Faculty Bibliography

OBJECTIVES/OBJECTIVE:With the development of genomic technologies, the isolation of genomic DNA (gDNA) from clinical samples has become more important for both clinical diagnostics and research studies. Blood samples collected in PAXgene Blood RNA Tubes, which are typically used for RNA extraction, can be used for gDNA extraction, particularly in clinical studies when only such blood samples are available. METHODS:We optimized the pre-treatment of blood samples collected in PAXgene Blood RNA Tubes. For the first time, three magnetic bead-based automated platforms (QIAsymphony SP, Maxwell RSC and KingFisher Apex) were compared for gDNA extraction from these blood samples. Additionally, the effects of storage at 4 °C or freeze-thaw cycles of the blood samples on gDNA yield were investigated. High-throughput extraction in 96-well format was evaluated. RESULTS:Systematic optimization of blood sample pre-treatment shows that prolonged incubation at room temperature and/or increased centrifugation speed and time improved gDNA yield from blood samples collected in PAXgene Blood RNA Tubes. QIAsymphony SP (4.27 ± 2.19 µg) and Maxwell RSC (4.82 ± 2.96 µg) produced significantly higher gDNA yields than KingFisher Apex (1.09 ± 0.61 µg). Higher gDNA yields were obtained with shorter storage time at 4 °C or fewer freeze-thaw cycles of the blood samples. In the 96-well format extraction, gDNA yields ranged from 0.24 to 13.46 µg. CONCLUSIONS:Not all magnetic bead-based automated platforms are suitable for gDNA extraction from blood samples collected in PAXgene Blood RNA Tubes. Systematic pre-treatments optimization provides guidance for routine and high-throughput workflows, and storage conditions and freeze-thaw cycles offer practical reference for biobanking.

Lymphatic vessels activate anti-tumor immune surveillance and support metastasis. Whether there are distinct lymphatic phenotypes that govern immunity and metastasis remains unclear. Here we reveal that cytotoxic immunity normalizes lymphatic function and uncouples immune and metastatic potential. We demonstrate that intratumoral lymphatic vessel density negatively correlates with cytotoxic immunity and that IFNγ reprograms the intratumoral lymphatic state. Lymphatic deletion of Ifngr1 expanded the intratumoral lymphatic network and drove the emergence of a tip-like state that promotes lymph node metastasis but not dendritic cell migration or response to immune checkpoint blockade (ICB). Mechanistically, IFNγ restrains proliferation and cell state programs through inhibition of mitochondrial respiration. Lymphatic-specific inhibition of mitochondrial complex III restrained the intratumoral tip-like state, blocked metastasis, and enhanced the response to ICB. Our data reveal that IFNγ induces a metabolic and phenotypic switch in tumor-associated lymphatic vessels that blocks regional metastasis and reinforces immune surveillance.

PURPOSE/OBJECTIVE:Patients with stage II and resected stage III melanomas have variable clinical outcomes, evidence of underlying biological differences in tumors and/or the patients themselves, beyond stage. The approval of adjuvant immunotherapy for stage IIB/C and resected stage III/IV disease (and adjuvant targeted therapy for resected stage III disease), has created a pressing need to develop biomarkers to accurately distinguish patients at low- versus high-risk for recurrence and death from melanoma. MicroRNAs (miRNAs) are promising biomarkers because of their stability in tissues/fluids and their demonstrated functional and prognostic roles in melanoma. We hypothesized that miRNA expression could be integrated into prognostic models that would more accurately classify 5-year survival outcomes than clinical factors alone. PATIENTS AND METHODS/METHODS:Using a Nanostring miRNA Expression Assay, we analyzed 715 primary melanomas from patients with stage II or stage III disease within the InterMEL consortium, and examined associations between miRNA expression and melanoma-specific death. RESULTS:When integrated into clinical prognostic models for 5-year melanoma-specific survival, miRNA signatures improved the area under the receiver operating characteristic curve (AUC) for stage II patients from 0.71 for a 'clinical factors-only' model to 0.81 for a 'clinical plus miRNA' model in an independent test set, an improvement of 0.10 with 95% CI (0.03, 0.19). The improvement was more modest for stage III patients that were included in the analysis. CONCLUSIONS:Incorporating miRNA expression in primary melanomas may enhance the accuracy of clinical prognostic models, and potentially aid the selection of melanoma patients for adjuvant treatment and clinical trials.

Thoracic SMARCA4-deficient undifferentiated tumor (SMARCA4d-UT) is an uncommon, aggressive lung neoplasm associated with smoking and characterized by loss of SMARCA4 (BRG-1) expression. Although originally considered to be a primary sarcoma, there is growing evidence that these lesions may represent transformation of conventional non-small cell carcinoma. In this study, we probe this relationship based on the clinical, histologic and molecular findings of 18 SMARCA4-deficient malignancies of the lung. Cases diagnosed as SMARCA4d-UT and SMARCA4-deficient carcinoma were retrospectively reviewed, including histologic and immunophenotypic features, and next generation sequencing studies. Of the 18 tumors, 5 were considered to represent undifferentiated SMARCA4d-UT, and 13 SMARCA4-deficient carcinomas, including 11 adenocarcinomas, 1 squamous cell carcinoma, and 1 poorly differentiated non-small cell carcinoma. All 13 carcinomas had a morphologically identifiable undifferentiated component. Survival outcomes were similar in both SMARCA4d-UT and carcinomas. Genetic alterations often seen in lung cancer were identified in 8 cases, including mutations in EGFR (in 2 SMARCA4-deficient adenocarcinomas), KRAS (1 SMARCA4d-UT and 1 SMARCA4-deficient adenocarcinoma), MAP2K1 (1 SMARCA4-deficient adenocarcinoma), and a gene fusion involving EML4::ALK (1 SMARCA4d-UT). The patient with EML4::ALK fusion was treated with alectinib with partial response. Fusions involving BRAF::CHCHD3 and FGFR1::FILIP1 were identified in 2 SMARCA4-deficient adenocarcinomas. High expression of PD-L1 (TPS >50 %) was seen in 12 cases (67 %). These finding further suggest that SMARCA4d-UT and carcinomas with SMARCA4 loss may be on the same spectrum of disease, and accurate histologic distinction between these lesions may be challenging. A unified terminology may be beneficial for appropriate diagnosis and treatment.

There is a body-wide network of interstitial spaces that includes three components: a large-scale fascial network made up of fluid-filled spaces containing collagens and other extracellular matrix components like hyaluronic acid (HA), the peri-vascular/capillary interstitium, and intercellular interstitial spaces. Staining for HA within the colon, skin, and liver has demonstrated spatial continuity of the fascial interstitium across tissue layers and between organs, while continuity of HA staining between perineurial and adventitial sheathes beyond organ boundaries confirmed that they also participate in this body-wide network. We asked whether the pulmonary interstitium comprises a continuous organ-wide network that also connects to the body-wide interstitium via routes along nerves and the vasculature. We studied archival lung lobectomy specimens containing normal tissues inclusive of all lung anatomical units from six females and three males (mean age 53+/- 16.5 years). For comparison, we also studied normal mouse lung. Multiplex immunohistochemical cocktails were used to identify: (1) HA, CD34, and vimentin - highlighting interstitium; (2) HA, CD34, and podoplanin (D2-40) - highlighting relationships between the interstitium, vasculature, and lymphatics. Sizes of extracellular APP were measured. Tissues from nine patients (six females, three males, mean age 53+/- 16.5 years) were studied. HA staining was continuous throughout the five major anatomic compartments of the lung: alveolar walls, subpleural connective tissue, centrilobular peribronchovascular compartment, interlobular septal compartment, and axial peribronchovascular of the hilum, with similar findings in murine lung tissue. Continuity with interstitial spaces of the perineurium and adventitia was confirmed. The distribution of APP corresponded to known routes of lymphatic drainage, superficial and deep. APP within perineurium and perivascular adventitia further demonstrated continuity between intra- and extrapulmonary interstitium. To conclude, all segments of the lung interstitium are connected and are linked along nerves and the vascular tree to a body-wide communication network. These findings have significant implications for understanding lung physiology and pathobiology, suggesting routes of passage for inflammatory cells and mediators, malignant cells, and infectious agents. Interstitial spaces may be important in microbiome signaling within and beyond the lung and may be a component of the lung-brain axis.

BACKGROUND:The introduction of Trastuzumab deruxtecan (T-Dxd) has exposed clinically significant limitations in accurately detecting HER2-low expression testing when using immunohistochemistry (IHC) assays originally developed to detect HER2 over-expression. While HER2 testing is widely used to determine T-Dxd eligibility, no HER2-low assay was ever validated against HER2 protein expression. METHODS:To address this pressing need, the Consortium for Analytic Standardization in Immunohistochemistry (CASI) conducted the CASI-01 study, involving 54 IHC laboratories across Europe and the U.S. The study aimed to identify optimal assay conditions for accurate HER2 testing, differentiating between HER2 overexpression (3+) for Trastuzumab eligibility and HER2-low expression (1+ or ultra-low) for T-Dxd eligibility. The conventional FDA-cleared HER2 assay ("predicate") was compared with higher-sensitivity assays using pathologist versus image analysis readouts. HER2 overexpression was validated against HER2 gene amplification via in situ hybridisation (ISH), while HER2-low accuracy was evaluated using newly introduced HER2 reference standards and a novel IHC parameter-dynamic range. FINDINGS/RESULTS:CASI-01 revealed variability in predicate HER2 assays, with detection thresholds ranging from 30,000 to 60,000 among laboratories. Despite this variability, these assays demonstrated high accuracy for identifying HER2 overexpression (3+), with 85.7% (18/21) sensitivity (95% confidence limits 63.66-96.95%) and 100% (49/49) specificity (95% confidence limits 92.75-100%), though sensitivity may have been limited by the use of older tissue specimens, with loss or reduced expression levels of the HER2 protein. However, these same assays exhibited poor dynamic range for detecting HER2-low scores. Enhanced analytic sensitivity of IHC assays combined with image analysis overcame this limitation with HER2-low scores, achieving a six-fold improvement (p = 0.0017). INTERPRETATION/CONCLUSIONS:IHC assays with detection thresholds in the range of 30,000-60,000 HER2 molecules per cell yield accurate results for determination of Trastuzumab eligibility (HER2 3+) but fail to demonstrate the dynamic range for accurate HER2-low scores. Enhanced analytic sensitivity of HER2 assays combined with image analysis addresses this critical gap in HER2-low testing. More generally, CASI-01 introduces pivotal advancements in precision medicine: (a) the importance of reporting IHC analytic sensitivity and ability to demonstrate an assay dynamic range, and (b) image analysis can surpass pathologist readout accuracy in specific clinical contexts. FUNDING/BACKGROUND:This work was supported by the National Cancer Institute of the National Institutes of Health under Award Number R44CA268484.

Targeted therapies and immunotherapy have improved treatment outcomes for many melanoma patients. However, patients whose melanomas harbor driver mutations in the neurofibromin 1 (NF1) tumor suppressor gene often lack effective targeted treatment options when their tumors do not respond to immunotherapy. In this study, we utilized patient-derived short-term cultures (STCs) and multiomics approaches to identify molecular features that could inform the development of therapies for patients with NF1 mutant melanoma. Differential gene expression analysis revealed that the epidermal growth factor receptor (EGFR) is highly expressed and active in NF1 mutant melanoma cells, where it hyper-activates ERK and AKT, leading to increased tumor cell proliferation, survival, and growth. In contrast, genetic or pharmacological inhibition of EGFR hindered cell proliferation and survival and suppressed tumor growth in patient-derived NF1 mutant melanoma models but not in NF1 wild-type models. These results reveal a connection between NF1 loss and increased EGFR expression that is critical for the survival and growth of NF1 mutant melanoma cells in patient-derived culture and xenograft models, irrespective of their BRAF and NRAS mutation status.

The suspicious for malignancy category is used by pathologists to indicate a certain degree of uncertainty but is still able to convey to the treating physician a risk stratification of the deferred diagnosis. The category of suspicious for malignancy can be used in a vast possibility of cytomorphological features and clinical scenarios. Suspicious for malignancy is often used when there is an insufficient number of neoplastic cells for the establishment of a final diagnosis, but in many situations, the number of suspicious cells may be abundant but discrepant with the clinical presentation. In addition, the pathologist must be aware of the many mimickers of malignancy that, when present, may prompt the use of the category. Therefore, there is a need for better illustrations of the use of the category, its pitfalls and suggestions on when the category of suspicious for malignancy can be upgraded for a more definite diagnosis using ancillary studies, even in scant material.

PURPOSE/UNASSIGNED:Cancer treatment has been revolutionized by immune checkpoint inhibitors (ICI). However, a subset of patients do not respond and/or they experience significant adverse events. Attempts to integrate reliable biomarkers of ICI response as part of standard care have been hampered by limited generalizability. We previously reported our supervised machine learning (ML) model in a retrospective cohort of metastatic melanoma. EXPERIMENTAL DESIGN/UNASSIGNED:In this study, we expanded our testing to include larger cohorts of patients with melanoma accrued at several sites, including patients enrolled in clinical trials in both adjuvant and metastatic settings. We examined pretreatment hematoxylin and eosin slides from 639 patients with stage III/IV melanoma treated with ICIs [anti-cytotoxic T-lymphocyte-associated protein 4 (n = 212), anti-programmed death 1 (n = 271), or the combination (n = 156)]. We tested the generalizability of our supervised ML algorithm to predict response to ICIs in the metastatic melanoma cohort and then developed a self-supervised ML model to identify the histologic morphologies associated with patients' survival following ICI use in adjuvant and metastatic melanoma cohorts. RESULTS/UNASSIGNED:We predicted the response to ICI treatment with an AUC of 0.72. The deep convolutional neural network classified patients into high and low risk based on their likelihood of progression-free survival (P < 0.0001). We uncovered a novel association of specific histomorphologic tumor features-epithelioid histology and a low tumor-stroma ratio-with survival following ICI treatment. CONCLUSIONS/UNASSIGNED:Our data support the generalizability of our developed ML algorithm in predicting response to ICI treatment in patients with metastatic unresectable melanoma. We also showed, for the first time, tumor features associated with patients' overall survival.

BACKGROUND AND OBJECTIVE/OBJECTIVE:The diagnosis of interstitial lung diseases (ILDs) often relies on the integration of various clinical, radiological, and histopathological data. Achieving high diagnostic accuracy in ILDs, particularly for distinguishing usual interstitial pneumonia (UIP), is challenging and requires a multidisciplinary approach. Therefore, this study aimed to develop a multimodal artificial intelligence (AI) algorithm that combines computed tomography (CT) and histopathological images to improve the accuracy and consistency of UIP diagnosis. METHODS:A dataset of CT and pathological images from 324 patients with ILD between 2009 and 2021 was collected. The CT component of the model was trained to identify 28 different radiological features. The pathological counterpart was developed in our previous study. A total of 114 samples were selected and used for testing the multimodal AI model. The performance of the multimodal AI was assessed through comparisons with expert pathologists and general pathologists. RESULTS:The developed multimodal AI demonstrated a substantial improvement in distinguishing UIP from non-UIP, achieving an AUC of 0.92. When applied by general pathologists, the diagnostic agreement rate improved significantly, with a post-model κ score of 0.737 compared to 0.273 pre-model integration. Additionally, the diagnostic consensus rate with expert pulmonary pathologists increased from κ scores of 0.278-0.53 to 0.474-0.602 post-model integration. The model also increased diagnostic confidence among general pathologists. CONCLUSION/CONCLUSIONS:Combining CT and histopathological images, the multimodal AI algorithm enhances pathologists' diagnostic accuracy, consistency, and confidence in identifying UIP, even in cases where specialised expertise is limited.