Slideflow: deep learning for digital histopathology with real-time whole-slide visualization.

Deep learning methods have emerged as powerful tools for analyzing histopathological images, but current methods are often specialized for specific domains and software environments, and few open-source options exist for deploying models in an interactive interface. Experimenting with different deep learning approaches typically requires switching software libraries and reprocessing data, reducing the feasibility and practicality of experimenting with new architectures. We developed a flexible deep learning library for histopathology called Slideflow, a package which supports a broad array of deep learning methods for digital pathology and includes a fast whole-slide interface for deploying trained models. Slideflow includes unique tools for whole-slide image data processing, efficient stain normalization and augmentation, weakly-supervised whole-slide classification, uncertainty quantification, feature generation, feature space analysis, and explainability. Whole-slide image processing is highly optimized, enabling whole-slide tile extraction at 40x magnification in 2.5 s per slide. The framework-agnostic data processing pipeline enables rapid experimentation with new methods built with either Tensorflow or PyTorch, and the graphical user interface supports real-time visualization of slides, predictions, heatmaps, and feature space characteristics on a variety of hardware devices, including ARM-based devices such as the Raspberry Pi.

Induction Chemotherapy in Low-Risk HPV+ Oropharyngeal Cancer.

OPINION STATEMENT: Human papillomavirus (HPV) associated oropharyngeal squamous cell carcinoma (OPSCC) is rapidly increasing in incidence, and has now become the most common head and neck cancer (HNC). Studies have demonstrated that HPV associated OPSCC is associated with a favorable prognosis compared with its HPV-negative counterparts, yet standard multimodality therapy is often associated with substantial acute and late treatment-related toxicity. While locoregional control is improved in HPV+ OPSCC, distant metastasis rate has gained recognition as a major cause of death in this population, with some studies suggesting similar rates as non-HPV-related cancers. Induction chemotherapy has been of long-standing interest in locoregionally advanced HNC, yet its use in combination with concomitant chemoradiation remains an area of controversy as a survival benefit remains unproven following randomized trials. Nevertheless, response to induction chemotherapy remains an important dynamic and prognostic biomarker, with response-adaptive de-intensified therapy in HPV+ OPSCC gaining traction in single-arm phase II studies demonstrating promising results. The emergence of immunotherapy in the recurrent/metastatic setting for HNC has led to enthusiasm to incorporate in the curative setting, yet its role remains undefined. Our institutional paradigm for HPV+ OPSCC incorporates induction therapy followed by risk and response adaptive locoregional treatment. Ultimately, the role of induction therapy in HPV+ OPSCC will need to be investigated in a randomized setting to be incorporated routinely into clinical practice.

Deep learning prediction of BRAF-RAS gene expression signature identifies noninvasive follicular thyroid neoplasms with papillary-like nuclear features.

Noninvasive follicular thyroid neoplasms with papillary-like nuclear features (NIFTP) are follicular-patterned thyroid neoplasms defined by nuclear atypia and indolent behavior. They harbor RAS mutations, rather than BRAFV600E mutations as is observed in papillary thyroid carcinomas with extensive follicular growth. Reliably identifying NIFTPs aids in safe therapy de-escalation, but has proven to be challenging due to interobserver variability and morphologic heterogeneity. The genomic scoring system BRS (BRAF-RAS score) was developed to quantify the extent to which a tumor's expression profile resembles a BRAFV600E or RAS-mutant neoplasm. We proposed that deep learning prediction of BRS could differentiate NIFTP from other follicular-patterned neoplasms. A deep learning model was trained on slides from a dataset of 115 thyroid neoplasms to predict tumor subtype (NIFTP, PTC-EFG, or classic PTC), and was used to generate predictions for 497 thyroid neoplasms within The Cancer Genome Atlas (TCGA). Within follicular-patterned neoplasms, tumors with positive BRS (RAS-like) were 8.5 times as likely to carry an NIFTP prediction than tumors with negative BRS (89.7% vs 10.5%, P < 0.0001). To test the hypothesis that BRS may serve as a surrogate for biological processes that determine tumor subtype, a separate model was trained on TCGA slides to predict BRS as a linear outcome. This model performed well in cross-validation on the training set (R2 = 0.67, dichotomized AUC = 0.94). In our internal cohort, NIFTPs were near universally predicted to have RAS-like BRS; as a sole discriminator of NIFTP status, predicted BRS performed with an AUC of 0.99 globally and 0.97 when restricted to follicular-patterned neoplasms. BRAFV600E-mutant PTC-EFG had BRAFV600E-like predicted BRS (mean -0.49), nonmutant PTC-EFG had more intermediate predicted BRS (mean -0.17), and NIFTP had RAS-like BRS (mean 0.35; P < 0.0001). In summary, histologic features associated with the BRAF-RAS gene expression spectrum are detectable by deep learning and can aid in distinguishing indolent NIFTP from PTCs.

Myc and ChREBP transcription factors cooperatively regulate normal and neoplastic hepatocyte proliferation in mice.

Analogous to the c-Myc (Myc)/Max family of bHLH-ZIP transcription factors, there exists a parallel regulatory network of structurally and functionally related proteins with Myc-like functions. Two related Myc-like paralogs, termed MondoA and MondoB/carbohydrate response element-binding protein (ChREBP), up-regulate gene expression in heterodimeric association with the bHLH-ZIP Max-like factor Mlx. Myc is necessary to support liver cancer growth, but not for normal hepatocyte proliferation. Here, we investigated ChREBP's role in these processes and its relationship to Myc. Unlike Myc loss, ChREBP loss conferred a proliferative disadvantage to normal murine hepatocytes, as did the combined loss of ChREBP and Myc. Moreover, hepatoblastomas (HBs) originating in myc-/-, chrebp-/-, or myc-/-/chrebp-/- backgrounds grew significantly more slowly. Metabolic studies on livers and HBs in all three genetic backgrounds revealed marked differences in oxidative phosphorylation, fatty acid ß-oxidation (FAO), and pyruvate dehydrogenase activity. RNA-Seq of livers and HBs suggested seven distinct mechanisms of Myc-ChREBP target gene regulation. Gene ontology analysis indicated that many transcripts deregulated in the chrebp-/- background encode enzymes functioning in glycolysis, the TCA cycle, and ß- and ω-FAO, whereas those dysregulated in the myc-/- background encode enzymes functioning in glycolysis, glutaminolysis, and sterol biosynthesis. In the myc-/-/chrebp-/- background, additional deregulated transcripts included those involved in peroxisomal ß- and α-FAO. Finally, we observed that Myc and ChREBP cooperatively up-regulated virtually all ribosomal protein genes. Our findings define the individual and cooperative proliferative, metabolic, and transcriptional roles for the "Extended Myc Network" under both normal and neoplastic conditions.

Sequential adaptive changes in a c-Myc-driven model of hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is a common cancer that frequently overexpresses the c-Myc (Myc) oncoprotein. Using a mouse model of Myc-induced HCC, we studied the metabolic, biochemical, and molecular changes accompanying HCC progression, regression, and recurrence. These involved altered rates of pyruvate and fatty acid ß-oxidation and the likely re-directing of glutamine into biosynthetic rather than energy-generating pathways. Initial tumors also showed reduced mitochondrial mass and differential contributions of electron transport chain complexes I and II to respiration. The uncoupling of complex II's electron transport function from its succinate dehydrogenase activity also suggested a mechanism by which Myc generates reactive oxygen species. RNA sequence studies revealed an orderly progression of transcriptional changes involving pathways pertinent to DNA damage repair, cell cycle progression, insulin-like growth factor signaling, innate immunity, and further metabolic re-programming. Only a subset of functions deregulated in initial tumors was similarly deregulated in recurrent tumors thereby indicating that the latter can "normalize" some behaviors to suit their needs. An interactive and freely available software tool was developed to allow continued analyses of these and other transcriptional profiles. Collectively, these studies define the metabolic, biochemical, and molecular events accompanyingHCCevolution, regression, and recurrence in the absence of any potentially confounding therapies.

Diagnostic and prognostic implications of ribosomal protein transcript expression patterns in human cancers.

BACKGROUND: Ribosomes, the organelles responsible for the translation of mRNA, are comprised of four rRNAs and ~ 80 ribosomal proteins (RPs). Although canonically assumed to be maintained in equivalent proportions, some RPs have been shown to possess differential expression across tissue types. Dysregulation of RP expression occurs in a variety of human diseases, notably in many cancers, and altered expression of some RPs correlates with different tumor phenotypes and patient survival. Little work has been done, however, to characterize overall patterns of RP transcript (RPT) expression in human cancers. METHODS: To investigate the impact of global RPT expression patterns on tumor phenotypes, we analyzed RPT expression of ~ 10,000 human tumors and over 700 normal tissues from The Cancer Genome Atlas (TCGA) using t-distributed stochastic neighbor embedding (t-SNE). Clusters of tumors identified by t-SNE were then analyzed with chi-squared and t-tests to compare phenotypic data, ANOVA to compare individual RPT expression, and Kaplan-Meier curves to assess survival differences. RESULTS: Normal tissues and cancers possess distinct and readily discernible RPT expression patterns that are independent of their absolute levels of expression. In tumors, RPT patterning is distinct from that of normal tissues, identifies heretofore unrecognized tumor subtypes, and in many cases correlates with molecular, pathological, and clinical features, including survival. CONCLUSIONS: RPT expression patterns are both tissue-specific and tumor-specific. These could be used as a powerful and novel method of tumor classification, offering a potential clinical tool for prognosis and therapeutic stratification.

Developing a low-cost, open-source, locally manufactured workstation and computational pipeline for automated histopathology evaluation using deep learning.

BACKGROUND: Deployment and access to state-of-the-art precision medicine technologies remains a fundamental challenge in providing equitable global cancer care in low-resource settings. The expansion of digital pathology in recent years and its potential interface with diagnostic artificial intelligence algorithms provides an opportunity to democratize access to personalized medicine. Current digital pathology workstations, however, cost thousands to hundreds of thousands of dollars. As cancer incidence rises in many low- and middle-income countries, the validation and implementation of low-cost automated diagnostic tools will be crucial to helping healthcare providers manage the growing burden of cancer. METHODS: Here we describe a low-cost ($230) workstation for digital slide capture and computational analysis composed of open-source components. We analyze the predictive performance of deep learning models when they are used to evaluate pathology images captured using this open-source workstation versus images captured using common, significantly more expensive hardware. Validation studies assessed model performance on three distinct datasets and predictive models: head and neck squamous cell carcinoma (HPV positive versus HPV negative), lung cancer (adenocarcinoma versus squamous cell carcinoma), and breast cancer (invasive ductal carcinoma versus invasive lobular carcinoma). FINDINGS: When compared to traditional pathology image capture methods, low-cost digital slide capture and analysis with the open-source workstation, including the low-cost microscope device, was associated with model performance of comparable accuracy for breast, lung, and HNSCC classification. At the patient level of analysis, AUROC was 0.84 for HNSCC HPV status prediction, 1.0 for lung cancer subtype prediction, and 0.80 for breast cancer classification. INTERPRETATION: Our ability to maintain model performance despite decreased image quality and low-power computational hardware demonstrates that it is feasible to massively reduce costs associated with deploying deep learning models for digital pathology applications. Improving access to cutting-edge diagnostic tools may provide an avenue for reducing disparities in cancer care between high- and low-income regions. FUNDING: Funding for this project including personnel support was provided via grants from NIH/NCIR25-CA240134, NIH/NCIU01-CA243075, NIH/NIDCRR56-DE030958, NIH/NCIR01-CA276652, NIH/NCIK08-CA283261, NIH/NCI-SOAR25CA240134, SU2C (Stand Up to Cancer) Fanconi Anemia Research Fund - Farrah Fawcett Foundation Head and Neck Cancer Research Team Grant, and the European UnionHorizon Program (I3LUNG).

Evaluation of Major Pathologic Response and Pathologic Complete Response as Surrogate End Points for Survival in Randomized Controlled Trials of Neoadjuvant Immune Checkpoint Blockade in Resectable in NSCLC.

INTRODUCTION: Controversy remains as to whether pathologic complete response (pCR) and major pathologic response (MPR) represent surrogate end points for event-free survival (EFS) and overall survival (OS) in neoadjuvant trials for resectable NSCLC. METHODS: A search of PubMed and archives of international conference abstracts was performed from June 2017 through October 31, 2023. Studies incorporating a neoadjuvant arm with immune checkpoint blockade alone or in combination with chemotherapy were included. Those not providing information regarding pCR, MPR, EFS, or OS were excluded. For trial-level surrogacy, log ORs for pCR and MPR and log hazard ratios for EFS and OS were analyzed using a linear regression model weighted by sample size. The regression coefficient and R2 with 95% confidence interval were calculated by the bootstrapping approach. RESULTS: Seven randomized clinical trials were identified for a total of 2385 patients. At the patient level, the R2 of pCR and MPR with 2-year EFS were 0.82 (0.66-0.94) and 0.81 (0.63-0.93), respectively. The OR of 2-year EFS rates by response status was 0.12 (0.07-0.19) and 0.11 (0.05-0.22), respectively. For the 2-year OS, the R2 of pCR and MPR were 0.55 (0.09-0.98) and 0.52 (0.10-0.96), respectively. At the trial level, the R2 for the association of OR for response and HR for EFS was 0.58 (0.00-0.97) and 0.61 (0.00-0.97), respectively. CONCLUSIONS: Our analyses reveal a robust correlation between pCR and MPR with 2-year EFS but not OS. Trial-level surrogacy was moderate but imprecise. More mature follow-up and data to assess the impact of study crossover are needed.

Applications of Deep Learning in Endocrine Neoplasms.

Machine learning methods have been growing in prominence across all areas of medicine. In pathology, recent advances in deep learning (DL) have enabled computational analysis of histological samples, aiding in diagnosis and characterization in multiple disease areas. In cancer, and particularly endocrine cancer, DL approaches have been shown to be useful in tasks ranging from tumor grading to gene expression prediction. This review summarizes the current state of DL research in endocrine cancer histopathology with an emphasis on experimental design, significant findings, and key limitations.

Data augmentation and multimodal learning for predicting drug response in patient-derived xenografts from gene expressions and histology images.

Patient-derived xenografts (PDXs) are an appealing platform for preclinical drug studies. A primary challenge in modeling drug response prediction (DRP) with PDXs and neural networks (NNs) is the limited number of drug response samples. We investigate multimodal neural network (MM-Net) and data augmentation for DRP in PDXs. The MM-Net learns to predict response using drug descriptors, gene expressions (GE), and histology whole-slide images (WSIs). We explore whether combining WSIs with GE improves predictions as compared with models that use GE alone. We propose two data augmentation methods which allow us training multimodal and unimodal NNs without changing architectures with a single larger dataset: 1) combine single-drug and drug-pair treatments by homogenizing drug representations, and 2) augment drug-pairs which doubles the sample size of all drug-pair samples. Unimodal NNs which use GE are compared to assess the contribution of data augmentation. The NN that uses the original and the augmented drug-pair treatments as well as single-drug treatments outperforms NNs that ignore either the augmented drug-pairs or the single-drug treatments. In assessing the multimodal learning based on the MCC metric, MM-Net outperforms all the baselines. Our results show that data augmentation and integration of histology images with GE can improve prediction performance of drug response in PDXs.

What is the ideal endpoint in early-stage immunotherapy neoadjuvant trials in lung cancer?

Numerous clinical trials investigating neoadjuvant immune checkpoint inhibitors (ICI) have been performed over the last 5 years. As the number of neoadjuvant trials increases, attention must be paid to identifying informative trial endpoints. Complete pathologic response has been shown to be an appropriate surrogate endpoint for clinical outcomes, such as event-free survival or overall survival, in breast cancer and bladder cancer, but it is less established for non-small-cell lung cancer (NSCLC). The simultaneous advances reported with adjuvant ICI make the optimal strategy for early-stage disease debatable. Considering the long time required to conduct trials, it is important to identify optimal endpoints and discover surrogate endpoints for survival that can help guide ongoing clinical research. Endpoints can be grouped into two categories: medical and surgical. Medical endpoints are measures of survival and drug activity; surgical endpoints describe the feasibility of neoadjuvant approaches at a surgical level as well as perioperative attrition and complications. There are also several exploratory endpoints, including circulating tumor DNA clearance and radiomics. In this review, we outline the advantages and disadvantages of commonly reported endpoints for clinical trials of neoadjuvant regimens in NSCLC.

Deep learning generates synthetic cancer histology for explainability and education.

Artificial intelligence methods including deep neural networks (DNN) can provide rapid molecular classification of tumors from routine histology with accuracy that matches or exceeds human pathologists. Discerning how neural networks make their predictions remains a significant challenge, but explainability tools help provide insights into what models have learned when corresponding histologic features are poorly defined. Here, we present a method for improving explainability of DNN models using synthetic histology generated by a conditional generative adversarial network (cGAN). We show that cGANs generate high-quality synthetic histology images that can be leveraged for explaining DNN models trained to classify molecularly-subtyped tumors, exposing histologic features associated with molecular state. Fine-tuning synthetic histology through class and layer blending illustrates nuanced morphologic differences between tumor subtypes. Finally, we demonstrate the use of synthetic histology for augmenting pathologist-in-training education, showing that these intuitive visualizations can reinforce and improve understanding of histologic manifestations of tumor biology.

Uncertainty-informed deep learning models enable high-confidence predictions for digital histopathology.

A model's ability to express its own predictive uncertainty is an essential attribute for maintaining clinical user confidence as computational biomarkers are deployed into real-world medical settings. In the domain of cancer digital histopathology, we describe a clinically-oriented approach to uncertainty quantification for whole-slide images, estimating uncertainty using dropout and calculating thresholds on training data to establish cutoffs for low- and high-confidence predictions. We train models to identify lung adenocarcinoma vs. squamous cell carcinoma and show that high-confidence predictions outperform predictions without uncertainty, in both cross-validation and testing on two large external datasets spanning multiple institutions. Our testing strategy closely approximates real-world application, with predictions generated on unsupervised, unannotated slides using predetermined thresholds. Furthermore, we show that uncertainty thresholding remains reliable in the setting of domain shift, with accurate high-confidence predictions of adenocarcinoma vs. squamous cell carcinoma for out-of-distribution, non-lung cancer cohorts.

Ribosomopathy-like properties of murine and human cancers.

Ribosomopathies comprise a heterogeneous group of hematologic and developmental disorders, often characterized by bone marrow failure, skeletal and other developmental abnormalities and cancer predisposition. They are associated with mutations and/or haplo-insufficiencies of ribosomal proteins (RPs) and inefficient ribosomal RNA (rRNA) processing. The resulting ribosomal stress induces the canonical p19ARF/Mdm2/p53 tumor suppressor pathway leading to proliferative arrest and/or apoptosis. It has been proposed that this pathway is then inactivated during subsequent neoplastic evolution. We show here that two murine models of hepatoblastoma (HB) and hepatocellular carcinoma (HCC) unexpectedly possess features that mimic the ribosomopathies. These include loss of the normal stoichiometry of RP transcripts and proteins and the accumulation of unprocessed rRNA precursors. Silencing of p19ARF, cytoplasmic sequestration of p53, binding to and inactivation of Mdm2 by free RPs, and up-regulation of the pro-survival protein Bcl-2 may further cooperate to drive tumor growth and survival. Consistent with this notion, re-instatement of constitutive p19ARF expression in the HB model completely suppressed tumorigenesis. In >2000 cases of human HCC, colorectal, breast, and prostate cancer, RP transcript deregulation was a frequent finding. In HCC and breast cancer, the severity of this dysregulation was associated with inferior survival. In HCC, the presence of RP gene mutations, some of which were identical to those previously reported in ribosomopathies, were similarly negatively correlated with long-term survival. Taken together, our results indicate that many if not all cancers possess ribosomopathy-like features that may affect their biological behaviors.

Genetic Dissociation of Glycolysis and the TCA Cycle Affects Neither Normal nor Neoplastic Proliferation.

Rapidly proliferating cells increase glycolysis at the expense of oxidative phosphorylation (oxphos) to generate sufficient levels of glycolytic intermediates for use as anabolic substrates. The pyruvate dehydrogenase complex (PDC) is a critical mitochondrial enzyme that catalyzes pyruvate's conversion to acetyl coenzyme A (AcCoA), thereby connecting these two pathways in response to complex energetic, enzymatic, and metabolic cues. Here we utilized a mouse model of hepatocyte-specific PDC inactivation to determine the need for this metabolic link during normal hepatocyte regeneration and malignant transformation. In PDC "knockout" (KO) animals, the long-term regenerative potential of hepatocytes was unimpaired, and growth of aggressive experimental hepatoblastomas was only modestly slowed in the face of 80%-90% reductions in AcCoA and significant alterations in the levels of key tricarboxylic acid (TCA) cycle intermediates and amino acids. Overall, oxphos activity in KO livers and hepatoblastoma was comparable with that of control counterparts, with evidence that metabolic substrate abnormalities were compensated for by increased mitochondrial mass. These findings demonstrate that the biochemical link between glycolysis and the TCA cycle can be completely severed without affecting normal or neoplastic proliferation, even under the most demanding circumstances. Cancer Res; 77(21); 5795-807. ©2017 AACR.

Abnormal lipid processing but normal long-term repopulation potential of myc-/- hepatocytes.

Establishing c-Myc's (Myc) role in liver regeneration has proven difficult particularly since the traditional model of partial hepatectomy may provoke an insufficiently demanding proliferative stress. We used a model of hereditary tyrosinemia whereby the affected parenchyma can be gradually replaced by transplanted hepatocytes, which replicate 50-100-fold, over several months. Prior to transplantation, livers from myc-/- (KO) mice were smaller in young animals and larger in older animals relative to myc+/+ (WT) counterparts. KO mice also consumed more oxygen, produced more CO2 and generated more heat. Although WT and KO hepatocytes showed few mitochondrial structural differences, the latter demonstrated defective electron transport chain function. RNAseq revealed differences in transcripts encoding ribosomal subunits, cytochrome p450 members and enzymes for triglyceride and sterol biosynthesis. KO hepatocytes also accumulated neutral lipids. WT and KO hepatocytes repopulated recipient tyrosinemic livers equally well although the latter were associated with a pro-inflammatory hepatic environment that correlated with worsening lipid accumulation, its extracellular deposition and parenchymal oxidative damage. Our results show Myc to be dispensable for sustained in vivo hepatocyte proliferation but necessary for maintaining normal lipid homeostasis. myc-/- livers resemble those encountered in non-alcoholic fatty liver disease and, under sustained proliferative stress, gradually acquire the features of non-alcoholic steatohepatitis.

c-Myc and AMPK Control Cellular Energy Levels by Cooperatively Regulating Mitochondrial Structure and Function.

The c-Myc (Myc) oncoprotein and AMP-activated protein kinase (AMPK) regulate glycolysis and oxidative phosphorylation (Oxphos) although often for different purposes. Because Myc over-expression depletes ATP with the resultant activation of AMPK, we explored the potential co-dependency of and cross-talk between these proteins by comparing the consequences of acute Myc induction in ampk+/+ (WT) and ampk-/- (KO) murine embryo fibroblasts (MEFs). KO MEFs showed a higher basal rate of glycolysis than WT MEFs and an appropriate increase in response to activation of a Myc-estrogen receptor (MycER) fusion protein. However, KO MEFs had a diminished ability to increase Oxphos, mitochondrial mass and reactive oxygen species in response to MycER activation. Other differences between WT and KO MEFs, either in the basal state or following MycER induction, included abnormalities in electron transport chain function, levels of TCA cycle-related oxidoreductases and cytoplasmic and mitochondrial redox states. Transcriptional profiling of pathways pertinent to glycolysis, Oxphos and mitochondrial structure and function also uncovered significant differences between WT and KO MEFs and their response to MycER activation. Finally, an unbiased mass-spectrometry (MS)-based survey capable of quantifying ~40% of all mitochondrial proteins, showed about 15% of them to be AMPK- and/or Myc-dependent in their steady state. Significant differences in the activities of the rate-limiting enzymes pyruvate kinase and pyruvate dehydrogenase, which dictate pyruvate and acetyl coenzyme A abundance, were also differentially responsive to Myc and AMPK and could account for some of the differences in basal metabolite levels that were also detected by MS. Thus, Myc and AMPK are highly co-dependent and appear to engage in significant cross-talk across numerous pathways which support metabolic and ATP-generating functions.