Haseeb Rahman
A5055641836- Renal cell carcinoma treatment
- Cancer Genomics and Diagnostics
- Acute Myeloid Leukemia Research
- Single-cell and spatial transcriptomics
- Genetic factors in colorectal cancer
- TGF-β signaling in diseases
- Chemical Reactions and Isotopes
- Myeloproliferative Neoplasms: Diagnosis and Treatment
- Genomics and Phylogenetic Studies
- Protein Degradation and Inhibitors
- Chronic Myeloid Leukemia Treatments
- Hematopoietic Stem Cell Transplantation
- Oral and Maxillofacial Pathology
- Cancer Cells and Metastasis
- Ferroptosis and cancer prognosis
The Francis Crick Institute
2025
MRC Weatherall Institute of Molecular Medicine
2020-2023
Medical Research Council
2020-2023
National Institute for Health Research
2023
University of Oxford
2020-2023
Oxford BioMedica (United Kingdom)
2020-2022
Oxford Brookes University
2017
Abstract Understanding the genetic and nongenetic determinants of tumor protein 53 ( TP53 ) - mutation-driven clonal evolution subsequent transformation is a crucial step toward design rational therapeutic strategies. Here we carry out allelic resolution single-cell multi-omic analysis hematopoietic stem/progenitor cells (HSPCs) from patients with myeloproliferative neoplasm who transform to TP53- mutant secondary acute myeloid leukemia (sAML). All showed dominant ‘multihit’ HSPC clones at...
Abstract Although the key aspects of genetic evolution and their clinical implications in clear-cell renal cell carcinoma (ccRCC) are well documented, how features coevolve with phenotype tumor microenvironment (TME) remains elusive. Here, through joint genomic–transcriptomic analysis 243 samples from 79 patients recruited to TRACERx Renal study, we identify pervasive nongenetic intratumor heterogeneity, over 40% not attributable alterations. By integrating transcriptomes phylogenetic...
Abstract The functional role of bone morphogenetic protein ( BMP ) signalling in colorectal cancer CRC is poorly defined, with contradictory results cell line models reflecting the inherent difficulties assessing a pathway that context‐dependent and subject to genetic constraints. By transcriptional response diploid human colonic epithelial ligand stimulation, we generated prognostic signature, which was applied multiple datasets investigate heterogeneity across molecular subtypes. We linked...
<p>Heterogeneity of the TME in ccRCC. <b>A,</b> Association cell abundance estimates from consensus and evolutionary trajectory tumor which sample is taken (in 171 primary samples). Comparisons are one against all using a linear mixed-effects model to control for inclusion multiple samples same patient. <i>P</i>-values corrected hypothesis testing by Benjamini–Hochberg (FDR). Significant associations (FDR < 0.05) highlighted with thicker borders. Negative...
<p>Evaluation of the potential to reconstruct TCR repertiore from bulk RNA-Sequencing data</p>
<p>Transcriptional evolution mirrors clonal structure and follows recurrent trends. <b>A,</b> Transcriptional distance between primary tumor samples other or adjacent normal kidney (12 patients with both available primary–primary primary–normal pairs). <b>B,</b> Proportion of genes significantly associated changes in expression normal–primary pairs using a gene-specific linear regression framework (see “Methods”). <b>C,</b> matched metastases (seven...
<p>Greater overall HERV expression, strongly associated with VHL loss of function, correlates longer progression-free survival in ccRCC. <b>A</b>, Distribution TRACERx Renal (<i>n</i> = 243 samples) the median expression across 615 transcripts overlapping annotated retroelements (HERVs and LTRs) by sample genotype. Boxes extend from lower to upper quartiles, line inside box representing median, whiskers indicating data within 1.5 times IQR quartiles....
<p>All Supplementary Figures are provided in PDF format with the corresponding legend after each figure. Figure 1. Genetic and clinical composition of TRACERx Renal cohort 2. Comparison between transcriptional inter intratumour heterogeneity 3. Representation I-TED to measure robustness analysis 4. Transcriptional ITH is not associated poorer outcomes ccRCC 5. Subclonal 9p loss subclonal somatic copy-number alteration greatest association 6. Variance explained by major clinico-genomic...
<p>Transcriptional inter- and intratumor heterogeneity is pervasive in TRACERx Renal. <b>A,</b> UMAP visualizing the transcriptional variation across 231 tumor samples (gray points). Samples from patients K390, K243, K153 are highlighted to illustrate varied levels of ITH distinct patients. For patient K153, we highlight phylogenetic tree branch containing clones observed adjacent sample points. <b>B,</b> Schematic representation calculation distance between two...
<div>Abstract<p>Although the key aspects of genetic evolution and their clinical implications in clear-cell renal cell carcinoma (ccRCC) are well documented, how features coevolve with phenotype tumor microenvironment (TME) remains elusive. Here, through joint genomic–transcriptomic analysis 243 samples from 79 patients recruited to TRACERx Renal study, we identify pervasive nongenetic intratumor heterogeneity, over 40% not attributable alterations. By integrating transcriptomes...
<p>Inputting information from previous TRACERx Renal studies</p>
<p>Spatial diversity of the TCR repertoire suggests heritable nature antigenic source in ccRCC. <b>A,</b> and BCR similarity between pairs samples across 60 TRACERx Renal patients with at least two regions sampled. Dark purple diamonds represent median (per-patient estimate TCR/BCR similarity). Pale squares minimum maximum similarities; blue points rest sample pairs, when available. Patients both plots are ordered by increasing similarity. <b>B,</b> Kaplan–Meier...
<p>Canonical ccRCC subclonal drivers and aneuploidy burden drive specific changes to the tumor transcriptome. <b>A,</b> Illustration of procedure analyze transcriptional association a copy number alteration without confounding effect patient-specific factors followed in this study. <b>B,</b> Association 50 different hallmark signatures with 9p 14q loss ccRCC. FDR was calculated by correcting <i>P</i>-values obtained via paired Wilcoxon tests...
Single-cell RNA-sequencing technologies are ideally placed to resolve intratumoral heterogeneity. However, the lack of coverage across key mutation hotspots has precluded correlation genetic and transcriptional readouts from same single cell. To overcome this, we developed TARGET-seq, a protocol for TARGETed high-sensitivity single-cell mutational analysis with extremely low allelic dropout rates, parallel RNA SEQuencing, cell-surface proteomics. Here, present detailed step-by-step including...