Decoding the functional impact of the cancer genome through protein–protein interactions
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Enhancer reprogramming: critical roles in cancer and promising therapeutic strategies
Transcriptional dysregulation is a hallmark of cancer initiation and progression, driven by genetic and epigenetic alterations. Enhancer reprogramming has emerged as a pivotal driver of carcinogenesis, with cancer cells often relying on aberrant transcriptional programs. The advent of high-throughput sequencing technologies has provided critical insights into enhancer reprogramming events and their role in malignancy. While targeting enhancers presents a promising therapeutic strategy, significant challenges remain. These include the off-target effects of enhancer-targeting technologies, the complexity and redundancy of enhancer networks, and the dynamic nature of enhancer reprogramming, which may contribute to therapeutic resistance. This review comprehensively encapsulates the structural attributes of enhancers, delineates the mechanisms underlying their dysregulation in malignant transformation, and evaluates the therapeutic opportunities and limitations associated with targeting enhancers in cancer.
Targeting of TAMs: can we be more clever than cancer cells?
With increasing incidence and geography, cancer is one of the leading causes of death, reduced quality of life and disability worldwide. Principal progress in the development of new anticancer therapies, in improving the efficiency of immunotherapeutic tools, and in the personification of conventional therapies needs to consider cancer-specific and patient-specific programming of innate immunity. Intratumoral TAMs and their precursors, resident macrophages and monocytes, are principal regulators of tumor progression and therapy resistance. Our review summarizes the accumulated evidence for the subpopulations of TAMs and their increasing number of biomarkers, indicating their predictive value for the clinical parameters of carcinogenesis and therapy resistance, with a focus on solid cancers of non-infectious etiology. We present the state-of-the-art knowledge about the tumor-supporting functions of TAMs at all stages of tumor progression and highlight biomarkers, recently identified by single-cell and spatial analytical methods, that discriminate between tumor-promoting and tumor-inhibiting TAMs, where both subtypes express a combination of prototype M1 and M2 genes. Our review focuses on novel mechanisms involved in the crosstalk among epigenetic, signaling, transcriptional and metabolic pathways in TAMs. Particular attention has been given to the recently identified link between cancer cell metabolism and the epigenetic programming of TAMs by histone lactylation, which can be responsible for the unlimited protumoral programming of TAMs. Finally, we explain how TAMs interfere with currently used anticancer therapeutics and summarize the most advanced data from clinical trials, which we divide into four categories: inhibition of TAM survival and differentiation, inhibition of monocyte/TAM recruitment into tumors, functional reprogramming of TAMs, and genetic enhancement of macrophages.
Bayesian p-curve mixture models as a tool to dissociate effect size and effect prevalence
Much research in the behavioral sciences aims to characterize the “typical” person. A statistically significant group-averaged effect size is often interpreted as evidence that the typical person shows an effect, but that is only true under certain distributional assumptions for which explicit evidence is rarely presented. Mean effect size varies with both within-participant effect size and population prevalence (proportion of population showing effect). Few studies consider how prevalence affects mean effect size estimates and existing estimators of prevalence are, conversely, confounded by uncertainty about effect size. We introduce a widely applicable Bayesian method, the p-curve mixture model, that jointly estimates prevalence and effect size by probabilistically clustering participant-level data based on their likelihood under a null distribution. Our approach, for which we provide a software tool, outperforms existing prevalence estimation methods when effect size is uncertain and is sensitive to differences in prevalence or effect size across groups or conditions.
EV DNA from pancreatic cancer patient-derived cells harbors molecular, coding, non-coding signatures and mutational hotspots
DNA packaged into cancer cell-derived EV is not well appreciated. Here, we uncovered signatures of EV DNA secreted by pancreatic cancer cells. The cancer cells and non-cancer counterparts exhibit distinct low vs. high molecular weight (LMW vs. HMW) EV DNA fragments distribution, respectively. Genome sequencing and Single Nucleotide Variants analysis revealed that 95% of reads and 94% of SNVs map to noncoding regions of the genome. Given that ~1% of the human genome represents coding regions, the 5% mapping rate to coding regions suggests a non-random enrichment of certain coding regions and mutations. The LMW DNA fragments not only set cancer cells apart, but also harbor cancer specific enrichment of unique coding regions, the top nine being FAM135B, COL22A1, TSNARE1, KCNK9, ZFAT, JRK, MROH5, GSDMD, and MIR3667HG. Additionally, the cancer cells’ LMW DNA fragments exhibit dense centromeric mapping more strikingly on chromosomes 3, 7, 9, 10, 11, 13, 17, and 20. Mutational profiling turned up close to 200 mutations specific for the cancer cells. Altogether, our analyses suggest that centromeric regions might hold clues to EV DNA content from pancreatic cancer, the molecular, mutational signatures thereof, and rationalizes the need for a new approach to DNA biomarker research.
Targeting the splicing factor SNRPB inhibits endometrial cancer progression by retaining the POLD1 intron
Dysregulated alternative splicing has been closely linked to the initiation and progression of tumors. Nevertheless, the precise molecular mechanisms through which splicing factors regulate endometrial cancer progression are still not fully understood. This study demonstrated elevated expression of the splicing factor SNRPB in endometrial cancer samples. Furthermore, our findings indicate that high SNRPB expression is correlated with poor prognosis in patients with endometrial cancer. Functionally, SNRPB inhibition hindered the proliferative and metastatic capacities of endometrial cancer cells. Mechanistically, we revealed that SNRPB knockdown decreased POLD1 expression and that POLD1 intron 22 was retained after SNRPB silencing in endometrial cancer cells, as determined via RNA sequencing data analysis. The retained intron 22 of POLD1 created a premature termination codon, leading to the absence of amino acids 941–1,107 and the loss of the site of interaction with PCNA, which is essential for POLD1 enzyme activity. In addition, POLD1 depletion decreased the increase in the malignancy of endometrial cancer cells overexpressing SNRPB. Furthermore, miR-654-5p was found to bind directly to the 3′ untranslated region of SNRPB, resulting in SNRPB expression inhibition in endometrial cancer. Antisense oligonucleotide-mediated SNRPB inhibition led to a decrease in the growth capacity of a cell-derived xenograft model and a patient with endometrial cancer-derived xenograft model. Overall, SNRPB promotes the efficient splicing of POLD1 by regulating intron retention, ultimately contributing to high POLD1 expression in endometrial cancer. The oncogenic SNRPB–POLD1 axis is an interesting therapeutic target for endometrial cancer, and antisense oligonucleotide-mediated silencing of SNRPB may constitute a promising therapeutic approach for treating patients with endometrial cancer.
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