MRD in ALL: Optimization and Innovations

PURPOSE OF REVIEW

Measurable residual disease (MRD) is an important monitoring parameter that can help predict survival outcomes in acute lymphoblastic leukemia (ALL). Identifying patients with MRD has the potential to decrease the risk of relapse with the initiation of early salvage therapy and to help guide decision making regarding allogeneic hematopoietic cell transplantation. In this review, we discuss MRD in ALL, focusing on advantages and limitations between MRD testing techniques and how to monitor MRD in specific patient populations.

RECENT FINDINGS

MRD has traditionally been measured through bone marrow samples, but more data for evaluation of MRD via peripheral blood is emerging. Current and developmental testing strategies for MRD include multiparametric flow cytometry (MFC), next-generation sequencing (NGS), quantitative polymerase chain reaction (qPCR), and ClonoSeq. Novel therapies are incorporating MRD as an outcome measure to demonstrate efficacy, including blinatumomab, inotuzumab ozogamicin, and chimeric antigen receptor T (CAR-T) cell therapy. Understanding how to incorporate MRD testing into the management of ALL could improve patient outcomes and predict efficacy of new therapy options.

Machine learning methods for prediction of cancer driver genes: a survey paper

Identifying the genes and mutations that drive the emergence of tumors is a critical step to improving our understanding of cancer and identifying new directions for disease diagnosis and treatment. Despite the large volume of genomics data, the precise detection of driver mutations and their carrying genes, known as cancer driver genes, from the millions of possible somatic mutations remains a challenge. Computational methods play an increasingly important role in discovering genomic patterns associated with cancer drivers and developing predictive models to identify these elements. Machine learning (ML), including deep learning, has been the engine behind many of these efforts and provides excellent opportunities for tackling remaining gaps in the field. Thus, this survey aims to perform a comprehensive analysis of ML-based computational approaches to identify cancer driver mutations and genes, providing an integrated, panoramic view of the broad data and algorithmic landscape within this scientific problem. We discuss how the interactions among data types and ML algorithms have been explored in previous solutions and outline current analytical limitations that deserve further attention from the scientific community. We hope that by helping readers become more familiar with significant developments in the field brought by ML, we may inspire new researchers to address open problems and advance our knowledge towards cancer driver discovery.

Personalized Medicine in Gynecologic Cancer: Fact or Fiction?

Personalized medicine in gynecologic oncology is an evolving field. In recent years, tumor profiling and large databases such as TCGA and NCI-Match have provided us with enormous amounts of molecular data. Several therapies that capitalize on novel genetic and immune discoveries including VEGF inhibitors, PARP inhibitors, and cancer vaccinations are discussed in this article. Additionally, we have seen direct to consumer marketing play an important role in cancer care and prevention as patients have increased ability to access genetic testing. This presents a unique challenge to gynecologic oncology providers as we learn to navigate the world of personalized medicine.

Approaches for the identification of driver mutations in cancer: A tutorial from a computational perspective

Cancer is a complex disease caused by the accumulation of genetic alterations during the individual's life. Such alterations are called genetic mutations and can be divided into two groups: (1) Passenger mutations, which are not responsible for cancer and (2) Driver mutations, which are significant for cancer and responsible for its initiation and progression. Cancer cells undergo a large number of mutations, of which most are passengers, and few are drivers. The identification of driver mutations is a key point and one of the biggest challenges in Cancer Genomics. Many computational methods for such a purpose have been developed in Cancer Bioinformatics. Such computational methods are complex and are usually described in a high level of abstraction. This tutorial details some classical computational methods, from a computational perspective, with the transcription in an algorithmic format towards an easy access by researchers.

Clinical Impact of Tumor Mutational Burden in Neuroblastoma

BACKGROUND

Neuroblastoma is the most common pediatric extracranial solid tumor. Within conventional risk groups, there is considerable heterogeneity in outcomes, indicating the need for improved risk stratification.

METHODS

In this study we analyzed the somatic mutational burden of 515 primary, untreated neuroblastoma tumors from three independent cohorts. Mutations in coding regions were determined by whole-exome/genome sequencing of tumor samples compared to matched blood leukocytes. Survival data for 459 patients were available for analysis of 5-year overall survival using the Kaplan-Meier method and log-rank test. All statistical tests were two-sided.

RESULTS

Despite a low overall somatic mutational burden (mean = 3, range = 0-56), 107 patients were considered to have high mutational burden (>3 mutations). Unfavorable histology and age 18 months and older were associated with high mutational burden. Patients with high mutational burden had inferior 5-year overall survival (29.0%, 95% confidence interval [CI] = 17.2 to 41.8%) vs those with three or fewer somatic mutations (76.2%, 95% CI = 71.5 to 80.3%) (log-rank P < .001) and this association persisted when limiting the analysis to genes included on a 447-gene panel commonly used in clinical practice. On multivariable analysis, mutational burden remained prognostic independent of age, stage, histology and MYCN status.

CONCLUSIONS

This study demonstrates that mutational burden of primary neuroblastoma may be useful in combination with conventional risk factors to optimize risk stratification and guide treatment decisions, pending prospective validation.

Interrogating Mutant Allele Expression via Customized Reference Genomes to Define Influential Cancer Mutations

Genetic alterations are essential for cancer initiation and progression. However, differentiating mutations that drive the tumor phenotype from mutations that do not affect tumor fitness remains a fundamental challenge in cancer biology. To better understand the impact of a given mutation within cancer, RNA-sequencing data was used to categorize mutations based on their allelic expression. For this purpose, we developed the MAXX (Mutation Allelic Expression Extractor) software, which is highly effective at delineating the allelic expression of both single nucleotide variants and small insertions and deletions. Results from MAXX demonstrated that mutations can be separated into three groups based on their expression of the mutant allele, lack of expression from both alleles, or expression of only the wild-type allele. By taking into consideration the allelic expression patterns of genes that are mutated in PDAC, it was possible to increase the sensitivity of widely used driver mutation detection methods, as well as identify subtypes that have prognostic significance and are associated with sensitivity to select classes of therapeutic agents in cell culture. Thus, differentiating mutations based on their mutant allele expression via MAXX represents a means to parse somatic variants in tumor genomes, helping to elucidate a gene’s respective role in cancer.

Intraventricular meningiomas frequently harbor NF2 mutations but lack common genetic alterations in TRAF7, AKT1, SMO, KLF4, PIK3CA, and TERT

Intraventricular meningiomas (IVMs) account for less than 5% of all intracranial meningiomas; hence their molecular phenotype remains unknown. In this study, we were interested whether genetic alterations in IVMs differ from meningiomas in other locations and analyzed our institutional series with respect to clinical and molecular characteristics. A total of 25 patients with surgical removal of an IVM at our department between 1986 and 2018 were identified from our institutional database. Median progression-free survival (PFS) was 79 months (range of 2–319 months) and PFS at 5 years was 86%. Corresponding tumor tissue was available for 18 patients including one matching recurrence and was subjected to targeted panel sequencing of 130 selected genes frequently mutated in brain cancers by applying a custom hybrid capture approach on a NextSeq500 instrument. Loss of chromosome 22q and 1p occurred frequently in 89 and 44% of cases. Deleterious NF2 mutations were found in 44% of IVMs (n = 8/18). In non-NF2-mutated IVMs, previously reported genetic alterations including TRAF7, AKT1, SMO, KLF4, PIK3CA, and TERT were lacking, suggesting alternative genes in the pathogenesis of non-NF2 IVMs. In silico analysis revealed possible damaging mutations of APC, GABRA6, GSE1, KDR, and two SMO missense mutations differing from previously reported ones. Interestingly, all WHO°II IVMs (n = 3) harbored SMARCB1 and SMARCA4 mutations, indicating a role of the SWI/SNF chromatin remodeling complex in aggressive IVMs.

Understanding the impacts of missense mutations on structures and functions of human cancer-related genes: A preliminary computational analysis of the COSMIC Cancer Gene Census

Genomics and genome screening are proving central to the study of cancer. However, a good appreciation of the protein structures coded by cancer genes is also invaluable, especially for the understanding of functions, for assessing ligandability of potential targets, and for designing new drugs. To complement the wealth of information on the genetics of cancer in COSMIC, the most comprehensive database for cancer somatic mutations available, structural information obtained experimentally has been brought together recently in COSMIC-3D. Even where structural information is available for a gene in the Cancer Gene Census, a list of genes in COSMIC with substantial evidence supporting their impacts in cancer, this information is quite often for a single domain in a larger protein or for a single protomer in a multiprotein assembly. Here, we show that over 60% of the genes included in the Cancer Gene Census are predicted to possess multiple domains. Many are also multicomponent and membrane-associated molecular assemblies, with mutations recorded in COSMIC affecting such assemblies. However, only 469 of the gene products have a structure represented in the PDB, and of these only 87 structures have 90–100% coverage over the sequence and 69 have less than 10% coverage. As a first step to bridging gaps in our knowledge in the many cases where individual protein structures and domains are lacking, we discuss our attempts of protein structure modelling using our pipeline and investigating the effects of mutations using two of our in-house methods (SDM2 and mCSM) and identifying potential driver mutations. This allows us to begin to understand the effects of mutations not only on protein stability but also on protein-protein, protein-ligand and protein-nucleic acid interactions. In addition, we consider ways to combine the structural information with the wealth of mutation data available in COSMIC. We discuss the impacts of COSMIC missense mutations on protein structure in order to identify and assess the molecular consequences of cancer-driving mutations.

Personalized Medicine in Gynecologic Cancer: Fact or Fiction?

Personalized medicine in gynecologic oncology is an evolving field. In recent years, tumor profiling and large databases such as TCGA and NCI-Match have provided us with enormous amounts of molecular data. Several therapies that capitalize on novel genetic and immune discoveries including VEGF inhibitors, PARP inhibitors, and cancer vaccinations are discussed in this article. Additionally, we have seen direct to consumer marketing play an important role in cancer care and prevention as patients have increased ability to access genetic testing. This presents a unique challenge to gynecologic oncology providers as we learn to navigate the world of personalized medicine.

Challenges in the introduction of next-generation sequencing (NGS) for diagnostics of myeloid malignancies into clinical routine use

Given the vast phenotypic and genetic heterogeneity of acute and chronic myeloid malignancies, hematologists have eagerly awaited the introduction of next-generation sequencing (NGS) into the routine diagnostic armamentarium to enable a more differentiated disease classification, risk stratification, and improved therapeutic decisions. At present, an increasing number of hematologic laboratories are in the process of integrating NGS procedures into the diagnostic algorithms of patients with acute myeloid leukemia (AML), myelodysplastic syndromes (MDS), and myeloproliferative neoplasms (MPNs). Inevitably accompanying such developments, physicians and molecular biologists are facing unexpected challenges regarding the interpretation and implementation of molecular genetic results derived from NGS in myeloid malignancies. This article summarizes typical challenges that may arise in the context of NGS-based analyses at diagnosis and during follow-up of myeloid malignancies.

Identifying Cancer Driver Genes Using Replication-Incompetent Retroviral Vectors

Identifying novel genes that drive tumor metastasis and drug resistance has significant potential to improve patient outcomes. High-throughput sequencing approaches have identified cancer genes, but distinguishing driver genes from passengers remains challenging. Insertional mutagenesis screens using replication-incompetent retroviral vectors have emerged as a powerful tool to identify cancer genes. Unlike replicating retroviruses and transposons, replication-incompetent retroviral vectors lack additional mutagenesis events that can complicate the identification of driver mutations from passenger mutations. They can also be used for almost any human cancer due to the broad tropism of the vectors. Replication-incompetent retroviral vectors have the ability to dysregulate nearby cancer genes via several mechanisms including enhancer-mediated activation of gene promoters. The integrated provirus acts as a unique molecular tag for nearby candidate driver genes which can be rapidly identified using well established methods that utilize next generation sequencing and bioinformatics programs. Recently, retroviral vector screens have been used to efficiently identify candidate driver genes in prostate, breast, liver and pancreatic cancers. Validated driver genes can be potential therapeutic targets and biomarkers. In this review, we describe the emergence of retroviral insertional mutagenesis screens using replication-incompetent retroviral vectors as a novel tool to identify cancer driver genes in different cancer types.

Next-Generation Sequencing and Applications to the Diagnosis and Treatment of Lung Cancer

Cancer is a genetic disease characterized by uncontrolled growth of abnormal cells. Over time, somatic mutations accumulate in the cells of an individual due to replication errors, chromosome segregation errors, or DNA damage. When not caught by traditional mechanisms, these somatic mutations can lead to cellular proliferation, the hallmark of cancer. Lung cancer is the leading cause of cancer-related mortality in the United States, accounting for approximately 160,000 deaths annually. Five year survival rates for lung cancer remain low (<50 %) for all stages, with even worse prognosis (<15 %) in late stage cases. Technological advances, including advances in next-generation sequencing (NGS), offer the vision of personalized medicine or precision oncology, wherein an individual's treatment can be based on his or her individual molecular profile, rather than on historical population-based medicine. Towards this end, NGS has already been used to identify new biomarker candidates for the early diagnosis of lung cancer and is increasingly used to guide personalized treatment decisions. In this review we will provide a high-level overview of NGS technology and summarize its application to the diagnosis and treatment of lung cancer. We will also describe how NGS can drive advances that bring us closer to precision oncology and discuss some of the technical challenges that will need to be overcome in order to realize this ultimate goal.

A novel gammaretroviral shuttle vector insertional mutagenesis screen identifies SHARPIN as a breast cancer metastasis gene and prognostic biomarker

Breast cancer (BC) is the second leading cause of malignancy among U.S. women. Metastasis results in a poor prognosis and increased mortality, but the molecular mechanisms by which metastatic tumors occur are not well understood. Identifying the genes that drive the metastatic process could provide targets for improved therapy and biomarkers to improve BC patient outcomes. Using a forward mutagenesis screen, BC cells mutagenized with a replication-incompetent gammaretroviral vector (γRV) were xenotransplanted into the mammary fat pad of immunodeficient mice. In this approach the vector provirus dysregulates nearby genes, providing a selective advantage to transduced cells to form metastases. Metastatic tumors were analyzed for proviral integration sites to identify nearby candidate metastasis genes. The γRV has a transgene cassette that allows for rescue in bacteria and rapid identification of vector integration sites. Using this approach, we identified the previously described metastasis gene WWTR1 (TAZ), and three other novel candidate metastasis genes including SHARPIN. SHARPIN was independently validated in vivo as a BC metastasis gene. Analysis of patient data showed that SHARPIN expression predicts metastasis-free survival after adjuvant therapy. Our approach has broad potential to identify genes involved in oncogenic processes for BC and other cancers. We show here it can identify both known (WWTR1) and novel (SHARPIN) BC metastasis genes.

Exploring preferred amino acid mutations in cancer genes: Applications to identify potential drug targets

Somatic mutations developed with missense, silent, insertions and deletions have varying effects on the resulting protein and are one of the important reasons for cancer development. In this study, we have systematically analysed the effect of these mutations at protein level in 41 different cancer types from COSMIC database on different perspectives: (i) Preference of residues at the mutant positions, (ii) probability of substitutions, (iii) influence of neighbouring residues in driver and passenger mutations, (iv) distribution of driver and passenger mutations around hotspot site in five typical genes and (v) distribution of silent and missense substitutions. We observed that R→H substitution is dominant in drivers followed by R→Q and R→C whereas E→K has the highest preference in passenger mutations. A set of 17 mutations including R→Y, W→A and V→R are specific to driver mutations and 31 preferred substitutions are observed only in passenger mutations. These frequencies of driver mutations vary across different cancer types and are selective to specific tissues. Further, driver missense mutations are mainly surrounded with silent driver mutations whereas the passenger missense mutations are surrounded with silent passenger mutations. This study reveals the variation of mutations at protein level in different cancer types and their preferences in cancer genes and provides new insights for understanding cancer mutations and drug development.

A cancer theory kerfuffle can lead to new lines of research

The standard viewpoint that cancer is a genetic disease is often stated as a fact rather than a theory. By not acknowledging that it is a theory, namely the Somatic Mutation Theory (SMT), researchers are limiting their progress. An attractive alternative to SMT is the tissue organization field theory (TOFT), which is summarized as "development gone awry." To initiate a kerfuffle, I discuss the interpretation of various results under both TOFT and SMT, including recurrent mutations, hereditary cancers, induction of tumors in transgenic experiments, remission of tumors following the inhibition of enzymes activated by mutated genes, nongenotoxic carcinogens, denervation experiments, foreign-body carcinogenesis, transplantation experiments, and tumors with zero mutations. Thinking in terms of TOFT can spur new lines of research; examples are given related to the early detection of cancer.

HotSpotter: efficient visualization of driver mutations

BACKGROUND

Driver mutations are positively selected during the evolution of cancers. The relative frequency of a particular mutation within a gene is typically used as a criterion for identifying a driver mutation. However, driver mutations may occur with relative infrequency at a particular site, but cluster within a region of the gene. When analyzing across different cancers, particular mutation sites or mutations within a particular region of the gene may be of relatively low frequency in some cancers, but still provide selective growth advantage.

RESULTS

This paper presents a method that allows rapid and easy visualization of mutation data sets and identification of potential gene mutation hotspot sites and/or regions. As an example, we identified hotspot regions in the NFE2L2 gene that are potentially functionally relevant in endometrial cancer, but would be missed using other analyses.

CONCLUSIONS

HotSpotter is a quick, easy-to-use visualization tool that delivers gene identities with associated mutation locations and frequencies overlaid upon a large cancer mutation reference set. This allows the user to identify potential driver mutations that are less frequent in a cancer or are localized in a hotspot region of relatively infrequent mutations.

Identification and analysis of driver missense mutations using rotation forest with feature selection

Identifying cancer-associated mutations (driver mutations) is critical for understanding the cellular function of cancer genome that leads to activation of oncogenes or inactivation of tumor suppressor genes. Many approaches are proposed which use supervised machine learning techniques for prediction with features obtained by some databases. However, often we do not know which features are important for driver mutations prediction. In this study, we propose a novel feature selection method (called DX) from 126 candidate features' set. In order to obtain the best performance, rotation forest algorithm was adopted to perform the experiment. On the train dataset which was collected from COSMIC and Swiss-Prot databases, we are able to obtain high prediction performance with 88.03% accuracy, 93.9% precision, and 81.35% recall when the 11 top-ranked features were used. Comparison with other various techniques in the TP53, EGFR, and Cosmic2plus datasets shows the generality of our method.

Functional annotation of putative regulatory elements at cancer susceptibility Loci

Most cancer-associated genetic variants identified from genome-wide association studies (GWAS) do not obviously change protein structure, leading to the hypothesis that the associations are attributable to regulatory polymorphisms. Translating genetic associations into mechanistic insights can be facilitated by knowledge of the causal regulatory variant (or variants) responsible for the statistical signal. Experimental validation of candidate functional variants is onerous, making bioinformatic approaches necessary to prioritize candidates for laboratory analysis. Thus, a systematic approach for recognizing functional (and, therefore, likely causal) variants in noncoding regions is an important step toward interpreting cancer risk loci. This review provides a detailed introduction to current regulatory variant annotations, followed by an overview of how to leverage these resources to prioritize candidate functional polymorphisms in regulatory regions.

Standardized decision support in next generation sequencing reports of somatic cancer variants

Of hundreds to thousands of somatic mutations that exist in each cancer genome, a large number are unique and non-recurrent variants. Prioritizing genetic variants identified via next generation sequencing technologies remains a major challenge. Many such variants occur in tumor genes that have well-established biological and clinical relevance and are putative targets of molecular therapy, however, most variants are still of unknown significance. With large amounts of data being generated as high throughput sequencing assays enter the clinical realm, there is a growing need to better communicate relevant findings in a timely manner while remaining cognizant of the potential consequences of misuse or overinterpretation of genomic information. Herein we describe a systematic framework for variant annotation and prioritization, and we propose a structured molecular pathology report using standardized terminology in order to best inform oncology clinical practice. We hope that our experience developing a comprehensive knowledge database of emerging predictive markers matched to targeted therapies will help other institutions implement similar programs.