Bioinformatics roadmap for therapy selection in cancer genomics

Jiménez-Santos, María José; García-Martín, Santiago; Fustero‐Torre, Coral; Domenico, Tomás Di; Gómez‐López, Gonzalo; Al‐Shahrour, Fátima

doi:10.1002/1878-0261.13286

Cited by 11 publications

(5 citation statements)

References 340 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the early stages of research, statistical approaches were employed, as demonstrated by Anderson et al [23] in their meta-analysis of long-term weight loss trends. A further breakthrough was made by Jiménez-Santos et al in federated learning for secure medical data sharing [24]. The inclusion of genetic and non-genetic attributes has enriched predictive models.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Unraveling the Potential of Attentive Bi-LSTM for Accurate Obesity Prognosis: Advancing Public Health towards Sustainable Cities

Ayub,

Khan,

Shehryar Ali Naqvi

et al. 2024

Bioengineering

View full text Add to dashboard Cite

The global prevalence of obesity presents a pressing challenge to public health and healthcare systems, necessitating accurate prediction and understanding for effective prevention and management strategies. This article addresses the need for improved obesity prediction models by conducting a comprehensive analysis of existing machine learning (ML) and deep learning (DL) approaches. This study introduces a novel hybrid model, Attention-based Bi-LSTM (ABi-LSTM), which integrates attention mechanisms with bidirectional Long Short-Term Memory (Bi-LSTM) networks to enhance interpretability and performance in obesity prediction. Our study fills a crucial gap by bridging healthcare and urban planning domains, offering insights into data-driven approaches to promote healthier living within urban environments. The proposed ABi-LSTM model demonstrates exceptional performance, achieving a remarkable accuracy of 96.5% in predicting obesity levels. Comparative analysis showcases its superiority over conventional approaches, with superior precision, recall, and overall classification balance. This study highlights significant advancements in predictive accuracy and positions the ABi-LSTM model as a pioneering solution for accurate obesity prognosis. The implications extend beyond healthcare, offering a precise tool to address the global obesity epidemic and foster sustainable development in smart cities.

show abstract

Section: Related Workmentioning

confidence: 99%

“…A further breakthrough was made by Jiménez-Santos et al. in federated learning for secure medical data sharing [ 24 ]. The inclusion of genetic and non-genetic attributes has enriched predictive models.…”

Section: Related Workmentioning

confidence: 99%

Unraveling the Potential of Attentive Bi-LSTM for Accurate Obesity Prognosis: Advancing Public Health towards Sustainable Cities

Ayub,

Khan,

Shehryar Ali Naqvi

et al. 2024

Bioengineering

View full text Add to dashboard Cite

show abstract

“…Novel Precision Medicine therapies, such as BRAF V600E inhibitors in patients with melanoma and PD1/PD-L1 immunotherapy for melanoma, lung, kidney, and other types of cancer [ 109 , 110 ], are enabling more efficient treatment with minimized side effects. In the following years, with the evolution of translational bioinformatics, advances in data sharing and integration are expected [ 111 ], improving collaborative and multidisciplinary teams [ 112 ], advances in data mining [ 113 ], expanding the ability to generate and analyze data [ 114 ], and planning therapies through better diagnosis [ 115 ] with the identification of new biomarkers and drug targets [ 116 , 117 ].…”

Section: Computational Perspectivesmentioning

confidence: 99%

Computational Biology Helps Understand How Polyploid Giant Cancer Cells Drive Tumor Success

Casotti

Meira

Zetum

et al. 2023

Genes

View full text Add to dashboard Cite

Precision and organization govern the cell cycle, ensuring normal proliferation. However, some cells may undergo abnormal cell divisions (neosis) or variations of mitotic cycles (endopolyploidy). Consequently, the formation of polyploid giant cancer cells (PGCCs), critical for tumor survival, resistance, and immortalization, can occur. Newly formed cells end up accessing numerous multicellular and unicellular programs that enable metastasis, drug resistance, tumor recurrence, and self-renewal or diverse clone formation. An integrative literature review was carried out, searching articles in several sites, including: PUBMED, NCBI-PMC, and Google Academic, published in English, indexed in referenced databases and without a publication time filter, but prioritizing articles from the last 3 years, to answer the following questions: (i) “What is the current knowledge about polyploidy in tumors?”; (ii) “What are the applications of computational studies for the understanding of cancer polyploidy?”; and (iii) “How do PGCCs contribute to tumorigenesis?”.

show abstract

“…Cancer is a group of genetic diseases that has been classically approached with a one-size-fits-all strategy, which often resulted in treatment inefficacy and adverse drug reactions [1], underlining the existence of intertumour heterogeneity [2]. Personalised precision oncology is a novel therapeutic strategy that aims to provide tailored treatments to cancer patients according to the molecular characteristics of individual tumours [3,4]. Breast cancer research has contributed to the foundations of personalised precision oncology by identifying biomarkers to guide patient stratification and selection of targeted therapies.…”

Section: Introductionmentioning

confidence: 99%

Spatial Transcriptomics in Breast Cancer Reveals Tumour Microenvironment-Driven Drug Responses and Clonal Therapeutic Heterogeneity

Jiménez-Santos,

García-Martín,

Rubio-Fernández

et al. 2024

Preprint

Self Cite

View full text Add to dashboard Cite

Breast cancer is a heterogeneous disease that has the highest incidence and mortality rate among cancers in women worldwide. Breast cancer patients are stratified into three clinical subtypes with different treatment strategies and prognostic values. The development of targeted therapies against the biomarkers that define these strata constitutes one of the precedents of precision oncology, which aims to provide tailored treatments to cancer patients by targeting the molecular alterations found in each tumour. Although this approach has increased patient outcomes, many treatment failure cases still exist. Drug ineffectiveness and relapse have been associated with the coexistence of several malignant subpopulations with different drug sensitivities within the same lesion, a phenomenon known as intratumor heterogeneity. This heterogeneity has been extensively studied from a tumour-centric view, but recently, it has become evident that the tumour microenvironment plays a crucial role in intratumor heterogeneity. However, few studies consider the tumour-microenvironment interplay and its influence on drug sensitivity. In this work, we predict the sensitivity of 10x Visium spatial transcriptomics data from 9 breast cancer patients to >1,200 drugs and verify different response patterns across the tumour, interphase and microenvironment regions. We uncover a sensitivity continuum from the tumour core to the periphery accompanied by a functional gradient. Moreover, we identify conserved therapeutic clusters with distinct response patterns within the tumour region. We link the specific drug sensitivities of each therapeutic cluster to different ligand-receptor interactions that underpin distinct biological functions. Finally, we demonstrate that genetically identical cancer spots may belong to different therapeutic clusters and that this therapeutic heterogeneity is related to their location at the edge or core of tumour ducts. These results highlight the importance of considering the distance to the tumour core and the microenvironment composition when identifying suitable treatments to target intratumor heterogeneity.

show abstract

Bioinformatics roadmap for therapy selection in cancer genomics

Abstract: Bioinformatics roadmap for cancer therapy selection M. J. Jim enez-Santos et al.

Cited by 11 publications

References 340 publications

Unraveling the Potential of Attentive Bi-LSTM for Accurate Obesity Prognosis: Advancing Public Health towards Sustainable Cities

Unraveling the Potential of Attentive Bi-LSTM for Accurate Obesity Prognosis: Advancing Public Health towards Sustainable Cities

Computational Biology Helps Understand How Polyploid Giant Cancer Cells Drive Tumor Success

Spatial Transcriptomics in Breast Cancer Reveals Tumour Microenvironment-Driven Drug Responses and Clonal Therapeutic Heterogeneity

Contact Info

Product

Resources

About