Biology subjects

Radovich, M.

Publications and source records attributed to Radovich, M..

3 recordsLinked to original sources

bioRxiv2024

Inhibition of PIM kinase in tumor associated macrophages suppresses inflammasome 1 activation and sensitizes prostate cancer to immunotherapy

Immunotherapy has changed the treatment paradigm for many types of cancer, but immune checkpoint inhibitors (ICIs) have not shown benefit in prostate cancer (PCa). Chronic inflammation contributes to the immunosuppressive prostate tumor microenvironment (TME) and is associated with poor response to ICIs. The primary source of inflammatory cytokine production is the inflammasome. Here, we identify PIM kinases as important regulators of inflammasome activation in tumor associated macrophages (TAMs). Analysis of clinical data from a cohort of treatment naive, hormone responsive PCa patients revealed that tumors from patients with high PIM1/2/3 display an immunosuppressive TME characterized by high inflammation (IL-1{beta} and TNF) and a high density of repressive immune cells, most notably TAMs. Strikingly, macrophage-specific knockout of PIM reduced tumor growth in syngeneic models of prostate cancer. Transcriptional analyses indicate that eliminating PIM from macrophages enhanced the adaptive immune response and increased cytotoxic immune cells. Combined treatment with PIM inhibitors and ICIs synergistically reduced tumor growth. Immune profiling revealed that PIM inhibitors sensitized PCa tumors to ICIs by increasing tumor suppressive TAMs and increasing the activation of cytotoxic T cells. Collectively, our data implicate macrophage PIM as a driver of inflammation that limits the potency of ICIs and provides preclinical evidence that PIM inhibitors are an effective strategy to improve the efficacy of immunotherapy in prostate cancer.

cancer biology↗

bioRxiv2021

Redefining tumor classification and clinical stratification through a colorectal cancer single-cell atlas

Colorectal cancer (CRC), a disease of high incidence and mortality, has had few treatment advances owing to a large degree of inter- and intratumoral heterogeneity. Attempts to classify subtypes of colorectal cancer to develop treatment strategies has been attempted by Consensus Molecular Subtypes (CMS) classification. However, the cellular etiology of CMS classification is incompletely understood and controversial. Here, we generated and analyzed a single-cell transcriptome atlas of 49,859 CRC cells from 16 patients, validated with an additional 31,383 cells from an independent CRC patient cohort. We describe subclonal transcriptomic heterogeneity of CRC tumor epithelial cells, as well as discrete stromal populations of cancer-associated fibroblasts (CAFs). Within CRC CAFs, we identify the transcriptional signature of specific subtypes (CAF-S1 and CAF-S4) in more than 1,500 CRC patients using bulk transcriptomic data that significantly stratifies overall survival in multiple independent cohorts. We also uncovered two CAF-S1 subpopulations, ecm-myCAF and TGF{beta}-myCAF, known to be associated with primary resistance to immunotherapies. We demonstrate that scRNA analysis of malignant, stromal, and immune cells exhibit a more complex picture than portrayed by bulk transcriptomic-based Consensus Molecular Subtypes (CMS) classification. By demonstrating an abundant degree of heterogeneity amongst these cell types, our work shows that CRC is best represented in a transcriptomic continuum crossing traditional classification systems boundaries. Overall, this CRC cell map provides a framework to re-evaluate CRC tumor biology with implications for clinical trial design and therapeutic development.

cancer biology↗

bioRxiv2017

Computational proteogenomic identification and functional interpretation of translated fusions and micro structural variations in cancer

MotivationRapid advancement in high throughput genome and transcriptome sequencing (HTS) and mass spectrometry (MS) technologies has enabled the acquisition of the genomic, transcriptomic and proteomic data from the same tissue sample. In this paper we introduce a novel computational framework which can integratively analyze all three types of omics data to obtain a complete molecular profile of a tissue sample, in normal and disease conditions. Our framework includes MiStrVar, an algorithmic method we developed to identify micro structural variants (microSVs) on genomic HTS data. Coupled with deFuse, a popular gene fusion detection method we developed earlier, MiStrVar can provide an accurate profile of structurally aberrant transcripts in cancer samples. Given the breakpoints obtained by MiStrVar and deFuse, our framework can then identify all relevant peptides that span the breakpoint junctions and match them with unique proteomic signatures in the respective proteomics data sets. Our framework's ability to observe structural aberrations at three levels of omics data provides means of validating their presence.\n\nResultsWe have applied our framework to all The Cancer Genome Atlas (TCGA) breast cancer Whole Genome Sequencing (WGS) and/or RNA-Seq data sets, spanning all four major subtypes, for which proteomics data from Clinical Proteomic Tumor Analysis Consortium (CPTAC) have been released. A recent study on this dataset focusing on SNVs has reported many that lead to novel peptides [1]. Complementing and significantly broadening this study, we detected 244 novel peptides from 432 candidate genomic or transcriptomic sequence aberrations. Many of the fusions and microSVs we discovered have not been reported in the literature. Interestingly, the vast majority of these translated aberrations (in particular, fusions) were private, demonstrating the extensive inter-genomic heterogeneity present in breast cancer. Many of these aberrations also have matching out-of-frame downstream peptides, potentially indicating novel protein sequence and structure. Moreover, the most significantly enriched genes involved in translated fusions are cancer-related. Furthermore a number of the somatic, translated microSVs are observed in tumor suppressor genes.\n\nContactcenksahi@indiana.edu

bioinformatics↗