Intratumor Heterogeneity Drives Lung Cancer Evolution and Therapy Resistance - medtigo



Intratumor Heterogeneity Drives Lung Cancer Evolution and Therapy Resistance

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Lung cancer is a complex and heterogeneous disease that can evolve through genetic and epigenetic changes, resulting in cellular and molecular diversity within tumors. The causes of cancer cell-to-cell variation are essential to understanding tumor evolution, including the development of metastases. Recent research has highlighted that much of this variation is transcriptomic, arising from diverse mechanisms that relate to, or are independent of, genomic variation.

This has led to a shift towards studying transcriptomic plasticity in non-small cell lung cancer (NSCLC) models. While genomic variation reflects past somatic events, transcriptomic variation may provide a more accurate approximation of the phenotypic state of a tumor at the time of sampling. However, to date, most studies of tumor evolution in humans have focused on the impact of genomic alterations on cancer, and transcriptomic studies that leverage bulk tumor RNA sequencing data tend to focus on the amplitude of gene expression in a single biopsy taken at a single time point, which might fail to capture poorly understood transcriptomic processes.  

A recent study published in Nature has shed light on the causes of cancer cell-to-cell variation, which is essential to understand tumor evolution. The study emphasizes that much of this variation is transcriptomic, arising from diverse mechanisms that relate to or are independent of genomic variation. Transcriptomic plasticity has been shown to underpin non-small cell lung cancer (NSCLC) in mouse models. However, most studies of human tumor evolution have focused on the impact of genomic alterations on cancer.  

Researchers used multi-region sequencing data from TRACERx patients to gain a better understanding of the role of various transcriptome characteristics and their interactions with genomic and phenotypic variability in NSCLC progression at different geographical and temporal scales. The study comprised 354 cases of non-small cell lung cancer (NSCLC) tumors and 96 cases with normal lung tissue around the lesions. At the time of original tumor excision, there were 886 initial tumor sites in 344 patients, 29 metastatic lymph node (LN) areas in 21 patients, and 30 metastatic tumor regions in 24 patients at the time of recurrence or advancement.  

First, they investigated how genes were expressed in diverse tumor types. Lung adenocarcinomas (LUADs), squamous cell carcinomas (LUSCs), and normal lung tissue around tumors were discovered to predominate in their respective sample groups. 27 of the 184 non-LUAD cancers were clustered with LUADs and were 23 times more likely to contain a LUAD-specific driver mutation. Sixty-seven percent of these tumors showed a LUAD-like morphology or were positive for LUAD immunohistochemical staining markers such as TTF-1, however they were not classified as LUADs. An enrichment for LUAD driver mutations among non-LUAD NSCLC tumors that cluster with LUADs suggests that this class of tumors may be phenotypically akin to LUADs.  

Following that, the researchers performed independent principal component analyses (PCAs) within the two most frequent NSCLC histologies (LUAD and LUSC) to discover characteristics that influence intratumor and intratumor transcriptome variation. These findings were connected to 39 underlying genetic and clinicopathological variables. Inside LUADs, more deterministic genomic-transcriptomic interactions exist, as indicated by PCs with lower relative ITH in LUADs compared to LUSCs.

Furthermore, orthogonal signatures that assess RAS pathway activation were linked to LUAD PC activity, suggesting that PCs may reflect transcriptional programs that are consistent across datasets. Overall, this study offers light on the variables that contribute to NSCLC development over several temporal and space scales, as well as how various transcriptome features interact with genomic and phenotypic heterogeneity. The findings might aid clinicians in making more exact diagnoses, providing more accurate prognoses, and developing better therapies for non-small cell lung cancer. 


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