A gene expression‐based single sample predictor of lung adenocarcinoma molecular subtype and prognosis

Abstract

Disease recurrence in surgically treated lung adenocarcinoma (AC) remains high. New approaches for risk stratification beyond tumor stage are needed. Gene expressionbased AC subtypes such as the Cancer Genome Atlas Network (TCGA) terminalrespiratory unit (TRU), proximal-inflammatory (PI) and proximal-proliferative (PP) subtypes have been associated with prognosis, but show methodological limitations for robust clinical use. We aimed to derive a platform independent single sample

Pan-cancer application of a lung-adenocarcinoma-derived gene-expression-based prognostic predictor

“…CLAMS [ 13 ] was installed as a package in RStudio running R v3.6.3 [ 24 ]. This SSP classifies lung adenocarcinoma samples into known expression subtypes in a two-class (TRU/nonTRU) or three-class (TRU/PP/PI) approach.…”

“…This SSP classifies lung adenocarcinoma samples into known expression subtypes in a two-class (TRU/nonTRU) or three-class (TRU/PP/PI) approach. In this study, the two-class CLAMS SSP was used to classify all samples from the above data sets into better prognosis (referred to as TRU in the original report for CLAMS [ 13 ]) or worse prognosis (originally referred to as nonTRU by CLAMS) groups. All 36 genes used by CLAMS for classification were present in all included data sets.…”

“…To try to overcome the problems with applying gene expression signatures in a clinical context in lung cancer, we recently reported a classifier for lung adenocarcinoma molecular subtypes (CLAMS) [ 13 ]. CLAMS is a true SSP that can identify lung adenocarcinoma gene-expression subtypes as proposed by The Cancer Genome Atlas (TCGA) consortium [ 6 ] either in a three-group (Terminal Respiratory Unit (TRU), Proximal-Proliferative (PP) and Proximal-Inflammatory (PI)) or two-group (TRU, nonTRU) context.…”

“…CLAMS was successfully trained and evaluated using a variety of different technical gene-expression platforms including Affymetrix, Illumina and Agilent microarrays, as well as RNA sequencing. Independent validation of CLAMS prediction in lung adenocarcinoma showed that predicted classes were associated with specific clinicopathological and molecular features, as well as with patient outcome with TRU-classified tumors showing superior survival compared to nonTRU cases [ 13 ]. For the two-class (TRU/nonTRU) classification approach, CLAMS uses the expression of 36 genes that are highly associated with cell proliferation arranged into 18 pair rules.…”

“…To resolve the knowledge gap of how rule-based SSP models work pan-cancer, we collected publicly available gene-expression data from nearly 15 000 patients representing 32 solid cancer types. For each malignancy, we classified tumors according to the reported two-class CLAMS SSP [ 13 ] into better (TRU used as acronym) or worse (nonTRU used as acronym) prognosis subgroups and compared the overall survival of patients in these groups. In this context, we used CLAMS as a case example of applying a rule-based SSP pan-cancer.…”