ESQmodel: biologically informed evaluation of 2-D cell segmentation quality in multiplexed tissue images.

MOTIVATION: Single cell segmentation is critical in the processing of spatial omics data to accurately perform cell type identification and analyze spatial expression patterns. Segmentation methods often rely on semi-supervised annotation or labeled training data which are highly dependent on user expertise. To ensure the quality of segmentation, current evaluation strategies quantify accuracy by assessing cellular masks or through iterative inspection by pathologists. While these strategies each address either the statistical or biological aspects of segmentation, there lacks a unified approach to evaluating segmentation accuracy. RESULTS: In this article, we present ESQmodel, a Bayesian probabilistic method to evaluate single cell segmentation using expression data. By using the extracted cellular data from segmentation and a prior belief of cellular composition as input, ESQmodel computes per cell entropy to assess segmentation quality by how consistent cellular expression profiles match with cell type expectations. AVAILABILITY AND IMPLEMENTATION: Source code is available on Github at: https://github.com/Roth-Lab/ESQmodel.

Breast Cancer.

Breast cancer kills hundreds of thousands of people every year. Rapid progress over the past two decades has increased our understanding of the genetic and environmental risk factors for disease. It has also shed light on drivers of tumor progression and the molecular landscape underpinning tumor heterogeneity, as well as the role of the microenvironment and the immune system. These strides forward should lead to more effective and tailored therapies for early- and late-stage patients.

Disentangling oncogenic amplicons in esophageal adenocarcinoma.

Esophageal adenocarcinoma is a prominent example of cancer characterized by frequent amplifications in oncogenes. However, the mechanisms leading to amplicons that involve breakage-fusion-bridge cycles and extrachromosomal DNA are poorly understood. Here, we use 710 esophageal adenocarcinoma cases with matched samples and patient-derived organoids to disentangle complex amplicons and their associated mechanisms. Short-read sequencing identifies ERBB2, MYC, MDM2, and HMGA2 as the most frequent oncogenes amplified in extrachromosomal DNAs. We resolve complex extrachromosomal DNA and breakage-fusion-bridge cycles amplicons by integrating of de-novo assemblies and DNA methylation in nine long-read sequenced cases. Complex amplicons shared between precancerous biopsy and late-stage tumor, an enrichment of putative enhancer elements and mobile element insertions are potential drivers of complex amplicons' origin. We find that patient-derived organoids recapitulate extrachromosomal DNA observed in the primary tumors and single-cell DNA sequencing capture extrachromosomal DNA-driven clonal dynamics across passages. Prospectively, long-read and single-cell DNA sequencing technologies can lead to better prediction of clonal evolution in esophageal adenocarcinoma.

Luminal breast epithelial cells from wildtype and BRCA mutation carriers harbor copy number alterations commonly associated with breast cancer.

Cancer-associated mutations have been documented in normal tissues, but the prevalence and nature of somatic copy number alterations and their role in tumor initiation and evolution is not well understood. Here, using single cell DNA sequencing, we describe the landscape of CNAs in >42,000 breast epithelial cells from women with normal or high risk of developing breast cancer. Accumulation of individual cells with one or two of a specific subset of CNAs (e.g. 1q gain and 16q, 22q, 7q, and 10q loss) is detectable in almost all breast tissues and, in those from BRCA1 or BRCA2 mutations carriers, occurs prior to loss of heterozygosity (LOH) of the wildtype alleles. These CNAs, which are among the most common associated with ductal carcinoma in situ (DCIS) and malignant breast tumors, are enriched almost exclusively in luminal cells not basal myoepithelial cells. Allele-specific analysis of the enriched CNAs reveals that each allele was independently altered, demonstrating convergent evolution of these CNAs in an individual breast. Tissues from BRCA1 or BRCA2 mutation carriers contain a small percentage of cells with extreme aneuploidy, featuring loss of TP53 , LOH of BRCA1 or BRCA2 , and multiple breast cancer-associated CNAs in addition to one or more of the common CNAs in 1q, 10q or 16q. Notably, cells with intermediate levels of CNAs are not detected, arguing against a stepwise gradual accumulation of CNAs. Overall, our findings demonstrate that chromosomal alterations in normal breast epithelium partially mirror those of established cancer genomes and are chromosome- and cell lineage-specific.

Single-cell decoding of drug induced transcriptomic reprogramming in triple negative breast cancers.

BACKGROUND: The encoding of cell intrinsic drug resistance states in breast cancer reflects the contributions of genomic and non-genomic variations and requires accurate estimation of clonal fitness from co-measurement of transcriptomic and genomic data. Somatic copy number (CN) variation is the dominant mutational mechanism leading to transcriptional variation and notably contributes to platinum chemotherapy resistance cell states. Here, we deploy time series measurements of triple negative breast cancer (TNBC) single-cell transcriptomes, along with co-measured single-cell CN fitness, identifying genomic and transcriptomic mechanisms in drug-associated transcriptional cell states. RESULTS: We present scRNA-seq data (53,641 filtered cells) from serial passaging TNBC patient-derived xenograft (PDX) experiments spanning 2.5 years, matched with genomic single-cell CN data from the same samples. Our findings reveal distinct clonal responses within TNBC tumors exposed to platinum. Clones with high drug fitness undergo clonal sweeps and show subtle transcriptional reversion, while those with weak fitness exhibit dynamic transcription upon drug withdrawal. Pathway analysis highlights convergence on epithelial-mesenchymal transition and cytokine signaling, associated with resistance. Furthermore, pseudotime analysis demonstrates hysteresis in transcriptional reversion, indicating generation of new intermediate transcriptional states upon platinum exposure. CONCLUSIONS: Within a polyclonal tumor, clones with strong genotype-associated fitness under platinum remained fixed, minimizing transcriptional reversion upon drug withdrawal. Conversely, clones with weaker fitness display non-genomic transcriptional plasticity. This suggests CN-associated and CN-independent transcriptional states could both contribute to platinum resistance. The dominance of genomic or non-genomic mechanisms within polyclonal tumors has implications for drug sensitivity, restoration, and re-treatment strategies.

Circulating tumour mutation detection in triple-negative breast cancer as an adjunct to tissue response assessment.

Circulating tumour DNA (ctDNA) detection via liquid biopsy is an emerging alternative to tissue biopsy, but its potential in treatment response monitoring and prognosis in triple negative breast cancer (TNBC) is not yet well understood. Here we determined the prevalence of actionable mutations detectable in ctDNA using a clinically validated cancer gene panel assay in patients with TNBC, without recurrence at the time of study entry. Sequencing of plasma DNA and validation of variants from 130 TNBC patients collected within 7 months of primary treatment completion revealed that 7.7% had detectable residual disease with a hotspot panel. Among neoadjuvant treated patients, we observed a trend where patients with incomplete pathologic response and positive ctDNA within 7 months of treatment completion were at much higher risk of reduced progression free survival. We propose that a high risk subset of early TNBC patients treated in neoadjuvant therapy protocols may be identifiable by combining tissue response and sensitive ctDNA detection.

Ongoing genome doubling promotes evolvability and immune dysregulation in ovarian cancer.

Whole-genome doubling (WGD) is a critical driver of tumor development and is linked to drug resistance and metastasis in solid malignancies. Here, we demonstrate that WGD is an ongoing mutational process in tumor evolution. Using single-cell whole-genome sequencing, we measured and modeled how WGD events are distributed across cellular populations within tumors and associated WGD dynamics with properties of genome diversification and phenotypic consequences of innate immunity. We studied WGD evolution in 65 high-grade serous ovarian cancer (HGSOC) tissue samples from 40 patients, yielding 29,481 tumor cell genomes. We found near-ubiquitous evidence of WGD as an ongoing mutational process promoting cell-cell diversity, high rates of chromosomal missegregation, and consequent micronucleation. Using a novel mutation-based WGD timing method, doubleTime , we delineated specific modes by which WGD can drive tumor evolution: (i) unitary evolutionary origin followed by significant diversification, (ii) independent WGD events on a pre-existing background of copy number diversity, and (iii) evolutionarily late clonal expansions of WGD populations. Additionally, through integrated single-cell RNA sequencing and high-resolution immunofluorescence microscopy, we found that inflammatory signaling and cGAS-STING pathway activation result from ongoing chromosomal instability and are restricted to tumors that remain predominantly diploid. This contrasted with predominantly WGD tumors, which exhibited significant quiescent and immunosuppressive phenotypic states. Together, these findings establish WGD as an evolutionarily 'active' mutational process that promotes evolvability and dysregulated immunity in late stage ovarian cancer.

Imaging and Molecular Annotation of Xenographs and Tumours (IMAXT): High throughput data and analysis infrastructure.

With the aim of producing a 3D representation of tumors, imaging and molecular annotation of xenografts and tumors (IMAXT) uses a large variety of modalities in order to acquire tumor samples and produce a map of every cell in the tumor and its host environment. With the large volume and variety of data produced in the project, we developed automatic data workflows and analysis pipelines. We introduce a research methodology where scientists connect to a cloud environment to perform analysis close to where data are located, instead of bringing data to their local computers. Here, we present the data and analysis infrastructure, discuss the unique computational challenges and describe the analysis chains developed and deployed to generate molecularly annotated tumor models. Registration is achieved by use of a novel technique involving spherical fiducial marks that are visible in all imaging modalities used within IMAXT. The automatic pipelines are highly optimized and allow to obtain processed datasets several times quicker than current solutions narrowing the gap between data acquisition and scientific exploitation.