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• Functional Mapping of AKT Signaling and Biomarkers of Response From the FAIRLANE Trial of Neoadjuvant Ipatasertib Plus Paclitaxel for Triple-Negative Breast Cancer

  Purpose: Despite extensive genomic and transcriptomic profiling, it remains unknown how signaling pathways are differentially activated and how tumors are differentially sensitized to certain perturbations. Here, we aim to characterize AKT signaling activity and its association with other genomic or immunohistochemistry-based PI3K/AKT pathway biomarkers as well as the clinical activity of ipatasertib (AKT inhibitor) in the FAIRLANE trial. Experimental Design: In FAIRLANE, 151 patients with early triple-negative breast cancer were randomized 1:1 to receive paclitaxel with ipatasertib or placebo for 12 weeks prior to surgery. Adding ipatasertib did not increase pathologic complete response rate and numerically improved overall response rate by magnetic resonance imaging (MRI). We used reverse-phase protein microarrays (RPPA) to examine the total level and/or phosphorylation states of over 100 proteins in various signaling or cell processes including PI3K/AKT and mTOR signaling. 125 baseline and 127 on-treatment samples were evaluable by RPPA, with 110 paired samples at both time points. Results: Tumors with genomic/protein alterations in PIK3CA/AKT1/PTEN were associated with higher levels of AKT phosphorylation. In addition, phosphorylated(p)AKT levels exhibited a significant association with enriched clinical benefit of ipatasertib, and identified patients who received benefit in the absence of PIK3CA/AKT1/PTEN alterations. Ipatasertib treatment led to a down-regulation of AKT/mTORC1 signaling, which was more pronounced among the tumors with PIK3CA/AKT1/PTEN alterations or among the responders to the treatment. Conclusions: We showed that the high baseline pAKT levels are associated with the alterations of PI3K/AKT pathway components and enriched benefit of ipatasertib in TNBC.

  Study EGAS00001005892

• Dataset of scRNAseq from 56 CRC, treatment-naive and post-chemotherapy

  The dataset includes FASTQ source files of scRNA-seq for 56 CRC patients, sequenced with Illumina 10x platform. All patients were diagnosed with colorectal cancer between 2014 and 2022 at the Gustave Roussy Institute. Cohort includes 32 samples from untreated patients and 24 samples from patients who received chemotherapy. Chemotherapy included FOLFOX or FOLFIRI as first line, in some cases in combination with anti-VEGF or anti-EGRF drugs (avastin, bevacizumab or cetuximab) as the second and third lines. The post-treatment cohort included 18 responders (partial response, stable disease and complete response) and 6 progressors (progressive disease).

  Dataset EGAD50000001218

• Primary breast tumor heterogeneity through therapy

  We performed whole-exome sequencing on multiple regions (n=2-3) from four primary untreated breast tumors (n=1 HER2+, n=2 ER+/HER2-, n=1 triple-negative), as well as matched normal. We also performed whole-exome sequencing on one region from the pre-treatment diagnostic core biopsy and multiple regions (n=2-6) from the post-treatment surgical specimen for five HER2+ primary breast tumors, as well as matched normal; all were treated with combination chemotherapy and trastuzumab. Analysis of these specimens allows characterization of breast tumor heterogeneity and clonal evolution.

  Study EGAS00001003168

• Profiling of gene expression and epigenomics in the fetal brain

  scRNA-seq and Cut&Tag from a sample of the human fetal brain. To compare neurogenesis in the primary human brain with that in organoids, we profiled transcriptome and chromatin accessibility (10X multiome) as well as bulk Cut&Tag of histone modifications H3K27me3, H3K27ac and H3K4me3. We used a human brain sample from GW 19 for all experiments.

  Dataset EGAD50000000225

• Whole exome data from PMID27216186

  This dataset comprises complete exome data from from the study PMID27216186 (Harbst & Lauss et al, Cancer Research 2016). These data are from 49 samples (tumor and matched normal) from 8 patients representing multi-region sequencing of human melanoma. Files are in the BAM format and contain aligned and processed data used for e.g. somatic variant calling. The sequencing libraries were constructed using SureSelect target enrichment with Clinical Research Exome Panel (Agilent) and sequenced on a HiSeq2500 (Illumina).

  Dataset EGAD00001008149

• Tumor microenvironment study of ovarian granulosa cell tumors

  Adult-type granulosa cell tumors (aGCT) are rare ovarian sex cord tumors with few 41 effective treatments for recurrent disease. The objective of this study was to compare global gene expression profiles and characterize the tumor microenvironment (TME) of primary and recurrent aGCTs in order to identify correlates of disease recurrence. Total RNA sequencing was performed on 24 pathologically confirmed, cryopreserved aGCT samples, including 8 primary and 16 recurrent tumors. A total of 31 differentially expressed genes were identified between primary and recurrent aGCT. TME analysis demonstrated significantly higher fractions of neutrophils and macrophages in recurrent tumors in conjunction with significantly lower fractions of cancer-associated fibroblasts and endothelial cells.

  Study EGAS00001006478

• human CMV-specific CD8+ T cells

  In this study we established a comprehensive workflow to collect multi-omics single-cell data using a commercially available micro-well based platform. This included whole transcriptome, cell surface markers (targeted sequencing-based cell surface proteomics), T cell specificities, adaptive immune receptor repertoire (AIRR) profiles and sample multiplexing. With this technique we identified novel paired T cell receptor sequences for three prominent human CMV epitopes. In addition, we review the ability of dCODE dextramers to detect antigenspecific T cells at low frequencies by estimating sensitivities and specificities when used as reagents for single-cell multi-omics.

  Study EGAS50000000633

• 10x Multiome from Human Fetal Heart

  We hope to uncover the genetic basis of congenital heart disease by examining how genetic variants, especially those in non-coding regions, influence heart development. To achieve this goal, our research team created a single-cell atlas of human fetal heart development. The study population consists of 41 structurally normal fetal hearts spanning 6 to 22 weeks post-conception. We profiled close to 750,000 using single-cell RNA sequencing and ATAC sequencing technologies via the 10x Genomics Multiome platform.The molecular technologies employed allowed for simultaneous analysis of gene expression and chromatin accessibility at the single-cell level. This approach identified numerous distinct cell types and states within the developing heart, offering detailed insights into gene activity and enhancer usage during critical stages of heart development.Principal findings include the characterization of diverse cell populations and the identification of specific gene expression patterns and regulatory elements active during heart formation. This high-resolution atlas provides valuable insights into how genetic variants may disrupt normal heart development, particularly those in non-coding regions that affect gene regulation.

  Study phs003778

• High-coverage whole-genome sequencing reveals structural variations in triple-negative breast cancer

  Triple-negative breast cancer (TNBC) cells do not express estrogen receptors, progesterone receptors or human epidermal growth factor receptor 2 (HER2). Currently, apart from poly ADP-ribose polymerase (PARP) inhibitors, there are few effective therapeutic options for this cancer type. Here, we present a comprehensive characterization of genetic alterations in TNBC via high-coverage whole-genome sequencing along with transcriptomic and whole-exome sequencing. Silencing of BRCA1 gene impaired the homologous recombination (HR) pathway in a subset of TNBC, which exhibited similar phenotypes with tumors with BRCA1 mutations; they harbored many structural variations (SVs) with relative enrichment for tandem duplication. Clonal analysis suggested that TP53 mutations and methylation of CpG dinucleotides in the BRCA1 promoter were the early events of carcinogenesis. SVs were associated with driver oncogenic events such as amplification of MYC, NOTCH2 or NOTCH3 and impacted tumor suppressor genes including RB1, PTEN and KMT2C. We also identified a variety of oncogenes that could transform 3T3 mouse fibroblast cells, suggesting that individual TNBC tumors may undergo a unique driver event that can be targetable. Thus, we revealed several features of TNBC that had important clinical implications.

  Study JGAS000095

• Precise reconstruction of the tumor microenvironment using bulk RNA-seq and a unique machine learning-based algorithm trained on artificial transcriptomes

  Cellular deconvolution algorithms virtually reconstruct tissue composition by analyzing the gene expression of complex tissues. Here, we present the decision tree machine learning algorithm, Kassandra, trained on a broad collection of > 9,400 tissue and blood sorted cell RNA profiles to accurately reconstruct the tumor microenvironment (TME). Bioinformatic correction for technical and biological variability, aberrant cancer cell expression inclusion, and the accurate quantification and normalization of transcript expression increased the stability and robustness of Kassandra. Performance was validated on over 4,000 H&E tissue slides and more than 1,000 normal and tumor tissues by comparison with cytometric, immunohistochemical or single-cell RNA-seq measurements. Kassandra accurately deconvolved stromal and immune elements of blood and tumors, revealing the role of the TME in tumor pathogenesis. Digital TME reconstruction revealed that the presence of PD1-positive CD8+ T cells strongly correlated with immunotherapy response and increased the predictive potential of established biomarkers, indicating that Kassandra could potentially be utilized in future clinical applications.

  Dataset EGAD00001008776