Using large-scale genomics data to identify driver mutations in lung cancer: methods and challenges

A New View of Activating Mutations in Cancer

A vast effort has been invested in the identification of driver mutations of cancer. However, recent studies and observations call into question whether the activating mutations or the signal strength are the major determinant of tumor development. The data argue that signal strength determines cell fate, not the mutation that initiated it. In addition to activating mutations, factors that can impact signaling strength include (i) homeostatic mechanisms that can block or enhance the signal, (ii) the types and locations of additional mutations, and (iii) the expression levels of specific isoforms of genes and regulators of proteins in the pathway. Because signal levels are largely decided by chromatin structure, they vary across cell types, states, and time windows. A strong activating mutation can be restricted by low expression, whereas a weaker mutation can be strengthened by high expression. Strong signals can be associated with cell proliferation, but too strong a signal may result in oncogene-induced senescence. Beyond cancer, moderate signal strength in embryonic neural cells may be associated with neurodevelopmental disorders, and moderate signals in aging may be associated with neurodegenerative diseases, like Alzheimer's disease. The challenge for improving patient outcomes therefore lies in determining signaling thresholds and predicting signal strength.

Open Structural Data in Precision Medicine

Three-dimensional protein structural data at the molecular level are pivotal for successful precision medicine. Such data are crucial not only for discovering drugs that act to block the active site of the target mutant protein but also for clarifying to the patient and the clinician how the mutations harbored by the patient work. The relative paucity of structural data reflects their cost, challenges in their interpretation, and lack of clinical guidelines for their utilization. Rapid technological advancements in experimental high-resolution structural determination increasingly generate structures. Computationally, modeling algorithms, including molecular dynamics simulations, are becoming more powerful, as are compute-intensive hardware, particularly graphics processing units, overlapping with the inception of the exascale era. Accessible, freely available, and detailed structural and dynamical data can be merged with big data to powerfully transform personalized pharmacology. Here we review protein and emerging genome high-resolution data, along with means, applications, and examples underscoring their usefulness in precision medicine. Expected final online publication date for the Annual Review of Biomedical Data Science, Volume 5 is August 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Why Are Some Driver Mutations Rare?

Understanding why driver mutations that promote cancer are sometimes rare is important for precision medicine since it would help in their identification. Driver mutations are largely discovered through their frequencies. Thus, rare mutations often escape detection. Unlike high-frequency drivers, low-frequency drivers can be tissue specific; rare drivers have extremely low frequencies. Here, we discuss rare drivers and strategies to discover them. We suggest that allosteric driver mutations shift the protein ensemble from the inactive to the active state. Rare allosteric drivers are statistically rare since, to switch the protein functional state, they cooperate with additional mutations, and these are not considered in the patient cancer-specific protein sequence analysis. A complete landscape of mutations that drive cancer will reveal tumor-specific therapeutic vulnerabilities.

Molecular heterogeneity in lung cancer: from mechanisms of origin to clinical implications

Molecular heterogeneity is a frequent event in cancer responsible of several critical issues in diagnosis and treatment of oncologic patients. Lung tumours are characterized by high degree of molecular heterogeneity associated to different mechanisms of origin including genetic, epigenetic and non-genetic source. In this review, we provide an overview of recognized mechanisms underlying molecular heterogeneity in lung cancer, including epigenetic mechanisms, mutant allele specific imbalance, genomic instability, chromosomal aberrations, tumor mutational burden, somatic mutations. We focus on the role of spatial and temporal molecular heterogeneity involved in therapeutic implications in lung cancer patients.

Recognition of Lung Adenocarcinoma-specific Gene Pairs Based on Genetic Algorithm and Establishment of a Deep Learning Prediction Model

AIM AND OBJECTIVE

Lung cancer is a disease with a dismal prognosis and is the major cause of cancer deaths in many countries. Nonetheless, rapid technological developments in genome science guarantees more effective prevention and treatment strategies.

MATERIALS AND METHODS

In this study, genes were pair-matched and screened for lung adenocarcinomaspecific gene relationships. False positives due to fluctuations in single gene expression were avoided and the stability and accuracy of the results was improved.

RESULTS

Finally, a deep learning model was constructed with machine learning algorithm to realize the clinical diagnosis of lung adenocarcinoma in patients.

CONCLUSION

Comparing with the traditional methods which takes ingle gene as a feature, the relative difference between gene pairs is a higher order feature, leverage high-order features to build the model can avoid instability caused by a single gene mutation, making the prediction results more reliable.

Review: Precision medicine and driver mutations: Computational methods, functional assays and conformational principles for interpreting cancer drivers

At the root of the so-called precision medicine or precision oncology, which is our focus here, is the hypothesis that cancer treatment would be considerably better if therapies were guided by a tumor’s genomic alterations. This hypothesis has sparked major initiatives focusing on whole-genome and/or exome sequencing, creation of large databases, and developing tools for their statistical analyses—all aspiring to identify actionable alterations, and thus molecular targets, in a patient. At the center of the massive amount of collected sequence data is their interpretations that largely rest on statistical analysis and phenotypic observations. Statistics is vital, because it guides identification of cancer-driving alterations. However, statistics of mutations do not identify a change in protein conformation; therefore, it may not define sufficiently accurate actionable mutations, neglecting those that are rare. Among the many thematic overviews of precision oncology, this review innovates by further comprehensively including precision pharmacology, and within this framework, articulating its protein structural landscape and consequences to cellular signaling pathways. It provides the underlying physicochemical basis, thereby also opening the door to a broader community.

Constructing a Risk Prediction Model for Lung Cancer Recurrence by Using Gene Function Clustering and Machine Learning

OBJECTIVE

A significant proportion of patients with early non-small cell lung cancer (NSCLC) can be cured by surgery. The distant metastasis of tumors is the most common cause of treatment failure. Precisely predicting the likelihood that a patient develops distant metastatic risk will help identify patients who can further intervene, such as conventional adjuvant chemotherapy or experimental drugs.

METHODS

Current molecular biology techniques enable the whole genome screening of differentially expressed genes, and rapid development of a large number of bioinformatics methods to improve prognosis.

RESULTS

The genes associated with metastasis do not necessarily play a role in the pathogenesis of the disease, but rather reflect the activation of specific signal transduction pathways associated with enhanced migration and invasiveness.

CONCLUSION

In this study, we discovered several genes related to lung cancer resistance and established a risk model to predict high-risk patients.

The Roles of Common Variation and Somatic Mutation in Cancer Pharmacogenomics

Cancer pharmacogenomics is the science concerned with understanding genetic alterations and its effects on the pharmacokinetics and pharmacodynamics of anti-cancer drugs, with the aim to provide cancer patients with the precise medication that will achieve a good response and cause low/no incidence of adverse events. Advances in biotechnology and bioinformatics have enabled genomic research to evolve from the evaluation of alterations at the single-gene level to studies on the whole-genome scale using large-scale genotyping and next generation sequencing techniques. International collaborative efforts have resulted in the construction of databases to curate the identified genetic alterations that are clinically significant, and these are currently utilized in clinical sequencing and liquid biopsy screening/monitoring. Furthermore, countless clinical studies have accumulated sufficient evidence to match cancer patients to therapies by utilizing the information of clinical-relevant alterations. In this review we summarize the importance of germline alterations that act as predictive biomarkers for drug-induced toxicity and drug response as well as somatic mutations in cancer cells that function as drug targets. The integration of genomics into the medical field has transformed the era of cancer therapy from one-size-fits-all to cancer precision medicine.

Targeted next-generation sequencing reveals recurrence-associated genomic alterations in early-stage non-small cell lung cancer

Purpose

The identification of genomic alterations related to recurrence in early-stage non-small cell lung cancer (NSCLC) patients may help better stratify high-risk individuals and guide treatment strategies. This study aimed to identify the molecular biomarkers of recurrence in early-stage NSCLC.

Results

Of the 42 tumors evaluable for genomic alterations, TP53 and EGFR were the most frequent alterations with population frequency 52.4% and 50.0%, respectively. Fusion genes were detected in four patients, which had lower mutational burden and relatively better genomic stability. EGFR mutation and fusion gene were mutually exclusive in this study. CDKN2A, FAS, SUFU and SMARCA4 genomic alterations were only observed in the relapsed patients. Increased copy number alteration index was observed in early relapsed patients. Among these genomic alterations, early-stage NSCLCs harboring CDKN2A, FAS, SUFU and SMARCA4 genomic alterations were found to be significantly associated with recurrence. Some of these new findings were validated using The Cancer Genome Atlas (TCGA) dataset.

Conclusions

The genomic alterations of CDKN2A, FAS, SUFU and SMARCA4 in early-stage NSCLC are found to be associated with recurrence, but confirmation in a larger independent cohort is required to define the clinical impact.

Materials and Methods

Paired primary tumor and normal lung tissue samples were collected for targeted next-generation sequencing analysis. A panel targets exons for 440 genes was used to assess the mutational and copy number status of selected genes in three clinically relevant groups of stage I/II NSCLC patients: 1) Early relapse; 2) Late relapse; and 3) No relapse.

Truncation- and motif-based pan-cancer analysis reveals tumor-suppressing kinases

A major challenge in cancer genomics is identifying "driver" mutations from the many neutral "passenger" mutations within a given tumor. To identify driver mutations that would otherwise be lost within mutational noise, we filtered genomic data by motifs that are critical for kinase activity. In the first step of our screen, we used data from the Cancer Cell Line Encyclopedia and The Cancer Genome Atlas to identify kinases with truncation mutations occurring within or before the kinase domain. The top 30 tumor-suppressing kinases were aligned, and hotspots for loss-of-function (LOF) mutations were identified on the basis of amino acid conservation and mutational frequency. The functional consequences of new LOF mutations were biochemically validated, and the top 15 hotspot LOF residues were used in a pan-cancer analysis to define the tumor-suppressing kinome. A ranked list revealed MAP2K7, an essential mediator of the c-Jun N-terminal kinase (JNK) pathway, as a candidate tumor suppressor in gastric cancer, despite its mutational frequency falling within the mutational noise for this cancer type. The majority of mutations in MAP2K7 abolished its catalytic activity, and reactivation of the JNK pathway in gastric cancer cells harboring LOF mutations in MAP2K7 or the downstream kinase JNK suppressed clonogenicity and growth in soft agar, demonstrating the functional relevance of inactivating the JNK pathway in gastric cancer. Together, our data highlight a broadly applicable strategy to identify functional cancer driver mutations and define the JNK pathway as tumor-suppressive in gastric cancer.

Is heterogeneity in stage 3 non-small cell lung cancer obscuring the potential benefits of dose-escalated concurrent chemo-radiotherapy in clinical trials?

The current standard of care for the management of inoperable stage 3 non-small cell lung cancer (NSCLC) is concurrent chemoradiotherapy (cCRT) using radiotherapy dose-fractionation and chemotherapy regimens that were established 3 decades ago. In an attempt to improve the chances of long-term control from cCRT, dose-escalation of the radiotherapy dose was assessed in the RTOG 0617 randomised control study comparing the standard 60 Gy in 30 fractions with a high-dose arm receiving 74 Gy in 37 fractions. Following the publication of this trial the thoracic oncology community were surprised to learn that there was worse survival in the dose-escalated arm and that for now the standard of care must remain with the lower dose. In this article we review the RTOG 0617 paper with subsequent analyses and studies to explore why the use of dose-escalated cCRT in stage 3 NSCLC has not shown the benefits that were expected. The overarching theme of this opinion piece is how heterogeneity between stage 3 NSCLC cases in terms of patient, tumour, and clinical factors may obscure the potential benefits of dose-escalation by causing imbalances in the arms of studies such as RTOG 0617. We also examine recent advances in the staging, management, and technological delivery of radiotherapy in NSCLC and how these may be employed to optimise cCRT trials in the future and ensure that any potential benefits of dose-escalation can be detected.