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. 2021 Dec 6;12(1):7081.
doi: 10.1038/s41467-021-27341-1.

The histologic phenotype of lung cancers is associated with transcriptomic features rather than genomic characteristics

Ming Tang #  1 , Hussein A Abbas #  2 , Marcelo V Negrao  3 , Maheshwari Ramineni  4 , Xin Hu  1 , Shawna Marie Hubert  3 , Junya Fujimoto  5 , Alexandre Reuben  3 , Susan Varghese  3 , Jianhua Zhang  1 , Jun Li  1 , Chi-Wan Chow  5 , Xizeng Mao  1 , Xingzhi Song  1 , Won-Chul Lee  1 , Jia Wu  6 , Latasha Little  1 , Curtis Gumbs  1 , Carmen Behrens  3 , Cesar Moran  7 , Annikka Weissferdt  7 , J Jack Lee  8 , Boris Sepesi  9 , Stephen Swisher  9 , Chao Cheng  10 , Jonathan Kurie  3 , Don Gibbons  3 , John V Heymach  3 , Ignacio I Wistuba  3   5 , P Andrew Futreal  11 , Neda Kalhor  12 , Jianjun Zhang  13   14
Affiliations

The histologic phenotype of lung cancers is associated with transcriptomic features rather than genomic characteristics

Ming Tang et al. Nat Commun. .

Abstract

Histology plays an essential role in therapeutic decision-making for lung cancer patients. However, the molecular determinants of lung cancer histology are largely unknown. We conduct whole-exome sequencing and microarray profiling on 19 micro-dissected tumor regions of different histologic subtypes from 9 patients with lung cancers of mixed histology. A median of 68.9% of point mutations and 83% of copy number aberrations are shared between different histologic components within the same tumors. Furthermore, different histologic components within the tumors demonstrate similar subclonal architecture. On the other hand, transcriptomic profiling reveals shared pathways between the same histologic subtypes from different patients, which is supported by the analyses of the transcriptomic data from 141 cell lines and 343 lung cancers of different histologic subtypes. These data derived from mixed histologic subtypes in the setting of identical genetic background and exposure history support that the histologic fate of lung cancer cells is associated with transcriptomic features rather than the genomic profiles in most tumors.

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Conflict of interest statement

Jianjun Zhang reports research funding from Merck, Johnson and Johnson, and consultant fees from BMS, Johnson and Johnson, AstraZeneca, Geneplus, OrigMed, Innovent outside the submitted work. J. V. H. reports research funding from AstraZeneca, GlaxoSmithKline, and Spectrum; consultant fees from AstraZeneca, Boehringer Ingelheim, Bristol-Myers Squibb, Catalyst, EMD Serono, Foundation Medicine, Hengrui Therapeutics, Genentech, GSK, Guardant Health, Eli Lilly, Merck, Novartis, Pfizer, Roche, Sanofi, Seattle Genetics, Spectrum, and Takeda; licensing fees from Spectrum. B. S. reports consultant fees from BMS. M. V. N. reports research funding from Mirati, Novartis, Checkmate, Ziopharm, AstraZeneca, Pfizer, and Genentech; consultant fees from Mirati, Merck/MSD. The other authors declare neither financial nor non-financial interests in the submitted work.

Figures

Fig. 1
Fig. 1. Overlapping number of somatic mutations across the samples.
The upset plot demonstrates the shared mutations across samples. Blue bars in the y-axis represent the total number of mutations in each sample. Black bars in the x-axis represent the number of mutations shared across samples connected by the black dots in the body of the plot. Source data are provided as a Source Data file.
Fig. 2
Fig. 2. Mutational spectrums and signatures are similar across different histologic components within the same patient.
a Bar plots represent the mutational spectrum decomposed by trinucleotide context. b Heatmap of the contribution of the 30 COSMIC mutation signatures in each sample. c Stacked barplot for the contribution of the top 10 mutation signatures in each sample. Source data are provided as a Source Data file.
Fig. 3
Fig. 3. Somatic copy number aberration (SCNA) analysis demonstrated similar copy number changes between different histologic components within the same patient.
a IGV screenshot of genome-wide SCNA profile for each sample. b Heatmap of the correlation of SCNA at the gene level. c Heatmap of copy number changes from canonical cancer genes of the COSMIC database. Source data are provided as a Source Data file.
Fig. 4
Fig. 4. Clonality analysis revealed shared clonal mutations between different histologic components within the same patients.
a–k Scatter plots of the cellular prevalence of somatic mutations calculated by PyClone for the two histological components within the same patient. Mutations were clustered by PyClone and mutations of the same cluster were labeled with the same color. Source data are provided as a Source Data file.
Fig. 5
Fig. 5. Gene expression profile revealed some extent of similarity of the same histologic components across different patients.
a Principal component analysis (PCA) of all histologic components based on gene expression data. b Heatmap of the top 500 most variable genes across the samples clustered by both genes and the samples. c Commonly upregulated and downregulated pathways comparing SCLC with LUAD across public datasets and in-house dataset. d Commonly upregulated and downregulated pathways comparing LCNEC with LUAD across public datasets and in-house dataset. Source data are provided as a Source Data file.
See this image and copyright information in PMC

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