Gene pathways associated with prognosis and chemotherapy sensitivity in molecular subtypes of 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.

Coagulation factor V in breast cancer: a p53-regulated tumor suppressor and predictive marker for treatment response to chemotherapy

BACKGROUND

Patients with cancer are at an increased risk of developing coagulation complications, and chemotherapy treatment increases the risk. Tumor progression is closely linked to the hemostatic system. Breast cancer tumors express coagulation factor V (FV), an essential factor in blood coagulation. The functional role of FV during treatment with chemotherapy is poorly understood and was explored in this study.

OBJECTIVES

We aimed to investigate the role of FV in breast cancer progression by exploring associations with treatment response, gene regulation, and the functional effects of FV.

METHODS

The receiver operating characteristic plotter was used to explore the predictive value of FV mRNA (F5) expression for treatment with FEC (5-fluorouracil, anthracycline, and cyclophosphamide). Breast cancer cohorts were analyzed to study treatment response to FEC. The effect of chemotherapy on F5 expression, the regulation of F5, and the functional effects of FV dependent and independent of chemotherapy were studied in breast cancer cell lines.

RESULTS

F5 tumor expression was significantly higher in responders to FEC than in nonresponders. In vitro experiments revealed that anthracycline treatment increased the expression of F5. Inhibition and knockdown of p53 reduced the anthracycline-induced F5 expression. Mutation of a p53 half-site (c.158+1541/158+1564) in a luciferase plasmid reduced luciferase activity, suggesting that p53 plays a role in regulating F5. FV overexpression increased apoptosis and reduced proliferation slightly during anthracycline treatment.

CONCLUSION

Our study identified F5 as a p53-regulated tumor suppressor candidate and a promising marker for response to chemotherapy. FV may have functional effects that are therapeutically relevant in breast cancer.

Oestrogen receptor positive breast cancer and its embedded mechanism: breast cancer resistance to conventional drugs and related therapies, a review

Traditional medication and alternative therapies have long been used to treat breast cancer. One of the main problems with current treatments is that there is an increase in drug resistance in the cancer cells owing to genetic differences such as mutational changes, epigenetic changes and miRNA (microRNA) alterations such as miR-1246, miR-298, miR-27b and miR-33a, along with epigenetic modifications, such as Histone3 acetylation and CCCTC-Binding Factor (CTCF) hypermethylation for drug resistance in breast cancer cell lines. Certain forms of conventional drug resistance have been linked to genetic changes in genes such as ABCB1, AKT, S100A8/A9, TAGLN2 and NPM. This review aims to explore the current approaches to counter breast cancer, the action mechanism, along with novel therapeutic methods endowing potential drug resistance. The investigation of novel therapeutic approaches sheds light on the phenomenon of drug resistance including genetic variations that impact distinct forms of oestrogen receptor (ER) cancer, genetic changes, epigenetics-reported resistance and their identification in patients. Long-term effective therapy for breast cancer includes selective oestrogen receptor modulators, selective oestrogen receptor degraders and genetic variations, such as mutations in nuclear genes, epigenetic modifications and miRNA alterations in target proteins. Novel research addressing combinational therapies including maytansine, photodynamic therapy, guajadiol, talazoparib, COX2 inhibitors and miRNA 1246 inhibitors have been developed to improve patient survival rates.

Molecular subtypes of breast cancer predicting clinical benefits of radiotherapy after breast-conserving surgery: a propensity-score-matched cohort study

BACKGROUND

Based on the molecular expression of cancer cells, molecular subtypes of breast cancer have been applied to classify patients for predicting clinical outcomes and prognosis. However, further evidence is needed regarding the influence of molecular subtypes on the efficacy of radiotherapy (RT) after breast-conserving surgery (BCS), particularly in a population-based context. Hence, the present study employed a propensity-score-matched cohort design to investigate the potential role of molecular subtypes in stratifying patient outcomes for post-BCS RT and to identify the specific clinical benefits that may emerge.

METHODS

From 2006 to 2019, the present study included 59,502 breast cancer patients who underwent BCS from the Taiwan National Health Insurance Research Database. Propensity scores were utilized to match confounding variables between patients with and without RT within each subtype of breast cancer, namely luminal A, luminal B/HER2-negative, luminal B/HER2-positive, basal-like, and HER2-enriched ones. Several clinical outcomes were assessed, in terms of local recurrence (LR), regional recurrence (RR), distant metastasis (DM), disease-free survival (DFS), and overall survival (OS).

RESULTS

After post-BCS RT, patients with luminal A and luminal B/HER2-positive breast cancers exhibited a decrease in LR (adjusted hazard ratio [aHR] = 0.18, p < 0.0001; and, 0.24, p = 0.0049, respectively). Furthermore, reduced RR and improved DFS were observed in patients with luminal A (aHR = 0.15, p = 0.0004; and 0.29, p < 0.0001), luminal B/HER2-negative (aHR = 0.06, p = 0.0093; and, 0.46, p = 0.028), and luminal B/HER2-positive (aHR = 0.14, p = 0.01; and, 0.38, p < 0.0001) breast cancers. Notably, OS benefits were found in patients with luminal A (aHR = 0.62, p = 0.002), luminal B/HER2-negative (aHR = 0.30, p < 0.0001), basal-like (aHR = 0.40, p < 0.0001), and HER2-enriched (aHR = 0.50, p = 0.03), but not luminal B/HER2-positive diseases. Remarkably, when considering DM, luminal A patients who received RT demonstrated a lower cumulative incidence of DM than those without RT (p = 0.02).

CONCLUSION

In patients with luminal A breast cancer who undergo BCS, RT could decrease the likelihood of tumor metastasis. After RT, the tumor's hormone receptor status may predict tumor control regarding LR, RR, and DFS. Besides, the HER2 status of luminal breast cancer patients may serve as an additional predictor of OS after post-BCS RT. However, further prospective studies are required to validate these findings.

Hypoxia induced responses are reflected in the stromal proteome of breast cancer

Cancers are often associated with hypoxia and metabolic reprogramming, resulting in enhanced tumor progression. Here, we aim to study breast cancer hypoxia responses, focusing on secreted proteins from low-grade (luminal-like) and high-grade (basal-like) cell lines before and after hypoxia. We examine the overlap between proteomics data from secretome analysis and laser microdissected human breast cancer stroma, and we identify a 33-protein stromal-based hypoxia profile (33P) capturing differences between luminal-like and basal-like tumors. The 33P signature is associated with metabolic differences and other adaptations following hypoxia. We observe that mRNA values for 33P predict patient survival independently of molecular subtypes and basic prognostic factors, also among low-grade luminal-like tumors. We find a significant prognostic interaction between 33P and radiation therapy.

The importance of batch sensitization in missing value imputation

Data analysis is complex due to a myriad of technical problems. Amongst these, missing values and batch effects are endemic. Although many methods have been developed for missing value imputation (MVI) and batch correction respectively, no study has directly considered the confounding impact of MVI on downstream batch correction. This is surprising as missing values are imputed during early pre-processing while batch effects are mitigated during late pre-processing, prior to functional analysis. Unless actively managed, MVI approaches generally ignore the batch covariate, with unknown consequences. We examine this problem by modelling three simple imputation strategies: global (M1), self-batch (M2) and cross-batch (M3) first via simulations, and then corroborated on real proteomics and genomics data. We report that explicit consideration of batch covariates (M2) is important for good outcomes, resulting in enhanced batch correction and lower statistical errors. However, M1 and M3 are error-generating: global and cross-batch averaging may result in batch-effect dilution, with concomitant and irreversible increase in intra-sample noise. This noise is unremovable via batch correction algorithms and produces false positives and negatives. Hence, careless imputation in the presence of non-negligible covariates such as batch effects should be avoided.

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.

CD4+ T cells drive an inflammatory, TNF-α/IFN-rich tumor microenvironment responsive to chemotherapy

While chemotherapy remains the first-line treatment for many cancers, it is still unclear what distinguishes responders from non-responders. Here, we characterize the chemotherapy-responsive tumor microenvironment in mice, using RNA sequencing on tumors before and after cyclophosphamide, and compare the gene expression profiles of responders with progressors. Responsive tumors have an inflammatory and highly immune infiltrated pre-treatment tumor microenvironment characterized by the enrichment of pathways associated with CD4⁺ T cells, interferons (IFNs), and tumor necrosis factor alpha (TNF-α). The same gene expression profile is associated with response to cyclophosphamide-based chemotherapy in patients with breast cancer. Finally, we demonstrate that tumors can be sensitized to cyclophosphamide and 5-FU chemotherapy by pre-treatment with recombinant TNF-α, IFNγ, and poly(I:C). Thus, a CD4⁺ T cell-inflamed pre-treatment tumor microenvironment is necessary for response to chemotherapy, and this state can be therapeutically attained by targeted immunotherapy.

Molecular differences between younger versus older ER-positive and HER2-negative breast cancers

The RxPONDER and TAILORx trials demonstrated benefit from adjuvant chemotherapy in patients age ≤ 50 with node-positive breast cancer and Recurrence Score (RS) 0-26, and in node-negative disease with RS 16-25, respectively, but no benefit in older women with the same clinical features. We analyzed transcriptomic and genomic data of ER+/HER2- breast cancers with in silico RS < 26 from TCGA (n = 530), two microarray cohorts (A: n = 865; B: n = 609), the METABRIC (n = 867), and the SCAN-B (n = 1636) datasets. There was no difference in proliferation-related gene expression between age groups. Older patients had higher mutation burden and more frequent ESR1 copy number gain, but lower frequency of GATA3 mutations. Younger patients had higher rate of ESR1 copy number loss. In all datasets, younger patients had significantly lower mRNA expression of ESR1 and ER-associated genes, and higher expression of immune-related genes. The ER- and immune-related gene signatures showed negative correlation and defined three subpopulations in younger women: immune-high/ER-low, immune-intermediate/ER-intermediate, and immune-low/ER-intermediate. We hypothesize that in immune-high cancers, the cytotoxic effect of chemotherapy may drive the benefit, whereas in immune-low/ER-intermediate cancers chemotherapy induced ovarian suppression may play important role.

E2F1 Affects the Therapeutic Response to Neoadjuvant Therapy in Breast Cancer

This study is aimed at screening genes for predicting the sensitivity response and favorable outcome of neoadjuvant therapy in breast cancer. We downloaded neoadjuvant therapy genetic data of breast cancer and separated it into the pathological complete response (pCR) group and the non-pCR group. Differential expression analysis was performed to select the differentially expressed genes (DEGs). After that, we investigated the enriched biological processes and pathways of DEGs. Then, core up/down protein-protein interaction (PPI) network was, respectively, constructed to identify the hub genes. A transcription factor-target gene regulation network was built to screen core transcription factors (TFs). We found one upregulated DEG (KLHDC7B) and four downregulated DEGs (TFF1, LOC440335, SLC39A6, and MLPH) overlapped in three datasets. All DEGs were mainly enriched in pathways related to DNA biosynthesis, cell cycle, immune response, metabolism, and angiogenesis. The hub genes were KRT18, IL7R, HIST1H1A, and E2F1. The core TFs were HOXA9, SPDEF, FOXA1, E2F1, and PGR. RT-qPCR suggested that E2F1 was overexpressed in MCF-7, but HOXA9 was low-expressed. Western blot suggested that the MAPK signal pathway was inhibited in MCF-7/ADR. That is to say, some genes and core TFs can predict the sensitivity response of neoadjuvant therapy in breast cancer. And E2F1 may be involved in the process of drug resistance by regulating the MAPK signaling pathway. These might be useful as sensitive genes for the efficacy evaluation of neoadjuvant chemotherapy in breast cancer.

Machine learning models based on immunological genes to predict the response to neoadjuvant therapy in breast cancer patients

Breast cancer (BC) is the most common malignancy worldwide and neoadjuvant therapy (NAT) plays an important role in the treatment of patients with early BC. However, only a subset of BC patients can achieve pathological complete response (pCR) and benefit from NAT. It is therefore necessary to predict the responses to NAT. Although many models to predict the response to NAT based on gene expression determined by the microarray platform have been proposed, their applications in clinical practice are limited due to the data normalization methods during model building and the disadvantages of the microarray platform compared with the RNA-seq platform. In this study, we first reconfirmed the correlation between immune profiles and pCR in an RNA-seq dataset. Then, we employed multiple machine learning algorithms and a model stacking strategy to build an immunological gene based model (Ipredictor model) and an immunological gene and receptor status based model ICpredictor model) in the RNA-seq dataset. The areas under the receiver operator characteristic curves for the Ipredictor model and ICpredictor models were 0.745 and 0.769 in an independent external test set based on the RNA-seq platform, and were 0.716 and 0.752 in another independent external test set based on the microarray platform. Furthermore, we found that the predictive score of the Ipredictor model was correlated with immune microenvironment and genomic aberration markers. These results demonstrated that the models can accurately predict the response to NAT for BC patients and will contribute to individualized therapy.

Association between tumor 18F-fluorodeoxyglucose metabolism and survival in women with estrogen receptor-positive, HER2-negative breast cancer

We examined whether ¹⁸F-fluorodeoxyglucose metabolism is associated with distant relapse-free survival (DRFS) and overall survival (OS) in women with estrogen receptor (ER)-positive, HER2-negative breast cancer. This was a cohort study examining the risk factors for survival that had occurred at the start of the study. A cohort from Asan Medical Center, Korea, recruited between November 2007 and December 2014, was included. Patients received anthracycline-based neoadjuvant chemotherapy. The maximum standardized uptake value (SUV) of ¹⁸F-fluorodeoxyglucose positron emission tomography/computed tomography (PET/CT) was measured. The analysis included 466 women. The median (interquartile range) follow-up period without distant metastasis or death was 6.2 (5.3-7.6) years. Multivariable analysis of hazard ratio (95% confidence interval [CI]) showed that the middle and high tertiles of SUV were prognostic for DRFS (2.93, 95% CI 1.62-5.30; P < 0.001) and OS (4.87, 95% CI 1.94-12.26; P < 0.001). The 8-year DRFS rates were 90.7% (95% CI 85.5-96.1%) for those in the low tertile of maximum SUV vs. 73.7% (95% CI 68.0-79.8%) for those in the middle and high tertiles of maximum SUV. ¹⁸F-fluorodeoxyglucose PET/CT may assess the risk of distant metastasis and death in ER-positive, HER2-negative patients.

A Novel Strategy to Identify Prognosis-Relevant Gene Sets in Cancers

Molecular prognosis markers hold promise for improved prediction of patient survival, and a pathway or gene set may add mechanistic interpretation to their prognostic prediction power. In this study, we demonstrated a novel strategy to identify prognosis-relevant gene sets in cancers. Our study consists of a first round of gene-level analyses and a second round of gene-set-level analyses, in which the Composite Gene Expression Score critically summarizes a surrogate expression value at gene set level and a permutation procedure is exerted to assess prognostic significance of gene sets. An optional differential coexpression module is appended to the two phases of survival analyses to corroborate and refine prognostic gene sets. Our strategy was demonstrated in 33 cancer types across 32,234 gene sets. We found oncogenic gene sets accounted for an increased proportion among the final gene sets, and genes involved in DNA replication and DNA repair have ubiquitous prognositic value for multiple cancer types. In summary, we carried out the largest gene set based prognosis study to date. Compared to previous similar studies, our approach offered multiple improvements in design and methodology implementation. Functionally relevant gene sets of ubiquitous prognostic significance in multiple cancer types were identified.

Correlation Between Immune Cell Infiltration and PD-L1 Expression and Immune-Related lncRNA Determination in Triple-Negative Breast Cancer

As a key element of the tumor microenvironment (TME), immune cell infiltration (ICI) is a frequently observed histologic finding in people with triple-negative breast cancer (TNBC), and it is linked to immunotherapy sensitivity. Nonetheless, the ICI in TNBC, to the best of our knowledge, has not been comprehensively characterized. In our current work, computational algorithms based on biological data from next-generation sequencing were employed to characterize ICI in a large cohort of TNBC patients. We defined various ICI patterns by unsupervised clustering and constructed the ICI scores using the principal component analysis (PCA). We observed patients with different clustering patterns had distinct ICI profiles and different signatures of differentially expressed genes. Patients with a high ICI score tended to have an increased PD-L1 expression and improved outcomes, and these patients were associated with decreased tumor mutational burden (TMB). Interestingly, it was showed that patients with high TMB exhibited an ameliorated overall survival (OS) than patients with low TMB. Furthermore, TMB scores only affected the prognosis of TNBC patients in the low-ICI score group but not in the high group. Finally, we identified a new immune-related lncRNA (irlncRNA) signature and established a risk model for the TNBC prognosis prediction. In addition, the high-risk group was related to poor prognosis, a high infiltration level of plasma B cells, monocytes, M2 macrophages, and neutrophils and a low PD-L1 expression. Therefore, the characterization and systematic evaluation of ICI patterns might potentially predict the prognosis and immunotherapy response in TNBC patients.

Tumor cell intrinsic and extrinsic features predict prognosis in estrogen receptor positive breast cancer

Although estrogen-receptor-positive (ER+) breast cancer is generally associated with favorable prognosis, clinical outcome varies substantially among patients. Genomic assays have been developed and applied to predict patient prognosis for personalized treatment. We hypothesize that the recurrence risk of ER+ breast cancer patients is determined by both genomic mutations intrinsic to tumor cells and extrinsic immunological features in the tumor microenvironment. Based on the Cancer Genome Atlas (TCGA) breast cancer data, we identified the 72 most common genomic aberrations (including gene mutations and indels) in ER+ breast cancer and defined sample-specific scores that systematically characterized the deregulated pathways intrinsic to tumor cells. To further consider tumor cell extrinsic features, we calculated immune infiltration scores for six major immune cell types. Many individual intrinsic features are predictive of patient prognosis in ER+ breast cancer, and some of them achieved comparable accuracy with the Oncotype DX assay. In addition, statistical learning models that integrated these features predicts the recurrence risk of patients with significantly better performance than the Oncotype DX assay (our optimized random forest model AUC = 0.841, Oncotype DX model AUC = 0.792, p = 0.04). As a proof-of-concept, our study indicates the great potential of genomic and immunological features in prognostic prediction for improving breast cancer precision medicine. The framework introduced in this work can be readily applied to other cancers.

Pathologic complete response (pCR) to neoadjuvant treatment with or without atezolizumab in triple negative, early high-risk and locally advanced breast cancer. NeoTRIP Michelangelo randomized study

BACKGROUND

High-risk triple negative breast cancers (TNBC) are characterized by poor prognosis, rapid progression to metastatic stage and onset of resistance to chemotherapy, thus representing an area in need of new therapeutic approaches. PD-L1 expression is an adaptive mechanism of tumour resistance to tumour infiltrating lymphocytes, which in turn are needed for response to chemotherapy. Overall, available data support the concept that blockade of PD-L1/PD-1 check-point may improve efficacy of classical chemotherapy.

PATIENTS AND METHODS

Two-hundred-eighty patients with TNBC were enrolled in this multicentre study (NCT002620280) and randomized to neoadjuvant carboplatin AUC 2 and nab-paclitaxel 125 mg/m² iv on days 1 and 8, without (N = 142) or with (N = 138) atezolizumab 1200 mg iv on day 1. Both regimens were given q3 weeks for 8 cycles before surgery and 4 cycles of an adjuvant anthracycline regimen. The primary aim of the study is to compare event-free survival, an important secondary aim was the rate of pathological complete remission (pCR defined as absence of invasive cells in breast and lymph nodes). The primary population for all efficacy endpoints is the intention-to-treat population.

RESULTS

The intention-to-treat analysis revealed that pCR rate after treatment with atezolizumab (48.6%) did not reach statistical significance compared to no atezolizumab [44.4%: odds ratio (OR) 1.18; 95% CI 0.74-1.89; P = 0.48]. Treatment-related adverse events were similar with either regimen except for a significantly higher overall incidence of serious adverse events and liver transaminases abnormalities with atezolizumab.

CONCLUSIONS

The addition of atezolizumab to nab-paclitaxel and carboplatin did not significantly increase the rate of pCR in women with TNBC. In multivariate analysis the presence of PD-L1 expression was the most significant factor influencing rate of pCR (OR 2.08). Continuing follow up for the event-free survival is ongoing, and molecular studies are under way.

A framework to predict the applicability of Oncotype DX, MammaPrint, and E2F4 gene signatures for improving breast cancer prognostic prediction

To improve cancer precision medicine, prognostic and predictive biomarkers are critically needed to aid physicians in deciding treatment strategies in a personalized fashion. Due to the heterogeneous nature of cancer, most biomarkers are expected to be valid only in a subset of patients. Furthermore, there is no current approach to determine the applicability of biomarkers. In this study, we propose a framework to improve the clinical application of biomarkers. As part of this framework, we develop a clinical outcome prediction model (CPM) and a predictability prediction model (PPM) for each biomarker and use these models to calculate a prognostic score (P-score) and a confidence score (C-score) for each patient. Each biomarker’s P-score indicates its association with patient clinical outcomes, while each C-score reflects the biomarker applicability of the biomarker’s CPM to a patient and therefore the confidence of the clinical prediction. We assessed the effectiveness of this framework by applying it to three biomarkers, Oncotype DX, MammaPrint, and an E2F4 signature, which have been used for predicting patient response, pathologic complete response versus residual disease to neoadjuvant chemotherapy (a classification problem), and recurrence-free survival (a Cox regression problem) in breast cancer, respectively. In both applications, our analyses indicated patients with higher C scores were more likely to be correctly predicted by the biomarkers, indicating the effectiveness of our framework. This framework provides a useful approach to develop and apply biomarkers in the context of cancer precision medicine.

Plasma Proteome Signature to Predict the Outcome of Breast Cancer Patients Receiving Neoadjuvant Chemotherapy

The plasma proteome of 51 non-metastatic breast cancer patients receiving neoadjuvant chemotherapy (NCT) was prospectively analyzed by high-resolution mass spectrometry coupled with nano-flow liquid chromatography using blood drawn at the time of diagnosis. Plasma proteins were identified as potential biomarkers, and their correlation with clinicopathological variables and survival outcomes was analyzed. Of 51 patients, 20 (39.2%) were HR+/HER2-, five (9.8%) were HR+/HER2+, five (9.8%) were HER2+, and 21 (41.2%) were triple-negative subtype. During a median follow-up of 52.0 months, there were 15 relapses (29.4%) and eight deaths (15.7%). Four potential biomarkers were identified among differentially expressed proteins: APOC3 had higher plasma concentrations in the pathological complete response (pCR) group, whereas MBL2, ENG, and P4HB were higher in the non-pCR group. Proteins statistically significantly associated with survival and capable of differentiating low- and high-risk groups were MBL2 and P4HB for disease-free survival, P4HB for overall survival, and MBL2 for distant metastasis-free survival (DMFS). In the multivariate analysis, only MBL2 was a consistent risk factor for DMFS (HR: 9.65, 95% CI 2.10-44.31). The results demonstrate that the proteomes from non-invasive sampling correlate with pCR and survival in breast cancer patients receiving NCT. Further investigation may clarify the role of these proteins in predicting prognosis and thus their therapeutic potential for the prevention of recurrence.

In-Silico Analysis of Differentially Expressed Genes and Their Regulating microRNA Involved in Lymph Node Metastasis in Invasive Breast Carcinoma

Axillary nodal metastasis is related to poor prognosis in breast cancer (BC). Key candidate genes in BC lymph node metastasis have been identified from Gene Expression Omnibus datasets and explored through functional enrichment database for annotation, visualization and integrated discovery (DAVID) , protein-protein interaction by Search Tool for the Retrieval of Interacting Genes and proteins (STRING), network visualization (Cytoscape), survival analysis (GEPIA, KM Plotter), and target prediction (miRNet). A total of 102 overlapping differentially expressed genes were found. In-silico survival and expression analyses revealed six candidate hub genes, Desmocollin 3 (DSC3), KRT5, KRT6B, KRT17, KRT81, and SERPINB5, to be significantly associated with nodal metastasis and overall survival, and 83 MicroRNA (miRNAs), which may be potential diagnostic markers and therapeutic targets in BC patients.

Expression of X-Linked Inhibitor of Apoptosis Protein (XIAP) in Breast Cancer Is Associated with Shorter Survival and Resistance to Chemotherapy

XIAP, the most potent inhibitor of cell death pathways, is linked to chemotherapy resistance and tumor aggressiveness. Currently, multiple XIAP-targeting agents are in clinical trials. However, the characterization of XIAP expression in relation to clinicopathological variables in large clinical series of breast cancer is lacking. We retrospectively analyzed non-metastatic, non-inflammatory, primary, invasive breast cancer samples for XIAP mRNA (n = 2341) and protein (n = 367) expression. XIAP expression was analyzed as a continuous value and correlated with clinicopathological variables. XIAP mRNA expression was heterogeneous across samples and significantly associated with younger patients' age (≤50 years), pathological ductal type, lower tumor grade, node-positive status, HR+/HER2- status, and PAM50 luminal B subtype. Higher XIAP expression was associated with shorter DFS in uni- and multivariate analyses in 909 informative patients. Very similar correlations were observed at the protein level. This prognostic impact was significant in the HR+/HER2- but not in the TN subtype. Finally, XIAP mRNA expression was associated with lower pCR rate to anthracycline-based neoadjuvant chemotherapy in both uni- and multivariate analyses in 1203 informative patients. Higher XIAP expression in invasive breast cancer is independently associated with poorer prognosis and resistance to chemotherapy, suggesting the potential therapeutic benefit of targeting XIAP.

A nomogram for predicting axillary pathologic complete response in hormone receptor-positive breast cancer with cytologically proven axillary lymph node metastases

BACKGROUND

The objective of this study was to determine an axillary pathologic complete response (pCR) and its influencing factors in patients with hormone receptor (HR)-positive breast cancer and cytologically proven axillary lymph node metastases. A prediction nomogram was established to provide information for the de-escalation of axillary management in these patients after neoadjuvant chemotherapy.

METHODS

The authors retrospectively enrolled all patients with HR-positive breast cancer in the neoadjuvant chemotherapy data set of Fudan University Shanghai Cancer Center. All data were prospectively collected. From 2007 to 2016, 533 consecutive patients were included. Multivariate logistic regression analysis was performed, after which a nomogram was constructed and validated.

RESULTS

An axillary pCR was achieved in 168 patients (31.5%), the which was much higher than the proportion of those who achieved a breast pCR (103 patients; 19.3%). Patients who had human epidermal growth factor receptor 2-positive disease (P = .004), a better primary tumor response (P = .001), earlier clinical stage (P = .045), and lower estrogen receptor expression (P < .001) were more likely to achieve a lymph node pCR. The nomogram indicated an area under the receiver operating characteristic curve (AUC) of 0.84 (95% CI, 0.78-0.89) in the training set. The validation set showed good discrimination with an AUC of 0.75 (95% CI, 0.69-0.81). The C-index was 0.834 and 0.756 in the training and validation cohort, respectively. The nomogram was well calibrated.

CONCLUSIONS

The authors developed and validated a nomogram for predicting axillary pCR in patients with HR-positive disease accurately by using clinicopathologic factors available before surgery. The model will facilitate logical clinical decision making and clinical trial design.

Pan-cancer application of a lung-adenocarcinoma-derived gene-expression-based prognostic predictor

Gene-expression profiling can be used to classify human tumors into molecular subtypes or risk groups, representing potential future clinical tools for treatment prediction and prognostication. However, it is less well-known how prognostic gene signatures derived in one malignancy perform in a pan-cancer context. In this study, a gene-rule-based single sample predictor (SSP) called classifier for lung adenocarcinoma molecular subtypes (CLAMS) associated with proliferation was tested in almost 15 000 samples from 32 cancer types to classify samples into better or worse prognosis. Of the 14 malignancies that presented both CLAMS classes in sufficient numbers, survival outcomes were significantly different for breast, brain, kidney and liver cancer. Patients with samples classified as better prognosis by CLAMS were generally of lower tumor grade and disease stage, and had improved prognosis according to other type-specific classifications (e.g. PAM50 for breast cancer). In all, 99.1% of non-lung cancer cases classified as better outcome by CLAMS were comprised within the range of proliferation scores of lung adenocarcinoma cases with a predicted better prognosis by CLAMS. This finding demonstrates the potential of tuning SSPs to identify specific levels of for instance tumor proliferation or other transcriptional programs through predictor training. Together, pan-cancer studies such as this may take us one step closer to understanding how gene-expression-based SSPs act, which gene-expression programs might be important in different malignancies, and how to derive tools useful for prognostication that are efficient across organs.

Laboratory indicators predict axillary nodal pathologic complete response after neoadjuvant therapy in breast cancer

The purpose was to integrate clinicopathological and laboratory indicators to predict axillary nodal pathologic complete response (apCR) after neoadjuvant therapy (NAT). The pretreatment clinicopathological and laboratory indicators of 416 clinical nodal-positive breast cancer patients who underwent surgery after NAT were analyzed from April 2015 to 2020. Predictive factors of apCR were examined by logistic analysis. A nomogram was built according to logistic analysis. Among the 416 patients, 37.3% achieved apCR. Multivariate analysis showed that age, pathological grading, molecular subtype and neutrophil-to-lymphocyte ratio were independent predictors of apCR. A nomogram was established based on these four factors. The area under the curve (AUC) was 0.758 in the training set. The validation set showed good discrimination, with AUC of 0.732. In subtype analysis, apCR was 23.8, 47.1 and 50.8% in hormone receptor-positive/HER2-, HER2+ and triple-negative subgroups, respectively. According to the results of the multivariate analysis, pathological grade and fibrinogen level were independent predictors of apCR after NAT in HER2+ patients. Except for traditional clinicopathological factors, laboratory indicators could also be identified as predictive factors of apCR after NAT. The nomogram integrating pretreatment indicators demonstrated its distinguishing capability, with a high AUC, and could help to guide individualized treatment options.

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.

High expression levels of centromere protein A plus upregulation of the phosphatidylinositol 3-kinase/Akt/mammalian target of rapamycin signaling pathway affect chemotherapy response and prognosis in patients with breast cancer

Centromere proteins (CENPs) are involved in mitosis, and CENP gene expression levels are associated with chemotherapy responses in patients with breast cancer. The present study aimed to examine the roles and underlying mechanisms of the effects of CENP genes on chemotherapy responses and breast cancer prognosis. Using data obtained from the Gene Expression Omnibus (GEO) database, correlation and Cox multivariate regression analyses were used to determine the CENP genes associated with chemotherapy responses and survival in patients with breast cancer. Weighted gene co-expression network and correlation analyses were used to determine the gene modules co-expressed with the identified genes and the differential expression of gene modules associated with the pathological complete response (PCR) and residual disease (RD) subgroups. CENPA, CENPE, CENPF, CENPI, CENPJ and CENPN were associated with a high nuclear grade and low estrogen and progesterone receptor expression levels. In addition, CENPA, CENPB, CENPC and CENPO were independent factors affecting the distant relapse-free survival (DRFS) rates in patients with breast cancer. Patients with high expression levels of CENPA or CENPO exhibited poor prognoses, whereas those with high expression levels of CENPB or CENPC presented with favorable prognoses. For validation between databases, the Cancer Genome Atlas (TCGA) database analysis also revealed that CENPA, CENPB and CENPO exerted similar effects on overall survival. However, according to the multivariate analyses, only CENPA was an independent risk factor associated with DRFS in GEO database. In addition, in the RD subgroup, patients with higher CENPA expression levels had a worse prognosis compared with those with lower CENPA expression levels. Among patients with high expression levels of CENPA, the PI3K/Akt/mTOR pathway was more likely to be activated in the RD compared with the PCR subgroup. The same trend was observed in TCGA data. These results suggested that high CENPA expression levels plus upregulation of the PI3K/Akt/mTOR signaling pathway may affect DRFS in patients with breast cancer.

A Correlation Analysis Between Metabolism-related Genes and Treatment Response to S-1 as First-line Chemotherapy for Metastatic Breast Cancer: The SELECT BC-EURECA Study

INTRODUCTION

The previous randomized phase 3 trial (SELECT BC) showed that S-1 as a first-line chemotherapy for metastatic breast cancer (MBC) is non-inferior to taxane with respect to overall survival. This study aimed to identify the usefulness of metabolism-related genes as predictive biomarkers for the response to S-1 compared with taxane using tumor tissue samples from the previous trial.   PATIENTS AND METHODS: In this SELECT BC-EURECA study, 147 patients with human epidermal growth factor 2 (HER2)-negative MBC who received either S-1 or taxane were evaluated. Formalin-fixed paraffin-embedded specimens were collected, and 14 genes involved in the pyrimidine metabolic pathway, estrogen receptor, progesterone receptor, HER2, Ki67, and beta-tubulin were measured using reverse transcription polymerase chain reaction in microdissected tumor specimens. The expression of each gene was categorized as low, intermediate, and high by tertile values.   RESULTS: Interaction tests to identify biomarkers for the response to S-1 compared with taxane, revealed the following as the top 3 biomarkers: RRM1 (P value = 0.24), GGH (P value = 0.25), and MTHFR (P value = 0.28). In the S-1 group, lower GGH and higher MTHFR expression were significantly correlated with better time to treatment failure. In the taxane group, there was no gene that was identified as a significant indicator of treatment failure.

CONCLUSION

This biomarker analysis from SELECT BC did not identify any predictive biomarkers for the response to S-1 compared with taxane. Future studies with larger sample size and information on not only mRNA, but also protein and DNA for broad functional analyses are needed.

Role of FUT8 expression in clinicopathology and patient survival for various malignant tumor types: a systematic review and meta-analysis

Dysregulation of α(1,6)-fucosyltransferase (FUT8) plays significant roles in development of a variety of malignant tumor types. We collected as many relevant articles and microarray datasets as possible to assess the prognostic value of FUT8 expression in malignant tumors. For this purpose, we systematically searched PubMed, Embase, Web of Science, Springer, Chinese National Knowledge Infrastructure (CNKI), and Wan Fang, and eventually identified 7 articles and 35 microarray datasets (involving 6124 patients and 10 tumor types) for inclusion in meta-analysis. In each tumor type, FUT8 expression showed significant (p< 0.05) correlation with one or more clinicopathological parameters; these included patient gender, molecular subgroup, histological grade, TNM stage, estrogen receptor, progesterone receptor, and recurrence status. In regard to survival prognosis, FUT8 expression level was associated with overall survival in non-small cell lung cancer (NSCLC), breast cancer, diffuse large B cell lymphoma, gastric cancer, and glioma. FUT8 expression was also correlated with disease-free survival in NSCLC, breast cancer, and colorectal cancer, and with relapse-free survival in pancreatic ductal adenocarcinoma. For most tumor types, survival prognosis of patients with high FUT8 expression was related primarily to clinical features such as gender, tumor stage, age, and pathological category. Our systematic review and meta-analysis confirmed the association of FUT8 with clinicopathological features and patient survival rates for numerous malignant tumor types. Verification of prognostic value of FUT8 in these tumor types will require a large-scale study using standardized methods of detection and analysis.

Combining Immunoscore with Clinicopathologic Features in Cholangiocarcinoma: An Influential Prognostic Nomogram

Purpose

The aim of this study was to determine the Immunoscore as an independent prognostic factor for cholangiocarcinoma and establish a useful prognostic model for postoperative patients.

Methods

This retrospective study was performed to assess the correlation between the clinicopathological features, tumor immune microenvironment, and prognosis of 76 patients with cholangiocarcinoma. Multivariate analysis was used to identify independent factors significantly associated with local recurrence-free survival (LRFS) and overall survival (OS). Finally, we constructed a nomogram combining the Immunoscore with clinicopathologic features to predict postoperative recurrence and OS.

Results

The present study showed that immune cell infiltration was negatively correlated with tumor size, peripheral vascular invasion, lymph node metastasis, and tumor staging. Kaplan-Meier curves indicated that a decreased Immunoscore was associated with poor prognosis. Multivariate analysis demonstrated that resection type, number of tumors, lymph node metastasis, TNM staging, and the Immunoscore were significantly associated with LRFS. For OS, the significantly correlated factors included resection type, peripheral vascular invasion, TNM staging, and the Immunoscore. Immunoscore was superior to TNM staging in predicting both LRFS and OS according to the receiver operating characteristic analysis. Based on the results of the Cox regression analysis, a prognostic nomogram for the postoperative recurrence of cholangiocarcinoma and OS of patients was established.

Conclusion

The results of this study suggest that the Immunoscore may be used as an independent predictor of postoperative recurrence and OS of patients with cholangiocarcinoma. The Immunoscore appears to offer distinct advantages over the TNM staging system. By combining the Immunoscore and clinicopathological features, the proposed nomogram provides a more accurate predictive tool for postoperative patients with cholangiocarcinoma.

Baseline immunity and impact of chemotherapy on immune microenvironment in cervical cancer

Background

We aimed to comprehensively evaluate the immunologic landscape at baseline and upon chemotherapy in cervical cancer. The information should aid ongoing clinical investigations of checkpoint blockade immunotherapies in this disease setting.

Methods

A series of 109 cervical carcinoma patients was retrospectively assayed before and after neoadjuvant chemotherapy. Tumour-infiltrating immune markers (CD3, CD4, CD8, CD20, CD56, CD68, PD-1, PD-L1) were assessed by immunohistochemistry. RNA sequencing analysis was performed on matched pre- and post-treatment fresh-frozen tissues.

Results

At diagnosis, diverse immune cell types including CD20+ B cells, CD3+ T cells, CD56+ natural killer (NK) cells, and CD68+ macrophages were detected in different proportions of cervical carcinoma. Unsupervised hierarchical clustering evidently showed that CD4+ and CD8+ T cell abundance correlated with PD-L1 expression. Based on the immune infiltration patterns, the patients could be stratified into four groups with prognostic relevance, namely, ‘immuno-active’, ‘immuno-medial’, ‘immuno-NK’, and ‘immuno-deficient’. Neoadjuvant chemotherapy was associated with increased CD4, CD8, CD20, and CD56 signals, most prominently in good responders. Transcriptomic data corroborated the improved anticancer immunity and identified immunosuppressive CD200 upregulation following chemotherapeutic intervention.

Conclusions

A subset of cervical cancer harbours active immune microenvironment, and chemotherapy treatment may further exert locoregional immunostimulation. Immune checkpoint inhibitors as combination or maintenance therapies warrant future exploration in clinic.

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.

Gene signature-based prediction of triple-negative breast cancer patient response to Neoadjuvant chemotherapy

Neoadjuvant chemotherapy is the current standard of care for large, advanced, and/or inoperable tumors, including triple‐negative breast cancer. Although the clinical benefits of neoadjuvant chemotherapy have been illustrated through numerous clinical trials, more than half of the patients do not experience therapeutic benefit and needlessly suffer from side effects. Currently, no clinically applicable biomarkers are available for predicting neoadjuvant chemotherapy response in triple‐negative breast cancer; the discovery of such a predictive biomarker or marker profile is an unmet need. In this study, we introduce a generic computational framework to calculate a response‐probability score (RPS), based on patient transcriptomic profiles, to predict their response to neoadjuvant chemotherapy. We first validated this framework in ER‐positive breast cancer patients and showed that it predicted neoadjuvant chemotherapy response with equal performance to several clinically used gene signatures, including Oncotype DX and MammaPrint. Then, we applied this framework to triple‐negative breast cancer data and, for each patient, we calculated a response probability score (TNBC‐RPS). Our results indicate that the TNBC‐RPS achieved the highest accuracy for predicting neoadjuvant chemotherapy response compared to previously proposed 143 gene signatures. When combined with additional clinical factors, the TNBC‐RPS achieved a high prediction accuracy for triple‐negative breast cancer patients, which was comparable to the prediction accuracy of Oncotype DX and MammaPrint in ER‐positive patients. In conclusion, the TNBC‐RPS accurately predicts neoadjuvant chemotherapy response in triple‐negative breast cancer patients and has the potential to be clinically used to aid physicians in stratifying patients for more effective neoadjuvant chemotherapy.

Immunological Differences Between Immune-Rich Estrogen Receptor-Positive and Immune-Rich Triple-Negative Breast Cancers

PURPOSE

A subset of estrogen receptor–positive (ER-positive) breast cancer (BC) contains high levels of tumor-infiltrating lymphocytes (TILs), similar to triple-negative BC (TNBC). The majority of immuno-oncology trials target TNBCs because of the greater proportion of TIL-rich TNBCs. The extent to which the immune microenvironments of immune-rich ER-positive BC and TNBC differ is unknown.

PATIENTS AND METHODS

RNA sequencing data from The Cancer Genome Atlas (TCGA; n = 697 ER-positive BCs; n = 191 TNBCs) were used for discovery; microarray expression data from Molecular Taxonomy of Breast Cancer International Consortium (METABRIC; n = 1,186 ER-positive BCs; n = 297 TNBCs) was used for validation. Patients in the top 25th percentile of a previously published total TIL metagene score distribution were considered immune rich. We compared expression of immune cell markers, immune function metagenes, and immuno-oncology therapeutic targets among immune-rich subtypes.

RESULTS

Relative fractions of resting mast cells (TCGA P_adj = .009; METABRIC P_adj = 4.09E-15), CD8⁺ T cells (TCGA P_adj = .015; METABRIC P_adj = 0.390), and M2-like macrophages (TCGA P_adj= 4.68E-05; METABRIC P_adj = .435) were higher in immune-rich ER-positive BCs, but M0-like macrophages (TCGA P_adj = 0.015; METABRIC P_adj = .004) and M1-like macrophages (TCGA P_adj = 9.39E-08; METABRIC P_adj = 6.24E-11) were higher in immune-rich TNBCs. Ninety-one immune-related genes (eg, CXCL14, CSF3R, TGF-B3, LRRC32/GARP, TGFB-R2) and a transforming growth factor β (TGF-β) response metagene were significantly overexpressed in immune-rich ER-positive BCs, whereas 41 immune-related genes (eg, IFNG, PD-L1, CTLA4, MAGEA4) were overexpressed in immune-rich TNBCs in both discovery and validation data sets. TGF-β pathway member genes correlated negatively with expression of immune activation markers (IFNG, granzyme-B, perforin) and positively with M2-like macrophages (IL4, IL10, and MMP9) and regulatory T-cell (FOXP3) markers in both subtypes.

CONCLUSION

Different immunotherapy strategies may be optimal in immune-rich ER-positive BC and TNBC. Drugs targeting the TGF-β pathway and M2-like macrophages are promising strategies in immune-rich ER-positive BCs to augment antitumor immunity.

Visceral obesity and cell cycle pathways serve as links in the association between bisphenol A exposure and breast cancer

It has been identified that bisphenol A (BPA) exposure causes developmental toxicity in breast cells. However, the exact molecular mechanisms underlying the association between exposure to BPA and breast cancer remain unclear. The aim of the present study was to investigate the BPA-regulated signaling pathways associated with the aggressiveness and the development of breast cancer. Microarray technology and functional gene set analyses were used to evaluate BPA and breast cancer-associated biomarkers and pathways in a discovery-driven manner. Using individual dataset analyses, it was indicated that two BPA-associated gene sets, the visceral obesity pathway, involved in visceral fat deposits and the metabolic syndrome, and the cell cycle pathway, involved in cyclins and cell cycle regulation, were significantly associated with a high grade of aggressiveness and the development of estrogen receptor (ER)-positive breast cancer (between P<0.05 and 0.0001). The pooled analysis indicated that the most significant pathway was G₁/S checkpoint regulation, and the cyclin and cell cycle regulation pathway for BPA-associated ER-positive cancer. Cancer cell signaling pathways were associated with healthy breast cells developing into breast cancer. The visceral obesity and the cell cycle pathways were indicated to link BPA exposure to breast cancer. The results of the present study demonstrate a significant association between breast cancer and BPA-regulated gene pathways.

Immune phenotype of tumor microenvironment predicts response to bevacizumab in neoadjuvant treatment of ER-positive breast cancer

Antiangiogenic drugs are potentially a useful supplement to neoadjuvant chemotherapy for a subgroup of patients with human epidermal growth factor receptor 2 (HER2) negative breast cancer, but reliable biomarkers for improved response are lacking. Here, we report on a randomized phase II clinical trial to study the added effect of bevacizumab in neoadjuvant chemotherapy with FEC100 (5-fluorouracil, epirubicin and cyclophosphamide) and taxanes (n = 132 patients). Gene expression from the tumors was obtained before neoadjuvant treatment, and treatment response was evaluated by residual cancer burden (RCB) at time of surgery. Bevacizumab increased the proportion of complete responders (RCB class 0) from 5% to 20% among patients with estrogen receptor (ER) positive tumors (P = .02). Treatment with bevacizumab was associated with improved 8-year disease-free survival (P = .03) among the good responders (RCB class 0 or I). Patients treated with paclitaxel (n = 45) responded better than those treated with docetaxel (n = 21; P = .03). Improved treatment response was associated with higher proliferation rate and an immune phenotype characterized by high presence of classically activated M1 macrophages, activated NK cells and memory activated CD4 T cells. Treatment with bevacizumab increased the number of adverse events, including hemorrhage, hypertension, infection and febrile neutropenia, but despite this, the ECOG status was not affected.

Immunotherapy and targeted therapy combinations in metastatic breast cancer

Immunotherapy is emerging as a new treatment modality in breast cancer. After long-standing use of endocrine therapy and targeted biological therapy, improved understanding of immune evasion by cancer cells and the discovery of selective immune checkpoint inhibitors have created novel opportunities for treatment. Single-drug therapies with monoclonal antibodies against programmed death-1 (PD-1) and programmed death ligand-1 (PD-L1) have shown little efficacy in patients with metastatic breast cancer, in part because of the low number of tumour-infiltrating lymphocytes in most breast cancers. There is growing interest in the development of combinations of immunotherapy and molecularly targeted therapies for metastatic breast cancer. In this Personal View, we review the available data and ongoing efforts to establish the safety and efficacy of immunotherapeutic approaches in combination with HER2-targeted therapy, inhibitors of cyclin-dependent kinases 4 and 6, angiogenesis inhibitors, poly(ADP-ribose) polymerase inhibitors, as well as chemotherapy and radiotherapy.

A Novel 4-Gene Score to Predict Survival, Distant Metastasis and Response to Neoadjuvant Therapy in Breast Cancer

We generated a 4-gene score with genes upregulated in LM2-4, a metastatic variant of MDA-MB-231 (DOK 4, HCCS, PGF, and SHCBP1) that was strongly associated with disease-free survival (DFS) in TCGA cohort (hazard ratio [HR]>1.2, p < 0.02). The 4-gene score correlated with overall survival of TCGA (HR = 1.44, p < 0.001), which was validated with DFS and disease-specific survival of METABRIC cohort. The 4-gene score was able to predict worse survival or clinically aggressive tumors, such as high Nottingham pathological grade and advanced cancer staging. High score was associated with worse survival in the hormonal receptor (HR)-positive/Her2-negative subtype. High score enriched cell proliferation-related gene sets in GSEA. The score was high in primary tumors that originated, in and metastasized to, brain and lung, and it predicted worse progression-free survival for metastatic tumors. Good tumor response to neoadjuvant chemotherapy or hormonal therapy was accompanied by score reduction. High scores were also predictive of response to neoadjuvant chemotherapy for HR-positive/Her2-negative subtype. High score tumors had increased expression of T cell exhaustion marker genes, suggesting that the score may also be a biomarker for immunotherapy response. Our novel 4-gene score with both prognostic and predictive values may, therefore, be clinically useful particularly in HR-positive breast cancer.

Multi-Gene Prognostic Signatures and Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy in ER-positive, HER2-negative Breast Cancer Patients

Determining which patients with early-stage breast cancer should receive chemotherapy is an important clinical issue. Chemotherapy has several adverse side effects, impacting on quality of life, along with significant economic consequences. There are a number of multi-gene prognostic signatures for breast cancer recurrence but there is less evidence that these prognostic signatures are predictive of therapy benefit. Biomarkers that can predict patient response to chemotherapy can help avoid ineffective over-treatment. The aim of this work was to assess if the OncoMasTR prognostic signature can predict pathological complete response (pCR) to neoadjuvant chemotherapy, and to compare its predictive value with other prognostic signatures: EndoPredict, Oncotype DX and Tumor Infiltrating Leukocytes. Gene expression datasets from ER-positive, HER2-negative breast cancer patients that had pre-treatment biopsies, received neoadjuvant chemotherapy and an assessment of pCR were obtained from the Gene Expression Omnibus repository. A total of 813 patients with 66 pCR events were included in the analysis. OncoMasTR, EndoPredict, Oncotype DX and Tumor Infiltrating Leukocytes numeric risk scores were approximated by applying the gene coefficients to the corresponding mean probe expression values. OncoMasTR, EndoPredict and Oncotype DX prognostic scores were moderately well correlated according to the Pearson's correlation coefficient. Association with pCR was estimated using logistic regression. The odds ratio for a 1 standard deviation increase in risk score, adjusted for cohort, were similar in magnitude for all four signatures. Additionally, the four signatures were significant predictors of pCR. OncoMasTR added significant predictive value to EndoPredict, Oncotype DX and Tumor Infiltrating Leukocytes signatures as determined by bivariable and trivariable analysis. In this in silico analysis, OncoMasTR, EndoPredict, Oncotype DX, and Tumor Infiltrating Leukocytes were significantly predictive of pCR to neoadjuvant chemotherapy in ER-positive and HER2-negative breast cancer patients.

Analysis of Pre- and Posttreatment Tissues from the SWOG S0800 Trial Reveals an Effect of Neoadjuvant Chemotherapy on the Breast Cancer Genome

PURPOSE

We performed whole-exome sequencing (WES) of pre- and posttreatment cancer tissues to assess the somatic mutation landscape of tumors before and after neoadjuvant taxane and anthracycline chemotherapy with or without bevacizumab.

EXPERIMENTAL DESIGN

Twenty-nine pretreatment biopsies from the SWOG S0800 trial were subjected to WES to identify mutational patterns associated with response to neoadjuvant chemotherapy. Nine matching samples with residual cancer after therapy were also analyzed to assess changes in mutational patterns in response to therapy.

RESULTS

In pretreatment samples, a higher proportion of mutation signature 3, a BRCA-mediated DNA repair deficiency mutational signature, was associated with higher rate of pathologic complete response (pCR; median signature weight 24%, range 0%-38% in pCR vs. median weight 0%, range 0%-19% in residual disease, Wilcoxon rank sum, Bonferroni P = 0.007). We found no biological pathway level mutations associated with pCR or enriched in posttreatment samples. We observed statistically significant enrichment of high functional impact mutations in the "E2F targets" and "G2-M checkpoint" pathways in residual cancer samples implicating these pathways in resistance to therapy and a significant depletion of mutations in the "myogenesis pathway" suggesting the cells harboring these variants were effectively eradicated by therapy.

CONCLUSIONS

These results suggest that genomic disturbances in BRCA-related DNA repair mechanisms, reflected by a dominant mutational signature 3, confer increased chemotherapy sensitivity. Cancers that survive neoadjuvant chemotherapy frequently have alterations in cell-cycle-regulating genes but different genes of the same pathways are affected in different 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.

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.

Differential DNA methylation following chemotherapy for breast cancer is associated with lack of memory improvement at one year

The biological basis underlying cognitive dysfunction in women with early-stage breast cancer (BC) remains unclear, but could reflect gene expression changes that arise from the acquisition and long-term retention of soma-wide alterations in DNA methylation in response to chemotherapy. In this longitudinal study, we identified differences in peripheral methylation patterns present in women prior to treatment (T1) and 1 year after receiving chemotherapy (T4) and evaluated relationships among the differential methylation (DM) ratios with changes in cognitive function. A total of 58 paired (T1 and T4) blood specimens were evaluated. Methylation values were determined for DNA isolated from whole blood using a genome-wide array . Cognitive function was measured using the validated, computerized CNS Vital Signs platform. Relationships between methylation patterns and cognitive domain scores were compared using a stepwise linear regression analysis, with demographic variables as covariates. The symptom comparison analysis was restricted to 2,199 CpG positions showing significant methylation ratio changes between T1 and T4. The positions with DM were enriched for genes involved in the modulation of cytokine concentrations. Significant DM ratios were associated with memory domain (56 CpGs). Eight of the ten largest DM ratio changes associated with lack of memory improvement were localized to genes involved in either neural function (ECE2, PPFIBP2) or signalling processes (USP6NL, RIPOR2, KLF5, UBE2V1, DGKA, RPS6KA1). These results suggest that epigenetic changes acquired and retained for at least one year in non-tumour cells following chemotherapy may be associated with a lack of memory improvement following treatment in BC survivors.

Identification of key genes as potential biomarkers for triple‑negative breast cancer using integrating genomics analysis

Triple-negative breast cancer (TNBC) accounts for the worst prognosis of all types of breast cancers due to a high risk of recurrence and a lack of targeted therapeutic options. Extensive effort is required to identify novel targets for TNBC. In the present study, a robust rank aggregation (RRA) analysis based on genome-wide gene expression datasets involving TNBC patients from the Gene Expression Omnibus (GEO) database was performed to identify key genes associated with TNBC. A total of 194 highly ranked differentially expressed genes (DEGs) were identified in TNBC vs. non-TNBC. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analysis was utilized to explore the biological functions of the identified genes. These DEGs were mainly involved in the biological processes termed positive regulation of transcription from RNA polymerase II promoter, negative regulation of apoptotic process, response to drug, response to estradiol and negative regulation of cell growth. Genes were mainly involved in the KEGG pathway termed estrogen signaling pathway. The aberrant expression of several randomly selected DEGs were further validated in cell lines, clinical tissues and The Cancer Genome Atlas (TCGA) cohort. Furthermore, all the top-ranked DEGs underwent survival analysis using TCGA database, of which overexpression of 4 genes (FABP7, ART3, CT83, and TTYH1) were positively correlated to the life expectancy (P<0.05) of TNBC patients. In addition, a model consisting of two genes (FABP7 and CT83) was identified to be significantly associated with the overall survival (OS) of TNBC patients by means of Cox regression, Kaplan-Meier, and receiver operating characteristic (ROC) analyses. In conclusion, the present study identified a number of key genes as potential biomarkers involved in TNBC, which provide novel insights into the tumorigenesis of TNBC at the gene level and may serve as independent prognostic factors for TNBC prognosis.

Tumor Characterization in Breast Cancer Identifies Immune-Relevant Gene Signatures Associated With Prognosis

There has been increasing attention on immune-oncology for its impressive clinical benefits in many different malignancies. However, due to molecular and genetic heterogeneity of tumors, the activities of traditional clinical and pathological criteria are far from satisfactory. Immune-based strategies have re-ignited hopes for the treatment and prevention of breast cancer. Prognostic or predictive biomarkers, associated with tumor immune microenvironment, may have great prospects in guiding patient management, identifying new immune-related molecular markers, establishing personalized risk assessment of breast cancer. Therefore, in this study, weighted gene co-expression network analysis (WGCNA), single-sample gene set enrichment analysis (ssGSEA), multivariate COX analysis, least absolute shrinkage, and selection operator (LASSO), and support vector machine-recursive feature elimination (SVM-RFE) algorithm, along with a series of analyses were performed, and four immune-related genes (APOD, CXCL14, IL33, and LIFR) were identified as biomarkers correlated with breast cancer prognosis. The findings may provide different insights into prognostic monitoring of immune-related targets for breast cancer or can be served as reference for the further research and validation of biomarkers.

Tumor-associated macrophages expressing galectin-9 identify immunoevasive subtype muscle-invasive bladder cancer with poor prognosis but favorable adjuvant chemotherapeutic response

PURPOSE

Tumor-associated macrophages (TAMs) exist as heterogeneous subsets and have dichotomous roles in cancer-immune evasion. This study aims to assess the clinical effects of Galectin-9⁺ tumor-associated macrophages (Gal-9⁺TAMs) in muscle-invasive bladder cancer (MIBC).

EXPERIMENTAL DESIGN

We identified Gal-9⁺TAMs by immunohistochemistry (IHC) analysis of a tumor microarray (TMA) (n = 141) from the Zhongshan Hospital and by flow cytometric analysis of tumor specimens (n = 20) from the Shanghai Cancer Center. The survival benefit of platinum-based chemotherapy in this subpopulation was evaluated. The effect of the tumor-immune microenvironment with different percentages of Gal-9⁺TAMs was explored.

RESULTS

The frequency of Gal-9⁺TAMs increased with tumor stage and grade. Gal-9⁺TAMs predicted poor overall survival (OS) and recurrence-free survival (RFS) and were better than Gal-9^-TAMs and TAMs to discriminate prognostic groups. In univariate and multivariate Cox regression analyses, patients with high percentages of Gal-9⁺TAMs showed the prominent survival benefit after receiving adjuvant chemotherapy (ACT). High Gal-9⁺TAM infiltration correlated with increasing numbers of regulatory T cells (Tregs) and mast cells and decreasing numbers of CD8⁺T and dendritic cells (DCs). Dense infiltration of Gal-9⁺TAMs was related to reduced cytotoxic molecules, enhanced immune checkpoints or immunosuppressive cytokines expressed by immune cells, as well as active proliferation of tumor cells. Additionally, the subpopulation accumulated was strongly associated with PD-1⁺TIM-3⁺CD8⁺T cells.

CONCLUSIONS

Gal-9⁺TAMs predicted OS and RFS and response to ACT in MIBC patients. High Gal-9⁺TAMs were associated with a pro-tumor immune contexture concomitant with T cell exhaustion.

FusionLearn: a biomarker selection algorithm on cross-platform data

MOTIVATION

In high dimensional genetic data analysis, the objective is to select important biomarkers which are involved in some biological processes, such as disease progression, immune response, etc. The experimental data are often collected from different platforms including microarray experiments and proteomic experiments. The conventional single-platform approach lacks the capability to learn from multiple platforms, and the resulted lists of biomarkers vary across different platforms. There is a great need to develop an algorithm which can aggregate information across platforms and provide a consolidated list of biomarkers across different platforms.

RESULTS

In this paper, we introduce an R package FusionLearn, which implements a fusion learning algorithm to analyze cross-platform data. The consolidated list of biomarkers is selected by the technique of group penalization. We first apply the algorithm on a collection of breast cancer microarray experiments from the NCBI (National Centre for Biotechnology Information) microarray database and the resulted list of selected genes have higher classification accuracy rate across different datasets than the lists generated from each single dataset. Secondly, we use the software to analyze a combined microarray and proteomic dataset for the study of the growth phase versus the stationary phase in Streptomyces coelicolor. The selected biomarkers demonstrate consistent differential behavior across different platforms.

AVAILABILITY AND IMPLEMENTATION

R package: https://cran.r-project.org/package=FusionLearn.

Evaluation of Therapeutic Target Gene Expression Based on Residual Cancer Burden Classification After Neoadjuvant Chemotherapy for HER2-Negative Breast Cancer

INTRODUCTION

Patients with residual disease usually have a poor prognosis after neoadjuvant chemotherapy for breast cancer. The aim of this study was to explore therapeutic targets and potential additional adjuvant treatments for patients with residual disease after standard neoadjuvant chemotherapy.

PATIENTS AND METHODS

We retrieved publicly available complementary DNA microarray data from 399 human epidermal growth factor receptor 2 (HER2)-negative primary breast cancer samples from patients who underwent standard neoadjuvant chemotherapy. We analyzed the messenger RNA (mRNA) expression levels of key breast cancer markers and therapeutic target genes according to residual cancer burden (RCB) classification: RCB-0/I, RCB-II, and RCB-III.

RESULTS

Among hormone receptor-positive samples, there were more luminal A tumors by PAM50 (Prediction Analysis of Microarray 50 [Prosigna], aka Prosigna Breast Cancer Prognostic Gene Signature Assay) in RCB-III than in RCB-0/I and RCB-II (P < .01). The mRNA expressions of ESR1 and PGR were significantly higher, and that of MKI67 was lower in RCB-II and RCB-III than in RCB-0/I. The mRNA expression of cyclin D1 was up-regulated in RCB-III and that of CDKN2A was down-regulated in RCB-III (P = .027 and < .01). Among triple-negative (TN) samples, RCB-III had higher clinical stage and more lymph node-positive samples than RCB-0/1 and RCB-II (P < .01). In both subtypes, VEGF-C expression was significantly higher in RCB-III than in RCB-0/I and RCB-II.

CONCLUSION

In hormone receptor-positive breast cancer, biological features such as luminal A were associated with RCB; this trend was not observed in TN breast cancer. Further, some targeted therapies should be tested as new strategies after standard neoadjuvant chemotherapy in future clinical trials.

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.