cancer patients. Importantly, high expression of STAMBP predicted poor overall survival (OS) for breast cancer patients. Furthermore, knockdown of STAMBP expression reduced cell mobility and invasion of breast cancer cells. Notably, the phosphorylation of EGFR and ERK was markedly reduced in STAMBP-knockdown cells.

CONCLUSION

STAMBP plays a critical role in the progression of breast cancer and may serve as a biomarker to monitor the progression of the disease.