Performance of somatic structural variant calling in lung cancer using Oxford Nanopore sequencing technology.

Background: Lung cancer is a heterogeneous disease and the primary cause of cancer-related mortality worldwide. Somatic mutations, including large structural variants, are important biomarkers in lung cancer for selecting targeted therapy. Genomic studies in lung cancer have been conducted using short-read sequencing.

Spatial intra-tumour heterogeneity and treatment-induced genomic evolution in oesophageal adenocarcinoma: implications for prognosis and therapy.

Background: Oesophageal adenocarcinoma (OAC) is a highly heterogeneous cancer with poor survival. Standard curative treatment is chemotherapy with or without radiotherapy followed by oesophagectomy. Genomic heterogeneity is a feature of OAC and has been linked to treatment resistance.

Evaluation of Endobronchial Ultrasound-Guided Transbronchial Needle Aspiration (EBUS-TBNA) Samples from Advanced Non-Small Cell Lung Cancer for Whole Genome, Whole Exome and Comprehensive Panel Sequencing.

Endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) is often the only source of tumor tissue from patients with advanced, inoperable lung cancer. EBUS-TBNA aspirates are used for the diagnosis, staging, and genomic testing to inform therapy options. Here we extracted DNA and RNA from 220 EBUS-TBNA aspirates to evaluate their suitability for whole genome (WGS), whole exome (WES), and comprehensive panel sequencing.

The oesophageal adenocarcinoma tumour immune microenvironment dictates outcomes with different modalities of neoadjuvant therapy - results from the AGITG DOCTOR trial and the cancer evolution biobank.

A plateau in treatment effect can be seen for the current 'one-size-fits-all' approach to oesophageal adenocarcinoma (OAC) management using neoadjuvant chemoradiotherapy (nCRT) or chemotherapy (nCT). In OAC, the tumour microenvironment (TME) is largely immunosuppressed, however a subgroup of patients with an immune-inflamed TME exist and show improved outcomes. We aimed to understand the overall immune-based mechanisms underlying treatment responses and patient outcomes in OAC, and in relation to neoadjuvant therapy modality.

Performance of tumour microenvironment deconvolution methods in breast cancer using single-cell simulated bulk mixtures.

Cells within the tumour microenvironment (TME) can impact tumour development and influence treatment response. Computational approaches have been developed to deconvolve the TME from bulk RNA-seq. Using scRNA-seq profiling from breast tumours we simulate thousands of bulk mixtures, representing tumour purities and cell lineages, to compare the performance of nine TME deconvolution methods (BayesPrism, Scaden, CIBERSORTx, MuSiC, DWLS, hspe, CPM, Bisque, and EPIC).

Circulating Tumor DNA: A Promising Biomarker for Predicting Recurrence in Patients with BRAF-Negative Melanoma.

For patients with BRAF wild-type stage III and IV melanoma, there is an urgent clinical need to identify prognostic biomarkers and biomarkers predictive of treatment response. Circulating tumor DNA (ctDNA) is emerging as a blood-based biomarker and has shown promising results for many cancers, including melanoma. The purpose of this study was to identify targetable, tumor-derived mutations in patient blood that may lead to treatment alternatives and improved outcomes for patients with BRAF-negative melanoma.

Multi-omic features of oesophageal adenocarcinoma in patients treated with preoperative neoadjuvant therapy.

Oesophageal adenocarcinoma is a poor prognosis cancer and the molecular features underpinning response to treatment remain unclear. We investigate whole genome, transcriptomic and methylation data from 115 oesophageal adenocarcinoma patients mostly from the DOCTOR phase II clinical trial (Australian New Zealand Clinical Trials Registry-ACTRN12609000665235), with exploratory analysis pre-specified in the study protocol of the trial. We report genomic features associated with poorer overall survival, such as the APOBEC mutational and RS3-like rearrangement signatures.

Generalising uncertainty improves accuracy and safety of deep learning analytics applied to oncology.

Uncertainty estimation is crucial for understanding the reliability of deep learning (DL) predictions, and critical for deploying DL in the clinic. Differences between training and production datasets can lead to incorrect predictions with underestimated uncertainty. To investigate this pitfall, we benchmarked one pointwise and three approximate Bayesian DL models for predicting cancer of unknown primary, using three RNA-seq datasets with 10,968 samples across 57 cancer types.

Intratumoral CD16+ Macrophages Are Associated with Clinical Outcomes of Patients with Metastatic Melanoma Treated with Combination Anti-PD-1 and Anti-CTLA-4 Therapy.

Purpose: This study characterizes intratumoral macrophage populations within baseline melanoma biopsies from patients with advanced melanoma who received either anti-PD-1 monotherapy or a combination with anti-CTLA-4. Particularly, FcγRIIIa (CD16)-expressing macrophage densities were investigated for associations with response and progression-free survival.

Experimental Design: Patients with advanced melanoma who received either anti-PD-1 monotherapy or combination anti-PD-1 and anti-CTLA-4 were retrospectively identified.

Efficient detection and monitoring of pediatric brain malignancies with liquid biopsy based on patient-specific somatic mutation screening.

Background: Brain cancer is the leading cause of cancer-related death in children. Early detection and serial monitoring are essential for better therapeutic outcomes. Liquid biopsy has recently emerged as a promising approach for detecting these tumors by screening body fluids for the presence of circulating tumor DNA (ctDNA).

IL-10-producing Th1 cells possess a distinct molecular signature in malaria.

Control of intracellular parasites responsible for malaria requires host IFN-γ+T-bet+CD4+ T cells (Th1 cells) with IL-10 produced by Th1 cells to mitigate the pathology induced by this inflammatory response. However, these IL-10-producing Th1 (induced type I regulatory [Tr1]) cells can also promote parasite persistence or impair immunity to reinfection or vaccination. Here, we identified molecular and phenotypic signatures that distinguished IL-10-Th1 cells from IL-10+Tr1 cells in Plasmodium falciparum-infected people who participated in controlled human malaria infection studies, as well as C57BL/6 mice with experimental malaria caused by P.

Comparative Genomics Provides Etiologic and Biological Insight into Melanoma Subtypes.

Unlabelled: Melanoma is a cancer of melanocytes, with multiple subtypes based on body site location. Cutaneous melanoma is associated with skin exposed to ultraviolet radiation; uveal melanoma occurs in the eyes; mucosal melanoma occurs in internal mucous membranes; and acral melanoma occurs on the palms, soles, and nail beds. Here, we present the largest whole-genome sequencing study of melanoma to date, with 570 tumors profiled, as well as methylation and RNA sequencing for subsets of tumors.

Increased amphiregulin expression by CD4 T cells from individuals with asymptomatic infection.

Objectives: There is an urgent need to be able to identify individuals with asymptomatic infection, so their risk of progressing to VL and transmitting parasites can be managed. This study examined transcriptional markers expressed by CD4 T cells that could distinguish asymptomatic individuals from endemic controls and visceral leishmaniasis (VL) patients.

Methods: CD4 T cells were isolated from individuals with asymptomatic .

Comprehensive genomic and tumour immune profiling reveals potential therapeutic targets in malignant pleural mesothelioma.

Background: Malignant pleural mesothelioma (MPM) has a poor overall survival with few treatment options. Whole genome sequencing (WGS) combined with the immune features of MPM offers the prospect of identifying changes that could inform future clinical trials.

Methods: We analysed somatic mutations from 229 MPM samples, including previously published data and 58 samples that had undergone WGS within this study.

Patient-derived xenograft models capture genomic heterogeneity in endometrial cancer.

Background: Endometrial cancer (EC) is a major gynecological cancer with increasing incidence. It comprises four molecular subtypes with differing etiology, prognoses, and responses to chemotherapy. In the future, clinical trials testing new single agents or combination therapies will be targeted to the molecular subtype most likely to respond.

Multiomic profiling of checkpoint inhibitor-treated melanoma: Identifying predictors of response and resistance, and markers of biological discordance.

We concurrently examine the whole genome, transcriptome, methylome, and immune cell infiltrates in baseline tumors from 77 patients with advanced cutaneous melanoma treated with anti-PD-1 with or without anti-CTLA-4. We show that high tumor mutation burden (TMB), neoantigen load, expression of IFNγ-related genes, programmed death ligand expression, low PSMB8 methylation (therefore high expression), and T cells in the tumor microenvironment are associated with response to immunotherapy. No specific mutation correlates with therapy response.

Donor bone marrow-derived macrophage MHC II drives neuroinflammation and altered behavior during chronic GVHD in mice.

Graft-versus-host disease (GVHD) remains the leading cause of nonrelapse mortality after allogeneic stem cell transplantation for hematological malignancies. Manifestations of GVHD in the central nervous system (CNS) present as neurocognitive dysfunction in up to 60% of patients; however, the mechanisms driving chronic GVHD (cGVHD) in the CNS are yet to be elucidated. Our studies of murine cGVHD revealed behavioral deficits associated with broad neuroinflammation and persistent Ifng upregulation.

Targeting novel LSD1-dependent ACE2 demethylation domains inhibits SARS-CoV-2 replication.

Treatment options for COVID-19 remain limited, especially during the early or asymptomatic phase. Here, we report a novel SARS-CoV-2 viral replication mechanism mediated by interactions between ACE2 and the epigenetic eraser enzyme LSD1, and its interplay with the nuclear shuttling importin pathway. Recent studies have shown a critical role for the importin pathway in SARS-CoV-2 infection, and many RNA viruses hijack this axis to re-direct host cell transcription.

Radiomics Biomarkers Correlate with CD8 Expression and Predict Immune Signatures in Melanoma Patients.

Treatment for metastatic melanoma includes targeted and/or immunotherapy. Although many patients respond, only a subset has complete response. As late-stage patients often have multiple tumors in difficult access sites, non-invasive techniques are necessary for the development of predictive/prognostic biomarkers.

Considerations for using population frequency data in germline variant interpretation: Cancer syndrome genes as a model.

Aggregate population genomics data from large cohorts are vital for assessing germline variant pathogenicity. However, there are no specifications on how sequencing quality metrics should be considered, and whether exome-derived and genome-derived allele frequencies should be considered in isolation. Germline genome sequence data were simulated for nine read-depths to identify a minimum acceptable read-depth for detecting variants.

DNA methylation patterns identify subgroups of pancreatic neuroendocrine tumors with clinical association.

Here we report the DNA methylation profile of 84 sporadic pancreatic neuroendocrine tumors (PanNETs) with associated clinical and genomic information. We identified three subgroups of PanNETs, termed T1, T2 and T3, with distinct patterns of methylation. The T1 subgroup was enriched for functional tumors and ATRX, DAXX and MEN1 wild-type genotypes.

Verifying explainability of a deep learning tissue classifier trained on RNA-seq data.

For complex machine learning (ML) algorithms to gain widespread acceptance in decision making, we must be able to identify the features driving the predictions. Explainability models allow transparency of ML algorithms, however their reliability within high-dimensional data is unclear. To test the reliability of the explainability model SHapley Additive exPlanations (SHAP), we developed a convolutional neural network to predict tissue classification from Genotype-Tissue Expression (GTEx) RNA-seq data representing 16,651 samples from 47 tissues.