Pla Indira, Szabolcs Botond L, Péter Petra Nikolett, Ujfaludi Zsuzsanna, Kim Yonghyo, Horvatovich Peter, Sanchez Aniel, Pawlowski Krzysztof, Wieslander Elisabet, Guedes Jéssica, Pál Dorottya Mp, Ascsillán Anna A, Betancourt Lazaro Hiram, Németh István Balázs, Gil Jeovanis, de Almeida Natália Pinto, Szeitz Beáta, Szadai Leticia, Doma Viktória, Woldmar Nicole, Bartha Áron, Pahi Zoltan, Pankotai Tibor, Győrffy Balázs, Szasz A Marcell, Domont Gilberto, Nogueira Fábio, Kwon Ho Jeong, Appelqvist Roger, Kárpáti Sarolta, Fenyö David, Malm Johan, Marko-Varga György, Kemény Lajos V
bioRxiv. 2024 Feb 12:2024.02.08.579424. doi: 10.1101/2024.02.08.579424.
The utilization of PD1 and CTLA4 inhibitors has revolutionized the treatment of malignant melanoma (MM). However, resistance to targeted and immune-checkpoint-based therapies still poses a significant problem. Here we mine large scale MM proteogenomic data integrating it with MM cell line dependency screen, and drug sensitivity data to identify druggable targets and forecast treatment efficacy and resistance. Leveraging protein profiles from established MM subtypes and molecular structures of 82 cancer treatment drugs, we identified nine candidate hub proteins, mTOR, FYN, PIK3CB, EGFR, MAPK3, MAP4K1, MAP2K1, SRC and AKT1, across five distinct MM subtypes. These proteins serve as potential drug targets applicable to one or multiple MM subtypes. By analyzing transcriptomic data from 48 publicly accessible melanoma cell lines sourced from Achilles and CRISPR dependency screens, we forecasted 162 potentially targetable genes. We also identified genetic resistance in 260 genes across at least one melanoma subtype. In addition, we employed publicly available compound sensitivity data (Cancer Therapeutics Response Portal, CTRPv2) on the cell lines to assess the correlation of compound effectiveness within each subtype. We have identified 20 compounds exhibiting potential drug impact in at least one melanoma subtype. Remarkably, employing this unbiased approach, we have uncovered compounds targeting ferroptosis, that demonstrate a striking 30x fold difference in sensitivity among different subtypes. This implies that the proteogenomic classification of melanoma has the potential to predict sensitivity to ferroptosis compounds. Our results suggest innovative and novel therapeutic strategies by stratifying melanoma samples through proteomic profiling, offering a spectrum of novel therapeutic interventions and prospects for combination therapy.
(1) Proteogenomic subtype classification can define the landscape of genetic dependencies in melanoma (2) Nine proteins from molecular subtypes were identified as potential drug targets for specified MM patients (3) 20 compounds identified that show potential effectiveness in at least one melanoma subtype (4) Proteogenomics can predict specific ferroptosis inducers, HDAC, and RTK Inhibitor sensitivity in melanoma subtypes.
PD1和CTLA4抑制剂的应用彻底改变了恶性黑色素瘤（MM）的治疗方式。然而，对靶向治疗和基于免疫检查点的治疗产生耐药性仍然是一个重大问题。在此，我们挖掘大规模MM蛋白质基因组数据，并将其与MM细胞系依赖性筛选及药物敏感性数据相结合，以识别可成药靶点并预测治疗效果及耐药性。利用已确立的MM亚型的蛋白质谱和82种癌症治疗药物的分子结构，我们在五种不同的MM亚型中鉴定出九个候选枢纽蛋白，即mTOR、FYN、PIK3CB、EGFR、MAPK3、MAP4K1、MAP2K1、SRC和AKT1。这些蛋白质可作为适用于一种或多种MM亚型的潜在药物靶点。通过分析来自阿喀琉斯项目和CRISPR依赖性筛选的48个可公开获取的黑色素瘤细胞系的转录组数据，我们预测了162个潜在可靶向基因。我们还在至少一种黑色素瘤亚型的260个基因中鉴定出遗传耐药性。此外，我们利用细胞系的公开可用化合物敏感性数据（癌症治疗反应门户，CTRPv2）评估每种亚型内化合物有效性的相关性。我们已鉴定出20种在至少一种黑色素瘤亚型中显示出潜在药物作用的化合物。值得注意的是，采用这种无偏倚方法，我们发现了靶向铁死亡的化合物，这些化合物在不同亚型之间的敏感性存在惊人的30倍差异。这意味着黑色素瘤的蛋白质基因组分类有可能预测对铁死亡化合物的敏感性。我们的结果表明，通过蛋白质组学分析对黑色素瘤样本进行分层，可为联合治疗提供一系列创新的治疗策略和前景。
（1）蛋白质基因组亚型分类可定义黑色素瘤中基因依赖性的格局（2）从分子亚型中鉴定出的九种蛋白质被确定为特定MM患者的潜在药物靶点（3）鉴定出20种在至少一种黑色素瘤亚型中显示出潜在有效性的化合物（4）蛋白质基因组学可预测黑色素瘤亚型中特定的铁死亡诱导剂、HDAC和RTK抑制剂敏感性。
