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Multi-omic signatures identify pan-cancer classes of tumors beyond tissue of origin
Agustín González-Reymúndez1,2, Ana I Vázquez3,4
1Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI, USA.
Abstract:
Despite recent advances in treatment, cancer continues to be one of the most lethal human maladies. One of the challenges of cancer treatment is the diversity among similar tumors that exhibit different clinical outcomes. Most of this variability comes from wide-spread molecular alterations that can be summarized by omic integration. Here, we have identified eight novel tumor groups (C1-8) via omic integration, characterized by unique cancer signatures and clinical characteristics. C3 had the best clinical outcomes, while C2 and C5 had poorest. C1, C7, and C8 were upregulated for cellular and mitochondrial translation, and relatively low proliferation. C6 and C4 were also downregulated for cellular and mitochondrial translation, and had high proliferation rates. C4 was represented by copy losses on chromosome 6, and had the highest number of metastatic samples. C8 was characterized by copy losses on chromosome 11, having also the lowest lymphocytic infiltration rate. C6 had the lowest natural killer infiltration rate and was represented by copy gains of genes in chromosome 11. C7 was represented by copy gains on chromosome 6, and had the highest upregulation in mitochondrial translation. We believe that, since molecularly alike tumors could respond similarly to treatment, our results could inform therapeutic action.
Insights
Researchers identified eight novel cancer groups using omic integration, revealing distinct molecular signatures and clinical outcomes. These findings could guide personalized cancer treatment strategies by matching tumor profiles to therapies.
Area of Science:
- Oncology
- Genomics
- Molecular Biology
Background:
- Cancer heterogeneity presents a significant challenge to effective treatment.
- Molecular alterations, summarized by omic integration, contribute to diverse clinical outcomes in similar tumors.
Purpose of the Study:
- To identify novel tumor groups based on molecular profiles.
- To characterize these groups by their unique signatures and clinical characteristics.
- To explore the potential of omic integration for informing therapeutic strategies.
Main Methods:
- Omic integration was employed to analyze tumor molecular data.
- Identification of eight distinct tumor groups (C1-C8).
- Correlation of molecular signatures with clinical characteristics, including proliferation rates, immune infiltration, and copy number alterations.
Main Results:
- Eight novel tumor groups (C1-C8) were identified with unique molecular signatures.
- Group C3 exhibited the best clinical outcomes, while C2 and C5 had the poorest.
- Specific molecular features, such as translation rates, proliferation, copy number alterations (chromosomes 6 and 11), and immune infiltration (lymphocytic and natural killer cells), were associated with distinct groups and clinical behaviors.
Conclusions:
- Omic integration successfully delineated novel cancer subgroups with distinct molecular and clinical features.
- Understanding these molecularly defined tumor groups can potentially predict treatment response.
- The identified tumor groups offer a foundation for developing more targeted and effective cancer therapies.
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