Biology subjects

Lindeman, N.

Publications and source records attributed to Lindeman, N..

4 recordsLinked to original sources

bioRxiv2025

Single-cell spatial mapping reveals dynamic bone marrow microarchitectural alterations and enhances clinical diagnostics in MDS

Myelodysplastic neoplasms (MDS) disrupt bone marrow hematopoiesis, yet clinical assessment relies largely on blast enumeration and qualitative morphology, which incompletely capture marrow architecture and disease state. We applied whole-slide multiplex immunofluorescence imaging with single-cell phenotyping to map bone marrow microarchitecture in MDS. Diagnostic biopsies (n=36), longitudinal treatment samples (n=29), precursor states (n=13), and normal controls (n=21) were analyzed, comprising >5 million spatially resolved cells. MDS marrow exhibited coordinated, genotype-imprinted architectural remodeling, including altered progenitor composition and spatial patterning, disrupted erythroid island organization, and displacement of hematopoietic stem and progenitor cells from perivascular niches. Interrogation of 82 cellular and spatial features yielded a composite Microarchitectural Perturbation Score (MDS-MAPS), derived from diagnostic samples and fixed prior to longitudinal analyses. In leave-one-patient-out cross-validation, MDS-MAPS discriminated remission from active disease more accurately than blast percentage (AUC 0.883 vs 0.660) and distinguished low-blast MDS from clonal cytopenia of undetermined significance (CCUS) (AUC 0.815). Mixed-effects modeling showed MAPS decreased in remission statistically independent of blast burden, with architectural normalization during remission and re-emergence at relapse. These findings define quantitative bone marrow architecture as a dynamic tissue-state biomarker that complements molecular and blast-based assessment in MDS.

pathology↗

bioRxiv2025

Pan-cancer spatial characterization of key immune biomarkers in the tumor microenvironment

Deciphering the composition and spatial organization of the tumor immune microenvironment (TiME) is key to uncovering the mechanisms driving cancer progression and treatment response. Spatial biology techniques like multiplex immunofluorescence (mIF) offer detailed insights into the TiME but are often limited to retrospective research studies of individual cancer types. Conversely, bulk omics techniques have been studied in pan-cancer settings but fail to capture single-cell spatial information. Here, we provide a pan-cancer spatial characterization of key biomarkers (CD8, FOXP3, PD-1, PD-L1) of the TiME using data from a mIF assay performed prospectively in a clinical setting on 2,019 tumors across 14 major cancer types. By integrating interpretable compositional and spatial metrics, we identified patterns of TiME variation that are conserved across cancer types and stages. We assess associations between these TiME spatial factors and tumor, genomic, and clinical features, where the results both extend prior findings and uncover new links. Altogether, our findings offer pan-cancer insights of the TiME to further the fields of spatial biology and cancer immunology.

cancer biology↗

bioRxiv2024

Clinicogenomic characterization of inflammatory breast cancer

BackgroundInflammatory breast cancer (IBC) is a rare and poorly characterized type of breast cancer with an aggressive clinical presentation. The biological mechanisms driving the IBC phenotype are relatively undefined--partially due to a lack of comprehensive, large-scale genomic studies and limited clinical cohorts. Patients and MethodsA retrospective analysis of 2457 patients with metastatic breast cancer who underwent targeted tumor-only DNA-sequencing was performed at Dana-Farber Cancer Institute. Clinicopathologic, single nucleotide variant (SNV), copy number variant (CNV) and tumor mutational burden (TMB) comparisons were made between clinically confirmed IBC cases within a dedicated IBC center versus non-IBC cases. ResultsClinicopathologic differences between IBC and non-IBC cases were consistent with prior reports--including IBC being associated with younger age at diagnosis, higher grade, and enrichment with hormone receptor (HR)-negative and HER2-positive tumors. The most frequent somatic alterations in IBC involved TP53 (72%), ERBB2 (32%), PIK3CA (24%), CCND1 (12%), MYC (9%), FGFR1 (8%) and GATA3 (8%). A multivariate logistic regression analysis revealed a significant enrichment in TP53 SNVs in IBC; particularly in HER2-positive and HR-positive disease which was associated with worse outcomes. Tumor mutational burden (TMB) did not differ substantially between IBC and non-IBC cases and a pathway analysis revealed an enrichment in NOTCH pathway alterations in HER2-positive disease. ConclusionTaken together, this study provides a comprehensive, clinically informed landscape of somatic alterations in a large cohort of patients with IBC. Our data support higher frequency of TP53 mutations and a potential enrichment in NOTCH pathway activation--but overall; a lack of major genomic differences. These results both reinforce the importance of TP53 alterations in IBC pathogenesis as well as their influence on clinical outcomes; but also suggest additional analyses beyond somatic DNA-level changes are warranted.

genomics↗

bioRxiv2017

MatchMiner: An open source computational platform for real-time matching of cancer patients to precision medicine clinical trials using genomic and clinical criteria

BackgroundMolecular profiling of cancers is now routine at many cancer centers, and the number of precision cancer medicine clinical trials, which are informed by profiling, is steadily rising. Additionally, these trials are becoming increasingly complex, often having multiple arms and many genomic eligibility criteria. Currently, it is a challenging for physicians to match patients to relevant clinical trials using the patients genomic profile, which can lead to missed opportunities. Automated matching against uniformly structured and encoded genomic eligibility criteria is essential to keep pace with the complex landscape of precision medicine clinical trials.\n\nResultsTo meet these needs, we built and deployed an automated clinical trial matching platform called MatchMiner at the Dana-Farber Cancer Institute (DFCI). The platform has been integrated with Profile, DFCIs enterprise genomic profiling project, which contains tumor profile data for >20,000 patients, and has been made available to physicians across the Institute. As no current standard exists for encoding clinical trial eligibility criteria, a new language called Clinical Trial Markup Language (CTML) was developed, and over 178 genomically-driven clinical trials were encoded using this language. The platform is open source and freely available for adoption by other institutions.\n\nConclusionMatchMiner is the first open platform developed to enable computational matching of patient-specific genomic profiles to precision cancer medicine clinical trials. Creating MatchMiner required developing clinical trial eligibility standards to support genome-driven matching and developing intuitive interfaces to support practical use-cases. Given the complexity of tumor profiling and the rapidly changing multi-site nature of genome-driven clinical trials, open source software is the most efficient, scalable, and economical option for matching cancer patients to clinical trials.

bioinformatics↗