Sherman Morton Weissman

Chronic myeloid leukemia (CML) is a myeloproliferative disease characterized by a unique BCR-ABL fusion gene. Tyrosine kinase inhibitors (TKIs) were developed to target the BCR-ABL oncoprotein, inhibiting its abnormal kinase activity. TKI treatments have significantly improved CML patient outcomes. However, the patients can develop drug resistance and relapse after therapy discontinues largely due to intratumor heterogeneity. It is critical to understand the differences in therapeutic responses among subpopulations of cells. Single-cell RNA sequencing measures the transcriptome of individual cells, allowing us to differentiate and analyze individual cell populations. Here, we integrated a single-cell RNA sequencing profile of CML stem cells and network analysis to decipher the mechanisms of distinct TKI responses. Compared to normal hematopoietic stem cells, a set of genes that were concordantly differentially expressed in various types of stem cells of CML patients was revealed. Further transcription regulatory network analysis found that most of these genes were directly controlled by one or more transcript factors and the genes have more regulators in the cells of the patients who responded to the treatment. The molecular markers including a known drug-resistance gene and novel gene signatures for treatment response were also identified. Moreover, we combined protein–protein interaction network construction with a cancer drug database and uncovered the drugs that target the marker genes directly or indirectly via the protein interactions. The gene signatures and their interacted proteins identified by this work can be used for treatment response prediction and lead to new strategies for drug resistance monitoring and prevention. Our single-cell-based findings offered novel insights into the mechanisms underlying the therapeutic response of CML.

Sherman Morton Weissman; Precision medicine: a few thoughts from 2022, Precision Clinical Medicine, , pbac003, https://doi.org/10.1093/pcmedi/pbac003

Cancer stem cells (CSCs) presumably contribute to tumor progression and drug resistance, yet their definitive features have remained elusive. Here, simultaneous measurement of telomere length and transcriptome in the same cells enables systematic assessment of CSCs in primary colorectal cancer (CRC). The in‐depth transcriptome profiled by SMART‐seq2 is independently validated by high‐throughput scRNA‐seq using 10 × Genomics. It is found that rare CSCs exist in dormant state and display plasticity toward cancer epithelial cells (EPCs) that essentially are presumptive tumor‐initiating cells (TICs), while both retaining the prominent signaling pathways including WNT, TGF‐β, and HIPPO/YAP. Moreover, CSCs exhibit chromosome copy number variation (CNV) pattern resembling cancer EPCs but distinct from normal stem cells, suggesting the phylogenetic relationship between CSCs and cancer EPCs. Notably, CSCs maintain shorter telomeres and possess minimal telomerase activity consistent with their nonproliferative nature, unlike cancer EPCs. Additionally, the specific signature of CSCs particularly NOTUM, SMOC2, BAMBI, PHLDA1, and TNFRSF19 correlates with the prognosis of CRC. These findings characterize the heterogeneity of CSCs and their linkage to cancer EPCs/TICs, some of which are conventionally regarded as CSCs.

It was highlighted that the original article [1] contained a typesetting error in the last name of Allon Canaan. This was incorrectly captured as Allon Canaann in the original article which has since been updated.

Single-cell RNA sequencing (scRNA-seq) technology provides an effective way to study cell heterogeneity. However, due to the low capture efficiency and stochastic gene expression, scRNA-seq data often contains a high percentage of missing values. It has been showed that the missing rate can reach approximately 30% even after noise reduction. To accurately recover missing values in scRNA-seq data, we need to know where the missing data is; how much data is missing; and what are the values of these data. To solve these three problems, we propose a novel model with a hybrid machine learning method, namely, missing imputation for single-cell RNA-seq (MISC). To solve the first problem, we transformed it to a binary classification problem on the RNA-seq expression matrix. Then, for the second problem, we searched for the intersection of the classification results, zero-inflated model and false negative model results. Finally, we used the regression model to recover the data in the missing elements. We compared the raw data without imputation, the mean-smooth neighbor cell trajectory, MISC on chronic myeloid leukemia data (CML), the primary somatosensory cortex and the hippocampal CA1 region of mouse brain cells. On the CML data, MISC discovered a trajectory branch from the CP-CML to the BC-CML, which provides direct evidence of evolution from CP to BC stem cells. On the mouse brain data, MISC clearly divides the pyramidal CA1 into different branches, and it is direct evidence of pyramidal CA1 in the subpopulations. In the meantime, with MISC, the oligodendrocyte cells became an independent group with an apparent boundary. Our results showed that the MISC model improved the cell type classification and could be instrumental to study cellular heterogeneity. Overall, MISC is a robust missing data imputation model for single-cell RNA-seq data.

The development of low-cost high-throughput nucleic acid sequencing has had transformative effects on multiple aspects of biological science and medicine. Prominent among these is an enormous impact on human genetics. We are close to a time when the birthright of each individual, at least in more prosperous countries, will be a complete knowledge of their genomic DNA sequence. Every patient with a diagnosed malignancy may well have a genomic sequence of cells from their neoplasm. A challenge for modern medicine is to convert this information into guides to predict the incidence and consequences of disease with more individual precision than previously possible, and to use these data to design optimal approaches for therapy of disorders including cancer and autoimmune diseases. Glimmerings of such an approach have been around for decades, in pharmacogenetics.¹ Single mutations in a number of genes have been associated with abnormal reactions to drugs, inherited as simple Mendelian disorders. Similarly, specific single gene mutations have been known for some time to predispose to serious reactions to environmental influences. Perhaps the most striking of these are mutations that predispose to development of sudden, potentially fatal, cardiac arrhythmias.²

Background Large copy number variants (CNVs) in the human genome are strongly associated with common neurodevelopmental, neuropsychiatric disorders such as schizophrenia and autism. Using Hi-C analysis of long-range chromosome interactions, including haplotype-specific Hi-C analysis, and ChIP-Seq analysis of regulatory histone marks, we studied the epigenomic effects of the prominent heterozygous large deletion CNV on chromosome 22q11.2 and also replicated a subset of the findings for the heterozygous large deletion CNV on chromosome 1q21.1. Results There are local and global gene expression changes as well as pronounced and multilayered effects on chromatin states, chromosome folding and topological domains of the chromatin, that emanate from the large CNV locus. Regulatory histone marks are altered in the deletion flanking regions, and in opposing directions for activating and repressing marks. Histone marks are changed along chromosome 22q and genome wide. Chromosome interaction patterns are weakened within the deletion boundaries and strengthened between the deletion flanking regions. The long-range folding contacts between the telomeric end of chromosome 22q and the distal deletion-flanking region are increased. On the chromosome 22q with deletion the topological domain spanning the CNV boundaries is deleted in its entirety while neighboring domains interact more intensely with each other. Finally, there is a widespread and complex effect on chromosome interactions genome-wide, i.e. involving all other autosomes, with some of the effect directly tied to the deletion region on 22q11.2. Conclusions These findings suggest novel principles of how such large genomic deletions can alter nuclear organization and affect genomic molecular activity.

Molecular changes underlying stem cell differentiation are of fundamental interest. scRNA-seq on murine hematopoietic stem cells (HSC) and their progeny MPP1 separated the cells into 3 main clusters with distinct features: active, quiescent, and an un-characterized cluster. Induction of anemia resulted in mobilization of the quiescent to the active cluster and of the early to later stage of cell cycle, with marked increase in expression of certain transcription factors (TFs) while maintaining expression of interferon response genes. Cells with surface markers of long term HSC increased the expression of a group of TFs expressed highly in normal cycling MPP1 cells. However, at least Id1 and Hes1 were significantly activated in both HSC and MPP1 cells in anemic mice. Lineage-specific genes were differently expressed between cells, and correlated with the cell cycle stages with a specific augmentation of erythroid related genes in the G2/M phase. Most lineage specific TFs were stochastically expressed in the early precursor cells, but a few, such as Klf1, were detected only at very low levels in few precursor cells. The activation of these factors may correlate with stages of differentiation. This study reveals effects of cell cycle progression on the expression of lineage specific genes in precursor cells, and suggests that hematopoietic stress changes the balance of renewal and differentiation in these homeostatic cells.

Tumor genomes evolve through a selection of mutations. These mutations may complement each other to promote tumorigenesis. To better understand the functional interactions of different processes in cancer, we studied mutation data of a set of tumors and identified significantly co-mutated pathways. Fisher’s exact test is a standard approach that can be used to assess the significance of the joint dysregulation of pathways pairs across a patient population. We developed a robust test to identify co-occurrence using DNA mutations, which overcomes deficiencies of the Fisher’s exact test by taking into account the large variability in overall mutation load and sequencing depth. Applying our method to a study of six common cancer types, we identify enrichment of co-mutated signal transduction pathways such as IP3 synthesis and PI3K and pairs of co-mutated pathways involving other processes such as immunity and development. We observed enrichment of clonal co-mutation of the proteasome and apoptosis pathways in colorectal cancer, which suggests potential mechanisms for immune evasion.