腫瘤進(jìn)展是一個(gè)復(fù)雜的過(guò)程，需要癌細(xì)胞、微環(huán)境和免疫系統(tǒng)相互作用，影響腫瘤的啟動(dòng)和進(jìn)展。免疫細(xì)胞對(duì)癌癥具有重要的輔助功能并影響臨床結(jié)局，對(duì)于免疫細(xì)胞浸潤(rùn)豐度較高的患者，治療效果和臨床結(jié)局往往較好。近年來(lái)，免疫治療已經(jīng)成為癌癥精準(zhǔn)治療的一大熱點(diǎn)，故有必要充分考慮癌癥的腫瘤浸潤(rùn)免疫細(xì)胞。對(duì)此，大連醫(yī)科大學(xué)附屬第二醫(yī)院李曼教授團(tuán)隊(duì)開(kāi)展研究，構(gòu)建了由腫瘤浸潤(rùn)免疫細(xì)胞生物標(biāo)志組成的免疫評(píng)分模型，用于預(yù)測(cè)和改善乳腺癌的治療效果和生存結(jié)局，并探討了乳腺癌腫瘤浸潤(rùn)免疫細(xì)胞的預(yù)后價(jià)值和臨床意義。

　　該研究分析數(shù)據(jù)來(lái)自美國(guó)國(guó)家癌癥研究所和國(guó)家人類基因組研究所癌癥基因組圖譜TCGA數(shù)據(jù)庫(kù)、美國(guó)國(guó)家生物技術(shù)信息中心基因表達(dá)數(shù)據(jù)庫(kù)GEO、歐洲生物信息研究所功能基因組學(xué)數(shù)據(jù)庫(kù)Array Express、國(guó)際癌癥基因組聯(lián)盟ICGC數(shù)據(jù)庫(kù)、國(guó)際乳腺癌分子分類學(xué)聯(lián)盟METABRIC數(shù)據(jù)庫(kù)的29個(gè)數(shù)據(jù)集，共包括6844個(gè)樣本，同時(shí)還包括臨床收集的183個(gè)樣本。通過(guò)CIBERSORT軟件評(píng)估樣本的22種免疫細(xì)胞浸潤(rùn)豐度，剔除CIBERSORT分析P值>0.05的樣本。通過(guò)隨機(jī)分層將5038例樣本以7∶3的比例分為訓(xùn)練集和驗(yàn)證集，并收集中國(guó)醫(yī)院173例乳腺癌組織作為測(cè)試集。隨后，在訓(xùn)練集中應(yīng)用包括單因素比例風(fēng)險(xiǎn)回歸、多因素比例風(fēng)險(xiǎn)回歸、最小絕對(duì)收縮選擇算子（LASSO）回歸等構(gòu)建由6種腫瘤浸潤(rùn)免疫細(xì)胞組成的免疫模型，用于預(yù)測(cè)患者的化療效果和總生存結(jié)局。最后，對(duì)驗(yàn)證集和測(cè)試集進(jìn)一步驗(yàn)證和測(cè)試該模型預(yù)測(cè)結(jié)局的準(zhǔn)確性和有效性。

　　結(jié)果，該研究根據(jù)單因素比例風(fēng)險(xiǎn)回歸分析，確定了14種免疫細(xì)胞與乳腺癌患者的總生存顯著相關(guān)，進(jìn)一步采用LASSO分析和多元比例風(fēng)險(xiǎn)回歸分析，構(gòu)建了由6種免疫細(xì)胞（靜息CD4陽(yáng)性T淋巴細(xì)胞、調(diào)節(jié)型T淋巴細(xì)胞、γδT淋巴細(xì)胞、活化自然殺傷細(xì)胞、單核細(xì)胞、M0型巨噬細(xì)胞）組成的免疫評(píng)分模型。在訓(xùn)練集中，免疫低風(fēng)險(xiǎn)組和高風(fēng)險(xiǎn)組的總生存差異具有顯著意義，其20年總生存率分別為42.6%和26.3%。隨后，在驗(yàn)證集和測(cè)試集中，也證實(shí)了該模型可預(yù)測(cè)乳腺癌患者總生存，且與乳腺癌的分子分型無(wú)關(guān)。用免疫評(píng)分模型預(yù)測(cè)乳腺癌患者化療效果，結(jié)果發(fā)現(xiàn)無(wú)論接受何種化療方案，低風(fēng)險(xiǎn)組接受化療的患者都有顯著生存優(yōu)勢(shì)。改研究還整合了免疫評(píng)分及其他臨床病理預(yù)后因素，包括年齡、腫瘤分級(jí)和TNM分期，構(gòu)建了列線圖。列線圖預(yù)后系統(tǒng)總體上改善了乳腺癌的預(yù)后模型。根據(jù)校準(zhǔn)曲線，列線圖對(duì)訓(xùn)練組和驗(yàn)證組的5年、10年和20年生存率預(yù)測(cè)良好，決策曲線表明該列線圖比標(biāo)準(zhǔn)TNM分期系統(tǒng)的預(yù)測(cè)精度更好，充分體現(xiàn)了免疫細(xì)胞的臨床意義。

　　因此，該研究結(jié)果表明，由免疫浸潤(rùn)細(xì)胞組成的免疫評(píng)分模型可以有效地用于預(yù)測(cè)乳腺癌患者的化療效果和生存結(jié)局。

Theranostics. 2020 Oct 25;10(26):11938-11949.

An immune cell infiltration-based immune score model predicts prognosis and chemotherapy effects in breast cancer.

Sui S, An X, Xu C, Li Z, Hua Y, Huang G, Sui S, Long Q, Sui Y, Xiong Y, Ntim M, Guo W, Chen M, Deng W, Xiao X, Li M.

The Second Affiliated Hospital of Dalian Medical University; Institute of Cancer Stem Cell, Dalian Medical University, Dalian, China; Sun Yat-Sen University Cancer Center, Guangzhou, China; The First Affiliated Hospital of Dalian Medical University, Dalian, China; Institute (College) of Integrative Medicine, Dalian Medical University, Dalian, China; The First People's Hospital of Yichang (People's Hospital of Three Gorges University), Yichang, Hubei, China; Vagelos College of Physicians and Surgeons, Columbia University, New York, USA.

BACKGROUND: Immune cells have essential auxiliary functions and influence clinical outcomes in cancer, with high immune infiltration being associated with improved clinical outcomes and better response to treatment in breast cancer (BC). However, studies to date have not fully considered the tumor-infiltrating immune cell (TIIC) landscape in tumors. This study investigated potential biomarkers based on TIICs to improve prognosis and treatment effect in BC.

RESULTS: We enrolled 5112 patients for analysis and used cell type identification by estimating relative subsets of RNA transcripts (CIBERSORT), a new computational algorithm, to quantify 22 TIICs in primary BC. From the results of univariate Cox regression, 12 immune cells were determined to be significantly related to the overall survival (OS) of BC patients. Furthermore, least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analyses were applied to construct an immune prognostic model based on six potential biomarkers. By dividing patients into low- and high-risk groups, a significant distinction in OS was found in the training cohort, with 20-year survival rates of 42.6% and 26.3%, respectively. Applying a similar protocol to validation and test cohorts, we found that OS was significantly shorter in the high-risk group than in the low-risk group, regardless of the molecular subtype of BC. Using the immune score model to predict the effect of BC patients to chemotherapy, the survival advantage for the low-risk group was evident among those who received chemotherapy, regardless of the chemotherapy regimen. In evaluating the predictive value of the nomogram, a decision curve showed better predictive accuracy than the standard tumor-node-metastasis (TNM) staging system.

CONCLUSION: The immune cell infiltration-based immune score model can be effectively and efficiently used to predict the prognosis of BC patients as well as the effect of chemotherapy.

KEYWORDS: CIBERSORT; breast cancer; immune score; prognostic; tumor-infiltrating immune cells

PMID: 33204321

PMCID: PMC7667685

DOI: 10.7150/thno.49451