Synthetic lethal strategies for the development of cancer therapeutics
Related Articles
The transcriptomic architecture of common cancers reflects synthetic lethal interactions
To maintain cell fitness, deleterious genetic alterations are buffered by compensatory changes in additional genes. In cancer, buffering processes could be targeted by synthetic lethality. However, despite the large-scale identification of synthetic lethal effects in preclinical models, evidence that these operate clinically is limited. This impedes the application of synthetic lethal approaches. By integrating molecular profiling data from >9,000 cancers with synthetic lethal screens, we show that transcriptomic buffering of tumor suppressor gene (TSG) loss by hyperexpression of synthetic lethal partners is a common phenomenon, extending to multiple TSGs and histotypes. Transcriptomic buffering is also notable in cancers that phenocopy TSG loss, such as BRCAness cancers, where expression of BRCA1/2 synthetic lethal genes correlates with clinical outcome. Synthetic lethal genes that exhibit transcriptomic buffering also represent more robust synthetic lethal effects. These observations have implications for understanding how tumor cells tolerate TSG loss, in part explain transcriptomic architectures in cancer and provide insight into target selection.
Inhibition of GSK3β is synthetic lethal with FHIT loss in lung cancer by blocking homologous recombination repair
FHIT is a fragile site tumor suppressor that is primarily inactivated upon tobacco smoking. FHIT loss is frequently observed in lung cancer, making it an important biomarker for the development of targeted therapy for lung cancer. Here, we report that inhibitors of glycogen synthase kinase 3 beta (GSK3β) and the homologous recombination DNA repair (HRR) pathway are synthetic lethal with FHIT loss in lung cancer. Pharmacological inhibition or siRNA depletion of GSK3β selectively suppressed the growth of FHIT-deficient lung cancer tumors in vitro and in animal models. We further showed that FHIT inactivation leads to the activation of DNA damage repair pathways, including the HRR and NHEJ pathways, in lung cancer cells. Conversely, FHIT-deficient cells are highly dependent on HRR for survival under DNA damage stress. The inhibition of GSK3β in FHIT-deficient cells suppressed the ATR/BRCA1/RAD51 axis in HRR signaling via two distinct pathways and suppressed DNA double-strand break repair, leading to the accumulation of DNA damage and apoptosis. Small molecule inhibitors of HRR, but not NHEJ or PARP, induced synthetic lethality in FHIT-deficient lung cancer cells. The findings of this study suggest that the GSK3β and HRR pathways are potential drug targets in lung cancer patients with FHIT loss.
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.
Deep learning-based image analysis in muscle histopathology using photo-realistic synthetic data
Artificial intelligence (AI), specifically Deep learning (DL), has revolutionized biomedical image analysis, but its efficacy is limited by the need for representative, high-quality large datasets with manual annotations. While latest research on synthetic data using AI-based generative models has shown promising results to tackle this problem, several challenges such as lack of interpretability and need for vast amounts of real data remain. This study aims to introduce a new approach—SYNTA—for the generation of photo-realistic synthetic biomedical image data to address the challenges associated with state-of-the art generative models and DL-based image analysis.
Responses