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Heejoon Jo

Publications and source records attributed to Heejoon Jo.

2 recordsLinked to original sources

RAS signaling in lung adenocarcinoma is defined by lineage context and DUSP4 loss.

BACKGROUNDThe molecular landscape of lung adenocarcinoma (LUAD) is often illustrated as a driver-oncogene pie chart, but identical mutations exhibit heterogeneous signaling shaped by comutations, transcriptional programs, and lineage context. We propose a lineage-integrated signaling framework using an EGFR mutation signature (mSig).METHODSWe defined EGFR mSig using differentially expressed genes in EGFR-mutant (EGFR-mt) LUADs. Semisupervised clustering and machine learning models were used to test reproducibility in different combinations of datasets. We analyzed molecular subtypes, lineage markers, co-occurring mutations, and EGFR copy number alterations in EGFR mSig-defined subtypes of LUAD.RESULTSEGFR mSig showed robust classification performance (area under receiver operating characteristic curve = 0.83-0.95; mean negative predictive value = 96.3%). Validated gene expression subtypes and lung lineage markers were closely aligned with EGFR mSig status. Most EGFR mSig+ tumors, including many without EGFR mutations, belonged to the bronchioid subtype. A subset of canonical RAS mutations were mSig+ and mirrored the EGFR mutation pattern. EGFR WT/mSig- tumors were enriched for nonbronchioid subtypes and had comutations in TP53 or RAS/RAF/RTKs. We highlight a parsimonious collection of coordinated mutations, including RAS, KEAP1, STK11, TP53, and CDKN2A, that taken together suggest coordination of tumor signaling previously suggested but now reproduced and expanded.CONCLUSIONA potentially novel EGFR mSig that captures the transcriptional footprint of EGFR activation revealed a subset of EGFR WT LUADs with mt-like features. mSig refines LUAD taxonomy beyond mutation-only pie-chart models by incorporating lineage and comutation context. Lineage-directed stratification with coalteration identifies clinically relevant groups across EGFR and RAS states and highlights treatment opportunities for patients currently considered oncogene-negative.FUNDINGNational Cancer Institute (NCI) U01CA272541, R01CA262296, U24CA264021, UG1CA233333, R01CA211939.

Humans

The offonome reveals on and off states of gene expression near the detection limit of RNA-seq.

RNA-seq, widely used for gene expression profiling, provides nucleotide level genome coverage and summary gene expression values. Generally, low-expressed genes are ignored due to their unfavorable signal-to-noise ratio, however, these genes may offer crucial information, such as detecting rare cells in bulk tissues. In this study, we applied an approach that transforms the expression levels of low-expressed genes into a robust dichotomized on/off state by leveraging similarities in transcript coverage shape. Applied to three human cancer cohorts from the Cancer Genome Atlas (TCGA), chosen based on tissue morphology and anatomic site, we identified genes, the "offonome" near the detection limit, consistently or occasionally off across samples. Genes in the offonome spectrum proved useful for supervised and unsupervised applications, including characterizing oncogenic pathways, and identifying rare populations of cells in bulk tissue. Interrogating the offonome is relevant to bulk tumor analyses like TCGA, potentially expediting gene investigation in low-input situations like single cell RNA-seq.

Humans