Cancer driver mutations: predictions and reality

Cancer cells accumulate many genetic alterations throughout their lifetime, but only a few of them drive cancer progression, termed driver mutations. Driver mutations may vary between cancer types and patients, can remain latent for a long time and become drivers at particular cancer stages, or may drive oncogenesis only in conjunction with other mutations. The high mutational, biochemical, and histological tumor heterogeneity makes driver mutation identification very challenging. In this review we summarize recent efforts to identify driver mutations in cancer and annotate their effects. We underline the success of computational methods to predict driver mutations in finding novel cancer biomarkers, including in circulating tumor DNA (ctDNA). We also report on the boundaries of their applicability in clinical research.     

A genetic view of laryngeal cancer heterogeneity

During the recent decades significant improvements in the understanding of laryngeal molecular biology allowed a better characterization of the tumor. However, despite increased molecular knowledge and clinical efforts, survival of patients with laryngeal cancer remains the same as 30 years ago. Although this result may not make major conclusions as preservation approaches were not broadly used until the time of database collection, it seems to be clear that there is still window for improvement. Although the cornerstone for laryngeal cancer eradication is to implement smoking cessation programs, survival progresses will be hopefully seen in the future. Introducing molecular biomarkers as predictive factors to determine which patients will benefit of preservation treatments may become one of the next steps to improve survival. Furthermore, the development of new therapeutic modalities joint to biomarkers to selectively apply such new therapy in these patients may help to define new modalities with improved survival. New inhibitors against Notch pathway, EGFR, VRK1 or DNA damage repair may become gold standard if we are able to identify patients that may benefit from them, either on survival or functional larynx preservation. It is the moment for an inflexion point on the way laryngeal cancer is clinically managed.     

Somatic variation and cancer: therapies lost in the mix

Cancer arises as a consequence of mutations in genomes of cancer cells, which over time allow them to proliferate and spread to distant sites. Large-scale sequencing of cancer genomes is revealing an increasing number of potential driver mutations that may allow specific targeting of cancer genes, proteins, and pathways. Comprehensive views of cancer genomes are also revealing enormous heterogeneity of mutation profiles, even among tumours derived from the same organs and having similar pathological characteristics. There are now many examples where mutation profiles observed in tumours have been shown to correlate with clinical features of disease, drug response, and patient outcomes. When ignored, molecular heterogeneity can lead to failures in drug development, as drugs that may have efficacy in subgroups of patients with specific molecular phenotypes may show marginal response when tested in large groups of unselected patients. This article explores issues relevant to the clinical translation of sequence-based mutation profiles in the clinical development of targeted therapies and in the future management of cancer patients.     

Assessing the contribution of tumor mutational phenotypes to cancer progression risk

Cancer occurs via an accumulation of somatic genomic alterations in a process of clonal evolution. There has been intensive study of potential causal mutations driving cancer development and progression. However, much recent evidence suggests that tumor evolution is normally driven by a variety of mechanisms of somatic hypermutability, which act in different combinations or degrees in different cancers. These variations in mutability phenotypes are predictive of progression outcomes independent of the specific mutations they have produced to date. Here we explore the question of how and to what degree these differences in mutational phenotypes act in a cancer to predict its future progression. We develop a computational paradigm using evolutionary tree inference (tumor phylogeny) algorithms to derive features quantifying single-tumor mutational phenotypes, followed by a machine learning framework to identify key features predictive of progression. Analyses of breast invasive carcinoma and lung carcinoma demonstrate that a large fraction of the risk of future clinical outcomes of cancer progression—overall survival and disease-free survival—can be explained solely from mutational phenotype features derived from the phylogenetic analysis. We further show that mutational phenotypes have additional predictive power even after accounting for traditional clinical and driver gene-centric genomic predictors of progression. These results confirm the importance of mutational phenotypes in contributing to cancer progression risk and suggest strategies for enhancing the predictive power of conventional clinical data or driver-centric biomarkers.     

Molecular Typing of Lung Adenocarcinoma on Cytological Samples Using a Multigene Next Generation Sequencing Panel

Identification of driver mutations in lung adenocarcinoma has led to development of targeted agents that are already approved for clinical use or are in clinical trials. Therefore, the number of biomarkers that will be needed to assess is expected to rapidly increase. This calls for the implementation of methods probing the mutational status of multiple genes for inoperable cases, for which limited cytological or bioptic material is available. Cytology specimens from 38 lung adenocarcinomas were subjected to the simultaneous assessment of 504 mutational hotspots of 22 lung cancer-associated genes using 10 nanograms of DNA and Ion Torrent PGM next-generation sequencing. Thirty-six cases were successfully sequenced (95%). In 24/36 cases (67%) at least one mutated gene was observed, including EGFR, KRAS, PIK3CA, BRAF, TP53, PTEN, MET, SMAD4, FGFR3, STK11, MAP2K1. EGFR and KRAS mutations, respectively found in 6/36 (16%) and 10/36 (28%) cases, were mutually exclusive. Nine samples (25%) showed concurrent alterations in different genes. The next-generation sequencing test used is superior to current standard methodologies, as it interrogates multiple genes and requires limited amounts of DNA. Its applicability to routine cytology samples might allow a significant increase in the fraction of lung cancer patients eligible for personalized therapy.     

Trying to compose the puzzle with all the pieces: epidermal growth factor receptor tyrosine kinase inhibitors in non-small cell lung cancer

Small molecule epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) have shown promising activity in patients with non-small cell lung cancer (NSCLC). Gefitinib has been the first of these drugs to be licensed for third-line treatment of advanced NSCLC patients. More recently, erlotinib has been shown to be more effective than placebo in increasing overall survival (OAS) and has been approved for NSCLC patients after failure of chemotherapy. However, a large body of clinical and experimental evidence suggests that the benefit from these drugs is limited to a subgroup of patients. The availability of clinical or molecular criteria for predicting sensitivity to EGFR-TKIs is the most relevant issue for their correct use and for planning future research. Determination of EGFR expression is not sufficient to predict sensitivity to EGFR-TKIs. However, several clinical features (female gender, adenocarcinoma/bronchioloalveolar histotype, never-smoking status, Oriental Asian origin) are associated with major clinical responses. The identification of somatic mutations in the tyrosine kinase domain of the EGFR gene represents the most important molecular marker of sensitivity to EGFR-TKIs. These "activating" mutations can be found in a high proportion of gefitinib- or erlotinib-responding patients. However, clinical effectiveness might not be limited to patients carrying EGFR mutations, in which the objective response is probably the detectable effect of apoptosis induction in cancer cells. In fact, clinical efficacy with gefitinib or erlotinib is also observed in another subgroup of patients, in which a tumor growth delay, determined by a block in cancer cell proliferation, could induce a prolonged and clinically relevant disease stabilization.     

Towards defining biomarkers indicating resistances to targeted therapies

An impressive, but often short objective response was obtained in many tumor patients treated with different targeted therapies, but most of the patients develop resistances against these drugs. So far, a number of distinct mechanisms leading to intrinsic as well as acquired resistances have been identified in tumors of distinct origin. These can arise from genetic alterations, like mutations, truncations, and amplifications or due to deregulated expression of various proteins and signal transduction pathways, but also from cellular heterogeneity within tumors after an initial response. Therefore, biomarkers are urgently needed for cancer prognosis and personalized cancer medicine. The application of “ome”-based technologies including cancer (epi)genomics, next generation sequencing, cDNA microarrays and proteomics might led to the predictive or prognostic stratification of patients to categorize resistance mechanisms and to postulate combinations of treatment strategies. This review discusses the implementation of proteome-based analysis to identify markers of pathway (in)activation in tumors and the resistance mechanisms, which represent major clinical problems as a tool to optimize individually tailored therapies based on targeted drugs. This article is part of a Special Issue entitled: Biomarkers: A Proteomic Challenge.     

Chronic Obstructive Pulmonary Disease-Related Non-Small-Cell Lung Cancer Exhibits a Low Prevalence of EGFR and ALK Driver Mutations

Lung cancer and chronic obstructive pulmonary disease (COPD) are two major lung diseases. Epidermal growth factor receptor (EGFR) mutations, v‐Ki‐ras2 Kirsten rat sarcoma (KRAS) mutations and anaplastic lymphoma kinase (ALK) gene rearrangements represent driver mutations that are frequently assessed on initial evaluation of non-small-cell lung cancer (NSCLC). The present study focused on the expression of driver mutations in NSCLC patients presenting with COPD and further evaluated the association between NSCLC and COPD. Data from 501 consecutive patients with histologically proven recurrent or metastatic NSCLC were analyzed retrospectively. The patients underwent spirometry and genotyping of EGFR, ALK, and KRAS in tissue samples. Patient characteristics and expression of driver mutations were compared between the COPD and non-COPD groups.Among 350 patients with spirometric results, 106 (30.3%) were diagnosed with COPD, 108 (30.9%) had EGFR mutations, 31 (8.9%) had KRAS mutations, and 34 (9.7%) showed ALK rearrangements. COPD was independently associated with lower prevalences of EGFR mutations (95% confidence interval [CI], 0.254–0.931, p = 0.029) and ALK rearrangements (95% CI, 0.065–0.600, p = 0.004). The proportions of EGFR mutations and ALK rearrangements decreased as the severity of airflow obstruction increased (p = 0.001). In never smokers, the prevalence of EGFR mutations was significantly lower in the COPD group than in the non-COPD group (12.7% vs. 49.0%, p = 0.002). COPD-related NSCLC patients exhibited low prevalences of EGFR mutations and ALK rearrangements compared with the non-COPD group. Further studies are required regarding the molecular mechanisms underlying lung cancer associated with COPD.     

Molecular biomarker‐guided anti‐angiogenic targeted therapy for malignant glioma

Despite aggressive multimodality treatment, the prognosis of glioma, especially malignant glioma, remains very poor. After decades of effort, anti‐angiogenic therapy has become an important method of cancer treatment in addition to surgery, radiotherapy and chemotherapy. Although the performance of anti‐angiogenic therapy in colorectal cancer is good, its performance in malignant glioma remains unsatisfactory. Several phase III clinical trials showed no overall survival benefits. To solve this problem, the division of patients into groups based on their molecular biomarkers is an important step. This paper provides current insights into anti‐angiogenic drugs undergoing clinical trials and discusses the potential of molecular biomarkers to guide glioma diagnosis.     

The Use of a Two-Tiered Testing Strategy for the Simultaneous Detection of Small EGFR Mutations and EGFR Amplification in Lung Cancer

Lung cancer is the leading cause of cancer-related death worldwide. Recent progress in lung cancer diagnosis and treatment has been achieved due to a better understanding the molecular mechanisms of the disease and the identification of biomarkers that allow more specific cancer treatments. One of the best known examples of personalized therapy is the use of tyrosine kinase inhibitors, such as gefitinib and erlotinib, for the successful treatment of non-small-cell lung cancer patients selected based on the specific EGFR mutations. Therefore, the reliable detection of mutations is critical for the application of appropriate therapy. In this study, we tested a two-tiered mutation detection strategy using real-time PCR assays as a well-validated high-sensitivity method and multiplex ligation-dependent probe amplification (MLPA)-based EGFRmut+ assay as a second-tier standard-sensitivity method. One additional advantage of the applied MLPA method is that it allows the simultaneous detection of EGFR mutations and copy-number alterations (i.e., amplifications) in EGFR, MET and ERBB2. Our analysis showed high concordance between these two methods. With the use of this two-tier strategy, we reliably determined the frequency of EGFR mutations and EGFR, MET and ERBB2 amplifications in over 200 lung cancer samples. Additionally, taking advantage of simultaneous copy number and small mutation analyses, we showed a very strong correlation between EGFR mutations and EGFR amplifications and a mutual exclusiveness of EGFR mutations/amplifications with MET and ERBB2 amplifications. Our results proved the reliability and usefulness of the two-tiered EGFR testing strategy.     

Overcoming acquired resistance to kinase inhibition: The cases of EGFR,ALK and BRAF

In the past decade, several kinase inhibitors have been approved based on their clinical benefit for cancer patients. Unfortunately, in many cases, patients develop resistance to these agents via secondary mutations and alternative mechanisms. This review will focus on the cases of acquired resistance to EGFR and ALK inhibitors for non-small cell lung cancer patients and BRAF inhibitors for melanoma patients. I will overview the main causes of acquired resistance, and explore the chemical scaffolds as well as combination of drugs, used to tackle these major causes of resistance.     

Cancer‐associated mutations are preferentially distributed in protein kinase functional sites

Protein kinases are a superfamily involved in many crucial cellular processes, including signal transmission and regulation of cell cycle. As a consequence of this role, kinases have been reported to be associated with many types of cancer and are considered as potential therapeutic targets. We analyzed the distribution of pathogenic somatic point mutations (drivers) in the protein kinase superfamily with respect to their location in the protein, such as in structural, evolutionary, and functionally relevant regions. We find these driver mutations are more clearly associated with key protein features than other somatic mutations (passengers) that have not been directly linked to tumor progression. This observation fits well with the expected implication of the alterations in protein kinase function in cancer pathogenicity. To explain the relevance of the detected association of cancer driver mutations at the molecular level in the human kinome, we compare these with genetically inherited mutations (SNPs). We find that the subset of nonsynonymous SNPs that are associated to disease, but sufficiently mild to the point of being widespread in the population, tend to avoid those key protein regions, where they could be more detrimental for protein function. This tendency contrasts with the one detected for cancer associated‐driver‐mutations, which seems to be more directly implicated in the alteration of protein function. The detailed analysis of protein kinase groups and a number of relevant examples, confirm the relation between cancer associated‐driver‐mutations and key regions for protein kinase structure and function. Proteins 2009. © 2009 Wiley‐Liss, Inc.     

Pharmacogenomics of epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors

The EGFR is a validated anticancer target whose successful exploitation has added novel agents to our current treatment protocols. Subsets of patients have shown to benefit the most from these therapies, and though these differential responses have yet to be completely defined, they are mostly of genetic nature. Egfr amplifications have shown to increase sensitivity to both small molecule inhibitors and specific monoclonal antibodies targeting the EGFR. A somatic/germline egfr intron 1 CA repeat sequence polymorphism has shown to have an important role in the control of EGFR protein expression, and has been linked to an increased risk of familial breast cancer, a worse outcome in patients with colorectal cancer, and anti-EGFR treatment efficacy in preclinical models. Egfr activating mutations have been recently described in lung cancer linking a cluster of genotypes with sensitivity to EGFR tyrosine kinase pharmacological inhibition. Despite the initial excitement that this discovery elicited, follow-up reports have not unequivocally confirmed this finding, and these drugs have been solidly efficacious both in individual patients and in diseases generally lacking egfr mutations such as pancreas cancer. We are witnessing exciting developments in the field of the pharmacogenomics of cancer, and this has particularly evolved in the area pertaining EGFR tyrosine kinase inhibitors. This review will discuss the background and currently available preclinical and clinical data.     

The methylation landscape of tumour metastasis

The metastatic cascade which leads to the death of cancer patients results from a multi‐step process of tumour progression caused by genetic and epigenetic alterations in key regulatory molecules. It is, therefore, crucial to improve our understanding of the regulation of genes controlling the metastatic process to identify predictive biomarkers and to develop more effective therapies to treat advanced disease. The study of epigenetic mechanisms of gene regulation offers a novel approach for innovative diagnosis and treatment of cancer patients. Recent discoveries provide compelling evidence that the methylation landscape (changes in both DNA methylation and histone post‐translational modifications) is profoundly altered in cancer cells and contributes to the altered expression of genes regulating tumour phenotypes. However, the impact of methylation events specifically on the advanced metastatic process is poorly understood compared with the initial oncogenic events. Moreover, the characterisation of a large number of histone‐modifying enzymes has revealed their active roles in cancer progression, via the regulation of specific target genes controlling different metastatic phenotypes. Here, we discuss two main methylating events (DNA methylation and histone‐tail methylation) involved in oncogenesis and metastasis formation. The potential reversibility of these molecular events makes them promising biomarkers of metastatic potential and potential therapeutic targets.     

Recent Development of the Second and Third Generation Irreversible Epidermal Growth Factor Receptor Inhibitors

Recent reports suggested that essential directions for new lung cancer, breast carcinoma therapies, as well as the roomier realm of targeted cancer therapies were provided through targeting the epidermal growth factor receptor (EGFR). Patients who carrying non‐small cell lung carcinoma (NSCLC) with activating mutations in EGFR initially respond well to the EGFR inhibitors erlotinib and gefitinib, which were located the active site of the EGFR kinase and designed to act as competitive inhibitors of combining with the ATP. However, patients who were treated with the erlotinib and gefitinib will relapse because of the emergence of drug‐resistant mutations, with T790M mutations accounting for approximately 60% of all resistance. In order to overcome drug resistance, Pharmaceutical chemistry experts recently devoted great endeavors to the development of second‐generation irreversible selective inhibitors which covalently modify Cys797 or Cys773 at the ATP binding cleft. Nevertheless, these inhibitors have not reached ideal effect of experts in patients with T790M positive mutation and apparently because of the dose‐limiting toxicities associated with inhibition of wild type EGFR. A novel class of ‘third generation’ EGFR TKIs have been developed that is sensitising and T790M mutant‐specific whilst sparing WT EGFR, representing a significant breakthrough in the treatment in NSCLC patients with acquired resistance harboring these genotypes. Herein, we provides an overview of the second and third generation inhibitors currently approved, in clinical trial and also encompasses novel structures of discovery. This review mainly focuses on drug resistance, their mechanisms of action, development of structure–activity relationships and binding modes.     

Preclinical strategies to define predictive biomarkers for therapeutically relevant cancer subtypes

Defining key driver mutations in cancer, the resulting aberrations in molecular mechanisms and the subsequent phenotype underpins the development and implementation of novel personalized medicine strategies. The literature is replete with biomarkers of prognosis and therapeutic responsiveness identified in single cohorts of patients that have not been independently validated and as a consequence, not developed. Integrating companion biomarker discovery with therapeutic development at the preclinical stage creates the opportunity to identify candidate biomarkers early, which would significantly facilitate both biomarker and therapeutic development. Advances in “-omic” technologies have led to large-scale efforts in characterizing and cataloguing the full range of aberrations in cancer. These include the International Cancer Genome Consortium and The Cancer Genome Atlas, which aim to comprehensively catalogue the range of genomic aberrations for large numbers of cancers for a progressively increasing range of cancer types and subtypes. The technical challenges associated with achieving these goals in some instances have required the generation of primary xenografts and cell lines. These extensively characterized model systems will provide an unprecedented resource for the discovery of biomarkers of therapeutic responsiveness for established therapies, and the development of companion biomarkers linked with preclinical novel therapeutic development in the future.     

Design and Inference for Cancer Biomarker Study with an Outcome and Auxiliary‐Dependent Subsampling

Summary In cancer research, it is important to evaluate the performance of a biomarker (e.g., molecular, genetic, or imaging) that correlates patients' prognosis or predicts patients' response to treatment in a large prospective study. Due to overall budget constraint and high cost associated with bioassays, investigators often have to select a subset from all registered patients for biomarker assessment. To detect a potentially moderate association between the biomarker and the outcome, investigators need to decide how to select the subset of a fixed size such that the study efficiency can be enhanced. We show that, instead of drawing a simple random sample from the study cohort, greater efficiency can be achieved by allowing the selection probability to depend on the outcome and an auxiliary variable; we refer to such a sampling scheme as outcome and auxiliary‐dependent subsampling (OADS). This article is motivated by the need to analyze data from a lung cancer biomarker study that adopts the OADS design to assess epidermal growth factor receptor (EGFR) mutations as a predictive biomarker for whether a subject responds to a greater extent to EGFR inhibitor drugs. We propose an estimated maximum‐likelihood method that accommodates the OADS design and utilizes all observed information, especially those contained in the likelihood score of EGFR mutations (an auxiliary variable of EGFR mutations) that is available to all patients. We derive the asymptotic properties of the proposed estimator and evaluate its finite sample properties via simulation. We illustrate the proposed method with a data example.