Ruoff Cole, Mitchell Allison, Mondal Priya, Gopalan Vishaka, Singh Arashdeep, Gottesman Michael, Hannenhalli Sridhar
Cancer Data Science Lab, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA.
Laboratory of Cell Biology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA.
Cancer Drug Resist. 2025 Aug 26;8:44. doi: 10.20517/cdr.2025.112. eCollection 2025.
Growing evidence points to non-genetic mechanisms underlying long-term resistance to cancer therapies. These mechanisms involve pre-existing or therapy-induced transcriptional cell states that confer resistance. However, the relationship between early transcriptional responses to treatment and the eventual emergence of resistant states remains poorly understood. Furthermore, it is unclear whether such early resistance-associated transcriptional responses are evolutionarily conserved. In this study, we examine the similarity between early transcriptional responses and long-term resistant states, assess their clinical relevance, and explore their evolutionary conservation across species. We integrated datasets on early drug responses and long-term resistance from multiple cancer cell lines, bacteria, and yeast to identify early transcriptional changes predictive of long-term resistance and assess their evolutionary conservation. Using genome-wide CRISPR-Cas9 knockout screens, we evaluated the impact of genes associated with resistant transcriptional states on drug sensitivity. Clinical datasets were analyzed to explore the prognostic value of the identified resistance-associated gene signatures. We found that transcriptional states observed in drug-naive cells and shortly after treatment overlapped with those seen in fully resistant populations. Some of these shared features appear to be evolutionarily conserved. Knockout of genes marking resistant states sensitized ovarian cancer cells to Prexasertib. Moreover, early resistance gene signatures effectively distinguished therapy responders from non-responders in multiple clinical cancer trials and differentiated premalignant breast lesions that progressed to malignancy from those that remained benign. Early cellular transcriptional responses to therapy exhibit key similarities to fully resistant states across different drugs, cancer types, and species. Gene signatures defining these early resistance states have prognostic value in clinical settings.
越来越多的证据表明，癌症治疗长期耐药存在非遗传机制。这些机制涉及预先存在的或治疗诱导的赋予耐药性的转录细胞状态。然而，治疗的早期转录反应与耐药状态最终出现之间的关系仍知之甚少。此外，尚不清楚这种与早期耐药相关的转录反应是否在进化上保守。在本研究中，我们检验了早期转录反应与长期耐药状态之间的相似性，评估它们的临床相关性，并探索它们在不同物种间的进化保守性。我们整合了来自多个癌细胞系、细菌和酵母的早期药物反应及长期耐药数据集，以识别预测长期耐药的早期转录变化并评估其进化保守性。利用全基因组CRISPR-Cas9敲除筛选，我们评估了与耐药转录状态相关的基因对药物敏感性的影响。分析临床数据集以探索所识别的耐药相关基因特征的预后价值。我们发现，在未接触过药物的细胞中以及治疗后不久观察到的转录状态与在完全耐药群体中看到的转录状态重叠。其中一些共同特征似乎在进化上是保守的。敲除标记耐药状态的基因可使卵巢癌细胞对普瑞赛替尼敏感。此外，在多项临床癌症试验中，早期耐药基因特征能有效区分治疗反应者与无反应者，并区分进展为恶性的癌前乳腺病变与保持良性的病变。针对治疗的早期细胞转录反应在不同药物、癌症类型和物种中与完全耐药状态表现出关键的相似性。定义这些早期耐药状态的基因特征在临床环境中具有预后价值。
