Improved detection of clinically relevant fusion transcripts in cancer by machine learning classification

BACKGROUND

Genomic rearrangements in cancer cells can create fusion genes that encode chimeric proteins or alter the expression of coding and non-coding RNAs. In some cancer types, fusions involving specific kinases are used as targets for therapy. Fusion genes can be detected by whole genome sequencing (WGS) and targeted fusion panels, but RNA sequencing (RNA-Seq) has the advantageous capability of broadly detecting expressed fusion transcripts.

RESULTS

We developed a pipeline for validation of fusion transcripts identified in RNA-Seq data using matched WGS data from The Cancer Genome Atlas (TCGA) and applied it to 910 tumors from 11 different cancer types. This resulted in 4237 validated gene fusions, 3049 of them with at least one identified genomic breakpoint. Utilizing validated fusions as true positive events, we trained a machine learning classifier to predict true and false positive fusion transcripts from RNA-Seq data. The final precision and recall metrics of the classifier were 0.74 and 0.71, respectively, in an independent dataset of 249 breast tumors. Application of this classifier to all samples with RNA-Seq data from these cancer types vastly extended the number of likely true positive fusion transcripts and identified many potentially targetable kinase fusions. Further analysis of the validated gene fusions suggested that many are created by intrachromosomal amplification events with microhomology-mediated non-homologous end-joining.

CONCLUSIONS

A classifier trained on validated fusion events increased the accuracy of fusion transcript identification in samples without WGS data. This allowed the analysis to be extended to all samples with RNA-Seq data, facilitating studies of tumor biology and increasing the number of detected kinase fusions. Machine learning could thus be used in identification of clinically relevant fusion events for targeted therapy. The large dataset of validated gene fusions generated here presents a useful resource for development and evaluation of fusion transcript detection algorithms.

Fast and sensitive validation of fusion transcripts in whole-genome sequencing data

BACKGROUND

In cancer, genomic rearrangements can create fusion genes that either combine protein-coding sequences from two different partner genes or place one gene under the control of the promoter of another gene. These fusion genes can act as oncogenic drivers in tumor development and several fusions involving kinases have been successfully exploited as drug targets. Expressed fusions can be identified in RNA sequencing (RNA-Seq) data, but fusion prediction software often has a high fraction of false positive fusion transcript predictions. This is problematic for both research and clinical applications.

RESULTS

We describe a method for validation of fusion transcripts detected by RNA-Seq in matched whole-genome sequencing (WGS) data. Our pipeline uses discordant read pairs to identify supported fusion events and analyzes soft-clipped read alignments to determine genomic breakpoints. We have tested it on matched RNA-Seq and WGS data for both tumors and cancer cell lines and show that it can be used to validate both new predicted gene fusions and experimentally validated fusion events. It was considerably faster and more sensitive than using BreakDancer and Manta, software that is instead designed to detect many different types of structural variants on a genome-wide scale.

CONCLUSIONS

We have developed a fast and very sensitive pipeline for validation of gene fusions detected by RNA-Seq in matched WGS data. It can be used to identify high-quality gene fusions for further bioinformatic and experimental studies, including validation of genomic breakpoints and studies of the mechanisms that generate fusions. In a clinical setting, it could help find expressed gene fusions for personalized therapy.

RNA sequencing-based single sample predictors of molecular subtype and risk of recurrence for clinical assessment of early-stage breast cancer

Multigene assays for molecular subtypes and biomarkers can aid management of early invasive breast cancer. Using RNA-sequencing we aimed to develop single-sample predictor (SSP) models for clinical markers, subtypes, and risk of recurrence (ROR). A cohort of 7743 patients was divided into training and test set. We trained SSPs for subtypes and ROR assigned by nearest-centroid (NC) methods and SSPs for biomarkers from histopathology. Classifications were compared with Prosigna in two external cohorts (ABiM, n = 100 and OSLO2-EMIT0, n = 103). Prognostic value was assessed using distant recurrence-free interval. Agreement between SSP and NC for PAM50 (five subtypes) was high (85%, Kappa = 0.78) for Subtype (four subtypes) very high (90%, Kappa = 0.84) and for ROR risk category high (84%, Kappa = 0.75, weighted Kappa = 0.90). Prognostic value was assessed as equivalent and clinically relevant. Agreement with histopathology was very high or high for receptor status, while moderate for Ki67 status and poor for Nottingham histological grade. SSP and Prosigna concordance was high for subtype (OSLO-EMIT0 83%, Kappa = 0.73 and ABiM 80%, Kappa = 0.72) and moderate and high for ROR risk category (68 and 84%, Kappa = 0.50 and 0.70, weighted Kappa = 0.70 and 0.78). Pooled concordance for emulated treatment recommendation dichotomized for chemotherapy was high (85%, Kappa = 0.66). Retrospective evaluation suggested that SSP application could change chemotherapy recommendations for up to 17% of postmenopausal ER+/HER2-/N0 patients with balanced escalation and de-escalation. Results suggest that NC and SSP models are interchangeable on a group-level and nearly so on a patient level and that SSP models can be derived to closely match clinical tests.

Regulatory networks and 5' partner usage of miRNA host gene fusions in breast cancer

Genomic rearrangements in cancer cells can create gene fusions where the juxtaposition of two different genes leads to the production of chimeric proteins or altered gene expression through promoter‐swapping. We have previously shown that fusion transcripts involving microRNA (miRNA) host genes contribute to deregulation of miRNA expression regardless of the protein‐coding potential of these transcripts. Many different genes can also be used as 5′ partners by a miRNA host gene in what we named recurrent miRNA‐convergent fusions. Here, we have explored the properties of 5′ partners in fusion transcripts that involve miRNA hosts in breast tumours from The Cancer Genome Atlas (TCGA). We hypothesised that firstly, 5′ partner genes should belong to pathways and transcriptional programmes that reflect the tumour phenotype and secondly, there should be a selection for fusion events that shape miRNA expression to benefit the tumour cell through the known hallmarks of cancer. We found that the set of 5′ partners in miRNA host fusions is non‐random, with overrepresentation of highly expressed genes in pathways active in cancer including epithelial‐to‐mesenchymal transition, translational regulation and oestrogen signalling. Furthermore, many miRNAs were upregulated in samples with host gene fusions, including established oncogenic miRNAs such as mir‐21 and the mir‐106b~mir‐93~mir‐25 cluster. To the list of mechanisms for deregulation of miRNA expression, we have added fusion transcripts that change the promoter region. We propose that this adds material for genetic selection and tumour evolution in cancer cells and that miRNA host fusions can act as tumour ‘drivers’.

Preexisting Somatic Mutations of Estrogen Receptor Alpha (ESR1) in Early-Stage Primary Breast Cancer

Background

More than three-quarters of primary breast cancers are positive for estrogen receptor alpha (ER; encoded by the gene ESR1), the most important factor for directing anti-estrogenic endocrine therapy (ET). Recently, mutations in ESR1 were identified as acquired mechanisms of resistance to ET, found in 12% to 55% of metastatic breast cancers treated previously with ET.

Methods

We analyzed 3217 population-based invasive primary (nonmetastatic) breast cancers (within the SCAN-B study, ClinicalTrials.gov NCT02306096), sampled from initial diagnosis prior to any treatment, for the presence of ESR1 mutations using RNA sequencing. Mutations were verified by droplet digital polymerase chain reaction on tumor and normal DNA. Patient outcomes were analyzed using Kaplan-Meier estimation and a series of 2-factor Cox regression multivariable analyses.

Results

We identified ESR1 resistance mutations in 30 tumors (0.9%), of which 29 were ER positive (1.1%). In ET-treated disease, presence of ESR1 mutation was associated with poor relapse-free survival and overall survival (2-sided log-rank test P < .001 and P = .008, respectively), with hazard ratios of 3.00 (95% confidence interval = 1.56 to 5.88) and 2.51 (95% confidence interval = 1.24 to 5.07), respectively, which remained statistically significant when adjusted for other prognostic factors.

Conclusions

These population-based results indicate that ESR1 mutations at diagnosis of primary breast cancer occur in about 1% of women and identify for the first time in the adjuvant setting that such preexisting mutations are associated to eventual resistance to standard hormone therapy. If replicated, tumor ESR1 screening should be considered in ER-positive primary breast cancer, and for patients with mutated disease, ER degraders such as fulvestrant or other therapeutic options may be considered as more appropriate.

Molecular analyses of triple-negative breast cancer in the young and elderly

Background

Breast cancer in young adults has been implicated with a worse outcome. Analyses of genomic traits associated with age have been heterogenous, likely because of an incomplete accounting for underlying molecular subtypes. We aimed to resolve whether triple-negative breast cancer (TNBC) in younger versus older patients represent similar or different molecular diseases in the context of genetic and transcriptional subtypes and immune cell infiltration.

Patients and methods

In total, 237 patients from a reported population-based south Swedish TNBC cohort profiled by RNA sequencing and whole-genome sequencing (WGS) were included. Patients were binned in 10-year intervals. Complimentary PD-L1 and CD20 immunohistochemistry and estimation of tumor-infiltrating lymphocytes (TILs) were performed. Cases were analyzed for differences in patient outcome, genomic, transcriptional, and immune landscape features versus age at diagnosis. Additionally, 560 public WGS breast cancer profiles were used for validation.

Results

Median age at diagnosis was 62 years (range 26–91). Age was not associated with invasive disease-free survival or overall survival after adjuvant chemotherapy. Among the BRCA1-deficient cases (82/237), 90% were diagnosed before the age of 70 and were predominantly of the basal-like subtype. In the full TNBC cohort, reported associations of patient age with changes in Ki67 expression, PIK3CA mutations, and a luminal androgen receptor subtype were confirmed. Within DNA repair deficiency or gene expression defined molecular subgroups, age-related alterations in, e.g., overall gene expression, immune cell marker gene expression, genetic mutational and rearrangement signatures, amount of copy number alterations, and tumor mutational burden did, however, not appear distinct. Similar non-significant associations for genetic alterations with age were obtained for other breast cancer subgroups in public WGS data. Consistent with age-related immunosenescence, TIL counts decreased linearly with patient age across different genetic TNBC subtypes.

Conclusions

Age-related alterations in TNBC, as well as breast cancer in general, need to be viewed in the context of underlying genomic phenotypes. Based on this notion, age at diagnosis alone does not appear to provide an additional layer of biological complexity above that of proposed genetic and transcriptional phenotypes of TNBC. Consequently, treatment decisions should be less influenced by age and more driven by tumor biology.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13058-021-01392-0.

The mutational landscape of the SCAN-B real-world primary breast cancer transcriptome

Breast cancer is a disease of genomic alterations, of which the panorama of somatic mutations and how these relate to subtypes and therapy response is incompletely understood. Within SCAN‐B (ClinicalTrials.gov: NCT02306096), a prospective study elucidating the transcriptomic profiles for thousands of breast cancers, we developed a RNA‐seq pipeline for detection of SNVs/indels and profiled a real‐world cohort of 3,217 breast tumors. We describe the mutational landscape of primary breast cancer viewed through the transcriptome of a large population‐based cohort and relate it to patient survival. We demonstrate that RNA‐seq can be used to call mutations in genes such as PIK3CA,TP53, and ERBB2, as well as the status of molecular pathways and mutational burden, and identify potentially druggable mutations in 86.8% of tumors. To make this rich dataset available for the research community, we developed an open source web application, the SCAN‐B MutationExplorer (http://oncogenomics.bmc.lu.se/MutationExplorer). These results add another dimension to the use of RNA‐seq as a clinical tool, where both gene expression‐ and mutation‐based biomarkers can be interrogated in real‐time within 1 week of tumor sampling.

Abstract CT074: Pre-existing ESR1 mutations in early-stage primary breast cancer predict failure of endocrine therapy and poor survival

Background: More than three-quarters of primary breast cancers are positive for estrogen receptor alpha (ER; encoded by the gene ESR1), the most important factor for directing anti-estrogenic endocrine therapy. Although mutation in ESR1 is known as an acquired mechanism of resistance to endocrine therapy (ET), found in 12-55% of metastatic breast cancers treated previously with ET, the impact of ESR1 mutation on therapy response in primary breast cancer is unclear. Patients and methods: In this study we analyzed 3217 real-world and population-based early-stage primary breast cancers (within the SCAN-B study, ClinicalTrials.gov NCT02306096). Tissues were sampled from initial diagnosis prior to any treatment and analyzed for the presence of ESR1 mutations using RNA sequencing. Mutations were verified by SAGAsafe droplet digital PCR. Results: We identified ESR1 resistance mutations in 30 cases (0.9%), of which 29 were ER-positive (1.1%). In ER-positive disease, presence of ESR1 mutation was significantly associated to poor relapse-free survival (RFS) and overall survival (OS) (p=0.011 and p=0.019, respectively), and moreover predicted poor RFS and OS within the patient group that received ET (p=0.007 and p=0.010, respectively). Conclusions: These results indicate that ESR1 mutations at diagnosis of untreated primary breast cancer are rare, however we confirm for the first time that such early mutations predict eventual resistance to standard hormone therapy in the adjuvant setting. If replicated, tumor ESR1 screening may be considered in ER-positive primary breast cancer and, in mutated cases, ER-degraders such as fulvestrant or other therapeutic options may be considered as more appropriate.

Citation Format: Malin Dahlgren, Anthony M. George, Christian Brueffer, Sergii Gladchuk, Yilun Chen, Johan Vallon-Christersson, Cecilia Hegardt, Jari Häkkinen, Lisa Rydén, Martin Malmberg, Christer Larsson, Sofia K. Gruvberger-Saal, Anna Ehinger, Niklas Loman, Åke Borg, Lao H. Saal. Pre-existing ESR1 mutations in early-stage primary breast cancer predict failure of endocrine therapy and poor survival [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr CT074.

The X-Linked DDX3X RNA Helicase Dictates Translation Reprogramming and Metastasis in Melanoma

The X-linked DDX3X gene encodes an ATP-dependent DEAD-box RNA helicase frequently altered in various human cancers, including melanomas. Despite its important roles in translation and splicing, how DDX3X dysfunction specifically rewires gene expression in melanoma remains completely unknown. Here, we uncover a DDX3X-driven post-transcriptional program that dictates melanoma phenotype and poor disease prognosis. Through an unbiased analysis of translating ribosomes, we identified the microphthalmia-associated transcription factor, MITF, as a key DDX3X translational target that directs a proliferative-to-metastatic phenotypic switch in melanoma cells. Mechanistically, DDX3X controls MITF mRNA translation via an internal ribosome entry site (IRES) embedded within the 5' UTR. Through this exquisite translation-based regulatory mechanism, DDX3X steers MITF protein levels dictating melanoma metastatic potential in vivo and response to targeted therapy. Together, these findings unravel a post-transcriptional layer of gene regulation that may provide a unique therapeutic vulnerability in aggressive male melanomas.

Comprehensive molecular comparison of BRCA1 hypermethylated and BRCA1 mutated triple negative breast cancers

Homologous recombination deficiency (HRD) is a defining characteristic in BRCA-deficient breast tumors caused by genetic or epigenetic alterations in key pathway genes. We investigated the frequency of BRCA1 promoter hypermethylation in 237 triple-negative breast cancers (TNBCs) from a population-based study using reported whole genome and RNA sequencing data, complemented with analyses of genetic, epigenetic, transcriptomic and immune infiltration phenotypes. We demonstrate that BRCA1 promoter hypermethylation is twice as frequent as BRCA1 pathogenic variants in early-stage TNBC and that hypermethylated and mutated cases have similarly improved prognosis after adjuvant chemotherapy. BRCA1 hypermethylation confers an HRD, immune cell type, genome-wide DNA methylation, and transcriptional phenotype similar to TNBC tumors with BRCA1-inactivating variants, and it can be observed in matched peripheral blood of patients with tumor hypermethylation. Hypermethylation may be an early event in tumor development that progress along a common pathway with BRCA1-mutated disease, representing a promising DNA-based biomarker for early-stage TNBC.

Prognostic implications of the expression levels of different immunoglobulin heavy chain-encoding RNAs in early breast cancer

The extent and composition of the immune response in a breast cancer is one important prognostic factor for the disease. The aim of the current work was to refine the analysis of the humoral component of an immune response in breast tumors by quantifying mRNA expression of different immunoglobulin classes and study their association with prognosis. We used RNA-Seq data from two local population-based breast cancer cohorts to determine the expression of IGJ and immunoglobulin heavy (IGH) chain-encoding RNAs. The association with prognosis was investigated and public data sets were used to corroborate the findings. Except for IGHE and IGHD, mRNAs encoding heavy chains were generally detected at substantial levels and correlated with other immune-related genes. High IGHG1 mRNA was associated with factors related to poor prognosis such as estrogen receptor negativity, HER2 amplification, and high grade, whereas high IGHA2 mRNA levels were primarily associated with lower age at diagnosis. High IGHA2 and IGJ mRNA levels were associated with a more favorable prognosis both in univariable and multivariable Cox models. When adjusting for other prognostic factors, high IGHG1 mRNA levels were positively associated with improved prognosis. To our knowledge, these results are the first to demonstrate that expression of individual Ig class types has prognostic implications in breast cancer.

Defining the mutational landscape of 3,217 primary breast cancer transcriptomes through large-scale RNA-seq within the Sweden Cancerome Analysis Network: Breast Project (SCAN-B; NCT03430492)

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Background: Breast cancer is a disease of genomic alterations, of which the complete panorama of somatic mutations and how these relate to molecular subtypes and therapy response is incompletely understood. The Sweden Cancerome Analysis Network-Breast project (SCAN-B; ClinicalTrials.gov NCT02306096) is a multi-center population-based ongoing prospective observational study elucidating the global transcriptomic profiles for thousands of patients and tumors using RNA sequencing. Since September 2010, over 15,000 patients with breast cancer have been enrolled at 9 hospitals across a wide geography of Sweden, comprising greater than 90% of all eligible patients in the catchment area. Methods: Within SCAN-B, we developed an optimized bioinformatics pipeline for detection of single nucleotide variants and small insertions and deletions from RNA-seq data. From this, we describe the mutational landscape of 3,217 primary breast cancer transcriptomes, and relate it to patient overall survival in a real-world setting (median follow-up 75 months, range 2-105 months). Results: We demonstrate that RNA-seq can be used to call mutations in important breast cancer genes such as PIK3CA, TP53, ESR1, and ERBB2, as well as mutation status of key molecular pathways and tumor mutational burden, identify mutations in one or more potentially druggable genes in 85.3% percent of cases, and reveal significant relationships to patient outcome within specific treatment groups. To make this rich and growing mutational portraiture of breast cancer available for the wider research community, we developed an open source interactive web application, SCAN-B MutationExplorer, publicly accessible at http://oncogenomics.bmc.lu.se/MutationExplorer . Conclusions: These results add another dimension to the use of RNA-seq as a potential clinical tool, where both gene expression-based signatures and gene mutation-based biomarkers can be interrogated simultaneously and in real-time within one week of tumor sampling.

Analysis of fusion transcripts indicates widespread deregulation of snoRNAs and their host genes in breast cancer

Genomic rearrangements in cancer can join the sequences of two separate genes. Studies of such gene fusion events have mainly focused on identification of fusion proteins from the chimeric transcripts. We have previously investigated how fusions instead can affect the expression of intronic microRNA (miRNA) genes that are encoded within fusion gene partners. Here, we extend our analysis to small nucleolar RNAs (snoRNAs) that also are embedded within protein-coding or noncoding host genes. We found that snoRNA hosts are selectively enriched in fusion transcripts, like miRNA host genes, and that this enrichment is associated with all snoRNA classes. These structural changes may have functional consequences for the cell; proteins involved in the protein translation machinery are overrepresented among snoRNA host genes, a gene architecture assumed to be needed for closely coordinated expression of snoRNAs and host proteins. Our data indicate that this structure is frequently disrupted in cancer. We furthermore observed that snoRNA genes involved in fusions tend to associate with stronger promoters than the natural host, suggesting a mechanism that selects for snoRNA overexpression. In summary, we highlight a previously unexplored frequent structural change in cancer that affects important components of cellular physiology.

Tumor genetic heterogeneity analysis of chronic sun-damaged melanoma

Chronic sun‐damaged (CSD) melanoma represents 10%–20% of cutaneous melanomas and is characterized by infrequent BRAF V600E mutations and high mutational load. However, the order of genetic events or the extent of intra‐tumor heterogeneity (ITH) in CSD^high melanoma is still unknown. Ultra‐deep targeted sequencing of 40 cancer‐associated genes was performed in 72 in situ or invasive CMM, including 23 CSD^high cases. In addition, we performed whole exome and RNA sequencing on multiple regions of primary tumor and multiple in‐transit metastases from one CSD^high melanoma patient. We found no significant difference in mutation frequency in melanoma‐related genes or in mutational load between in situ and invasive CSD^high lesions, while this difference was observed in CSD^low lesions. In addition, increased frequency of BRAF V600K, NF1, and TP53 mutations (p < .01, Fisher's exact test) was found in CSD^high melanomas. Sequencing of multiple specimens from one CSD^high patient revealed strikingly limited ITH with >95% shared mutations. Our results provide evidence that CSD^high and CSD^low melanomas are distinct molecular entities that progress via different genetic routes.

Agreement between molecular subtyping and surrogate subtype classification: a contemporary population-based study of ER-positive/HER2-negative primary breast cancer

PURPOSE

Oestrogen receptor-positive (ER+) and human epidermal receptor 2-negative (HER2-) breast cancers are classified as Luminal A or B based on gene expression, but immunohistochemical markers are used for surrogate subtyping. The aims of this study were to examine the agreement between molecular subtyping (MS) and surrogate subtyping and to identify subgroups consisting mainly of Luminal A or B tumours.

METHODS

The cohort consisted of 2063 patients diagnosed between 2013-2017, with primary ER+/HER2- breast cancer, analysed by RNA sequencing. Surrogate subtyping was performed according to three algorithms (St. Gallen 2013, Maisonneuve and our proposed Grade-based classification). Agreement (%) and kappa statistics (κ) were used as concordance measures and ROC analysis for luminal distinction. Ki67, progesterone receptor (PR) and histological grade (HG) were further investigated as surrogate markers.

RESULTS

The agreement rates between the MS and St. Gallen 2013, Maisonneuve and Grade-based classifications were 62% (κ = 0.30), 66% (κ = 0.35) and 70% (κ = 0.41), respectively. PR did not contribute to distinguishing Luminal A from B tumours (auROC = 0.56). By classifying HG1-2 tumours as Luminal A-like and HG3 as Luminal B-like, agreement with MS was 80% (κ = 0.46). Moreover, by combining HG and Ki67 status, a large subgroup of patients (51% of the cohort) having > 90% Luminal A tumours could be identified.

CONCLUSIONS

Agreement between MS and surrogate classifications was generally poor. However, a post hoc analysis showed that a combination of HG and Ki67 could identify patients very likely to have Luminal A tumours according to MS.

Whole-genome sequencing of triple-negative breast cancers in a population-based clinical study

Whole-genome sequencing (WGS) brings comprehensive insights to cancer genome interpretation. To explore the clinical value of WGS, we sequenced 254 triple-negative breast cancers (TNBCs) for which associated treatment and outcome data were collected between 2010 and 2015 via the population-based Sweden Cancerome Analysis Network-Breast (SCAN-B) project (ClinicalTrials.gov ID:NCT02306096). Applying the HRDetect mutational-signature-based algorithm to classify tumors, 59% were predicted to have homologous-recombination-repair deficiency (HRDetect-high): 67% explained by germline/somatic mutations of BRCA1/BRCA2, BRCA1 promoter hypermethylation, RAD51C hypermethylation or biallelic loss of PALB2. A novel mechanism of BRCA1 abrogation was discovered via germline SINE-VNTR-Alu retrotransposition. HRDetect provided independent prognostic information, with HRDetect-high patients having better outcome on adjuvant chemotherapy for invasive disease-free survival (hazard ratio (HR) = 0.42; 95% confidence interval (CI) = 0.2-0.87) and distant relapse-free interval (HR = 0.31, CI = 0.13-0.76) compared to HRDetect-low, regardless of whether a genetic/epigenetic cause was identified. HRDetect-intermediate, some possessing potentially targetable biological abnormalities, had the poorest outcomes. HRDetect-low cancers also had inadequate outcomes: ~4.7% were mismatch-repair-deficient (another targetable defect, not typically sought) and they were enriched for (but not restricted to) PIK3CA/AKT1 pathway abnormalities. New treatment options need to be considered for now-discernible HRDetect-intermediate and HRDetect-low categories. This population-based study advocates for WGS of TNBC to better inform trial stratification and improve clinical decision-making.

Cross comparison and prognostic assessment of breast cancer multigene signatures in a large population-based contemporary clinical series

Multigene expression signatures provide a molecular subdivision of early breast cancer associated with patient outcome. A gap remains in the validation of such signatures in clinical treatment groups of patients within population-based cohorts of unselected primary breast cancer representing contemporary disease stages and current treatments. A cohort of 3520 resectable breast cancers with RNA sequencing data included in the population-based SCAN-B initiative (ClinicalTrials.gov ID NCT02306096) were selected from a healthcare background population of 8587 patients diagnosed within the years 2010-2015. RNA profiles were classified according to 19 reported gene signatures including both gene expression subtypes (e.g. PAM50, IC10, CIT) and risk predictors (e.g. Oncotype DX, 70-gene, ROR). Classifications were analyzed in nine adjuvant clinical assessment groups: TNBC-ACT (adjuvant chemotherapy, n = 239), TNBC-untreated (n = 82), HER2+/ER- with anti-HER2+ ACT treatment (n = 110), HER2+/ER+ with anti-HER2 + ACT + endocrine treatment (n = 239), ER+/HER2-/LN- with endocrine treatment (n = 1113), ER+/HER2-/LN- with endocrine + ACT treatment (n = 243), ER+/HER2-/LN+ with endocrine treatment (n = 423), ER+/HER2-/LN+ with endocrine + ACT treatment (n = 433), and ER+/HER2-/LN- untreated (n = 200). Gene signature classification (e.g., proportion low-, high-risk) was generally well aligned with stratification based on current immunohistochemistry-based clinical practice. Most signatures did not provide any further risk stratification in TNBC and HER2+/ER- disease. Risk classifier agreement (low-, medium/intermediate-, high-risk groups) in ER+ assessment groups was on average 50-60% with occasional pair-wise comparisons having <30% agreement. Disregarding the intermediate-risk groups, the exact agreement between low- and high-risk groups was on average ~80-95%, for risk prediction signatures across all assessment groups. Outcome analyses were restricted to assessment groups of TNBC-ACT and endocrine treated ER+/HER2-/LN- and ER+/HER2-/LN+ cases. For ER+/HER2- disease, gene signatures appear to contribute additional prognostic value even at a relatively short follow-up time. Less apparent prognostic value was observed in the other groups for the tested signatures. The current study supports the usage of gene expression signatures in specific clinical treatment groups within population-based breast cancer. It also stresses the need of further development to reach higher consensus in individual patient classifications, especially for intermediate-risk patients, and the targeting of patients where current gene signatures and prognostic variables provide little support in clinical decision-making.

Prediction of Lymph Node Metastasis in Breast Cancer by Gene Expression and Clinicopathological Models: Development and Validation within a Population-Based Cohort

PURPOSE

More than 70% of patients with breast cancer present with node-negative disease, yet all undergo surgical axillary staging. We aimed to define predictors of nodal metastasis using clinicopathological characteristics (CLINICAL), gene expression data (GEX), and mixed features (MIXED) and to identify patients at low risk of metastasis who might be spared sentinel lymph node biopsy (SLNB).Experimental Design: Breast tumors (n = 3,023) from the population-based Sweden Cancerome Analysis Network-Breast initiative were profiled by RNA sequencing and linked to clinicopathologic characteristics. Seven machine-learning models present the discriminative ability of N0/N+ in development (n = 2,278) and independent validation cohorts (n = 745) stratified as ER⁺HER2^-, HER2⁺, and TNBC. Possible SLNB reduction rates are proposed by applying CLINICAL and MIXED predictors.

RESULTS

In the validation cohort, the MIXED predictor showed the highest area under ROC curves to assess nodal metastasis; AUC = 0.72. For the subgroups, the AUCs for MIXED, CLINICAL, and GEX predictors ranged from 0.66 to 0.72, 0.65 to 0.73, and 0.58 to 0.67, respectively. Enriched proliferation metagene and luminal B features were noticed in node-positive ER⁺HER2^- and HER2⁺ tumors, while upregulated basal-like features were observed in node-negative TNBC tumors. The SLNB reduction rates in patients with ER⁺HER2^- tumors were 6% to 7% higher for the MIXED predictor compared with the CLINICAL predictor accepting false negative rates of 5% to 10%.

CONCLUSIONS

Although CLINICAL and MIXED predictors of nodal metastasis had comparable accuracy, the MIXED predictor identified more node-negative patients. This translational approach holds promise for development of classifiers to reduce the rates of SLNB for patients at low risk of nodal involvement.

Microenvironmental control of breast cancer subtype elicited through paracrine platelet-derived growth factor-CC signaling

Breast tumors of the basal-like, hormone receptor-negative, subtype remain an unmet clinical challenge, as patients exhibit a high rate of recurrence and poor survival. Co-evolution of the malignant mammary epithelium and its underlying stroma instigates cancer-associated fibroblasts (CAFs) to endorse most, if not all, hallmarks of cancer progression. Here, we delineate a previously unappreciated role for CAFs as determinants of the molecular subtype of breast cancer. We identified a paracrine cross-talk between cancer cells expressing platelet-derived growth factor (PDGF)-CC and CAFs expressing the cognate receptors in human basal-like mammary carcinomas. Genetic or pharmacological intervention with PDGF-CC activity in mouse models of cancer resulted in conversion of basal-like breast cancers into a hormone receptor-positive state that conferred sensitivity to endocrine therapy in previously impervious tumors. We conclude that specification of the basal-like subtype of breast cancer is under microenvironmental control and therapeutically actionable in order to achieve sensitivity to endocrine therapy.

Clinical Value of RNA Sequencing-Based Classifiers for Prediction of the Five Conventional Breast Cancer Biomarkers: A Report From the Population-Based Multicenter Sweden Cancerome Analysis Network-Breast Initiative

Purpose

In early breast cancer (BC), five conventional biomarkers-estrogen receptor (ER), progesterone receptor (PgR), human epidermal growth factor receptor 2 (HER2), Ki67, and Nottingham histologic grade (NHG)-are used to determine prognosis and treatment. We aimed to develop classifiers for these biomarkers that were based on tumor mRNA sequencing (RNA-seq), compare classification performance, and test whether such predictors could add value for risk stratification.

Methods

In total, 3,678 patients with BC were studied. For 405 tumors, a comprehensive multi-rater histopathologic evaluation was performed. Using RNA-seq data, single-gene classifiers and multigene classifiers (MGCs) were trained on consensus histopathology labels. Trained classifiers were tested on a prospective population-based series of 3,273 BCs that included a median follow-up of 52 months (Sweden Cancerome Analysis Network-Breast [SCAN-B], ClinicalTrials.gov identifier: NCT02306096), and results were evaluated by agreement statistics and Kaplan-Meier and Cox survival analyses.

Results

Pathologist concordance was high for ER, PgR, and HER2 (average κ, 0.920, 0.891, and 0.899, respectively) but moderate for Ki67 and NHG (average κ, 0.734 and 0.581). Concordance between RNA-seq classifiers and histopathology for the independent cohort of 3,273 was similar to interpathologist concordance. Patients with discordant classifications, predicted as hormone responsive by histopathology but non-hormone responsive by MGC, had significantly inferior overall survival compared with patients who had concordant results. This extended to patients who received no adjuvant therapy (hazard ratio [HR], 3.19; 95% CI, 1.19 to 8.57), or endocrine therapy alone (HR, 2.64; 95% CI, 1.55 to 4.51). For cases identified as hormone responsive by histopathology and who received endocrine therapy alone, the MGC hormone-responsive classifier remained significant after multivariable adjustment (HR, 2.45; 95% CI, 1.39 to 4.34).

Conclusion

Classification error rates for RNA-seq-based classifiers for the five key BC biomarkers generally were equivalent to conventional histopathology. However, RNA-seq classifiers provided added clinical value in particular for tumors determined by histopathology to be hormone responsive but by RNA-seq to be hormone insensitive.

Abstract P3-02-02: Concordance between immunohistochemical and gene-expression based subtyping of early breast cancer using core needle biopsies and surgical specimens - experices from SCAN-B

Introduction: Preoperative chemotherapy in early breast cancer increases the rate of breast preservation and provides prognostic information. Treatment decisions in these cases rely on biomarker assessments and subtyping from tissue acquired through core needle biopsies. Tumor heterogeneity and representativity are pit-falls when limited tissue is available. Biomarker expression may change considerably as a result of preoperative chemotherapy, and in a subset of cases a complete pathological response at time of surgery may even preclude any further assessment. Therefore, the reliability and reproducibility of biomarkers in base-line core biopsies are of utmost importance for patients treated with preoperative chemotherapy.

Material and Methods: In an ongoing population-based study of early breast cancer, the SCAN-B (NCT02306096), patients were identified for whom an ultra-sound guided core needle biopsy was analyzed for biomarkers during primary clinical work-up and the patient was offered primary surgery as initial treatment. Clinical biomarker profiles including immunohistochemical (IHC) determinations of ER, PgR, HER2 and Ki67 were translated to subtypes according to modified St Gallen criteria (2013) and compared with paired samples from surgical specimens. In addition, tumor specimens for biomolecule extraction and RNA sequencing were collected fresh in RNAlater.

Results: IHC data was available from 51 paired samples. The subtype distribution in core needle biopsies was DCIS in 1 case (2 %), LCIS in 1 case (2 %) Luminal A-like in 16 cases (31 %), Luminal B-like (HER2 negative) in 26 cases (51 %), Luminal B-HER2-like (HER2 positive) in 4 cases (8 %), HER2-positive (non-luminal) in 1 case (2 %) and triple negative (ductal) breast cancer in 2 cases (4 %). The subtype distribution in surgical specimens was DCIS in 0 case (0 %), LCIS in 1 case (2 %) Luminal A-like in 18 cases (35 %), Luminal B-like (HER2negative) in 23 cases (45 %), Luminal B--like (HER2 positive) in 6 cases (12 %), HER2-positive (non-luminal) in 1 case (2 %) and triple negative (ductal) breast cancer in 2 cases (4 %). Notably, 5/16 cases classified as Luminal A-like in the core needle biopsy were reclassified as Luminal B-like (HER2-negative) in the surgical specimen, whereas 9/26 cases classified as Luminal B-like (HER2-negative) in the core needle biopsy were reclassified as either Luminal A-like (7 cases) or Luminal B-like (HER2 positive) (2 cases) in the surgical specimen. In all instances, except one, transition between Luminal A-like and Luminal B-like was due to recorded Ki67 expression. One case that was classified as a DCIS in the core needle was reclassified as Luminal B-like (HER2 negative) at time of surgery.

Discussion: In this limited material, discordance between evaluations regarding Luminal A-like and Luminal B-like was considerable. Especially the misclassification of primary HER2-positive breast cancer needs further evaluation. These findings may be caused by tumor heterogeneity, and highlight the risk of both over- and under-treatment upon biomarker assessment from core needle biopsies. Data from gene expression based subtype classifications will be presented during the meeting.

Citation Format: Morgan G, Larsson C, Tahin B, Vallon-Christersson J, Häkkinen J, Ehinger A, Malmberg M, Hegardt C, Borg Å, Rydén L, Saal LH, Hedenfalk I, Loman N. Concordance between immunohistochemical and gene-expression based subtyping of early breast cancer using core needle biopsies and surgical specimens - experices from SCAN-B [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr P3-02-02.

Abstract P1-06-01: Putting multigene signatures to the test: Prognostic assessment in population-based contemporary clinical breast cancer

Background

Gene expression signatures hold promise for a molecularly driven division of primary breast cancer with clinical implications. A gap still remains in the application/validation of such signatures in actual clinical treatment groups from unselected, population-based, primary breast cancer receiving current standard of care therapy. We analyzed classification proportions and overall survival (OS) of 14 reported gene expression phenotypes (GEPs) and risk predictors (RPs) in seven clinical treatments groups from an 3273-sample breast cancer cohort representative of population-based disease in the South Swedish healthcare region.

Patients and methods

Between 2010-09-01 to 2015-03-31, 5101 (87%) of 5892 patients with invasive primary disease in the healthcare region were included in the SCAN-B study (ClinicalTrials.gov ID: NCT02306096). Inclusion criteria included no generalized/prior contralateral disease and known surgery/treatment status (neo- or adjuvant). 3273 tumors were profiled by RNA sequencing and matched to clinicopathological patient data from the National Breast Cancer Register, with distribution of clinicopathological characteristics reflecting proportions in the catchment region. RNA profiles were classified according to 14 reported gene signatures featuring both GEPs (PAM50, IC10, CIT, TNBCtype) and specific risk predictors (e.g. Oncotype Dx, 70-gene, 76-gene, ROR-variants, genomic grade index). Classifications were investigated for association with patient OS by univariate and multivariate analyses in seven adjuvant clinical treatment groups: TNBC-ACT (adjuvant chemotherapy, n=228), TNBC-untreated (n=83), HER2+/ER- with trastuzumab + ACT treatment (n=101), HER2+/ER+ with trastuzumab + ACT + endocrine treatment (n=210), ER+/HER2- with endocrine treatment (n=1477), ER+/HER2- with endocrine + ACT treatment (n=637), and ER+/HER2- untreated (n=216).

Results

For the majority of signatures, analysis of classification demonstrated prognostic value limited to ER+/HER2- tumors given follow-up time. Several signatures (including Oncotype Dx, 70-gene, ROR-variants) showed strong predictive value in identifying a subset of ER+/HER2- patients receiving a combination of endocrine and ACT therapy with excellent overall survival (&gt;96%), indicating appropriate therapy selection. In addition, for both ER+/HER2- treatment groups signature analysis identified high-risk groups of patients in clear need of additional treatment beyond standard therapeutic regimes, even with less than 5-years of follow-up.

Conclusions

Our results support the prognostic association of gene expression signatures in large unselected population-based primary breast cancer cohorts even with a short follow-up of OS.Importantly, prognostic associations are limited to specific subgroups for different classifiers and in population-based breast cancer some clinically important subgroups constitute a small proportion of cases. In this context, continued population-based inclusion and broad transcriptional profiling of breast cancer patients provides an opportunity for application to broader patient groups (e.g. TNBC and HER2+), and for consensus classification of individual risk assessments that could potentially provide more stable predictions.

Citation Format: Staaf J, Vallon-Christersson J, Häkkinen J, Saal LH, Hegardt C, Larsson C, Ehinger A, Ryden L, Loman N, Malmberg M, Borg Å. Putting multigene signatures to the test: Prognostic assessment in population-based contemporary clinical breast cancer [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr P1-06-01.

Abstract PD2-08: Predictors of axillary nodal metastasis based on gene expression and clinicopathological characteristics: Data from a population-based prospective study, the Sweden Cancerome Analysis Network – Breast (SCAN-B)

Background

Gene expression patterns show promise in estimating prognosis and directing adjuvant therapy, but its significance in guiding axillary treatment is sparsely evaluated. We aimed to identify predictors for nodal status based on gene expression patterns alongside clinicopathological characteristics, and to validate the performances as well as the prognostic importance of the predictors in a population-based context.

Material and Methods

The study assigned consecutive patients with primary breast cancer enrolled in the SCAN-B study (ClinicalTrials.gov ID: NCT02306096)in South Sweden between September 2010-March 2015. Exclusion criteria were: prior breast cancer, neoadjuvant therapy or unknown nodal status after surgical staging. Data on age, tumour size, multifocality, vascular invasion, NHG and ER/PR/HER2 status were retrieved. 3026 patients were successfully profiled by RNA sequencing (RNA-seq) forming the study analysis cohort. Patients enrolled during 2011 (n=1206) were excluded from predictor training/test sets and kept as an independent validation set. Seven machine-based learning algorithms were evaluated for all samples and for each of the molecular subtypes based on routine analysis: ER+/HER2-, HER2+ and TNBC. Primary outcome was discrimination (AUC) for N0/N+ based on either clinicopathological parameters, RNA-seq data or mixed data. Secondary outcome was to evaluate the prognostic value of the predictors. Kaplan-Meier estimates were used to portray univariate survival data in subgroups stratified by nodal status.

Results

The Swedish National Quality Registry for Breast Cancer revealed 5235 patients eligible for study inclusion, of which 89% were enrolled in the SCAN-B study. Distribution of clinicopathological characteristics for the 3026 RNA-sequenced patients reflected features in the catchment region, and were similar for the training/test sets (n = 1820) as well as the validation set (n = 1206). Mean AUCs from 10 iterative assessments in the training/test sets identified Generalized Boosted Regression Models having the highest performance. AUCs for clinicopathological predictors in the validation set were 0.73, 0.75, 0.71 and 0.66 for all samples, ER+/HER2-, HER2+ and TNBC, respectively. Corresponding AUCs for gene expression predictors were 0.66, 0.66, 0.62 and 0.57, respectively, while the best predictive performances were achieved with mixed predictors, revealing AUCs 0.75, 0.75, 0.78 and 0.68, respectively. Preliminary results indicated prognostic value of the predictors; patients with stated N0 but predicted N+ by the models had worse survival rates. On the contrary, a trend towards better survival was observed for those with stated N+ but predicted N0 by the models.

Conclusions

Subgroup-specific predictors for nodal status based on gene expression data alongside traditional clinicopathological characteristics were developed, and independently validated regarding performance and prognostic value, in a population-based breast cancer cohort. Integrating gene expression data in the preoperative setting may improve decision-making on the required extent of axillary surgery and systemic therapy needed.

Citation Format: Dihge L, Staaf J, Vallon-Christersson J, Hegardt C, Häkkinen J, Borg Å, Rydén L. Predictors of axillary nodal metastasis based on gene expression and clinicopathological characteristics: Data from a population-based prospective study, the Sweden Cancerome Analysis Network – Breast (SCAN-B) [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr PD2-08.

Abstract P2-02-09: Breast cancer subtype distribution and circulating tumor DNA in response to neoadjuvant chemotherapy: Experiences from a preoperative cohort within SCAN-B

Introduction: Preoperative chemotherapy in early breast cancer increases the rate of breast preservation and provides prognostic information. In the case of residual disease, a change in subtypes may be observed. Sensitive and reproducible biomarkers predicting treatment response early during the treatment course are needed in order to better exploit the potential benefit of an individualized preoperative treatment.

Material and Methods: In an ongoing prospective study within the population-based SCAN-B project (NCT02306096), patients undergoing preoperative chemotherapy for early or locally recurrent breast cancer have been treated with iv Epirubicin and Cyclophosphamide q3w x 3 in sequence with either Docetaxel q3w x 3 or Paclitaxel q1w x 9 with a preoperative intent. HER2-positive cases also received HER2-directed treatment. At baseline, patients were staged using sentinel node biopsy for clinically node-negative patients and CT scan for cytologically confirmed node-positive cases. A clinical core needle biopsy as well as tissue from the surgical specimen was collected for determination of conventional biomarkers including ER, PgR, HER2 and Ki67. Tumor biopsies for biomolecule-extraction and RNA-sequencing were taken using ultrasound guidance and collected fresh in RNAlater at baseline, after 2 treatment cycles, as well as at surgery. Blood plasma samples were collected at baseline, after one-, three-, and six- 3w treatment cycles, and post-surgery. Using RNA-sequencing data, somatic mutations were identified in the tumor biopsies and personalized analyses for circulating tumor DNA (ctDNA) were performed. A pathological complete remission (pCR) was defined as the complete disappearance of invasive breast cancer in the breast and axilla at time of definitive surgery. Subtyping was performed using modified St Gallen criteria (2013).

Results: Thus far, 45 patients aged 24-74 years have been included, of which 34 (76 %) were clinical stage 2 and 11 (24%) were stage 3. The subtype distribution at baseline was five Luminal A-like (11 %), 21 Luminal B-like (HER2 negative) (47 %), 8 HER2-positive (18 %) and 11 Triple-negative (ductal) (24 %). The rates of pCR in 38 operated cases to date were 0/3 Luminal A-like, 3/19 Luminal B-like (HER2 negative), 2/8 HER2-positive, and 4/7 Triple-negative (overall 24 % pCR rate). One patient did not undergo surgery due to clinically progressive disease. In 25 cases with evaluable residual disease at surgery, there was a shift in the subtype in 13 (52 %), the majority of which represented a transition from Luminal B to Luminal A. No Triple-negative cases underwent a change in subtype during treatment. Results of the ctDNA analyses will be presented at the meeting.

Discussion: We have established an infrastructure allowing for an extensive evaluation of preoperative chemotherapy in early breast cancer. The goal is to develop methods to refine response-guided treatment in early breast cancer using molecular responses in the tumor as well as in the blood circulation. The patients continue to be prospectively monitored with iterative ctDNA analyses during follow-up.

Citation Format: Loman N, Chen Y, Aaltonen K, Brueffer C, George AM, Zander L, Vallon-Christersson J, Häkkinen J, Förnvik D, Rigo R, Ehinger A, Malmberg M, Larsson C, Hegardt C, Borg Å, Rydén L, Saal LH. Breast cancer subtype distribution and circulating tumor DNA in response to neoadjuvant chemotherapy: Experiences from a preoperative cohort within SCAN-B [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr P2-02-09.

Abstract P4-09-03: On the development and clinical value of RNA-sequencing-based classifiers for prediction of the five conventional breast cancer biomarkers: A report from the population-based multicenter SCAN-B study

Background:

In early breast cancer, five histopathological biomarkers are part of current clinical routines and used for determining prognosis and treatment: estrogen receptor (ER), progesterone receptor (PgR), human epidermal growth factor receptor 2 (ERBB2/HER2), Ki67, and Nottingham histological grade (NHG). We aimed to develop classifiers for these biomarkers based on tumor mRNA-sequencing (RNA-seq), compare classification performance to conventional histopathology, and test whether RNA-seq-based predictors could add value for patient risk-stratification.

Patients and Methods:

In total, 3678 breast tumors were studied. For 405 breast tumors in the training cohort, a comprehensive histopathological biomarker evaluation was performed by three pathology readings to estimate inter-pathologist variability on the original diagnostic slides as well as on repeat immunostains for this study, and the consensus biomarker status for all five conventional biomarkers was determined. Whole transcriptome gene expression profiling was performed by RNA-sequencing on the Illumina platform. Using RNA-seq-derived tumor gene expression data as input, single-gene classifiers (SGC) and multi-gene classifiers (MGC) were trained on the consensus pathology biomarker labels. The trained classifiers were tested on an independent prospective population-based series of 3273 primary breast cancer cases from the multicenter SCAN-B study with median 41 months follow-up (ClinicalTrials.gov identifier NCT02306096), and classifications were evaluated by agreement statistics and by Kaplan-Meier and Cox regression survival analyses.

Results:

For the histopathological evaluation, pathologist evaluation concordance was high for ER, PgR, and HER2 (average kappa values of .920, .891, and .899, respectively), but moderate for Ki67 and NHG (.734 and .581). Classification concordance between RNA-seq classifiers and histopathology for the independent 3273-cohort was similar to that within histopathology assessments, with SGCs slightly outperforming MGCs. Importantly, patients with discordant results, classified as hormone responsive (HoR+) by histopathology but non-hormone responsive by MGC, presented with significantly inferior overall survival compared to patients with concordant results. These results extended to patients with no adjuvant systemic therapy (hazard ratio, HR, 4.54; 95% confidence interval, CI, 1.42-14.5), endocrine therapy alone (HR 3.46; 95% CI, 2.01-5.95), or receiving chemotherapy (HR 2.57; 95% CI 1.13-5.86). For HoR+ cases receiving endocrine therapy alone, the MGC HoR classifier remained significant after multivariable adjustment (HR 3.14; 95% CI, 1.75-5.65).

Conclusions:

RNA-seq-based classifiers for the five key early breast cancer biomarkers were generally equivalent to conventional histopathology with regards to classification error rate. However, when benchmarked using overall survival, our RNA-seq classifiers provided added clinical value in particular for cases that are determined by histopathology to be hormone-responsive but by RNA-seq appear hormone-insensitive and have a significantly poorer outcome when treated with endocrine therapy alone.

Citation Format: Brueffer C, Vallon-Christersson J, Grabau D, Ehinger A, Häkkinen J, Hegardt C, Malina J, Chen Y, Bendahl P-O, Manjer J, Malmberg M, Larsson C, Loman N, Ryden L, Borg Å, Saal LH. On the development and clinical value of RNA-sequencing-based classifiers for prediction of the five conventional breast cancer biomarkers: A report from the population-based multicenter SCAN-B study [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr P4-09-03.

Abstract P1-07-17: The SCAN-B study: 5-year summary of a large-scale population-based prospective breast cancer translational genomics platform covering a wide geography of Sweden (NCT02306096)

Background:

Breast cancer exhibits significant molecular, pathological, and clinical heterogeneity. Current clinicopathological evaluation is imperfect for predicting outcome, which results in overtreatment for many patients, and for others, leads to death from recurrent disease. Therefore, additional criteria are needed to better personalize care and maximize treatment effectiveness and survival. To address these challenges, large-scale population-based studies are needed to develop and evaluate new predictive biomarker tests under real-world conditions.

Methods:

In 2010 we initiated the Sweden Cancerome Analysis Network - Breast (SCAN-B) multicenter prospective study (ClinicalTrials.gov identifier NCT02306096) with longsighted aims to 1) analyze breast cancers with next-generation genomic technologies for translational research in a population-based manner and integrated with healthcare; 2) decipher fundamental tumor biology from these analyses; 3) utilize genomic data to develop and validate new clinically-actionable biomarker assays; and 4) establish real-time clinical implementation of molecular diagnostic and treatment-predictive tests. Eligibility criteria are suspicion or confirmed diagnosis of primary breast cancer. Eligibility will be extended to recurrent breast cancer in late 2016. For all patients, tumor biopsy and/or surgical tumor specimen and baseline blood samples are collected, as well as follow-up blood samples at defined intervals, and clinical data are obtained from regional and national databases. From all samples, DNA, RNA, and protein fractions are isolated, and tissue arrays are constructed. In the first phase, we focus on molecular profiling of tumor tissue by next-generation RNA-sequencing.

Results:

From August 2010 through May 2016, we have consented and enrolled 8,669 patients with primary breast cancer at 9 hospital sites in Sweden, representing approximately 85% of eligible patients in the catchment area. Preoperative blood samples have been collected for 8,288 (96%) patients and primary fresh-frozen tumor specimens collected for 6,129 (71%) patients. All tumors have been RNA-sequenced, and newly enrolled cases are analyzed in “real-time” within an average of 7 days after biopsy/surgery. Herein we describe the study infrastructure and protocols and present initial proof of concept results from prospective RNA-sequencing including tumor molecular subtyping, detection of driver gene mutations, and determination of ER, PgR, HER2, Ki67, and tumor grade from RNA-seq data. Prospective patient enrollment is ongoing and pilot clinical reports are being evaluated at multidisciplinary breast cancer conferences.

Conclusions:

We demonstrate that population-based collection and real-time RNA-sequencing analysis of breast cancer is feasible at large-scale. The SCAN-B Study should significantly reduce the time to discovery, validation, and clinical implementation of novel molecular diagnostic and predictive tests. We welcome the participation of additional comprehensive cancer treatment centers.

Citation Format: Saal LH, Hegardt C, Vallon-Christersson J, Häkkinen J, Ehinger A, Manjer J, Larsson C, Loman N, Rydén L, Malmberg M, Borg Å. The SCAN-B study: 5-year summary of a large-scale population-based prospective breast cancer translational genomics platform covering a wide geography of Sweden (NCT02306096) [abstract]. In: Proceedings of the 2016 San Antonio Breast Cancer Symposium; 2016 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2017;77(4 Suppl):Abstract nr P1-07-17.

Targeted sequencing of BRCA1 and BRCA2 across a large unselected breast cancer cohort suggests that one-third of mutations are somatic

We carried out targeted sequencing of BRCA1/2 in an unselected cohort of patients diagnosed with primary breast cancer within a population without strong founder mutations. Eleven percent of cases harbored a germline or somatic BRCA1/2 mutation, and the ratio of germline versus somatic mutation was 2 : 1. This has implications for treatment, genetic counseling, and interpretation of tumor-only testing.

Background

A mutation found in the BRCA1 or BRCA2 gene of a breast tumor could be either germline or somatically acquired. The prevalence of somatic BRCA1/2 mutations and the ratio between somatic and germline BRCA1/2 mutations in unselected breast cancer patients are currently unclear.

Patients and methods

Paired normal and tumor DNA was analyzed for BRCA1/2 mutations by massively parallel sequencing in an unselected cohort of 273 breast cancer patients from south Sweden.

Results

Deleterious germline mutations in BRCA1 (n = 10) or BRCA2 (n = 10) were detected in 20 patients (7%). Deleterious somatic mutations in BRCA1 (n = 4) or BRCA2 (n = 5) were detected in 9 patients (3%). Accordingly, about 1 in 9 breast carcinomas (11%) in our cohort harbor a BRCA1/2 mutation. For each gene, the tumor phenotypes were very similar regardless of the mutation being germline or somatically acquired, whereas the tumor phenotypes differed significantly between wild-type and mutated cases. For age at diagnosis, the patients with somatic BRCA1/2 mutations resembled the wild-type patients (median age at diagnosis, germline BRCA1: 41.5 years; germline BRCA2: 49.5 years; somatic BRCA1/2: 65 years; wild-type BRCA1/2: 62.5 years).

Conclusions

In a population without strong germline founder mutations, the likelihood of a BRCA1/2 mutation found in a breast carcinoma being somatic was ∼1/3 and germline 2/3. This may have implications for treatment and genetic counseling.

The Sweden Cancerome Analysis Network - Breast (SCAN-B) Initiative: a large-scale multicenter infrastructure towards implementation of breast cancer genomic analyses in the clinical routine

BACKGROUND

Breast cancer exhibits significant molecular, pathological, and clinical heterogeneity. Current clinicopathological evaluation is imperfect for predicting outcome, which results in overtreatment for many patients, and for others, leads to death from recurrent disease. Therefore, additional criteria are needed to better personalize care and maximize treatment effectiveness and survival.

METHODS

To address these challenges, the Sweden Cancerome Analysis Network - Breast (SCAN-B) consortium was initiated in 2010 as a multicenter prospective study with longsighted aims to analyze breast cancers with next-generation genomic technologies for translational research in a population-based manner and integrated with healthcare; decipher fundamental tumor biology from these analyses; utilize genomic data to develop and validate new clinically-actionable biomarker assays; and establish real-time clinical implementation of molecular diagnostic, prognostic, and predictive tests. In the first phase, we focus on molecular profiling by next-generation RNA-sequencing on the Illumina platform.

RESULTS

In the first 3 years from 30 August 2010 through 31 August 2013, we have consented and enrolled 3,979 patients with primary breast cancer at the seven hospital sites in South Sweden, representing approximately 85% of eligible patients in the catchment area. Preoperative blood samples have been collected for 3,942 (99%) patients and primary tumor specimens collected for 2,929 (74%) patients. Herein we describe the study infrastructure and protocols and present initial proof of concept results from prospective RNA sequencing including tumor molecular subtyping and detection of driver gene mutations. Prospective patient enrollment is ongoing.

CONCLUSIONS

We demonstrate that large-scale population-based collection and RNA-sequencing analysis of breast cancer is feasible. The SCAN-B Initiative should significantly reduce the time to discovery, validation, and clinical implementation of novel molecular diagnostic and predictive tests. We welcome the participation of additional comprehensive cancer treatment centers.

TRIAL REGISTRATION

ClinicalTrials.gov identifier NCT02306096.

Molecular and genetic diversity in the metastatic process of melanoma

Diversity between metastatic melanoma tumours in individual patients is known; however, the molecular and genetic differences remain unclear. To examine the molecular and genetic differences between metastatic tumours, we performed gene-expression profiling of 63 melanoma tumours obtained from 28 patients (two or three tumours/patient), followed by analysis of their mutational landscape, using targeted deep sequencing of 1697 cancer genes and DNA copy number analysis. Gene-expression signatures revealed discordant phenotypes between tumour lesions within a patient in 50% of the cases. In 18 of 22 patients (where matched normal tissue was available), we found that the multiple lesions within a patient were genetically divergent, with one or more melanoma tumours harbouring 'private' somatic mutations. In one case, the distant subcutaneous metastasis of one patient occurring 3 months after an earlier regional lymph node metastasis had acquired 37 new coding sequence mutations, including mutations in PTEN and CDH1. However, BRAF and NRAS mutations, when present in the first metastasis, were always preserved in subsequent metastases. The patterns of nucleotide substitutions found in this study indicate an influence of UV radiation but possibly also DNA alkylating agents. Our results clearly demonstrate that metastatic melanoma is a molecularly highly heterogeneous disease that continues to progress throughout its clinical course. The private aberrations observed on a background of shared aberrations within a patient provide evidence of continued evolution of individual tumours following divergence from a common parental clone, and might have implications for personalized medicine strategies in melanoma treatment.

Molecular microheterogeneity of prostate specific antigen in seminal fluid by mass spectrometry

OBJECTIVES

Prostate specific antigen (PSA) is a widely used and clinically valuable marker for prostate disease. In order to enable the development of new PSA assays and progress the understanding of the biology of PSA we have analyzed PSA in seminal plasma.

DESIGN AND METHODS

PSA in seminal plasma from men attending a fertility clinic and healthy controls was analyzed using SDS-PAGE, Western blotting and mass spectrometry.

RESULTS

Using mass spectrometry, different forms of PSA could be identified in 1-9 bands seen on SDS-PAGE analysis of the respective sample. However, a majority of these molecular forms of PSA were not observed on Western blots. Enzymatic activity of PSA isoforms was demonstrated by sequencing data in zymogram gels. Multivariate analysis of clinical data revealed well-separated patient groups.

CONCLUSIONS

We demonstrated that PSA in seminal plasma occurs in several isoforms, yet not all were detectable using an antibody based clinical routine method. The heterogeneity of PSA expression might be of clinical significance, by an improved patient phenotyping.

GOBO: gene expression-based outcome for breast cancer online

Microarray-based gene expression analysis holds promise of improving prognostication and treatment decisions for breast cancer patients. However, the heterogeneity of breast cancer emphasizes the need for validation of prognostic gene signatures in larger sample sets stratified into relevant subgroups. Here, we describe a multifunctional user-friendly online tool, GOBO (http://co.bmc.lu.se/gobo), allowing a range of different analyses to be performed in an 1881-sample breast tumor data set, and a 51-sample breast cancer cell line set, both generated on Affymetrix U133A microarrays. GOBO supports a wide range of applications including: 1) rapid assessment of gene expression levels in subgroups of breast tumors and cell lines, 2) identification of co-expressed genes for creation of potential metagenes, 3) association with outcome for gene expression levels of single genes, sets of genes, or gene signatures in multiple subgroups of the 1881-sample breast cancer data set. The design and implementation of GOBO facilitate easy incorporation of additional query functions and applications, as well as additional data sets irrespective of tumor type and array platform.

BASE--2nd generation software for microarray data management and analysis

BACKGROUND

Microarray experiments are increasing in size and samples are collected asynchronously over long time. Available data are re-analysed as more samples are hybridized. Systematic use of collected data requires tracking of biomaterials, array information, raw data, and assembly of annotations. To meet the information tracking and data analysis challenges in microarray experiments we reimplemented and improved BASE version 1.2.

RESULTS

The new BASE presented in this report is a comprehensive annotable local microarray data repository and analysis application providing researchers with an efficient information management and analysis tool. The information management system tracks all material from biosource, via sample and through extraction and labelling to raw data and analysis. All items in BASE can be annotated and the annotations can be used as experimental factors in downstream analysis. BASE stores all microarray experiment related data regardless if analysis tools for specific techniques or data formats are readily available. The BASE team is committed to continue improving and extending BASE to make it usable for even more experimental setups and techniques, and we encourage other groups to target their specific needs leveraging on the infrastructure provided by BASE.

CONCLUSION

BASE is a comprehensive management application for information, data, and analysis of microarray experiments, available as free open source software at http://base.thep.lu.se under the terms of the GPLv3 license.

Proteomics Data Collection – The 1st ProDaC workshop 26 April 2007 Ecole Normale Supérieur, Lyon, France

Proteomics Data Collection (ProDaC) is an EU funded "Coordination Action" within the 6(th) framework programme. It aims to simplify the publication, dissemination and utilization of proteomics data by establishing standards that will support broad data collection from the research community. The 1(st) ProDaC workshop 2007 (succeeding the kick-off meeting last year at the HUPO World Congress 2006) took place at the Ecole Normale Supérieur in Lyon, France. These workshops take place as regular meetings on a half-year basis. On Thursday April 26(th) 2007 the progress of the first six months of the project was presented by the leaders of each of the seven work packages.

Proteomic Variation Is as Large within as between Strawberry Varieties

In search for a strawberry (Fragaria ananassa) with low allergen content, we determined the proteomic variation within and between different varieties. Proteomics data were generated by DIGE and proteins identified with MALDI-MS/MS. The amount of the strawberry allergen Fra a 1 varied between different strawberry varieties (CV = 39%). The variation was at the same level, or even slightly larger, due to different growth conditions (CV = 43%). For 153 other proteins, the biological variation was more affected by different growth conditions than by different varieties (mean CV = 52% and 43%, respectively) due to variation in a subset of proteins. Thus, the allergen variation due to growth conditions must be taken into consideration in attempts to obtain a low-allergen strawberry. However, the allergen content was always lower in colorless (white) strawberry varieties than in the red ones. Moreover, of the spots whose expression correlated with the allergen and the color (32 and 68, respectively), only 3 were the same. This implies that these two phenotypic traits are not inseparable, and it may be possible to breed a red strawberry with low amount of allergen.

The Mass Distance Fingerprint: A statistical framework for de novo detection of predominant modifications using high-accuracy mass spectrometry

We describe a statistical measure, Mass Distance Fingerprint, for automatic de novo detection of predominant peptide mass distances, i.e., putative protein modifications. The method's focus is to globally detect mass differences, not to assign peptide sequences or modifications to individual spectra. The Mass Distance Fingerprint is calculated from high accuracy measured peptide masses. For the data sets used in this study, known mass differences are detected at electron mass accuracy or better. The proposed method is novel because it works independently of protein sequence databases and without any prior knowledge about modifications. Both modified and unmodified peptides have to be present in the sample to be detected. The method can be used for automated detection of chemical/post-translational modifications, quality control of experiments and labeling approaches, and to control the modification settings of protein identification tools. The algorithm is implemented as a web application and is distributed as open source software.

Automated reporting from gel-based proteomics experiments using the open source Proteios database application

The assembly of data from different parts of proteomics workflow is often a major bottleneck in proteomics. Furthermore, there is an increasing demand for the publication of details about protein identifications due to the problems with false-positive and false-negative identifications. In this report, we describe how the open-source Proteios software has been expanded to automate the assembly of the different parts of a gel-based proteomics workflow. In Proteios it is possible to generate protein identification reports that contain all the information currently required by proteomics journals. It is also possible for the user to specify maximum allowed false positive ratios, and reports are automatically generated with the corresponding score cut-offs calculated. When protein identification is conducted using multiple search engines, the score thresholds that correlate to the predetermined error rate are also explicitly calculated for proteins that appear on the result lists of more than one search engine.

Erectile dysfunction influences the subsequent incidence of lower urinary tract symptoms and bother

It is unclear whether lower urinary tract symptoms (LUTS) cause erectile dysfunction (ED) independently or through common underlying pathophysiology and shared risk factors. The aim of this study was to investigate the effect of ED on the incidence of frequency and bother of LUTS. Target population consisted of men aged 50, 60 or 70 years residing in the study area in Finland in 1994. Questionnaires were mailed to 3143 men in 1994 and to 2837 of them 5 years later. The follow-up sample comprised 1683 men who responded to both baseline and follow-up surveys. ED was assessed by two questions on subject's ability to achieve or maintain an erection sufficient for intercourse and LUTS by the Danish Prostatic Symptom Score questionnaire. A dose-response relation was found between the severity of ED at baseline and the incidence of LUTS or bother during follow-up. After adjustment for the confounders, the incidence rate ratio (RR) of LUTS was higher in men with moderate (RR 1.5, 95% confidence interval (CI) 1.0-2.3) or severe ED (RR 2.3, 95% CI 1.4-3.8) than in those free of ED at entry. Compared with men free of ED at baseline, the RRs of urinary bother were 1.6 (95% CI 1.1-2.4), 1.9 (95% CI 1.1-3.2) and 2.2 (95% CI 1.1-4.3) for minimal, moderate or severe ED, respectively. In summary, ED is associated with an increased incidence of LUTS and bother. ED and LUTS may have a common underlying pathophysiology or shared risk factors.

Cardiovascular drug use and the incidence of erectile dysfunction

It is unclear whether high blood pressure per se or antihypertensive drug use causes erectile dysfunction (ED). The aim of this study was to investigate the effect of cardiovascular diseases and their concomitant medications use on the incidence of ED. The target population consisted of men aged 55, 65 or 75 years old residing in the study area in Finland in 1999. Questionnaires were mailed to 2837 men in 1999 and to 2510 of them 5 years later. The follow-up sample consisted of 1665 men (66% of those eligible) who responded to both baseline and follow-up questionnaires. Men free of moderate or severe ED at baseline (N=1000) were included in the study. ED was assessed by two questions on subject ability to achieve or maintain an erection sufficient for intercourse. Poisson regression model was used in the multivariable analyses. The risk of ED was higher in men suffering from treated hypertension or heart disease than in those with the untreated condition. The risk of ED was higher in men using calcium channel inhibitor (adjusted relative risk (RR)=1.6, 95% confidence interval (CI) 1.0-2.4), angiotensin II antagonist (RR=2.2, 95% CI 1.0-4.7), non-selective beta-blocker (RR=1.7, 95% CI 0.9-3.2) or diuretic (RR=1.3, CI 0.7-2.4) compared with non-users. ED was not associated with using organic nitrates, angiotensin-converting enzyme inhibitors, selective beta-blockers and serum lipid-lowering agents. In summary, calcium channel inhibitors, angiotensin II antagonists, non-selective beta-blockers and diuretics may increase the risk of ED.

Improving missing value imputation of microarray data by using spot quality weights

Background

Microarray technology has become popular for gene expression profiling, and many analysis tools have been developed for data interpretation. Most of these tools require complete data, but measurement values are often missing A way to overcome the problem of incomplete data is to impute the missing data before analysis. Many imputation methods have been suggested, some naïve and other more sophisticated taking into account correlation in data. However, these methods are binary in the sense that each spot is considered either missing or present. Hence, they are depending on a cutoff separating poor spots from good spots. We suggest a different approach in which a continuous spot quality weight is built into the imputation methods, allowing for smooth imputations of all spots to larger or lesser degree.

Results

We assessed several imputation methods on three data sets containing replicate measurements, and found that weighted methods performed better than non-weighted methods. Of the compared methods, best performance and robustness were achieved with the weighted nearest neighbours method (WeNNI), in which both spot quality and correlations between genes were included in the imputation.

Conclusion

Including a measure of spot quality improves the accuracy of the missing value imputation. WeNNI, the proposed method is more accurate and less sensitive to parameters than the widely used kNNimpute and LSimpute algorithms.

Effect of nonsteroidal anti-inflammatory drug use on the incidence of erectile dysfunction

PURPOSE

We estimated the effect of nonsteroidal anti-inflammatory drug use on the incidence of erectile dysfunction.

MATERIALS AND METHODS

The target population consisted of men 50, 60 or 70 years old residing in the study area in Finland in 1994. Questionnaires were mailed to 3,143 men in 1994 and to 2,864 men 5 years later. The followup sample consisted of 1,683 men who responded to baseline and followup questionnaires. We estimated the effect of NSAIDs on the incidence of ED in men free from moderate or complete ED at baseline (in 1,126). ED was assessed by 2 questions on subject ability to achieve or maintain an erection sufficient for intercourse. Confounding was assessed by stratification and by adjustment in multivariate Poisson regression model.

RESULTS

The incidence of ED was 93 cases per 1,000 person-years in men who used and 35 in those who did not use NSAIDs. Among men with arthritis, the most common indication for NSAID use, ED incidence was 97 cases per 1,000 in those using and 52 in men who did not use NSAIDs. Compared with men who did not use NSAIDs and were free from arthritis, the relative risk of ED after controlling for the effects of age, smoking, and other medical conditions and medications was higher in men who used NSAIDs but were free of arthritis (IDR 2.0, 95% CI 1.2-3.5) and in those who used NSAIDs and had arthritis (IDR 1.9, 95% CI 1.2-3.1). The relative risk was only somewhat higher in men who had arthritis but did not use NSAIDs (IDR 1.3, 95% CI 0.9-1.8).

CONCLUSIONS

The use of nonsteroidal anti-inflammatory drugs increases the risk of ED and the effect is independent of indication.

Detection and Identification of Protein Isoforms Using Cluster Analysis of MALDI−MS Mass Spectra

We describe an approach to screen large sets of MALDI-MS mass spectra for protein isoforms separated on two-dimensional electrophoresis gels. Mass spectra are matched against each other by utilizing extracted peak mass lists and hierarchical clustering. The output is presented as dendrograms in which protein isoforms cluster together. Clustering could be applied to mass spectra from different sample sets, dates, and instruments, revealed similarities between mass spectra, and was a useful tool to highlight peptide peaks of interest for further investigation. Shared peak masses in a cluster could be identified and were used to create novel peak mass lists suitable for protein identification using peptide mass fingerprinting. Complex mass spectra consisting of more than one protein were deconvoluted using information from other mass spectra in the same cluster. The number of peptide peaks shared between mass spectra in a cluster was typically found to be larger than the number of peaks that matched to calculated peak masses in databases, thus modified peaks are probably among the shared peptides. Clustering increased the number of peaks associated with a given protein.

Are questions on both achieving and maintaining an erection needed to define erectile dysfunction?

Our aim was to ascertain if the assessment of erectile dysfunction (ED) should include questions on difficulty in both achieving and maintaining erection. A population-based study of 3143 men in Tampere region in Finland was conducted by mailed questionnaire. The 1983 men who responded questions on erectile function were included in the analysis. Different levels in the severity of ED were used to examine the agreement between the two questions. Consistency was measured by kappa coefficient. Consistency was fairly high (kappa=0.71) when the severity of ED was examined in four groups. In those cases where alternative responses were categorized into three groups, consistency was best (kappa=0.86) when the alternatives 'never' and 'sometimes' were combined. Consistency was almost perfect (kappa=0.97) when ED dichotomized into two groups and the cutoff was set between the alternatives 'quite often' and 'intercourse does succeed'. The kappa index was 0.86 with the cutoff level between 'sometimes' and 'quite often' and 0.67 with the cutoff between 'never' and 'sometimes'. When ED was examined in different age groups, the consistency was the highest among the oldest respondents. Responses on both symptoms are needed to distinguish between normal erectile function and minimal ED, and also between minimal and moderate ED, especially in young men. When the aim is to distinguish between complete ED and a milder form, the information from one question is the same as that from two questions.