Tumor Gene Expression and Prognosis in Breast Cancer Patients with 10 or More Positive Lymph Nodes

The digital revolution in pathology: Towards a smarter approach to research and treatment

Artificial intelligence (AI) applications in oncology are at the forefront of transforming healthcare during the Fourth Industrial Revolution, driven by the digital data explosion. This review provides an accessible introduction to the field of AI, presenting a concise yet structured overview of the foundations of AI, including expert systems, classical machine learning, and deep learning, along with their contextual application in clinical research and healthcare. We delve into the current applications of AI in oncology, with a particular focus on diagnostic imaging and pathology. Numerous AI tools have already received regulatory approval, and more are under active development, bringing clear benefits but not without challenges. We discuss the importance of data security, the need for transparent and interpretable models, and the ethical considerations that must guide AI development in healthcare. By providing a perspective on the opportunities and challenges, this review aims to inform and guide researchers, clinicians, and policymakers in the adoption of AI in oncology.

Unveiling the HER2-low phenomenon: exploring immunohistochemistry and gene expression to characterise HR-positive HER2-negative early breast cancer

PURPOSE

HER2-low breast cancer (BC) is a novel entity with relevant therapeutic implications, especially in hormone receptor (HR) positive BC. This study examines whether HER2 mRNA through the 21-gene assay, Oncotype DX (ODX), can refine the diagnosis of HER2-low and HER2-zero, obtained by immunohistochemistry (IHC).

METHODS

Between Jan 2021 and Jan 2023, 229 consecutive HR-positive HER2-negative early BC (T1-3 N0-1) have been characterised by IHC and ODX. HER2 status by IHC was either zero (IHC-0) or low (IHC-1 + and IHC-2 + /ISH-negative) while HER2-zero was further divided into HER2-null (IHC-0) and HER2-ultralow (IHC-1-10%). HER2 gene expression by ODX was negative if lower 10.7.

RESULTS

The distribution of HER2 IHC was as follows: 53.3% HER2-0, 29.25% HER2-1 + , and 17.5% HER2-2 + . The clinicopathological characteristics were similar in the three groups, with higher PgR-negative rate in HER2-zero (13.9% vs 3% vs 5%). The distribution of RS was homogeneous in the three groups with the median HER2 gene expression of 9.20 [IQR: 8.70-9.60]. HER2 gene expression gradually increased as the IHC score, with substantial overlap. After adjusting for confounders, HER2-1 + and HER2 2 + had a significant positive correlation between HER2 gene expression and IHC [OR 1.42, 95% CI 1.21 to 1.68, p < 0.001; OR 1.96, 95% CI 1.61 to 2.37, p < 0.001] compared to the HER2-zero group. HER2 gene expression did not differ between HER2-null and HER2-ultralow subgroups.

CONCLUSION

Due to the substantial overlap, the HER2 gene expression is unable to properly distinguish HER2-low and HER2-zero IHC whose accurate identification is critical in the context of HER2-negative BC.

Assessment of Background Parenchymal Enhancement at Dynamic Contrast-enhanced MRI in Predicting Breast Cancer Recurrence Risk

Background Background parenchymal enhancement (BPE) at dynamic contrast-enhanced (DCE) MRI of cancer-free breasts increases the risk of developing breast cancer; implications of quantitative BPE in ipsilateral breasts with breast cancer are largely unexplored. Purpose To determine whether quantitative BPE measurements in one or both breasts could be used to predict recurrence risk in women with breast cancer, using the Oncotype DX recurrence score as the reference standard. Materials and Methods This HIPAA-compliant retrospective single-institution study included women diagnosed with breast cancer between January 2007 and January 2012 (development set) and between January 2012 and January 2017 (internal test set). Quantitative BPE was automatically computed using an in-house-developed computer algorithm in both breasts. Univariable logistic regression was used to examine the association of BPE with Oncotype DX recurrence score binarized into high-risk (recurrence score >25) and low- or intermediate-risk (recurrence score ≤25) categories. Models including BPE measures were assessed for their ability to distinguish patients with high risk versus those with low or intermediate risk and the actual recurrence outcome. Results The development set included 127 women (mean age, 58 years ± 10.2 [SD]; 33 with high risk and 94 with low or intermediate risk) with an actual local or distant recurrence rate of 15.7% (20 of 127) at a minimum 10 years of follow-up. The test set included 60 women (mean age, 57.8 years ± 11.6; 16 with high risk and 44 with low or intermediate risk). BPE measurements quantified in both breasts were associated with increased odds of a high-risk Oncotype DX recurrence score (odds ratio range, 1.27-1.66 [95% CI: 1.02, 2.56]; P < .001 to P = .04). Measures of BPE combined with tumor radiomics helped distinguish patients with a high-risk Oncotype DX recurrence score from those with a low- or intermediate-risk score, with an area under the receiver operating characteristic curve of 0.94 in the development set and 0.79 in the test set. For the combined models, the negative predictive values were 0.97 and 0.93 in predicting actual distant recurrence and local recurrence, respectively. Conclusion Ipsilateral and contralateral DCE MRI measures of BPE quantified in patients with breast cancer can help distinguish patients with high recurrence risk from those with low or intermediate recurrence risk, similar to Oncotype DX recurrence score. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Zhou and Rahbar in this issue.

A cost-consequence model of using the 21-gene assay to identify patients with early-stage node-positive breast cancer who benefit from adjuvant chemotherapy in the Netherlands

BACKGROUND

Patients with early-stage hormone receptor positive, human epidermal growth factor receptor-2 (HER2) negative invasive breast cancer with 1-3 positive lymph nodes (N1) often undergo surgical excisions followed by adjuvant chemotherapy (ACT). Many patients have no benefit from ACT and receive unnecessary, costly treatment often associated with short- and long-term adverse events (AEs). Gene expression profiling (GEP) assays, such as the 21-gene assay (i.e. the Oncotype DX assay), can identify patients at higher risk for recurrence who may benefit from ACT. However, the budgetary consequence of using the Oncotype DX assay versus no GEP testing in the Netherlands is unknown. Our study therefore assessed it using a cost-consequence model.

METHODS

A validated model was used to create the N1 model. The model compared the costs and consequences of using the Oncotype DX assay versus no GEP testing and MammaPrint, and subsequent ACT use with corresponding costs for chemotherapy, treatment of AEs, productivity losses, GEP testing, and treatment of recurrences, according to the Oncotype DX results. The model time horizon was 5 years.

RESULTS

Costs for the total population amounted to €8.0 million (M), €16.2 M, and €9.5 M, and cost per patient amounted to €13,540, €27,455, and €16,154 for using the Oncotype DX assay, no GEP testing, and MammaPrint, respectively. Total cost savings of using the Oncotype DX assay amounted to €8.2 M versus no GEP testing and €1.5 M versus MammaPrint. Using the Oncotype DX assay would result in fewer patients receiving ACT and thus fewer AEs, sick days, and hospitalizations, leading to overall cost savings compared with no GEP testing and MammaPrint.

CONCLUSIONS

Implementing Oncotype DX testing in this population can prevent unnecessary overtreatment, reducing clinical and economic burden on the patient and Dutch healthcare system.

Detecting Human Epidermal Growth Factor Receptor 2 (HER2) Amplification: Proof of Concept of an Alternative Approach

BACKGROUND

There are multiple genes that are co-amplified along with human epidermal growth factor receptor 2 (HER2) in chromosome 17. GRB7 and PGAP3 are two such genes. We hypothesize that the protein products of these genes may serve as immunohistochemistry (IHC) markers for detecting HER2 amplification in breast cancer.

METHODS

Tissue sections from one hundred and thirty-five primary breast carcinoma cases were subjected to immunohistochemical staining for antibodies against HER2, GRB7, and PGAP3 and graded on a scale of 1 to 3. Both membranous staining and cytoplasmic staining were assessed for GRB7 and PGAP3. For equivocal HER2 IHC positivity, fluorescent in situ hybridization was performed to get the final HER2 status.

RESULTS

IHC staining for GRB7 and PGAP 3 was a moderate to strong predictor for HER2 status (area under the curve (AUC) of 0.768, 0.868,0.754, and 0.790 for GRB7 membranous staining, GRB7 cytoplasmic staining, PGAP3 membranous staining, and PGAP3 cytoplasmic staining respectively). A combination of GRB7 cytoplasmic and PGAP3 membranous staining resulted in an AUC of 0.905 (95% CI 0.855-0.954), while a combination of GRB7 and PGAP3 cytoplasmic staining resulted in an AUC of 0.902 (95% CI 0.851-0.953).

CONCLUSION

The point estimates for the AUC of GRB7 and combined GRB7 and PGAP3 in predicting the AUC suggest a strong predictive ability of these markers to predict HER2. With further refinement in technique, cytoplasmic staining and membranous IHC staining for GRB7 and PGAP3 have potential to serve as surrogate markers for HER2 status. The strategy of using protein products of co-amplified genes of HER2 is likely to be successful in technical validation.

Predicting Chemotherapy Benefit across Different Races in Early-Stage Breast Cancer Patients Using the Oncotype DX Score

BACKGROUND

Oncotype DX assay, a multigene molecular test, has been widely used to stratify relapse risk and guide chemotherapy treatment in breast cancer. However, the optimal threshold of the Oncotype DX score in predicting chemotherapy benefit and its racial variation has not been investigated.

METHODS

In this study, we apply a random forest survival model to the SEER-Oncotype cohort data (Surveillance, Epidemiology, and End Results with Oncotype DX test information for breast cancer patients) and determine chemotherapy benefit thresholds in early-stage, estrogen-receptor-positive (ER+), and HER2-negative (HER2-) patients of different races.

RESULTS

Our results indicate that early-stage ER+, HER2-, and LN-/LN+ patients may benefit from receiving chemotherapy at a lower Oncotype DX score than current guidelines (Recurrence Score, RS > 25 or RS > 30) suggest. According to the estimated chemotherapy sensitivity thresholds from our models, 2.05-2.72-fold more lymph-node-negative (LN-) and 2.08-5.02-fold more lymph-node-positive (LN+) patients who may not currently be recommended for chemotherapy by their Oncotype DX test result may actually have the potential to benefit from chemotherapy. Furthermore, our models indicate a racial difference in chemotherapy benefit: white, black, and Asian women with early-stage ER+/LN- tumors benefit from chemotherapy when their Oncotype DX scores are greater than 19.9, 37.2, and 18.0, respectively.

CONCLUSIONS

Our study provides a method for calibrating multigene molecular tests to help guide treatment decisions in racially and ethnically diverse patients with cancer. Specifically, we identify key chemotherapy sensitivity thresholds for the Oncotype DX recurrence score test in breast cancer patients and provide evidence that certain patients may benefit from receiving chemotherapy at a lower threshold than the current clinical guidelines suggest.

Leveraging transcriptomics for precision diagnosis: Lessons learned from cancer and sepsis

Diagnostics require precision and predictive ability to be clinically useful. Integration of multi-omic with clinical data is crucial to our understanding of disease pathogenesis and diagnosis. However, interpretation of overwhelming amounts of information at the individual level requires sophisticated computational tools for extraction of clinically meaningful outputs. Moreover, evolution of technical and analytical methods often outpaces standardisation strategies. RNA is the most dynamic component of all -omics technologies carrying an abundance of regulatory information that is least harnessed for use in clinical diagnostics. Gene expression-based tests capture genetic and non-genetic heterogeneity and have been implemented in certain diseases. For example patients with early breast cancer are spared toxic unnecessary treatments with scores based on the expression of a set of genes (e.g., Oncotype DX). The ability of transcriptomics to portray the transcriptional status at a moment in time has also been used in diagnosis of dynamic diseases such as sepsis. Gene expression profiles identify endotypes in sepsis patients with prognostic value and a potential to discriminate between viral and bacterial infection. The application of transcriptomics for patient stratification in clinical environments and clinical trials thus holds promise. In this review, we discuss the current clinical application in the fields of cancer and infection. We use these paradigms to highlight the impediments in identifying useful diagnostic and prognostic biomarkers and propose approaches to overcome them and aid efforts towards clinical implementation.

Impact of the 21-Gene Assay in Patients with High-Clinical Risk ER-Positive and HER2-Negative Early Breast Cancer: Results of the KARMA Dx Study

BACKGROUND

The 21-gene Oncotype DX Breast Recurrence Score^® assay is prognostic and predictive of chemotherapy benefit for patients with estrogen receptor-positive, HER2- early breast cancer (EBC). The KARMA Dx study evaluated the impact of the Recurrence Score^® results (RS) on the treatment decision for patients with EBC and high-risk clinicopathological characteristics for whom chemotherapy (CT) was considered.

METHODS

Eligible patients with EBC were candidates for the study if CT was considered standard recommendation by local guidelines. Three high-risk EBC cohorts were predefined: (A) pT1-2, pN0/N1mi, and grade 3; (B) pT1-2, pN1, and grades 1-2; and (C) neoadjuvant cT2-3, cN0, and Ki67 ≤ 30%. Treatment recommendations before and after 21-gene testing were registered, as well as treatment received and physicians' confidence levels in their final recommendations.

RESULTS

A total of 219 consecutive patients were included from eight Spanish centers: 30 in cohort A, 158 in cohort B, and 31 in cohort C. Ten patients were excluded from the final analysis as CT was not initially recommended. After 21-gene testing, treatment decisions changed from CT + endocrine therapy (ET) to ET alone for 67% of the whole group. In total, 30% (95% confidence interval [CI] 15% to 49%), 73% (95% CI 65% to 80%), and 76% (95% CI 56% to 90%) of patients ultimately received ET alone in cohorts A, B, and C, respectively. Physicians' confidence in their final recommendations increased in 34% of cases.

CONCLUSIONS

Use of the 21-gene test resulted in an overall 67% reduction in CT recommendation in patients considered candidates for CT. Our findings indicate the substantial potential of the 21-gene test to guide CT recommendations in patients with EBC considered to be at high risk of recurrence based on clinicopathological parameters, regardless of nodal status or treatment setting.

Cost and Clinical Benefits Associated with Oncotype DX® Test in Patients with Early-Stage HR+/HER2- Node-Negative Breast Cancer in the Netherlands

Objectives

Patients with early-stage HR+/HER2- N0 breast cancer may receive adjuvant chemotherapy in combination with surgery. However, chemotherapy does not always lead to improved survival and incurs high healthcare costs and increased adverse events. To support decision-making regarding adjuvant chemotherapy, genomic profile testing performed with tests such as the Oncotype DX® test can help healthcare practitioners decide whether chemotherapy provides any benefit to these patients. As such, a cost-consequence model was developed with the aim to estimate the economic impact of using different gene expression tests or no testing, in patients with node-negative early-stage breast cancer.

Methods

A cost-consequence model was developed to estimate the economic impact of three different scenarios in the Dutch setting: (1) Oncotype DX® test, (2) MammaPrint®, and (3) and no genomic profile testing. The model included chemotherapy costs, administration costs, short- and long-term adverse event costs, productivity loss, genomic profiling testing costs, cost of cancer recurrence, and hospitalization costs.

Results

A treatment paradigm with Oncotype DX resulted in average savings per patient of €6,768 vs. a paradigm with MammaPrint and €13,125 vs. a paradigm with no genomic testing. Furthermore, due to less patients receiving adjuvant chemotherapy through better targeting by the Oncotype DX test, fewer adverse events, sick days, practice visits, and hospitalizations were required compared to MammaPrint and no genomic profiling.

Conclusions

Testing with Oncotype DX test in Dutch clinical practice in patients with early-stage breast cancer proved to be cost-saving versus MammaPrint and no genomic profiling tests. Introducing the Oncotype DX test to the Dutch setting will likely reduce the economic resources that are required.

Prognostic Value of Copy Number Alteration Burden in Early-Stage Breast Cancer and the Construction of an 11-Gene Copy Number Alteration Model

The increasing burden of breast cancer has prompted a wide range of researchers to search for new prognostic markers. Considering that tumor mutation burden (TMB) is low and copy number alteration burden (CNAB) is high in breast cancer, we built a CNAB-based model using a public database and validated it with a Chinese population. We collected formalin-fixed, paraffin-embedded (FFPE) tissue samples from 31 breast cancer patients who were treated between 2010 and 2014 at the National Cancer Center (CICAMS). METABRIC and TCGA data were downloaded via cBioPortal. In total, 2295 patients with early-stage breast cancer were enrolled in the study, including 1427 in the METABRIC cohort, 837 in the TCGA cohort, and 31 in the CICAMS cohort. Based on the ROC curve, we consider 2.2 CNA/MBp as the threshold for the CNAB-high and CNAB-low groupings. In both the TCGA cohort and the CICAMS cohort, CNAB-high had a worse prognosis than CNAB-low. We further simplified this model by establishing a prognostic nomogram for early breast cancer patients by 11 core genes, and this nomogram was highly effective in both the TCGA cohort and the CICAMS cohort. We hope that this model will subsequently help clinicians with prognostic assessments.

Functional Drug Screening in the Era of Precision Medicine

The focus of precision medicine is providing the right treatment to each unique patient. This scientific movement has incited monumental advances in oncology including the approval of effective, targeted agnostic therapies. Yet, precision oncology has focused largely on genomics in the treatment decision making process, and several recent clinical trials demonstrate that genomics is not the only variable to be considered. Drug screening in three dimensional (3D) models, including patient derived organoids, organs on a chip, xenografts, and 3D-bioprinted models provide a functional medicine perspective and necessary complement to genomic testing. In this review, we discuss the practicality of various 3D drug screening models and each model's ability to capture the patient's tumor microenvironment. We highlight the potential for enhancing precision medicine that personalized functional drug testing holds in combination with genomic testing and emerging mathematical models.

Molecular Biomarkers Predict Pathological Complete Response of Neoadjuvant Chemotherapy in Breast Cancer Patients: Review

Simple Summary

Breast cancer is the most common cancer in women worldwide. Although many studies have aimed to understand the genetic basis of breast cancer, leading to increasingly accurate diagnoses, only a few molecular biomarkers are used in clinical practice to predict response to therapy. Current studies aim to develop more personalized therapies to decrease the adverse effects of chemotherapy. Personalized medicine not only requires clinical, but also molecular characterization of tumors, which allows the use of more effective drugs for each patient. The aim of this study was to identify potential molecular biomarkers that can predict the response to therapy after neoadjuvant chemotherapy in patients with breast cancer. In this review, we summarize genomic, transcriptomic, and proteomic biomarkers that can help predict the response to therapy.

Abstract

Neoadjuvant chemotherapy (NAC) is often used to treat locally advanced disease for tumor downstaging, thus improving the chances of breast-conserving surgery. From the NAC response, it is possible to obtain prognostic information as patients may reach a pathological complete response (pCR). Those who do might have significant advantages in terms of survival rates. Breast cancer (BC) is a heterogeneous disease that requires personalized treatment strategies. The development of targeted therapies depends on identifying biomarkers that can be used to assess treatment efficacy as well as the discovery of new and more accurate therapeutic agents. With the development of new “OMICS” technologies, i.e., genomics, transcriptomics, and proteomics, among others, the discovery of new biomarkers is increasingly being used in the context of clinical practice, bringing us closer to personalized management of BC treatment. The aim of this review is to compile the main biomarkers that predict pCR in BC after NAC.

6q deletion is frequent but unrelated to patient prognosis in breast cancer

BACKGROUND

Deletions involving the long arm of chromosome 6 have been reported to occur in breast cancer, but little is known about the clinical relevance of this alteration.

METHODS

We made use of a pre-existing tissue microarray with 2197 breast cancers and employed a 6q15/centromere 6 dual-labeling probe for fluorescence in situ (FISH) analysis RESULTS: Heterozygous 6q15 deletions were found in 202 (18%) of 1099 interpretable cancers, including 19% of 804 cancers of no special type (NST), 3% of 29 lobular cancers, 7% of 41 cribriform cancers, and 28% of 18 cancers with papillary features. Homozygous deletions were not detected. In the largest subset of NST tumors, 6q15 deletions were significantly linked to advanced tumor stage and high grade (p < 0.0001 each). 6q deletions were also associated with estrogen receptor negativity (p = 0.0182), high Ki67 proliferation index (p < 0.0001), amplifications of HER2 (p = 0.0159), CCND1 (p = 0.0069), and cMYC (p = 0.0411), as well as deletions of PTEN (p = 0.0003), 8p21 (p < 0.0001), and 9p21 (p = 0.0179). However, 6q15 deletion was unrelated to patient survival in all cancers, in NST cancers, or in subsets of cancers defined by the presence or absence of lymph-node metastases.

CONCLUSION

Our data demonstrate that 6q deletion is a frequent event in breast cancer that is statistically linked to unfavorable tumor phenotype and features of genomic instability. The absence of any prognostic impact argues against a clinical applicability of 6q15 deletion testing in breast cancer patients.

A matrisome RNA signature from early-pregnancy mouse mammary fibroblasts predicts distant metastasis-free breast cancer survival in humans

BACKGROUND

During pregnancy, the mouse mammary ductal epithelium branches and grows into the surrounding stroma, requiring extensive extracellular matrix (ECM) and tissue remodelling. It therefore shows parallels to cancer invasion. We hypothesised that similar molecular mechanisms may be utilised in both processes, and that assessment of the stromal changes during pregnancy-associated branching may depict the stromal involvement during human breast cancer progression.

METHODS

Immunohistochemistry (IHC) was employed to assess the alterations within the mouse mammary gland extracellular matrix during early pregnancy when lateral branching of the primary ductal epithelium is initiated. Primary mouse mammary fibroblasts from three-day pregnant and age-matched non-pregnant control mice, respectively, were 3D co-cultured with mammary epithelial cells to assess differences in their abilities to induce branching morphogenesis in vitro. Transcriptome analysis was performed to identify the underlying molecular changes. A signature of the human orthologues of the differentially expressed matrisome RNAs was analysed by Kaplan-Meier and multi-variate analysis in two large breast cancer RNA datasets (Gene expression-based Outcome for Breast cancer Online (GOBO) und Kaplan-Meier Plotter), respectively, to test for similarities in expression between early-pregnancy mouse mammary gland development and breast cancer progression.

RESULTS

The ECM surrounding the primary ductal network showed significant differences in collagen and basement membrane protein distribution early during pregnancy. Pregnancy-associated fibroblasts (PAFs) significantly enhanced branching initiation compared to age-matched control fibroblast. A combined signature of 64 differentially expressed RNAs, encoding matrisome proteins, was a strong prognostic indicator of distant metastasis-free survival (DMFS) independent of other clinical parameters. The prognostic power could be significantly strengthened by using only a subset of 18 RNAs (LogRank P ≤ 1.00e-13; Hazard ratio (HR) = 2.42 (1.8-3.26); p = 5.61e-09). The prognostic power was confirmed in a second breast cancer dataset, as well as in datasets from ovarian and lung cancer patients.

CONCLUSIONS

Our results describe for the first time the early stromal changes that accompany pregnancy-associated branching morphogenesis in mice, specify the early pregnancy-associated molecular alterations in mouse mammary fibroblasts, and identify a matrisome signature as a strong prognostic indicator of human breast cancer progression, with particular strength in oestrogen receptor (ER)-negative breast cancers.

High mitochondrial content is associated with breast cancer aggressiveness

Mitochondria are relevant for cancer initiation and progression. Antibodies against mitochondrially encoded cytochrome c oxidase II (MTCO2), targeting a mitochondria specific epitope, can be used to quantitate the mitochondria content of tumor cells. The present study evaluated the impact of the cellular mitochondrial content on the prognosis of patients with breast cancer using immunohistochemical analysis on 2,197 arrayed breast cancer specimens. Results were compared with histological tumor parameters, patient overall survival, tumor cell proliferation using Ki67 labeling index (Ki67LI) and various other molecular features. Tumor cells exhibited stronger MTCO2 expression than normal breast epithelial cells. MTCO2 immunostaining was largely absent in normal breast epithelium, but was observed in 71.9% of 1,797 analyzable cancer specimens, including 34.6% tumors with weak expression, 22.3% with moderate expression and 15.0% with strong expression. High MTCO2 expression was significantly associated with advanced tumor stage, high Bloom-Richardson-Elston/Nottingham (BRE) grade, nodal metastasis and shorter overall survival (P<0.0001 each). In multivariate analysis, MTCO2 expression did not provide prognostic information independent of BRE grade, pathological tumor and pathological lymph node status. Additionally, significant associations were observed for high MTCO2 expression and various molecular features, including high Ki67LI, amplifications of HER2, MYC, CCND1 and MDM2, deletions of PTEN, 8p21 and 9p, low estrogen receptor expression (P<0.0001 each) and progesterone receptor expression (P<0.0001). The present study demonstrated that high MTCO2 expression was strongly associated with a poor prognosis and unfavorable phenotypical and molecular tumor features in patients with breast cancer. This suggests that the mitochondrial content may have a pivotal role in breast cancer progression.

The Role of Tumor Necrosis Factor in Manipulating the Immunological Response of Tumor Microenvironment

The tumor microenvironment (TME) is an intricate system within solid neoplasms. In this review, we aim to provide an updated insight into the TME with a focus on the effects of tumor necrosis factor-α (TNF-α) on its various components and the use of TNF-α to improve the efficiency of drug delivery. The TME comprises the supporting structure of the tumor, such as its extracellular matrix and vasculature. In addition to cancer cells and cancer stem cells, the TME contains various other cell types, including pericytes, tumor-associated fibroblasts, smooth muscle cells, and immune cells. These cells produce signaling molecules such as growth factors, cytokines, hormones, and extracellular matrix proteins. This review summarizes the intricate balance between pro-oncogenic and tumor-suppressive functions that various non-tumor cells within the TME exert. We focused on the interaction between tumor cells and immune cells in the TME that plays an essential role in regulating the immune response, tumorigenesis, invasion, and metastasis. The multifunctional cytokine, TNF-α, plays essential roles in diverse cellular events within the TME. The uses of TNF-α in cancer treatment and to facilitate cancer drug delivery are discussed. The effects of TNF-α on tumor neovasculature and tumor interstitial fluid pressure that improve treatment efficacy are summarized.

Cancer Cell Intrinsic and Immunologic Phenotypes Determine Clinical Outcomes in Basal-like Breast Cancer

PURPOSE

Basal-like breast cancer (BLBC) is a particularly aggressive intrinsic molecular subtype of breast cancer that lacks targeted therapies. There is also no clinically useful test to risk stratify patients with BLBC. We hypothesized that a transcriptome-based phenotypic characterization of BLBC tumors and their microenvironments may overcome these challenges.

EXPERIMENTAL DESIGN

We conducted a retrospective correlative genomic sequencing study using a matched pairs design with validation in five independent cohorts. The study was conducted on a large population-based prospective cohort of the major molecular subtypes of breast cancer conducted in the greater Seattle-Puget Sound metropolitan area. Cases consisted of women 20-69 years of age first diagnosed with invasive breast cancer identified through the population-based Surveillance Epidemiology and End Results program. Patients for this analysis (n = 949) were identified from the 1,408 patients with stage I-III triple-negative breast cancer [estrogen receptor-negative (ER^-), progesterone receptor-negative (PR^-), HER2^-]. Of the 949 women, 248 developed a recurrence after their initial diagnosis. A matched set of 67 recurrent and nonrecurrent BLBC tumors was subjected to transcriptome sequencing. Through RNA sequencing of the matched sets of recurrent and nonrecurrent BLBC tumors, we aimed to identify prognostic phenotypes.To identify nonredundant and uncorrelated prognostic genes, we used an ensemble of variable selection algorithms, which resulted in a ranking of genes on the basis of their expected utility in classification. Using leave-one-out cross-validation, we trained a random forest classifier on the basis of the top 21 genes (BRAVO-DX). Validations were performed in five independent triple-negative or BLBC cohorts, and biomarker robustness and transferability were demonstrated by employing real-time PCR.

RESULTS

We found that cancer cell intrinsic and immunologic phenotypes are independent predictors of recurrence. By simultaneously interrogating the tumor and its microenvironment, we developed a compound risk model that stratified patients into low-, medium-, and high-risk groups, with a 14%/56%/74% chance of recurrence, respectively. Biologically, the primary tumors of patients who developed a recurrence had increased growth factor signaling and stem-like features, while nonrecurrent tumors showed high lymphocyte infiltration with clonal expansion of T and B cells, as well as antitumor polarization of macrophages. We validated our model in five independent cohorts, including three large cohorts, where BRAVO-DX was highly informative in identifying patients with disease recurrence [HR, 6.79 (95% confidence interval (CI), 1.89-24.37); HR, 3.45 (95% CI, 2.41-4.93); and HR, 1.69 (95% CI, 1.17-2.46)]. A smaller gene set focused on the tumor immunophenotype, BRAVO-IMMUNE, was highly prognostic in all five cohorts.

CONCLUSIONS

Together, these results indicate that phenotypic characteristics of BLBCs and their microenvironment are associated with recurrence-free survival and demonstrate the utility of intrinsic and extrinsic phenotypes as independent prognostic biomarkers in BLBC. Pending further evaluation and validation, our prognostic model has the potential to inform clinical decision-making for patients with BLBC as it identifies those at high risk of rapidly progressing on standard chemotherapy, as well as those who may benefit from alternative first-line therapies.

Uridine Glucuronosyltransferase 2B7 Polymorphism-Based Pharmacogenetic Dosing of Epirubicin in FEC Chemotherapy for Early-Stage Breast Cancer

BACKGROUND

Epirubicin is metabolized by uridine glucuronosyltransferase 2B7 (UGT2B7). Patients homozygous for the minor allele (CC) in the UGT2B7 -161 promoter polymorphism have lower clearance and significantly higher rates of leukopenia compared to wild-type homozygote (TT) or heterozygote (CT) patients. This study was designed to determine if TT and CT genotype patients could tolerate a higher epirubicin dose compared to CC genotype patients.

PATIENTS AND METHODS

We studied women with histologically confirmed non-metastatic, invasive breast cancer who were scheduled to receive at least three cycles of FE₁₀₀C in the (neo)adjuvant setting. Patients received standard-dose FE₁₀₀C during the first 21-day cycle. Based on genotype, the epirubicin dose was escalated in the second and third cycles to 115 and 130 mg/m² or to 120 and 140 mg/m² for CT and TT genotype patients, respectively. The main outcome measurements were myelosuppression and dose-limiting toxicity. These were analyzed for relationships with the three genotypes.

RESULTS

Forty-five patients were enrolled (10 CC, 21 CT, and 14 TT genotypes) and received 100 mg/m² of epirubicin in the first cycle. Twelve and 10 TT patients were dose escalated at the second and third cycles, respectively; 16 CT patients were dose escalated at the second and third cycles. Leukopenia, but not febrile neutropenia, was genotype and dose dependent and increased in patients with CT and TT genotypes as their dose was increased. However, the third-cycle leukopenia rates were comparable to patients with the CC genotype receiving standard-dose epirubicin.

CONCLUSION

Pharmacogenetically guided epirubicin dosing is well tolerated and allowed dose escalation without increased toxicity.

Association of Race/Ethnicity and the 21-Gene Recurrence Score With Breast Cancer-Specific Mortality Among US Women

Importance

Given the widespread use of the 21-gene recurrence score for identifying candidates for adjuvant chemotherapy, it is important to examine the performance of the Oncotype DX Breast Recurrence Score test in diverse patient populations to validate this approach for tailoring treatment in women in racial/ethnic minority groups.

Objective

To examine whether breast cancer-specific mortality for women with hormone-dependent breast cancer differs by race/ethnicity across risk categories defined by the Oncotype DX Breast Recurrence Score test and whether the prognostic accuracy of the 21-gene recurrence score differs by race/ethnicity.

Design, Setting, and Participants

This retrospective, population-based cohort study used the Surveillance, Epidemiology, and End Results Oncotype DX 2004-2015 database to obtain breast cancer-specific survival data on US women 18 years and older who were diagnosed with first primary stage I to III, estrogen receptor-positive breast cancer between January 1, 2004, and December 31, 2015, and had tumor testing through the Genomic Health Clinical Laboratory. Data were analyzed from April 20 to September 27, 2020.

Main Outcomes and Measures

The primary outcome was breast cancer-specific mortality among women from different racial/ethnic groups stratified by the 21-gene recurrence score risk categories. Secondary analyses compared the prognostic accuracy of the recurrence score among the different racial/ethnic groups.

Results

A total of 86 033 patients with breast cancer (mean [SD] age, 57.6 [10.6] years) with Oncotype DX Breast Recurrence Score test information were available for the analysis, including 64 069 non-Hispanic White women (74.4%), 6719 non-Hispanic Black women (7.8%), 7944 Hispanic women (9.2%), 6950 Asian/Pacific Islander women (8.0%), and 351 American Indian/Alaska Native women (0.4%). Black women were significantly more likely than non-Hispanic White women to have a recurrence score greater than 25 (17.7% vs 13.7%; P < .001). Among women with axillary node-negative tumors, competing risk models adjusted for age, tumor characteristics, and treatment found higher breast cancer-specific mortality for Black compared with non-Hispanic White women within each recurrence score risk stratum, with subdistribution hazard ratios of 2.54 (95% CI, 1.44-4.50) for Black women with recurrence scores of 0 to 10, 1.64 (95% CI, 1.23-2.18) for Black women with recurrence scores of 11 to 25, and 1.48 (95% CI, 1.10-1.98) for Black women with scores greater than 25. The prognostic accuracy of the recurrence score was significantly lower for Black women, with a C index of 0.656 (95% CI, 0.592-0.720) compared with 0.700 (95% CI, 0.677-0.722) (P = .002) for non-Hispanic Whites.

Conclusions and Relevance

In this cohort study, Black women in the US were more likely to have a high-risk recurrence score and to die of axillary node-negative breast cancer compared with non-Hispanic White women with comparable recurrence scores. The Oncotype DX Breast Recurrence Score test has lower prognostic accuracy in Black women, suggesting that genomic assays used to identify candidates for adjuvant chemotherapy may require model calibration in populations with greater racial/ethnic diversity.

Knockout of ASPP2 promotes DEN-induced hepatocarcinogenesis via the NF-κB pathway in mice

Apoptosis-stimulating protein p53 2 (ASPP2) is a member of the p53-binding protein family, which is closely related to tumor development. However, the precise mechanism of ASPP2 in liver inflammation and tumorigenesis remains largely unclear. We aimed to characterize the mechanistic significance and clinical implication of ASPP2 in hepatitis and hepatocellular carcinoma (HCC). In this study, ASPP2 knockout (APKO) mice were generated to confirm the role of ASPP2 in the development of hepatitis and HCC. Liver tissues from mice were analyzed by immunohistochemistry, Western blotting, proteomic analysis, ChIP-Seq, and qRT-PCR to evaluate the role of ASPP2 in DEN-induced hepatitis and HCC. We found that APKO promoted the formation of hepatitis/hepatocarcinoma and the increased expression of proinflammatory factors. The proteomics and Western blotting results showed that APKO activated the NF-κB signaling pathway. Further, ChIP-Seq results revealed that NF-κB target genes were dramatically increased in APKO mice. In contrast, blockade of the NF-κB pathway by QNZ reduced the expression of proinflammatory factors and the susceptibility of APKO mice to DEN-induced hepatocarcinogenesis. These results suggested that the absence of ASPP2 activates the NF-κB pathway to promote the occurrence of DEN-induced hepatocarcinogenesis, indicating that ASPP2 may be a potential target for the treatment of hepatocarcinoma.

Significance of Oncotype DX 21-Gene Test and Expression of Long Non-Coding RNA MALAT1 in Early and Estrogen Receptor-Positive Breast Cancer Patients

Objective

To investigate the association between the recurrence score (RS) obtained by Oncotype DX 21-gene test and long non-coding RNA (lncRNA) MALAT1 expression in early and estrogen receptor-positive (ER⁺) breast cancer.

Materials and Methods

The Oncotype DX 21-gene test and MALAT1 expression detection were performed in tumor samples from 76 ER⁺ and early breast cancer patients with the Surplex liquid chip. The RS value was calculated based on the expression of total 21 genes. The level of MALAT1 was measured in both tumor tissue and para-tumor tissue, and relatively quantified with an internal control gene. Mann–Whitney U-test or Kruskal–Wallis test were used to analyze the association between MALAT1 level and different clinical pathological characteristics, including age, tumor stage, disease grade, lymph node status, Ki-67 expression, and progesterone receptor (PR) status. The association between the RS and different characteristics was analyzed by Wilcoxon rank-sum test. Correlation between two parameters was analyzed by Spearman’s rank correlation analysis.

Results

The expression of MALAT1 was more abundant in tumor tissue (2.992 ± 2.256) than that in adjacent normal tissue (1.641±1.438, Z=−2.594, p=0.009), and it was not correlated with any clinical pathological characteristics. According to the old criteria for RS stratification, 52.7% of patients were in low risk (RS<18), 36.8% of patients were in medium risk (18≤RS≤30), and 10.5% of patients were in high risk (RS>30). While under the new criteria, 18.4% were in low risk group (RS<11), 63.2% were in a medium risk group (11≤RS≤26), and 18.4% were in a high risk group (RS>26). The Oncotype DX 21-gene results only correlated with Ki-67 expression under both new and old criteria, and it was not related with other cancer characteristics. The expression of lncRNA MALAT1 was significantly correlated with the Oncotype DX 21-gene results under the old criteria.

Conclusion

MALAT1 is a novel breast cancer biomarker independent of tumor stage, disease grade and lymph node status. MALAT1 level is associated with the Oncotype DX 21-gene RS value. Therefore, combination of MALAT1 and the Oncotype DX 21-gene test may be used to predict prognosis in ER⁺ and early stage breast cancer.

Activating p53 function by targeting RLIP

Aberrations in RLIP, p53, and PKCα represent essentially the entire spectrum of all human neoplasms. Elevated PKCα expression, failure of the cell cycle checkpoint (p53 dysfunction), and abnormal glutathione (GSH) metabolism are fundamental hallmarks of carcinogenesis and drug/radiation resistance. However, a lack of investigations into the interactions between these important regulatory nodes has fundamentally limited our understanding of carcinogenesis and the development of effective interventions for cancer prevention and therapy. Loss of p53, perhaps the most powerful tumor suppressor gene, predisposes rodents to spontaneous cancer and humans to familial, as well as acquired, cancers. Until recently, no genetic manipulation of any oncogene had been reported to abrogate spontaneous carcinogenesis in p53^-/- rodent models. However, the overexpression of RLIP, a GSH-electrophile conjugate (GS-E) transporter, has been found to enhance cancer cell proliferation and confer drug/radiation resistance, whereas its depletion causes tumor regression, suggesting its importance in cancer and drug/radiation resistance. Indeed, RLIP is an essential effector of p53 that is necessary for broad cancer-promoting epigenetic remodeling. Interestingly, through a haploinsufficiency mechanism, the partial depletion of RLIP in p53^-/- mice provides complete protection from neoplasia. Furthermore, RLIP^-/- mice exhibit altered p53 and PKCα function, marked deficiency in clathrin-dependent endocytosis (CDE), and almost total resistance to chemical carcinogenesis. Based on these findings, in this review, we present a novel and radical hypothesis that expands our understanding of the highly significant cross-talk between p53, PKCα, and GSH signaling by RLIP in multiple tumor models.

Gene Expression Along with Genomic Copy Number Variation and Mutational Analysis Were Used to Develop a 9-Gene Signature for Estimating Prognosis of COAD

PURPOSE

This study aims to systematically analyze multi-omics data to explore new prognosis biomarkers in colon adenocarcinoma (COAD).

MATERIALS AND METHODS

Multi-omics data of COAD and clinical information were obtained from The Cancer Genome Atlas (TCGA). Univariate Cox analysis was used to select genes which significantly related to the overall survival. GISTIC 2.0 software was used to identify significant amplification or deletion. Mutsig 2.0 software was used to identify significant mutation genes. The 9-gene signature was screened by random forest algorithm and Cox regression analysis. GSE17538 dataset was used as an external dataset to verify the predictive ability of 9-gene signature. qPCR was used to detect the expression of 9 genes in clinical specimens.

RESULTS

A total of 71 candidate genes are obtained by integrating genomic variation, mutation and prognostic data. Then, 9-gene signature was established, which includes HOXD12, RNF25, CBLN3, DOCK3, DNAJB13, PYGO2, CTNNA1, PTPRK, and NAT1. The 9-gene signature is an independent prognostic risk factor for COAD patients. In addition, the signature shows good predicting performance and clinical practicality in training set, testing set and external verification set. The results of qPCR based on clinical samples showed that the expression of HOXD12, RNF25, CBLN3, DOCK3, DNAJB13, and PYGO2 was increased in colon cancer tissues and the expression of CTNNA1, PTPRK, NAT1 was decreased in colon cancer tissues.

CONCLUSION

In this study, 9-gene signature is constructed as a new prognostic marker to predict the survival of COAD patients.

A Novel Prognostic Scoring System Integrating Gene Expressions and Clinicopathological Characteristics to Predict Very Early Relapse in Node-Negative Estrogen Receptor-Positive/HER2-Negative Breast Cancer

Background: Despite low aggressiveness in tumor biology and high responsiveness to endocrine therapy, subgroups of patients with estrogen receptor-positive/HER2-negative (ER+/HER2-) breast cancer relapse early in the first two years after initiation of endocrine therapy, indicating potential endocrine resistance. Accordingly, we attempted to establish a scoring system to inform the first-2-year prognosis (F2P Score). Methods: Patients with node-negative ER+/HER2- breast cancer and complete data of gene expressions in a 21-gene panel were retrospectively retrieved from Shanghai Jiao Tong University Breast Cancer Database (SJTU-BCDB). The F2P Score was established based on the clinical and genomic variables associated with the first-2-year relapse after shrinkage correction and validated using the bootstrap resampling method. Model performance was quantified by Harrell's concordance-index (C-index) and Bayesian information criteria (BIC). Results: The F2P Score was established by integrating the clinical (age and tumor size) and genomic (ESR1, PGR, BCL2, CD68, GSTM1, and BAG1) variables with a C-index of 0.71 and BIC of 397.46. Bootstrap C-index was 0.72 (95% CI, 0.62-0.81) and BIC was 396.75 (95% CI, 252.37-541.13). A higher score indicated an increased likelihood of a first-2-year relapse, when used as continuous (HR, 2.94; 95% CI, 1.87-4.61) or categorical (HR, 3.68; 95% CI, 1.70-8.00) predictors in multivariate analysis. Both continuous and categorical F2P Score also remained prognostic for overall survival and other endpoints. No significant interaction was observed between the F2P Score and treatment subgroups. Additionally, the F2P Score outperformed the IHC4, clinical treatment score and 21-gene test in predicting first-2-year relapse. Conclusion: The F2P Score reported herein, integrating the clinicopathological and genomic variables, may inform prognosis and endocrine responsiveness. After the benefits and risks have been considered, treatment escalation may be an alternative strategy for patients with a higher score.

Primary 21-Gene Recurrence Score and Disease Outcome in Loco-Regional and Distant Recurrent Breast Cancer Patients

Background: The 21-gene recurrence score (RS) assay has been proven prognostic and predictive for hormone receptor-positive/HER2-negative, node-negative early breast cancer patients. However, whether primary 21-gene RS can predict prognosis in recurrent breast cancer patients remained unknown.

Patients and Methods: Consecutive breast cancer patients operated in Comprehensive Breast Health Center, Shanghai Ruijin Hospital between January 2009 and December 2018 were retrospectively analyzed. Patients with available 21-gene RS result for the primary tumor and reporting disease recurrence during follow-up were included. Association of 21-gene RS and overall survival (OS), post-recurrence overall survival (PR-OS), post-recurrence progression-free survival (PR-PFS), and first-line systemic treatment after recurrence were compared among different groups.

Results: A total of 74 recurrent patients were included, with 10, 27, 37 patients in the RS <18, 18–30, and ≥ 31 groups, respectively. Recurrent patients with RS ≥ 31 were more likely to receive chemotherapy as their first-line treatment compared to those with RS <31 (P = 0.025). Compared to those with RS <31, patients with RS ≥ 31 had significantly worse OS (P = 0.025), worse PR-OS (P = 0.026), and a trend of inferior PR-PFS (P = 0.106). Multivariate analysis demonstrated that primary ER expression level (OS: P = 0.009; PR-OS: P = 0.017) and histological grade (OS: P = 0.003; PR-OS: P = 0.009), but not primary 21-gene RS (OS: P = 0.706; PR-OS: P = 0.120), were independently associated with worse OS and PR-OS.

Conclusions: High primary 21-gene RS tended to be associated with worse disease outcome in loco-regional and distant recurrent breast cancer patients, which could influence the first-line systemic treatment after relapse, warranting further clinical evaluation.

DGM-CM6: A New Model to Predict Distant Recurrence Risk in Operable Endocrine-Responsive Breast Cancer

To investigate the prognostic value of DGM-CM6 (Distant Genetic Model-Clinical variable Model 6) for endocrine-responsive breast cancer (ERBC) patients, we analyzed 752 operable breast cancer patients treated in a Taiwan cancer center from 2005 to 2014. Among them, 490 ERBC patients (identified by the PAM50 or immunohistochemistry method) were classified by DGM-CM6 into low- and high-risk groups (cutoff <33 and ≥33, respectively). Significant differences were observed between the DGM-CM6 low- and high-risk groups for 10-year distant recurrence-free survival (DRFS) in both lymph node (LN)- (P < 0.05) and LN+ patients (P < 0.05). Multivariate analysis confirmed the independent strength of DGM-CM6 for the prediction of high- vs. low- risk groups for DRFS (P < 0.0001, HR: 6.76, 95% CI, 1.8–25.42) and overall survival (P = 0.01, HR: 6.06, 95% CI:1.55–23.47), respectively. In summary, DGM-CM6 may be used to classify low- and high-risk groups for 10-year distant recurrence in both LN- and LN+ ERBC patients in the Asian population. A large scale clinical trial is warranted.

Oncotype DX Breast Cancer recurrence score resists inter-assay reproducibility with RT2-Profiler Multiplex RT-PCR

The Oncotype Dx assay is frequently used to test if breast cancer patients can be spared from chemotherapy without negative effects for their future clinical course. However, due to conflicting data on the assay utility, in the recent past its reimbursement situation in Germany was revised; due to continued requests by clinicians for predictive values, our group decided to implement an Oncotype Dx like alternative assay with the objective of obtaining quality and cost optimization. Customized RT²-Profiler assays covering the 21 gene panel of the Oncotype Dx assay were applied to a pilot cohort of breast cancer patients with known Oncotype Dx Recurrence Score (RS). The Ct values obtained with RT²-Profiler-assays were used to calculate the unscaled Recurrence Score (RSu) values and the thereon based RS according to the Oncotype DX assay rules if available. Despite consistent assay performance it was impossible to establish correlations between RT²-Profiler recurrence scores with the respective Oncotype DX values not to mention exact matches. By following the Oncotype DX assay and its interpretation as close as possible we faced several obstructions such as lack of information on RNA amount used, missing units in the single gene expression report, missing references cited in the original study that should explain the determination of the recurrence score formula, and vague information on the normalization of the gene expression impeding the reproduction of Oncotype Dx results in other laboratories. Unfortunately, the Oncotype Dx assay cannot be confirmed by the customized RT²-profiler assay, not least because of the fact that the individual gene measurements are not provided in the medical report, although these are mandatory for the RS calculation. In fact, the "single gene report" only contains unscaled scores of the ER, PR, and Her2 genes without any internationally accepted unit used to describe a transcript quantity. Therefore a direct comparison with the in-house measurement to evaluate its performance is impossible. With regard to our findings and the fact that the Oncotype RS represents a likelihood of the risk of relapse it thus remains impossible to assess the clinical necessity of this assay.

Panoptic View of Prognostic Models for Personalized Breast Cancer Management

The efforts to personalize treatment for patients with breast cancer have led to a focus on the deeper characterization of genotypic and phenotypic heterogeneity among breast cancers. Traditional pathology utilizes microscopy to profile the morphologic features and organizational architecture of tumor tissue for predicting the course of disease, and is the first-line set of guiding tools for customizing treatment decision-making. Currently, clinicians use this information, combined with the disease stage, to predict patient prognosis to some extent. However, tumoral heterogeneity stubbornly persists among patient subgroups delineated by these clinicopathologic characteristics, as currently used methodologies in diagnostic pathology lack the capability to discern deeper genotypic and subtler phenotypic differences among individual patients. Recent advancements in molecular pathology, however, are poised to change this by joining forces with multiple-omics technologies (genomics, transcriptomics, epigenomics, proteomics, and metabolomics) that provide a wealth of data about the precise molecular complement of each patient's tumor. In addition, these technologies inform the drivers of disease aggressiveness, the determinants of therapeutic response, and new treatment targets in the individual patient. The tumor architecture information can be integrated with the knowledge of the detailed mutational, transcriptional, and proteomic phenotypes of cancer cells within individual tumors to derive a new level of biologic insight that enables powerful, data-driven patient stratification and customization of treatment for each patient, at each stage of the disease. This review summarizes the prognostic and predictive insights provided by commercially available gene expression-based tests and other multivariate or clinical -omics-based prognostic/predictive models currently under development, and proposes a more inclusive multiplatform approach to tackling the challenging heterogeneity of breast cancer to individualize its management. "The future is already here-it's just not very evenly distributed."-William Ford Gibson.

Alternative splicing of the tumor suppressor ASPP2 results in a stress-inducible, oncogenic isoform prevalent in acute leukemia

Background

Apoptosis-stimulating Protein of TP53-2 (ASPP2) is a tumor suppressor enhancing TP53-mediated apoptosis via binding to the TP53 core domain. TP53 mutations found in cancers disrupt ASPP2 binding, arguing for an important role of ASPP2 in TP53-mediated tumor suppression. We now identify an oncogenic splicing variant, ASPP2κ, with high prevalence in acute leukemia.

Methods

An mRNA screen to detect ASPP2 splicing variants was performed and ASPP2κ was validated using isoform-specific PCR approaches. Translation into a genuine protein isoform was evaluated after establishing epitope-specific antibodies. For functional studies cell models with forced expression of ASPP2κ or isoform-specific ASPP2κ-interference were created to evaluate proliferative, apoptotic and oncogenic characteristics of ASPP2κ.

Findings

Exon skipping generates a premature stop codon, leading to a truncated C-terminus, omitting the TP53-binding sites. ASPP2κ translates into a dominant-negative protein variant impairing TP53-dependent induction of apoptosis. ASPP2κ is expressed in CD34+ leukemic progenitor cells and functional studies argue for a role in early oncogenesis, resulting in perturbed proliferation and impaired induction of apoptosis, mitotic failure and chromosomal instability (CIN) – similar to TP53 mutations.

Importantly, as expression of ASPP2κ is stress-inducible it defines a novel class of dynamic oncogenes not represented by genomic mutations.

Interpretation

Our data demonstrates that ASPP2κ plays a distinctive role as an antiapoptotic regulator of the TP53 checkpoint, rendering cells to a more aggressive phenotype as evidenced by proliferation and apoptosis rates – and ASPP2κ expression results in acquisition of genomic mutations, a first initiating step in leukemogenesis. We provide proof-of-concept to establish ASPP2κ as a clinically relevant biomarker and a target for molecule-defined therapy.

Fund

Unrestricted grant support from the Wilhelm Sander Foundation for Cancer Research, the IZKF Program of the Medical Faculty Tübingen, the Brigitte Schlieben-Lange Program and the Margarete von Wrangell Program of the State Ministry Baden-Wuerttemberg for Science, Research and Arts and the Athene Program of the excellence initiative of the Eberhard-Karls University, Tübingen.

Biomarkers in breast cancer: A consensus statement by the Spanish Society of Medical Oncology and the Spanish Society of Pathology

This consensus statement revises and updates the recommendations for biomarkers use in the diagnosis and treatment of breast cancer, and is a joint initiative of the Spanish Society of Medical Oncology and the Spanish Society of Pathology. This expert group recommends determining in all cases of breast cancer the histologic grade and the alpha-estrogen receptor (ER), progesterone receptor, Ki-67 and HER2 status, in order to assist prognosis and establish therapeutic options, including hormone therapy, chemotherapy and anti-HER2 therapy. One of the four available genetic prognostic platforms (MammaPrint^®, Oncotype DX^®, Prosigna^® or EndoPredict^®) may be used in node-negative ER-positive patients to establish a prognostic category and decide with the patient whether adjuvant treatment may be limited to hormonal therapy. Newer technologies including next-generation sequencing, liquid biopsy, tumour-infiltrating lymphocytes or PD-1 determination are at this point investigational.

The family of apoptosis-stimulating proteins of p53 is dysregulated in colorectal cancer patients

The apoptosis-stimulating protein of p53 (ASPP) family is a newly identified family protein including ASPP1, ASPP2 and inhibitor of ASPP (iASPP), by which the tumor protein 53 (TP53)-mediated apoptotic process is selectively regulated. Downregulation of ASPP1/ASPP2 and upregulation of iASPP were revealed to be associated with a poor prognosis and metastasis in several types of cancer. However, to the best of our knowledge, the expression of ASPP in colorectal cancer (CRC) has not previously been investigated. The present study analyzed ASPP expression in human CRC tissues with multiple clinical and pathological profiles. A total of 41 patients diagnosed with CRC were enrolled in the present study. The expression of ASPP was detected by immunohistochemistry, immunofluorescence and reverse transcription-quantitative polymerase chain reaction. In addition, the variation in ASPP expression was examined in a number of pathological groups. The associations among ASPP expression, and the expression of TP53, plasma carcinoembryonic antigen (CEA) levels and α-fetoprotein (AFP) levels were also investigated. ASPP1 and ASPP2 expression was significantly reduced, while iASPP expression was elevated in CRC samples compared with expression in adjacent non-cancerous tissues. Downregulation of ASPP1 was detected in the TP53-positive group compared with the TP53-negative group. The increase in iASPP expression was correlated with the grade of malignancy, but not with regional lymph node status or metastases. The expression of ASPP2 was negatively correlated with plasma CEA levels. The results of the present study, not only enrich CRC epidemic and pathological data, but also provide valuable indices for CRC clinical treatment and prognosis.

[Consensus statement on biomarkers in breast cancer by the Spanish Society of Pathology and the Spanish Society of Medical Oncology]

This consensus statement is a joint initiative of the Spanish Society of Pathology (SEAP) and the Spanish Society of Medical Oncology (SEOM). It revises and updates the recommendations for the use of biomarkers in the diagnosis and treatment of breast cancer. The group of experts recommends that, in all cases of breast cancer, the histologic grade and the alpha-estrogen receptor (ER), progesterone receptor, Ki-67 and HER2 status should be determined, in order to assist prognosis and establish therapeutic options, including hormone therapy, chemotherapy and anti-HER2 therapy. One of the four available genetic prognostic platforms (MammaPrint^®, Oncotype DX^®, Prosigna^® or EndoPredict^®) may be used in node-negative ER-positive patients to establish a prognostic category and decide, together with the patient, whether adjuvant treatment be limited to hormonal therapy. Newer technologies, including next generation sequencing, liquid biopsy, tumour infiltrating lymphocytes or PD-1 determination, are still investigational.

The Application of Gene Expression Profiling in Predictions of Occult Lymph Node Metastasis in Colorectal Cancer Patients

A key factor in determining the likely outcome for a patient with colorectal cancer is whether or not the tumour has metastasised to the lymph nodes-information which is also important in assessing any possibilities of lymph node resection so as to improve survival. In this review we perform a wide-range assessment of literature relating to recent developments in gene expression profiling (GEP) of the primary tumour, to determine their utility in assessing node status. A set of characteristic genes seems to be involved in the prediction of lymph node metastasis (LNM) in colorectal patients. Hence, GEP is applicable in personalised/individualised/tailored therapies and provides insights into developing novel therapeutic targets. Not only is GEP useful in prediction of LNM, but it also allows classification based on differences such as sample size, target gene expression, and examination method.

Exome analysis of carotid body tumor

Background

Carotid body tumor (CBT) is a form of head and neck paragangliomas (HNPGLs) arising at the bifurcation of carotid arteries. Paragangliomas are commonly associated with germline and somatic mutations involving at least one of more than thirty causative genes. However, the specific functionality of a number of these genes involved in the formation of paragangliomas has not yet been fully investigated.

Methods

Exome library preparation was carried out using Nextera® Rapid Capture Exome Kit (Illumina, USA). Sequencing was performed on NextSeq 500 System (Illumina).

Results

Exome analysis of 52 CBTs revealed potential driver mutations (PDMs) in 21 genes: ARNT, BAP1, BRAF, BRCA1, BRCA2, CDKN2A, CSDE1, FGFR3, IDH1, KIF1B, KMT2D, MEN1, RET, SDHA, SDHB, SDHC, SDHD, SETD2, TP53BP1, TP53BP2, and TP53I13. In many samples, more than one PDM was identified. There are also 41% of samples in which we did not identify any PDM; in these cases, the formation of CBT was probably caused by the cumulative effect of several not highly pathogenic mutations. Estimation of average mutation load demonstrated 6–8 mutations per megabase (Mb). Genes with the highest mutation rate were identified.

Conclusions

Exome analysis of 52 CBTs for the first time revealed the average mutation load for these tumors and also identified potential driver mutations as well as their frequencies and co-occurrence with the other PDMs.

Electronic supplementary material

The online version of this article (10.1186/s12920-018-0327-0) contains supplementary material, which is available to authorized users.

Molecular Genomic Testing for Breast Cancer: Utility for Surgeons

Molecular genomic testing provides clinicians with both prognostic and (sometimes) predictive information that can help individualize treatment and decrease the risk of over- or under-treatment. We review the genomic tests that are currently available for clinical use in management of breast cancer, discuss ongoing research related to validating and expanding their utility in different patient populations, and explain why it is important for surgeons to know how to incorporate these tools into their clinical practice in order to individualize patient treatment, reduce unnecessary morbidity, and, accordingly, improve outcomes.

Evaluation of nipple aspirate fluid as a diagnostic tool for early detection of breast cancer

There has been tremendous progress in detection of breast cancer in postmenopausal women, resulting in two-thirds of women surviving more than 20 years after treatment. However, breast cancer remains the leading cause of cancer-related deaths in premenopausal women. Breast cancer is increasing in younger women due to changes in life-style as well as those at high risk as carriers of mutations in high-penetrance genes. Premenopausal women with breast cancer are more likely to be diagnosed with aggressive tumours and therefore have a lower survival rate. Mammography plays an important role in detecting breast cancer in postmenopausal women, but is considerably less sensitive in younger women. Imaging techniques, such as contrast-enhanced MRI improve sensitivity, but as with all imaging approaches, cannot differentiate between benign and malignant growths. Hence, current well-established detection methods are falling short of providing adequate safety, convenience, sensitivity and specificity for premenopausal women on a global level, necessitating the exploration of new methods. In order to detect and prevent the disease in high risk women as early as possible, methods that require more frequent monitoring need to be developed. The emergence of "omics" strategies over the last 20 years, enabling the characterisation and understanding of breast cancer at the molecular level, are providing the potential for long term, longitudinal monitoring of the disease. Tissue and serum biomarkers for breast cancer stratification, diagnosis and predictive outcome have emerged, but have not successfully translated into clinical screening for early detection of the disease. The use of breast-specific liquid biopsies, such as nipple aspirate fluid (NAF), a natural secretion produced by breast epithelial cells, can be collected non-invasively for biomarker profiling. As we move towards an age of active surveillance, home-based liquid biopsy collection kits are increasingly being applied and these could provide a paradigm shift where NAF biomarker profiling is used for routine breast health monitoring. The current status of established and newly emerging imaging techniques for early detection of breast cancer and the potential for alternative biomarker screening of liquid biopsies, particularly those applied to high-risk, premenopausal women, will be reviewed.

Addressing the challenges of applying precision oncology

Precision oncology is described as the matching of the most accurate and effective treatments with the individual cancer patient. Identification of important gene mutations, such as BRCA1/2 that drive carcinogenesis, helped pave the way for precision diagnosis in cancer. Oncoproteins and their signaling pathways have been extensively studied, leading to the development of target-based precision therapies against several types of cancers. Although many challenges exist that could hinder the success of precision oncology, cutting-edge tools for precision diagnosis and precision therapy will assist in overcoming many of these difficulties. Based on the continued rapid progression of genomic analysis, drug development, and clinical trial design, precision oncology will ultimately become the standard of care in cancer therapeutics.

Prognostic values of apoptosis-stimulating P53-binding protein 1 and 2 and their relationships with clinical characteristics of esophageal squamous cell carcinoma patients: a retrospective study

BACKGROUND

Esophageal squamous cell carcinoma (ESCC) is a leading cause of cancer-related death, and new prognostic biomarkers are urgently needed. Apoptosis-stimulating P53-binding protein 1 (ASPP1) and 2 (ASPP2) have been reported to play important roles in the development, progression, metastasis, and prognosis of cancers, but their roles in ESCC have not been elucidated. In this study, we examined the expression of ASPP1 and ASPP2 in ESCC to evaluate their prognostic values.

METHODS

The protein expression of ASPP1, ASPP2, and P53 in 175 specimens of ESCC was detected using immunohistochemical staining; their expression in cancerous and noncancerous tissues was scored according to the staining intensity and the percentage of stained cells. The associations of ASPP1, ASPP2, and P53 with clinicopathologic parameters, overall survival (OS), and disease-free survival (DFS) were analyzed.

RESULTS

The protein expression levels of ASPP2 and P53 were significantly higher in cancerous tissues than in paired noncancerous tissues (P < 0.001), whereas the expression levels of ASPP1 in the two groups were similar. In ESCCs, ASPP1 expression was significantly associated with histological differentiation (P = 0.002) and invasive depth (P = 0.014); ASPP2 expression was associated with age (P = 0.029) and histological differentiation (P < 0.001); and P53 expression was associated with age (P = 0.021) and tumor size (P = 0.040). No correlations were found between ASPP1, ASPP2, and P53 expression. Survival analysis revealed that high ASPP2 expression was significantly associated with increased 5-year OS (P = 0.001) and DFS rates (P = 0.010) and that high P53 expression was significantly associated with a reduced 5-year DFS rate of ESCC patients (P = 0.015). Multivariate Cox analysis indicated that ASPP2 was an independent predictor of OS [hazard ratio (HR): 0.541, 95% confidence interval (CI) 0.363-0.804] and DFS (HR: 0.599, 95% CI 0.404-0.888) of ESCC patients and that P53 was an independent predictor of DFS (HR: 2.161, 95% CI 1.100-4.245).

CONCLUSIONS

ASPP1 might be involved in the progression of ESCC, and ASPP2 was a potential prognostic biomarker of ESCC and should be evaluated in future studies.

ΔN-ASPP2, a novel isoform of the ASPP2 tumor suppressor, promotes cellular survival

ASPP2 is a tumor suppressor that works, at least in part, through enhancing p53-dependent apoptosis. We now describe a new ASPP2 isoform, ΔN-ASPP2, generated from an internal transcription start site that encodes an N-terminally truncated protein missing a predicted 254 amino acids. ΔN-ASPP2 suppresses p53 target gene transactivation, promoter occupancy, and endogenous p53 target gene expression in response to DNA damage. Moreover, ΔN-ASPP2 promotes progression through the cell cycle, as well as resistance to genotoxic stress-induced growth inhibition and apoptosis. Additionally, we found that ΔN-ASPP2 expression is increased in human breast tumors as compared to adjacent normal breast tissue; in contrast, ASPP2 is suppressed in the majority of these breast tumors. Together, our results provide insight into how this new ASPP2 isoform may play a role in regulating the ASPP2-p53 axis.

A systematic comparison of copy number alterations in four types of female cancer

Background

Detection and localization of genomic alterations and breakpoints are crucial in cancer research. The purpose of this study was to investigate, in a methodological and biological perspective, different female, hormone-dependent cancers to identify common and diverse DNA aberrations, genes, and pathways.

Methods

In this work, we analyzed tissue samples from patients with breast (n = 112), ovarian (n = 74), endometrial (n = 84), or cervical (n = 76) cancer. To identify genomic aberrations, the Circular Binary Segmentation (CBS) and Piecewise Constant Fitting (PCF) algorithms were used and segmentation thresholds optimized. The Genomic Identification of Significant Targets in Cancer (GISTIC) algorithm was applied to the segmented data to identify significantly altered regions and the associated genes were analyzed by Ingenuity Pathway Analysis (IPA) to detect over-represented pathways and functions within the identified gene sets.

Results and Discussion

Analyses of high-resolution copy number alterations in four different female cancer types are presented. For appropriately adjusted segmentation parameters the two segmentation algorithms CBS and PCF performed similarly. We identified one region at 8q24.3 with focal aberrations that was altered at significant frequency across all four cancer types. Considering both, broad regions and focal peaks, three additional regions with gains at significant frequency were revealed at 1p21.1, 8p22, and 13q21.33, respectively. Several of these events involve known cancer-related genes, like PPP2R2A, PSCA, PTP4A3, and PTK2. In the female reproductive system (ovarian, endometrial, and cervix [OEC]), we discovered three common events: copy number gains at 5p15.33 and 15q11.2, further a copy number loss at 8p21.2. Interestingly, as many as 75% of the aberrations (75% amplifications and 86% deletions) identified by GISTIC were specific for just one cancer type and represented distinct molecular pathways.

Conclusions

Our results disclose that some prominent copy number changes are shared in the four examined female, hormone-dependent cancer whereas others are definitive to specific cancer types.

Electronic supplementary material

The online version of this article (doi:10.1186/s12885-016-2899-4) contains supplementary material, which is available to authorized users.

The analytical validation of the Oncotype DX Recurrence Score assay

In vitro diagnostic multivariate index assays are highly complex molecular assays that can provide clinically actionable information regarding the underlying tumour biology and facilitate personalised treatment. These assays are only useful in clinical practice if all of the following are established: analytical validation (i.e., how accurately/reliably the assay measures the molecular characteristics), clinical validation (i.e., how consistently/accurately the test detects/predicts the outcomes of interest), and clinical utility (i.e., how likely the test is to significantly improve patient outcomes). In considering the use of these assays, clinicians often focus primarily on the clinical validity/utility; however, the analytical validity of an assay (e.g., its accuracy, reproducibility, and standardisation) should also be evaluated and carefully considered. This review focuses on the rigorous analytical validation and performance of the Oncotype DX^® Breast Cancer Assay, which is performed at the Central Clinical Reference Laboratory of Genomic Health, Inc. The assay process includes tumour tissue enrichment (if needed), RNA extraction, gene expression quantitation (using a gene panel consisting of 16 cancer genes plus 5 reference genes and quantitative real-time RT-PCR), and an automated computer algorithm to produce a Recurrence Score^® result (scale: 0–100). This review presents evidence showing that the Recurrence Score result reported for each patient falls within a tight clinically relevant confidence interval. Specifically, the review discusses how the development of the assay was designed to optimise assay performance, presents data supporting its analytical validity, and describes the quality control and assurance programmes that ensure optimal test performance over time.

Molecular Phenotype, Multigene Assays, and the Locoregional Management of Breast Cancer

Molecular profiling has revealed that breast cancer is not a single disease entity, but rather a class of heterogeneous subtypes, each with its own inherent biology and natural history. As a result, different treatment approaches have been optimized for the various subtypes and, in turn, the ability to identify subtypes has become a critical element in the management of breast cancer. Comprehensive transcriptional profiling studies have revealed at least 4 principal subtypes that, in practice, are often distinguished by immunohistochemical staining of the estrogen receptor (ER), progesterone receptor (PR), and HER2, along with a determination of histologic grade or Ki-67 staining: luminal A (ER+/HER2-/grade 1 or 2), luminal B (ER+/HER2-/grade 3), HER2 enriched (any HER2+ tumor), and basal like (ER-/PR-/HER2-). Although these immunohistochemically derived subtypes show robust prognostic and predictive ability, there remain many cases that demand profiling that more closely approximates the original transcriptionally derived definitions of the intrinsic subtypes. The need for improved prognostication and risk stratification has led to the development of several multigene assays in breast cancer. Although there is little molecular overlap between current assays, they all rely heavily on quantifying the transcriptional output of ER signaling and proliferation-related genes. These data are typically then used in multivariate prediction models that incorporate other canonical risk factors such as the tumor size, lymph node involvement, and patient demographic parameters, among others. Indeed, the advent of scalable molecular profiling technologies has brought a number of assays into routine clinical use for optimizing risk prediction and treatment assignment. The landscape of these assays and the clinical utility of contemporary molecular profiles are the main focus of this overview. In addition to the clinical advances in transcriptional subtyping, recent reports have characterized the most common genomic and epigenomic alterations that are likely to drive certain breast cancers. The identification of these "driver" lesions has heralded an era of precision medicine in which vulnerable oncogenic pathways may be targeted to disrupt the etiologic lesion(s) of a specific tumor. A number of such early targeted approaches have yielded success in treating breast cancer, demonstrating the critical need for molecular diagnostics in this disease.

Clinical evidence supporting genomic tests in early breast cancer: Do all genomic tests provide the same information?

Breast cancer (BC) has historically been treated as a single disease entity; however, in the last decade, insights into its molecular heterogeneity have underpinned the development/commercialisation of several genomic tools whose goal is to guide patient management in early BC. These include the Oncotype DX^® Breast Recurrence Score™ assay, MammaPrint^®, Prosigna^®, and EndoPredict^®. Although these assays are similar in that they are all multigene assays reflecting risk of recurrence, they differ substantially in the technological platform used to measure gene expression; the number and identity of genes assessed; the patient populations used for development and validation; and the level of evidence supporting clinical utility. They also differ in the amount of evidence demonstrating their impact on treatment decisions and cost effectiveness in different countries. This review discusses these 4 assays, highlighting the clinical evidence that supports each of them, while focussing on the Recurrence Score assay. This assay has the greatest body of evidence supporting its clinical utility and decision impact/effectiveness, and currently is the only one validated as a predictor of response to adjuvant chemotherapy in hormone-receptor positive early BC patients treated with endocrine therapy and to be included as such in international/national BC treatment guidelines. The review also discusses ongoing prospective trials investigating the 4 assays, recent outcome studies, as well as analyses comparing different assays on the same tumour blocks.

Endocrine therapy resistance in estrogen receptor (ER)-positive breast cancer

Estrogen receptor (ER)-positive breast cancer represents the majority (∼70%) of all breast malignancies. In this subgroup of breast cancers, endocrine therapies are effective both in the adjuvant and recurrent settings, although resistance remains a major issue. Several high-throughput approaches have been used to elucidate mechanisms of resistance and to derive potential predictive markers or alternative therapies. In this review, we cover the state-of-the-art of endocrine-resistance biomarker discovery with regard to the latest technological developments, and discuss current opportunities and restrictions for their implementation into a clinical setting.

The first decade of estrogen receptor cistromics in breast cancer

The advent of genome-wide transcription factor profiling has revolutionized the field of breast cancer research. Estrogen receptor α (ERα), the major drug target in hormone receptor-positive breast cancer, has been known as a key transcriptional regulator in tumor progression for over 30 years. Even though this function of ERα is heavily exploited and widely accepted as an Achilles heel for hormonal breast cancer, only since the last decade we have been able to understand how this transcription factor is functioning on a genome-wide scale. Initial ChIP-on-chip (chromatin immunoprecipitation coupled with tiling array) analyses have taught us that ERα is an enhancer-associated factor binding to many thousands of sites throughout the human genome and revealed the identity of a number of directly interacting transcription factors that are essential for ERα action. More recently, with the development of massive parallel sequencing technologies and refinements thereof in sample processing, a genome-wide interrogation of ERα has become feasible and affordable with unprecedented data quality and richness. These studies have revealed numerous additional biological insights into ERα behavior in cell lines and especially in clinical specimens. Therefore, what have we actually learned during this first decade of cistromics in breast cancer and where may future developments in the field take us?

Signatures of breast cancer metastasis at a glance

Gene expression profiling has yielded expression signatures from which prognostic tests can be derived to facilitate clinical decision making in breast cancer patients. Some of these signatures are based on profiling of whole tumor tissue (tissue signatures), which includes all tumor and stromal cells. Prognostic markers have also been derived from the profiling of metastasizing tumor cells, including circulating tumor cells (CTCs) and migratory-disseminating tumor cells within the primary tumor. The metastasis signatures based on CTCs and migratory-disseminating tumor cells have greater potential for unraveling cell biology insights and mechanistic underpinnings of tumor cell dissemination and metastasis. Of clinical interest is the promise that stratification of patients into high or low metastatic risk, as well as assessing the need for cytotoxic therapy, might be improved if prognostics derived from these two types of signatures are used in a combined way. The aim of this Cell Science at a Glance article and accompanying poster is to navigate through both types of signatures and their derived prognostics, as well as to highlight biological insights and clinical applications that could be derived from them, especially when they are used in combination.

Equivalence of MammaPrint array types in clinical trials and diagnostics

MammaPrint is an FDA-cleared microarray-based test that uses expression levels of the 70 MammaPrint genes to assess distant recurrence risk in early-stage breast cancer. The prospective RASTER study proved that MammaPrint Low Risk patients can safely forgo chemotherapy, which is further subject of the prospective randomized MINDACT trial. While MammaPrint diagnostic results are obtained from mini-arrays, clinical trials may be performed on whole-genome arrays. Here we demonstrate the equivalence and reproducibility of the MammaPrint test. MammaPrint indices were collected for breast cancer samples: (i) on both customized certified array types (n = 1,897 sample pairs), (ii) with matched fresh and FFPE tissues (n = 552 sample pairs), iii) for control samples replicated over a period of 10 years (n = 11,333), and iv) repeated measurements (n = 280). The array type indicated a near perfect Pearson correlation of 0.99 (95 % CI: 0.989-0.991). Paired fresh and FFPE samples showed an excellent Pearson correlation of 0.93 (95 % CI 0.92-0.94), in spite of the variability introduced by intratumoral tissue heterogeneity. Control samples showed high consistency over 10 year's time (overall reproducibility of 97.4 %). Precision and repeatability are overall 98.2 and 98.3 %, respectively. Results confirm that the combination of the near perfect correlation between array types, excellent equivalence between tissue types, and a very high stability, precision, and repeatability demonstrate that results from clinical trials (such as MINDACT and I-SPY 2) are equivalent to current MammaPrint FFPE and fresh diagnostics, and can be used interchangeably.