Biology subjects

Sachs, Z.

Publications and source records attributed to Sachs, Z..

3 recordsLinked to original sources

bioRxiv2026

Single cell Correlation Analysis (SCA): Identifying self-renewing subpopulation of human acute myeloid leukemia stem cells using single cell RNA sequencing analysis

Leukemia stem cells (LSCs), a rare and self-renewing subpopulation, drive Acute myeloid leukemia (AML) relapse and therapy resistance. While single-cell gene expression profiling (scGEP) offers high-resolution insights into LSC biology, existing computational tools rely on relative and arbitrary similarity metrics. This limits their ability to definitively identify true biological cell identities or compare rare cell populations across independent datasets. To overcome these limitations, we developed Single cell Correlation Analysis (SCA), a novel computational method that utilizes a permutation-based false discovery rate (FDR) framework and common background datasets to establish standardized, statistically rigorous thresholds of similarity. We applied SCA to query human AML scRNA-seq datasets using an experimentally validated murine scGEP of LSC self-renewal as our reference. SCA demonstrated superior specificity and precision, maintaining low false positive rates compared to existing reference-based annotation tools. Using SCA, we successfully identified cells expressing a conserved self-renewal program (scaLSC-SR) in both adult and pediatric human AML samples. These scaLSC-SR cells share classical LSC immunophenotypic markers (e.g., CD34, CD96, CD200) and exhibit transcriptomic hallmarks of LSC biology, including evasion of apoptosis and immune responses. Furthermore, this self-renewal program is significantly enriched in poor-risk genetic subtypes, specifically TP53 and NRAS mutated AML. Finally, we derived a 28-gene signature (LSC-SR28) from human AML patients that accurately captures LSC stemness and is highly prognostic across multiple independent clinical cohorts. SCA provides a robust, statistically principled framework for identifying rare and biologically meaningful cell populations across heterogeneous single-cell datasets. By successfully identifying a functional self-renewal profile to human AML, SCA reveals conserved, clinically relevant LSC populations that drive relapse and therapy resistance across different age groups.

cancer biology↗

bioRxiv2023

Machine Learning Investigation Of Gene Expression Datasets Reveals TP53 Mutant-like AML With Wild Type TP53 And Poor Prognosis

Acute myeloid leukemia (AML) with TP53 mutations (TP53Mut) has poor clinical outcomes with 1-year survival rates of less than 10%. We investigated whether this AML subtype harbors a distinct gene expression profiling (GEP), what this GEP reveals about TP53Mut AML pathophysiology, and whether this GEP is prognostic in TP53 wild type (TP53WT) AML. We applied a supervised machine-learning approach to assess whether a unique TP53Mut GEP could be detected. Using the BEAT-AML dataset, we randomly divided the samples into training and testing datasets, while the TCGA dataset was reserved as a validation dataset. We trained a ridge regression machine learning model to classify TP53Mut and TP53WT cases. This model was highly accurate in distinguishing TP53Mut versus TP53WT cases in both the test and validation data sets. Additionally, we noted a cohort of TP53WT samples with high ridge regression scores and poor overall survival, suggesting share clinical and GEP features with TP53Mut AML. We defined these TP53WT samples as TP53 mutant-like (TP53Mut-like) AMLs. We trained a second ridge regression model to specifically detect TP53Mut-like samples in the BEAT AML dataset and found that TCGA data also harbors TP53Mut-like samples. The TP53Mut-like samples in the TCGA also have a worse OS rate than TP53WT cases. Using drug sensitivity data from 122 small molecules in the BEAT AML dataset, we found TP53Mut-like AMLs have distinct drug sensitivity patterns compared to TP53WT. Finally, we identified a 25 gene signature that can identify TP53Mut-like cases. This signature could be used clinically to identify this novel subset of poor-prognosis AML.

cancer biology↗

bioRxiv2018

Multi-omic profiling of TKI resistant K562 cells suggests metabolic reprogramming to promote cell survival

Resistance to chemotherapy can occur through a wide variety of mechanisms. Resistance to tyrosine kinase inhibitors (TKIs) often arises from kinase mutations-however, \"off-target\" resistance occurs but is poorly understood. Previously, we established cell line resistance models for three TKIs used in chronic myeloid leukemia treatment, and found that resistance was not attributed entirely to failure of kinase inhibition. Here, we performed global, integrated proteomic and transcriptomic profiling of these cell lines to describe mechanisms of resistance at the protein and gene expression level. We used whole transcriptome sequencing and SWATH-based data-independent acquisition mass spectrometry (DIA-MS), which does not require isotopic labels and provides quantitative measurements of proteins in a comprehensive, unbiased fashion. The proteomic and transcriptional data were correlated to generate an integrated understanding of the gene expression and protein alterations associated with TKI resistance. We defined mechanisms of resistance and two novel markers, CA1 and alpha-synuclein, that were common to all TKIs tested. Resistance to all of the TKIs was associated with oxidative stress responses, hypoxia signatures, and apparent metabolic reprogramming of the cells. Metabolite profiling and glucose-dependence experiments showed that resistant cells had routed their metabolism through glycolysis (particularly through the pentose phosphate pathway) and exhibited disruptions in mitochondrial metabolism. These experiments are the first to report a global, integrated proteomic, transcriptomic and metabolic analysis of TKI resistance. These data suggest that although the mechanisms are complex, targeting metabolic pathways along with TKI treatment may overcome pan-TKI resistance.\n\nKey PointsO_LIAlterations to metabolism are a common feature of target-mutation-independent resistance in CML cells across multiple clinically relevant TKIs.\nC_LIO_LICarbonic anhydrase 1 (CA1) and a-synuclein (SNCA) are novel markers of metabolic reprogramming in TKI resistant CML cells.\nC_LI

cancer biology↗