Multi-omic signatures identify pan-cancer classes of tumors beyond tissue of origin.

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 extreme diversity among seemingly identical tumors: while some tumors may have good prognosis and are treatable, others are quite aggressive, and may lack of effective therapies. Most of this variability comes from wide-spread mutations and epigenetic alterations. Using a novel omic-integration method, we have exploited this molecular information to re-classify tumors beyond the constraints of cell type. Eight novel tumor groups (C1-8) emerged, characterized by unique cancer signatures. C3 had better prognosis, genome stability, and immune infiltration. C2 and C5 had higher genome instability and poorer clinical outcomes. Remaining clusters were characterized by worse outcomes, along with higher genome instability. 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.\n\nSignificanceCancer has been traditionally studied as a family of different diseases from different anatomical sites. Nevertheless, regardless of the tissue of origin, cancer can be characterized by molecular alterations on mechanisms controlling cell fate and progression. In this study, we integrate 33 cancer types and show the existence of eight clusters with unique genomic signatures and clinical characteristics, beyond the site of origin of the tumor. The study and treatment of cancer, based on predominant molecular features, rather than site of origin, can potentially aid in the discovery of novel therapeutic alternatives.