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"Giorgio, E"
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The origins and genetic interactions of KRAS mutations are allele- and tissue-specific
Mutational activation of
KRAS
promotes the initiation and progression of cancers, especially in the colorectum, pancreas, lung, and blood plasma, with varying prevalence of specific activating missense mutations. Although epidemiological studies connect specific alleles to clinical outcomes, the mechanisms underlying the distinct clinical characteristics of mutant
KRAS
alleles are unclear. Here, we analyze 13,492 samples from these four tumor types to examine allele- and tissue-specific genetic properties associated with oncogenic
KRAS
mutations. The prevalence of known mutagenic mechanisms partially explains the observed spectrum of
KRAS
activating mutations. However, there are substantial differences between the observed and predicted frequencies for many alleles, suggesting that biological selection underlies the tissue-specific frequencies of mutant alleles. Consistent with experimental studies that have identified distinct signaling properties associated with each mutant form of KRAS, our genetic analysis reveals that each
KRAS
allele is associated with a distinct tissue-specific comutation network. Moreover, we identify tissue-specific genetic dependencies associated with specific mutant
KRAS
alleles. Overall, this analysis demonstrates that the genetic interactions of oncogenic
KRAS
mutations are allele- and tissue-specific, underscoring the complexity that drives their clinical consequences.
The
KRAS
gene is often mutated at several hotspot codons in cancer, resulting in similar, yet distinct, functional impacts on the KRAS protein. Here, the authors examine the genetic interactions of the different
KRAS
mutations across multiple cancer types and discover that
KRAS
mutations have allele- and tissue-specific mutagenic origins, comutation patterns, and dependency interactions.
Journal Article
Detecting the mutational signature of homologous recombination deficiency in clinical samples
Mutations in
BRCA1
and/or
BRCA2
(
BRCA1/2
) are the most common indication of deficiency in the homologous recombination (HR) DNA repair pathway. However, recent genome-wide analyses have shown that the same pattern of mutations found in
BRCA1
/
2
-mutant tumors is also present in several other tumors. Here, we present a new computational tool called Signature Multivariate Analysis (SigMA), which can be used to accurately detect the mutational signature associated with HR deficiency from targeted gene panels. Whereas previous methods require whole-genome or whole-exome data, our method detects the HR-deficiency signature even from low mutation counts, by using a likelihood-based measure combined with machine-learning techniques. Cell lines that we identify as HR deficient show a significant response to poly (ADP-ribose) polymerase (PARP) inhibitors; patients with ovarian cancer whom we found to be HR deficient show a significantly longer overall survival with platinum regimens. By enabling panel-based identification of mutational signatures, our method substantially increases the number of patients that may be considered for treatments targeting HR deficiency.
Signature Multivariate Analysis is a new computational tool that detects the mutational signature of homologous-recombination deficiency in clinical samples sequenced with targeted panels, enabling the identification of patients who are responsive to poly (ADP-ribose) polymerase inhibition therapy.
Journal Article
Credit Supply and the Housing Boom
An increase in credit supply driven by looser lending constraints in the mortgage market is the key force behind four empirical features of the housing boom before the Great Recession: the unprecedented rise in home prices, the surge in household debt, the stability of debt relative to house values, and the fall in mortgage rates. These facts are more difficult to reconcile with the popular view that attributes the housing boom only to looser borrowing constraints associated with lower collateral requirements, because they shift the demand for credit.
Journal Article
PRIOR SELECTION FOR VECTOR AUTOREGRESSIONS
Vector autoregressions (VARs) are flexible time series models that can capture complex dynamic interrelationships among macroeconomic variables. However, their dense parameterization leads to unstable inference and inaccurate out-of-sample forecasts, particularly for models with many variables. A solution to this problem is to use informative priors in order to shrink the richly parameterized unrestricted model toward a parsimonious naive benchmark, and thus reduce estimation uncertainty. This paper studies the optimal choice of the informativeness of these priors, which we treat as additional parameters, in the spirit of hierarchical modeling. This approach, theoretically grounded and easy to implement, greatly reduces the number and importance of subjective choices in the setting of the prior. Moreover, it performs very well in terms of both out-of-sample forecasting—as well as factor models—and accuracy in the estimation of impulse response functions.
Journal Article
Time Varying Structural Vector Autoregressions and Monetary Policy: A Corrigendum
This note shows how to apply the procedure of Kim et al. (1998) to the estimation of VAR, DSGE, factor, and unobserved components models with stochastic volatility. In particular, it revisits the estimation algorithm of the time-varying VAR model of Primiceri (2005). The main difference of the new algorithm is the ordering of the various MCMC steps, with each individual step remaining the same.
Journal Article
ECONOMIC PREDICTIONS WITH BIG DATA
We compare sparse and dense representations of predictive models in macroeconomics, microeconomics, and finance. To deal with a large number of possible predictors, we specify a prior that allows for both variable selection and shrinkage. The posterior distribution does not typically concentrate on a single sparse model, but on a wide set of models that often include many predictors.
Journal Article
Computational analysis of cancer genome sequencing data
Distilling biologically meaningful information from cancer genome sequencing data requires comprehensive identification of somatic alterations using rigorous computational methods. As the amount and complexity of sequencing data have increased, so has the number of tools for analysing them. Here, we describe the main steps involved in the bioinformatic analysis of cancer genomes, review key algorithmic developments and highlight popular tools and emerging technologies. These tools include those that identify point mutations, copy number alterations, structural variations and mutational signatures in cancer genomes. We also discuss issues in experimental design, the strengths and limitations of sequencing modalities and methodological challenges for the future.In this Review the authors provide an overview of key algorithmic developments, popular tools and emerging technologies used in the bioinformatic analysis of genomes. They also describe how such analysis can identify point mutations, copy number alterations, structural variations and mutational signatures in cancer genomes.
Journal Article
The Time-Varying Volatility of Macroeconomic Fluctuations
We investigate the sources of the important shifts in the volatility of US macroeconomic variables in the postwar period. To this end, we propose the estimation of DSGE models allowing for time variation in the volatility of the structural innovations. We apply our estimation strategy to a large-scale model of the business cycle and find that shocks specific to the equilibrium condition of investment account for most of the sharp decline in volatility of the last two decades.
Journal Article
Time Varying Structural Vector Autoregressions and Monetary Policy
Monetary policy and the private sector behaviour of the U.S. economy are modelled as a time varying structural vector autoregression, where the sources of time variation are both the coefficients and the variance covariance matrix of the innovations. The paper develops a new, simple modelling strategy for the law of motion of the variance covariance matrix and proposes an efficient Markov chain Monte Carlo algorithm for the model likelihood/posterior numerical evaluation. The main empirical conclusions are: (1) both systematic and non-systematic monetary policy have changed during the last 40 years-in particular, systematic responses of the interest rate to inflation and unemployment exhibit a trend toward a more aggressive behaviour, despite remarkable oscillations; (2) this has had a negligible effect on the rest of the economy. The role played by exogenous non-policy shocks seems more important than interest rate policy in explaining the high inflation and unemployment episodes in recent U.S. economic history.
Journal Article