Pooling breast cancer datasets has a synergetic effect on classification performance and improves signature stability

Background: Michiels et al. (Lancet 2005; 365: 488-92) employed a resampling strategy to show that the genes identified as predictors of prognosis from resamplings of a single gene expression dataset are highly variable. The genes most frequently identified in the separate resamplings were put forward as a 'gold standard'. On a higher level, breast cancer datasets collected by different institutions can be considered as resamplings from the underlying breast cancer population. The limited overlap between published prognostic signatures confirms the trend of signature instability identified by the resampling strategy. Six breast cancer datasets, totaling 947 samples, all measured on the Affymetrix platform, are currently available. This provides a unique opportunity to employ a substantial dataset to investigate the effects of pooling datasets on classifier accuracy, signature stability and enrichment of functional categories. Results: We show that the resampling strategy produces a suboptimal ranking of genes, which can not be considered to be a 'gold standard'. When pooling breast cancer datasets, we observed a synergetic effect on the classification performance in 73% of the cases. We also observe a significant positive correlation between the number of datasets that is pooled, the validation performance, the number of genes selected, and the enrichment of specific functional categories. In addition, we have evaluated the support for five explanations that have been postulated for the limited overlap of signatures. Conclusion: The limited overlap of current signature genes can be attributed to small sample size. Pooling datasets results in more accurate classification and a convergence of signature genes. We therefore advocate the analysis of new data within the context of a compendium, rather than analysis in isolatio

SIRAC: Supervised Identification of Regions of Aberration in aCGH datasets

<p>Abstract</p> <p>Background</p> <p>Array comparative genome hybridization (aCGH) provides information about genomic aberrations. Alterations in the DNA copy number may cause the cell to malfunction, leading to cancer. Therefore, the identification of DNA amplifications or deletions across tumors may reveal key genes involved in cancer and improve our understanding of the underlying biological processes associated with the disease.</p> <p>Results</p> <p>We propose a supervised algorithm for the analysis of aCGH data and the identification of regions of chromosomal alteration (SIRAC). We first determine the DNA-probes that are important to distinguish the classes of interest, and then evaluate in a systematic and robust scheme if these relevant DNA-probes are closely located, i.e. form a region of amplification/deletion. SIRAC does not need any preprocessing of the aCGH datasets, and requires only few, intuitive parameters.</p> <p>Conclusion</p> <p>We illustrate the features of the algorithm with the use of a simple artificial dataset. The results on two breast cancer datasets show promising outcomes that are in agreement with previous findings, but SIRAC better pinpoints the dissimilarities between the classes of interest.</p

Comprehensive evaluation of methodology to assess abundance of immune infiltrates in breast cancer

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Does "one Size Fits All"?:Rethinking FIGO Depth of Invasion Measurements in Vulvar Cancer

Depth of invasion (DOI) is an important diagnostic parameter in patients with vulvar carcinoma, where a cutoff value of 1 mm largely determines the tumor stage and the need for groin surgery. DOI measurement should be reproducible and straightforward. In light of the new recommendation on how to measure DOI in the International Federation of Gynecology and Obstetrics (FIGO) staging system 2021, an exploratory study was conducted on the current practice of DOI measurement in vulvar cancer. In this study of 26 selected cases, 10 pathologists with high exposure to vulvar cancer cases in daily practice assessed both the conventional (FIGO 2009) and alternative (FIGO 2021) DOI methods for applicability and preference. In this set of cases, the DOI measurement according to FIGO 2009 was generally considered easier to apply than the measurement according to FIGO 2021, with applicability being rated as "easy to reasonable"in 76.9% versus 38.5% of cases, respectively (P=0.005). The preferred method was FIGO 2009 or tumor thickness in 14 cases and FIGO 2021 in 6 cases. No invasion was preferred in 1 case. For the remaining 5 cases, half of the pathologists opted for the FIGO 2009 method and half for the FIGO 2021 method. Although the FIGO 2009 method proved to be more readily applicable in most of the cases studied, the method may differ for each case. There may not be a "one size fits all"solution for all cases of vulvar cancer.</p

Selective inhibition of microRNA accessibility by RBM38 is required for p53 activity

MicroRNAs (miRNAs) interact with 3'-untranslated regions of messenger RNAs to restrict expression of most protein-coding genes during normal development and cancer. RNA-binding proteins (RBPs) can control the biogenesis, stability and activity of miRNAs. Here we identify RBM38 in a genetic screen for RBPs whose expression controls miRNA access to target mRNAs. RBM38 is induced by p53 and its ability to modulate miRNA-mediated repression is required for proper p53 function. In contrast, RBM38 shows lower propensity to block the action of the p53-controlled miR-34a on SIRT1. Target selectivity is determined by the interaction of RBM38 with uridine-rich regions near miRNA target sequences. Furthermore, in large cohorts of human breast cancer, reduced RBM38 expression by promoter hypermethylation correlates with wild-type p53 status. Thus, our results indicate a novel layer of p53 gene regulation, which is required for its tumour suppressive function

Functional Profiling of FSH and Estradiol in Ovarian Granulosa Cell Tumors

Adult-type granulosa cell tumors (AGCTs) are sex-cord derived neoplasms with a propensity for late relapse. Hormonal modulators have been used empirically in the treatment of recurrent AGCT, albeit with limited success. To provide a more rigorous foundation for hormonal therapy in AGCT, we used a multi-modal approach to characterize the expressions of key hormone biomarkers in 175 tumor specimens and 51 serum samples using RNA sequencing, immunohistochemistry, RNA in situ hybridization, quantitative PCR, and circulating biomarker analysis, and correlated these results with clinical data. We show that FSH receptor and estrogen receptor beta (ER beta) are highly expressed in the majority of AGCTs, whereas the expressions of estrogen receptor alpha (ER alpha) and G-protein coupled estrogen receptor 1 are less prominent. ER beta protein expression is further increased in recurrent tumors. Aromatase expression levels show high variability between tumors. None of the markers examined served as prognostic biomarkers for progression-free or overall survival. In functional experiments, we assessed the effects of FSH, estradiol (E2), and the aromatase inhibitor letrozole on AGCT cell viability using 2 in vitro models: KGN cells and primary cultures of AGCT cells. FSH increased cell viability in a subset of primary AGCT cells, whereas E2 had no effect on cell viability at physiological concentrations. Letrozole suppressed E2 production in AGCTs; however, it did not impact cell viability. We did not find preclinical evidence to support the clinical use of aromatase inhibitors in AGCT treatment, and thus randomized, prospective clinical studies are needed to clarify the role of hormonal treatments in AGCTs. (C) Endocrine Society 2020.Peer reviewe

A comprehensive analysis of prognostic signatures reveals the high predictive capacity of the proliferation, immune response and RNA splicing modules in breast cancer.

INTRODUCTION: Several gene expression signatures have been proposed and demonstrated to be predictive of outcome in breast cancer. In the present article we address the following issues: Do these signatures perform similarly? Are there (common) molecular processes reported by these signatures? Can better prognostic predictors be constructed based on these identified molecular processes? METHODS: We performed a comprehensive analysis of the performance of nine gene expression signatures on seven different breast cancer datasets. To better characterize the functional processes associated with these signatures, we enlarged each signature by including all probes with a significant correlation to at least one of the genes in the original signature. The enrichment of functional groups was assessed using four ontology databases. RESULTS: The classification performance of the nine gene expression signatures is very similar in terms of assigning a sample to either a poor outcome group or a good outcome group. Nevertheless the concordance in classification at the sample level is low, with only 50% of the breast cancer samples classified in the same outcome group by all classifiers. The predictive accuracy decreases with the number of poor outcome assignments given to a sample. The best classification performance was obtained for the group of patients with only good outcome assignments. Enrichment analysis of the enlarged signatures revealed 11 functional modules with prognostic ability. The combination of the RNA-splicing and immune modules resulted in a classifier with high prognostic performance on an independent validation set. CONCLUSIONS: The study revealed that the nine signatures perform similarly but exhibit a large degree of discordance in prognostic group assignment. Functional analyses indicate that proliferation is a common cellular process, but that other functional categories are also enriched and show independent prognostic ability. We provide new evidence of the potentially promising prognostic impact of immunity and RNA-splicing processes in breast cancer.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

IHC-based Ki67 as response biomarker to tamoxifen in breast cancer window trials enrolling premenopausal women

Window studies are gaining traction to assess (molecular) changes in short timeframes. Decreased tumor cell positivity for the proliferation marker Ki67 is often used as a proxy for treatment response. Immunohistochemistry (IHC)-based Ki67 on tissue from neo-adjuvant trials was previously reported to be predictive for long-term response to endocrine therapy for breast cancer in postmenopausal women, but none of these trials enrolled premenopausal women. Nonetheless, the marker is being used on this subpopulation. We compared pathologist assessed IHC-based Ki67 in samples from pre- and postmenopausal women in a neo-adjuvant, endocrine therapy focused trial (NCT00738777), randomized between tamoxifen, anastrozole, or fulvestrant. These results were compared with (1) IHC-based Ki67 scoring by AI, (2) mitotic figures, (3) mRNA-based Ki67, (4) five independent gene expression signatures capturing proliferation, and (5) blood levels for tamoxifen and its metabolites as well as estradiol. Upon tamoxifen, IHC-based Ki67 levels were decreased in both pre- and postmenopausal breast cancer patients, which was confirmed using mRNA-based cell proliferation markers. The magnitude of decrease of Ki67 IHC was smaller in pre- versus postmenopausal women. We found a direct relationship between post-treatment estradiol levels and the magnitude of the Ki67 decrease in tumors. These data suggest IHC-based Ki67 may be an appropriate biomarker for tamoxifen response in premenopausal breast cancer patients, but anti-proliferative effect size depends on estradiol levels.</p

Tumour microenvironment characterisation to stratify patients for hyperthermic intraperitoneal chemotherapy in high-grade serous ovarian cancer (OVHIPEC-1).

BACKGROUND Hyperthermic intraperitoneal chemotherapy (HIPEC) improves survival in patients with Stage III ovarian cancer following interval cytoreductive surgery (CRS). Optimising patient selection is essential to maximise treatment efficacy and avoid overtreatment. This study aimed to identify biomarkers that predict HIPEC benefit by analysing gene signatures and cellular composition of tumours from participants in the OVHIPEC-1 trial. METHODS Whole-transcriptome RNA sequencing data were retrieved from high-grade serous ovarian cancer (HGSOC) samples from 147 patients obtained during interval CRS. We performed differential gene expression analysis and applied deconvolution methods to estimate cell-type proportions in bulk mRNA data, validated by histological assessment. We tested the interaction between treatment and potential predictors on progression-free survival using Cox proportional hazards models. RESULTS While differential gene expression analysis did not yield any predictive biomarkers, the cellular composition, as characterised by deconvolution, indicated that the absence of macrophages and the presence of B cells in the tumour microenvironment are potential predictors of HIPEC benefit. The histological assessment confirmed the predictive value of macrophage absence. CONCLUSION Immune cell composition, in particular macrophages absence, may predict response to HIPEC in HGSOC and these hypothesis-generating findings warrant further investigation. CLINICAL TRIAL REGISTRATION NCT00426257

Low probability of disease cure in advanced ovarian carcinomas before the PARP inhibitor era

BACKGROUND: In ovarian carcinomas, the likelihood of disease cure following first-line medical-surgical treatment has been poorly addressed. The objective was to: (a) assess the likelihood of long-term disease-free (LDF) > 5 years; and (b) evaluate the impact of the tumour primary chemosensitivity (assessed with the modelled CA-125 KELIM) with respect to disease stage, and completeness of debulking surgery. METHODS: Three Phase III trial datasets (AGO-OVAR 9; AGO-OVAR 7; ICON-7) were retrospectively investigated in an "adjuvant dataset", whilst the Netherlands Cancer Registry was used in a "neoadjuvant dataset". The prognostic values of KELIM, disease stage and surgery outcomes regarding the likelihood of LDF were assessed using univariate/multivariate analyses. RESULTS: Of 2029 patients in the "adjuvant dataset", 82 (4.0%) experienced LDF (Stage I-II: 25.9%; III: 2.1%; IV: 0.5%). Multivariate analyses identified disease stage and KELIM (OR = 4.24) as independent prognostic factors. Among the 1452 patients from the "neoadjuvant dataset", 36 (2.4%) had LDF (Stage II-III: 3.3%; IV: 1.3%). Using multivariate tests, high-risk diseases (OR = 0.18) and KELIM (OR = 2.96) were significant. CONCLUSION: The probability of LDF > 5 years after first-line treatment in 3486 patients (<4%) was lower than thought. These data could represent a reference for future studies meant to assess progress related to PARP inhibitors