Biology subjects

Ramus, S. J.

Publications and source records attributed to Ramus, S. J..

2 recordsLinked to original sources

bioRxiv2024

IdentifiHR: predicting homologous recombination deficiency in high-grade serous ovarian carcinoma using gene expression

BackgroundApproximately half of all high-grade serous ovarian carcinomas (HGSCs) have a therapeutically targetable defect in the homologous recombination (HR) DNA repair mechanism. While there are genomic and transcriptomic methods, developed for other cancer types, to identify HR deficient (HRD) samples, there are no gene expression-based tools to predict HR repair status in HGSC specifically. We have built the first HGSC-specific model to predict HR repair status using gene expression. MethodsWe separated The Cancer Genome Atlas (TCGA) cohort of HGSCs (n = 361) into training (n = 288) and testing (n = 73) sets and labelled each case as HRD or HR proficient (HRP) based on the clinical standard for classification, being a score of HRD genomic damage. Using the training set, we performed differential gene expression analysis between HRD and HRP cases. The 2604 significantly differentially expressed genes were then used to tune and train a penalised logistic regression model. ResultsIdentifiHR is an elastic net penalised logistic regression model that uses the expression of 209 genes to predict HR status in HGSC. These genes capture known regions of HR-specific copy number alteration, which impact gene expression levels, and preserve the genomic damage signal. IdentifiHR has an accuracy of 85% in the TCGA test set and of 91% in an independent cohort of 99 samples, collected from primary tumours before (n = 74/99) and after autopsy (n = 6/99), in addition to ascites (n = 12/99) and normal fallopian tube samples (n = 7/99). Further, IdentifiHR is 84% accurate in pseudobulked single-cell HGSC sequencing from 37 patients and outperforms existing gene expression-based methods to predict HR status, being BRCAness, MutliscaleHRD and expHRD. ConclusionsIdentifiHR is an accurate model to predict HR status in HGSC using gene expression alone, that is available as an R package from https://github.com/DavidsonGroup/IdentifiHR.

cancer biology↗

bioRxiv2018

Rat orbitofrontal ensemble activity contains a multiplexed but value-invariant representation of task structure in an odor sequence task

The orbitofrontal cortex (OFC) has long been implicated in signaling information about expected outcomes to facilitate adaptive or flexible behavior. Current proposals focus on signaling of expected reward values versus the representation of a value-agnostic cognitive map of the task. While often suggested as mutually exclusive, these alternatives may represent two extreme ends of a continuum determined by the complexity of the environment and the subjects experience in it. As learning proceeds, an initial, detailed cognitive map might be acquired, based largely on external information. With more experience, this hypothesized map can then be tailored to include relevant abstract hidden cognitive constructs. This might default to expected values in situations where other attributes are minimized or largely irrelevant, whereas in richer tasks, a more detailed structure might continue to be represented, at least where relevant to behavior, and possibly alongside value. Here we sought to arbitrate between these options by recording single unit activity from the OFC in rats navigating an odor sequence task analogous to a spatial maze. The odor sequences provided a clearly mappable state space, with 24 unique \"positions\" defined by sensory information, likelihood of reward, or both. Consistent with the hypothesis that the OFC represents a cognitive map tailored to the subjects intentions or plans, we found a close correspondence between how subjects behavior suggested they were using the sequences, and the neural representations of the sequences in OFC ensembles. Multiplexed with this value-invariant representation of the task, we also found a representation of the expected value at each location. Thus value and task structure are co-existing and potentially dissociable components of the neural code in OFC.

neuroscience↗