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Biology subjects

Saeed, K.

Publications and source records attributed to Saeed, K..

2 recordsLinked to original sources

Single-cell functional genomics of natural killer cell evasion in blood cancers

Natural killer (NK) cells are emerging as a promising therapeutic option in cancer. To better understand how cancer cells evade NK cells, we studied interacting NK and blood cancer cells using single-cell and genome-scale functional genomics screens. At single-cell resolution, interaction of NK and cancer cells induced distinct activation states in both cell types depending on the cancer cell lineage and molecular phenotype, ranging from more sensitive myeloid to more resistant B-lymphoid cancers. CRISPR screens uncovered cancer cell-intrinsic genes driving sensitivity and resistance, including antigen presentation and death receptor signaling mediators, adhesion molecules, protein fucosylation genes, and transcriptional regulators. CRISPR screens with a single-cell transcriptomic readout revealed how these cancer cell genes influenced the gene expression landscape of both cell types, including regulation of activation states in both cancer and NK cells by IFN{gamma} signaling. Our findings provide a resource for rational design of NK cell-based therapies in blood cancers. HIGHLIGHTSO_LITranscriptomic states of interacting NK cells and cancer cells depend on cancer cell lineage C_LIO_LIMolecular correlates of increased sensitivity of myeloid compared to B-lymphoid cancers include activating receptor ligands NCR3LG1, PVR, and ULBP1 C_LIO_LINew regulators of NK cell resistance from 12 genome-scale CRISPR screens include blood cancer-specific regulators SELPLG, SPN, and MYB C_LIO_LISingle-cell transcriptomics CRISPR screens targeting 65 genome-wide screen hits identify MHC-I, IFNy, and NF-{kappa}B regulation as underlying mechanisms C_LI

immunology↗

Tracing back primed resistance in cancer via sister cells

Exploring non-genetic evolution of cell states during cancer treatments has become attainable by recent advances in lineage-tracing methods. However, transcriptional changes that drive cells into resistant fates may be subtle, necessitating high resolution analysis. We developed ReSisTrace that uses shared transcriptomic features of synchronised sister cells to predict the states that prime treatment resistance. We applied ReSisTrace in ovarian cancer cells perturbed with olaparib, carboplatin or natural killer (NK) cells. The pre-resistant phenotypes were defined by cell cycle and proteostatic features, reflecting the traits enriched in the upcoming subclonal selection. Furthermore, DNA repair deficiency rendered cells susceptible to both DNA damaging agents and NK killing in a context-dependent manner. Finally, we leveraged the pre-resistance profiles to predict and validate small molecules driving cells to sensitive states prior to treatment. In summary, ReSisTrace resolves pre-existing transcriptional features of treatment vulnerability, facilitating both molecular patient stratification and discovery of synergistic pre-sensitizing therapies.

cancer biology↗