KRAS is the most frequently mutated oncogene in cancer and encodes a key signalling protein in tumours1,2. The KRAS(G12C) mutant has a cysteine residue that has been exploited to design covalent inhibitors that have promising preclinical activity3–5. Here we optimized a series of inhibitors, using novel binding interactions to markedly enhance their potency and selectivity. Our efforts have led to the discovery of AMG 510, which is, to our knowledge, the first KRAS(G12C) inhibitor in clinical development. In preclinical analyses, treatment with AMG 510 led to the regression of KRASG12C tumours and improved the anti-tumour efficacy of chemotherapy and targeted agents. In immune-competent mice, treatment with AMG 510 resulted in a pro-inflammatory tumour microenvironment and produced durable cures alone as well as in combination with immune-checkpoint inhibitors. Cured mice rejected the growth of isogenic KRASG12D tumours, which suggests adaptive immunity against shared antigens. Furthermore, in clinical trials, AMG 510 demonstrated anti-tumour activity in the first dosing cohorts and represents a potentially transformative therapy for patients for whom effective treatments are lacking. Treatment of KRASG12C-mutant cancer cells with the KRAS(G12C) inhibitor AMG 510 leads to durable response in mice, and anti-tumour activity in patients suggests that AMG 510 could be effective in patients for whom treatments are currently lacking.

The clinical KRAS(G12C) inhibitor AMG 510 drives anti-tumour immunity

RAS (KRAS, NRAS and HRAS) is the most frequently mutated gene family in cancers, and, consequently, investigators have sought an effective RAS inhibitor for more than three decades. Even 10 years ago, RAS inhibitors were so elusive that RAS was termed ‘undruggable’. Now, with the success of allele-specific covalent inhibitors against the most frequently mutated version of RAS in non-small-cell lung cancer, KRASG12C, we have the opportunity to evaluate the best therapeutic strategies to treat RAS-driven cancers. Mutation-specific biochemical properties, as well as the tissue of origin, are likely to affect the effectiveness of such treatments. Currently, direct inhibition of mutant RAS through allele-specific inhibitors provides the best therapeutic approach. Therapies that target RAS-activating pathways or RAS effector pathways could be combined with these direct RAS inhibitors, immune checkpoint inhibitors or T cell-targeting approaches to treat RAS-mutant tumours. Here we review recent advances in therapies that target mutant RAS proteins and discuss the future challenges of these therapies, including combination strategies. RAS proteins, which are frequently altered in cancer, were once considered undruggable, but compounds targeting some mutant RAS proteins have recently demonstrated clinical efficacy. In this Review, Malek and colleagues explore how these and other drugs that target RAS or associated pathways might be used effectively, particularly in combinations, and discuss other RAS-targeted therapies in the pipeline.

RAS-targeted therapies: is the undruggable drugged?

Artificial intelligence (AI), and, in particular, deep learning as a subcategory of AI, provides opportunities for the discovery and development of innovative drugs. Various machine learning approaches have recently (re)emerged, some of which may be considered instances of domain-specific AI which have been successfully employed for drug discovery and design. This review provides a comprehensive portrayal of these machine learning techniques and of their applications in medicinal chemistry. After introducing the basic principles, alongside some application notes, of the various machine learning algorithms, the current state-of-the art of AI-assisted pharmaceutical discovery is discussed, including applications in structure- and ligand-based virtual screening, de novo drug design, physicochemical and pharmacokinetic property prediction, drug repurposing, and related aspects. Finally, several challenges and limitations of the current methods are summarized, with a view to potential future directions for AI-assisted drug discovery and design.

Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery

A framework to rank genomic alterations as targets for cancer precision medicine: the ESMO Scale for Clinical Actionability of molecular Targets (ESCAT)

While tumor genome sequencing has become widely available in clinical and research settings, the interpretation of tumor somatic variants remains an important bottleneck. Here we present the Cancer Genome Interpreter, a versatile platform that automates the interpretation of newly sequenced cancer genomes, annotating the potential of alterations detected in tumors to act as drivers and their possible effect on treatment response. The results are organized in different levels of evidence according to current knowledge, which we envision can support a broad range of oncology use cases. The resource is publicly available at http://www.cancergenomeinterpreter.org
 .

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Cancer Genome Interpreter annotates the biological and clinical relevance of tumor alterations

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K. Shaw

Papers

AACR project genie: Powering precision medicine through an international consortium