KMT2A-rearranged (KMT2A-r) infant ALL is a devastating malignancy with a dismal outcome, and younger age at diagnosis is associated with increased risk of relapse. To discover age-specific differences and critical drivers that mediate poor outcome in KMT2A-r ALL, we subjected KMT2A-r leukemias and normal hematopoietic cells from patients of different ages to single cell multi-omics analyses. We uncovered the following critical new insights: leukemia cells from patients younger than 6 months have significantly increased lineage plasticity. Steroid response pathways are downregulated in the most immature blasts from younger patients. We identify a hematopoietic stem and progenitor-like (HSPC-like) population in the blood of younger patients that contains leukemic blasts and form an immunosuppressive signaling circuit with cytotoxic lymphocytes. These observations offer a compelling explanation for the ability of leukemias in young patients to evade chemotherapy and immune mediated control. Our analysis also revealed pre-existing lymphomyeloid primed progenitors and myeloid blasts at initial diagnosis of B-ALL. Tracking of leukemic clones in two patients whose leukemia underwent a lineage switch documented the evolution of such clones into frank AML. These findings provide critical insights into KMT2A-r ALL and have clinical implications for molecularly targeted and immunotherapy approaches. Beyond infant ALL, our study demonstrates the power of single cell multi-omics to detect tumor intrinsic and extrinsic factors affecting rare but critical subpopulations within a malignant population that ultimately determines patient outcome.
Accurate prognosis of patients with cancer is important for the stratification of patients, the optimization of treatment strategies, and the design of clinical trials. Both clinical features and molecular data can be used for this purpose, for instance, to predict the survival of patients censored at specific time points. Multi-omics data, including genome-wide gene expression, methylation, protein expression, copy number alteration, and somatic mutation data, are becoming increasingly common in cancer studies. To harness the rich information in multi-omics data, we developed GDP (Group lass regularized Deep learning for cancer Prognosis), a computational tool for survival prediction using both clinical and multi-omics data. GDP integrated a deep learning framework and Cox proportional hazard model (CPH) together, and applied group lasso regularization to incorporate gene-level group prior knowledge into the model training process. We evaluated its performance in both simulated and real data from The Cancer Genome Atlas (TCGA) project. In simulated data, our results supported the importance of group prior information in the regularization of the model. Compared to the standard lasso regularization, we showed that group lasso achieved higher prediction accuracy when the group prior knowledge was provided. We also found that GDP performed better than CPH for complex survival data. Furthermore, analysis on real data demonstrated that GDP performed favorably against other methods in several cancers with large-scale omics data sets, such as glioblastoma multiforme, kidney renal clear cell carcinoma, and bladder urothelial carcinoma. In summary, we demonstrated that GDP is a powerful tool for prognosis of patients with cancer, especially when large-scale molecular features are available.
Despite recent improvements in basecalling accuracy, nanopore sequencing still has higher error rates on short-tandem repeats (STRs). Instead of using basecalled reads, we developed DeepRepeat which converts ionic current signals into red-green-blue channels, thus transforming the repeat detection problem into an image recognition problem. DeepRepeat identifies and accurately quantifies telomeric repeats in the CHM13 cell line and achieves higher accuracy in quantifying repeats in long STRs than competing methods. We also evaluate DeepRepeat on genome-wide or candidate region datasets from seven different sources. In summary, DeepRepeat enables accurate quantification of long STRs and complements existing methods relying on basecalled reads.
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