Classification Of Triple-negative Breast Cancer Research Articles

Comparative Evaluation of Machine Learning Models for Subtyping Triple-Negative Breast Cancer: A Deep Learning-Based Multi-Omics Data Integration Approach.

Objective: Triple-negative breast cancer (TNBC) poses significant diagnostic challenges due to its aggressive nature. This research develops an innovative deep learning (DL) model based on the latest multi-omics data to enhance the accuracy of TNBC subtype and prognosis prediction. The study focuses on addressing the constraints of prior studies by showcasing a model with substantial advancements in data integration, statistical performance, and algorithmic optimization. Methods: Breast cancer-related molecular characteristic data, including mRNA, miRNA, gene mutations, DNA methylation, and magnetic resonance imaging (MRI) images, were retrieved from the TCGA and TCIA databases. This study not only compared single-omics with multi-omics machine learning models but also applied Bayesian optimization to innovatively optimize the neural network structure of a DL model for multi-omics data. Results: The DL model for multi-omics data significantly outperformed single-omics models in subtype prediction, achieving a 98.0% accuracy in cross-validation, 97.0% in the validation set, and 91.0% in an external test set. Additionally, the MRI radiomics model showed promising performance, especially with the training set; however, a decrease in performance during transfer testing underscored the advantages of the DL model for multi-omics data in data consistency and digital processing. Conclusion: Our multi-omics DL model presents notable innovations in statistical performance and transfer learning capability, bearing significant clinical relevance for TNBC classification and prognosis prediction. While the MRI radiomics model proved effective, it requires further optimization for cross-dataset application to enhance accuracy and consistency. Our findings offer new insights into improving TNBC classification and prognosis through multi-omics data and DL algorithms.

Target-Oriented Classification of Triple-negative Breast Cancer.

Breast cancer that is estrogen receptor (ER)-negative, progesterone receptor (PR)-negative, and human epidermal growth factor receptor-2 (HER2)-negative is termed triple-negative breast cancer (TNBC). Cytotoxic chemotherapy remains the first choice of treatment against TNBC due to lack of specific therapeutic targets. TNBC is not classified based on therapeutic targets, but recently, the development of targeted therapies - including immune checkpoint inhibitors and poly (adenosine diphosphate-ribose) polymerase inhibitors - has gained attention. This study aimed to examine a novel target-oriented TNBC classification to further facilitate targeted therapy by classifying TNBC based on the breast cancer 1 (BRCA1)-like as well as the protein expression of HER2, programmed death ligand 1 (PD-L1), androgen receptor (AR), cytokeratin 5/6, and epidermal growth factor receptor (EGFR). We enrolled 17 patients with primary TNBC who did not receive preoperative chemotherapy and underwent surgery at the Kobe University Hospital, Japan, between January 1, 2018, and July 31, 2019. Immunohistochemical staining was performed on tumor specimens, while a BRCAness test was performed using multiplex ligation-dependent probe amplification (MLPA) analysis. A BRCAness score 0.5 or higher was considered BRCA1-like. Tumors were classified as HER2-low (immunohistochemistry score 1+ or 2+ and FISH negative), PD-L1 positive, AR positive, or BRCA1-like. HER2-low, PD-L1 positive, AR positive, and BRCA1-like were detected in 11 (64.7%), 4 (23.5%), 6 (35.3%), and 6 (35.3%) samples. The tumor of only one patient could not be classified into any of these categories. Almost all TNBC cases can be classified according to treatable targets.

Dual Prognostic Classification of Triple-Negative Breast Cancer by DNA Damage Immune Response and Homologous Recombination Deficiency.

Triple-negative breast cancer (TNBC) is a heterogeneous disease. We previously showed that homologous recombination deficiency (HRD) and the DNA damage immune response (DDIR) signature are prognostic in TNBC. We hypothesized that these biomarkers reflect related but not completely interdependent biological processes, that their combined use would be prognostic, and that simultaneous assessment of the immunologic microenvironment and susceptibility to DNA damaging therapies might be able to identify subgroups with distinct therapeutic vulnerabilities. We analyzed the dual DDIR/HRD classification in 341 patients with TNBC treated with adjuvant anthracycline-based chemotherapy on the SWOG S9313 trial and corroborated our findings in The Cancer Genome Atlas breast cancer data set. DDIR/HRD classification is highly prognostic in TNBC and identifies biologically and immunologically distinct subgroups. Immune-enriched DDIR+/HRD+ TNBCs have the most favorable prognosis, and DDIR+/HRD- and DDIR-/HRD+ TNBCs have favorable intermediate prognosis, despite the latter being immune-depleted. DDIR-/HRD- TNBCs have the worst prognosis and represent an internally heterogeneous group of immune-depleted chemoresistant tumors. Our findings propose DDIR/HRD classification as a potentially clinically relevant approach to categorize tumors on the basis of therapeutic vulnerabilities.

Research Progress on Molecular Subtyping and Modern Treatment of Triple-Negative Breast Cancer.

Breast cancer has become the most common malignant tumor worldwide. Triple-negative breast cancer (TNBC) is a type of breast cancer that is negative for estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2). Compared with other molecular subtypes of breast cancer, TNBC is the most aggressive and highly heterogeneous. TNBC is insensitive to endocrine and anti-HER2 therapy, and chemotherapy is currently the main systemic treatment. With the continuous development of detection techniques and deepening research on TNBC molecular subtypes, drugs targeting immune checkpoints and different targets have emerged, such as atezolizumab, pembrolizumab, poly (ADP-ribose) polymerase (PARP) inhibitors, trophoblast cell-surface antigen 2 (TROP-2), and antibody-drug conjugates. These therapies provide new hope for TNBC treatment. Based on the analysis and classification of TNBC, this article summarizes the immunotherapy, targeted therapy, and new treatment combinations, providing references for the precise treatment of TNBC in the future.

Retrospective study of transcriptomic profiling identifies Thai triple-negative breast cancer patients who may benefit from immune checkpoint and PARP inhibitors.

Triple-negative breast cancer (TNBC) is a rare and aggressive breast cancer subtype. Unlike the estrogen receptor-positive subtype, whose recurrence risk can be predicted by gene expression-based signature, TNBC is more heterogeneous, with diverse drug sensitivity levels to standard regimens. This study explored the benefit of gene expression-based profiling for classifying the molecular subtypes of Thai TNBC patients. The nCounter-based Breast 360 gene expression was used to classify Thai TNBC retrospective cohort subgroups. Their expression profiles were then compared against the previously established TNBC classification system. The differential characteristics of the tumor microenvironment and DNA damage repair signatures across subgroups were also explored. Thai TNBC cohort could be classified into four main subgroups, corresponding to the LAR, BL-2, and M subtypes based on Lehmann's TNBC classification. The PAM50 gene set classified most samples as basal-like subtypes except for Group 1. Group 1 exhibited similar enrichment of the metabolic and hormone response pathways to the LAR subtype. Group 2 shared pathway activation with the BL-2 subtype. Group 3 showed an increase in the EMT pathway, similar to the M subtype. Group 4 showed no correlation with Lehmann's TNBC. The tumor microenvironment (TME) analysis showed high TME cell abundance with increased expression of immune blockade genes in Group 2. Group 4 exhibited low TME cell abundance and reduced immune blockade gene expressions. We also observed distinct signatures of the DNA double-strand break repair genes in Group 1. Our study reported unique characteristics between the four TNBC subgroups and showed the potential use of immune checkpoint and PARP inhibitors in subsets of Thai TNBC patients. Our findings warrant further clinical investigation to validate TNBC's sensitivity to these regimens.

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: 49.6years) than other subtypes (mean: 51-57.7years). 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.

Classification of triple-negative breast cancer based on pathway enrichment levels.

Triple-negative breast cancer (TNBC) is highly heterogeneous in the stromal and immune microenvironment, genome integrity, oncogenic signatures, and metabolic processes. However, a classification of TNBC by combining these features remains lacking. Here we classified TNBC based on the enrichment levels of 14 pathways related to immune, stromal, oncogenic and metabolic signatures by unsupervised clustering. In three bulk tumor transcriptome datasets and a single cell RNA-Seq (scRNA-seq) dataset, we consistently identified four TNBC subtypes: Imm-E, Str-E, DR-E, and Met-E. Imm-E displayed the strongest immune signatures, the lowest intratumor heterogeneity (ITH), the highest global methylation levels, the lowest tumor purity, and the best prognosis. Str-E showed the strongest stromal signatures and TGF-β signaling. DR-E had the highest DNA repair activity, cell cycle activity, and genomic instability. Met-E demonstrated the strongest hormone metabolism and signaling, the highest ITH, the lowest genomic instability, and the worst prognosis. The subtyping of TNBC provides novel insights into tumor biology and clinical implications for this disease.

Mesenchymal Subtype of Triple-Negative Breast Cancer: A Review of Specific Features

Triple-negative breast cancer (TNBC) is characterized by high invasiveness, high metastatic potential, proneness to relapse, and poor prognosis. Currently, four subtypes in the classification of triple-negative breast cancer (TNBC) are distinguished, which differ from each other in morphological manifestations, molecular genetic features, survival rates, prognosis parameters, and tumor resistance to therapy. A special place in this breast tumors group is occupied by the mesenchymal subtype, the frequency percentage of which varies from 7% to 28%, according to different data. The mesenchymal subtype of TNBC (M-TNBC) is characterized by the expression of molecular markers related to the epithelial-mesenchymal transition (EMT) program and cancer stem cells. M-TNBC has a highly aggressive behavior and worse prognosis due to its invasive and stem-like features, which correlate with metastatic dissemination and resistance to therapies. This review discusses the current knowledge regarding the mesenchymal TNBC subtype and its response to conventional therapeutic strategies. The complex approach to finding effective treatment options to restore immunocompetence in mesenchymal breast cancer patients is the final goal for further extended studies.

Abstract PD4-01: PD4-01 Spatial transcriptomics reveals a substantial heterogeneity in TNBC tumor and stroma compartments with potential clinical implications

Abstract Background Triple negative breast cancer (TNBC) is a heterogeneous disease characterized by at least five molecular subtypes, namely basal like (BL), immunomodulatory (IM), luminal androgen receptor (LAR), mesenchymal (M) and mesenchymal stem like (MSL), associated with distinct gene expression, genomic and tumor microenvironment (TME) profiles. Recent technological advances allow to investigate intratumor geographic heterogeneity ignored by bulk tumor analyses. Here, we deployed spatial transcriptomics (ST) to interrogate tumor and stroma compartments heterogeneity and assess its association with clinical outcome. Methods Spatial transcriptomics (Visium® Spatial Gene Expression, 10X Genomics) was performed on a retrospective series of 94 case-control TNBC samples matched for known clinic-pathological parameters with available long term outcome. Detailed morphological annotations spanning 11 histomorphological categories were performed by a breast dedicated pathologist assisted by the automated QuPath digital pathology software. Bioinformatic analyses were performed using in house pipelines. Results We investigated the distribution of each morphological category across the five TNBC molecular subtypes. We found that LAR, M and MSL, even though they had less tumor cells, had more patches of very small size (p< 0.0001). On the other hand, BL and IM had few large and dense patches. Stroma had an opposite distribution compared to tumor, with LAR and MSL being enriched with stroma (p< 00001). Tumor infiltrating lymphocytes were specific of the IM (p< 0.0001) and to a lesser extent the BL subtype. Normal structures, like fat tissue (p< 0.008), lactiferous ducts (p< 0.03) and vessels (p< 0.02), were more present in MSL and LAR. The differences between the molecular subtypes are mirrored at the level of their cell composition and tumor organization, suggesting the possibility to assess TNBC molecular subtypes from imaging data alone. At the gene expression level, spatial deconvolution analyses revealed the co-existence of tumor and stroma compartments from different TNBC subtypes within a tumor sample of a given subtype as defined by bulk tumor analysis. Interestingly, these different tumor-stroma combinations were associated with prognosis. For example, M tumors associated with MSL stroma seem to have a better prognosis than M tumors with an M stroma (p=0.001). Furthermore, spatial resolution of the gene expression identified 418 individual clusters (median 4 clusters per sample) associated with specific molecular and cellular features highlighting a substantial intra-patient heterogeneity. These clusters were further grouped into 11 ecotypes associated with distinct hallmarks and pathways, including EMT, angiogenesis, DNA repair and immune profiles revealing an important inter-patient heterogeneity beyond TNBC classification. Interestingly, 2 ecotypes were identified within the IM subtype associated with distinct clinical outcome, with ecotype 6 characterized by high EMT, mesenchymal stroma and worse prognosis (p = 0,021). Conclusion To our knowledge, this is the largest study demonstrating the substantial intra- and inter-patient heterogeneity characterizing TNBC at an unprecedented level, with differences both in tumor and stroma composition as well as spatial organization and clinical outcome. Our results hightlight the need to consider TNBC heterogeneity for patient care and future clinical development including immunotherapy. Citation Format: Xiaoxiao Wang, David Venet, Frédéric Lifrange, Denis Larsimont, Mattia Rediti, Linnea Stenbeck, David Gacquer, Floriane Dupont, Ghizlane Rouas, Matteo Serra, Joakim Lundeberg, Françoise Rothé, Christos Sotiriou. PD4-01 Spatial transcriptomics reveals a substantial heterogeneity in TNBC tumor and stroma compartments with potential clinical implications [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr PD4-01.

Abstract P4-09-04: Correlation between histology and molecular subtypes in triple negative breast cancer

Abstract Correlation between histology and molecular subtypes in triple negative breast cancer Authors: Tabata Alves Domingos, MD; 1* Roberto Bonfim Pimenta Peixoto, MD; 1* Ashka Patel, BS1,2; Krishan Taneja, PhD; 1 Wendy Y. Chen, MD, MPH; 4,5,6 Elizabeth A. Mittendorf, MD, PhD; 5,6,7 Alexa Zimbalist, MS; 3 Elizabeth M. Cespedes Feliciano, ScD SM; 3 Deborah A. Dillon, MD 1,2 *equal contribution Author Affiliations 1. Department of Pathology, Brigham and Women’s Hospital, Boston, MA 02115 2. Breast Oncology, Dana-Farber Brigham Cancer Center, Boston, MA 02215 3. Division of Research, Kaiser Permanente Northern California, Oakland, CA 94612 4. Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA 02115 5. Department of Medical Oncology Dana Farber Cancer Institute, Boston, MA 02215 6. Harvard Medical School, Boston, MA 02215 7. Division of Breast Surgery, Brigham and Women’s Hospital, Boston, MA 02215 Background Recently defined molecular subtypes of triple negative breast cancer (TNBC) show distinct clinical outcomes and suggest new therapeutic targets but have not been integrated into current pathologic classification systems. Here, we describe the histopathologic features of TNBC according to four molecular subtypes: basal-like immune activated (BLIA), basal-like immune suppressed (BLIS), luminal androgen receptor (LAR) and mesenchymal (MES), classified using the NanoString BC360 gene expression assay. Methods Stage II and III invasive breast cancers were identified in the Kaiser Permanente (KP) clinical pathology archives (2005-2015) and triple negative status was determined from the KP Northern California Cancer Registry data on immunohistochemistry. Selected slides were reviewed by two pathologists who recorded key histopathologic features [histologic subtype, presence of apocrine, metaplastic or micropapillary features, nuclear grade, mitotic score, and Tumor Infiltrating Lymphocytes (TILs)] and marked the best tumor areas for molecular analysis. TILs were evaluated according to the guidelines of the International TILs Working Group. Cases were macrodissected and evaluated using the NanoString BC360 gene expression assay. Histologic features were then summarized according to molecular subtype. Results Of 72 TNBCs, 60 were classified as Basal-like (83.3%), 7 as HER2-enriched (9.7%) and 5 as Luminal A (6.9%). 41 cases were classified as BLIA (56.9%), 14 as BLIS (19.4%), 13 as LAR (18.0%) and 4 as MES (5.5%). Both BLIA and BLIS tumors showed uniformly high nuclear grade and high mitotic score but differed significantly in TILs (BLIA average 32% vs BLIS average 9%; p value< 0.001, t-test for mean difference in TILs). The majority of LAR cases (69%) showed apocrine differentiation, not present in any other molecular subtype (p value< 0.001, chi-square test for presence of apocrine differentiation). LAR cases showed high nuclear grade but a lower average mitotic score (average score of 2) compared with the basal-like subtypes. TILs in LAR tumors were intermediate (17%) between BLIA and BLIS tumors. Of the 4 MES cases, all showed high nuclear grade. TILs in the MES cases were also intermediate (14%) between BLIA and BLIS tumors. Other histologic features, including lobular subtype, metaplastic and micropapillary features were not associated with specific triple negative molecular subtypes. Conclusion TILs are high in the BLIA molecular subtype (average 32%), low in the BLIS subtype (average 9%) and intermediate in LAR (average 17%) and MES (average 14%) subtypes. Apocrine features, if present in a TNBC, are a strong predictor of LAR molecular subtype. The inclusion of TILs and apocrine features (both easily derived from H&E slides) in routine pathology reporting could improve the classification of TNBC and aid in the identification of patients more likely to respond to specific therapies for the BLIA, BLIS and LAR subtypes, especially in resource-limited settings. Citation Format: Tabata Alves Domingos, Roberto Bonfim Pimenta Peixoto, Ashka Patel, Krishan Taneja, Wendy Y. Chen, Elizabeth A. Mittendorf, Alexa Zimbalist, Elizabeth M. Cespedes Feliciano, Deborah A. Dillon. Correlation between histology and molecular subtypes in triple negative breast cancer [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P4-09-04.

Characterization of Hormone Receptor and HER2 Status in Breast Cancer Using Mass Spectrometry Imaging.

Immunohistochemical evaluation of estrogen receptor, progesterone receptor, and human epidermal growth factor receptor-2 status stratify the different subtypes of breast cancer and define the treatment course. Triple-negative breast cancer (TNBC), which does not register receptor overexpression, is often associated with worse patient prognosis. Mass spectrometry imaging transcribes the molecular content of tissue specimens without requiring additional tags or preliminary analysis of the samples, being therefore an excellent methodology for an unbiased determination of tissue constituents, in particular tumor markers. In this study, the proteomic content of 1191 human breast cancer samples was characterized by mass spectrometry imaging and the epithelial regions were employed to train and test machine-learning models to characterize the individual receptor status and to classify TNBC. The classification models presented yielded high accuracies for estrogen and progesterone receptors and over 95% accuracy for classification of TNBC. Analysis of the molecular features revealed that vimentin overexpression is associated with TNBC, supported by immunohistochemistry validation, revealing a new potential target for diagnosis and treatment.

Targeting triple-negative breast cancer: A clinical perspective.

Triple-negative breast cancer (TNBC) is a disease with often an aggressive course and a poor prognosis compared to other subtypes of breast cancer. TNBC accounts for approximately 10%-15% of all diagnosed breast cancer cases and represents a high unmet need in the field. Up to just a few years ago, chemotherapy was the only systemic treatment option for this subtype (1). To date, TNBC is considered a heterogeneous disease. One of the existing classifications is based on the analysis of mRNA expression in 587 TNBC cases, in which Lehman et al. proposed six subtypes of TNBC as follows: two basal-like (BL1 and BL2) subtypes, a mesenchymal (M) subtype, a mesenchymal stem-like (MSL) subtype, an immunomodulatory (IM) subtype, and a luminal androgen receptor (LAR) subtype (2). Later studies have demonstrated that the IM and MSL subtypes do not correlate with independent subtypes but reflect background expression by dense infiltration of tumor-infiltrating lymphocytes (TILs) or stromal cells. According to this finding, the classification of TNBC has been revised into the following four subtypes: basal 1, basal 2, LAR, and mesenchymal subtypes (3). Over the last years, several new strategies have been investigated for the treatment of patients with TNBC. Among them, immunotherapy, antibody drug conjugates, new chemotherapy agents, and targeted therapy have been and are currently being developed. The present article aims to provide an updated overview on the different treatment options that are now available or are still under investigation for patients with TNBC.

Molecular Classification, Treatment, and Genetic Biomarkers in Triple-Negative Breast Cancer: A Review.

Breast cancer is the most common malignancy and the second most common cause of cancer-related mortality in women. Triple-negative breast cancers do not express estrogen receptors, progesterone receptors, or human epidermal growth factor receptor 2 and have a higher recurrence rate, greater metastatic potential, and lower overall survival rate than those of other breast cancers. Treatment of triple-negative breast cancer is challenging; molecular-targeted therapies are largely ineffective and there is no standard treatment. In this review, we evaluate current attempts to classify triple-negative breast cancers based on their molecular features. We also describe promising treatment methods with different advantages and discuss genetic biomarkers and other prediction tools. Accurate molecular classification of triple-negative breast cancers is critical for patient risk categorization, treatment decisions, and surveillance. This review offers new ideas for more effective treatment of triple-negative breast cancer and identifies novel targets for drug development.

LncRNA-Based Classification of Triple Negative Breast Cancer Revealed Inherent Tumor Heterogeneity and Vulnerabilities.

Triple negative breast cancer (TNBC) represents a diverse group of cancers based on their gene expression profiles. While the current mRNA-based classification of TNBC has contributed to our understanding of the heterogeneity of this disease, whether such heterogeneity can be resolved employing a long noncoding RNA (lncRNA) transcriptome has not been established thus far. Herein, we used iterative clustering and guide-gene selection (ICGS) and uniform manifold approximation and projection (UMAP) dimensionality reduction analysis on a large cohort of TNBC transcriptomic data (TNBC = 360, normal = 88) and classified TNBC into four main clusters: LINC00511-enriched, LINC00393-enriched, FIRRE-enriched, and normal tissue-like. Delving into associated gene expression profiles revealed remarkable differences in canonical, casual, upstream, and functional categories among different lncRNA-derived TNBC clusters, suggesting functional consequences for altered lncRNA expression. Correlation and survival analysis comparing mRNA- and lncRNA-based clustering revealed similarities and differences between the two classification approaches. To provide insight into the potential role of the identified lncRNAs in TNBC biology, CRISPR-Cas9 mediated LINC00511 promoter deletion reduced colony formation and enhanced the sensitivity of TNBC cells to paclitaxel, suggesting a role for LINC00511 in conferring tumorigenicity and resistance to therapy. Our data revealed a novel lncRNA-based classification of TNBC and suggested their potential utilization as disease biomarkers and therapeutic targets.

Improved pathologic complete response rates for triple-negative breast cancer in the I-SPY2 Trial.

591 Background: The I-SPY2 Trial evaluates multiple investigative agents in neoadjuvant breast cancer therapy with the primary endpoint of estimated pathologic complete response (pCR) rate. As a platform phase 2 trial it utilizes an adaptive design to compare new regimens with control chemotherapy (weekly paclitaxel followed by AC). Methods: Specific regimens are assigned based on clinically relevant signatures, including triple negative breast cancer (TNBC). Drug regimens graduate from the trial when the predicted pCR rate in any signature meets the pre-specified threshold of 85% probability of success in a hypothetical 300-patient, 1:1 randomized, phase 3 trial. The strong correlation between pCR rate and event free survival has been reported. To establish the benefit of administering investigational agents in combination with control weekly paclitaxel x 12 in TNBC, we report estimated pCR rates for the first 7 investigational agents. Results: TNBC accounted for 37% (363/987) of enrolled patients. Only veliparib and carboplatin (VC) and pembrolizumab (Pembro) met the graduation criteria for TNBC. However, compared to control chemotherapy, each drug tested in TNBC resulted in a numerically superior pCR rate compared to control. These findings imply that stratification of TNBC by response-predictive biomarkers may lead to improved pCR rates. For example, we have used gene expression profiling to further refine TNBC classification into Immune enhanced (Immune+), Immune-/DNA Repair Deficient (DRD)+, and Immune-/DRD- classes. TNBC identified as immune enhanced (63%) have an 89% pCR rate to pembrolizumab, while VC is less effective with pCR rate of 71%. Similarly, Immune-/DRD+ (11%) identifies TNBCs with a 80% pCR rate to VC, while pembrolizumab’s pCR rate in this group is only 33%. For tumors that are neither immune enhanced or DRD-positive (Immune-/DRD-; 25%) show numerically improved pCR rates for neratinib (20%), MK2206 (25%), ganitumab (24%), and ganetespib (22%) compared to control (12%). pCR rates for VC (10%) and pembrolizumab (20%) in this group were similar to drugs that did not graduate. For TNBC, many agents in I-SPY2 showed numerically improved pCR rates compared to conventional chemotherapy even when they did not meet our specified definition of graduation. Conclusions: Further refinement of TNBC signatures should yield improved therapeutic strategies while also sparing women unnecessary systemic therapy. Clinical trial information: NCT01042379. [Table: see text]

Phase 2 study of anlotinib combined with taxanes and lobaplatin in the neoadjuvant treatment of triple-negative breast cancer: Efficacy, safety and biomarker analysis.

e12605 Background: Pathological complete response (pCR) is associated with improved prognosis in triple-negative breast cancer (TNBC). Anlotinib, a novel multi-target tyrosine kinase inhibitor that effectively inhibits VEGFR, FGFR, c-KIT, c-MET, and RET, monotherapy has been proven effective in HER-2 negative metastatic breast cancer, but its efficacy in early-stage TNBC is unknown. This phase 2 study aims to evaluate the efficacy and safety of adding anlotinib to neoadjuvant chemotherapy in patients (pts) with primary TNBC. Methods: Pts with clinical stage II/III TNBC were to be treated with 5 cycles of anlotinib (12mg, d1-14, q3w) plus 6 cycles of taxanes (docetaxel 75 mg/m2 or nab-paclitaxel 260 mg/m2, d1, q3w) and lobaplatin (30 mg/m2, d1, q3w), followed by surgery. The primary endpoint was the total pCR (tpCR; ypT0/is ypN0). A Simon’s two-stage optimum design was used, and ＞5 of 11 pts were required to achieve tpCR in the first stage, with a pre-specified tpCR rate of 54.5% before proceeding to the second stage. A total of 31 participants was required for the study. Results: Six out of 11 pts achieved tpCR in the first stage, reaching the threshold for the second stage. From Jan 2021 to Jan 2022, a total of 22 pts were enrolled and 12 received surgery after the completion of neoadjuvant therapy, but a total of 2 pts withdrew from the study due to the COVID-19 pandemic or serious adverse events. Of the 22 eligible pts, the median age was 49 years (range, 29-64), 64% were postmenopausal, and 73% were nodal involved. At the time of surgery, 58.3% (7/12) achieved tpCR. Of the 9 pts with the node-positive disease at diagnosis, 88.9% (8/9) became ypN0. The results of FUSCC TNBC classification (IHC-based) revealed the tpCR rates were 57.1% (4/7), 100% (3/3), and 0% (0/2) for BLIS subtype, IM subtype and LAR/unknown subtypes, respectively. Biomarker analysis showed the tpCR rates were 100% (3/3) and 100% (4/4) in patients with gBRCA1 mutation and MYC amplification, respectively. The most common grade 3 or 4 treatment-related adverse events were leucopenia (6/22, 27%), neutropenia (6/22, 27%), anemia (5/22, 23%), decreased appetite (5/22, 23%), hypertension (2/22, 9%), ALT increased (1/22, 5%) and oral mucositis (1/22, 5%). No treatment-related deaths occurred. The trial is ongoing. Conclusions: The addition of anlotinib to neoadjuvant chemotherapy showed manageable toxicity and promising antitumor activity for patients with early-stage TNBC. Clinical trial information: ChiCTR2100043027.

Update of molecular classification of triple-negative breast cancer

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Triple-negative breast cancer: A run-through of features, classification and current therapies.

Breast cancer is the most prevalent cancer in women worldwide. Triple-negative breast cancer (TNBC) is characterized by the lack of expression of estrogen receptor, progesterone receptor, and human epidermal growth factor receptor 2. It is the most aggressive subtype of breast cancer and accounts for 12–20% of all breast cancer cases. TNBC is associated with younger age of onset, greater metastatic potential, higher incidence of relapse, and lower overall survival rates. Based on molecular phenotype, TNBC has been classified into six subtypes (BL1, BL2, M, MES, LAR, and IM). TNBC treatment is challenging due to its heterogeneity, highly invasive nature, and relatively poor therapeutics response. Chemotherapy and radiotherapy are conventional strategies for the treatment of TNBC. Recent research in TNBC and mechanistic understanding of disease pathogenesis using cutting-edge technologies has led to the unfolding of new lines of therapies that have been incorporated into clinical practice. Poly (ADP-ribose) polymerase and immune checkpoint inhibitors have made their way to the current TNBC treatment paradigm. This review focuses on the classification, features, and treatment progress in TNBC. Histological subtypes connected to recurrence, molecular classification of TNBC, targeted therapy for early and advanced TNBC, and advances in non-coding RNA in therapy are the key highlights in this review.

Spectrum of PIK3CA/AKT mutations across molecular subtypes of triple-negative breast cancer.

The heterogeneity of triple-negative breast cancer (TNBC) confers variable response to chemotherapy that results in poor outcome and relapse. Due to lack of targeted therapy, there is a need to provide molecular classification of TNBC and identify probable therapeutic targets. We classified TNBC into surrogate molecular subtypes by immunohistochemistry and evaluated hotspot mutations (N = 80) in PIK3CA (exon 4, 9, and 20) and AKT1 (exon 2) in TNBC subtypes by Sanger sequencing. TNBCs were classified into Basal-like 1(BL1) (n = 20, 25%), Mesenchymal (n = 19, 23.75%), Luminal Androgen (LAR) (n = 12, 15%), Basal+Mesenchymal (Mixed type) (n = 10, 12.5%), and unclassified subtype (n = 19, 23.75%). PIK3CA mutations were observed in 16.25% (13/80) TNBC cases. PIK3CA mutations were more frequent in exon 20 (8.7%) than in exon 9 (5%) and exon 4 (2.5%). PIK3CA mutations were frequent in LAR subtype (33.3%) followed by unclassified type (31.5%), Mesenchymal (10.5%), and BL1 (5%) subtypes. Two hotspot mutations were found in AKT1 (T21I, E17K) in mixed and unclassified subtype. This study highlights the heterogeneity within TNBCs. Higher frequencies of PIK3CA mutations were noted in LAR subtypes and unclassified type, comparable to their incidence reported in literature in ER-positive tumors. The mutation status can be used as potential biomarker for PI3K inhibitors in TNBC subgroups.

Classification of triple-negative breast cancers through a Boolean network model of the epithelial-mesenchymal transition.

Predicting the metastasis risk in patients with a primary breast cancer tumor is of fundamental importance to decide the best therapeutic strategy in the framework of personalized medicine. Here, we present ARIADNE, a general algorithmic strategy to assess the risk of metastasis from transcriptomic data of patients with triple-negative breast cancer, a subtype of breast cancer with poorer prognosis with respect to the other subtypes. ARIADNE identifies hybrid epithelial/mesenchymal phenotypes by mapping gene expression data into the states of a Boolean network model of the epithelial-mesenchymal pathway. Using this mapping, it is possible to stratify patients according to their prognosis, as we show by validating the strategy with three independent cohorts of triple-negative breast cancer patients. Our strategy provides a prognostic tool that could be applied to other biologically relevant pathways, in order to estimate the metastatic risk for other breast cancer subtypes or other tumor types. A record of this paper's transparent peer review process is included in the supplemental information.