Search

Background: In recent years, a relationship between the tumor microenvironment (TME) and patient response to targeted cancer immunotherapy has been suggested. We applied machine-learning algorithms on H&E stained tissue to study the TME in metastatic non-small cell lung cancer (NSCLC) patients. Our goal was to identify digital pathology (DP) features associated with outcome under combination treatment or monotherapy with atezolizumab (atezo), an anti-PD-L1 therapy, and relate those features to other data modalities. We analyzed patient data from two phase 3 clinical trials, OAK (docetaxel versus atezo in 2L+ NSCLC) and IMpower150 (bevacizumab, carboplatin, and paclitaxel (BCP) versus BCP+atezo (ABCP) in advanced 1L non-squamous NSCLC). Methods: As part of our effort to build a DP-based tumor-immune microenvironment atlas, digitized H&E images were registered onto the PathAI research platform. Over 200K annotations from 90 pathologists were used to train convolutional neural networks (CNNs) that classify slide-level human-interpretable features (HIFs) of cells and tissue structures from images and deployed on images from OAK and IMpower150. HIFs and PD-L1 status were associated with outcome in all samples in each arm in OAK and results were validated in IMpower150, using Cox proportional hazard models. Bulk RNAseq was run using samples extracted from the same area as the H&E slide. Results: We identified a composite feature capturing the ratio of immune cells to fibroblasts in the stroma predictive of both overall survival (OS) (HR=0.74 p=0.0046) and progression-free survival (PFS) (HR=0.87 p=0.14). While patients primarily benefit from atezo if they are PD-L1 high, we found that even PD-L1 negative patients benefited from atezo when enriched for this feature (22C3 PD-L1 assay: OS HR=0.59 p=0.015, PFS HR=0.8 p=0.25; SP142 PD-L1 assay: OS HR=0.74 p=0.12, PFS HR=0.88 p=0.45). We thus recognized a DP feature that was predictive for positive outcome with atezo treatment, independent of PD-L1 levels. This association was then validated in IMpower150 comparing ABCP to BCP, both overall (OS HR=0.69 p=0.012) and in PD-L1 negative patients (SP263 assay OS HR=0.56 p=0.034). Integrating with RNAseq, patients enriched for this DP feature showed similar enrichment for B and T gene signatures and depletion in CAF-related gene signatures, thus showing the harmonization of TME between different data modalities. Conclusions: Using a deep learning-based assay for quantifying pathology features of the TME from H&E images in two NSCLC trials, we identified a novel biomarker predictive of outcome to PD-L1 targeting therapy, even in PD-L1 low & negative patients. Importantly, our work shows how different data modalities (DP, gene expression) can be integrated to further our understanding of the TME. Citation Format: Aditi Qamra, Minu K. Srivastava, Eloisa Fuentes, Ben Trotter, Raymond Biju, Guillaume Chhor, James Cowan, Steven Gendreau, Webster Lincoln, Lisa McGinnis, Luciana Molinero, Namrata S. Patil, Amber Schedlbauer, Katja Schulze, Adam Stanford-Moore, Laura Chambre, Ilan Wapinski, David S. Shames, Hartmut Koeppen, Stephanie Hennek, Jane Fridlyand, Jennifer M. Giltnane, Assaf Amitai. Digital pathology based prognostic & predictive biomarkers in metastatic non-small cell lung cancer. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5705.

Memory encoding and retrieval involve distinct interactions between multiple brain areas, yet the flexible structure of corresponding large-scale networks during such memory processing remains unclear. Using functional magnetic resonance imaging, we employed a spatiotemporal encoding and retrieval task, detecting functional community structure across the multiple components of our task. Consistent with past work, we identified a set of stable subnetworks, mostly belonging to primary motor and sensory cortices but also identified a subset of flexible hubs, mostly belonging to higher association areas. These “mover” hubs changed connectivity patterns across spatial and temporal memory encoding and retrieval, engaging in an integrative role within the network. Global encoding network and subnetwork dissimilarity predicted retrieval performance. Together, our findings emphasize the importance of flexible network allegiance among some hubs and the importance of network reconfiguration to human episodic memory., Author Summary The degree to which task-related functional connectivity patterns remain stable or are dynamic when people learn and remember information remains largely untested. We investigated this issue by collecting fMRI while participants performed a memory encoding and retrieval task. Our results suggested that subnetworks are dynamic and tend to fragment relative to a resting-state network partition. From these changes in connectivity, we identified a subset of “movers,” or in other words, nodes that changed their allegiance to subnetworks across all aspects of the task. These findings emphasize that memory is a dynamic process involving changes in task-related functional connectivity across the brain.

Summary Focal epilepsies represent approximately half of all diagnoses, and more than one‐third of these patients are refractory to pharmacologic treatment. Although resection can result in seizure freedom, many patients do not meet surgical criteria, as seizures may be multifocal in origin or have a focus in an eloquent region of the brain. For these individuals, several U.S. Food and Drug Administration (FDA)–approved electrical stimulation paradigms serve as alternative options, including vagus nerve stimulation, responsive neurostimulation, and stimulation of the anterior nucleus of the thalamus. All of these are safe, flexible, and lead to progressive seizure control over time when used as an adjunctive therapy to antiepileptic drugs. Focal epilepsies frequently involve significant comorbidities such as cognitive decline. Similar to antiepilepsy medications and surgical resection, current stimulation targets and parameters have yet to address cognitive impairments directly, with patients reporting persistent comorbidities associated with focal epilepsy despite a significant reduction in the number of their seizures. Although low‐frequency theta oscillations of the septohippocampal network are critical for modulating cellular activity and, in turn, cognitive processing, the coordination of neural excitability is also imperative for preventing seizures. In this review, we summarize current FDA‐approved electrical stimulation paradigms and propose that theta oscillations of the medial septal nucleus represent a novel neuromodulation target for concurrent seizure reduction and cognitive improvement in epilepsy. Ultimately, further advancements in clinical neurostimulation strategies will allow for the efficient treatment of both seizures and comorbidities, thereby improving overall quality of life for patients with epilepsy.

Background Direct brain stimulation via electrodes implanted for intracranial electroencephalography (iEEG) permits the modulation of endogenous electrical signals with significantly greater spatial and temporal specificity than non-invasive approaches. It also allows for the stimulation of deep brain structures important to memory, such as the hippocampus, that are difficult, if not impossible, to target non-invasively. Direct stimulation studies of these deep memory structures, though, have produced mixed results, with some reporting improvement, some impairment, and others, no consistent changes. Objective/hypothesis We hypothesize that to modulate cognitive function using brain stimulation, it is essential to modulate connected nodes comprising a network, rather than just alter local activity. Methods iEEG data collected while patients performed a spatiotemporal memory retrieval task were used to map frequency-specific, coherent oscillatory activity between different brain regions associated with successful memory retrieval. We used these to identify two target nodes that exhibited selectively stronger coupling for spatial vs. temporal retrieval. In a subsequent session, electrical stimulation - theta-bursts with a fixed phase-lag (0° or 180°) – was applied to the two target regions while patients performed spatiotemporal retrieval. Results Stimulation selectively impaired spatial retrieval while not affecting temporal retrieval, and this selective impairment was associated with theta decoupling of the spatial retrieval network. Conclusion These findings suggest that stimulating tightly connected nodes in a functional network at the appropriate phase-lag may effectively modulate the network function, and while in this case it impaired memory processes, it sets a foundation for further network-based perturbation studies.

Research on the neural basis of episodic memory, which refers to memories involving contextual details of past events, has classically focused on the primary role of a single brain region, the hippocampus (HPC). Recent advances in multivariate analysis methods have provided novel insight into the large-scale organization of task-related functional networks important to memory, shedding new light on the role of extrahippocampal “hubs.” Here, we review the emerging perspective from this work, which considers the HPC as one of several different “connector hubs” central to memory processing. Together, these findings provide further insight into spatially distributed and temporally evolving memory processes underlying episodic memory and how these processes change following brain lesions.

Numerous studies indicate the importance of the hippocampus to temporal order retrieval. However, behavioral studies suggest that there are different ways to retrieve temporal order information from encoded sequences, one involving an associative strategy (retrieving associations using neighboring items in a list) and another involving a recency strategy (determining which of two items came first). It remains unresolved, however, whether both strategies recruit the hippocampus or only associative strategies, consistent with the hippocampus's role in relational processing. To address this, we developed a paradigm in which we dissociated associative versus recency-based retrieval, involving the same stimulus presentation during retrieval. Associative retrieval involved an increase in RT (and decrease in performance) with greater distances between intervals, consistent with the need to retrieve intervening associations. Recency-based retrieval involved an increase in RT (and decrease in performance) with shorter distances between intervals, suggesting the use of a strength-based coding mechanism to retrieve information. We employed fMRI to determine the neural basis of the different strategies. Both strategies showed significant levels of hippocampal activation and connectivity that did not differ between tasks. In contrast, both univariate and connectivity pattern analyses revealed differences in extrahippocampal areas such as parietal and frontal cortices. A covariate analysis suggested that differences could not be explained by task difficulty alone. Together, these findings suggest that the hippocampus plays a role in both forms of temporal order retrieval, with neocortical networks mediating the different cognitive demands for associative versus recency-based temporal order retrieval.

Emerging evidence suggests that our memories for recent events depend on a dynamic interplay between multiple cortical brain regions, although previous research has also emphasized a primary role for the hippocampus in episodic memory. One challenge in determining the relative importance of interactions between multiple brain regions versus a specific brain region is a lack of analytic approaches to address this issue. Participants underwent neuroimaging while retrieving the spatial and temporal details of a recently experienced virtual reality environment; we then employed graph theory to analyze functional connectivity patterns across multiple lobes. Dense, large-scale increases in connectivity during successful memory retrieval typified network topology, with individual participant performance correlating positively with overall network density. Within this dense network, the hippocampus, prefrontal cortex, precuneus, and visual cortex served as “hubs” of high connectivity. Spatial and temporal retrieval were characterized by distinct but overlapping “subnetworks” with higher connectivity within posterior and anterior brain areas, respectively. Together, these findings provide new insight into the neural basis of episodic memory, suggesting that the interactions of multiple hubs characterize successful memory retrieval. Furthermore, distinct subnetworks represent components of spatial versus temporal retrieval, with the hippocampus acting as a hub integrating information between these two subnetworks.