Abstract Background: Cervical cancer patients undergoing CRT face a 40% mortality rate, and the genetic underpinnings of radiation response variability remain under-explored. Our laboratory has developed a noninvasive cervical swab biopsy method, complemented by a custom computational pipeline that facilitates longitudinal whole-exome sequencing (WES) from samples with minimal tumor purity. This investigation used the pipeline for the identification of persistent or clonally expanded genes during CRT. The aim is to identify genes and pathways associated with radiation resistance. Methods: Tumor swabs from 30 cervical cancer patients were collected at baseline (week 1) and CRT completion (week 5) paired with corresponding buccal samples. We performed whole exome sequencing, adjusting for calculated tumor purity with strict mutation calling using several tools. Results: Our analysis revealed genes in known DNA damage and repair (DDR) pathways, in addition to unique genes not previously associated with DDR and radiation response, including BAGE3, CGREF1, XRCC5, ATM, LINP1, etc. Future work will include validation of these identified genes in vitro radiation sensitivity screening and validation in large-scale datasets, such as The Cancer Genome Atlas, and network and pathway analysis. Discussion: This exploratory analysis suggests that serial sequencing during chemoradiation to identify novel radiation sensitization targets is feasible and further study is needed. Their recurring involvement in DDR pathways underscores their pivotal role in CRT resistance mechanisms. Conclusions: Through longitudinal WES, we've deepened the understanding of CRT resistance and flagged potential targets for improved radiosensitization strategies in cervical cancer. Future Directions: We are moving towards experimental validation of these genes in patient-derived organoids using CRISPR/Cas9 and CyTOF. Additionally, we'll be screening FDA-approved drugs to pinpoint effective radiosensitizers. A major upcoming effort is the development of a CRT resistance map, enhanced by machine learning, promising a transformative impact on cervical cancer treatment. Citation Format: Shafqat F. Ehsan, Rui Wang, Xiaogang Wu, Tatiana V. Karpinets, Julianna K. Bronk, Chiraag Kapadia, Xingzhi Song, Andrew M. Futreal, Ann H. Klopp, Tim Harris, Jianhua Zhang, Lauren E. Colbert. DDR pathway genes in CRT resistance: Insights from longitudinal whole exome sequencing in cervical cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: DNA Damage Repair: From Basic Science to Future Clinical Application; 2024 Jan 9-11; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2024;84(1 Suppl):Abstract nr A029.
<p>Supplementary Table 1: Baseline characteristics for patients included in the integrated gene set enrichment analysis (Figure 2-3) by cohort; Supplementary Table 6: The most frequent somatic alterations (alts) in patients with regionally metastatic melanoma by body mass index from The Cancer Genome Atlas cohort; Supplemental Table 7: Integrated gene set enrichment analysis for pathways associated with fatty acid metabolism by body mass index; Supplemental Table 8: Gene expression of selected genes of interest involved in fatty acid metabolism.</p>
Adenoid cystic carcinoma (ACC) is a rare, usually slow-growing yet aggressive head and neck malignancy. Despite its clinical significance, our understanding of the cellular evolution and microenvironment in ACC remains limited. We investigated the intratumoral microbiomes of 50 ACC tumor tissues and 33 adjacent normal tissues using 16S rRNA gene sequencing. This allowed us to characterize the bacterial communities within the ACC and explore potential associations between the bacterial community structure, patient clinical characteristics, and tumor molecular features obtained through RNA sequencing. The bacterial composition in the ACC was significantly different from that in adjacent normal salivary tissue, and the ACC exhibited diverse levels of species richness. We identified two main microbial subtypes within the ACC: oral-like and gut-like. Oral-like microbiomes, characterized by increased diversity and abundance of Neisseria, Leptotrichia, Actinomyces, Streptococcus, Rothia, and Veillonella (commonly found in healthy oral cavities), were associated with a less aggressive ACC-II molecular subtype and improved patient outcomes. Notably, we identified the same oral genera in oral cancer and head and neck squamous cell carcinomas. In both cancers, they were part of shared oral communities associated with a more diverse microbiome, less aggressive tumor phenotype, and better survival that reveal the genera as potential pancancer biomarkers for favorable microbiomes in ACC and other head and neck cancers. Conversely, gut-like intratumoral microbiomes, which feature low diversity and colonization by gut mucus layer-degrading species, such as Bacteroides, Akkermansia, Blautia, Bifidobacterium, and Enterococcus, were associated with poorer outcomes. Elevated levels of Bacteroides thetaiotaomicron were independently associated with significantly worse survival and positively correlated with tumor cell biosynthesis of glycan-based cell membrane components.
Abstract High-throughput assay systems have had a disproportionally large impact on uncovering how cells function, as well as how misregulation can lead to disease. However, no high-throughput assay systems have been developed to systematically address how mutations impact molecular functions or cell processes in human cells. This is arguably one of the most critical assays because human pathology and treatment are largely based upon molecular functions. To address this challenge, herein we engineered, developed, and tested the first modular high-throughput molecular function assay system. Note that this is not a selection lethality screen! This “GigaAssay” single cell / one-pot assay system was adapted to study how variants impact HIV Tat-driven transactivation of a green fluorescent protein (GFP) reporter. We assayed all 1,615 Tat single and 3,429 double amino acid substitutions with no single mutant dropout. Each mutant was assayed with replicate observations in LentiX293T and Jurkat cells with an average of 100s of separately barcoded cDNA molecules and cell groups for each mutant. Each mutant had ~2,000X-90,000X sequencing coverage to measure its transcriptional activity and had p value ranging as low as 10-271. Five independent assay performance assessments with benchmark data, individually tested clones, and replicate comparisons all indicate exceptional reproducibility, accuracy, and robustness. The shortcomings of alanine scanning mutagenesis and protein truncation studies are revealed by including exhaustive substitution tolerance and intragenic epistasis in the typical structure/function analysis(structure/function/tolerance/epistasis). This flexible and extensible technology enables a far more comprehensive holistic view of protein molecular function and yet with a highly simplified single-pot assay.
Summary In many cases, crucial genes show relatively slight changes between groups of samples (e.g. normal vs. disease), and many genes selected from microarray differential analysis by measuring the expression level statistically are also poorly annotated and lack of biological significance. In this paper, we present an innovative approach - network expansion and pathway enrichment analysis (NEPEA) for integrative microarray analysis. We assume that organized knowledge will help microarray data analysis in significant ways, and the organized knowledge could be represented as molecular interaction networks or biological pathways. Based on this hypothesis, we develop the NEPEA framework based on network expansion from the human annotated and predicted protein interaction (HAPPI) database, and pathway enrichment from the human pathway database (HPD). We use a recently-published microarray dataset (GSE24215) related to insulin resistance and type 2 diabetes (T2D) as case study, since this study provided a thorough experimental validation for both genes and pathways identified computationally from classical microarray analysis and pathway analysis. We perform our NEPEA analysis for this dataset based on the results from the classical microarray analysis to identify biologically significant genes and pathways. Our findings are not only consistent with the original findings mostly, but also obtained more supports from other literatures.
In many cases, crucial genes show relatively slight changes between groups of samples (e.g. normal vs. disease), and many genes selected from microarray differential analysis by measuring the expression level statistically are also poorly annotated and lack of biological significance. In this paper, we present an innovative approach - network expansion and pathway enrichment analysis (NEPEA) for integrative microarray analysis. We assume that organized knowledge will help microarray data analysis in significant ways, and the organized knowledge could be represented as molecular interaction networks or biological pathways. Based on this hypothesis, we develop the NEPEA framework based on network expansion from the human annotated and predicted protein interaction (HAPPI) database, and pathway enrichment from the human pathway database (HPD). We use a recently-published microarray dataset (GSE24215) related to insulin resistance and type 2 diabetes (T2D) as case study, since this study provided a thorough experimental validation for both genes and pathways identified computationally from classical microarray analysis and pathway analysis. We perform our NEPEA analysis for this dataset based on the results from the classical microarray analysis to identify biologically significant genes and pathways. Our findings are not only consistent with the original findings mostly, but also obtained more supports from other literatures.
It has been challenging to develop enhanced pathway tools and methods that could expand the coverage and improve the quality of existing annotated human pathway data. We aim to quantitatively evaluate the processes of merging similar or functionally-related signaling pathways together by linking them with protein-protein interaction (PPI) data. We presented a concept of pathway mergeability to examine the merging potential between two different pathways. We analyzed the mergeability variation of existing pathways in an integrated human pathway database (HPD) and potential pathways expended from a human annotated and predicted protein interaction (HAPPI) database with confidence score for each interaction. Furthermore, by comparing the mergeability variation between expanding existing pathways in the HAPPI and expanding existing pathways in randomly-permuted PPI networks, we revealed a quantitative relationship between signaling pathway data and high-quality PPI data. This quantitative relationship will further guide pathway merging processes and also pathway tool development.
'/%3e%3cuse xlink:href='%23a' transform='rotate(23.036 .093 25.536)'/%3e%3cuse xlink:href='%23a' transform='rotate(45.87 1.273 16.18)'/%3e%3cuse xlink:href='%23a' transform='rotate(69.945 .996 12.078)'/%3e%3cuse xlink:href='%23a' transform='rotate(20.66 -19.689 31.932)'/%3e%3c/svg%3e)