Clonal Evolution of Acute Myeloid Leukemia Revealed by High-Throughput Single-Cell Genomics, bioRxiv, 2020-02-08

SummaryOne of the pervasive features of cancer is the diversity of mutations found in malignant cells within the same tumor; a phenomenon called clonal diversity or intratumor heterogeneity. Clonal diversity allows tumors to adapt to the selective pressure of treatment and likely contributes to the development of treatment resistance and cancer recurrence. Thus, the ability to precisely delineate the clonal substructure of a tumor, including the evolutionary history of its development and the co-occurrence of its mutations, is necessary to understand and overcome treatment resistance. However, DNA sequencing of bulk tumor samples cannot accurately resolve complex clonal architectures. Here, we performed high-throughput single-cell DNA sequencing to quantitatively assess the clonal architecture of acute myeloid leukemia (AML). We sequenced a total of 556,951 cells from 77 patients with AML for 19 genes known to be recurrently mutated in AML. The data revealed clonal relationship among AML driver mutations and identified mutations that often co-occurred (e.g., NPM1FLT3-ITD, DNMT3ANPM1, SRSF2IDH2, and WT1FLT3-ITD) and those that were mutually exclusive (e.g., NRASKRAS, FLT3-D835ITD, and IDH1IDH2) at single-cell resolution. Reconstruction of the tumor phylogeny uncovered history of tumor development that is characterized by linear and branching clonal evolution patterns with latter involving functional convergence of separately evolved clones. Analysis of longitudinal samples revealed remodeling of clonal architecture in response to therapeutic pressure that is driven by clonal selection. Furthermore, in this AML cohort, higher clonal diversity (≥4 subclones) was associated with significantly worse overall survival. These data portray clonal relationship, architecture, and evolution of AML driver genes with unprecedented resolution, and illuminate the role of clonal diversity in therapeutic resistance, relapse and clinical outcome in AML.

biorxiv cancer-biology 0-100-users 2020

Non-oncology drugs are a source of previously unappreciated anti-cancer activity, bioRxiv, 2019-08-09

ABSTRACTAnti-cancer uses of non-oncology drugs have been found on occasion, but such discoveries have been serendipitous and rare. We sought to create a public resource containing the growth inhibitory activity of 4,518 drugs tested across 578 human cancer cell lines. To accomplish this, we used PRISM, which involves drug treatment of molecularly barcoded cell lines in pools. Relative barcode abundance following treatment thus reflects cell line viability. We found that an unexpectedly large number of non-oncology drugs selectively inhibited subsets of cancer cell lines. Moreover, the killing activity of the majority of these drugs was predictable based on the molecular features of the cell lines. Follow-up of several of these compounds revealed novel mechanisms. For example, compounds that kill by inducing PDE3A-SLFN12 complex formation; vanadium-containing compounds whose killing is dependent on the sulfate transporter SLC26A2; the alcohol dependence drug disulfiram, which kills cells with low expression of metallothioneins; and the anti-inflammatory drug tepoxalin, whose killing is dependent on high expression of the multi-drug resistance gene ABCB1. These results illustrate the potential of the PRISM drug repurposing resource as a starting point for new oncology therapeutic development. The resource is available at <jatsext-link xmlnsxlink=httpwww.w3.org1999xlink ext-link-type=uri xlinkhref=httpsdepmap.org>httpsdepmap.org<jatsext-link>.

biorxiv cancer-biology 100-200-users 2019

Deep learning detects virus presence in cancer histology, bioRxiv, 2019-07-06

AbstractOncogenic viruses like human papilloma virus (HPV) or Epstein Barr virus (EBV) are a major cause of human cancer. Viral oncogenesis has a direct impact on treatment decisions because virus-associated tumors can demand a lower intensity of chemotherapy and radiation or can be more susceptible to immune check-point inhibition. However, molecular tests for HPV and EBV are not ubiquitously available.We hypothesized that the histopathological features of virus-driven and non-virus driven cancers are sufficiently different to be detectable by artificial intelligence (AI) through deep learning-based analysis of images from routine hematoxylin and eosin (HE) stained slides. We show that deep transfer learning can predict presence of HPV in head and neck cancer with a patient-level 3-fold cross validated area-under-the-curve (AUC) of 0.89 [0.82; 0.94]. The same workflow was used for Epstein-Barr virus (EBV) driven gastric cancer achieving a cross-validated AUC of 0.80 [0.70; 0.92] and a similar performance in external validation sets. Reverse-engineering our deep neural networks, we show that the key morphological features can be made understandable to humans.This workflow could enable a fast and low-cost method to identify virus-induced cancer in clinical trials or clinical routine. At the same time, our approach for feature visualization allows pathologists to look into the black box of deep learning, enabling them to check the plausibility of computer-based image classification.

biorxiv cancer-biology 0-100-users 2019

Serum Flt3 ligand is biomarker of progenitor cell mass and prognosis in acute myeloid leukemia, bioRxiv, 2019-03-25

AbstractFms-like tyrosine kinase 3 (Flt3) is a hematopoietic growth factor receptor expressed on lymphomyeloid progenitors and frequently, by AML blasts. Its ligand, Flt3L, has non-hematopoietic and lymphoid origins, is detectable during homeostasis and increases to high levels in states of hypoplasia due to genetic defects or treatment with cytoreductive agents. Measurement of Flt3L by ELISA reveals that Flt3+AML, is associated with depletion of Flt3L to undetectable levels. After induction chemotherapy, Flt3L is restored in patients entering CR, but remains depressed in those with refractory disease. Weekly sampling reveals marked differences in the kinetics of Flt3L response during the first 6 weeks of treatment, proportionate to the clearance of blasts and cellularity of the BM. In the UK NCRI AML17 trial, Flt3L was measured at day 26 in a subgroup of 135 patients with Flt3 mutation randomized to the tyrosine kinase inhibitor lestaurtinib. In these patients, attainment of CR was associated with higher Flt3L at day 26 (Mann-Whitney p &lt; 0.0001). Day 26 Flt3L was also associated with survival Flt3L ≤ 291pgml was associated with inferior event-free survival; and, Flt3L &gt;1185pgml was associated with higher overall survival (p = 0.0119). Serial measurement of Flt3L in patients who had received a hematopoietic stem cell transplant for AML further illustrated the potential value of declining Flt3L to identify relapse. Together these observations suggest that measurement of Flt3L provides a non-invasive estimate of progenitor cell mass in most patients with AML, with the potential to inform clinical decisions.Graphical abstract<jatsfig id=ufig1 position=float fig-type=figure orientation=portrait><jatsgraphic xmlnsxlink=httpwww.w3.org1999xlink xlinkhref=588319_ufig1 position=float orientation=portrait >

biorxiv cancer-biology 100-200-users 2019

 

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