Characterizing genomic alterations in cancer by complementary functional associations.

Systematic efforts to sequence the cancer genome have identified large numbers of mutations and copy number alterations in human cancers. However, elucidating the functional consequences of these variants, and their interactions to drive or maintain oncogenic states, remains a challenge in cancer research. We developed REVEALER, a computational method that identifies combinations of mutually exclusive genomic alterations correlated with functional phenotypes, such as the activation or gene dependency of oncogenic pathways or sensitivity to a drug treatment. We used REVEALER to uncover complementary genomic alterations associated with the transcriptional activation of β-catenin and NRF2, MEK-inhibitor sensitivity, and KRAS dependency. REVEALER successfully identified both known and new associations, demonstrating the power of combining functional profiles with extensive characterization of genomic alterations in cancer genomes

Ambiguity in logic-based models of gene regulatory networks: An integrative multi-perturbation analysis.

Most studies of gene regulatory network (GRN) inference have focused extensively on identifying the interaction map of the GRNs. However, in order to predict the cellular behavior, modeling the GRN in terms of logic circuits, i.e., Boolean networks, is necessary. The perturbation techniques, e.g., knock-down and over-expression, should be utilized for identifying the underlying logic behind the interactions. However, we will show that by using only transcriptomic data obtained by single-perturbation experiments, we cannot observe all regulatory interactions, and this invisibility causes ambiguity in our model. Consequently, we need to employ the data of multiple omics layers (genome, transcriptome, and proteome) as well as multiple perturbation experiments to reduce or eliminate ambiguity in our modeling. In this paper, we introduce a multi-step perturbation experiment to deal with ambiguity. Moreover, we perform a thorough analysis to investigate which types of perturbations and omics layers play the most important role in the unambiguous modeling of the GRNs and how much ambiguity will be eliminated by considering more perturbations and more omics layers. Our analysis shows that performing both knock-down and over-expression is necessary in order to achieve the least ambiguous model. Moreover, the more steps of the perturbation are taken, the more ambiguity is eliminated. In addition, we can even achieve an unambiguous model of the GRN by using multi-step perturbation and integrating transcriptomic, protein-protein interaction, and cis-element data. Finally, we demonstrate the effect of utilizing different types of perturbation experiment and integrating multi-omics data on identifying the logic behind the regulatory interactions in a synthetic GRN. In conclusion, relying on the results of only knock-down experiments and not including as many omics layers as possible in the GRN inference, makes the results ambiguous, unreliable, and less accurate

The Python code simulating the results presented in this study.

(PY)</p

The effect of , , , , and <i>T</i>_<i>E</i> on the total number of infected cases in a 2-strain viral epidemic.

The fraction of the population who are infected with strains 1 and 2 and the total percentage of the infected cases are depicted for three cases of (A), (B), and (C). Also, these figures demonstrate that the emergence time of the new strain should be considered to determine the winner of the viral competition. In other words, a new, more contingent strain will not be necessarily dominant in the population if it emerges late. The legends of all figures are the same as those in panel A.</p

The effect of , , , , and <i>T</i>_<i>E</i> on the cumulative mortality.

These figures indicate that in the presence of asymptomatic transmission, the emergence of a more-transmissible strain does not necessarily reflect more disease severity. A) The cumulative mortality vs. TE for the case of all-symptomatic infections in the population for different levels of transmissibility of the new variant ( = 1.3, 2, and 2.7). In this case, the cumulative mortality is higher than the case of having asymptomatic infection. B-D) For and , these figures show the effect of increase in the fraction of asymptomatic cases of the emergent strain ( = 0.1 (B), 0.2 (C), and 0.4 (D)), on the cumulative mortality. The increase in the fraction of asymptomatic cases can reduce the cumulative mortality.</p

Explanation of the symbols of the 2-SEICARD model.

Explanation of the symbols of the 2-SEICARD model.</p

Schematic of the 2-SEICARD model.

For simplicity, the lower branch, corresponding to the second strain, is not depicted, and it is denoted by EICARD2. The EICARD2 branch appears in the model for t ≥ TE.</p

Characterizing genomic alterations in cancer by complementary functional associations.

Systematic efforts to sequence the cancer genome have identified large numbers of mutations and copy number alterations in human cancers. However, elucidating the functional consequences of these variants, and their interactions to drive or maintain oncogenic states, remains a challenge in cancer research. We developed REVEALER, a computational method that identifies combinations of mutually exclusive genomic alterations correlated with functional phenotypes, such as the activation or gene dependency of oncogenic pathways or sensitivity to a drug treatment. We used REVEALER to uncover complementary genomic alterations associated with the transcriptional activation of β-catenin and NRF2, MEK-inhibitor sensitivity, and KRAS dependency. REVEALER successfully identified both known and new associations, demonstrating the power of combining functional profiles with extensive characterization of genomic alterations in cancer genomes