No abstract

High-grade serous ovarian cancer (HGSOC) is an archetypal cancer of genomic instability1–4 patterned by distinct mutational processes5,6, tumour heterogeneity7–9 and intraperitoneal spread7,8,10. Immunotherapies have had limited efficacy in HGSOC11–13, highlighting an unmet need to assess how mutational processes and the anatomical sites of tumour foci determine the immunological states of the tumour microenvironment. Here we carried out an integrative analysis of whole-genome sequencing, single-cell RNA sequencing, digital histopathology and multiplexed immunofluorescence of 160 tumour sites from 42 treatment-naive patients with HGSOC. Homologous recombination-deficient HRD-Dup (BRCA1 mutant-like) and HRD-Del (BRCA2 mutant-like) tumours harboured inflammatory signalling and ongoing immunoediting, reflected in loss of HLA diversity and tumour infiltration with highly differentiated dysfunctional CD8+ T cells. By contrast, foldback-inversion-bearing tumours exhibited elevated immunosuppressive TGFβ signalling and immune exclusion, with predominantly naive/stem-like and memory T cells. Phenotypic state associations were specific to anatomical sites, highlighting compositional, topological and functional differences between adnexal tumours and distal peritoneal foci. Our findings implicate anatomical sites and mutational processes as determinants of evolutionary phenotypic divergence and immune resistance mechanisms in HGSOC. Our study provides a multi-omic cellular phenotype data substrate from which to develop and interpret future personalized immunotherapeutic approaches and early detection research.

Patients with high-grade serous ovarian cancer suffer poor prognosis and variable response to treatment. Known prognostic factors for this disease include homologous recombination deficiency status, age, pathological stage and residual disease status after debulking surgery. Recent work has highlighted important prognostic information captured in computed tomography and histopathological specimens, which can be exploited through machine learning. However, little is known about the capacity of combining features from these disparate sources to improve prediction of treatment response. Here, we assembled a multimodal dataset of 444 patients with primarily late-stage high-grade serous ovarian cancer and discovered quantitative features, such as tumor nuclear size on staining with hematoxylin and eosin and omental texture on contrast-enhanced computed tomography, associated with prognosis. We found that these features contributed complementary prognostic information relative to one another and clinicogenomic features. By fusing histopathological, radiologic and clinicogenomic machine-learning models, we demonstrate a promising path toward improved risk stratification of patients with cancer through multimodal data integration.

Immunotherapy is used to treat almost all patients with advanced non-small cell lung cancer (NSCLC); however, identifying robust predictive biomarkers remains challenging. Here we show the predictive capacity of integrating medical imaging, histopathologic and genomic features to predict immunotherapy response using a cohort of 247 patients with advanced NSCLC with multimodal baseline data obtained during diagnostic clinical workup, including computed tomography scan images, digitized programmed death ligand-1 immunohistochemistry slides and known outcomes to immunotherapy. Using domain expert annotations, we developed a computational workflow to extract patient-level features and used a machine-learning approach to integrate multimodal features into a risk prediction model. Our multimodal model (area under the curve (AUC) = 0.80, 95% confidence interval (CI) 0.74–0.86) outperformed unimodal measures, including tumor mutational burden (AUC = 0.61, 95% CI 0.52–0.70) and programmed death ligand-1 immunohistochemistry score (AUC = 0.73, 95% CI 0.65–0.81). Our study therefore provides a quantitative rationale for using multimodal features to improve prediction of immunotherapy response in patients with NSCLC using expert-guided machine learning.

Reverse phase protein microarrays (RPMA) are designed for quantitative, multiplexed analysis of proteins, and their posttranslational modified forms, from a limited amount of sample. To correct for sample to sample variability due to the number of cells in each lysate and the presence of extracellular proteins or red blood cells, a normalization method is required that is independent of these potentially confounding parameters. We adopted a gene microarray algorithm for use with RPMA to optimize the proteomic data normalization process and developed a systematic approach to RPMA processing and analysis, tailored to the study set. Our approach capitalizes on the gene microarray algorithms geNorm and NormFinder to identify the normalization parameter with the lowest variability across a proteomic sample set. Seven analytes (ssDNA, glyceraldehyde 3-phosphate dehydrogenase, α/β-tubulin, mitochondrial ribosomal protein L11, ribosomal protein L13a, β-actin, and total protein) were compared across sample sets including cell lines, tissues subjected to laser capture microdissection, and blood-contaminated tissues. We examined normalization parameters to correct for red blood cell content. We show that single-stranded DNA (ssDNA) is proportional to total non-red blood cell content and is a suitable RPMA normalization parameter. Simple modifications to RPMA processing allow flexibility in using ssDNA- or protein-based normalization molecules.

BackgroundTo further our understanding of immunopeptidomics, improved tools are needed to identify peptides presented by major histocompatibility complex class I (MHC-I). Many existing tools are limited by their reliance upon chemical affinity data, which is less biologically relevant than sampling by mass spectrometry, and other tools are limited by incomplete exploration of machine learning approaches. Herein, we assemble publicly available data describing human peptides discovered by sampling the MHC-I immunopeptidome with mass spectrometry and use this database to train random forest classifiers (ForestMHC) to predict presentation by MHC-I.ResultsAs measured by precision in the top 1% of predictions, our method outperforms NetMHC and NetMHCpan on test sets, and it outperforms both these methods and MixMHCpred on new data from an ovarian carcinoma cell line. We also find that random forest scores correlate monotonically, but not linearly, with known chemical binding affinities, and an information-based analysis of classifier features shows the importance of anchor positions for our classification. The random-forest approach also outperforms a deep neural network and a convolutional neural network trained on identical data. Finally, we use our large database to confirm that gene expression partially determines peptide presentation.ConclusionsForestMHC is a promising method to identify peptides bound by MHC-I. We have demonstrated the utility of random forest-based approaches in predicting peptide presentation by MHC-I, assembled the largest known database of MS binding data, and mined this database to show the effect of gene expression on peptide presentation. ForestMHC has potential applicability to basic immunology, rational vaccine design, and neoantigen binding prediction for cancer immunotherapy. This method is publicly available for applications and further validation.Electronic supplementary materialThe online version of this article (10.1186/s12859-018-2561-z) contains supplementary material, which is available to authorized users.

Acute ischemic stroke resulting from intracranial vessel occlusion is associated with high morbidity and mortality. The mainstays of therapy are fibrinolytics and mechanical thrombectomy in properly selected patients. A new Food and Drug Administration-approved technology to perform thrombectomy, retrievable stenting, may provide superior revascularization rates and improved patient outcomes. We analyzed the cumulative human experience reported for the Trevo Pro Retrieval System (Stryker, Kalamazoo, MI, USA) and the Solitaire FR Revascularization Device (ev3, Irvine, CA, USA) as the definitive treatment for acute ischemic stroke. A literature search was undertaken to identify studies using the retrievable stents published up to September 2012. Nineteen studies identified a total of 576 patients treated with either the Trevo (n = 221) or Solitaire (n = 355) devices. Pooled data analysis identified baseline National Institutes of Health Stroke Scale scores of 18.5 ± 0.289 (standard error of the mean) and 17.9 ± 0.610, and time to recanalization of 53.9 ± 23.6 minutes and 59.0 ± 8.0 minutes for the Trevo and Solitaire groups, respectively. Recanalization was variably defined by individual studies, most commonly achieving at least a thrombolysis in cerebral infarction score of 2a–3 or a thrombolysis in myocardial infarction score of 2–3. Revascularization (83%, 82%), mortality (31%, 14%), hemorrhage (8%, 6%), device complications (5%, 6%), and good patient outcomes (51%, 47%) were found with the Trevo and Solitaire devices, respectively. Preliminary analysis reveals excellent clinical outcomes for retrievable stent technology. This may be attributable to both high rates of revascularization with a relatively short time to perfusion restoration.

High-grade serous ovarian cancer (HGSOC) is an archetypal cancer of genomic instability patterned by distinct mutational processes, intratumoral heterogeneity and intraperitoneal spread. We investigated determinants of immune recognition and evasion in HGSOC to elucidate co-evolutionary processes underlying malignant progression and tumor immunity. Mutational processes and anatomic sites of tumor foci were key determinants of tumor microenvironment cellular phenotypes, inferred from whole genome sequencing, single-cell RNA sequencing, digital histopathology and multiplexed immunofluorescence of 160 tumor sites from 42 treatment-naive HGSOC patients. Homologous recombination-deficient (HRD)-Dup (BRCA1 mutant-like) and HRD-Del (BRCA2 mutant-like) tumors harbored increased neoantigen burden, inflammatory signaling and ongoing immunoediting, reflected in loss of HLA diversity and tumor infiltration with highly-differentiated dysfunctional CD8+ T cells. Foldback inversion (FBI, non-HRD) tumors exhibited elevated TGFβ signaling and immune exclusion, with predominantly naive/stem-like and memory T cells. Our findings implicate distinct immune resistance mechanisms across HGSOC subtypes which can inform future immunotherapeutic strategies.