Biology subjects

Khan, S. R.

Publications and source records attributed to Khan, S. R..

4 recordsLinked to original sources

bioRxiv2026

Aging restricts colorectal tumor growth by epigenetically silencing developmental gene programs

The incidence of early-onset colorectal cancer (CRC) has risen sharply in recent decades1, yet the biological basis underlying the distinct behavior of tumors arising in young versus aged tissues remains poorly understood. Here we show that aging reprograms the epigenetic landscape of the colon, restricting colon tumor growth through stable silencing of developmental and fetal gene programs. We find that colon tumors arising in aged mice are intrinsically less proliferative than those arising in young animals. Multi-omic profiling of normal colon and colon tumors reveals that aging drives DNA hypermethylation, loss of Polycomb-associated chromatin states, and reduced chromatin accessibility at a defined set of developmental genes that are bivalent (marked by both H3K27me3 and H3K4 methylation), transcriptionally active in colon tumors from young animals and repressed in both tumors and normal tissue from old animals. Among the genes most strongly repressed in old animals is Tacstd2 (Trop2), a regulator of fetal intestinal programs and epithelial stemness. Pharmacologic inhibition of DNA methylation reactivates the aging-silenced gene network in organoids from old animals, whereas genetic disruption of Tacstd2 suppresses growth and developmental transcriptional programs in young tumor organoids. TACSTD2, fetal gene signatures, and the aging-associated bivalent gene program are likewise repressed in late-onset vs. early-onset human colorectal cancers. Collectively, these findings identify age-associated epigenetic silencing of developmental gene programs as a causal mechanism that constrains colorectal tumor growth and provide a mechanistic framework for understanding the distinct biology of early-onset colorectal cancer.

cancer biology↗

bioRxiv2026

Inferring Multi-Stage Pathway Progression Models from Tumor Phylogenies

Cancer progression is an evolutionary process driven by the accumulation and selection of somatic mutations, giving rise to genetically diverse subclonal populations within tumors. Understanding the dependencies among mutations and identifying recurrent evolutionary trajectories is critical for understanding cancer progression and informing therapeutic strategies. Recent advances in genomic sequencing and phylogenetic reconstruction now enable large-scale inference of tumor phylogenies, providing detailed representations of intratumor evolutionary histories across patient cohorts. However, modeling cancer progression from these data remains challenging due to extensive inter- and intratumor heterogeneity, often arising from mutations in different genes within the same pathway that confer similar fitness advantages. Existing methods to infer pathway-level progression models summarize each tumor by a single consensus genotype, ignoring intra-tumor heterogeneity, while phylogeny-based methods typically focus on individual mutations and do not model pathways. We introduce PhyloStage, an algorithm for inferring multi-stage pathway-level cancer progression models from large cohorts of tumor phylogenies. PhyloStage represents progression as a partial order over pathways, permitting independent mutations in incomparable pathways while constraining the order of mutations within the same or dependent pathways. The framework also incorporates uncertainty in tumor phylogenies, resolves mutation clusters with unknown ordering, and stratifies patients by progression stage. Applied to a cohort of 120 acute myeloid leukemia (AML) tumor phylogenies, PhyloStage infers progression models that are aligned with known AML progression. On 99 non-small cell lung cancer (NSCLC) patients, PhyloStage stratifies patients into progression stages such that later stages have larger tumor sizes, corroborating phenotypic tumor progression.

bioinformatics↗

bioRxiv2026

Exploring the Space of Tumor Phylogenies Consistent with Single-Cell Whole-Genome Sequencing Data

Tumors comprise subpopulations of cells that harbor distinct collections of somatic mutations, ranging from single-nucleotide variants (SNVs) to large-scale copy-number aberrations (CNAs). Single-cell whole-genome sequencing (scWGS) enables direct measurement of these mutations; however, inferring tumor phylogenies from scWGS data remains challenging due to ultra-low coverage ([~]0.05 x). There may be multiple ways of imputing missing information in the data leading to distinct tumor phylogenies that are equally well supported by the data. Existing methods produce a single phylogeny and overlook this uncertainty in reconstructing evolutionary histories from sparse scWGS data. We present SCOPE, a novel algorithmic framework that characterizes the space of tumor phylogenies consistent with scWGS data under a copy-number constrained version of the perfect phylogeny model. Our approach relies on estimating the cell fraction of each mutation, i.e. the proportion of cells within each copy-number cluster that carry the mutation. We derive the necessary and sufficient conditions these fractions must satisfy to admit a copy-number constrained perfect phylogeny. This yields a complete combinatorial description of all tumor phylogenies that are supported by the data under our model. We prove that identifying the largest subset of mutations with cell fractions satisfy model constraints using noisy measurements of cell fractions is NP-hard. On simulated data, SCOPE outperforms existing methods in accuracy with faster runtime in particular on the larger simulations. On scWGS data from a patient-derived ovarian cancer cell line, SCOPE infers a more resolved phylogeny with stronger statistical support compared to existing methods. Using SCOPE to analyze a larger dataset of 4 triple negative breast cancer (TNBC) and 8 high-grade serous ovarian cancer (HGSOC) samples, we show that several samples admit multiple phylogenies. We further find that number of admissible phylogenies increases with lower sequencing coverage and is negatively correlated with the number of copy-number clusters and number of distinct loss of heterozygosity (LOH) events in the clusters, highlighting how data quality and evolutionary constraints jointly shape uncertainty in tumor phylogeny reconstruction. By providing a principled framework for exploring and quantifying phylogenetic uncertainty, SCOPE establishes a new foundation for robust inference of tumor evolution from scWGS data. Code availabilitySoftware is available at https://github.com/sashittal-group/SCOPE

bioinformatics↗

bioRxiv2024

Quantifying Pathological Progression from Single-Cell Data

The surge in single-cell datasets and reference atlases has enabled the comparison of cell states across conditions, yet a gap persists in quantifying pathological shifts from healthy cell states. To address this gap, we introduce single-cell Pathological Shift Scoring (scPSS) which provides a statistical measure for how much a "query" cell from a diseased sample has been shifted away from a reference group of healthy cells. In scPSS, The distance of a query cell to its k-th nearest reference cell is considered as its pathological shift score. Euclidean distances in the top n principal component space of the gene expressions are used to measure distances between cells. The p-value of a query pathological shift score belonging to the null distribution of intra-reference cell shift scores provides a statistical significance measure of the query cell being in the reference cell group. This makes our method both simple and statistically rigorous. Comparative evaluations against a state-of-the-art contrastive variational inference model, modified for shift scores, demonstrate our methods accuracy and efficiency. Additionally, we have also shown that the aggregation of cell-level pathological scores from scPSS can be used to predict health conditions at the individual level.

genomics↗