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Endometrial carcinomas (ECs) classified by The Cancer Genome Atlas (TCGA) as copy number-low (also referred to as “no specific molecular profile” [NSMP]) have a prognosis intermediate between POLE-mutated and copy number-high ECs. NSMP-ECs are a heterogeneous group, however, comprising both relatively indolent and aggressive ECs. We identified a total of 472 NSMP-ECs among 1,239 ECs that underwent clinical sequencing of 410–468 cancer-related genes. Somatic mutation and copy number alteration data were subjected to unsupervised hierarchical clustering, which identified three genomic clusters. Random sampling with stratification was used to choose ~80 endometrioid ECs from each cluster, resulting in a study size of 240 endometrioid ECs as well as an additional 44 non-endometrioid NSMP-ECs. Cluster 1 (C1, n = 80) consisted primarily of NSMP-ECs with PTEN and PIK3R1 mutations, Cluster 2 (C2, n = 81) of tumors with PTEN and PIK3CA mutations and Cluster 3 (C3, n = 79) of NSMP-ECs with chromosome 1q high-level gain and lack of PTEN mutations. The majority (72.7%) of non-endometrioid NSMP-ECs mapped to C3. NSMP-ECs from C3 were more likely to be FIGO grade 3 (30%), estrogen receptor-negative/weak (54.5%) and FIGO stages III or IV. In multivariate analysis, molecular clusters were associated with worse overall survival outcomes with C3 tumors having the worst (hazard ratio: 4) and C1 tumors having the best outcome. In conclusion, NSMP-ECs are a heterogenous group of tumors and comprise both aggressive and clinically low-risk ECs that can be identified based on mutation and copy number data.

The use of self-supervised learning to train pathology foundation models has increased substantially in the past few years. Notably, several models trained on large quantities of clinical data have been made publicly available in recent months. This will significantly enhance scientific research in computational pathology and help bridge the gap between research and clinical deployment. With the increase in availability of public foundation models of different sizes, trained using different algorithms on different datasets, it becomes important to establish a benchmark to compare the performance of such models on a variety of clinically relevant tasks spanning multiple organs and diseases. In this work, we present a collection of pathology datasets comprising clinical slides associated with clinically relevant endpoints including cancer diagnoses and a variety of biomarkers generated during standard hospital operation from three medical centers. We leverage these datasets to systematically assess the performance of public pathology foundation models and provide insights into best practices for training foundation models and selecting appropriate pretrained models. To enable the community to evaluate their models on our clinical datasets, we make available an automated benchmarking pipeline for external use. Self-supervised learning (SSL) is increasingly used to train pathology foundation models. Here, the authors introduce a pathology benchmark set generated during standard clinical workflows that includes multiple cancer and disease types; then leverage it to assess the performance of multiple public SSL pathology foundation models and to provide best practices for model training and selection.

Myxoid pleomorphic liposarcoma (MPLPS) is a recently described and extremely rare subtype of liposarcoma with a predilection for the mediastinum. However, the genomic features of MPLPS remain poorly understood. We performed comprehensive genomic profiling of MPLPS in comparison with pleomorphic liposarcoma (PLPS) and myxoid/round cell liposarcoma (MRLPS). Of the 8 patients with MPLPS, 5 were female and 3 were male, with a median age of 32 years old (range 10–68). All except one were located in the mediastinum, with invasion of surrounding anatomic structures, including chest wall, pleura, spine, and large vessels. All cases showed an admixture of morphologies reminiscent of PLPS and MRLPS, including myxoid areas with plexiform vasculature admixed with uni- and/or multivacuolated pleomorphic lipoblasts. Less common features included well-differentiated liposarcoma-like areas, and in one case fascicular spindle cell sarcoma reminiscent of dedifferentiated LPS. Clinically, 4 experienced local recurrence, 4 had distant metastases and 5 died of disease. Compared to PLPS and MRLPS, patients with MPLPS had worse overall and progression-free survival. Recurrent TP53 mutations were present in all 8 MPLPS cases. In contrast, in PLPS, which also showed recurrent TP53 mutations (83%), RB1 and ATRX losses were more common. MRLPS was highly enriched in TERT promoter mutations (88%) and PI3K/AKT pathway mutations. Copy number profiling in MPLPS revealed multiple chromosomal gains with recurrent amplifications of chromosomes 1, 19 and 21. Importantly, allele-specific copy number analysis revealed widespread loss of heterozygosity (80% of the genome on average) in MPLPS, but not in PLPS or MRLPS. Our findings revealed genome-wide loss of heterozygosity co-existing with TP53 mutations as a characteristic genomic signature distinct from other liposarcoma subtypes, which supports the current classification of MPLPS as a stand-alone pathologic entity. These results further expand the clinicopathologic features of MPLPS, including older age, extra-mediastinal sites, and a highly aggressive outcome.

Comprehensive molecular profiling by next-generation sequencing has revolutionized tumor classification and biomarker evaluation. However, routine implementation is challenged by the scant nature of diagnostic material obtained through minimally invasive procedures. Here, we describe our long-term experience in profiling cytology samples with an in-depth assessment of the performance, quality metrics, biomarker identification capabilities, and potential pitfalls. We highlight the impact of several optimization strategies to maximize performance with 4,871 prospectively sequenced clinical cytology samples tested by MSK-IMPACT TM . Special emphasis is given to the use of residual supernatant cell-free DNA (ScfDNA) as a valuable source of tumor DNA. Overall, cytology samples are similar in performance to surgical samples in identifying clinically relevant genomic alterations, achieving success rates up to 93% with full optimization. While cell block (CB) samples have excellent performance overall, low-level cross-contamination is identified in a small proportion of cases (4.7%), a common pitfall intrinsic to the processing of paraffin blocks, suggesting that more stringent precautions and processing modifications should be considered in quality control initiatives. By contrast ScfDNA samples have negligible contamination. Finally, ScfDNA testing exclusively used as a rescue strategy, delivered successful results in 71% of cases where tumor tissue from CB was depleted. Tumour profiling with next-generation sequencing is challenging due to the low amounts of diagnostic material in cytology samples that are obtained through non-invasive procedures. Here, the authors report on the optimization of this process across 4,871 cancer cytology samples profiled by MSK-IMPACT, with emphasis on the successful use of supernatant cell-free DNA.

Anti-PD(L)-1 inhibition combined with platinum doublet chemotherapy (Chemo-IO) has become the most frequently used standard of care regimen in patients with non-small cell lung cancer (NSCLC). The negative impact of antibiotics on clinical outcomes prior to anti-PD(L)-1 inhibition monotherapy (IO) has been demonstrated in multiple studies, but the impact of antibiotic exposure prior to initiation of Chemo-IO is controversial. We assessed antibiotic exposures at two time windows: within 60 days prior to therapy (-60 d window) and within 60 days prior to therapy and 42 days after therapy (-60 + 42d window) in 2028 patients with advanced NSCLC treated with Chemo-IO and IO monotherapy focusing on objective response rate (ORR: rate of partial response and complete response), progression-free survival (PFS), and overall survival (OS). We also assessed impact of antibiotic exposure in an independent cohort of 53 patients. Univariable and multivariable analyses were conducted along with a meta-analysis from similar studies. For the -60 d window, in the Chemo-IO group ( N  = 769), 183 (24%) patients received antibiotics. Antibiotic exposure was associated with worse ORR (27% vs 40%, p  = 0.001), shorter PFS (3.9 months vs. 5.9 months, hazard ratio [HR] 1.35, 95%CI 1.1,1.6, p  = 0.0012), as well as shorter OS (10 months vs. 15 months, HR 1.50, 95%CI 1.2,1.8, p  = 0.00014). After adjusting for known prognostic factors in NSCLC, antibiotic exposure was independently associated with worse PFS (HR 1.39, 95%CI 1.35,1.7, p  = 0.002) and OS (HR 1.61, 95%CI 1.28,2.03, p  < 0.001). Similar results were obtained in the -60 + 42d window, and also in an independent cohort. In a meta-analysis of patients with NSCLC treated with Chemo-IO ( N  = 4) or IO monotherapy ( N  = 13 studies) antibiotic exposure before treatment was associated with worse OS among all patients ( n  = 11,351) (HR 1.93, 95% CI 1.52, 2.45) and Chemo-IO-treated patients ( n  = 1201) (HR 1.54, 95% CI 1.28, 1.84). Thus, antibiotics exposure prior to Chemo-IO is common and associated with worse outcomes, even after adjusting for other factors. These results highlight the need to implement antibiotic stewardship in routine oncology practice.

Microsatellite instability (MSI) is a critical phenotype of cancer genomes and an FDA-recognized biomarker that can guide treatment with immune checkpoint inhibitors. Previous work has demonstrated that next-generation sequencing data can be used to identify samples with MSI-high phenotype. However, low tumor purity, as frequently observed in routine clinical samples, poses a challenge to the sensitivity of existing algorithms. To overcome this critical issue, we developed MiMSI, an MSI classifier based on deep neural networks and trained using a dataset that included low tumor purity MSI cases in a multiple instance learning framework. On a challenging yet representative set of cases, MiMSI showed higher sensitivity (0.895) and auROC (0.971) than MSISensor (sensitivity: 0.67; auROC: 0.907), an open-source software previously validated for clinical use at our institution using MSK-IMPACT large panel targeted NGS data. In a separate, prospective cohort, MiMSI confirmed that it outperforms MSISensor in low purity cases ( P  = 8.244e-07). Identifying microsatellite instability (MSI) from routine next generation sequencing assays is an important part of clinical patient care. Here, authors develop a deep-learning based algorithm, highlighting its performance in a large validation cohort.

Circulating cell-free DNA from blood plasma of cancer patients can be used to non-invasively interrogate somatic tumor alterations. Here we develop MSK-ACCESS (Memorial Sloan Kettering - Analysis of Circulating cfDNA to Examine Somatic Status), an NGS assay for detection of very low frequency somatic alterations in 129 genes. Analytical validation demonstrated 92% sensitivity in de-novo mutation calling down to 0.5% allele frequency and 99% for a priori mutation profiling. To evaluate the performance of MSK-ACCESS, we report results from 681 prospective blood samples that underwent clinical analysis to guide patient management. Somatic alterations are detected in 73% of the samples, 56% of which have clinically actionable alterations. The utilization of matched normal sequencing allows retention of somatic alterations while removing over 10,000 germline and clonal hematopoiesis variants. Our experience illustrates the importance of analyzing matched normal samples when interpreting cfDNA results and highlights the importance of cfDNA as a genomic profiling source for cancer patients. Liquid biopsies allow the non-invasive detection of somatic mutations from tumours. Here, the authors develop and test MSK-ACCESS, an NGS-based clinical assay for identifying low frequency mutations in 129 genes and describe how it benefits patients in the clinic.

A subset of glioblastomas (GBMs) harbors potentially druggable oncogenic FGFR3 - TACC3 (F3T3) fusions. However, their associated molecular and clinical features are poorly understood. Here we analyze the frequency of F3T3-fusion positivity, its associated genetic and methylation profiles, and its impact on survival in 906 IDH-wildtype GBM patients. We establish an F3T3 prevalence of 4.1% and delineate its associations with cancer signaling pathway alterations. F3T3-positive GBMs had lower tumor mutational and copy-number alteration burdens than F3T3-wildtype GBMs. Although F3T3 fusions were predominantly mutually exclusive with other oncogenic RTK pathway alterations, they did rarely co-occur with EGFR amplification. They were less likely to harbor TP53 alterations. By methylation profiling, they were more likely to be assigned the mesenchymal or RTK II subclass. Despite being older at diagnosis and having similar frequencies of MGMT promoter hypermethylation, patients with F3T3-positive GBMs lived about 8 months longer than those with F3T3-wildtype tumors. While consistent with IDH-wildtype GBM, F3T3-positive GBMs exhibit distinct biological features, underscoring the importance of pursuing molecular studies prior to clinical trial enrollment and targeted treatment.

Abstract Objectives We implemented a policy of reflex serotonin-release assay (SRA) testing for all patients with a positive heparin-induced thrombocytopenia (HIT) immunoassay. Methods We identified all patients who had SRA testing sent as a consequence of a positive HIT immunoassay test. We reviewed charts of patients to calculate the 4Ts clinical score, determined the effect of testing on clinical management, and documented the change in utilization of direct thrombin inhibitors (DTIs). Results The likelihood of a positive SRA varied with the optical density (OD) of the immunoassay. The performance of the immunoglobulin G (IgG)–specific and polytypic enzyme-linked immunosorbent assay was not statistically different. Both OD and 4Ts score correlated with the likelihood of a positive SRA but demonstrated poor specificity. Discontinuation of DTIs in patients with negative SRAs resulted in decreased drug utilization. Conclusions The IgG-specific HIT immunoassay OD correlates with the likelihood of a positive SRA but does not achieve high specificity. The reflex testing algorithm allows for definitive classification of patients, and the cost of such a reflex testing program may be offset by decreased utilization of DTIs.