Translating insights into tumor evolution to clinical practice: promises and challenges

CaClust: linking genotype to transcriptional heterogeneity of follicular lymphoma using BCR and exomic variants

Tumours exhibit high genotypic and transcriptional heterogeneity. Both affect cancer progression and treatment, but have been predominantly studied separately in follicular lymphoma. To comprehensively investigate the evolution and genotype-to-phenotype maps in follicular lymphoma, we introduce CaClust, a probabilistic graphical model integrating deep whole exome, single-cell RNA and B-cell receptor sequencing data to infer clone genotypes, cell-to-clone mapping, and single-cell genotyping. CaClust outperforms a state-of-the-art model on simulated and patient data. In-depth analyses of single cells from four samples showcase effects of driver mutations, follicular lymphoma evolution, possible therapeutic targets, and single-cell genotyping that agrees with an independent targeted resequencing experiment.

Blending space and time to talk about cancer in extended reality

We introduce a proof-of-concept extended reality (XR) environment for discussing cancer, presenting genomic information from multiple tumour sites in the context of 3D tumour models generated from CT scans. This tool enhances multidisciplinary discussions. Clinicians and cancer researchers explored its use in oncology, sharing perspectives on XR's potential for use in molecular tumour boards, clinician-patient communication, and education. XR serves as a universal language, fostering collaborative decision-making in oncology.

Genetic variation drives cancer cell adaptation to ECM stiffness

The progression of many solid tumors is accompanied by temporal and spatial changes in the stiffness of the extracellular matrix (ECM). Cancer cells adapt to soft and stiff ECM through mechanisms that are not fully understood. It is well known that there is significant genetic heterogeneity from cell to cell in tumors, but how ECM stiffness as a parameter might interact with that genetic variation is not known. Here, we employed experimental evolution to study the response of genetically variable and clonal populations of tumor cells to variable ECM stiffness. Proliferation rates of genetically variable populations cultured on soft ECM increased over a period of several weeks, whereas clonal populations did not evolve. Tracking of DNA barcoded cell lineages revealed that soft ECM consistently selected for the same few variants. These data provide evidence that ECM stiffness exerts natural selection on genetically variable tumor populations. Soft-selected cells were highly migratory, with enriched oncogenic signatures and unusual behaviors such as spreading and traction force generation on ECMs with stiffness as low as 1 kPa. Rho-regulated cell spreading was found to be the directly selected trait, with yes-associated protein 1 translocation to the nucleus mediating fitness on soft ECM. Overall, these data show that genetic variation can drive cancer cell adaptation to ECM stiffness.

Genomic and transcriptomic landscape of HER2-low breast cancer

BACKGROUND

Novel agents have expanded the traditional HER2 definitions to include HER2-Low (HER2L) Breast Cancer (BC). We sought to evaluate the distinct molecular characteristics of HER2L BC to understand potential clinical/biologic factors driving resistance and clinical outcomes.

METHODS

Retrospective analysis was performed on 13,613 BC samples, tested at Caris Life Sciences via NextGen DNA/RNA Sequencing. BC subtypes were defined by IHC/ISH. CODEai database was used to access clinical outcomes from insurance claims data.

RESULTS

Overall, mutational landscape was similar between HER2L and classical subsets of HR+and HRneg cohorts. TP53 mutations were significantly higher in HRneg/HER2L group vs. HR+/HER2L tumors (p<0.001). A higher mutation rate of PIK3CA was observed in HRneg/HER2L tumors compared to TNBC subtype (p=0.016). PD-L1 positivity was elevated in HRneg/HER2L tumors compared to HR+/HER2L tumors, all p<0.01. Patients with HR+/HER2L tumors treated with CDK4/6 inhibitors had similar OS compared to pts with HR+/HER2-0 (HR=0.89, p=0.012). 27.2% of HR+/HER2L pts had activating PIK3CA mutations. Among HR+PIK3CA mutated tumors, HER2L pts treated with alpelisib showed no difference in OS vs. HER2-0 alpelisib-treated pts (HR=1.23, p=0.517). 13.9% of HER2L TNBC pts were PD-L1+. Interestingly, pts with PD-L1+ HER2L/HRneg (TNBC) treated with immune checkpoint inhibitors (ICI) showed improved OS than HER2-0 TNBC (HR=0.61, p=0.046).

CONCLUSION

Our findings expand the understanding of the molecular profile of the HER2L subgroup and comparison to the classically defined breast cancer subgroups. Genomic risk assessments after progression on novel therapeutics can be assessed to better define implications for mechanisms of resistance.

Inference of chromosome selection parameters and missegregation rate in cancer from DNA-sequencing data

Aneuploidy is frequently observed in cancers and has been linked to poor patient outcome. Analysis of aneuploidy in DNA-sequencing (DNA-seq) data necessitates untangling the effects of the Copy Number Aberration (CNA) occurrence rates and the selection coefficients that act upon the resulting karyotypes. We introduce a parameter inference algorithm that takes advantage of both bulk and single-cell DNA-seq cohorts. The method is based on Approximate Bayesian Computation (ABC) and utilizes CINner, our recently introduced simulation algorithm of chromosomal instability in cancer. We examine three groups of statistics to summarize the data in the ABC routine: (A) Copy Number-based measures, (B) phylogeny tip statistics, and (C) phylogeny balance indices. Using these statistics, our method can recover both the CNA probabilities and selection parameters from ground truth data, and performs well even for data cohorts of relatively small sizes. We find that only statistics in groups A and C are well-suited for identifying CNA probabilities, and only group A carries the signals for estimating selection parameters. Moreover, the low number of CNA events at large scale compared to cell counts in single-cell samples means that statistics in group B cannot be estimated accurately using phylogeny reconstruction algorithms at the chromosome level. As data from both bulk and single-cell DNA-sequencing techniques becomes increasingly available, our inference framework promises to facilitate the analysis of distinct cancer types, differentiation between selection and neutral drift, and prediction of cancer clonal dynamics.

Frequency-dependent selection of neoantigens fosters tumor immune escape and predicts immunotherapy response

Cancer is an evolutionary process shaped by selective pressure from the microenvironments. However, recent studies reveal that certain tumors undergo neutral evolution where there is no detectable fitness difference amongst the cells following malignant transformation. Here, through computational modeling, we demonstrate that negative frequency-dependent selection (or NFDS), where the immune response against cancer cells depends on the clonality of neoantigens, can lead to an immunogenic landscape that is highly similar to neutral evolution. Crucially, NFDS promotes high antigenic heterogeneity and early immune evasion in hypermutable tumors, leading to poor responses to immune checkpoint blockade (ICB) therapy. Our model also reveals that NFDS is characterized by a negative association between average clonality and total burden of neoantigens. Indeed, this unique feature of NFDS is common in the whole-exome sequencing (WES) datasets (357 tumor samples from 275 patients) from four melanoma cohorts with ICB therapy and a non-small cell lung cancer (NSCLC) WES dataset (327 tumor samples from 100 patients). Altogether, our study provides quantitative evidence supporting the theory of NFDS in cancer, explaining the high prevalence of neutral-looking tumors. These findings also highlight the critical role of frequency-dependent selection in devising more efficient and predictive immunotherapies.

Cetuximab chemotherapy resistance: Insight into the homeostatic evolution of head and neck cancer (Review)

The complex evolution of genetic alterations in cancer that occurs in vivo is a selective process involving numerous factors and mechanisms. Chemotherapeutic agents that prevent the growth and spread of cancer cells induce selective pressure, leading to rapid artificial selection of resistant subclones. This rapid evolution is possible because antineoplastic drugs promote alterations in tumor‑cell metabolism, thus creating a bottleneck event. The few resistant cells that survive in this new environment obtain differential reproductive success that enables them to pass down the newly selected resistant gene pool. The present review aims to summarize key findings of tumor evolution, epithelial‑mesenchymal transition and resistance to cetuximab therapy in head and neck squamous cell carcinoma.

The molecular map of CLL and Richter's syndrome

Clonal expansion of B-cells, from the early stages of monoclonal B-cell lymphocytosis through to chronic lymphocytic leukemia (CLL), and then in some cases to Richter's syndrome (RS) provides a comprehensive model of cancer evolution, notable for the marked morphological transformation and distinct clinical phenotypes. High-throughput sequencing of large cohorts of patients and single-cell studies have generated a molecular map of CLL and more recently, of RS, yielding fundamental insights into these diseases and of clonal evolution. A selection of CLL driver genes have been functionally interrogated to yield novel insights into the biology of CLL. Such findings have the potential to impact patient care through risk stratification, treatment selection and drug discovery. However, this molecular map remains incomplete, with extant questions concerning the origin of the B-cell clone, the role of the TME, inter- and intra-compartmental heterogeneity and of therapeutic resistance mechanisms. Through the application of multi-modal single-cell technologies across tissues, disease states and clinical contexts, these questions can now be addressed with the answers holding great promise of generating translatable knowledge to improve patient care.

ConDoR: tumor phylogeny inference with a copy-number constrained mutation loss model

A tumor contains a diverse collection of somatic mutations that reflect its past evolutionary history and that range in scale from single nucleotide variants (SNVs) to large-scale copy-number aberrations (CNAs). However, no current single-cell DNA sequencing (scDNA-seq) technology produces accurate measurements of both SNVs and CNAs, complicating the inference of tumor phylogenies. We introduce a new evolutionary model, the constrained k-Dollo model, that uses SNVs as phylogenetic markers but constrains losses of SNVs according to clusters of cells. We derive an algorithm, ConDoR, that infers phylogenies from targeted scDNA-seq data using this model. We demonstrate the advantages of ConDoR on simulated and real scDNA-seq data.

Comprehensive and realistic simulation of tumour genomic sequencing data

Accurate identification of somatic mutations and allele frequencies in cancer has critical research and clinical applications. Several computational tools have been developed for this purpose but, in the absence of comprehensive 'ground truth' data, assessing the accuracy of these methods is challenging. We created a computational framework to simulate tumour and matched normal sequencing data for which the source of all loci that contain non-reference bases is known, based on a phased, personalized genome. Unlike existing methods, we account for sampling errors inherent in the sequencing process. Using this framework, we assess accuracy and biases in inferred mutations and their frequencies in an established somatic mutation calling pipeline. We demonstrate bias in existing methods of mutant allele frequency estimation and show, for the first time, the observed mutation frequency spectrum corresponding to a theoretical model of tumour evolution. We highlight the impact of quality filters on detection sensitivity of clinically actionable variants and provide definitive assessment of false positive and false negative mutation calls. Our simulation framework provides an improved means to assess the accuracy of somatic mutation calling pipelines and a detailed picture of the effects of technical parameters and experimental factors on somatic mutation calling in cancer samples.

XMR: an explainable multimodal neural network for drug response prediction

Introduction: Existing large-scale preclinical cancer drug response databases provide us with a great opportunity to identify and predict potentially effective drugs to combat cancers. Deep learning models built on these databases have been developed and applied to tackle the cancer drug-response prediction task. Their prediction has been demonstrated to significantly outperform traditional machine learning methods. However, due to the "black box" characteristic, biologically faithful explanations are hardly derived from these deep learning models. Interpretable deep learning models that rely on visible neural networks (VNNs) have been proposed to provide biological justification for the predicted outcomes. However, their performance does not meet the expectation to be applied in clinical practice. Methods: In this paper, we develop an XMR model, an eXplainable Multimodal neural network for drug Response prediction. XMR is a new compact multimodal neural network consisting of two sub-networks: a visible neural network for learning genomic features and a graph neural network (GNN) for learning drugs' structural features. Both sub-networks are integrated into a multimodal fusion layer to model the drug response for the given gene mutations and the drug's molecular structures. Furthermore, a pruning approach is applied to provide better interpretations of the XMR model. We use five pathway hierarchies (cell cycle, DNA repair, diseases, signal transduction, and metabolism), which are obtained from the Reactome Pathway Database, as the architecture of VNN for our XMR model to predict drug responses of triple negative breast cancer. Results: We find that our model outperforms other state-of-the-art interpretable deep learning models in terms of predictive performance. In addition, our model can provide biological insights into explaining drug responses for triple-negative breast cancer. Discussion: Overall, combining both VNN and GNN in a multimodal fusion layer, XMR captures key genomic and molecular features and offers reasonable interpretability in biology, thereby better predicting drug responses in cancer patients. Our model would also benefit personalized cancer therapy in the future.

Growth exponents reflect evolutionary processes and treatment response in brain metastases

Tumor growth is the result of the interplay of complex biological processes in huge numbers of individual cells living in changing environments. Effective simple mathematical laws have been shown to describe tumor growth in vitro, or simple animal models with bounded-growth dynamics accurately. However, results for the growth of human cancers in patients are scarce. Our study mined a large dataset of 1133 brain metastases (BMs) with longitudinal imaging follow-up to find growth laws for untreated BMs and recurrent treated BMs. Untreated BMs showed high growth exponents, most likely related to the underlying evolutionary dynamics, with experimental tumors in mice resembling accurately the disease. Recurrent BMs growth exponents were smaller, most probably due to a reduction in tumor heterogeneity after treatment, which may limit the tumor evolutionary capabilities. In silico simulations using a stochastic discrete mesoscopic model with basic evolutionary dynamics led to results in line with the observed data.

A weighted distance-based approach for deriving consensus tumor evolutionary trees

MOTIVATION

The acquisition of somatic mutations by a tumor can be modeled by a type of evolutionary tree. However, it is impossible to observe this tree directly. Instead, numerous algorithms have been developed to infer such a tree from different types of sequencing data. But such methods can produce conflicting trees for the same patient, making it desirable to have approaches that can combine several such tumor trees into a consensus or summary tree. We introduce The Weighted m-Tumor Tree Consensus Problem (W-m-TTCP) to find a consensus tree among multiple plausible tumor evolutionary histories, each assigned a confidence weight, given a specific distance measure between tumor trees. We present an algorithm called TuELiP that is based on integer linear programming which solves the W-m-TTCP, and unlike other existing consensus methods, allows the input trees to be weighted differently.

RESULTS

On simulated data we show that TuELiP outperforms two existing methods at correctly identifying the true underlying tree used to create the simulations. We also show that the incorporation of weights can lead to more accurate tree inference. On a Triple-Negative Breast Cancer dataset, we show that including confidence weights can have important impacts on the consensus tree identified.

AVAILABILITY

An implementation of TuELiP and simulated datasets are available at https://bitbucket.org/oesperlab/consensus-ilp/src/main/.

Tracking the evolution of therapy-related myeloid neoplasms using chemotherapy signatures

Patients treated with cytotoxic therapies, including autologous stem cell transplantation, are at risk for developing therapy-related myeloid neoplasms (tMN). Preleukemic clones (ie, clonal hematopoiesis [CH]) are detectable years before the development of these aggressive malignancies, although the genomic events leading to transformation and expansion are not well defined. Here, by leveraging distinctive chemotherapy-associated mutational signatures from whole-genome sequencing data and targeted sequencing of prechemotherapy samples, we reconstructed the evolutionary life-history of 39 therapy-related myeloid malignancies. A dichotomy was revealed, in which neoplasms with evidence of chemotherapy-induced mutagenesis from platinum and melphalan were hypermutated and enriched for complex structural variants (ie, chromothripsis), whereas neoplasms with nonmutagenic chemotherapy exposures were genomically similar to de novo acute myeloid leukemia. Using chemotherapy-associated mutational signatures as temporal barcodes linked to discrete clinical exposure in each patient's life, we estimated that several complex events and genomic drivers were acquired after chemotherapy was administered. For patients with prior multiple myeloma who were treated with high-dose melphalan and autologous stem cell transplantation, we demonstrate that tMN can develop from either a reinfused CH clone that escapes melphalan exposure and is selected after reinfusion, or from TP53-mutant CH that survives direct myeloablative conditioning and acquires melphalan-induced DNA damage. Overall, we revealed a novel mode of tMN progression that is not reliant on direct mutagenesis or even exposure to chemotherapy. Conversely, for tMN that evolve under the influence of chemotherapy-induced mutagenesis, distinct chemotherapies not only select preexisting CH but also promote the acquisition of recurrent genomic drivers.

Clonal selection parallels between normal and cancer tissues

Clonal selection and drift drive both normal tissue and cancer development. However, the biological mechanisms and environmental conditions underpinning these processes remain to be elucidated. Clonal selection models are centered in Darwinian evolutionary theory, where some clones with the fittest features are selected and populate the tissue or tumor. We suggest that different subclasses of stem cells, each of which is responsible for a distinct feature of the selection process, share common features between normal and cancer conditions. While active stem cells populate the tissue, dormant cells account for tissue replenishment/regeneration in both normal and cancerous tissues. We also discuss potential mechanisms that drive clonal drift, their interactions with clonal selection, and their similarities during normal and cancer tissue development.

Clonal evolution and expansion associated with therapy resistance and relapse of colorectal cancer

Colorectal cancer (CRC) arises by a continuous process of genetic diversification and clonal evolution. Multiple genes and pathways have a role in tumor initiation and progression. The gradual accumulation of genetic and epigenetic processes leads to the establishment of adenoma and cancer. The important 'driver' mutations in tumor suppressor genes (such as TP53, APC, and SMAD4) and oncogenes (such as KRAS, NRAS, MET, and PIK3CA) confer selective growth advantages and cause CRC advancement. Clonal evolution induced by therapeutic pressure, as well as intra-tumoral heterogeneity, has been a great challenge in the treatment of metastatic CRC. Tumors often develop resistance to treatments as a result of intra-tumor heterogeneity, clonal evolution, and selection. Hence, the development of a multidrug personalized approach should be prioritized to pave the way for therapeutics repurposing and combination therapy to arrest tumor progression. This review summarizes how selective drug pressure can impact tumor evolution, resulting in the formation of polyclonal resistance mechanisms, ultimately promoting cancer progression. Current strategies for targeting clonal evolution are described. By understanding sources and consequences of tumor heterogeneity, customized and effective treatment plans to combat drug resistance may be devised.

Comprehensive Genomic Profiling of High-Risk Pediatric Cancer Patients Has a Measurable Impact on Clinical Care

PURPOSE

Profiling of pediatric cancers through deep sequencing of large gene panels and whole exomes is rapidly being adopted in many clinical settings. However, the most impactful approach to genomic profiling of pediatric cancers remains to be defined.

METHODS

We conducted a prospective precision medicine trial, using whole-exome sequencing of tumor and germline tissue and whole-transcriptome sequencing (RNA Seq) of tumor tissue to characterize the mutational landscape of 127 tumors from 126 unique patients across the spectrum of pediatric brain tumors, hematologic malignancies, and extracranial solid tumors.

RESULTS

We identified somatic tumor alterations in 121/127 (95.3%) tumor samples and identified cancer predisposition syndromes on the basis of known pathogenic or likely pathogenic germline mutations in cancer predisposition genes in 9/126 patients (7.1%). Additionally, we developed a novel scoring system for measuring the impact of tumor and germline sequencing, encompassing therapeutically relevant genomic alterations, cancer-related germline findings, recommendations for treatment, and refinement of risk stratification or prognosis. At least one impactful finding from the genomic results was identified in 108/127 (85%) samples sequenced. A recommendation to consider a targeted agent was provided for 82/126 (65.1%) patients. Twenty patients ultimately received therapy with a molecularly targeted agent, representing 24% of those who received a targeted agent recommendation and 16% of the total cohort.

CONCLUSION

Paired tumor/normal whole-exome sequencing and tumor RNA Seq of de novo or relapsed/refractory tumors was feasible and clinically impactful in high-risk pediatric cancer patients.

Leveraging single cell sequencing to unravel intra-tumour heterogeneity and tumour evolution in human cancers

Intra-tumour heterogeneity and tumour evolution are well-documented phenomena in human cancers. While the advent of next-generation sequencing technologies has facilitated the large-scale capture of genomic data, the field of single cell genomics is nascent but rapidly advancing and generating many new insights into the complex molecular mechanisms of tumour biology. In this review, we provide an overview of current single cell DNA sequencing technologies, exploring how recent methodological advancements have enumerated new insights into intra-tumour heterogeneity and tumour evolution. Areas highlighted include the potential power of single cell genome sequencing studies to explore evolutionary dynamics contributing to tumourigenesis through to progression, metastasis and therapy resistance. We also explore the use of in-situ sequencing technologies to study intra-tumour heterogeneity in a spatial context, as well as examining the use of single cell genomics to perform lineage tracing in both normal and malignant tissues. Finally, we consider the use of multi-modal single cell sequencing technologies. Taken together, it is hoped that these many facets of single cell genome sequencing will improve our understanding of tumourigenesis, progression and lethality in cancer leading to the development of novel therapies. This article is protected by copyright. All rights reserved.

Colorectal Cancer Is Associated with the Presence of Cancer Driver Mutations in Normal Colon

Although somatic mutations in colorectal cancer are well characterized, little is known about the accumulation of cancer mutations in the normal colon before cancer. Here, we have developed and applied an ultrasensitive, single-molecule mutational test based on CRISPR-DS technology, which enables mutation detection at extremely low frequency (<0.001) in normal colon from patients with and without colorectal cancer. This testing platform revealed that normal colon from patients with and without colorectal cancer carries mutations in common colorectal cancer genes, but these mutations are more abundant in patients with cancer. Oncogenic KRAS mutations were observed in the normal colon of about one third of patients with colorectal cancer but in none of the patients without colorectal cancer. Patients with colorectal cancer also carried more TP53 mutations than patients without cancer and these mutations were more pathogenic and formed larger clones, especially in patients with early-onset colorectal cancer. Most mutations in the normal colon were different from the driver mutations in tumors, suggesting that the occurrence of independent clones with pathogenic KRAS and TP53 mutations is a common event in the colon of individuals who develop colorectal cancer. These results indicate that somatic evolution contributes to clonal expansions in the normal colon and that this process is enhanced in individuals with cancer, particularly in those with early-onset colorectal cancer.

SIGNIFICANCE

This work suggests prevalent somatic evolution in the normal colon of patients with colorectal cancer, highlighting the potential of using ultrasensitive gene sequencing to predict disease risk.

Inferring ongoing cancer evolution from single tumour biopsies using synthetic supervised learning

Variant allele frequencies (VAF) encode ongoing evolution and subclonal selection in growing tumours. However, existing methods that utilize VAF information for cancer evolutionary inference are compressive, slow, or incorrectly specify the underlying cancer evolutionary dynamics. Here, we provide a proof-of-principle synthetic supervised learning method, TumE, that integrates simulated models of cancer evolution with Bayesian neural networks, to infer ongoing selection in bulk-sequenced single tumour biopsies. Analyses in synthetic and patient tumours show that TumE significantly improves both accuracy and inference time per sample when detecting positive selection, deconvoluting selected subclonal populations, and estimating subclone frequency. Importantly, we show how transfer learning can leverage stored knowledge within TumE models for related evolutionary inference tasks-substantially reducing data and computational time for further model development and providing a library of recyclable deep learning models for the cancer evolution community. This extensible framework provides a foundation and future directions for harnessing progressive computational methods for the benefit of cancer genomics and, in turn, the cancer patient.

A Liquid Biopsy-Based Approach for Monitoring Treatment Response in Post-Operative Colorectal Cancer Patients

Monitoring the therapeutic response of colorectal cancer (CRC) patients is crucial to determine treatment strategies; therefore, we constructed a liquid biopsy-based approach for tracking tumor dynamics in non-metastatic (nmCRC) and metastatic (mCRC) patients (n = 55). Serial blood collections were performed during chemotherapy for measuring the amount and the global methylation pattern of cell-free DNA (cfDNA), the promoter methylation of SFRP2 and SDC2 genes, and the plasma homocysteine level. The average cfDNA amount was higher (p < 0.05) in nmCRC patients with recurrent cancer (30.4 ± 17.6 ng) and mCRC patients with progressive disease (PD) (44.3 ± 34.5 ng) compared to individuals with remission (13.2 ± 10.0 ng) or stable disease (12.5 ± 3.4 ng). More than 10% elevation of cfDNA from first to last sample collection was detected in all recurrent cases and 92% of PD patients, while a decrease was observed in most patients with remission. Global methylation level changes indicated a decline (75.5 ± 3.4% vs. 68.2 ± 8.4%), while the promoter methylation of SFRP2 and SDC2 and homocysteine level (10.9 ± 3.4 µmol/L vs. 13.7 ± 4.3 µmol/L) presented an increase in PD patients. In contrast, we found exact opposite changes in remission cases. Our study offers a more precise blood-based approach to monitor the treatment response to different chemotherapies than the currently used markers.

Prognostic Value of Circulating Tumour DNA in Asian Patients with Hepatocellular Carcinoma: A Systematic Review and Meta-Analysis

BACKGROUND

Circulating tumour DNA (ctDNA) is a noninvasive method of detecting tumours, and its prognostic significance in hepatocellular carcinoma (HCC) patients is controversial. We conducted a systematic review of published research data to evaluate the prognostic value of ctDNA in HCC patients.

METHODS

The PubMed, Embase, Web of Science, Cochrane Library, and Scopus databases were searched to identify eligible studies reporting disease-free survival (DFS) and overall survival (OS) stratified by ctDNA prior to January 2022. We evaluated the quality and design of these studies. The hazard ratio (HR) was used to combine the survivorship curve and univariate and multivariate results of the included studies.

RESULTS

In total, 8 articles were included, encompassing 577 HCC patients. The results of survival curve analysis showed that ctDNA was related to poor OS and DFS, and the effect sizes were HR = 2.44, 95% CI (1.42, 4.20), P=0.001; HR = 2.63, 95% CI (1.96, 3.53), P < 0.001. The univariate analysis results showed that ctDNA was related to poor OS (HR = 4.48, 95% CI (1.17, 13.70), P=0.003). The combined results of multivariate analysis showed that ctDNA was related to a shorter risk of OS (HR = 3.74, 95% CI (1.45, 9.65), P=0.006). The univariate and multivariate descriptive analysis results showed that ctDNA was related to shorter DFS, and the effect sizes were HR = 3.28, 95% CI (1.23, 11.30), P=0.011; HR = 3.01, 95% CI (1.11, 10.5), P < 0.001.

CONCLUSION

The evidence provided by this analysis suggests that ctDNA may be a prognostic biomarker and is negatively correlated with the survival of HCC patients. Mutations in the TERT and SOCS3 promoters in ctDNA are associated with poor prognosis and are expected to become good targets for liquid biopsy and to help select treatment strategies.

Image Analysis of Circulating Tumor Cells and Leukocytes Predicts Survival and Metastatic Pattern in Breast Cancer Patients

Background

The purpose of the present work was to test whether quantitative image analysis of circulating cells can provide useful clinical information targeting bone metastasis (BM) and overall survival (OS >30 months) in metastatic breast cancer (MBC).

Methods

Starting from cell images of epithelial circulating tumor cells (eCTC) and leukocytes (CD45pos) obtained with DEPArray, we identified the most significant features and applied single-variable and multi-variable methods, screening all combinations of four machine-learning approaches (Naïve Bayes, Logistic regression, Decision Trees, Random Forest).

Results

Best predictive features were circularity (OS) and diameter (BM), in both eCTC and CD45pos. Median difference in OS was 15 vs. 43 (months), p = 0.03 for eCTC and 19 vs. 36, p = 0.16 for CD45pos. Prediction for BM showed low accuracy (64%, 53%) but strong positive predictive value PPV (79%, 91%) for eCTC and CD45, respectively. Best machine learning model was Naïve Bayes, showing 46 vs 11 (months), p <0.0001 for eCTC; 12.5 vs. 45, p = 0.0004 for CD45pos and 11 vs. 45, p = 0.0003 for eCTC + CD45pos. BM prediction reached 91% accuracy with eCTC, 84% with CD45pos and 91% with combined model.

Conclusions

Quantitative image analysis and machine learning models were effective methods to predict survival and metastatic pattern, with both eCTC and CD45pos containing significant and complementary information.

Clone decomposition based on mutation signatures provides novel insights into mutational processes

Intra-tumor heterogeneity is a phenomenon in which mutation profiles differ from cell to cell within the same tumor and is observed in almost all tumors. Understanding intra-tumor heterogeneity is essential from the clinical perspective. Numerous methods have been developed to predict this phenomenon based on variant allele frequency. Among the methods, CloneSig models the variant allele frequency and mutation signatures simultaneously and provides an accurate clone decomposition. However, this method has limitations in terms of clone number selection and modeling. We propose SigTracer, a novel hierarchical Bayesian approach for analyzing intra-tumor heterogeneity based on mutation signatures to tackle these issues. We show that SigTracer predicts more reasonable clone decompositions than the existing methods against artificial data that mimic cancer genomes. We applied SigTracer to whole-genome sequences of blood cancer samples. The results were consistent with past findings that single base substitutions caused by a specific signature (previously reported as SBS9) related to the activation-induced cytidine deaminase intensively lie within immunoglobulin-coding regions for chronic lymphocytic leukemia samples. Furthermore, we showed that this signature mutates regions responsible for cell-cell adhesion. Accurate assignments of mutations to signatures by SigTracer can provide novel insights into signature origins and mutational processes.

ParsVNN: parsimony visible neural networks for uncovering cancer-specific and drug-sensitive genes and pathways

Prediction of cancer-specific drug responses as well as identification of the corresponding drug-sensitive genes and pathways remains a major biological and clinical challenge. Deep learning models hold immense promise for better drug response predictions, but most of them cannot provide biological and clinical interpretability. Visible neural network (VNN) models have emerged to solve the problem by giving neurons biological meanings and directly casting biological networks into the models. However, the biological networks used in VNNs are often redundant and contain components that are irrelevant to the downstream predictions. Therefore, the VNNs using these redundant biological networks are overparameterized, which significantly limits VNNs' predictive and explanatory power. To overcome the problem, we treat the edges and nodes in biological networks used in VNNs as features and develop a sparse learning framework ParsVNN to learn parsimony VNNs with only edges and nodes that contribute the most to the prediction task. We applied ParsVNN to build cancer-specific VNN models to predict drug response for five different cancer types. We demonstrated that the parsimony VNNs built by ParsVNN are superior to other state-of-the-art methods in terms of prediction performance and identification of cancer driver genes. Furthermore, we found that the pathways selected by ParsVNN have great potential to predict clinical outcomes as well as recommend synergistic drug combinations.

Genomic landscape of circulating tumour DNA in metastatic extramammary Paget's disease

Although cancer personalized profiling by deep sequencing (CAPP-Seq) of cell-free DNA (cfDNA) has gained attention, the clinical utility of circulating tumour DNA (ctDNA) in extramammary Paget's disease (EMPD) has not been investigated. In this study, genomic alterations in the cfDNA and tumour tissue DNA were investigated in seven patients with metastatic EMPD. CAPP-Seq revealed mutations in 18 genes, 11 of which have not yet been reported in EMPD. The variant allele frequency of some of the mutated genes reflected the disease course in patients with EMPD. In one patient, the mutation was detected even though imaging findings revealed no metastasis. In another patient with triple EMPD (genital area and both axilla), cfDNA sequencing detected the mutation in a rib metastatic lesion, which was also detected in both axilla lesions but not the genital region. Investigations of the ctDNA may be useful towards the elucidation of clonal evolution in EMPD.

Implications of Antigen Selection on T Cell-Based Immunotherapy

Many immunotherapies rely on CD8+ effector T cells to recognize and kill cognate tumor cells. These T cell-based immunotherapies include adoptive cell therapy, such as CAR T cells or transgenic TCR T cells, and anti-cancer vaccines which expand endogenous T cell populations. Tumor mutation burden and the choice of antigen are among the most important aspects of T cell-based immunotherapies. Here, we highlight various classes of cancer antigens, including self, neojunction-derived, human endogenous retrovirus (HERV)-derived, and somatic nucleotide variant (SNV)-derived antigens, and consider their utility in T cell-based immunotherapies. We further discuss the respective anti-tumor/anti-self-properties that influence both the degree of immunotolerance and potential off-target effects associated with each antigen class.

Pan-cancer proteogenomic investigations identify post-transcriptional kinase targets

Identifying genomic alterations of cancer proteins has guided the development of targeted therapies, but proteomic analyses are required to validate and reveal new treatment opportunities. Herein, we develop a new algorithm, OPPTI, to discover overexpressed kinase proteins across 10 cancer types using global mass spectrometry proteomics data of 1,071 cases. OPPTI outperforms existing methods by leveraging multiple co-expressed markers to identify targets overexpressed in a subset of tumors. OPPTI-identified overexpression of ERBB2 and EGFR proteins correlates with genomic amplifications, while CDK4/6, PDK1, and MET protein overexpression frequently occur without corresponding DNA- and RNA-level alterations. Analyzing CRISPR screen data, we confirm expression-driven dependencies of multiple currently-druggable and new target kinases whose expressions are validated by immunochemistry. Identified kinases are further associated with up-regulated phosphorylation levels of corresponding signaling pathways. Collectively, our results reveal protein-level aberrations-sometimes not observed by genomics-represent cancer vulnerabilities that may be targeted in precision oncology.

Cancer evolution: Darwin and beyond

Clinical and laboratory studies over recent decades have established branched evolution as a feature of cancer. However, while grounded in somatic selection, several lines of evidence suggest a Darwinian model alone is insufficient to fully explain cancer evolution. First, the role of macroevolutionary events in tumour initiation and progression contradicts Darwin's central thesis of gradualism. Whole-genome doubling, chromosomal chromoplexy and chromothripsis represent examples of single catastrophic events which can drive tumour evolution. Second, neutral evolution can play a role in some tumours, indicating that selection is not always driving evolution. Third, increasing appreciation of the role of the ageing soma has led to recent generalised theories of age-dependent carcinogenesis. Here, we review these concepts and others, which collectively argue for a model of cancer evolution which extends beyond Darwin. We also highlight clinical opportunities which can be grasped through targeting cancer vulnerabilities arising from non-Darwinian patterns of evolution.

CloneSig can jointly infer intra-tumor heterogeneity and mutational signature activity in bulk tumor sequencing data

Systematic DNA sequencing of cancer samples has highlighted the importance of two aspects of cancer genomics: intra-tumor heterogeneity (ITH) and mutational processes. These two aspects may not always be independent, as different mutational processes could be involved in different stages or regions of the tumor, but existing computational approaches to study them largely ignore this potential dependency. Here, we present CloneSig, a computational method to jointly infer ITH and mutational processes in a tumor from bulk-sequencing data. Extensive simulations show that CloneSig outperforms current methods for ITH inference and detection of mutational processes when the distribution of mutational signatures changes between clones. Applied to a large cohort of 8,951 tumors with whole-exome sequencing data from The Cancer Genome Atlas, and on a pan-cancer dataset of 2,632 whole-genome sequencing tumor samples from the Pan-Cancer Analysis of Whole Genomes initiative, CloneSig obtains results overall coherent with previous studies.

Best Practices for Spatial Profiling for Breast Cancer Research with the GeoMx® Digital Spatial Profiler

Breast cancer is a heterogenous disease with variability in tumor cells and in the surrounding tumor microenvironment (TME). Understanding the molecular diversity in breast cancer is critical for improving prediction of therapeutic response and prognostication. High-plex spatial profiling of tumors enables characterization of heterogeneity in the breast TME, which can holistically illuminate the biology of tumor growth, dissemination and, ultimately, response to therapy. The GeoMx Digital Spatial Profiler (DSP) enables researchers to spatially resolve and quantify proteins and RNA transcripts from tissue sections. The platform is compatible with both formalin-fixed paraffin-embedded and frozen tissues. RNA profiling was developed at the whole transcriptome level for human and mouse samples and protein profiling of 100-plex for human samples. Tissue can be optically segmented for analysis of regions of interest or cell populations to study biology-directed tissue characterization. The GeoMx Breast Cancer Consortium (GBCC) is composed of breast cancer researchers who are developing innovative approaches for spatial profiling to accelerate biomarker discovery. Here, the GBCC presents best practices for GeoMx profiling to promote the collection of high-quality data, optimization of data analysis and integration of datasets to advance collaboration and meta-analyses. Although the capabilities of the platform are presented in the context of breast cancer research, they can be generalized to a variety of other tumor types that are characterized by high heterogeneity.

PolyG-DS: An ultrasensitive polyguanine tract-profiling method to detect clonal expansions and trace cell lineage

Polyguanine tracts (PolyGs) are short guanine homopolymer repeats that are prone to accumulating mutations when cells divide. This feature makes them especially suitable for cell lineage tracing, which has been exploited to detect and characterize precancerous and cancerous somatic evolution. PolyG genotyping, however, is challenging because of the inherent biochemical difficulties in amplifying and sequencing repetitive regions. To overcome this limitation, we developed PolyG-DS, a next-generation sequencing (NGS) method that combines the error-correction capabilities of duplex sequencing (DS) with enrichment of PolyG loci using CRISPR-Cas9-targeted genomic fragmentation. PolyG-DS markedly reduces technical artifacts by comparing the sequences derived from the complementary strands of each original DNA molecule. We demonstrate that PolyG-DS genotyping is accurate, reproducible, and highly sensitive, enabling the detection of low-frequency alleles (<0.01) in spike-in samples using a panel of only 19 PolyG markers. PolyG-DS replicated prior results based on PolyG fragment length analysis by capillary electrophoresis, and exhibited higher sensitivity for identifying clonal expansions in the nondysplastic colon of patients with ulcerative colitis. We illustrate the utility of this method for resolving the phylogenetic relationship among precancerous lesions in ulcerative colitis and for tracing the metastatic dissemination of ovarian cancer. PolyG-DS enables the study of tumor evolution without prior knowledge of tumor driver mutations and provides a tool to perform cost-effective and easily scalable ultra-accurate NGS-based PolyG genotyping for multiple applications in biology, genetics, and cancer research.

Liquid Biopsy: A New Tool for Overcoming CDKi Resistance Mechanisms in Luminal Metastatic Breast Cancer

Breast cancer (BC) is the most common cancer diagnosed in women worldwide. Approximately 70% of BC patients have the luminal subtype, which expresses hormone receptors (HR+). Adjuvant endocrine treatments are the standard of care for HR+/HER2− BC patients. Over time, approximately 30% of those patients develop endocrine resistance and metastatic disease. Cyclin-dependent kinase inhibitors (CDKi), in combination with an aromatase inhibitor or fulvestrant, have demonstrated superior efficacies in increasing progression-free survival, with a safe toxicity profile, in HR+/HER2− metastatic BC patients. CDKi blocks kinases 4/6, preventing G1/S cell cycle transition. However, not all of the patients respond to CDKi, and those who do respond ultimately develop resistance to the combined therapy. Studies in tumour tissues and cell lines have tried to elucidate the mechanisms that underlie this progression, but there are still no conclusive data. Over the last few years, liquid biopsy has contributed relevant information. Circulating tumour materials are potential prognostic markers for determining patient prognosis in metastatic luminal BC, for monitoring disease, and for treatment selection. This review outlines the different studies performed using liquid biopsy in patients with HR+ metastatic BC treated with CDKi plus endocrine therapy. We mainly focus on those studies that describe the possible resistance mechanisms in circulating tumour-derived material.

Promises and challenges of adoptive T-cell therapies for solid tumours

Cancer is a leading cause of death worldwide and, despite new targeted therapies and immunotherapies, many patients with advanced-stage- or high-risk cancers still die, owing to metastatic disease. Adoptive T-cell therapy, involving the autologous or allogeneic transplant of tumour-infiltrating lymphocytes or genetically modified T cells expressing novel T-cell receptors or chimeric antigen receptors, has shown promise in the treatment of cancer patients, leading to durable responses and, in some cases, cure. Technological advances in genomics, computational biology, immunology and cell manufacturing have brought the aspiration of individualised therapies for cancer patients closer to reality. This new era of cell-based individualised therapeutics challenges the traditional standards of therapeutic interventions and provides opportunities for a paradigm shift in our approach to cancer therapy. Invited speakers at a 2020 symposium discussed three areas-cancer genomics, cancer immunology and cell-therapy manufacturing-that are essential to the effective translation of T-cell therapies in the treatment of solid malignancies. Key advances have been made in understanding genetic intratumour heterogeneity, and strategies to accurately identify neoantigens, overcome T-cell exhaustion and circumvent tumour immunosuppression after cell-therapy infusion are being developed. Advances are being made in cell-manufacturing approaches that have the potential to establish cell-therapies as credible therapeutic options. T-cell therapies face many challenges but hold great promise for improving clinical outcomes for patients with solid tumours.

Extracellular Vesicles and Their Role in the Spatial and Temporal Expansion of Tumor-Immune Interactions

Extracellular vesicles (EVs) serve as trafficking vehicles and intercellular communication tools. Their cargo molecules directly reflect characteristics of their parental cell. This includes information on cell identity and specific cellular conditions, ranging from normal to pathological states. In cancer, the content of EVs derived from tumor cells is altered and can induce oncogenic reprogramming of target cells. As a result, tumor-derived EVs compromise antitumor immunity and promote cancer progression and spreading. However, this pro-oncogenic phenotype is constantly being challenged by EVs derived from the local tumor microenvironment and from remote sources. Here, we summarize the role of EVs in the tumor–immune cross-talk that includes, but is not limited to, immune cells in the tumor microenvironment. We discuss the potential of remotely released EVs from the microbiome and during physical activity to shape the tumor–immune cross-talk, directly or indirectly, and confer antitumor activity. We further discuss the role of proinflammatory EVs in the temporal development of the tumor–immune interactions and their potential use for cancer diagnostics.

CACTUS: integrating clonal architecture with genomic clustering and transcriptome profiling of single tumor cells

Background

Drawing genotype-to-phenotype maps in tumors is of paramount importance for understanding tumor heterogeneity. Assignment of single cells to their tumor clones of origin can be approached by matching the genotypes of the clones to the mutations found in RNA sequencing of the cells. The confidence of the cell-to-clone mapping can be increased by accounting for additional measurements. Follicular lymphoma, a malignancy of mature B cells that continuously acquire mutations in parallel in the exome and in B cell receptor loci, presents a unique opportunity to join exome-derived mutations with B cell receptor sequences as independent sources of evidence for clonal evolution.

Methods

Here, we propose CACTUS, a probabilistic model that leverages the information from an independent genomic clustering of cells and exploits the scarce single cell RNA sequencing data to map single cells to given imperfect genotypes of tumor clones.

Results

We apply CACTUS to two follicular lymphoma patient samples, integrating three measurements: whole exome, single-cell RNA, and B cell receptor sequencing. CACTUS outperforms a predecessor model by confidently assigning cells and B cell receptor-based clusters to the tumor clones.

Conclusions

The integration of independent measurements increases model certainty and is the key to improving model performance in the challenging task of charting the genotype-to-phenotype maps in tumors. CACTUS opens the avenue to study the functional implications of tumor heterogeneity, and origins of resistance to targeted therapies. CACTUS is written in R and source code, along with all supporting files, are available on GitHub (https://github.com/LUMC/CACTUS).

Supplementary Information

The online version contains supplementary material available at (10.1186/s13073-021-00842-w).

Understanding tumour cell heterogeneity and its implication for immunotherapy in liver cancer using single-cell analysis

Over the last decade, precision medicine and immunotherapeutic approaches have become increasingly popular in oncology. Early clinical trials reported promising results, but response rates in phase III clinical trials have been suboptimal. Knowledge gained from subsequent translational studies indicates the importance of targeting the tumour microenvironment to overcome resistance to immunotherapy. In this era of precision medicine, it is crucial to consider inter- as well as intratumoural heterogeneity. Single-cell analysis is a cutting-edge technology that enables us to better define the tumour cell community and to identify potential targets for immunotherapy or combination treatments. This review focuses on single-cell analysis in the context of immunotherapy in liver cancer, including the rationale behind studying hepatocellular carcinoma biology at a single-cell level. Single-cell technologies have the potential to revolutionise our understanding of resistance mechanisms and to guide drug discovery efforts, leading to further advances in personalised medicine.

A practical guide to cancer subclonal reconstruction from DNA sequencing

Subclonal reconstruction from bulk tumor DNA sequencing has become a pillar of cancer evolution studies, providing insight into the clonality and relative ordering of mutations and mutational processes. We provide an outline of the complex computational approaches used for subclonal reconstruction from single and multiple tumor samples. We identify the underlying assumptions and uncertainties in each step and suggest best practices for analysis and quality assessment. This guide provides a pragmatic resource for the growing user community of subclonal reconstruction methods.

Evolution of Lung Cancer in the Context of Immunotherapy

Immunotherapy, as a novel treatment, has brought new hope to many patients with cancer, including patients with lung cancer. However, the overall cure rate and survival rate of lung cancer are still not satisfactory. The process of evolution has improved the ability of tumors to adapt to immunotherapy, which induces drug resistance. Many studies have focused on immunoresistance and achieved meaningful results. Therefore, it is necessary to have an in-depth understanding of the current research progress in immunoresistance, which will help to achieve good clinical results more efficiently.

Real-Time Molecular Monitoring in Acute Myeloid Leukemia With Circulating Tumor DNA

The clonal evolution of acute myeloid leukemia (AML), an oligoclonal hematological malignancy, is driven by a plethora of cytogenetic abnormalities, gene mutations, abnormal epigenetic patterns, and aberrant gene expressions. These alterations in the leukemic blasts promote clinically diverse manifestations with common characteristics of high relapse and drug resistance. Defining and real-time monitoring of a personalized panel of these predictive genetic biomarkers is rapidly being adapted in clinical setting for diagnostic, prognostic, and therapeutic decision-making in AML. A major challenge remains the frequency of invasive biopsy procedures that can be routinely performed for monitoring of AML disease progression. Moreover, a single-site biopsy is not representative of the tumor heterogeneity as it is spatially and temporally constrained and necessitates the understanding of longitudinal and spatial subclonal dynamics in AML. Hematopoietic cells are a major contributor to plasma cell-free DNA, which also contain leukemia-specific aberrations as the circulating tumor-derived DNA (ctDNA) fraction. Plasma cell-free DNA analysis holds immense potential as a minimally invasive tool for genomic profiling at diagnosis as well as clonal evolution during AML disease progression. With the technological advances and increasing sensitivity for detection of ctDNA, both genetic and epigenetic aberrations can be qualitatively and quantitatively evaluated. However, challenges remain in validating the utility of liquid biopsy tools in clinics, and universal recommendations are still awaited towards reliable diagnostics and prognostics. Here, we provide an overview on the scope of ctDNA analyses for prognosis, assessment of response to treatment and measurable residual disease, prediction of disease relapse, development of acquired resistance and beyond in AML.

TERT promoter mutated circulating tumor DNA as a biomarker for prognosis in hepatocellular carcinoma

BACKGROUND AND AIMS

Plasma circulating tumor DNA (ctDNA) with tumor-specific mutations is an attractive biomarker. The telomerase reverse transcriptase (TERT) C228T promoter mutation is the most prevalent tumor-associated mutation in hepatocellular carcinoma (HCC). We evaluated the presence and prognostic value of the TERT C228T mutation in plasma and tissue in a Danish HCC cohort.

METHODS

We analyzed ctDNA from 95 HCC patients and 45 liver cirrhotic patients without HCC for the TERT mutation using droplet digital polymerase chain reaction. We also analyzed DNA from the corresponding primary tumor tissues in 34 HCC patients.

RESULTS

The plasma TERT C228T mutation was detected in 42/95 HCC patients (44%) but in none of the non-HCC patients. The TERT mutation was detected in 23/34 tumor samples (68%). The TERT mutation was associated with increased mortality when detected in plasma (adjusted HR 2.16 (1.20-3.88), p = .010) but not in tumor tissue (adjusted HR 1.11 (0.35-3.56), p = .860). There was a positive correlation between the presence of the TERT mutation in plasma and an advanced TNM stage (p < .0001) and vascular invasion (p = .005). Analysis of the TERT mutation in plasma and tumor DNA from the same patient was concordant in 21/34 samples (62%; kappa value 0.31, p = .014). Non-concordance was associated with an early TNM stage.

CONCLUSION

The plasma TERT mutation was detected in 44% of HCC patients and in none of non-HCC cirrhotic patients; and was associated with increased mortality. We propose the TERT C228T mutation in ctDNA as a promising HCC biomarker for prognosis.

Genomic profiling of platinum-resistant ovarian cancer: The road into druggable targets

Ovarian cancer is the most lethal gynecologic cancer. High-grade serous carcinoma (HGSC) is the most frequent histologic subtype and while it is a highly platinum-sensitive cancer at initial treatment, nearly 90 % of stage IIIC patients recur in 5 years and eventually become resistant to platinum treatment. Historically, the definition of platinum-resistant disease is based on the time interval between last platinum therapy and recurrence shorter than 6 months. Nowadays the use of sophisticated imaging techniques and serum markers to detect recurrence makes the accuracy of this clinical definition less clear and even more debatable as we begin to better understand the molecular landscape of HGSC and markers of platinum resistance and sensitivity. HGSC is characterized by a low frequency of recurrent mutations, great genomic instability with widespread copy number variations, universal TP53 mutations, and homologous recombination deficiency in more than 50 % of cases. Platinum agents form DNA adducts and intra- and inter-strand cross-links in the DNA. Most of DNA repair pathways are involved at some point in the repair of platinum induced DNA damaging, most notably homologous recombination, Fanconi Anemia, and nucleotide excision repair pathways. Mechanisms of platinum resistance are related mostly to the limitation of platinum-DNA adduct formation by changing cellular pharmacology, and to the prevention of cell death after DNA damage due to alterations in DNA repair pathways and cell cycle regulation. Understanding these mechanisms of sensitivity and resistance may help to define the utility of platinum re-challenge in each situation and guide new therapeutic opportunities. Moreover, the discovery of mechanisms of synthetic lethality related to alterations in DNA repair and cell cycle regulation pathways has opened up a new avenue for drug therapy in the last decade. In the present article, we review pathways involved in platinum-induced DNA damage repair and their relationship with genomic alterations present in HGSC. Moreover, we report new treatment strategies that are underway to target these alterations.

An enhanced genetic model of relapsed IGH-translocated multiple myeloma evolutionary dynamics

Most patients with multiple myeloma (MM) die from progressive disease after relapse. To advance our understanding of MM evolution mechanisms, we performed whole-genome sequencing of 80 IGH-translocated tumour-normal newly diagnosed pairs and 24 matched relapsed tumours from the Myeloma XI trial. We identify multiple events as potentially important for survival and therapy-resistance at relapse including driver point mutations (e.g., TET2), translocations (MAP3K14), lengthened telomeres, and increased genomic instability (e.g., 17p deletions). Despite heterogeneous mutational processes contributing to relapsed mutations across MM subtypes, increased AID/APOBEC activity is particularly associated with shorter progression time to relapse, and contributes to higher mutational burden at relapse. In addition, we identify three enhanced major clonal evolution patterns of MM relapse, independent of treatment strategies and molecular karyotypes, questioning the viability of "evolutionary herding" approach in treating drug-resistant MM. Our data show that MM relapse is associated with acquisition of new mutations and clonal selection, and suggest APOBEC enzymes among potential targets for therapy-resistant MM.

Exploiting Cancer's Tactics to Make Cancer a Manageable Chronic Disease

The history of modern oncology started around eighty years ago with the introduction of cytotoxic agents such as nitrogen mustard into the clinic, followed by multi-agent chemotherapy protocols. Early success in radiation therapy in Hodgkin lymphoma gave birth to the introduction of radiation therapy into different cancer treatment protocols. Along with better understanding of cancer biology, we developed drugs targeting cancer-related cellular and genetic aberrancies. Discovery of the crucial role of vasculature in maintenance, survival, and growth of a tumor opened the way to the development of anti-angiogenic agents. A better understanding of T-cell regulatory pathways advanced immunotherapy. Awareness of stem-like cancer cells and their role in cancer metastasis and local recurrence led to the development of drugs targeting them. At the same time, sequential and rapidly accelerating advances in imaging and surgical technology have markedly increased our ability to safely remove ≥90% of tumor cells. While we have advanced our ability to kill cells from multiple directions, we have still failed to stop most types of cancer from recurring. Here we analyze the tactics employed in cancer evolution; namely, chromosomal instability (CIN), intra-tumoral heterogeneity (ITH), and cancer-specific metabolism. These tactics govern the resistance to current cancer therapeutics. It is time to focus on maximally delaying the time to recurrence, with drugs that target these fundamental tactics of cancer evolution. Understanding the control of CIN and the optimal state of ITH as the most important tactics in cancer evolution could facilitate the development of improved cancer therapeutic strategies designed to transform cancer into a manageable chronic disease.

Use of an Integrated Pan-Cancer Oncology Enrichment Next-Generation Sequencing Assay to Measure Tumour Mutational Burden and Detect Clinically Actionable Variants

INTRODUCTION

The identification of tumour mutational burden (TMB) as a biomarker of response to programmed cell death protein 1 (PD-1) immunotherapy has necessitated the development of genomic assays to measure this. We carried out comprehensive molecular profiling of cancers using the Illumina TruSight Oncology 500 (TSO500) panel and compared these to whole-genome sequencing (WGS).

METHODS

Cancer samples derived from formalin-fixed material were profiled on the TSO500 panel, sequenced on an Illumina NextSeq 500 instrument and processed through the TSO500 Docker pipeline. Either FASTQ files (PierianDx) or vcf files (OncoKDM) were processed to understand clinical actionability.

RESULTS

In total, 108 samples (a mixture of colorectal, lung, oesophageal and control samples) were processed via the DNA panel. There was good correlation between TMB, single-nucleotide variants (SNVs), indels and copy-number variations as predicted by TSO500 and WGS (R2 > 0.9) and good reproducibility, with less than 5% variability between repeated controls. For the RNA panel, 13 samples were processed, with all known fusions observed via orthogonal techniques. For clinical actionability, 72 tier 1 variants and 297 tier 2 variants were detected, with clinical trials identified for all patients.

CONCLUSIONS

The TSO500 assay accurately measures TMB, microsatellite instability, SNVs, indels, copy-number/structural variation and gene fusions when compared to WGS and orthogonal technologies. Coupled with a clinical annotation pipeline, this provides a powerful methodology for identification of clinically actionable variants.

Timing the initiation of multiple myeloma

The evolution and progression of multiple myeloma and its precursors over time is poorly understood. Here, we investigate the landscape and timing of mutational processes shaping multiple myeloma evolution in a large cohort of 89 whole genomes and 973 exomes. We identify eight processes, including a mutational signature caused by exposure to melphalan. Reconstructing the chronological activity of each mutational signature, we estimate that the initial transformation of a germinal center B-cell usually occurred during the first 2nd-3rd decades of life. We define four main patterns of activation-induced deaminase (AID) and apolipoprotein B mRNA editing catalytic polypeptide-like (APOBEC) mutagenesis over time, including a subset of patients with evidence of prolonged AID activity during the pre-malignant phase, indicating antigen-responsiveness and germinal center reentry. Our findings provide a framework to study the etiology of multiple myeloma and explore strategies for prevention and early detection.