Bioinformatics Tools and Resources for Cancer Immunotherapy Study

IHGA: An interactive web server for large-scale and comprehensive discovery of genes of interest in hepatocellular carcinoma

Mining gene expression data is valuable for discovering novel biomarkers and therapeutic targets in hepatocellular carcinoma (HCC). Although emerging data mining tools are available for pan-cancer-related gene data analysis, few tools are dedicated to HCC. Moreover, tools specifically designed for HCC have restrictions such as small data scale and limited functionality. Therefore, we developed IHGA, a new interactive web server for discovering genes of interest in HCC on a large-scale and comprehensive basis. Integrative HCC Gene Analysis (IHGA) contains over 100 independent HCC patient-derived datasets (with over 10,000 tissue samples) and more than 90 cell models. IHGA allows users to conduct a series of large-scale and comprehensive analyses and data visualizations based on gene mRNA levels, including expression comparison, correlation analysis, clinical characteristics analysis, survival analysis, immune system interaction analysis, and drug sensitivity analysis. This method notably enhanced the richness of clinical data in IHGA. Additionally, IHGA integrates artificial intelligence (AI)-assisted gene screening based on natural language models. IHGA is free, user-friendly, and can effectively reduce time spent during data collection, organization, and analysis. In conclusion, IHGA is competitive in terms of data scale, data diversity, and functionality. It effectively alleviates the obstacles caused by HCC heterogeneity to data mining work and helps advance research on the molecular mechanisms of HCC.

Connecting omics signatures and revealing biological mechanisms with iLINCS

There are only a few platforms that integrate multiple omics data types, bioinformatics tools, and interfaces for integrative analyses and visualization that do not require programming skills. Here we present iLINCS ( http://ilincs.org ), an integrative web-based platform for analysis of omics data and signatures of cellular perturbations. The platform facilitates mining and re-analysis of the large collection of omics datasets (>34,000), pre-computed signatures (>200,000), and their connections, as well as the analysis of user-submitted omics signatures of diseases and cellular perturbations. iLINCS analysis workflows integrate vast omics data resources and a range of analytics and interactive visualization tools into a comprehensive platform for analysis of omics signatures. iLINCS user-friendly interfaces enable execution of sophisticated analyses of omics signatures, mechanism of action analysis, and signature-driven drug repositioning. We illustrate the utility of iLINCS with three use cases involving analysis of cancer proteogenomic signatures, COVID 19 transcriptomic signatures and mTOR signaling.

Liposomes as Versatile Platform for Cancer Theranostics: Therapy, Bio-imaging, and Toxicological Aspects

Liposomes are nano-sized formulations having the benefits of site-specificity, biocompatibility, and biodegradability, which make them useful for the therapy and diagnosis of major diseases like cancer. In this review, various synthetic strategies of liposomes and their biomedical application in special concern to cancer are discussed. In context to the biomedical application, this article gives a detailed insight into subcellular targeted therapy and several therapeutic modifications like immunotherapy, receptor-based therapy, phototherapy, and combination therapy. The review also describes the liposome-based imaging platforms and the toxicity associated with liposomes. Owing to a significant amount of benefits of this carrier system, several products have been approved to be launched in the market and several others have already been marketed for clinical use.

Computational resources for identification of cancer biomarkers from omics data

Cancer is one of the most prevailing, deadly and challenging diseases worldwide. The advancement in technology led to the generation of different types of omics data at each genome level that may potentially improve the current status of cancer patients. These data have tremendous applications in managing cancer effectively with improved outcome in patients. This review summarizes the various computational resources and tools housing several types of omics data related to cancer. Major categorization of resources includes-cancer-associated multiomics data repositories, visualization/analysis tools for omics data, machine learning-based diagnostic, prognostic, and predictive biomarker tools, and data analysis algorithms employing the multiomics data. The review primarily focuses on providing comprehensive information on the open-source multiomics tools and data repositories, owing to their broader applicability, economic-benefit and usability. Sections including the comparative analysis, tools applicability and possible future directions have also been discussed in detail. We hope that this information will significantly benefit the researchers and clinicians, especially those with no sound background in bioinformatics and who lack sufficient data analysis skills to interpret something from the plethora of cancer-specific data generated nowadays.

The Importance of STK11/LKB1 Assessment in Non-Small Cell Lung Carcinomas

Despite the recent implementation of immunotherapy as a single treatment or in combination with chemotherapy for first-line treatment of advanced non-small cell lung cancer (NSCLC), many patients do not benefit from this regimen due to primary treatment resistance or toxicity. Consequently, there is an urgent need to develop efficient biomarkers that can select patients who will benefit from immunotherapy thereby providing the appropriate treatment and avoiding toxicity. One of the biomarkers recently described for the stratification of NSCLC patients undergoing immunotherapy are mutations in STK11/LKB1, which are often associated with a lack of response to immunotherapy in some patients. Therefore, the purpose of this review is to describe the different cellular mechanisms associated with STK11/LKB1 mutations, which may explain the lack of response to immunotherapy. Moreover the review addresses the co-occurrence of additional mutations that may influence the response to immunotherapy and the current clinical studies that have further explored STK11/LKB1 as a predictive biomarker. Additionally this work includes the opportunities and limitations to look for the STK11/LKB1 status in the therapeutic strategy for NSCLC patients.

Precision transplant pathology

PURPOSE OF REVIEW

Transplant pathology contributes substantially to personalized treatment of organ allograft recipients. Rapidly advancing next-generation human leukocyte antigen (HLA) sequencing and pathology are enhancing the abilities to improve donor/recipient matching and allograft monitoring.

RECENT FINDINGS

The present review summarizes the workflow of a prototypical patient through a pathology practice, highlighting histocompatibility assessment and pathologic review of tissues as areas that are evolving to incorporate next-generation technologies while emphasizing critical needs of the field.

SUMMARY

Successful organ transplantation starts with the most precise pratical donor-recipient histocompatibility matching. Next-generation sequencing provides the highest resolution donor-recipient matching and enables eplet mismatch scores and more precise monitoring of donor-specific antibodies (DSAs) that may arise after transplant. Multiplex labeling combined with hand-crafted machine learning is transforming traditional histopathology. The combination of traditional blood/body fluid laboratory tests, eplet and DSA analysis, traditional and next-generation histopathology, and -omics-based platforms enables risk stratification and identification of early subclinical molecular-based changes that precede a decline in allograft function. Needs include software integration of data derived from diverse platforms that can render the most accurate assessment of allograft health and needs for immunosuppression adjustments.