Accelerating Discovery of Functional Mutant Alleles in Cancer

Oncogenic composite mutations can be predicted by co-mutations and their chromosomal location

Genetic heterogeneity in tumors can show a remarkable selectivity when two or more independent genetic events occur in the same gene. This phenomenon, called composite mutation, points toward a selective pressure, which frequently causes therapy resistance to mutation-specific drugs. Since composite mutations have been described to occur in sub-clonal populations, they are not always captured through biopsy sampling. Here, we provide a proof of concept to predict composite mutations to anticipate which patients might be at risk for sub-clonally driven therapy resistance. We found that composite mutations occur in 5% of cancer patients, mostly affecting the PIK3CA, EGFR, BRAF, and KRAS genes, which are common precision medicine targets. Furthermore, we found a strong and significant relationship between the frequencies of composite mutations with commonly co-occurring mutations in a non-composite context. We also found that co-mutations are significantly enriched on the same chromosome. These observations were independently confirmed using cell line data. Finally, we show the feasibility of predicting compositive mutations based on their co-mutations (AUC 0.62, 0.81, 0.82, and 0.91 for EGFR, PIK3CA, KRAS, and BRAF, respectively). This prediction model could help to stratify patients who are at risk of developing therapy resistance-causing mutations.

Defining the heterogeneous molecular landscape of lung cancer cell responses to epigenetic inhibition

Epigenetic inhibitors exhibit powerful antiproliferative and anticancer activities. However, cellular responses to small-molecule epigenetic inhibition are heterogenous and dependent on factors such as the genetic background, metabolic state, and on-/off-target engagement of individual small-molecule compounds. The molecular study of the extent of this heterogeneity often measures changes in a single cell line or using a small number of compounds. To more comprehensively profile the effects of small-molecule perturbations and their influence on these heterogeneous cellular responses, we present a molecular resource based on the quantification of chromatin, proteome, and transcriptome remodeling due to histone deacetylase inhibitors (HDACi) in non-isogenic cell lines. Through quantitative molecular profiling of 10,621 proteins, these data reveal coordinated molecular remodeling of HDACi treated cancer cells. HDACi-regulated proteins differ greatly across cell lines with consistent (JUN, MAP2K3, CDKN1A) and divergent (CCND3, ASF1B, BRD7) cell-state effectors. Together these data provide valuable insight into cell-type driven and heterogeneous responses that must be taken into consideration when monitoring molecular perturbations in culture models.

Histopathologic and Molecular Characterization of IDH-Mutant Prostatic Adenocarcinoma

Gain-of-function isocitrate dehydrogenase (IDH) mutations are pathogenically significant in many tumor types and are actionable in cholangiocarcinoma, low-grade glioma, and acute myeloid leukemia. Rare IDH mutations have been described in prostatic adenocarcinoma (PCa). Recent publications have suggested that psammomatous calcifications in PCa are associated with IDH1 mutations. In this retrospective study, we queried our institutional clinical sequencing database (cohort 1), and previously published PCa data sets in cBioPortal (cohort 2). Samples were stratified based on oncogenic hotspot IDH mutations at IDH1 R132 and IDH2 R140/R172, and other nonhotspot IDH mutations. Seventeen (0.4%) cases were identified from 4033 PCa cases in cohort 1 harboring mutually exclusive oncogenic hotspot IDH1 (N = 15, 1 of which was subclonal) or IDH2 (N = 2) mutations, and 20 (0.5%) cases had nonhotspot IDH1/2 mutations. A histologic review of 13 cases with IDH1 hotspot mutations and available material showed grade group 3 or higher disease. Immunohistochemistry was performed on cases with IDH1 hotspot mutations when possible and showed AR, PSA, PSMA, and NKX3.1 positive in all the 4 cases stained. In cohort 2, 9 cases (0.3%) harboring IDH1 hotspot mutations were identified from 2749 patients, and 9 cases carried nonhotspot IDH1/2 mutations. The combined cohorts of 23 PCa cases with clonal IDH1 hotspot mutations had no ETS fusions, SPOP hotspot mutations, and somatic or germline alterations in BRCA1/2, ATM, RB1, or AR; 19 cases with successful microsatellite instability testing were all microsatellite stable. Conversely, among 29 cases with nonhotspot IDH mutations, there were 4 with TMPRSS2::ERG fusions, 6 with SPOP hotspot mutations, and 10 with AR amplifications/hotspot mutations; 8 were microsatellite instability high. Notably, two cases with IDH1 hotspot mutations had psammomatous calcifications. Our findings provide evidence that IDH1 hotspot mutations serve as driver alterations in this rare yet distinct molecular subset of PCa. Further studies are warranted to correlate response to androgen deprivation and IDH inhibitors.

The tRNA Gm18 methyltransferase TARBP1 promotes hepatocellular carcinoma progression via metabolic reprogramming of glutamine

Cancer cells rely on metabolic reprogramming to sustain the prodigious energetic requirements for rapid growth and proliferation. Glutamine metabolism is frequently dysregulated in cancers and is being exploited as a potential therapeutic target. Using CRISPR/Cas9 interference (CRISPRi) screening, we identified TARBP1 (TAR (HIV-1) RNA Binding Protein 1) as a critical regulator involved in glutamine reliance of cancer cell. Consistent with this discovery, TARBP1 amplification and overexpression are frequently observed in various cancers. Knockout of TARBP1 significantly suppresses cell proliferation, colony formation and xenograft tumor growth. Mechanistically, TARBP1 selectively methylates and stabilizes a small subset of tRNAs, which promotes efficient protein synthesis of glutamine transporter-ASCT2 (also known as SLC1A5) and glutamine import to fuel the growth of cancer cell. Moreover, we found that the gene expression of TARBP1 and ASCT2 are upregulated in combination in clinical cohorts and their upregulation is associated with unfavorable prognosis of HCC (hepatocellular carcinoma). Taken together, this study reveals the unexpected role of TARBP1 in coordinating the tRNA availability and glutamine uptake during HCC progression and provides a potential target for tumor therapy.

Pharmacogenomic discovery of genetically targeted cancer therapies optimized against clinical outcomes

Despite the clinical success of dozens of genetically targeted cancer therapies, the vast majority of patients with tumors caused by loss-of-function (LoF) mutations do not have access to these treatments. This is primarily due to the challenge of developing a drug that treats a disease caused by the absence of a protein target. The success of PARP inhibitors has solidified synthetic lethality (SL) as a means to overcome this obstacle. Recent mapping of SL networks using pooled CRISPR-Cas9 screens is a promising approach for expanding this concept to treating cancers driven by additional LoF drivers. In practice, however, translating signals from cell lines, where these screens are typically conducted, to patient outcomes remains a challenge. We developed a pharmacogenomic (PGx) approach called "Clinically Optimized Driver Associated-PGx" (CODA-PGX) that accurately predicts genetically targeted therapies with clinical-stage efficacy in specific LoF driver contexts. Using approved targeted therapies and cancer drugs with available real-world evidence and molecular data from hundreds of patients, we discovered and optimized the key screening principles predictive of efficacy and overall patient survival. In addition to establishing basic technical conventions, such as drug concentration and screening kinetics, we found that replicating the driver perturbation in the right context, as well as selecting patients where those drivers are genuine founder mutations, were key to accurate translation. We used CODA-PGX to screen a diverse collection of clinical stage drugs and report dozens of novel LoF genetically targeted opportunities; many validated in xenografts and by real-world evidence. Notable examples include treating STAG2-mutant tumors with Carboplatin, SMARCB1-mutant tumors with Oxaliplatin, and TP53BP1-mutant tumors with Etoposide or Bleomycin.

Variant Impact Predictor database (VIPdb), version 2: trends from three decades of genetic variant impact predictors

BACKGROUND

Variant interpretation is essential for identifying patients' disease-causing genetic variants amongst the millions detected in their genomes. Hundreds of Variant Impact Predictors (VIPs), also known as Variant Effect Predictors (VEPs), have been developed for this purpose, with a variety of methodologies and goals. To facilitate the exploration of available VIP options, we have created the Variant Impact Predictor database (VIPdb).

RESULTS

The Variant Impact Predictor database (VIPdb) version 2 presents a collection of VIPs developed over the past three decades, summarizing their characteristics, ClinGen calibrated scores, CAGI assessment results, publication details, access information, and citation patterns. We previously summarized 217 VIPs and their features in VIPdb in 2019. Building upon this foundation, we identified and categorized an additional 190 VIPs, resulting in a total of 407 VIPs in VIPdb version 2. The majority of the VIPs have the capacity to predict the impacts of single nucleotide variants and nonsynonymous variants. More VIPs tailored to predict the impacts of insertions and deletions have been developed since the 2010s. In contrast, relatively few VIPs are dedicated to the prediction of splicing, structural, synonymous, and regulatory variants. The increasing rate of citations to VIPs reflects the ongoing growth in their use, and the evolving trends in citations reveal development in the field and individual methods.

CONCLUSIONS

VIPdb version 2 summarizes 407 VIPs and their features, potentially facilitating VIP exploration for various variant interpretation applications. VIPdb is available at  https://genomeinterpretation.org/vipdb.

Alterations of the AKT Pathway in Sporadic Human Tumors, Inherited Susceptibility to Cancer, and Overgrowth Syndromes

The AKT kinases are critical signaling molecules that regulate cellular physiology upon the activation of tyrosine kinase receptors and phosphatidylinositol 3-kinases (PI3K). AKT kinases govern many cellular processes considered hallmarks of cancer, including cell proliferation and survival, cell size, tumor invasion, metastasis, and angiogenesis. AKT signaling is regulated by multiple tumor suppressors and oncogenic proteins whose loss or activation, respectively, leads to dysregulation of this pathway, thereby contributing to oncogenesis. Herein, we review the enormous body of literature documenting how the AKT pathway becomes hyperactivated in sporadic human tumors and various hereditary cancer syndromes. We also discuss the role of activating mutations of AKT pathway genes in various chimeric overgrowth disorders, including Proteus syndrome, hypoglycemia with hypertrophy, CLOVES and SOLAMEN syndromes, and hemimegalencephaly.

CTNNB1 exon 3 mutations in metastatic solid pseudopapillary neoplasm of the pancreas

BACKGROUND AND OBJECTIVES

Solid pseudopapillary neoplasm (SPN) of the pancreas demonstrates an indolent disease course; however, some patients present with a "malignant" phenotype, including distant metastases resistant to chemotherapy. This analysis identifies molecular drivers of metastatic SPN using the world's largest clinicogenomics database.

METHODS

The American Association for Cancer Research Project Genomics Evidence Neoplasia Information Exchange was queried for primary and metastatic SPN samples. Sample-level genomic alterations were compared. A pan-pancreatic cancer analysis assessed relevant mutations among all metastatic pancreatic malignancies.

RESULTS

Among 28 SPN samples identified (n = 17 primary, n = 11 metastatic), the most commonly mutated gene was CTNNB1, (24/28 samples; 85.7%). Most mutations were missense (21/24; 87.5%) or in-frame deletions (3/24; 12.5%). The most common CTNNB1 mutations in primary SPN were exon 3 S37F/C missense mutations (6/16 profiled patients, 37.5%), contrasting exon 3 D32N/Y/H missense mutations in metastatic samples (6/11 profiled patients, 54.5%). Metastatic SPN had higher rates of CTNNB1 mutations than metastases from pancreatic ductal adenocarcinoma (72.7% vs. 1.1%; q < 0.0001), pancreatic neuroendocrine tumor (72.7% vs. 2.5%; q < 0.0001), and pancreatic acinar cell carcinoma (72.7% vs. 11.5%; q = 0.0254).

CONCLUSIONS

Missense mutations along exon 3 of CTNNB1 predominate metastatic SPN, differentiating these patients from those with metastases from analogous pancreatic malignancies.

Mutational Landscapes of Normal Skin and Their Potential Implications in the Development of Skin Cancer: A Comprehensive Narrative Review

Recent evidence suggests that physiologically normal skin harbors pervasive mutant clones with cancer drivers. Normal skin has the highest burden of somatic mutations due to persistent ultraviolet exposure throughout life. The mutation burden exponentially increases with age and is further modified by skin site, sun-damage history, and skin phototype. Driver gene profiles in normal skin are similar to those in cutaneous squamous cell carcinoma where NOTCH family, FAT family, and TP53 are consistently reported, while other reported profiles include PPM1D, KMT2D, ASXL1, and RBM10. Normal skin seldom harbors canonical hotspot mutations with therapeutic relevance. The pathologic role of mutant clones with cancer drivers in normal skin is classically considered precursors for skin cancer; however, recent evidence also suggests their putative cancer-protective role. Copy number alterations and other structural variants are rare in normal skin with loss in 9q region encompassing NOTCH1 being the most common. Study methodologies should be carefully designed to obtain an adequate number of cells for sequencing, and a comparable number of cells and read depth across samples. In conclusion, this review provides mutational landscapes of normal skin and discusses their potential implications in the development of skin cancer, highlighting the role of driver genes in early malignant progression.

Identify truly high-risk TP53-mutated diffuse large B cell lymphoma patients and explore the underlying biological mechanisms

TP53 mutation (TP53-mut) correlates with inferior survival in many cancers, whereas its prognostic role in diffuse large B-cell lymphoma (DLBCL) is still in controversy. Therefore, more precise risk stratification needs to be further explored for TP53-mut DLBCL patients. A set of 2637 DLBCL cases from multiple cohorts, was enrolled in our analysis. Among the 2637 DLBCL patients, 14.0% patients (370/2637) had TP53-mut. Since missense mutations account for the vast majority of TP53-mut DLBCL patients, and most non-missense mutations affect the function of the P53 protein, leading to worse survival rates, we distinguished patients with missense mutations. A TP53 missense mutation risk model was constructed based on a 150-combination machine learning computational framework, demonstrating excellent performance in predicting prognosis. Further analysis revealed that patients with high-risk missense mutations are significantly associated with early progression and exhibit dysregulation of multiple immune and metabolic pathways at the transcriptional level. Additionally, the high-risk group showed an absolutely suppressed immune microenvironment. To stratify the entire cohort of TP53-mut DLBCL, we combined clinical characteristics and ultimately constructed the TP53 Prognostic Index (TP53PI) model. In summary, we identified the truly high-risk TP53-mut DLBCL patients and explained this difference at the mutation and transcriptional levels.

Comprehensive genetic profiling and molecularly guided treatment for patients with primary CNS tumors

Despite major advances in molecular profiling and classification of primary brain tumors, personalized treatment remains limited for most patients. Here, we explored the feasibility of individual molecular profiling and the efficacy of biomarker-guided therapy for adult patients with primary brain cancers in the real-world setting within the molecular tumor board Freiburg, Germany. We analyzed genetic profiles, personalized treatment recommendations, and clinical outcomes of 102 patients with 21 brain tumor types. Alterations in the cell cycle, BRAF, and mTOR pathways most frequently led to personalized treatment recommendations. Molecularly informed therapies were recommended in 71% and implemented in 32% of patients with completed molecular diagnostics. The disease control rate following targeted treatment was 50% and the overall response rate was 30%, with a progression-free survival 2/1 ratio of at least 1.3 in 31% of patients. This study highlights the efficacy of molecularly guided treatment and the need for biomarker-stratified trials in brain cancers.

Understanding variants of unknown significance and classification of genomic alterations

Despite recent efforts to issue clinical guidelines outlining strategies to define the pathogenicity of genomic variants, there is currently no standardized framework for which to make these assertions. This review does not present a step-by-step methodology, but rather takes a holistic approach to discuss many aspects which should be taken into consideration when determining variant pathogenicity. Categorization should be curated to reflect relevant findings within the scope of the specific medical context. Functional characterization should evaluate all available information, including results from literature reviews, different classes of genomic data repositories, and applicable computational predictive algorithms. This article further proposes a multidimensional view to infer pathogenic status from many genomic measurements across multiple axes. Notably, tumor suppressors and oncogenes exhibit fundamentally different biology which helps refine the importance of effects on splicing, mutation interactions, copy number thresholds, rearrangement annotations, germline status, and genome-wide signatures. Understanding these relevant datapoints with thoughtful perspective could aid in the reclassification of variants of unknown significance (VUS), which are ambiguously understood and currently have uncertain clinical implications. Ongoing assessments of VUS examining these relevant biological axes could lead to more accurate classification of variant pathogenicity interpretation in diagnostic oncology.

Identification of TP53 germline variants in pediatric patients undergoing tumor testing: strategy and prevalence

BACKGROUND

TP53 alterations are common in certain pediatric cancers, making identification of putative germline variants through tumor genomic profiling crucial for disease management.

METHODS

We analyzed TP53 alterations in 3123 tumors from 2788 pediatric patients sequenced using tumor-only or tumor-normal paired panels. Germline confirmatory testing was performed when indicated. Somatic and germline variants were classified based on published guidelines.

RESULTS

In 248 tumors from 222 patients, 284 tier 1/2 TP53 sequence and small copy number variants were detected. Following germline classification, 86.6% of 142 unique variants were pathogenic or likely pathogenic. Confirmatory testing on 118 patients revealed germline TP53 variants in 28 of them (23 pathogenic or likely pathogenic and 5 of uncertain significance), suggesting a minimum Li-Fraumeni syndrome incidence of 0.8% (23/2788) in this cohort, 10.4% (23/222) in patients with TP53 variant-carrying tumors, and 19.5% (23/118) with available normal samples. About 25% (7/28) of patients with germline TP53 variants did not meet Li-Fraumeni syndrome diagnostic or testing criteria, while 20.9% (28/134) with confirmed or inferred somatic origins did. TP53 biallelic inactivation occurred in 75% of germline carrier tumors and was also prevalent in other groups, causing an elevated tumor-observed variant allelic fraction. Somatic evidence, however, including low variant allele fraction correctly identified only 27.8% (25/90) of patients with confirmed somatic TP53 variants.

CONCLUSION

The high incidence and variable phenotype of Li-Fraumeni syndrome in this cohort highlights the importance of assessing germline status of TP53 variants identified in all pediatric tumors. Without clear somatic evidence, distinguishing somatic from germline origins is challenging. Classifying germline and somatic variants should follow appropriate guidelines.

Systematic discovery of neoepitope-HLA pairs for neoantigens shared among patients and tumor types

The broad application of precision cancer immunotherapies is limited by the number of validated neoepitopes that are common among patients or tumor types. To expand the known repertoire of shared neoantigen-human leukocyte antigen (HLA) complexes, we developed a high-throughput platform that coupled an in vitro peptide-HLA binding assay with engineered cellular models expressing individual HLA alleles in combination with a concatenated transgene harboring 47 common cancer neoantigens. From more than 24,000 possible neoepitope-HLA combinations, biochemical and computational assessment yielded 844 unique candidates, of which 86 were verified after immunoprecipitation mass spectrometry analyses of engineered, monoallelic cell lines. To evaluate the potential for immunogenicity, we identified T cell receptors that recognized select neoepitope-HLA pairs and elicited a response after introduction into human T cells. These cellular systems and our data on therapeutically relevant neoepitopes in their HLA contexts will aid researchers studying antigen processing as well as neoepitope targeting therapies.

Diffuse Pleural Mesotheliomas with Genomic Near-Haploidization: A Newly Recognized Subset with Distinct Clinical, Histologic, and Molecular Features

PURPOSE

Diffuse pleural mesotheliomas (DPM) with genomic near-haploidization (GNH) represent a novel subtype first recognized by The Cancer Genome Atlas project; however, its clinicopathologic and molecular features remain poorly defined.

EXPERIMENTAL DESIGN

We analyzed clinical genomic profiling data from 290 patients with DPM using the Memorial Sloan Kettering-Integrated Mutation Profiling of Actionable Cancer Targets (MSK-IMPACT) assay. Allele-specific copy number analysis was performed using the Fraction and Allele-Specific Copy Number Estimates from Tumor Sequencing (FACETS) algorithm.

RESULTS

A total of 210 patients were evaluable for loss of heterozygosity (LOH) analysis using FACETS from MSK-IMPACT tumor:normal sequencing data. In this cohort, GNH, defined as LOH across >80% of the genome, was detected in 10 cases (4.8%). Compared with non-GNH tumors, GNH DPMs were associated with younger age and less frequent self-reported history of occupational asbestos exposure. Histologically, GNH DPMs were enriched in biphasic subtype (80% vs. 14.5%) and showed abundant tumor-infiltrating lymphocytes (TILs). Genomic analysis revealed a higher frequency of TP53 alterations, whereas SETDB1 mutations were present in nearly all and only in this subset. The clinicopathologic and molecular findings were further validated in a separate cohort. Despite the younger age, patients with GNH DPMs had a shorter overall survival (10.9 vs. 25.4 months, P = 0.004); the poor prognostic impact of GNH remained significant after controlling for biphasic histology. Of three patients with GNH DPMs who received immune checkpoint blockade, two achieved a clinician-assessed partial response.

CONCLUSIONS

GNH defines an aggressive subtype of mainly biphasic DPMs in younger patients with recurrent alterations in SETDB1 and TP53. The enrichment in biphasic histology and TILs, together with our preliminary immune checkpoint blockade response data and anecdotal clinical trial data, suggests that further evaluation of immunotherapy may be warranted in this subset.

Variant Impact Predictor database (VIPdb), version 2: Trends from 25 years of genetic variant impact predictors

Background

Variant interpretation is essential for identifying patients' disease-causing genetic variants amongst the millions detected in their genomes. Hundreds of Variant Impact Predictors (VIPs), also known as Variant Effect Predictors (VEPs), have been developed for this purpose, with a variety of methodologies and goals. To facilitate the exploration of available VIP options, we have created the Variant Impact Predictor database (VIPdb).

Results

The Variant Impact Predictor database (VIPdb) version 2 presents a collection of VIPs developed over the past 25 years, summarizing their characteristics, ClinGen calibrated scores, CAGI assessment results, publication details, access information, and citation patterns. We previously summarized 217 VIPs and their features in VIPdb in 2019. Building upon this foundation, we identified and categorized an additional 186 VIPs, resulting in a total of 403 VIPs in VIPdb version 2. The majority of the VIPs have the capacity to predict the impacts of single nucleotide variants and nonsynonymous variants. More VIPs tailored to predict the impacts of insertions and deletions have been developed since the 2010s. In contrast, relatively few VIPs are dedicated to the prediction of splicing, structural, synonymous, and regulatory variants. The increasing rate of citations to VIPs reflects the ongoing growth in their use, and the evolving trends in citations reveal development in the field and individual methods.

Conclusions

VIPdb version 2 summarizes 403 VIPs and their features, potentially facilitating VIP exploration for various variant interpretation applications.

Availability

VIPdb version 2 is available at https://genomeinterpretation.org/vipdb.

Deep-Learning Model for Tumor-Type Prediction Using Targeted Clinical Genomic Sequencing Data

Tumor type guides clinical treatment decisions in cancer, but histology-based diagnosis remains challenging. Genomic alterations are highly diagnostic of tumor type, and tumor-type classifiers trained on genomic features have been explored, but the most accurate methods are not clinically feasible, relying on features derived from whole-genome sequencing (WGS), or predicting across limited cancer types. We use genomic features from a data set of 39,787 solid tumors sequenced using a clinically targeted cancer gene panel to develop Genome-Derived-Diagnosis Ensemble (GDD-ENS): a hyperparameter ensemble for classifying tumor type using deep neural networks. GDD-ENS achieves 93% accuracy for high-confidence predictions across 38 cancer types, rivaling the performance of WGS-based methods. GDD-ENS can also guide diagnoses of rare type and cancers of unknown primary and incorporate patient-specific clinical information for improved predictions. Overall, integrating GDD-ENS into prospective clinical sequencing workflows could provide clinically relevant tumor-type predictions to guide treatment decisions in real time.

SIGNIFICANCE

We describe a highly accurate tumor-type prediction model, designed specifically for clinical implementation. Our model relies only on widely used cancer gene panel sequencing data, predicts across 38 distinct cancer types, and supports integration of patient-specific nongenomic information for enhanced decision support in challenging diagnostic situations. See related commentary by Garg, p. 906. This article is featured in Selected Articles from This Issue, p. 897.

Adenoid cystic carcinoma of the Bartholin's gland is underpinned by MYB- and MYBL1- rearrangements

OBJECTIVE

Adenoid cystic carcinoma (AdCC) of the Bartholin's gland (AdCC-BG) is a very rare gynecologic vulvar malignancy. AdCC-BGs are slow-growing but locally aggressive and are associated with high recurrence rates. Here we sought to characterize the molecular underpinning of AdCC-BGs.

METHODS

AdCC-BGs (n = 6) were subjected to a combination of RNA-sequencing, targeted DNA-sequencing, reverse-transcription PCR, fluorescence in situ hybridization (FISH) and MYB immunohistochemistry (IHC). Clinicopathologic variables, somatic mutations, copy number alterations and chimeric transcripts were assessed.

RESULTS

All six AdCC-BGs were biphasic, composed of ductal and myoepithelial cells. Akin to salivary gland and breast AdCCs, three AdCC-BGs had the MYB::NFIB fusion gene with varying breakpoints, all of which were associated with MYB overexpression by IHC. Two AdCC-BGs were underpinned by MYBL1 fusion genes with different gene partners, including MYBL1::RAD51B and MYBL1::EWSR1 gene fusions, and showed MYB protein expression. Although the final AdCC-BG studied had MYB protein overexpression, no gene fusion was identified. AdCC-BGs harbored few additional somatic genetic alterations, and only few mutations in cancer-related genes were identified, including GNAQ, GNAS, KDM6A, AKT1 and BCL2, none of which were recurrent. Two AdCC-BGs, both with a MYB::NFIB fusion gene, developed metastatic disease.

CONCLUSIONS

AdCC-BGs constitute a convergent phenotype, whereby activation of MYB or MYBL1 can be driven by the MYB::NFIB fusion gene or MYBL1 rearrangements. Our observations further support the notion that AdCCs, irrespective of organ site, constitute a genotypic-phenotypic correlation. Assessment of MYB or MYBL1 rearrangements may be used as an ancillary marker for the diagnosis of AdCC-BGs.

Basket Trials: Past, Present, and Future

Large-scale tumor molecular profiling has revealed that diverse cancer histologies are driven by common pathways with unifying biomarkers that can be exploited therapeutically. Disease-agnostic basket trials have been increasingly utilized to test biomarker-driven therapies across cancer types. These trials have led to drug approvals and improved the lives of patients while simultaneously advancing our understanding of cancer biology. This review focuses on the practicalities of implementing basket trials, with an emphasis on molecularly targeted trials. We examine the biologic subtleties of genomic biomarker and patient selection, discuss previous successes in drug development facilitated by basket trials, describe certain novel targets and drugs, and emphasize practical considerations for participant recruitment and study design. This review also highlights strategies for aiding patient access to basket trials. As basket trials become more common, steps to ensure equitable implementation of these studies will be critical for molecularly targeted drug development.

Prioritizing Clinically Significant Lung Cancer Somatic Mutations for Targeted Therapy Through Efficient NGS Data Filtering System

In the realm of lung cancer treatment, where genetic heterogeneity presents formidable challenges, precision oncology demands an exacting approach to identify and hierarchically sort clinically significant somatic mutations. Current Next-Generation Sequencing (NGS) data filtering pipelines, while utilizing various external databases for mutation screening, often fall short in comprehensive integration and flexibility needed to keep pace with the evolving landscape of clinical data. Our study introduces a sophisticated NGS data filtering system, which not only aggregates but effectively synergizes diverse data sources, encompassing genetic variants, gene functions, clinical evidence, and an extensive body of literature. This system is distinguished by a unique algorithm that facilitates a rigorous, multi-tiered filtration process. This allows for the efficient prioritization of 420 genes and 1,193 variants from large datasets, with a particular focus on 80 variants demonstrating high clinical actionability. These variants have been aligned with FDA approvals, NCCN guidelines, and thoroughly reviewed literature, thereby equipping oncologists with a refined arsenal for targeted therapy decisions. The innovation of our system lies in its dynamic integration framework and its algorithm, tailored to emphasize clinical utility and actionability-a nuanced approach often lacking in existing methodologies. Our validation on real-world lung adenocarcinoma NGS datasets has shown not only an enhanced efficiency in identifying genetic targets but also the potential to streamline clinical workflows, thus propelling the advancement of precision oncology. Planned future enhancements include expanding the range of integrated data types and developing a user-friendly interface, aiming to facilitate easier access to data and promote collaborative efforts in tailoring cancer treatments.

Predicted mechanistic impacts of human protein missense variants

Genome sequencing efforts have led to the discovery of tens of millions of protein missense variants found in the human population with the majority of these having no annotated role and some likely contributing to trait variation and disease. Sequence-based artificial intelligence approaches have become highly accurate at predicting variants that are detrimental to the function of proteins but they do not inform on mechanisms of disruption. Here we combined sequence and structure-based methods to perform proteome-wide prediction of deleterious variants with information on their impact on protein stability, protein-protein interactions and small-molecule binding pockets. AlphaFold2 structures were used to predict approximately 100,000 small-molecule binding pockets and stability changes for over 200 million variants. To inform on protein-protein interfaces we used AlphaFold2 to predict structures for nearly 500,000 protein complexes. We illustrate the value of mechanism-aware variant effect predictions to study the relation between protein stability and abundance and the structural properties of interfaces underlying trans protein quantitative trait loci (pQTLs). We characterised the distribution of mechanistic impacts of protein variants found in patients and experimentally studied example disease linked variants in FGFR1.

Assessment of gene-disease associations and recommendations for genetic testing for somatic variants in vascular anomalies by VASCERN-VASCA

BACKGROUND

Vascular anomalies caused by somatic (postzygotic) variants are clinically and genetically heterogeneous diseases with overlapping or distinct entities. The genetic knowledge in this field is rapidly growing, and genetic testing is now part of the diagnostic workup alongside the clinical, radiological and histopathological data. Nonetheless, access to genetic testing is still limited, and there is significant heterogeneity across the approaches used by the diagnostic laboratories, with direct consequences on test sensitivity and accuracy. The clinical utility of genetic testing is expected to increase progressively with improved theragnostics, which will be based on information about the efficacy and safety of the emerging drugs and future molecules. The aim of this study was to make recommendations for optimising and guiding the diagnostic genetic testing for somatic variants in patients with vascular malformations.

RESULTS

Physicians and lab specialists from 11 multidisciplinary European centres for vascular anomalies reviewed the genes identified to date as being involved in non-hereditary vascular malformations, evaluated gene-disease associations, and made recommendations about the technical aspects for identification of low-level mosaicism and variant interpretation. A core list of 24 genes were selected based on the current practices in the participating laboratories, the ISSVA classification and the literature. In total 45 gene-phenotype associations were evaluated: 16 were considered definitive, 16 strong, 3 moderate, 7 limited and 3 with no evidence.

CONCLUSIONS

This work provides a detailed evidence-based view of the gene-disease associations in the field of vascular malformations caused by somatic variants. Knowing both the gene-phenotype relationships and the strength of the associations greatly help laboratories in data interpretation and eventually in the clinical diagnosis. This study reflects the state of knowledge as of mid-2023 and will be regularly updated on the VASCERN-VASCA website (VASCERN-VASCA, https://vascern.eu/groupe/vascular-anomalies/ ).

CardioHotspots: a database of mutational hotspots for cardiac disorders

Mutational hotspots are DNA regions with an abnormally high frequency of genetic variants. Identifying whether a variant is located in a mutational hotspot is critical for determining the variant's role in disorder predisposition, development, and treatment response. Despite their significance, current databases on mutational hotspots are limited to the oncology domain. However, identifying mutational hotspots is critical for any disorder in which genetics plays a role. This is true for the world's leading cause of death: cardiac disorders. In this work, we present CardioHotspots, a literature-based database of manually curated hotspots for cardiac diseases. This is the only database we know of that provides high-quality and easily accessible information about hotspots associated with cardiac disorders. CardioHotspots is publicly accessible via a web-based platform (https://genomics-hub.pros.dsic.upv.es:3099/). Database URL: https://genomics-hub.pros.dsic.upv.es:3099/.

PreMode predicts mode-of-action of missense variants by deep graph representation learning of protein sequence and structural context

Accurate prediction of the functional impact of missense variants is important for disease gene discovery, clinical genetic diagnostics, therapeutic strategies, and protein engineering. Previous efforts have focused on predicting a binary pathogenicity classification, but the functional impact of missense variants is multi-dimensional. Pathogenic missense variants in the same gene may act through different modes of action (i.e., gain/loss-of-function) by affecting different aspects of protein function. They may result in distinct clinical conditions that require different treatments. We developed a new method, PreMode, to perform gene-specific mode-of-action predictions. PreMode models effects of coding sequence variants using SE(3)-equivariant graph neural networks on protein sequences and structures. Using the largest-to-date set of missense variants with known modes of action, we showed that PreMode reached state-of-the-art performance in multiple types of mode-of-action predictions by efficient transfer-learning. Additionally, PreMode's prediction of G/LoF variants in a kinase is consistent with inactive-active conformation transition energy changes. Finally, we show that PreMode enables efficient study design of deep mutational scans and optimization in protein engineering.

Nivolumab for mismatch-repair-deficient or hypermutated gynecologic cancers: a phase 2 trial with biomarker analyses

Programmed death-1 (PD-1) inhibitors are approved for therapy of gynecologic cancers with DNA mismatch repair deficiency (dMMR), although predictors of response remain elusive. We conducted a single-arm phase 2 study of nivolumab in 35 patients with dMMR uterine or ovarian cancers. Co-primary endpoints included objective response rate (ORR) and progression-free survival at 24 weeks (PFS24). Secondary endpoints included overall survival (OS), disease control rate (DCR), duration of response (DOR) and safety. Exploratory endpoints included biomarkers and molecular correlates of response. The ORR was 58.8% (97.5% confidence interval (CI): 40.7-100%), and the PFS24 rate was 64.7% (97.5% one-sided CI: 46.5-100%), meeting the pre-specified endpoints. The DCR was 73.5% (95% CI: 55.6-87.1%). At the median follow-up of 42.1 months (range, 8.9-59.8 months), median OS was not reached. One-year OS rate was 79% (95% CI: 60.9-89.4%). Thirty-two patients (91%) had a treatment-related adverse event (TRAE), including arthralgia (n = 10, 29%), fatigue (n = 10, 29%), pain (n = 10, 29%) and pruritis (n = 10, 29%); most were grade 1 or grade 2. Ten patients (29%) reported a grade 3 or grade 4 TRAE; no grade 5 events occurred. Exploratory analyses show that the presence of dysfunctional (CD8+PD-1+) or terminally dysfunctional (CD8+PD-1+TOX+) T cells and their interaction with programmed death ligand-1 (PD-L1)+ cells were independently associated with PFS24. PFS24 was associated with presence of MEGF8 or SETD1B somatic mutations. This trial met its co-primary endpoints (ORR and PFS24) early, and our findings highlight several genetic and tumor microenvironment parameters associated with response to PD-1 blockade in dMMR cancers, generating rationale for their validation in larger cohorts.ClinicalTrials.gov identifier: NCT03241745 .

Molecular underpinnings of dedifferentiation and aggressiveness in chromophobe renal cell carcinoma

Sarcomatoid dedifferentiation is common to multiple renal cell carcinoma (RCC) subtypes, including chromophobe RCC (ChRCC), and is associated with increased aggressiveness, resistance to targeted therapies, and heightened sensitivity to immunotherapy. To study ChRCC dedifferentiation, we performed multiregion integrated paired pathological and genomic analyses. Interestingly, ChRCC dedifferentiates not only into sarcomatoid but also into anaplastic and glandular subtypes, which are similarly associated with increased aggressiveness and metastases. Dedifferentiated ChRCC shows loss of epithelial markers, convergent gene expression, and whole genome duplication from a hypodiploid state characteristic of classic ChRCC. We identified an intermediate state with atypia and increased mitosis but preserved epithelial markers. Our data suggest that dedifferentiation is initiated by hemizygous mutation of TP53, which can be observed in differentiated areas, as well as mutation of PTEN. Notably, these mutations become homozygous with duplication of preexisting monosomes (i.e., chromosomes 17 and 10), which characterizes the transition to dedifferentiated ChRCC. Serving as potential biomarkers, dedifferentiated areas become accentuated by mTORC1 activation (phospho-S6) and p53 stabilization. Notably, dedifferentiated ChRCC share gene enrichment and pathway activation features with other sarcomatoid RCC, suggesting convergent evolutionary trajectories. This study expands our understanding of aggressive ChRCC, provides insight into molecular mechanisms of tumor progression, and informs pathologic classification and diagnostics.

CRISPR/Cas9 targeting of passenger single nucleotide variants in haploinsufficient or essential genes expands cancer therapy prospects

CRISPR/Cas9 technology has effectively targeted cancer-specific oncogenic hotspot mutations or insertion-deletions. However, their limited prevalence in tumors restricts their application. We propose a novel approach targeting passenger single nucleotide variants (SNVs) in haploinsufficient or essential genes to broaden therapeutic options. By disrupting haploinsufficient or essential genes through the cleavage of DNA in the SNV region using CRISPR/Cas9, we achieved the selective elimination of cancer cells without affecting normal cells. We found that, on average, 44.8% of solid cancer patients are eligible for our approach, a substantial increase compared to the 14.4% of patients with CRISPR/Cas9-applicable oncogenic hotspot mutations. Through in vitro and in vivo experiments, we validated our strategy by targeting a passenger mutation in the essential ribosomal gene RRP9 and haploinsufficient gene SMG6. This demonstrates the potential of our strategy to selectively eliminate cancer cells and expand therapeutic opportunities.

Singleton mutations in large-scale cancer genome studies: uncovering the tail of cancer genome

Singleton or low-frequency driver mutations are challenging to identify. We present a domain driver mutation estimator (DOME) to identify rare candidate driver mutations. DOME analyzes positions analogous to known statistical hotspots and resistant mutations in combination with their functional and biochemical residue context as determined by protein structures and somatic mutation propensity within conserved PFAM domains, integrating the CADD scoring scheme. Benchmarked against seven other tools, DOME exhibited superior or comparable accuracy compared to all evaluated tools in the prediction of functional cancer drivers, with the exception of one tool. DOME identified a unique set of 32 917 high-confidence predicted driver mutations from the analysis of whole proteome missense variants within domain boundaries across 1331 genes, including 1192 noncancer gene census genes, emphasizing its unique place in cancer genome analysis. Additionally, analysis of 8799 TCGA (The Cancer Genome Atlas) and in-house tumor samples revealed 847 potential driver mutations, with mutations in tyrosine kinase members forming the dominant burden, underscoring its higher significance in cancer. Overall, DOME complements current approaches for identifying novel, low-frequency drivers and resistant mutations in personalized therapy.

A prime editor mouse to model a broad spectrum of somatic mutations in vivo

Genetically engineered mouse models only capture a small fraction of the genetic lesions that drive human cancer. Current CRISPR-Cas9 models can expand this fraction but are limited by their reliance on error-prone DNA repair. Here we develop a system for in vivo prime editing by encoding a Cre-inducible prime editor in the mouse germline. This model allows rapid, precise engineering of a wide range of mutations in cell lines and organoids derived from primary tissues, including a clinically relevant Kras mutation associated with drug resistance and Trp53 hotspot mutations commonly observed in pancreatic cancer. With this system, we demonstrate somatic prime editing in vivo using lipid nanoparticles, and we model lung and pancreatic cancer through viral delivery of prime editing guide RNAs or orthotopic transplantation of prime-edited organoids. We believe that this approach will accelerate functional studies of cancer-associated mutations and complex genetic combinations that are challenging to construct with traditional models.

Microsatellite Instability and Mismatch Repair Deficiency Define a Distinct Subset of Lung Cancers Characterized by Smoking Exposure, High Tumor Mutational Burden, and Recurrent Somatic MLH1 Inactivation

INTRODUCTION

Microsatellite instability (MSI) and mismatch repair (MMR) deficiency represent a distinct oncogenic process and predict response to immune checkpoint inhibitors (ICIs). The clinicopathologic features of MSI-high (MSI-H) and MMR deficiency (MMR-D) in lung cancers remain poorly characterized.

METHODS

MSI status from 5171 patients with NSCLC and 315 patients with SCLC was analyzed from targeted next-generation sequencing data using two validated bioinformatic pipelines.

RESULTS

MSI-H and MMR-D were identified in 21 patients with NSCLC (0.41%) and six patients with SCLC (1.9%). Notably, all patients with NSCLC had a positive smoking history, including 11 adenocarcinomas. Compared with microsatellite stable cases, MSI-H was associated with exceptionally high tumor mutational burden (37.4 versus 8.5 muts/Mb, p < 0.0001), MMR mutational signatures (43% versus 0%, p < 0.0001), and somatic biallelic alterations in MLH1 (52% versus 0%, p < 0.0001). Loss of MLH1 and PMS2 expression by immunohistochemistry was found in MLH1 altered and wild-type cases. Similarly, the majority of patients with MSI-H SCLC had evidence of MLH1 inactivation, including two with MLH1 promoter hypermethylation. A single patient with NSCLC with a somatic MSH2 mutation had Lynch syndrome as confirmed by the presence of a germline MSH2 mutation. Among patients with advanced MSI-H lung cancers treated with ICIs, durable clinical benefit was observed in three of eight patients with NSCLC and two of two patients with SCLC. In NSCLC, STK11, KEAP1, and JAK1 were mutated in nonresponders but wild type in responders.

CONCLUSIONS

We present a comprehensive clinicogenomic landscape of MSI-H lung cancers and reveal that MSI-H defines a rare subset of lung cancers associated with smoking, high tumor mutational burden, and MLH1 inactivation. Although durable clinical benefit to ICI was observed in some patients, the broad range of responses suggests that clinical activity may be modulated by co-mutational landscapes.

A comprehensive clinically informed map of dependencies in cancer cells and framework for target prioritization

Genetic screens in cancer cell lines inform gene function and drug discovery. More comprehensive screen datasets with multi-omics data are needed to enhance opportunities to functionally map genetic vulnerabilities. Here, we construct a second-generation map of cancer dependencies by annotating 930 cancer cell lines with multi-omic data and analyze relationships between molecular markers and cancer dependencies derived from CRISPR-Cas9 screens. We identify dependency-associated gene expression markers beyond driver genes, and observe many gene addiction relationships driven by gain of function rather than synthetic lethal effects. By combining clinically informed dependency-marker associations with protein-protein interaction networks, we identify 370 anti-cancer priority targets for 27 cancer types, many of which have network-based evidence of a functional link with a marker in a cancer type. Mapping these targets to sequenced tumor cohorts identifies tractable targets in different cancer types. This target prioritization map enhances understanding of gene dependencies and identifies candidate anti-cancer targets for drug development.

BRCA1, BRCA2, and TP53 germline and somatic variants and clinicopathological characteristics of Brazilian patients with epithelial ovarian cancer

BACKGROUND

Approximately 3/4 of ovarian cancers are diagnosed in advanced stages, with the high-grade epithelial ovarian carcinoma (EOC) accounting for 90% of the cases. EOC present high genomic instability and somatic loss-of-function variants in genes associated with homologous recombination mutational repair pathway (HR), such as BRCA1 and BRCA2, and in TP53. The identification of germline variants in HR genes in EOC is relevant for treatment of platinum resistant tumors and relapsed tumors with therapies based in synthetic lethality such as PARP inhibitors. Patients with somatic variants in HR genes may also benefit from these therapies. In this work was analyzed the frequency of somatic variants in BRCA1, BRCA2, and TP53 in an EOC cohort of Brazilian patients, estimating the proportion of variants in tumoral tissue and their association with progression-free survival and overall survival.

METHODS

The study was conducted with paired blood/tumor samples from 56 patients. Germline and tumoral sequences of BRCA1, BRCA2, and TP53 were obtained by massive parallel sequencing. The HaplotypeCaller method was used for calling germline variants, and somatic variants were called with Mutect2.

RESULTS

A total of 26 germline variants were found, and seven patients presented germline pathogenic or likely pathogenic variants in BRCA1 or BRCA2. The analysis of tumoral tissue identified 52 somatic variants in 41 patients, being 43 somatic variants affecting or likely affecting protein functionality. Survival analyses showed that tumor staging was associated with overall survival (OS), while the presence of somatic mutation in TP53 was not associated with OS or progression-free survival.

CONCLUSION

Frequency of pathogenic or likely pathogenic germline variants in BRCA1 and BRCA2 (12.5%) was lower in comparison with other studies. TP53 was the most altered gene in tumors, with 62.5% presenting likely non-functional or non-functional somatic variants, while eight 14.2% presented likely non-functional or non-functional somatic variants in BRCA1 or BRCA2.

Integrating Clinical Cancer and PTM Proteomics Data Identifies a Mechanism of ACK1 Kinase Activation

Beyond the most common oncogenes activated by mutation (mut-drivers), there likely exists a variety of low-frequency mut-drivers, each of which is a possible frontier for targeted therapy. To identify new and understudied mut-drivers, we developed a machine learning (ML) model that integrates curated clinical cancer data and posttranslational modification (PTM) proteomics databases. We applied the approach to 62,746 patient cancers spanning 84 cancer types and predicted 3,964 oncogenic mutations across 1,148 genes, many of which disrupt PTMs of known and unknown function. The list of putative mut-drivers includes established drivers and others with poorly understood roles in cancer. This ML model is available as a web application. As a case study, we focused the approach on nonreceptor tyrosine kinases (NRTK) and found a recurrent mutation in activated CDC42 kinase-1 (ACK1) that disrupts the Mig6 homology region (MHR) and ubiquitin-association (UBA) domains on the ACK1 C-terminus. By studying these domains in cultured cells, we found that disruption of the MHR domain helps activate the kinase while disruption of the UBA increases kinase stability by blocking its lysosomal degradation. This ACK1 mutation is analogous to lymphoma-associated mutations in its sister kinase, TNK1, which also disrupt a C-terminal inhibitory motif and UBA domain. This study establishes a mut-driver discovery tool for the research community and identifies a mechanism of ACK1 hyperactivation shared among ACK family kinases.

IMPLICATIONS

This research identifies a potentially targetable activating mutation in ACK1 and other possible oncogenic mutations, including PTM-disrupting mutations, for further study.

Lung cancer in patients who have never smoked - an emerging disease

Lung cancer is the most common cause of cancer-related deaths globally. Although smoking-related lung cancers continue to account for the majority of diagnoses, smoking rates have been decreasing for several decades. Lung cancer in individuals who have never smoked (LCINS) is estimated to be the fifth most common cause of cancer-related deaths worldwide in 2023, preferentially occurring in women and Asian populations. As smoking rates continue to decline, understanding the aetiology and features of this disease, which necessitate unique diagnostic and treatment paradigms, will be imperative. New data have provided important insights into the molecular and genomic characteristics of LCINS, which are distinct from those of smoking-associated lung cancers and directly affect treatment decisions and outcomes. Herein, we review the emerging data regarding the aetiology and features of LCINS, particularly the genetic and environmental underpinnings of this disease as well as their implications for treatment. In addition, we outline the unique diagnostic and therapeutic paradigms of LCINS and discuss future directions in identifying individuals at high risk of this disease for potential screening efforts.

Comprehensive Genomic and Transcriptomic Analysis of Sclerosing Pneumocytoma

Sclerosing pneumocytoma is a rare and distinct lung neoplasm whose histogenesis and molecular alterations are the subject of ongoing research. Our recent study revealed that AKT1 internal tandem duplications (ITD), point mutations, and short indels were present in almost all tested sclerosing pneumocytomas, suggesting that AKT1 mutations are a major driving oncogenic event in this tumor. Although the pathogenic role of AKT1 point mutations is well established, the significance of AKT1 ITD in oncogenesis remains largely unexplored. We conducted comprehensive genomic and transcriptomic analyses of sclerosing pneumocytoma to address this knowledge gap. RNA-sequencing data from 23 tumors and whole-exome sequencing data from 44 tumors were used to obtain insights into their genetic and transcriptomic profiles. Our analysis revealed a high degree of genetic and transcriptomic similarity between tumors carrying AKT1 ITD and those with AKT1 point mutations. Mutational signature analysis revealed COSMIC signatures 1 and 5 as the prevailing signatures of sclerosing pneumocytoma, associated with the spontaneous deamination of 5-methylcytosine and an unknown etiology, respectively. RNA-sequencing data analysis revealed that the sclerosing pneumocytoma gene expression profile is characterized by activation of the PI3K/AKT/mTOR pathway, which exhibits significant similarity between tumors harboring AKT1 ITD and those with AKT1 point mutations. Notably, an upregulation of SOX9, a transcription factor known for its involvement in fetal lung development, was observed in sclerosing pneumocytoma. Specifically, SOX9 expression was prominent in the round cell component, whereas it was relatively lower in the surface cell component of the tumor. To the best of our knowledge, this is the first comprehensive investigation of the genomic and transcriptomic characteristics of sclerosing pneumocytoma. Results of the present study provide insights into the molecular attributes of sclerosing pneumocytoma and a basis for future studies of this enigmatic tumor.

Comprehensive genomic profiling of breast cancers characterizes germline-somatic mutation interactions mediating therapeutic vulnerabilities

Germline-somatic mutation interactions are universal and associated with tumorigenesis, but their role in breast cancer, especially in non-Caucasians, remains poorly characterized. We performed large-scale prospective targeted sequencing of matched tumor-blood samples from 4079 Chinese females, coupled with detailed clinical annotation, to map interactions between germline and somatic alterations. We discovered 368 pathogenic germline variants and identified 5 breast cancer DNA repair-associated genes (BCDGs; BRCA1/BRCA2/CHEK2/PALB2/TP53). BCDG mutation carriers, especially those with two-hit inactivation, demonstrated younger onset, higher tumor mutation burden, and greater clinical benefits from platinum drugs, PARP inhibitors, and immune checkpoint inhibitors. Furthermore, we leveraged a multiomics cohort to reveal that clinical benefits derived from two-hit events are associated with increased genome instability and an immune-activated tumor microenvironment. We also established an ethnicity-specific tool to predict BCDG mutation and two-hit status for genetic evaluation and therapeutic decisions. Overall, this study leveraged the large sequencing cohort of Chinese breast cancers, optimizing genomics-guided selection of DNA damaging-targeted therapy and immunotherapy within a broader population.

Analysis and Visualization of Longitudinal Genomic and Clinical Data from the AACR Project GENIE Biopharma Collaborative in cBioPortal

International cancer registries make real-world genomic and clinical data available, but their joint analysis remains a challenge. AACR Project GENIE, an international cancer registry collecting data from 19 cancer centers, makes data from >130,000 patients publicly available through the cBioPortal for Cancer Genomics (https://genie.cbioportal.org). For 25,000 patients, additional real-world longitudinal clinical data, including treatment and outcome data, are being collected by the AACR Project GENIE Biopharma Collaborative using the PRISSMM data curation model. Several thousand of these cases are now also available in cBioPortal. We have significantly enhanced the functionalities of cBioPortal to support the visualization and analysis of this rich clinico-genomic linked dataset, as well as datasets generated by other centers and consortia. Examples of these enhancements include (i) visualization of the longitudinal clinical and genomic data at the patient level, including timelines for diagnoses, treatments, and outcomes; (ii) the ability to select samples based on treatment status, facilitating a comparison of molecular and clinical attributes between samples before and after a specific treatment; and (iii) survival analysis estimates based on individual treatment regimens received. Together, these features provide cBioPortal users with a toolkit to interactively investigate complex clinico-genomic data to generate hypotheses and make discoveries about the impact of specific genomic variants on prognosis and therapeutic sensitivities in cancer.

SIGNIFICANCE

Enhanced cBioPortal features allow clinicians and researchers to effectively investigate longitudinal clinico-genomic data from patients with cancer, which will improve exploration of data from the AACR Project GENIE Biopharma Collaborative and similar datasets.

A Novel Type of IDH-wildtype Glioma Characterized by Gliomatosis Cerebri-like Growth Pattern, TERT Promoter Mutation, and Distinct Epigenetic Profile

Diffuse gliomas in adults encompass a heterogenous group of central nervous system neoplasms. In recent years, extensive (epi-)genomic profiling has identified several glioma subgroups characterized by distinct molecular characteristics, most importantly IDH1/2 and histone H3 mutations. A group of 16 diffuse gliomas classified as "adult-type diffuse high-grade glioma, IDH-wildtype, subtype F (HGG-F)" was identified by the DKFZ v12.5 Brain Tumor Classifier . Histopathologic characterization, exome sequencing, and review of clinical data was performed in all cases. Based on unsupervised t -distributed stochastic neighbor embedding and clustering analysis of genome-wide DNA methylation data, HGG-F shows distinct epigenetic profiles separate from established central nervous system tumors. Exome sequencing demonstrated frequent TERT promoter (12/15 cases), PIK3R1 (11/16), and TP53 mutations (5/16). Radiologic characteristics were reminiscent of gliomatosis cerebri in 9/14 cases (64%). Histopathologically, most cases were classified as diffuse gliomas (7/16, 44%) or were suspicious for the infiltration zone of a diffuse glioma (5/16, 31%). None of the cases demonstrated microvascular proliferation or necrosis. Outcome of 14 patients with follow-up data was better compared to IDH-wildtype glioblastomas with a median progression-free survival of 58 months and overall survival of 74 months (both P <0.0001). Our series represents a novel type of adult-type diffuse glioma with distinct molecular and clinical features. Importantly, we provide evidence that TERT promoter mutations in diffuse gliomas without further morphologic or molecular signs of high-grade glioma should be interpreted in the context of the clinicoradiologic presentation as well as epigenetic profile and may not be suitable as a standalone marker for glioblastoma, IDH-wildtype.

Genetic analysis of low-grade adenosquamous carcinoma of the breast progressing to high-grade metaplastic carcinoma

PURPOSE

Low-grade adenosquamous carcinoma (LGASC) is a rare type of metaplastic carcinoma of the breast (MBC) with an indolent clinical course. A few LGASC cases with high-grade transformation have been reported; however, the genetics underlying malignant progression of LGASC remain unclear.

METHODS

We performed whole-genome sequencing analysis on five MBCs from four patients, including one case with matching primary LGASC and a lymph node metastatic tumor consisting of high-grade MBC with a predominant metaplastic squamous cell carcinoma component (MSC) that progressed from LGASC and three cases of independent de novo MSC.

RESULTS

Unlike de novo MSC, LGASC and its associated MSC showed no TP53 mutation and tended to contain fewer structural variants than de novo MSC. Both LGASC and its associated MSC harbored the common GNAS c.C2530T:p.Arg844Cys mutation, which was more frequently detected in the cancer cell fraction of MSC. MSC associated with LGASC showed additional pathogenic deletions of multiple tumor-suppressor genes, such as KMT2D and BTG1. Copy number analysis revealed potential 18q loss of heterozygosity in both LGASC and associated MSC. The frequency of SMAD4::DCC fusion due to deletions increased with progression to MSC; however, chimeric proteins were not detected. SMAD4 protein expression was already decreased at the LGASC stage due to unknown mechanisms.

CONCLUSION

Not only LGASC but also its associated high-grade MBC may be genetically different from de novo high-grade MBC. Progression from LGASC to high-grade MBC may involve the concentration of driver mutations caused by clonal selection and inactivation of tumor-suppressor genes.

ATM deficiency confers specific therapeutic vulnerabilities in bladder cancer

Ataxia-telangiectasia mutated (ATM) plays a central role in the cellular response to DNA damage and ATM alterations are common in several tumor types including bladder cancer. However, the specific impact of ATM alterations on therapy response in bladder cancer is uncertain. Here, we combine preclinical modeling and clinical analyses to comprehensively define the impact of ATM alterations on bladder cancer. We show that ATM loss is sufficient to increase sensitivity to DNA-damaging agents including cisplatin and radiation. Furthermore, ATM loss drives sensitivity to DNA repair-targeted agents including poly(ADP-ribose) polymerase (PARP) and Ataxia telangiectasia and Rad3 related (ATR) inhibitors. ATM loss alters the immune microenvironment and improves anti-PD1 response in preclinical bladder models but is not associated with improved anti-PD1/PD-L1 response in clinical cohorts. Last, we show that ATM expression by immunohistochemistry is strongly correlated with response to chemoradiotherapy. Together, these data define a potential role for ATM as a predictive biomarker in bladder cancer.

Molecular Characterization of Endometrial Carcinomas in Black and White Patients Reveals Disparate Drivers with Therapeutic Implications

Although the incidence of endometrial carcinoma (EC) is similar in Black and White women, racial disparities are stark, with the highest mortality rates observed among Black patients. Here, analysis of 1,882 prospectively sequenced ECs using a clinical FDA-authorized tumor-normal panel revealed a significantly higher prevalence of high-risk histologic and molecular EC subtypes in self-identified Black (n = 259) compared with White (n = 1,623) patients. Clinically actionable alterations, including high tumor mutational burden/microsatellite instability, which confer benefit from immunotherapy, were less frequent in ECs from Black than from White patients. Ultramutated POLE molecular subtype ECs associated with favorable outcomes were rare in Black patients. Results were confirmed by genetic ancestry analysis. CCNE1 gene amplification, which is associated with aggressive clinical behavior, was more prevalent in carcinosarcomas occurring in Black than in White patients. ECs from Black and White patients display important differences in their histologic types, molecular subtypes, driver genetic alterations, and therapeutic targets.

SIGNIFICANCE

Our comprehensive analysis of prospectively clinically sequenced ECs revealed significant differences in their histologic and molecular composition and in the presence of therapeutic targets in Black versus White patients. These findings emphasize the importance of incorporating diverse populations into molecular studies and clinical trials to address EC disparities. This article is featured in Selected Articles from This Issue, p. 2293.

NCT/DKFZ MASTER handbook of interpreting whole-genome, transcriptome, and methylome data for precision oncology

Analysis of selected cancer genes has become an important tool in precision oncology but cannot fully capture the molecular features and, most importantly, vulnerabilities of individual tumors. Observational and interventional studies have shown that decision-making based on comprehensive molecular characterization adds significant clinical value. However, the complexity and heterogeneity of the resulting data are major challenges for disciplines involved in interpretation and recommendations for individualized care, and limited information exists on how to approach multilayered tumor profiles in clinical routine. We report our experience with the practical use of data from whole-genome or exome and RNA sequencing and DNA methylation profiling within the MASTER (Molecularly Aided Stratification for Tumor Eradication Research) program of the National Center for Tumor Diseases (NCT) Heidelberg and Dresden and the German Cancer Research Center (DKFZ). We cover all relevant steps of an end-to-end precision oncology workflow, from sample collection, molecular analysis, and variant prioritization to assigning treatment recommendations and discussion in the molecular tumor board. To provide insight into our approach to multidimensional tumor profiles and guidance on interpreting their biological impact and diagnostic and therapeutic implications, we present case studies from the NCT/DKFZ molecular tumor board that illustrate our daily practice. This manual is intended to be useful for physicians, biologists, and bioinformaticians involved in the clinical interpretation of genome-wide molecular information.

Functional analysis reveals driver cooperativity and novel mechanisms in endometrial carcinogenesis

High-risk endometrial cancer has poor prognosis and is increasing in incidence. However, understanding of the molecular mechanisms which drive this disease is limited. We used genetically engineered mouse models (GEMM) to determine the functional consequences of missense and loss of function mutations in Fbxw7, Pten and Tp53, which collectively occur in nearly 90% of high-risk endometrial cancers. We show that Trp53 deletion and missense mutation cause different phenotypes, with the latter a substantially stronger driver of endometrial carcinogenesis. We also show that Fbxw7 missense mutation does not cause endometrial neoplasia on its own, but potently accelerates carcinogenesis caused by Pten loss or Trp53 missense mutation. By transcriptomic analysis, we identify LEF1 signalling as upregulated in Fbxw7/FBXW7-mutant mouse and human endometrial cancers, and in human isogenic cell lines carrying FBXW7 mutation, and validate LEF1 and the additional Wnt pathway effector TCF7L2 as novel FBXW7 substrates. Our study provides new insights into the biology of high-risk endometrial cancer and suggests that targeting LEF1 may be worthy of investigation in this treatment-resistant cancer subgroup.

Integration of exome-seq and mRNA-seq using DawnRank, identified genes involved in innate immunity as drivers of breast cancer in the Indian cohort

Genetic heterogeneity influences the prognosis and therapy of breast cancer. The cause of disease progression varies and can be addressed individually. To identify the mutations and their impact on disease progression at an individual level, we sequenced exome and transcriptome from matched normal-tumor samples. We utilised DawnRank to prioritise driver genes and identify specific mutations in Indian patients. Mutations in the C3 and HLA genes were identified as drivers of disease progression, indicating the involvement of the innate immune system. We performed immune profiling on 16 matched normal/tumor samples using CIBERSORTx. We identified CD8+ve T cells, M2 macrophages, and neutrophils to be enriched in luminal A and T cells CD4+naïve, natural killer (NK) cells activated, T follicular helper (Tfh) cells, dendritic cells activated, and neutrophils in triple-negative breast cancer (TNBC) subtypes. Weighted gene co-expression network analysis (WGCNA) revealed activation of T cell-mediated response in ER positive samples and Interleukin and Interferons in ER negative samples. WGCNA analysis also identified unique pathways for each individual, suggesting that rare mutations/expression signatures can be used to design personalised treatment.

HER2 status as a potential predictive biomarker for ovarian clear cell carcinoma

Ovarian clear cell carcinoma (OCCC) is a subtype of ovarian carcinoma characterized by unique biological features and highly malignant characteristics including low chemosensitivity. Therefore, new therapeutic targets are needed. These could include the downstream pathways of receptor tyrosine kinases, especially the human epidermal growth factor receptor 2 (HER2). Our main objective was to characterize the HER2 status using immunohistochemistry (IHC) and FISH on 118 OCCCs, also considering the novel paradigm of HER2-zero and HER2-low status. Other aims included determination of the association between HER2 status and survival, HER2 gene DNA and RNA NGS analysis, HER2 gene expression analysis, and correlation between IHC and gene expression in HER2-zero and HER2-low cases. Cases with HER2 overexpression/amplification accounted for 5.1% (6/118), with additional 3% harbouring HER2 gene mutation. The remaining 112 (94.9%) cases were HER2-negative. Of these, 75% were classified as HER2-zero and 25% as HER2-low. This percentage of HER2 aberrations is significant concerning their possible therapeutic influence. Cases from the HER2-zero group showed significantly better survival. Although this relationship lost statistical significance in multivariate analysis, the results have potential therapeutic significance. HER2 gene expression analysis showed a significant correlation with HER2 IHC status in the entire cohort (HER2-positive vs. HER2-negative), while in the cohort of only HER2-negative cases, the results did not reach statistical significance, suggesting that gene expression analysis would not be suitable to confirm the subdivision into HER2-low and HER2-zero. Our results also emphasize the need for standardized HER2 testing in OCCC to determine the best predictor of clinical response.

AMBAR - Interactive Alteration annotations for molecular tumor boards

MOTIVATION

Personalized decision-making for cancer therapy relies on molecular profiling from sequencing data in combination with database evidence and expert knowledge. Molecular tumor boards (MTBs) bring together clinicians and scientists with diverse expertise and are increasingly established in the clinical routine for therapeutic interventions. However, the analysis and documentation of patients data are still time-consuming and difficult to manage for MTBs, especially as few tools are available for the amount of information required.

RESULTS

To overcome these limitations, we developed an interactive web application AMBAR (Alteration annotations for Molecular tumor BoARds), for therapeutic decision-making support in MTBs. AMBAR is an R shiny-based application that allows customization, interactive filtering, visualization, adding expert knowledge, and export to clinical systems of annotated mutations.

AVAILABILITY

AMBAR is dockerized, open source and available at https://sysbio.uni-ulm.de/?Software:Ambar Contact:hans.kestler@uni-ulm.de.

Accurate proteome-wide missense variant effect prediction with AlphaMissense

The vast majority of missense variants observed in the human genome are of unknown clinical significance. We present AlphaMissense, an adaptation of AlphaFold fine-tuned on human and primate variant population frequency databases to predict missense variant pathogenicity. By combining structural context and evolutionary conservation, our model achieves state-of-the-art results across a wide range of genetic and experimental benchmarks, all without explicitly training on such data. The average pathogenicity score of genes is also predictive for their cell essentiality, capable of identifying short essential genes that existing statistical approaches are underpowered to detect. As a resource to the community, we provide a database of predictions for all possible human single amino acid substitutions and classify 89% of missense variants as either likely benign or likely pathogenic.

Deep Learning Model for Tumor Type Prediction using Targeted Clinical Genomic Sequencing Data

Tumor type guides clinical treatment decisions in cancer, but histology-based diagnosis remains challenging. Genomic alterations are highly diagnostic of tumor type, and tumor type classifiers trained on genomic features have been explored, but the most accurate methods are not clinically feasible, relying on features derived from whole genome sequencing (WGS), or predicting across limited cancer types. We use genomic features from a dataset of 39,787 solid tumors sequenced using a clinical targeted cancer gene panel to develop Genome-Derived-Diagnosis Ensemble (GDD-ENS): a hyperparameter ensemble for classifying tumor type using deep neural networks. GDD-ENS achieves 93% accuracy for high-confidence predictions across 38 cancer types, rivalling performance of WGS-based methods. GDD-ENS can also guide diagnoses on rare type and cancers of unknown primary, and incorporate patient-specific clinical information for improved predictions. Overall, integrating GDD-ENS into prospective clinical sequencing workflows has enabled clinically-relevant tumor type predictions to guide treatment decisions in real time.

Standing in the canine precision medicine knowledge gap: Improving annotation of canine cancer genomic biomarkers through systematic comparative analysis of human cancer mutations in COSMIC

The accrual of cancer mutation data and related functional and clinical associations have revolutionised human oncology, enabling the advancement of precision medicine and biomarker-guided clinical management. The catalogue of cancer mutations is also growing in canine cancers. However, without direct high-powered functional data in dogs, it remains challenging to interpret and utilise them in research and clinical settings. It is well-recognised that canine and human cancers share genetic, molecular and phenotypic similarities. Therefore, leveraging the massive wealth of human mutation data may help advance canine oncology. Here, we present a structured analysis of sequence conservation and conversion of human mutations to the canine genome through a 'caninisation' process. We applied this analysis to COSMIC, the Catalogue of Somatic Mutations in Cancer, the most prominent human cancer mutation database. For the project's initial phase, we focused on the subset of the COSMIC data corresponding to Cancer Gene Census (CGC) genes. A total of 670 canine orthologs were found for 721 CGC genes. In these genes, 365 K unique mutations across 160 tumour types were converted successfully to canine coordinates. We identified shared putative cancer-driving mutations, including pathogenic and hotspot mutations and mutations bearing similar biomarker associations with diagnostic, prognostic and therapeutic utility. Thus, this structured caninisation of human cancer mutations facilitates the interpretation and annotation of canine mutations and helps bridge the knowledge gap to enable canine precision medicine.

Pan-cancer proteogenomics connects oncogenic drivers to functional states

Cancer driver events refer to key genetic aberrations that drive oncogenesis; however, their exact molecular mechanisms remain insufficiently understood. Here, our multi-omics pan-cancer analysis uncovers insights into the impacts of cancer drivers by identifying their significant cis-effects and distal trans-effects quantified at the RNA, protein, and phosphoprotein levels. Salient observations include the association of point mutations and copy-number alterations with the rewiring of protein interaction networks, and notably, most cancer genes converge toward similar molecular states denoted by sequence-based kinase activity profiles. A correlation between predicted neoantigen burden and measured T cell infiltration suggests potential vulnerabilities for immunotherapies. Patterns of cancer hallmarks vary by polygenic protein abundance ranging from uniform to heterogeneous. Overall, our work demonstrates the value of comprehensive proteogenomics in understanding the functional states of oncogenic drivers and their links to cancer development, surpassing the limitations of studying individual cancer types.