The 41-gene classifier TRAR predicts response of HER2 positive breast cancer patients in the NeoALTTO study

Dual HER2 inhibition: mechanisms of synergy, patient selection, and resistance

HER2-targeted therapies for patients with HER2+ breast cancer are rapidly evolving, offering a range of more complex and personalized treatment options. Currently, an array of anti-HER2 monoclonal antibodies, tyrosine kinase inhibitors and antibody-drug conjugates are administered, sometimes alongside chemotherapy or endocrine therapy, both in curative and palliative contexts. However, the heterogeneous nature of HER2+ breast cancer demands a deeper understanding of disease biology and its role in responsiveness to novel HER2-targeted agents, as well as non-HER2-targeted therapies, in order to optimize patient outcomes. In this Review, we revisit the mechanisms of action of HER2-targeted agents, examine the evidence supporting the use of dual HER2 blockade in patients with HER2-amplified tumours, and explore the role of biomarkers in guiding future treatment strategies. We also discuss potential implications for the future treatment of patients with HER2+ breast cancer.

Identification of the shared gene MXD3 signatures and biological mechanism in patients with hip pain and prostate cancer

Prostate cancer (PRAD) is recognized as having a significant effect on systemic illnesses. This study examined possible immune cells, metabolic pathways, and genes that may explain the interaction between PRAD and hip pain. We used information retrieved from the Cancer Genome Atlas and the Gene Expression Omnibus databases. To find common genes, we utilized differential expression analysis and weighted gene co-expression network analysis. The genes that were shared were subjected to pathway enrichment studies using Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes. Additionally, hub genes were analyzed using LASSO regression, and a receiver operating characteristic curve was generated based on the screening outcomes. The genes for the nodes were chosen in a protein-protein interaction network that was built. Single-sample gene-set enrichment analysis was performed to identify the differentially expressed genes. Immunohistochemistry staining confirmed hub gene expression, and single-sample gene-set enrichment analysis assessed immune cell infiltration. We concluded by comparing MAX dimerization protein 3 (MXD3) and MAX interactor 1 (MXI1) expression in tumor tissues using Uniform Manifold Approximation and Projection and violin plots in the Tumor lmmune Single-cell Hub database. After analyzing the intersection of the differentially expressed genes and weighted gene co-expression network analysis-significant module genes, we determined that MXD3 was the best shared diagnostic biomarker for PRAD and hip pain. One potential predictor of PRAD development was the MXI1 node gene, which was found in the protein-protein interaction network. The analyses revealed that MXD3 had a relatively positive correlation with neutrophil and T-helper cell infiltration levels, whereas MXI1 had a negative correlation with mast and Tgd cell levels. Tumors had lower levels of MXI1 expression and higher levels of MXD3 expression compared to normal tissues. Endothelial cells, induced pluripotent stem cells, and smooth muscle cells were all found to express MXI1. This is the first study to investigate the close genetic link between hip pain and PRAD using bioinformatics technologies. The 2 most significant genes involved in crosstalk between PRAD and hip pain were MXD3 and MXI1. The immunological responses triggered by T cells, mast cells, and neutrophils may be crucial in the relationship between PRAD and hip pain.

Application of triple evaluation method in predicting the efficacy of neoadjuvant therapy for breast cancer

OBJECTIVE

To analyze the factors related to the efficacy of neoadjuvant therapy for breast cancer and find appropriate evaluation methods for evaluating the efficacy of neoadjuvant therapy METHODS: A total of 143 patients with breast cancer treated by neoadjuvant chemotherapy at Baotou Cancer Hospital were retrospectively analyzed. The chemotherapy regimen was mainly paclitaxel combined with carboplatin for 1 week, docetaxel combined with carboplatin for 3 weeks, and was replaced with epirubicin combined with cyclophosphamide after evaluation of disease progression. All HER2-positive patients were treated with simultaneous targeted therapy, including trastuzumab single-target therapy and trastuzumab combined with pertuzumab double-target therapy. Combined with physical examination, color Doppler ultrasound, and magnetic resonance imaging (MRI), a systematic evaluation system was initially established-the "triple evaluation method." A baseline evaluation was conducted before treatment. The efficacy was evaluated by physical examination and color Doppler every cycle, and the efficacy was evaluated by physical examination, color Doppler, and MRI every two cycles.

RESULTS

The increase in ultrasonic blood flow after treatment could affect the efficacy of monitoring. The presence of two preoperative time-signal intensity curves is a therapeutically effective protective factor for inflow. The triple evaluation determined by physical examination, color Doppler ultrasound, and MRI in determining clinical efficacy is consistent with the effectiveness of the pathological gold standard.

CONCLUSION

The therapeutic effect of neoadjuvant therapy can be better evaluated by combining clinical physical examination, color ultrasound, and nuclear magnetic resonance evaluation. The three methods complement each other to avoid the insufficient evaluation of a single method, which is convenient for most prefecty-level hospitals. Additionally, this method is simple, feasible, and suitable for promotion.

Predictive Role of CD36 Expression in HER2-Positive Breast Cancer Patients Receiving Neoadjuvant Trastuzumab

BACKGROUND

Despite huge efforts to identify biomarkers associated with long-term clinical outcomes in patients with early-stage HER2-positive breast cancer (HER2+ BC) treated with (neo)adjuvant anti-HER2 therapy, no reliable predictors have been identified so far. Fatty acid uptake, a process mediated by the transmembrane transporter CD36, has recently emerged as a potential determinant of resistance to anti-HER2 treatments in preclinical HER2+ BC models.

METHODS

Here, we investigated the association between baseline intratumor CD36 gene expression and event-free survival in 180 patients enrolled in the phase III trial Neoadjuvant Lapatinib and/or Trastuzumab Treatment Optimization (NeoALTTO), which randomly assigned stage II-III HER2+ BC patients to receive neoadjuvant lapatinib, trastuzumab, or lapatinib-trastuzumab in combination with chemotherapy. To this aim, we selected NeoALTTO trial patients for whom pretreatment whole transcriptomic data were available. The main study results were validated in an independent cohort of patients enrolled in the neoadjuvant phase II trial NeoSphere.

RESULTS

In 180 NeoALTTO patients, high intratumor CD36 expression was independently associated with worse event-free survival in patients treated with trastuzumab-based therapy (hazard ratio [HR] = 1.72, 95% confidence interval [CI] = 1.20 to 2.46), but not with lapatinib-based (HR = 1.02, 95% CI = 0.68 to 1.53) or trastuzumab-lapatinib-based (HR = 1.08, 95% CI = 0.60 to 1.94) therapy. Among 331 NeoSphere patients evaluated, high CD36 expression was independently associated with worse patient disease-free survival in both the whole study cohort (HR = 1.197, 95% CI = 1.002 to 1.428) and patients receiving trastuzumab-based neoadjuvant therapy (HR = 1.282, 95% CI = 1.049 to 1.568).

CONCLUSIONS

High CD36 expression predicts worse clinical outcomes in early-stage HER2+ BC treated with trastuzumab-based neoadjuvant therapy.

The TRAR gene classifier to predict response to neoadjuvant therapy in HER2-positive and ER-positive breast cancer patients: an explorative analysis from the NeoSphere trial

As most erb-b2 receptor tyrosine kinase 2 (HER2)-positive breast cancer (BC) patients currently receive dual HER2-targeting added to neoadjuvant chemotherapy, improved methods for identifying individual response, and assisting postsurgical salvage therapy, are needed. Herein, we evaluated the 41-gene classifier trastuzumab advantage risk model (TRAR) as a predictive marker for patients enrolled in the NeoSphere trial. TRAR scores were computed from RNA of 350 pre- and 166 post-treatment tumor specimens. Overall, TRAR score was significantly associated with pathological complete response (pCR) rate independently of other predictive clinico-pathological variables. Separate analyses according to estrogen receptor (ER) status showed a significant association between TRAR score and pCR in ER-positive specimens but not in ER-negative counterparts. Among ER-positive BC patients not achieving a pCR, those with TRAR-low scores in surgical specimens showed a trend for lower distant event-free survival. In conclusion, in HER2-positive/ER-positive BC, TRAR is an independent predictor of pCR and represents a promising tool to select patients responsive to anti-HER2-based neoadjuvant therapy and to assist treatment escalation and de-escalation strategies in this setting.

Integrated Molecular and Immune Phenotype of HER2-Positive Breast Cancer and Response to Neoadjuvant Therapy: A NeoALTTO Exploratory Analysis

BACKGROUND

Little is known about the efficacy of HER2-targeted therapy in patients with breast cancer showing different HER2-pathway dependence and immune phenotypes. Herein, we report a NeoALTTO exploratory analysis evaluating the clinical value of 22 types of tumor-infiltrating immune cells by CIBERSORT and 5 immune-related metagenes in the overall patient population, and in subgroups defined by the TRAR classifier as HER2-addicted (TRAR-low) or not (TRAR-high).

METHODS

Association of baseline TRAR, immune-related metagenes, and CIBERSORT data with pathologic complete response (pCR) and event-free survival (EFS) were assessed using logistic and Cox regression models. Corrections for multiple testing were performed by the Bonferroni method.

RESULTS

A total of 226 patients were analyzed: 80 (35%) achieved a pCR, and 64 (28%) experienced a relapse with a median follow-up of 6.7 (interquartile range 6.1-6.8) years; 108 cases were classified as TRAR-low, and 118 TRAR-high. Overall, γδ T-cell fraction [OR = 2.69; 95% confidence interval (CI), 1.40-5.18], and no immune-related metagenes were predictive of pCR. Notably, lymphocyte-specific kinase (LCK) predicted pCR to combination (OR = 2.53; 95% CI, 1.12-5.69), but not to single-agent trastuzumab or lapatinib [OR = 0.74; 95% CI, 0.45-1.22 (P _interaction = 0.01)]. Integrating LCK with γδ T cells in a multivariate model added to the discriminatory capability of clinical and molecular variables with a shift in AUC from 0.80 (95% CI, 0.74-0.86) to 0.83 (95% CI, 0.78-0.89). In TRAR-low cases, activated mast cells, IFN and MHCII were reduced, and STAT1, HCK1, and γδ T cells were associated with pCR. STAT1 was broadly associated with improved EFS regardless of pCR, and nodal status in overall (HR = 0.68; 95% CI, 0.49-0.94) and in TRAR-low cases (HR = 0.50; 95% CI, 0.30-0.86).

CONCLUSIONS

Immuno-phenotyping holds the promise to complement current predictive models in HER2-positive breast cancer and to assist in new therapeutic development.

A 10-Gene Signature for Predicting the Response to Neoadjuvant Trastuzumab Therapy in HER2-Positive Breast Cancer

BACKGROUND AND PURPOSE

Dual-target therapy may increase the incidence of adverse events and cause economic burden to patients with human epidermal growth factor receptor 2 (HER2)-positive breast cancer. It is necessary to identify the patients who could benefit greatly from a single-target neoadjuvant therapy in order to avoid overtreatment of patients.

PATIENTS AND METHODS

The baseline transcriptome data and clinical characteristics of patients with HER2-positive breast cancer who received neoadjuvant trastuzumab therapy were obtained from the Gene Expression Omnibus database. Least absolute shrinkage and selection operator (LASSO) regression analyses were used to construct the predictive model for pathologic complete response (pCR).

RESULTS

A 10-gene signature model for predicting pCR rate after neoadjuvant trastuzumab therapy was constructed by LASSO regression. The areas under the receiver operating characteristics (ROC) curves in the training set and validation set were 0.896 (95% confidence interval [CI], 0.8165-0.9758) and 0.775 (95% CI, 0.5402-1), respectively. The result of logistic regression analysis showed that the risk score calculated by the 10-gene signature model was a potential predictor for pCR. Among the 10-gene signature, TFAP2B, SUSD2, AQP3, MUCL1, and ANKRD30A were found to be predictors for worse relapse-free survival (RFS) in patients with HER2-positive breast cancer, whereas MGP, YIF1B, ANKRD36BP2, and FBXO6 were found to be predictors for favorable RFS.

CONCLUSION

A novel 10-gene signature that could predict the response of neoadjuvant anti-HER2 therapy in patients with HER2-positive breast cancer was developed, and the risk score of the 10-gene signature could be calculated to guide the selection of anti-HER2 therapy regimens.

NSABP B-41, a Randomized Neoadjuvant Trial: Genes and Signatures Associated with Pathologic Complete Response

PURPOSE

In NSABP B-41, pathologic complete response (pCR) was associated with prolonged survival among women with HER2-positive operable breast cancer treated with neoadjuvant chemotherapy and lapatinib, trastuzumab, or the combination. We used a large human breast cancer gene expression panel to select candidate prognostic biomarkers for pCR among women treated with trastuzumab in NSABP B-41.

PATIENTS AND METHODS

Eligible patients had a baseline preadjuvant treatment core biopsy sample, known pCR status, and no withdrawal of consent. We analyzed extracted RNA using the human nCounter Breast Cancer 360 gene expression panel. Gene counts were normalized to housekeeping genes and transformed into logarithmic scale with base 2. To screen for candidate genes and metagene signatures prognostic of pCR, we used univariate logistic regression. Variable selection was done by multivariable logistic regression with lasso regularization.

RESULTS

Analyses of data from 130 patients revealed that a composite of gene expression from 19 genes and one gene signature appeared to predict pCR in women with HER2-positive early-stage breast cancer undergoing neoadjuvant chemotherapy with trastuzumab-containing regimens. The identified genes are involved in important pathways such as epithelial-mesenchymal transition, adhesion and migration, estrogen receptor signaling, DNA damage and repair, apoptosis, and proliferation. The AUC from a 10-fold cross-validation on predicting pCR, with these 20 genomic markers in a logistic regression model, was 0.73.

CONCLUSIONS

The expression level of ERBB2, ESR1, and a few other genomic markers was highly predictive of pCR after trastuzumab-containing regimens. These findings need to be validated and calibrated in future studies.

Resistance to Neoadjuvant Treatment in Breast Cancer: Clinicopathological and Molecular Predictors

Neoadjuvant Chemotherapy (NAC) in Breast Cancer (BC) has proved useful for the reduction in tumor burden prior to surgery, allowing for a more extensive breast preservation and the eradication of subjacent micrometastases. However, the impact on prognosis is highly dependent on the establishment of Pathological Complete Response (pCR), in particular for Triple Negative (TN) and Hormonal Receptor negative/Human Epidermal growth factor Receptor 2 positive (HR-/HER2+) subtypes. Several pCR predictors, such as PAM50, Integrative Cluster (IntClust), mutations in PI3KCA, or the Trastuzumab Risk model (TRAR), are useful molecular tools for estimating response to treatment and are prognostic. Major evolution events during BC NAC that feature the Residual Disease (RD) are the loss of HR and HER2, which are prognostic of bad outcome, and stemness and immune depletion-related gene expression aberrations. This dynamic nature of the determinants of response to BC NAC, together with the extensive heterogeneity of BC, raises the need to discern the individual and subtype-specific determinants of resistance. Moreover, refining the current approaches for a comprehensive monitoring of tumor evolution during treatment, RD, and eventual recurrences is essential for identifying new actionable alterations and the integral best management of the disease.

Breast Cancer: A Molecularly Heterogenous Disease Needing Subtype-Specific Treatments

Breast cancer is the most commonly occurring cancer in women. There were over two-million new cases in world in 2018. It is the second leading cause of death from cancer in western countries. At the molecular level, breast cancer is a heterogeneous disease, which is characterized by high genomic instability evidenced by somatic gene mutations, copy number alterations, and chromosome structural rearrangements. The genomic instability is caused by defects in DNA damage repair, transcription, DNA replication, telomere maintenance and mitotic chromosome segregation. According to molecular features, breast cancers are subdivided in subtypes, according to activation of hormone receptors (estrogen receptor and progesterone receptor), of human epidermal growth factors receptor 2 (HER2), and or BRCA mutations. In-depth analyses of the molecular features of primary and metastatic breast cancer have shown the great heterogeneity of genetic alterations and their clonal evolution during disease development. These studies have contributed to identify a repertoire of numerous disease-causing genes that are altered through different mutational processes. While early-stage breast cancer is a curable disease in about 70% of patients, advanced breast cancer is largely incurable. However, molecular studies have contributed to develop new therapeutic approaches targeting HER2, CDK4/6, PI3K, or involving poly(ADP-ribose) polymerase inhibitors for BRCA mutation carriers and immunotherapy.

Extracellular Matrix Features Discriminate Aggressive HER2-Positive Breast Cancer Patients Who Benefit from Trastuzumab Treatment

We previously identified an extracellular matrix (ECM) gene expression pattern in breast cancer (BC), called ECM3, characterized by a high expression of genes encoding structural ECM proteins. Since ECM is reportedly implicated in response to therapy of BCs, the aim of this work is to investigate the prognostic and predictive value of ECM3 molecular classification in HER2-positive BCs. ECM3 resulted in a robust cluster that identified a subset of 25-37% of HER2-positive tumors with molecular aggressive features. ECM3 was significantly associated with worse prognosis in two datasets of HER2-positive BCs untreated with adjuvant therapy. Analyses carried out on two of our cohorts of patients treated or not with adjuvant trastuzumab showed association of ECM3 with worse prognosis only in patients not treated with trastuzumab. Moreover, investigating a dataset that includes gene profile data of tumors treated with neoadjuvant trastuzumab plus chemotherapy or chemotherapy alone, ECM3 was associated with increased pathological complete response if treated with trastuzumab. In the in vivo experiments, increased diffusion and trastuzumab activity were found in tumors derived from injection of HER2-positive cells with Matrigel that creates an ECM-rich tumor environment. Taken together, these results indicate that HER2-positive BCs classified as ECM3 have an aggressive phenotype but they are sensitive to trastuzumab treatment.