Are Positive Biopsy Margins in Melanoma Significant?: A Cohort Study of Micro- Versus Macroscopic Margin Status and Their Impact on Residual Disease and Survival.

BACKGROUND: Presence of positive biopsy margins in melanoma can provoke anxiety over potential disease progression from delays to surgical excision, but their impact on outcomes is unknown. We aimed to compare the presence of residual melanoma in the surgical excision specimen and survival between patients with negative, microscopically positive, and macroscopically positive biopsy margins. METHODS: Patients with cutaneous melanoma who underwent surgical excision over a 13-year period were included. Biopsy characteristics, residual disease in the surgical specimen, and overall and recurrence-free survival were compared between patients with negative, microscopically positive (only scar visible), and macroscopically positive (visible remaining melanoma) biopsy margins. RESULTS: Of 901 patients, 42.4%, 33.3%, and 24.3% had negative, microscopically positive, and macroscopically positive margins, respectively. The incidence of residual invasive melanoma in the surgical specimen varied (P < 0.001), occurring in 5.5%, 17.0%, and 74.9% of patients, respectively. Both microscopically and macroscopically positive margins were associated with residual disease (P < 0.001) but only the latter predicted worse overall (P = 0.013) and recurrence-free survival (P = 0.009). Kaplan-Meier estimated survival was comparable between those with negative and microscopically positive margins, but overall (P = 0.006) and recurrence-free survival (P = 0.004) were significantly worse in the macroscopically positive margin group. These patients had worse prognosis melanoma, with 33.8% being stage III disease, and 23.2% having positive sentinel lymph nodes. CONCLUSIONS: Patients and physicians may be reassured in the presence of microscopically positive biopsy margins which are not associated with worse survival, However, patients with macroscopically positive margins have poorer prognosis and should be treated within an acceptable time frame.

Immunologic signatures of response and resistance to nivolumab with ipilimumab in advanced metastatic cancer.

Identifying pan-tumor biomarkers that predict responses to immune checkpoint inhibitors (ICI) is critically needed. In the AMADEUS clinical trial (NCT03651271), patients with various advanced solid tumors were assessed for changes in intratumoral CD8 percentages and their response to ICI. Patients were grouped based on tumoral CD8 levels: those with CD8 <15% (CD8-low) received nivolumab (anti-PD-1) plus ipilimumab (anti-CTLA4) and those with CD8 ≥15% (CD8-high) received nivolumab monotherapy. 79 patients (72 CD8-low and 7 CD8-high) were treated. The disease control rate was 25.0% (18/72; 95% CI: 15.8-35.2) in CD8-low and 14.3% (1/7; 95% CI: 1.1-43.8) in CD8-high. Tumors from 35.9% (14/39; 95% CI: 21.8-51.4) of patients converted from CD8 <15% pretreatment to ≥15% after treatment. Multiomic analyses showed that CD8-low responders had an inflammatory tumor microenvironment pretreatment, enhanced by an influx of CD8 T cells, CD4 T cells, B cells, and macrophages upon treatment. These findings reveal crucial pan-cancer immunological features for ICI response in patients with metastatic disease.

Analysis of bariatric surgical complications requiring interhospital transfers using a modified Victorian Audit of Surgical Mortality framework.

BACKGROUND: Surgical audit is key in upholding the standards of surgical care but may be inadequate in capturing morbidity experienced by patients being transferred across different health systems. The aim of this study was to assess the utility of an objective framework in the evaluation of clinical issues surrounding interhospital transfers (IHTs). METHODS: A retrospective cohort study was conducted at a Victorian state bariatric hospital. Patients transferred with bariatric surgery related complications between 2014 and 2021 were included. Each case was reviewed by two surgeons using an objective framework developed via a modified Delphi-process. Key issues and preventability surrounding each transfer were evaluated. Inter-observer agreement was assessed using weighted Cohen's Kappa coefficient. RESULTS: Seventy-three patients were included. The most common indication for transfer was proximal staple line leak post sleeve gastrectomy (34/73, 46.6%). Length of stay was 38.3 ± 58.8 days. Cost of care amounted to AUD $110 666.18 per patient. Delay in transfer and complication recognition were present in 20% of cases (Cohen's Kappa 0.51;0.61). Human factors and patient related factors were the most common principal underlying causes (Cohen's Kappa 0.59). A third of the complications (n = 25/73, 34.2%), were potentially preventable (Cohen's Kappa 0.58) and more than half (39/73, 53.4%) did not have documented objective feedback to referring clinicians. CONCLUSION: IHTs associated with bariatric surgery complications have significant morbidity and costs. A structured framework in reviewing IHT can consistently identify potentially modifiable factors that improve clinical outcomes, and constructive feedback to the referring clinician should be actively facilitated and documented.

Deep Learning-based Modeling for Preclinical Drug Safety Assessment.

In drug development, assessing the toxicity of candidate compounds is crucial for successfully transitioning from preclinical research to early-stage clinical trials. Drug safety is typically assessed using animal models with a manual histopathological examination of tissue sections to characterize the dose-response relationship of the compound - a time-intensive process prone to inter-observer variability and predominantly involving tedious review of cases without abnormalities. Artificial intelligence (AI) methods in pathology hold promise to accelerate this assessment and enhance reproducibility and objectivity. Here, we introduce TRACE, a model designed for toxicologic liver histopathology assessment capable of tackling a range of diagnostic tasks across multiple scales, including situations where labeled data is limited. TRACE was trained on 15 million histopathology images extracted from 46,734 digitized tissue sections from 157 preclinical studies conducted on Rattus norvegicus. We show that TRACE can perform various downstream toxicology tasks spanning histopathological response assessment, lesion severity scoring, morphological retrieval, and automatic dose-response characterization. In an independent reader study, TRACE was evaluated alongside ten board-certified veterinary pathologists and achieved higher concordance with the consensus opinion than the average of the pathologists. Our study represents a substantial leap over existing computational models in toxicology by offering the first framework for accelerating and automating toxicological pathology assessment, promoting significant progress with faster, more consistent, and reliable diagnostic processes.

AI-driven Discovery of Morphomolecular Signatures in Toxicology.

Early identification of drug toxicity is essential yet challenging in drug development. At the preclinical stage, toxicity is assessed with histopathological examination of tissue sections from animal models to detect morphological lesions. To complement this analysis, toxicogenomics is increasingly employed to understand the mechanism of action of the compound and ultimately identify lesion-specific safety biomarkers for which in vitro assays can be designed. However, existing works that aim to identify morphological correlates of expression changes rely on qualitative or semi-quantitative morphological characterization and remain limited in scale or morphological diversity. Artificial intelligence (AI) offers a promising approach for quantitatively modeling this relationship at an unprecedented scale. Here, we introduce GEESE, an AI model designed to impute morphomolecular signatures in toxicology data. Our model was trained to predict 1,536 gene targets on a cohort of 8,231 hematoxylin and eosin-stained liver sections from Rattus norvegicus across 127 preclinical toxicity studies. The model, evaluated on 2,002 tissue sections from 29 held-out studies, can yield pseudo-spatially resolved gene expression maps, which we correlate with six key drug-induced liver injuries (DILI). From the resulting 25 million lesion-expression pairs, we established quantitative relations between up and downregulated genes and lesions. Validation of these signatures against toxicogenomic databases, pathway enrichment analyses, and human hepatocyte cell lines asserted their relevance. Overall, our study introduces new methods for characterizing toxicity at an unprecedented scale and granularity, paving the way for AI-driven discovery of toxicity biomarkers.

Circulating KRAS G12D but not G12V is associated with survival in metastatic pancreatic ductal adenocarcinoma.

While high circulating tumor DNA (ctDNA) levels are associated with poor survival for multiple cancers, variant-specific differences in the association of ctDNA levels and survival have not been examined. Here we investigate KRAS ctDNA (ctKRAS) variant-specific associations with overall and progression-free survival (OS/PFS) in first-line metastatic pancreatic ductal adenocarcinoma (mPDAC) for patients receiving chemoimmunotherapy ("PRINCE", NCT03214250), and an independent cohort receiving standard of care (SOC) chemotherapy. For PRINCE, higher baseline plasma levels are associated with worse OS for ctKRAS G12D (log-rank p = 0.0010) but not G12V (p = 0.7101), even with adjustment for clinical covariates. Early, on-therapy clearance of G12D (p = 0.0002), but not G12V (p = 0.4058), strongly associates with OS for PRINCE. Similar results are obtained for the SOC cohort, and for PFS in both cohorts. These results suggest ctKRAS G12D but not G12V as a promising prognostic biomarker for mPDAC and that G12D clearance could also serve as an early biomarker of response.

A multimodal generative AI copilot for human pathology.

Computational pathology1,2 has witnessed considerable progress in the development of both task-specific predictive models and task-agnostic self-supervised vision encoders3,4. However, despite the explosive growth of generative artificial intelligence (AI), there have been few studies on building general-purpose multimodal AI assistants and copilots5 tailored to pathology. Here we present PathChat, a vision-language generalist AI assistant for human pathology. We built PathChat by adapting a foundational vision encoder for pathology, combining it with a pretrained large language model and fine-tuning the whole system on over 456,000 diverse visual-language instructions consisting of 999,202 question and answer turns. We compare PathChat with several multimodal vision-language AI assistants and GPT-4V, which powers the commercially available multimodal general-purpose AI assistant ChatGPT-4 (ref. 6). PathChat achieved state-of-the-art performance on multiple-choice diagnostic questions from cases with diverse tissue origins and disease models. Furthermore, using open-ended questions and human expert evaluation, we found that overall PathChat produced more accurate and pathologist-preferable responses to diverse queries related to pathology. As an interactive vision-language AI copilot that can flexibly handle both visual and natural language inputs, PathChat may potentially find impactful applications in pathology education, research and human-in-the-loop clinical decision-making.

Generation of Rhesus Macaque Embryos with Expanded CAG Trinucleotide Repeats in the Huntingtin Gene.

Huntington's disease (HD) arises from expanded CAG repeats in exon 1 of the Huntingtin (HTT) gene. The resultant misfolded HTT protein accumulates within neuronal cells, negatively impacting their function and survival. Ultimately, HTT accumulation results in cell death, causing the development of HD. A nonhuman primate (NHP) HD model would provide important insight into disease development and the generation of novel therapies due to their genetic and physiological similarity to humans. For this purpose, we tested CRISPR/Cas9 and a single-stranded DNA (ssDNA) containing expanded CAG repeats in introducing an expanded CAG repeat into the HTT gene in rhesus macaque embryos. Analyses were conducted on arrested embryos and trophectoderm (TE) cells biopsied from blastocysts to assess the insertion of the ssDNA into the HTT gene. Genotyping results demonstrated that 15% of the embryos carried an expanded CAG repeat. The integration of an expanded CAG repeat region was successfully identified in five blastocysts, which were cryopreserved for NHP HD animal production. Some off-target events were observed in biopsies from the cryopreserved blastocysts. NHP embryos were successfully produced, which will help to establish an NHP HD model and, ultimately, may serve as a vital tool for better understanding HD's pathology and developing novel treatments.

Enhancing Gluten Network Formation and Bread-Making Performance of Wheat Flour Using Wheat Bran Aqueous Extract.

Wheat bran possesses diverse nutritional and functional properties. In this study, wheat bran aqueous extract (WBE) was produced and thoroughly characterized as a functional ingredient and improver for bakery application. The WBE contained 50.3% total carbohydrate, 24.5% protein, 13.0% ash, 6.7% soluble fiber, 2.9% insoluble fiber, and 0.5% ß-glucan. Notably, adding 7.5% WBE significantly increased the bread-specific volume to 4.84 cm3/g, compared with the control of 4.18 cm3/g. Adding WBE also resulted in a remarkable improvement in dough properties. The WBE-enriched dough showed increased peak, setback, breakdown, and final viscosities, along with higher storage and loss modulus. Scanning electron microscopy analysis further revealed that the WBE promoted the aggregation of protein and starch within the dough. The extractable gliadin to glutenin ratio increased with 5 and 7.5% WBE additions, compared with the control and 2.5% WBE addition. WBE did not significantly alter the starch gelatinization temperature or dough extension properties. These findings demonstrate that the inclusion of WBE in wheat flour is a promising approach for producing high-quality bread that is enriched with dietary fiber and protein.

Demographic bias in misdiagnosis by computational pathology models.

Despite increasing numbers of regulatory approvals, deep learning-based computational pathology systems often overlook the impact of demographic factors on performance, potentially leading to biases. This concern is all the more important as computational pathology has leveraged large public datasets that underrepresent certain demographic groups. Using publicly available data from The Cancer Genome Atlas and the EBRAINS brain tumor atlas, as well as internal patient data, we show that whole-slide image classification models display marked performance disparities across different demographic groups when used to subtype breast and lung carcinomas and to predict IDH1 mutations in gliomas. For example, when using common modeling approaches, we observed performance gaps (in area under the receiver operating characteristic curve) between white and Black patients of 3.0% for breast cancer subtyping, 10.9% for lung cancer subtyping and 16.0% for IDH1 mutation prediction in gliomas. We found that richer feature representations obtained from self-supervised vision foundation models reduce performance variations between groups. These representations provide improvements upon weaker models even when those weaker models are combined with state-of-the-art bias mitigation strategies and modeling choices. Nevertheless, self-supervised vision foundation models do not fully eliminate these discrepancies, highlighting the continuing need for bias mitigation efforts in computational pathology. Finally, we demonstrate that our results extend to other demographic factors beyond patient race. Given these findings, we encourage regulatory and policy agencies to integrate demographic-stratified evaluation into their assessment guidelines.

Size-structure-property relationship of wood particles in aqueous and dry insulative foams.

Three size-fractionated samples of pine beetle-killed wood particles were used to prepare lightweight insulative foams. The foams were produced by foam-forming an aqueous slurry containing wood particles (125-1000 µm), a polymer binder, and surfactant, followed by oven drying. The effect of wood particle size on the aqueous foam stability, structure, and performance of insulative foams was investigated. While all aqueous foams were highly stable, aqueous foam stability increased with decreasing particle size. For dry foams, the cell size distribution was similar for all particle sizes as it was primarily controlled by the surfactant; differences occurred within the cell wall structure. A size-structure-property relationship was identified using x-ray micro-computed tomography where smaller particles produced lighter cell wall frameworks, leading to lower densities and decreased thermal conductivity and compressive strength. Larger particles produced denser cell wall frameworks that were more resistant to deformation, although all dry foams had sufficient mechanical properties for use as insulation panels. Thermal conductivity for all wood particle size-fractionated samples was <0.047 W m-1 K-1 making the foams similar to expanded polystyrene/polyurethane and supporting their use as thermal insulation in buildings.

Analytical validation of NeXT Personal®, an ultra-sensitive personalized circulating tumor DNA assay.

We describe the analytical validation of NeXT Personal®, an ultra-sensitive, tumor-informed circulating tumor DNA (ctDNA) assay for detecting residual disease, monitoring therapy response, and detecting recurrence in patients diagnosed with solid tumor cancers. NeXT Personal uses whole genome sequencing of tumor and matched normal samples combined with advanced analytics to accurately identify up to ~1,800 somatic variants specific to the patient's tumor. A personalized panel is created, targeting these variants and then used to sequence cell-free DNA extracted from patient plasma samples for ultra-sensitive detection of ctDNA. The NeXT Personal analytical validation is based on panels designed from tumor and matched normal samples from two cell lines, and from 123 patients across nine cancer types. Analytical measurements demonstrated a detection threshold of 1.67 parts per million (PPM) with a limit of detection at 95% (LOD95) of 3.45 PPM. NeXT Personal showed linearity over a range of 0.8 to 300,000 PPM (Pearson correlation coefficient = 0.9998). Precision varied from a coefficient of variation of 12.8% to 3.6% over a range of 25 to 25,000 PPM. The assay targets 99.9% specificity, with this validation study measuring 100% specificity and in silico methods giving us a confidence interval of 99.92 to 100%. In summary, this study demonstrates NeXT Personal as an ultra-sensitive, highly quantitative and robust ctDNA assay that can be used to detect residual disease, monitor treatment response, and detect recurrence in patients.

A visual-language foundation model for computational pathology.

The accelerated adoption of digital pathology and advances in deep learning have enabled the development of robust models for various pathology tasks across a diverse array of diseases and patient cohorts. However, model training is often difficult due to label scarcity in the medical domain, and a model's usage is limited by the specific task and disease for which it is trained. Additionally, most models in histopathology leverage only image data, a stark contrast to how humans teach each other and reason about histopathologic entities. We introduce CONtrastive learning from Captions for Histopathology (CONCH), a visual-language foundation model developed using diverse sources of histopathology images, biomedical text and, notably, over 1.17 million image-caption pairs through task-agnostic pretraining. Evaluated on a suite of 14 diverse benchmarks, CONCH can be transferred to a wide range of downstream tasks involving histopathology images and/or text, achieving state-of-the-art performance on histology image classification, segmentation, captioning, and text-to-image and image-to-text retrieval. CONCH represents a substantial leap over concurrent visual-language pretrained systems for histopathology, with the potential to directly facilitate a wide array of machine learning-based workflows requiring minimal or no further supervised fine-tuning.

Towards a general-purpose foundation model for computational pathology.

Quantitative evaluation of tissue images is crucial for computational pathology (CPath) tasks, requiring the objective characterization of histopathological entities from whole-slide images (WSIs). The high resolution of WSIs and the variability of morphological features present significant challenges, complicating the large-scale annotation of data for high-performance applications. To address this challenge, current efforts have proposed the use of pretrained image encoders through transfer learning from natural image datasets or self-supervised learning on publicly available histopathology datasets, but have not been extensively developed and evaluated across diverse tissue types at scale. We introduce UNI, a general-purpose self-supervised model for pathology, pretrained using more than 100 million images from over 100,000 diagnostic H&E-stained WSIs (>77 TB of data) across 20 major tissue types. The model was evaluated on 34 representative CPath tasks of varying diagnostic difficulty. In addition to outperforming previous state-of-the-art models, we demonstrate new modeling capabilities in CPath such as resolution-agnostic tissue classification, slide classification using few-shot class prototypes, and disease subtyping generalization in classifying up to 108 cancer types in the OncoTree classification system. UNI advances unsupervised representation learning at scale in CPath in terms of both pretraining data and downstream evaluation, enabling data-efficient artificial intelligence models that can generalize and transfer to a wide range of diagnostically challenging tasks and clinical workflows in anatomic pathology.

Sox9 regulates alternative splicing and pancreatic beta cell function.

Despite significant research, mechanisms underlying the failure of islet beta cells that result in type 2 diabetes (T2D) are still under investigation. Here, we report that Sox9, a transcriptional regulator of pancreas development, also functions in mature beta cells. Our results show that Sox9-depleted rodent beta cells have defective insulin secretion, and aging animals develop glucose intolerance, mimicking the progressive degeneration observed in T2D. Using genome editing in human stem cells, we show that beta cells lacking SOX9 have stunted first-phase insulin secretion. In human and rodent cells, loss of Sox9 disrupts alternative splicing and triggers accumulation of non-functional isoforms of genes with key roles in beta cell function. Sox9 depletion reduces expression of protein-coding splice variants of the serine-rich splicing factor arginine SRSF5, a major splicing enhancer that regulates alternative splicing. Our data highlight the role of SOX9 as a regulator of alternative splicing in mature beta cell function.

Search of inhibitors of aldose reductase for treatment of diabetic cataracts using machine learning.

Purpose: Patients with diabetes mellitus have an elevated chance of developing cataracts, a degenerative vision-impairing condition often needing surgery. The process of the reduction of glucose to sorbitol in the lens of the human eye that causes cataracts is managed by the Aldose Reductase Enzyme (AR), and it is been found that AR inhibitors may mitigate the onset of diabetic cataracts. There exists a large pool of natural and synthetic AR inhibitors that can prevent diabetic complications, and the development of a machine-learning (ML) prediction model may bring new AR inhibitors with better characteristics into clinical use. Methods: Using known AR inhibitors and their chemical-physical descriptors we created the ML model for prediction of new AR inhibitors. The predicted inhibitors were tested by computational docking to the binding site of AR. Results: Using cross-validation in order to find the most accurate ML model, we ended with final cross-validation accuracy of 90%. Computational docking testing of the predicted inhibitors gave a high level of correlation between the ML prediction score and binding free energy. Conclusions: Currently known AR inhibitors are not used yet for patients for several reasons. We think that new predicted AR inhibitors have the potential to possess more favorable characteristics to be successfully implemented after clinical testing. Exploring new inhibitors can improve patient well-being and lower surgical complications all while decreasing long-term medical expenses.

A General-Purpose Self-Supervised Model for Computational Pathology.

Tissue phenotyping is a fundamental computational pathology (CPath) task in learning objective characterizations of histopathologic biomarkers in anatomic pathology. However, whole-slide imaging (WSI) poses a complex computer vision problem in which the large-scale image resolutions of WSIs and the enormous diversity of morphological phenotypes preclude large-scale data annotation. Current efforts have proposed using pretrained image encoders with either transfer learning from natural image datasets or self-supervised pretraining on publicly-available histopathology datasets, but have not been extensively developed and evaluated across diverse tissue types at scale. We introduce UNI, a general-purpose self-supervised model for pathology, pretrained using over 100 million tissue patches from over 100,000 diagnostic haematoxylin and eosin-stained WSIs across 20 major tissue types, and evaluated on 33 representative CPath clinical tasks in CPath of varying diagnostic difficulties. In addition to outperforming previous state-of-the-art models, we demonstrate new modeling capabilities in CPath such as resolution-agnostic tissue classification, slide classification using few-shot class prototypes, and disease subtyping generalization in classifying up to 108 cancer types in the OncoTree code classification system. UNI advances unsupervised representation learning at scale in CPath in terms of both pretraining data and downstream evaluation, enabling data-efficient AI models that can generalize and transfer to a gamut of diagnostically-challenging tasks and clinical workflows in anatomic pathology.