Tracking genomic cancer evolution for precision medicine: the lung TRACERx study

Unlocking the future of cancer diagnosis - promises and challenges of ctDNA-based liquid biopsies in non-small cell lung cancer

The advent of liquid biopsies has brought significant changes to the diagnosis and monitoring of non-small cell lung cancer (NSCLC), presenting both promise and challenges. Molecularly targeted drugs, capable of enhancing survival rates, are now available to around a quarter of NSCLC patients. However, to ensure their effectiveness, precision diagnosis is essential. Circulating tumor DNA (ctDNA) analysis as the most advanced liquid biopsy modality to date offers a non-invasive method for tracking genomic changes in NSCLC. The potential of ctDNA is particularly rooted in its ability to furnish comprehensive (epi-)genetic insights into the tumor, thereby aiding personalized treatment strategies. One of the key advantages of ctDNA-based liquid biopsies in NSCLC is their ability to capture tumor heterogeneity. This capability ensures a more precise depiction of the tumor's (epi-)genomic landscape compared to conventional tissue biopsies. Consequently, it facilitates the identification of (epi-)genetic alterations, enabling informed treatment decisions, disease progression monitoring, and early detection of resistance-causing mutations for timely therapeutic interventions. Here we review the current state-of-the-art in ctDNA-based liquid biopsy technologies for NSCLC, exploring their potential to revolutionize clinical practice. Key advancements in ctDNA detection methods, including PCR-based assays, next-generation sequencing (NGS), and digital PCR (dPCR), are discussed, along with their respective strengths and limitations. Additionally, the clinical utility of ctDNA analysis in guiding treatment decisions, monitoring treatment response, detecting minimal residual disease, and identifying emerging resistance mechanisms is examined. Liquid biopsy analysis bears the potential of transforming NSCLC management by enabling non-invasive monitoring of Minimal Residual Disease and providing early indicators for response to targeted treatments including immunotherapy. Furthermore, considerations regarding sample collection, processing, and data interpretation are highlighted as crucial factors influencing the reliability and reproducibility of ctDNA-based assays. Addressing these challenges will be essential for the widespread adoption of ctDNA-based liquid biopsies in routine clinical practice, ultimately paving the way toward personalized medicine and improved outcomes for patients with NSCLC.

The genomic landscape of 2,023 colorectal cancers

Colorectal carcinoma (CRC) is a common cause of mortality1, but a comprehensive description of its genomic landscape is lacking2-9. Here we perform whole-genome sequencing of 2,023 CRC samples from participants in the UK 100,000 Genomes Project, thereby providing a highly detailed somatic mutational landscape of this cancer. Integrated analyses identify more than 250 putative CRC driver genes, many not previously implicated in CRC or other cancers, including several recurrent changes outside the coding genome. We extend the molecular pathways involved in CRC development, define four new common subgroups of microsatellite-stable CRC based on genomic features and show that these groups have independent prognostic associations. We also characterize several rare molecular CRC subgroups, some with potential clinical relevance, including cancers with both microsatellite and chromosomal instability. We demonstrate a spectrum of mutational profiles across the colorectum, which reflect aetiological differences. These include the role of Escherichia colipks+ colibactin in rectal cancers10 and the importance of the SBS93 signature11-13, which suggests that diet or smoking is a risk factor. Immune-escape driver mutations14 are near-ubiquitous in hypermutant tumours and occur in about half of microsatellite-stable CRCs, often in the form of HLA copy number changes. Many driver mutations are actionable, including those associated with rare subgroups (for example, BRCA1 and IDH1), highlighting the role of whole-genome sequencing in optimizing patient care.

Representation of genomic intratumor heterogeneity in multi-region non-small cell lung cancer patient-derived xenograft models

Patient-derived xenograft (PDX) models are widely used in cancer research. To investigate the genomic fidelity of non-small cell lung cancer PDX models, we established 48 PDX models from 22 patients enrolled in the TRACERx study. Multi-region tumor sampling increased successful PDX engraftment and most models were histologically similar to their parent tumor. Whole-exome sequencing enabled comparison of tumors and PDX models and we provide an adapted mouse reference genome for improved removal of NOD scid gamma (NSG) mouse-derived reads from sequencing data. PDX model establishment caused a genomic bottleneck, with models often representing a single tumor subclone. While distinct tumor subclones were represented in independent models from the same tumor, individual PDX models did not fully recapitulate intratumor heterogeneity. On-going genomic evolution in mice contributed modestly to the genomic distance between tumors and PDX models. Our study highlights the importance of considering primary tumor heterogeneity when using PDX models and emphasizes the benefit of comprehensive tumor sampling.

The two sides of chromosomal instability: drivers and brakes in cancer

Chromosomal instability (CIN) is a hallmark of cancer and is associated with tumor cell malignancy. CIN triggers a chain reaction in cells leading to chromosomal abnormalities, including deviations from the normal chromosome number or structural changes in chromosomes. CIN arises from errors in DNA replication and chromosome segregation during cell division, leading to the formation of cells with abnormal number and/or structure of chromosomes. Errors in DNA replication result from abnormal replication licensing as well as replication stress, such as double-strand breaks and stalled replication forks; meanwhile, errors in chromosome segregation stem from defects in chromosome segregation machinery, including centrosome amplification, erroneous microtubule-kinetochore attachments, spindle assembly checkpoint, or defective sister chromatids cohesion. In normal cells, CIN is deleterious and is associated with DNA damage, proteotoxic stress, metabolic alteration, cell cycle arrest, and senescence. Paradoxically, despite these negative consequences, CIN is one of the hallmarks of cancer found in over 90% of solid tumors and in blood cancers. Furthermore, CIN could endow tumors with enhanced adaptation capabilities due to increased intratumor heterogeneity, thereby facilitating adaptive resistance to therapies; however, excessive CIN could induce tumor cells death, leading to the "just-right" model for CIN in tumors. Elucidating the complex nature of CIN is crucial for understanding the dynamics of tumorigenesis and for developing effective anti-tumor treatments. This review provides an overview of causes and consequences of CIN, as well as the paradox of CIN, a phenomenon that continues to perplex researchers. Finally, this review explores the potential of CIN-based anti-tumor therapy.

Characterizing chromosomal instability-driven cancer evolution and cell fitness at a glance

Chromosomal instability (CIN), an increased rate of chromosome segregation errors during mitosis, is a hallmark of cancer cells. CIN leads to karyotype differences between cells and thus large-scale heterogeneity among individual cancer cells; therefore, it plays an important role in cancer evolution. Studying CIN and its consequences is technically challenging, but various technologies have been developed to track karyotype dynamics during tumorigenesis, trace clonal lineages and link genomic changes to cancer phenotypes at single-cell resolution. These methods provide valuable insight not only into the role of CIN in cancer progression, but also into cancer cell fitness. In this Cell Science at a Glance article and the accompanying poster, we discuss the relationship between CIN, cancer cell fitness and evolution, and highlight techniques that can be used to study the relationship between these factors. To that end, we explore methods of assessing cancer cell fitness, particularly for chromosomally unstable cancer.

Transcriptional intra-tumour heterogeneity predicted by deep learning in routine breast histopathology slides provides independent prognostic information

BACKGROUND

Intra-tumour heterogeneity (ITH) causes diagnostic challenges and increases the risk for disease recurrence. Quantification of ITH is challenging and has not been demonstrated in large studies. It has previously been shown that deep learning can enable spatially resolved prediction of molecular phenotypes from digital histopathology whole slide images (WSIs). Here we propose a novel method (Deep-ITH) to predict and measure ITH, and we evaluate its prognostic performance in breast cancer.

METHODS

Deep convolutional neural networks were used to spatially predict gene-expression (PAM50 set) from WSIs. For each predicted transcript, 12 measures of heterogeneity were extracted in the training data set (N = 931). A prognostic score to dichotomise patients into Deep-ITH low- and high-risk groups was established using an elastic-net regularised Cox proportional hazards model (recurrence-free survival). Prognostic performance was evaluated in two independent data sets: SöS-BC-1 (N = 1358) and SCAN-B-Lund (N = 1262).

RESULTS

We observed an increase in risk of recurrence in the high-risk group with hazard ratio (HR) 2.11 (95%CI:1.22-3.60; p = 0.007) using nested cross-validation. Subgroup analyses confirmed the prognostic performance in oestrogen receptor (ER)-positive, human epidermal growth factor receptor 2 (HER2)-negative, grade 3, and large tumour subgroups. The prognostic value was confirmed in the independent SöS-BC-1 cohort (HR=1.84; 95%CI:1.03-3.3; p = 3.99 ×10-2). In the other external cohort, significant HR was observed in the subgroup of histological grade 2 patients, as well as in the subgroup of patients with small tumours (<20 mm).

CONCLUSION

We developed a novel method for an automated, scalable, and cost-efficient measure of ITH from WSIs that provides independent prognostic value for breast cancer.

SIGNIFICANCE

Transcriptional ITH predicted by deep learning models enables prediction of patient survival from routine histopathology WSIs in breast cancer.

The Future of Precision Oncology

Our understanding of the molecular mechanisms underlying cancer development and evolution have evolved rapidly over recent years, and the variation from one patient to another is now widely recognized. Consequently, one-size-fits-all approaches to the treatment of cancer have been superseded by precision medicines that target specific disease characteristics, promising maximum clinical efficacy, minimal safety concerns, and reduced economic burden. While precision oncology has been very successful in the treatment of some tumors with specific characteristics, a large number of patients do not yet have access to precision medicines for their disease. The success of next-generation precision oncology depends on the discovery of new actionable disease characteristics, rapid, accurate, and comprehensive diagnosis of complex phenotypes within each patient, novel clinical trial designs with improved response rates, and worldwide access to novel targeted anticancer therapies for all patients. This review outlines some of the current technological trends, and highlights some of the complex multidisciplinary efforts that are underway to ensure that many more patients with cancer will be able to benefit from precision oncology in the near future.

Phenotyping of lymphoproliferative tumours generated in xenografts of non-small cell lung cancer

Background

Patient-derived xenograft (PDX) models involve the engraftment of tumour tissue in immunocompromised mice and represent an important pre-clinical oncology research method. A limitation of non-small cell lung cancer (NSCLC) PDX model derivation in NOD-scid IL2Rgammanull (NSG) mice is that a subset of initial engraftments are of lymphocytic, rather than tumour origin.

Methods

The immunophenotype of lymphoproliferations arising in the lung TRACERx PDX pipeline were characterised. To present the histology data herein, we developed a Python-based tool for generating patient-level pathology overview figures from whole-slide image files; PATHOverview is available on GitHub (https://github.com/EpiCENTR-Lab/PATHOverview).

Results

Lymphoproliferations occurred in 17.8% of lung adenocarcinoma and 10% of lung squamous cell carcinoma transplantations, despite none of these patients having a prior or subsequent clinical history of lymphoproliferative disease. Lymphoproliferations were predominantly human CD20+ B cells and had the immunophenotype expected for post-transplantation diffuse large B cell lymphoma with plasma cell features. All lymphoproliferations expressed Epstein-Barr-encoded RNAs (EBER). Analysis of immunoglobulin light chain gene rearrangements in three tumours where multiple tumour regions had resulted in lymphoproliferations suggested that each had independent clonal origins.

Discussion

Overall, these data suggest that B cell clones with lymphoproliferative potential are present within primary NSCLC tumours, and that these are under continuous immune surveillance. Since these cells can be expanded following transplantation into NSG mice, our data highlight the value of quality control measures to identify lymphoproliferations within xenograft pipelines and support the incorporation of strategies to minimise lymphoproliferations during the early stages of xenograft establishment pipelines.

Prognostic subtypes of thyroid cancer was constructed based on single cell and bulk-RNA sequencing data and verified its authenticity

There has been an increase in the mortality rate of thyroid cancer (THCA), which is the most common endocrine malignancy. We identified six distinct cell types in the THAC microenvironment by analyzing single-cell RNA sequencing (Sc-RNAseq) data from 23 THCA tumor samples, indicating high intratumoral heterogeneity. Through re-dimensional clustering of immune subset cells, myeloid cells, cancer-associated fibroblasts, and thyroid cell subsets, we deeply reveal differences in the tumor microenvironment of thyroid cancer. Through an in-depth analysis of thyroid cell subsets, we identified the process of thyroid cell deterioration (normal, intermediate, malignant cells). Through cell-to-cell communication analysis, we found a strong link between thyroid cells and fibroblasts and B cells in the MIF signaling pathway. In addition, we found a strong correlation between thyroid cells and B cells, TampNK cells, and bone marrow cells. Finally, we developed a prognostic model based on differentially expressed genes in thyroid cells from single-cell analysis. Both in the training set and the testing set, it can effectively predict the survival of thyroid patients. In addition, we identified significant differences in the composition of immune cell subsets between high-risk and low-risk patients, which may be responsible for their different prognosis. Through in vitro experiments, we identify that knockdown of NPC2 can significantly promote thyroid cancer cell apoptosis, and NPC2 may be a potential therapeutic target for thyroid cancer. In this study, we developed a well-performing prognostic model based on Sc-RNAseq data, revealing the cellular microenvironment and tumor heterogeneity of thyroid cancer. This will help to provide more accurate personalized treatment for patients in clinical diagnosis.

Imaging genomics: data fusion in uncovering disease heritability

Sequencing of the human genome in the early 2000s enabled probing of the genetic basis of disease on a scale previously unimaginable. Now, two decades later, after interrogating millions of markers in thousands of individuals, a significant portion of disease heritability still remains hidden. Recent efforts to unravel this 'missing heritability' have focused on garnering new insight from merging different data types, including medical imaging. Imaging offers promising intermediate phenotypes to bridge the gap between genetic variation and disease pathology. In this review we outline this fusion and provide examples of imaging genomics in a range of diseases, from oncology to cardiovascular and neurodegenerative disease. Finally, we discuss how ongoing revolutions in data science and sharing are primed to advance the field.

Clonal transcriptomics identifies mechanisms of chemoresistance and empowers rational design of combination therapies

Tumour heterogeneity is thought to be a major barrier to successful cancer treatment due to the presence of drug resistant clonal lineages. However, identifying the characteristics of such lineages that underpin resistance to therapy has remained challenging. Here, we utilise clonal transcriptomics with WILD-seq; Wholistic Interrogation of Lineage Dynamics by sequencing, in mouse models of triple-negative breast cancer (TNBC) to understand response and resistance to therapy, including BET bromodomain inhibition and taxane-based chemotherapy. These analyses revealed oxidative stress protection by NRF2 as a major mechanism of taxane resistance and led to the discovery that our tumour models are collaterally sensitive to asparagine deprivation therapy using the clinical stage drug L-asparaginase after frontline treatment with docetaxel. In summary, clonal transcriptomics with WILD-seq identifies mechanisms of resistance to chemotherapy that are also operative in patients and pin points asparagine bioavailability as a druggable vulnerability of taxane-resistant lineages.

Cellular barcoding to decipher clonal dynamics in disease

Cellular barcodes are distinct DNA sequences that enable one to track specific cells across time or space. Recent advances in our ability to detect natural or synthetic cellular barcodes, paired with single-cell readouts of cell state, have markedly increased our knowledge of clonal dynamics and genealogies of the cells that compose a variety of tissues and organs. These advances hold promise to redefine our view of human disease. Here, we provide an overview of cellular barcoding approaches, discuss applications to gain new insights into disease mechanisms, and provide an outlook on future applications. We discuss unanticipated insights gained through barcoding in studies of cancer and blood cell production and describe how barcoding can be applied to a growing array of medical fields, particularly with the increasing recognition of clonal contributions in human diseases.

Multi-omics analysis: Paving the path toward achieving precision medicine in cancer treatment and immuno-oncology

The acceleration of large-scale sequencing and the progress in high-throughput computational analyses, defined as omics, was a hallmark for the comprehension of the biological processes in human health and diseases. In cancerology, the omics approach, initiated by genomics and transcriptomics studies, has revealed an incredible complexity with unsuspected molecular diversity within a same tumor type as well as spatial and temporal heterogeneity of tumors. The integration of multiple biological layers of omics studies brought oncology to a new paradigm, from tumor site classification to pan-cancer molecular classification, offering new therapeutic opportunities for precision medicine. In this review, we will provide a comprehensive overview of the latest innovations for multi-omics integration in oncology and summarize the largest multi-omics dataset available for adult and pediatric cancers. We will present multi-omics techniques for characterizing cancer biology and show how multi-omics data can be combined with clinical data for the identification of prognostic and treatment-specific biomarkers, opening the way to personalized therapy. To conclude, we will detail the newest strategies for dissecting the tumor immune environment and host–tumor interaction. We will explore the advances in immunomics and microbiomics for biomarker identification to guide therapeutic decision in immuno-oncology.

Circulating tumor DNA-minimal residual disease: An up-and-coming nova in resectable non-small-cell lung cancer

Circulating tumor DNA (ctDNA) in the bloodstream can be used to reliably identify a minimal residual disease (MRD). ctDNA-MRD has demonstrated clinical values as a predictive and prognostic marker for resectable non-small cell lung cancer (NSCLC) regarding efficacy evaluation, recurrence monitoring, risk classification, and adjuvant treatment choices, and it has the advantage of being non-invasive, real-time, and dynamic. A recent large-scale prospective study of patients with resectable NSCLC revealed that patients with longitudinal undetectable MRD might represent a potentially curable population, benefiting many patients by eliminating wasteful therapies and side effects. However, there are still barriers to using ctDNA-MRD in clinical management, and the most significant is the lack of high-sensitivity detection technologies and consistent detection times. Herein, we defined the clinical significance of ctDNA-MRD in resectable NSCLC, summarized the available next-generation sequencing (NGS) detection approaches, and speculated on future clinical trial design and detection technology optimization.

Biomarkers and the microbiome in the detection and treatment of early-stage non-small cell lung cancer

Lung cancer is one of the most common and deadly cancers in the world. However, over the last several years, research into lung cancer screening and novel therapeutic approaches have provided promise that earlier detection combined with new treatment strategies may result in significantly improved outcomes. Biomarkers will most certainly play a major role in identifying those who may benefit from, and how to apply, these new treatment strategies. Here we discuss potential biomarkers, including the microbiome, in both detection and treatment strategies for early stage lung cancer.

Neoadjuvant Immunotherapy in Gastrointestinal Cancers - The New Standard of Care?

The development of immune checkpoint inhibitors (ICI) offers novel treatment possibilities for solid cancers, with the crucial benefit of providing higher cure rates. These agents have become part of standard treatments in the metastatic and adjuvant setting for select cancers, such as melanoma, non-small cell lung cancer (NSCLC) or urological malignancies. Currently, there is ample clinical interest in employing ICI in a neoadjuvant setting with a curative intent. This approach is especially supported by the scientific rationale that ICI primarily stimulate the host immune system to eradicate tumor cells, rather than being inherently cytotoxic. Aside from tumor downstaging, neoadjuvant immunotherapy offers the potential of an in situ cancer vaccination, leading to a systemic adjuvant immunological effect after tumor resection. Moreover, preclinical data clearly demonstrate a synergistic effect of ICI with radiotherapy (RT), chemoradiotherapy (CRT) or chemotherapy (ChT). This review harmonizes preclinical concepts with real world data (RWD) in the field of neoadjuvant ICI in gastrointestinal (GI) cancers and discusses their limitations. We believe this is a crucial approach, since up to now, neoadjuvant strategies have been primarily developed by clinicians, whereas the advances in immunotherapy primarily originate from preclinical research. Currently there is limited published data on neoadjuvant ICI in GI cancers, even though neoadjuvant treatments including RT, CRT or ChT are frequently employed in locally advanced/oligometastatic GI cancers (i.e. rectal, pancreatic, esophagus, stomach, etc.). Utilizing established therapies in combination with ICI provides an abundance of opportunities for innovative treatment regimens to further improve survival rates.

Research Progress on Postoperative Minimal/Molecular Residual Disease Detection in Lung Cancer

Lung cancer is the leading cause of cancer-related deaths worldwide. Approximately 10%-50% of patients experience relapse after radical surgery, which may be attributed to the persistence of minimal/molecular residual disease (MRD). Circulating tumor DNA (ctDNA), a common liquid biopsy approach, has been demonstrated to have significant clinical merit. In this study, we review the evidence supporting the use of ctDNA for MRD detection and discuss the potential clinical applications of postoperative MRD detection, including monitoring recurrence, guiding adjuvant treatment, and driving clinical trials in lung cancer. We will also discuss the problems that prevent the routine application of ctDNA MRD detection. Multi-analyte methods and identification of specific genetic and molecular alterations, especially methylation, are effective detection strategies and show considerable prospects for future development. Interventional prospective studies based on ctDNA detection are needed to determine whether the application of postoperative MRD detection can improve the clinical outcomes of lung cancer patients, and the accuracy, sensitivity, specificity, and robustness of different detection methods still require optimization and refinement.

Early-Stage Lung Adenocarcinoma MDM2 Genomic Amplification Predicts Clinical Outcome and Response to Targeted Therapy

Simple Summary

Invasive subtypes of lung adenocarcinoma (LUAD) show MDM2 amplification that is associated with poor survival. Mouse double minute 2 (MDM2) is frequently amplified in lung adenocarcinoma (LUAD) and is a negative regulator of p53, which binds to p53 and regulates its activity and stability. Genomic amplification and overexpression of MDM2, together with genetic alterations in p53, leads to genomic and genetic heterogeneity in LUAD that represents a therapeutic target. In vitro assays in a panel of LUAD cell lines showed that tumor cell response to MDM2-targeted therapy is associated with MDM2 amplification.

Abstract

Lung cancer is the most common cause of cancer-related deaths in both men and women, accounting for one-quarter of total cancer-related mortality globally. Lung adenocarcinoma is the major subtype of non-small cell lung cancer (NSCLC) and accounts for around 40% of lung cancer cases. Lung adenocarcinoma is a highly heterogeneous disease and patients often display variable histopathological morphology, genetic alterations, and genomic aberrations. Recent advances in transcriptomic and genetic profiling of lung adenocarcinoma by investigators, including our group, has provided better stratification of this heterogeneous disease, which can facilitate devising better treatment strategies suitable for targeted patient cohorts. In a recent study we have shown gene expression profiling identified novel clustering of early stage LUAD patients and correlated with tumor invasiveness and patient survival. In this study, we focused on copy number alterations in LUAD patients. SNP array data identified amplification at chromosome 12q15 on MDM2 locus and protein overexpression in a subclass of LUAD patients with an invasive subtype of the disease. High copy number amplification and protein expression in this subclass correlated with poor overall survival. We hypothesized that MDM2 copy number and overexpression predict response to MDM2-targeted therapy. In vitro functional data on a panel of LUAD cells showed that MDM2-targeted therapy effectively suppresses cell proliferation, migration, and invasion in cells with MDM2 amplification/overexpression but not in cells without MDM2 amplification, independent of p53 status. To determine the key signaling mechanisms, we used RNA sequencing (RNA seq) to examine the response to therapy in MDM2-amplified/overexpressing p53 mutant and wild-type LUAD cells. RNA seq data shows that in MDM2-amplified/overexpression with p53 wild-type condition, the E2F → PEG10 → MMPs pathway is operative, while in p53 mutant genetic background, MDM2-targeted therapy abrogates tumor progression in LUAD cells by suppressing epithelial to mesenchymal transition (EMT) signaling. Our study provides a potentially clinically relevant strategy of selecting LUAD patients for MDM2-targeted therapy that may provide for increased response rates and, thus, better survival.

Sensei: how many samples to tell a change in cell type abundance?

Cellular heterogeneity underlies cancer evolution and metastasis. Advances in single-cell technologies such as single-cell RNA sequencing and mass cytometry have enabled interrogation of cell type-specific expression profiles and abundance across heterogeneous cancer samples obtained from clinical trials and preclinical studies. However, challenges remain in determining sample sizes needed for ascertaining changes in cell type abundances in a controlled study. To address this statistical challenge, we have developed a new approach, named Sensei, to determine the number of samples and the number of cells that are required to ascertain such changes between two groups of samples in single-cell studies. Sensei expands the t-test and models the cell abundances using a beta-binomial distribution. We evaluate the mathematical accuracy of Sensei and provide practical guidelines on over 20 cell types in over 30 cancer types based on knowledge acquired from the cancer cell atlas (TCGA) and prior single-cell studies. We provide a web application to enable user-friendly study design via https://kchen-lab.github.io/sensei/table_beta.html .

Tumors Are Evolutionary Island-Like Ecosystems

Integration of ecological and evolutionary features has begun to understand the interplay of tumor heterogeneity, microenvironment, and metastatic potential. Developing a theoretical framework is intrinsic to deciphering tumors' tremendous spatial and longitudinal genetic variation patterns in patients. Here, we propose that tumors can be considered evolutionary island-like ecosystems, that is, isolated systems that undergo evolutionary and spatiotemporal dynamic processes that shape tumor microenvironments and drive the migration of cancer cells. We examine attributes of insular systems and causes of insularity, such as physical distance and connectivity. These properties modulate migration rates of cancer cells through processes causing spatial and temporal isolation of the organs and tissues functioning as a supply of cancer cells for new colonizations. We discuss hypotheses, predictions, and limitations of tumors as islands analogy. We present emerging evidence of tumor insularity in different cancer types and discuss their relevance to the islands model. We suggest that the engagement of tumor insularity into conceptual and mathematical models holds promise to illuminate cancer evolution, tumor heterogeneity, and metastatic potential of cells.

Genetic Polymorphisms in Pharmaceuticals and Chemotherapy

The study of genetic polymorphisms has significantly advanced the field of personalized medicine. Polymorphism of genes influence the efficacy of drugs used for treating medical conditions such as depression, cardiac diseases, thromboembolic disorders, oncological diseases, etc. The study of genetic polymorphism is beneficial for drug safety as well as for assessing therapeutic outcomes. Understanding and detecting genetic polymorphisms early on in patients can be useful in selecting the correct chemotherapeutic agent and appropriate dosage for a patient. Knowing the genetic profile of a patient and the interindividual response to various drugs significantly influences the proper selection of medication - a key step towards personalized medicine. Polymorphisms also make patients susceptible to certain cancers and identification of these polymorphisms early can be useful for a personalized treatment plan. The Genome-Wide Association Studies (GWAS) project where millions of genetic variants in the genomes of many individuals are studied to identify connections between what is present on the gene and the phenotype of the patient has enhanced the prospect of personalized medicine. GWAS has been used to identify hundreds of diseases associated to genetic polymorphisms. Individual pharmacokinetic profiles of patients to drugs enable the development of early surveillance protocols to prophylactically prevent patients from having adverse reactions. Furthermore, patient-derived cellular organoids are another advancement that allows researchers to screen for polymorphisms of the patient for adverse reactions from chemotherapy and will allow for the development of new medications that are specific to the profile of the patient’s tumor. These advances have led to significant progress towards personalized medicine. The functional consequences of genetic polymorphism on cancer drugs and treatment are studied here.

Epigenetic Alterations and Mechanisms That Drive Resistance to Targeted Cancer Therapies

Cancer is a complex disease and cancer cells typically harbor multiple genetic and epigenetic alterations. Large-scale sequencing of patient-derived cancer samples has identified several druggable driver oncogenes. Many of these oncogenes can be pharmacologically targeted to provide effective therapies for breast cancer, leukemia, lung cancer, melanoma, lymphoma, and other cancer types. Initial responses to these agents can be robust in many cancer types and some patients with cancer experience sustained tumor inhibition. However, resistance to these targeted therapeutics frequently emerges, either from intrinsic or acquired mechanisms, posing a major clinical hurdle for effective treatment. Several resistance mechanisms, both cell autonomous and cell nonautonomous, have been identified in different cancer types. Here we describe how alterations of the transcriptome, transcription factors, DNA, and chromatin regulatory proteins confer resistance to targeted therapeutic agents. We also elaborate on how these studies have identified underlying epigenetic factors that drive drug resistance and oncogenic pathways, with direct implications for the prevention and treatment of drug-resistant cancer.

Tracing the evolution of aneuploid cancers by multiregional sequencing with CRUST

Clonal deconvolution of mutational landscapes is crucial to understand the evolutionary dynamics of cancer. Two limiting factors for clonal deconvolution that have remained unresolved are variation in purity and chromosomal copy number across different samples of the same tumor. We developed a semi-supervised algorithm that tracks variant calls through multi-sample spatiotemporal tumor data. While normalizing allele frequencies based on purity, it also adjusts for copy number changes at clonal deconvolution. Absent à priori copy number data, it renders in silico copy number estimations from bulk sequences. Using published and simulated tumor sequences, we reliably segregated clonal/subclonal variants even at a low sequencing depth (~50×). Given at least one pure tumor sample (>70% purity), we could normalize and deconvolve paired samples down to a purity of 40%. This renders a reliable clonal reconstruction well adapted to multi-regionally sampled solid tumors, which are often aneuploid and contaminated by non-cancer cells.

Integrating NGS-derived mutational profiling in the diagnosis of multiple lung adenocarcinomas

MICROABSTRACT

Integration of Next Generation Sequencing (NGS) information for use in distinguishing between Multiple Primary Lung Cancer and intrapulmonary metastasis was evaluated. We used a probabilistic model, comprehensive histologic assessment and NGS to classify patients. Integrating NGS data confirmed initial diagnosis (n = 41), revised the diagnosis (n = 12), while resulted in non-informative data (n = 8). Accuracy of diagnosis can be significantly improved with integration of NGS data.

BACKGROUND

Distinguishing between multiple primary lung cancers (MPLC) and intrapulmonary metastases (IPM) is challenging. The goal of this study was to evaluate how Next Generation Sequencing (NGS) information may be integrated in the diagnostic strategy.

PATIENTS AND METHODS

Patients with multiple lung adenocarcinomas were classified using both the comprehensive histologic assessment and NGS. We computed the joint probability of each pair having independent mutations by chance (thus being classified as MPLC). These probabilities were computed using the marginal mutation rates of each mutation, and the known negative dependencies between driver genes and different gene loci. With these NGS-driven data, cases were re-classified as MPLC or IPM.

RESULTS

We analyzed 61 patients with a total of 131 tumors. The most frequent mutation was KRAS (57.3%) which occured at a rate higher than expected (p < 0.001) in lung cancer. No mutation was detected in 25/131 tumors (19.1%). Discordant molecular findings between tumor sites were found in 46 patients (75.4%); 11 patients (18.0%) had concordant molecular findings, and 4 patients (6.6%) had concordant molecular findings at 2 of the 3 sites. After integration of the NGS data, the initial diagnosis was confirmed for 41 patients (67.2%), the diagnosis was revised for 12 patients (19.7%) or was considered as non-informative for 8 patients (13.1%).

CONCLUSION

Integrating the information of NGS data may significantly improve accuracy of diagnosis and staging.

Update on Molecular Imaging and Precision Medicine in Lung Cancer

Precision medicine integrates molecular pathobiology, genetic make-up, and clinical manifestations of disease in order to classify patients into subgroups for the purposes of predicting treatment response and suggesting outcome. By identifying those patients who are most likely to benefit from a given therapy, interventions can be tailored to avoid the expense and toxicity of futile treatment. Ultimately, the goal is to offer the right treatment, to the right patient, at the right time. Lung cancer is a heterogeneous disease both functionally and morphologically. Further, over time, clonal proliferations of cells may evolve, becoming resistant to specific therapies. PET is a sensitive imaging technique with an important role in the precision medicine algorithm of lung cancer patients. It provides anatomo-functional insight during diagnosis, staging, and restaging of the disease. It is a prognostic biomarker in lung cancer patients that characterizes tumoral heterogeneity, helps predict early response to therapy, and may direct the selection of appropriate treatment.

Detection of Tumor Recurrence via Circulating Tumor DNA Profiling in Patients with Localized Lung Cancer: Clinical Considerations and Challenges

Simple Summary

Circulating tumor DNA is a novel biomarker with emerging uses in the clinical care of patients with cancer, including non-small-cell lung cancer. Already approved for use in various clinical settings in patients with metastatic non-small-cell lung cancer, recent research has focused on the ability of circulating tumor DNA to predict relapse of patients with localized disease after treatment with curative intent. Identifying patients at increased risk of relapse after treatment with curative intent remains challenging, but several groups have identified circulating tumor DNA kinetics as a potential means of aiding our risk stratification. Herein, we discuss current research that identifies longitudinal circulating tumor DNA kinetics as a highly sensitive and specific marker for relapse. Then, we identify important clinical considerations and challenges for moving forward with further studying and eventually using this biomarker for patients with localized disease in clinic.

Abstract

Approximately 30% of patients with non-small-cell lung cancer (NSCLC) present with localized/non-metastatic disease and are eligible for surgical resection or other “treatment with curative intent”. Due to the high prevalence of recurrence after treatment, adjuvant therapy is standard care for most patients. The effect of adjuvant chemotherapy is, however, modest, and new tools are needed to identify candidates for adjuvant treatments (chemotherapy, immunotherapy, or targeted therapies), especially since expanded lung cancer screening programs will increase the rate of patients detected with localized NSCLC. Circulating tumor DNA (ctDNA) has shown strong potential to detect minimal residual disease (MRD) and to guide adjuvant therapies. In this manuscript, we review the technical aspects and performances of the main ctDNA sequencing platforms (TRACERx, CAPP-seq) investigated in this purpose, and discuss the potential of this approach to guide or spare adjuvant therapies after definitive treatment of NSCLC.

Progress in Treatment of Non-Small Cell Lung Cancer Harboring HER2 Aberrations

Epidermal growth factor receptor 2 (HER2/ErbB2/neu), a member of ErbB receptor tyrosine kinase family, forms homo- or heterodimers with ErbB1 (HER1/EGFR), ErbB3 (HER3), or ErbB4 (HER4), to activate signal transduction pathways and promote proliferation, differentiation and tumorigenesis. Preliminary clinical trials of monoclonal antibodies, antibody conjugates and small-molecule tyrosine kinase inhibitors targeting HER2 have indicated that HER2 is a potential therapeutic target in non-small cell lung cancer (NSCLC). HER2 aberrations in NSCLC patients mainly include mutation, amplification, and overexpression. While there are significant differences in the outcome of NSCLC with these HER2 changes, no consensus has been reached for the incidence, detection method and targeted treatments for the three types of HER2 aberration. HER2 mutation is generally considered to have more clinical relevance and response to HER2-targeted therapies. In this review, we discuss HER2 alterations in NSCLC, including diagnostic challenges and treatment strategies particular to the HER2 mutation.

Future Directions in the Use of SAbR for the Treatment of Oligometastatic Cancers

The role of local therapy as a sole therapy or part of a combined approach in treating metastatic cancer continues to evolve. The most obvious requirements for prudent implementation of local therapies like stereotactic ablative radiotherapy (SAbR) to become mainstream in treating oligometastases are (1) Clear guidance as to what particular patients might benefit, and (2) Confirmation of improvements in outcome after such treatments via clinical trials. These future directional requirements are non-negotiable. However, innovation and research offer many more opportunities to understand and improve therapy. Identifying candidates and personalizing their therapy can be afforded via proteomic, genomic and epigenomic characterization techniques. Such molecular profiling along with liquid biopsy opportunities will both help select best therapies and facilitate ongoing monitoring of response. Technologies both to find targets and help deliver less-toxic therapy continue to improve and will be available in the marketplace. These technologies include molecular-based imaging (eg, PET-PSMA), FLASH ultra-high dose rate platforms, Grid therapy, PULSAR adaptive dosing, and MRI/PET guided linear accelerators. Importantly, a treatment approach beyond oligometastastic could evolve including a rationale for using SAbR in the oligoprogressive, oligononresponsive, oligobulky and oligolethal settings as well as expansion beyond oligo- toward even plurimetastastic disease. In any case, lessons learned and experiences required by the implementation of using SAbR in oligometastatic cancer will be revisited.

Programmed cell death ligand-1 expression and survival in a cohort of patients with non-small cell lung cancer receiving first-line through third-line therapy in Denmark

BACKGROUND

PD-L1 expression on tumor cells (TCs) or immune cells (ICs) may be used as a prognostic marker for survival in patients with NSCLC. We characterized PD-L1 expression on TCs or ICs in a patient cohort with NSCLC to determine associations between PD-L1 expression and overall survival (OS), according to EGFR and KRAS mutation status.

METHODS

Danish patients aged >18 years diagnosed with NSCLC before 2014 on first- (N = 491), second- (N = 368), or third-line (N = 498) therapy were included. Data were extracted from population-based medical registries. Tumor samples from pathology archives were tested for biomarkers. High PD-L1 expression was defined as expression on ≥25 % of TCs or ICs based on first diagnostic biopsy or surgical resection. KRAS and EGFR mutation status were tested using PCR-based assays. Cox regression analysis was used to compute adjusted HRs and associated 95 % CIs.

RESULTS

PD-L1 TC and IC ≥ 25 % were observed in 24.3 %-31.0 % and 11.7-14.7 % of patients, respectively. EGFR and KRAS mutations were detected in 4.7 %-8.8 % and 26.5 %-30.7 % of patients, respectively. PD-L1 TC ≥ 25 % was not associated with survival advantage in first- (HR = 0.96, 95 % CI: 0.75-1.22), second- (1.08, 0.81-1.42), or third-line (0.94, 0.74-1.20) therapy. PD-L1 IC ≥ 25 % was associated with survival advantage in second-line (HR = 0.56, 95 % CI: 0.36-0.86) and third-line (0.69, 0.49-0.97) but not first-line (1.00, 0.70-1.41) therapy.

CONCLUSION

No association was observed between PD-L1 TC ≥ 25 % and OS in any therapy line. PD-L1 IC ≥ 25 % may confer survival benefit among some patients who reach second-line therapy.

Tracing Lung Cancer Risk Factors Through Mutational Signatures in Never-Smokers

Epidemiologic studies often rely on questionnaire data, exposure measurement tools, and/or biomarkers to identify risk factors and the underlying carcinogenic processes. An emerging and promising complementary approach to investigate cancer etiology is the study of somatic "mutational signatures" that endogenous and exogenous processes imprint on the cellular genome. These signatures can be identified from a complex web of somatic mutations thanks to advances in DNA sequencing technology and analytical algorithms. This approach is at the core of the Sherlock-Lung study (2018-ongoing), a retrospective case-only study of over 2,000 lung cancers in never-smokers (LCINS), using different patterns of mutations observed within LCINS tumors to trace back possible exposures or endogenous processes. Whole genome and transcriptome sequencing, genome-wide methylation, microbiome, and other analyses are integrated with data from histological and radiological imaging, lifestyle, demographic characteristics, environmental and occupational exposures, and medical records to classify LCINS into subtypes that could reveal distinct risk factors. To date, we have received samples and data from 1,370 LCINS cases from 17 study sites worldwide and whole-genome sequencing has been completed on 1,257 samples. Here, we present the Sherlock-Lung study design and analytical strategy, also illustrating some empirical challenges and the potential for this approach in future epidemiologic studies.

Mapping Spatial Genetic Landscapes in Tissue Sections through Microscale Integration of Sampling Methodology into Genomic Workflows

In cancer research, genomic profiles are often extracted from homogenized macrodissections of tissues, with the histological context lost and a large fraction of material underutilized. Pertinently, the spatial genomic landscape provides critical complementary information in deciphering disease heterogeneity and progression. Microscale sampling methods such as microdissection to obtain such information are often destructive to a sizeable fraction of the biopsy sample, thus showing limited multiplexability and adaptability to different assays. A modular microfluidic technology is here implemented to recover cells at the microscale from tumor tissue sections, with minimal disruption of unsampled areas and tailored to interface with genome profiling workflows, which is directed here toward evaluating intratumoral genomic heterogeneity. The integrated workflow-GeneScape-is used to evaluate heterogeneity in a metastatic mammary carcinoma, showing distinct single nucleotide variants and copy number variations in different tumor tissue regions, suggesting the polyclonal origin of the metastasis as well as development driven by multiple location-specific drivers.

Tracking Cancer Evolution through the Disease Course

During cancer evolution, constituent tumor cells compete under dynamic selection pressures. Phenotypic variation can be observed as intratumor heterogeneity, which is propagated by genome instability leading to mutations, somatic copy-number alterations, and epigenomic changes. TRACERx was set up in 2014 to observe the relationship between intratumor heterogeneity and patient outcome. By integrating multiregion sequencing of primary tumors with longitudinal sampling of a prospectively recruited patient cohort, cancer evolution can be tracked from early- to late-stage disease and through therapy. Here we review some of the key features of the studies and look to the future of the field. SIGNIFICANCE: Cancers evolve and adapt to environmental challenges such as immune surveillance and treatment pressures. The TRACERx studies track cancer evolution in a clinical setting, through primary disease to recurrence. Through multiregion and longitudinal sampling, evolutionary processes have been detailed in the tumor and the immune microenvironment in non-small cell lung cancer and clear-cell renal cell carcinoma. TRACERx has revealed the potential therapeutic utility of targeting clonal neoantigens and ctDNA detection in the adjuvant setting as a minimal residual disease detection tool primed for translation into clinical trials.

Single-cell lineages reveal the rates, routes, and drivers of metastasis in cancer xenografts

Detailed phylogenies of tumor populations can recount the history and chronology of critical events during cancer progression, such as metastatic dissemination. We applied a Cas9-based, single-cell lineage tracer to study the rates, routes, and drivers of metastasis in a lung cancer xenograft mouse model. We report deeply resolved phylogenies for tens of thousands of cancer cells traced over months of growth and dissemination. This revealed stark heterogeneity in metastatic capacity, arising from preexisting and heritable differences in gene expression. We demonstrate that these identified genes can drive invasiveness and uncovered an unanticipated suppressive role for KRT17 We also show that metastases disseminated via multidirectional tissue routes and complex seeding topologies. Overall, we demonstrate the power of tracing cancer progression at subclonal resolution and vast scale.

Reviving the Autopsy for Modern Cancer Evolution Research

Outstanding questions plaguing oncologists, centred around tumour evolution and heterogeneity, include the development of treatment resistance, immune evasion, and optimal drug targeting strategies. Such questions are difficult to study in limited cancer tissues collected during a patient's routine clinical care, and may be better investigated in the breadth of cancer tissues that may be permissible to collect during autopsies. We are starting to better understand key tumour evolution challenges based on advances facilitated by autopsy studies completed to date. This review article explores the great progress in understanding that cancer tissues collected at autopsy have already enabled, including the shared origin of metastatic cells, the importance of early whole-genome doubling events for amplifying genes needed for tumour survival, and the creation of a wealth of tissue resources powered to answer future questions, including patient-derived xenografts, cell lines, and a wide range of banked tissues. We also highlight the future role of these programmes in advancing our understanding of cancer evolution. The research autopsy provides a special opportunity for cancer patients to give the ultimate gift-to selflessly donate their tissues towards better cancer care.

Reconstructing tumor evolutionary histories and clone trees in polynomial-time with SubMARine

Tumors contain multiple subpopulations of genetically distinct cancer cells. Reconstructing their evolutionary history can improve our understanding of how cancers develop and respond to treatment. Subclonal reconstruction methods cluster mutations into groups that co-occur within the same subpopulations, estimate the frequency of cells belonging to each subpopulation, and infer the ancestral relationships among the subpopulations by constructing a clone tree. However, often multiple clone trees are consistent with the data and current methods do not efficiently capture this uncertainty; nor can these methods scale to clone trees with a large number of subclonal populations.

Here, we formalize the notion of a partially-defined clone tree (partial clone tree for short) that defines a subset of the pairwise ancestral relationships in a clone tree, thereby implicitly representing the set of all clone trees that have these defined pairwise relationships. Also, we introduce a special partial clone tree, the Maximally-Constrained Ancestral Reconstruction (MAR), which summarizes all clone trees fitting the input data equally well. Finally, we extend commonly used clone tree validity conditions to apply to partial clone trees and describe SubMARine, a polynomial-time algorithm producing the subMAR, which approximates the MAR and guarantees that its defined relationships are a subset of those present in the MAR. We also extend SubMARine to work with subclonal copy number aberrations and define equivalence constraints for this purpose. Further, we extend SubMARine to permit noise in the estimates of the subclonal frequencies while retaining its validity conditions and guarantees. In contrast to other clone tree reconstruction methods, SubMARine runs in time and space that scale polynomially in the number of subclones.

We show through extensive noise-free simulation, a large lung cancer dataset and a prostate cancer dataset that the subMAR equals the MAR in all cases where only a single clone tree exists and that it is a perfect match to the MAR in most of the other cases. Notably, SubMARine runs in less than 70 seconds on a single thread with less than one Gb of memory on all datasets presented in this paper, including ones with 50 nodes in a clone tree. On the real-world data, SubMARine almost perfectly recovers the previously reported trees and identifies minor errors made in the expert-driven reconstructions of those trees.

The freely-available open-source code implementing SubMARine can be downloaded at https://github.com/morrislab/submarine.

Cancer cells accumulate mutations over time and consist of genetically distinct subpopulations. Their evolutionary history (as represented by tumor phylogenies) can be inferred from bulk cancer genome sequencing data. Current tumor phylogeny reconstruction methods have two main issues: they are slow, and they do not efficiently represent uncertainty in the reconstruction.

To address these issues, we developed SubMARine, a fast algorithm that summarizes all valid phylogenies in an intuitive format. SubMARine solved all reconstruction problems in this manuscript in less than 70 seconds, orders of magnitude faster than other methods. These reconstruction problems included those with up to 50 subclones; problems that are too large for other algorithms to even attempt. SubMARine achieves these result because, unlike other algorithms, it performs its reconstruction by identifying an upper-bound on the solution set of trees and the amount of noise in the estimates of the subclonal frequencies. In the vast majority of cases we checked, i. e. an extensive noise-free simulation, a lung cancer and a prostate cancer dataset, this upper bound is tight: when only a single solution exists, SubMARine converges to it every time. When multiple solutions exist, our algorithm correctly recovers the uncertain relationships in 71% of cases.

In addition to solving these two major challenges, we introduce some useful new concepts for and open research problems in the field of tumor phylogeny reconstruction. Specifically, we formalize the concept of a partial clone tree which provides a set of constraints on the solution set of clone trees; and provide a complete set of conditions under which a partial clone tree is valid. These conditions guarantee that all trees in the solution set satisfy the constraints implied by the partial clone tree.

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.

Targeting Loss of Heterozygosity: A Novel Paradigm for Cancer Therapy

Loss of heterozygosity (LOH) is a common genetic event in the development of cancer. In certain tumor types, LOH can affect more than 20% of the genome, entailing loss of allelic variation in thousands of genes. This reduction of heterozygosity creates genetic differences between tumor and normal cells, providing opportunities for development of novel cancer therapies. Here, we review and summarize (1) mutations associated with LOH on chromosomes which have been shown to be promising biomarkers of cancer risk or the prediction of clinical outcomes in certain types of tumors; (2) loci undergoing LOH that can be targeted for development of novel anticancer drugs as well as (3) LOH in tumors provides up-and-coming possibilities to understand the underlying mechanisms of cancer evolution and to discover novel cancer vulnerabilities which are worth a further investigation in the near future.

Comparative analysis of target gene exon sequencing by cognitive technology using a next generation sequencing platform in patients with lung cancer

Next generation sequencing (NGS) technology is an increasingly important clinical tool for therapeutic decision-making. However, interpretation of NGS data presents challenges at the point of care, due to limitations in understanding the clinical importance of gene variants and efficiently translating results into actionable information for the clinician. The present study compared two approaches for annotating and reporting actionable genes and gene mutations from tumor samples: The traditional approach of manual curation, annotation and reporting using an experienced molecular tumor bioinformationist; and a cloud-based cognitive technology, with the goal to detect gene mutations of potential significance in Chinese patients with lung cancer. Data from 285 gene-targeted exon sequencing previously conducted on 115 patient tissue samples between 2014 and 2016 and subsequently manually annotated and evaluated by the Guangdong Lung Cancer Institute (GLCI) research team were analyzed by the Watson for Genomics (WfG) cognitive genomics technology. A comparative analysis of the annotation results of the two methods was conducted to identify quantitative and qualitative differences in the mutations generated. The complete congruence rate of annotation results between WfG analysis and the GLCI bioinformatician was 43.48%. In 65 (56.52%) samples, WfG analysis identified and interpreted, on average, 1.54 more mutation sites in each sample than the manual GLCI review. These mutation sites were located on 27 genes, including EP300, ARID1A, STK11 and DNMT3A. Mutations in the EP300 gene were most prevalent, and present in 30.77% samples. The Tumor Mutation Burden (TMB) interpreted by WfG analysis (1.82) was significantly higher than the TMB (0.73) interpreted by GLCI review. Compared with manual curation by a bioinformatician, WfG analysis provided comprehensive insights and additional genetic alterations to inform clinical therapeutic strategies for patients with lung cancer. These findings suggest the valuable role of cognitive computing to increase efficiency in the comprehensive detection and interpretation of genetic alterations which may inform opportunities for targeted cancer therapies.

Revealing the subtyping of non-small cell lung cancer based on genomic evolutionary patterns by multi-region sequencing

Accurately classifying patients with non-small cell lung cancer (NSCLC) from the perspective of tumor evolution has not been systematically studied to date. Here, we reconstructed phylogenetic relationships of somatic mutations in 100 early NSCLC patients (327 lesions) through reanalyzing the TRACERx data. Based on the genomic evolutionary patterns presented on the phylogenetic trees, we grouped NSCLC patients into three evolutionary subtypes. The phylogenetic trees among three subtypes exhibited distinct branching structures, with one subtype representing branched evolution and another reflecting the early accumulation of genomic variation. However, in the evolutionary pattern of the third subtype, some mutations experienced selective sweeps and were gradually replaced by multiple newly formed subclonal populations. The subtype patients with poor prognosis had higher intra-tumor heterogeneity and subclonal diversity. We combined genomic heterogeneity with clinical phenotypes analysis and found that subclonal expansion results in the progression and deterioration of the tumor. The molecular mechanisms of subtype-specific Early Driver Feature (EDF) genes differed across the evolutionary subtypes, reflecting the characteristics of the subtype itself. In summary, our study provided new insights on the stratification of NSCLC patients based on genomic evolution that can be valuable for us to understand the development of pulmonary tumor profoundly.

Radiomic Detection of EGFR Mutations in NSCLC

Radiomics is defined as the use of automated or semi-automated post-processing and analysis of multiple features derived from imaging exams. Extracted features might generate models able to predict the molecular profile of solid tumors. The aim of this study was to develop a predictive algorithm to define the mutational status of EGFR in treatment-naïve patients with advanced non-small cell lung cancer (NSCLC). CT scans from 109 treatment-naïve patients with NSCLC (21 EGFR-mutant and 88 EGFR-wild type) underwent radiomics analysis to develop a machine learning model able to recognize EGFR-mutant from EGFR-WT patients via CT scans. A "test-retest" approach was used to identify stable radiomics features. The accuracy of the model was tested on an external validation set from another institution and on a dataset from the Cancer Imaging Archive (TCIA). The machine learning model that considered both radiomic and clinical features (gender and smoking status) reached a diagnostic accuracy of 88.1% in our dataset with an AUC at the ROC curve of 0.85, whereas the accuracy values in the datasets from TCIA and the external institution were 76.6% and 83.3%, respectively. Furthermore, 17 distinct radiomics features detected at baseline CT scan were associated with subsequent development of T790M during treatment with an EGFR inhibitor. In conclusion, our machine learning model was able to identify EGFR-mutant patients in multiple validation sets with globally good accuracy, especially after data optimization. More comprehensive training sets might result in further improvement of radiomics-based algorithms. SIGNIFICANCE: These findings demonstrate that data normalization and "test-retest" methods might improve the performance of machine learning models on radiomics images and increase their reliability when used on external validation datasets.

Liquid Biopsies Using Circulating Tumor DNA in Non-Small Cell Lung Cancer

Liquid biopsies for the diagnosis and treatment of lung cancer have developed rapidly, driven primarily by technical advances in sensitivity to detect circulating tumor DNA (ctDNA). Still, technical limitations such as the challenge of detecting low-level ctDNA variants and distinguishing tumor-related variants from clonal hematopoiesis remain. With further technical advancements, new applications for ctDNA analysis are emerging including detection of post-treatment molecular residual disease (MRD), clinical trial selection, and early cancer detection. This chapter reviews the current state of ctDNA testing in NSCLC, the underlying technological advances enabling ctDNA detection, and the potential to expand ctDNA analysis to new applications.

Prognostic implications of preoperative versus postoperative circulating tumor DNA in surgically resected lung cancer patients: a pilot study

Background

Recent studies of advanced lung cancer patients have shown that circulating tumor DNA (ctDNA) analysis is useful for molecular profiling, monitoring tumor burden, and predicting therapeutic efficacies and disease progression. However, the usefulness of ctDNA analysis in surgically resected lung cancers is unclear.

Methods

This study included 20 lung cancer patients with clinical stage IIA–IIIA disease. Preoperative and postoperative (3–12 days) plasma samples were collected for ctDNA analysis. Cancer personalized profiling by deep sequencing, which can detect mutations in 197 cancer-related genes, was used for ctDNA detection. The cohort consisted of 18 men and 2 women with a median age of 69 (range, 37–88) years. Sixteen patients (80%) had a history of smoking. Histologically, there were four squamous cell carcinomas, 13 adenocarcinomas, two adenosquamous cell carcinomas, and one small cell carcinoma.

Results

At the time of data analysis, the 20 patients had been monitored for a median follow-up of 12 months. Eight patients (40%) were positive for preoperative ctDNA, and this was significantly correlated with tumor size (≥5 vs. <5 cm, P=0.018). Four patients (20%) were positive for postoperative ctDNA, and this was significantly correlated with histological grade (3 vs. 1 or 2, P=0.032). Postoperative positivity for ctDNA also predicted shorter recurrence-free survival (RFS) (P=0.015), while pre- and post-operative carcinoembryonic antigen levels (P=0.150 and P=0.533, respectively) and preoperative positivity for ctDNA (P=0.132) were not correlated with RFS.

Conclusions

Detecting ctDNA postoperatively was a poor prognostic factor in surgically resected lung cancer patients that may suggest there is minimal residual disease (MRD).

CancerTracer: a curated database for intrapatient tumor heterogeneity

Comprehensive genomic analyses of cancers have revealed substantial intrapatient molecular heterogeneities that may explain some instances of drug resistance and treatment failures. Examination of the clonal composition of an individual tumor and its evolution through disease progression and treatment may enable identification of precise therapeutic targets for drug design. Multi-region and single-cell sequencing are powerful tools that can be used to capture intratumor heterogeneity. Here, we present a database we’ve named CancerTracer (http://cailab.labshare.cn/cancertracer): a manually curated database designed to track and characterize the evolutionary trajectories of tumor growth in individual patients. We collected over 6000 tumor samples from 1548 patients corresponding to 45 different types of cancer. Patient-specific tumor phylogenetic trees were constructed based on somatic mutations or copy number alterations identified in multiple biopsies. Using the structured heterogeneity data, researchers can identify common driver events shared by all tumor regions, and the heterogeneous somatic events present in different regions of a tumor of interest. The database can also be used to investigate the phylogenetic relationships between primary and metastatic tumors. It is our hope that CancerTracer will significantly improve our understanding of the evolutionary histories of tumors, and may facilitate the identification of predictive biomarkers for personalized cancer therapies.

Co-Occurring Alterations of ERBB2 Exon 20 Insertion in Non-Small Cell Lung Cancer (NSCLC) and the Potential Indicator of Response to Afatinib

Background: Human epidermal growth factor receptor 2 (ERBB2, HER-2) exon 20 insertion (ERBB2ex20ins) remains a refractory oncogenic driver in lung cancer. So far there is limited data showing the co-occurring mutation background of ERBB2ex20ins in Chinese lung cancer and its relationship with response to afatinib. Patients and Methods: A total of 112 Chinese patients with ERBB2ex20ins identified by next-generation sequencing from 17 hospitals were enrolled. The clinical outcomes of 18 patients receiving afatinib treatment were collected. Results: Among the 112 patients, insertion-site subtypes comprised of A775ins (71%; 79/112), G776indel (17%; 19/112), and P780ins (12%; 14/112). There were 66.1% (74/112) of patients carrying TP53 co-mutation and FOXA1 was the most prevalent co-amplified gene (5.5%, 3/55). The co-occurring genomic feature was similar among three insertional-site subtypes and had an overall strong concordance with the western population from the MSKCC cohort (R ² = 0.74, P < 0.01). For the prognosis, patients with co-occurring mutation in cell-cycle pathway especially TP53 showed shorter OS than patients without [median OS: 14.5 m (95% CI:12.7-16.3 m) vs. 30.3 m (95% CI: not reached), p = 0.04], while the OS was comparable among three subtypes. For the response to afatinib, ERBB2ex20ins as a subclonal variant was an independent factor relating to shorter PFS [median PFS: 1.2 m (95% CI: 0.8-1.6 m) vs. 4.3 m (95% CI: 3.3-5.3 m), p < 0.05]. Conclusion: Our data revealed co-occurring TP53 represent an unfavorable prognosis of patients with ERBB2ex20ins, emphasizing the more valuable role of the co-mutation patterns than insertion-site subtypes in predicting prognosis of this group of patients. Moreover, the clonality status of ERBB2ex20ins was identified as a potential indicator for response to afatinib.