Clinical evaluation of molecular surrogate subtypes in patients with ipsilateral multifocal primary breast cancer.

BACKGROUND: When ipsilateral multifocal primary breast cancer (IMBC) is detected, standard routine is to evaluate the largest tumor with immunohistochemistry (IHC). As all foci are not routinely characterized, many patients may not receive optimal adjuvant treatment. Here, we assess the clinical relevance of examining at least two foci present in patients with IMBC. METHODS: Patients diagnosed and treated for IMBC at Sahlgrenska University Hospital (Gothenburg, Sweden) between 2012 and 2017 were screened. In total, 180 patients with ≥ 2 invasive foci (183 specimens) were assessed with IHC and included in this study. Expression of the estrogen (ER) and progesterone (PR) receptors, Ki67, HER2, and tumor grade were used to determine the molecular surrogate subtypes and discordance among the foci was recorded. An additional multidisciplinary team board was then held to re-assess whether treatment recommendations changed due to discordances in molecular surrogate subtype between the different foci. RESULTS: Discordance in ER, PR, HER2, and Ki67 was found in 2.7%, 19.1%, 7.7%, and 16.9% of invasive foci, respectively. Discordance in the molecular surrogate subtypes was found in 48 of 180 (26.7%) patients, which resulted in therapy changes for 11 patients (6.1%). These patients received additional endocrine therapy (n = 2), chemotherapy (n = 3), and combined chemotherapy and trastuzumab (n = 6). CONCLUSION: Taken together, when assessing at least two tumor foci with IHC, regardless of shared morphology or tumor grade between the different foci, 6.1% of patients with IMBC were recommended additional adjuvant treatment. A pathologic assessment using IHC of all foci is therefore recommended to assist in individualized treatment decision making.

Pan-cancer analysis identifies BIRC5 as a prognostic biomarker.

BACKGROUND: The BIRC5 gene encodes for the Survivin protein, which is a member of the inhibitor of apoptosis family. Survivin is found in humans during fetal development, but generally not in adult cells thereafter. Previous studies have shown that Survivin is abundant in most cancer cells, thereby making it a promising target for anti-cancer drugs and a potential prognostic tool. METHODS: To assess genetic alterations and mutations in the BIRC5 gene as well as BIRC5 co-expression with other genes, genomic and transcriptomic data were downloaded via cBioPortal for approximately 9000 samples from The Cancer Genome Atlas (TCGA) representing 33 different cancer types and 11 pan-cancer organ systems, and validated using the ICGC Data Portal and COSMIC. TCGA BIRC5 RNA sequencing data from 33 different cancer types and matching normal tissue samples for 16 cancer types were downloaded from Broad GDAC Firehose and validated using breast cancer microarray data from our previous work and data sets from the GENT2 web-based tool. Survival data were analyzed with multivariable Cox proportional hazards regression analysis and validated using KM plotter for breast-, ovarian-, lung- and gastric cancer. RESULTS: Although genetic alterations in BIRC5 were not common in cancer, BIRC5 expression was significantly higher in cancer tissue compared to normal tissue in the 16 different cancer types. For 14/33 cancer types, higher BIRC5 expression was linked to worse overall survival (OS, 4/14 after adjusting for both age and tumor grade and 10/14 after adjusting only for age). Interestingly, higher BIRC5 expression was associated with better OS in lung squamous cell carcinoma and ovarian serous cystadenocarcinoma. Higher BIRC5 expression was also linked to shorter progressive-free interval (PFI) for 14/33 cancer types (4/14 after adjusting for both age and tumor grade and 10/14 after adjusting only for age). External validation showed that high BIRC5 expression was significantly associated with worse OS for breast-, lung-, and gastric cancer. CONCLUSIONS: Our findings suggest that BIRC5 overexpression is associated with the initiation and progression of several cancer types, and thereby a promising prognostic biomarker.

Pan-cancer analysis of genomic and transcriptomic data reveals the prognostic relevance of human proteasome genes in different cancer types.

BACKGROUND: The human proteasome gene family (PSM) consists of 49 genes that play a crucial role in cancer proteostasis. However, little is known about the effect of PSM gene expression and genetic alterations on clinical outcome in different cancer forms. METHODS: Here, we performed a comprehensive pan-cancer analysis of genetic alterations in PSM genes and the subsequent prognostic value of PSM expression using data from The Cancer Genome Atlas (TCGA) containing over 10,000 samples representing up to 33 different cancer types. External validation was performed using a breast cancer cohort and KM plotter with four cancer types. RESULTS: The PSM genetic alteration frequency was high in certain cancer types (e.g. 67%; esophageal adenocarcinoma), with DNA amplification being most common. Compared with normal tissue, most PSM genes were predominantly overexpressed in cancer. Survival analysis also established a relationship with PSM gene expression and adverse clinical outcome, where PSMA1 and PSMD11 expression were linked to more unfavorable prognosis in ≥ 30% of cancer types for both overall survival (OS) and relapse-free interval (PFI). Interestingly, PSMB5 gene expression was associated with OS (36%) and PFI (27%), and OS for PSMD2 (42%), especially when overexpressed. CONCLUSION: These findings indicate that several PSM genes may potentially be prognostic biomarkers and novel therapeutic targets for different cancer forms.

Comparison of breast cancer surrogate subtyping using a closed-system RT-qPCR breast cancer assay and immunohistochemistry on 100 core needle biopsies with matching surgical specimens.

BACKGROUND: Routine clinical management of breast cancer (BC) currently depends on surrogate subtypes according to estrogen- (ER) and progesterone (PR) receptor, Ki-67, and HER2-status. However, there has been growing demand for reduced immunohistochemistry (IHC) turnaround times. The Xpert® Breast Cancer STRAT4* Assay (STRAT4)*, a standardized test for ESR1/PGR/MKi67/ERBB2 mRNA biomarker assessment, takes less than 2 hours. Here, we compared the concordance between the STRAT4 and IHC/SISH, thereby evaluating the effect of method choice on surrogate subtype assessment and adjuvant treatment decisions. METHODS: In total, 100 formalin-fixed paraffin-embedded core needle biopsy (CNB) samples and matching surgical specimens for 98 patients with primary invasive BC were evaluated using the STRAT4 assay. The concordance between STRAT4 and IHC was calculated for individual markers for the CNB and surgical specimens. In addition, we investigated whether changes in surrogate BC subtyping based on the STRAT4 results would change adjuvant treatment recommendations. RESULTS: The overall percent agreement (OPA) between STRAT4 and IHC/SISH ranged between 76 and 99% for the different biomarkers. Concordance for all four biomarkers in the surgical specimens and CNBs was only 66 and 57%, respectively. In total, 74% of surgical specimens were concordant for subtype, regardless of the method used. IHC- and STRAT4-based subtyping for the surgical specimen were shown to be discordant for 25/98 patients and 18/25 patients would theoretically have been recommended a different adjuvant treatment, primarily receiving more chemotherapy and trastuzumab. CONCLUSIONS: A comparison of data from IHC/in situ hybridization and STRAT4 demonstrated that subsequent changes in surrogate subtyping for the surgical specimen may theoretically result in more adjuvant treatment given, primarily with chemotherapy and trastuzumab.

A 17-marker panel for global genomic instability in breast cancer.

Genomic instability is a hallmark of cancer that plays a pivotal role in breast cancer development and evolution. A number of existing prognostic gene expression signatures for breast cancer are based on proliferation-related genes. Here, we identified a 17-marker panel associated with genome stability. A total of 136 primary breast carcinomas were stratified by genome stability. Matched gene expression profiles showed an innate segregation based on genome stability. We identified a 17-marker panel stratifying the training and validation cohorts into high- and low-risk patients. The 17 genes associated with genomic instability strongly impacted clinical outcome in breast cancer. Pathway analyses determined chromosome organisation, cell cycle regulation, and RNA processing as the underlying biological processes, thereby offering options for drug development and treatment tailoring. Our work supports the applicability of the 17-marker panel to improve clinical outcome prediction for breast cancer patients based on a signature accounting for genomic instability.

Previously diagnosed multiple primary malignancies in patients with breast carcinoma in Western Sweden between 2007 and 2018.

PURPOSE: Multiple primary malignancies (MPMs) caused by breast cancer treatment are well described, but only few studies to date describe which other previous primary malignancies (OPPMs) occur before breast cancer. The purpose of the present study was to evaluate the prevalence of OPPMs in patients with breast cancer between 2007 and 2018 in Western Sweden. METHODS: Patient selection was performed using both pathology reports at Sahlgrenska University Hospital (Sweden) and the Swedish Cancer Registry. All newly diagnosed breast cancer patients were screened for presence of OPPM. RESULTS: In total, 8031 breast cancer patients were diagnosed at Sahlgrenska University Hospital between 2007 and 2018. The prevalence of breast cancer patients with OPPMs (n = 414) increased from on average 2.6% to 8.2% during this 12-year period and ranged from 17 to 59 patients annually. The most striking increase in prevalence was found among the gynecological tumors (endometrium and ovarian adenocarcinomas), malignant melanomas and gastrointestinal malignancies. These findings were validated using data of the Swedish Cancer Registry. CONCLUSIONS: The overall survival rates for cancer patients have improved tremendously during the past 40 years, in part due to individually tailored therapies and screening programs. Our study revealed an increasing trend of OPPMs in breast cancer patients.

Radiation-induced genomic instability in breast carcinomas of the Swedish hemangioma cohort.

Radiation-induced genomic instability (GI) is hypothesized to persist after exposure and ultimately promote carcinogenesis. Based on the absorbed dose to the breast, an increased risk of developing breast cancer was shown in the Swedish hemangioma cohort that was treated with radium-226 for skin hemangioma as infants. Here, we screened 31 primary breast carcinomas for genetic alterations using the OncoScan CNV Plus Assay to assess GI and chromothripsis-like patterns associated with the absorbed dose to the breast. Higher absorbed doses were associated with increased numbers of copy number alterations in the tumor genome and thus a more unstable genome. Hence, the observed dose-dependent GI in the tumor genome is a measurable manifestation of the long-term effects of irradiation. We developed a highly predictive Cox regression model for overall survival based on the interaction between absorbed dose and GI. The Swedish hemangioma cohort is a valuable cohort to investigate the biological relationship between absorbed dose and GI in irradiated humans. This work gives a biological basis for improved risk assessment to minimize carcinogenesis as a secondary disease after radiation therapy.

Immunohistochemical validation of COL3A1, GPR158 and PITHD1 as prognostic biomarkers in early-stage ovarian carcinomas.

BACKGROUND: Ovarian cancer is the main cause of gynecological cancer-associated death. However, 5-year survival rates differ dramatically between the five main ovarian carcinoma histotypes. Therefore, we need to have a better understanding of the mechanisms that promote histotype-specific ovarian carcinogenesis and identify novel prognostic biomarkers. METHODS: Here, we evaluated the prognostic role of 29 genes for early-stage (I and II) ovarian carcinomas (n = 206) using immunohistochemistry (IHC). RESULTS: We provide evidence of aberrant protein expression patterns for Collagen type III alpha 1 chain (COL3A1), G protein-coupled receptor 158 (GPR158) and PITH domain containing 1 (PITHD1). Kaplan-Meier survival analysis revealed that COL3A1 expression was associated with shorter overall survival in the four major histotypes of epithelial ovarian carcinoma patients (P value = 0.026, HR = 2.99 (95% CI 1.089-8.19)). Furthermore, GPR158 and PITHD1 were shown to be histotype-specific prognostic biomarkers, with elevated GPR158 expression patterns in mucinous ovarian carcinoma patients with unfavorable overall survival (P value = 0.00043, HR = 6.13 (95% CI 1.98-18.98)), and an association with lower PITHD1 protein expression and unfavorable overall and disease-specific survival in clear-cell ovarian carcinoma patients (P value = 0.012, HR = 0.22 (95% CI 0.058-0.80); P value = 0.003, HR = 0.17 (95% CI 0.043-0.64)). CONCLUSIONS: The novel biomarkers identified here may improve prognostication at the time of diagnosis and may assist in the development of future individualized therapeutic strategies for ovarian carcinoma patients.

The prognostic relevance of FOXA1 and Nestin expression in breast cancer metastases: a retrospective study of 164 cases during a 10-year period (2004-2014).

BACKGROUND: Current prognostic markers cannot adequately predict the clinical outcome of breast cancer patients. Therefore, additional biomarkers need to be included in routine immune panels. FOXA1 was a significant predictor of favorable outcome in primary breast cancer, while Nestin expression is preferentially found in triple-negative tumors with increased rate of nodal metastases, and reduced survival. No studies have investigated the prognostic value of FOXA1 and Nestin expression in breast cancer metastases. METHODS: Breast cancer metastases (n = 164) from various anatomical sites were retrospectively analyzed by immunohistochemistry for FOXA1, Nestin and GATA3 expression. Cox regression analysis assessed the prognostic value of FOXA1 and Nestin expression. RESULTS: In breast cancer metastases, FOXA1 expression was associated with Nestin-negativity, GATA3-positivity, ER-positivity, HER2-positivity and non-triple-negative status (P < 0.05). In contrast, Nestin expression was associated with FOXA1-negative, GATA3-negative, ER-negative, and triple-negative metastases (P < 0.05). Univariate Cox regression analysis showed FOXA1 expression was predictive of overall survival (OS, P = 0.00048) and metastasis-free survival (DMFS, P = 0.0011), as well as, distant metastasis-free survival in ER-positive patients (P = 0.036) and overall survival in ER-negative patients (P = 0.024). Multivariate analysis confirmed the significance of FOXA1 for both survival endpoints in metastatic breast cancer patients (OS, P = 0.0033; DMFS, P = 0.015). CONCLUSIONS: In our study, FOXA1 was expressed mostly in ER-positive breast cancer metastases. Expression of Nestin was related to triple-negative metastases, where brain was the most frequent metastatic site. These findings highlight the clinical utility of FOXA1 and Nestin expression and warrant their inclusion in routine immunohistochemical panels for breast carcinoma.

Clonal relatedness in tumour pairs of breast cancer patients.

BACKGROUND: Molecular classification of tumour clonality is currently not evaluated in multiple invasive breast carcinomas, despite evidence suggesting common clonal origins. There is no consensus about which type of data (e.g. copy number, mutation, histology) and especially which statistical method is most suitable to distinguish clonal recurrences from independent primary tumours. METHODS: Thirty-seven invasive breast tumour pairs were stratified according to laterality and time interval between the diagnoses of the two tumours. In a multi-omics approach, tumour clonality was analysed by integrating clinical characteristics (n = 37), DNA copy number (n = 37), DNA methylation (n = 8), gene expression microarray (n = 7), RNA sequencing (n = 3), and SNP genotyping data (n = 3). Different statistical methods, e.g. the diagnostic similarity index (SI), were used to classify the tumours as clonally related recurrences or independent primary tumours. RESULTS: The SI and hierarchical clustering showed similar tendencies and the highest concordance with the other methods. Concordant evidence for tumour clonality was found in 46% (17/37) of patients. Notably, no association was found between the current clinical guidelines and molecular tumour features. CONCLUSIONS: A more accurate classification of clonal relatedness between multiple breast tumours may help to mitigate treatment failure and relapse by integrating tumour-associated molecular features, clinical parameters, and statistical methods. Guidelines need to be defined with exact thresholds to standardise clonality testing in a routine diagnostic setting.

GATA3 as a putative marker of breast cancer metastasis-A retrospective immunohistochemical study.

Diagnostic verification of breast cancer metastasis with histopathology and imaging analysis is essential to determine tumor staging. The aim of this study was to validate the utility of GATA3 immunohistochemistry as a diagnostic marker for breast cancer metastases and metastases of unknown primary origin. Retrospective immunohistochemical analysis of GATA3 expression in 164 breast cancer metastases diagnosed between 2004 and 2014 showed a striking difference between mammaglobin and GATA3 expression (51.2% vs 94% positivity). These findings highlight GATA3 as a more reliable and sensitive diagnostic marker for breast cancer metastases and possibly metastatic tumors of unknown origin than mammaglobin.

Hedgehog inhibitor sonidegib potentiates 177Lu-octreotate therapy of GOT1 human small intestine neuroendocrine tumors in nude mice.

BACKGROUND: 177Lu-octreotate can be used to treat somatostatin receptor expressing neuroendocrine tumors. It is highly effective in animal models, but clinical studies have so far only demonstrated low cure rates. Hedgehog inhibitors have shown therapeutic effect as monotherapy in neuroendocrine tumor model systems and might be one option to enhance the efficacy of 177Lu-octreotate therapy. The aim of this study was to determine the therapeutic effect of combination therapy using 177Lu-octreotate and the Hedgehog signaling pathway inhibitor sonidegib. METHODS: GOT1-bearing BALB/c nude mice were treated with either sonidegib (80 mg/kg twice a week via oral gavage), a single injection of 30 MBq 177Lu-octreotate i.v., or a combination of both. Untreated animals served as controls. Tumor size was measured twice-weekly using calipers. The animals were killed 41 d after injection followed by excision of the tumors. Total RNA was extracted from each tumor sample and then subjected to gene expression analysis. Gene expression patterns were compared with those of untreated controls using Nexus Expression 3.0, IPA and Gene Ontology terms. Western blot was carried out on total protein extracted from the tumor samples to analyze activation-states of the Hh and PI3K/AKT/mTOR pathways. RESULTS: Sonidegib monotherapy resulted in inhibition of tumor growth, while a significant reduction in mean tumor volume was observed after 177Lu-octreotate monotherapy and combination therapy. Time to progression was prolonged in the combination therapy group compared with 177Lu-octreotate monotherapy. Gene expression analysis revealed a more pronounced response following combination therapy compared with both monotherapies, regarding the number of regulated genes and biological processes. Several cancer-related signaling pathways (i.e. Wnt/ß-catenin, PI3K/AKT/mTOR, G-protein coupled receptor, and Notch) were affected by the combination therapy, but not by either monotherapy. Protein expression analysis revealed an activation of the Hh- and PI3K/AKT/mTOR pathways in tumors exposed to 177Lu-octreotate monotherapy and combination therapy. CONCLUSIONS: A comparative analysis of the different treatment groups showed that combination therapy using sonidegib and 177Lu-octreotate could be beneficial to patients with neuroendocrine tumors. Gene expression analysis revealed a functional interaction between sonidegib and 177Lu-octreotate, i.e. several cancer-related signaling pathways were modulated that were not affected by either monotherapy. Protein expression analysis indicated a possible PI3K/AKT/mTOR-dependent activation of the Hh pathway, independent of SMO.

Additive effect of the AZGP1, PIP, S100A8 and UBE2C molecular biomarkers improves outcome prediction in breast carcinoma.

The deregulation of key cellular pathways is fundamental for the survival and expansion of neoplastic cells, which in turn can have a detrimental effect on patient outcome. To develop effective individualized cancer therapies, we need to have a better understanding of which cellular pathways are perturbed in a genetically defined subgroup of patients. Here, we validate the prognostic value of a 13-marker signature in independent gene expression microarray datasets (n = 1,141) and immunohistochemistry with full-faced FFPE samples (n = 71). The predictive performance of individual markers and panels containing multiple markers was assessed using Cox regression analysis. In the external gene expression dataset, six of the 13 genes (AZGP1, NME5, S100A8, SCUBE2, STC2 and UBE2C) retained their prognostic potential and were significantly associated with disease-free survival (p < 0.001). Protein analyses refined the signature to a four-marker panel [AZGP1, Prolactin-inducible protein (PIP), S100A8 and UBE2C] significantly correlated with cycling, high grade tumors and lower disease-specific survival rates. AZGP1 and PIP were found in significantly lower levels in invasive breast tissue as compared with adjacent normal tissue, whereas elevated levels of S100A8 and UBE2C were observed. A predictive model containing the four-marker panel in conjunction with established clinical variables outperformed a model containing the clinical variables alone. Our findings suggest that deregulated AZGP1, PIP, S100A8 and UBE2C are critical for the aggressive breast cancer phenotype, which may be useful as novel therapeutic targets for drug development to complement established clinical variables.

Clinical relevance of breast cancer-related genes as potential biomarkers for oral squamous cell carcinoma.

BACKGROUND: Squamous cell carcinoma of the oral cavity (OSCC) is a common cancer form with relatively low 5-year survival rates, due partially to late detection and lack of complementary molecular markers as targets for treatment. Molecular profiling of head and neck cancer has revealed biological similarities with basal-like breast and lung carcinoma. Recently, we showed that 16 genes were consistently altered in invasive breast tumors displaying varying degrees of aggressiveness. METHODS: To extend our findings from breast cancer to another cancer type with similar characteristics, we performed an integrative analysis of transcriptomic and proteomic data to evaluate the prognostic significance of the 16 putative breast cancer-related biomarkers in OSCC using independent microarray datasets and immunohistochemistry. Predictive models for disease-specific (DSS) and/or overall survival (OS) were calculated for each marker using Cox proportional hazards models. RESULTS: We found that CBX2, SCUBE2, and STK32B protein expression were associated with important clinicopathological features for OSCC (peritumoral inflammatory infiltration, metastatic spread to the cervical lymph nodes, and tumor size). Consequently, SCUBE2 and STK32B are involved in the hedgehog signaling pathway which plays a pivotal role in metastasis and angiogenesis in cancer. In addition, CNTNAP2 and S100A8 protein expression were correlated with DSS and OS, respectively. CONCLUSIONS: Taken together, these candidates and the hedgehog signaling pathway may be putative targets for drug development and clinical management of OSCC patients.

A diagnostic algorithm to identify paired tumors with clonal origin.

Despite practical implications we still lack standardized methods for clonality testing of tumor pairs. Each tumor is characterized by a set of chromosomal abnormalities, nonrandom changes preferentially involving specific chromosomes and chromosomal regions. Although tumors accumulate chromosomal abnormalities during their development, the majority of these alterations is specific and characteristic for each individual tumor is not exhibited at the population level. Assumingly, secondary tumors that develop from disseminated cells from the primary tumor inherit not only chromosomal changes specific for the cancerous process but also random chromosomal changes that accumulate during tumor development. Based on this assumption, we adopted an intuitive index for genomic similarities of paired tumors, which ranges between zero (completely different genomic profiles) and one (identical genomic profiles). To test the assumption that two tumors have clonal origins if they share a higher degree of genomic similarity than two randomly paired tumors, we built a permutation-based null-hypothesis procedure. The procedure is demonstrated using two publicly available data sets. The article highlights the complexities of clonality testing and aims to offer an easy to follow blueprint that will allow researchers to test genomic similarities of paired tumors, with the proposed index or any other index that fits their need.

Digital RNA sequencing using unique molecular identifiers enables ultrasensitive RNA mutation analysis.

Mutation analysis is typically performed at the DNA level since most technical approaches are developed for DNA analysis. However, some applications, like transcriptional mutagenesis, RNA editing and gene expression analysis, require RNA analysis. Here, we combine reverse transcription and digital DNA sequencing to enable low error digital RNA sequencing. We evaluate yield, reproducibility, dynamic range and error correction rate for seven different reverse transcription conditions using multiplexed assays. The yield, reproducibility and error rate vary substantially between the specific conditions, where the yield differs 9.9-fold between the best and worst performing condition. Next, we show that error rates similar to DNA sequencing can be achieved for RNA using appropriate reverse transcription conditions, enabling detection of mutant allele frequencies <0.1% at RNA level. We also detect mutations at both DNA and RNA levels in tumor tissue using a breast cancer panel. Finally, we demonstrate that digital RNA sequencing can be applied to liquid biopsies, analyzing cell-free gene transcripts. In conclusion, we demonstrate that digital RNA sequencing is suitable for ultrasensitive RNA mutation analysis, enabling several basic research and clinical applications.

Repurposing proteasome inhibitors for improved treatment of triple-negative breast cancer.

Triple-negative breast cancer (TNBC) is associated with poor prognosis and limited treatment options due to the lack of important receptors (estrogen receptor [ER], progesterone receptor [PR], and human epidermal growth factor receptor 2 [HER2]) used for targeted therapy. However, high-throughput in vitro drug screening of cell lines is a powerful tool for identifying effective drugs for a disease. Here, we determine the intrinsic chemosensitivity of TNBC cell lines to proteasome inhibitors (PIs), thereby identifying potentially potent 2-drug combinations for TNBC. Eight TNBC cell lines (BT-549, CAL-148, HCC1806, HCC38, HCC70, MDA-MB-436, MDA-MB-453, and MDA-MB-468) and two controls (MCF-10A and MCF-7) were first exposed to 18 drugs (11 PIs and 7 clinically relevant chemotherapeutic agents) as monotherapy, followed by prediction of potent 2-drug combinations using the IDACombo pipeline. The synergistic effects of the 2-drug combinations were evaluated with SynergyFinder in four TNBC cell lines (CAL-148, HCC1806, HCC38, and MDA-MB-468) and three controls (BT-474, MCF-7, and T47D) in vitro, followed by further evaluation of tumor regression in zebrafish tumor models established using HCC1806 and MCF-7 cells. Monotherapy identified nine effective drugs (bortezomib, carfilzomib, cisplatin, delanzomib, docetaxel, epoxomicin, MLN-2238, MLN-9708, and nedaplatin) across all cell lines. PIs (e.g., bortezomib, delanzomib, and epoxomicin) were highly potent drugs in TNBC cells, of which bortezomib and delanzomib inhibited the chymotrypsin-like activity of the 20 S proteasome by 100% at 10 µM. Moreover, several potent 2-drug combinations (e.g., bortezomib+nedaplatin and epoxomicin+epirubicin) that killed virtually 100% of cells were also identified. Although HCC1806- and MCF-7-derived xenografts treated with bortezomib+nedaplatin and carboplatin+paclitaxel were smaller, HCC1806 cells frequently metastasized to the trunk region. Taken together, we show that PIs used in combination with platinum agents or topoisomerase inhibitors exhibit increased efficiency with almost 100% inhibition in TNBC cell lines, indicating that PIs are therefore promising compounds to use as combination therapy for TNBC.

Elevated cyclin B2 expression in invasive breast carcinoma is associated with unfavorable clinical outcome.

BACKGROUND: Breast cancer is a potentially fatal malignancy in females despite the improvement in therapeutic techniques. The identification of novel molecular signatures is needed for earlier detection, monitoring effects of treatment, and predicting prognosis. We have previously used microarray analysis to identify differentially expressed genes in aggressive breast tumors. The purpose of the present study was to investigate the prognostic value of the candidate biomarkers CCNB2, ASPM, CDCA7, KIAA0101, and SLC27A2 in breast cancer. METHODS: The expression levels and subcellular localization of the CCNB2, ASPM, CDCA7, KIAA0101, and SLC27A2 proteins were measured using immunohistochemistry (IHC) on a panel of 80 primary invasive breast tumors. Furthermore, the mRNA levels of CCNB2, KIAA0101, and SLC27A2 were subsequently examined by qRT-PCR to validate IHC results. Patient disease-specific survival (DSS) was evaluated in correlation to protein levels using the Kaplan-Meier method. Multivariate Cox regression analysis was used to determine the impact of aberrant protein expression of the candidate biomarkers on patient DSS and to estimate the hazard ratio at 8-year follow-up. RESULTS: Elevated cytoplasmic CCNB2 protein levels were strongly associated with short-term disease-specific survival of breast cancer patients (≤ 8 years; P<0.001) and with histological tumor type (P= 0.04). However, no association with other clinicopathological parameters was observed. Multivariate Cox regression analysis specified that CCNB2 protein expression is an independent prognostic marker of DSS in breast cancer. The predictive ability of several classical clinicopathological parameters was improved when used in conjunction with CCNB2 protein expression (C-index = 0.795) in comparison with a model without CCNB2 expression (C-index = 0.698). The protein levels of ASPM, CDCA7, KIAA0101, and SLC27A2 did not correlate with any clinicopathological parameter and had no influence on DSS. However, a significant correlation between the expression of the CCNB2 and ASPM proteins was detected (P = 0.03). CONCLUSION: These findings suggest that cytoplasmic CCNB2 may function as an oncogene and could serve as a potential biomarker of unfavorable prognosis over short-term follow-up in breast cancer.

Segmented regression, a versatile tool to analyze mRNA levels in relation to DNA copy number aberrations.

DNA copy number aberrations (CNA) and subsequent altered gene expression profiles (mRNA levels) are characteristic features of cancerous cells. Integrative genomic analysis aims to identify recurrent CNA that may have a potential role in cancer development, assuming that gene amplification is accompanied by overexpression, while deletions give rise to downregulation of gene expression. We propose a segmented regression-based approach to identify CNA-driven alteration of gene expression profiles. Segmented regression allows to fit piecewise linear models in different domains of CNA joined by a change-point, where the mRNA-CNA relationship undergoes structural changes. Here, we illustrate the implementation and applicability of the proposed model using 1,161 chromosome fragments detected as DNA CNA in primary tumors from 97 breast cancer patients. We identified significant CNA-driven changes in gene expression levels for 341 chromosome fragments, of which 72 showed a nonlinear relationship to CNA. For 59 of 72 chromosome fragments (82%), we observed an initial increase in mRNA levels due to changes in CNA. After the change-point was passed, the mRNA levels reached a plateau, and a further increase in DNA copy numbers did not induce further elevation in mRNA levels. In contrast, for 13 chromosome fragments, the change-point marked the point where mRNA production accelerated. We conclude that segmented regression modeling may provide valuable insights into the impact CNA have on gene expression in cancer cells.

Optimization of Cell Viability Assays for Drug Sensitivity Screens.

During the preclinical stages of the drug discovery process, cell viability assays are fundamental tools for studying the phenotypic properties and overall health of cells following in vitro drug sensitivity screens. Therefore, it is important to optimize your viability assay of choice to obtain reproducible and replicable results, as well as use relevant drug response metrics (e.g., IC50, AUC, GR50, and GRmax) to identify candidate drugs for further evaluation in vivo. Herein, we used the resazurin reduction assay which is a quick, cost-effective, simple-to-use, and sensitive method for examining the phenotypic properties of cells. Using the MCF7 breast cancer cell line, we provide a detailed step-by-step protocol for optimizing drug sensitivity screens using the resazurin assay.