Molecular Subtyping of Triple-Negative Breast Cancers by Immunohistochemistry: Molecular Basis and Clinical Relevance

Molecular methods in cancer diagnostics: a short review

BACKGROUND

One of the ailments with the greatest fatality rates in the 21st century is cancer. Globally, molecular methods are widely employed to treat cancer-related disorders, and the body of research on this subject is growing yearly. A thorough and critical summary of the data supporting molecular methods for illnesses linked to cancer is required.

OBJECTIVE

In order to guide clinical practice and future research, it is important to examine and summarize the systematic reviews (SRs) that evaluate the efficacy and safety of molecular methods for disorders associated to cancer.

METHODS

We developed a comprehensive search strategy to find relevant articles from electronic databases like PubMed, Google Scholar, Web of Science (WoS), or Scopus. We looked through the literature and determined which diagnostic methods in cancer genetics were particularly reliable. We used phrases like 'cancer genetics', genetic susceptibility, Hereditary cancer, cancer risk assessment, 'cancer diagnostic tools', cancer screening', biomarkers, and molecular diagnostics, reviews and meta-analyses evaluating the efficacy and safety of molecular therapies for cancer-related disorders. Research that only consider treatment modalities that don't necessitate genetic or molecular diagnostics fall under the exclusion criteria.

RESULTS

The results of this comprehensive review clearly demonstrate the transformative impact of molecular methods in the realm of cancer genetics.This review underscores how these technologies have empowered researchers and clinicians to identify and understand key genetic alterations that drive malignancy, ranging from point mutations to structural variations. Such insights are instrumental in pinpointing critical oncogenic drivers and potential therapeutic targets, thus opening the door for methods in precision medicine that can significantly improve patient outcomes.

LIMITATION

The search does not specify a timeframe for publication inclusion, it may have missed recent advancements or changes in the field's landscape of molecular methods for cancer. As a result, it may not have included the most recent developments in the field.

CONCLUSION

After conducting an in-depth study on the molecular methods in cancer genetics, it is evident that these cutting-edge technologies have revolutionized the field of oncology, providing researchers and clinicians with powerful tools to unravel the complexities of cancer at the genetic level. The integration of molecular methods techniques has not only enhanced our understanding of cancer etiology, progression, and treatment response but has also opened new avenues for personalized medicine and targeted therapies, leading to improved patient outcomes.

Neoadjuvant anthracycline followed by toripalimab combined with nab-paclitaxel in patients with early triple-negative breast cancer (NeoTENNIS): a single-arm, phase II study

Background

Toripalimab, a novel PD-1 antibody, is approved for treatment of multiple solid tumors; however, its neoadjuvant use with chemotherapy for triple-negative breast cancer (TNBC) remains unevaluated. Additionally, induction chemotherapy followed by de-escalation of neoadjuvant immunotherapy remains underexplored. Therefore, we conducted a phase II trial investigating a novel neoadjuvant chemoimmunotherapy regimen including de-escalation of immunotherapy for early-stage TNBC.

Methods

Chemotherapy and anti-PD-1 therapy were sequentially administered in a neoadjuvant setting to female patients with histologically confirmed stage II-III TNBC between June 9, 2020, and March 24, 2022. Patients received neoadjuvant therapy with four cycles of epirubicin-cyclophosphamide every 2 weeks, followed by toripalimab (240 mg) every 3 weeks plus nab-paclitaxel weekly for 12 weeks. The primary endpoint was total pathological complete response (tpCR; ypT0/is ypN0). Key secondary endpoints included breast pCR (bpCR; ypT0/is), event-free survival and biomarker analysis. Safety was also assessed. This study was registered with ClinicalTrials.gov (NCT04418154).

Findings

Among 70 enrolled patients (median age, 51 years; 62.9% stage III), 66 completed treatment without progression and subsequently underwent surgery. The percentages of patients with a tpCR and bpCR were 39 of 70 (55.7%, 95% confidence interval [CI]: 43.3-67.6) and 41 of 70 (58.6%, 95% CI 46.2-70.2), respectively. Sixteen (22.9%) patients experienced grade ≥3 adverse events (AEs), frequently neutropenia (12, 17.1%) and leukopenia (11, 15.7%). The most common immune-related AE was hypothyroidism (5, 7.1%, all grade 1-2).

Interpretation

Including 12 weeks of toripalimab in neoadjuvant chemotherapy conferred encouraging activity and manageable toxicity in patients with early TNBC, and this regimen warrants further investigation.

Funding

National Natural Science Foundation of China, Junshi Biosciences, and Jiangsu Hengrui Pharmaceuticals.

Patient subtyping analysis of baseline multi-omic data reveals distinct pre-immune states associated with antibody response to seasonal influenza vaccination

Understanding the molecular mechanisms underpinning diverse vaccination responses is critical for developing efficient vaccines. Molecular subtyping can offer insights into heterogeneous nature of responses and aid in vaccine design. We analyzed multi-omic data from 62 haemagglutinin seasonal influenza vaccine recipients (2019-2020), including transcriptomics, proteomics, glycomics, and metabolomics data collected pre-vaccination. We performed a subtyping analysis on the integrated data revealing five subtypes with distinct molecular signatures. These subtypes differed in the expression of pre-existing adaptive or innate immunity signatures, which were linked to significant variation in baseline immunoglobulin A (IgA) and hemagglutination inhibition (HAI) titer levels. It is worth noting that these differences persisted through day 28 post-vaccination, indicating the effect of initial immune state on vaccination response. These findings highlight the significance of interpersonal variation in baseline immune status as a crucial factor in determining the effectiveness of seasonal vaccines. Ultimately, incorporating molecular profiling could enable personalized vaccine optimization.

Genomic characterization reveals distinct mutational landscapes and therapeutic implications between different molecular subtypes of triple-negative breast cancer

Triple-negative breast cancer (TNBC) has high heterogeneity, poor prognosis, and limited treatment success. Recently, an immunohistochemistry-based surrogate classification for the "Fudan University Shanghai Cancer Center (FUSCC) subtyping" has been developed and is considered more suitable for clinical application. Seventy-one paraffin-embedded sections of surgically resected TNBC were classified into four molecular subtypes using the IHC-based surrogate classification. Genomic analysis was performed by targeted next-generation sequencing and the specificity of the subtypes was explored by bioinformatics, including survival analysis, multivariate Cox regression, pathway enrichment, Pyclone analysis, mutational signature analysis and PHIAL analysis. AKT1 and BRCA1 mutations were identified as independent prognostic factors in TNBC. TNBC molecular subtypes encompass distinct genomic landscapes that show specific heterogeneities. The luminal androgen receptor (LAR) subtype was associated with mutations in PIK3CA and PI3K pathways, which are potentially sensitive to PI3K pathway inhibitors. The basal-like immune-suppressed (BLIS) subtype was characterized by high genomic instability and the specific possession of signature 19 while patients in the immunomodulatory (IM) subtype belonged to the PD-L1 ≥ 1% subgroup with enrichment in Notch signaling, suggesting a possible benefit of immune checkpoint inhibitors and Notch inhibitors. Moreover, mesenchymal-like (MES) tumors displayed enrichment in the receptor tyrosine kinase (RTK)-RAS pathway and potential sensitivity to RTK pathway inhibitors. The findings suggest potential treatment targets and prognostic factors, indicating the possibility of TNBC stratified therapy in the future.

Triple-Negative Breast Cancer Subclassified by Immunohistochemistry: Correlation with Clinical and Pathological Outcomes in Patients Receiving Neoadjuvant Chemotherapy

The intrinsic subtype of triple-negative breast cancer (TNBC) is based on genomic evaluation. In this study, we report the survival and pathological complete response (pCR) rates of TNBC patients subtyped by IHC and treated with neoadjuvant chemotherapy (NACT). A retrospective cohort of 187 TNBC patients who received NACT between 2008 and 2017 was used, and IHC subtyping was performed on biopsy specimens before chemotherapy. The subtyping revealed predominantly basal-like tumors (IHC-BL, 61%), followed by basal-like immune-suppressed tumors (IHC-BLIS, 31%), mesenchymal tumors (12.5%), luminal androgen receptor tumors (IHC-LAR, 12%), and basal-like immune-activated tumors (IHC-BLIA, 10.9%). The pCR rate varied among subtypes, with IHC-BLIA showing the highest (30.0%) and IHC-LAR showing the lowest (4.5%). IHC-BLIS led in recurrence sites. Overall and disease-free survival analyses did not show significant differences among subtypes, although IHC-BLIA demonstrated a trend toward better survival, and IHC-mesenchymal, worse. Patients who achieved pCR exhibited significantly better disease-free survival and overall survival than non-responders. This study underscores the potential of IHC-based subtyping in TNBC management, highlighting distinct response patterns to neoadjuvant chemotherapy and potential implications for treatment strategies. Further research is warranted to validate these findings and explore tailored therapeutic approaches for specific TNBC subtypes.

Tumor biomarkers for diagnosis, prognosis and targeted therapy

Tumor biomarkers, the substances which are produced by tumors or the body's responses to tumors during tumorigenesis and progression, have been demonstrated to possess critical and encouraging value in screening and early diagnosis, prognosis prediction, recurrence detection, and therapeutic efficacy monitoring of cancers. Over the past decades, continuous progress has been made in exploring and discovering novel, sensitive, specific, and accurate tumor biomarkers, which has significantly promoted personalized medicine and improved the outcomes of cancer patients, especially advances in molecular biology technologies developed for the detection of tumor biomarkers. Herein, we summarize the discovery and development of tumor biomarkers, including the history of tumor biomarkers, the conventional and innovative technologies used for biomarker discovery and detection, the classification of tumor biomarkers based on tissue origins, and the application of tumor biomarkers in clinical cancer management. In particular, we highlight the recent advancements in biomarker-based anticancer-targeted therapies which are emerging as breakthroughs and promising cancer therapeutic strategies. We also discuss limitations and challenges that need to be addressed and provide insights and perspectives to turn challenges into opportunities in this field. Collectively, the discovery and application of multiple tumor biomarkers emphasized in this review may provide guidance on improved precision medicine, broaden horizons in future research directions, and expedite the clinical classification of cancer patients according to their molecular biomarkers rather than organs of origin.

Luminal androgen receptor subtype and tumor-infiltrating lymphocytes groups based on triple-negative breast cancer molecular subclassification

In our previous study, we developed a triple-negative breast cancer (TNBC) subtype classification that correlated with the TNBC molecular subclassification. In this study, we aimed to evaluate the predictor variables of this subtype classification on the whole slide and to validate the model's performance by using an external test set. We explored the characteristics of this subtype classification and investigated genomic alterations, including genomic scar signature scores. First, TNBC was classified into the luminal androgen receptor (LAR) and non-luminal androgen receptor (non-LAR) subtypes based on the AR Allred score (≥ 6 and < 6, respectively). Then, the non-LAR subtype was further classified into the lymphocyte-predominant (LP), lymphocyte-intermediate (LI), and lymphocyte-depleted (LD) groups based on stromal tumor-infiltrating lymphocytes (TILs) (< 20%, > 20% but < 60%, and ≥ 60%, respectively). This classification showed fair agreement with the molecular classification in the test set. The LAR subtype was characterized by a high rate of PIK3CA mutation, CD274 (encodes PD-L1) and PDCD1LG2 (encodes PD-L2) deletion, and a low homologous recombination deficiency (HRD) score. The non-LAR LD TIL group was characterized by a high frequency of NOTCH2 and MYC amplification and a high HRD score.

Safety and efficacy of anlotinib combined with taxane and lobaplatin in neoadjuvant treatment of clinical stage II/III triple-negative breast cancer in China (the neoALTAL trial): a single-arm, phase 2 trial

Background

Anlotinib is a new type of tyrosine kinase inhibitor that targets vascular endothelial growth factor receptors 1/2/3, platelet-derived growth factor receptors α/β, and fibroblast growth factor receptors 1-4 and c-Kit, with a broad spectrum of inhibitory effects on tumor angiogenesis and growth. It has been proven effective in HER2-negative metastatic breast cancer, but its efficacy in early-stage triple-negative breast cancer (TNBC) is unknown. This phase 2 study aims to evaluate the efficacy and safety of adding anlotinib to neoadjuvant chemotherapy in patients with TNBC.

Methods

Patients with clinical stage II/III TNBC were treated with 5 cycles of anlotinib (12 mg, d1-14, q3w) plus 6 cycles of taxanes (docetaxel 75 mg/m2 ,d1, q3w or nab-paclitaxel 125 mg/m2, d1 and d8, q3w) and lobaplatin (30 mg/m2, d1, q3w), followed by surgery. The primary endpoint was pathological complete response (pCR; ypT0/is ypN0) and the secondary endpoints include breast pCR (bpCR), axillary pCR (apCR), residual cancer burden (RCB), objective response rate (ORR), survival, and safety. Exploratory endpoints were efficacy biomarkers based on Fudan University Shanghai Cancer Center Immunohistochemical (FUSCC IHC) classification for TNBC and next-generation sequencing (NGS) of DNA from tumor tissue and blood samples of patients with 425-gene panel. This trial is registered with www.chictr.org.cn (ChiCTR2100043027).

Findings

From Jan 2021 to Aug 2022, 48 patients were assessed and 45 were enrolled. All patients received at least one dose of study treatment and underwent surgery. The median age was 48.5 years (SD: 8.7), 71% were nodal involved, and 20% had stage III. In the intention-to-treat population, 26 out of 45 patients achieved pCR (57.8%; 90% CI, 44.5%-70.3%), and 39 achieved residual cancer burden class 0-I (86.7%; 95% CI, 73.2%-94.9%). The bpCR and apCR rate were 64.4% (29/45) and 71.9% (23/32), respectively. No recurrence or metastasis occurred during the short-term follow-up. Based on the FUSCC IHC-based subtypes, the pCR rates were 68.8% (11/16) for immunomodulatory subtype, 58.3% (7/12) for basal-like immune-suppressed subtype and 33.3% (4/12) for luminal androgen receptor subtype, respectively. NGS revealed that the pCR were 77% (10/13) and 50% (14/28) in MYC-amplified and wild-type patients, respectively, and 78% (7/9) and 53% (17/32) in gBRCA1/2-mutated and wild-type patients, respectively. The median follow-up time of the study was 14.9 months (95% CI: 13.5-16.3 months). There was no disease progression or death during neoadjuvant therapy. No deaths occurred during postoperative follow-up. In the safety population (N = 45), Grade 3 or 4 treatment emergent adverse events occurred in 29 patients (64%), and the most common events were neutropenia (38%), leukopenia (27%), thrombocytopenia (25%), anemia (13%), and hypertension (13%), respectively.

Interpretation

The addition of anlotinib to neoadjuvant chemotherapy showed manageable toxicity and encouraging antitumor activity for patients with clinical stage II/III TNBC.

Funding

Chongqing Talents Project, Chongqing Key Project of Technology Innovation and Application Development and Chongqing Outstanding Youth Natural Science Foundation.

Therapeutic vulnerabilities in triple negative breast cancer: Stem-like traits explored within molecular classification

Triple Negative Breast Cancer (TNBC) is the most aggressive type of breast cancer (BC). Despite advances in the clinical management of TNBC, recurrence-related mortality remains a challenge. The stem-like phenotype of TNBC plays a significant role in the persistence of minimal disease residue after therapy. Individuals exhibiting stem-like characteristics are particularly prone to inducing malignant relapse accompanied by strong resistance. Therefore, stem-like traits have been broadly proposed as therapeutic vulnerabilities to treat TNBC and reduce recurrence. However, heterogeneity within TNBC often generally restricts the stability of the therapeutic efficacy. To understand the heterogeneity and manage TNBC more precisely, multiple TNBC subtyping categories have been reported, providing the basis for profile-according therapeutic regimens. To provide more insight into targeting stem-like traits to ablate TNBC and reduce recurrence in the context of heterogeneity, this paper reviewed the molecular subtyping of TNBC, identified the consensus subtypes with distinct stem-like phenotypes, characterized the stemness hierarchy of TNBC, outlined the biological models for stem-like TNBC subtypes, summarized the therapeutic vulnerabilities in stem-like traits of the subtypes, and proposed potential therapeutic regimens targeting stem-like characteristics to improve TNBC prognosis.

Deep learning framework for comprehensive molecular and prognostic stratifications of triple-negative breast cancer

Triple-negative breast cancer (TNBC) is the most challenging breast cancer subtype. Molecular stratification and target therapy bring clinical benefit for TNBC patients, but it is difficult to implement comprehensive molecular testing in clinical practice. Here, using our multi-omics TNBC cohort (N = 425), a deep learning-based framework was devised and validated for comprehensive predictions of molecular features, subtypes and prognosis from pathological whole slide images. The framework first incorporated a neural network to decompose the tissue on WSIs, followed by a second one which was trained based on certain tissue types for predicting different targets. Multi-omics molecular features were analyzed including somatic mutations, copy number alterations, germline mutations, biological pathway activities, metabolomics features and immunotherapy biomarkers. It was shown that the molecular features with therapeutic implications can be predicted including the somatic PIK3CA mutation, germline BRCA2 mutation and PD-L1 protein expression (area under the curve [AUC]: 0.78, 0.79 and 0.74 respectively). The molecular subtypes of TNBC can be identified (AUC: 0.84, 0.85, 0.93 and 0.73 for the basal-like immune-suppressed, immunomodulatory, luminal androgen receptor, and mesenchymal-like subtypes respectively) and their distinctive morphological patterns were revealed, which provided novel insights into the heterogeneity of TNBC. A neural network integrating image features and clinical covariates stratified patients into groups with different survival outcomes (log-rank P < 0.001). Our prediction framework and neural network models were externally validated on the TNBC cases from TCGA (N = 143) and appeared robust to the changes in patient population. For potential clinical translation, we built a novel online platform, where we modularized and deployed our framework along with the validated models. It can realize real-time one-stop prediction for new cases. In summary, using only pathological WSIs, our proposed framework can enable comprehensive stratifications of TNBC patients and provide valuable information for therapeutic decision-making. It had the potential to be clinically implemented and promote the personalized management of TNBC.

Subtyping of triple-negative breast cancers: its prognostication and implications in diagnosis of breast origin

BACKGROUND

Triple-negative breast cancer (TNBC) subtyping by gene profiling has provided valuable clinical information. Here, we aimed to evaluate the relevance of TNBC subtyping using immunohistochemistry (IHC), which could be a more clinically practical approach, for prognostication and applications in patient management.

METHODS

A total of 123 TNBC cases were classified using androgen receptor (AR), CD8, Forkhead box C1 protein (FOXC1), and doublecortin-like kinase 1 (DCLK1) into luminal androgen receptor (LAR), basal-like immunosuppressive (BLIS), mesenchymal-like (MES), and immunomodulatory (IM) subtypes. The IM cases were further divided into the IM-excluded and IM-inflamed categories by CD8 spatial distribution. Their clinicopathological and biomarker profiles and prognoses were evaluated.

RESULTS

LAR (28.6%) and MES (11.2%) were the most and least frequent subtypes. The IHC-TNBC subtypes demonstrated distinct clinicopathological features and biomarker profiles, corresponding to the reported features in gene profiling studies. IM-inflamed subtype had the best outcome, while BLIS had a significantly poorer survival. Differential breast-specific marker expressions were found. Trichorhinophalangeal syndrome type 1 (TRPS1) was more sensitive for IM-inflamed and BLIS, GATA-binding protein 3 (GATA3) for IM-excluded and MES, and gross cystic disease fluid protein 15 (GCDFP15) for LAR subtypes.

CONCLUSIONS

Our findings demonstrated the feasibility of IHC surrogates to stratify TNBC subtypes with distinct features and prognoses. The IM subtype can be refined by its CD8 spatial pattern. Breast-specific marker expression varied among the subtypes. Marker selection should be tailored accordingly.

The molecular subtyping and precision medicine in triple-negative breast cancer---based on Fudan TNBC classification

Triple-negative breast cancer (TNBC) is widely recognized as the most aggressive form of breast cancer, occurring more frequently in younger patients and characterized by high heterogeneity, early distant metastases and poor prognosis. Multiple treatment options have failed to achieve the expected therapeutic effects due to the lack of clear molecular targets. Based on genomics, transcriptomics and metabolomics, the multi-omics analysis further clarifies TNBC subtyping, which provides a greater understanding of tumour heterogeneity and targeted therapy sensitivity. For instance, the luminal androgen receptor subtype (LAR) exhibits responsiveness to anti-AR therapy, and the basal-like immune-suppressed subtype (BLIS) tends to benefit from poly (ADP-ribose) polymerase inhibitors (PARPis) and anti-angiogenic therapy. The efficacy of multi-dimensional combination therapy holds immense importance in guiding personalized and precision medicine for TNBC. This review offers a systematic overview of recent FuDan TNBC molecular subtyping and its role in the instruction of clinical precision therapy.

GraphPath: a graph attention model for molecular stratification with interpretability based on the pathway-pathway interaction network

MOTIVATION

Studying the molecular heterogeneity of cancer is essential for achieving personalized therapy. At the same time, understanding the biological processes that drive cancer development can lead to the identification of valuable therapeutic targets. Therefore, achieving accurate and interpretable clinical predictions requires paramount attention to thoroughly characterizing patients at both the molecular and biological pathway levels.

RESULTS

Here, we present GraphPath, a biological knowledge-driven graph neural network with multi-head self-attention mechanism that implements the pathway-pathway interaction network. We train GraphPath to classify the cancer status of patients with prostate cancer based on their multi-omics profiling. Experiment results show that our method outperforms P-NET and other baseline methods. Besides, two external cohorts are used to validate that the model can be generalized to unseen samples with adequate predictive performance. We reduce the dimensionality of latent pathway embeddings and visualize corresponding classes to further demonstrate the optimal performance of the model. Additionally, since GraphPath's predictions are interpretable, we identify target cancer-associated pathways that significantly contribute to the model's predictions. Such a robust and interpretable model has the potential to greatly enhance our understanding of cancer's biological mechanisms and accelerate the development of targeted therapies.

AVAILABILITY AND IMPLEMENTATION

https://github.com/amazingma/GraphPath.

TROP2 is highly expressed in triple-negative breast cancer CTCs and is a potential marker for epithelial mesenchymal CTCs

Circulating tumor cells (CTCs) are the seeds of distant metastases of malignant tumors and are associated with malignancy and risk of metastasis. However, tumor cells undergo epithelial-mesenchymal transition (EMT) during metastasis, leading to the emergence of different types of CTCs. Real-time dynamic molecular and functional typing of CTCs is necessary to precisely guide personalized treatment. Most CTC detection systems are based on epithelial markers that may fail to detect EMT CTCs. Therefore, it is clinically important to identify new markers of different CTC types. In this study, bioinformatics analysis and experimental assays showed that trophoblast cell surface antigen 2 (TROP2), a target molecule for advanced palliative treatment of triple-negative breast cancer (TNBC), was highly expressed in TNBC tissues and tumor cells. Furthermore, TROP2 can promote the migration and invasion of TNBC cells by upregulating EMT markers. The specificity and potential of TROP2 as an EMT-associated marker of TNBC CTCs were evaluated by flow cytometry, immunofluorescence, spiking experiments, and a well-established CTC assay. The results indicated that TROP2 is a potential novel CTC marker associated with EMT, providing a basis for more efficacious markers that encompass CTC heterogeneity in patients with TNBC.

Characteristics of Ovarian Cancer Immune Cell Invasion and Bioinformatics to Predict the Effect of Immunotherapy

Recent studies have confirmed that tumor immune cell infiltration (ICI) is associated with sensitivity of ovarian cancer (OC) immunotherapy and disease progression of OC patients. However, studies related to immune infiltration in OC, has not been elucidated. Two algorithms are used to analyze the OC data in the TCGA and GEO databases. After combining the two data sets, the immune cell content of the sample was estimated by Cell-type Identification By Estimate Relative Subsets of RNA Transcripts (CIBERSORT method). An unsupervised consistent clustering algorithm was used to analyze ICI subtypes and their differentially expressed genes (DEGs). Two subgroups and three ICI gene clusters were identified by unsupervised consensus clustering algorithm. The ICI score was obtained by analyzing the gene characteristics through principal component analysis (PCA). The ICI score ranged from -15.8132 to 18.7211, which was associated with the prognosis of OC patients with immunotherapy. The Toll-like receptor pathway, B-cell receptor pathway, antigen processing and presentation pathway, NK-cell-mediated cytotoxicity pathway, and arginine-proline metabolism pathway were activated in the high ICI score group, suggesting that immune cells in the high ICI score group were activated, thus leading to a better prognosis in this group of patients. Patients with G3-G4 in the high ICI rating group were more sensitive to immunotherapy and had a better prognosis in patients with high tumor mutation burden (TMB). This study suggests that ICI scores can be used as a feasible auxiliary indicator for predicting the prognosis of patients with OC.

Establishment and analysis of artificial neural network diagnosis model for coagulation-related molecular subgroups in coronary artery disease

Background: Coronary artery disease (CAD) is the most common type of cardiovascular disease and cause significant morbidity and mortality. Abnormal coagulation cascade is one of the high-risk factors in CAD patients, but the molecular mechanism of coagulation in CAD is still limited. Methods: We clustered and categorized 352 CAD paitents based on the expression patterns of coagulation-related genes (CRGs), and then we explored the molecular and immunological variations across the subgroups to reveal the underlying biological characteristics of CAD patients. The feature genes between CRG-subgroups were further identified using a random forest model (RF) and least absolute shrinkage and selection operator (LASSO) regression, and an artificial neural network prediction model was constructed. Results: CAD patients could be divided into the C1 and C2 CRG-subgroups, with the C1 subgroup highly enriched in immune-related signaling pathways. The differential expressed genes between the two CRG-subgroups (DE-CRGs) were primarily enriched in signaling pathways connected to signal transduction and energy metabolism. Subsequently, 10 feature DE-CRGs were identified by RF and LASSO. We constructed a novel artificial neural network model using these 10 genes and evaluated and validated its diagnostic performance on a public dataset. Conclusion: Diverse molecular subgroups of CAD patients may each have a unique gene expression pattern. We may identify subgroups using a few feature genes, providing a theoretical basis for the precise treatment of CAD patients with different molecular subgroups.

Rational and trial design of FASCINATE-N: a prospective, randomized, precision-based umbrella trial

Background

With our growing insight into the molecular heterogeneity and biological characteristics of breast cancer, individualized treatment is the future of cancer treatment. In this prospective Fudan University Shanghai Cancer Center Breast Cancer Precision Platform Series study - neoadjuvant therapy (FASCINATE-N) trial, we classify breast cancer patients using multiomic characteristics into different subtypes to evaluate the efficacy of precision-based targeted therapies compared to standard neoadjuvant chemotherapy.

Methods and design

The FASCINATE-N trial is a prospective, randomized, precision-based umbrella trial that plans to enroll 716 women with early breast cancer. After enrollment, patients will first be divided into three groups: hormone receptor (HR)+/human epidermal growth factor receptor 2 (HER2)-, HER2+, and HR-/HER2-. The HR+/HER2- patients are further stratified using fusion and clustering of similarity network fusion (SNF) algorithm into four subtypes; HER2+ patients are divided into HR+/HER2+ and HR-/HER2+ subtypes; and HR-/HER2- patients are stratified using the Fudan University Shanghai Cancer Center classification. For the assignment of drugs to patients, Bayesian methods of adaptive randomization will be used. The primary endpoint is pathological complete response rate; secondary endpoints include 3-year invasive disease-free survival, overall response rate, and toxicities according to common terminology criteria for adverse events (CTCAE) scale version 4.0 and the ratio of patients with complete cell cycle arrest (Ki67 < 2.7%) in HR+/HER2+ breast cancer.

Discussion

The goal of our trial is to test the efficacy of our subtyping-based treatment in a neoadjuvant setting and to conduct a pilot study into the efficacy of targeted therapies within each precision-based subtype. The precision-based treatment arm can be updated with the refinement of our subtyping method, the discovery of new targets, and the development of novel targeted drugs. Our trial offers a unique opportunity to provide patients with individualized neoadjuvant therapy and test promising novel treatments that may further benefit patients.

Trial registration

ClinicalTrials.gov identifier: NCT05582499 (https://classic.clinicaltrials.gov/ct2/show/NCT05582499).

Optimising first-line subtyping-based therapy in triple-negative breast cancer (FUTURE-SUPER): a multi-cohort, randomised, phase 2 trial

BACKGROUND

Triple-negative breast cancers display heterogeneity in molecular drivers and immune traits. We previously classified triple-negative breast cancers into four subtypes: luminal androgen receptor (LAR), immunomodulatory, basal-like immune-suppressed (BLIS), and mesenchymal-like (MES). Here, we aimed to evaluate the efficacy and safety of subtyping-based therapy in the first-line treatment of triple-negative breast cancer.

METHODS

FUTURE-SUPER is an ongoing, open-label, randomised, controlled phase 2 trial being conducted at Fudan University Shanghai Cancer Center (FUSCC), Shanghai, China. Eligible participants were females aged 18-70 years, with an Eastern Cooperative Oncology Group performance status of 0-1, and histologically confirmed, untreated metastatic or recurrent triple-negative breast cancer. After categorising participants into five cohorts according to molecular subtype and genomic biomarkers, participants were randomly assigned (1:1) with a block size of 4, stratified by subtype, to receive, in 28-day cycles, nab-paclitaxel (100 mg/m2, intravenously on days 1, 8, and 15) alone (control group) or with a subtyping-based regimen (subtyping-based group): pyrotinib (400 mg orally daily) for the LAR-HER2mut subtype, everolimus (10 mg orally daily) for the LAR-PI3K/AKTmut and MES-PI3K/AKTmut subtypes, camrelizumab (200 mg intravenously on days 1 and 15) and famitinib (20 mg orally daily) for the immunomodulatory subtype, and bevacizumab (10 mg/kg intravenously on days 1 and 15) for the BLIS/MES-PI3K/AKTWT subtype. The primary endpoint was investigator-assessed progression-free survival for the pooled subtyping-based group versus the control group in the intention-to-treat population (all randomly assigned participants). Safety was analysed in all patients with safety records who received at least one dose of study drug. This study is registered with ClinicalTrials.gov (NCT04395989).

FINDINGS

Between July 28, 2020, and Oct 16, 2022, 139 female participants were enrolled and randomly assigned to the subtyping-based group (n=69) or control group (n=70). At the data cutoff (May 31, 2023), the median follow-up was 22·5 months (IQR 15·2-29·0). Median progression-free survival was significantly longer in the pooled subtyping-based group (11·3 months [95% CI 8·6-15·2]) than in the control group (5·8 months [4·0-6·7]; hazard ratio 0·44 [95% CI 0·30-0·65]; p<0·0001). The most common grade 3-4 treatment-related adverse events were neutropenia (21 [30%] of 69 in the pooled subtyping-based group vs 16 [23%] of 70 in the control group), anaemia (five [7%] vs none), and increased alanine aminotransferase (four [6%] vs one [1%]). Treatment-related serious adverse events were reported for seven (10%) of 69 patients in the subtyping-based group and none in the control group. No treatment-related deaths were reported in either group.

INTERPRETATION

These findings highlight the potential clinical benefits of using molecular subtype-based treatment optimisation in patients with triple-negative breast cancer, suggesting a path for further clinical investigation. Phase 3 randomised clinical trials assessing the efficacy of subtyping-based regimens are now underway.

FUNDING

National Natural Science Foundation of China, Natural Science Foundation of Shanghai, Shanghai Hospital Development Center, and Jiangsu Hengrui Pharmaceuticals.

TRANSLATION

For the Chinese translation of the abstract see Supplementary Materials section.

Development of a machine learning-based radiomics signature for estimating breast cancer TME phenotypes and predicting anti-PD-1/PD-L1 immunotherapy response

BACKGROUNDS

Since breast cancer patients respond diversely to immunotherapy, there is an urgent need to explore novel biomarkers to precisely predict clinical responses and enhance therapeutic efficacy. The purpose of our present research was to construct and independently validate a biomarker of tumor microenvironment (TME) phenotypes via a machine learning-based radiomics way. The interrelationship between the biomarker, TME phenotypes and recipients' clinical response was also revealed.

METHODS

In this retrospective multi-cohort investigation, five separate cohorts of breast cancer patients were recruited to measure breast cancer TME phenotypes via a radiomics signature, which was constructed and validated by integrating RNA-seq data with DCE-MRI images for predicting immunotherapy response. Initially, we constructed TME phenotypes using RNA-seq of 1089 breast cancer patients in the TCGA database. Then, parallel DCE-MRI images and RNA-seq of 94 breast cancer patients obtained from TCIA were applied to develop a radiomics-based TME phenotypes signature using random forest in machine learning. The repeatability of the radiomics signature was then validated in an internal validation set. Two additional independent external validation sets were analyzed to reassess this signature. The Immune phenotype cohort (n = 158) was divided based on CD8 cell infiltration into immune-inflamed and immune-desert phenotypes; these data were utilized to examine the relationship between the immune phenotypes and this signature. Finally, we utilized an Immunotherapy-treated cohort with 77 cases who received anti-PD-1/PD-L1 treatment to evaluate the predictive efficiency of this signature in terms of clinical outcomes.

RESULTS

The TME phenotypes of breast cancer were separated into two heterogeneous clusters: Cluster A, an "immune-inflamed" cluster, containing substantial innate and adaptive immune cell infiltration, and Cluster B, an "immune-desert" cluster, with modest TME cell infiltration. We constructed a radiomics signature for the TME phenotypes ([AUC] = 0.855; 95% CI 0.777-0.932; p < 0.05) and verified it in an internal validation set (0.844; 0.606-1; p < 0.05). In the known immune phenotypes cohort, the signature can identify either immune-inflamed or immune-desert tumor (0.814; 0.717-0.911; p < 0.05). In the Immunotherapy-treated cohort, patients with objective response had higher baseline radiomics scores than those with stable or progressing disease (p < 0.05); moreover, the radiomics signature achieved an AUC of 0.784 (0.643-0.926; p < 0.05) for predicting immunotherapy response.

CONCLUSIONS

Our imaging biomarker, a practicable radiomics signature, is beneficial for predicting the TME phenotypes and clinical response in anti-PD-1/PD-L1-treated breast cancer patients. It is particularly effective in identifying the "immune-desert" phenotype and may aid in its transformation into an "immune-inflamed" phenotype.

Patient Subtyping Analysis of Baseline Multi-omic Data Reveals Distinct Pre-immune States Predictive of Vaccination Responses

Understanding the molecular mechanisms that underpin diverse vaccination responses is a critical step toward developing efficient vaccines. Molecular subtyping approaches can offer valuable insights into the heterogeneous nature of responses and aid in the design of more effective vaccines. In order to explore the molecular signatures associated with the vaccine response, we analyzed baseline transcriptomics data from paired samples of whole blood, proteomics and glycomics data from serum, and metabolomics data from urine, obtained from influenza vaccine recipients (2019-2020 season) prior to vaccination. After integrating the data using a network-based model, we performed a subtyping analysis. The integration of multiple data modalities from 62 samples resulted in five baseline molecular subtypes with distinct molecular signatures. These baseline subtypes differed in the expression of pre-existing adaptive or innate immunity signatures, which were linked to significant variation across subtypes in baseline immunoglobulin A (IgA) and hemagglutination inhibition (HAI) titer levels. It is worth noting that these significant differences persisted through day 28 post-vaccination, indicating the effect of initial immune state on vaccination response. These findings highlight the significance of interpersonal variation in baseline immune status as a crucial factor in determining vaccine response and efficacy. Ultimately, incorporating molecular profiling could enable personalized vaccine optimization.

A real-world study of the effectiveness and safety of apatinib-based regimens in metastatic triple-negative breast cancer

PURPOSE

This investigation sought to examine the efficacy and safety of low-dose apatinib used alongside chemotherapy in the clinical management of patients with metastatic triple-negative breast cancer (TNBC) within a real-world setting, whilst comparing the outcomes with those treated solely with chemotherapy.

METHODS

This case series study analyzed clinical data and treatment outcomes of 163 patients with metastatic TNBC who underwent rescue treatment at the Medical Oncology Department of Clinical Oncology, Fujian Cancer Hospital, School of Fujian Medical University, China, between October 2011 and January 2023. All the patients underwent rescue treatment with either chemotherapy alone or apatinib (250 mg/day) combined with chemotherapy. The study's primary outcome was progression-free survival (PFS), whereas the secondary outcomes included overall survival (OS), objective response rate (ORR), disease control rate (DCR), and safety profiles.

RESULTS

The study was designed to compare two groups [1]. Out of the 163 TNBC patients who participated in the study, 107 individuals (65.6%) received treatment based on chemotherapy, whereas 56 patients (34.4%) were given treatment based on a combination of low-dose apatinib (250 mg/day) and other treatments, including chemotherapy. After propensity score matching (PSM), the objective response rate (ORR) and disease control rate (DCR) of patients with advanced triple-negative breast cancer (TNBC) who received apatinib-based treatment were 50.0 and 90.0%, respectively, while they were 6.7 and 20.0%, respectively, for the chemotherapy-based group (P < 0.001). The group that received apatinib-based treatment showed superior results in both PFS and OS compared to the group that received chemotherapy. The median PFS and OS for the apatinib-based group were 7.8 and 20.3 months, respectively, while they were only 2.2 months and 9.0 months, respectively, for the chemotherapy-based group (P < 0.001) [2]. Patients who were administered combo therapies, including PD-1 inhibitors, were excluded. In total, 97 patients received chemotherapy alone, while 34 patients were treated with apatinib in combination with chemotherapy. After propensity score matching (PSM), the ORR and DCR for the total group who received combo therapies were 44.4 and 81.5%, respectively, while they were 11.1 and 22.2%, respectively, for the chemotherapy alone group (P < 0.001). The group receiving both apatinib and chemotherapy displayed notable advantages over the group solely receiving chemotherapy in regards to PFS and OS for the entirety of the population. The PFS was found to be 7.8 months in comparison to 2.1 months (P < 0.001) and the OS was 21.1 months in contrast to 9.0 months (P < 0.001). Apatinib combined with chemotherapy induced grade 3/4 hematological toxicities, including neutropenia (8.8%) and thrombocytopenia (2.9%). Additionally, non-hematological toxicities were commonly observed, such as Hand-foot syndrome (35.3%), proteinuria (26.5%), hypertension (61.8%), higher alanine aminotransferase levels (26.5%), and fatigue (35.3%). The most frequent non-hematological grade 3/4 toxicities were Hand-foot syndrome (2.9%) and hypertension (5.9%). The study did not report any fatal adverse effects.

CONCLUSIONS

The combination of low-dose apatinib with chemotherapy has proven to be more effective than chemotherapy alone in treating metastatic triple-negative breast cancer (TNBC). Additionally, the occurrence of grade 3/4 non-hematologic toxicities was significantly lower compared to the recommended dose of apatinib.

Prognostic Value of Immunohistochemistry-based Subtyping Before and After Neoadjuvant Chemotherapy in Patients with Triple-negative Breast Cancer

To assess the predictive and prognostic value of a subtyping method based on immunohistochemistry in patients with triple-negative breast cancer (TNBC) treated with neoadjuvant chemotherapy (NAC). This study included patients with TNBC treated with anthracycline- and taxane-based NAC and curative surgery. Immunohistochemical (IHC) subtyping was performed using core needle biopsy specimens before NAC (pre-NAC) and residual tumors after NAC (post-NAC). Logistic regression was performed to identify predictive biomarkers of pathological complete response (pCR). Invasive disease-free survival (iDFS), distant disease-free survival (DDFS), and overall survival (OS) were assessed using the log-rank test and Cox proportional hazards regression. A total of 230 patients were followed up for a median of 59 months. Clinical lymph node status and the pre-NAC subtype were independent predictors of pCR (P=0.006 and 0.005, respectively). The pre-NAC subtype was an independent prognostic factor for long-term survival (iDFS: P < 0.001, DDFS: P=0.010, and OS: P=0.044). Among patients with residual disease (RD) after NAC, approximately 45% of tumors changed their IHC subtype. Furthermore, the post-NAC subtype, but not the pre-NAC subtype, was strongly associated with the survival of patients with RD (iDFS: P < 0.001, DDFS: P=0.005, and OS: P=0.006). The IHC subtype predicted response to NAC and long-term survival in patients with early TNBC. In patients with RD, almost 45% of the tumors changed subtype after NAC. The IHC subtype should be considered when planning additional therapies pre- and post-NAC.

Risk factors and survival of triple-negative breast cancer among breast cancer patients: Ten-year cross-sectional study in the southwestern Iranian population

Background

Breast cancer results from genetic and epigenetic mutations, contributing significantly to cancer-related morbidity and mortality. This study aimed to determine the prevalence and survival rates of triple-negative breast cancer (TNBC) among breast cancer patients in southwestern Iran over a ten-year period.

Methods

This retrospective cross-sectional study aims to assess prognostic factors associated with survival in women diagnosed with breast cancer in Iran's southwestern region over a ten-year period (2007-2017). Data were collected from patients who visited the Clinical Oncology Department at Golestan Hospital in Ahvaz (the breast cancer center of the Southwestern country). The study enrolled women diagnosed with TNBC using a census method and data from medical records. The primary outcome (survival rates) and secondary outcomes (demographic data, diagnostic stages, and three receptors estrogen receptors [ER], progesterone receptor [PR], human epidermal growth factor receptor 2 [HER2] status) were collected.

Results

Breast cancer was diagnosed in 2641 women over ten years; TNBC was diagnosed in 227 individuals (8.59%). Statistical analysis revealed a significant correlation between negative ER status and TNBC (p > 0.05). Furthermore, the prevalence of TNBC differed significantly from that of other types of breast cancer (p = 0.0001). The variables of age, HER2, PR, and TNBC grade did not differ significantly (p > 0.05). The overall disease-free survival rate over 5 years was 88.1%, while the rate for individuals without recurrence was 77.97%.

Conclusion

This study highlights a differentially low incidence of TNBC in the southwestern part of Iran when compared to other regions; genetic or epigenetic influences may explain this discrepancy. ER-negative status is a crucial prognostic indicator in diagnosing TNBC. The incidence of this disease is expected to rise by 100% in 2 years and 77.97% in 5 years.

Components of the tumor immune microenvironment based on m-IHC correlate with prognosis and subtype of triple-negative breast cancer

BACKGROUND AND AIM

The spatial distribution and interactions of cells in the tumor immune microenvironment (TIME) might be related to the different responses of triple-negative breast cancer (TNBC) to immunomodulators. The potential of multiplex IHC (m-IHC) in evaluating the TIME has been reported, but the efficacy is insufficient. We aimed to research whether m-IHC results could be used to reflect the TIME, and thus to predict prognosis and complement the TNBC subtyping system.

METHODS

The clinical, imaging, and prognosis data for 86 TNBC patients were retrospectively reviewed. CD3, CD4, CD8, Foxp3, PD-L1, and Pan-CK markers were stained by m-IHC. Particular cell spatial distributions and interactions in the TIME were evaluated with the HALO multispectral analysis platform. Then, we calculated the prognostic value of components of the TIME and their correlations with TNBC transcriptomic subtypes and MRI radiomic features reflecting TNBC subtypes.

RESULTS

The components of the TIME score were established by m-IHC and demonstrated positive prognostic value for TNBC (p = 0.0047, 0.039, <0.0001 for DMFS, RFS, and OS). The score was calculated from several indicators, including Treg% in the tumor core (TC) or stromal area (SA), PD-L1+ cell% in the SA, CD3 + cell% in the TC, and PD-L1+ /CD8+ cells in the invasive margin and SA. According to the TNBC subtyping system, a few TIME indicators were significantly different in different subtypes and significantly correlated with MRI radiomic features reflecting TNBC subtypes.

CONCLUSION

We demonstrated that the m-IHC-based quantitative score and indicators related to the spatial distribution and interactions of cells in the TIME can aid in the accurate diagnosis of TNBC in terms of prognosis and classification.

Artificial intelligence in breast imaging: potentials and challenges

Breast cancer, which is the most common type of malignant tumor among humans, is a leading cause of death in females. Standard treatment strategies, including neoadjuvant chemotherapy, surgery, postoperative chemotherapy, targeted therapy, endocrine therapy, and radiotherapy, are tailored for individual patients. Such personalized therapies have tremendously reduced the threat of breast cancer in females. Furthermore, early imaging screening plays an important role in reducing the treatment cycle and improving breast cancer prognosis. The recent innovative revolution in artificial intelligence (AI) has aided radiologists in the early and accurate diagnosis of breast cancer. In this review, we introduce the necessity of incorporating AI into breast imaging and the applications of AI in mammography, ultrasonography, magnetic resonance imaging, and positron emission tomography/computed tomography based on published articles since 1994. Moreover, the challenges of AI in breast imaging are discussed.

Triple-negative breast cancer: from none to multiple therapeutic targets in two decades

Triple-negative breast cancers (TNBCs) are more likely to occur in younger patients and have a poor prognosis. They are highly heterogeneous tumors consisting of different molecular subtypes. The only common characteristic among them is the absence of targets for endocrine therapy and human epidermal growth factor receptor 2 (HER2) blockade. In the past two decades, there has been an increased understanding of these tumors from a molecular perspective, leading to their stratification according to new therapeutic strategies. TNBC has ushered breast carcinomas into the era of immunotherapy. The higher frequency of germline BRCA mutations in these tumors enables targeting this repair defect by drugs like PARP inhibitors, resulting in synthetic lethality in neoplastic cells. Additionally, we have the identification of new molecules to which this generation of smart drugs, such as antibody-drug conjugates (ADCs), are directed. In this review, we will discuss the trajectory of this knowledge in a systematic manner, presenting the molecular bases, therapeutic possibilities, and biomarkers.

Research progress of Claudin-low breast cancer

Claudin-low breast cancer (CLBC) is a subgroup of breast cancer discovered at the molecular level in 2007. Claudin is one of the primary proteins that make up tight junctions, and it plays crucial roles in anti-inflammatory and antitumor responses as well as the maintenance of water and electrolyte balance. Decreased expression of claudin results in the disruption of tight junction structures and the activation of downstream signaling pathways, which can lead to tumor formation. The origin of Claudin-low breast cancer is still in dispute. Claudin-low breast cancer is characterized by low expression of Claudin3, 4, 7, E-cadherin, and HER2 and high expression of Vimentin, Snai 1/2, Twist 1/2, Zeb 1/2, and ALDH1, as well as stem cell characteristics. The clinical onset of claudin-low breast cancer is at menopause age, and its histological grade is higher. This subtype of breast cancer is more likely to spread to lymph nodes than other subtypes. Claudin-low breast cancer is frequently accompanied by increased invasiveness and a poor prognosis. According to a clinical retrospective analysis, claudin-low breast cancer can achieve low pathological complete remission. At present, although several therapeutic targets of claudin-low breast cancer have been identified, the effective treatment remains in basic research stages, and no animal studies or clinical trials have been designed. The origin, molecular biological characteristics, pathological characteristics, treatment, and prognosis of CLBC are extensively discussed in this article. This will contribute to a comprehensive understanding of CLBC and serve as the foundation for the individualization of breast cancer treatment.

Integrin β4 promotes DNA damage-related drug resistance in triple-negative breast cancer via TNFAIP2/IQGAP1/RAC1

Anti-tumor drug resistance is a challenge for human triple-negative breast cancer (TNBC) treatment. Our previous work demonstrated that TNFAIP2 activates RAC1 to promote TNBC cell proliferation and migration. However, the mechanism by which TNFAIP2 activates RAC1 is unknown. In this study, we found that TNFAIP2 interacts with IQGAP1 and Integrin β4. Integrin β4 activates RAC1 through TNFAIP2 and IQGAP1 and confers DNA damage-related drug resistance in TNBC. These results indicate that the Integrin β4/TNFAIP2/IQGAP1/RAC1 axis provides potential therapeutic targets to overcome DNA damage-related drug resistance in TNBC.

N-3, a novel synthetic derivative of bifendate, inhibits metastasis of triple-negative breast cancer via decreasing p38-regulated FOXC1 protein stability

Triple-negative breast cancer (TNBC) is an aggressive breast cancer subtype with high invasiveness, metastatic potential, and poor prognosis. Epithelial-mesenchymal transition (EMT) is pivotal in TNBC progression, becoming a promising target for TNBC treatment. Our study evaluated N-3, a novel synthetic bifendate derivative, which inhibited the EMT-associated migration and invasion of MDA-MB-231 and 4T1 TNBC cells. The results were consistent with the suppression of FOXC1 expression and transcriptional activity. Additional studies indicated that N-3 reduced the protein stability of FOXC1 by enhancing ubiquitination and degradation. Moreover, N-3 downregulated p-p38 expression and FOXC1 interaction, decreasing the stability of p38-regulated FOXC1. Further, N-3 blocked TNBC metastasis with an artificial lung metastasis model in vivo, related to FOXC1 suppression and EMT. These results highlight the potential of N-3 as a TNBC metastasis treatment. Therefore, FOXC1 regulation could be a novel targeted therapeutic strategy for TNBC metastasis.

Prognostic and predictive biomarkers with therapeutic targets in breast cancer: A 2022 update on current developments, evidence, and recommendations

OBJECTIVE

To evaluate and validate the recent and emerging data for prognostic and predictive biomarkers with therapeutic targets in breast cancer.

DATA SOURCES

A literature search from January 2015 to March 2022 was performed using the key terms breast cancer, clinical practice guidelines, gene mutations, genomic assay, immune cancer therapy, predictive and/or prognostic biomarkers, and targeted therapies.

STUDY SELECTION AND DATA EXTRACTION

Relevant clinical trials, meta-analyses, seminal articles, and published evidence- and consensus-based clinical practice guidelines in the English language were identified, reviewed and evaluated.

DATA SYNTHESIS

Breast cancer is a biologically heterogeneous disease, leading to wide variability in treatment responses and survival outcomes. Biomarkers for breast cancer are evolving from traditional biomarkers in immunohistochemistry (IHC) such as estrogen receptor (ER), progesterone receptor (PR) and epidermal growth factor receptor type 2 (HER2) to genetic biomarkers with therapeutic implications (e.g. breast cancer susceptibility gene 1/2 [BRCA1/2], estrogen receptor α [ESR1] gene mutation, HER2 gene mutation, microsatellite instability [MSI], phosphatidylinositol 3-kinase catalytic subunit 3Cα [PIK3CA] gene mutation, neurotrophic tyrosine receptor kinase [NTRK] gene mutation). In addition, current data are most robust for biomarkers in immunotherapy (e.g. programmed cell death receptor ligand-1 [PD-L1], microsatellite instability-high [MSI-H] or deficient mismatch repair [dMMR]). Oncotype DX assay remains the best validated gene expression assay that is both predictive and prognostic whereas MammaPrint is prognostic for genomic risk.

CONCLUSIONS

Biomarker-driven therapies have the potential to confer greater therapeutic advantages than standard-of-care therapies. The purported survival benefits associated with biomarker-driven therapies should be weighed against their potential harms.

Recent advances in targeted strategies for triple-negative breast cancer

Triple-negative breast cancer (TNBC), a highly aggressive subtype of breast cancer, negatively expresses estrogen receptor, progesterone receptor, and the human epidermal growth factor receptor 2 (HER2). Although chemotherapy is the main form of treatment for patients with TNBC, the effectiveness of chemotherapy for TNBC is still limited. The search for more effective therapies is urgent. Multiple targeted therapeutic strategies have emerged according to the specific molecules and signaling pathways expressed in TNBC. These include PI3K/AKT/mTOR inhibitors, epidermal growth factor receptor inhibitors, Notch inhibitors, poly ADP-ribose polymerase inhibitors, and antibody-drug conjugates. Moreover, immune checkpoint inhibitors, for example, pembrolizumab, atezolizumab, and durvalumab, are widely explored in the clinic. We summarize recent advances in targeted therapy and immunotherapy in TNBC, with the aim of serving as a reference for the development of individualized treatment of patients with TNBC in the future.

Omics Technologies Improving Breast Cancer Research and Diagnostics

Breast cancer (BC) has yielded approximately 2.26 million new cases and has caused nearly 685,000 deaths worldwide in the last two years, making it the most common diagnosed cancer type in the world. BC is an intricate ecosystem formed by both the tumor microenvironment and malignant cells, and its heterogeneity impacts the response to treatment. Biomedical research has entered the era of massive omics data thanks to the high-throughput sequencing revolution, quick progress and widespread adoption. These technologies-liquid biopsy, transcriptomics, epigenomics, proteomics, metabolomics, pharmaco-omics and artificial intelligence imaging-could help researchers and clinicians to better understand the formation and evolution of BC. This review focuses on the findings of recent multi-omics-based research that has been applied to BC research, with an introduction to every omics technique and their applications for the different BC phenotypes, biomarkers, target therapies, diagnosis, treatment and prognosis, to provide a comprehensive overview of the possibilities of BC research.

Identification of DDIT4 as a potential prognostic marker associated with chemotherapeutic and immunotherapeutic response in triple-negative breast cancer

BACKGROUND

Triple-negative breast cancer (TNBC) is the most heterogenous and aggressive subtype of breast cancer. Chemotherapy remains the standard treatment option for patients with TNBC owing to the unavailability of acceptable targets and biomarkers in clinical practice. Novel biomarkers and targets for patient stratification and treatment of TNBC are urgently needed. It has been reported that the overexpression of DNA damage-inducible transcript 4 gene (DDIT4) is associated with resistance to neoadjuvant chemotherapy and poor prognosis in patients with TNBC. In this study, we aimed to identify novel biomarkers and therapeutic targets using RNA sequencing (RNA-seq) and data mining using data from public databases.

METHODS

RNA sequencing (RNA-Seq) was performed to detect the different gene expression patterns in the human TNBC cell line HS578T treated with docetaxel or doxorubicin. Sequencing data were further analyzed by the R package "edgeR" and "clusterProfiler" to identify the profile of differentially expressed genes (DEGs) and annotate gene functions. The prognostic and predictive value of DDIT4 expression in patients with TNBC was further validated by published online data resources, including TIMER, UALCAN, Kaplan-Meier plotter, and LinkedOmics, and GeneMANIA and GSCALite were used to investigate the functional networks and hub genes related to DDIT4, respectively.

RESULTS

Through the integrative analyses of RNA-Seq data and public datasets, we observed the overexpression of DDIT4 in TNBC tissues and found that patients with DDIT4 overexpression showed poor survival outcomes. Notably, immune infiltration analysis showed that the levels of DDIT4 expression correlated negatively with the abundance of tumor-infiltrating immune cells and immune biomarker expression, but correlated positively with immune checkpoint molecules. Furthermore, DDIT4 and its hub genes (ADM, ENO1, PLOD1, and CEBPB) involved in the activation of apoptosis, cell cycle, and EMT pathways. Eventually, we found ADM, ENO1, PLOD1, and CEBPB showed poor overall survival in BC patients.

CONCLUSION

In this study, we found that DDIT4 expression is associated with the progression, therapeutic efficacy, and immune microenvironment of patients with TNBC, and DDIT4 would be as a potential prognostic biomarker and therapeutic target. These findings will help to identify potential molecular targets and improve therapeutic strategies against TNBC.

The tumor-nerve circuit in breast cancer

It is well established that innervation is one of the updated hallmarks of cancer and that psychological stress promotes the initiation and progression of cancer. The breast tumor environment includes not only fibroblasts, adipocytes, endothelial cells, and lymphocytes but also neurons, which is increasingly discovered important in breast cancer progression. Peripheral nerves, especially sympathetic, parasympathetic, and sensory nerves, have been reported to play important but different roles in breast cancer. However, their roles in the breast cancer progression and treatment are still controversial. In addition, the brain is one of the favorite sites of breast cancer metastasis. In this review, we first summarize the innervation of breast cancer and its mechanism in regulating cancer growth and metastasis. Next, we summarize the neural-related molecular markers in breast cancer diagnosis and treatment. In addition, we review drugs and emerging technologies used to block the interactions between nerves and breast cancer. Finally, we discuss future research directions in this field. In conclusion, the further research in breast cancer and its interactions with innervated neurons or neurotransmitters is promising in the clinical management of breast cancer.

Multiomics technologies for comprehensive tumor microenvironment analysis in triple-negative breast cancer under neoadjuvant chemotherapy

Triple-negative breast cancer (TNBC) is one of the most aggressive breast cancer subtypes and is characterized by abundant infiltrating immune cells within the microenvironment. As standard care, chemotherapy remains the fundamental neoadjuvant treatment in TNBC, and there is increasing evidence that supplementation with immune checkpoint inhibitors may potentiate the therapeutic efficiency of neoadjuvant chemotherapy (NAC). However, 20-60% of TNBC patients still have residual tumor burden after NAC and require additional chemotherapy; therefore, it is critical to understand the dynamic change in the tumor microenvironment (TME) during treatment to help improve the rate of complete pathological response and long-term prognosis. Traditional methods, including immunohistochemistry, bulk tumor sequencing, and flow cytometry, have been applied to elucidate the TME of breast cancer, but the low resolution and throughput may overlook key information. With the development of diverse high-throughput technologies, recent reports have provided new insights into TME alterations during NAC in four fields, including tissue imaging, cytometry, next-generation sequencing, and spatial omics. In this review, we discuss the traditional methods and the latest advances in high-throughput techniques to decipher the TME of TNBC and the prospect of translating these techniques to clinical practice.

Immunohistochemistry-based molecular subtyping of triple-negative breast cancer and its prognostic significance

Background: Immunohistochemistry (IHC)-based protein markers representing molecular subtypes are of great value for routine use. This study aimed to evaluate the frequency distributions of the molecular subtypes of triple-negative breast cancer (TNBC) using IHC-based surrogate markers and examined their prognostic value. Methods: Patients with TNBC treated at a university hospital in Southern Thailand were included in this study. Expression levels of androgen receptor, CD8, Forkhead box transcription factor C1, and Doublecortin-like kinase 1 were detected in tumor tissue to classify them into luminal androgen receptor (LAR), immunomodulatory (IM), basal-like immunosuppressed (BLIS), mesenchymal-like (MES), and unclassifiable (UC) subtypes. The association between variables and disease-free survival (DFS) and overall survival (OS) was analyzed using Cox proportional hazards regression. Results: Among the 195 cases of TNBC, the frequency distribution of the IHC-based subtype was as follows: BLIS, 52.8%; LAR, 19.0%; IM, 17.4%; MES, 0.5%; and un-classifiable, 10.3%. BLIS subtype was significantly found in younger ages (mean: 49.6 years) than other subtypes (mean: 51-57.7 years). LAR and BLIS subtypes were significantly associated with poorer OS compared to the IM subtype in univariate analysis, however, only BLIS was significant in multivariate analysis (HR: 3.29, 95% CI: 1.01-10.72). IHC-based subtype was not found to be associated with DFS. Conclusion: This study revealed the differences in the proportion frequency of IHC-based TNBC subtypes in Thai patients compared to other populations. IHC-based molecular subtyping may be beneficial for prognosis. However further refinement of the molecular classification of TNBC is needed for better clinical relevance.

Subtyping-based platform guides precision medicine for heavily pretreated metastatic triple-negative breast cancer: The FUTURE phase II umbrella clinical trial

Triple-negative breast cancer (TNBC) is a heterogeneous disease and lacks effective treatment. Our previous study classified TNBCs into four subtypes with putative therapeutic targets. Here, we report the final results of FUTURE, a phase II umbrella trial designed to explore whether the subtyping-based strategy may improve the outcomes in metastatic TNBC patients. A total of 141 patients with a median of three previous lines of therapies in the metastatic setting were enrolled in seven parallel arms. Confirmed objective responses were achieved in 42 patients (29.8%; 95% confidence interval [CI], 22.4-38.1). The median values of progression-free survival and overall survival were 3.4 (95% CI: 2.7-4.2) and 10.7 (95% CI: 9.1-12.3) months, respectively. Given Bayesian predictive probability, efficacy boundaries were achieved in four arms. Furthermore, integrated genomic and clinicopathological profiling illustrated associations of clinical and genomic parameters with treatment efficacy, and the efficacy of novel antibody-drug conjugates was explored in preclinical TNBC models of subtypes for which treatment was futile. In general, the FUTURE strategy recruits patients efficiently and provides promising efficacy with manageable toxicities, outlining a direction for further clinical exploration.

Targeting DCLK1 attenuates tumor stemness and evokes antitumor immunity in triple-negative breast cancer by inhibiting IL-6/STAT3 signaling

Triple-negative breast cancer (TNBC) exhibits the poorest outcomes among breast cancer subtypes due to the high heterogeneity and a lasting scarcity of effectual treatments. Targeted therapies based on molecular subtypes of TNBC are critical step toward tailoring treatments to improve clinical outcomes. Gastrointestinal cancer stem cell (CSC) marker DCLK1 was reported to be highly expressed in stem cell-rich subtype of TNBC. Here, we firstly explored the impacts of DCLK1 on tumor cells as well as their immune microenvironment in TNBC and potential therapeutic strategies for TNBC patients with high DCLK1 expression. Our results disclosed that DCLK1 overexpression promoted, while knockout of DCLK1 suppressed the CSC-like traits of TNBC cells and resistance to chemotherapeutics. Besides, DCLK1 supported immune escape by inhibiting intratumoral cytotoxic T cell infiltration in TNBC and hence limited immune checkpoint inhibitors efficacy. Mechanistically, bioinformatics analysis revealed that IL-6/STAT3 signaling was significantly enriched in high DCLK1-expressing patients, and our results further revealed that DCLK1 enhanced IL-6 expression and STAT3 activation in TNBC cells, which finally gave rise to upregulated CSC traits and suppressed CD8+ T-cell activity. Inhibiting IL-6/STAT3 pathway by IL-6R antagonist, Tocilizumab or STAT3 inhibitor, S31-201 could abolish DCLK1-promoted malignant phenotypes of TNBC cells. Finally, DCLK1 was identified to be specifically and highly expressed in the mesenchymal-like subtype of TNBC and targeting DCLK1 could improve chemotherapy efficacy and activate antitumor immunity. Overall, our study revealed the potential clinical benefits of targeting DCLK1 in TNBC treatment.

Clinical and biological heterogeneities in triple-negative breast cancer reveals a non-negligible role of HER2-low

Background

HER2-low could be found in some patients with triple-negative breast cancer (TNBC). However, its potential impacts on clinical features and tumor biological characteristics in TNBC remain unclear.

Methods

We enrolled 251 consecutive TNBC patients retrospectively, including 157 HER2-low (HER2low) and 94 HER2-negtive (HER2neg) patients to investigate the clinical and prognostic features. Then, we performed single-cell RNA sequencing (scRNA-seq) with another seven TNBC samples (HER2negvs. HER2low, 4 vs. 3) prospectively to further explore the differences of tumor biological properties between the two TNBC phenotypes. The underlying molecular distinctions were also explored and then verified in the additional TNBC samples.

Results

Compared with HER2neg TNBC, HER2low TNBC patients exhibited malignant clinical features with larger tumor size (P = 0.04), more lymph nodes involvement (P = 0.02), higher histological grade of lesions (P < 0.001), higher Ki67 status (P < 0.01), and a worse prognosis (P < 0.001; HR [CI 95%] = 3.44 [2.10–5.62]). Cox proportional hazards analysis showed that neoadjuvant systemic therapy, lymph nodes involvement and Ki67 levels were prognostic factors in HER2low TNBC but not in HER2neg TNBC patients. ScRNA-seq revealed that HER2low TNBC which showed more metabolically active and aggressive hallmarks, while HER2neg TNBC exhibited signatures more involved in immune activities with higher expressions of immunoglobulin-related genes (IGHG1, IGHG4, IGKC, IGLC2); this was further confirmed by immunofluorescence in clinical TNBC samples. Furthermore, HER2low and HER2neg TNBC exhibited distinct tumor evolutionary characteristics. Moreover, HER2neg TNBC revealed a potentially more active immune microenvironment than HER2low TNBC, as evidenced by positively active regulation of macrophage polarization, abundant CD8+ effector T cells, enriched diversity of T-cell receptors and higher levels of immunotherapy-targeted markers, which contributed to achieve immunotherapeutic response.

Conclusions

This study suggests that HER2low TNBC patients harbor more malignant clinical behavior and aggressive tumor biological properties than the HER2neg phenotype. The heterogeneity of HER2 may be a non-negligible factor in the clinical management of TNBC patients. Our data provide new insights into the development of a more refined classification and tailored therapeutic strategies for TNBC patients.

Predictive Biomarkers for Response to Immunotherapy in Triple Negative Breast Cancer: Promises and Challenges

Triple negative breast cancer (TNBC) is a highly heterogeneous disease with a poor prognosis and a paucity of therapeutic options. In recent years, immunotherapy has emerged as a new treatment option for patients with TNBC. However, this therapeutic evolution is paralleled by a growing need for biomarkers which allow for a better selection of patients who are most likely to benefit from this immune checkpoint inhibitor (ICI)-based regimen. These biomarkers will not only facilitate a better optimization of treatment strategies, but they will also avoid unnecessary side effects in non-responders, and limit the increasing financial toxicity linked to the use of these agents. Huge efforts have been deployed to identify predictive biomarkers for the ICI, but until now, the fruits of this labor remained largely unsatisfactory. Among clinically validated biomarkers, only programmed death-ligand 1 protein (PD-L1) expression has been prospectively assessed in TNBC trials. In addition to this, microsatellite instability and a high tumor mutational burden are approved as tumor agnostic biomarkers, but only a small percentage of TNBC fits this category. Furthermore, TNBC should no longer be approached as a single biological entity, but rather as a complex disease with different molecular, clinicopathological, and tumor microenvironment subgroups. This review provides an overview of the validated and evolving predictive biomarkers for a response to ICI in TNBC.

Ferroptosis heterogeneity in triple-negative breast cancer reveals an innovative immunotherapy combination strategy

Treatment of triple-negative breast cancer (TNBC) remains challenging. Deciphering the orchestration of metabolic pathways in regulating ferroptosis will provide new insights into TNBC therapeutic strategies. Here, we integrated the multiomics data of our large TNBC cohort (n = 465) to develop the ferroptosis atlas. We discovered that TNBCs had heterogeneous phenotypes in ferroptosis-related metabolites and metabolic pathways. The luminal androgen receptor (LAR) subtype of TNBC was characterized by the upregulation of oxidized phosphatidylethanolamines and glutathione metabolism (especially GPX4), which allowed the utilization of GPX4 inhibitors to induce ferroptosis. Furthermore, we verified that GPX4 inhibition not only induced tumor ferroptosis but also enhanced antitumor immunity. The combination of GPX4 inhibitors and anti-PD1 possessed greater therapeutic efficacy than monotherapy. Clinically, higher GPX4 expression correlated with lower cytolytic scores and worse prognosis in immunotherapy cohorts. Collectively, this study demonstrated the ferroptosis landscape of TNBC and revealed an innovative immunotherapy combination strategy for refractory LAR tumors.

Potential Therapeutic Targets for Luminal Androgen Receptor Breast Cancer: What We Know so Far

Luminal Androgen Receptor Breast Cancers (LAR BCs) are characterized by a triple negative phenotype and by the expression of Androgen Receptor (AR), coupled with luminal-like genomic features. This unique BC subtype, accounting for about 10% of all triple negative BC, has raised considerable interest given its ill-defined clinical behavior and the chance to exploit AR as a therapeutic target. The complexity of AR activity in BC cells, as revealed by decades of mechanistic studies, holds promise to offer additional therapeutic options beyond mere AR inhibition. Indeed, preclinical and translational evidence showed that several pathways and mediators, including PI3K/mToR, HER2, BRCA1, cell cycle and immune modulation, can be tackled in LAR BCs. Moving from bench to bedside, several clinical trials tested anti-androgen therapies in LAR BCs, but their results are inconsistent and often disappointing. More recently, studies exploring combinations of anti-androgen agents with other targeted therapies have been designed and are currently ongoing. While the results from these trials are awaited, a concerted effort will be needed to find the biological vulnerabilities of LAR BCs which may disclose new and effective therapeutic targets, eventually improving patients' outcomes.

Update on triple-negative breast cancers - highlighting subtyping update and treatment implication

Triple-negative breast cancer (TNBC) remains a major challenge in breast cancer management. Continuing research in the past years aimed at understanding the biology of this tumour and developing more effective therapeutic options. It is now clear that TNBC is vastly heterogeneous with diverse histological, molecular, immunological profiles and clinical differences. Current evidence suggested the existence of at least four predominant subtypes based on expression profiling across studies. These subtypes exhibited specific genomic alterations and tumour microenvironment. Subtype-specific therapeutic strategies were identified. Recognising these subtypes allows not only an improved prognostication but also a better treatment decision. Herein, we provide an overview of the recent findings on TNBC heterogeneity at different levels and corresponding subtyping. The characteristic of subtypes and the implication of these subtypings in therapeutic approaches are also discussed.

Anti-Androgenic Therapies Targeting the Luminal Androgen Receptor of a Typical Triple-Negative Breast Cancer

Triple-negative tumors are progressively delineating their existence over the extended spectrum of breast cancers, marked by intricate molecular heterogeneity, a low overall survival rate, and an unexplored therapeutic approach. Although the basal subtype transcends the group and contributes approximately 80% to triple-negative breast cancer (TNBC) cases, the exceptionally appearing mesenchymal and luminal androgen receptor (LAR) subtypes portray an unfathomable clinical course. LAR with a distinct generic profile frequently metastasizes to regional lymph nodes and bones. This subtype is minimally affected by chemotherapy and shows the lowest pathologic complete response. The androgen receptor is the only sex steroid receptor that plays a cardinal role in the progression of breast cancers and is typically overexpressed in LAR. The partial AR antagonist bicalutamide and the next-generation AR inhibitor enzalutamide are being assessed in standard protocols for the mitigation of TNBC. There arises an inevitable need to probe into the strategies that could neutralize these androgen receptors and alleviate the trajectory of concerning cancer. This paper thus focuses on reviewing literature that provides insights into the anti-androgenic elements against LAR typical TNBC that could pave the way for clinical advancements in this dynamic sphere of oncology.

Modulating the Activity of Androgen Receptor for Treating Breast Cancer

The androgen receptor (AR) is a steroid hormone receptor widely detected in breast cancer. Evidence suggests that the AR might be a tumor suppressor in estrogen receptor alpha-positive (ERα+ve) breast cancer but a tumor promoter in estrogen receptor alpha-negative (ERα-ve) breast cancer. Modulating AR activity could be a potential strategy for treating breast cancer. For ERα+ve breast cancer, activation of the AR had been demonstrated to suppress the disease. In contrast, for ERα-ve breast cancer, blocking the AR could confer better prognosis to patients. These studies support the feasibility of utilizing AR modulators as anti-cancer drugs for different subtypes of breast cancer patients. Nevertheless, several issues still need to be addressed, such as the lack of standardization in the determination of AR positivity and the presence of AR splice variants. In future, the inclusion of the AR status in the breast cancer report at the time of diagnosis might help improve disease classification and treatment decision, thereby providing additional treatment strategies for breast cancer.

Establishment and verification of a nomogram to predict tumor-specific mortality risk in triple-negative breast cancer: a competing risk model based on the SEER cohort study

Background

Triple-negative breast cancer (TNBC) is the subtype of breast cancer with the worst prognosis, and traditional survival analysis methods are biased when predicting mortality. To predict the risk of death in patients with triple-negative breast cancer more precisely, a competing risk model was developed.

Methods

The clinicopathological data of the TNBC patients from 2010 to 2015 were collected from the Surveillance, Epidemiology, and End Results (SEER) database. The data were assigned into a training set and testing set at a ratio of 7:3 in a randomized pattern. Univariate and multivariate competing risk models were applied to find the independent prognostic factors. A prediction nomogram for cancer-specific mortality (CSM) risk was constructed. The accuracy and discrimination of the nomogram were assessed using receiver operating characteristic (ROC) area under the curve (AUC), concordance index (C-index), and a calibration curve using the training and testing sets, respectively.

Results

A total of 28,430 TNBC patients were randomly grouped into the training set (n=19,900) and the testing set (n=8,530). The median time for follow-up was 59 [1-107] months. A total of 7,014 (24.67%) patients died, among whom 4,801 (68.45%) died from breast cancer and 2,213 (31.55%) due to non-breast cancer events. The independent risk factors were primary site of tumor, grade, tumor-node-metastasis (TNM) stage, T stage, approach of surgery, chemotherapy, axillary lymph node metastases, brain metastases, and liver metastases. The prediction nomogram was constructed by using the aforementioned variables. The 1-, 3-, and 5-year AUC of CSM were predicted to be 0.856, 0.81, and 0.782, respectively, in the training set, and 0.856, 0.81, and 0.782 in the testing set, respectively. The C-index of the nomogram was 0.801 and 0.799 in the training and testing sets, respectively. As confirmed by the validation and training calibration curves, the nomogram was consistent with the results.

Conclusions

We used competing risk models to identify risk factors for CSM and constructed a CSM risk prediction nomogram for TNBC patients, that may be utilized to predict CSM risk in TNBC patients clinically and assist in the creation of individualised clinical treatment options.

Value of genomics- and radiomics-based machine learning models in the identification of breast cancer molecular subtypes: a systematic review and meta-analysis

Background

In the era of precision therapy, early classification of breast cancer (BRCA) molecular subtypes has clinical significance for disease management and prognosis. We explored the accuracy of machine learning (ML) models for early classification of BRCA molecular subtypes through a systematic review of the literature currently available.

Methods

We retrieved relevant studies published in PubMed, EMBASE, Cochrane, and Web of Science until 15 April 2022. A prediction model risk of bias assessment tool (PROBAST) was applied for the assessment of risk of bias of a genomics-based ML model, and the Radiomics Quality Score (RQS) was simultaneously used to evaluate the quality of this radiomics-based ML model. A random effects model was adopted to analyze the predictive accuracy of genomics-based ML and radiomics-based ML for Luminal A, Luminal B, Basal-like or triple-negative breast cancer (TNBC), and human epidermal growth factor receptor 2 (HER2). The PROSPERO of our study was prospectively registered (CRD42022333611).

Results

Of the 38 studies were selected for analysis, 14 ML models were based on gene-transcriptomic, with only 4 external validations; and 43 ML models were based on radiomics, with only 14 external validations. Meta-analysis results showed that c-statistic values of the ML based on radiomics for the identification of BRCA molecular subtypes Luminal A, Luminal B, Basal-like or TNBC, and HER2 were 0.76 [95% confidence interval (CI): 0.60-0.96], 0.78 (95% CI: 0.69-0.87), 0.89 (95% CI: 0.83-0.91), and 0.83 (95% CI: 0.81-0.86), respectively. The c-statistic values of ML based on the gene-transcriptomic analysis cohort for the identification of the previously described BRCA molecular subtypes were 0.96 (95% CI: 0.93-0.99), 0.96 (95% CI: 0.93-0.99), 0.98 (95% CI: 0.95-1.00), and 0.97 (95% CI: 0.96-0.98) respectively. Additionally, the sensitivity of the ML model based on radiomics for each molecular subtype ranged from 0.79 to 0.85, while the sensitivity of the ML model based on gene-transcriptomic was between 0.92 and 0.99.

Conclusions

Both radiomics and gene transcriptomics produced ideal effects on BRCA molecular subtype prediction. Compared with radiomics, gene transcriptomics yielded better prediction results, but radiomics was simpler and more convenient from a clinical point of view.

Androgen receptor function and targeted therapeutics across breast cancer subtypes

Despite significant progress in breast cancer (BC) therapy, it is globally the most commonly diagnosed cancer and leads to the death of over 650,000 women annually. Androgen receptor (AR) is emerging as a potential new therapeutic target in BC. While the role of AR is well established in prostate cancer (PCa), its function in BC remains incompletely understood. Emerging data show that AR's role in BC is dependent on several factors including, but not limited to, disease subtype, tumour microenvironment, and levels of circulating oestrogens and androgens. While targeting AR in PCa is becoming increasingly effective, these advances have yet to make any significant impact on the care of BC patients. However, this approach is increasingly being evaluated in BC and it is clear that improvements in our understanding of AR's role in BC will increase the likelihood of success for AR-targeted therapies. This review summarizes our current understanding of the function of AR across BC subtypes. We highlight limitations in our current knowledge and demonstrate the importance of categorizing BC subtypes effectively, in relation to determining AR activity. Further, we describe the current state of the art regarding AR-targeted approaches for BC as monotherapy or in combination with radiotherapy.