Driver mutations promote initiation and progression of cancer. Pharmacological treatment can inhibit the action of the mutant protein; however, drug resistance almost invariably emerges. Multiple studies revealed that cancer drug resistance is based upon a plethora of distinct mechanisms. Drug resistance mutations can occur in the same protein or in different proteins; as well as in the same pathway or in parallel pathways, bypassing the intercepted signaling. The dilemma that the clinical oncologist is facing is that not all the genomic alterations as well as alterations in the tumor microenvironment that facilitate cancer cell proliferation are known, and neither are the alterations that are likely to promote metastasis. For example, the common KRas^G12C driver mutation emerges in different cancers. Most occur in NSCLC, but some occur, albeit to a lower extent, in colorectal cancer and pancreatic ductal carcinoma. The responses to KRas^G12C inhibitors are variable and fall into three categories, (i) new point mutations in KRas, or multiple copies of KRAS G12C which lead to higher expression level of the mutant protein; (ii) mutations in genes other than KRAS; (iii) original cancer transitioning to other cancer(s). Resistance to adagrasib, an experimental antitumor agent exerting its cytotoxic effect as a covalent inhibitor of the G12C KRas, indicated that half of the cases present multiple KRas mutations as well as allele amplification. Redundant or parallel pathways included MET amplification; emerging driver mutations in NRAS, BRAF, MAP2K1, and RET; gene fusion events in ALK, RET, BRAF, RAF1, and FGFR3; and loss-of-function mutations in NF1 and PTEN tumor suppressors.

In the current review we discuss the molecular mechanisms underlying drug resistance while focusing on those emerging to common targeted cancer drivers. We also address questions of why cancers with a common driver mutation are unlikely to evolve a common drug resistance mechanism, and whether one can predict the likely mechanisms that the tumor cell may develop. These vastly important and tantalizing questions in drug discovery, and broadly in precision medicine, are the focus of our present review. We end with our perspective, which calls for target combinations to be selected and prioritized with the help of the emerging massive compute power which enables artificial intelligence, and the increased gathering of data to overcome its insatiable needs.

驱动突变促进癌症的发生和进展。药物治疗可以抑制突变蛋白的作用；然而，耐药性几乎总是会出现。多项研究表明，癌症耐药性基于多种不同的机制。耐药突变可以发生在同一蛋白质上，也可以发生在不同蛋白质上；以及在同一途径或平行途径中，绕过被拦截的信号传导。临床肿瘤学家面临的困境是，并非所有促进癌细胞增殖的基因组改变和肿瘤微环境的改变都是已知的，而且可能促进转移的改变也是未知的。例如，常见的 KRas ^G12C驱动突变出现在不同的癌症中。大多数发生在非小细胞肺癌中，但也有一些发生在结直肠癌和胰腺导管癌中，尽管程度较低。^{对 KRas G12C}抑制剂的反应各不相同，分为三类：(i) KRas 中的新点突变，或KRAS G12C 的多个拷贝，导致突变蛋白的表达水平更高；(ii) KRAS以外的基因突变；(iii) 原发癌症转变为其他癌症。阿达格拉西（adagrasib）是一种实验性抗肿瘤药物，作为 G12C KRas 的共价抑制剂发挥其细胞毒性作用，对阿达格拉西的耐药性表明，一半的病例存在多种 KRas 突变以及等位基因扩增。冗余或平行途径包括MET扩增；NRAS、BRAF、MAP2K1和RET中出现的驱动突变；ALK、RET、BRAF、RAF1和FGFR3中的基因融合事件；以及NF1和PTEN肿瘤抑制因子的功能丧失突变。

在当前的综述中，我们讨论了耐药性背后的分子机制，同时重点关注常见目标癌症驱动因素中出现的分子机制。我们还解决了为什么具有共同驱动突变的癌症不太可能进化出共同的耐药机制，以及是否可以预测肿瘤细胞可能发展的可能机制的问题。这些在药物发现和广泛的精准医学中极其重要和诱人的问题是我们当前综述的重点。我们以我们的观点结束，它要求借助新兴的大规模计算能力来选择目标组合并确定优先级，从而实现人工智能，并增加数据收集以克服其永不满足的需求。