A method to reduce ancestry related germline false positives in tumor only somatic variant calling

Analytic validation of NeXT Dx™, a comprehensive genomic profiling assay

We describe the analytic validation of NeXT Dx, a comprehensive genomic profiling assay to aid therapy and clinical trial selection for patients diagnosed with solid tumor cancers. Proprietary methods were utilized to perform whole exome and whole transcriptome sequencing for detection of single nucleotide variants (SNVs), insertions/deletions (indels), copy number alterations (CNAs), and gene fusions, and determination of tumor mutation burden and microsatellite instability. Variant calling is enhanced by sequencing a patient-specific normal sample from, for example, a blood specimen. This provides highly accurate somatic variant calls as well as the incidental reporting of pathogenic and likely pathogenic germline alterations. Fusion detection via RNA sequencing provides more extensive and accurate fusion calling compared to DNA-based tests. NeXT Dx features the proprietary Accuracy and Content Enhanced technology, developed to optimize sequencing and provide more uniform coverage across the exome. The exome was validated at a median sequencing depth of >500x. While variants from 401 cancer-associated genes are currently reported from the assay, the exome/transcriptome assay is broadly validated to enable reporting of additional variants as they become clinically relevant. NeXT Dx demonstrated analytic sensitivities as follows: SNVs (99.4%), indels (98.2%), CNAs (98.0%), and fusions (95.8%). The overall analytic specificity was >99.0%.

DeepSom: a CNN-based approach to somatic variant calling in WGS samples without a matched normal

MOTIVATION

Somatic mutations are usually called by analyzing the DNA sequence of a tumor sample in conjunction with a matched normal. However, a matched normal is not always available, for instance, in retrospective analysis or diagnostic settings. For such cases, tumor-only somatic variant calling tools need to be designed. Previously proposed approaches demonstrate inferior performance on whole-genome sequencing (WGS) samples.

RESULTS

We present the convolutional neural network-based approach called DeepSom for detecting somatic single nucleotide polymorphism and short insertion and deletion variants in tumor WGS samples without a matched normal. We validate DeepSom by reporting its performance on five different cancer datasets. We also demonstrate that on WGS samples DeepSom outperforms previously proposed methods for tumor-only somatic variant calling.

AVAILABILITY AND IMPLEMENTATION

DeepSom is available as a GitHub repository at https://github.com/heiniglab/DeepSom.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Artificial intelligence applied in neoantigen identification facilitates personalized cancer immunotherapy

The field of cancer neoantigen investigation has developed swiftly in the past decade. Predicting novel and true neoantigens derived from large multi-omics data became difficult but critical challenges. The rise of Artificial Intelligence (AI) or Machine Learning (ML) in biomedicine application has brought benefits to strengthen the current computational pipeline for neoantigen prediction. ML algorithms offer powerful tools to recognize the multidimensional nature of the omics data and therefore extract the key neoantigen features enabling a successful discovery of new neoantigens. The present review aims to outline the significant technology progress of machine learning approaches, especially the newly deep learning tools and pipelines, that were recently applied in neoantigen prediction. In this review article, we summarize the current state-of-the-art tools developed to predict neoantigens. The standard workflow includes calling genetic variants in paired tumor and blood samples, and rating the binding affinity between mutated peptide, MHC (I and II) and T cell receptor (TCR), followed by characterizing the immunogenicity of tumor epitopes. More specifically, we highlight the outstanding feature extraction tools and multi-layer neural network architectures in typical ML models. It is noted that more integrated neoantigen-predicting pipelines are constructed with hybrid or combined ML algorithms instead of conventional machine learning models. In addition, the trends and challenges in further optimizing and integrating the existing pipelines are discussed.

Fast, accurate, and racially unbiased pan-cancer tumor-only variant calling with tabular machine learning

Accurately identifying somatic mutations is essential for precision oncology and crucial for calculating tumor-mutational burden (TMB), an important predictor of response to immunotherapy. For tumor-only variant calling (i.e., when the cancer biopsy but not the patient's normal tissue sample is sequenced), accurately distinguishing somatic mutations from germline variants is a challenging problem that, when unaddressed, results in unreliable, biased, and inflated TMB estimates. Here, we apply machine learning to the task of somatic vs germline classification in tumor-only solid tumor samples using TabNet, XGBoost, and LightGBM, three machine-learning models for tabular data. We constructed a training set for supervised classification using features derived exclusively from tumor-only variant calling and drawing somatic and germline truth labels from an independent pipeline using the patient-matched normal samples. All three trained models achieved state-of-the-art performance on two holdout test datasets: a TCGA dataset including sarcoma, breast adenocarcinoma, and endometrial carcinoma samples (AUC > 94%), and a metastatic melanoma dataset (AUC > 85%). Concordance between matched-normal and tumor-only TMB improves from R2 = 0.006 to 0.71-0.76 with the addition of a machine-learning classifier, with LightGBM performing best. Notably, these machine-learning models generalize across cancer subtypes and capture kits with a call rate of 100%. We reproduce the recent finding that tumor-only TMB estimates for Black patients are extremely inflated relative to that of white patients due to the racial biases of germline databases. We show that our approach with XGBoost and LightGBM eliminates this significant racial bias in tumor-only variant calling.

Genetic Ancestry Correlates with Somatic Differences in a Real-World Clinical Cancer Sequencing Cohort

Accurate ancestry inference is critical for identifying genetic contributors of cancer disparities among populations. Although methods to infer genetic ancestry have historically relied upon genome-wide markers, the adaptation to targeted clinical sequencing panels presents an opportunity to incorporate ancestry inference into routine diagnostic workflows. We show that global ancestral contributions and admixture of continental populations can be quantitatively inferred using markers captured by the MSK-IMPACT clinical panel. In a pan-cancer cohort of 45,157 patients, we observed differences by ancestry in the frequency of somatic alterations, recapitulating known associations and revealing novel associations. Despite the comparable overall prevalence of driver alterations by ancestry group, the proportion of patients with clinically actionable alterations was lower for African (30%) compared with European (33%) ancestry. Although this result is largely explained by population-specific cancer subtype differences, it reveals an inequity in the degree to which different populations are served by existing precision oncology interventions.

SIGNIFICANCE

We performed a comprehensive analysis of ancestral associations with somatic mutations in a real-world pan-cancer cohort, including >5,000 non-European individuals. Using an FDA-authorized tumor sequencing panel and an FDA-recognized oncology knowledge base, we detected differences in the prevalence of clinically actionable alterations, potentially contributing to health care disparities affecting underrepresented populations. This article is highlighted in the In This Issue feature, p. 2483.

Transcriptome analysis provides critical answers to the "variants of uncertain significance" conundrum

While whole-genome and exome sequencing have transformed our collective understanding of genetics' role in disease pathogenesis, there are certain conditions and populations for whom DNA-level data fails to identify the underlying genetic etiology. Specifically, patients of non-White race and non-European ancestry are disproportionately affected by "variants of unknown/uncertain significance" (VUS), limiting the scope of precision medicine for minority patients and perpetuating health disparities. VUS often include deep intronic and splicing variants which are difficult to interpret from DNA data alone. RNA analysis can illuminate the consequences of VUS, thereby allowing for their reclassification as pathogenic versus benign. Here we review the critical role transcriptome analysis plays in clarifying VUS in both neoplastic and non-neoplastic diseases.

PRAME Expression Correlates With Genomic Aberration and Malignant Diagnosis of Spitzoid Melanocytic Neoplasms

ABSTRACT

Spitzoid melanocytic neoplasms are a diagnostically challenging class of lesions in dermatopathology. Recently, molecular assays and immunohistochemical markers have been explored as ancillary methods to assist in the diagnostic workup. Specifically, preferentially expressed antigen in melanoma (PRAME) immunohistochemistry is a nuclear stain commonly positive in melanomas, but not in nevi. This study investigates PRAME immunoreactivity (≥75% positive nuclear staining in tumor cells) in a set of 59 spitzoid melanocytic neoplasms with known clinical outcomes. We compared PRAME status with (1) the clinical outcomes, (2) the morphologic diagnoses, and (3) the status of TERT promoter mutation. Regarding clinical outcomes, 3 cases developed metastatic disease, of which 2 expressed diffusely positive PRAME staining. Of the 56 cases that did not show evidence of metastasis, 6 expressed diffusely positive PRAME staining. Morphologically, diffusely positive PRAME staining was seen in 7 of 21 cases (33.3%) diagnosed as melanoma and only 1 benign tumor 1 of 38 (2.6%). There were 4 of 8 cases with a TERT promoter mutation which were diffusely PRAME-positive compared with 4 of 51 cases without TERT promoter mutation ( P = 0.001). Our results show a statistically significant correlation between PRAME expression and the diagnosis, outcome, and TERT promoter mutation status of atypical spitzoid melanocytic neoplasms, suggesting immunohistochemistry for PRAME can help support a suspected diagnosis. However, because of occasional false-positive and negative test results, correlation with the clinical and histologic findings as well as results from other tests is needed for the interpretation of diagnostically challenging spitzoid melanocytic neoplasms.

Analysis of the genomic landscapes of Barbadian and Nigerian women with triple negative breast cancer

PURPOSE

Triple negative breast cancer (TNBC) is an aggressive breast cancer subtype that disproportionately affects women of African ancestry (WAA) and is often associated with poor survival. Although there is a high prevalence of TNBC across West Africa and in women of the African diaspora, there has been no comprehensive genomics study to investigate the mutational profile of ancestrally related women across the Caribbean and West Africa.

METHODS

This multisite cross-sectional study used 31 formalin-fixed paraffin-embedded (FFPE) samples from Barbadian and Nigerian TNBC participants. High-resolution whole exome sequencing (WES) was performed on the Barbadian and Nigerian TNBC samples to identify their mutational profiles and comparisons were made to African American, European American and Asian American sequencing data obtained from The Cancer Genome Atlas (TCGA). Whole exome sequencing was conducted on tumors with an average of 382 × coverage and 4335 × coverage for pooled germline non-tumor samples.

RESULTS

Variants detected at high frequency in our WAA cohorts were found in the following genes NBPF12, PLIN4, TP53 and BRCA1. In the TCGA TNBC cases, these genes had a lower mutation rate, except for TP53 (32% in our cohort; 63% in TCGA-African American; 67% in TCGA-European American; 63% in TCGA-Asian). For all altered genes, there were no differences in frequency of mutations between WAA TNBC groups including the TCGA-African American cohort. For copy number variants, high frequency alterations were observed in PIK3CA, TP53, FGFR2 and HIF1AN genes.

CONCLUSION

This study provides novel insights into the underlying genomic alterations in WAA TNBC samples and shines light on the importance of inclusion of under-represented populations in cancer genomics and biomarker studies.

Cancer Neoantigens: Challenges and Future Directions for Prediction, Prioritization, and Validation

Prioritization of immunogenic neoantigens is key to enhancing cancer immunotherapy through the development of personalized vaccines, adoptive T cell therapy, and the prediction of response to immune checkpoint inhibition. Neoantigens are tumor-specific proteins that allow the immune system to recognize and destroy a tumor. Cancer immunotherapies, such as personalized cancer vaccines, adoptive T cell therapy, and immune checkpoint inhibition, rely on an understanding of the patient-specific neoantigen profile in order to guide personalized therapeutic strategies. Genomic approaches to predicting and prioritizing immunogenic neoantigens are rapidly expanding, raising new opportunities to advance these tools and enhance their clinical relevance. Predicting neoantigens requires acquisition of high-quality samples and sequencing data, followed by variant calling and variant annotation. Subsequently, prioritizing which of these neoantigens may elicit a tumor-specific immune response requires application and integration of tools to predict the expression, processing, binding, and recognition potentials of the neoantigen. Finally, improvement of the computational tools is held in constant tension with the availability of datasets with validated immunogenic neoantigens. The goal of this review article is to summarize the current knowledge and limitations in neoantigen prediction, prioritization, and validation and propose future directions that will improve personalized cancer treatment.

Impact of Next-generation Sequencing on Interobserver Agreement and Diagnosis of Spitzoid Neoplasms

Atypical Spitzoid melanocytic tumors are diagnostically challenging. Many studies have suggested various genomic markers to improve classification and prognostication. We aimed to assess whether next-generation sequencing studies using the Tempus xO assay assessing mutations in 1711 cancer-related genes and performing whole transcriptome mRNA sequencing for structural alterations could improve diagnostic agreement and accuracy in assessing neoplasms with Spitzoid histologic features. Twenty expert pathologists were asked to review 70 consultation level cases with Spitzoid features, once with limited clinical information and again with additional genomic information. There was an improvement in overall agreement with additional genomic information. Most significantly, there was increase in agreement of the diagnosis of conventional melanoma from moderate (κ=0.470, SE=0.0105) to substantial (κ=0.645, SE=0.0143) as measured by an average Cohen κ. Clinical follow-up was available in all 70 cases which substantiated that the improved agreement was clinically significant. Among 3 patients with distant metastatic disease, there was a highly significant increase in diagnostic recognition of the cases as conventional melanoma with genomics (P<0.005). In one case, none of 20 pathologists recognized a tumor with BRAF and TERT promoter mutations associated with fatal outcome as a conventional melanoma when only limited clinical information was provided, whereas 60% of pathologists correctly diagnosed this case when genomic information was also available. There was also a significant improvement in agreement of which lesions should be classified in the Spitz category/WHO Pathway from an average Cohen κ of 0.360 (SE=0.00921) to 0.607 (SE=0.0232) with genomics.

UNMASC: tumor-only variant calling with unmatched normal controls

Despite years of progress, mutation detection in cancer samples continues to require significant manual review as a final step. Expert review is particularly challenging in cases where tumors are sequenced without matched normal control DNA. Attempts have been made to call somatic point mutations without a matched normal sample by removing well-known germline variants, utilizing unmatched normal controls, and constructing decision rules to classify sequencing errors and private germline variants. With budgetary constraints related to computational and sequencing costs, finding the appropriate number of controls is a crucial step to identifying somatic variants. Our approach utilizes public databases for canonical somatic variants as well as germline variants and leverages information gathered about nearby positions in the normal controls. Drawing from our cohort of targeted capture panel sequencing of tumor and normal samples with varying tumortypes and demographics, these served as a benchmark for our tumor-only variant calling pipeline to observe the relationship between our ability to correctly classify variants against a number of unmatched normals. With our benchmarked samples, approximately ten normal controls were needed to maintain 94% sensitivity, 99% specificity and 76% positive predictive value, far outperforming comparable methods. Our approach, called UNMASC, also serves as a supplement to traditional tumor with matched normal variant calling workflows and can potentially extend to other concerns arising from analyzing next generation sequencing data.

Analytic validation and clinical utilization of the comprehensive genomic profiling test, GEM ExTra®

We developed and analytically validated a comprehensive genomic profiling (CGP) assay, GEM ExTra, for patients with advanced solid tumors that uses Next Generation Sequencing (NGS) to characterize whole exomes employing a paired tumor-normal subtraction methodology. The assay detects single nucleotide variants (SNV), indels, focal copy number alterations (CNA), TERT promoter region, as well as tumor mutation burden (TMB) and microsatellite instability (MSI) status. Additionally, the assay incorporates whole transcriptome sequencing of the tumor sample that allows for the detection of gene fusions and select special transcripts, including AR-V7, EGFR vIII, EGFRvIV, and MET exon 14 skipping events. The assay has a mean target coverage of 180X for the normal (germline) and 400X for tumor DNA including enhanced probe design to facilitate the sequencing of difficult regions. Proprietary bioinformatics, paired with comprehensive clinical curation results in reporting that defines clinically actionable, FDA-approved, and clinical trial drug options for the management of the patient's cancer. GEM ExTra demonstrated analytic specificity (PPV) of > 99.9% and analytic sensitivity of 98.8%. Application of GEM ExTra to 1,435 patient samples revealed clinically actionable alterations in 83.9% of reports, including 31 (2.5%) where therapeutic recommendations were based on RNA fusion findings only.

Reliable Analysis of Clinical Tumor-Only Whole-Exome Sequencing Data

PURPOSE

Allele-specific copy number alteration (CNA) analysis is essential to study the functional impact of single-nucleotide variants (SNVs) and the process of tumorigenesis. However, controversy over whether it can be performed with sufficient accuracy in data without matched normal profiles and a lack of open-source implementations have limited its application in clinical research and diagnosis.

METHODS

We benchmark allele-specific CNA analysis performance of whole-exome sequencing (WES) data against gold standard whole-genome SNP6 microarray data and against WES data sets with matched normal samples. We provide a workflow based on the open-source PureCN R/Bioconductor package in conjunction with widely used variant-calling and copy number segmentation algorithms for allele-specific CNA analysis from WES without matched normals. This workflow further classifies SNVs by somatic status and then uses this information to infer somatic mutational signatures and tumor mutational burden (TMB).

RESULTS

Application of our workflow to tumor-only WES data produces tumor purity and ploidy estimates that are highly concordant with estimates from SNP6 microarray data and matched normal WES data. The presence of cancer type–specific somatic mutational signatures was inferred with high accuracy. We also demonstrate high concordance of TMB between our tumor-only workflow and matched normal pipelines.

CONCLUSION

The proposed workflow provides, to our knowledge, the only open-source option with demonstrated high accuracy for comprehensive allele-specific CNA analysis and SNV classification of tumor-only WES. An implementation of the workflow is available on the Terra Cloud platform of the Broad Institute (Cambridge, MA).

Tumor Mutational Burden From Tumor-Only Sequencing Compared With Germline Subtraction From Paired Tumor and Normal Specimens

IMPORTANCE

Tumor mutation burden (TMB) is an emerging factor associated with survival with immunotherapy. When tumor-normal pairs are available, TMB is determined by calculating the difference between somatic and germline sequences. In the case of commonly used tumor-only sequencing, additional steps are needed to estimate the somatic alterations. Computational tools have been developed to determine germline contribution based on sample copy state, purity estimates, and occurrence of the variant in population databases; however, there is potential for sampling bias in population data sets.

OBJECTIVE

To investigate whether tumor-only filtering approaches overestimate TMB.

DESIGN, SETTING, AND PARTICIPANTS

This was a retrospective cohort study of 50 tumor samples from 10 different tumor types. A 595-gene panel test was used to assess TMB by adding all missense, indels, and frameshift variants with an allelic fraction of at least 5% and coverage of at least 100× within each tumor. Tumor-only TMB was evaluated against the criterion standard of matched germline-subtracted TMB at 3 levels. Level 1 removed all the tumor-only variants with allelic fraction of at least 1% in the Exome Aggregation Consortium database (with the Cancer Genome Atlas cohort removed). Level 2 removed all variants observed in population databases, simulating a naive approach of removing germline variation. Level 3 used an internal tumor-only pipeline for calculating TMB. These specimens were processed with a commercially available panel, and results were analyzed at the Mayo Clinic. Data were analyzed between December 1, 2018, and May 28, 2019.

MAIN OUTCOMES AND MEASURES

Tumor mutation burden per megabase (Mb) as determined by 3 levels of filtering and germline subtraction.

RESULTS

There were significantly higher estimates of TMB with level 1 (median [range] mutations per Mb, 28.8 [17.5-67.1]), level 2 (median [range] mutations per Mb, 20.8 [10.4-30.8]), and level 3 (median [range] mutations per Mb, 3.8 [0.8-12.1]) tumor-only filtering approaches than those determined by germline subtraction (median [range] mutations per Mb, 1.7 [0.4-9.2]). There were no strong associations between TMB estimates and tumor-germline TMB for level 1 filtering (r = 0.008; 95% CI, -0.004 to 0.020), level 2 filtering (r = 0.018; 95% CI, 0.003 to 0.033), or level 3 filtering (r = 0.54; 95% CI, 0.36 to 0.68).

CONCLUSIONS AND RELEVANCE

The findings of this study indicate that tumor-only approaches that filter variants in population databases can overestimate TMB compared with germline subtraction methods. Despite improved association with more stringent filtering approaches, these falsely elevated estimates may result in the inappropriate categorization of tumor specimens and negatively affect clinical trial results and patient outcomes.

Multiscale, multimodal analysis of tumor heterogeneity in IDH1 mutant vs wild-type diffuse gliomas

Glioma is recognized to be a highly heterogeneous CNS malignancy, whose diverse cellular composition and cellular interactions have not been well characterized. To gain new clinical- and biological-insights into the genetically-bifurcated IDH1 mutant (mt) vs wildtype (wt) forms of glioma, we integrated data from protein, genomic and MR imaging from 20 treatment-naïve glioma cases and 16 recurrent GBM cases. Multiplexed immunofluorescence (MxIF) was used to generate single cell data for 43 protein markers representing all cancer hallmarks, Genomic sequencing (exome and RNA (normal and tumor) and magnetic resonance imaging (MRI) quantitative features (protocols were T1-post, FLAIR and ADC) from whole tumor, peritumoral edema and enhancing core vs equivalent normal region were also collected from patients. Based on MxIF analysis, 85,767 cells (glioma cases) and 56,304 cells (GBM cases) were used to generate cell-level data for 24 biomarkers. K-means clustering was used to generate 7 distinct groups of cells with divergent biomarker profiles and deconvolution was used to assign RNA data into three classes. Spatial and molecular heterogeneity metrics were generated for the cell data. All features were compared between IDH mt and IDHwt patients and were finally combined to provide a holistic/integrated comparison. Protein expression by hallmark was generally lower in the IDHmt vs wt patients. Molecular and spatial heterogeneity scores for angiogenesis and cell invasion also differed between IDHmt and wt gliomas irrespective of prior treatment and tumor grade; these differences also persisted in the MR imaging features of peritumoral edema and contrast enhancement volumes. A coherent picture of enhanced angiogenesis in IDHwt tumors was derived from multiple platforms (genomic, proteomic and imaging) and scales from individual proteins to cell clusters and heterogeneity, as well as bulk tumor RNA and imaging features. Longer overall survival for IDH1mt glioma patients may reflect mutation-driven alterations in cellular, molecular, and spatial heterogeneity which manifest in discernable radiological manifestations.

Calling Variants in the Clinic: Informed Variant Calling Decisions Based on Biological, Clinical, and Laboratory Variables

Deep sequencing genomic analysis is becoming increasingly common in clinical research and practice, enabling accurate identification of diagnostic, prognostic, and predictive determinants. Variant calling, distinguishing between true mutations and experimental errors, is a central task of genomic analysis and often requires sophisticated statistical, computational, and/or heuristic techniques. Although variant callers seek to overcome noise inherent in biological experiments, variant calling can be significantly affected by outside factors including those used to prepare, store, and analyze samples. The goal of this review is to discuss known experimental features, such as sample preparation, library preparation, and sequencing, alongside diverse biological and clinical variables, and evaluate their effect on variant caller selection and optimization.

Leveraging Spatial Variation in Tumor Purity for Improved Somatic Variant Calling of Archival Tumor Only Samples

Archival tumor samples represent a rich resource of annotated specimens for translational genomics research. However, standard variant calling approaches require a matched normal sample from the same individual, which is often not available in the retrospective setting, making it difficult to distinguish between true somatic variants and individual-specific germline variants. Archival sections often contain adjacent normal tissue, but this tissue can include infiltrating tumor cells. As existing comparative somatic variant callers are designed to exclude variants present in the normal sample, a novel approach is required to leverage adjacent normal tissue with infiltrating tumor cells for somatic variant calling. Here we present lumosVar 2.0, a software package designed to jointly analyze multiple samples from the same patient, built upon our previous single sample tumor only variant caller lumosVar 1.0. The approach assumes that the allelic fraction of somatic variants and germline variants follow different patterns as tumor content and copy number state change. lumosVar 2.0 estimates allele specific copy number and tumor sample fractions from the data, and uses a to model to determine expected allelic fractions for somatic and germline variants and to classify variants accordingly. To evaluate the utility of lumosVar 2.0 to jointly call somatic variants with tumor and adjacent normal samples, we used a glioblastoma dataset with matched high and low tumor content and germline whole exome sequencing data (for true somatic variants) available for each patient. Both sensitivity and positive predictive value were improved when analyzing the high tumor and low tumor samples jointly compared to analyzing the samples individually or in-silico pooling of the two samples. Finally, we applied this approach to a set of breast and prostate archival tumor samples for which tumor blocks containing adjacent normal tissue were available for sequencing. Joint analysis using lumosVar 2.0 detected several variants, including known cancer hotspot mutations that were not detected by standard somatic variant calling tools using the adjacent tissue as presumed normal reference. Together, these results demonstrate the utility of leveraging paired tissue samples to improve somatic variant calling when a constitutional sample is not available.

Precision medicine for locally advanced breast cancer: frontiers and challenges in Latin America

Advances in high-throughput technologies and their involvement in the 'omics' of cancer have made possible the identification of hundreds of biomarkers and the development of predictive and prognostic platforms that model the management of cancer from evidence-based medicine to precision medicine. Latin America (LATAM) is a region characterised by fragmented healthcare, high rates of poverty and disparities to access to a basic standard of care not only for cancer but also for other complex diseases. Patients from the public setting cannot afford targeted therapy, the facilities offering genomic platforms are scarce and the use of high-precision radiotherapy is limited to few facilities. Despite the fact that LATAM oncologists are well-trained in the use of genomic platforms and constantly participate in genomic projects, a medical practice based in precision oncology is a great challenge and frequently limited to private practice. In breast cancer, we are waiting for the results of large basket trials to incorporate the detection of actionable mutations to select targeted treatments, in a similar way to the management of lung cancer. On the other hand and paradoxically, in the 'one fit is not for all' era, clinical and genomic studies continue grouping our patients under the single label 'Latin American' or 'Hispanic' despite the different ancestries and genomic backgrounds seen in the region. More regional cancer genomic initiatives and public availability of this data are needed in order to develop more precise oncology in locally advanced breast cancer.

De novo variants in GREB1L are associated with non-syndromic inner ear malformations and deafness

Congenital inner ear malformations affecting both the osseous and membranous labyrinth can have a devastating impact on hearing and language development. With the exception of an enlarged vestibular aqueduct, non-syndromic inner ear malformations are rare, and their underlying molecular biology has thus far remained understudied. To identify molecular factors that might be important in the developing inner ear, we adopted a family-based trio exome sequencing approach in young unrelated subjects with severe inner ear malformations. We identified two previously unreported de novo loss-of-function variants in GREB1L [c.4368G>T;p.(Glu1410fs) and c.982C>T;p.(Arg328*)] in two affected subjects with absent cochleae and eighth cranial nerve malformations. The cochlear aplasia in these affected subjects suggests that a developmental arrest or problem at a very early stage of inner ear development exists, e.g., during the otic pit formation. Craniofacial Greb1l RNA expression peaks in mice during this time frame (E8.5). It also peaks in the developing inner ear during E13-E16, after which it decreases in adulthood. The crucial function of Greb1l in craniofacial development is also evidenced in knockout mice, which develop severe craniofacial abnormalities. In addition, we show that Greb1l-/- zebrafish exhibit a loss of abnormal sensory epithelia innervation. An important role for Greb1l in sensory epithelia innervation development is supported by the eighth cranial nerve deficiencies seen in both affected subjects. In conclusion, we demonstrate that GREB1L is a key player in early inner ear and eighth cranial nerve development. Abnormalities in cochleovestibular anatomy can provide challenges for cochlear implantation. Combining a molecular diagnosis with imaging techniques might aid the development of individually tailored therapeutic interventions in the future.

Comprehensive genomic transcriptomic tumor-normal gene panel analysis for enhanced precision in patients with lung cancer

A CMS approved test for lung cancer is based on tumor-only analysis of a targeted 35 gene panel, specifically excluding the use of the patient's normal germline tissue. However, this tumor-only approach increases the risk of mistakenly identifying germline single nucleotide polymorphisms (SNPs) as somatically-derived cancer driver mutations (false positives). 621 patients with 30 different cancer types, including lung cancer, were studied to compare the precision of tumor somatic variant calling in 35 genes using tumor-only DNA sequencing versus tumor-normal DNA plus RNA sequencing. When sequencing of lung cancer was performed using tumor genomes alone without normal germline controls, 94% of variants identified were SNPs and thus false positives. Filtering for common SNPs still resulted in as high as 48% false positive variant calling. With tumor-only sequencing, 29% of lung cancer patients had a false positive variant call in at least one of twelve genes with directly targetable drugs. RNA analysis showed 18% of true somatic variants were not expressed. Thus, sequencing and analysis of both normal germline and tumor genomes is necessary for accurate identification of molecular targets. Treatment decisions based on tumor-only analysis may result in the administration of ineffective therapies while also increasing the risk of negative drug-related side effects.