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Charles Swanton and colleagues used multiregion exome sequencing to study the evolutionary histories of ten clear cell renal cell carcinomas. They observed marked intratumoral heterogeneity in all cases, with extensive evidence of parallel evolution of tumor subclones and only a small number of truncal driver events. Clear cell renal carcinomas (ccRCCs) can display intratumor heterogeneity (ITH). We applied multiregion exome sequencing (M-seq) to resolve the genetic architecture and evolutionary histories of ten ccRCCs. Ultra-deep sequencing identified ITH in all cases. We found that 73–75% of identified ccRCC driver aberrations were subclonal, confounding estimates of driver mutation prevalence. ITH increased with the number of biopsies analyzed, without evidence of saturation in most tumors. Chromosome 3p loss and VHL aberrations were the only ubiquitous events. The proportion of C>T transitions at CpG sites increased during tumor progression. M-seq permits the temporal resolution of ccRCC evolution and refines mutational signatures occurring during tumor development.

Spatial and temporal dissection of the genomic changes occurring during the evolution of human non–small cell lung cancer (NSCLC) may help elucidate the basis for its dismal prognosis. We sequenced 25 spatially distinct regions from seven operable NSCLCs and found evidence of branched evolution, with driver mutations arising before and after subclonal diversification. There was pronounced intratumor heterogeneity in copy number alterations, translocations, and mutations associated with APOBEC cytidine deaminase activity. Despite maintained carcinogen exposure, tumors from smokers showed a relative decrease in smoking-related mutations over time, accompanied by an increase in APOBEC-associated mutations. In tumors from former smokers, genome-doubling occurred within a smoking-signature context before subclonal diversification, which suggested that a long period of tumor latency had preceded clinical detection. The regionally separated driver mutations, coupled with the relentless and heterogeneous nature of the genome instability processes, are likely to confound treatment success in NSCLC.

The importance of intratumour genetic and functional heterogeneity is increasingly recognised as a driver of cancer progression and survival outcome. Understanding how tumour clonal heterogeneity impacts upon therapeutic outcome, however, is still an area of unmet clinical and scientific need. TRACERx (TRAcking non-small cell lung Cancer Evolution through therapy [Rx]), a prospective study of patients with primary non-small cell lung cancer (NSCLC), aims to define the evolutionary trajectories of lung cancer in both space and time through multiregion and longitudinal tumour sampling and genetic analysis. By following cancers from diagnosis to relapse, tracking the evolutionary trajectories of tumours in relation to therapeutic interventions, and determining the impact of clonal heterogeneity on clinical outcomes, TRACERx may help to identify novel therapeutic targets for NSCLC and may also serve as a model applicable to other cancer types.

Neoantigen immunogenicity prediction is a highly challenging problem in the development of personalised medicines. Low reactivity rates in called neoantigens result in a difficult prediction scenario with limited training datasets. Here we describe ImmugenX, a modular protein language modelling approach to immunogenicity prediction for CD8+ reactive epitopes. ImmugenX comprises of a pMHC encoding module trained on three pMHC prediction tasks, an optional TCR encoding module and a set of context specific immunogenicity prediction head modules. Compared with state-of-the-art models for each task, ImmugenX’s encoding module performs comparably or better on pMHC binding affinity, eluted ligand prediction and stability tasks. ImmugenX outperforms all compared models on pMHC immunogenicity prediction (Area under the receiver operating characteristic curve = 0.619, average precision: 0.514), with a 7% increase in average precision compared to the next best model. ImmugenX shows further improved performance on immunogenicity prediction with the integration of TCR context information. ImmugenX performance is further analysed for interpretability, which locates areas of weakness found across existing immunogenicity models and highlight possible biases in public datasets.

BackgroundA significant challenge within the field of personalized neoantigen therapies is the determination of which neoantigen targets will elicit durable, therapeutically relevant immune responses. T cell responses can be detected for circa 10–20% of neoepitopes selected for use in vaccines. Screening of memory responses in tumor-infiltrating T cells show much lower rates of 1–2%. Of this small percentage of neoantigens capable of driving an immune response, only a subset will be resistant to methods of tumor immune evasion. Therefore, it is paramount that both these challenges are faced in order to obtain a durable clinical response.Across different types of neoantigens, the relationship between clonal neoantigens and response to immunotherapy has previously been demonstrated across multiple indications supporting the key role of clonal neoantigens as substrate for T cell recognition of tumors.MethodsAchilles Therapeutics aims to deliver precision immunotherapies specifically targeting clonal neoantigens identified through the Achilles Clonality Engine methodology within our PELEUSTM bioinformatics platform. The PELEUSTM platform incorporates a Bayesian approach allowing for the determination of the probability of each potential neoantigen being clonal.In addition to clonality, and to improve our ability to select for immunogenic neoantigens, we have developed an extensive pipeline for identification of tumor-derived memory T cell responses to clonal neoantigens.ResultsThrough the use of data obtained by screening circa 10,000 neoantigens for T cell reactivity in expanded tumor-infiltrating lymphocytes, we developed and validated an AI method, NeoRanker, for predicting neoantigen immunogenicity. Using a small set of features incorporating genomic, transcriptomic and proteomic data for training purposes, NeoRanker is able to preferentially enrich our clonal neoantigen list for those capable of driving either CD8+ or CD4+ T cell responses. When benchmarked against well-known tools in the field including BigMHC and Prime, NeoRanker displayed the best performance as measured by the area under the receiver operator characteristic curve.ConclusionsWe believe this technology has broad applicability for optimising target selection across all types of personalized neoantigen vaccines and cell therapies.Trial RegistrationNCT03997474; NCT04032847; NCT03517917

Distinct genes are mutated in different regions of a single patient’s tumor. Point mutations seem to have less adverse effect on relapse-free survival than copy-number heterogeneity. Chromosome instability appears to be an important adverse prognostic factor. Lung cancer is the leading cause of cancer-related death worldwide, 1 , 2 with non–small-cell lung cancer (NSCLC) being the most common type. Large-scale sequencing studies have revealed the complex genomic landscape of NSCLC 3 – 6 and genomic differences between lung adenocarcinomas and lung squamous-cell carcinomas. 7 However, in-depth exploration of NSCLC intratumor heterogeneity (which provides the fuel for tumor evolution and drug resistance) and cancer genome evolution has been limited to small retrospective cohorts. 8 , 9 Therefore, the clinical significance of intratumor heterogeneity and the potential for clonality of driver events to guide therapeutic strategies have not yet been defined. Tracking Non–Small-Cell Lung Cancer . . .

Papillary renal cell carcinoma (pRCC) is an important subtype of kidney cancer with a problematic pathological classification and highly variable clinical behaviour. Here we sequence the genomes or exomes of 31 pRCCs, and in four tumours, multi-region sequencing is undertaken. We identify BAP1 , SETD2 , ARID2 and Nrf2 pathway genes ( KEAP1 , NHE2L2 and CUL3 ) as probable drivers, together with at least eight other possible drivers. However, only ~10% of tumours harbour detectable pathogenic changes in any one driver gene, and where present, the mutations are often predicted to be present within cancer sub-clones. We specifically detect parallel evolution of multiple SETD2 mutations within different sub-regions of the same tumour. By contrast, large copy number gains of chromosomes 7, 12, 16 and 17 are usually early, monoclonal changes in pRCC evolution. The predominance of large copy number variants as the major drivers for pRCC highlights an unusual mode of tumorigenesis that may challenge precision medicine approaches. Papillary renal cell carcinoma (pRCC) is a subtype of kidney cancer characterized by highly variable clinical behaviour. Here the authors sequence either the genomes or exomes of 31 pRCCs and identify several genes in sub-clones and large copy number variants in major clones that may be important drivers of pRCC.

Neoantigen immunogenicity prediction is a burgeoning field with vast potential; however, the shortage of high-quality data and biases in current datasets limit model generalizability. Here we discuss some of the pitfalls that may underly this limited performance and propose a path forward.

The importance of intratumour genetic and functional heterogeneity is increasingly recognised as a driver of cancer progression and survival outcome. Understanding how tumour clonal heterogeneity impacts upon therapeutic outcome, however, is still an area of unmet clinical and scientific need. TRACERx (TRAcking non-small cell lung Cancer Evolution through therapy [Rx]), a prospective study of patients with primary non-small cell lung cancer (NSCLC), aims to define the evolutionary trajectories of lung cancer in both space and time through multiregion and longitudinal tumour sampling and genetic analysis. By following cancers from diagnosis to relapse, tracking the evolutionary trajectories of tumours in relation to therapeutic interventions, and determining the impact of clonal heterogeneity on clinical outcomes, TRACERx may help to identify novel therapeutic targets for NSCLC and may also serve as a model applicable to other cancer types.