开发创新的精准和个性化癌症疗法对于提高癌症生存率至关重要，特别是对于结直肠癌等常见癌症类型。本研究旨在展示在波兰结直肠癌患者队列中使用人工智能 (AI) 发现精准治疗新靶点的各种方法。我们分析了 71 名经组织病理学证实的晚期可切除结直肠腺癌患者。对肿瘤和外周血样本进行全外显子组测序，对肿瘤样本进行RNA测序（RNAseq）。我们采用三种方法来确定个性化和精准治疗的潜在目标。首先，使用我们内部的新抗原识别管道 ARDentify，结合基于免疫肽组学质谱数据 (ARDisplay) 训练的人工智能模型，我们鉴定了队列中的新表位。其次，根据在我们的患者队列中发现的反复突变，我们选择了相应的癌细胞系，并利用敲除基因依赖性评分来识别合成致死基因。第三，采用基于癌细胞系数据训练的基于人工智能的模型来识别与选定患者基因组图谱相似的细胞系。这些细胞系中的拷贝数变异和重复的单核苷酸变异以及基因依赖性数据被用来寻找个性化的合成致死对。我们鉴定了大约 8,700 个独特的新表位，但没有一个被超过两名患者共享，这表明共享的潜力有限。我们队列中的新抗原目标。此外，我们还确定了三个合成致死对：众所周知的 APC-CTNNB1 和 BRAF-DUSP4 对，以及最近描述的 APC-TCF7L2 对，这对 APC 和 BRAF 变异患者可能具有重要意义。此外，通过利用相似癌细胞系的识别，我们发现了具有治疗意义的潜在基因对 VPS4A 和 VPS4B。我们的研究强调了识别癌症患者潜在治疗靶点的三种不同方法。每种方法都为我们的队列提供了宝贵的见解，强调了这些方法在开发精准和个性化癌症疗法中的相关性和实用性。重要的是，我们开发了一种新颖的 AI 模型，可以使用 RNAseq 和甲基化数据将肿瘤与代表性细胞系进行比对。该模型使我们能够识别与患者肿瘤非常相似的细胞系，从而有助于准确选择体外验证所需的模型。版权所有 © 2024 Murcia Pienkowski, Skoczylas, Zaremba, Kłęk, Balawejder, Biernat, Czarnocka, Gniewek, Grochowalski, Kamuda, Król- Józaga、Marczyńska-Grzelak、Mazzocco、Szatanek、Widawski、Welanyk、Orzeszko、Szura、Torbicz、Borys、Wohadlo、Wysocki、Karczewski、Markowska、Kucharczyk、Piatek、Jasiński、Warchoł、Kaczmarczyk、Blum 和 Sanecka-Duin。

Developing innovative precision and personalized cancer therapeutics is essential to enhance cancer survivability, particularly for prevalent cancer types such as colorectal cancer. This study aims to demonstrate various approaches for discovering new targets for precision therapies using artificial intelligence (AI) on a Polish cohort of colorectal cancer patients.We analyzed 71 patients with histopathologically confirmed advanced resectional colorectal adenocarcinoma. Whole exome sequencing was performed on tumor and peripheral blood samples, while RNA sequencing (RNAseq) was conducted on tumor samples. We employed three approaches to identify potential targets for personalized and precision therapies. First, using our in-house neoantigen calling pipeline, ARDentify, combined with an AI-based model trained on immunopeptidomics mass spectrometry data (ARDisplay), we identified neoepitopes in the cohort. Second, based on recurrent mutations found in our patient cohort, we selected corresponding cancer cell lines and utilized knock-out gene dependency scores to identify synthetic lethality genes. Third, an AI-based model trained on cancer cell line data was employed to identify cell lines with genomic profiles similar to selected patients. Copy number variants and recurrent single nucleotide variants in these cell lines, along with gene dependency data, were used to find personalized synthetic lethality pairs.We identified approximately 8,700 unique neoepitopes, but none were shared by more than two patients, indicating limited potential for shared neoantigenic targets across our cohort. Additionally, we identified three synthetic lethality pairs: the well-known APC-CTNNB1 and BRAF-DUSP4 pairs, along with the recently described APC-TCF7L2 pair, which could be significant for patients with APC and BRAF variants. Furthermore, by leveraging the identification of similar cancer cell lines, we uncovered a potential gene pair, VPS4A and VPS4B, with therapeutic implications.Our study highlights three distinct approaches for identifying potential therapeutic targets in cancer patients. Each approach yielded valuable insights into our cohort, underscoring the relevance and utility of these methodologies in the development of precision and personalized cancer therapies. Importantly, we developed a novel AI model that aligns tumors with representative cell lines using RNAseq and methylation data. This model enables us to identify cell lines closely resembling patient tumors, facilitating accurate selection of models needed for in vitro validation.Copyright © 2024 Murcia Pienkowski, Skoczylas, Zaremba, Kłęk, Balawejder, Biernat, Czarnocka, Gniewek, Grochowalski, Kamuda, Król-Józaga, Marczyńska-Grzelak, Mazzocco, Szatanek, Widawski, Welanyk, Orzeszko, Szura, Torbicz, Borys, Wohadlo, Wysocki, Karczewski, Markowska, Kucharczyk, Piatek, Jasiński, Warchoł, Kaczmarczyk, Blum and Sanecka-Duin.