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Purpose:Detection of leptomeningeal metastasis is hampered by limited sensitivities of currently used techniques: MRI and cytology of cerebrospinal fluid (CSF). Detection of cell-free tumor DNA in CSF has been proposed as a tumor-specific candidate to detect leptomeningeal metastasis at an earlier stage. The aim of this study was to investigate mutation and aneuploidy status in CSF-derived cell-free DNA (cfDNA) of patients with breast cancer with a clinical suspicion of leptomeningeal metastasis.Experimental Design:cfDNA was isolated from stored remnant CSF and analyzed by targeted next-generation sequencing (NGS; n = 30) and the modified fast aneuploidy screening test-sequencing system (mFAST-SeqS; n = 121). The latter method employs selective amplification of long interspaced nuclear elements sequences that are present throughout the genome and allow for fast and cheap detection of aneuploidy. We compared these results with the gold standard to diagnose leptomeningeal metastasis: cytology.Results:Leptomeningeal metastasis was cytology proven in 13 of 121 patients. Low DNA yields resulted in insufficient molecular coverage of NGS for the majority of samples (success rate, 8/30). The mFAST-SeqS method, successful in 112 of 121 (93%) samples, detected genome-wide aneuploidy in 24 patients. Ten of these patients had cytology-proven leptomeningeal metastasis; 8 additional patients were either concurrently diagnosed with central nervous system metastases by radiological means or developed these soon after the lumbar puncture. The remaining six cases were suspected of leptomeningeal metastasis, but could not be confirmed by cytology or imaging. Aneuploidy was associated with development of leptomeningeal metastasis and significantly worse overall survival.Conclusions:Aneuploidy in CSF-derived cfDNA may provide a promising biomarker to improve timely detection of leptomeningeal metastasis.

Purpose: The MGMT promoter methylation status has been suggested to be predictive for outcome to temozolomide chemotherapy in patients with glioblastoma (GBM). Subsequent studies indicated that MGMT promoter methylation is a prognostic marker even in patients treated with radiotherapy alone, both in GBMs and in grade III gliomas.Experimental Design: To help determine the molecular mechanism behind this prognostic effect, we have conducted genome-wide methylation profiling and determined the MGMT promoter methylation status, 1p19q LOH, IDH1 mutation status, and expression profile on a series of oligodendroglial tumors [anaplastic oligodendrogliomas (AOD) and anaplastic oligoastrocytomas (AOA)] within EORTC study 26951. The series was expanded with tumors of the same histology and treatment from our own archive.Results: Methylation profiling identified two main subgroups of oligodendroglial brain tumors of which survival in the CpG island hypermethylation phenotype (CIMP+) subgroup was markedly better than the survival of the unmethylated (CIMP−) subgroup (5.62 vs. 1.24 years; P < 0.0001). CIMP status correlated with survival, MGMT promoter methylation, 1p19q LOH, and IDH1 mutation status. CIMP status strongly increases the predictive accuracy of survival in a model including known clinical prognostic factors such as age and performance score. We validated our results on an independent data set from the Cancer Genome Atlas (TCGA).Conclusion: The strong association between CIMP status and MGMT promoter methylation suggests that the MGMT promoter methylation status is part of a more general, prognostically favorable genome-wide methylation profile. Methylation profiling therefore may help identify AODs and AOAs with improved prognosis. Clin Cancer Res; 17(22); 7148–55. ©2011 AACR.

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Supplementary Table 2 - PDF file 58K, Comparison of baseline characteristics between patients of the EORTC26951 clinical trial that were included v. not included for genome wide methylation analysis

Supplementary Table 3 - PDF file 61K, Molecular markers associated with methylation status

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Supplementary Table 4 - PDF file 69K, CpGs associated with benefit from adjuvant PCV chemotherapy

Supplementary Table 1 - PDF file 67K, Clinical and molecular characteristics of patients

Correlation between input ng cfDNA in the Oncomine Breast V2 breast panel and the median molecular coverage (i.e., the median number of uniquely

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PDF file - 73KB, Correlation between clinical and molecular markers.

PDF file - 50KB, Comparison of baseline characteristics.

PDF file - 175KB, Multivariate analysis.

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Purpose: The long-term follow-up results from the EORTC-26951 trial showed that the addition of procarbazine, CCNU, and vincristine (PCV) after radiotherapy increases survival in anaplastic oligodendrogliomas/oligoastrocytomas (AOD/AOA). However, some patients appeared to benefit more from PCV treatment than others.Experimental Design: We conducted genome-wide methylation profiling of 115 samples included in the EORTC-26951 trial and extracted the CpG island hypermethylated phenotype (CIMP) and MGMT promoter methylation (MGMT-STP27) status.Results: We first show that methylation profiling can be conducted on archival tissues with a performance that is similar to snap-frozen tissue samples. We then conducted methylation profiling on EORTC-26951 clinical trial samples. Univariate analysis indicated that CIMP+ or MGMT-STP27 methylated tumors had an improved survival compared with CIMP− and/or MGMT-STP27 unmethylated tumors [median overall survival (OS), 1.05 vs. 6.46 years and 1.06 vs. 3.8 years, both P < 0.0001 for CIMP and MGMT-STP27 status, respectively]. Multivariable analysis indicates that CIMP and MGMT-STP27 are significant prognostic factors for survival in presence of age, sex, performance score, and review diagnosis in the model. CIMP+ and MGMT-STP27 methylated tumors showed a clear benefit from adjuvant PCV chemotherapy: the median OS of CIMP+ samples in the RT and RT-PCV arms was 3.27 and 9.51 years, respectively (P = 0.0033); for MGMT-STP27 methylated samples, it was 1.98 and 8.65 years. There was no such benefit for CIMP- or for MGMT-STP27 unmethylated tumors. MGMT-STP27 status remained significant in an interaction test (P = 0.003). Statistical analysis of microarray (SAM) identified 259 novel CpGs associated with treatment response.Conclusions:MGMT-STP27 may be used to guide treatment decisions in this tumor type. Clin Cancer Res; 19(19); 5513–22. ©2013 AACR.

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Supplementary Tables

Supplementary Figure 3 - PDF file 186K, SAM analysis of survival within CIMP+ or CIMP- tumors

Supplementary figure legends - PDF file 23K, Supplementary figure legends

Supplementary figure 1 - PDF file 810K, Comparison of baseline variables on Humanmethylation450 beadchips

Correlation in sample assignment between DASL and RNA-seq.

Supplementary Figure 7 from Intrinsic Gene Expression Profiles of Gliomas Are a Better Predictor of Survival than Histology

Performance of DASL and RNA-seq on RNA isolated from FFPE tissues, correlation with snap frozen samples.

Supplementary Figure 5 from Intrinsic Gene Expression Profiles of Gliomas Are a Better Predictor of Survival than Histology

Supplementary Figure 10 from Intrinsic Gene Expression Profiles of Gliomas Are a Better Predictor of Survival than Histology

Supplementary Tables 1-2 from Gene Expression Profiles Associated with Treatment Response in Oligodendrogliomas

Supplementary Figures 1-3 from Identification of Differentially Regulated Splice Variants and Novel Exons in Glial Brain Tumors Using Exon Expression Arrays

Supplementary Figure 6 from Intrinsic Gene Expression Profiles of Gliomas Are a Better Predictor of Survival than Histology

The results from the randomized phase II BELOB trial provided evidence for a potential benefit of bevacizumab (beva), a humanized monoclonal antibody against circulating VEGF-A, when added to CCNU chemotherapy in patients with recurrent glioblastoma (GBM). In this study, we performed gene expression profiling (DASL and RNA-seq) of formalin-fixed, paraffin-embedded tumor material from participants of the BELOB trial to identify patients with recurrent GBM who benefitted most from beva+CCNU treatment. We demonstrate that tumors assigned to the IGS-18 or “classical” subtype and treated with beva+CCNU showed a significant benefit in progression-free survival and a trend toward benefit in overall survival, whereas other subtypes did not exhibit such benefit. In particular, expression of FMO4 and OSBPL3 was associated with treatment response. Importantly, the improved outcome in the beva+CCNU treatment arm was not explained by an uneven distribution of prognostically favorable subtypes as all molecular glioma subtypes were evenly distributed along the different study arms. The RNA-seq analysis also highlighted genetic alterations, including mutations, gene fusions, and copy number changes, within this well-defined cohort of tumors that may serve as useful predictive or prognostic biomarkers of patient outcome. Further validation of the identified molecular markers may enable the future stratification of recurrent GBM patients into appropriate treatment regimens. Cancer Res; 76(3); 525–34. ©2016 AACR.

Supplementary Figure 8 from Intrinsic Gene Expression Profiles of Gliomas Are a Better Predictor of Survival than Histology

Supplementary Figure 3 from Intrinsic Gene Expression Profiles of Gliomas Are a Better Predictor of Survival than Histology

Gliomas are the most common primary brain tumors with heterogeneous morphology and variable prognosis. Treatment decisions in patients rely mainly on histologic classification and clinical parameters. However, differences between histologic subclasses and grades are subtle, and classifying gliomas is subject to a large interobserver variability. To improve current classification standards, we have performed gene expression profiling on a large cohort of glioma samples of all histologic subtypes and grades. We identified seven distinct molecular subgroups that correlate with survival. These include two favorable prognostic subgroups (median survival, >4.7 years), two with intermediate prognosis (median survival, 1–4 years), two with poor prognosis (median survival

Supplementary Figure 2 from Intrinsic Gene Expression Profiles of Gliomas Are a Better Predictor of Survival than Histology

Supplementary Figure 4 from Intrinsic Gene Expression Profiles of Gliomas Are a Better Predictor of Survival than Histology

Supplementary Table 3 from Gene Expression Profiles Associated with Treatment Response in Oligodendrogliomas

Supplementary Table 3 from Identification of Differentially Regulated Splice Variants and Novel Exons in Glial Brain Tumors Using Exon Expression Arrays

Supplementary Tables 1-6 from Intrinsic Gene Expression Profiles of Gliomas Are a Better Predictor of Survival than Histology

Oligodendrogliomas are a specific subtype of brain tumor of which the majority responds favorably to chemotherapy. In this study, we made use of expression profiling to identify chemosensitive oligodendroglial tumors. Correlation of expression profiles to loss of heterozygosity on 1p and 19q, common chromosomal aberrations associated with response to treatment, identified 376, 64, and 60 differentially expressed probe sets associated with loss of 1p, 19q or 1p, and 19q, respectively. Correlation of expression profiles to the tumors' response to treatment identified 16 differentially expressed probe sets. Because transcripts associated with chemotherapeutic response were identified independent of common chromosomal aberrations, expression profiling may be used as an alternative approach to the tumors' 1p status to identify chemosensitive oligodendroglial tumors. Finally, we correlated expression profiles to survival of the patient after diagnosis and identified 103 differentially expressed probe sets. The observation that many genes are differentially expressed between long and short survivors indicates that the genetic background of the tumor is an important factor in determining the prognosis of the patient. Furthermore, these transcripts can help identify patient subgroups that are associated with favorable prognosis. Our study is the first to correlate gene expression with chromosomal aberrations and clinical performance (response to treatment and survival) in oligodendrogliomas. The differentially expressed transcripts can help identify patient subgroups with good prognosis and those that will benefit from chemotherapeutic treatments. (Cancer Res 2005; 65(24): 11335-44)

Legends for Supplementary Figures S1-S13.

Supplementary Table 1 from Identification of Differentially Regulated Splice Variants and Novel Exons in Glial Brain Tumors Using Exon Expression Arrays

Aberrant splice variants are involved in the initiation and/or progression of glial brain tumors. We therefore set out to identify splice variants that are differentially expressed between histologic subgroups of gliomas. Splice variants were identified using a novel platform that profiles the expression of virtually all known and predicted exons present in the human genome. Exon-level expression profiling was done on 26 glioblastomas, 22 oligodendrogliomas, and 6 control brain samples. Our results show that Human Exon arrays can identify subgroups of gliomas based on their histologic appearance and genetic aberrations. We next used our expression data to identify differentially expressed splice variants. In two independent approaches, we identified 49 and up to 459 exons that are differentially spliced between glioblastomas and oligodendrogliomas, a subset of which (47% and 33%) were confirmed by reverse transcription-PCR (RT-PCR). In addition, exon level expression profiling also identified >700 novel exons. Expression of ∼67% of these candidate novel exons was confirmed by RT-PCR. Our results indicate that exon level expression profiling can be used to molecularly classify brain tumor subgroups, can identify differentially regulated splice variants, and can identify novel exons. The splice variants identified by exon level expression profiling may help to detect the genetic changes that cause or maintain gliomas and may serve as novel treatment targets. [Cancer Res 2007;67(12):5635–8]

Supplementary Figure 9 from Intrinsic Gene Expression Profiles of Gliomas Are a Better Predictor of Survival than Histology

Supplementary Table 2 from Identification of Differentially Regulated Splice Variants and Novel Exons in Glial Brain Tumors Using Exon Expression Arrays

Supplementary Methods from Intrinsic Gene Expression Profiles of Gliomas Are a Better Predictor of Survival than Histology

Background: Spatially resolved transcriptomics is a novel and already highly recognized method that allows RNA sequencing results to be annotated with local tissue phenotypes. The NanoString GeoMx Digital Spatial Profiling (DSP) Platform allows users to collect RNA expression data from manually selected Regions of Interest (ROIs) on FFPE tissue sections. Here, we extensively evaluated data from the DSP platform with its associated pipeline and identify significant background noise interference issues which compromise data interpretation. Alternative and more suitable workflows are presented for correct data analysis. Methods: In this study, 12 paired tumor samples were collected from six glioma patients who underwent two separate resections. For all patients, the first resection was a low grade astrocytoma (WHO grade II or III) and the second resection was a high grade astrocytoma (WHO grade IV). The DSP platform was used to collect expression data of 1,800 genes from 72 ROIs (i.e. 6 per sample). Biological replicates were made of eight tumors from four patients. Gene expression data was normalized with both standard NanoString methods and several alternative methods (e.g. DeSeq2, gamma fit correction and quantile normalization). Weighted Gene Co-expression Network analysis (WGCNA) was used for biological validation. In addition to our own study, six publicly available NanoString DSP datasets were evaluated. Results: Data distributions of all glioma samples, when exposed to standard data processing, were burdened with significant background noise interference. Notably, differences in noise interference were largest between biologically distinct tumor subgroups (i.e. between first and second glioma resections), which was confirmed in replicate experiments. The noise interference patterns were also present in all six publicly available NanoString DSP datasets which will invariably lead to incorrect interpretation of the underlying biology. To correct for noise interference, we tested several normalization methods. The relatively crude quantile normalization method provided the least biased result and showed the highest concordance with bulk RNA sequencing data. To evaluate the biological validity of our alternative approach, we used T cell counts from our tissue regions as an independent parameter, that were quantified using immune fluorescence. Unsupervised WGCNA identified gene clusters enriched for lymphocyte genes that highly correlated with T cell quantities in ROIs, confirming that alternative normalization can extract a biological signal from the DSP platform. Conclusion: The DSP Platform platform suffers from significant noise interference when using standard analysis tools that obscure its results. Here, we revised the workflow and provide an alternative normalization that adequately addresses noise interference and enables correct interpretation of gene expression data. Citation Format: Levi van Hijfte, Marjolein Geurts, Wies R. Vallentgoed, Paul H. Eilers, Peter A. Sillevis Smitt, Reno Debets, Pim J. French. Spatial transcriptomics: Data processing revisited to address noise interference [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1228.