中国抗癌协会

概述
日月其迈，时盛岁新。回顾过去的2022年，全国病理同仁踔厉奋发，从临床问题入手，发挥学科优势，综合应用多种检测手段及分析方法，从多个层面丰富了肿瘤精准诊断的内涵，为临床的精准治疗提供了充分的有效信息，促进产研转化，创造了一定的社会价值。本报告收集整理了2022年度肿瘤病理学科的主要进展及研究成果，抛砖引玉，以期提供给读者更多的启发和思考。
1. 精准诊断的根基——形态学基础
形态学是病理诊断的根基，在“准确”的前提下追求“精细”始终是病理医师孜孜以求的目标。一方面，部分研究对常见肿瘤的特殊亚型进行了归纳总结，如：①作为一种特殊的霍奇金淋巴瘤，合体型结节硬化型霍奇金淋巴瘤常伴有坏死，而且预后较差[1]。②SWI/SNF复合物缺陷型胃肠道未分化癌一般表现为SMARCA4和SMARCA2缺失，形态多样，预后差[2]。③富马酸水合酶（FH）缺陷型子宫平滑肌瘤通常表现出独特的形态特征和高频率的FH胚系突变[3]。④CD5阳性的弥漫性大B细胞淋巴瘤预后不良，常有中枢系统受累[4]。⑤乳腺纤维瘤病样梭形细胞癌具有惰性行为，常有PIK3CA和TERT启动子突变的高频率[5]。另一方面，部分研究归纳总结了少见肿瘤的形态及分子特征，包括硬化性上皮样纤维肉瘤[6]、心脏未分化多形性肉瘤[7]、原发性成人鞍区SMARCB1/INI1缺陷肿瘤[8]、类似于卵巢性索肿瘤的子宫肿瘤[9]、GLI1变异的间叶肿瘤[10]、胃肠道双/三打击淋巴瘤[11]、浆母细胞淋巴瘤[12]、具有广泛上皮包涵体的孤立性纤维性肿瘤[13]、乳腺化生性鳞状细胞癌[14]等。

2. 精准诊断的框架——新分类实践
近几年多个亚专业更新了第五版WHO肿瘤分类指南。过去一年中，国内同仁从国内实际出发，对新分类进行了验证、补充及拓展：①对肾的嗜酸性肿瘤进行精准诊断是近几年的研究热点。国内多中心协作通过二代测序等方法对TSC/MTOR相关嗜酸性肾肿瘤的形态学特征及分子特征进行了细致分析[15]。另外有研究证实，核极向倒置特征的乳头状肾肿瘤不同于乳头状肾细胞癌，预后良好[16]，而且常常有KRAS突变[17]。②在中枢神经系统肿瘤分类的重要改动是应用整合诊断。有研究通过对H3 K27M突变弥漫性中线胶质瘤的大宗病例回顾，发现成人型肿瘤和儿童型肿瘤在临床病理特点及分子特征上具有显著差异[18]。还有研究发现成人型H3.3 G34突变弥漫性胶质瘤也具有独特的病理学和分子特征[19]。③在胸部肿瘤中肺腺癌的新组织学分级系统已经逐渐推广应用，多家单位通过对国内人群的回顾性分析，对肺腺癌新的组织学分级系统的应用价值做了验证和补充[20-22]。④随着研究的深入，学界已逐渐认可伴神经内分泌表达/分化的癌与真正的神经内分泌癌是不同的。在基因变异上，双性癌不同于混合性腺-神经内分泌癌[23]，而全外显子检测证实了乳腺等系统的神经内分泌癌在基因组上具有特殊性，应作为一种单独类型[24, 25]。

3. 精准诊断的关键——分子检测应用
分子检测可以从微观层面揭示肿瘤的发生发展过程，已经成为大部分病理研究中的常用方法。一部分研究分析了肿瘤形成的分子特点：①食管原发性恶性黑色素瘤与皮肤恶性黑色素瘤具有相似的基因特点[26]。②肺细支气管腺瘤具有独特的高频驱动基因突变[27]。③部分基因突变在肺浸润前病变发挥主导作用[28]。④NF2/22q异常在脑膜瘤中应被视为一个特殊亚群[29]。⑤多原发食管鳞状细胞癌与食管癌壁内转移分子特征不同[30]。⑥miR-20b-5p[31]、LINC01554[32]、ncRNA paGLI1[33]、circPTEN1[34]、circRHOTBB3[35]等非编码RNA在调控肿瘤进展中发挥了重要作用。另外一些研究则关注了肿瘤的分子分型，基于转录组、蛋白质组等高通量方法将肿瘤膀胱尿路上皮[36]、小B细胞淋巴瘤[37]、同时性多原发结直肠癌[38]、NK/T细胞淋巴瘤[39]分为了不同分子亚型。
质量控制对于分子检测尤为重要，部分研究探讨了检测方法的可靠性：①非小细胞肺癌中DNA检测出的断点融合对于预测靶向治疗疗效是不可靠的[40]。②在非小细胞癌中NGS是检测ALK基因重排的可靠方法[41]；在炎性肌纤维母细胞肿瘤中，FISH检测不能确定ALK状态时可以通过RNA seq进一步明确[42]。③有研究通过DNA和RNA测序鉴定和验证非小细胞肺癌中的非典型RET融合[43]。④来自国内多中心协作分析了卵巢癌中同源重组修复基因突变情况及临床病理意义[44]。⑤有研究提出了一种新的单核苷酸变异(SNV)的新型统计验证方法在检测可能遗漏的低频变异时更有价值[45]。

4. 精准诊断的外延——丰富预后信息
病理学的蛛丝马迹为临床提供充分的预后参考信息。一方面是通过形态学线索建立预后相关指标。比如：①宫颈活检组织中的核分级等因素可以预测淋巴结转移情况[46]。②在宫颈腺癌中，肿瘤出芽及肿瘤细胞簇可以预测预后[47]。③宫颈腺癌的输卵管播散提示具有卵巢转移的风险[48]。④子宫内膜乳头状增生的形态学分级可以预测肿瘤风险[49]。⑤有特殊形态学特点的乳腺叶状肿瘤更容易局部复发[50]。另一方面，分子病理检测为判断预后提供了更丰富的信息：①LEF1-Nup93-β-catenin通路促进癌细胞癌进展[51]。②DGKA与SRC/FAK相互作用促进非小细胞肺癌转移[52]。③JAK2/TOPK/组蛋白H3轴促进伯基特淋巴瘤进展[53]。④基于同源重组缺陷（HRD）计算的评分在前列腺癌中具有预后价值[54]。⑤B7-H4高表达通过AMPK/mTOR通路促进非小细胞肺癌进展[55]。⑥Fbw7可抑制弥漫性大B细胞淋巴瘤进展[56]。⑦LRP5、SETD5等多种分子上调可以促进糖酵解进而促进肿瘤生长[29, 57]。⑧甲状腺乳头状癌中CEMIP可激活NF-κB通路发挥致癌作用[58]。⑨GOLM1和FAM49B可作为头颈鳞癌的预后标志[59]。⑩另外，在细胞学诊断上，研究发现在ASC-US及L-SIL病例中使用HPV基因分型可以预测进展风险[60, 61]。

5. 精准诊断的重点——免疫治疗
免疫治疗已经成为多个癌种中肿瘤综合治疗的重要组成部分。然而现行治疗标准（主要基于PD-L1判断）仍存在一定局限性，如何精准筛选出免疫治疗的获益人群仍然是临床治疗中的关键问题之一。病理医师对此做出了卓有成效的研究：①通过对疗前活检组织中的坏死、嗜酸性粒细胞、中性粒细胞等因素分析，建立免疫相关组织学表型，可以预测肺鳞状细胞癌的PD-1新辅助免疫治疗疗效[62]。②通过免疫组化组合将三阴性乳腺癌分为管腔雄激素受体（LAR）、免疫调节（IM）、基底样免疫抑制（BLIS）和间充质（MES）四种亚型，其中IM型可能从免疫治疗中获益[63]。③中国人群散发性甲状腺髓样癌中PD-L1的表达与HRAS突变呈正相关，提示免疫治疗可能对HRAS突变者更有效[64]。④在宫颈腺癌中，HPV基因型与PD-L1表达存在一定相关性[65]。⑤在非小细胞肺癌原发灶和转移灶中PD-L1表达和TILs数量存在异质性[66]。⑥三级淋巴结构在EBV相关胃癌中具有重要意义[67]。⑦肥大细胞可以抑制结直肠癌进展[68]。另外，多个研究基于公共数据库分析筛选了可能预测免疫治疗疗效的标记物，如NK细胞相关标记物特征可以预测肺腺癌的免疫治疗反应[69]、免疫相关标记物组合预测胃癌免疫治疗反应[70]等。除了免疫治疗相关研究外，还有研究关注了免疫微环境中影响预后的相关因素，如子宫内膜癌中的VISTA表达[71]、乳腺癌中的ME3方法[72]等。

6. 精准诊断的助力——免疫组化与人工智能
免疫组化作为最常用的病理辅助诊断技术，其研究成果具有较高的临床应用价值：一方面是作为诊断及鉴别诊断的标记物研究，如：①HE4可作为神经内分泌分化标记物[73]、NR4A3可作为鼻窦腺泡细胞癌诊断标记物[74]。②PRAME可鉴别肢端雀斑样黑色素瘤与肢端痣[75]、parafibromin可鉴别甲状旁腺腺瘤与甲状旁腺癌[76]、NKX6-1可鉴别嫌色细胞癌和其他嗜酸性肾肿瘤[77]、D2-40可鉴别肺腺癌前驱病变[78]等。另一方面是作为治疗或预后相关标记物的研究，如①Pan-TRK免疫组化染色可作为错配修复缺陷型结直肠癌中NTRK融合的常规筛查可靠策略[79]。②UCH-L1在非小细胞癌中提示预后不良[80]。③乳腺黏液癌中WT1表达提示预后较好[81]。④NRF2高表达提示食管鳞癌预后不良[82]等。
随着深度学习技术的不断进步，人工智能在数字病理中的应用逐渐突破了“肿瘤vs非肿瘤”的桎梏，向精准诊断的目标大步迈进。人工智能辅助精准诊断主要体现在以下几个方面：①对不同肿瘤亚型的进一步细分，如胸腔积液[83]、胃癌[84, 85]、甲状腺癌[86]等。②对不同预后人群的进一步筛选，如肾透明细胞癌[87]、宫颈细胞学ASC-US[88]、结直肠腺瘤[89]等。③对免疫微环境的数字化分析，尤其是对非小细胞癌中PD-L1、TILs等相关因素的分析等[90, 91]。

【主编】

应建明     中国医学科学院肿瘤医院

刘艳辉     广东省人民医院

【副主编】

王   哲     空军军医大学西京医院

王   坚     复旦大学附属肿瘤医院

云径平     中山大学附属肿瘤医院

孟   斌     天津医科大学肿瘤医院

周   桥     四川大学华西医院

【编委】（按姓氏拼音排序）

冯晓莉     中国医学科学院肿瘤医院

刘尚梅     中国医学科学院肿瘤医院

鲁海珍     中国医学科学院肿瘤医院

石素胜     中国医学科学院肿瘤医院

宋   艳     中国医学科学院肿瘤医院

薛丽燕     中国医学科学院肿瘤医院

张宏图     中国医学科学院肿瘤医院

张智慧     中国医学科学院肿瘤医院

郑   波     中国医学科学院肿瘤医院

郑   闪     中国医学科学院肿瘤医院

邹霜梅     中国医学科学院肿瘤医院

【秘书】

王炳智     中国医学科学院肿瘤医院

郭嫦媛     中国医学科学院肿瘤医院

李文斌     中国医学科学院肿瘤医院

曲   扬     中国医学科学院肿瘤医院

薛学敏     中国医学科学院肿瘤医院

胡春芳     中国医学科学院肿瘤医院

李丽红     中国医学科学院肿瘤医院

赵祖璇     中国医学科学院肿瘤医院

孙子涵     中国医学科学院肿瘤医院

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