Faculty Bibliography

High-dimensional multiplexed imaging can reveal the spatial organization of tumour tissues at the molecular level. However, owing to the scale and information complexity of the imaging data, it is challenging to discover and thoroughly characterize the heterogeneity of tumour microenvironments. Here we show that self-supervised representation learning on data from imaging mass cytometry can be leveraged to distinguish morphological differences in tumour microenvironments and to precisely characterize distinct microenvironment signatures. We used self-supervised masked image modelling to train a vision transformer that directly takes high-dimensional multiplexed mass-cytometry images. In contrast with traditional spatial analyses relying on cellular segmentation, the vision transformer is segmentation-free, uses pixel-level information, and retains information on the local morphology and biomarker distribution. By applying the vision transformer to a lung-tumour dataset, we identified and validated a monocytic signature that is associated with poor prognosis.

INTRODUCTION/BACKGROUND:The phase 3 randomized controlled trial of extended pleurectomy decortication and chemotherapy versus chemotherapy alone for pleural mesothelioma (PM) (MARS2) reported "extended pleurectomy decortication was associated with worse survival to 2 years, and more serious adverse events for individuals with resectable PM, compared with chemotherapy alone." These results have led to considerable discourse regarding the future role of surgery for PM, and there has not been unanimity in the mesothelioma surgical community regarding the trial interpretation. This "perspective" evaluates MARS2 using internationally renowned PM experts who either agreed with the trial interpretation or who found issues with its conduct which may have influenced the results. METHODS:A facilitator (HP) worked with team leaders (GW, IO) to assemble individuals offering opinions regarding the trial and its conclusions. Arguments agreeing or not agreeing with the trial interpretation were written only after publication of the full trial. Once both arguments were received by the facilitator, the individual team manuscripts were combined and sent to each team allowing editing for changes in perceived factual errors. FINDINGS/RESULTS:Insightful arguments include (but were not limited to) the difficulties yet advantages of randomization, quality assurance, selection of histologic subtypes, the timing of randomization, use of preoperative staging, statistical methods, and reasons for surgical mortality. CONCLUSIONS:The decision to operate for PM in the future will continue to be defined by consensus guidelines and health payer willingness, and the interpretation of MARS2 may play an important role in modulating the role of surgery in the future.

RATIONALE/BACKGROUND:Lower airway enrichment with oral commensals has been previously associated with grade 3 severe primary graft dysfunction (PGD) after lung transplantation (LT). We aimed to determine whether this dysbiotic signature is present across all PGD severity grades, including milder forms, and whether it is associated with a distinct host inflammatory endotype. METHODS:Lower airway samples from 96 LT recipients with varying degrees of PGD were used to evaluate the lung allograft microbiota via 16S rRNA gene sequencing. Bronchoalveolar lavage (BAL) cytokine concentrations and cell differential percentages were compared across PGD grades. In a subset of samples, we evaluated the lower airway host transcriptome using RNA sequencing methods. RESULTS:Differential analyses demonstrated lower airway enrichment with supraglottic-predominant taxa (SPT) in both moderate and severe PGD. Dirichlet Multinomial Mixtures (DMM) modeling identified two distinct microbial clusters. A greater percentage of subjects with moderate-severe PGD were identified within the dysbiotic cluster (C-SPT) than within the no PGD group (48 and 29%, respectively) though this difference did not reach statistical significance (p=0.06). PGD severity associated with increased BAL neutrophil concentration (p=0.03) and correlated with BAL concentrations of MCP-1/CCL2, IP-10/CXCL10, IL-10, and TNF-α (p<0.05). Furthermore, microbial signatures of dysbiosis correlated with neutrophils, MCP-1/CCL-2, IL-10, and TNF-α (p<0.05). C-SPT exhibited differential expression of TNF, SERPINE1 (PAI-1), MPO, and MMP1 genes and upregulation of MAPK pathways, suggesting that dysbiosis regulates host signaling to promote neutrophilic inflammation. CONCLUSIONS:Lower airway dysbiosis within the lung allograft is associated with a neutrophilic inflammatory endotype, an immune profile commonly recognized as the hallmark for PGD pathogenesis. This data highlights a putative role for lower airway microbial dysbiosis in the pathogenesis of this syndrome.

Over the last decade, Hippo signaling has emerged as a major tumor-suppressing pathway. Its dysregulation is associated with abnormal expression of YAP1 and TEAD-family genes. Recent works have highlighted the role of YAP1/TEAD activity in several cancers and its potential therapeutic implications. Therefore, identifying patients with a dysregulated Hippo pathway is key to enhancing treatment impact. Although recent studies have derived RNA-seq-based signatures, there remains a need for a reproducible and cost-effective method to measure the pathway activation. In recent years, deep learning applied to histology slides have emerged as an effective way to predict molecular information from a data modality available in clinical routine. Here, we trained models to predict YAP1/TEAD activity from H&E-stained histology slides in multiple cancers. The robustness of our approach was assessed in seven independent validation cohorts. Finally, we showed that histological markers of disease aggressiveness were associated with dysfunctional Hippo signaling.

BACKGROUND:Risk of early-stage lung adenocarcinoma (LUAD) recurrence after surgical resection is significant, and post-recurrence median survival is approximately two years. Currently there are no commercially available biomarkers that predict recurrence. Here, we investigated whether microbial and host genomic signatures in the lung can predict recurrence. METHODS:In 91 early-stage (Stage IA/IB) LUAD-patients with extensive follow-up, we used 16s rRNA gene sequencing and host RNA-sequencing to map the microbial and host transcriptomic landscape in tumor and adjacent unaffected lung samples. RESULTS:23 out of 91 subjects had tumor recurrence over 5-year period. In tumor samples, LUAD recurrence was associated with enrichment with Dialister, Prevotella, while in unaffected lung, recurrence was associated with enrichment with Sphyngomonas and Alloiococcus. The strengths of the associations between microbial and host genomic signatures with LUAD recurrence were greater in adjacent unaffected lung samples than in the primary tumor. Among microbial-host features in the unaffected lung samples associated with recurrence, enrichment with Stenotrophomonas geniculata and Chryseobacterium were positively correlated with upregulation of IL-2, IL-3, IL-17, EGFR, HIF-1 signaling pathways among the host transcriptome. In tumor samples, enrichment with Veillonellaceae Dialister, Ruminococcacea, Haemophilus Influenza, and Neisseria were positively correlated with upregulation of IL-1, IL-6, IL17, IFN, and Tryptophan metabolism pathways. CONCLUSIONS:Overall, modeling suggested that a combined microbial/transcriptome approach using unaffected lung samples had the best biomarker performance (AUC=0.83). IMPACT/CONCLUSIONS:This study suggests that LUAD recurrence is associated with distinct pathophysiological mechanisms of microbial-host interactions in the unaffected lung rather than those present in the resected tumor.

INTRODUCTION/BACKGROUND:The International Association for the Study of Lung Cancer developed a global multicenter database to propose evidence-based revisions for the ninth edition of the TNM classification of pleural mesothelioma (PM). This study analyzes the M category to validate eighth edition M category recommendations. METHODS:Cases were submitted electronically or by transfer of existing institutional databases for patients with histologically or cytologically confirmed PM. The presence and number of metastases (single versus multiple) in each of eight organ systems were reported for patients with M1 disease at diagnosis. Overall survival (OS) was calculated by the Kaplan-Meier method. Differences in OS were assessed by log-rank test. RESULTS:Of 7338 submitted cases, 3598 were eligible and 3221 had sufficient data for clinical staging; 228 cases (7%) were M1. Median overall estimated survival was inferior for M1 compared with M0 patients: 10.5 months versus 21.5 months, respectively (p < 0.0001); estimated 1-year survival was 46% versus 71%, respectively. OS differences between M categories were preserved within histologic subgroups. Among 158 patients with organ-specific documentation of M1 disease, there was no statistically significant difference in OS between those with intrathoracic versus more distant metastatic disease (14.4 mo versus 10.9 mo, p = 0.64). No significant survival difference was detected between patients with metastatic disease in a single-organ system versus multiple-organ systems (12.6 mo versus 8.8 mo, p = 0.45). CONCLUSIONS:This evidence-based analysis of the M category for PM conforms with the eighth edition M descriptors. No changes are proposed in the ninth edition of the mesothelioma M category.

The interaction between tumors and their microenvironment is complex and heterogeneous. Recent developments in high-dimensional multiplexed imaging have revealed the spatial organization of tumor tissues at the molecular level. However, the discovery and thorough characterization of the tumor microenvironment (TME) remains challenging due to the scale and complexity of the images. Here, we propose a self-supervised representation learning framework, CANVAS, that enables discovery of novel types of TMEs. CANVAS is a vision transformer that directly takes high-dimensional multiplexed images and is trained using self-supervised masked image modeling. In contrast to traditional spatial analysis approaches which rely on cell segmentations, CANVAS is segmentation-free, utilizes pixel-level information, and retains local morphology and biomarker distribution information. This approach allows the model to distinguish subtle morphological differences, leading to precise separation and characterization of distinct TME signatures. We applied CANVAS to a lung tumor dataset and identified and validated a monocytic signature that is associated with poor prognosis.

INTRODUCTION/BACKGROUND:The International Association for the Study of Lung Cancer developed an international database to inform potential revisions in the ninth edition of the TNM classification of diffuse pleural mesothelioma (PM). This study analyzed the clinical and pathologic N categories to determine whether revisions were indicated relative to the eighth edition staging system. METHODS:Of 7338 PM cases diagnosed from 2013 to 2022 and 3598 met all inclusion criteria for planned analyses. Data on 2836 patients without metastases were included in this study. Overall survival (OS) was measured from date of diagnosis. Patients were included regardless of whether they received neoadjuvant treatment. For the pathologic N analysis, patients who underwent resection (extrapleural pneumonectomy or pleurectomy/decortication) were included. N subgroups were analyzed and OS assessed by the Kaplan-Meier method. RESULTS:The existing eighth edition N categories were performed adequately in the ninth edition data set. A median OS advantage was noted for clinical and pathologic N0 versus N1 patients: 23.2 versus 18.5 and 33.8 versus 25.0 months, respectively. Patients with resected pN0 had a 3-year OS of 48%. No difference in OS was noted for single- versus multiple-station nodal metastases. The number of nodal stations sampled at the time of resection was not associated with a difference in OS. CONCLUSIONS:Data regarding clinical and pathologic N categories corroborate those used in the eighth edition. No changes in the N categories are recommended in the ninth edition of PM staging system.

INTRODUCTION/BACKGROUND:The eighth edition of the TNM classification of pleural mesothelioma (PM) saw substantial changes in T and N components and stage groupings. The International Association for the Study of Lung Cancer collected data into a multinational database to further refine this classification. This ninth edition proposal incorporates changes proposed in the clinical (c)T component but not the pathologic T component, to include size criteria, and further refines TNM stage groupings for PM. METHODS:Data were submitted through electronic data capture or batch transfer from institutional databases. Survival was measured from diagnosis date. Candidate stage groups were developed using a recursive partitioning and amalgamation algorithm applied to all cM0 cases for clinical stage and subsequently for pathologic stage. Cox models were developed to estimate survival for each stage group. RESULTS:Of 3598 submitted cases, 2192 were analyzable for overall clinical stage and 445 for overall pathologic stage. Recursive partitioning and amalgamation generated survival tree on overall survival outcomes restricted to cM0, with newly proposed (ninth edition) cT and cN component-derived optimal stage groupings of stage I (T1N0), II (T1N1; T2N0), IIIA (T1N2; T2N1/2; any T3), IIIB (any T4), and IV (any M1). Although cT and pathologic T descriptors are different in the ninth edition, aligning pathologic stage groupings with clinical stage produced better discrimination than did retaining eighth edition pathologic stage groupings. CONCLUSIONS:To our knowledge, this revision of the clinical TNM classification for PM is the first to incorporate the measurement-based proposed changes in cT category. The pathologic TNM aligns with clinical TNM.

BACKGROUND:edition of the PM staging system. METHODS:edition data. Overall survival (OS) was calculated by the Kaplan-Meier method and differences in OS assessed by the log-rank test. RESULTS:edition analyses. CONCLUSION/CONCLUSIONS:Given reproducible prognostication by Psum, size criteria will be incorporated into cT1-T3 categories in the 9th edition. Current cT4 category and all pT descriptors will be maintained, with reclassification of fissural invasion as pT2.