Biology subjects

Olopade, O. I.

Publications and source records attributed to Olopade, O. I..

4 recordsLinked to original sources

bioRxiv2023

PTPN2 copper-sensing rapidly relays copper level fluctuations into EGFR/CREB activation and associated CTR1 transcriptional repression

Fluxes in human intra- and extracellular copper levels recently garnered attention for roles in cellular signaling, including affecting levels of the signaling molecule cyclic adenosine monophosphate (cAMP). We herein applied an unbiased temporal evaluation of the whole-genome transcriptional activities modulated by fluctuations in copper levels to identify the copper sensor proteins responsible for driving these activities. We found that fluctuations in physiologically-relevant copper levels rapidly modulate EGFR/MAPK/ERK signal transduction and activation of the transcription factor cAMP response element-binding protein (CREB). Both intracellular and extracellular assays support Cu1+ inhibition of the EGFR-phosphatase PTPN2 (and potentially the homologous PTPN1)-via direct ligation to the PTPN2 active site cysteine side chain-as the underlying mechanism of copper-stimulated EGFR signal transduction activation. Depletion of copper represses this signaling pathway. We additionally show i) copper supplementation drives transcriptional repression of the copper importer CTR1 and ii) CREB activity is inversely correlated with CTR1 expression. In summary, our study reveals PTPN2 as a physiological copper sensor and defines a regulatory mechanism linking feedback control of copper-stimulated MAPK/ERK/CREB-signaling and CTR1 expression, thereby uncovering a previously unrecognized link between copper levels and cellular signal transduction.

biochemistry↗

bioRxiv2022

Multimodal Prediction of Breast Cancer Recurrence Assays and Risk of Recurrence

Gene expression-based recurrence assays are strongly recommended to guide the use of chemotherapy in hormone receptor-positive, HER2-negative breast cancer, but such testing is expensive, can contribute to delays in care, and may not be available in low-resource settings. Here, we describe the training and independent validation of a deep learning model that predicts recurrence assay result and risk of recurrence using both digital histology and clinical risk factors. We demonstrate that this approach outperforms an established clinical nomogram (area under the receiver operating characteristic curve of 0.833 versus 0.765 in an external validation cohort, p = 0.003), and can identify a subset of patients with excellent prognoses who may not need further genomic testing.

cancer biology↗

bioRxiv2020

The Impact of Digital Histopathology Batch Effect on Deep Learning Model Accuracy and Bias

The Cancer Genome Atlas (TCGA) is one of the largest biorepositories of digital histology. Deep learning (DL) models have been trained on TCGA to predict numerous features directly from histology, including survival, gene expression patterns, and driver mutations. However, we demonstrate that these features vary substantially across tissue submitting sites in TCGA for over 3,000 patients with six cancer subtypes. Additionally, we show that histologic image differences between submitting sites can easily be identified with DL. This site detection remains possible despite commonly used color normalization and augmentation methods, and we quantify the digital image characteristics constituting this histologic batch effect. As an example, we show that patient ethnicity within the TCGA breast cancer cohort can be inferred from histology due to site-level batch effect, which must be accounted for to ensure equitable application of DL. Batch effect also leads to overoptimistic estimates of model performance, and we propose a quadratic programming method to guide validation that abrogates this bias.

bioinformatics↗

bioRxiv2020

Whole-genome analysis of Nigerian patients with breast cancer reveals ethnic-driven somatic evolution and distinct genomic subtypes

Black women of African ancestry experience more aggressive breast cancer with higher mortality rates than White women of European ancestry. Although inter-ethnic germline variation is known, differential somatic evolution has not been investigated in detail. Analysis of deep whole genomes of 97 breast tumors, with RNA-seq in a subset, from indigenous African patients in Nigeria in comparison to The Cancer Genome Atlas (n=76) revealed a higher rate of genomic instability and increased intra-tumoral heterogeneity as well as a unique genomic subtype defined by early clonal GATA3 mutations and a 10.5-year younger age at diagnosis. We also found evidence for non-coding mutations in two novel drivers (ZNF217 and SYPL1) and a novel INDEL signature strongly associated with African ancestry proportion. This comprehensive analysis of an understudied population underscores the need to incorporate diversity of genomes as a key parameter in fundamental research with potential to tailor clinical intervention and promote equity in precision oncology care.

genomics↗