On the instability and constraints of the interaction between number representation and spatial attention in healthy humans. A concise review of the literature and new experimental evidence

.The relationship between number and space representation is still one of the most debated topics in studies of mathematical cognition. Here we offer a concise review of two important behavioral effects that have pointed out the use of a spatially left-to-right oriented mental number line (MNL) in healthy participants: the SNARC effect and the attentional SNARC effect (Att-SNARC). Following a brief summary of seminal investigations on the introspective properties of the MNL, we review recent empirical evidence and theories on the functional origin of the SNARC effect, where upon left/right response choices faster reaction times are found for small numbers with left-side responses and for large numbers with right-side responses. Then we offer a summary of the studies that have investigated whether the mere perception of visual Arabic numbers presented at central fixation can engender spatially congruent lateral shifts of attention, ie, leftward for small numbers and rightward for large ones, ie, the Att-SNARC effect. Finally, we summarize four experiments that tested whether the Att-SNARC depends on an active rather than passive processing of centrally presented digit cues. In line with other recent studies, these experiment do not replicate the original Att-SNARC and show that the mere perception of Arabic numerals does not trigger automatic shifts of attention. These shifts are instead found when the task requires the explicit left/right spatial coding of digit cues, ie, Spatial Att-SNARC (Fattorini et al., 2015b). Nonetheless, the reliability of the Spatial Att-SNARC effect seems not as strong as that of conventional SNARC effects where left/ right codes are mapped onto responses rather than directly mapped on digit cues. Comparing the magnitude of digits to a numerical reference, ie, "5," also produced a Magnitude Comparison Att-SNARC that was weaker than the spatial one. However, the reliability of this Magnitude Comparison Att-SNARC should be considered with caution because, like in a study by Zanolie and Pecher (2014), we recently failed to replicate this effect in a separate behavioral-eventrelated potentials study in preparation (Fattorini et al., 2015a). All together the results from the present series of experiments support the hypothesis that spatial coding is not an intrinsic part of number representation and that number-space interaction is determined by the use of stimulus-or response-related spatial codes in the task at han

No inherent left and right side in human ‘mental number line': evidence from right brain damage

Spatial reasoning has a relevant role in mathematics and helps daily computational activities. It is widely assumed that in cultures with left-to-right reading, numbers are organized along the mental equivalent of a ruler, the mental number line, with small magnitudes located to the left of larger ones. Patients with right brain damage can disregard smaller numbers while mentally setting the midpoint of number intervals. This has been interpreted as a sign of spatial neglect for numbers on the left side of the mental number line and taken as a strong argument for the intrinsic left-to-right organization of the mental number line. Here, we put forward the understanding of this cognitive disability by discovering that patients with right brain damage disregard smaller numbers both when these are mapped on the left side of the mental number line and on the right side of an imagined clock face. This shows that the right hemisphere supports the representation of small numerical magnitudes independently from their mapping on the left or the right side of a spatial-mental layout. In addition, the study of the anatomical correlates through voxel-based lesion-symptom mapping and the mapping of lesion peaks on the diffusion tensor imaging-based reconstruction of white matter pathways showed that the rightward bias in the imagined clock-face was correlated with lesions of high-level middle temporal visual areas that code stimuli in object-centred spatial coordinates, i.e. stimuli that, like a clock face, have an inherent left and right side. In contrast, bias towards higher numbers on the mental number line was linked to white matter damage in the frontal component of the parietal-frontal number network. These anatomical findings show that the human brain does not represent the mental number line as an object with an inherent left and right side. We conclude that the bias towards higher numbers in the mental bisection of number intervals does not depend on left side spatial, imagery or object-centred neglect and that it rather depends on disruption of an abstract non-spatial representation of small numerical magnitude

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment

Integrated Genomic Analysis of the Ubiquitin Pathway across Cancer Types

Protein ubiquitination is a dynamic and reversibleprocess of adding single ubiquitin molecules orvarious ubiquitin chains to target proteins. Here,using multidimensional omic data of 9,125 tumorsamples across 33 cancer types from The CancerGenome Atlas, we perform comprehensive molecu-lar characterization of 929 ubiquitin-related genesand 95 deubiquitinase genes. Among them, we sys-tematically identify top somatic driver candidates,including mutatedFBXW7with cancer-type-specificpatterns and amplifiedMDM2showing a mutuallyexclusive pattern withBRAFmutations. Ubiquitinpathway genes tend to be upregulated in cancermediated by diverse mechanisms. By integratingpan-cancer multiomic data, we identify a group oftumor samples that exhibit worse prognosis. Thesesamples are consistently associated with the upre-gulation of cell-cycle and DNA repair pathways, char-acterized by mutatedTP53,MYC/TERTamplifica-tion, andAPC/PTENdeletion. Our analysishighlights the importance of the ubiquitin pathwayin cancer development and lays a foundation fordeveloping relevant therapeutic strategies

Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation.

Cancer progression involves the gradual loss of a differentiated phenotype and acquisition of progenitor and stem-cell-like features. Here, we provide novel stemness indices for assessing the degree of oncogenic dedifferentiation. We used an innovative one-class logistic regression (OCLR) machine-learning algorithm to extract transcriptomic and epigenetic feature sets derived from non-transformed pluripotent stem cells and their differentiated progeny. Using OCLR, we were able to identify previously undiscovered biological mechanisms associated with the dedifferentiated oncogenic state. Analyses of the tumor microenvironment revealed unanticipated correlation of cancer stemness with immune checkpoint expression and infiltrating immune cells. We found that the dedifferentiated oncogenic phenotype was generally most prominent in metastatic tumors. Application of our stemness indices to single-cell data revealed patterns of intra-tumor molecular heterogeneity. Finally, the indices allowed for the identification of novel targets and possible targeted therapies aimed at tumor differentiation

Molecular characterization and clinical relevance of metabolic expression subtypes in human cancers.

Metabolic reprogramming provides critical information for clinical oncology. Using molecular data of 9,125 patient samples from The Cancer Genome Atlas, we identified tumor subtypes in 33 cancer types based on mRNA expression patterns of seven major metabolic processes and assessed their clinical relevance. Our metabolic expression subtypes correlated extensively with clinical outcome: subtypes with upregulated carbohydrate, nucleotide, and vitamin/cofactor metabolism most consistently correlated with worse prognosis, whereas subtypes with upregulated lipid metabolism showed the opposite. Metabolic subtypes correlated with diverse somatic drivers but exhibited effects convergent on cancer hallmark pathways and were modulated by highly recurrent master regulators across cancer types. As a proof-of-concept example, we demonstrated that knockdown of SNAI1 or RUNX1—master regulators of carbohydrate metabolic subtypes-modulates metabolic activity and drug sensitivity. Our study provides a system-level view of metabolic heterogeneity within and across cancer types and identifies pathway cross-talk, suggesting related prognostic, therapeutic, and predictive utility