Accurate Prediction and Validation of Response to Endocrine Therapy in Breast Cancer

Inflammatory response signature score model for predicting immunotherapy response and pan-cancer prognosis

Background

Inflammatory responses influence the outcome of immunotherapy and tumorigenesis by modulating host immunity. However, systematic inflammatory response assessment models for predicting cancer immunotherapy (CIT) responses and survival across human cancers remain unexplored. Here, we investigated an inflammatory response score model to predict CIT responses and patient survival in a pan-cancer analysis.

Methods

We retrieved 12 CIT response gene expression datasets from the Gene Expression Omnibus database (GSE78220, GSE19423, GSE100797, GSE126044, GSE35640, GSE67501, GSE115821 and GSE168204), Tumor Immune Dysfunction and Exclusion database (PRJEB23709, PRJEB25780 and phs000452.v2.p1), European Genome-phenome Archive database (EGAD00001005738), and IMvigor210 cohort. The tumor samples from six cancers types: metastatic urothelial cancer, metastatic melanoma, gastric cancer, primary bladder cancer, renal cell carcinoma, and non-small cell lung cancer.We further established a binary classification model to predict CIT responses using the least absolute shrinkage and selection operator (LASSO) computational algorithm.

Findings

The model had high predictive accuracy in both the training and validation cohorts. During sub-group analysis, area under the curve (AUC) values of 0.82, 0.80, 0.71, 0.7, 0.67, and 0.64 were obtained for the non-small cell lung cancer, gastric cancer, metastatic urothelial cancer, primary bladder cancer, metastatic melanoma, and renal cell carcinoma cohorts, respectively. CIT response rates were higher in the high-scoring training cohort subjects (51%) than the low-scoring subjects (27%). The five-year survival rates in the high- and low score groups of the training cohorts were 62% and 21%, respectively, while those of the validation cohorts were 54% and 22%, respectively (P < 0·001 in all cases). Inflammatory response signature score derived from on-treatment tumor specimens are highly predictive of response to CIT in patients with metastatic melanoma. A significant correlation was observed between the inflammatory response scores and tumor purity. Regardless of the tumor purity, patients in the low score group had a significantly poorer prognosis than those in the high score group. Immune cell infiltration analysis indicated that in the high score cohort, tumor-infiltrating lymphocytes were significantly enriched, particularly effector and natural killer cells. Inflammatory response scores were positively correlated with immune checkpoint genes, suggesting that immune checkpoint inhibitors may have benefited patients with high scores. Analysis of signature scores across different cancer types from The Cancer Genome Atlas revealed that the prognostic performance of inflammatory response scores for survival in patients who have not undergone immunotherapy can be affected by tumor purity. Interleukin 21 (IL21) had the highest weight in the inflammatory response model, suggesting its vital role in the prediction mode. Since the number of metastatic melanoma patients (n = 429) was relatively large among CIT cohorts, we further performed a co-culture experiment using a melanoma cell line and CD8 + T cell populations generated from peripheral blood monocytes. The results showed that IL21 therapy combined with anti-PD1 (programmed cell death 1) antibodies (trepril monoclonal antibodies) significantly enhanced the cytotoxic activity of CD8 + T cells against the melanoma cell line.

Conclusion

In this study, we developed an inflammatory response gene signature model that predicts patient survival and immunotherapy response in multiple malignancies. We further found that the predictive performance in the non-small cell lung cancer and gastric cancer group had the highest value among the six different malignancy subgroups. When compared with existing signatures, the inflammatory response gene signature scores for on-treatment samples were more robust predictors of the response to CIT in metastatic melanoma.

Clinical efficacy of combined goserelin and anastrozole in neoadjuvant endocrine therapy for premenopausal women with hormone receptor-positive breast cancer

OBJECTIVE

The objective of this study is to assess the efficacy and safety of combining goserelin with anastrozole in neoadjuvant endocrine therapy (NET) for patients diagnosed with premenopausal breast cancer.

METHODS

A retrospective analysis was conducted on the clinicopathological data of 34 patients diagnosed with premenopausal breast cancer who underwent NET in the Department of Breast Surgery at Baotou Cancer Hospital between March 2016 and December 2019. Additionally, the feasibility of using goserelin combined with anastrozole for premenopausal endocrine therapy was assessed.

RESULTS

The duration of NET ranged from 6 to 72 months, with a mean of 22.5 months and a median of 18 months. In patients with progressive disease, endocrine therapy was assessed over a period of 6 to 18 months, with a mean of 13.1 months and a median of 13 months. Among the 28 patients assessed, 12 (42.86%) were found to have stable disease, subsequently receiving chemotherapy. Of these, seven patients demonstrated good compliance, and 5 achieved a pathological complete response. Including the 2 patients who responded favorably to NET alone, a total of 7 patients attained a pathological complete response. Additionally, 16 patients achieved complete cell cycle arrest following treatment. A significant correlation was observed between the clinical efficacy assessment and the pathological assessment of NET (P < 0.05).

CONCLUSION

Although NET was safe for patients diagnosed with premenopausal breast cancer, it should not be considered in isolation from chemotherapy. Transitioning to chemotherapy in a timely manner can significantly enhance treatment outcomes. The duration of NET should be guided by clinical assessment rather than being constrained by a predetermined time frame.

On-treatment biopsies to predict response to neoadjuvant chemotherapy for breast cancer

BACKGROUND

Patients with pathologic complete response (pCR) to neoadjuvant chemotherapy for invasive breast cancer (BC) have better outcomes, potentially warranting less extensive surgical and systemic treatments. Early prediction of treatment response could aid in adapting therapies.

METHODS

On-treatment biopsies from 297 patients with invasive BC in three randomized, prospective neoadjuvant trials were assessed (GeparQuattro, GeparQuinto, GeparSixto). BC quantity, tumor-infiltrating lymphocytes (TILs), and the proliferation marker Ki-67 were compared to pre-treatment samples. The study investigated the correlation between residual cancer, changes in Ki-67 and TILs, and their impact on pathologic complete response (pCR) and disease-free survival (DFS).

RESULTS

Among the 297 samples, 138 (46%) were hormone receptor-positive (HR+)/human epidermal growth factor 2-negative (HER2-), 87 (29%) were triple-negative (TNBC), and 72 (24%) were HER2+. Invasive tumor cells were found in 70% of on-treatment biopsies, with varying rates across subtypes (HR+/HER2-: 84%, TNBC: 62%, HER2+: 51%; p < 0.001). Patients with residual tumor on-treatment had an 8% pCR rate post-treatment (HR+/HER2-: 3%, TNBC: 19%, HER2+: 11%), while those without any invasive tumor had a 50% pCR rate (HR+/HER2-: 27%; TNBC: 48%, HER2+: 66%). Sensitivity for predicting residual disease was 0.81, with positive and negative predictive values of 0.92 and 0.50, respectively. Increasing TILs from baseline to on-treatment biopsy (if residual tumor was present) were linked to higher pCR likelihood in the overall cohort (OR 1.034, 95% CI 1.013-1.056 per % increase; p = 0.001) and with a longer DFS in TNBC (HR 0.980, 95% CI 0.963-0.997 per % increase; p = 0.026). Persisting or increased Ki-67 was associated with with lower pCR probability in the overall cohort (OR 0.957, 95% CI 0.928-0.986; p = 0.004) and shorter DFS in TNBC (HR 1.023, 95% CI 1.001-1.047; p = 0.04).

CONCLUSION

On-treatment biopsies can predict patients unlikely to achieve pCR post-therapy. This could facilitate therapy adjustments for TNBC or HER2 + BC. They also might offer insights into therapy resistance mechanisms. Future research should explore whether standardized or expanded sampling enhances the accuracy of on-treatment biopsy procedures. Trial registration GeparQuattro (EudraCT 2005-001546-17), GeparQuinto (EudraCT 2006-005834-19) and GeparSixto (EudraCT 2011-000553-23).

BCAR4 Expression as a Predictive Biomarker for Endocrine Therapy Resistance in Breast Cancer

BACKGROUND

Breast cancer, particularly the estrogen receptor positive (ER+) subtype, remains a leading cause of cancer-related death among women. Endocrine therapy is the most effective treatment for ER+ breast cancer; however, the development of resistance presents a significant challenge. This study explored the role of the breast cancer antiestrogen resistance 4 (BCAR4) gene as a potential driver of resistance and a pivotal biomarker in breast cancer.

PATIENTS AND METHODS

The researchers undertook a comprehensive analysis of 1743 patients spanning 6 independent cohorts. They examined the association of BCAR4 expression with patient outcomes across all breast cancer types and the PAM50 molecular subtypes. The relationship between elevated BCAR4 expression and resistance to endocrine therapy including AIs, the prevailing standard-of-care for endocrine therapy, was also investigated.

RESULTS

This meta-analysis corroborated the link between BCAR4 expression and adverse outcomes as well as resistance to endocrine therapy in breast cancer. Notably, BCAR4 expression is clinically significant in luminal A and B subtypes. Additionally, an association between BCAR4 expression and resistance to AI treatment was discerned.

CONCLUSION

This study expands on previous findings by demonstrating that BCAR4 expression is associated with resistance to newer therapies. The identification of patients with intrinsic resistance to hormone therapy is crucial to avoid ineffective treatment strategies. These findings contribute to our understanding of endocrine therapy resistance in breast cancer and could potentially guide the development of more effective treatment strategies.

Estrogen regulation and functional role of FGFR4 in estrogen receptor positive breast cancer

Background

Resistance to endocrine therapy is a major challenge of managing estrogen receptor positive (ER+) breast cancer. We previously reported frequent overexpression of FGFR4 in endocrine resistant cell lines and breast cancers that recurred and metastasized following endocrine therapy, suggesting FGFR4 as a potential driver of endocrine resistance. In this study, we investigated the role of FGFR4 in mediating endocrine resistance and explored the therapeutic potential of targeting FGFR4 in advanced breast cancer.

Methods

A gene expression signature of FGFR4 activity was examined in ER+ breast cancer pre- and post-neoadjuvant endocrine therapy and the association between FGFR4 expression and patient survival was examined. A correlation analysis was used to uncover potential regulators of FGFR4 overexpression. To investigate if FGFR4 is necessary to drive endocrine resistance, we tested response to FGFR4 inhibition in long term estrogen deprived (LTED) cells and their paired parental cells. Doxycycline inducible FGFR4 overexpression and knockdown cell models were generated to examine if FGFR4 was sufficient to confer endocrine resistance. Finally, we examined response to FGFR4 monotherapy or combination therapy with fulvestrant in breast cancer cell lines to explore the potential of FGFR4 targeted therapy for advanced breast cancer and assessed the importance of PAM50 subtype in response to FGFR4 inhibition.

Results

A FGFR4 activity gene signature was significantly upregulated post neoadjuvant aromatase inhibitor treatment, and high FGFR4 expression predicted poorer survival in patients with ER+ breast cancer. Gene expression association analysis using TCGA, METABRIC and SCAN-B datasets uncovered ER as the most significant gene negatively correlated with FGFR4 expression. ER negatively regulates FGFR4 expression at both the mRNA and protein level across multiple ER+ breast cancer cell lines. Despite robust overexpression of FGFR4, LTED cells did not show enhanced responses to FGFR4 inhibition compared to parental cells. Similarly, FGFR4 overexpression, knockdown or hotspot mutations did not significantly alter response to endocrine treatment in ER+ cell lines, nor did FGFR4 and fulvestrant combination treatment show synergistic effects. The HER2-like subtype of breast cancer showed elevated expression of FGFR4 and an increased response to FGFR4 inhibition relative to other breast cancer subtypes.

Conclusions

Despite ER-mediated upregulation of FGFR4 post endocrine therapy, our study does not support a general role of FGFR4 in mediating endocrine resistance in ER+ breast cancer. Our data suggests that specific genomic backgrounds such as HER2 expression may be required for FGFR4 function in breast cancer and should be further explored.

B3GALT6 promotes dormant breast cancer cell survival and recurrence by enabling heparan sulfate-mediated FGF signaling

Breast cancer mortality results from incurable recurrences thought to be seeded by dormant, therapy-refractory residual tumor cells (RTCs). Understanding the mechanisms enabling RTC survival is therefore essential for improving patient outcomes. Here, we derive a dormancy-associated RTC signature that mirrors the transcriptional response to neoadjuvant therapy in patients and is enriched for extracellular matrix-related pathways. In vivo CRISPR-Cas9 screening of dormancy-associated candidate genes identifies the galactosyltransferase B3GALT6 as a functional regulator of RTC fitness. B3GALT6 is required for glycosaminoglycan (GAG) linkage to proteins to generate proteoglycans, and its germline loss of function in patients causes skeletal dysplasias. We find that B3GALT6-mediated biosynthesis of heparan sulfate GAGs predicts poor patient outcomes and promotes tumor recurrence by enhancing dormant RTC survival in multiple contexts, and does so via a B3GALT6-heparan sulfate/HS6ST1-heparan 6-O-sulfation/FGF1-FGFR2 signaling axis. These findings implicate B3GALT6 in cancer and nominate FGFR2 inhibition as a promising approach to eradicate dormant RTCs and prevent recurrence.

The Role of Nodes and Nodal Assessment in Diagnosis, Treatment and Prediction in ER+, Node-Positive Breast Cancer

The majority of breast cancers are oestrogen receptor-positive (ER+). In ER+ cancers, oestrogen acts as a disease driver, so these tumours are likely to be susceptible to endocrine therapy (ET). ET works by blocking the hormone's synthesis or effect. A significant number of patients diagnosed with breast cancer will have the spread of tumour cells into regional lymph nodes either at the time of diagnosis, or as a recurrence some years later. Patients with node-positive disease have a poorer prognosis and can respond less well to ET. The nodal metastases may be genomically similar or, as is becoming more evident, may differ from the primary tumour. However, nodal metastatic disease is often not assessed, and treatment decisions are almost always based on biomarkers evaluated in the primary tumour. This review will summarise the evidence in the field on ER+, node-positive breast cancer, including diagnosis, treatment, prognosis and predictive tools.

A tumor-associated endothelial signature score model in immunotherapy and prognosis across pan-cancers

Background: The tumor-associated endothelial cell (TAE) component plays a vital role in tumor immunity. However, systematic tumor-associated endothelial-related gene assessment models for predicting cancer immunotherapy (CIT) responses and survival across human cancers have not been explored. Herein, we investigated a TAE gene risk model to predict CIT responses and patient survival in a pan-cancer analysis. Methods: We analyzed publicly available datasets of tumor samples with gene expression and clinical information, including gastric cancer, metastatic urothelial cancer, metastatic melanoma, non-small cell lung cancer, primary bladder cancer, and renal cell carcinoma. We further established a binary classification model to predict CIT responses using the least absolute shrinkage and selection operator (LASSO) computational algorithm. Results: The model demonstrated a high predictive accuracy in both training and validation cohorts. The response rate of the high score group to immunotherapy in the training cohort was significantly higher than that of the low score group, with CIT response rates of 51% and 27%, respectively. The survival analysis showed that the prognosis of the high score group was significantly better than that of the low score group (all p < 0·001). Tumor-associated endothelial gene signature scores positively correlated with immune checkpoint genes, suggesting that immune checkpoint inhibitors may benefit patients in the high score group. The analysis of TAE scores across 33 human cancers revealed that the TAE model could reflect immune cell infiltration and predict the survival of cancer patients. Conclusion: The TAE signature model could represent a CIT response prediction model with a prognostic value in multiple cancer types.

Molecular profiling of aromatase inhibitor sensitive and resistant ER+HER2- postmenopausal breast cancers

Aromatase inhibitors (AIs) reduce recurrences and mortality in postmenopausal patients with oestrogen receptor positive (ER+) breast cancer (BC), but >20% of patients will eventually relapse. Given the limited understanding of intrinsic resistance in these tumours, here we conduct a large-scale molecular analysis to identify features that impact on the response of ER + HER2- BC to AI. We compare the 15% of poorest responders (PRs, n = 177) as measured by proportional Ki67 changes after 2 weeks of neoadjuvant AI to good responders (GRs, n = 190) selected from the top 50% responders in the POETIC trial and matched for baseline Ki67 categories. In this work, low ESR1 levels are associated with poor response, high proliferation, high expression of growth factor pathways and non-luminal subtypes. PRs having high ESR1 expression have similar proportions of luminal subtypes to GRs but lower plasma estradiol levels, lower expression of estrogen response genes, higher levels of tumor infiltrating lymphocytes and immune markers, and more TP53 mutations.

Neoadjuvant endocrine therapy in postmenopausal women with HR+/HER2- breast cancer

INTRODUCTION

While endocrine therapy is the standard-of-care adjuvant treatment for hormone receptor-positive (HR+) breast cancers, there is also extensive evidence for the role of pre-operative (or neoadjuvant) endocrine therapy (NET) in HR+ postmenopausal women.

AREAS COVERED

We conducted a thorough review of the published literature, to summarize the evidence to date, including studies of how NET compares to neoadjuvant chemotherapy, which NET agents are preferable, and the optimal duration of NET. We describe the importance of on-treatment assessment of response, the different predictors available (including Ki67, PEPI score, and molecular signatures) and the research opportunities the pre-operative setting offers. We also summarize recent combination trials and discuss how the COVID-19 pandemic led to increases in NET use for safe management of cases with deferred surgery and adjuvant treatments.

EXPERT OPINION

NET represents a safe and effective tool for the management of postmenopausal women with HR+/HER2- breast cancer, enabling disease downstaging and a wider range of surgical options. Aromatase inhibitors are the preferred NET, with evidence suggesting that longer regimens might yield optimal results. However, NET remains currently underutilised in many territories and institutions. Further validation of predictors for treatment response and benefit is needed to help standardise and fully exploit the potential of NET in the clinic.

Comprehensive analysis of glycoprotein VI-mediated platelet activation signaling pathway for predicting pan-cancer survival and response to anti-PD-1 immunotherapy

Platelets play a vital role in cancer and immunity. However, few comprehensive studies have been conducted on the role of platelet-related signaling pathways in various cancers and their responses to immune checkpoint blockade (ICB) therapy. In the present study, we focused on the glycoprotein VI-mediated platelet activation (GMPA) signaling pathway and comprehensively evaluated its roles in 19 types of cancers listed in The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO). Cox regression and meta-analyses showed that for all 19 types of cancers, patients with high GMPA scores tended to have a good prognosis. Furthermore, the GMPA signature score could serve as an independent prognostic factor for patients with skin cutaneous melanoma (SKCM). The GMPA signature was linked to tumor immunity in all 19 types of cancers, and was correlated with SKCM tumor histology. Compared to other signature scores, the GMPA signature scores for on-treatment samples were more robust predictors of the response to anti-PD-1 blockade in metastatic melanoma. Moreover, the GMPA signature scores were significantly negatively correlated with EMMPRIN (CD147) and positively correlated with CD40LG expression at the transcriptomic level in most cancer patient samples from the TCGA cohort and on-treatment samples from anti-PD1 therapy cohorts. The results of this study provide an important theoretical basis for the use of GMPA signatures, as well as GPVI-EMMPRIN and GPVI-CD40LG pathways, to predict the responses of cancer patients to various types of ICB therapy.

Functional Gene Expression Signatures from On-Treatment Tumor Specimens Predict Anti-PD1 Blockade Response in Metastatic Melanoma

Functional gene expression signatures (FGES) from pretreatment biopsy samples have been used to predict the responses of metastatic melanoma to immune checkpoint blockade (ICB) therapies. However, there are no predictive FGE signatures from patients receiving treatment. Here, using the Elastic Net Regression (ENLR) algorithm, we analyzed transcriptomic and matching clinical data from a dataset of patients with metastatic melanoma treated with ICB therapies and produced an FGE signature for pretreatment (FGES-PRE) and on-treatment (FGES-ON). Both the FGES-PRE and FGES-ON signatures are validated in three independent datasets of metastatic melanoma as the validation set, achieving area under the curve (AUC) values of 0.44-0.81 and 0.82-0.83, respectively. Then, we combined all test samples and obtained AUCs of 0.71 and 0.82 for the FGES-PRE and FGES-ON signatures, respectively. The FGES-ON signatures had a higher predictive value for prognosis than the FGES-PRE signatures. The FGES-PRE and FGES-ON signatures were divided into high- and low-risk scores using the signature score mean value. Patients with a high FGE signature score had better survival outcomes than those with low scores. Overall, we determined that the FGES-ON signature is an effective biomarker for metastatic melanoma patients receiving ICB therapy. This work would provide an important theoretical basis for applying FGE signatures derived from on-treatment tumor samples to predict patients' therapeutic response to ICB therapies.

Treatment strategies for hormone receptor-positive, human epidermal growth factor receptor 2-positive (HR+/HER2+) metastatic breast cancer: A review

Hormone receptor-positive HER2-positive (HR+/HER2+) metastatic breast cancer (MBC) is a unique subtype of breast cancer. Most current guidelines recommend that combination regimens based on anti-HER2 therapy should be used as first-line treatment for HER2+ MBC, irrespective of HR status. Endocrine therapy can be applied as maintenance therapy for patients who are intolerant to chemotherapy or post-chemotherapy. Increasing evidence suggests that complex molecular crosstalk between HR and HER2 pathways may affect the sensitivity to both HER2-targeted and endocrine therapy in patients with HR+/HER2+ breast cancer. Recent research and clinical trials have revealed that a combination of endocrine therapy and anti-HER2 approaches without chemotherapy provides along-term disease control for some patients, but the challenge lies in how to accurately identify the subsets of patients who can benefit from such a de-chemotherapy treatment strategy. In this review, we aim to summarize the results of preclinical and clinical studies in HR+/HER2+ MBC and discuss the possibility of sparing chemotherapy in this subgroup of patients.

Insights and Strategies of Melanoma Immunotherapy: Predictive Biomarkers of Response and Resistance and Strategies to Improve Response Rates

Despite the recent successes and durable responses with immune checkpoint inhibitors (ICI), many cancer patients, including those with melanoma, do not derive long-term benefits from ICI therapies. The lack of predictive biomarkers to stratify patients to targeted treatments has been the driver of primary treatment failure and represents an unmet medical need in melanoma and other cancers. Understanding genomic correlations with response and resistance to ICI will enhance cancer patients' benefits. Building on insights into interplay with the complex tumor microenvironment (TME), the ultimate goal should be assessing how the tumor 'instructs' the local immune system to create its privileged niche with a focus on genomic reprogramming within the TME. It is hypothesized that this genomic reprogramming determines the response to ICI. Furthermore, emerging genomic signatures of ICI response, including those related to neoantigens, antigen presentation, DNA repair, and oncogenic pathways, are gaining momentum. In addition, emerging data suggest a role for checkpoint regulators, T cell functionality, chromatin modifiers, and copy-number alterations in mediating the selective response to ICI. As such, efforts to contextualize genomic correlations with response into a more insightful understanding of tumor immune biology will help the development of novel biomarkers and therapeutic strategies to overcome ICI resistance.

Predictive biomarkers for personalized medicine in breast cancer

Breast cancer is one of the most frequent malignancies among women worldwide. Based on clinical and molecular features of breast tumors, patients are treated with chemotherapy, hormonal therapy and/or radiotherapy and more recently with immunotherapy or targeted therapy. These different therapeutic options have markedly improved patient outcomes. However, further improvement is needed to fight against resistance to treatment. In the rapidly growing area of research for personalized medicine, predictive biomarkers - which predict patient response to therapy - are essential tools to select the patients who are most likely to benefit from the treatment, with the aim to give the right therapy to the right patient and avoid unnecessary overtreatment. The search for predictive biomarkers is an active field of research that includes genomic, proteomic and/or machine learning approaches. In this review, we describe current strategies and innovative tools to identify, evaluate and validate new biomarkers. We also summarize current predictive biomarkers in breast cancer and discuss companion biomarkers of targeted therapy in the context of precision medicine.

Endocrine Therapy Response and 21-Gene Expression Assay for Therapy Guidance in HR+/HER2- Early Breast Cancer

PURPOSE

To our knowledge, WSG-ADAPT-HR+/HER2- (NCT01779206; n = 5,625 registered) is the first trial combining the 21-gene expression assay (recurrence score [RS]) and response to 3-week preoperative endocrine therapy (ET) to guide systemic therapy in early breast cancer.

MATERIALS AND METHODS

Baseline and postendocrine Ki67 (Ki67_post) were evaluated centrally. In the endocrine trial, all patients received exclusively ET: patients with pathologic regional lymph node status (pN) 0-1 (ie, 0-3 involved lymph nodes) entered control arm if RS ≤ 11 and experimental arm if RS12-25 with ET response (Ki67_post ≤ 10%). All other patients (including N0-1 RS12-25 without ET response) received dose-dense chemotherapy (CT) followed by ET in the CT trial. Primary end point of the endocrine trial was noninferiority of 5-year invasive disease-free survival (5y-iDFS) in experimental (v control) arm; secondary end points included distant DFS, overall survival, and translational research.

RESULTS

Intention-to-treat population comprised 2,290 patients (n = 1,422 experimental v n = 868 control): 26.3% versus 34.6% premenopausal and 27.4% versus 24.0% pN1. One-sided 95% lower confidence limit of the 5y-iDFS difference was -3.3%, establishing prespecified noninferiority (P = .05). 5y-iDFS was 92.6% (95% CI, 90.8 to 94.0) in experimental versus 93.9% (95% CI, 91.8 to 95.4) in control arm; 5-year distant DFS was 95.6% versus 96.3%, and 5-year overall survival 97.3% versus 98.0%, respectively. Differences were similar in age and nodal subgroups. In N0-1 RS12-25, outcome of ET responders (ET alone) was comparable with that of ET nonresponders (CT) for age > 50 years and superior for age ≤ 50 years. ET response was more likely with aromatase inhibitors (mostly postmenopausal) than with tamoxifen (mostly premenopausal): 78.1% versus 41.1% (P < .001). ET response was 78.8% in RS0-11, 62.2% in RS12-25, and 32.7% in RS > 25 (n = 4,203, P < .001).

CONCLUSION

WSG-ADAPT-HR+/HER2- demonstrates that guiding systemic treatment by both RS and ET response is feasible in clinical routine and spares CT in pre- and postmenopausal patients with ≤ 3 involved lymph nodes.

Circulating Exosomal microRNAs as Predictive Biomarkers of Neoadjuvant Chemotherapy Response in Breast Cancer

Background: Neoadjuvant chemotherapy (NACT) is an increasingly used approach for treatment of breast cancer. The pathological complete response (pCR) is considered a good predictor of disease-specific survival. This study investigated whether circulating exosomal microRNAs could predict pCR in breast cancer patients treated with NACT. Method: Plasma samples of 20 breast cancer patients treated with NACT were collected prior to and after the first cycle. RNA sequencing was used to determine microRNA profiling. The Cancer Genome Atlas (TCGA) was used to explore the expression patterns and survivability of the candidate miRNAs, and their potential targets based on the expression levels and copy number variation (CNV) data. Results: Three miRNAs before that NACT (miR-30b, miR-328 and miR-423) predicted pCR in all of the analyzed samples. Upregulation of miR-127 correlated with pCR in triple-negative breast cancer (TNBC). After the first NACT dose, pCR was predicted by exo-miR-141, while miR-34a, exo-miR182, and exo-miR-183 predicted non-pCR. A significant correlation between the candidate miRNAs and the overall survival, subtype, and metastasis in breast cancer, suggesting their potential role as predictive biomarkers of pCR. Conclusions: If the miRNAs identified in this study are validated in a large cohort of patients, they might serve as predictive non-invasive liquid biopsy biomarkers for monitoring pCR to NACT in breast cancer.

Neoadjuvant Endocrine Therapy in Clinical Practice: A Review

Importance

In clinical practice, neoadjuvant endocrine therapy (NET) is rarely used despite being an effective treatment modality able to downstage tumors and facilitate breast-conserving surgery.

Observations

Using data from studies conducted since 2000, we provide readers with a critical in-depth review on clinical aspects related to the application of NET in the treatment of hormone receptor (HR)-positive/ERBB2 (formerly HER2)-negative breast cancer. This includes an overview of patient-selection criteria, regimen choice, treatment duration, evaluation of response by imaging, interpretation of pathology after treatment, and surgical considerations. Areas of controversy include the use of gene-expression tests for patient selection, treatment of premenopausal women, surgical management of the axilla after NET, and adjuvant systemic therapy decision-making, including the use of chemotherapy.

Conclusions and Relevance

NET is an optimal treatment modality for a considerable proportion of postmenopausal women diagnosed with HR-positive tumors. The treatment landscape for HR-positive breast cancer is evolving, with novel agents and the growing use of gene expression profiling to define treatment selection. As such, it is likely that NET use will increase and the practical considerations outlined here will become more important.

Pathway signatures derived from on-treatment tumor specimens predict response to anti-PD1 blockade in metastatic melanoma

Both genomic and transcriptomic signatures have been developed to predict responses of metastatic melanoma to immune checkpoint blockade (ICB) therapies; however, most of these signatures are derived from pre-treatment biopsy samples. Here, we build pathway-based super signatures in pre-treatment (PASS-PRE) and on-treatment (PASS-ON) tumor specimens based on transcriptomic data and clinical information from a large dataset of metastatic melanoma treated with anti-PD1-based therapies as the training set. Both PASS-PRE and PASS-ON signatures are validated in three independent datasets of metastatic melanoma as the validation set, achieving area under the curve (AUC) values of 0.45-0.69 and 0.85-0.89, respectively. We also combine all test samples and obtain AUCs of 0.65 and 0.88 for PASS-PRE and PASS-ON signatures, respectively. When compared with existing signatures, the PASS-ON signature demonstrates more robust and superior predictive performance across all four datasets. Overall, we provide a framework for building pathway-based signatures that is highly and accurately predictive of response to anti-PD1 therapies based on on-treatment tumor specimens. This work would provide a rationale for applying pathway-based signatures derived from on-treatment tumor samples to predict patients' therapeutic response to ICB therapies.

Identification of candidate biomarkers correlated with poor prognosis of breast cancer based on bioinformatics analysis

Breast cancer (BC) is a malignancy with high incidence among women in the world. This study aims to screen key genes and potential prognostic biomarkers for BC using bioinformatics analysis. Total 58 normal tissues and 203 cancer tissues were collected from three Gene Expression Omnibus (GEO) gene expression profiles, and then the differential expressed genes (DEGs) were identified. Subsequently, the Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genome (KEGG) pathway were analyzed to investigate the biological function of DEGs. Additionally, hub genes were screened by constructing a protein–protein interaction (PPI) network. Then, we explored the prognostic value and molecular mechanism of these hub genes using Kaplan–Meier (KM) curve and Gene Set Enrichment Analysis (GSEA). As a result, 42 up-regulated and 82 down-regulated DEGs were screened out from GEO datasets. The DEGs were mainly related to cell cycles and cell proliferation by GO and KEGG pathway analysis. Furthermore, 12 hub genes (FN1, AURKA, CCNB1, BUB1B, PRC1, TPX2, NUSAP1, TOP2A, KIF20A, KIF2C, RRM2, ASPM) with a high degree were identified initially, among which, 11 hub genes were significantly correlated with the prognosis of BC patients based on the Kaplan–Meier-plotter. GSEA reviewed that these hub genes correlated with KEGG_CELL_CYCLE and HALLMARK_P53_PATHWAY. In conclusion, this study identified 11 key genes as BC potential prognosis biomarkers on the basis of integrated bioinformatics analysis. This finding will improve our knowledge of the BC progress and mechanisms.

Steroid Ligands, the Forgotten Triggers of Nuclear Receptor Action; Implications for Acquired Resistance to Endocrine Therapy

PURPOSE

There is strong epidemiologic evidence indicating that estrogens may not be the sole steroid drivers of breast cancer. We hypothesize that abundant adrenal androgenic steroid precursors, acting via the androgen receptor (AR), promote an endocrine-resistant breast cancer phenotype.

EXPERIMENTAL DESIGN

AR was evaluated in a primary breast cancer tissue microarray (n = 844). Androstenedione (4AD) levels were evaluated in serum samples (n = 42) from hormone receptor-positive, postmenopausal breast cancer. Levels of androgens, progesterone, and estradiol were quantified using LC/MS-MS in serum from age- and grade-matched recurrent and nonrecurrent patients (n = 6) before and after aromatase inhibitor (AI) therapy (>12 months). AR and estrogen receptor (ER) signaling pathway activities were analyzed in two independent AI-treated cohorts.

RESULTS

AR protein expression was associated with favorable progression-free survival in the total population (Wilcoxon, P < 0.001). Pretherapy serum samples from breast cancer patients showed decreasing levels of 4AD with age only in the nonrecurrent group (P < 0.05). LC/MS-MS analysis of an AI-sensitive and AI-resistant cohort demonstrated the ability to detect altered levels of steroids in serum of patients before and after AI therapy. Transcriptional analysis showed an increased ratio of AR:ER signaling pathway activities in patients failing AI therapy (t test P < 0.05); furthermore, 4AD mediated gene changes associated with acquired AI resistance.

CONCLUSIONS

This study highlights the importance of examining the therapeutic consequences of the steroid microenvironment and demonstrable receptor activation using indicative gene expression signatures.

The Signal Transducer IL6ST (gp130) as a Predictive and Prognostic Biomarker in Breast Cancer

Novel biomarkers are needed to continue to improve breast cancer clinical management and outcome. IL6-like cytokines, whose pleiotropic functions include roles in many hallmarks of malignancy, rely on the signal transducer IL6ST (gp130) for all their signalling. To date, 10 separate independent studies based on the analysis of clinical breast cancer samples have identified IL6ST as a predictor. Consistent findings suggest that IL6ST is a positive prognostic factor and is associated with ER status. Interestingly, these studies include 4 multigene signatures (EndoPredict, EER4, IRSN-23 and 42GC) that incorporate IL6ST to predict risk of recurrence or outcome from endocrine or chemotherapy. Here we review the existing evidence on the promising predictive and prognostic value of IL6ST. We also discuss how this potential could be further translated into clinical practice beyond the EndoPredict tool, which is already available in the clinic. The most promising route to further exploit IL6ST's promising predicting power will likely be through additional hybrid multifactor signatures that allow for more robust stratification of ER+ breast tumours into discrete groups with distinct outcomes, thus enabling greater refinement of the treatment-selection process.

The Present and Future of Neoadjuvant Endocrine Therapy for Breast Cancer Treatment

Simple Summary

The treatment of breast cancer has evolved considerably over the last two decades, leading toward individualized disease management. Hormone-sensitive breast cancers constitute the vast majority of cases and endocrine therapy is the mainstay of their treatment. On the other hand, neoadjuvant or pre-surgical treatments provide a number of advantages for tumor management. In this review we will discuss the existing evidence on neoadjuvant endocrine therapy, as well as its possible future indications.

Abstract

Endocrine therapy (ET) has established itself as an efficacious treatment for estrogen receptor-positive (ER+) breast cancers, with a reduction in recurrence rates and increased survival rates. The pre-surgical approach with chemotherapy (NCT) has become a common form of management for large, locally advanced, or high-risk tumors. However, a good response to NCT is not usually expected in ER+ tumors. Good results with primary ET, mainly in elderly women, have encouraged studies in other stages of life, and nowadays neoadjuvant endocrine treatment (NET) has become a useful approach to many ER+ breast cancers. The aim of this review is to provide an update on the current state of art regarding the present and the future role of NET.

MCM4 in human hepatocellular carcinoma: a potent prognostic factor associated with cell proliferation

Hepatocellular carcinoma (HCC) remains a major public health problem. MCM4, a constitutive member of the minichromosomal maintenance protein family, has been reported to play a vital role in cancer malignancy behavior. However, the function of MCM4 in HCC remains largely unknown. The present study explored the specific role of MCM4 in HCC. The data from public datasets including TCGA and GTEx showed that MCM4 was overexpressed in HCC and significantly associated with poor prognosis. Immunohistochemistry results from 102 HCC patients suggested that high-level expression of MCM4 was correlated with tumor size. Then a series of in vivo and in vitro experiments were performed to investigate the function of MCM4 in HCC tumor cells. MCM4 silencing suppressed the cell proliferation and sphere formation of hepatoma cells. Moreover, silencing MCM4 significantly decreased the growth of tumors in a xenograft tumor model. In conclusion, the results of the present study indicated that MCM4 was a potential prognostic predictor associated with poor outcomes of HCC patients and even a therapeutic target for HCC.

Association between the nucleosome footprint of plasma DNA and neoadjuvant chemotherapy response for breast cancer

Gene expression signatures have been used to predict the outcome of chemotherapy for breast cancer. The nucleosome footprint of cell-free DNA (cfDNA) carries gene expression information of the original tissues and thus may be used to predict the response to chemotherapy. Here we carried out the nucleosome positioning on cfDNA from 85 breast cancer patients and 85 healthy individuals and two cancer cell lines T-47D and MDA-MB-231 using low-coverage whole-genome sequencing (LCWGS) method. The patients showed distinct nucleosome footprints at Transcription Start Sites (TSSs) compared with normal donors. In order to identify the footprints of cfDNA corresponding with the responses to neoadjuvant chemotherapy in patients, we mapped on nucleosome positions on cfDNA of patients with different responses: responders (pretreatment, n = 28; post-1 cycle, post-3/4 cycles, and post-8 cycles of treatment, n = 12) and nonresponders (pretreatment, n = 10; post-1 cycle, post-3/4 cycles, and post-8 cycles of treatment, n = 10). The coverage depth near TSSs in plasma cfDNA differed significantly between responders and nonresponders at pretreatment, and also after neoadjuvant chemotherapy treatment cycles. We identified 232 TSSs with differential footprints at pretreatment and 321 after treatment and found enrichment in Gene Ontology terms such as cell growth inhibition, tumor suppressor, necrotic cell death, acute inflammatory response, T cell receptor signaling pathway, and positive regulation of vascular endothelial growth factor production. These results suggest that cfDNA nucleosome footprints may be used to predict the efficacy of neoadjuvant chemotherapy for breast cancer patients and thus may provide help in decision making for individual patients.

Current trends in the treatment of HR+/HER2+ breast cancer

Treatment for HR+/HER2+ patients has been debated, as some tumors within this luminal HER2+ subtype behave like luminal A cancers, whereas others behave like non-luminal HER2+ breast cancers. Recent research and clinical trials have revealed that a combination of hormone and targeted anti-HER2 approaches without chemotherapy provides long-term disease control for at least some HR+/HER2+ patients. Novel anti-HER2 therapies, including neratinib and trastuzumab emtansine, and new agents that are effective in HR+ cancers, including the next generation of oral selective estrogen receptor downregulators/degraders and CDK4/6 inhibitors such as palbociclib, are now being evaluated in combination. This review discusses current trials and results from previous studies that will provide the basis for current recommendations on how to treat newly diagnosed patients with HR+/HER2+ disease.

Amphiregulin retains ERα expression in acquired aromatase inhibitor resistant breast cancer cells

Acquired resistance to aromatase inhibitors (AIs) is a significant clinical issue in endocrine therapy for estrogen receptor (ER) positive breast cancer which accounts for the majority of breast cancer. Despite estrogen production being suppressed, ERα signaling remains active and plays a key role in most AI-resistant breast tumors. Here, we found that amphiregulin (AREG), an ERα transcriptional target and EGF receptor (EGFR) ligand, is crucial for maintaining ERα expression and signaling in acquired AI-resistant breast cancer cells. AREG was deregulated and critical for cell viability in ER+ AI-resistant breast cancer cells, and ectopic expression of AREG in hormone responsive breast cancer cells promoted endocrine resistance. RNA-sequencing and reverse phase protein array analyses revealed that AREG maintains ERα expression and signaling by activation of PI3K/Akt/mTOR signaling and upregulation of forkhead box M1 (FOXM1) and serum- and glucocorticoid-inducible kinase 3 (SGK3) expression. Our study uncovers a previously unappreciated role of AREG in maintaining ERα expression and signaling, and establishes the AREG-ERα crosstalk as a driver of acquired AI resistance in breast cancer.

Time-Resolved Profiling Reveals ATF3 as a Novel Mediator of Endocrine Resistance in Breast Cancer

Breast cancer is one of the leading causes of death for women worldwide. Patients whose tumors express Estrogen Receptor α account for around 70% of cases and are mostly treated with targeted endocrine therapy. However, depending on the degree of severity of the disease at diagnosis, 10 to 40% of these tumors eventually relapse due to resistance development. Even though recent novel approaches as the combination with CDK4/6 inhibitors increased the overall survival of relapsing patients, this remains relatively short and there is a urgent need to find alternative targetable pathways. In this study we profiled the early phases of the resistance development process to uncover drivers of this phenomenon. Time-resolved analysis revealed that ATF3, a member of the ATF/CREB family of transcription factors, acts as a novel regulator of the response to therapy via rewiring of central signaling processes towards the adaptation to endocrine treatment. ATF3 was found to be essential in controlling crucial processes such as proliferation, cell cycle, and apoptosis during the early response to treatment through the regulation of MAPK/AKT signaling pathways. Its essential role was confirmed in vivo in a mouse model, and elevated expression of ATF3 was verified in patient datasets, adding clinical relevance to our findings. This study proposes ATF3 as a novel mediator of endocrine resistance development in breast cancer and elucidates its role in the regulation of downstream pathways activities.

Cancer gene expression profiles associated with clinical outcomes to chemotherapy treatments

Background

Machine learning (ML) methods still have limited applicability in personalized oncology due to low numbers of available clinically annotated molecular profiles. This doesn’t allow sufficient training of ML classifiers that could be used for improving molecular diagnostics.

Methods

We reviewed published datasets of high throughput gene expression profiles corresponding to cancer patients with known responses on chemotherapy treatments. We browsed Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA) and Tumor Alterations Relevant for GEnomics-driven Therapy (TARGET) repositories.

Results

We identified data collections suitable to build ML models for predicting responses on certain chemotherapeutic schemes. We identified 26 datasets, ranging from 41 till 508 cases per dataset. All the datasets identified were checked for ML applicability and robustness with leave-one-out cross validation. Twenty-three datasets were found suitable for using ML that had balanced numbers of treatment responder and non-responder cases.

Conclusions

We collected a database of gene expression profiles associated with clinical responses on chemotherapy for 2786 individual cancer cases. Among them seven datasets included RNA sequencing data (for 645 cases) and the others – microarray expression profiles. The cases represented breast cancer, lung cancer, low-grade glioma, endothelial carcinoma, multiple myeloma, adult leukemia, pediatric leukemia and kidney tumors. Chemotherapeutics included taxanes, bortezomib, vincristine, trastuzumab, letrozole, tipifarnib, temozolomide, busulfan and cyclophosphamide.

ASPM predicts poor prognosis and regulates cell proliferation in bladder cancer

Bladder cancer (BCa) is one of the most common malignancies with high morbidity and mortality worldwide. In recent years, it is of great importance to investigate the molecular etiology associated with of BCa. Abnormal spindle-like microcephaly associated gene (ASPM) is the human orthologous of the Drosophila abnormal spindle (asp) and the most commonly mutated gene of autosomal recessive primary microcephaly. ASPM is overexpressed in several types of cancer cell lines and affects the progression and development of multiple types of cancers. However, its possible role in BCa progression is still unclear. Herein, we demonstrated the possible involvement of ASPM in the progression of BCa. We noticed that high expression of ASPM was positively associated with the poor prognosis. Its knockdown could significantly inhibit the proliferation of BCa cells in vitro and in mice. Therefore, we thought ASPM could act as a promising therapeutic target for BCa.

The Immune Checkpoint Kick Start: Optimization of Neoadjuvant Combination Therapy Using Game Theory

PURPOSE

In an upcoming clinical trial at the Moffitt Cancer Center for women with stage 2/3 estrogen receptor-positive breast cancer, treatment with an aromatase inhibitor and a PD-L1 checkpoint inhibitor combination will be investigated to lower a preoperative endocrine prognostic index (PEPI) that correlates with relapse-free survival. PEPI is fundamentally a static index, measured at the end of neoadjuvant therapy before surgery. We have developed a mathematical model of the essential components of the PEPI score to identify successful combination therapy regimens that minimize tumor burden and metastatic potential, on the basis of time-dependent trade-offs in the system.

METHODS

We considered two molecular traits, CCR7 and PD-L1, which correlate with treatment response and increased metastatic risk. We used a matrix game model with the four phenotypic strategies to examine the frequency-dependent interactions of cancer cells. This game was embedded in an ecological model of tumor population-growth dynamics. The resulting model predicts evolutionary and ecological dynamics that track with changes in the PEPI score.

RESULTS

We considered various treatment regimens on the basis of combinations of the two therapies with drug holidays. By considering the trade off between tumor burden and metastatic potential, the optimal therapy plan was a 1-month kick start of the immune checkpoint inhibitor followed by 5 months of continuous combination therapy. Relative to a protocol giving both therapeutics together from the start, this delayed regimen resulted in transient suboptimal tumor regression while maintaining a phenotypic constitution that is more amenable to fast tumor regression for the final 5 months of therapy.

CONCLUSION

The mathematical model provides a useful abstraction of clinical intuition, enabling hypothesis generation and testing of clinical assumptions.

Targeting STAT3 signaling using stabilised sulforaphane (SFX-01) inhibits endocrine resistant stem-like cells in ER-positive breast cancer

Estrogen receptor (ER) positive breast cancer is frequently sensitive to endocrine therapy. Multiple mechanisms of endocrine therapy resistance have been identified, including cancer stem-like cell (CSC) activity. Here we investigate SFX-01, a stabilised formulation of sulforaphane (SFN), for its effects on breast CSC activity in ER+ preclinical models. SFX-01 reduced mammosphere formation efficiency (MFE) of ER+ primary and metastatic patient samples. Both tamoxifen and fulvestrant increased MFE and aldehyde dehydrogenase (ALDH) activity of patient-derived xenograft (PDX) tumors, which was reversed by combination with SFX-01. SFX-01 significantly reduced tumor-initiating cell frequency in secondary transplants and reduced the formation of spontaneous lung micrometastases by PDX tumors in mice. Mechanistically, we establish that both tamoxifen and fulvestrant induce STAT3 phosphorylation. SFX-01 suppressed phospho-STAT3 and SFN directly bound STAT3 in patient and PDX samples. Analysis of ALDH+ cells from endocrine-resistant patient samples revealed activation of STAT3 target genes MUC1 and OSMR, which were inhibited by SFX-01 in patient samples. Increased expression of these genes after 3 months' endocrine treatment of ER+ patients (n = 68) predicted poor prognosis. Our data establish the importance of STAT3 signaling in CSC-mediated resistance to endocrine therapy and the potential of SFX-01 for improving clinical outcomes in ER+ breast cancer.

Neoadjuvant Endocrine Therapy in Breast Cancer: Current Knowledge and Future Perspectives

In locally advanced (LA) breast cancer (BC), neoadjuvant treatments have led to major achievements, which hold particular relevance in HER2-positive and triple-negative BC. Conversely, their role in hormone receptor positive (HR+), hormone epidermal growth factor 2 negative (HER2-) BC is still under debate, mainly due to the generally low rates of pathological complete response (pCR) and lower accuracy of pCR as predictors of long-term outcomes in this patient subset. While administration of neoadjuvant chemotherapy (NCT) in LA, HR+, HER2- BC patients is widely used in clinical practice, neoadjuvant endocrine therapy (NET) still retains an unfulfilled potential in the management of these subgroups, particularly in elderly and unfit patients. In addition, NET has gained a central role as a platform to test new drugs and predictive biomarkers in previously untreated patients. We herein present historical data regarding Tamoxifen and/or Aromatase Inhibitors and a debate on recent evidence regarding agents such as CDK4/6 and PI3K/mTOR inhibitors in the neoadjuvant setting. We also discuss key issues concerning the optimal treatment length, appropriate comparisons with NCT efficacy and use of NET in premenopausal patients.

Unlocking the transcriptomic potential of formalin-fixed paraffin embedded clinical tissues: comparison of gene expression profiling approaches

BACKGROUND

High-throughput transcriptomics has matured into a very well established and widely utilised research tool over the last two decades. Clinical datasets generated on a range of different platforms continue to be deposited in public repositories provide an ever-growing, valuable resource for reanalysis. Cost and tissue availability normally preclude processing samples across multiple technologies, making it challenging to directly evaluate performance and whether data from different platforms can be reliably compared or integrated.

METHODS

This study describes our experiences of nine new and established mRNA profiling techniques including Lexogen QuantSeq, Qiagen QiaSeq, BioSpyder TempO-Seq, Ion AmpliSeq, Nanostring, Affymetrix Clariom S or U133A, Illumina BeadChip and RNA-seq of formalin-fixed paraffin embedded (FFPE) and fresh frozen (FF) sequential patient-matched breast tumour samples.

RESULTS

The number of genes represented and reliability varied between the platforms, but overall all methods provided data which were largely comparable. Crucially we found that it is possible to integrate data for combined analyses across FFPE/FF and platforms using established batch correction methods as required to increase cohort sizes. However, some platforms appear to be better suited to FFPE samples, particularly archival material.

CONCLUSIONS

Overall, we illustrate that technology selection is a balance between required resolution, sample quality, availability and cost.

Flexible Data Trimming Improves Performance of Global Machine Learning Methods in Omics-Based Personalized Oncology

(1) Background: Machine learning (ML) methods are rarely used for an omics-based prescription of cancer drugs, due to shortage of case histories with clinical outcome supplemented by high-throughput molecular data. This causes overtraining and high vulnerability of most ML methods. Recently, we proposed a hybrid global-local approach to ML termed floating window projective separator (FloWPS) that avoids extrapolation in the feature space. Its core property is data trimming, i.e., sample-specific removal of irrelevant features. (2) Methods: Here, we applied FloWPS to seven popular ML methods, including linear SVM, k nearest neighbors (kNN), random forest (RF), Tikhonov (ridge) regression (RR), binomial naïve Bayes (BNB), adaptive boosting (ADA) and multi-layer perceptron (MLP). (3) Results: We performed computational experiments for 21 high throughput gene expression datasets (41–235 samples per dataset) totally representing 1778 cancer patients with known responses on chemotherapy treatments. FloWPS essentially improved the classifier quality for all global ML methods (SVM, RF, BNB, ADA, MLP), where the area under the receiver-operator curve (ROC AUC) for the treatment response classifiers increased from 0.61–0.88 range to 0.70–0.94. We tested FloWPS-empowered methods for overtraining by interrogating the importance of different features for different ML methods in the same model datasets. (4) Conclusions: We showed that FloWPS increases the correlation of feature importance between the different ML methods, which indicates its robustness to overtraining. For all the datasets tested, the best performance of FloWPS data trimming was observed for the BNB method, which can be valuable for further building of ML classifiers in personalized oncology.

Identification of potential key genes for HER-2 positive breast cancer based on bioinformatics analysis

BACKGROUNDS

HER-2 positive breast cancer is a subtype of breast cancer with poor clinical outcome. The aim of this study was to identify differentially expressed genes (DEGs) for HER-2 positive breast cancer and elucidate the potential interactions among them.

MATERIAL AND METHODS

Three gene expression profiles (GSE29431, GSE45827, and GSE65194) were derived from the Gene Expression Omnibus (GEO) database. GEO2R tool was applied to obtain DEGs between HER-2 positive breast cancer and normal breast tissues. Gene ontology (GO) annotation analysis and Kyoto Encyclopedia of Genes and Genome (KEGG) pathway enrichment analysis was performed by the Database for Annotation, Visualization and Integrated Discovery (David) online tool. Protein-protein interaction (PPI) network, hub gene identification and module analysis was conducted by Cytoscape software. Online Kaplan-Meier plotter survival analysis tool was also used to investigate the prognostic values of hub genes in HER-2 positive breast cancer patients.

RESULTS

A total of 54 upregulated DEGs and 269 downregulated DEGs were identified. Among them, 10 hub genes including CCNB1, RAC1, TOP2A, KIF20A, RRM2, ASPM, NUSAP1, BIRC5, BUB1B, and CEP55 demonstrated by connectivity degree in the PPI network were screened out. In Kaplan-Meier plotter survival analysis, the overexpression of RAC1 and RRM2 were shown to be associated with an unfavorable prognosis in HER-2 positive breast cancer patients.

CONCLUSIONS

This present study identified a number of potential target genes and pathways which might impact the oncogenesis and progression of HER-2 positive breast cancer. These findings could provide new insights into the detection of novel diagnostic and therapeutic biomarkers for this disease.

Estrogen Receptor Pathway Activity Score to Predict Clinical Response or Resistance to Neoadjuvant Endocrine Therapy in Primary Breast Cancer

Endocrine therapy is important for management of patients with estrogen receptor (ER)-positive breast cancer; however, positive ER staining does not reliably predict therapy response. We assessed the potential to improve prediction of response to endocrine treatment of a novel test that quantifies functional ER pathway activity from mRNA levels of ER pathway-specific target genes. ER pathway activity was assessed on datasets from three neoadjuvant-treated ER-positive breast cancer patient cohorts: Edinburgh: 3-month letrozole, 55 pre-/2-week/posttreatment matched samples; TEAM IIa: 3- to 6-month exemestane, 49 pre-/28 posttreatment paired samples; and NEWEST: 16-week fulvestrant, 39 pretreatment samples. ER target gene mRNA levels were measured in fresh-frozen tissue (Edinburgh, NEWEST) with Affymetrix microarrays, and in formalin-fixed paraffin-embedded samples (TEAM IIa) with qRT-PCR. Approximately one third of ER-positive patients had a functionally inactive ER pathway activity score (ERPAS), which was associated with a nonresponding status. Quantitative ERPAS decreased significantly upon therapy (P < 0.001 Edinburgh and TEAM IIa). Responders had a higher pretreatment ERPAS and a larger 2-week decrease in activity (P = 0.02 Edinburgh). Progressive disease was associated with low baseline ERPAS (P = 0.03 TEAM IIa; P = 0.02 NEWEST), which did not decrease further during treatment (P = 0.003 TEAM IIa). In contrast, the staining-based ER Allred score was not significantly associated with therapy response (P = 0.2). The ERPAS identified a subgroup of ER-positive patients with a functionally inactive ER pathway associated with primary endocrine resistance. Results confirm the potential of measuring functional ER pathway activity to improve prediction of response and resistance to endocrine therapy.

Clinical Implications of Transcriptomic Changes After Neoadjuvant Chemotherapy in Patients with Triple-Negative Breast Cancer

BACKGROUND

Pathological response to neoadjuvant chemotherapy (NAC) is critical in prognosis and selection of systemic treatments for patients with triple-negative breast cancer (TNBC). The aim of this study is to identify gene expression-based markers to predict response to NAC.

PATIENTS AND METHODS

A survey of 43 publicly available gene expression datasets was performed. We identified a cohort of TNBC patients treated with NAC (n = 708). Gene expression data from different studies were renormalized, and the differences between pretreatment (pre-NAC), on-treatment (post-C1), and surgical (Sx) specimens were evaluated. Euclidean statistical distances were calculated to estimate changes in gene expression patterns induced by NAC. Hierarchical clustering and pathway enrichment analyses were used to characterize relationships between differentially expressed genes and affected gene pathways. Machine learning was employed to refine a gene expression signature with the potential to predict response to NAC.

RESULTS

Forty nine genes consistently affected by NAC were involved in enhanced regulation of wound response, chemokine release, cell division, and decreased programmed cell death in residual invasive disease. The statistical distances between pre-NAC and post-C1 significantly predicted pathological complete response [area under the curve (AUC) = 0.75; p = 0.003; 95% confidence interval (CI) 0.58-0.92]. Finally, the expression of CCND1, a cyclin that forms complexes with CDK4/6 to promote the cell cycle, was the most informative feature in pre-NAC biopsies to predict response to NAC.

CONCLUSIONS

The results of this study reveal significant transcriptomic changes induced by NAC and suggest that chemotherapy-induced gene expression changes observed early in therapy may be good predictors of response to NAC.

MCM2, MCM4, and MCM6 in Breast Cancer: Clinical Utility in Diagnosis and Prognosis

Breast cancer is a heterogeneous disease comprising the estrogen receptor (ER)-positive luminal subtype which is subdivided into luminal A and luminal B and ER-negative breast cancer which includes the triple-negative subtype. This study has four aims: 1) to examine whether Minichromosome Maintenance (MCM)2, MCM4, and MCM6 can be used as markers to differentiate between luminal A and luminal B subtypes; 2) to study whether MCM2, MCM4, and MCM6 are highly expressed in triple-negative breast cancer, as there is an urgent need to search for surrogate markers in this aggressive subtype, for drug development purposes; 3) to compare the prognostic values of these markers in predicting relapse-free survival; and 4) to compare the three approaches used for scoring the protein expression of these markers by immunohistochemistry (IHC). MCM2, MCM4, MCM6, and MKI67 mRNA expression was first studied using in silico analysis of available breast cancer datasets. We next used IHC to evaluate their protein expression on tissue microarrays using three scoring methods. MCM2, MCM4, and MCM6 can help in distinction between luminal A and luminal B whose therapeutic management and clinical outcomes are different. MCM2, MCM4, MCM6, and Ki-67 are highly expressed in breast cancer of high histological grades that comprise clinically aggressive tumors such as luminal B, HER2-positive, and triple-negative subtypes. Low transcript expression of these markers is associated with increased probability of relapse-free survival. A positive relationship exists among the three scoring methods of each of the four markers. An independent validation cohort is needed to confirm their clinical utility.

Neoadjuvant endocrine therapy: A potential strategy for ER-positive breast cancer

A potential strategy for patients with estrogen receptor (ER)-positive breast cancer is necessary to replace neoadjuvant chemotherapy which has limited benefit. Neoadjuvant endocrine therapy (NAE) has been indicated to be a favorable alternate approach to downstage large or locally advanced breast cancer in ER-positive, human epidermal growth factor receptor 2 (HER2)-negative (ER+/HER2-) patients, especially postmenopausal women. Previous studies have demonstrated the efficacy of various endocrine agents in NAE. Aromatase inhibitors (AIs) have proven superiority over tamoxifen as a suitable choice to optimize treatment efficacy. Fulvestrant was recently reported as an effective agent, similar to AIs. Furthermore, the addition of targeted agents exerts synergistic antiproliferative effects with endocrine agents and rapidly improves response rates in both endocrine sensitive and resistant tumors. The neoadjuvant platform provides a unique opportunity to define the appropriate strategy and address the mechanisms of endocrine resistance. In addition, the predictive value of biomarkers and genomic assays in NAE is under investigation to evaluate individual effects and validate biomarker-based strategies. In this review, we discuss the most relevant evidence on the potential of NAE for ER+ breast cancer. The current understanding also offers new insights into the identification of the optimal settings and valuable predictive tools of NAE to guide clinical treatment decisions and achieve beneficial therapeutic effects.

Altered Steroid Milieu in AI-Resistant Breast Cancer Facilitates AR Mediated Gene-Expression Associated with Poor Response to Therapy

Divergent roles for androgen receptor (AR) in breast cancer have been reported. Following aromatase inhibitor (AI) treatment, the conversion of circulating androgens into estrogens can be diminished by >99%. We wished to establish whether the steroid environment can dictate the role of AR and the implications of this for subsequent therapy. This study utilizes models of AI resistance to explore responsiveness to PI3K/mTOR and anti-AR therapy when cells are exposed to unconverted weak androgens. Transcriptomic alterations driven by androstenedione (4AD) were assessed by RNA-sequencing. AR and estrogen receptor (ER) recruitment to target gene promoters was evaluated using ChIP, and relevance to patient profiles was performed using publicly available data sets. Although BEZ235 showed decreased viability across AI-sensitive and -resistant cell lines, anti-AR treatment elicited a decrease in cell viability only in the AI-resistant model. Serum and glucocorticoid-regulated kinase 3 (SGK3) and cAMP-dependent protein kinase inhibitor β (PKIB) were confirmed to be regulated by 4AD and shown to be mediated by AR; crucially, reexposure to estradiol suppressed expression of these genes. Meta-analysis of transcript levels showed high expression of SGK3 and PKIB to be associated with poor response to endocrine therapy (HR = 2.551, P = 0.003). Furthermore, this study found levels of SGK3 to be sustained in patients who do not respond to AI therapy. This study highlights the importance of the tumor steroid environment. SGK3 and PKIB are associated with poor response to endocrine therapy and could have utility in tailoring therapeutic approaches.

On-treatment biomarkers can improve prediction of response to neoadjuvant chemotherapy in breast cancer

Background

Neoadjuvant chemotherapy is increasingly given preoperatively to shrink breast tumours prior to surgery. This approach also provides the opportunity to study the molecular changes associated with treatment and evaluate whether on-treatment sequential samples can improve response and outcome predictions over diagnostic or excision samples alone.

Methods

This study included a total of 97 samples from a cohort of 50 women (aged 29–76, with 46% ER+ and 20% HER2+ tumours) with primary operable breast cancer who had been treated with neoadjuvant chemotherapy. Biopsies were taken at diagnosis, at 2 weeks on-treatment, mid-chemotherapy, and at resection. Fresh frozen samples were sequenced with Ion AmpliSeq Transcriptome yielding expression values for 12,635 genes. Differential expression analysis was performed across 16 patients with a complete pathological response (pCR) and 34 non-pCR patients, and over treatment time to identify significantly differentially expressed genes, pathways, and markers indicative of response status. Prediction accuracy was compared with estimations of established gene signatures, for this dataset and validated using data from the I-SPY 1 Trial.

Results

Although changes upon treatment are largely similar between the two cohorts, very few genes were found to be consistently different between responders and non-responders, making the prediction of response difficult. AAGAB was identified as a novel potential on-treatment biomarker for pathological complete response, with an accuracy of 100% in the NEO training dataset and 78% accuracy in the I-SPY 1 testing dataset. AAGAB levels on-treatment were also significantly predictive of outcome (p = 0.048, p = 0.0036) in both cohorts. This single gene on-treatment biomarker had greater predictive accuracy than established prognostic tests, Mammaprint and PAM50 risk of recurrence score, although interestingly, both of these latter tests performed better in the on-treatment rather than the accepted pre-treatment setting.

Conclusion

Changes in gene expression measured in sequential samples from breast cancer patients receiving neoadjuvant chemotherapy resulted in the identification of a potentially novel on-treatment biomarker and suggest that established prognostic tests may have greater prediction accuracy on than before treatment. These results support the potential use and further evaluation of on-treatment testing in breast cancer to improve the accuracy of tumour response prediction.

Electronic supplementary material

The online version of this article (10.1186/s13058-019-1159-3) contains supplementary material, which is available to authorized users.

Neoadjuvant Therapy for Breast Cancer as a Model for Translational Research

Neoadjuvant therapy, where patients receive systemic therapy before surgical removal of the tumour, can downstage tumours allowing breast-conserving surgery, rather than mastectomy. In addition to its impact on surgery, the neoadjuvant setting offers a valuable opportunity to monitor individual tumour response. The effectiveness of standard and/or potential new therapies can be tested in the neoadjuvant pre-surgical setting. It can potentially help to identify markers differentiating patients that will potentially benefit from continuing with the same or a different adjuvant treatment enabling personalised treatment. Characterising the molecular response to treatment over time can more accurately identify the significant differences between baseline samples that would not be identified without post-treatment samples. In this review, we discuss the potential and challenges of using the neoadjuvant setting in translational breast cancer research, considering the implications for improving our understanding of response to treatment, predicting therapy benefit, modelling breast cancer dormancy, and the development of drug resistance.

Network-guided prediction of aromatase inhibitor response in breast cancer

Prediction of response to specific cancer treatments is complicated by significant heterogeneity between tumors in terms of mutational profiles, gene expression, and clinical measures. Here we focus on the response of Estrogen Receptor (ER)+ post-menopausal breast cancer tumors to aromatase inhibitors (AI). We use a network smoothing algorithm to learn novel features that integrate several types of high throughput data and new cell line experiments. These features greatly improve the ability to predict response to AI when compared to prior methods. For a subset of the patients, for which we obtained more detailed clinical information, we can further predict response to a specific AI drug.

Breast cancer is the second most common type of cancer in women, with an incidence rate of over 250,000 cases per year, and breast cancer cases show significant heterogeneity in clinical and omic measures. Estrogen receptor positive (ER+) tumors typically grow in response to estrogen, and in post menopausal women, estrogen is only produced in peripheral tissues via the aromatase enzyme. Inhibition of aromatase is often an effective treatment for ER+ tumors, but aromatase inhibitor therapy is not effective for all tumors, and causes of this heterogeneity in response are largely not known. In this work, we present a feature construction and classification method to predict response to aromatase inhibitor therapy. We use network smoothing techniques to combine tumor omic data into predictive features, which we use as input to standard machine learning algorithms. We train predictive models using clinical data, including high-quality clinical data from UPMC patients, and show that our method outperforms previous approaches in predicting response to aromatase inhibitor therapy.

Evaluating ZNF217 mRNA Expression Levels as a Predictor of Response to Endocrine Therapy in ER+ Breast Cancer

ZNF217 is a candidate oncogene with a wide variety of deleterious functions in breast cancer. Here, we aimed at investigating in a pilot prospective study the association between ZNF217 mRNA expression levels and the clinical response to neoadjuvant endocrine therapy (ET) in postmenopausal ER-positive (ER+) breast cancer patients. Core surgical biopsy samples before treatment initiation and post-treatment were obtained from 68 patients, and Ki-67 values measured by immunohistochemistry (IHC) were used to identify responders (n = 59) and non-responders (n = 9) after 4 months of ET. We report for the first time that high ZNF217 mRNA expression level measured by RT-qPCR in the initial tumor samples (pre-treatment) is associated with poor response to neoadjuvant ET. Indeed, the clinical positive response rate in patients with low ZNF217 expression levels was significantly higher than that in those with high ZNF217 expression levels (P = 0.027). Additionally, a retrospective analysis evaluating ZNF217 expression levels in primary breast tumor of ER+/HER2-/LN0 breast cancer patients treated with adjuvant ET enabled the identification of poorer responders prone to earlier relapse (P = 0.013), while ZNF217 did not retain any prognostic value in the ER+/HER2-/LN0 breast cancer patients who did not receive any treatment. Altogether, these data suggest that ZNF217 expression might be predictive of clinical response to ET.

Molecular changes during extended neoadjuvant letrozole treatment of breast cancer: distinguishing acquired resistance from dormant tumours

BACKGROUND

The risk of recurrence for endocrine-treated breast cancer patients persists for many years or even decades following surgery and apparently successful adjuvant therapy. This period of dormancy and acquired resistance is inherently difficult to investigate; previous efforts have been limited to in-vitro or in-vivo approaches. In this study, sequential tumour samples from patients receiving extended neoadjuvant aromatase inhibitor therapy were characterised as a novel clinical model.

METHODS

Consecutive tumour samples from 62 patients undergoing extended (4-45 months) neoadjuvant aromatase inhibitor therapy with letrozole were subjected to transcriptomic and proteomic analysis, representing before (≤ 0), early (13-120 days), and long-term (> 120 days) neoadjuvant aromatase inhibitor therapy with letrozole. Patients with at least a 40% initial reduction in tumour size by 4 months of treatment were included. Of these, 42 patients with no subsequent progression were classified as "dormant", and the remaining 20 patients as "acquired resistant".

RESULTS

Changes in gene expression in dormant tumours begin early and become more pronounced at later time points. Therapy-induced changes in resistant tumours were common features of treatment, rather than being specific to the resistant phenotype. Comparative analysis of long-term treated dormant and resistant tumours highlighted changes in epigenetics pathways including DNA methylation and histone acetylation. The DNA methylation marks 5-methylcytosine and 5-hydroxymethylcytosine were significantly reduced in resistant tumours compared with dormant tissues after extended letrozole treatment.

CONCLUSIONS

This is the first patient-matched gene expression study investigating long-term aromatase inhibitor-induced dormancy and acquired resistance in breast cancer. Dormant tumours continue to change during treatment whereas acquired resistant tumours more closely resemble their diagnostic samples. Global loss of DNA methylation was observed in resistant tumours under extended treatment. Epigenetic alterations may lead to escape from dormancy and drive acquired resistance in a subset of patients, supporting a potential role for therapy targeted at these epigenetic alterations in the management of resistance to oestrogen deprivation therapy.

Neoadjuvant Model as a Platform for Research in Breast Cancer and Novel Targets under Development in this Field

For decades, the neoadjuvant setting has provided a useful scenario for research in breast cancer. Historically, neoadjuvant clinical trials, either hormone therapy-based or chemotherapy-based, have tried to recapitulate the results of their counterpart adjuvant studies, but with smaller patient numbers, more rapid outcomes (clinical response and/or pathologic complete response (pCR)), together with additional biologic information. As for neoadjuvant chemotherapy trials, the increase in pCR rates has been recently accepted as an appropriate surrogate marker to accelerate drug approval in high-risk breast cancer patients. In this setting, with the exception of luminal A tumors, pCR has been associated with improved long-term outcomes, particularly when the analysis is based on specific trials for each breast cancer subtype. For luminal tumors receiving neoadjuvant endocrine therapy, Ki67 at 2-4 weeks and the preoperative endocrine prognostic index score are the most accepted intermediate markers of efficacy, which will be validated in ongoing larger trials. In this review, we describe the different neoadjuvant designs: from the classical randomized trials in which treatment is delivered for 6 or more months to short non-therapeutic presurgical studies lasting just 2 or 3 weeks. We also review the main neoadjuvant trials, either ongoing or completed, for luminal, triple-negative, and HER2-positive breast cancer. The translational effort and research of biomarkers conducted in these studies will be particularly addressed.

Molecular profiling of hormone receptor-positive, HER2-negative breast cancers from patients treated with neoadjuvant endocrine therapy in the CARMINA 02 trial (UCBG-0609)

BACKGROUND

Postmenopausal women with large, hormone receptor (HR)-positive/HER2-negative and low-proliferative breast cancer derived a benefit from neoadjuvant endocrine therapy (NET) in the CARMINA02 trial. This study was designed to correlate gene expression and mutation profiles with both response to NET and prognosis.

METHODS

Gene expression profiling using RNA sequencing was performed in 86 pre-NET and post-NET tumor samples. Targeted next-generation sequencing of 91 candidate breast cancer-associated genes was performed on DNA samples from 89 patients. Molecular data were correlated with radiological response and relapse-free survival.

RESULTS

The transcriptional profile of tumors to NET in responders involved immune-associated genes enriched in activated Th1 pathway, which remained unchanged in non-responders. Immune response was confirmed by analysis of tumor-infiltrating lymphocytes (TILs). The percentage of TILs was significantly increased post-NET compared to pre-NET samples in responders (p = 0.0071), but not in non-responders (p = 0.0938). Gene expression revealed that lipid metabolism was the main molecular function related to prognosis, while PPARγ is the most important upstream regulator gene. The most frequently mutated genes were PIK3CA (48.3%), CDH1 (20.2%), PTEN (15.7%), TP53 (10.1%), LAMA2 (10.1%), BRCA2 (9.0%), MAP3K1 (7.9%), ALK (6.7%), INPP4B (6.7%), NCOR1 (6.7%), and NF1 (5.6%). Cell cycle and apoptosis pathway and PIK3CA/AKT/mTOR pathway were altered significantly more frequently in non-responders than in responders (p = 0.0017 and p = 0.0094, respectively). The average number of mutations per sample was significantly higher in endocrine-resistant tumors (2.88 vs. 1.64, p = 0.03), but no difference was observed in terms of prognosis. ESR1 hotspot mutations were detected in 3.4% of treatment-naive tumors.

CONCLUSIONS

The Th1-related immune system and lipid metabolism appear to play key roles in the response to endocrine therapy and prognosis in HR-positive/HER2-negative breast cancer. Deleterious somatic mutations in the cell cycle and apoptosis pathway and PIK3CA/AKT/mTOR pathway may be relevant for clinical management.

TRIAL REGISTRATION

This trial is registered with ClinicalTrials.gov ( NCT00629616 ) on March 6, 2008, retrospectively registered.

Predictive markers of endocrine response in breast cancer

Ongoing clinical and research efforts seek to optimise the use of endocrine therapy in the treatment of breast cancer. Accurate biomarkers are needed that predict response for individual patients. The presence of the estrogen receptor (ER) as the direct (for tamoxifen and fulvestrant) or indirect (for aromatase inhibitors) target molecule for endocrine therapy remains the foremost biomarker and determinant of response. However, ER expression only poorly predicts outcome and further indicators of response or resistance are required. The development and application of molecular signature assays such as Oncotype Dx, Prosigna, Mammaprint and Endopredict have provided valuable information on prognosis and these are being used to support clinical decision making on whether endocrine therapy alone alongside surgery is sufficient for ER-positive early stage breast cancers or whether combination of endocrine with chemotherapy are also warranted. Ki67, the proliferation marker, has been widely used in the neo-adjuvant (pre-operative) setting to help predict response and long term outcome. Gene expression studies within the same setting have allowed monitoring of changes of potential predictive markers. These have identified frequent changes in estrogen-regulated and proliferation genes. Specific molecules such as mutant ER may also prove helpful biomarkers in predicting outcome and monitoring response to treatment.