Biology subjects

Sokac, M.

Publications and source records attributed to Sokac, M..

4 recordsLinked to original sources

bioRxiv2024

Advancing Oceanic Studies with HyperOCR Sensors and Non-Negative Matrix Factorization: A Cost-Effective, Data-Driven Approach for Analyzing Light in Marine Water Column

Understanding the intricate dynamics of ocean biogeochemistry is crucial for deciphering its role in climate change. Our study addresses this challenge by integrating advanced computational techniques and innovative sensor technology to enhance remote sensing capabilities. Drawing on recent insights into the vast carbon reservoirs within the ocean, particularly within the dissolved organic matter (DOM) pool, we highlight the pressing need for comprehensive spatial and temporal understanding facilitated by a combination of satellite and in situ data. However, existing remote sensing methods face limitations in capturing subsurface processes, hindering our ability to grasp carbon fluxes within the oceanic water column fully. Recent advancements in remote sensing offer promising avenues for addressing these challenges. Studies investigating polarized radiance distribution and Chromophoric Dissolved Organic Matter (CDOM) provide valuable insights into improving remote sensing capabilities. Building upon these advancements, we propose a novel data-driven approach utilizing HyperOCR sensors and non-negative matrix factorization (NMF). Non-negative matrix factorization (NMF) is a powerful tool for extracting meaningful biological signatures from hyperspectral data, offering a granular yet comprehensive view of spectral diversity. Our study showcases the potential of NMF in elucidating spatial and temporal variations in biogeochemical processes within the ocean. Leveraging HyperOCR sensors, our approach offers a cost-effective and efficient means of enhancing remote sensing capabilities, enabling the rapid deployment and identification of seasonal patterns in the water column. Through extensive validation against field data from the Adriatic Sea, we demonstrate the utility of our approach in refining satellite measurements and improving algorithms for analyzing ocean color data. Our findings underscore the importance of integrating multiple observational platforms and advanced computational techniques to enhance the accuracy and reliability of remote sensing in ocean biogeochemistry studies. In conclusion, our study contributes to a deeper understanding of marine ecosystems responses to environmental changes and offers a new perspective on remote sensing capabilities, particularly in challenging coastal waters. By bridging the gap between satellite and in situ measurements, our approach exemplifies a promising pathway for advancing remote sensing of ocean biogeochemistry.

ecology↗

bioRxiv2023

Endogenous viral elements constitute a complementary source of antigens for personalized cancer immunotherapy

Personalized cancer vaccines (PCVs) largely leverage neoantigens arising from somatic mutations limiting their application to patients with relatively high tumor mutational burden (TMB). This underscores the need for alternative antigens to design PCVs for low TMB cancers. To this end, we substantiate endogenous retroviral elements (EVEs) as tumor antigens through large-scale genomic analyses of healthy tissues and solid cancers. These analyses revealed that the breadth of EVE expression in tumors stratify checkpoint inhibitor treated melanoma patients into groups with differential overall and progression-free survival. To enable the design of PCVs containing EVE-derived epitopes with therapeutic potential, we developed a computational pipeline, ObsERV. We show that EVE-derived peptides are presented as epitopes on tumors and can be predicted by ObsERV. Preclinical testing of ObsERV demonstrates induction of sustained poly-functional CD4+ and CD8+ T-cell responses as well as long-term tumor protection. As such, EVEs may facilitate and improve PCVs especially for low-TMB patients.

cancer biology↗

bioRxiv2023

GENIUS: GEnome traNsformatIon and spatial representation of mUltiomicS data

The application of next-generation sequencing (NGS) has transformed cancer research. As costs have decreased, NGS has increasingly been applied to generate multiple layers of molecular data from the same samples, covering genomics, transcriptomics, and methylomics. Integrating these types of multi-omics data in a combined analysis is now becoming a common issue with no obvious solution, often handled on an ad-hoc basis, with multi-omics data arriving in a tabular format and analyzed using computationally intensive statistical methods. These methods particularly ignore the spatial orientation of the genome and often apply stringent p-value corrections that likely result in the loss of true positive associations. Here, we present GENIUS (GEnome traNsformatIon and spatial representation of mUltiomicS data), a framework for integrating multi-omics data using deep learning models developed for advanced image analysis. The GENIUS framework is able to transform multi-omics data into images with genes displayed as spatially connected pixels and successfully extract relevant information with respect to the desired output. Here, we demonstrate the utility of GENIUS by applying the framework to multi-omics datasets from the Cancer Genome Atlas. Our results are focused on predicting the development of metastatic cancer from primary tumors, and demonstrate how through model inference, we are able to extract the genes which are driving the model prediction and likely associated with metastatic disease progression. We anticipate our framework to be a starting point and strong proof of concept for multi-omics data transformation and analysis without the need for statistical correction.

bioinformatics↗

bioRxiv2022

The ratio of adaptive to innate immune cells differs between genders and associates with improved prognosis and response to immunotherapy

Immunotherapy has revolutionised cancer treatment. However, not all cancer patients benefit, and current stratification strategies based primarily on PD1 status and mutation burden is far from perfect. We hypothesised that high activation of an innate response relative to the adaptive response may prevent proper tumour neoantigen identification and decrease the specific anticancer response, both in the presence and absence of immunotherapy. To investigate this, we obtained transcriptomic data from three large publicly available cancer datasets, the Cancer Genome Atlas (TCGA), the Hartwig Medical Foundation (HMF), and a recently published cohort of metastatic bladder cancer patients treated with immunotherapy. To analyse immune infiltration into bulk tumours, we developed an RNAseq-based model based on previously published definitions to estimate the overall level of infiltrating innate and adaptive immune cells from bulk tumour RNAseq data. From these, the adaptive-to-innate immune ratio (A/I ratio) was defined. A meta-analysis of 32 cancer types from TCGA overall showed improved overall survival in patients with an A/I ratio above median (Hazard ratio (HR) females 0.73, HR males 0.86, P < 0.05). Of particular interest, we found that the association was different for males and females for eight cancer types, demonstrating a gender bias in the relative balance of the infiltration of innate and adaptive immune cells. For patients with metastatic disease, we found that responders to immunotherapy had a significantly higher A/I ratio than non-responders in HMF (P = 0.036) and a significantly higher ratio in complete responders in a separate metastatic bladder cancer dataset (P = 0.022). Overall, the adaptive-to-innate immune ratio seems to define separate states of immune activation, likely linked to fundamental immunological reactions to cancer. This ratio was associated with improved prognosis and improved response to immunotherapy, demonstrating potential relevance to patient stratification. Furthermore, by demonstrating a significant difference between males and females that associates with response, we highlight an important gender bias which likely has direct clinical relevance.

bioinformatics↗