Biomarker analysis of neoadjuvant doxorubicin/cyclophosphamide followed by ixabepilone or Paclitaxel in early-stage breast cancer

GSDME-mediated pyroptosis promotes anti-tumor immunity of neoadjuvant chemotherapy in breast cancer

Paclitaxel and anthracycline-based chemotherapy is one of the standard treatment options for breast cancer. However, only about 6-30% of breast cancer patients achieved a pathological complete response (pCR), and the mechanism responsible for the difference is still unclear. In this study, random forest algorithm was used to screen feature genes, and artificial neural network (ANN) algorithm was used to construct an ANN model for predicting the efficacy of neoadjuvant chemotherapy for breast cancer. Furthermore, digital pathology, cytology, and molecular biology experiments were used to verify the relationship between the efficacy of neoadjuvant chemotherapy and immune ecology. It was found that paclitaxel and doxorubicin, an anthracycline, could induce typical pyroptosis and bubbling in breast cancer cells, accompanied by gasdermin E (GSDME) cleavage. Paclitaxel with LDH release and Annexin V/PI doubule positive cell populations, and accompanied by the increased release of damage-associated molecular patterns, HMGB1 and ATP. Cell coculture experiments also demonstrated enhanced phagocytosis of macrophages and increased the levels of IFN-γ and IL-2 secretion after paclitaxel treatment. Mechanistically, GSDME may mediate paclitaxel and doxorubicin-induced pyroptosis in breast cancer cells through the caspase-9/caspase-3 pathway, activate anti-tumor immunity, and promote the efficacy of paclitaxel and anthracycline-based neoadjuvant chemotherapy. This study has practical guiding significance for the precision treatment of breast cancer, and can also provide ideas for understanding molecular mechanisms related to the chemotherapy sensitivity.

Network-based approach elucidates critical genes in BRCA subtypes and chemotherapy response in triple negative breast cancer

Breast cancers (BRCA) exhibit substantial transcriptional heterogeneity, posing a significant clinical challenge. The global transcriptional changes in a disease context, however, are likely mediated by few key genes which reflect disease etiology better than the differentially expressed genes (DEGs). We apply our network-based tool PathExt to 1,059 BRCA tumors across 4 subtypes to identify key mediator genes in each subtype. Compared to conventional differential expression analysis, PathExt-identified genes exhibit greater concordance across tumors, revealing shared and subtype-specific biological processes; better recapitulate BRCA-associated genes in multiple benchmarks, and are more essential in BRCA subtype-specific cell lines. Single-cell transcriptomic analysis reveals a subtype-specific distribution of PathExt-identified genes in multiple cell types from the tumor microenvironment. Application of PathExt to a TNBC chemotherapy response dataset identified subtype-specific key genes and biological processes associated with resistance. We described putative drugs that target key genes potentially mediating drug resistance.

Programming a Ferroptosis-to-Apoptosis Transition Landscape Revealed Ferroptosis Biomarkers and Repressors for Cancer Therapy

Ferroptosis and apoptosis are key cell-death pathways implicated in several human diseases including cancer. Ferroptosis is driven by iron-dependent lipid peroxidation and currently has no characteristic biomarkers or gene signatures. Here a continuous phenotypic gradient between ferroptosis and apoptosis coupled to transcriptomic and metabolomic landscapes is established. The gradual ferroptosis-to-apoptosis transcriptomic landscape is used to generate a unique, unbiased transcriptomic predictor, the Gradient Gene Set (GGS), which classified ferroptosis and apoptosis with high accuracy. Further GGS optimization using multiple ferroptotic and apoptotic datasets revealed highly specific ferroptosis biomarkers, which are robustly validated in vitro and in vivo. A subset of the GGS is associated with poor prognosis in breast cancer patients and PDXs and contains different ferroptosis repressors. Depletion of one representative, PDGFA-assaociated protein 1(PDAP1), is found to suppress basal-like breast tumor growth in a mouse model. Omics and mechanistic studies revealed that ferroptosis is associated with enhanced lysosomal function, glutaminolysis, and the tricarboxylic acid (TCA) cycle, while its transition into apoptosis is attributed to enhanced endoplasmic reticulum(ER)-stress and phosphatidylethanolamine (PE)-to-phosphatidylcholine (PC) metabolic shift. Collectively, this study highlights molecular mechanisms underlying ferroptosis execution, identified a highly predictive ferroptosis gene signature with prognostic value, ferroptosis versus apoptosis biomarkers, and ferroptosis repressors for breast cancer therapy.

Exploring miRNA‑target gene profiles associated with drug resistance in patients with breast cancer receiving neoadjuvant chemotherapy

Exosomal microRNAs (miRNAs) are closely related to drug resistance in patients with breast cancer (BC); however, only a few roles of the exosomal miRNA-target gene networks have been clinically implicated in drug resistance in BC. Therefore, the present study aimed to identify the differential expression of exosomal miRNAs associated with drug resistance and their target mRNAs. In vitro microarray analysis was used to verify differentially expressed miRNAs (DEMs) in drug-resistant BC. Next, tumor-derived exosomes (TDEs) were isolated. Furthermore, it was determined whether the candidate drug-resistant miRNAs were also significant in TDEs, and then putative miRNAs in TDEs were validated in plasma samples from 35 patients with BC (20 patients with BC showing no response and 15 patients with BC showing a complete response). It was confirmed that the combination of five exosomal miRNAs, including miR-125b-5p, miR-146a-5p, miR-484, miR-1246-5p and miR-1260b, was effective for predicting therapeutic response to neoadjuvant chemotherapy, with an area under the curve value of 0.95, sensitivity of 75%, and specificity of 95%. Public datasets were analyzed to identify differentially expressed genes (DEGs) related to drug resistance and it was revealed that BAK1, NOVA1, PTGER4, RTKN2, AGO1, CAP1, and ETS1 were the target genes of exosomal miRNAs. Networks between DEMs and DEGs were highly correlated with mitosis, metabolism, drug transport, and immune responses. Consequently, these targets could be used as predictive markers and therapeutic targets for clinical applications to enhance treatment outcomes for patients with BC.

Txnrd1 as a prognosticator for recurrence, metastasis and response to neoadjuvant chemotherapy and radiotherapy in breast cancer patients

Thioredoxin reductase 1 (Txnrd1) is known to have prognostic significance in a subset of breast cancer patients. Despite the pivotal role of Txnrd1 in regulating several cellular and physiological processes in cancer progression and metastasis, its clinical significance is largely unrecognized. Here, we undertook a retrospective comprehensive meta-analysis of 13,322 breast cancer patients from 43 independent cohorts to assess prognostic and predictive roles of Txnrd1. We observed that Txnrd1 has a positive correlation with tumor grade and size and it is over-expressed in higher-grade and larger tumors. Further, hormone receptor-negative and HER2-positive tumors exhibit elevated Txnrd1 gene expression. Patients with elevated Txnrd1 expression exhibit significant hazards for shorter disease-specific and overall survival. While Txnrd1 has a positive correlation with tumor recurrence and metastasis, it has a negative correlation with time to recurrence and metastasis. Txnrd1High patients exhibit 2.5 years early recurrence and 1.3 years early metastasis as compared to Txnrd1Low cohort. Interestingly, patients with high Txnrd1 gene expression exhibit a pathologic complete response (pCR) to neoadjuvant chemotherapy, but they experience early recurrence after radiotherapy. Txnrd1High MDA-MB-231 cells exhibit significant ROS generation and reduced viability after doxorubicin treatment compared to Txnrd1Low MCF7 cells. Corroborating with findings from meta-analysis, Txnrd1 depletion leads to decreased survival, enhanced sensitivity to radiation induced killing, poor scratch-wound healing, and reduced invasion potential in MDA-MB-231 cells. Thus, Txnrd1 appears to be a potential predictor of recurrence, metastasis and therapy response in breast cancer patients.

Integrated Proteogenomic Analysis Reveals Distinct Potentially Actionable Therapeutic Vulnerabilities in Triple-Negative Breast Cancer Subtypes

Triple-negative breast cancer (TNBC) is characterized by an aggressive clinical presentation and a paucity of clinically actionable genomic alterations. Here, we utilized the Cancer Genome Atlas (TCGA) to explore the proteogenomic landscape of TNBC subtypes to see whether genomic alterations can be inferred from proteomic data. We found only 4% of the protein level changes are explained by mutations, while 21% of the protein and 35% of the transcriptomics changes were determined by copy number alterations (CNAs). We found tighter coupling between proteome and genome in some genes that are predicted to be the targets of drug inhibitors, including CDKs, PI3K, tyrosine kinase (TKI), and mTOR. The validation of our proteogenomic workflow using mass spectrometry Clinical Proteomic Tumor Analysis Consortium (MS-CPTAC) data also demonstrated the highest correlation between protein-RNA-CNA. The integrated proteogenomic approach helps to prioritize potentially actionable targets and may enable the acceleration of personalized cancer treatment.

Transcriptomic correlates of cell cycle checkpoints with distinct prognosis, molecular characteristics, immunological regulation, and therapeutic response in colorectal adenocarcinoma

Backgrounds

Colorectal adenocarcinoma (COAD), accounting for the most common subtype of colorectal cancer (CRC), is a kind of malignant digestive tumor. Some cell cycle checkpoints (CCCs) have been found to contribute to CRC progression, whereas the functional roles of a lot of CCCs, especially the integrated role of checkpoint mechanism in the cell cycle, remain unclear.

Materials and methods

The Genomic Data Commons (GDC) The Cancer Genome Atlas (TCGA) COAD cohort was retrieved as the training dataset, and GSE24551 and GSE29623 were downloaded from Gene Expression Omnibus (GEO) as the validation datasets. A total of 209 CCC-related genes were derived from the Gene Ontology Consortium and were subsequently enrolled in the univariate, multivariate, and least absolute shrinkage and selection operator (LASSO) Cox regression analyses, finally defining a CCC signature. Cell proliferation and Transwell assay analyses were utilized to evaluate the functional roles of signature-related CCCs. The underlying CCC signature, molecular characteristics, immune-related features, and therapeutic response were finally estimated. The Genomics of Drug Sensitivity in Cancer (GDSC) database was employed for the evaluation of chemotherapeutic responses.

Results

The aberrant gene expression of CCCs greatly contributed to COAD development and progression. Univariate Cox regression analysis identified 27 CCC-related genes significantly affecting the overall survival (OS) of COAD patients; subsequently, LASSO analysis determined a novel CCC signature. Noticeably, CDK5RAP2, MAD1L1, NBN, RGCC, and ZNF207 were first identified to be correlated with the prognosis of COAD, and it was proven that all of them were significantly correlated with the proliferation and invasion of HCT116 and SW480 cells. In TCGA COAD cohort, CCC signature robustly stratified COAD patients into high and low CCC score groups (median OS: 57.24 months vs. unreached, p< 0.0001), simultaneously, with the good AUC values for OS prediction at 1, 2, and 3 years were 0.74, 0.78, and 0.77. Furthermore, the prognostic capacity of the CCC signature was verified in the GSE24551 and GSE29623 datasets, and the CCC signature was independent of clinical features. Moreover, a higher CCC score always indicated worse OS, regardless of clinical features, histological subtypes, or molecular subgroups. Intriguingly, functional enrichment analysis confirmed the CCC score was markedly associated with extracellular, matrix and immune (chemokine)-related signaling, cell cycle-related signaling, and metabolisms. Impressively, a higher CCC score was positively correlated with a majority of chemokines, receptors, immunostimulators, and anticancer immunity, indicating a relatively immune-promoting microenvironment. In addition, GSE173839, GSE25066, GSE41998, and GSE194040 dataset analyses of the underlying CCC signature suggested that durvalumab with olaparib and paclitaxel, taxane-anthracycline chemotherapy, neoadjuvant cyclophosphamide/doxorubicin with ixabepilone or paclitaxel, and immunotherapeutic strategies might be suitable for COAD patients with higher CCC score. Eventually, the GDSC database analysis showed that lower CCC scores were likely to be more sensitive to 5-fluorouracil, bosutinib, gemcitabine, gefitinib, methotrexate, mitomycin C, and temozolomide, while patients with higher CCC score seemed to have a higher level of sensitivity to bortezomib and elesclomol.

Conclusion

The novel CCC signature exhibited a good ability for prognosis prediction for COAD patients, and the CCC score was found to be highly correlated with molecular features, immune-related characteristics, and therapeutic responses, which would greatly promote clinical management and precision medicine for COAD.

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.

Network-based approach elucidates critical genes in BRCA subtypes and chemotherapy response in Triple Negative Breast Cancer

Breast cancers exhibit substantial transcriptional heterogeneity, posing a significant challenge to the prediction of treatment response and prognostication of outcomes. Especially, translation of TNBC subtypes to the clinic remains a work in progress, in part because of a lack of clear transcriptional signatures distinguishing the subtypes. Our recent network-based approach, PathExt, demonstrates that global transcriptional changes in a disease context are likely mediated by a small number of key genes, and these mediators may better reflect functional or translationally relevant heterogeneity. We apply PathExt to 1059 BRCA tumors and 112 healthy control samples across 4 subtypes to identify frequent, key-mediator genes in each BRCA subtype. Compared to conventional differential expression analysis, PathExt-identified genes (1) exhibit greater concordance across tumors, revealing shared as well as BRCA subtype-specific biological processes, (2) better recapitulate BRCA-associated genes in multiple benchmarks, and (3) exhibit greater dependency scores in BRCA subtype-specific cancer cell lines. Single cell transcriptomes of BRCA subtype tumors reveal a subtype-specific distribution of PathExt-identified genes in multiple cell types from the tumor microenvironment. Application of PathExt to a TNBC chemotherapy response dataset identified TNBC subtype-specific key genes and biological processes associated with resistance. We described putative drugs that target top novel genes potentially mediating drug resistance. Overall, PathExt applied to breast cancer refines previous views of gene expression heterogeneity and identifies potential mediators of TNBC subtypes, including potential therapeutic targets.

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.

Applicability of Anticancer Drugs for the Triple-Negative Breast Cancer Based on Homologous Recombination Repair Deficiency

Triple-negative breast cancer (TNBC) is a highly aggressive disease with historically poor outcomes, primarily due to the lack of effective targeted therapies. Here, we established a drug sensitivity prediction model based on the homologous recombination deficiency (HRD) using 83 TNBC patients from TCGA. Through analyzing the effect of HRD status on response efficacy of anticancer drugs and elucidating its related mechanisms of action, we found rucaparib (PARP inhibitor) and doxorubicin (anthracycline) sensitive in HR-deficient patients, while paclitaxel sensitive in the HR-proficient. Further, we identified a HRD signature based on gene expression data and constructed a transcriptomic HRD score, for analyzing the functional association between anticancer drug perturbation and HRD. The results revealed that CHIR99021 (GSK3 inhibitor) and doxorubicin have similar expression perturbation patterns with HRD, and talazoparib (PARP inhibitor) could kill tumor cells by reversing the HRD activity. Genomic characteristics indicated that doxorubicin inhibited tumor cells growth by hindering the process of DNA damage repair, while the resistance of cisplatin was related to the activation of angiogenesis and epithelial-mesenchymal transition. The negative correlation of HRD signature score could interpret the association of doxorubicin pIC50 with worse chemotherapy response and shorter survival of TNBC patients. In summary, these findings explain the applicability of anticancer drugs in TNBC and underscore the importance of HRD in promoting personalized treatment development.

Reduced Expression of Annexin A6 Induces Metabolic Reprogramming That Favors Rapid Fatty Acid Oxidation in Triple-Negative Breast Cancer Cells

The ability of cancer cells to alter their metabolism is one of the major mechanisms underlying rapid tumor progression and/or therapeutic resistance in solid tumors, including the hard-to-treat triple-negative breast cancer (TNBC) subtype. Here, we assessed the contribution of the tumor suppressor, Annexin A6 (AnxA6), in the metabolic adaptation of basal-like (AnxA6-low) versus mesenchymal-like (AnxA6-high), as well as in lapatinib-resistant TNBC cells. Using model basal-like and mesenchymal-like TNBC cell lines, we show that TNBC cells also exhibit metabolic heterogeneity. The downregulation of AnxA6 in TNBC cells generally attenuated mitochondrial respiration, glycolytic flux, and cellular ATP production capacity resulting in a quiescent metabolic phenotype. We also show that AnxA6 depletion in mesenchymal-like TNBC cells was associated with a rapid uptake and mitochondrial fatty acid oxidation and diminished lipid droplet accumulation and altered the lipogenic metabolic phenotype of these cells to a lypolytic metabolic phenotype. The overexpression or chronic lapatinib-induced upregulation of AnxA6 in AnxA6-low TNBC cells reversed the quiescent/lypolytic phenotype to a more lipogenic/glycolytic phenotype with gluconeogenic precursors as additional metabolites. Collectively, these data suggest that the expression status of AnxA6 in TNBC cells underlies distinct metabolic adaptations of basal-like and mesenchymal-like TNBC subsets in response to cellular stress and/or therapeutic intervention and suggest AnxA6 as a biomarker for metabolic subtyping of TNBC subsets.

Multi-omic machine learning predictor of breast cancer therapy response

Breast cancers are complex ecosystems of malignant cells and the tumour microenvironment1. The composition of these tumour ecosystems and interactions within them contribute to responses to cytotoxic therapy2. Efforts to build response predictors have not incorporated this knowledge. We collected clinical, digital pathology, genomic and transcriptomic profiles of pre-treatment biopsies of breast tumours from 168 patients treated with chemotherapy with or without HER2 (encoded by ERBB2)-targeted therapy before surgery. Pathology end points (complete response or residual disease) at surgery3 were then correlated with multi-omic features in these diagnostic biopsies. Here we show that response to treatment is modulated by the pre-treated tumour ecosystem, and its multi-omics landscape can be integrated in predictive models using machine learning. The degree of residual disease following therapy is monotonically associated with pre-therapy features, including tumour mutational and copy number landscapes, tumour proliferation, immune infiltration and T cell dysfunction and exclusion. Combining these features into a multi-omic machine learning model predicted a pathological complete response in an external validation cohort (75 patients) with an area under the curve of 0.87. In conclusion, response to therapy is determined by the baseline characteristics of the totality of the tumour ecosystem captured through data integration and machine learning. This approach could be used to develop predictors for other cancers.

Computationally scalable regression modeling for ultrahigh-dimensional omics data with ParProx

Statistical analysis of ultrahigh-dimensional omics scale data has long depended on univariate hypothesis testing. With growing data features and samples, the obvious next step is to establish multivariable association analysis as a routine method to describe genotype-phenotype association. Here we present ParProx, a state-of-the-art implementation to optimize overlapping and non-overlapping group lasso regression models for time-to-event and classification analysis, with selection of variables grouped by biological priors. ParProx enables multivariable model fitting for ultrahigh-dimensional data within an architecture for parallel or distributed computing via latent variable group representation. It thereby aims to produce interpretable regression models consistent with known biological relationships among independent variables, a property often explored post hoc, not during model estimation. Simulation studies clearly demonstrate the scalability of ParProx with graphics processing units in comparison to existing implementations. We illustrate the tool using three different omics data sets featuring moderate to large numbers of variables, where we use genomic regions and biological pathways as variable groups, rendering the selected independent variables directly interpretable with respect to those groups. ParProx is applicable to a wide range of studies using ultrahigh-dimensional omics data, from genome-wide association analysis to multi-omics studies where model estimation is computationally intractable with existing implementation.

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.

An Immune-Associated Genomic Signature Effectively Predicts Pathologic Complete Response to Neoadjuvant Paclitaxel and Anthracycline-Based Chemotherapy in Breast Cancer

Breast cancer is now the leading cause of cancer morbidity and mortality among women worldwide. Paclitaxel and anthracycline-based neoadjuvant chemotherapy is widely used for the treatment of breast cancer, but its sensitivity remains difficult to predict for clinical use. In our study, a LASSO logistic regression method was applied to develop a genomic classifier for predicting pathologic complete response (pCR) to neoadjuvant chemotherapy in breast cancer. The predictive accuracy of the signature classifier was further evaluated using four other independent test sets. Also, functional enrichment analysis of genes in the signature was performed, and the correlations between the prediction score of the signature classifier and immune characteristics were explored. We found a 25-gene signature classifier through the modeling, which showed a strong ability to predict pCR to neoadjuvant chemotherapy in breast cancer. For T/FAC-based training and test sets, and a T/AC-based test set, the AUC of the signature classifier is 1.0, 0.9071, 0.9683, 0.9151, and 0.7350, respectively, indicating that it has good predictive ability for both T/FAC and T/AC schemes. The multivariate model showed that 25-gene signature was far superior to other clinical parameters as independent predictor. Functional enrichment analysis indicated that genes in the signature are mainly enriched in immune-related biological processes. The prediction score of the classifier was significantly positively correlated with the immune score. There were also significant differences in immune cell types between pCR and residual disease (RD) samples. Conclusively, we developed a 25-gene signature classifier that can effectively predict pCR to paclitaxel and anthracycline-based neoadjuvant chemotherapy in breast cancer. Our study also suggests that the immune ecosystem is actively involved in modulating clinical response to neoadjuvant chemotherapy and is beneficial to patient outcomes.

Combined homologous recombination repair deficiency and immune activation analysis for predicting intensified responses of anthracycline, cyclophosphamide and taxane chemotherapy in triple-negative breast cancer

BACKGROUND

Triple-negative breast cancer (TNBC) is a clinically aggressive disease with abundant variants that cause homologous recombination repair deficiency (HRD). Whether TNBC patients with HRD are sensitive to anthracycline, cyclophosphamide and taxane (ACT), and whether the combination of HRD and tumour immunity can improve the recognition of ACT responders are still unknown.

METHODS

Data from 83 TNBC patients in The Cancer Genome Atlas (TCGA) was used as a discovery cohort to analyse the association between HRD and ACT chemotherapy benefits. The combined effects of HRD and immune activation on ACT chemotherapy were explored at both the genome and the transcriptome levels. Independent cohorts from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) and Gene Expression Omnibus (GEO) were adopted to validate our findings.

RESULTS

HRD was associated with a longer ACT chemotherapy failure-free interval (FFI) with a hazard ratio of 0.16 (P = 0.004) and improved patient prognosis (P = 0.0063). By analysing both HRD status and ACT response, we identified patients with a distinct TNBC subtype (ACT-S&HR-P) that showed higher tumour lymphocyte infiltration, IFN-γ activity and NK cell levels. Patients with ACT-S&HR-P had significantly elevated immune inhibitor levels and presented immune activation associated with the increased activities of both innate immune cells and adaptive immune cells, which suggested treatment with immune checkpoint blockade as an option for this subtype. Our analysis revealed that the combination of HRD and immune activation enhanced the efficiency of identifying responders to ACT chemotherapy (AUC = 0.91, P = 1.06e-04) and synergistically contributed to the clinical benefits of TNBC patients. A transcriptional HRD signature of ACT response-related prognostic factors was identified and independently validated to be significantly associated with improved survival in the GEO cohort (P = 0.0038) and the METABRIC dataset (P < 0.0001).

CONCLUSIONS

These findings highlight that HR deficiency prolongs FFI and predicts intensified responses in TNBC patients by combining HRD and immune activation, which provides a molecular basis for identifying ACT responders.

Synthetic lethality-mediated precision oncology via the tumor transcriptome

Precision oncology has made significant advances, mainly by targeting actionable mutations in cancer driver genes. Aiming to expand treatment opportunities, recent studies have begun to explore the utility of tumor transcriptome to guide patient treatment. Here, we introduce SELECT (synthetic lethality and rescue-mediated precision oncology via the transcriptome), a precision oncology framework harnessing genetic interactions to predict patient response to cancer therapy from the tumor transcriptome. SELECT is tested on a broad collection of 35 published targeted and immunotherapy clinical trials from 10 different cancer types. It is predictive of patients' response in 80% of these clinical trials and in the recent multi-arm WINTHER trial. The predictive signatures and the code are made publicly available for academic use, laying a basis for future prospective clinical studies.

Extensive analysis of the molecular biomarkers excision repair cross complementing 1, ribonucleotide reductase M1, β-tubulin III, thymidylate synthetase, and topoisomerase IIα in breast cancer: Association with clinicopathological characteristics

Excision repair cross complementing 1 (ERCC1), ribonucleotide reductase M1 (RRM1), β-tubulin III (TUBB3), thymidylate synthetase (TYMS), and topoisomerase IIα (TOP2A) genes have been shown to be associated with the pathogenesis and prognosis of various types of carcinomas; however, their roles in breast cancer have not been fully validated. In this study, we evaluated the correlations among these biomarkers and the associations between their expression intensity and the clinicopathological characteristics to investigate whether the above genes are underlying biomarkers for patients with breast cancer.Ninety-seven tissue specimens collected from breast cancer patients. The expression levels of these biomarkers were measured by the multiplex branched DNA liquidchip (MBL) technology and clinicopathological characteristics were collected simultaneously.The expression levels of ERCC1, TUBB3, TYMS, and TOP2A were significantly associated with the characteristics of menopausal status, tumor size, lymph node metastasis, hormone receptor status, triple-negative status, Ki-67 index, and epidermal growth factor receptor. The expression intensity of ERCC1 negatively associated with that of TUBB3 and TYMS, and positively associated with that of RRM1. The expression intensity of TOP2A positively associated with that of TYMS. Hierarchical clustering analysis and difference test indicated that breast cancer with higher levels of TUBB3, TYMS, and TOP2A, as well as lower levels of ERCC1 and RRM1 tended to have higher histological grade and Ki-67 index.Our studies showed that ERCC1, TYMS, TUBB3, and TOP2A may be potential biomarkers for prognosis and individualized chemotherapy guidance, while there may be interactions between ERCC1 and RRM1, or TUBB3, or TYMS, as well as between TOP2A and TYMS in pathogenesis and development of breast cancer.

Systems Medicine Design for Triple-Negative Breast Cancer and Non-Triple-Negative Breast Cancer Based on Systems Identification and Carcinogenic Mechanisms

Triple-negative breast cancer (TNBC) is a heterogeneous subtype of breast cancers with poor prognosis. The etiology of triple-negative breast cancer (TNBC) is involved in various biological signal cascades and multifactorial aberrations of genetic, epigenetic and microenvironment. New therapeutic for TNBC is urgently needed because surgery and chemotherapy are the only available modalities nowadays. A better understanding of the molecular mechanisms would be a great challenge because they are triggered by cascade signaling pathways, genetic and epigenetic regulations, and drug–target interactions. This would allow the design of multi-molecule drugs for the TNBC and non-TNBC. In this study, in terms of systems biology approaches, we proposed a systematic procedure for systems medicine design toward TNBC and non-TNBC. For systems biology approaches, we constructed a candidate genome-wide genetic and epigenetic network (GWGEN) by big databases mining and identified real GWGENs of TNBC and non-TNBC assisting with corresponding microarray data by system identification and model order selection methods. After that, we applied the principal network projection (PNP) approach to obtain the core signaling pathways denoted by KEGG pathway of TNBC and non-TNBC. Comparing core signaling pathways of TNBC and non-TNBC, essential carcinogenic biomarkers resulting in multiple cellular dysfunctions including cell proliferation, autophagy, immune response, apoptosis, metastasis, angiogenesis, epithelial-mesenchymal transition (EMT), and cell differentiation could be found. In order to propose potential candidate drugs for the selected biomarkers, we designed filters considering toxicity and regulation ability. With the proposed systematic procedure, we not only shed a light on the differences between carcinogenetic molecular mechanisms of TNBC and non-TNBC but also efficiently proposed candidate multi-molecule drugs including resveratrol, sirolimus, and prednisolone for TNBC and resveratrol, sirolimus, carbamazepine, and verapamil for non-TNBC.

An Improved, Assay Platform Agnostic, Absolute Single Sample Breast Cancer Subtype Classifier

While intrinsic molecular subtypes provide important biological classification of breast cancer, the subtype assignment of individuals is influenced by assay technology and study cohort composition. We sought to develop a platform-independent absolute single-sample subtype classifier based on a minimal number of genes. Pairwise ratios for subtype-specific differentially expressed genes from un-normalized expression data from 432 breast cancer (BC) samples of The Cancer Genome Atlas (TCGA) were used as inputs for machine learning. The subtype classifier with the fewest number of genes and maximal classification power was selected during cross-validation. The final model was evaluated on 5816 samples from 10 independent studies profiled with four different assay platforms. Upon cross-validation within the TCGA cohort, a random forest classifier (MiniABS) with 11 genes achieved the best accuracy of 88.2%. Applying MiniABS to five validation sets of RNA-seq and microarray data showed an average accuracy of 85.15% (vs. 77.72% for Absolute Intrinsic Molecular Subtype (AIMS)). Only MiniABS could be applied to five low-throughput datasets, showing an average accuracy of 87.93%. The MiniABS can absolutely subtype BC using the raw expression levels of only 11 genes, regardless of assay platform, with higher accuracy than existing methods.

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.

Radiation-Related Deregulation of TUBB3 and BRCA1/2 and Risk of Secondary Lung Cancer in Women With Breast Cancer

INTRODUCTION

Breast cancer survivors are at increased risk of developing unrelated primary cancers, particularly lung cancer. Evidence indicates that sex hormones as well as a deregulation of DNA-repair pathways may contribute to lung cancer onset. We investigated whether the hormone status and expression of markers involved in DNA repair (BRCA1/2, ERCC1, and P53R2), synthesis (TS and RRM1), and cell division (TUBB3) might be linked to lung cancer risk.

PATIENTS AND METHODS

Thirty-seven breast cancer survivors with unrelated lung cancer and 84 control subjects comprising women with breast cancer (42/84) or lung cancer (42/84) were enrolled. Immunohistochemistry on tumor tissue was performed. Geometric mean ratio was used to assess the association of marker levels with patient groups.

RESULTS

Estrogen receptor was expressed in approximately 90% of the breast cancer group but was negative in the majority of the lung cancer group, a result similar to the lung cancer control group. Likewise, ER isoform β was weakly expressed in the lung cancer group. Protein analysis of breast cancer versus control had a significantly lower expression of BRCA1, P53R2, and TUBB3. Likewise, a BRCA1 reduction was observed in the lung cancer group concomitant with a BRCA2 increase. Furthermore, BRCA2 and TUBB3 increased in ipsilateral lung cancer in women who had previously received radiotherapy for breast cancer.

CONCLUSION

The decrease of DNA-repair proteins in breast cancer could make these women more susceptible to therapy-related cancer. The increase of BRCA2 and TUBB3 in lung cancer from patients who previously received radiotherapy for breast cancer might reflect a tissue response to exposure to ionizing radiation.

PDXGEM: patient-derived tumor xenograft-based gene expression model for predicting clinical response to anticancer therapy in cancer patients

Background

Cancer is a highly heterogeneous disease with varying responses to anti-cancer drugs. Although several attempts have been made to predict the anti-cancer therapeutic responses, there remains a great need to develop highly accurate prediction models of response to the anti-cancer drugs for clinical applications toward a personalized medicine. Patient derived xenografts (PDXs) are preclinical cancer models in which the tissue or cells from a patient’s tumor are implanted into an immunodeficient or humanized mouse. In the present study, we develop a bioinformatics analysis pipeline to build a predictive gene expression model (GEM) for cancer patients’ drug responses based on gene expression and drug activity data from PDX models.

Results

Drug sensitivity biomarkers were identified by performing an association analysis between gene expression levels and post-treatment tumor volume changes in PDX models. We built a drug response prediction model (called PDXGEM) in a random-forest algorithm by using a subset of the drug sensitvity biomarkers with concordant co-expression patterns between the PDXs and pretreatment cancer patient tumors. We applied the PDXGEM to several cytotoxic chemotherapies as well as targeted therapy agents that are used to treat breast cancer, pancreatic cancer, colorectal cancer, or non-small cell lung cancer. Significantly accurate predictions of PDXGEM for pathological response or survival outcomes were observed in extensive independent validations on multiple cancer patient datasets obtained from retrospective observational studies and prospective clinical trials.

Conclusion

Our results demonstrated the strong potential of using molecular profiles and drug activity data of PDX tumors in developing a clinically translatable predictive cancer biomarkers for cancer patients. The PDXGEM web application is publicly available at http://pdxgem.moffitt.org.

Predictors of Neoadjuvant Chemotherapy Response in Breast Cancer: A Review

Neoadjuvant chemotherapy (NAC) largely increases operative chances and improves prognosis of the local advanced breast cancer patients. However, no specific means have been invented to predict the therapy responses of patients receiving NAC. Therefore, we focus on the alterations of tumor tissue-related microenvironments such as stromal tumor-infiltrating lymphocytes status, cyclin-dependent kinase expression, non-coding RNA transcription or other small molecular changes, in order to detect potentially predicted biomarkers which reflect the therapeutic efficacy of NAC in different subtypes of breast cancer. Further, possible mechanisms are also discussed to discover feasible treatment targets. Thus, these findings will be helpful to promote the prognosis of breast cancer patients who received NAC and summarized in this review.

A Qualitative Transcriptional Signature for Predicting Extreme Resistance of ER-Negative Breast Cancer to Paclitaxel, Doxorubicin, and Cyclophosphamide Neoadjuvant Chemotherapy

For estrogen receptor (ER)-negative breast cancer patients, paclitaxel (P), doxorubicin (A) and cyclophosphamide (C) neoadjuvant chemotherapy (NAC) is the standard therapeutic regimen. Pathologic complete response (pCR) and residual disease (RD) are common surrogate measures of chemosensitivity. After NAC, most patients still have RD; of these, some partially respond to NAC, whereas others show extreme resistance and cannot benefit from NAC but only suffer complications resulting from drug toxicity. Here we developed a qualitative transcriptional signature, based on the within-sample relative expression ordering (REO) of gene pairs, to identify extremely resistant samples to PAC NAC. Using gene expression data for ER-negative breast cancer patients including 113 pCR samples and 137 RD samples from four datasets, we selected 61 gene pairs with reversal REO patterns between the two groups as the resistance signature, denoted as NR61. Samples with more than 37 signature gene pairs that had the same REO patterns within the extremely resistant group were defined as having extreme resistance; otherwise, they were considered responders. In the GSE25055 and GSE25065 dataset, the NR61 signature could correctly identify 44 (97.8%) of the 45 pCR samples and 22 (95.7%) of the 23 pCR samples as responder samples, respectively; it also identified 13 (16.9%) of 77 RD samples and 8 (21.1%) of 38 RD samples as extremely resistant samples, respectively. Survival analysis showed that the distant relapse-free survival (DRFS) time of the 14 extremely resistant cases was significantly shorter than that of the 108 responders (P < 0.01; HR = 3.84; 95% CI = 1.91–7.70) in GSE25055. Similar results were obtained in GSE25065. Moreover, in the integrated data of the two datasets with 94 responders and 21 extremely resistant samples identified from RD patients, the former had significantly longer DRFS than the latter (P < 0.01; HR = 2.22; 95% CI = 1.26–3.90). In summary, our signature could effectively identify patients who completely respond to PAC NAC, as well as cases of extreme resistance, which can assist decision-making on the clinical therapy for these patients.

A 3-gene biomarker signature to predict response to taxane-based neoadjuvant chemotherapy in breast cancer

Breast cancer is the most common cancer in women worldwide, affecting one in eight women in their lifetime. Taxane-based chemotherapy is routinely used in the treatment of breast cancer. The purpose of this study was to develop and validate a predictive biomarker to improve the benefit/risk ratio for that cytotoxic chemotherapy. We explicitly strived for a biomarker that enables secure translation into clinical practice. We used genome-wide gene expression data of the Hatzis et al. discovery cohort of 310 patients for biomarker development and three independent cohorts with a total of 567 breast cancer patients for validation. We were able to develop a biomarker signature that consists of just the three gene products ELF5, SCUBE2 and NFIB, measured on RNA level. Compared to Hatzis et al., we achieved a significant improvement in predicting responders and non-responders in the Hatzis et al. validation cohort with an area under the receiver operating characteristics curve of 0.73 [95% CI, 69%—77%]. Moreover, we could confirm the performance of our biomarker on two further independent validation cohorts. The overall performance on all three validation cohorts expressed as area under the receiver operating characteristics curve was 0.75 [95% CI, 70%—80%]. At the clinically relevant classifier’s operation point to optimize the exclusion of non-responders, the biomarker correctly predicts three out of four patients not responding to neoadjuvant taxane-based chemotherapy, independent of the breast cancer subtype. At the same time, the response rate in the group of predicted responders increased to 42% compared to 23% response rate in all patients of the validation cohorts.

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.

Downregulation of GPSM2 is associated with primary resistance to paclitaxel in breast cancer

Paclitaxel is one of the most effective chemotherapy drugs for breast cancer worldwide but 20–30% patients show primary resistance to the drug. Screening and identification of markers that facilitate effective and rapid prediction of sensitivity to paclitaxel is therefore an urgent medical requirement. In the present study, G protein signaling modulator 2 (GPSM2) mRNA levels were significantly associated with taxane sensitivity in experiments based on the Gene Expression Omnibus (GEO) online database. Immunohistochemical analysis consistently revealed a significant association of GPSM2 protein levels with paclitaxel sensitivity in breast cancer patients. Knockdown of GPSM2 reduced the sensitivity of breast cancer cells to paclitaxel via regulation of the cell cycle. Animal experiments further corroborated our in vitro findings. These results suggest that GPSM2 plays an important role in breast cancer resistance, supporting its utility as a potential target for improving drug susceptibility in patients as well as a marker of paclitaxel sensitivity.

Evaluation of the 12-Gene Molecular Score and the 21-Gene Recurrence Score as Predictors of Response to Neo-adjuvant Chemotherapy in Estrogen Receptor-Positive, HER2-Negative Breast Cancer

Background

Neo-adjuvant chemotherapy (NaCT) facilitates complete surgical resection in locally advanced breast cancer. Due to its association with improved outcome, complete pathologic response (pCR) to neo-adjuvant treatment has been accepted as a surrogate for long-term outcome in clinical trials of human epidermal growth factor receptor 2 (HER2)-positive, triple-negative, or luminal B breast cancer patients. In contrast, NaCT is effective in only ~ 7–10% of estrogen receptor (ER)-positive, HER2-negative disease. Response biomarkers would enable such patients to be selected for NaCT.

Methods

Two commercially available breast cancer prognostic signatures [12-gene molecular score (MS) and the 21-gene Recurrence Score (RS)] were compared in their ability to predict pCR to NaCT in ER-positive, HER2-negative breast cancer in six public RNA expression microarray data sets. Scores were approximated according to published algorithms and analyzed by logistic regression.

Results

Expression data were available for 764 ER-positive, HER2-negative breast cancer samples, including 59 patients with pCR. The two scores were well correlated. Either score was a significant predictor of pCR (12-gene MS p = 9.4 × 10⁻⁵; 21-gene RS p = 0.0041). However, in a model containing both scores, the 12-gene MS remained significant (p = 0.0079), while the 21-gene RS did not (p = 0.79).

Conclusions

In this microarray study, two commercial breast cancer prognostic scores were significant predictors of response to NaCT. In direct comparison, the 12-gene MS outperformed the 21-gene RS as a predictive marker for NaCT. Considering pCR as surrogate for improved survival, these results support the ability of both scores to predict chemotherapy sensitivity.

Electronic supplementary material

The online version of this article (10.1245/s10434-019-08039-7) contains supplementary material, which is available to authorized users.

Long noncoding RNA FER1L4 acts as an oncogenic driver in human pan-cancer

The function of Fer-1 like family member 4 (FER1L4) in human pan-cancer is unknown. Expression of FER1L4 in tumor tissues and nontumor tissues, upstream regulation of FER1L4, and the relationship between its expression with prognosis and chemoresistance were examined by The Cancer Genome Atlas and Gene Expression Omnibus databases. Next, these results were validated in breast tumor and paired nontumor tissues in our cohort. FER1L4 expression is higher in tumor tissues compared with the adjacent nontumor tissues. High FER1L4 expression is associated with worse disease outcomes. Hypomethylation and H3K4me3 accumulation in FER1L4 promoter locus activate its transcriptional expression. Moreover, FER1L4 may trigger chemoresistance in human cancer. Gene Ontology enrichment analysis revealed that FER1L4 may be involved in processes associated with tumorigenesis. These results indicated that FER1L4 may act as an oncogenic driver and it may be a potential therapy target in human cancer.

ALDH1A1 in patient-derived bladder cancer spheroids activates retinoic acid signaling leading to TUBB3 overexpression and tumor progression

Acquired chemoresistance is a critical issue for advanced bladder cancer patients during long-term treatment. Recent studies reveal that a fraction of tumor cells with enhanced tumor-initiating potential, or cancer stem-like cells (CSCs), may particularly contribute to acquired chemoresistance and recurrence. Thus, CSC characterization will be the first step towards understanding the mechanisms underlying advanced disease. Here we generated long-term patient-derived cancer cells (PDCs) from bladder cancer patient specimens in spheroid culture, which is favorable for CSC enrichment. Pathological features of bladder cancer PDCs and PDC-dependent patient-derived xenografts (PDXs) were basically similar to those of their corresponding patients' specimens. Notably, CSC marker aldehyde dehydrogenase 1A1 (ALDH1A1), a critical enzyme that synthesizes retinoic acid (RA), was abundantly expressed in PDCs. ALDH1A1 inhibitors and shRNAs repressed both PDC proliferation and spheroid formation, whereas all-trans RA could rescue ALDH1A1 shRNA-suppressed spheroid formation. ALDH inhibitor also reduced the in vivo growth of PDC-derived xenografts. ALDH1A1 knockdown study showed that tubulin beta III (TUBB3) was one of the downregulated genes in PDCs. We identified functional RA response elements in TUBB3 promoter, whose transcriptional activities were substantially activated by RA. Clinical survival database reveals that TUBB3 expression may associate with poor prognosis in bladder cancer patients. Moreover, TUBB3 knockdown was sufficient to suppress PDC proliferation and spheroid formation. Taken together, our results indicate that ALDH1A1 and its putative downstream target TUBB3 are overexpressed in bladder cancer, and those molecules could be applied to alternative diagnostic and therapeutic options for advanced disease.

FLOating-Window Projective Separator (FloWPS): A Data Trimming Tool for Support Vector Machines (SVM) to Improve Robustness of the Classifier

Here, we propose a heuristic technique of data trimming for SVM termed FLOating Window Projective Separator (FloWPS), tailored for personalized predictions based on molecular data. This procedure can operate with high throughput genetic datasets like gene expression or mutation profiles. Its application prevents SVM from extrapolation by excluding non-informative features. FloWPS requires training on the data for the individuals with known clinical outcomes to create a clinically relevant classifier. The genetic profiles linked with the outcomes are broken as usual into the training and validation datasets. The unique property of FloWPS is that irrelevant features in validation dataset that don’t have significant number of neighboring hits in the training dataset are removed from further analyses. Next, similarly to the k nearest neighbors (kNN) method, for each point of a validation dataset, FloWPS takes into account only the proximal points of the training dataset. Thus, for every point of a validation dataset, the training dataset is adjusted to form a floating window. FloWPS performance was tested on ten gene expression datasets for 992 cancer patients either responding or not on the different types of chemotherapy. We experimentally confirmed by leave-one-out cross-validation that FloWPS enables to significantly increase quality of a classifier built based on the classical SVM in most of the applications, particularly for polynomial kernels.

High expression of class III β-tubulin in upper gastrointestinal cancer types

Class III β-tubulin (TUBB3) is a component of microtubules of neuronal cells that is upregulated in various cancer entities. To better understand the role of TUBB3 in upper gastrointestinal tract cancer types, the present study assessed TUBB3 expression in tissue microarrays including 189 gastric and 428 esophageal cancer. TUBB3 expression was detected in 62.4% of gastric cancer, 73.8% of esophageal adenocarcinoma and 88.7% of esophageal squamous cell cancer, while control samples of normal esophageal and gastric epithelium were TUBB3-negative. TUBB3 positivity was not associated with the International Union Against Cancer classification, World Health Organization grading, lymph node involvement or distant metastasis in any entity. Of note, TUBB3 expression was associated with tumor localization and prognosis in gastric cancer, with the tumor stage in esophageal adenocarcinoma, and with the resection margin in esophageal squamous cell cancer. In conclusion, the substantial rate of positivity for TUBB3 already in early stages of gastric cancer in combination with the lack of a further increase in frequency with tumor stage, may suggest, that TUBB3 upregulation is rather relevant for cancer development than for cancer progression. TUBB3 might be a suitable prognostic biomarker in gastric cancer types.

The effect of participation in neoadjuvant clinical trials on outcomes in patients with early breast cancer

BACKGROUND

Clinical trials can offer novel and more advanced and/or novel treatments to cancer patients in advance of them being approved and available for all patients. While several studies have examined the effect of clinical trial participation on prognosis, there has been no clear conclusion from these studies. Therefore, we chose to test the influence of trial participation on pathological complete response (pCR) and mastectomy rates after neoadjuvant chemotherapy.

METHODS

In this retrospective study, all patients treated with neoadjuvant chemotherapy from 2001 to 2014 were selected. A total of 1038 patients with complete treatment, patient, and tumor characteristics were included. A total of 260 of those were treated in clinical trials. We examined whether study participation status in addition to commonly known predictors for pCR improves prediction of pCR. Similar analyses were conducted for the mastectomy rate outcome measure. Finally, survival analyses were also conducted as part of an exploratory analysis.

RESULTS

Study participation was an independent predictor of pCR in addition to commonly known predictors. Adjusted odds ratio (OR) for trial participants versus non-participants was 1.53 (95% CI 1.03-2.28). Additionally, study participation improved the prediction of mastectomy risk. The adjusted OR for trial participants versus non-participants was 0.62 (95% CI 0.42-0.90). Subgroup-specific differences concerning the impact of study participation could not be shown for either pCR or mastectomy rate. Survival comparisons could not be conducted due to large differences in follow-up data in patients participating in clinical trials versus those who did not participate; however, pCR was a predictor of prognosis in both groups.

CONCLUSION

Patients taking part in neoadjuvant chemotherapy clinical trials have a higher pCR rate and a lower mastectomy risk than patients not participating in clinical trials for their cancer care. This finding is a supporting factor for trial participation in neoadjuvant chemotherapy trials.

Probabilistic graphical models relate immune status with response to neoadjuvant chemotherapy in breast cancer

Breast cancer is the most frequent tumor in women and its incidence is increasing. Neoadjuvant chemotherapy has become standard of care as a complement to surgery in locally advanced or poor-prognosis early stage disease. The achievement of a complete response to neoadjuvant chemotherapy correlates with prognosis but it is not possible to predict who will obtain an excellent response. The molecular analysis of the tumor offers a unique opportunity to unveil predictive factors. In this work, gene expression profiling in 279 tumor samples from patients receiving neoadjuvant chemotherapy was performed and probabilistic graphical models were used. This approach enables addressing biological and clinical questions from a Systems Biology perspective, allowing to deal with large gene expression data and their interactions. Tumors presenting complete response to neoadjuvant chemotherapy had a higher activity of immune related functions compared to resistant tumors. Similarly, samples from complete responders presented higher expression ​​of lymphocyte cell lineage markers, immune-activating and immune-suppressive markers, which may correlate with tumor infiltration by lymphocytes (TILs). These results suggest that the patient's immune system plays a key role in tumor response to neoadjuvant treatment. However, future studies with larger cohorts are necessary to validate these hypotheses.

Single drug biomarker prediction for ER- breast cancer outcome from chemotherapy

ER-negative breast cancer includes most aggressive subtypes of breast cancer such as triple negative (TN) breast cancer. Excluded from hormonal and targeted therapies effectively used for other subtypes of breast cancer, standard chemotherapy is one of the primary treatment options for these patients. However, as ER- patients have shown highly heterogeneous responses to different chemotherapies, it has been difficult to select most beneficial chemotherapy treatments for them. In this study, we have simultaneously developed single drug biomarker models for four standard chemotherapy agents: paclitaxel (T), 5-fluorouracil (F), doxorubicin (A) and cyclophosphamide (C) to predict responses and survival of ER- breast cancer patients treated with combination chemotherapies. We then flexibly combined these individual drug biomarkers for predicting patient outcomes of two independent cohorts of ER- breast cancer patients who were treated with different drug combinations of neoadjuvant chemotherapy. These individual and combined drug biomarker models significantly predicted chemotherapy response for 197 ER- patients in the Hatzis cohort (AUC = 0.637, P = 0.002) and 69 ER- patients in the Hess cohort (AUC = 0.635, P = 0.056). The prediction was also significant for the TN subgroup of both cohorts (AUC = 0.60, 0.72, P = 0.043, 0.009). In survival analysis, our predicted responder patients showed significantly improved survival with a >17 months longer median PFS than the predicted non-responder patients for both ER- and TN subgroups (log-rank test P-value = 0.018 and 0.044). This flexible prediction capability based on single drug biomarkers may allow us to even select new drug combinations most beneficial to individual patients with ER- breast cancer.

Targeting of apoptotic pathways by SMAC or BH3 mimetics distinctly sensitizes paclitaxel-resistant triple negative breast cancer cells

Standard chemotherapy is the only systemic treatment for triple-negative breast cancer (TNBC), and despite the good initial response, resistance remains a major therapeutic obstacle. Here, we employed a High-Throughput Screen to identify targeted therapies that overcome chemoresistance in TNBC. We applied short-term paclitaxel treatment and screened 320 small-molecule inhibitors of known targets to identify drugs that preferentially and efficiently target paclitaxel-treated TNBC cells. Among these compounds the SMAC mimetics (BV6, Birinapant) and BH3-mimetics (ABT-737/263) were recognized as potent targeted therapy for multiple paclitaxel-residual TNBC cell lines. However, acquired paclitaxel resistance through repeated paclitaxel pulses result in desensitization to BV6, but not to ABT-263, suggesting that short- and long-term paclitaxel resistance are mediated by distinct mechanisms. Gene expression profiling of paclitaxel-residual, -resistant and naïve MDA-MB-231 cells demonstrated that paclitaxel-residual, as opposed to -resistant cells, were characterized by an apoptotic signature, with downregulation of anti-apoptotic genes (BCL2, BIRC5), induction of apoptosis inducers (IL24, PDCD4), and enrichment of TNFα/NF-κB pathway, including upregulation of TNFSF15, coupled with cell-cycle arrest. BIRC5 and FOXM1 downregulation and IL24 induction was also evident in breast cancer patient datasets following taxane treatment. Exposure of naïve or paclitaxel-resistant cells to supernatants of paclitaxel-residual cells sensitized them to BV6, and treatment with TNFα enhanced BV6 potency, suggesting that sensitization to BV6 is mediated, at least partially, by secreted factor(s). Our results suggest that administration of SMAC or BH3 mimetics following short-term paclitaxel treatment could be an effective therapeutic strategy for TNBC, while only BH3-mimetics could effectively overcome long-term paclitaxel resistance.

Chemical genomics reveals inhibition of breast cancer lung metastasis by Ponatinib via c-Jun

Metastasis is the leading cause of human cancer deaths. Unfortunately, no approved drugs are available for anti-metastatic treatment. In our study, high-throughput sequencing-based high-throughput screening (HTS²) and a breast cancer lung metastasis (BCLM)-associated gene signature were combined to discover anti-metastatic drugs. After screening of thousands of compounds, we identified Ponatinib as a BCLM inhibitor. Ponatinib significantly inhibited the migration and mammosphere formation of breast cancer cells in vitro and blocked BCLM in multiple mouse models. Mechanistically, Ponatinib represses the expression of BCLM-associated genes mainly through the ERK/c-Jun signaling pathway by inhibiting the transcription of JUN and accelerating the degradation of c-Jun protein. Notably, JUN expression levels were positively correlated with BCLM-associated gene expression and lung metastases in breast cancer patients. Collectively, we established a novel approach for the discovery of anti-metastatic drugs, identified Ponatinib as a new drug to inhibit BCLM and revealed c-Jun as a crucial factor and potential drug target for BCLM. Our study may facilitate the therapeutic treatment of BCLM as well as other metastases.

Compound A attenuates toll-like receptor 4-mediated paclitaxel resistance in breast cancer and melanoma through suppression of IL-8

Background

Paclitaxel (PTX) is a potent anti-cancer drug commonly used for the treatment of advanced breast cancer (BCA) and melanoma. Toll-like receptor 4 (TLR4) promotes the production of pro-inflammatory cytokines associated with cancer chemoresistance. This study aims to explore the effect of TLR4 in PTX resistance in triple-negative BCA and advanced melanoma and the effect of compound A (CpdA) to attenuate this resistance.

Methods

BCA and melanoma cell lines were checked for the response to PTX by cytotoxic assay. The response to PTX of TLR4-transient knockdown cells by siRNA transfection was evaluated compared to the control cells. Levels of pro-inflammatory cytokines, IL-6 and IL-8, and anti-apoptotic protein, XIAP were measured by real-time PCR whereas the secreted IL-8 was quantitated by ELISA in TLR4-transient knockdown cancer cells with or without CpdA treatment. The apoptotic cells after adding PTX alone or in combination with CpdA were detected by caspase-3/7 assay.

Results

PTX could markedly induce TLR4 expression in both MDA-MB-231 BCA and MDA-MB-435 melanoma cell lines having a basal level of TLR4 whereas no significant induction in TLR4-transient knockdown cells occurred. The siTLR4-treated BCA cells revealed more dead cells after PTX treatment than that of mock control cells. IL-6, IL-8 and XIAP showed increased expressions in PTX-treated cells and this over-production effect was inhibited in TLR4-transient knockdown cells. Apoptotic cells were detected higher when PTX and CpdA were combined than PTX treatment alone. Isobologram exhibited the synergistic effect of CpdA and PTX. CpdA could significantly decrease expressions of IL-6, XIAP and IL-8, as well as excreted IL-8 levels together with reduced cancer viability after PTX treatment.

Conclusions

The acquired TLR4-mediated PTX resistance in BCA and melanoma is explained partly by the paracrine effect of IL-6 and IL-8 released into the tumor microenvironment and over-production of anti-apoptotic protein, XIAP, in BCA cells and importantly CpdA could reduce this effect and sensitize PTX-induced apoptosis in a synergistic manner. In conclusion, the possible impact of TLR4-dependent signaling pathway in PTX resistance in BCA and melanoma is proposed and using PTX in combination with CpdA may attenuate TLR4-mediated PTX resistance in the treatment of the patients.

Electronic supplementary material

The online version of this article (10.1186/s12885-018-4155-6) contains supplementary material, which is available to authorized users.

PP2A as the Main Node of Therapeutic Strategies and Resistance Reversal in Triple-Negative Breast Cancer

Triple negative breast cancer (TNBC), is defined as a type of tumor lacking the expression of estrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor 2 (HER2). The ER, PR and HER2 are usually the molecular therapeutic targets for breast cancers, but they are ineffective for TNBC because of their negative expressions, so chemotherapy is currently the main treatment strategy in TNBC. However, drug resistance remains a major impediment to TNBC chemotherapeutic treatment. Recently, the protein phosphatase 2A (PP2A) has been found to regulate the phosphorylation of some substrates involved in the relevant target of TNBC, such as cell cycle control, DNA damage responses, epidermal growth factor receptor, immune modulation and cell death resistance, which may be the effective therapeutic strategies or influence drug sensitivity to TNBCs. Furthermore, PP2A has also been found that could induce ER re-expression in ER-negative breast cancer cells, and which suggests PP2A could promote the sensitivity of tamoxifen to TNBCs as a resistance reversal agent. In this review, we will summarize the potential therapeutic value of PP2A as the main node in developing targeting agents, disrupting resistance or restoring drug sensitivity in TNBC.

CONCORD biomarker prediction for novel drug introduction to different cancer types

Many cancer therapeutic agents have shown to be effective for treating multiple cancer types. Yet major challenges exist toward introducing a novel drug used in one cancer type to different cancer types, especially when a relatively small number of patients with the other cancer type often benefit from anti-cancer therapy with the drug. Recently, many novel agents were introduced to different cancer types together with companion biomarkers which were obtained or biologically assumed from the original cancer type. However, there is no guarantee that biomarkers from one cancer can directly predict a therapeutic response in another. To tackle this challenging question, we have developed a concordant expression biomarker-based technique ("CONCORD") that overcomes these limitations. CONCORD predicts drug responses from one cancer type to another by identifying concordantly co-expressed biomarkers across different cancer systems. Application of CONCORD to three standard chemotherapeutic agents and two targeted agents demonstrated its ability to accurately predict the effectiveness of a drug against new cancer types and predict therapeutic response in patients.

Genome-wide analysis of the three-way interplay among gene expression, estrogen receptor expression and chemotherapeutic sensitivity in breast cancer

The expression of estrogen receptor α (ER) in breast cancers may be indicative of a favorable prognosis and most of these cancers respond to anti-estrogens or aromatase inhibitors. However, ER-positive (ER+) breast cancers receiving anti-hormone and/or chemotherapy sometimes lose their ER expression, which leads to the evolution of the disease to higher aggressiveness and drug resistance. In the present study, an ER-modified signature (EMS) was developed from the expression profile of a chemoresistant MCF-7 breast cancer cell line that lost ER expression during long-term treatment with a chemotherapeutic agent. The EMS could discriminate the ER-negative (ER-) breast cancer cells from the ER+ ones, which included seven pathways essential for the ER- cell development. Furthermore, the EMS indicated a more malignant subgroup of the ER- cells by discriminating the chemoresistant ER- cells from the chemosensitive ones. In addition, the classified chemoresistant ER- patients demonstrated worse prognosis. In conclusion, we developed a new method to discriminate subgroups of ER- breast cancer cells.

Role of high mobility group A1 and body mass index in the prognosis of patients with breast cancer

The high mobility group A1 (HMGA1) protein is associated with poor prognosis in patients with a wide range of cancers. However, the affect of HMGA1 on the risk of mortality from breast cancer (BC) has not been fully characterized. In the present retrospective multiple center study, the HMGA1 expression level was determined by performing immunohistochemistry on surgical tissue samples of 273 BC specimens from the Second Affiliated Hospital of Zhejiang University (Zhejiang, China) and 310 BCs from the National Engineering Center for Biochip (Shanghai, China). Kaplan-Meier analysis and Cox proportional hazard model were employed to analyze the survivability. HMGA1 expression was significantly associated with tumor histological degree and body mass index (BMI). However, HMGA1 expression showed no prognostic value in patients with BC. Combined evaluation of HMGA1 expression and high BMI (≥24 kg/m²) predicted worse overall survival of BC. Therefore, HMGA1 and BMI were considered to serve synergistic roles in the development and progression of BC, and combined evaluation of HMGA1 expression and high BMI may be an effective marker in predicting poor prognosis of BC patients.

A PAM50-Based Chemoendocrine Score for Hormone Receptor-Positive Breast Cancer with an Intermediate Risk of Relapse

Purpose: Hormone receptor-positive (HR+) breast cancer is clinically and biologically heterogeneous, and subgroups with different prognostic and treatment sensitivities need to be identified.Experimental Design: Research-based PAM50 subtyping and expression of additional genes was performed on 63 patients with HR+/HER2- disease randomly assigned to neoadjuvant multiagent chemotherapy versus endocrine therapy in a phase II trial. The biology associated with treatment response was used to derive a PAM50-based chemoendocrine score (CES). CES's predictive ability was evaluated in 4 independent neoadjuvant data sets (n = 675) and 4 adjuvant data sets (n = 1,505). The association of CES, intrinsic biology, and PAM50 risk of relapse (ROR) was explored across 6,007 tumors.Results: Most genes associated with endocrine sensitivity were also found associated with chemotherapy resistance. In the chemotherapy test/validation data sets, CES was independently associated with pathologic complete response (pCR), even after adjusting for intrinsic subtype. pCR rates of the CES endocrine-sensitive (CES-E), uncertain (CES-U), and chemotherapy-sensitive (CES-C) groups in both data sets combined were 25%, 11%, and 2%, respectively. In the endocrine test/validation data sets, CES was independently associated with response. Compared with ROR, >90% of ROR-low and ROR-high tumors were identified as CES-E and CES-C, respectively; however, each CES group represented >25% of ROR-intermediate disease. In terms of survival outcome, CES-C was associated with poor relapse-free survival in patients with ROR-intermediate disease treated with either adjuvant endocrine therapy only or no adjuvant systemic therapy, but not in patients treated with (neo)adjuvant chemotherapy.Conclusions: CES is a genomic signature capable of estimating chemoendocrine sensitivity in HR+ breast cancer beyond intrinsic subtype and risk of relapse. Clin Cancer Res; 23(12); 3035-44. ©2016 AACR.

The E2F4 prognostic signature predicts pathological response to neoadjuvant chemotherapy in breast cancer patients

Background

Neoadjuvant chemotherapy is a key component of breast cancer treatment regimens and pathologic complete response to this therapy varies among patients. This is presumably due to differences in the molecular mechanisms that underlie each tumor’s disease pathology. Developing genomic clinical assays that accurately categorize responders from non-responders can provide patients with the most effective therapy for their individual disease.

Methods

We applied our previously developed E2F4 genomic signature to predict neoadjuvant chemotherapy response in breast cancer. E2F4 individual regulatory activity scores were calculated for 1129 patient samples across 5 independent breast cancer neoadjuvant chemotherapy datasets. Accuracy of the E2F4 signature in predicting neoadjuvant chemotherapy response was compared to that of the Oncotype DX and MammaPrint predictive signatures.

Results

In all datasets, E2F4 activity level was an accurate predictor of neoadjuvant chemotherapy response, with high E2F4 scores predictive of achieving pathologic complete response and low scores predictive of residual disease. These results remained significant even after stratifying patients by estrogen receptor (ER) status, tumor stage, and breast cancer molecular subtypes. Compared to the Oncotype DX and MammaPrint signatures, our E2F4 signature achieved similar performance in predicting neoadjuvant chemotherapy response, though all signatures performed better in ER+ tumors compared to ER- ones. The accuracy of our signature was reproducible across datasets and was maintained when refined from a 199-gene signature down to a clinic-friendly 33-gene panel.

Conclusion

Overall, we show that our E2F4 signature is accurate in predicting patient response to neoadjuvant chemotherapy. As this signature is more refined and comparable in performance to other clinically available gene expression assays in the prediction of neoadjuvant chemotherapy response, it should be considered when evaluating potential treatment options.

Electronic supplementary material

The online version of this article (doi:10.1186/s12885-017-3297-2) contains supplementary material, which is available to authorized users.