Research Laboratory Center, Guizhou Provincial People's Hospital, Guiyang, Guizhou, China.
NHC Key Laboratory of Pulmonary Immune-related Diseases, Guizhou Provincial People's Hospital, Guizhou University, Guiyang, Guizhou, China.
Front Immunol. 2024 Aug 6;15:1414450. doi: 10.3389/fimmu.2024.1414450. eCollection 2024.
In the ongoing battle against breast cancer, a leading cause of cancer-related mortality among women globally, the urgent need for innovative prognostic markers and therapeutic targets is undeniable. This study pioneers an advanced methodology by integrating machine learning techniques to unveil a vascular mimicry signature, offering predictive insights into breast cancer outcomes. Vascular mimicry refers to the phenomenon where cancer cells mimic blood vessel formation absent of endothelial cells, a trait associated with heightened tumor aggression and diminished response to conventional treatments.
The study's comprehensive analysis spanned data from over 6,000 breast cancer patients across 12 distinct datasets, incorporating both proprietary clinical data and single-cell data from 7 patients, accounting for a total of 43,095 cells. By employing an integrative strategy that utilized 10 machine learning algorithms across 108 unique combinations, the research scrutinized 100 existing breast cancer signatures. Empirical validation was sought through immunohistochemistry assays, alongside explorations into potential immunotherapeutic and chemotherapeutic avenues.
The investigation successfully identified six genes related to vascular mimicry from multi-center cohorts, laying the groundwork for a novel predictive model. This model outstripped the prognostic accuracy of traditional clinical and molecular indicators in forecasting recurrence and mortality risks. High-risk individuals identified by our model faced worse outcomes. Further validation through IHC assays in 30 patients underscored the model's extensive applicability. Notably, the model unveiled varying therapeutic responses; low-risk patients might achieve greater benefits from immunotherapy, whereas high-risk patients demonstrated a particular sensitivity to certain chemotherapies, such as ispinesib.
This model marks a significant step forward in the precise evaluation of breast cancer prognosis and therapeutic responses across different patient groups. It heralds the possibility of refining patient outcomes through tailored treatment strategies, accentuating the potential of machine learning in revolutionizing cancer prognosis and management.
在全球范围内，乳腺癌是女性癌症相关死亡的主要原因，因此迫切需要创新的预后标志物和治疗靶点。本研究通过整合机器学习技术，揭示了一种血管模拟特征，为乳腺癌的预后提供了预测性见解，这是一种先进的方法。血管模拟是指癌细胞在没有内皮细胞的情况下模拟血管形成的现象，这种特性与肿瘤侵袭性增强和对常规治疗反应减弱有关。
该研究的综合分析涵盖了来自 12 个不同数据集的超过 6000 名乳腺癌患者的数据，包括专有临床数据和 7 名患者的单细胞数据，共包含 43095 个细胞。通过使用 10 种机器学习算法和 108 种独特组合的综合策略，研究人员对 100 种现有的乳腺癌特征进行了分析。通过免疫组织化学检测进行了实证验证，并探讨了潜在的免疫治疗和化学治疗途径。
研究成功地从多中心队列中鉴定出与血管模拟相关的六个基因，为建立一个新的预测模型奠定了基础。该模型在预测复发和死亡风险方面优于传统的临床和分子指标。通过对 30 名患者的 IHC 检测进一步验证了该模型的广泛适用性。值得注意的是，该模型揭示了不同的治疗反应；低风险患者可能从免疫治疗中获得更大的益处，而高风险患者对某些化疗药物（如异博定）表现出特殊的敏感性。
该模型标志着在不同患者群体中精确评估乳腺癌预后和治疗反应方面迈出了重要的一步。它有可能通过量身定制的治疗策略来改善患者的预后，突出了机器学习在癌症预后和管理方面的变革潜力。
