Determinants of Survival with Combined HER2 and PD-1 Blockade in Metastatic Esophagogastric Cancer.

PURPOSE: We report updated clinical outcomes from a phase II study of pembrolizumab, trastuzumab, and chemotherapy (PTC) in metastatic esophagogastric cancer in conjunction with outcomes from an independent Memorial Sloan Kettering (MSK) cohort. PATIENTS AND METHODS: The significance of pretreatment 89Zr-trastuzumab PET, plasma circulating tumor DNA (ctDNA) dynamics, and tumor HER2 expression and whole exome sequencing was evaluated to identify prognostic biomarkers and mechanisms of resistance in patients treated on-protocol with PTC. Additional prognostic features were evaluated using a multivariable Cox regression model of trastuzumab-treated MSK patients (n = 226). Single-cell RNA sequencing (scRNA-seq) data from MSK and Samsung were evaluated for mechanisms of therapy resistance. RESULTS: 89Zr-trastuzumab PET, scRNA-seq, and serial ctDNA with CT imaging identified how pre-treatment intrapatient genomic heterogeneity contributes to inferior progression-free survival (PFS). We demonstrated that the presence of intensely avid lesions by 89Zr-trastuzumab PET declines in tumor-matched ctDNA by 3 weeks, and clearance of tumor-matched ctDNA by 9 weeks were minimally invasive biomarkers of durable PFS. Paired pre- and on-treatment scRNA-seq identified rapid clearance of HER2-expressing tumor clones with expansion of clones expressing a transcriptional resistance program, which was associated with MT1H, MT1E, MT2A, and MSMB expression. Among trastuzumab-treated patients at MSK, ERBB2 amplification was associated with improved PFS, while alterations in MYC and CDKN2A/B were associated with inferior PFS. CONCLUSIONS: These findings highlight the clinical relevance of identifying baseline intrapatient heterogeneity and serial ctDNA monitoring of HER2-positive esophagogastric cancer patients to identify early evidence of treatment resistance, which could guide proactive therapy escalation or deescalation.

Identification of transcriptional programs using dense vector representations defined by mutual information with GeneVector.

Deciphering individual cell phenotypes from cell-specific transcriptional processes requires high dimensional single cell RNA sequencing. However, current dimensionality reduction methods aggregate sparse gene information across cells, without directly measuring the relationships that exist between genes. By performing dimensionality reduction with respect to gene co-expression, low-dimensional features can model these gene-specific relationships and leverage shared signal to overcome sparsity. We describe GeneVector, a scalable framework for dimensionality reduction implemented as a vector space model using mutual information between gene expression. Unlike other methods, including principal component analysis and variational autoencoders, GeneVector uses latent space arithmetic in a lower dimensional gene embedding to identify transcriptional programs and classify cell types. In this work, we show in four single cell RNA-seq datasets that GeneVector was able to capture phenotype-specific pathways, perform batch effect correction, interactively annotate cell types, and identify pathway variation with treatment over time.

Immune profiling after allogeneic hematopoietic cell transplantation in pediatric acute myeloid leukemia.

Although allogeneic hematopoietic cell transplant (allo-HCT) is curative for high-risk pediatric acute myeloid leukemia (AML), disease relapse remains the primary cause of posttransplant mortality. To identify pressures imposed by allo-HCT on AML cells that escape the graft-versus-leukemia effect, we evaluated immune signatures at diagnosis and posttransplant relapse in bone marrow samples from 4 pediatric patients using a multimodal single-cell proteogenomic approach. Downregulation of major histocompatibility complex class II expression was most profound in progenitor-like blasts and accompanied by correlative changes in transcriptional regulation. Dysfunction of activated natural killer cells and CD8+ T-cell subsets at relapse was evidenced by the loss of response to interferon gamma, tumor necrosis factor α signaling via NF-κB, and interleukin-2/STAT5 signaling. Clonotype analysis of posttransplant relapse samples revealed an expansion of dysfunctional T cells and enrichment of T-regulatory and T-helper cells. Using novel computational methods, our results illustrate a diverse immune-related transcriptional signature in posttransplant relapses not previously reported in pediatric AML.

Ovarian cancer mutational processes drive site-specific immune evasion.

High-grade serous ovarian cancer (HGSOC) is an archetypal cancer of genomic instability1-4 patterned by distinct mutational processes5,6, tumour heterogeneity7-9 and intraperitoneal spread7,8,10. Immunotherapies have had limited efficacy in HGSOC11-13, highlighting an unmet need to assess how mutational processes and the anatomical sites of tumour foci determine the immunological states of the tumour microenvironment. Here we carried out an integrative analysis of whole-genome sequencing, single-cell RNA sequencing, digital histopathology and multiplexed immunofluorescence of 160 tumour sites from 42 treatment-naive patients with HGSOC. Homologous recombination-deficient HRD-Dup (BRCA1 mutant-like) and HRD-Del (BRCA2 mutant-like) tumours harboured inflammatory signalling and ongoing immunoediting, reflected in loss of HLA diversity and tumour infiltration with highly differentiated dysfunctional CD8+ T cells. By contrast, foldback-inversion-bearing tumours exhibited elevated immunosuppressive TGFß signalling and immune exclusion, with predominantly naive/stem-like and memory T cells. Phenotypic state associations were specific to anatomical sites, highlighting compositional, topological and functional differences between adnexal tumours and distal peritoneal foci. Our findings implicate anatomical sites and mutational processes as determinants of evolutionary phenotypic divergence and immune resistance mechanisms in HGSOC. Our study provides a multi-omic cellular phenotype data substrate from which to develop and interpret future personalized immunotherapeutic approaches and early detection research.

Single-cell genomic variation induced by mutational processes in cancer.

How cell-to-cell copy number alterations that underpin genomic instability1 in human cancers drive genomic and phenotypic variation, and consequently the evolution of cancer2, remains understudied. Here, by applying scaled single-cell whole-genome sequencing3 to wild-type, TP53-deficient and TP53-deficient;BRCA1-deficient or TP53-deficient;BRCA2-deficient mammary epithelial cells (13,818 genomes), and to primary triple-negative breast cancer (TNBC) and high-grade serous ovarian cancer (HGSC) cells (22,057 genomes), we identify three distinct 'foreground' mutational patterns that are defined by cell-to-cell structural variation. Cell- and clone-specific high-level amplifications, parallel haplotype-specific copy number alterations and copy number segment length variation (serrate structural variations) had measurable phenotypic and evolutionary consequences. In TNBC and HGSC, clone-specific high-level amplifications in known oncogenes were highly prevalent in tumours bearing fold-back inversions, relative to tumours with homologous recombination deficiency, and were associated with increased clone-to-clone phenotypic variation. Parallel haplotype-specific alterations were also commonly observed, leading to phylogenetic evolutionary diversity and clone-specific mono-allelic expression. Serrate variants were increased in tumours with fold-back inversions and were highly correlated with increased genomic diversity of cellular populations. Together, our findings show that cell-to-cell structural variation contributes to the origins of phenotypic and evolutionary diversity in TNBC and HGSC, and provide insight into the genomic and mutational states of individual cancer cells.

Clonal Decomposition and DNA Replication States Defined by Scaled Single-Cell Genome Sequencing.

Accurate measurement of clonal genotypes, mutational processes, and replication states from individual tumor-cell genomes will facilitate improved understanding of tumor evolution. We have developed DLP+, a scalable single-cell whole-genome sequencing platform implemented using commodity instruments, image-based object recognition, and open source computational methods. Using DLP+, we have generated a resource of 51,926 single-cell genomes and matched cell images from diverse cell types including cell lines, xenografts, and diagnostic samples with limited material. From this resource we have defined variation in mitotic mis-segregation rates across tissue types and genotypes. Analysis of matched genomic and image measurements revealed correlations between cellular morphology and genome ploidy states. Aggregation of cells sharing copy number profiles allowed for calculation of single-nucleotide resolution clonal genotypes and inference of clonal phylogenies and avoided the limitations of bulk deconvolution. Finally, joint analysis over the above features defined clone-specific chromosomal aneuploidy in polyclonal populations.