Biology subjects

Graham, T. A.

Publications and source records attributed to Graham, T. A..

At least 19 recordsLinked to original sources

bioRxiv2026

Spatiotemporal profiling of T cell repertoires in Lynch syndrome reveals convergent immune responses through neoplastic progression

Surveillance of individuals with Lynch syndrome (LS), who are carriers of heterozygous inactivating mismatch repair (MMR) gene mutations that predispose to colorectal cancer, enables the study of tumour-immune coevolution during tumour initiation. Somatic loss of MMR activity in the LS colon leads to the accrual of cancer driver mutations, and also of neoantigens likely to generate an adaptive immune response. However, the spatial and temporal immune dynamics in colon and peripheral blood are uncharacterised. Here we show that the neoplastic evolution of mismatch repair deficient (MMRd) clones leads to large-scale and sustained reorganisation of the T-cell repertoire in colon and peripheral blood. We performed dense spatiotemporal profiling of T cell receptor (TCR) repertoires from 109 colonic biopsies (11 tumour samples, 98 normal bowel) from seven individuals, with eight matched blood repertoires. We additionally analysed 13 colorectal polyp repertoires from eight participants, 286 cross sectional blood repertoires from 242 participants (198 LS and 44 non-LS) and 52 longitudinal peripheral blood samples from 26 individuals with LS. We detected tumour-associated T cell clones, some of which were widely present in normal bowel tissue and also detectable in peripheral blood. Sequence similarity analysis revealed conserved TCR architectures across tissue compartments and recurrent clustering patterns between patients, that were not present in non-Lynch controls, consistent with convergent MMRd-associated antigen-driven immune selection. We identified systemic signatures associated with local tumour immune activity and revealed evidence of structured repertoire remodelling before overt malignancy. Longitudinal profiling revealed frequent turnover of low-abundance clonotypes, whereas highly abundant clonotypes persisted for several years. Collectively, our data defines the local and circulating TCR landscape associated with colonic neoplasia in LS, and provides a framework for LS blood-based immune monitoring, early interception and biomarker development.

immunology↗

bioRxiv2026

Spatially resolved transcriptomic and proteomic profiling reveals cell interaction programs that predict Barrett's esophagus progression

Barretts esophagus (BE) is the precursor lesion of esophageal adenocarcinoma (EAC). It affects approximately 5% of adults in the United States and significantly increases the risk of developing EAC. However, current surveillance strategies cannot reliably distinguish patients who will progress from those who will remain stable. Direct studies of progressor BE are extremely limited due to availability of tissue with known progression outcomes, and have largely been restricted to genomic profiling approaches. The premalignant cellular landscape of progressor BE remains poorly understood. Here, we used complementary spatial transcriptomic and proteomic imaging to profile 34 non-dysplastic BE patients under endoscopic surveillance, including those who subsequently progressed to dysplasia or EAC, termed "Progressors" and those who remained stable, termed "Non-progressors". Transcriptomics based Xenium analysis captured 974,604 cells across 70 whole-biopsy regions, while protein based imaging mass cytometry profiled 372,242 cells across 119 selected regions. FUME-TCRseq further quantified T cell clonotypes from matched tissues scrolls. Cellular composition was generally similar between Progressors and Non-progressors. However, Progressors showed increased intestinal Barretts columnar cells, B cells and gastric progenitor-like cells, together with enhanced immune-epithelial interactions, whereas Non-progressors retained coordinated stromal organization. Spatial interaction features strongly outperformed cell composition and density for progression prediction. Combined spatial interaction model achieved an area under the curve (AUC) of 0.97, compared with 0.62 and 0.68 for comparison and density alone. Complementary imaging mass cytometry further resolved the underlying immune programs, identifying cytotoxic and antigen presenting myeloid features enriched in progressors, and CD56 associated memory T cell interactions enriched in non progressors. Together, these findings support a model that BE progression is driven by progressive remodeling of epithelial-immune-stromal architecture rather than emergence of distinct dysplasia-like cell subsets. Increased T cell clonal diversity and recruitment of cytotoxic and antigen-presenting immune niches may also reflect an evolving response to genomic alteration prior to dysplasia. These results establish spatial tissue architecture, rather than specific cell types, captures progression associated microenvironmental states in BE and provides a framework for spatially informed patient stratification and early cancer risk assessment.

systems biology↗

bioRxiv2026

PHYFUM: Phylogenetic Reconstruction of Normal and Pre-malignant Tissue Evolution Using Fluctuating Methylation

We present PHYFUM, a novel Bayesian phylogenetic method for methylation data that reconstructs the evolutionary history of stem cells and the glandular structures they reside within in normal tissue. Using simulations, we validated this phylogenetic method and confirmed its accuracy. A re-analysis of 22 patients unveiled early gland divergence in the human gut, in contrast to a much later common ancestor in the endometrium, and yielded strong evidence against gland division by segregation of individual stem cells.

bioinformatics↗

bioRxiv2026

Inference of Cancer Drug Cross Resistance Using Only Single-Drug Exposure Data

Combining drugs may prevent resistance in cancer treatment. However, the evolution of resistance to more than one drug simultaneously - cross resistance - remains a major obstacle to durable clinical response. Here, we show that we can identify the presence and strength of cross resistance using lineage tracing data from cell populations exposed only to each drug independently, removing the need for complex combinatorial treatment experiments. Simulations where the ground truth was known showed that the method accurately recovered the true underlying resistance dynamics. We applied the framework to breast cancer, lung cancer and lymphoma datasets, quantifying cross resistance across diverse therapy types. Our predictions align with biological expectations and reveal that even drugs with shared putative targets can exhibit variable cross resistance across pre-clinical models. Finally, we used inferred cross resistance strengths to evaluate treatment strategies, predicting that high cross resistance contributes to the limited efficacy of switching between CDK4/6 inhibitors. By enabling the identification of cross resistance without requiring multi-drug exposure experiments, our framework provides a method for prioritising drug combinations and screening for novel resistance-minimising drug targets.

cancer biology↗

bioRxiv2025

Classpose: foundation model-driven whole slide image-scale cell phenotyping in H&E

Cell phenotyping in histopathology samples is essential for diagnostic and research workflows. However, human expert annotation requires significant time and expertise while being affected by inter-observer variability. Here, we present Classpose, an easily trainable framework for cell segmenting and phenotyping built on top of Cellpose-SAM with state-of-the-art performance across 6 distinct datasets, outperforming competing methods. We show that this requires fine-tuning the entire network, highlighting how instance segmentation is a poor objective for downstream cellular classification. We apply it to a large whole slide image (WSI) colorectal cancer (CRC) cohort (SurGen) and show that Classpose-derived cellular organisation and morphology features can be used to determine novel spatial morphological phenotypes for clinically relevant molecular conditions (MMR deficiency, BRAF mutations, KRAS mutations) and to predict these same molecular conditions. We make Classpose models available and provide a user-friendly QuPath extension for widespread use by the digital pathology community.

bioinformatics↗

bioRxiv2025

PKN2 regulates cell-junctions to limit colitis and colon tumour formation

Intestinal barrier failure is a defining feature of inflammatory bowel disease and a key driver of inflammation-associated colorectal cancer. However, the epithelial mechanisms that preserve barrier stability during inflammatory stress remain incompletely defined. Here, we identify the Rho-regulated kinase PKN2 as a non-redundant safeguard of tight junction integrity and a candidate tumour suppressor in colitis-associated colorectal cancer. Conditional PKN2 deletion in mice sensitised the colon to inflammatory injury and significantly increased adenoma formation in an inflammatory cancer model, with tumour burden tightly correlating with colitis severity. Mechanistically, PKN2 localises to tight junctions and is required to stabilise barrier integrity during injury in mouse and organoid models. PKN2 loss induces transcriptional programs conserved in human inflammatory bowel disease, where reduced PKN2 expression associates with increased disease severity and altered therapeutic response. Notably, PKN2 is encoded within genomic loci previously linked to intestinal inflammation susceptibility and tumour suppression in both humans and mice. Our findings identify PKN2 as a functional effector underlying these phenotypes and demonstrate that even heterozygous loss is sufficient to confer heightened sensitivity to inflammatory injury and tumour initiation, positioning PKN2 as a central regulator of tight junction stability that shapes disease severity, treatment response and cancer risk.

cell biology↗

bioRxiv2025

Quantitative measurement of phenotype dynamics during cancer drug resistance evolution using genetic barcoding

Cancer treatment frequently fails due to the evolution of drug-resistant cell phenotypes driven by genetic or non-genetic changes. The origin, timing, and rate of spread of these adaptations are critical for understanding drug resistance mechanisms but remain challenging to observe directly. We present a mathematical framework to infer drug resistance dynamics from genetic lineage tracing and population size data without direct measurement of resistance phenotypes. Simulation experiments demonstrate that the framework accurately recovers ground-truth evolutionary dynamics. Experimental evolution to 5-Fu chemotherapy in colorectal cancer cell lines SW620 and HCT116 validates the framework. In SW620 cells, a stable pre-existing resistant subpopulation was inferred, whereas in HCT116 cells, resistance emerged through phenotypic switching into a slow-growing resistant state with stochastic progression to full resistance. Functional assays, including scRNA-seq and scDNA-seq, validate these distinct evolutionary routes. This framework facilitates rapid characterisation of resistance mechanisms across diverse experimental settings.

cancer biology↗

bioRxiv2025

Deep Learning for Biomarker Discovery in Cancer Genomes

BackgroundAccurate determination of genomic biomarkers from tumor sequencing is fundamental to precision oncology, informing disease classification and treatment decisions. In practice, biomarker inference relies on computational pipelines that often compress high-dimensional mutation data into predefined summaries such as mutational signatures or composite genomic features. While robust and widely adopted, these representations may not fully capture the complexity of cancer genomes. Deep learning (DL) offers an end-to-end alternative by learning features directly from raw genomic data. However, clinical translation remains challenging due to limited empirical validation of new DL models and a lack of systematic comparisons with established machine learning (ML) baselines, particularly when transitioning from information-rich genome or exome data to real-world targeted sequencing profiles. Here, we compare state-of-the-art DL architectures with classical ML models across variant-level, copy-number (CNV), and multimodal inputs, using microsatellite instability (MSI) and homologous recombination deficiency (HRD) prediction as oncologically relevant tasks. We aim to derive practical guidance on modelling strategies across different data modalities and clinical sequencing contexts. MethodsFor MSI and HRD prediction, we trained multiple DL models, including supervised and self-supervised encoders, alongside feature-based ML approaches using tumor mutation data, copy-number alterations, and their multimodal combinations. Analyses were conducted on 5,647 patients in The Cancer Genome Atlas (TCGA), the Clinical Proteomic Tumor Analysis Consortium (CPTAC), and two targeted sequencing panel cohorts. Model performance was evaluated on both whole-exome and panel-based datasets, and explainability analysis were performed for both DL and ML models. ResultsFor MSI, DL demonstrated stronger generalization than ML on external validation data (F1 0.97 vs 0.76) and maintained comparatively high performance under pseudo-panels conditions, whereas ML performance dropped. In a real-world targeted panel cohort, DL again showed more robust generalization than ML, with performance partly affected by cross-assay variability. For HRD, incorporation of CNV data was the primary determinant of predictive performance. Once CNVs were included, DL and ML achieved similar accuracy on external datasets (F1 0.61 vs 0.58). In panel-based settings, DL retained an advantage over ML (F1 0.78 vs 0.62). Model interpretation analyses indicated that both DL and ML relied on mutation and chromosomal patterns consistent with established MSI and HRD biology. ConclusionOverall, predictive performance depended strongly on data availability and clinical sequencing context. When information-rich inputs were available, both DL and classical ML achieved robust biomarker prediction, with DL generally matching or exceeding ML performance. The most pronounced advantages of DL emerged in cross-assay evaluations and data-sparse settings, where generalization was more reliable. Notably, the best-performing DL models were lightweight and interpretable, supporting practical deployment. In clinical genomics workflows, such models may complement established pipelines by leveraging patient sequencing data to provide additional evidence for treatment-relevant biomarker assessment.

bioinformatics↗

bioRxiv2024

Asymmetric learning and adaptability to changes in relational structure during transitive inference

Humans and other animals can generalise from local to global relationships in a transitive manner. Recent research has shown that asymmetrically biased learning, where beliefs about only the winners (or losers) of local comparisons are updated, is well-suited for inferring relational structures from sparse feedback. However, less is known about how belief-updating biases intersect with humans capacity to adapt to changes in relational structure, where re-valuing an item may have downstream implications for inferential knowledge pertaining to unchanged items. We designed a transitive inference paradigm involving one of two possible changepoints for which an asymmetric (winner-or loser-biased) learning policy was more or less optimal. Participants (N=83) exhibited differential sensitivity to changes in relational structure: whereas participants readily learned that a hitherto low-ranking item increased its rank, moving a high-ranking item down the hierarchy impaired downstream inferential knowledge. Behaviour best captured by an adaptive reinforcement learning model which exhibited a predominantly winner-biased learning policy but also modulated its degree of asymmetry as a function of its choice preference strength. Our results indicate that asymmetric learning not only accounts for efficient inference of latent relational structures, but also for differences in the ease with which learners accommodate structural changes. Author SummaryWhen reasoning about relationships between objects, events, or people, humans can readily use previous experiences to infer relations that they have never encountered before. For example, if Anna beats Bruce at tennis, and Bruce beats Clara, then one can predict that Anna will likely also beat Clara. Human learning in such transitive inference problems tends to be winner-biased - that is, upon observing Annas victory over Bruce, a spectator would be more likely to attribute this outcome to Annas skill than to Bruces lack thereof. However, in a constantly changing world whose comparative relations are rarely static, humans must also be able to infer how changes in the outcomes of certain comparisons bear on other relationships within a transitive hierarchy. Combining behavioural testing and computational modelling, we show that a learning strategy that preferentially focuses on the winners of comparisons induces greater flexibility for certain types of hierarchy changes than for others. In addition, we provide evidence that humans may dynamically adjust their degree of learning asymmetry according to the current strength of their beliefs about the relations under comparison.

neuroscience↗

bioRxiv2024

Epigenome and early selection determine the tumour-immune evolutionary trajectory of colorectal cancer

Immune system control is a major hurdle that cancer evolution must circumvent. The relative timing and evolutionary dynamics of subclones that have escaped immune control remain incompletely characterized, and how immune-mediated selection shapes the epigenome has received little attention. Here, we infer the genome- and epigenome-driven evolutionary dynamics of tumour-immune coevolution within primary colorectal cancers (CRCs). We utilise our existing CRC multi-region multi-omic dataset that we supplement with high-resolution spatially-resolved neoantigen sequencing data and highly multiplexed imaging of the tumour microenvironment (TME). Analysis of somatic chromatin accessibility alterations (SCAAs) reveals frequent somatic loss of accessibility at antigen presenting genes, and that SCAAs contribute to silencing of neoantigens. We observe that strong immune escape and exclusion occur at the outset of CRC formation, and that within tumours, including at the microscopic level of individual tumour glands, additional immune escape alterations have negligible consequences for the immunophenotype of cancer cells. Further minor immuno-editing occurs during local invasion and is associated with TME reorganisation, but that evolutionary bottleneck is relatively weak. Collectively, we show that immune evasion in CRC follows a "Big Bang" evolutionary pattern, whereby genetic, epigenetic and TME-driven immune evasion acquired by the time of transformation defines subsequent cancer-immune evolution.

cancer biology↗

bioRxiv2023

Mutational order and epistasis determine the consequences of FBXW7 mutations during colorectal cancer evolution

Somatic driver mutations, in genes such as FBXW7, have been discovered in phenotypically normal colonic tissue, however their role in cancer initiation remains elusive. Using normal and gene-edited patient-derived human colon organoids as models of early tumour evolution we observed that FBXW7-/- mutations exerted an epistatic effect on subsequent transcription depending on the mutational background of the cell. Specifically, the timing of acquiring an FBXW7-/- mutation respective to an APC mutation, led to profound phenotypic and transcriptomic differences. When FBXW7 was mutated before APC, a near-normal cell state was maintained alongside repression of the APC transcriptional response. However, when APC was mutated before FBXW7, cells acquired classical cancer-stem cell features. Single-cell RNA sequencing revealed that mutation of FBXW7 in normal tissue subtly switched cells from adult to a foetal/regenerative stem cell state. Further analysis using transposase-accessible chromatin sequencing identified this cellular plasticity was driven by changes in chromatin accessibility of transcriptional start site regions associated with TEAD, SNAI1 and AP-1 motifs, which in turn activate the foetal-like state. Taken together, we demonstrate a critical role of FBXW7 mutations in preventing colorectal cancer initiation and provide exemplar evidence for the importance of epistasis and mutational order in cancer biology.

cancer biology↗

bioRxiv2023

Spatially-resolved profiling of the T-cell receptor repertoire in archival clinical specimens using FUME-TCRseq

Genomic analysis of the T-cell receptor (TCR) reveals the strength, breadth and clonal dynamics of the adaptive immune response to pathogens or cancer. The diversity of the TCR repertoire, however, means that sequencing is technically challenging, particularly for samples with low quality, degraded nucleic acids. Here, we have developed and validated FUME-TCRseq, a robust and sensitive RNA-based TCR sequencing methodology that is suitable for formalin-fixed paraffin-embedded samples and low amounts of input material. FUME-TCRseq incorporates unique molecular identifiers into each molecule of cDNA, allowing correction for sequencing errors and PCR bias. We used RNA extracted from colorectal and head and neck cancers to benchmark the accuracy and sensitivity of FUME-TCRseq against existing methods, and found excellent concordance between the datasets. Furthermore, FUME-TCRseq detected more clonotypes than a commercial RNA-based alternative, with shorter library preparation time and significantly lower cost. The high sensitivity and the ability to sequence RNA of poor quality and limited amount enables quantitative analysis of small numbers of cells from archival tissue sections, which is not possible with other methods. To demonstrate this we performed spatially-resolved FUME-TCRseq of colorectal cancers using macrodissected archival samples, revealing the shifting T-cell landscapes at the transition to an invasive phenotype, and between tumour subclones containing distinct driver alterations. In summary, FUME-TCRseq represents an accurate, sensitive and low-cost tool for the characterisation of T-cell repertoires, particularly in samples with low quality RNA that have not been accessible using existing methodology.

immunology↗

bioRxiv2023

Cancer Prevalence Across Vertebrates

Cancer is pervasive across multicellular species, but what explains differences in cancer prevalence across species? Using 16,049 necropsy records for 292 species spanning three clades (amphibians, sauropsids and mammals) we found that neoplasia and malignancy prevalence increases with adult weight (contrary to Petos Paradox) and somatic mutation rate, but decreases with gestation time. Evolution of cancer susceptibility appears to have undergone sudden shifts followed by stabilizing selection. Outliers for neoplasia prevalence include the common porpoise (<1.3%), the Rodrigues fruit bat (<1.6%) the black-footed penguin (<0.4%), ferrets (63%) and opossums (35%). Discovering why some species have particularly high or low levels of cancer may lead to a better understanding of cancer syndromes and novel strategies for the management and prevention of cancer. Statement of SignificanceEvolution has discovered mechanisms for suppressing cancer in a wide variety of species. By analyzing veterinary necropsy records we can identify species with exceptionally high or low cancer prevalence. Discovering the mechanisms of cancer susceptibility and resistance may help improve cancer prevention and explain cancer syndromes.

cancer biology↗

bioRxiv2022

CNETML: Maximum likelihood inference of phylogeny from copy number profiles of spatio-temporal samples

Phylogenetic trees based on copy number alterations (CNAs) for multi-region samples of a single cancer patient are helpful to understand the spatio-temporal evolution of cancers, especially in tumours driven by chromosomal instability. Due to the high cost of deep sequencing data, low-coverage data are more accessible in practice, which only allow the calling of (relative) total copy numbers due to the lower resolution. However, methods to reconstruct sample phylogenies from CNAs often use allele-specific copy numbers and those using total copy number are mostly distance matrix or maximum parsimony methods which do not handle temporal data or estimate mutation rates. In this work, we developed a new maximum likelihood method based on a novel evolutionary model of CNAs, CNETML, to infer phylogenies from spatio-temporal samples taken within a single patient. CNETML is the first program to jointly infer the tree topology, node ages, and mutation rates from total copy numbers when samples were taken at different time points. Our extensive simulations suggest CNETML performed well even on relative copy numbers with subclonal whole genome doubling events and under slight violation of model assumptions. The application of CNETML to real data from Barretts esophagus patients also generated consistent results with previous discoveries and novel early CNAs for further investigations.

bioinformatics↗

bioRxiv2022

AI-powered pan-species computational pathology: bridging clinic and wildlife care

Cancers occur across species. Understanding what is consistent and varies across species can provide new insights into cancer initiation and evolution, with significant implications for animal welfare and wildlife conservation. We built the pan-species cancer digital pathology atlas (PANCAD) and conducted the first pan-species study of computational comparative pathology using a supervised convolutional neural network algorithm trained on human samples. The artificial intelligence algorithm achieves high accuracy in measuring immune response through single-cell classification for two transmissible cancers (canine transmissible venereal tumour, 0.94; Tasmanian devil facial tumour disease, 0.88). Furthermore, in 18 other vertebrate species (mammalia=11, reptilia=4, aves=2, and amphibia=1), accuracy (0.57-0.94) was influenced by cell morphological similarity preserved across different taxonomic groups, tumour sites, and variations in the immune compartment. A new metric, named morphospace overlap, was developed to guide veterinary pathologists towards rational deployment of this technology on new samples. This study provides the foundation and guidelines for transferring artificial intelligence technologies to veterinary pathology based on a new understanding of morphological conservation, which could vastly accelerate new developments in veterinary medicine and comparative oncology.

cancer biology↗

bioRxiv2022

Mutation Rate Evolution Drives Immune Escape In Mismatch Repair-Deficient Cancer

Mutation rate optimisation drives evolution and immune evasion of bacteria and lentiviral strains, including HIV. Whether evolving cancer lineages similarly adapt mutation rates to increase tumour cell fitness is unknown. Here, by mapping the clonal topography of mismatch repair-deficient (MMRd) colorectal cancer, we show that genomic MMRd mutability co-evolves with neoantigen selection to drive intratumour diversification and immune escape. Mechanistically, we find that microsatellite instability modulates subclonal DNA repair by toggling two hypermutable mononucleotide homopolymer runs in the mismatch repair genes MSH6 and MSH3 (C8 and A8, respectively) through stochastic frameshift switching. Spontaneous mutation and reversion at these evolvability switches modulates subclonal mutation rate, mutation bias, and clonal HLA diversity during MMRd cancer evolution. Combined experimental and simulation studies demonstrate that subclonal immune selection favours incremental MMR mutations. MMRd cancers thus fuel intratumour heterogeneity by adapting subclonal mutation rate and mutation bias to immune selection, revealing a conserved co-evolutionary arms race between neoantigen selection and adaptive genomic mutability. Our work reveals layers of mutational complexity and microsatellite biology in MMRd cancer evolution previously hidden in bulk analyses.

cancer biology↗

bioRxiv2022

First passage time analysis of spatial mutation patterns reveals evolutionary dynamics of pre-existing resistance in colorectal cancer

The footprint left by early cancer dynamics on the spatial arrangement of tumour cells is poorly understood, and yet could encode information about how therapy resistant sub-clones grew within the expanding tumour. Novel methods of quantifying spatial tumour data at the cellular scale are required to link evolutionary dynamics to the resulting spatial architecture of the tumour. Here, we propose a framework using first passage times of random walks to quantify the complex spatial patterns of tumour cell population mixing. First, using a toy model of cell mixing we demonstrate how first passage time statistics can distinguish between different pattern structures. We then apply our method to simulated patterns of wild-type and mutated tumour cell population mixing, generated using an agent-based model of expanding tumours, to explore how first passage times reflect mutant cell replicative advantage, time of emergence and strength of cell pushing. Finally, we analyse experimentally measured patterns of genetic point mutations in human colorectal cancer, and estimate parameters of early sub-clonal dynamics using our spatial computational model. We uncover a wide range of mutant cell replicative advantages and timings, with the majority of sampled tumours consistent with boundary driven growth or short-range cell pushing. By analysing multiple sub-sampled regions in a small number of samples, we explore how the distribution of inferred dynamics could inform about the initial mutational event. Our results demonstrate the efficacy of first passage time analysis as a new methodology for quantifying cell mixing patterns in vivo, and suggest that patterns of sub-clonal mixing can provide insights into early cancer dynamics.

cancer biology↗

bioRxiv2021

VALIS: Virtual Alignment of pathoLogy Image Series

Spatial analyses can reveal important interactions between and among cells and their microenvironment. However, most existing staining methods are limited to a handful of markers per slice, thereby limiting the number of interactions that can be studied. This limitation is frequently overcome by registering multiple images to create a single composite image containing many markers. While there are several existing image registration methods for whole slide images (WSI), most have specific use cases. Here, we present the Virtual Alignment of pathoLogy Image Series (VALIS), a fully automated pipeline that opens, registers (rigid and/or non-rigid), and saves aligned slides in the ome.tiff format. VALIS has been tested with 273 immunohistochemistry (IHC) samples and 340 immunofluorescence (IF) samples, each of which contained between 2-69 images per sample. The registered WSI tend to have low error and are completed within a matter of minutes. In addition to registering slides, VALIS can also using the registration parameters to warp point data, such as cell centroids previously determined via cell segmentation and phenotyping. VALIS is written in Python and requires only few lines of code for execution. VALIS therefore provides a free, opensource, flexible, and simple pipeline for rigid and non-rigid registration of IF and/or IHC that can facilitate spatial analyses of WSI from novel and existing datasets.

cancer biology↗