INTEGRATIVE ANALYSIS FOR LUNG ADENOCARCINOMA PREDICTS MORPHOLOGICAL FEATURES ASSOCIATED WITH GENETIC VARIATIONS

A Review of Fusion Methods for Omics and Imaging Data

The development of omics data and biomedical images has greatly advanced the progress of precision medicine in diagnosis, treatment, and prognosis. The fusion of omics and imaging data, i.e., omics-imaging fusion, offers a new strategy for understanding complex diseases. However, due to a variety of issues such as the limited number of samples, high dimensionality of features, and heterogeneity of different data types, efficiently learning complementary or associated discriminative fusion information from omics and imaging data remains a challenge. Recently, numerous machine learning methods have been proposed to alleviate these problems. In this review, from the perspective of fusion levels and fusion methods, we first provide an overview of preprocessing and feature extraction methods for omics and imaging data, and comprehensively analyze and summarize the basic forms and variations of commonly used and newly emerging fusion methods, along with their advantages, disadvantages and the applicable scope. We then describe public datasets and compare experimental results of various fusion methods on the ADNI and TCGA datasets. Finally, we discuss future prospects and highlight remaining challenges in the field.

Computational Image Analysis Identifies Histopathological Image Features Associated With Somatic Mutations and Patient Survival in Gastric Adenocarcinoma

Computational analysis of histopathological images can identify sub-visual objective image features that may not be visually distinguishable by human eyes, and hence provides better modeling of disease phenotypes. This study aims to investigate whether specific image features are associated with somatic mutations and patient survival in gastric adenocarcinoma (sample size = 310). An automated image analysis pipeline was developed to extract quantitative morphological features from H&E stained whole-slide images. We found that four frequently somatically mutated genes (TP53, ARID1A, OBSCN, and PIK3CA) were significantly associated with tumor morphological changes. A prognostic model built on the image features significantly stratified patients into low-risk and high-risk groups (log-rank test p-value = 2.6e-4). Multivariable Cox regression showed the model predicted risk index was an additional prognostic factor besides tumor grade and stage. Gene ontology enrichment analysis showed that the genes whose expressions mostly correlated with the contributing features in the prognostic model were enriched on biological processes such as cell cycle and muscle contraction. These results demonstrate that histopathological image features can reflect underlying somatic mutations and identify high-risk patients that may benefit from more precise treatment regimens. Both the image features and pipeline are highly interpretable to enable translational applications.

Plakophilin-2 Promotes Lung Adenocarcinoma Development via Enhancing Focal Adhesion and Epithelial-Mesenchymal Transition

Background

Lung cancer is one of the most aggressive tumors with high incidence and mortality, which could be classified into lung squamous cell carcinoma (LUSC) and lung adenocarcinoma (LUAD). Overexpression of Plakophilin-2 (PKP2) has been reported in multiple malignancies. However, the expression and function mechanism of PKP2 in LUAD remain illusive.

Methods

Real-time PCR (RT-PCR) was conducted to assess the expression of PKP2 in LUAD cells and tissues. An integrated analysis of PKP2 expression in The Cancer Genome Atlas (TCGA) was further performed. The effect of PKP2 on cell proliferation and invasion potential were then evaluated with loss-of-function assays in vitro. Xenograft nude mouse models were used to determine the role of PKP2 in LUAD tumorigenicity in vivo. Bioinformatics prediction, immunohistochemistry and Western blot were performed to examine whether PKP2 promoted LUAD development via enhancing focal adhesion and epithelial–mesenchymal transition.

Results

PKP2 expression was highly expressed in LUAD tissues compared with that in normal tissues and predicated poor prognosis of LUAD patients. TCGA LUAD cohort analysis also showed that high expression of PKP2 indicated unfavorable outcomes in LUAD patients. PKP2 expression was also upregulated in lung cancer cells. Functionally, knockdown of PKP2 suppressed lung cancer cell proliferation and invasion in vitro, while inhibited xenograft lung tumor development in vivo. Mechanistically, we demonstrated that high expression of PKP2 in LUAD was correlated with enhanced EMT and focal adhesion. Knockdown of PKP2 inhibited the expression of EMT-related Vimentin and N-cadherin and focal adhesion-associated expression of BMP4, ICAM1, and VCAM1 in xenograft tumors and lung cancer cells.

Conclusion

In summary, our findings indicate that PKP2 functions as an oncogene in LUAD, which could be utilized as a novel diagnostic and therapeutic marker for LUAD treatment.

Integrative analysis of histopathological images and chromatin accessibility data for estrogen receptor-positive breast cancer

BACKGROUND

Existing studies have demonstrated that the integrative analysis of histopathological images and genomic data can be used to better understand the onset and progression of many diseases, as well as identify new diagnostic and prognostic biomarkers. However, since the development of pathological phenotypes are influenced by a variety of complex biological processes, complete understanding of the underlying gene regulatory mechanisms for the cell and tissue morphology is still a challenge. In this study, we explored the relationship between the chromatin accessibility changes and the epithelial tissue proportion in histopathological images of estrogen receptor (ER) positive breast cancer.

METHODS

An established whole slide image processing pipeline based on deep learning was used to perform global segmentation of epithelial and stromal tissues. We then used canonical correlation analysis to detect the epithelial tissue proportion-associated regulatory regions. By integrating ATAC-seq data with matched RNA-seq data, we found the potential target genes that associated with these regulatory regions. Then we used these genes to perform the following pathway and survival analysis.

RESULTS

Using canonical correlation analysis, we detected 436 potential regulatory regions that exhibited significant correlation between quantitative chromatin accessibility changes and the epithelial tissue proportion in tumors from 54 patients (FDR < 0.05). We then found that these 436 regulatory regions were associated with 74 potential target genes. After functional enrichment analysis, we observed that these potential target genes were enriched in cancer-associated pathways. We further demonstrated that using the gene expression signals and the epithelial tissue proportion extracted from this integration framework could stratify patient prognoses more accurately, outperforming predictions based on only omics or image features.

CONCLUSION

This integrative analysis is a useful strategy for identifying potential regulatory regions in the human genome that are associated with tumor tissue quantification. This study will enable efficient prioritization of genomic regulatory regions identified by ATAC-seq data for further studies to validate their causal regulatory function. Ultimately, identifying epithelial tissue proportion-associated regulatory regions will further our understanding of the underlying molecular mechanisms of disease and inform the development of potential therapeutic targets.

AI in Medical Imaging Informatics: Current Challenges and Future Directions

This paper reviews state-of-the-art research solutions across the spectrum of medical imaging informatics, discusses clinical translation, and provides future directions for advancing clinical practice. More specifically, it summarizes advances in medical imaging acquisition technologies for different modalities, highlighting the necessity for efficient medical data management strategies in the context of AI in big healthcare data analytics. It then provides a synopsis of contemporary and emerging algorithmic methods for disease classification and organ/ tissue segmentation, focusing on AI and deep learning architectures that have already become the de facto approach. The clinical benefits of in-silico modelling advances linked with evolving 3D reconstruction and visualization applications are further documented. Concluding, integrative analytics approaches driven by associate research branches highlighted in this study promise to revolutionize imaging informatics as known today across the healthcare continuum for both radiology and digital pathology applications. The latter, is projected to enable informed, more accurate diagnosis, timely prognosis, and effective treatment planning, underpinning precision medicine.

SPOP regulates the DNA damage response and lung adenocarcinoma cell response to radiation

Speckle-type POZ protein (SPOP) plays an important role in maintaining genome stability. Disability or mutation of the SPOP gene has been reported to contribute to prostate cancer incidence and prognosis. However, the functions of SPOP in lung cancer remain poorly understood, especially in lung adenocarcinoma (LUAD). Here, we found that SPOP affects the LUAD cell response to radiation by regulating the DNA damage response (DDR) pathway. SPOP is widely expressed in lung cancer cell lines, and SPOP protein levels are upregulated when cells experience DNA damage. SPOP knockdown affects DDR repair kinetics, apoptosis and cell cycle checkpoints that are induced by IR (ionizing radiation). Furthermore, we found that SPOP positively regulates the expression of DDR factors Rad51 and Ku80. Taken together, these data indicate the essential roles of SPOP in the DDR signaling pathways and LUAD cell response to radiation.

Somatic genome alterations in relation to age in lung squamous cell carcinoma

Lung squamous cell carcinoma (LUSC) is the most common cause of global cancer-related mortality and the major risk factors is smoking consumption. By analyzing ∼500 LUSC samples from The Cancer Genome Atlas, we detected a higher mutational burden as well as a higher level of methylation changes in younger patients. The SNPs mutational profiling showed enrichments of smoking-related signature 4 and defective DNA mismatch repair (MMR)-related signature 6 in younger patients, while the defective DNA MMR signature 26 was enriched among older patients. Furthermore, gene set enrichment analysis was performed in order to explore functional effect of somatic alterations in relation to patient age. Extracellular Matrix-Receptor Interaction, Nucleotide Excision Repair and Axon Guidance seem crucial disrupted pathways in younger patients. We hypothesize that a higher sensitivity to smoking-related damages and the enrichment of defective DNA MMR related mutations may contribute to the higher mutational burden of younger patients. The two distinct age-related defective DNA MMR signatures 6 and 26 might be crucial mutational patterns in LUSC tumorigenesis which may develop distinct phenotypes. Our study provides indications of age-dependent differences in mutational backgrounds (SNPs and CNVs) as well as epigenetic patterns that might be relevant for age adjusted treatment approaches.