Anti-EGFR Antibody-Drug Conjugate Carrying an Inhibitor Targeting CDK Restricts Triple-Negative Breast Cancer Growth.

PURPOSE: Anti-EGFR antibodies show limited response in breast cancer, partly due to activation of compensatory pathways. Furthermore, despite the clinical success of cyclin-dependent kinase (CDK) 4/6 inhibitors in hormone receptor-positive tumors, aggressive triple-negative breast cancers (TNBC) are largely resistant due to CDK2/cyclin E expression, whereas free CDK2 inhibitors display normal tissue toxicity, limiting their therapeutic application. A cetuximab-based antibody drug conjugate (ADC) carrying a CDK inhibitor selected based on oncogene dysregulation, alongside patient subgroup stratification, may provide EGFR-targeted delivery. EXPERIMENTAL DESIGN: Expressions of G1/S-phase cell cycle regulators were evaluated alongside EGFR in breast cancer. We conjugated cetuximab with CDK inhibitor SNS-032, for specific delivery to EGFR-expressing cells. We assessed ADC internalization and its antitumor functions in vitro and in orthotopically grown basal-like/TNBC xenografts. RESULTS: Transcriptomic (6,173 primary, 27 baseline, and matched post-chemotherapy residual tumors), single-cell RNA sequencing (150,290 cells, 27 treatment-naïve tumors), and spatial transcriptomic (43 tumor sections, 22 TNBCs) analyses confirmed expression of CDK2 and its cyclin partners in basal-like/TNBCs, associated with EGFR. Spatiotemporal live-cell imaging and super-resolution confocal microscopy demonstrated ADC colocalization with late lysosomal clusters. The ADC inhibited cell cycle progression, induced cytotoxicity against high EGFR-expressing tumor cells, and bystander killing of neighboring EGFR-low tumor cells, but minimal effects on immune cells. Despite carrying a small molar fraction (1.65%) of the SNS-032 inhibitor, the ADC restricted EGFR-expressing spheroid and cell line/patient-derived xenograft tumor growth. CONCLUSIONS: Exploiting EGFR overexpression, and dysregulated cell cycle in aggressive and treatment-refractory tumors, a cetuximab-CDK inhibitor ADC may provide selective and efficacious delivery of cell cycle-targeted agents to basal-like/TNBCs, including chemotherapy-resistant residual disease.

Optimisation-based modelling for explainable lead discovery in malaria.

BACKGROUND: The search for new antimalarial treatments is urgent due to growing resistance to existing therapies. The Open Source Malaria (OSM) project offers a promising starting point, having extensively screened various compounds for their effectiveness. Further analysis of the chemical space surrounding these compounds could provide the means for innovative drugs. METHODS: We report an optimisation-based method for quantitative structure-activity relationship (QSAR) modelling that provides explainable modelling of ligand activity through a mathematical programming formulation. The methodology is based on piecewise regression principles and offers optimal detection of breakpoint features, efficient allocation of samples into distinct sub-groups based on breakpoint feature values, and insightful regression coefficients. Analysis of OSM antimalarial compounds yields interpretable results through rules generated by the model that reflect the contribution of individual fingerprint fragments in ligand activity prediction. Using knowledge of fragment prioritisation and screening of commercially available compound libraries, potential lead compounds for antimalarials are identified and evaluated experimentally via a Plasmodium falciparum asexual growth inhibition assay (PfGIA) and a human cell cytotoxicity assay. CONCLUSIONS: Three compounds are identified as potential leads for antimalarials using the methodology described above. This work illustrates how explainable predictive models based on mathematical optimisation can pave the way towards more efficient fragment-based lead discovery as applied in malaria.

Weakly supervised segmentation models as explainable radiological classifiers for lung tumour detection on CT images.

PURPOSE: Interpretability is essential for reliable convolutional neural network (CNN) image classifiers in radiological applications. We describe a weakly supervised segmentation model that learns to delineate the target object, trained with only image-level labels ("image contains object" or "image does not contain object"), presenting a different approach towards explainable object detectors for radiological imaging tasks. METHODS: A weakly supervised Unet architecture (WSUnet) was trained to learn lung tumour segmentation from image-level labelled data. WSUnet generates voxel probability maps with a Unet and then constructs an image-level prediction by global max-pooling, thereby facilitating image-level training. WSUnet's voxel-level predictions were compared to traditional model interpretation techniques (class activation mapping, integrated gradients and occlusion sensitivity) in CT data from three institutions (training/validation: n = 412; testing: n = 142). Methods were compared using voxel-level discrimination metrics and clinical value was assessed with a clinician preference survey on data from external institutions. RESULTS: Despite the absence of voxel-level labels in training, WSUnet's voxel-level predictions localised tumours precisely in both validation (precision: 0.77, 95% CI: [0.76-0.80]; dice: 0.43, 95% CI: [0.39-0.46]), and external testing (precision: 0.78, 95% CI: [0.76-0.81]; dice: 0.33, 95% CI: [0.32-0.35]). WSUnet's voxel-level discrimination outperformed the best comparator in validation (area under precision recall curve (AUPR): 0.55, 95% CI: [0.49-0.56] vs. 0.23, 95% CI: [0.21-0.25]) and testing (AUPR: 0.40, 95% CI: [0.38-0.41] vs. 0.36, 95% CI: [0.34-0.37]). Clinicians preferred WSUnet predictions in most instances (clinician preference rate: 0.72 95% CI: [0.68-0.77]). CONCLUSION: Weakly supervised segmentation is a viable approach by which explainable object detection models may be developed for medical imaging. CRITICAL RELEVANCE STATEMENT: WSUnet learns to segment images at voxel level, training only with image-level labels. A Unet backbone first generates a voxel-level probability map and then extracts the maximum voxel prediction as the image-level prediction. Thus, training uses only image-level annotations, reducing human workload. WSUnet's voxel-level predictions provide a causally verifiable explanation for its image-level prediction, improving interpretability. KEY POINTS: • Explainability and interpretability are essential for reliable medical image classifiers. • This study applies weakly supervised segmentation to generate explainable image classifiers. • The weakly supervised Unet inherently explains its image-level predictions at voxel level.

Human skin CD141+ dendritic cells regulate cutaneous immunity via the neuropeptide urocortin 2.

Skin immune homeostasis is a multi-faceted process where dermal dendritic cells (DDCs) are key in orchestrating responses to environmental stressors. We have previously identified CD141+CD14+ DDCs as a skin-resident immunoregulatory population that is vitamin-D3 (VitD3) inducible from monocyte-derived DCs (moDCs), termed CD141hi VitD3 moDCs. We demonstrate that CD141+ DDCs and CD141hi VitD3 moDCs share key immunological features including cell surface markers, reduced T cell stimulation, IL-10 production, and a common transcriptomic signature. Bioinformatic analysis identified the neuroactive ligand receptor pathway and the neuropeptide, urocortin 2 (UCN2), as a potential immunoregulatory candidate molecule. Incubation with VitD3 upregulated UCN2 in CD141+ DCs and UVB irradiation induced UCN2 in CD141+ DCs in healthy skin in vivo. Notably, CD141+ DDC generation of suppressive Tregs was dependent upon the UCN2 pathway as in vivo administration of UCN2 reversed skin inflammation in humanized mice. We propose the neuropeptide UCN2 as a novel skin DC-derived immunoregulatory mediator with a potential role in UVB and VitD3-dependent skin immune homeostasis.

B cell profiles, antibody repertoire and reactivity reveal dysregulated responses with autoimmune features in melanoma.

B cells are known to contribute to the anti-tumor immune response, especially in immunogenic tumors such as melanoma, yet humoral immunity has not been characterized in these cancers to detail. Here we show comprehensive phenotyping in samples of circulating and tumor-resident B cells as well as serum antibodies in melanoma patients. Memory B cells are enriched in tumors compared to blood in paired samples and feature distinct antibody repertoires, linked to specific isotypes. Tumor-associated B cells undergo clonal expansion, class switch recombination, somatic hypermutation and receptor revision. Compared with blood, tumor-associated B cells produce antibodies with proportionally higher levels of unproductive sequences and distinct complementarity determining region 3 properties. The observed features are signs of affinity maturation and polyreactivity and suggest an active and aberrant autoimmune-like reaction in the tumor microenvironment. Consistent with this, tumor-derived antibodies are polyreactive and characterized by autoantigen recognition. Serum antibodies show reactivity to antigens attributed to autoimmune diseases and cancer, and their levels are higher in patients with active disease compared to post-resection state. Our findings thus reveal B cell lineage dysregulation with distinct antibody repertoire and specificity, alongside clonally-expanded tumor-infiltrating B cells with autoimmune-like features, shaping the humoral immune response in melanoma.

Anti-cancer pro-inflammatory effects of an IgE antibody targeting the melanoma-associated antigen chondroitin sulfate proteoglycan 4.

Outcomes for half of patients with melanoma remain poor despite standard-of-care checkpoint inhibitor therapies. The prevalence of the melanoma-associated antigen chondroitin sulfate proteoglycan 4 (CSPG4) expression is ~70%, therefore effective immunotherapies directed at CSPG4 could benefit many patients. Since IgE exerts potent immune-activating functions in tissues, we engineer a monoclonal IgE antibody with human constant domains recognizing CSPG4 to target melanoma. CSPG4 IgE binds to human melanomas including metastases, mediates tumoricidal antibody-dependent cellular cytotoxicity and stimulates human IgE Fc-receptor-expressing monocytes towards pro-inflammatory phenotypes. IgE demonstrates anti-tumor activity in human melanoma xenograft models engrafted with human effector cells and is associated with enhanced macrophage infiltration, enriched monocyte and macrophage gene signatures and pro-inflammatory signaling pathways in the tumor microenvironment. IgE prolongs the survival of patient-derived xenograft-bearing mice reconstituted with autologous immune cells. No ex vivo activation of basophils in patient blood is measured in the presence of CSPG4 IgE. Our findings support a promising IgE-based immunotherapy for melanoma.

Optimisation Models for Pathway Activity Inference in Cancer.

BACKGROUND: With advances in high-throughput technologies, there has been an enormous increase in data related to profiling the activity of molecules in disease. While such data provide more comprehensive information on cellular actions, their large volume and complexity pose difficulty in accurate classification of disease phenotypes. Therefore, novel modelling methods that can improve accuracy while offering interpretable means of analysis are required. Biological pathways can be used to incorporate a priori knowledge of biological interactions to decrease data dimensionality and increase the biological interpretability of machine learning models. METHODOLOGY: A mathematical optimisation model is proposed for pathway activity inference towards precise disease phenotype prediction and is applied to RNA-Seq datasets. The model is based on mixed-integer linear programming (MILP) mathematical optimisation principles and infers pathway activity as the linear combination of pathway member gene expression, multiplying expression values with model-determined gene weights that are optimised to maximise discrimination of phenotype classes and minimise incorrect sample allocation. RESULTS: The model is evaluated on the transcriptome of breast and colorectal cancer, and exhibits solution results of good optimality as well as good prediction performance on related cancer subtypes. Two baseline pathway activity inference methods and three advanced methods are used for comparison. Sample prediction accuracy, robustness against noise expression data, and survival analysis suggest competitive prediction performance of our model while providing interpretability and insight on key pathways and genes. Overall, our work demonstrates that the flexible nature of mathematical programming lends itself well to developing efficient computational strategies for pathway activity inference and disease subtype prediction.

Folate receptor alpha in ovarian cancer tissue and patient serum is associated with disease burden and treatment outcomes.

BACKGROUND: Survival rates for ovarian cancer remain poor, and monitoring and prediction of therapeutic response may benefit from additional markers. Ovarian cancers frequently overexpress Folate Receptor alpha (FRα) and the soluble receptor (sFRα) is measurable in blood. Here we investigated sFRα as a potential biomarker. METHODS: We evaluated sFRα longitudinally, before and during neo-adjuvant, adjuvant and palliative therapies, and tumour FRα expression status by immunohistrochemistry. The impact of free FRα on the efficacy of anti-FRα treatments was evaluated by an antibody-dependent cellular cytotoxicity assay. RESULTS: Membrane and/or cytoplasmic FRα staining were observed in 52.7% tumours from 316 ovarian cancer patients with diverse histotypes. Circulating sFRα levels were significantly higher in patients, compared to healthy volunteers, specifically in patients sampled prior to neoadjuvant and palliative treatments. sFRα was associated with FRα cell membrane expression in the tumour. sFRα levels decreased alongside concurrent tumour burden in patients receiving standard therapies. High concentrations of sFRα partly reduced anti-FRα antibody tumour cell killing, an effect overcome by increased antibody doses. CONCLUSIONS: sFRα may present a non-invasive marker for tumour FRα expression, with the potential for monitoring patient response to treatment. Larger, prospective studies should evaluate FRα for assessing disease burden and response to systemic treatments.

TIGIT blockade repolarizes AML-associated TIGIT+ M2 macrophages to an M1 phenotype and increases CD47-mediated phagocytosis.

BACKGROUND: Leukemia-associated macrophages (LAMs) represent an important cell population within the tumor microenvironment, but little is known about the phenotype, function, and plasticity of these cells. The present study provides an extensive characterization of macrophages in patients with acute myeloid leukemia (AML). METHODS: The phenotype and expression of coregulatory markers were assessed on bone marrow (BM)-derived LAM populations, using multiparametric flow cytometry. BM and blood aspirates were obtained from patients with newly diagnosed acute myeloid leukemia (pAML, n=59), patients in long-term remission (lrAML, n=8), patients with relapsed acute myeloid leukemia (rAML, n=7) and monocyte-derived macrophages of the blood from healthy donors (HD, n=17). LAM subpopulations were correlated with clinical parameters. Using a blocking anti-T-cell immunoreceptor with Ig and ITIM domains (TIGIT) antibody or mouse IgG2α isotype control, we investigated polarization, secretion of cytokines, and phagocytosis on LAMs and healthy monocyte-derived macrophages in vitro. RESULTS: In pAML and rAML, M1 LAMs were reduced and the predominant macrophage population consisted of immunosuppressive M2 LAMs defined by expression of CD163, CD204, CD206, and CD86. M2 LAMs in active AML highly expressed inhibitory receptors such as TIGIT, T-cell immunoglobulin and mucin-domain containing-3 protein (TIM-3), and lymphocyte-activation gene 3 (LAG-3). High expression of CD163 was associated with a poor overall survival (OS). In addition, increased frequencies of TIGIT+ M2 LAMs were associated with an intermediate or adverse risk according to the European Leukemia Network criteria and the FLT3 ITD mutation. In vitro blockade of TIGIT shifted the polarization of primary LAMs or peripheral blood-derived M2 macrophages toward the M1 phenotype and increased secretion of M1-associated cytokines and chemokines. Moreover, the blockade of TIGIT augmented the anti-CD47-mediated phagocytosis of AML cell lines and primary AML cells. CONCLUSION: Our findings suggest that immunosuppressive TIGIT+ M2 LAMs can be redirected into an efficient effector population that may be of direct clinical relevance in the near future.

Influencing tumor-associated macrophages in malignant melanoma with monoclonal antibodies.

The application of monoclonal antibodies (mAbs) for the treatment of melanoma has significantly improved the clinical management of this malignancy over the last decade. Currently approved mAbs for melanoma enhance T cell effector immune responses by blocking immune checkpoint molecules PD-L1/PD-1 and CTLA-4. However, more than half of patients do not benefit from treatment. Targeting the prominent myeloid compartment within the tumor microenvironment, and in particular the ever-abundant tumor-associated macrophages (TAMs), may be a promising strategy to complement existing therapies and enhance treatment success. TAMs are a highly diverse and plastic subset of cells whose pro-tumor properties can support melanoma growth, angiogenesis and invasion. Understanding of their diversity, plasticity and multifaceted roles in cancer forms the basis for new promising TAM-centered treatment strategies. There are multiple mechanisms by which macrophages can be targeted with antibodies in a therapeutic setting, including by depletion, inhibition of specific pro-tumor properties, differential polarization to pro-inflammatory states and enhancement of antitumor immune functions. Here, we discuss TAMs in melanoma, their interactions with checkpoint inhibitor antibodies and emerging mAbs targeting different aspects of TAM biology and their potential to be translated to the clinic.

Design and manufacture of a lyophilised faecal microbiota capsule formulation to GMP standards.

Faecal microbiota transplant (FMT) is an established and effective treatment for recurrent Clostridioides difficile infection (CDI) and has many other potential clinical applications. However, preparation and quality of FMT is poorly standardised and clinical studies are hampered by a lack of well-defined FMT formulations that meet regulatory standards for medicines. As an alternative to FMT suspensions for administration by nasojejunal tube or colonoscopy, which is invasive and disliked by many patients, this study aimed to develop a well-controlled, standardised method for manufacture of lyophilised FMT capsules and to provide stability data allowing storage for extended time periods. Faecal donations were collected from healthy, pre-screened individuals, homogenised, filtered and centrifuged to remove dietary matter. The suspension was centrifuged to pellet bacteria, which were resuspended with trehalose and lyophilised to produce a powder which was filled into 5 enteric-coated capsules (size 0). Live-dead bacterial cell quantitative PCR assay showed <10 fold viable bacterial load reduction through the manufacturing process. No significant loss of viable bacterial load was observed after storage at -80 °C for 36 weeks (p = 0.24, n = 5). Initial clinical experience demonstrated that the capsules produced clinical cure in patients with CDI with no adverse events reported (n = 7). We provide the first report of a detailed manufacturing protocol and specification for an encapsulated lyophilised formulation of FMT. As clinical trials into intestinal microbiota interventions proceed, it is important to use a well-controlled investigational medicinal product in the studies so that any beneficial results can be replicated in clinical practice.

Enriched circulating and tumor-resident TGF-ß+ regulatory B cells in patients with melanoma promote FOXP3+ Tregs.

B cells are emerging as key players of anti-tumor adaptive immune responses. We investigated regulatory and pro-inflammatory cytokine-expressing B cells in patients with melanoma by flow cytometric intracellular cytokine, CyTOF, transcriptomic, immunofluorescence, single-cell RNA-seq, and B:T cell co-culture analyses. We found enhanced circulating regulatory (TGF-ß+ and PD-L1+) and reduced pro-inflammatory TNF-α+ B cell populations in patients compared with healthy volunteers (HVs), including lower IFN-γ+:IL-4+ and higher TGF-ß+:TNF-α+ B cell ratios in patients. TGF-ß-expressing B cells in the melanoma tumor microenvironment assembled in clusters and interacted with T cells via lymphoid recruitment (SELL, CXCL13, CCL4, CD74) signals and with Tregs via CD47:SIRP-γ, and FOXP3-promoting Galectin-9:CD44. While reduced in tumors compared to blood, TNF-α-expressing B cells engaged in crosstalk with Tregs via TNF-α signaling and the ICOS/ICOSL axis. Patient-derived B cells promoted FOXP3+ Treg differentiation in a TGF-ß-dependent manner, while sustaining expression of IFN-γ and TNF-α by autologous T-helper cells and promoting T-helper cell proliferation ex vivo, an effect further enhanced with anti-PD-1 checkpoint blockade. Our findings reveal cytokine-expressing B cell compartments skewed toward regulatory phenotypes in patient circulation and melanoma lesions, intratumor spatial localization, and bidirectional crosstalk between B and T cell subsets with immunosuppressive attributes.

Radiomic assessment of oesophageal adenocarcinoma: a critical review of 18F-FDG PET/CT, PET/MRI and CT.

OBJECTIVES: Radiomic models present an avenue to improve oesophageal adenocarcinoma assessment through quantitative medical image analysis. However, model selection is complicated by the abundance of available predictors and the uncertainty of their relevance and reproducibility. This analysis reviews recent research to facilitate precedent-based model selection for prospective validation studies. METHODS: This analysis reviews research on 18F-FDG PET/CT, PET/MRI and CT radiomics in oesophageal adenocarcinoma between 2016 and 2021. Model design, testing and reporting are evaluated according to the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) score and Radiomics Quality Score (RQS). Key results and limitations are analysed to identify opportunities for future research in the area. RESULTS: Radiomic models of stage and therapeutic response demonstrated discriminative capacity, though clinical applications require greater sensitivity. Although radiomic models predict survival within institutions, generalisability is limited. Few radiomic features have been recommended independently by multiple studies. CONCLUSIONS: Future research must prioritise prospective validation of previously proposed models to further clinical translation.

Influence of FLG loss-of-function mutations in host-microbe interactions during atopic skin inflammation.

BACKGROUND: Loss-of-function mutations in the filaggrin (FLG) gene directly alter skin barrier function and critically influence atopic inflammation. While skin barrier dysfunction, Th2-associated inflammation and bacterial dysbiosis are well-known characteristics of atopic dermatitis (AD), the mechanisms interconnecting genotype, transcriptome and microbiome remain largely elusive. OBJECTIVE: In-depth analysis of FLG genotype-associated skin gene expression alterations and host-microbe interactions in AD. METHODS: Multi-omics characterization of a cohort of AD patients carrying heterozygous loss-of-function mutations in the FLG gene (ADMut) (n = 15), along with matched wild-type (ADWt) patients and healthy controls. Detailed clinical characterization, microarray gene expression and 16 S rRNA-based microbial marker gene data were generated and analyzed. RESULTS: In the context of filaggrin dysfunction, the transcriptome was characterized by dysregulation of barrier function and water homeostasis, while the lesional skin of ADWt demonstrated the specific upregulation of pro-inflammatory cytokines and T-cell proliferation. S. aureus dominated the microbiome in both patient groups, however, shifting microbial communities could be observed when comparing healthy with non-lesional ADWt or ADMut skin, offering the opportunity to identify microbe-associated transcriptomic signatures. Moreover, an AD core signature with 28 genes, including CCL13, CCL18, BTC, SCIN, RAB31 and PCLO was identified. CONCLUSIONS: Our integrative approach provides molecular insights for the concept that FLG loss-of-function mutations are a genetic shortcut to atopic inflammation and unravels the complex interplay between genotype, transcriptome and microbiome in the human holobiont.

A Bayesian predictive analytics model for improving long range epidemic forecasting during an infection wave.

Following the outbreak of the coronavirus epidemic in early 2020, municipalities, regional governments and policymakers worldwide had to plan their Non-Pharmaceutical Interventions (NPIs) amidst a scenario of great uncertainty. At this early stage of an epidemic, where no vaccine or medical treatment is in sight, algorithmic prediction can become a powerful tool to inform local policymaking. However, when we replicated one prominent epidemiological model to inform health authorities in a region in the south of Brazil, we found that this model relied too heavily on manually predetermined covariates and was too reactive to changes in data trends. Our four proposed models access data of both daily reported deaths and infections as well as take into account missing data (e.g., the under-reporting of cases) more explicitly, with two of the proposed versions also attempting to model the delay in test reporting. We simulated weekly forecasting of deaths from the period from 31/05/2020 until 31/01/2021, with first week data being used as a cold-start to the algorithm, after which we use a lighter variant of the model for faster forecasting. Because our models are significantly more proactive in identifying trend changes, this has improved forecasting, especially in long-range predictions and after the peak of an infection wave, as they were quicker to adapt to scenarios after these peaks in reported deaths. Assuming reported cases were under-reported greatly benefited the model in its stability, and modelling retroactively-added data (due to the "hot" nature of the data used) had a negligible impact on performance.

Sparse Regression in Cancer Genomics: Comparing Variable Selection and Predictions in Real World Data.

BACKGROUND: Evaluation of gene interaction models in cancer genomics is challenging, as the true distribution is uncertain. Previous analyses have benchmarked models using synthetic data or databases of experimentally verified interactions - approaches which are susceptible to misrepresentation and incompleteness, respectively. The objectives of this analysis are to (1) provide a real-world data-driven approach for comparing performance of genomic model inference algorithms, (2) compare the performance of LASSO, elastic net, best-subset selection, L 0 L 1 penalisation and L 0 L 2 penalisation in real genomic data and (3) compare algorithmic preselection according to performance in our benchmark datasets to algorithmic selection by internal cross-validation. METHODS: Five large ( n 4000 ) genomic datasets were extracted from Gene Expression Omnibus. 'Gold-standard' regression models were trained on subspaces of these datasets ( n 4000 , p = 500 ). Penalised regression models were trained on small samples from these subspaces ( n ∈ { 25 , 75 , 150 } , p = 500 ) and validated against the gold-standard models. Variable selection performance and out-of-sample prediction were assessed. Penalty 'preselection' according to test performance in the other 4 datasets was compared to selection internal cross-validation error minimisation. RESULTS: L 1 L 2 -penalisation achieved the highest cosine similarity between estimated coefficients and those of gold-standard models. L 0 L 2 -penalised models explained the greatest proportion of variance in test responses, though performance was unreliable in low signal:noise conditions. L 0 L 2 also attained the highest overall median variable selection F1 score. Penalty preselection significantly outperformed selection by internal cross-validation in each of 3 examined metrics. CONCLUSIONS: This analysis explores a novel approach for comparisons of model selection approaches in real genomic data from 5 cancers. Our benchmarking datasets have been made publicly available for use in future research. Our findings support the use of L 0 L 2 penalisation for structural selection and L 1 L 2 penalisation for coefficient recovery in genomic data. Evaluation of learning algorithms according to observed test performance in external genomic datasets yields valuable insights into actual test performance, providing a data-driven complement to internal cross-validation in genomic regression tasks.

Tumor-Infiltrating B Lymphocyte Profiling Identifies IgG-Biased, Clonally Expanded Prognostic Phenotypes in Triple-Negative Breast Cancer.

In breast cancer, humoral immune responses may contribute to clinical outcomes, especially in more immunogenic subtypes. Here, we investigated B lymphocyte subsets, immunoglobulin expression, and clonal features in breast tumors, focusing on aggressive triple-negative breast cancers (TNBC). In samples from patients with TNBC and healthy volunteers, circulating and tumor-infiltrating B lymphocytes (TIL-B) were evaluated. CD20+CD27+IgD- isotype-switched B lymphocytes were increased in tumors, compared with matched blood. TIL-B frequently formed stromal clusters with T lymphocytes and engaged in bidirectional functional cross-talk, consistent with gene signatures associated with lymphoid assembly, costimulation, cytokine-cytokine receptor interactions, cytotoxic T-cell activation, and T-cell-dependent B-cell activation. TIL-B-upregulated B-cell receptor (BCR) pathway molecules FOS and JUN, germinal center chemokine regulator RGS1, activation marker CD69, and TNFα signal transduction via NFκB, suggesting BCR-immune complex formation. Expression of genes associated with B lymphocyte recruitment and lymphoid assembly, including CXCL13, CXCR4, and DC-LAMP, was elevated in TNBC compared with other subtypes and normal breast. TIL-B-rich tumors showed expansion of IgG but not IgA isotypes, and IgG isotype switching positively associated with survival outcomes in TNBC. Clonal expansion was biased toward IgG, showing expansive clonal families with specific variable region gene combinations and narrow repertoires. Stronger positive selection pressure was present in the complementarity determining regions of IgG compared with their clonally related IgA in tumor samples. Overall, class-switched B lymphocyte lineage traits were conspicuous in TNBC, associated with improved clinical outcomes, and conferred IgG-biased, clonally expanded, and likely antigen-driven humoral responses. SIGNIFICANCE: Tumor-infiltrating B lymphocytes assemble in clusters, undergoing B-cell receptor-driven activation, proliferation, and isotype switching. Clonally expanded, IgG isotype-biased humoral immunity associates with favorable prognosis primarily in triple-negative breast cancers.

Combined anti-PD-1 and anti-CTLA-4 checkpoint blockade: Treatment of melanoma and immune mechanisms of action.

Cytotoxic T-lymphocyte associated protein-4 (CTLA-4) and the Programmed Death Receptor 1 (PD-1) are immune checkpoint molecules that are well-established targets of antibody immunotherapies for the management of malignant melanoma. The monoclonal antibodies, Ipilimumab, Pembrolizumab, and Nivolumab, designed to interfere with T cell inhibitory signals to activate immune responses against tumors, were originally approved as monotherapy. Treatment with a combination of immune checkpoint inhibitors may improve outcomes compared to monotherapy in certain patient groups and these clinical benefits may be derived from unique immune mechanisms of action. However, treatment with checkpoint inhibitor combinations also present significant clinical challenges and increased rates of immune-related adverse events. In this review, we discuss the potential mechanisms attributed to single and combined checkpoint inhibitor immunotherapies and clinical experience with their use.

Microbial and transcriptional differences elucidate atopic dermatitis heterogeneity across skin sites.

It is well established that different sites in healthy human skin are colonized by distinct microbial communities due to different physiological conditions. However, few studies have explored microbial heterogeneity between skin sites in diseased skin, such as atopic dermatitis (AD) lesions. To address this issue, we carried out deep analysis of the microbiome and transcriptome in the skin of a large cohort of AD patients and healthy volunteers, comparing two physiologically different sites: upper back and posterior thigh. Microbiome samples and biopsies were obtained from both lesional and nonlesional skin to identify changes related to the disease process. Transcriptome analysis revealed distinct disease-related gene expression profiles depending on anatomical location, with keratinization dominating the transcriptomic signatures in posterior thigh, and lipid metabolism in the upper back. Moreover, we show that relative abundance of Staphylococcus aureus is associated with disease severity in the posterior thigh, but not in the upper back. Our results suggest that AD may select for similar microbes in different anatomical locations-an "AD-like microbiome," but distinct microbial dynamics can still be observed when comparing posterior thigh to upper back. This study highlights the importance of considering the variability across skin sites when studying the development of skin inflammation.

IgE Activates Monocytes from Cancer Patients to Acquire a Pro-Inflammatory Phenotype.

IgE contributes to host-protective functions in parasitic and bacterial infections, often by monocyte and macrophage recruitment. We previously reported that monocytes contribute to tumour antigen-specific IgE-mediated tumour growth restriction in rodent models. Here, we investigate the impact of IgE stimulation on monocyte response, cellular signalling, secretory and tumour killing functions. IgE cross-linking on human monocytes with polyclonal antibodies to mimic formation of immune complexes induced upregulation of co-stimulatory (CD40, CD80, CD86), and reduced expression of regulatory (CD163, CD206, MerTK) monocyte markers. Cross-linking and tumour antigen-specific IgE antibody-dependent cellular cytotoxicity (ADCC) of cancer cells by cancer patient-derived monocytes triggered release of pro-inflammatory mediators (TNFα, MCP-1, IL-10, CXCL-10, IL-1ß, IL-6, IL-23). High intratumoural gene expression of these mediators was associated with favourable five-year overall survival in ovarian cancer. IgE cross-linking of trimeric FcεRI on monocytes stimulated the phosphorylation of intracellular protein kinases widely reported to be downstream of mast cell and basophil tetrameric FcεRI signalling. These included recently-identified FcεRI pathway kinases Fgr, STAT5, Yes and Lck, which we now associate with monocytes. Overall, anti-tumour IgE can potentiate pro-inflammatory signals, and prime tumour cell killing by human monocytes. These findings will inform the development of IgE monoclonal antibody therapies for cancer.