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Background Elucidation of the genetic mechanisms underlying treatment response to standard induction chemotherapy in AML patients is warranted, in order to aid in risk-adapted treatment decisions as novel treatments are emerging. In this pilot study, we explored the treatment-induced expression patterns in a small cohort of AML patients by analyzing differential gene expression (DGE) over the first 2 days of induction chemotherapy. Methods Blood samples were collected from ten AML patients at baseline (before treatment initiation) and during the first 2 days of treatment (Day 1; approximately 24 h, and Day 2; approximately 48 h after treatment initiation, respectively) and RNA was extracted for subsequent RNA sequencing. DGE between time points were assessed by pairwise analysis using the R package edgeR version 3.18.1 in all patients as well as in relation to treatment response (complete remission, CR, vs non-complete remission, nCR). Ingenuity Pathway Analysis (Qiagen) software was used for pathway analysis and visualization. Results After initial data quality control, two patients were excluded from further analysis, resulting in a final cohort of eight patients with data from all three timepoints. DGE analysis demonstrated activation of pathways with genes directly or indirectly associated with NF-κB signaling. Significant activation of the NF-κB pathway was seen in 50% of the patients 2 days after treatment start, while iNOS pathway effects could be identified already after 1 day. nCR patients displayed activation of pathways associated with cell cycle progression, oncogenesis and anti-apoptotic behavior, including the STAT3 pathway and Salvage pathways of pyrimidine ribonucleotides. Notably, a significant induction of cytidine deaminase, an enzyme responsible for the deamination of Ara-C, could be observed between baseline and Day 2 in the nCR patients but not in patients achieving CR. Conclusions In conclusion, we show that time-course analysis of gene expression represents a feasible approach to identify relevant pathways affected by standard induction chemotherapy in AML patients. This poses as a potential method for elucidating new drug targets and biomarkers for categorizing disease aggressiveness and evaluating treatment response. However, more studies on larger cohorts are warranted to elucidate the transcriptional basis for drug response.

Tyrosine kinase inhibitor (TKI) treatment has dramatically improved the survival of chronic myeloid leukemia (CML) patients, but measurable residual disease typically persists. To more effectively eradicate leukemia cells, simultaneous targeting of BCR-ABL1 and additional CML-related survival proteins has been proposed. Notably, several highly specific myeloid cell leukemia 1 (MCL1) inhibitors have recently entered clinical trials for various hematologic malignancies, although not for CML, reflecting the insensitivity of CML cell lines to single MCL1 inhibition. Here, we show that combining TKI (imatinib, nilotinib, dasatinib, or asciminib) treatment with the small-molecule MCL1 inhibitor S63845 exerted strong synergistic antiviability and proapoptotic effects on CML lines and CD34+ stem/progenitor cells isolated from untreated CML patients in chronic phase. Using wild-type BCR-ABL1-harboring CML lines and their T315I-mutated sublines (generated by CRISPR/Cas9-mediated homologous recombination), we prove that the synergistic proapoptotic effect of the drug combination depended on TKI-mediated BCR-ABL1 inhibition, but not on TKI-related off-target mechanisms. Moreover, we demonstrate that colony formation of CML but not normal hematopoietic stem/progenitor cells became markedly reduced upon combination treatment compared to imatinib monotherapy. Our results suggest that dual targeting of MCL1 and BCR-ABL1 activity may efficiently eradicate residual CML cells without affecting normal hematopoietic stem/progenitors.

Persistent taxane-induced peripheral neuropathy (TIPN) is highly prevalent among early-stage breast cancer survivors (ESBCS) and has detrimental effect on quality of life. We leveraged logistic regression models to develop and validate polygenic prediction models to estimate the risk of persistent PN symptoms in a training cohort and validation cohort taking clinical risk factors into account. Based on 337 whole-exome sequenced ESBCS two of five prediction models for individual PN symptoms obtained AUC results above 60% when validated. Using the model for numbness in feet (35 SNVs) in the test cohort, 73% survivors were correctly predicted. For tingling in feet (55 SNVs) 70% were correctly predicted. Both models included SNVs from the ADAMTS20, APT6V0A2, CCDC88C, CYP2C8, EPHA5, NR1H3, PSKH2/APTV0D2 , and SCN10A genes. For cramps in feet , difficulty climbing stairs and difficulty opening a jar the validation was unsuccessful. Polygenic prediction models including clinical risk factors can estimate the risk of persistent taxane-induced numbness in feet and tingling in feet in ESBCS.

Non-small cell lung cancer is often diagnosed at advanced stages, and many patients are still treated with classical chemotherapy. The unselective nature of chemotherapy often results in severe myelosuppression. Previous studies showed that protein-coding mutations could not fully explain the predisposition to myelosuppression. Here, we investigate the possible role of enhancer mutations in myelosuppression susceptibility. We produced transcriptome and promoter-interaction maps (using HiCap) of three blood stem-like cell lines treated with carboplatin or gemcitabine. Taking advantage of publicly available enhancer datasets, we validated HiCap results in silico and in living cells using epigenetic CRISPR technology. We also developed a network approach for interactome analysis and detection of differentially interacting genes. Differential interaction analysis provided additional information on relevant genes and pathways for myelosuppression compared with differential gene expression analysis at the bulk level. Moreover, we showed that enhancers of differentially interacting genes are highly enriched for variants associated with differing levels of myelosuppression. Altogether, our work represents a prominent example of integrative transcriptome and gene regulatory datasets analysis for the functional annotation of noncoding mutations.

Treatments that include gemcitabine and carboplatin induce dose-limiting myelosuppression. The understanding of how human bone marrow is affected on a transcriptional level leading to the development of myelosuppression is required for the implementation of personalized treatments in the future. In this study, we treated human hematopoietic stem and progenitor cells (HSPCs) harvested from a patient with chronic myelogenous leukemia (CML) with gemcitabine/carboplatin. Thereafter, scRNA-seq was performed to distinguish transcriptional effects induced by gemcitabine/carboplatin. Gene expression was calculated and evaluated among cells within and between samples compared to untreated cells. Cell cycle analysis showed that the treatments effectively decrease cell proliferation, indicated by the proportion of cells in the G2M-phase dropping from 35% in untreated cells to 14.3% in treated cells. Clustering and t-SNE showed that cells within samples and between treated and untreated samples were affected differently. Enrichment analysis of differentially expressed genes showed that the treatments influence KEGG pathways and Gene Ontologies related to myeloid cell proliferation/differentiation, immune response, cancer, and the cell cycle. The present study shows the feasibility of using scRNA-seq and chemotherapy-treated HSPCs to find genes, pathways, and biological processes affected among and between treated and untreated cells. This indicates the possible gains of using single-cell toxicity studies for personalized medicine.

Chemotherapy-induced myelosuppression, including thrombocytopenia, is a recurrent problem during cancer treatments that may require dose alterations or cessations that could affect the antitumor effect of the treatment. To identify genetic markers associated with treatment-induced thrombocytopenia, we whole-exome sequenced 215 non-small cell lung cancer patients homogeneously treated with gemcitabine/carboplatin. The decrease in platelets (defined as nadir/baseline) was used to assess treatment-induced thrombocytopenia. Association between germline genetic variants and thrombocytopenia was analyzed at single-nucleotide variant (SNV) (based on the optimal false discovery rate, the severity of predicted consequence, and effect), gene, and pathway levels. These analyses identified 130 SNVs/INDELs and 25 genes associated with thrombocytopenia (P-value < 0.002). Twenty-three SNVs were validated in an independent genome-wide association study (GWAS). The top associations include rs34491125 in JMJD1C (P-value = 9.07 × 10−5), the validated variants rs10491684 in DOCK8 (P-value = 1.95 × 10−4), rs6118 in SERPINA5 (P-value = 5.83 × 10−4), and rs5877 in SERPINC1 (P-value = 1.07 × 10−3), and the genes CAPZA2 (P-value = 4.03 × 10−4) and SERPINC1 (P-value = 1.55 × 10−3). The SNVs in the top-scoring pathway “Factors involved in megakaryocyte development and platelet production” (P-value = 3.34 × 10−4) were used to construct weighted genetic risk score (wGRS) and logistic regression models that predict thrombocytopenia. The wGRS predict which patients are at high or low toxicity risk levels, for CTCAE (odds ratio (OR) = 22.35, P-value = 1.55 × 10−8), and decrease (OR = 66.82, P-value = 5.92 × 10−9). The logistic regression models predict CTCAE grades 3–4 (receiver operator characteristics (ROC) area under the curve (AUC) = 0.79), and large decrease (ROC AUC = 0.86). We identified and validated genetic variations within hematopoiesis-related pathways that provide a solid foundation for future studies using genetic markers for predicting chemotherapy-induced thrombocytopenia and personalizing treatments.

Gemcitabine/carboplatin chemotherapy commonly induces myelosuppression, including neutropenia, leukopenia, and thrombocytopenia. Predicting patients at risk of these adverse drug reactions (ADRs) and adjusting treatments accordingly is a long-term goal of personalized medicine. This study used whole-genome sequencing (WGS) of blood samples from 96 gemcitabine/carboplatin-treated non-small cell lung cancer (NSCLC) patients and gene network modules for predicting myelosuppression. Association of genetic variants in PLINK found 4594, 5019, and 5066 autosomal SNVs/INDELs with p  ≤ 1 × 10 −3 for neutropenia, leukopenia, and thrombocytopenia, respectively. Based on the SNVs/INDELs we identified the toxicity module, consisting of 215 unique overlapping genes inferred from MCODE-generated gene network modules of 350, 345, and 313 genes, respectively. These module genes showed enrichment for differentially expressed genes in rat bone marrow, human bone marrow, and human cell lines exposed to carboplatin and gemcitabine ( p  < 0.05). Then using 80% of the patients as training data, random LASSO reduced the number of SNVs/INDELs in the toxicity module into a feasible prediction model consisting of 62 SNVs/INDELs that accurately predict both the training and the test (remaining 20%) data with high (CTCAE 3–4) and low (CTCAE 0–1) maximal myelosuppressive toxicity completely, with the receiver-operating characteristic (ROC) area under the curve (AUC) of 100%. The present study shows how WGS, gene network modules, and random LASSO can be used to develop a feasible and tested model for predicting myelosuppressive toxicity. Although the proposed model predicts myelosuppression in this study, further evaluation in other studies is required to determine its reproducibility, usability, and clinical effect.

Cancer is a serious disease expected to be the world-leading cause of death in the 21st century. The use of harsh chemotherapies is motivated and accepted but, unfortunately, is often accompanied by severe toxicity and adverse drug reactions (ADRs). These occur because the classical chemotherapies’ common modes of action effectively kill and/or reduce the growth rate not only of tumor cells, but also of many other rapidly dividing healthy cells in the body. There are also considerable interindividual differences in ADRs, even between patients with similar cancers and disease stage treated with equal doses; some experience severe to life-threatening ADRs after one dose, leading to treatment delays, adjustments, or even discontinuation resulting in suboptimal treatment, while others remain unaffected through all treatment cycles. Being able to predict which patients are at high or low risk of ADRs, and to adjust doses accordingly before treatment, would probably decrease toxicity and patient suffering while also increasing treatment tolerability and effects. In this thesis, we have used next-generation sequencing (NGS) and bioinformatics for the prediction of myelosuppressive ADRs in lung and ovarian cancer patients treated with gemcitabine/carboplatin and paclitaxel/carboplatin.Paper I shows that ABCB1 and CYP2C8 genotypes have small effects inadequate for stratification of paclitaxel/carboplatin toxicity. This supports the transition to whole-exome sequencing (WES) and whole-genome sequencing (WGS). Papers II and IV, respectively, use WES and WGS, and demonstrate that genetic variation in or around genes involved in blood cell regulation and proliferation, or genes differentially expressed at chemotherapy exposure, can be used in polygenic prediction models for stratification of gemcitabine/carboplatininduced myelosuppression. Paper III reassuringly shows that WES and WGS are concordant and mostly yield comparable genotypes across the exome. Paper V proves that single-cell RNA sequencing of hematopoietic stem cells is a feasible method for elucidating differential transcriptional effects induced as a response to in vitro chemotherapy treatment.In conclusion, our results supports the transition to genome-wide approaches using WES, WGS, and RNA sequencing to establish polygenic models that combine effects of multiple pharmacogenetic biomarkers for predicting chemotherapy-induced ADRs. This approach could be applied to improve risk stratification and our understanding of toxicity and ADRs related to other drugs and diseases. We hope that our myelosuppression prediction models can be refined and validated to facilitate personalized treatments, leading to increased patient wellbeing and quality of life.

The efficiency of the adaptive immune system is dependent on the diverse repertoire of antigen receptors expressed on the surface of mature B lymphocytes, surface immunoglobulins (sIg). For a B lymphocyte to produce a virtually unique sIg receptor, it relies on a process of site-specific recombination to create the genes coding for this receptor. Rather than being specified in the germline, these are created through the somatic assembly of individual members of various germline gene segment families (V, D and J gene segments), a process commonly referred to as V(D)J recombination. This thesis describes studies on the control and regulation of V(D)J recombination at one of the human Ig loci, the Ig$\\lambda$ light chain locus. Chapter 2 describes a clonal cell culture system derived from a human B lymphoma cell line. This system, established from a single sIg$\\sp+$ B cell, is shown to exhibit sIg variation as a result of continuous rearrangement activity at the Ig$\\lambda$ locus. A correlation between Ig$\\lambda$ gene rearrangement, upregulated RAG expression and C$\\lambda$ germline transcription is observed. In chapter 3 the contribution of somatic hypermutation to this sIg variation is investigated and found to be undetectable. Chapter 4 shows that the process of Ig$\\lambda$ gene rearrangement is quite conservative, with an absence of N nucleotide insertions as well as a complete lack of deletions in the 5$\\sp\\prime$ end of the J$\\lambda$ gene segment, restricting junctional diversity to infrequent deletions in the 3$\\sp\\prime$ end of the V$\\lambda$ gene segment. This chapter also describes an unusual sIg receptor which lacks the majority of the IgL constant region, sIg$\\Delta$CL. Chapter 4 also provides an example of a functional V$\\lambda$ gene segment which is infrequently rearranged compared to other V$\\lambda$ gene segments. It is shown that, compared to a frequently rearranged V$\\lambda$ gene segment, this V$\\lambda$ gene segment is transcribed at significantly reduced levels in the germline. This is likely due to a mutated octamer motif in the promoter of this V$\\lambda$ gene segment. In chapter 5 this V$\\lambda$ gene segment is described in some detail and shown to belong to a previously unidentified V$\\lambda$ gene family, V$\\lambda$X.

Predation is thought to shape the macroscopic properties of animal groups, making moving groups more cohesive and coordinated. Precisely how predation has shaped individuals' fine-scale social interactions in natural populations, however, is unknown. Using high-resolution tracking data of shoaling fish (Poecilia reticulata) from populations differing in natural predation pressure, we show how predation adapts individuals' social interaction rules. Fish originating from high predation environments formed larger, more cohesive, but not more polarized groups than fish from low predation environments. Using a new approach to detect the discrete points in time when individuals decide to update their movements based on the available social cues, we determine how these collective properties emerge from individuals' microscopic social interactions. We first confirm predictions that predation shapes the attraction–repulsion dynamic of these fish, reducing the critical distance at which neighbours move apart, or come back together. While we find strong evidence that fish align with their near neighbours, we do not find that predation shapes the strength or likelihood of these alignment tendencies. We also find that predation sharpens individuals' acceleration and deceleration responses, implying key perceptual and energetic differences associated with how individuals move in different predation regimes. Our results reveal how predation can shape the social interactions of individuals in groups, ultimately driving differences in groups' collective behaviour.