Drivers postprocessing

The intOGen pipeline outputs a ranked list of driver genes for each input cohort. We aimed to create a comprehensive catalog of driver genes per tumor type from all the cohorts included in this version.

Then, we performed a filtering on automatically generated driver gene lists per cohort. This filtering is intended to reduce artifacts from the cohort-specific driver lists, due to e.g. errors in calling algorithms, local hypermutation effects, undocumented filtering of mutations.

We first created a collection of candidate driver genes by selecting either: i) significant non-CGC genes (q-value < 0.05) with at least two significant bidders (methods rendering the genes as significant); ii) significant CGC genes (either q-value < 0.05 or CGC q-value < 0.25) from individual cohorts. All genes that did not fulfill these requirements were discarded.

Additionally, candidate driver genes were further filtered using the following criteria:

  1. We discarded non-expressed genes using TCGA expression data. For tumor types directly mapping to cohorts from TCGA –including TCGA cohorts– we removed non-expressed genes in that tumor type. We used the following criterion for non-expressed genes: genes where at least 80% of the samples showed a negative log2 RSEM. For those tumor types which could not be mapped to TCGA cohorts this filtering step was not done.

  2. We also discarded genes highly tolerant to Single Nucleotide Polymorphisms (SNP) across human populations. Such genes are more susceptible to calling errors and should be taken cautiously. More specifically, we downloaded transcript specific constraints from gnomAD (release 2.1; 2018/02/14) and used the observed-to-expected ratio score (oe) of missense (mys), synonymous (syn) and loss-of-function (lof) variants to detect genes highly tolerant to SNPs. Genes enriched in SNPs (oe_mys > 1.5 or oe_lof > 1.5 or oe_syn > 1.5) with a number of mutations per sample greater than 1 were discarded. Additionally, we discarded mutations overlapping with germline variants (germline count > 5) from a panel of normals (PON) from Hartwig Medical Foundation (

  3. We also discarded genes that are likely false positives according to their known function from the literature. We convened that the following genes are likely false positives: i) known long genes such as TTN, OBSCN, RYR2, etc.; ii) olfactory receptors from HORDE (; download date 2018/02/14); iii) genes not belonging to Tier1 CGC genes lacking literature references according to CancerMine 2 (

  4. We also removed non CGC genes with more than 3 mutations in one sample. This abnormally high number of mutations in a sample may be the result of either a local hypermutation process or cross contamination from germline variants.

  5. Finally we discarded genes whose mutations are likely the result of local hypermutation activity. More specifically, some coding regions might be the target of mutations associated to COSMIC Signature 9 ( which is associated to non-canonical AID activity in lymphoid tumours. In those cancer types were Signature 9 is known to play a significant mutagenic role (i.e., AML, Non-Hodgkin Lymphomas, B-cell Lymphomas, CLL and Myelodysplastic syndromes) we discarded genes where more than 50% of mutations in a cohort of patients were associated with Signature 9.

Candidate driver genes that were not discarded composed the catalog of driver genes.

Classification according to annotation level from CGC

We then annotated the catalog of highly confident driver genes according to their annotation level in CGC 1. Specifically, we created a three-level annotation: i) the first level included driver genes with a reported involvement in the source tumor type according to the CGC; ii) the second group included CGC genes lacking reported association with the tumor type; iii) the third group included genes that were not present in CGC.

To match the tumor type of our analyzed cohorts and the nomenclature/acronyms of cancer types reported in the CGC we manually created a dictionary comprising all the names of tumor types from CGC and cancer types defined in our study, according to the following rules:

  1. All the equivalent terms for a cancer type reported in the CGC using the Somatic Tumor Type field (e.g. “breast”, “breast carcinoma”, “breast cancer”), were mapped into the same tumor type.

  2. CGC terms with an unequivocal mapping into our cancer types were automatically linked (e.g., “breast” with “BRCA”).

  3. CGC terms representing fine tuning classification of a more prevalent cancer type that did not represent an independent cohort in our study, were mapped to their closest parent tumor type in our study (e.g., “malignant melanoma of soft parts” into “cutaneous melanoma” or “alveolar soft part sarcoma” into “sarcoma”).

  4. Adenomas were mapped to carcinomas of the same cell type (e.g.,”hepatic adenoma” into “hepatic adenocarcinoma”, “salivary gland adenoma” into “salivary gland adenocarcinoma”).

  5. CGC parent terms mapping to several tumor types from our study were mapped to each of the potential child tumor types. For instance, the term “non small cell lung cancer” was mapped to “LUAD” (lung adenocarcinoma) and “LUSC” (lung squamous cell carcinoma).

  6. Finally, CGC terms associated with benign lesions, with unspecified tumor types (e.g., “other”, “other tumor types”, “other CNS”) or with tumor types with missing parent in our study were left unmatched.

Mode of action of driver genes

We computed the mode of action for highly confident driver genes. To do so, we first performed a pan-cancer run of dNdScv across all TCGA cohorts. We then applied the aforementioned algorithm (see Mode of action section below for more details on how the algorithm determines the role of driver genes according to their distribution of mutations in a cohort of samples) to classify driver genes into the three possible roles: Act (activating or oncogene), LoF (loss-of-function or tumor suppressor) or Amb (ambiguous or non-defined). We then combined these predictions with prior knowledge from the Cancer Genome Interpreter 3 according to the following rules: i) when the inferred mode of action matched the prior knowledge, we used the consensus mode of action; ii) when the gene was not included in the prior knowledge list, we selected the inferred mode of action; iii) when the inferred mode of action did not match the prior knowledge, we selected that of the prior knowledge list.


Sondka Z, et al. The COSMIC Cancer Gene Census: describing genetic dysfunction across all human cancers. Nat Rev Cancer. 2018;18(11):696–705. doi:10.1038/s41568-018-0060-1


Lever J, et al. CancerMine: a literature-mined resource for drivers, oncogenes and tumor suppressors in cancer. Nat Methods. 2019 Jun;16(6):505-507. doi: 10.1038/s41592-019-0422-y. Epub 2019 May 20.


Tamborero D, et al. Cancer Genome Interpreter annotates the biological and clinical relevance of tumor alterations. Genome Med. 2018;10(1):25. Published 2018 Mar 28. doi:10.1186/s13073-018-0531-8