Right-sided colon cancer (RCC) has worse prognosis compared to left-sided colon cancer (LCC) and rectal cancer. The reason for this difference in outcomes is not well understood. We performed comparative somatic and proteomic analyses of RCC, LCC and rectal cancers to understand the unique molecular features of each tumor sub-types. Utilizing a novel in silico clonal evolution algorithm, we identified common tumor-initiating events involving APC, KRAS and TP53 genes in RCC, LCC and rectal cancers. However, the individual role-played by each event, their order in tumor development and selection of downstream somatic alterations were distinct in all three anatomical locations. Some similarities were noted between LCC and rectal cancer. Hotspot mutation analysis identified a nonsense mutation, APC R1450* specific to RCC. In addition, we discovered new significantly mutated genes at each tumor location, Further in silico proteomic analysis, developed by our group, found distinct central or hub proteins with unique interactomes among each location. Our study revealed significant differences between RCC, LCC and rectal cancers not only at somatic but also at proteomic level that may have therapeutic relevance in these highly complex and heterogeneous tumors.Electronic supplementary materialThe online version of this article (10.1186/s12943-018-0923-9) contains supplementary material, which is available to authorized users.

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Next-generation Sequencing (NGS) of cancer tissues is increasingly being carried out to identify somatic genomic alterations that may guide physicians to make therapeutic decisions. However, a single tissue biopsy may not reflect complete genomic architecture due to the heterogeneous nature of tumors. Circulating tumor DNA (ctDNA) analysis is a robust noninvasive method to detect and monitor genomic alterations in blood in real time. We analyzed 28 matched tissue NGS and ctDNA from gastrointestinal and lung cancers for concordance of somatic genomic alterations, driver, and actionable alterations. Six patients (21%) had at least one concordant mutation between tissue and ctDNA sequencing. At the gene level, among all the mutations ( = 104) detected by tissue and blood sequencing, 7.7% ( = 8) of mutations were concordant. Tissue and ctDNA sequencing identified driver mutations in 60% and 64% of the tested samples, respectively. We found high discordance between tissue and ctDNA testing, especially with respect to the driver and actionable alterations. Both tissue and ctDNA NGS detected actionable alterations in 25% of patients. When somatic alterations identified by each test were combined, the total number of patients with actionable mutations increased to 32%. Our data show significant discordance between tissue NGS and ctDNA analysis. These results suggest tissue NGS and ctDNA NGS are complementary approaches rather than exclusive of each other. When performed in isolation, tissue and ctDNA NGS can each potentially miss driver and targetable alterations, suggesting that both approaches should be incorporated to enhance mutation detection rates. Larger prospective studies are needed to better clarify this emerging precision oncology landscape. .

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