IMPACT: a whole-exome sequencing analysis pipeline for integrating molecular profiles with actionable therapeutics in clinical samples

Mutations in ALK signaling pathways conferring resistance to ALK inhibitor treatment lead to collateral vulnerabilities in neuroblastoma cells

BACKGROUND

Development of resistance to targeted therapies has tempered initial optimism that precision oncology would improve poor outcomes for cancer patients. Resistance mechanisms, however, can also confer new resistance-specific vulnerabilities, termed collateral sensitivities. Here we investigated anaplastic lymphoma kinase (ALK) inhibitor resistance in neuroblastoma, a childhood cancer frequently affected by activating ALK alterations.

METHODS

Genome-wide forward genetic CRISPR-Cas9 based screens were performed to identify genes associated with ALK inhibitor resistance in neuroblastoma cell lines. Furthermore, the neuroblastoma cell line NBLW-R was rendered resistant by continuous exposure to ALK inhibitors. Genes identified to be associated with ALK inhibitor resistance were further investigated by generating suitable cell line models. In addition, tumor and liquid biopsy samples of four patients with ALK-mutated neuroblastomas before ALK inhibitor treatment and during tumor progression under treatment were genomically profiled.

RESULTS

Both genome-wide CRISPR-Cas9-based screens and preclinical spontaneous ALKi resistance models identified NF1 loss and activating NRASQ61K mutations to confer resistance to chemically diverse ALKi. Moreover, human neuroblastomas recurrently developed de novo loss of NF1 and activating RAS mutations after ALKi treatment, leading to therapy resistance. Pathway-specific perturbations confirmed that NF1 loss and activating RAS mutations lead to RAS-MAPK signaling even in the presence of ALKi. Intriguingly, NF1 loss rendered neuroblastoma cells hypersensitive to MEK inhibition.

CONCLUSIONS

Our results provide a clinically relevant mechanistic model of ALKi resistance in neuroblastoma and highlight new clinically actionable collateral sensitivities in resistant cells.

Bioinformatics Research on Drug Sensitivity Prediction

Modeling-based anti-cancer drug sensitivity prediction has been extensively studied in recent years. While most drug sensitivity prediction models only use gene expression data, the remarkable impacts of gene mutation, methylation, and copy number variation on drug sensitivity are neglected. Drug sensitivity prediction can both help protect patients from some adverse drug reactions and improve the efficacy of treatment. Genomics data are extremely useful for drug sensitivity prediction task. This article reviews the role of drug sensitivity prediction, describes a variety of methods for predicting drug sensitivity. Moreover, the research significance of drug sensitivity prediction, as well as existing problems are well discussed.

Targeting CDK4/6 Represents a Therapeutic Vulnerability in Acquired BRAF/MEK Inhibitor-Resistant Melanoma

There is a clear need to identify targetable drivers of resistance and potential biomarkers for salvage therapy for patients with melanoma refractory to the combination of BRAF and MEK inhibition. In this study, we performed whole-exome sequencing on BRAF-V600E-mutant melanoma patient tumors refractory to the combination of BRAF/MEK inhibition and identified acquired oncogenic mutations in NRAS and loss of the tumor suppressor gene CDKN2A We hypothesized the acquired resistance mechanisms to BRAF/MEK inhibition were reactivation of the MAPK pathway and activation of the cell-cycle pathway, which can both be targeted pharmacologically with the combination of a MEK inhibitor (trametinib) and a CDK4/6 inhibitor (palbociclib). In vivo, we found that combination of CDK4/6 and MEK inhibition significantly decreased tumor growth in two BRAF/MEK inhibitor-resistant patient-derived xenograft models. In vitro, we observed that the combination of CDK4/6 and MEK inhibition resulted in synergy and significantly reduced cellular growth, promoted cell-cycle arrest, and effectively inhibited downstream signaling of MAPK and cell-cycle pathways in BRAF inhibitor-resistant cell lines. Knockdown of CDKN2A in BRAF inhibitor-resistant cells increased sensitivity to CDK4/6 inhibition alone and in combination with MEK inhibition. A key implication of our study is that the combination of CDK4/6 and MEK inhibitors overcomes acquired resistance to BRAF/MEK inhibitors, and loss of CDKN2A may represent a biomarker of response to the combination. Inhibition of the cell-cycle and MAPK pathway represents a promising strategy for patients with metastatic melanoma who are refractory to BRAF/MEK inhibitor therapy.

dbMCS: A Database for Exploring the Mutation Markers of Anti-Cancer Drug Sensitivity

The identification of mutation markers and the selection of appropriate treatment for patients with specific genome mutations are important steps in the development of targeted therapies and the realization of precision medicine for human cancers. To investigate the baseline characteristics of drug sensitivity markers and develop computational methods of mutation effect prediction, we presented a manually curated online- based database of mutation Markers for anti-Cancer drug Sensitivity (dbMCS). Currently, dbMCS contains 1271 mutations and 4427 mutation-disease-drug associations (3151 and 1276 for sensitivity and resistance, respectively) with their PubMed indexed articles. By comparing the mutations in dbMCS with the putative neutral polymorphisms, we investigated the characteristics of drug sensitivity markers. We found that the mutation markers tend to significantly impact on high-conservative regions both in DNA sequences and protein domains. And some of them presented pleiotropic effects depending on the tumor context, appearing concurrently in the sensitivity and resistance categories. In addition, we preliminarily explored the machine learning-based methods for identifying mutation markers of anti-cancer drug sensitivity and produced optimistic results, which suggests that a reliable dataset may provide new insights and essential clues for future cancer pharmacogenomics studies. dbMCS is available at http://bioinfo.aielab.cc/dbMCS/.

A proteogenomic approach to target neoantigens in solid tumors

INTRODUCTION

Proteogenomic techniques find applications in identifying novel cancer-specific peptides called neoantigens; they are non-self peptides derived from tumor-specific non-synonymous mutations. These peptides with MHCs are recognized by the T cells and induce an antitumor response. Due to their selective expression of tumor cells, neoantigens are considered attractive targets for cancer immunotherapy.

AREAS COVERED

In this review, we have discussed the proteogenomic strategies to identify neoantigens. We have also provided a neoantigen identification pipeline using data from whole-exome sequencing, RNA sequencing, and MHC peptidomics. Further, we have reviewed recent tools for neoantigen discovery.

EXPERT COMMENTARY

The limitations in instrument sensitivity and availability of bioinformatics tools have restricted the identification of neoantigens from tumor samples. Nonetheless, the recent improvement in genome sequencing, mass spectrometry technologies, and the development of reliable algorithms for epitope prediction provide hope for efficient identification of neoantigens. Translating this workflow on patient samples would represent a massive advancement in neoantigen identification methods, leading to the constitution of novel personalized neoantigen cancer vaccines.

Methods and resources to access mutation-dependent effects on cancer drug treatment

In clinical cancer treatment, genomic alterations would often affect the response of patients to anticancer drugs. Studies have shown that molecular features of tumors could be biomarkers predictive of sensitivity or resistance to anticancer agents, but the identification of actionable mutations are often constrained by the incomplete understanding of cancer genomes. Recent progresses of next-generation sequencing technology greatly facilitate the extensive molecular characterization of tumors and promote precision medicine in cancers. More and more clinical studies, cancer cell lines studies, CRISPR screening studies as well as patient-derived model studies were performed to identify potential actionable mutations predictive of drug response, which provide rich resources of molecularly and pharmacologically profiled cancer samples at different levels. Such abundance of data also enables the development of various computational models and algorithms to solve the problem of drug sensitivity prediction, biomarker identification and in silico drug prioritization by the integration of multiomics data. Here, we review the recent development of methods and resources that identifies mutation-dependent effects for cancer treatment in clinical studies, functional genomics studies and computational studies and discuss the remaining gaps and future directions in this area.

Studying Immunotherapy Resistance in a Melanoma Autologous Humanized Mouse Xenograft

Resistance to immunotherapy is a significant challenge, and the scarcity of human models hinders the identification of the underlying mechanisms. To address this limitation, we constructed an autologous humanized mouse (aHM) model with hematopoietic stem and progenitor cells (HSPC) and tumors from 2 melanoma patients progressing to immunotherapy. Unlike mismatched humanized mouse (mHM) models, generated from cord blood-derived HSPCs and tumors from different donors, the aHM recapitulates a patient-specific tumor microenvironment (TME). When patient tumors were implanted on aHM, mHM, and NOD/SCID/IL2rg^-/- (NSG) cohorts, tumors appeared earlier and grew faster on NSG and mHM cohorts. We observed that immune cells differentiating in the aHM were relatively more capable of circulating peripherally, invading into tumors and interacting with the TME. A heterologous, human leukocyte antigen (HLA-A) matched cohort also yielded slower growing tumors than non-HLA-matched mHM, indicating that a less permissive immune environment inhibits tumor progression. When the aHM, mHM, and NSG cohorts were treated with immunotherapies mirroring what the originating patients received, tumor growth in the aHM accelerated, similar to the progression observed in the patients. This rapid growth was associated with decreased immune cell infiltration, reduced interferon gamma (IFNγ)-related gene expression, and a reduction in STAT3 phosphorylation, events that were replicated in vitro using tumor-derived cell lines. IMPLICATIONS: Engrafted adult HSPCs give rise to more tumor infiltrative immune cells, increased HLA matching leads to slower tumor initiation and growth, and continuing immunotherapy past progression can paradoxically lead to increased growth.

Mechanisms of Acquired BRAF Inhibitor Resistance in Melanoma: A Systematic Review

This systematic review investigated the literature on acquired v-raf murine sarcoma viral oncogene homolog B1 (BRAF) inhibitor resistance in patients with melanoma. We searched MEDLINE for articles on BRAF inhibitor resistance in patients with melanoma published since January 2010 in the following areas: (1) genetic basis of resistance; (2) epigenetic and transcriptomic mechanisms; (3) influence of the immune system on resistance development; and (4) combination therapy to overcome resistance. Common resistance mutations in melanoma are BRAF splice variants, BRAF amplification, neuroblastoma RAS viral oncogene homolog (NRAS) mutations and mitogen-activated protein kinase kinase 1/2 (MEK1/2) mutations. Genetic and epigenetic changes reactivate previously blocked mitogen-activated protein kinase (MAPK) pathways, activate alternative signaling pathways, and cause epithelial-to-mesenchymal transition. Once BRAF inhibitor resistance develops, the tumor microenvironment reverts to a low immunogenic state secondary to the induction of programmed cell death ligand-1. Combining a BRAF inhibitor with a MEK inhibitor delays resistance development and increases duration of response. Multiple other combinations based on known mechanisms of resistance are being investigated. BRAF inhibitor-resistant cells develop a range of 'escape routes', so multiple different treatment targets will probably be required to overcome resistance. In the future, it may be possible to personalize combination therapy towards the specific resistance pathway in individual patients.

DL, Esteban-Cardeñosa EM, Avila Cobos F, Lastra E, Abella LE, de la Cruz V, Lobatón CD, Claes KB, Durán M, Infante M. Germline Genetic Findings Which May Impact Therapeutic Decisions in Families with a Presumed Predisposition for Hereditary Breast and Ovarian Cancer

In this study, we aim to gain insight in the germline mutation spectrum of ATM, BARD1, BRIP1, ERCC4, PALB2, RAD51C and RAD51D in breast and ovarian cancer families from Spain. We have selected 180 index cases in whom a germline mutation in BRCA1 and BRCA2 was previously ruled out. The importance of disease-causing variants in these genes lies in the fact that they may have possible therapeutic implications according to clinical guidelines. All variants were assessed by combined annotation dependent depletion (CADD) for scoring their deleteriousness. In addition, we used the cancer genome interpreter to explore the implications of some variants in drug response. Finally, we compiled and evaluated the family history to assess whether carrying a pathogenic mutation was associated with age at diagnosis, tumour diversity of the pedigree and total number of cancer cases in the family. Eight unequivocal pathogenic mutations were found and another fourteen were prioritized as possible causal variants. Some of these molecular results could contribute to cancer diagnosis, treatment selection and prevention. We found a statistically significant association between tumour diversity in the family and carrying a variant with a high score predicting pathogenicity (p = 0.0003).

Simultaneous Detection of Gene Fusions and Base Mutations in Cancer Tissue Biopsies by Sequencing Dual Nucleic Acid Templates in Unified Reaction

BACKGROUND

Targeted next-generation sequencing is a powerful method to comprehensively identify biomarkers for cancer. Starting material is currently either DNA or RNA for different variations, but splitting to 2 assays is burdensome and sometimes unpractical, causing delay or complete lack of detection of critical events, in particular, potent and targetable fusion events. An assay that analyzes both templates in a streamlined process is eagerly needed.

METHODS

We developed a single-tube, dual-template assay and an integrated bioinformatics pipeline for relevant variant calling. RNA was used for fusion detection, whereas DNA was used for single-nucleotide variations (SNVs) and insertion and deletions (indels). The reaction chemistry featured barcoded adaptor ligation, multiplexed linear amplification, and multiplexed PCR for noise reduction and novel fusion detection. An auxiliary quality control assay was also developed.

RESULTS

In a 1000-sample lung tumor cohort, we identified all major SNV/indel hotspots and fusions, as well as MET exon 14 skipping and several novel or rare fusions. The occurrence frequencies were in line with previous reports and were verified by Sanger sequencing. One noteworthy fusion event was HLA-DRB1-MET that constituted the second intergenic MET fusion ever detected in lung cancer.

CONCLUSIONS

This method should benefit not only a majority of patients carrying core actionable targets but also those with rare variations. Future extension of this assay to RNA expression and DNA copy number profiling of target genes such as programmed death-ligand 1 may provide additional biomarkers for immune checkpoint therapies.

Pre-Treatment Mutational and Transcriptomic Landscape of Responding Metastatic Melanoma Patients to Anti-PD1 Immunotherapy

Immunotherapy, such as anti-PD1, has improved the survival of patients with metastatic melanoma. However, predicting which patients will respond to immunotherapy remains a significant knowledge gap. In this study we analyzed pre-immunotherapy treated tumors from 52 patients with metastatic melanoma and monitored their response based on RECIST 1.1 criteria. The responders group contained 21 patients that had a complete or partial response, while the 31 non-responders had stable or progressive disease. Whole exome sequencing (WES) was used to identify biomarkers of anti-PD1 response from somatic mutations between the two groups. Variants in codons G34 and G41 in NFKBIE, a negative regulator of NFkB, were found exclusively in the responders. Mutations in NKBIE-related genes were also enriched in the responder group compared to the non-responders. Patients that harbored NFKBIE-related gene mutations also had a higher mutational burden, decreased tumor volume with treatment, and increased progression-free survival. RNA sequencing on a subset of tumor samples identified that CD83 was highly expressed in our responder group. Additionally, Gene Set Enrichment Analysis showed that the TNFalpha signaling via NFkB pathway was one of the top pathways with differential expression in responders vs. non-responders. In vitro NFkB activity assays indicated that the G34E variant caused loss-of-function of NFKBIE, and resulted in activation of NFkB signaling. Flow cytometry assays indicated that G34E variant was associated with upregulation of CD83 in human melanoma cell lines. These results suggest that NFkB activation and signaling in tumor cells contributes to a favorable anti-PD1 treatment response, and clinical screening to include aberrations in NFkB-related genes should be considered.

A comprehensive custom panel evaluation for routine hereditary cancer testing: improving the yield of germline mutation detection

Background

In the context of our Regional Program of Hereditary Cancer, individuals fulfilling the criteria are tested for germline mutations to subsequently establish the clinical management. Our standard diagnostic approach focuses on sequencing a few classic high-risk genes, a method that frequently renders uninformative genetic results. This study aims to examine the improved yield offered by an On-Demand panel.

Methods

We designed an On-Demand panel for the analysis of 35-genes associated with inherited cancer susceptibility in a total of 128 cases of Hereditary Breast and Ovarian Cancer (HBOC) and Hereditary Nonpolyposis Colorectal Cancer (HNPCC).

Results

Eighteen deleterious mutations were detected, in both routinely (BRCA2, MLH1, MSH2, PMS2) and non-routinely (ATM, BLM, BRIP1, CHEK2, MUTYH) tested genes. The screening extended to 35 genes rendered by patients carrying several- up to 6-Variants of Unknown Significance (VUS). Moreover, we confirmed the splicing disruption at RNA level for a not previously reported BRIP1 splicing mutation. Using an On-Demand panel, we identified 18 pathogenic mutation carriers, seven of which would have gone unnoticed with traditional analysis.

Conclusions

Our results reinforce the utility of NGS gene panels in the diagnostic routine to increase the performance of genetic testing, especially in individuals from families with overlapping cancer phenotypes.

Patient-specific multi-omics models and the application in personalized combination therapy

The rapid advancement of high-throughput technologies and sharp decrease in cost have opened up the possibility to generate large amount of multi-omics data on an individual basis. The development of high-throughput -omics, including genomics, epigenomics, transcriptomics, proteomics, metabolomics and microbiomics, enables the application of multi-omics technologies in the clinical settings. Combination therapy, defined as disease treatment with two or more drugs to achieve efficacy with lower doses or lower drug toxicity, is the basis for the care of diseases like cancer. Patient-specific multi-omics data integration can help the identification and development of combination therapies. In this review, we provide an overview of different -omics platforms, and discuss the methods for multi-omics, high-throughput, data integration, personalized combination therapy.

Monocytic Subclones Confer Resistance to Venetoclax-Based Therapy in Patients with Acute Myeloid Leukemia

Venetoclax-based therapy can induce responses in approximately 70% of older previously untreated patients with acute myeloid leukemia (AML). However, up-front resistance as well as relapse following initial response demonstrates the need for a deeper understanding of resistance mechanisms. In the present study, we report that responses to venetoclax +azacitidine in patients with AML correlate closely with developmental stage, where phenotypically primitive AML is sensitive, but monocytic AML is more resistant. Mechanistically, resistant monocytic AML has a distinct transcriptomic profile, loses expression of venetoclax target BCL2, and relies on MCL1 to mediate oxidative phosphorylation and survival. This differential sensitivity drives a selective process in patients which favors the outgrowth of monocytic subpopulations at relapse. Based on these findings, we conclude that resistance to venetoclax + azacitidine can arise due to biological properties intrinsic to monocytic differentiation. We propose that optimal AML therapies should be designed so as to independently target AML subclones that may arise at differing stages of pathogenesis. SIGNIFICANCE: Identifying characteristics of patients who respond poorly to venetoclax-based therapy and devising alternative therapeutic strategies for such patients are important topics in AML. We show that venetoclax resistance can arise due to intrinsic molecular/metabolic properties of monocytic AML cells and that such properties can potentially be targeted with alternative strategies.

Metabolomics technology and bioinformatics for precision medicine

Precision medicine is rapidly emerging as a strategy to tailor medical treatment to a small group or even individual patients based on their genetics, environment and lifestyle. Precision medicine relies heavily on developments in systems biology and omics disciplines, including metabolomics. Combination of metabolomics with sophisticated bioinformatics analysis and mathematical modeling has an extreme power to provide a metabolic snapshot of the patient over the course of disease and treatment or classifying patients into subpopulations and subgroups requiring individual medical treatment. Although a powerful approach, metabolomics have certain limitations in technology and bioinformatics. We will review various aspects of metabolomics technology and bioinformatics, from data generation, bioinformatics analysis, data fusion and mathematical modeling to data management, in the context of precision medicine.

mTCTScan: a comprehensive platform for annotation and prioritization of mutations affecting drug sensitivity in cancers

Cancer therapies have experienced rapid progress in recent years, with a number of novel small-molecule kinase inhibitors and monoclonal antibodies now being widely used to treat various types of human cancers. During cancer treatments, mutations can have important effects on drug sensitivity. However, the relationship between tumor genomic profiles and the effectiveness of cancer drugs remains elusive. We introduce Mutation To Cancer Therapy Scan (mTCTScan) web server (http://jjwanglab.org/mTCTScan) that can systematically analyze mutations affecting cancer drug sensitivity based on individual genomic profiles. The platform was developed by leveraging the latest knowledge on mutation-cancer drug sensitivity associations and the results from large-scale chemical screening using human cancer cell lines. Using an evidence-based scoring scheme based on current integrative evidences, mTCTScan is able to prioritize mutations according to their associations with cancer drugs and preclinical compounds. It can also show related drugs/compounds with sensitivity classification by considering the context of the entire genomic profile. In addition, mTCTScan incorporates comprehensive filtering functions and cancer-related annotations to better interpret mutation effects and their association with cancer drugs. This platform will greatly benefit both researchers and clinicians for interrogating mechanisms of mutation-dependent drug response, which will have a significant impact on cancer precision medicine.

WDR72 Mutations Associated with Amelogenesis Imperfecta and Acidosis

Dental enamel malformations, or amelogenesis imperfecta (AI), can be isolated or syndromic. To improve the prospects of making a successful diagnosis by genetic testing, it is important that the full range of genes and mutations that cause AI be determined. Defects in WDR72 (WD repeat-containing protein 72; OMIM *613214) cause AI, type IIA3 (OMIM #613211), which follows an autosomal recessive pattern of inheritance. The defective enamel is normal in thickness, severely hypomineralized, orange-brown stained, and susceptible to attrition. We identified 6 families with biallelic WDR72 mutations by whole exome sequence analyses that perfectly segregated with the enamel phenotype. The novel mutations included 3 stop-gains [NM_182758.2: c.377G>A/p.(Trp126*), c.1801C>T/p.(Arg601*), c.2350A>T/p.(Arg784*)], a missense mutation [c.1265G>T/p.(Gly422Val)], and a 62,138-base pair deletion (NG_017034.2: g.35441_97578del62138) that removed WDR72 coding exons 3 through 13. A previously reported WDR72 frameshift was also observed [c.1467_1468delAT/p.(Val491Aspfs*8)]. Three of the affected patients showed decreased serum pH, consistent with a diagnosis of renal tubular acidosis. Percentiles of stature and body weight varied among 8 affected individuals but did not show a consistent trend. These studies support that WDR72 mutations cause a syndromic form of AI and improve our ability to diagnose AI caused by WDR72 defects.

Data mining for mutation-specific targets in acute myeloid leukemia

Three mutation-specific targeted therapies have recently been approved by the FDA for the treatment of acute myeloid leukemia (AML): midostaurin for FLT3 mutations, enasidenib for relapsed or refractory cases with IDH2 mutations, and ivosidenib for cases with an IDH1 mutation. Together, these agents offer a mutation-directed treatment approach for up to 45% of de novo adult AML cases, a welcome deluge after a prolonged drought. At the same time, a number of computational tools have recently been developed that promise to further accelerate progress in mutation-specific therapy for AML and other cancers. Technical advances together with comprehensively annotated AML tissue banks have resulted in the availability of large and complex data sets for exploration by the end-user, including (i) microarray gene expression, (ii) exome sequencing, (iii) deep sequencing data of sub-clone heterogeneity, (iv) RNA sequencing of gene expression (bulk and single cell), (v) DNA methylation and chromatin, (vi) and germline quantitative trait loci. Yet few clinicians or experimental hematologists have the time or the training to access or analyze these repositories. This review summarizes the data sets and bioinformatic tools currently available to further the discovery of mutation-specific targets with an emphasis on web-based applications that are open, accessible, user-friendly, and do not require coding experience to navigate. We show examples of how available data can be mined to identify potential targets using synthetic lethality, drug repurposing, epigenetic sub-grouping, and proteomic networks while also highlighting strengths and limitations and the need for superior models for validation.

Clinical exome sequencing in France and Quebec: what are the challenges? What does the future hold?

BACKGROUND

The decreasing cost of next-generation sequencing technologies (NGS) has resulted in their increased use in research, and in the clinic. However, France and Quebec have not yet implemented nation-wide personalized medicine programs using NGS. To produce policies on the large-scale implementation of NGS, decision makers could benefit from a detailed understanding of how these technologies are currently used, their limitations, and the benefits they could bring to patients.

OBJECTIVES

We aimed at answering two research questions: How are patients' NGS data currently managed in healthcare institutions in Quebec and in France? What issues do technology users identify which should be solved in order to implement clinical genomics at the national level?

METHOD

Through a multiple case study method, we analysed interviews and documentation from four teams that use whole-exome sequencing in hybrid clinical research projects focusing on cancer and rare diseases.

RESULTS

Interviewees detailed numerous challenges linked with managing the complexity of the process of collecting and interpreting data in a relevant manner for patients, and described how obtaining buy-in from multiple stakeholders was necessary.

CONCLUSION

A strong political will is essential for personalized medicine to be implemented efficiently in France and Quebec.

Innate Genetic Evolution of Lung Cancers and Spatial Heterogeneity: Analysis of Treatment-Naïve Lesions

INTRODUCTION

Data regarding the pre-treatment intertumor heterogeneity of potential biomarkers in advanced-stage lung cancers is limited. A finding of such heterogeneity between primary and metastatic lesions would prove valuable to determine if a metastatic lesion can be a surrogate for the primary tumor, as more biomarkers will likely be used in the future to inform treatment decisions.

METHODS

We performed RNA sequencing to analyze intertumor heterogeneity in 30 specimens (primary tumors, intrathoracic, and extrathoracic metastatic lesions) obtained from five treatment-naïve lung cancer patients.

RESULTS

The global unsupervised clustering analysis showed that the lesions clustered at the individual patient level rather than on the metastatic sites, suggesting that the characteristics of specific tumor cells have a greater impact on the gene expression signature than the microenvironment in which the metastasis develops. The mutational and transcriptional data highlight the presence of intertumor heterogeneity showing that the primary tumors are usually distinct from metastatic lesions. Through a comparison between metastatic lesions and the primary tumors, we observed that pathways related to cell proliferation were upregulated, whereas immune-related pathways were downregulated in metastatic lesions.

CONCLUSION

These data not only provide insight into the evolution of lung cancers, but also imply possibilities and limitations of biomarker-based treatment in lung cancers.

IMPACT web portal: oncology database integrating molecular profiles with actionable therapeutics

Background

With the advancement of next generation sequencing technology, researchers are now able to identify important variants and structural changes in DNA and RNA in cancer patient samples. With this information, we can now correlate specific variants and/or structural changes with actionable therapeutics known to inhibit these variants. We introduce the creation of the IMPACT Web Portal, a new online resource that connects molecular profiles of tumors to approved drugs, investigational therapeutics and pharmacogenetics associated drugs.

Results

IMPACT Web Portal contains a total of 776 drugs connected to 1326 target genes and 435 target variants, fusion, and copy number alterations. The online IMPACT Web Portal allows users to search for various genetic alterations and connects them to three levels of actionable therapeutics. The results are categorized into 3 levels: Level 1 contains approved drugs separated into two groups; Level 1A contains approved drugs with variant specific information while Level 1B contains approved drugs with gene level information. Level 2 contains drugs currently in oncology clinical trials. Level 3 provides pharmacogenetic associations between approved drugs and genes.

Conclusion

IMPACT Web Portal allows for sequencing data to be linked to actionable therapeutics for translational and drug repurposing research. The IMPACT Web Portal online resource allows users to query genes and variants to approved and investigational drugs. We envision that this resource will be a valuable database for personalized medicine and drug repurposing. IMPACT Web Portal is freely available for non-commercial use at http://tanlab.ucdenver.edu/IMPACT.

Whole-exome sequencing identifies recurrent SF3B1 R625 mutation and comutation of NF1 and KIT in mucosal melanoma

Mucosal melanomas are a rare subtype of melanoma, arising in mucosal tissues, which have a very poor prognosis due to the lack of effective targeted therapies. This study aimed to better understand the molecular landscape of these cancers and find potential new therapeutic targets. Whole-exome sequencing was performed on mucosal melanomas from 19 patients and 135 sun-exposed cutaneous melanomas, with matched peripheral blood samples when available. Mutational profiles were compared between mucosal subgroups and sun-exposed cutaneous melanomas. Comparisons of molecular profiles identified 161 genes enriched in mucosal melanoma (P<0.05). KIT and NF1 were frequently comutated (32%) in the mucosal subgroup, with a significantly higher incidence than that in cutaneous melanoma (4%). Recurrent SF3B1 R625H/S/C mutations were identified and validated in 7 of 19 (37%) mucosal melanoma patients. Mutations in the spliceosome pathway were found to be enriched in mucosal melanomas when compared with cutaneous melanomas. Alternative splicing in four genes were observed in SF3B1-mutant samples compared with the wild-type samples. This study identified potential new therapeutic targets for mucosal melanoma, including comutation of NF1 and KIT, and recurrent R625 mutations in SF3B1. This is the first report of SF3B1 R625 mutations in vulvovaginal mucosal melanoma, with the largest whole-exome sequencing project of mucosal melanomas to date. The results here also indicated that the mutations in SF3B1 lead to alternative splicing in multiple genes. These findings expand our knowledge of this rare disease.

The use of whole exome sequencing and murine patient derived xenografts as a method of chemosensitivity testing in sarcoma

Background

Soft tissue and bone sarcoma represent a broad spectrum of different pathology and genetic variance. Current chemotherapy regimens are derived from randomised trials and represent empirical treatment. Chemosensitivity testing and whole exome sequencing (WES) may offer personalized chemotherapy treatment based on genetic mutations.

Methods

A pilot, prospective, non-randomised control experimental study was conducted. Twelve patients with metastatic bone or soft tissue sarcoma that had failed first line chemotherapy treatment were enrolled for this study. Human tissue taken at surgical biopsy under general anaesthetic was divided between two arms of the trial. Subsections of the tumour were used for WES and the remainder was implanted subcutaneously in immunodeficient mice (PDX). Results of WES were analysed using a bioinformatics pipeline to identify mutations conferring susceptibility to kinase inhibitors and common chemotherapeutic agents. PDX models exhibiting successful growth underwent WES of the tumour and subsequent chemosensitivity testing.

Results

WES was successful in all 12 patients, with successful establishment PDX tumours models in seven patients. WES identified potential actionable therapeutics in all patients. Significant variation in predicted therapeutics was demonstrated between three PDX samples and their matched tumour samples.

Conclusion

Analysis of WES of fresh tumour specimens via a bioinformatics pipeline may identify potential actionable chemotherapy agents. Further research into this field may lead to the development of personalized cancer therapy for sarcoma.

Electronic supplementary material

The online version of this article (10.1186/s13569-018-0090-1) contains supplementary material, which is available to authorized users.

ALK Inhibitor Response in Melanomas Expressing EML4-ALK Fusions and Alternate ALK Isoforms

Oncogenic ALK fusions occur in several types of cancer and can be effectively treated with ALK inhibitors; however, ALK fusions and treatment response have not been characterized in malignant melanomas. Recently, a novel isoform of ALK (ALKATI ) was reported in 11% of melanomas but the response of melanomas expressing ALKATI to ALK inhibition has not been well characterized. We analyzed 45 melanoma patient-derived xenograft models for ALK mRNA and protein expression. ALK expression was identified in 11 of 45 (24.4%) melanomas. Ten melanomas express wild-type (wt) ALK and/or ALKATI and one mucosal melanoma expresses multiple novel EML4-ALK fusion variants. Melanoma cells expressing different ALK variants were tested for response to ALK inhibitors. Whereas the melanoma expressing EML4-ALK were sensitive to ALK inhibitors in vitro and in vivo, the melanomas expressing wt ALK or ALKATI were not sensitive to ALK inhibitors. In addition, a patient with mucosal melanoma expressing ALKATI was treated with an ALK/ROS1/TRK inhibitor (entrectinib) on a phase I trial but did not respond. Our results demonstrate ALK fusions occur in malignant melanomas and respond to targeted therapy, whereas melanomas expressing ALKATI do not respond to ALK inhibitors. Targeting ALK fusions is an effective therapeutic option for a subset of melanoma patients, but additional clinical studies are needed to determine the efficacy of targeted therapies in melanomas expressing wt ALK or ALKATIMol Cancer Ther; 17(1); 222-31. ©2017 AACR.

Kinase gene fusions in defined subsets of melanoma

Genomic rearrangements resulting in activating kinase fusions have been increasingly described in a number of cancers including malignant melanoma, but their frequency in specific melanoma subtypes has not been reported. We used break-apart fluorescence in situ hybridization (FISH) to identify genomic rearrangements in tissues from 59 patients with various types of malignant melanoma including acral lentiginous, mucosal, superficial spreading, and nodular. We identified four genomic rearrangements involving the genes BRAF, RET, and ROS1. Of these, three were confirmed by Immunohistochemistry (IHC) or sequencing and one was found to be an ARMC10-BRAF fusion that has not been previously reported in melanoma. These fusions occurred in different subtypes of melanoma but all in tumors lacking known driver mutations. Our data suggest gene fusions are more common than previously thought and should be further explored particularly in melanomas lacking known driver mutations.

A Survey of Computational Tools to Analyze and Interpret Whole Exome Sequencing Data

Whole Exome Sequencing (WES) is the application of the next-generation technology to determine the variations in the exome and is becoming a standard approach in studying genetic variants in diseases. Understanding the exomes of individuals at single base resolution allows the identification of actionable mutations for disease treatment and management. WES technologies have shifted the bottleneck in experimental data production to computationally intensive informatics-based data analysis. Novel computational tools and methods have been developed to analyze and interpret WES data. Here, we review some of the current tools that are being used to analyze WES data. These tools range from the alignment of raw sequencing reads all the way to linking variants to actionable therapeutics. Strengths and weaknesses of each tool are discussed for the purpose of helping researchers make more informative decisions on selecting the best tools to analyze their WES data.