Shifting Gears in Precision Oncology—Challenges and Opportunities of Integrative Data Analysis

Abstract

For decades, research relating to modification of host immunity towards antitumor response activation has been ongoing, with the breakthrough discovery of immune-checkpoint blockers. Several biomarkers with potential predictive value have been reported in recent studies for these novel therapies. However, with the plethora of therapeutic options existing for a given cancer entity, modern oncology is now being confronted with multifactorial interpretation to devise “the best therapy” for the individual patient. Into the bargain come the multiverse guidelines for established and emerging diagnostic biomarkers, as well as the complex interplay between cancer cells and tumor microenvironment, provoking immense challenges in the therapy decision-making process. Through this review, we present various molecular diagnostic modalities and techniques, such as genomics, immunohistochemistry and quantitative image analysis, which have the potential of becoming powerful tools in the development of an optimal treatment regime when analogized with patient characteristics. We will summarize the underlying complexities of these methods and shed light upon the necessary considerations and requirements for data integration. It is our hope to provide compelling evidence to emphasize on the need for inclusion of integrative data analysis in modern cancer therapy, and thereupon paving a path towards precision medicine and better patient outcomes.

Challenges and solutions to system-wide use of precision oncology as the standard of care paradigm

“…As discussed, addressing clinical workflow inefficiencies also necessitates a substantial investment in data integration platforms that import, integrate and aggregate various data types. On a practical level, clinicians would benefit from a comprehensive data view of the patient journey, streamlined care coordination and better decision-making with less administrative and cognitive burden (Noh et al, 2021 ; Patel et al, 2023 ). With the rising complexity of interpreting molecular and clinical data, there is an increasing demand for the adoption of clinical decision support systems (CDS) that can assist treatment choices, enhance adherence to clinical guidelines and reduce variability (Scudder et al, 2020 ; Serramito-Gómez et al, 2021 ; Halligan et al, 2023 ).…”

Spatially Exploring RNA Biology in Archival Formalin-Fixed Paraffin-Embedded Tissues

“…Spatial transcriptomics is revolutionizing our understanding of developmental biology, oncology, and disease pathology, mapping intricate gene expression patterns within their native tissue context 1 . This innovation is instrumental in discerning subtle nuances of transcriptional diversity, cellular pathways, and microscopic niches within complex tissues 2 , promising to refine diagnostic precision and guide the creation of targeted treatment modalities 3,4 . However, to date, the scope of the field primarily revolves around the analysis of messenger RNA (mRNA) expression.…”