LOBICO is used to find the logic combinations of binary input features (e.g. mutations, CNAs) that best explain the response of cancer cell lines to anticancer drugs. These logic models that are described in the disjunctive normal form (DNF), a standard notation in which every logic function can be expressed. The DNF is parameterized by two parameters: K, the number of disjuncts, and M, the number of terms per disjunct. LOBICO is applied with eight different parameter settings, i.e. from simple single-predictor models (K=1, M=1) to more complex multi-predictor models ( K>1, M>1). Tables with logic symbols and the possible 8 models are given below.

Logic Symbol Name | Logic Symbol |
---|---|

AND | & |

OR | | |

NOT | ¬ |

Model complexity | Drug name | Drug target | Optimal logic model |
---|---|---|---|

K=1,M=1 (Single predictor) | PLX4720 | RAF | BRAF |

K=1,M=2 (2-input AND) | Paclitaxel | Microtubules | CDKN2A & TP53 |

K=1,M=3 (3-input AND) | Cytarabine | DNA synthesis | CDKN2A & ¬EGFR & ¬SMAD4 |

K=1,M=4 (4-input AND) | KIN001-102 | Akt1 | ¬APC & ¬BRAF & ¬EGFR & ¬KRAS |

K=2,M=1 (2-input OR) | BEZ235 | PI3K,MTORC | PIK3CA | PTEN |

K=3,M=1 (3-input OR) | AZD6244 | MEK 1/2 | BRAF | KRAS | NRAS |

K=4,M=1 (3-input OR) | Afatinib | EGFR, ERBB2 | EGFR | ERBB2 | JAK2 | SMAD4 |

K=2,M=2 (2-by-2) | JQ12 | HDAC | (CDKN2A & ¬SMAD4) | (¬KRAS & ¬TP53) |

We describe here the details and specifications of the LOBICO plots.

The input plot is split into 3 parts: i) the drug sensitivity histogram for all cell lines ii) the tissue type heatmap for the sensitive cell lines iii) the input (e.g. mutations) heatmap for the sensitive cell lines.

We describe here the details and specifications of the GDSC dataset.

The input samples used for generating the logic models using LOBICO algorithm are cell lines from the GDSC dataset (GDSC complete dataset contains primary tumors and cell lines). These cell lines are originated from 21 tissue types. An overview of the tissue types and cancer types (TCGA labels) are given here.

You can download the complete sample landscape for the GDSC dataset here.

The main input features for the cell lines in this dataset:

- Cancer Genes (CGs) with point mutations, small insertions or deletions (e.g. SMAD4, ATM)
- Recurrently aberrant copy number segments (RACSs) of a cancer gene or genes, locus / chromosome locations. Amplifications are represented as a(g1,g2,..) (e.g. a(CCND1,CTTN)) and deletions as d(g1,g2) (e.g. d(FAT1), d18q22.1).
- Activation signature of signaling pathways containing well-known mutational activations, copy number alterations and other signaling aberrations. (e.g. TNFa-UP, TGFB-DOWN). Click here to download the pathway activity scores across cell lines.

LOBICO generates logic models to explain drug response based on binarized mutation data of cancer samples. A gene was called mutated when it had a point mutation, a small insertion or deletion as determined by capillary sequencing, or when it was highly amplified or homozygously deleted based on copy number arrays. LOBICO was executed for each drug separately utilizing pan-cancer and cancer-specific molecular datasets. This led to the inference of 1,080 logic models. In the GDSC dataset, a total of 265 drug compounds are screened. A summary of the complete screened compounds can be downloaded from here.

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