SomaticCombiner: improving the performance of somatic variant calling based on evaluation tests and a consensus approach

Molecular profiling of a bladder cancer with very high tumour mutational burden

The increasing incidence of urothelial bladder cancer is a notable global concern, as evidenced by the epidemiological data in terms of frequency, distribution, as well as mortality rates. Although numerous molecular alterations have been linked to the occurrence and progression of bladder cancer, currently there is a limited knowledge on the molecular signature able of accurately predicting clinical outcomes. In this report, we present a case of a pT3b high-grade infiltrating urothelial carcinoma with areas of squamous differentiation characterized by very high tumor mutational burden (TMB), with up-regulations of immune checkpoints. The high TMB, along with elevated expressions of PD-L1, PD-L2, and PD1, underscores the rationale for developing a personalized immunotherapy focused on the use of immune-checkpoint inhibitors. Additionally, molecular analysis revealed somatic mutations in several other cancer-related genes, including TP53, TP63 and NOTCH3. Mutations of TP53 and TP63 genes provide mechanistic insights on the molecular mechanisms underlying disease development and progression. Notably, the above-mentioned mutations and the elevated hypoxia score make the targeting of p53 and/or hypoxia related pathways a plausible personalized medicine option for this bladder cancer, particularly in combination with immunotherapy. Our data suggest a requirement for molecular profiling in bladder cancer to possibly select appropriate immune-checkpoint therapy.

Improving somatic exome sequencing performance by biological replicates

BACKGROUND

Next-generation sequencing (NGS) technologies offer fast and inexpensive identification of DNA sequences. Somatic sequencing is among the primary applications of NGS, where acquired (non-inherited) variants are based on comparing diseased and healthy tissues from the same individual. Somatic mutations in genetic diseases such as cancer are tightly associated with genomic instability. Genomic instability increases heterogenity, complicating sequencing efforts further, a task already challenged by the presence of short reads and repetitions in human DNA. This leads to low concordance among studies and limits reproducibility. This limitation is a significant problem since identified mutations in somatic sequencing are major biomarkers for diagnosis and the primary input of targeted therapies. Benchmarking studies were conducted to assess the error rates and increase reproducibility. Unfortunately, the number of somatic benchmarking sets is very limited due to difficulties in validating true somatic variants. Moreover, most NGS benchmarking studies are based on relatively simpler germline (inherited) sequencing. Recently, a comprehensive somatic sequencing benchmarking set was published by Sequencing Quality Control Phase 2 (SEQC2). We chose this dataset for our experiments because it is a well-validated, cancer-focused dataset that includes many tumor/normal biological replicates. Our study has two primary goals. First goal is to determine how replicate-based consensus approaches can improve the accuracy of somatic variant detection systems. Second goal is to develop highly predictive machine learning (ML) models by employing replicate-based consensus variants as labels during the training phase.

RESULTS

Ensemble approaches that combine alternative algorithms are relatively common; here, as an alternative, we study the performance enhancement potential of biological replicates. We first developed replicate-based consensus approaches that utilize the biological replicates available in this study to improve variant calling performance. Subsequently, we trained ML models using these biological replicates and achieved performance comparable to optimal ML models, those trained using high-confidence variants identified in advance.

CONCLUSIONS

Our replicate-based consensus approach can be used to improve variant calling performance and develop efficient ML models. Given the relative ease of obtaining biological replicates, this strategy allows for the development of efficient ML models tailored to specific datasets or scenarios.

DNA polymerase ε and δ variants drive mutagenesis in polypurine tracts in human tumors

Alterations in the exonuclease domain of DNA polymerase ε cause ultramutated cancers. These cancers accumulate AGA>ATA transversions; however, their genomic features beyond the trinucleotide motifs are obscure. We analyze the extended DNA context of ultramutation using whole-exome sequencing data from 524 endometrial and 395 colorectal tumors. We find that G>T transversions in POLE-mutant tumors predominantly affect sequences containing at least six consecutive purines, with a striking preference for certain positions within polypurine tracts. Using this signature, we develop a machine-learning classifier to identify tumors with hitherto unknown POLE drivers and validate two drivers, POLE-E978G and POLE-S461L, by functional assays in yeast. Unlike other pathogenic variants, the E978G substitution affects the polymerase domain of Pol ε. We further show that tumors with POLD1 drivers share the extended signature of POLE ultramutation. These findings expand the understanding of ultramutation mechanisms and highlight peculiar mutagenic properties of polypurine tracts in the human genome.

Mismatch repair deficiency is not sufficient to elicit tumor immunogenicity

DNA mismatch repair deficiency (MMRd) is associated with a high tumor mutational burden (TMB) and sensitivity to immune checkpoint blockade (ICB) therapy. Nevertheless, most MMRd tumors do not durably respond to ICB and critical questions remain about immunosurveillance and TMB in these tumors. In the present study, we developed autochthonous mouse models of MMRd lung and colon cancer. Surprisingly, these models did not display increased T cell infiltration or ICB response, which we showed to be the result of substantial intratumor heterogeneity of mutations. Furthermore, we found that immunosurveillance shapes the clonal architecture but not the overall burden of neoantigens, and T cell responses against subclonal neoantigens are blunted. Finally, we showed that clonal, but not subclonal, neoantigen burden predicts ICB response in clinical trials of MMRd gastric and colorectal cancer. These results provide important context for understanding immune evasion in cancers with a high TMB and have major implications for therapies aimed at increasing TMB.

The mutational landscape of the adult healthy parous and nulliparous human breast

The accumulation of somatic mutations in healthy human tissues has been extensively characterized, but the mutational landscape of the healthy breast is still poorly understood. Our analysis of whole-genome sequencing shows that in line with other healthy organs, the healthy breast during the reproduction years accumulates mutations with age, with the rate of accumulation in the epithelium of 15.24 ± 5 mutations/year. Both epithelial and stromal compartments contain mutations in breast-specific driver genes, indicative of subsequent positive selection. Parity- and age-associated differences are evident in the mammary epithelium, partly explaining the observed difference in breast cancer risk amongst women of different childbearing age. Parity is associated with an age-dependent increase in the clone size of mutated epithelial cells, suggesting that older first-time mothers have a higher probability of accumulating oncogenic events in the epithelium compared to younger mothers or nulliparous women. In conclusion, we describe the reference genome of the healthy female human breast during reproductive years and provide evidence of how parity affects the genomic landscape of the mammary gland.

Simple combination of multiple somatic variant callers to increase accuracy

Publications comparing variant caller algorithms present discordant results with contradictory rankings. Caller performances are inconsistent and wide ranging, and dependent upon input data, application, parameter settings, and evaluation metric. With no single variant caller emerging as a superior standard, combinations or ensembles of variant callers have appeared in the literature. In this study, a whole genome somatic reference standard was used to derive principles to guide strategies for combining variant calls. Then, manually annotated variants called from the whole exome sequencing of a tumor were used to corroborate these general principles. Finally, we examined the ability of these principles to reduce noise in targeted sequencing.

Performance comparisons between clustering models for reconstructing NGS results from technical replicates

To improve the performance of individual DNA sequencing results, researchers often use replicates from the same individual and various statistical clustering models to reconstruct a high-performance callset. Here, three technical replicates of genome NA12878 were considered and five model types were compared (consensus, latent class, Gaussian mixture, Kamila–adapted k-means, and random forest) regarding four performance indicators: sensitivity, precision, accuracy, and F1-score. In comparison with no use of a combination model, i) the consensus model improved precision by 0.1%; ii) the latent class model brought 1% precision improvement (97%–98%) without compromising sensitivity (= 98.9%); iii) the Gaussian mixture model and random forest provided callsets with higher precisions (both >99%) but lower sensitivities; iv) Kamila increased precision (>99%) and kept a high sensitivity (98.8%); it showed the best overall performance. According to precision and F1-score indicators, the compared non-supervised clustering models that combine multiple callsets are able to improve sequencing performance vs. previously used supervised models. Among the models compared, the Gaussian mixture model and Kamila offered non-negligible precision and F1-score improvements. These models may be thus recommended for callset reconstruction (from either biological or technical replicates) for diagnostic or precision medicine purposes.

Exome sequencing reveals a distinct somatic genomic landscape in breast cancer from women with germline PTEN variants

Germline PTEN variants (PTEN hamartoma tumor syndrome [PHTS]) confer up to 85% lifetime risk of female breast cancer (BC). BCs arising in PHTS are clinically distinct from sporadic BCs, including younger age of onset, multifocality, and an increased risk of second primary BCs. Yet, there is no previous investigation into the underlying genomic landscape of this entity. We sought to address the hypothesis that BCs arising in PHTS have a distinct genomic landscape compared to sporadic counterparts. We performed and analyzed exome sequencing data from 44 women with germline PTEN variants who developed BCs. The control cohort comprised of 497 women with sporadic BCs from The Cancer Genome Atlas (TCGA) dataset. We demonstrate that PHTS-derived BCs have a distinct somatic mutational landscape compared to the sporadic counterparts, namely second somatic hits in PTEN, distinct mutational signatures, and increased genomic instability. The PHTS group had a significantly higher frequency of somatic PTEN variants compared to TCGA (22.7% versus 5.6%; odds ratio [OR] 4.93; 95% confidence interval [CI] 2.21 to 10.98; p < 0.001) and a lower mutational frequency in PIK3CA (22.7% versus 33.4%; OR 0.59; 95% CI 0.28 to 1.22; p = 0.15). Somatic variants in PTEN and PIK3CA were mutually exclusive in PHTS (p = 0.01) but not in TCGA. Our findings have important implications for the personalized management of PTEN-related BCs, especially in the context of more accessible genetic testing.

Enhanced neoepitope-specific immunity following neoadjuvant PD-L1 and TGF-b blockade in HPV-unrelated head and neck cancer

BACKGROUND

Head and neck squamous cell carcinoma not associated with human papillomavirus (HPV-unrelated HNSCC) is associated with high rates of recurrence and poor survival.

METHODS

We conducted a clinical trial in 14 patients with newly diagnosed, HPV-unrelated HNSCC to evaluate the safety and efficacy of neoadjuvant bintrafusp alfa, a bifunctional fusion protein that blocks programmed death-ligand 1 (PD-L1) and neutralizes transforming growth factor-beta (TGF-).

RESULTS

Bintrafusp alfa was well tolerated, and no treatment-associated surgical delays or complications occurred. Objective pathologic responses were observed and 12 of 14 patients (86%) were alive and disease free at one year. Alterations in regulatory T cell infiltration and spatial distribution relative to proliferating CD8 T cells indicated reversal of Treg immunosuppression in the primary tumor. Detection of neoepitope-specific tumor T cell responses, but not viral-specific responses, correlated with development of a pathologic response. Detection of neoepitope-specific responses and pathologic responses in tumors was not correlated with genomic features or tumor antigenicity but was associated with reduced pre-treatment myeloid cell tumor infiltration. These results indicate that dual PD-L1 and TGF- blockade can safely enhance tumor antigen-specific immunity and highlight the feasibility of multi-mechanism neoadjuvant immunotherapy in patients with HPV-unrelated HNSCC.

CONCLUSION

Our studies provide new insight into the ability of neoadjuvant immunotherapy to induce polyclonal neoadjuvant-specific T cell responses in tumors and suggest that features of the tumor microenvironment, such as myeloid cell infiltration, may be a major determinant of enhanced anti-tumor immunity following such treatment.

Prediction of risk-associated genes and high-risk liver cancer patients from their mutation profile: Benchmarking of mutation calling techniques

Identification of somatic mutations with high precision is one of the major challenges in the prediction of high-risk liver-cancer patients. In the past, number of mutations calling techniques have been developed that include MuTect2, MuSE, Varscan2, and SomaticSniper. In this study, an attempt has been made to benchmark the potential of these techniques in predicting the prognostic biomarkers for liver cancer. Initially, we extracted somatic mutations in liver cancer patients using Variant Call Format (VCF) and Mutation Annotation Format (MAF) files from the cancer genome atlas. In terms of size, the MAF files are 42 times smaller than VCF files and containing only high-quality somatic mutations. Further, machine learning based models have been developed for predicting high-risk cancer patients using mutations obtained from different techniques. The performance of different techniques and data files have been compared based on their potential to discriminate high and low-risk liver-cancer patients. Based on correlation analysis, we selected 80 genes having significant negative-correlation with the overall survival of liver cancer patients. The univariate survival analysis revealed the prognostic role of highly mutated genes. Single-gene based analysis showed that MuTect2 technique based MAF file has achieved maximum hazard ratio (HRLAMC3) of 9.25 with p-value 1.78E-06. Further, we developed various prediction models using risk-associated top-10 genes for each technique. Our results indicate that MuTect2 technique based VCF files outperform all other methods with maximum Area Under the Receiver-Operating Characteristic (AUROC) curve of 0.765 and HR 4.50 (p-value 3.83E-15). Eventually, VCF file generated using MuTect2 technique performs better among other mutation calling techniques for the prediction of high-risk liver cancer patients. We hope that our findings will provide a useful and comprehensive comparison of various mutation calling techniques for the prognostic analysis of cancer patients. In order to serve the scientific community, we have provided a Python-based pipeline to develop the prediction models using mutation profiles (VCF/MAF) of cancer patients. It is available on GitHub at https://github.com/raghavagps/mutation_bench.

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.

Halvade somatic: Somatic variant calling with Apache Spark

Background

The accurate detection of somatic variants from sequencing data is of key importance for cancer treatment and research. Somatic variant calling requires a high sequencing depth of the tumor sample, especially when the detection of low-frequency variants is also desired. In turn, this leads to large volumes of raw sequencing data to process and hence, large computational requirements. For example, calling the somatic variants according to the GATK best practices guidelines requires days of computing time for a typical whole-genome sequencing sample.

Findings

We introduce Halvade Somatic, a framework for somatic variant calling from DNA sequencing data that takes advantage of multi-node and/or multi-core compute platforms to reduce runtime. It relies on Apache Spark to provide scalable I/O and to create and manage data streams that are processed on different CPU cores in parallel. Halvade Somatic contains all required steps to process the tumor and matched normal sample according to the GATK best practices recommendations: read alignment (BWA), sorting of reads, preprocessing steps such as marking duplicate reads and base quality score recalibration (GATK), and, finally, calling the somatic variants (Mutect2). Our approach reduces the runtime on a single 36-core node to 19.5 h compared to a runtime of 84.5 h for the original pipeline, a speedup of 4.3 times. Runtime can be further decreased by scaling to multiple nodes, e.g., we observe a runtime of 1.36 h using 16 nodes, an additional speedup of 14.4 times. Halvade Somatic supports variant calling from both whole-genome sequencing and whole-exome sequencing data and also supports Strelka2 as an alternative or complementary variant calling tool. We provide a Docker image to facilitate single-node deployment. Halvade Somatic can be executed on a variety of compute platforms, including Amazon EC2 and Google Cloud.

Conclusions

To our knowledge, Halvade Somatic is the first somatic variant calling pipeline that leverages Big Data processing platforms and provides reliable, scalable performance. Source code is freely available.

Comprehensive multiomic characterization of human papillomavirus-driven recurrent respiratory papillomatosis reveals distinct molecular subtypes

Recurrent respiratory papillomatosis (RRP) is a debilitating neoplastic disorder of the upper aerodigestive tract caused by chronic infection with low-risk human papillomavirus types 6 or 11. Patients with severe RRP can require hundreds of lifetime surgeries to control their disease and pulmonary papillomatosis can be fatal. Here we report the comprehensive genomic and transcriptomic characterization of respiratory papillomas. We discovered and characterized distinct subtypes with transcriptional resemblance to either a basal or differentiated cell state that associate with disease aggressiveness and differ in key molecular, immune and APOBEC mutagenesis profiles. Through integrated comparison with high-risk HPV-associated head and neck squamous cell carcinoma, our analysis revealed divergent molecular and immune papilloma subtypes that form independent of underlying genomic alterations. Cumulatively our results support the development of dysregulated cellular proliferation and suppressed anti-viral immunity through distinct programs of squamous cell differentiation and associated expression of low-risk HPV genes. These analyses provide insight into the pathogenesis of respiratory papillomas and provide a foundation for the development of therapeutic strategies.

Cem Sievers et al. performed genomic and transcriptomic analysis in human recurrent respiratory papillomatosis (RRP). They found that RRP harbors few genomic alterations, but that distinct transcriptional subtypes correlate with HPV gene expression and frequency of clinically-indicated interventions.

Benchmarking pipelines for subclonal deconvolution of bulk tumour sequencing data

Intratumour heterogeneity provides tumours with the ability to adapt and acquire treatment resistance. The development of more effective and personalised treatments for cancers, therefore, requires accurate characterisation of the clonal architecture of tumours, enabling evolutionary dynamics to be tracked. Many methods exist for achieving this from bulk tumour sequencing data, involving identifying mutations and performing subclonal deconvolution, but there is a lack of systematic benchmarking to inform researchers on which are most accurate, and how dataset characteristics impact performance. To address this, we use the most comprehensive tumour genome simulation tool available for such purposes to create 80 bulk tumour whole exome sequencing datasets of differing depths, tumour complexities, and purities, and use these to benchmark subclonal deconvolution pipelines. We conclude that i) tumour complexity does not impact accuracy, ii) increasing either purity or purity-corrected sequencing depth improves accuracy, and iii) the optimal pipeline consists of Mutect2, FACETS and PyClone-VI. We have made our benchmarking datasets publicly available for future use.

Dual PD-L1 and TGF-b blockade in patients with recurrent respiratory papillomatosis

Background

Recurrent respiratory papillomatosis (RRP) is a human papillomavirus (HPV) driven neoplastic disorder of the upper aerodigestive tract that causes significant morbidity and can lead to fatal airway obstruction. Prior clinical study demonstrated clinical benefit with the programmed death-ligand 1 (PD-L1) monoclonal antibody avelumab. Bintrafusp alfa is a bifunctional inhibitor of PD-L1 and transforming growth factor-beta (TGF-b) that has shown clinical activity in several cancer types.

Methods

We conducted a phase II clinical trial evaluating bintrafusp alfa in adults with RRP. Papilloma samples before and after treatment with bintrafusp alfa were assessed for correlates of response with multiplex immunofluorescence as well as immunological and genomic analyses. Post hoc analyses of papilloma samples before and after treatment with avelumab were assessed for comparison.

Results

Dual PD-L1/TGF-b inhibition failed to abrogate papilloma growth in most subjects and increased the frequency of clinically indicated interventions after treatment in four of eight subjects based on each subject’s own historical control. TGF-b neutralization consistently decreased pSMAD3 and p21 and increased Ki67 expression within the basal layers of papillomas, indicating that TGF-b restrained proliferation. These alterations were not observed in papillomas treated with PD-L1 blockade alone. Dual PD-L1/TGF-b inhibition did not enhance anti-HPV immunity within papillomas beyond that observed with PD-L1 blockade. Genomic alterations in TGF-b superfamily genes were infrequent in papillomas and normal mucosa but present in a significant fraction of head and neck carcinomas.

Conclusions

Intact TGF-b signaling restrains proliferation within papillomas, and the use of clinical agents that abrogate this pathway should be avoided in patients with RRP.

Trial registration numbers

NCT03707587 and NCT02859454.