Biology subjects

Mohibullah, N.

Publications and source records attributed to Mohibullah, N..

7 recordsLinked to original sources

bioRxiv2026

Neoantigen-reactive CD8+ T cell engagement marks exceptional survivors of pancreatic cancer

Pancreatic cancer (PC) is largely refractory to immune checkpoint blockade (ICB), although homologous recombination-deficient (HRD) tumors may derive benefit. In the POLAR trial of maintenance pembrolizumab plus olaparib after platinum-based chemotherapy for metastatic PC, responses remained heterogeneous. To define determinants of productive antitumor immunity, we integrated longitudinal blood TCR sequencing with tumor single-cell and spatial profiling. Durable benefit was associated with rare tumor-infiltrating, peripherally expanding (TIE) CD8+ T cell clonotypes, a subset of which were functionally neoantigen-reactive. TIE patients showed markedly prolonged survival beyond established genomic and immune biomarkers. Conversely, resistance was associated with spatial T cell exclusion, myCAF-rich stromal remodeling, basal-like/KRAS-associated malignant-cell programs, and expansion of CTLA4 regulatory T cells linked to local immunosuppressive remodeling. These findings define a clonotype-resolved framework for immune monitoring and identify complementary stromal, tumor-intrinsic, and regulatory immune barriers that may guide rational combination immunotherapy in PC.

cancer biology↗

bioRxiv2026

Evolution of oncogene amplification across 86,000 cancer cell genomes

High-level copy-number (CN) amplification (HLAMP) is a major mechanism of oncogene activation in human cancer. Despite progress in therapeutically targeting amplified oncogenes, the processes underlying amplicon evolution remain incompletely understood, leaving critical knowledge gaps in their etiology and mechanisms of therapeutic response. To address this, we analyzed the evolutionary trajectories of HLAMPs using single-cell whole-genome sequencing data from 86,239 cancer cells across 93 patients and 9 experimental systems. We found that cell-to-cell CN variability provides a quantifiable readout of HLAMP mechanism, clearly distinguishing extrachromosomal circular DNA (ecDNA) from intrachromosomal amplification (ICamp) through characteristic CN distributions that reflect distinct modes of segregation and correspond to clonal architecture. Notably, ICamp events frequently showed multiple amplitude peaks specific to subclones, indicating punctuated shifts in oncogene dosage through numeric or structural modulatory mechanisms with transcriptional impact. In contrast, ecDNAs exhibited broad, continuous CN distribution with extreme high-copy outliers, consistent with asymmetric segregation. The CN and structural diversity of ecDNA regions enabled systematic deconvolution of ecDNA subspecies and estimation of their per-cell abundance, revealing the history of ecDNA-mediated oncogenesis at single-nucleotide resolution. We observed ecDNA diversification through internal rearrangements across cases and, notably, convergent evolution in glioblastoma cases marked by multiple, recurrent acquisition of EGFR-targeting ecDNAs. Finally, single-cell genome-based identification of ecDNAs showed substantial discrepancy with bulk genome graph-based predictions and reliably distinguished actively maintained ecDNAs from historical genomic footprints after chromosomal re-integration. These findings reveal marked tissue-type specificity of ecDNAs, suggesting that ecDNA-mediated oncogenesis may depend on a permissive tissue context.

genomics↗

bioRxiv2024

Chromatin architecture mapping by multiplex proximity tagging

Chromatin plays a pivotal role in genome expression, maintenance, and replication. To better understand chromatin organization, we developed a novel proximity-tagging method which assigns unique DNA barcodes to molecules that associate in 3D space. Using this method - Proximity Copy Paste (PCP) - we mapped the connectivity of individual nucleosomes in Saccharomyces cerevisiae. By analyzing nucleosome positions and spacing on single molecule fibers, we show that chromatin is predominantly organized into regularly spaced nucleosome arrays that can be positioned or delocalized. Basic features of nucleosome arrays are generally explained by gene size and transcription. PCP can also map long-range, multi-way interactions and we provide the first direct evidence supporting a model that metaphase chromosomes are compacted by cohesin loop clustering. Analyzing single-molecule nuclease footprinting data we define distinct chromatin states within a mixed population to show that non-canonical nucleosomes, notably Overlapping-Di-Nucleosomes (OLDN) are a stable feature of chromatin. PCP is a versatile method allowing the detection of the connectivity of individual molecules locally and over large distance to be mapped at high-resolution in a single experiment.

molecular biology↗

bioRxiv2024

Tracking clonal evolution of drug resistance in ovarian cancer patients by exploiting structural variants in cfDNA

Drug resistance is the major cause of therapeutic failure in high-grade serous ovarian cancer (HGSOC). Yet, the mechanisms by which tumors evolve to drug resistant states remains largely unknown. To address this, we aimed to exploit clone-specific genomic structural variations by combining scaled single-cell whole genome sequencing with longitudinally collected cell-free DNA (cfDNA), enabling clonal tracking before, during and after treatment. We developed a cfDNA hybrid capture, deep sequencing approach based on leveraging clone-specific structural variants as endogenous barcodes, with orders of magnitude lower error rates than single nucleotide variants in ctDNA (circulating tumor DNA) detection, demonstrated on 19 patients at baseline. We then applied this to monitor and model clonal evolution over several years in ten HGSOC patients treated with systemic therapy from diagnosis through recurrence. We found drug resistance to be polyclonal in most cases, but frequently dominated by a single high-fitness and expanding clone, reducing clonal diversity in the relapsed disease state in most patients. Drug-resistant clones frequently displayed notable genomic features, including high-level amplifications of oncogenes such as CCNE1, RAB25, NOTCH3, and ERBB2. Using a population genetics Wright-Fisher model, we found evolutionary trajectories of these features were consistent with drug-induced positive selection. In select cases, these alterations impacted selection of secondary lines of therapy with positive patient outcomes. For cases with matched single-cell RNA sequencing data, pre-existing and genomically encoded phenotypic states such as upregulation of EMT and VEGF were linked to drug resistance. Together, our findings indicate that drug resistant states in HGSOC pre-exist at diagnosis and lead to dramatic clonal expansions that alter clonal composition at the time of relapse. We suggest that combining tumor single cell sequencing with cfDNA enables clonal tracking in patients and harbors potential for evolution-informed adaptive treatment decisions.

cancer biology↗

bioRxiv2024

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.

cancer biology↗

bioRxiv2021

Immune and malignant cell phenotypes of ovarian cancer are determined by distinct mutational processes

High-grade serous ovarian cancer (HGSOC) is an archetypal cancer of genomic instability patterned by distinct mutational processes, intratumoral heterogeneity and intraperitoneal spread. We investigated determinants of immune recognition and evasion in HGSOC to elucidate co- evolutionary processes underlying malignant progression and tumor immunity. Mutational processes and anatomic sites of tumor foci were key determinants of tumor microenvironment cellular phenotypes, inferred from whole genome sequencing, single-cell RNA sequencing, digital histopathology and multiplexed immunofluorescence of 160 tumor sites from 42 treatment-naive HGSOC patients. Homologous recombination-deficient (HRD)-Dup (BRCA1 mutant-like) and HRD- Del (BRCA2 mutant-like) tumors harbored increased neoantigen burden, inflammatory signaling and ongoing immunoediting, reflected in loss of HLA diversity and tumor infiltration with highly- differentiated dysfunctional CD8+ T cells. Foldback inversion (FBI, non-HRD) tumors exhibited elevated TGF{beta} signaling and immune exclusion, with predominantly naive/stem-like and memory T cells. Our findings implicate distinct immune resistance mechanisms across HGSOC subtypes which can inform future immunotherapeutic strategies. HIGHLIGHTSO_LIMulti-region, multi-modal profiling of malignant and immune cell phenotypes in ovarian cancer C_LIO_LIAnatomic site specificity is a determinant of cancer cell and intratumoral immune phenotypes C_LIO_LITumor mutational processes impact mechanisms of immune control and immune evasion C_LIO_LISpatial topology of HR-deficient tumors is defined by immune interactions absent from immune inert HR-proficient subtypes C_LI

cancer biology↗

bioRxiv2020

Chromosome-autonomous feedback downregulates meiotic DSB competence upon synaptonemal complex formation

The number of DNA double-strand breaks (DSBs) initiating meiotic recombination is elevated in Saccharomyces cerevisiae mutants that are globally defective in forming crossovers and synaptonemal complex (SC), a protein scaffold juxtaposing homologous chromosomes. These mutants thus appear to lack a negative feedback loop that inhibits DSB formation when homologs engage one another. This feedback is predicted to be chromosome autonomous, but this has not been tested. Moreover, what chromosomal process is recognized as "homolog engagement" remains unclear. To address these questions, we evaluated effects of homolog engagement defects restricted to small portions of the genome using karyotypically abnormal yeast strains with a homeologous chromosome V pair, monosomic V, or trisomy XV. We found that homolog-engagement-defective chromosomes incurred more DSBs, concomitant with prolonged retention of the DSB-promoting protein Rec114, while the rest of the genome remained unaffected. SC-deficient, crossover-proficient mutants ecm11 and gmc2 experienced increased DSB numbers diagnostic of homolog engagement defects. These findings support the hypothesis that SC formation provokes DSB protein dissociation, leading in turn to loss of a DSB competent state. Our findings show that DSB number is regulated in a chromosome-autonomous fashion and provide insight into how homeostatic DSB controls respond to aneuploidy during meiosis. bioRxiv version 2 (September 2020) We added one bioRxiv citation in the discussion section which shows increased breaks in gmc2 and ecm11 mutants. This work was done independently by Keun Kim, Miki Shinohara and colleagues. The pulse gel electrophoresis result shown in Figure 1D has been re-quantified. The conclusion remains that homeologous and monosomic chromosome V generate more DSBs compared to internally controlled chromosome III. One zip3 map included in PCA (Figure 6C) and hierarchical clustering analysis (Figure S5B) has been deleted because this particular map has not yet been published. This change does not affect the conclusion. An additional supplemental table (S5) has been added to show Spo11-oligo datasets used in this study. Figures and preprint have been reformatted. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=193 SRC="FIGDIR/small/089367v2_fig1.gif" ALT="Figure 1"> View larger version (67K): org.highwire.dtl.DTLVardef@871eaforg.highwire.dtl.DTLVardef@ef7995org.highwire.dtl.DTLVardef@176441org.highwire.dtl.DTLVardef@1b6e772_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 1.C_FLOATNO Higher DSB levels on homolog-engagement defective chromosomes. (A) Cartoons of wild-type, homeologous, monosomic and trisomic chromosome configurations. Grey lines are S. cerevisiae chromosomes and orange lines are S. pastorianus. The homeologous chrV pair rarely synapses or recombines. The trisomic chromosomes can adopt different synaptic configurations ("II+I", partner switch, and triple synapsis). (B) Representative PFGE Southern blots probed for chrIII and chrV. P, signal from parental-length DNA; W, signal in wells. Asterisk indicates an ectopic recombination product between leu2::hisG (on chrIII) and ho::hisG (on chrIV) in the monosomic strain. The other strains do not form this product because they do not have a hisG insert at ho. (C) Poisson-corrected DSB quantification of PFGE Southern blots. (D) Quantification of time-averaged DSBs on chrV relative to chrIII for wild-type, homeologous and monosomic strains. * p<0.05, unpaired t test. Error bars in C, D are mean {+/-} SD except for the monosomic strain (mean {+/-} range). C_FIG O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=167 SRC="FIGDIR/small/089367v2_fig6.gif" ALT="Figure 6"> View larger version (36K): org.highwire.dtl.DTLVardef@1152edforg.highwire.dtl.DTLVardef@1b853daorg.highwire.dtl.DTLVardef@89392dorg.highwire.dtl.DTLVardef@108d65a_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 6.C_FLOATNO gmc2{Delta} and ecm11{Delta} mutants share similar homolog-engagement signatures as zip3{Delta}. (A, B) Representative labeling of Spo11-oligo complexes in gmc2{Delta} (A) and ecm11{Delta} (B) mutants and quantification relative to wild-type cultures processed in parallel. Error bars indicate mean {+/-} SD for 3 cultures. Radiolabeled Spo11-oligo complexes are detected by autoradiography (left panel, top) and total Spo11 is detected by anti-Flag western blot (WB; left panel, bottom). The two main labeled species differ in the sizes of oligos (Neale et al. 2005). Most Spo11 protein does not end up making DSBs, so nearly all of the visible western blot signal is from free Spo11 that does not have an oligo attached (Neale et al. 2005). (C) Principal component analysis of 21 wild-type and mutant Spo11-oligo maps. (D) Loss of anticorrelation between chromosome length and DSB density in homolog-engagement-defective mutants. Each point is one chromosome. Correlation coefficients (Pearsons r) are shown. (E) Fold change of Spo11-oligo counts in different chromosomal domains. Tel, within 20 kb of telomeres; Cen, within 10 kb of centromeres; rDNA, from 60 kb leftward to 30 kb rightward of rDNA; Interstitial, all others. Horizontal dashed lines mark values assumed as no change (black) and average change (1.8-fold for zip3{Delta}, cyan; 1.7-fold for ecm11{Delta}, purple). Boxes indicate median and interquartile range; whiskers indicate the most extreme data points that are [≤] 1.5 times the interquartile range from the box; individual points are outliers C_FIG O_FIG O_LINKSMALLFIG WIDTH=174 HEIGHT=200 SRC="FIGDIR/small/089367v2_figS5.gif" ALT="Figure 5"> View larger version (35K): org.highwire.dtl.DTLVardef@130cbf6org.highwire.dtl.DTLVardef@9ebe8forg.highwire.dtl.DTLVardef@1f0a2b4org.highwire.dtl.DTLVardef@14a0fcf_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOSupplemental Figure S5.C_FLOATNO DSB patterns in gmc2{Delta} and ecm11{Delta} mutants. (A) Meiotic progression showing percentage of cells completing the first divisions (total bi- and tetra-nucleate cells, mean {+/-} SD, 3 cultures each). At least 100 cells were counted at each time point for each culture. (B) Hierarchical clustering analysis using the "Ward D2" method for 21 wild-type and mutant maps. The y-axis represents height of the cluster dendrogram. In the label for each data set, the letters A, B, C and D stand for maps generated by Xiaojing Mu, Neeman Mohibullah, Megan van Overbeek, and Xuan Zhu, respectively, and the numerals distinguish biological replicate maps. (C) Regional variation in response to zip3{Delta} and ecm11{Delta} mutations for chromosome XII. Lines, local regression (loess) based on Spo11-oligo density changes in 5 kb-bins; green circle, centromere; rDNA location is labeled. (D, E) Fold change of Spo11-oligo densities for zip3{Delta} and ecm11{Delta} compared to wild type in pericentric (D) and telomere-proximal (E) regions. Lines, local regression (loess) for data in 5 kb-bins averaged across 32 chromosome arms; dashed line, no change in black and genome average change in cyan (1.8-fold, zip3{Delta}) and purple (1.7-fold, ecm11{Delta}); yellow shading, zones suppressed for DSB formation as previously defined (Pan etal. 2011). C_FIG O_TBL View this table: org.highwire.dtl.DTLVardef@15408baorg.highwire.dtl.DTLVardef@9171aorg.highwire.dtl.DTLVardef@cbabfaorg.highwire.dtl.DTLVardef@187d594org.highwire.dtl.DTLVardef@d6eb68_HPS_FORMAT_FIGEXP M_TBL O_FLOATNOSupplemental Table S5.C_FLOATNO O_TABLECAPTIONSpo11-oligo datasets used in this study C_TABLECAPTION C_TBL

genetics↗