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1. A weighted exact test for mutually exclusive mutations in cancer

   https://doi.org/10.1093/bioinformatics/btw462  

   Leiserson, Mark DM ; Reyna, Matthew A. ; Raphael, Benjamin J. ( August 2016 , Bioinformatics)

   The somatic mutations in the pathways that drive cancer development tend to be mutually exclusive across tumors, providing a signal for distinguishing driver mutations from a larger number of random passenger mutations. This mutual exclusivity signal can be confounded by high and highly variable mutation rates across a cohort of samples. Current statistical tests for exclusivity that incorporate both per-gene and per-sample mutational frequencies are computationally expensive and have limited precision.

   We formulate a weighted exact test for assessing the significance of mutual exclusivity in an arbitrary number of mutational events. Our test conditions on the number of samples with a mutation as well as per-event, per-sample mutation probabilities. We provide a recursive formula to compute P-values for the weighted test exactly as well as a highly accurate and efficient saddlepoint approximation of the test. We use our test to approximate a commonly used permutation test for exclusivity that conditions on per-event, per-sample mutation frequencies. However, our test is more efficient and it recovers more significant results than the permutation test. We use our Weighted Exclusivity Test (WExT) software to analyze hundreds of colorectal and endometrial samples from The Cancer Genome Atlas, which are two cancer types that often have extremely high mutation rates. On both cancer types, the weighted test identifies sets of mutually exclusive mutations in cancer genes with fewer false positives than earlier approaches.

   See http://compbio.cs.brown.edu/projects/wext for software.

   braphael@cs.brown.edu

   Supplementary data are available at Bioinformatics online.

    

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2. Oncogenetic network estimation with disjunctive Bayesian networks

   https://doi.org/10.1002/cso2.1027  

   Nicol, Phillip B. ; Coombes, Kevin R. ; Deaver, Courtney ; Chkrebtii, Oksana ; Paul, Subhadeep ; Toland, Amanda E. ; Asiaee, Amir ( June 2021 , Computational and Systems Oncology)

   Motivation: Cancer is the process of accumulating genetic alterations that confer selective advantages to tumor cells. The order in which aberrations occur is not arbitrary, and inferring the order of events is challenging due to the lack of longitudinal samples from tumors. Moreover, a network model of oncogenesis should capture biological facts such as distinct progression trajectories of cancer subtypes and patterns of mutual exclusivity of alterations in the same pathways.

   In this paper, we present the disjunctive Bayesian network (DBN), a novel oncogenetic model with a phylogenetic interpretation. DBN is expressive enough to capture cancer subtypes' trajectories and mutually exclusive relations between alterations from unstratified data.

   Results: In cases where the number of studied alterations is small (), we provide an efficient dynamic programming implementation of an exact structure learning method that finds a best DBN in the superexponential search space of networks. In rare cases that the number of alterations is large, we provided an efficient genetic algorithm in our software package, OncoBN. Through numerous synthetic and real data experiments, we show OncoBN's ability in inferring ground truth networks and recovering biologically meaningful progression networks.

   Availability: OncoBN is implemented in R and is available athttps://github.com/phillipnicol/OncoBN.

    

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3. Identification of prognostic factors in childhood T‐cell acute lymphoblastic leukemia: Results from DFCI ALL Consortium Protocols 05‐001 and 11‐001

   https://doi.org/10.1002/pbc.28719  

   Burns, Melissa A. ; Place, Andrew E. ; Stevenson, Kristen E. ; Gutiérrez, Alejandro ; Forrest, Suzanne ; Pikman, Yana ; Vrooman, Lynda M. ; Harris, Marian H. ; Weinberg, Olga K. ; Hunt, Sarah K. ; et al ( October 2020 , Pediatric Blood & Cancer)

   While outcomes for pediatric T‐cell acute lymphoblastic leukemia (T‐ALL) are favorable, there are few widely accepted prognostic factors, limiting the ability to risk stratify therapy.

   Dana‐Farber Cancer Institute (DFCI) Protocols 05‐001 and 11‐001 enrolled pediatric patients with newly diagnosed B‐ or T‐ALL from 2005 to 2011 and from 2012 to 2015, respectively. Protocol therapy was nearly identical for patients with T‐ALL (N = 123), who were all initially assigned to the high‐risk arm. End‐induction minimal residual disease (MRD) was assessed by reverse transcription polymerase chain reaction (RT‐PCR) or next‐generation sequencing (NGS), but was not used to modify postinduction therapy. Early T‐cell precursor (ETP) status was determined by flow cytometry. Cases with sufficient diagnostic DNA were retrospectively evaluated by targeted NGS of known genetic drivers of T‐ALL, including Notch, PI3K, and Ras pathway genes.

   The 5‐year event‐free survival (EFS) and overall survival (OS) for patients with T‐ALL was 81% (95% CI, 73‐87%) and 90% (95% CI, 83‐94%), respectively. ETP phenotype was associated with failure to achieve complete remission, but not with inferior OS. Low end‐induction MRD (<10⁻⁴) was associated with superior disease‐free survival (DFS). Pathogenic mutations of the PI3K pathway were mutually exclusive of ETP phenotype and were associated with inferior 5‐year DFS and OS.

   Together, our findings demonstrate that ETP phenotype, end‐induction MRD, and PI3K pathway mutation status are prognostically relevant in pediatric T‐ALL and should be considered for risk classification in future trials. DFCI Protocols 05‐001 and 11‐001 are registered atwww.clinicaltrials.govas NCT00165087 and NCT01574274, respectively.

    

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4. Algorithms for covering multiple submodular constraints and applications

   https://doi.org/10.1007/s10878-022-00874-x  

   Chekuri, Chandra ; Inamdar, Tanmay ; Quanrud, Kent ; Varadarajan, Kasturi ; Zhang, Zhao ( June 2022 , Journal of Combinatorial Optimization)

   We consider the problem of covering multiple submodular constraints. Given a finite ground setN, a weight function$$w: N \rightarrow \mathbb {R}_+$$$w:N\to R_{+}$,rmonotone submodular functions$$f_1,f_2,\ldots ,f_r$$$f_1,f_2,\dots ,f_r$overNand requirements$$k_1,k_2,\ldots ,k_r$$$k_1,k_2,\dots ,k_r$the goal is to find a minimum weight subset$$S \subseteq N$$$S\subseteq N$such that$$f_i(S) \ge k_i$$$f_i\left(S\right)\ge k_i$for$$1 \le i \le r$$$1\le i\le r$. We refer to this problem asMulti-Submod-Coverand it was recently considered by Har-Peled and Jones (Few cuts meet many point sets. CoRR.arxiv:abs1808.03260Har-Peled and Jones 2018) who were motivated by an application in geometry. Even with$$r=1$$$r=1$Multi-Submod-Covergeneralizes the well-known Submodular Set Cover problem (Submod-SC), and it can also be easily reduced toSubmod-SC. A simple greedy algorithm gives an$$O(\log (kr))$$$O(log(kr\left)\right)$approximation where$$k = \sum _i k_i$$$k={\sum }_i{k}_i$and this ratio cannot be improved in the general case. In this paper, motivated by several concrete applications, we consider two ways to improve upon the approximation given by the greedy algorithm. First, we give a bicriteria approximation algorithm forMulti-Submod-Coverthat covers each constraint to within a factor of$$(1-1/e-\varepsilon )$$$(1-1/e-\epsilon )$while incurring an approximation of$$O(\frac{1}{\epsilon }\log r)$$$O(\frac{1}{ϵ}logr)$in the cost. Second, we consider the special case when each$$f_i$$$f_i$is a obtained from a truncated coverage function and obtain an algorithm that generalizes previous work on partial set cover (Partial-SC), covering integer programs (CIPs) and multiple vertex cover constraints Bera et al. (Theoret Comput Sci 555:2–8 Bera et al. 2014). Both these algorithms are based on mathematical programming relaxations that avoid the limitations of the greedy algorithm. We demonstrate the implications of our algorithms and related ideas to several applications ranging from geometric covering problems to clustering with outliers. Our work highlights the utility of the high-level model and the lens of submodularity in addressing this class of covering problems.

    

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5. Prediction of evolutionary constraint by genomic annotations improves functional prioritization of genomic variants in maize

   https://doi.org/10.1186/s13059-022-02747-2  

   Ramstein, Guillaume P. ; Buckler, Edward S. ( September 2022 , Genome Biology)

   Crop improvement through cross-population genomic prediction and genome editing requires identification of causal variants at high resolution, within fewer than hundreds of base pairs. Most genetic mapping studies have generally lacked such resolution. In contrast, evolutionary approaches can detect genetic effects at high resolution, but they are limited by shifting selection, missing data, and low depth of multiple-sequence alignments. Here we use genomic annotations to accurately predict nucleotide conservation across angiosperms, as a proxy for fitness effect of mutations.

   Using only sequence analysis, we annotate nonsynonymous mutations in 25,824 maize gene models, with information from bioinformatics and deep learning. Our predictions are validated by experimental information: within-species conservation, chromatin accessibility, and gene expression. According to gene ontology and pathway enrichment analyses, predicted nucleotide conservation points to genes in central carbon metabolism. Importantly, it improves genomic prediction for fitness-related traits such as grain yield, in elite maize panels, by stringent prioritization of fewer than 1% of single-site variants.

   Our results suggest that predicting nucleotide conservation across angiosperms may effectively prioritize sites most likely to impact fitness-related traits in crops, without being limited by shifting selection, missing data, and low depth of multiple-sequence alignments. Our approach—Prediction of mutation Impact by Calibrated Nucleotide Conservation (PICNC)—could be useful to select polymorphisms for accurate genomic prediction, and candidate mutations for efficient base editing. The trained PICNC models and predicted nucleotide conservation at protein-coding SNPs in maize are publicly available in CyVerse (https://doi.org/10.25739/hybz-2957).

    

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