In the previous column we showed how hidden states driving observable changes in a cell can be modeled as a hidden Markov model (HMM). To confidently use the HMM for inference or prediction, we must first train it to accurately represent observed data.
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Head and neck squamous cell carcinoma (HNSCC) is one of the most common cancers worldwide and represents a heterogeneous group of tumours, the majority of which are treated with a combination of surgery, radiation and chemotherapy. Fluoropyrimidine (5-FU) and its oral pro-drug, capecitabine, are commonly prescribed treatments for several solid tumour types including HNSCC. 5-FU-associated toxicity is observed in approximately 30% of treated patients and is largely caused by germline polymorphisms in DPYD which encodes dihydropyrimidine dehydrogenase (DPD), a key enzyme of 5-FU catabolism and deactivation. Although the association of germline DPYD alterations with toxicity is well-described, the potential contribution of somatic DPYD alterations to 5-FU sensitivity has not been explored. In a patient with metastatic HNSCC, in-depth genomic and transcriptomic integrative analysis on a biopsy from a metastatic neck lesion revealed alterations in genes that are associated with 5-FU uptake and metabolism. These included a novel somatic structural variant resulting in a partial deletion affecting DPYD, a variant of unknown significance affecting SLC29A1 and homozygous deletion of MTAP. There was no evidence of deleterious germline polymorphisms that have been associated with 5-FU toxicity, indicating a potential vulnerability of the tumour to 5-FU therapy. The discovery of the novel DPYD variant led to the initiation of 5-FU treatment that resulted in a rapid response lasting 17 weeks, with subsequent relapse due to unknown resistance mechanisms. This suggests that somatic alterations present in this tumour may serve as markers for tumour sensitivity to 5-FU, aiding in selection of personalized treatment strategies.
Introduction Given the high level of uncertainty surrounding the outcomes of early phase clinical trials, whole genome and transcriptome analysis (WGTA) can be used to optimize patient selection and study assignment. In this retrospective analysis, we reviewed the impact of this approach on one such program. Methods Patients with advanced malignancies underwent fresh tumor biopsies as part of our personalized medicine program (NCT02155621). Tumour molecular data were reviewed for potentially clinically actionable findings and patients were referred to the developmental therapeutics program. Outcomes were reviewed in all patients, including those where trial selection was driven by molecular data (matched) and those where there was no clear molecular rationale (unmatched). Results From January 2014 to January 2018, 28 patients underwent WGTA and enrolled in clinical trials, including 2 patients enrolled in two trials. Fifteen patients were matched to a treatment based on a molecular target. Five patients were matched to a trial based upon single-gene DNA changes, all supported by RNA data. Ten cases were matched on the basis of genome-wide data (n = 4) or RNA gene expression only (n = 6). With a median follow-up of 6.7 months, the median time on treatment was 8.2 weeks. Discussion When compared to single-gene DNA-based data alone, WGTA led to a 3-fold increase in treatment matching. In a setting where there is a high level of uncertainty around both the investigational agents and the biomarkers, more data are needed to fully evaluate the impact of routine use of WGTA.
Genome-wide association studies have identified breast cancer risk variants in over 150 genomic regions, but the mechanisms underlying risk remain largely unknown. These regions were explored by combining association analysis with in silico genomic feature annotations. We defined 205 independent risk-associated signals with the set of credible causal variants in each one. In parallel, we used a Bayesian approach (PAINTOR) that combines genetic association, linkage disequilibrium and enriched genomic features to determine variants with high posterior probabilities of being causal. Potentially causal variants were significantly over-represented in active gene regulatory regions and transcription factor binding sites. We applied our INQUSIT pipeline for prioritizing genes as targets of those potentially causal variants, using gene expression (expression quantitative trait loci), chromatin interaction and functional annotations. Known cancer drivers, transcription factors and genes in the developmental, apoptosis, immune system and DNA integrity checkpoint gene ontology pathways were over-represented among the highest-confidence target genes.
Pancreatic adenocarcinoma presents as a spectrum of a highly aggressive disease in patients. The basis of this disease heterogeneity has proved difficult to resolve due to poor tumor cellularity and extensive genomic instability. To address this, a dataset of whole genomes and transcriptomes was generated from purified epithelium of primary and metastatic tumors. Transcriptome analysis demonstrated that molecular subtypes are a product of a gene expression continuum driven by a mixture of intratumoral subpopulations, which was confirmed by single-cell analysis. Integrated whole-genome analysis uncovered that molecular subtypes are linked to specific copy number aberrations in genes such as mutant KRAS and GATA6. By mapping tumor genetic histories, tetraploidization emerged as a key mutational process behind these events. Taken together, these data support the premise that the constellation of genomic aberrations in the tumor gives rise to the molecular subtype, and that disease heterogeneity is due to ongoing genomic instability during progression.
Lung group 2 innate lymphoid cells (ILC2s) drive allergic inflammation and promote tissue repair. ILC2 development is dependent on the transcription factor retinoic acid receptor-related orphan receptor (RORα), which is also expressed in common ILC progenitors. To elucidate the developmental pathways of lung ILC2s, we generated RORα lineage tracer mice and performed single-cell RNA sequencing, flow cytometry, and functional analyses. In adult mouse lungs, we found an IL-18Rα+ST2- population different from conventional IL-18Rα-ST2+ ILC2s. The former was GATA-3intTcf7EGFP+Kit+, produced few cytokines, and differentiated into multiple ILC lineages in vivo and in vitro. In neonatal mouse lungs, three ILC populations were identified, namely an ILC progenitor population similar to that in adult lungs and two distinct effector ILC2 subsets that differentially produced type 2 cytokines and amphiregulin. Lung ILC progenitors might actively contribute to ILC-poiesis in neonatal and inflamed adult lungs. In addition, neonatal lung ILC2s include distinct proinflammatory and tissue-repairing subsets.