Emerging landscape of oncogenic signatures across human cancers

By Giovanni Ciriello, Martin L. Miller, B. A. Aksoy, Y. Şenbabaoğlu, Nikolaus Schultz, Christopher Sander. Nature Genetics, 2013

1,314 citations recorded by the source corpus.

Cancer therapy is challenged by the diversity of molecular implementations of oncogenic processes and by the resulting variation in therapeutic responses. Projects such as The Cancer Genome Atlas (TCGA) provide molecular tumor maps in unprecedented detail. The interpretation of these maps remains a major challenge. Here we distilled thousands of genetic and epigenetic features altered in cancers to ∼500 selected functional events (SFEs). Using this simplified description, we derived a hierarchical classification of 3,299 TCGA tumors from 12 cancer types. The top classes are dominated by either mutations (M class) or copy number changes (C class). This distinction is clearest at the extremes of genomic instability, indicating the presence of different oncogenic processes. The full hierarchy shows functional event patterns characteristic of multiple cross-tissue groups of tumors, termed oncogenic signature classes. Targetable functional events in a tumor class are suggestive of class-specific combination therapy. These results may assist in the definition of clinical trials to match actionable oncogenic signatures with personalized therapies.

Subfields: Cancer Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/aa541b4da41869a35f8a6f1ea18c5f93cea0d9e1

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cancer Research

In Molecular Biology

Citations in the corpus

Precision Oncology: The Road Ahead (2017, methodology): “Some methods search for a “consensus” after clustering patients by each platform separately [37], or cluster with protein-protein interactions [29], or patient similarity networks [80, 81].”
Machine learning and feature selection for drug response prediction in precision oncology applications (2018, background): “For instance, how to deal with the redundancy between the predictive profiles in case of anti-correlations between CNV and somatic point mutations, which are widely observed both in tumor samples and in cancer cell lines (Ciriello et al. 2013; Iorio et al. 2016).”
Cell cycle regulation: p53-p21-RB signaling (2022, background): “According to a data analysis based on 12 tumor types, RB1 somatic mutations or deletions are detected in a large fraction of colorectal, breast, uterine, ovarian, bladder, and lung cancer [6].”
An Integrated Mechanistic Model of Pan-Cancer Driver Pathways Predicts Stochastic Proliferation and Death (2017, background): “TCGA identified pan-cancer driver pathways (Ciriello et al., 2013): multiple RTKs; proliferation and growth (Ras/Raf/MEK/ERK; PI-3K/PTEN/AKT/mTOR); cell cycle; DNA damage.”
The Cancer Genome Atlas (TCGA): an immeasurable source of knowledge (2015, neutral): “indicated the landscape of oncogenic signature [78].”
Development of tumor mutation burden as an immunotherapy biomarker: utility for the oncology clinic (2018, background): “these genetic alterations between individual tumors and between different tumor types [35-38].”
Tumor aneuploidy correlates with markers of immune evasion and with reduced response to immunotherapy (2017, result): “In contrast with a previous report showing that SCNAs are more abundant in tumors with a low mutation burden (33), we found a positive correlation between the number (n) of mutations and SCNA level (Fig.”
Comprehensive genomic characterization of head and neck squamous cell carcinomas (2015, background): “…Kyle Chang12, Jennifer A. Drummond12, Richard A. Gibbs12, Nipun Kakkar12, David Wheeler12, Liu Xi12; Memorial Sloan-Kettering Cancer Center Giovanni Ciriello13, Marc Ladanyi13, William Lee13, Ricardo Ramirez13, Chris Sander13, Ronglai Shen13, Rileen Sinha13, Nils Weinhold13, Barry S. Taylor13, B.…”
The MAPK pathway across different malignancies: A new perspective (2014, background): “Differentiation of targetable driver mutations from genetic and genomic noise and passenger mutations is 1 of the most important goals of genome and epigenome analysis.(2,3) Driver mutations lead to dysregulation of signaling pathways, increasing malignant behavior.”
Genomic and Molecular Landscape of DNA Damage Repair Deficiency across The Cancer Genome Atlas (2018, neutral): “We also found moderate, statistically signiﬁcant positive correlations between mutation burden and SCNA scores, contrary to a previously reported inverse correlation between SCNA and mutation frequency in 12 cancer types (Ciriello et al., 2013).”
Oncotator: cancer variant annotation tool (2015, neutral): “…2011; Banerji et al., 2012; Barbieri et al., 2012; Barretina et al., 2012; Berger et al., 2012; Hodis et al., 2012; Imielinski et al., 2012; Lee et al., 2012; Lohr et al., 2012; Pugh et al., 2012, 2013, 2014; Ciriello et al., 2013; Francis et al., 2013; Lawrence et al., 2013; Ojesina et al., 2013).”
Combining tumor genome simulation with crowdsourcing to benchmark somatic single-nucleotide-variant detection (2015, methodology): “The resulting predictions were compared using recall (equation (1)), precision (equation (2)) and F-score (equation (3)).”
The Landscape of Long Noncoding RNAs in the Human Transcriptome (2015, methodology): “Primer pairs with the following parameters were selected: (1) amplicon length between 80–140 bp (2) primer GC content between 35–65%, and (3) primer length greater than 20 bp.”