Gene expression pathway analysis to predict response to neoadjuvant docetaxel and capecitabine for breast cancer

Beyond CTS5 score: A novel nomogram predicting long-term prognosis in patients with hormone receptor-positive and human epidermal growth factor receptor 2-positive breast cancer

BACKGROUND

HR+/HER2+ breast cancer are exposed to high late-recurrence risk. CTS5 is widely used in predicting late recurrence of HR+/HER2- patients. This study aims to explore the application of CTS5 in HR+/HER2+ patients and develop a novel model with greater predictive efficacy.

METHODS

We collect 26605 HR+/HER2+ breast cancer patients diagnosed between 2010 and 2019 from SEER database. The main survival outcome was breast cancer-specific survival (BCSS) after 5 years of diagnosis. CTS5 score was calculated. Survival analysis was performed. Cox regression identified significant clinicopathological parameters, which were used to construct a nomogram.

RESULTS

Patients were stratified into CTS5 low- (n = 10,217, 38.4%), intermediate- (n = 9,257, 34.8%) and high-risk (n = 7,131, 26.8%) groups. Patients in CTS5 high-risk subgroup were more likely to be older at diagnosis, postmenopausal and have tumors with higher TN stage and grades (all p < 0.001). High-risk patients showed worse BCSS compared with intermediate- and low-risk patients (cumulative hazard: BCSS, 7.4%, 3.2% and 1.7%, p < 0.001). Cox regression suggested age, TN stage, chemotherapy and radiotherapy were BCSS associated (all p < 0.001) while grade wasn't. A nomogram based on age, tumor size and lymph nodes was constructed. The AUC values of the ROC curves for 6, 8, and 10-year BCSS were 0.687, 0.698, and 0.700. The nomogram demonstrated a significantly higher likelihood ratio statistic compared to CTS5 (518.9 vs. 483.8, p < 0.001).

CONCLUSIONS

We confirmed the prognostic value of CTS5 in HR+/HER2+ breast cancer and developed a new nomogram with superior predictive performance for long-term prognosis compared to CTS5.

MammOnc-DB, an integrative breast cancer data analysis platform for target discovery

Breast cancer (BCa), a leading malignancy among women, is characterized by morphological and molecular heterogeneity. While early-stage, hormone receptor, and HER2-positive BCa are treatable, triple-negative BCa and metastatic BCa remains largely untreatable. Advances in sequencing and proteomic technologies have improved our understanding of the molecular alterations that occur during BCa initiation and progression and enabled identification of subclass-specific biomarkers and therapeutic targets. Despite the availability of abundant omics data in public repositories, user-friendly tools for multi-omics data analysis and integration are scarce. To address this, we developed a comprehensive BCa data analysis platform called MammOnc-DB ( http://resource.path.uab.edu/MammOnc-Home.html ), comprising data from more than 20,000 BCa samples. MammOnc-DB facilitates hypothesis generation and testing, biomarker discovery, and therapeutic targets identification. The platform also includes pre- and post-treatment data, which can help users identify treatment resistance markers and support combination therapy strategies, offering researchers and clinicians a comprehensive tool for BCa data analysis and visualization.

Integrated single-cell and bulk transcriptomic analysis identifies a novel macrophage subtype associated with poor prognosis in breast cancer

BACKGROUND

Tumor-associated macrophages (TAMs) are pivotal components of the breast cancer (BC) tumor microenvironment (TME), significantly influencing tumor progression and response to therapy. However, the heterogeneity and specific roles of TAM subpopulations in BC remain inadequately understood.

METHODS

We performed an integrated analysis of single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing (RNA-seq) data from BC patients to comprehensively characterize TAM heterogeneity. Utilizing the MetaTiME computational framework and consensus clustering, we identified distinct TAM subtypes and assessed their associations with clinical outcomes and treatment responses. A machine learning-based predictive model was developed to evaluate the prognostic significance of TAM-related gene expression profiles.

RESULTS

Our analysis revealed three distinct TAM subgroups. Notably, we identified a novel macrophage subtype, M_Macrophage-SPP1-C1Q, characterized by high expression of SPP1 and C1QA, representing an intermediate differentiation state with unique proliferative and oncogenic properties. High infiltration of M_Macrophage-SPP1-C1Q was significantly associated with poor overall survival (OS) and chemotherapy resistance in BC patients. We developed a Random Forest (RF)-based predictive model, Macro.RF, which accurately stratified patients based on survival outcomes and chemotherapy responses, independent of established prognostic parameters.

CONCLUSION

This study uncovers a previously unrecognized TAM subtype that drives poor prognosis in BC. The identification of M_Macrophage-SPP1-C1Q enhances our understanding of TAM heterogeneity within the TME and offers a novel prognostic biomarker. The Macro.RF model provides a robust tool for predicting clinical outcomes and guiding personalized treatment strategies in BC patients.

Profiling Multiple CD8+ T-cell Functional Dimensions Enhances Breast Cancer Immune Assessment

CD8+ T-cell abundance is insufficient to assess antitumor immunity and shows poor performance in predicting breast cancer prognosis and immunotherapy response, presumably owing to the complexity of CD8+ T-cell functionalities. Although single-cell RNA sequencing can dissect the multifaceted functions of CD8+ T cells for better immune assessment, its clinical application is limited. In this study, we developed bulk RNA sequencing-based FuncDimen models from integrative analysis of single-cell RNA sequencing and matched bulk RNA sequencing data to evaluate CD8+ T-cell functionalities across five dimensions: tumor reactivity, cytotoxicity, IFNγ secretion, proliferation, and apoptosis. The FuncDimen models quantifying different functional dimensions of CD8+ T cells were validated in our breast cancer cohort and external databases using immunofluorescence and imaging mass cytometry. We calculated the FuncAggre score by weighted aggregation of all five FuncDimen models to encapsulate the overall antitumor immunity. In our breast cancer cohort and external databases, the FuncAggre score demonstrated superior predictive performance for breast cancer prognosis (time-dependent AUC: 0.56-0.70) and immunotherapy response (AUC: 0.71-0.83) over other immune biomarkers, regardless of the breast cancer molecular subtype. Together, the FuncDimen models offer a refined assessment of antitumor immunity mediated by CD8+ T cells in the clinic, enhancing prognostic prediction and aiding personalized immunotherapy in breast cancer.

MammOnc-DB, an integrative breast cancer data analysis platform for target discovery

Breast cancer (BCa) is one of the most common malignancies among women worldwide. It is a complex disease that is characterized by morphological and molecular heterogeneity. In the early stages of the disease, most BCa cases are treatable, particularly hormone receptor-positive and HER2-positive tumors. Unfortunately, triple-negative BCa and metastases to distant organs are largely untreatable with current medical interventions. Recent advances in sequencing and proteomic technologies have improved our understanding of the molecular changes that occur during breast cancer initiation and progression. In this era of precision medicine, researchers and clinicians aim to identify subclass-specific BCa biomarkers and develop new targets and drugs to guide treatment. Although vast amounts of omics data including single cell sequencing data, can be accessed through public repositories, there is a lack of user-friendly platforms that integrate information from multiple studies. Thus, to meet the need for a simple yet effective and integrative BCa tool for multi-omics data analysis and visualization, we developed a comprehensive BCa data analysis platform called MammOnc-DB (http://resource.path.uab.edu/MammOnc-Home.html), comprising data from more than 20,000 BCa samples. MammOnc-DB was developed to provide a unique resource for hypothesis generation and testing, as well as for the discovery of biomarkers and therapeutic targets. The platform also provides pre- and post-treatment data, which can help users identify treatment resistance markers and patient groups that may benefit from combination therapy.

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.

Transferrin receptor in primary and metastatic breast cancer: Evaluation of expression and experimental modulation to improve molecular targeting

BACKGROUND

Conjugation of transferrin (Tf) to imaging or nanotherapeutic agents is a promising strategy to target breast cancer. Since the efficacy of these biomaterials often depends on the overexpression of the targeted receptor, we set out to survey expression of transferrin receptor (TfR) in primary and metastatic breast cancer samples, including metastases and relapse, and investigate its modulation in experimental models.

METHODS

Gene expression was investigated by datamining in twelve publicly-available datasets. Dedicated Tissue microarrays (TMAs) were generated to evaluate matched primary and bone metastases as well as and pre and post chemotherapy tumors from the same patient. TMA were stained with the FDA-approved MRQ-48 antibody against TfR and graded by staining intensity (H-score). Patient-derived xenografts (PDX) and isogenic metastatic mouse models were used to study in vivo TfR expression and uptake of transferrin.

RESULTS

TFRC gene and protein expression were high in breast cancer of all subtypes and stages, and in 60-85% of bone metastases. TfR was detectable after neoadjuvant chemotherapy, albeit with some variability. Fluorophore-conjugated transferrin iron chelator deferoxamine (DFO) enhanced TfR uptake in human breast cancer cells in vitro and proved transferrin localization at metastatic sites and correlation of tumor burden relative to untreated tumor mice.

CONCLUSIONS

TfR is expressed in breast cancer, primary, metastatic, and after neoadjuvant chemotherapy. Variability in expression of TfR suggests that evaluation of the expression of TfR in individual patients could identify the best candidates for targeting. Further, systemic iron chelation with DFO may upregulate receptor expression and improve uptake of therapeutics or tracers that use transferrin as a homing ligand.

Multi-omics analyses unravel DNA damage repair-related clusters in breast cancer with experimental validation

Background

As one of the most common malignancies worldwide, breast cancer (BC) exhibits high heterogeneity of molecular phenotypes. The evolving view regarding DNA damage repair (DDR) is that it is context-specific and heterogeneous, but its role in BC remains unclear.

Methods

Multi-dimensional data of transcriptomics, genomics, and single-cell transcriptome profiling were obtained to characterize the DDR-related features of BC. We collected 276 DDR-related genes based on the Molecular Signature Database (MSigDB) database and previous studies. We acquired public datasets included the SCAN-B dataset (GEO: GSE96058), METABRIC database, and TCGA-BRCA database. Corresponding repositories such as transcriptomics, genomics, and clinical information were also downloaded. We selected scRNA-seq data from GEO: GSE176078, GSE114727, GSE161529, and GSE158724. Bulk RNA-seq data from GEO: GSE176078, GSE18728, GSE5462, GSE20181, and GSE130788 were extracted for independent analyses.

Results

The DDR classification was constructed in the SCAN-B dataset (GEO: GSE96058) and METABRIC database, Among BC patients, there were two clusters with distinct clinical and molecular characteristics: the DDR-suppressed cluster and the DDR-active cluster. A superior survival rate is found for tumors in the DDR-suppressed cluster, while those with the DDR-activated cluster tend to have inferior prognoses and clinically aggressive behavior. The DDR classification was validated in the TCGA-BRCA cohort and shown similar results. We also found that two clusters have different pathway activities at the genomic level. Based on the intersection of the different expressed genes among these cohorts, we found that PRAME might play a vital role in DDR. The DDR classification was then enabled by establishing a DDR score, which was verified through multilayer cohort analysis. Furthermore, our results revealed that malignant cells contributed more to the DDR score at the single-cell level than nonmalignant cells. Particularly, immune cells with immunosuppressive properties (such as FOXP3+ CD4+ T cells) displayed higher DDR scores among those with distinguishable characteristics.

Conclusion

Collectively, this study performs general analyses of DDR heterogeneity in BC and provides insight into the understanding of individualized molecular and clinicopathological mechanisms underlying unique DDR profiles.

Clinicopathological Characteristics of Breast Cancer Patients with HER-2 Low Expression Receiving Neoadjuvant Therapy

INTRODUCTION

Human epidermal growth factor receptor-2 (HER-2) low expression breast malignant tumors have become a research hotspot in recent years, but it is still unclear whether HER-2 low expression represents a special subtype of breast cancer. However, this molecular type requires more effective treatment regimens in the neoadjuvant therapy stage.

METHODS

This study enrolled breast cancer patients who were treated at Harbin Medical University Cancer Hospital with neoadjuvant treatment between October 2011 and May 2019 and was a single-center retrospective study.

RESULTS

A total of 1,053 breast cancer patients who received preoperative therapy, including 279 (26%) HER-2 low expression patients, were included in this retrospective study. The HER-2 low expression group had a higher proportion of patients under 50 years old than the other two molecular subtype groups (p = 0.047, 62.0% vs. 57.2% and 52.5%), and the percentage of patients with Ki67 index above 15% was lower than that in HER-2-negative and HER-2-positive patients (p < 0.001, 50.2% vs. 63.6% and 71.5%). Most of the patients with HER-2 low expression were hormone receptor (HR) positive (p < 0.001, 85.7% vs. 60.4% and 36.0%), and their pathologic complete response (pCR) rate after neoadjuvant therapy was significantly lower than that of HER-2-negative and HER-2-positive patients (p < 0.001, 5.7% vs. 11.8% and 20.5%). The results of the subgroup analysis showed HR-positive patients with HER-2 low expression had a lower pCR rate (p < 0.001, 4.6% vs. 14.6%) and objective response rate (p = 0.001, 77.8% vs. 91.0%) than HER-2-positive patients and had no significant difference in these rates compared to HER-2-negative patients. There were no significant differences in overall survival (OS) and disease-free survival (DFS) up to 67 months (the median follow-up time) among HER-2 low, HER-2-negative, and HER-2-positive patients. The results of Cox hazard proportional showed that the Ki67 index and T stage (T3) were independent influencing factors for DFS. In terms of OS, Ki67 index, P53, T stage, and objective response were independent influencing factors for OS in HER-2 low expression patients.

CONCLUSIONS

In general, further studies are needed to confirm that HER-2 low expression is a special breast cancer molecular subtype. The efficacy of neoadjuvant therapy in patients with HER-2 low expression is relatively poor, and the efficacy of neoadjuvant therapy can predict the prognosis of patients with HER-2 low expression.

The expression profiles of signature genes from CD103+LAG3+ tumour-infiltrating lymphocyte subsets predict breast cancer survival

BACKGROUND

Tumour-infiltrating lymphocytes (TILs), including T and B cells, have been demonstrated to be associated with tumour progression. However, the different subpopulations of TILs and their roles in breast cancer remain poorly understood. Large-scale analysis using multiomics data could uncover potential mechanisms and provide promising biomarkers for predicting immunotherapy response.

METHODS

Single-cell transcriptome data for breast cancer samples were analysed to identify unique TIL subsets. Based on the expression profiles of marker genes in these subsets, a TIL-related prognostic model was developed by univariate and multivariate Cox analyses and LASSO regression for the TCGA training cohort containing 1089 breast cancer patients. Multiplex immunohistochemistry was used to confirm the presence of TIL subsets in breast cancer samples. The model was validated with a large-scale transcriptomic dataset for 3619 breast cancer patients, including the METABRIC cohort, six chemotherapy transcriptomic cohorts, and two immunotherapy transcriptomic cohorts.

RESULTS

We identified two TIL subsets with high expression of CD103 and LAG3 (CD103+LAG3+), including a CD8+ T-cell subset and a B-cell subset. Based on the expression profiles of marker genes in these two subpopulations, we further developed a CD103+LAG3+ TIL-related prognostic model (CLTRP) based on CXCL13 and BIRC3 genes for predicting the prognosis of breast cancer patients. CLTRP-low patients had a better prognosis than CLTRP-high patients. The comprehensive results showed that a low CLTRP score was associated with a high TP53 mutation rate, high infiltration of CD8 T cells, helper T cells, and CD4 T cells, high sensitivity to chemotherapeutic drugs, and a good response to immunotherapy. In contrast, a high CLTRP score was correlated with a low TP53 mutation rate, high infiltration of M0 and M2 macrophages, low sensitivity to chemotherapeutic drugs, and a poor response to immunotherapy.

CONCLUSIONS

Our present study showed that the CLTRP score is a promising biomarker for distinguishing prognosis, drug sensitivity, molecular and immune characteristics, and immunotherapy outcomes in breast cancer patients. The CLTRP could serve as a valuable tool for clinical decision making regarding immunotherapy.

Gene Expression Profiles in Cancers and Their Therapeutic Implications

ABSTRACT

The vast amount of gene expression profiling data of bulk tumors and cell lines available in the public domain represents a tremendous resource. For any major cancer type, expression data can identify molecular subtypes, predict patient outcome, identify markers of therapeutic response, determine the functional consequences of somatic mutation, and elucidate the biology of metastatic and advanced cancers. This review provides a broad overview of gene expression profiling in cancer (which may include transcriptome and proteome levels) and the types of findings made using these data. This review also provides specific examples of accessing public cancer gene expression data sets and generating unique views of the data and the resulting genes of interest. These examples involve pan-cancer molecular subtyping, metabolism-associated expression correlates of patient survival involving multiple cancer types, and gene expression correlates of chemotherapy response in breast tumors.

Ethnic and racial-specific differences in levels of centrosome-associated mitotic kinases, proliferative and epithelial-to-mesenchymal markers in breast cancers

Molecular epidemiology evidence indicates racial and ethnic differences in the aggressiveness and survival of breast cancer. Hispanics/Latinas (H/Ls) and non-Hispanic Black women (NHB) are at higher risk of breast cancer (BC)-related death relative to non-Hispanic white (NHW) women in part because they are diagnosed with hormone receptor-negative (HR) subtype and at higher stages. Since the cell cycle is one of the most commonly deregulated cellular processes in cancer, we propose that the mitotic kinases TTK (or Mps1), TBK1, and Nek2 could be novel targets to prevent breast cancer progression among NHBs and H/Ls. In this study, we calculated levels of TTK, p-TBK1, epithelial (E-cadherin), mesenchymal (Vimentin), and proliferation (Ki67) markers through immunohistochemical (IHC) staining of breast cancer tissue microarrays (TMAs) that includes samples from 6 regions in the Southeast of the United States and Puerto Rico -regions enriched with NHB and H/L breast cancer patients. IHC analysis showed that TTK, Ki67, and Vimentin were significantly expressed in triple-negative (TNBC) tumors relative to other subtypes, while E-cadherin showed decreased expression. TTK correlated with all of the clinical variables but p-TBK1 did not correlate with any of them. TCGA analysis revealed that the mRNA levels of multiple mitotic kinases, including TTK, Nek2, Plk1, Bub1, and Aurora kinases A and B, and transcription factors that are known to control the expression of these kinases (e.g. FoxM1 and E2F1-3) were upregulated in NHBs versus NHWs and correlated with higher aneuploidy indexes in NHB, suggesting that these mitotic kinases may be future novel targets for breast cancer treatment in NHB women.

Subpathway Analysis of Transcriptome Profiles Reveals New Molecular Mechanisms of Acquired Chemotherapy Resistance in Breast Cancer

Chemoresistance has been a major challenge in the treatment of patients with breast cancer. The diverse omics platforms and small sample sizes reported in the current studies of chemoresistance in breast cancer limit the consensus regarding the underlying molecular mechanisms of chemoresistance and the applicability of these study findings. Therefore, we built two transcriptome datasets for patients with chemotherapy-resistant breast cancers—one comprising paired transcriptome samples from 40 patients before and after chemotherapy and the second including unpaired samples from 690 patients before and 45 patients after chemotherapy. Subsequent conventional pathway analysis and new subpathway analysis using these cohorts uncovered 56 overlapping upregulated genes (false discovery rate [FDR], 0.018) and 36 downregulated genes (FDR, 0.016). Pathway analysis revealed the activation of several pathways in the chemotherapy-resistant tumors, including those of drug metabolism, MAPK, ErbB, calcium, cGMP-PKG, sphingolipid, and PI3K-Akt, as well as those activated by Cushing’s syndrome, human papillomavirus (HPV) infection, and proteoglycans in cancers, and subpathway analysis identified the activation of several more, including fluid shear stress, Wnt, FoxO, ECM-receptor interaction, RAS signaling, Rap1, mTOR focal adhesion, and cellular senescence (FDR < 0.20). Among these pathways, those associated with Cushing’s syndrome, HPV infection, proteoglycans in cancer, fluid shear stress, and focal adhesion have not yet been reported in breast cancer chemoresistance. Pathway and subpathway analysis of a subset of triple-negative breast cancers from the two cohorts revealed activation of the identical chemoresistance pathways.

Decision Theory versus Conventional Statistics for Personalized Therapy of Breast Cancer

Estrogen and progesterone receptors being present or not represents one of the most important biomarkers for therapy selection in breast cancer patients. Conventional measurement by immunohistochemistry (IHC) involves errors, and numerous attempts have been made to increase precision by additional information from gene expression. This raises the question of how to fuse information, in particular, if there is disagreement. It is the primary domain of Dempster–Shafer decision theory (DST) to deal with contradicting evidence on the same item (here: receptor status), obtained through different techniques. DST is widely used in technical settings, such as self-driving cars and aviation, and is also promising to deliver significant advantages in medicine. Using data from breast cancer patients already presented in previous work, we focus on comparing DST with classical statistics in this work, to pave the way for its application in medicine. First, we explain how DST not only considers probabilities (a single number per sample), but also incorporates uncertainty in a concept of ‘evidence’ (two numbers per sample). This allows for very powerful displays of patient data in so-called ternary plots, a novel and crucial advantage for medical interpretation. Results are obtained according to conventional statistics (ODDS) and, in parallel, according to DST. Agreement and differences are evaluated, and the particular merits of DST discussed. The presented application demonstrates how decision theory introduces new levels of confidence in diagnoses derived from medical data.

Chemokines in Triple-Negative Breast Cancer Heterogeneity: New Challenges for Clinical Implications

Tumor heterogeneity is a hallmark of cancer and one of the primary causes of resistance to therapies. Triple-negative breast cancer (TNBC), which accounts for 15% to 20% of all breast cancers and is the most aggressive subtype, is very diverse, connected to metastatic potential and response to therapy. It is a very diverse disease at the molecular, pathologic, and clinical levels. TNBC is substantially more likely to recur and has a worse overall survival rate following diagnosis than other breast cancer subtypes. Chemokines, low molecular weight proteins that stimulate chemotaxis, have been shown to control the cues responsible for TNBC heterogeneity. In this review, we have focused on tumor heterogeneity and the role of chemokines in modulating tumor heterogeneity, since this is the most critical issue in treating TNBC. Additionally, we examined numerous cues mediated by chemokine networks that contribute to the heterogeneity of TNBC. Recent developments in our knowledge of the chemokine networks that regulate TNBC heterogeneity may pave the door for developing difficult-to-treat TNBC treatment options.

Early on-treatment transcriptional profiling as a tool for improving pathological response prediction in HER2-positive inflammatory breast cancer

Background

Inflammatory breast cancer (IBC) is a rare and understudied disease, with 40% of cases presenting with human epidermal growth factor receptor 2 (HER2)-positive subtype. The goals of this study were to (i) assess the pathologic complete response (pCR) rate of short-term neoadjuvant dual-HER2-blockade and paclitaxel, (ii) contrast baseline and on-treatment transcriptional profiles of IBC tumor biopsies associated with pCR, and (iii) identify biological pathways that may explain the effect of neoadjuvant therapy on tumor response.

Patients and Methods

A single-arm phase II trial of neoadjuvant trastuzumab (H), pertuzumab (P), and paclitaxel for 16 weeks was completed among patients with newly diagnosed HER2-positive IBC. Fresh-frozen tumor biopsies were obtained pretreatment (D1) and 8 days later (D8), following a single dose of HP, prior to adding paclitaxel. We performed RNA-sequencing on D1 and D8 tumor biopsies, identified genes associated with pCR using differential gene expression analysis, identified pathways associated with pCR using gene set enrichment and gene expression deconvolution methods, and compared the pCR predictive value of principal components derived from gene expression profiles by calculating and area under the curve for D1 and D8 subsets.

Results

Twenty-three participants were enrolled, of whom 21 completed surgery following neoadjuvant therapy. Paired longitudinal fresh-frozen tumor samples (D1 and D8) were obtained from all patients. Among the 21 patients who underwent surgery, the pCR and the 4-year disease-free survival were 48% (90% CI 0.29-0.67) and 90% (95% CI 66-97%), respectively. The transcriptional profile of D8 biopsies was found to be more predictive of pCR (AUC = 0.91, 95% CI: 0.7993-1) than the D1 biopsies (AUC = 0.79, 95% CI: 0.5905-0.9822).

Conclusions

In patients with HER2-positive IBC treated with neoadjuvant HP and paclitaxel for 16 weeks, gene expression patterns of tumor biopsies measured 1 week after treatment initiation not only offered different biological information but importantly served as a better predictor of pCR than baseline transcriptional analysis.

Trial Registration

ClinicalTrials.gov identifier: NCT01796197 (https://clinicaltrials.gov/ct2/show/NCT01796197); registered on February 21, 2013.

Dynamics of Serum Thymidine Kinase 1 at the First Cycle of Neoadjuvant Chemotherapy Predicts Outcome of Disease in Estrogen-Receptor-Positive Breast Cancer

Simple Summary

Chemotherapy before surgery (NAC) is an option for high-risk breast cancer (BC) patients. Pathologic complete response (pCR) predicts long-term outcome and has become a surrogate biomarker for survival. pCR is, however, reached in only <10% of hormone-receptor-positive (ER+) patients and is of limited prognostic value. Biomarkers able to predict outcome early during NAC would facilitate individualized therapy with the possibility to adjust or interrupt an ineffective therapy. Here, it is shown that differential response of the serum concentration of thymidine kinase 1, an enzyme involved in the DNA synthesis and released from the tumor into the blood, 48 h after the first cycle of NAC, predicts long-term outcome in localized advanced ER+/HER2-BC. The different reactions to chemotherapy could be used to guide this process early during NAC and utilized to identify mechanisms of tumor sensitivity that could provide a prediction of long-term outcome prior to chemotherapy.

Abstract

Pathologic complete response (pCR) predicts the long-term outcome of neoadjuvantly treated (NAC) breast cancer (BC) but is reached in <10% of hormone-receptor-positive patients. Biomarkers enabling adjustment or interruption of an ineffective therapy are desired. Here, we evaluated whether changes in the serum concentration of thymidine kinase 1 (sTK1) during NAC could be utilized as a biomarker. In the PROMIX trial, women with localized HER2- BC received neoadjuvant epirubicin/docetaxel in six cycles. sTK1 was measured with an ELISA in 54 patients at cycles 1–4 and in an additional 77 patients before and 48 h after treatment 1. Treatment resulted in a 2-fold increase of sTK1 before and a 3-fold increase 48 h after the cycles, except for the first cycle, where half of the patients reacted with a significant decrease and the other half with an increase of sTK1. In Kaplan–Meier estimates of ER+ patients divided by the median of the post/pre-treatment sTK1 ratio at the first treatment cycle, OS was 97.7% and 78% (p = 0.005), and DFS was 90.7% and 68% (p = 0.006), respectively. Thus, the response of sTK1 at the first cycle of chemotherapy could be used both as an early biomarker for the guidance of chemotherapy and for the study of inherent tumor chemo-sensitivity, which could predict long-term outcome prior to therapy.

From Cellular Infiltration Assessment to a Functional Gene Set-Based Prognostic Model for Breast Cancer

Background

Cancer heterogeneity is a major challenge in clinical practice, and to some extent, the varying combinations of different cell types and their cross-talk with tumor cells that modulate the tumor microenvironment (TME) are thought to be responsible. Despite recent methodological advances in cancer, a reliable and robust model that could effectively investigate heterogeneity with direct prognostic/diagnostic clinical application remained elusive.

Results

To investigate cancer heterogeneity, we took advantage of single-cell transcriptome data and constructed the first indication- and cell type-specific reference gene expression profile (RGEP) for breast cancer (BC) that can accurately predict the cellular infiltration. By utilizing the BC-specific RGEP combined with a proven deconvolution model (LinDeconSeq), we were able to determine the intrinsic gene expression of 15 cell types in BC tissues. Besides identifying significant differences in cellular proportions between molecular subtypes, we also evaluated the varying degree of immune cell infiltration (basal-like subtype: highest; Her2 subtype: lowest) across all available TCGA-BRCA cohorts. By converting the cellular proportions into functional gene sets, we further developed a 24 functional gene set-based prognostic model that can effectively discriminate the overall survival (P = 5.9 × 10-33, n = 1091, TCGA-BRCA cohort) and therapeutic response (chemotherapy and immunotherapy) (P = 6.5 × 10-3, n = 348, IMvigor210 cohort) in the tumor patients.

Conclusions

Herein, we have developed a highly reliable BC-RGEP that adequately annotates different cell types and estimates the cellular infiltration. Of importance, the functional gene set-based prognostic model that we have introduced here showed a great ability to screen patients based on their therapeutic response. On a broader perspective, we provide a perspective to generate similar models in other cancer types to identify shared factors that drives cancer heterogeneity.

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.

Immune Cell Infiltration-Based Characterization of Triple-Negative Breast Cancer Predicts Prognosis and Chemotherapy Response Markers

Breast cancer represents the number one cause of cancer-associated mortality globally. The most aggressive molecular subtype is triple negative breast cancer (TNBC), of which limited therapeutic options are available. It is well known that breast cancer prognosis and tumor sensitivity toward immunotherapy are dictated by the tumor microenvironment. Breast cancer gene expression profiles were extracted from the METABRIC dataset and two TNBC clusters displaying unique immune features were identified. Activated immune cells formed a large proportion of cells in the high infiltration cluster, which correlated to a good prognosis. Differentially expressed genes (DEGs) extracted between two heterogeneous subtypes were used to further explore the underlying immune mechanism and to identify prognostic biomarkers. Functional enrichment analysis revealed that the DEGs were predominately related to some processes involved in activation and regulation of innate immune signaling. Using network analysis, we identified two modules in which genes were selected for further prognostic investigation. Validation by independent datasets revealed that CXCL9 and CXCL13 were good prognostic biomarkers for TNBC. We also performed comparisons between the above two genes and immune markers (CYT, APM, TILs, and TIS), as well as cell checkpoint marker expressions, and found a statistically significant correlation between them in both METABRIC and TCGA datasets. The potential of CXCL9 and CXCL13 to predict chemotherapy sensitivity was also evaluated. We found that the CXCL9 and CXCL13 were good predictors for chemotherapy and their expressions were higher in chemotherapy-responsive patients in contrast to those who were not responsive. In brief, immune infiltrate characterization on TNBC revealed heterogeneous subtypes with unique immune features allowed for the identification of informative and reliable characteristics representative of the local immune tumor microenvironment and were potential candidates to guide the management of TNBC patients.

Decision theory for precision therapy of breast cancer

Correctly estimating the hormone receptor status for estrogen (ER) and progesterone (PGR) is crucial for precision therapy of breast cancer. It is known that conventional diagnostics (immunohistochemistry, IHC) yields a significant rate of wrongly diagnosed receptor status. Here we demonstrate how Dempster Shafer decision Theory (DST) enhances diagnostic precision by adding information from gene expression. We downloaded data of 3753 breast cancer patients from Gene Expression Omnibus. Information from IHC and gene expression was fused according to DST, and the clinical criterion for receptor positivity was re-modelled along DST. Receptor status predicted according to DST was compared with conventional assessment via IHC and gene-expression, and deviations were flagged as questionable. The survival of questionable cases turned out significantly worse (Kaplan Meier p < 1%) than for patients with receptor status confirmed by DST, indicating a substantial enhancement of diagnostic precision via DST. This study is not only relevant for precision medicine but also paves the way for introducing decision theory into OMICS data science.

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.

Revisiting the Concept of Stress in the Prognosis of Solid Tumors: A Role for Stress Granules Proteins?

Simple Summary

Stress Granules (SGs) were discovered in 1999 and while the first decade of research has focused on some fundamental questions, the field is now investigating their role in human pathogenesis. Since then, evidences of a link between SGs and cancerology are accumulating in vitro and in vivo. In this work we summarized the role of SGs proteins in cancer development and their prognostic values. We find that level of expression of protein involved in SGs formation (and not mRNA level) could serve a prognostic marker in cancer. With this review we strongly suggest that SGs (proteins) could be targets of choice to block cancer development and counteract resistance to improve patients care.

Abstract

Cancer treatments are constantly evolving with new approaches to improve patient outcomes. Despite progresses, too many patients remain refractory to treatment due to either the development of resistance to therapeutic drugs and/or metastasis occurrence. Growing evidence suggests that these two barriers are due to transient survival mechanisms that are similar to those observed during stress response. We review the literature and current available open databases to study the potential role of stress response and, most particularly, the involvement of Stress Granules (proteins) in cancer. We propose that Stress Granule proteins may have prognostic value for patients.

Transcriptomic and functional pathway features were associated with survival after pathological complete response to neoadjuvant chemotherapy in breast cancer

Pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) has been proposed as a surrogate endpoint for the prediction of long-term survival in breast cancer (BC); however, an increased pCR rate has not clearly correlated with improved survival. We hypothesized that some transcriptomic and functional pathway features correlate with survival after pCR in BC. We utilized 2 published NAC cohorts, 105 women with gene expression data before, "Baseline", and that changed during NAC, "Delta", and TCGA database with 1068 BC patients to investigate the relationship between the efficacy of NAC and survival utilizing differentially expressed-mRNAs, construction and analysis of the mRNA-hub gene network, and functional pathway analysis. In mRNA expression profiling, S100A8 was a gene involved in survival after pCR in Baseline and NDP was a gene involved in recurrence after pCR in Delta. In functional pathway analysis, we found multiple pathways involved in survival after pCR. In mRNA-hub gene analysis, HSP90AA1, EEF1A1, APP, and HSPA4 were related to recurrence in BC patients with pCR due to NAC. TP53, EGFR, CTNNB1, ERBB2, and HSPB1 may play a significant role in survival for patients with pCR. Interestingly, high HSP90AA1, HSPA4, S100A8, and TP53, and low EEF1A1, EGFR, and CTNNB1 expressing tumors have significantly worse overall survival in TCGA BC cohort. We demonstrated the genes and functional pathway features associated with pCR and survival utilizing the bioinformatics approach to public BC cohorts. Some genes involved in recurrence after pCR due to NAC also served as prognostic factors in primary BC.

Downregulation of beta-catenin in chemo-tolerant TNBC through changes in receptor and antagonist profiles of the WNT pathway: Clinical and prognostic implications

PURPOSE

In approximately 30% of triple-negative breast cancer (TNBC) patients a complete pathological response is achieved. However, after neo-adjuvant chemotherapy treatment (NACT) residual tumour cells can be intrinsically resistant to chemotherapy. In this study, associations of the WNT/beta-catenin pathway with chemo-tolerance of NACT treated TNBC patients were compared to that of pre-treatment TNBC patients.

METHODS

Expression analyses were performed in both pre-treatment and NACT treated TNBC samples using immunohistochemistry and qRT-PCR, along with DNA copy number variation (CNV) and promoter methylation analyses to elucidate the mechanism(s) underlying chemo-tolerance. In addition, in vitro validation experiments were performed in TNBC cells followed by in vivo clinicopathological correlation analyses.

RESULTS

A reduced expression (41.1%) of nuclear beta-catenin together with a low proliferation index was observed in NACT samples, whereas a high expression (59.0%) was observed in pre-treatment samples. The reduced nuclear expression of beta-catenin in the NACT samples showed concordance with reduced expression levels (47-52.9%) of its associated receptors (FZD7 and LRP6) and increased expression levels (35.2-41.1%) of its antagonists (SFRP1, SFRP2, DKK1) compared to those in the pre-treatment samples. The expression levels of the receptors showed no concordance with its respective gene copy number/mRNA expression statuses, regardless treatment. Interestingly, however, significant increases in promoter hypomethylation of the antagonists were observed in the NACT samples compared to the pre-treatment samples. Similar expression patterns of the antagonists, receptors and beta-catenin were observed in the TNBC-derived cell line MDA-MB-231 using the anthracyclines doxorubicin and nogalamycin, suggesting the importance of promoter hypomethylation in chemotolerance. NACT patients showing reduced receptor and/or beta-catenin expression levels and high antagonist expression levels exhibited a comparatively better prognosis than the pre-treatment patients.

CONCLUSIONS

Our data suggest that reduced nuclear expression of beta-catenin in NACT TNBC samples, due to downregulation of its receptors and upregulation of its antagonists through promoter hypomethylation of the WNT pathway, plays an important role in chemo-tolerance.

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.

ABCC9, NKAPL, and TMEM132C are potential diagnostic and prognostic markers in triple-negative breast cancer

Triple-negative breast cancer (TNBC) is a highly heterogeneous disease. The aim of this study is to identify the diagnostic and poor prognostic signatures in TNBC by exploring the aberrant DNA methylation and gene expression. Differential expression and methylation analysis of the TNBC and paracancer samples from The Cancer Genome Atlas were performed. Gene set enrichment and protein-protein interaction (PPI) network analysis was used to explore the mechanisms of TNBC. Methylation-gene expression correlation analysis was performed, and multivariate Cox analysis and receiver operating characteristics analysis were used to further screen the hub genes for TNBC. We identified 1,525 differentially expressed genes and 150 differentially methylated genes between TNBC and paracancer samples. About 96.64% of the methylation sites were located on the CpG island. A total of 17 Gene Ontology biological process terms and 18 signal pathways were significantly enriched. GNG4, GNG11, PENK, MAOA, and AOX1 were identified as the core genes of the PPI network. Methylation-expression correlations revealed that ABCC9 (cg06951626), NKAPL (cg18675097, cg01031101, and cg17384889), and TMEM132C (cg03530754) showed promise as diagnostic and prognostic markers in TNBC. ABCC9 (cg06951626), NKAPL (cg18675097, cg01031101, and cg17384889), and TMEM132C (cg03530754) were potential diagnostic and prognostic markers in TNBC.

The therapeutic response of ER+/HER2- breast cancers differs according to the molecular Basal or Luminal subtype

The genomics-based molecular classifications aim at identifying more homogeneous classes than immunohistochemistry, associated with a more uniform clinical outcome. We conducted an in silico analysis on a meta-dataset including gene expression data from 5342 clinically defined ER+/HER2- breast cancers (BC) and DNA copy number/mutational and proteomic data. We show that the Basal (16%) versus Luminal (74%) subtypes as defined using the 80-gene signature differ in terms of response/vulnerability to systemic therapies of BC. The Basal subtype is associated with better chemosensitivity, lesser benefit from adjuvant hormone therapy, and likely better sensitivity to PARP inhibitors, platinum salts and immune therapy, and other targeted therapies under development such as FGFR inhibitors. The Luminal subtype displays potential better sensitivity to CDK4/6 inhibitors and vulnerability to targeted therapies such as PIK3CA, AR and Bcl-2 inhibitors. Expression profiles are very different, showing an intermediate position of the ER+/HER2- Basal subtype between the ER+/HER2- Luminal and ER- Basal subtypes, and let suggest a different cell-of-origin. Our data suggest that the ER+/HER2- Basal and Luminal subtypes should not be assimilated and treated as a homogeneous group.

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.

Fatty acid synthase inhibitor orlistat impairs cell growth and down-regulates PD-L1 expression of a human T-cell leukemia line

Fatty Acid Synthase (FASN) is responsible for the de novo synthesis of fatty acids, which are involved in the preservation of biological membrane structure, energy storage and assembly of factors involved in signal transduction. FASN plays a critical role in supporting tumor cell growth, thus representing a potential target for anti-cancer therapies. Moreover, this enzyme has been recently associated with increased PD-L1 expression, suggesting a role for fatty acids in the impairment of the immune response in the tumor microenvironment. Orlistat, a tetrahydrolipstatin used for the treatment of obesity, has been reported to reduce FASN activity, while inducing a sensible reduction of the growth potential in different cancer models. We have analyzed the effect of orlistat on different features involved in the tumor cell biology of the T-ALL Jurkat cell line. In particular, we have observed that orlistat inhibits Jurkat cell growth and induces a perturbation of cell cycle along with a decline of FASN activity and protein levels. Moreover, the drug produces a remarkable impairment of PD-L1 expression. These findings suggest that orlistat interferes with different mechanisms involved in the control of tumor cell growth and can potentially contribute to decrease the tumor-associated immune-pathogenesis.

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.

Metabolic enzymes expressed by cancer cells impact the immune infiltrate

The expression of two metabolic enzymes, i.e., aldehyde dehydrogenase 7 family, member A1 (ALDH7A1) and lipase C, hepatic type (LIPC) by malignant cells, has been measured by immunohistochemical methods in non-small cell lung carcinoma (NSCLC) biopsies, and has been attributed negative and positive prognostic value, respectively. Here, we demonstrate that the protein levels of ALDH7A1 and LIPC correlate with the levels of the corresponding mRNAs. Bioinformatic analyses of gene expression data from 4921 cancer patients revealed that the expression of LIPC positively correlates with abundant tumor infiltration by myeloid and lymphoid cells in NSCLC, breast carcinoma, colorectal cancer and melanoma samples. In contrast, high levels of ALDH7A1 were associated with a paucity of immune effectors within the tumor bed. These data reinforce the notion that the metabolism of cancer cells has a major impact on immune and inflammatory processes in the tumor microenvironment, pointing to hitherto unsuspected intersections between oncometabolism and immunometabolism.

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.

Molecular characterization of papillary thyroid carcinoma: a potential three-lncRNA prognostic signature

Purpose

Papillary thyroid carcinoma (PTC), the most frequent type of malignant thyroid tumor, lacks novel and reliable biomarkers of patients’ prognosis. In the current study, we mined The Cancer Genome Atlas (TCGA) to develop lncRNA signature of PTC.

Patients and methods

The intersection of PTC lncRNAs was obtained from the TCGA database using integrative computational method. By the univariate and multivariate Cox analysis, key lncRNAs were identified to construct the prognostic model. Then, all patients were divided into the high-risk group and low-risk group to perform the Kaplan–Meier (K–M) survival curves and time-dependent receiver operating characteristic (ROC) curve, estimating the prognostic power of the prognostic model. Functional enrichment analysis was also performed. Finally, we verified the results of the TCGA analysis by the Gene Expression Omnibus (GEO) databases and quantitative real-time PCR (qRT-PCR).

Results

After the comprehensive analysis, a three-lncRNA signature (PRSS3P2, KRTAP5-AS1 and PWAR5) was obtained. Interestingly, patients with low-risk scores tended to gain obviously longer survival time, and the area under the time-dependent ROC curve was 0.739. Furthermore, gene ontology (GO) and pathway analysis revealed the tumorigenic and prognostic function of the three lncRNAs. We also found three potential transcription factors to help understand the mechanisms of the PTC-specific lncRNAs. Finally, the GEO databases and qRT-PCR validation were consistent with our TCGA bioinformatics results.

Conclusion

We built a three-lncRNA signature by mining the TCGA database, which could effectively predict the prognosis of PTC.

Polymorphisms in the 3'-UTR of SCD5 gene are associated with hepatocellular carcinoma in Korean population

The purpose of the study was to assess the relationship between polymorphisms of the SCD5 and MMP1 gene and hepatocellular carcinoma (HCC). The gene polymorphisms with a minor allele frequency (MAF) > 0.05 were selected eight SNPs (rs6840, rs1065403, rs3821974, and rs3733230 in 3'-UTR; rs4693472, rs3733227, rs1848067, and rs6535374 in intron region) of SCD5 gene and two SNPs (rs1799750 and rs1144393 in promoter region) of MMP1 gene. The genotype of SCD5 and MMP1 gene SNPs were determined by direct sequencing and pyrosequencing, respectively. One hundred sixty-two patients with HCC and two hundred twenty-five control subjects were recruited in Korean male population. In terms of genotype frequencies, SCD5 genotype TC, GA, AG, and CG of rs6840, rs1065403, rs3821974, and rs3733230, respectively were higher in control group than HCC. In addition, these genotype decreased the risk (rs6840; OR 0.55, 95% CI 0.31-0.99; rs1065403; OR 0.46, 95% CI 0.26-0.83; rs3821974; OR 0.56, 95% CI 0.31-0.99; rs3733230; OR 0.62, 95% CI 0.34-1.12) of HCC, which may work as a prevention of HCC. At least one minor allele carrier of SCD5 gene polymorphisms were related to decreased risk of HCC for AFP cut-point levels > 200 or > 400 ng/ml, respectively. Our results indicate that polymorphisms in the 3'-UTR of the SCD5 gene may associated with HCC in the Korean male population.

Impact of chemotactic factors and receptors on the cancer immune infiltrate: a bioinformatics study revealing homogeneity and heterogeneity among patient cohorts

Multiple soluble factors including proteins (in particular chemokines), non-proteinaceous factors released by dead cells, as well as receptors for such factors (in particular chemokine receptors, formyl peptide receptors and purinergic receptors), influence the recruitment of distinct cell subsets into the tumor microenvironment. We performed an extensive bioinformatic analysis on tumor specimens from 5953 cancer patients to correlate the mRNA expression levels of chemotactic factors/receptors with the density of immune cell types infiltrating the malignant lesions. This meta-analysis, which included specimens from breast, colorectal, lung, ovary and head and neck carcinomas as well as melanomas, revealed that a subset of chemotactic factors/receptors exhibited a positive and reproducible correlation with several infiltrating cell types across various solid cancers, revealing a universal pattern of association. Hence, this meta-analysis distinguishes between homogeneous associations that occur across different cancer types and heterogeneous correlations, that are specific of one organ. Importantly, in four out of five breast cancer cohorts for which clinical data were available, the levels of expression of chemotactic factors/receptors that exhibited universal (rather than organ-specific) positive correlations with the immune infiltrate had a positive impact on the response to neoadjuvant chemotherapy. These results support the notion that general (rather than organ-specific) rules governing the recruitment of immune cells into the tumor bed are particularly important in determining local immunosurveillance and response to therapy.

The actin cytoskeletal architecture of estrogen receptor positive breast cancer cells suppresses invasion

Estrogen promotes growth of estrogen receptor-positive (ER+) breast tumors. However, epidemiological studies examining the prognostic characteristics of breast cancer in postmenopausal women receiving hormone replacement therapy reveal a significant decrease in tumor dissemination, suggesting that estrogen has potential protective effects against cancer cell invasion. Here, we show that estrogen suppresses invasion of ER+ breast cancer cells by increasing transcription of the Ena/VASP protein, EVL, which promotes the generation of suppressive cortical actin bundles that inhibit motility dynamics, and is crucial for the ER-mediated suppression of invasion in vitro and in vivo. Interestingly, despite its benefits in suppressing tumor growth, anti-estrogenic endocrine therapy decreases EVL expression and increases local invasion in patients. Our results highlight the dichotomous effects of estrogen on tumor progression and suggest that, in contrast to its established role in promoting growth of ER+ tumors, estrogen has a significant role in suppressing invasion through actin cytoskeletal remodeling.

Whilst estrogen is known to be tumorigenic in some breast cancer, in some contexts it can be protective against invasion and dissemination. Here, the authors show estrogen can promote generation of Suppressive Cortical Actin Bundles that can inhibit motility dynamics through EVL-mediated actin cytoskeletal remodeling.

SPIN1, negatively regulated by miR-148/152, enhances Adriamycin resistance via upregulating drug metabolizing enzymes and transporter in breast cancer

Background

Spindlin1 (SPIN1), a protein highly expressed in several human cancers, has been correlated with tumorigenesis and development. Alterations of drug metabolizing enzymes and drug transporters are major determinants of chemoresistance in tumor cells. However, whether the metabolizing enzymes and transporters are under the control of SPIN1 in breast cancer chemoresistance has not yet been defined.

Methods

SPIN1 expression in breast cancer cells and tissues was detected by quantitative real-time PCR (qRT-PCR) and immunohistochemistry. Chemosensitivity assays in vitro and in vivo were performed to determine the effect of SPIN1 on Adriamycin resistance. Downstream effectors of SPIN1 were screened by microarray and confirmed by qRT-PCR and Western blot. Luciferase assay and Western blot were used to identify miRNAs regulating SPIN1.

Results

We showed that SPIN1 was significantly elevated in drug-resistant breast cancer cell lines and tissues, compared with the chemosensitive ones. SPIN1 enhanced Adriamycin resistance of breast cancer cells in vitro, and downregulation of SPIN1 by miRNA could decrease Adriamycin resistance in vivo. Mechanistically, drug metabolizing enzymes and transporter CYP2C8, UGT2B4, UGT2B17 and ABCB4 were proven to be downstream effectors of SPIN1. Notably, SPIN1 was identified as a direct target of the miR-148/152 family (miR-148a-3p, miR-148b-3p and miR-152-3p). As expected, miR-148a-3p, miR-148b-3p or miR-152-3p could increase Adriamycin sensitivity in breast cancer cells in vitro. Moreover, high expression of SPIN1 or low expression of the miR-148/152 family predicted poorer survival in breast cancer patients.

Conclusions

Our results establish that SPIN1, negatively regulated by the miR-148/152 family, enhances Adriamycin resistance in breast cancer via upregulating the expression of drug metabolizing enzymes and drug transporter.

Electronic supplementary material

The online version of this article (10.1186/s13046-018-0748-9) contains supplementary material, which is available to authorized users.

Identification of cancer genes that are independent of dominant proliferation and lineage programs

Large, multidimensional “landscaping” projects have provided datasets that can be mined to identify potential targets for subgroups of tumors. Here, we analyzed genomic and transcriptomic data from human breast tumors to identify genes whose expression is enriched in tumors harboring specific genetic alterations. However, this analysis revealed that two other factors, proliferation rate and tumor lineage, are more dominant factors in shaping tumor transcriptional programs than genetic alterations. This discovery shifted our attention to identifying genes that are independent of the dominant proliferation and lineage programs. A small subset of these genes represents candidate targets for combination cancer therapies because they are druggable, maintained after treatment with chemotherapy, essential for cell line survival, and elevated in drug-resistant stem-like cancer cells.

Large, multidimensional cancer datasets provide a resource that can be mined to identify candidate therapeutic targets for specific subgroups of tumors. Here, we analyzed human breast cancer data to identify transcriptional programs associated with tumors bearing specific genetic driver alterations. Using an unbiased approach, we identified thousands of genes whose expression was enriched in tumors with specific genetic alterations. However, expression of the vast majority of these genes was not enriched if associations were analyzed within individual breast tumor molecular subtypes, across multiple tumor types, or after gene expression was normalized to account for differences in proliferation or tumor lineage. Together with linear modeling results, these findings suggest that most transcriptional programs associated with specific genetic alterations in oncogenes and tumor suppressors are highly context-dependent and are predominantly linked to differences in proliferation programs between distinct breast cancer subtypes. We demonstrate that such proliferation-dependent gene expression dominates tumor transcriptional programs relative to matched normal tissues. However, we also identified a relatively small group of cancer-associated genes that are both proliferation- and lineage-independent. A subset of these genes are attractive candidate targets for combination therapy because they are essential in breast cancer cell lines, druggable, enriched in stem-like breast cancer cells, and resistant to chemotherapy-induced down-regulation.

Tumor Heterogeneity in Breast Cancer

Breast cancer is a heterogeneous disease and differs greatly among different patients (intertumor heterogeneity) and even within each individual tumor (intratumor heterogeneity). Clinical and morphologic intertumor heterogeneity is reflected by staging systems and histopathologic classification of breast cancer. Heterogeneity in the expression of established prognostic and predictive biomarkers, hormone receptors, and human epidermal growth factor receptor 2 oncoprotein is the basis for targeted treatment. Molecular classifications are indicators of genetic tumor heterogeneity, which is probed with multigene assays and can lead to improved stratification into low- and high-risk groups for personalized therapy. Intratumor heterogeneity occurs at the morphologic, genomic, transcriptomic, and proteomic levels, creating diagnostic and therapeutic challenges. Understanding the molecular and cellular mechanisms of tumor heterogeneity that are relevant to the development of treatment resistance is a major area of research. Despite the improved knowledge of the complex genetic and phenotypic features underpinning tumor heterogeneity, there has been only limited advancement in diagnostic, prognostic, or predictive strategies for breast cancer. The current guidelines for reporting of biomarkers aim to maximize patient eligibility for targeted therapy, but do not take into account intratumor heterogeneity. The molecular classification of breast cancer is not implemented in routine clinical practice. Additional studies and in-depth analysis are required to understand the clinical significance of rapidly accumulating data. This review highlights inter- and intratumor heterogeneity of breast carcinoma with special emphasis on pathologic findings, and provides insights into the clinical significance of molecular and cellular mechanisms of heterogeneity.

Distinct lymphocyte antigens 6 (Ly6) family members Ly6D, Ly6E, Ly6K and Ly6H drive tumorigenesis and clinical outcome

Stem cell antigen-1 (Sca-1) is used to isolate and characterize tumor initiating cell populations from tumors of various murine models [1]. Sca-1 induced disruption of TGF-β signaling is required in vivo tumorigenesis in breast cancer models [2, 3-5]. The role of human Ly6 gene family is only beginning to be appreciated in recent literature [6-9]. To study the significance of Ly6 gene family members, we have visualized one hundred thirty gene expression omnibus (GEO) dataset using Oncomine (Invitrogen) and Georgetown Database of Cancer (G-DOC). This analysis showed that four different members Ly6D, Ly6E, Ly6H or Ly6K have increased gene expressed in bladder, brain and CNS, breast, colorectal, cervical, ovarian, lung, head and neck, pancreatic and prostate cancer than their normal counter part tissues. Increased expression of Ly6D, Ly6E, Ly6H or Ly6K was observed in sub-set of cancer type. The increased expression of Ly6D, Ly6E, Ly6H and Ly6K was found to be associated with poor outcome in ovarian, colorectal, gastric, breast, lung, bladder or brain and CNS as observed by KM plotter and PROGgeneV2 platform. The remarkable findings of increased expression of Ly6 family members and its positive correlation with poor outcome on patient survival in multiple cancer type indicate that Ly6 family members Ly6D, Ly6E, Ly6K and Ly6H will be an important targets in clinical practice as marker of poor prognosis and for developing novel therapeutics in multiple cancer type.

Gene expression information improves reliability of receptor status in breast cancer patients

Immunohistochemical (IHC) determination of receptor status in breast cancer patients is frequently inaccurate. Since it directs the choice of systemic therapy, it is essential to increase its reliability.

We increase the validity of IHC receptor expression by additionally considering gene expression (GE) measurements. Crisp therapeutic decisions are based on IHC estimates, even if they are borderline reliable. We further improve decision quality by a responsibility function, defining a critical domain for gene expression. Refined normalization is devised to file any newly diagnosed patient into existing data bases. Our approach renders receptor estimates more reliable by identifying patients with questionable receptor status. The approach is also more efficient since the rate of conclusive samples is increased. We have curated and evaluated gene expression data, together with clinical information, from 2880 breast cancer patients. Combining IHC with gene expression information yields a method more reliable and also more efficient as compared to common practice up to now.

Several types of possibly suboptimal treatment allocations, based on IHC receptor status alone, are enumerated. A ‘therapy allocation check’ identifies patients possibly miss-classified. Estrogen: false negative 8%, false positive 6%. Progesterone: false negative 14%, false positive 11%. HER2: false negative 2%, false positive 50%. Possible implications are discussed.

We propose an ‘expression look-up-plot’, allowing for a significant potential to improve the quality of precision medicine.

Methods are developed and exemplified here for breast cancer patients, but they may readily be transferred to diagnostic data relevant for therapeutic decisions in other fields of oncology.

A novel FOXA1/ESR1 interacting pathway: A study of Oncomine™ breast cancer microarrays

Forkhead box protein A1 (FOXA1) is essential for the growth and differentiation of breast epithelium, and has a favorable outcome in breast cancer (BC). Elevated FOXA1 expression in BC also facilitates hormone responsiveness in estrogen receptor (ESR)-positive BC. However, the interaction between these two pathways is not fully understood. FOXA1 and GATA binding protein 3 (GATA3) along with ESR1 expression are responsible for maintaining a luminal phenotype, thus suggesting the existence of a strong association between them. The present study utilized the Oncomine™ microarray database to identify FOXA1:ESR1 and FOXA1:ESR1:GATA3 co-expression co-regulated genes. Oncomine™ analysis revealed 115 and 79 overlapping genes clusters in FOXA1:ESR1 and FOXA1:ESR1:GATA3 microarrays, respectively. Five ESR1 direct target genes [trefoil factor 1 (TFF1/PS2), B-cell lymphoma 2 (BCL2), seven in absentia homolog 2 (SIAH2), cellular myeloblastosis viral oncogene homolog (CMYB) and progesterone receptor (PGR)] were detected in the co-expression clusters. To further investigate the role of FOXA1 in ESR1-positive cells, MCF7 cells were transfected with a FOXA1 expression plasmid, and it was observed that the direct target genes of ESR1 (PS2, BCL2, SIAH2 and PGR) were significantly regulated upon transfection. Analysis of one of these target genes, PS2, revealed the presence of two FOXA1 binding sites in the vicinity of the estrogen response element (ERE), which was confirmed by binding assays. Under estrogen stimulation, FOXA1 protein was recruited to the FOXA1 site and could also bind to the ERE site (although in minimal amounts) in the PS2 promoter. Co-transfection of FOXA1/ESR1 expression plasmids demonstrated a significantly regulation of the target genes identified in the FOXA1/ESR1 multi-arrays compared with only FOXA1 transfection, which was suggestive of a synergistic effect of ESR1 and FOXA1 on the target genes. In summary, the present study identified novel FOXA1, ESR1 and GATA3 co-expressed genes that may be involved in breast tumorigenesis.

Transposon insertional mutagenesis in mice identifies human breast cancer susceptibility genes and signatures for stratification

Despite concerted efforts to identify causal genes that drive breast cancer (BC) initiation and progression, we have yet to establish robust signatures to stratify patient risk. Here we used in vivo transposon-based forward genetic screening to identify potentially relevant BC driver genes. Integrating this approach with survival prediction analysis, we identified six gene pairs that could prognose human BC subtypes into high-, intermediate-, and low-risk groups with high confidence and reproducibility. Furthermore, we identified susceptibility gene sets for basal and claudin-low subtypes (21 and 16 genes, respectively) that stratify patients into three relative risk subgroups. These signatures offer valuable prognostic insight into the genetic basis of BC and allow further exploration of the interconnectedness of BC driver genes during disease progression.

Robust prognostic gene signatures and therapeutic targets are difficult to derive from expression profiling because of the significant heterogeneity within breast cancer (BC) subtypes. Here, we performed forward genetic screening in mice using Sleeping Beauty transposon mutagenesis to identify candidate BC driver genes in an unbiased manner, using a stabilized N-terminal truncated β-catenin gene as a sensitizer. We identified 134 mouse susceptibility genes from 129 common insertion sites within 34 mammary tumors. Of these, 126 genes were orthologous to protein-coding genes in the human genome (hereafter, human BC susceptibility genes, hBCSGs), 70% of which are previously reported cancer-associated genes, and ∼16% are known BC suppressor genes. Network analysis revealed a gene hub consisting of E1A binding protein P300 (EP300), CD44 molecule (CD44), neurofibromin (NF1) and phosphatase and tensin homolog (PTEN), which are linked to a significant number of mutated hBCSGs. From our survival prediction analysis of the expression of human BC genes in 2,333 BC cases, we isolated a six-gene-pair classifier that stratifies BC patients with high confidence into prognostically distinct low-, moderate-, and high-risk subgroups. Furthermore, we proposed prognostic classifiers identifying three basal and three claudin-low tumor subgroups. Intriguingly, our hBCSGs are mostly unrelated to cell cycle/mitosis genes and are distinct from the prognostic signatures currently used for stratifying BC patients. Our findings illustrate the strength and validity of integrating functional mutagenesis screens in mice with human cancer transcriptomic data to identify highly prognostic BC subtyping biomarkers.

Progress in the clinical detection of heterogeneity in breast cancer

Breast cancer is currently the most common form of cancer and the second‐leading cause of death from cancer in women. Though considerable progress has been made in the treatment of breast cancer, the heterogeneity of tumors (both inter‐ and intratumor) remains a considerable diagnostic and prognostic challenge. From clinical observation to genetic mutations, the history of understanding the heterogeneity of breast cancer is lengthy and detailed. Effectively detecting heterogeneity in breast cancer is important during treatment. Various methods of depicting this heterogeneity are now available and include genetic, pathologic, and imaging analysis. These methods allow characterization of the heterogeneity of breast cancer on a genetic level, providing greater insight during the process of establishing an effective therapeutic plan. This study reviews how the understanding of tumor heterogeneity in breast cancer evolved, and further summarizes recent advances in the detection and monitoring of this heterogeneity in patients with breast cancer.

Dane M, Esch A, Feiler HS, Wang NJ, Hardwicke MA, Laquerre S, Jackson J, W. Wood K, Weber B, Spellman PT, Aparicio S, Wooster R, Caldas C, Gray JW. Genome co-amplification upregulates a mitotic gene network activity that predicts outcome and response to mitotic protein inhibitors in breast cancer

BACKGROUND

High mitotic activity is associated with the genesis and progression of many cancers. Small molecule inhibitors of mitotic apparatus proteins are now being developed and evaluated clinically as anticancer agents. With clinical trials of several of these experimental compounds underway, it is important to understand the molecular mechanisms that determine high mitotic activity, identify tumor subtypes that carry molecular aberrations that confer high mitotic activity, and to develop molecular markers that distinguish which tumors will be most responsive to mitotic apparatus inhibitors.

METHODS

We identified a coordinately regulated mitotic apparatus network by analyzing gene expression profiles for 53 malignant and non-malignant human breast cancer cell lines and two separate primary breast tumor datasets. We defined the mitotic network activity index (MNAI) as the sum of the transcriptional levels of the 54 coordinately regulated mitotic apparatus genes. The effect of those genes on cell growth was evaluated by small interfering RNA (siRNA).

RESULTS

High MNAI was enriched in basal-like breast tumors and was associated with reduced survival duration and preferential sensitivity to inhibitors of the mitotic apparatus proteins, polo-like kinase, centromere associated protein E and aurora kinase designated GSK462364, GSK923295 and GSK1070916, respectively. Co-amplification of regions of chromosomes 8q24, 10p15-p12, 12p13, and 17q24-q25 was associated with the transcriptional upregulation of this network of 54 mitotic apparatus genes, and we identify transcription factors that localize to these regions and putatively regulate mitotic activity. Knockdown of the mitotic network by siRNA identified 22 genes that might be considered as additional therapeutic targets for this clinically relevant patient subgroup.

CONCLUSIONS

We define a molecular signature which may guide therapeutic approaches for tumors with high mitotic network activity.