Integrative spatial analysis reveals a multi-layered organization of glioblastoma.

Glioma contains malignant cells in diverse states. Here, we combine spatial transcriptomics, spatial proteomics, and computational approaches to define glioma cellular states and uncover their organization. We find three prominent modes of organization. First, gliomas are composed of small local environments, each typically enriched with one major cellular state. Second, specific pairs of states preferentially reside in proximity across multiple scales. This pairing of states is consistent across tumors. Third, these pairwise interactions collectively define a global architecture composed of five layers. Hypoxia appears to drive the layers, as it is associated with a long-range organization that includes all cancer cell states. Accordingly, tumor regions distant from any hypoxic/necrotic foci and tumors that lack hypoxia such as low-grade IDH-mutant glioma are less organized. In summary, we provide a conceptual framework for the organization of cellular states in glioma, highlighting hypoxia as a long-range tissue organizer.

Deterministic Evolutionary Trajectories Influence Primary Tumor Growth: TRACERx Renal.

The evolutionary features of clear-cell renal cell carcinoma (ccRCC) have not been systematically studied to date. We analyzed 1,206 primary tumor regions from 101 patients recruited into the multi-center prospective study, TRACERx Renal. We observe up to 30 driver events per tumor and show that subclonal diversification is associated with known prognostic parameters. By resolving the patterns of driver event ordering, co-occurrence, and mutual exclusivity at clone level, we show the deterministic nature of clonal evolution. ccRCC can be grouped into seven evolutionary subtypes, ranging from tumors characterized by early fixation of multiple mutational and copy number drivers and rapid metastases to highly branched tumors with >10 subclonal drivers and extensive parallel evolution associated with attenuated progression. We identify genetic diversity and chromosomal complexity as determinants of patient outcome. Our insights reconcile the variable clinical behavior of ccRCC and suggest evolutionary potential as a biomarker for both intervention and surveillance.

Chemoresistance Evolution in Triple-Negative Breast Cancer Delineated by Single-Cell Sequencing.

Triple-negative breast cancer (TNBC) is an aggressive subtype that frequently develops resistance to chemotherapy. An unresolved question is whether resistance is caused by the selection of rare pre-existing clones or alternatively through the acquisition of new genomic aberrations. To investigate this question, we applied single-cell DNA and RNA sequencing in addition to bulk exome sequencing to profile longitudinal samples from 20 TNBC patients during neoadjuvant chemotherapy (NAC). Deep-exome sequencing identified 10 patients in which NAC led to clonal extinction and 10 patients in which clones persisted after treatment. In 8 patients, we performed a more detailed study using single-cell DNA sequencing to analyze 900 cells and single-cell RNA sequencing to analyze 6,862 cells. Our data showed that resistant genotypes were pre-existing and adaptively selected by NAC, while transcriptional profiles were acquired by reprogramming in response to chemotherapy in TNBC patients.

Multiclonal Invasion in Breast Tumors Identified by Topographic Single Cell Sequencing.

Ductal carcinoma in situ (DCIS) is an early-stage breast cancer that infrequently progresses to invasive ductal carcinoma (IDC). Genomic evolution has been difficult to delineate during invasion due to intratumor heterogeneity and the low number of tumor cells in the ducts. To overcome these challenges, we developed Topographic Single Cell Sequencing (TSCS) to measure genomic copy number profiles of single tumor cells while preserving their spatial context in tissue sections. We applied TSCS to 1,293 single cells from 10 synchronous patients with both DCIS and IDC regions in addition to exome sequencing. Our data reveal a direct genomic lineage between in situ and invasive tumor subpopulations and further show that most mutations and copy number aberrations evolved within the ducts prior to invasion. These results support a multiclonal invasion model, in which one or more clones escape the ducts and migrate into the adjacent tissues to establish the invasive carcinomas.

Impact of crowding on the diversity of expanding populations.

Crowding effects critically impact the self-organization of densely packed cellular assemblies, such as biofilms, solid tumors, and developing tissues. When cells grow and divide, they push each other apart, remodeling the structure and extent of the population's range. Recent work has shown that crowding has a strong impact on the strength of natural selection. However, the impact of crowding on neutral processes, which controls the fate of new variants as long as they are rare, remains unclear. Here, we quantify the genetic diversity of expanding microbial colonies and uncover signatures of crowding in the site frequency spectrum. By combining Luria-Delbrück fluctuation tests, lineage tracing in a novel microfluidic incubator, cell-based simulations, and theoretical modeling, we find that the majority of mutations arise behind the expanding frontier, giving rise to clones that are mechanically "pushed out" of the growing region by the proliferating cells in front. These excluded-volume interactions result in a clone-size distribution that solely depends on where the mutation first arose relative to the front and is characterized by a simple power law for low-frequency clones. Our model predicts that the distribution depends on a single parameter-the characteristic growth layer thickness-and hence allows estimation of the mutation rate in a variety of crowded cellular populations. Combined with previous studies on high-frequency mutations, our finding provides a unified picture of the genetic diversity in expanding populations over the whole frequency range and suggests a practical method to assess growth dynamics by sequencing populations across spatial scales.

The tumour ecology of quiescence: Niches across scales of complexity.

Quiescence is a state of cell cycle arrest, allowing cancer cells to evade anti-proliferative cancer therapies. Quiescent cancer stem cells are thought to be responsible for treatment resistance in glioblastoma, an aggressive brain cancer with poor patient outcomes. However, the regulation of quiescence in glioblastoma cells involves a myriad of intrinsic and extrinsic mechanisms that are not fully understood. In this review, we synthesise the literature on quiescence regulatory mechanisms in the context of glioblastoma and propose an ecological perspective to stemness-like phenotypes anchored to the contemporary concepts of niche theory. From this perspective, the cell cycle regulation is multiscale and multidimensional, where the niche dimensions extend to extrinsic variables in the tumour microenvironment that shape cell fate. Within this conceptual framework and powered by ecological niche modelling, the discovery of microenvironmental variables related to hypoxia and mechanosignalling that modulate proliferative plasticity and intratumor immune activity may open new avenues for therapeutic targeting of emerging biological vulnerabilities in glioblastoma.

Diagnostic leukapheresis reveals distinct phenotypes of NSCLC circulating tumor cells.

BACKGROUND: Circulating tumor cells (CTCs) hold immense promise for unraveling tumor heterogeneity and understanding treatment resistance. However, conventional methods, especially in cancers like non-small cell lung cancer (NSCLC), often yield low CTC numbers, hindering comprehensive analyses. This study addresses this limitation by employing diagnostic leukapheresis (DLA) to cancer patients, enabling the screening of larger blood volumes. To leverage DLA's full potential, this study introduces a novel approach for CTC enrichment from DLAs. METHODS: DLA was applied to six advanced stage NSCLC patients. For an unbiased CTC enrichment, a two-step approach based on negative depletion of hematopoietic cells was used. Single-cell (sc) whole-transcriptome sequencing was performed, and CTCs were identified based on gene signatures and inferred copy number variations. RESULTS: Remarkably, this innovative approach led to the identification of unprecedented 3,363 CTC transcriptomes. The extensive heterogeneity among CTCs was unveiled, highlighting distinct phenotypes related to the epithelial-mesenchymal transition (EMT) axis, stemness, immune responsiveness, and metabolism. Comparison with sc transcriptomes from primary NSCLC cells revealed that CTCs encapsulate the heterogeneity of their primary counterparts while maintaining unique CTC-specific phenotypes. CONCLUSIONS: In conclusion, this study pioneers a transformative method for enriching CTCs from DLA, resulting in a substantial increase in CTC numbers. This allowed the creation of the first-ever single-cell whole transcriptome in-depth characterization of the heterogeneity of over 3,300 NSCLC-CTCs. The findings not only confirm the diagnostic value of CTCs in monitoring tumor heterogeneity but also propose a CTC-specific signature that can be exploited for targeted CTC-directed therapies in the future. This comprehensive approach signifies a major leap forward, positioning CTCs as a key player in advancing our understanding of cancer dynamics and paving the way for tailored therapeutic interventions.

Changing the Landscape of Solid Tumor Therapy from Apoptosis-Promoting to Apoptosis-Inhibiting Strategies.

The many limitations of implementing anticancer strategies under the term "precision oncology" have been extensively discussed. While some authors propose promising future directions, others are less optimistic and use phrases such as illusion, hype, and false hypotheses. The reality is revealed by practicing clinicians and cancer patients in various online publications, one of which has stated that "in the quest for the next cancer cure, few researchers bother to look back at the graveyard of failed medicines to figure out what went wrong". The message is clear: Novel therapeutic strategies with catchy names (e.g., synthetic "lethality") have not fulfilled their promises despite decades of extensive research and clinical trials. The main purpose of this review is to discuss key challenges in solid tumor therapy that surprisingly continue to be overlooked by the Nomenclature Committee on Cell Death (NCCD) and numerous other authors. These challenges include: The impact of chemotherapy-induced genome chaos (e.g., multinucleation) on resistance and relapse, oncogenic function of caspase 3, cancer cell anastasis (recovery from late stages of apoptosis), and pitfalls of ubiquitously used preclinical chemosensitivity assays (e.g., cell "viability" and tumor growth delay studies in live animals) that score such pro-survival responses as "lethal" events. The studies outlined herein underscore the need for new directions in the management of solid tumors.

The Ecology and Evolution of Cancer: The Ultra-Microevolutionary Process.

Although tumorigenesis has been accepted as an evolutionary process ( 20 , 102 ), many forces may operate differently in cancers than in organisms, as they evolve at vastly different time scales. Among such forces, natural selection, here defined as differential cellular proliferation among distinct somatic cell genotypes, is particularly interesting because its action might be thwarted in multicellular organisms ( 20 , 29 ). In this review, selection is analyzed in two stages of cancer evolution: Stage I is the evolution between tumors and normal tissues, and Stage II is the evolution within tumors. The Cancer Genome Atlas (TCGA) data show a low degree of convergent evolution in Stage I, where genetic changes are not extensively shared among cases. An equally important, albeit much less highlighted, discovery using TCGA data is that there is almost no net selection in cancer evolution. Both positive and negative selection are evident but they neatly cancel each other out, rendering total selection ineffective in the absence of recombination. The efficacy of selection is even lower in Stage II, where neutral (non-Darwinian) evolution is increasingly supported by high-density sampling studies ( 81 , 123 ). Because natural selection is not a strong deterministic force, cancers usually evolve divergently even in similar tissue environments.

Mapping three-dimensional intratumor proteomic heterogeneity in uterine serous carcinoma by multiregion microsampling.

BACKGROUND: Although uterine serous carcinoma (USC) represents a small proportion of all uterine cancer cases, patients with this aggressive subtype typically have high rates of chemotherapy resistance and disease recurrence that collectively result in a disproportionately high death rate. The goal of this study was to provide a deeper view of the tumor microenvironment of this poorly characterized uterine cancer variant through multi-region microsampling and quantitative proteomics. METHODS: Tumor epithelium, tumor-involved stroma, and whole "bulk" tissue were harvested by laser microdissection (LMD) from spatially resolved levels from nine USC patient tumor specimens and underwent proteomic analysis by mass spectrometry and reverse phase protein arrays, as well as transcriptomic analysis by RNA-sequencing for one patient's tumor. RESULTS: LMD enriched cell subpopulations demonstrated varying degrees of relatedness, indicating substantial intratumor heterogeneity emphasizing the necessity for enrichment of cellular subpopulations prior to molecular analysis. Known prognostic biomarkers were quantified with stable levels in both LMD enriched tumor and stroma, which were shown to be highly variable in bulk tissue. These USC data were further used in a comparative analysis with a data generated from another serous gynecologic malignancy, high grade serous ovarian carcinoma, and have been added to our publicly available data analysis tool, the Heterogeneity Analysis Portal ( https://lmdomics.org/ ). CONCLUSIONS: Here we identified extensive three-dimensional heterogeneity within the USC tumor microenvironment, with disease-relevant biomarkers present in both the tumor and the stroma. These data underscore the critical need for upfront enrichment of cellular subpopulations from tissue specimens for spatial proteogenomic analysis.

Single-cell RNA sequencing of anaplastic ependymoma and H3K27M-mutant diffuse midline glioma.

BACKGROUND: Anaplastic ependymoma and H3K27M-mutant diffuse midline glioma are two common subtypes of brain tumors with poor long-term prognosis. The present study analyzed and compared the differences in cell types between two tumors by single-cell RNA sequencing (scRNA-seq) technology. METHODS: ScRNA-seq was performed to profile cells from cancer tissue from anaplastic ependymoma patient and H3K27M-mutant diffuse midline glioma patient. Cell clustering, marker gene identification, cell type annotation, copy number variation analysis and function analysis of differentially expressed genes were then performed. RESULTS: A total of 11,219 cells were obtained from anaplastic ependymoma and H3K27M mutant diffuse midline glioma, and these cells categorized into 12 distinct clusters. Each cell cluster could be characterized with specific cell markers to indicate cellular heterogeneity. Five cell types were annotated in each sample, including astrocyte, oligodendrocytes, microglial cell, neural progenitor cell and immune cell. The cluster types and proportion of cell types were not consistent between the two brain tumors. Functional analyses suggest that these cell clusters are involved in tumor-associated pathways, with slight differences in the cells of origin between the two tumors. In addition, cell communication analysis showed that the NRG3-ERBB4 pair is a key Ligand-receptor pair for anaplastic ependymoma, while in H3K27M-mutant diffuse midline glioma it is the PTN-PTPRZ1 pair that establishes contact with other cells. CONCLUSION: There was intratumor heterogeneity in anaplastic ependymoma and H3K27M mutant diffuse midline glioma, and that the subtype differences may be due to differences in the origin of the cells.

Ovarian high-grade serous carcinoma cells with low SMARCA4 expression and high SMARCA2 expression contribute to platinum resistance.

Platinum resistance is a major obstacle to the treatment of ovarian cancer and is correlated with poor clinical outcomes. Intratumor heterogeneity plays a key role in chemoresistance. Recent studies have emphasized the contributions of genetic and epigenetic factors to the development of intratumor heterogeneity. Although the clinical significance of multi-subunit chromatin remodeler, switch/sucrose nonfermenting (SWI/SNF) complexes in cancers has been reported, the impacts of SWI/SNF-related, matrix-associated, actin-dependent regulator of chromatin, subfamily A, member 4/subfamily A, member 2 (SMARCA4/A2) expression patterns in human cancer tissues have not been fully elucidated. Here, we show that low expression of SMARCA4 and high expression of SMARCA2 are associated with platinum resistance in ovarian high-grade serous carcinoma (HGSC) cells. We used fluorescence multiplex immunohistochemistry (fmIHC) to study resected specimens; we examined heterogeneity in human HGSC tissues at the single-cell level, which revealed that the proportion of cells with the SMARCA4low /SMARCA2high phenotype was positively correlated with clinical platinum-resistant recurrence. We used stable transfection of SMARCA2 and siRNA knockdown of SMARCA4 to generate HGSC cells with the SMARCA4low /SMARCA2high phenotype; these cells had the greatest resistance to carboplatin. Bioinformatics analyses revealed that the underlying mechanism involved in substantial alterations to chromatin accessibility and resultant fibroblast growth factor (FGF) signaling activation, MAPK pathway activation, BCL2 overexpression, and reduced carboplatin-induced apoptosis; these were confirmed by in vitro functional experiments. Furthermore, in vivo experiments in an animal model demonstrated that combination therapy with carboplatin and a fibroblast growth factor receptor (FGFR) inhibitor promoted cell death in HGSC xenografts. Taken together, these observations reveal a specific subpopulation of HGSC cells that is associated with clinical chemoresistance, which may lead to the establishment of a histopathological prediction system for carboplatin response. Our findings may facilitate the development of novel therapeutic strategies for platinum-resistant HGSC cells. © 2023 The Pathological Society of Great Britain and Ireland.

Intratumoral Cell Heterogeneity in Patient-Derived Glioblastoma Cell Lines Revealed by Single-Cell RNA-Sequencing.

Glioblastoma cell lines derived from different patients are widely used in tumor biology research and drug screening. A key feature of glioblastoma is the high level of inter- and intratumor heterogeneity that accounts for treatment resistance. Our aim was to investigate whether intratumor heterogeneity is maintained in cell models. Single-cell RNA sequencing was used to investigate the cellular composition of a tumor sample and six patient-derived glioblastoma cell lines. Three cell lines preserved the mutational profile of the original tumor, whereas three others differed from their precursors. Copy-number variation analysis showed significantly rearranged genomes in all the cell lines and in the tumor sample. The tumor had the most complex cell composition, including cancer cells and microenvironmental cells. Cell lines with a conserved genome had less diverse cellularity, and during cultivation, a relative increase in the stem-cell-derived progenitors was noticed. Cell lines with genomes different from those of the primary tumors mainly contained neural progenitor cells and microenvironmental cells. The establishment of cell lines without the driver mutations that are intrinsic to the original tumors may be related to the selection of clones or cell populations during cultivation. Thus, patient-derived glioblastoma cell lines differ substantially in their cellular profile, which should be taken into account in translational studies.

ATACAmp: a tool for detecting ecDNA/HSRs from bulk and single-cell ATAC-seq data.

BACKGROUND: High oncogene expression in cancer cells is a major cause of rapid tumor progression and drug resistance. Recent cancer genome research has shown that oncogenes as well as regulatory elements can be amplified in the form of extrachromosomal DNA (ecDNA) or subsequently integrated into chromosomes as homogeneously staining regions (HSRs). These genome-level variants lead to the overexpression of the corresponding oncogenes, resulting in poor prognosis. Most existing detection methods identify ecDNA using whole genome sequencing (WGS) data. However, these techniques usually detect many false positive regions owing to chromosomal DNA interference. RESULTS: In the present study, an algorithm called "ATACAmp" that can identify ecDNA/HSRs in tumor genomes using ATAC-seq data has been described. High chromatin accessibility, one of the characteristics of ecDNA, makes ATAC-seq naturally enriched in ecDNA and reduces chromosomal DNA interference. The algorithm was validated using ATAC-seq data from cell lines that have been experimentally determined to contain ecDNA regions. ATACAmp accurately identified the majority of validated ecDNA regions. AmpliconArchitect, the widely used ecDNA detecting tool, was used to detect ecDNA regions based on the WGS data of the same cell lines. Additionally, the Circle-finder software, another tool that utilizes ATAC-seq data, was assessed. The results showed that ATACAmp exhibited higher accuracy than AmpliconArchitect and Circle-finder. Moreover, ATACAmp supported the analysis of single-cell ATAC-seq data, which linked ecDNA to specific cells. CONCLUSIONS: ATACAmp, written in Python, is freely available on GitHub under the MIT license: https://github.com/chsmiss/ATAC-amp . Using ATAC-seq data, ATACAmp offers a novel analytical approach that is distinct from the conventional use of WGS data. Thus, this method has the potential to reduce the cost and technical complexity associated ecDNA analysis.

Phenotypically sorted highly and weakly migratory triple negative breast cancer cells exhibit migratory and metastatic commensalism.

BACKGROUND: Intratumor heterogeneity is a well-established hallmark of cancer that impedes cancer research, diagnosis, and treatment. Previously, we phenotypically sorted human breast cancer cells based on migratory potential. When injected into mice, highly migratory cells were weakly metastatic and weakly migratory cells were highly metastatic. The purpose of this study was to determine whether these weakly and highly migratory cells interact with each other in vitro or in vivo. METHODS: To assess the relationship between heterogeneity in cancer cell migration and metastatic fitness, MDA-MB-231 and SUM159PT triple negative breast cancer cells were phenotypically sorted into highly migratory and weakly migratory subpopulations and assayed separately and in a 1:1 mixture in vitro and in vivo for metastatic behaviors. Unpaired, two-tailed Student's t-tests, Mann-Whitney tests, ordinary, one-way ANOVAs, and Kruskal-Wallis H tests were performed as appropriate with p < 0.05 as the cutoff for statistical significance. RESULTS: When highly and weakly migratory cells are co-seeded in mixed spheroids, the weakly migratory cells migrated farther than weakly migratory only spheroids. In mixed spheroids, leader-follower behavior occurred with highly migratory cells leading the weakly migratory cells in migration strands. When cell suspensions of highly migratory, weakly migratory, or a 1:1 mixture of both subpopulations were injected orthotopically into mice, both the mixed cell suspensions and weakly migratory cells showed significant distal metastasis, but the highly migratory cells did not metastasize significantly to any location. Notably, significantly more distal metastasis was observed in mice injected with the 1:1 mixture compared to either subpopulation alone. CONCLUSIONS: This study suggests that weakly migratory cells interact with highly migratory cells in a commensal fashion resulting in increased migration and metastasis. Together, these findings indicate that cancer cell subpopulation migration ability does not correlate with metastatic potential and that cooperation between highly migratory and weakly migratory subpopulations can enhance overall metastatic fitness.

Transcriptomic intratumor heterogeneity of breast cancer patient-derived organoids may reflect the unique biological features of the tumor of origin.

BACKGROUND: The intratumor heterogeneity (ITH) of cancer cells plays an important role in breast cancer resistance and recurrence. To develop better therapeutic strategies, it is necessary to understand the molecular mechanisms underlying ITH and their functional significance. Patient-derived organoids (PDOs) have recently been utilized in cancer research. They can also be used to study ITH as cancer cell diversity is thought to be maintained within the organoid line. However, no reports investigated intratumor transcriptomic heterogeneity in organoids derived from patients with breast cancer. This study aimed to investigate transcriptomic ITH in breast cancer PDOs. METHODS: We established PDO lines from ten patients with breast cancer and performed single-cell transcriptomic analysis. First, we clustered cancer cells for each PDO using the Seurat package. Then, we defined and compared the cluster-specific gene signature (ClustGS) corresponding to each cell cluster in each PDO. RESULTS: Cancer cells were clustered into 3-6 cell populations with distinct cellular states in each PDO line. We identified 38 clusters with ClustGS in 10 PDO lines and used Jaccard similarity index to compare the similarity of these signatures. We found that 29 signatures could be categorized into 7 shared meta-ClustGSs, such as those related to the cell cycle or epithelial-mesenchymal transition, and 9 signatures were unique to single PDO lines. These unique cell populations appeared to represent the characteristics of the original tumors derived from patients. CONCLUSIONS: We confirmed the existence of transcriptomic ITH in breast cancer PDOs. Some cellular states were commonly observed in multiple PDOs, whereas others were specific to single PDO lines. The combination of these shared and unique cellular states formed the ITH of each PDO.

Quantification of subtype purity in Luminal A breast cancer predicts clinical characteristics and survival.

PURPOSE: PAM50 profiling assigns each breast cancer to a single intrinsic subtype based on a bulk tissue sample. However, individual cancers may show evidence of admixture with an alternate subtype that could affect prognosis and treatment response. We developed a method to model subtype admixture using whole transcriptome data and associated it with tumor, molecular, and survival characteristics for Luminal A (LumA) samples. METHODS: We combined TCGA and METABRIC cohorts and obtained transcriptome, molecular, and clinical data, which yielded 11,379 gene transcripts in common and 1,178 cases assigned to LumA. We used semi-supervised non-negative matrix factorization (ssNMF) to compute the subtype admixture proportions of the four major subtypes-pLumA, pLumB, pHER2, and pBasal-for each case and measured associations with tumor characteristics, molecular features, and survival. RESULTS: Luminal A cases in the lowest versus highest quartile for pLumA transcriptomic proportion had a 27% higher prevalence of stage > 1, nearly a threefold higher prevalence of TP53 mutation, and a hazard ratio of 2.08 for overall mortality. We found positive associations between pHER2 and HER2 positivity by IHC or FISH; between pLumB and PR negativity; and between pBasal and younger age, node positivity, TP53 mutation, and EGFR expression. Predominant basal admixture, in contrast to predominant LumB or HER2 admixture, was not associated with shorter survival. CONCLUSION: Bulk sampling for genomic analyses provides an opportunity to expose intratumor heterogeneity, as reflected by subtype admixture. Our results elucidate the striking extent of diversity among LumA cancers and suggest that determining the extent and type of admixture holds promise for refining individualized therapy. LumA cancers with a high degree of basal admixture appear to have distinct biological characteristics that warrant further study.

HER2 Genetic Intratumor Heterogeneity Is Associated With Resistance to Trastuzumab and Trastuzumab Emtansine Therapy in Recurrent High-Grade Endometrial Cancer.

Anti-HER2 targeted therapies have recently demonstrated clinical activity in the treatment of high-grade endometrial carcinomas (ECs), particularly serous carcinomas with HER2 amplification and/or overexpression. Intratumor heterogeneity of HER2 amplification or HER2 genetic intratumor heterogeneity (G-ITH) has been associated with resistance to anti-HER2 therapies in breast and gastroesophageal cancers; however, its clinical relevance in EC is unknown. To characterize HER2 G-ITH in EC, archival specimens from a clinically annotated cohort of 57 ECs treated with trastuzumab or trasutuzmab emtansine in the recurrent (n = 38) or adjuvant (n = 19) setting were subjected to central pathology review, HER2 assessment by immunohistochemistry (IHC) and fluorescence in situ hybridization (FISH), and next-generation sequencing. HER2 G-ITH, defined as HER2 amplification in 5% to 50% of tumor cells examined by FISH, was identified in 36% (19/53) of ECs and was associated with lower HER2 copy number and levels of protein expression. HER2 IHC revealed spatially distinct areas of strong expression juxtaposed with areas of low/absent expression in tumors with the "cluster" pattern of G-ITH, whereas the "mosaic" pattern was typically associated with a diffuse admixture of cells with variable levels of HER2 expression. HER2 G-ITH was frequently observed in cases with IHC/FISH or FISH/next-generation sequencing discrepancies and/or with an equivocal/negative FISH result (9/13, 69%). Although the objective response rate to anti-HER2 therapy in recurrent ECs was 52% (13/25) for tumors lacking HER2 G-ITH, none (0%, 0/10) of the patients with HER2 G-ITH achieved a complete or partial response (P = .005). HER2 G-ITH was significantly associated with worse progression-free survival (hazard ratio, 2.88; 95% CI, 1.33-6.27; P = .005) but not overall survival. HER2 IHC score, HER2/CEP17 ratio, HER2 copy number, histologic subtype, and other genetic alterations, including PIK3CA hotspot mutations, were not significantly associated with therapeutic response or survival outcomes. Treatment responses were not restricted to serous carcinomas, supporting consideration of anti-HER2 therapy in patients with HER2-positive high-grade ECs of non-serous histology. Our results demonstrate that HER2 G-ITH is an important determinant of response to trastuzumab and trastuzumab emtansine in EC, providing a rationale for the development of novel therapeutic strategies to target HER2-nonamplified resistant tumor subpopulations, such as HER2 antibody-drug conjugates with bystander effects.

Using Intratumor Heterogeneity of Immunohistochemistry Biomarkers to Classify Laryngeal and Hypopharyngeal Tumors Based on Histologic Features.

Haralick texture features are used to quantify the spatial distribution of signal intensities within an image. In this study, the heterogeneity of proliferation (Ki-67 expression) and immune cells (CD45 expression) within tumors was quantified and used to classify histologic characteristics of larynx and hypopharynx carcinomas. Of 21 laryngectomy specimens, 74 whole-mount tumor slides were scored on histologic characteristics. Ki-67 and CD45 immunohistochemistry was performed, and all sections were digitized. The tumor area was annotated in QuPath. Haralick features independent of the diaminobenzidine intensity were extracted from the isolated diaminobenzidine signal to quantify intratumor heterogeneity. Haralick features from both Ki-67 and CD45 were used as input for a principal component analysis. A linear support vector machine was fitted to the first 4 principal components for classification and validated with a leave-one-patient-out cross-validation method. Significant differences in individual Haralick features were found between cohesive and noncohesive tumors for CD45 (angular second motion: P =.03, inverse difference moment: P =.009, and entropy: P =.02) and between the larynx and hypopharynx tumors for both CD45 (angular second motion: P =.03, inverse difference moment: P =.007, and entropy: P =.005) and Ki-67 (correlation: P =.003). Therefore, these features were used for classification. The linear classifier resulted in a classification accuracy of 85% for site of origin and 81% for growth pattern. A leave-one-patient-out cross-validation resulted in an error rate of 0.27 and 0.35 for both classifiers, respectively. In conclusion, we show a method to quantify intratumor heterogeneity of immunohistochemistry biomarkers using Haralick features. This study also shows the feasibility of using these features to classify tumors by histologic characteristics. The classifiers created in this study are a proof of concept because more data are needed to create robust classifiers, but the method shows potential for automated tumor classification.

Analysis of Immune Intratumor Heterogeneity Highlights Immunoregulatory and Coinhibitory Lymphocytes as Hallmarks of Recurrence in Stage I Non-Small Cell Lung Cancer.

Our understanding of the molecular mechanisms underlying postsurgical recurrence of non-small cell lung cancer (NSCLC) is rudimentary. Molecular and T cell repertoire intratumor heterogeneity (ITH) have been reported to be associated with postsurgical relapse; however, how ITH at the cellular level impacts survival is largely unknown. Here we report the analysis of 2880 multispectral images representing 14.2% to 27% of tumor areas from 33 patients with stage I NSCLC, including 17 cases (relapsed within 3 years after surgery) and 16 controls (without recurrence ≥5 years after surgery) using multiplex immunofluorescence. Spatial analysis was conducted to quantify the minimum distance between different cell types and immune cell infiltration around malignant cells. Immune ITH was defined as the variance of immune cells from 3 intratumor regions. We found that tumors from patients having relapsed display different immune biology compared with nonrecurrent tumors, with a higher percentage of tumor cells and macrophages expressing PD-L1 (P =.031 and P =.024, respectively), along with an increase in regulatory T cells (Treg) (P =.018), antigen-experienced T cells (P =.025), and effector-memory T cells (P =.041). Spatial analysis revealed that a higher level of infiltration of PD-L1+ macrophages (CD68+PD-L1+) or antigen-experienced cytotoxic T cells (CD3+CD8+PD-1+) in the tumor was associated with poor overall survival (P =.021 and P =.006, respectively). A higher degree of Treg ITH was associated with inferior recurrence-free survival regardless of tumor mutational burden (P =.022), neoantigen burden (P =.021), genomic ITH (P =.012) and T cell repertoire ITH (P =.001). Using multiregion multiplex immunofluorescence, we characterized ITH at the immune cell level along with whole exome and T cell repertoire sequencing from the same tumor regions. This approach highlights the role of immunoregulatory and coinhibitory signals as well as their spatial distribution and ITH that define the hallmarks of tumor relapse of stage I NSCLC.