Informace o publikaci
An invasion front gene expression signature for higher-risk patient selection in stage IIA MSS colon cancer
| Autoři | |
|---|---|
| Rok publikování | 2024 |
| Druh | Článek v odborném periodiku |
| Časopis / Zdroj | Frontiers in Oncology |
| Fakulta / Pracoviště MU | |
| Citace | |
| www | https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2024.1367231/full |
| Doi | http://dx.doi.org/10.3389/fonc.2024.1367231 |
| Klíčová slova | colon cancer; invasion front; early stage; prognostic signature; stage II/MSS |
| Přiložené soubory | |
| Popis | Stage II colon cancer (CC) encompasses a heterogeneous group of patients with diverse survival experiences: 87% to 58% 5-year relative survival rates for stages IIA and IIC, respectively. While stage IIA patients are usually spared the adjuvant chemotherapy, some of them relapse and may benefit from it; thus, their timely identification is crucial. Current gene expression signatures did not specifically target this group nor did they find their place in clinical practice. Since processes at invasion front have also been linked to tumor progression, we hypothesize that aside from bulk tumor features, focusing on the invasion front may provide additional clues for this stratification. A retrospective matched case-control collection of 39 stage IIA microsatellite-stable (MSS) untreated CCs was analyzed to identify prognostic gene expression-based signatures. The endpoint was defined as relapse within 5 years vs. no relapse for at least 6 years. From the same tumors, three different classifiers (bulk tumor, invasion front, and constrained baseline on bulk tumor) were developed and their performance estimated. The baseline classifier, while the weakest, was validated in two independent data sets. The best performing signature was based on invasion front profiles [area under the receiver operating curve (AUC) = 0.931 (0.815-1.0)] and contained genes associated with KRAS pathway activation, apical junction complex, and heme metabolism. Its combination with bulk tumor classifier further improved the accuracy of the predictions. |
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