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Clear-cell renal cell carcinoma (ccRCC) is the most common origin of pancreatic metastases (PM). Distinct genomic aberrations, favorable prognosis, and clinical observations on high angiogenesis, and succeeding tyrosine kinase inhibitor (TKI) sensitivity have been reported in PM-ccRCC. However, no functional or single-cell studies have been conducted thus far. We recruited five PM-ccRCC patients and investigated the genomic, single-cell transcriptomic, and drug sensitivity profiles of their patient-derived cells (PDCs). The PM depicted both expected and novel genomic alterations. Further, the transcriptomics differed from both primary and metastatic ccRCC, with upregulations of the PI3K/mTOR and – supporting the clinical observations – angiogenesis pathways. Data integration at pathway level showed that transcriptomics explained drug sensitivities the best. Accordingly, PM-ccRCC PDCs shared sensitivity to many PI3K/mTOR inhibitors. Altogether, we show distinct genomic and transcriptomic signatures in PM-ccRCC, highlight the superiority of transcriptomics in interpreting drug sensitivities, and encourage the use of TKIs and PI3K/mTOR inhibitors in PM-ccRCC. Functional precision medicine approach reveals genomic and transcriptomic aberrations that distinguish pancreatic metastases from other types of ccRCC metastases and suggest potential therapeutic targets at the individual level. [ABSTRACT FROM AUTHOR]

Intratumoral cellular heterogeneity necessitates multi-targeting therapies for improved clinical benefits in advanced malignancies. However, systematic identification of patient-specific treatments that selectively co-inhibit cancerous cell populations poses a combinatorial challenge, since the number of possible drug-dose combinations vastly exceeds what could be tested in patient cells. Here, we describe a machine learning approach, scTherapy, which leverages single-cell transcriptomic profiles to prioritize multi-targeting treatment options for individual patients with hematological cancers or solid tumors. Patient-specific treatments reveal a wide spectrum of co-inhibitors of multiple biological pathways predicted for primary cells from heterogenous cohorts of patients with acute myeloid leukemia and high-grade serous ovarian carcinoma, each with unique resistance patterns and synergy mechanisms. Experimental validations confirm that 96% of the multi-targeting treatments exhibit selective efficacy or synergy, and 83% demonstrate low toxicity to normal cells, highlighting their potential for therapeutic efficacy and safety. In a pan-cancer analysis across five cancer types, 25% of the predicted treatments are shared among the patients of the same tumor type, while 19% of the treatments are patient-specific. Our approach provides a widely-applicable strategy to identify personalized treatment regimens that selectively co-inhibit malignant cells and avoid inhibition of non-cancerous cells, thereby increasing their likelihood for clinical success. The identification of treatments that selectively co-inhibit cancerous cell populations remains a challenge. Here, a machine learning approach, scTherapy, leverages single-cell transcriptomic profiles to prioritize multi-targeting treatment options for individual patients with hematological cancers or solid tumors. [ABSTRACT FROM AUTHOR]

Despite recent progress in acute lymphoblastic leukemia (ALL) therapies, a significant subset of adult and pediatric ALL patients has a dismal prognosis. Better understanding of leukemogenesis and recognition of germline genetic changes may provide new tools for treating patients. Given that hematopoietic stem cell transplantation, often from a family member, is a major form of treatment in ALL, acknowledging the possibility of hereditary predisposition is of special importance. Reports of comprehensive germline analyses performed in adult ALL patients are scarce. Aiming at fulfilling this gap of knowledge, we investigated variants in 93 genes predisposing to hematologic malignancies and 70 other cancer-predisposing genes from exome data obtained from 61 adult and 87 pediatric ALL patients. Our results show that pathogenic (P) or likely pathogenic (LP) germline variants in genes associated with predisposition to ALL or other cancers are prevalent in ALL patients: 8% of adults and 11% of children. Comparison of P/LP germline variants in patients to population-matched controls (gnomAD Finns) revealed a 2.6-fold enrichment in ALL cases (CI 95% 1.5–4.2, p = 0.00071). Acknowledging inherited factors is crucial, especially when considering hematopoietic stem cell transplantation and planning post-therapy follow-up. Harmful germline variants may also predispose patients to excessive toxicity potentially compromising the outcome. We propose integrating germline genetics into precise ALL patient care and providing families genetic counseling. [ABSTRACT FROM AUTHOR]

Combination therapy is preferred over single-targeted monotherapies for cancer treatment due to its efficiency and safety. However, identifying effective drug combinations costs time and resources. We propose a method for identifying potential drug combinations by bipartite network modelling of patient-related drug response data, specifically the Beat AML dataset. The median of cell viability is used as a drug potency measurement to reconstruct a weighted bipartite network, model drug-biological sample interactions, and find the clusters of nodes inside two projected networks. Then, the clustering results are leveraged to discover effective multi-targeted drug combinations, which are also supported by more evidence using GDSC and ALMANAC databases. The potency and synergy levels of selective drug combinations are corroborated against monotherapy in three cell lines for acute myeloid leukaemia in vitro. In this study, we introduce a nominal data mining approach to improving acute myeloid leukaemia treatment through combinatorial therapy. Identifying effective drug combinations to treat cancer is a challenging task, either experimentally or computationally. Here, the authors develop a bipartite network modelling approach to propose drug combination strategies in acute myeloid leukaemia using patient and cell line drug screening data. [ABSTRACT FROM AUTHOR]

FLT3 internal tandem duplication (FLT3-ITD) is a frequent mutation in acute myeloid leukemia (AML) and remains a strong prognostic factor due to high rate of disease recurrence. Several FLT3-targeted agents have been developed, but determinants of variable responses to these agents remain understudied. Here, we investigated the role FLT3-ITD allelic ratio (ITD-AR), ITD length, and associated gene expression signatures on FLT3 inhibitor response in adult AML. We performed fragment analysis, ex vivo drug testing, and next generation sequencing (RNA, exome) to 119 samples from 87 AML patients and 13 healthy bone marrow controls. We found that ex vivo response to FLT3 inhibitors is significantly associated with ITD-AR, but not with ITD length. Interestingly, we found that the HLF gene is overexpressed in FLT3-ITD+ AML and associated with ITD-AR. The retrospective analysis of AML patients treated with FLT3 inhibitor sorafenib showed that patients with high HLF expression and ITD-AR had better clinical response to therapy compared to those with low ITD-AR and HLF expression. Thus, our findings suggest that FLT3 ITD-AR together with increased HLF expression play a role in variable FLT3 inhibitor responses observed in FLT3-ITD+ AML patients. [ABSTRACT FROM AUTHOR]

The FDA recently approved eight targeted therapies for acute myeloid leukemia (AML), including the BCL-2 inhibitor venetoclax. Maximizing efficacy of these treatments requires refining patient selection. To this end, we analyzed two recent AML studies profiling the gene expression and ex vivo drug response of primary patient samples. We find that ex vivo samples often exhibit a general sensitivity to (any) drug exposure, independent of drug target. We observe that this "general response across drugs" (GRD) is associated with FLT3-ITD mutations, clinical response to standard induction chemotherapy, and overall survival. Further, incorporating GRD into expression-based regression models trained on one of the studies improved their performance in predicting ex vivo response in the second study, thus signifying its relevance to precision oncology efforts. We find that venetoclax response is independent of GRD but instead show that it is linked to expression of monocyte-associated genes by developing and applying a multi-source Bayesian regression approach. The method shares information across studies to robustly identify biomarkers of drug response and is broadly applicable in integrative analyses. [ABSTRACT FROM AUTHOR]

With the introduction of novel therapeutic agents, survival in Multiple Myeloma (MM) has increased in recent years. However, drug-resistant clones inevitably arise and lead to disease progression and death. The current International Myeloma Working Group response criteria are broad and make it difficult to clearly designate resistant and responsive patients thereby hampering proteo-genomic analysis for informative biomarkers for sensitivity. In this proof-of-concept study we addressed these challenges by combining an ex-vivo drug sensitivity testing platform with state-of-the-art proteomics analysis. 35 CD138-purified MM samples were taken from patients with newly diagnosed or relapsed MM and exposed to therapeutic agents from five therapeutic drug classes including Bortezomib, Quizinostat, Lenalidomide, Navitoclax and PF-04691502. Comparative proteomic analysis using liquid chromatography-mass spectrometry objectively determined the most and least sensitive patient groups. Using this approach several proteins of biological significance were identified in each drug class. In three of the five classes focal adhesion-related proteins predicted low sensitivity, suggesting that targeting this pathway could modulate cell adhesion mediated drug resistance. Using Receiver Operating Characteristic curve analysis, strong predictive power for the specificity and sensitivity of these potential biomarkers was identified. This approach has the potential to yield predictive theranostic protein panels that can inform therapeutic decision making. [ABSTRACT FROM AUTHOR]

Background: Esterase enzymes differ in substrate specificity and biological function and may display dysregulated expression in cancer. This study evaluated the biological significance of esterase expression in multiple myeloma (MM).Methods: For gene expression profiling and evaluation of genomic variants in the Institute for Molecular Medicine Finland (FIMM) cohort, bone marrow aspirates were obtained from patients with newly diagnosed MM (NDMM) or relapsed/refractory MM (RRMM). CD138+ plasma cells were enriched and used for RNA sequencing and analysis, and to evaluate genomic variation. The Multiple Myeloma Research Foundation (MMRF) Relating Clinical Outcomes in MM to Personal Assessment of Genetic Profile (CoMMpass) dataset was used for validation of the findings from FIMM.Results: MM patients (NDMM, n = 56; RRMM, n = 78) provided 171 bone marrow aspirates (NDMM, n = 56; RRMM, n = 115). Specific esterases exhibited relatively high or low expression in MM, and expression of specific esterases (UCHL5, SIAE, ESD, PAFAH1B3, PNPLA4 and PON1) was significantly altered on progression from NDMM to RRMM. High expression of OVCA2, PAFAH1B3, SIAE and USP4, and low expression of PCED1B, were identified as poor prognostic markers (P < 0.05). The MMRF CoMMpass dataset provided validation that higher expression of PAFAH1B3 and SIAE, and lower expression of PCED1B, were associated with poor prognosis.Conclusions: Esterase gene expression levels change as patients progress from NDMM to RRMM. High expression of OVCA2, PAFAH1B3, USP4 and SIAE, and low expression of PCED1B, are poor prognostic markers in MM, suggesting a role for these esterases in myeloma biology. [ABSTRACT FROM AUTHOR]

We developed a systematic algorithmic solution for quantitative drug sensitivity scoring (DSS), based on continuous modeling and integration of multiple dose-response relationships in high-throughput compound testing studies. Mathematical model estimation and continuous interpolation makes the scoring approach robust against sources of technical variability and widely applicable to various experimental settings, both in cancer cell line models and primary patient-derived cells. Here, we demonstrate its improved performance over other response parameters especially in a leukemia patient case study, where differential DSS between patient and control cells enabled identification of both cancer-selective drugs and drug-sensitive patient sub-groups, as well as dynamic monitoring of the response patterns and oncogenic driver signals during cancer progression and relapse in individual patient cells ex vivo. An open-source and easily extendable implementation of the DSS calculation is made freely available to support its tailored application to translating drug sensitivity testing results into clinically actionable treatment options. [ABSTRACT FROM AUTHOR]

The most important predictor of prognosis in breast cancer is lymph node status, yet little is known about molecular changes associated with lymph node metastasis. Here, gene expression analysis was performed on primary breast (PBT) and corresponding metastatic lymph node (MLN) tumors to identify molecular signatures associated with nodal metastasis. RNA was isolated after laser microdissection from frozen PBT and MLN from 20 patients with positive lymph nodes and hybridized to the microarray chips. Differential expression was determined using Mann–Whitney testing; Bonferroni corrected P values of 0.05 and 0.001 were calculated. Results were validated using TaqMan assays. Fifty-one genes were differentially expressed ( P < 1 × 10−5, less than twofold differences) between the PBT and paired MLN; 13 with significantly higher expression in the MLN and 38 in the PBT. qRT-PCR validated the differential expression of 40/51 genes. Of the 40 validated genes, NTS and PAX5 were found to have >100-fold higher expression in MLT while COL11A1, KRT14, MMP13, TAC1 and WNT2 had >100-fold higher expression in PBT. Gene expression differences between PBT and MLN suggests that expression of a unique set of genes is required for successful lymph node colonization. Genes expressed at higher levels in PBT are involved in degradation of the extracellular matrix, enabling cells with metastatic potential to disseminate, while genes expressed at higher levels in metastases are involved in transcription, signal transduction and immune response, providing cells with proliferation and survival advantages. These data improve our understanding of the biological processes involved in successful metastatis and provide new targets to arrest tumor cell dissemination and metastatic colonization. [ABSTRACT FROM AUTHOR]

In follicular lymphomas with the t(14;18) translocation, there is increased expression of the bcl-2 gene, which is dependent upon regulatory elements within the bcl-2 5' flanking region and the immunoglobulin heavy-chain gene enhancers. We found that t(14;18) lymphomas expressed C/EBPa, which is not normally expressed in B lymphocytes. Expression of C/EBPa increased bcl-2 expression, and two regions of the bcl-2 P2 promoter that mediated this effect were identified. C/EBPß was also able to increase bcl-2 promoter activity through these sites. The 5' site was GC-rich and did not contain a C/EBP consensus sequence; however, C/EBP was observed to interact with this site both in vitro by EMSA and in vivo by chromatin immunoprecipitation assay. The 3' region contained the Cdx site, which mediates the effect of A-Myb on the bcl-2 promoter. In vivo binding studies revealed that C/EBP interacted with this region of the bcl-2 promoter as well. Decreased expression of C/EBP factors due to targeting of their transcripts by siRNA molecules resulted in downregulation of Bcl-2 protein. We conclude that C/EBPa and C/EBPß contribute to the deregulated expression of Bcl-2 in t(14;18) lymphoma cells.Oncogene (2003) 22, 5947-5955. doi:10.1038/sj.onc.1206639 [ABSTRACT FROM AUTHOR]

Presents a study that examined the mechanism of bcl-2 regulation by nuclear factor kappa B (NF-κB) in t(14;18) lymphoma cells. Background on follicular lymphomas characterized by t(14;18) translocation; Analysis of the function of the bcl-2 proto-oncogene; Investigation of promoters mediating the transcriptional control of the bcl-2 gene; Discussion on NF-κB/Rel activity.

The p53 protein activates promoters containing p53 binding sites, and it represses other promoters. We examined the effect of p53 on bcl-2 expression in both the DHL-4 B cell line and the K562 erythroleukemia line. Transient transfection analyses revealed that wild-type p53 repressed the bcl-2 full-length promoter. The region of the bcl-2 promoter that was responsive to p53 was mapped to the bcl-2 P2 minimal promoter region, and we showed that p53 and the TATA binding protein bound to the bcl-2 TATA sequence. The TATA binding protein, p53, histone deacetylase-1 and mSin3a could be co-immunoprecipitated from K562 cell nuclear extract. The TATA binding protein and mSin3a could be recovered in a complex at the bcl-2 promoter TATA sequence, however, the formation of this complex was not dependent on the presence of p53. Treatment of K562 cells with the histone deacetylase inhibitor, trichostatin A, resulted in an increase in bcl-2 promoter activity whether p53 was present or not. Therefore, we demonstrated that p53 and the histone deacetylases repress the bcl-2 promoter independently. Similar results were obtained when endogenous bcl-2 mRNA or protein levels were measured in response to either p53 or trichostatin A, and p53 expression resulted in enhanced apoptosis. RNase protection assays demonstrated that transcription from the endogenous 3′ bcl-2 promoter was decreased by p53. The regions of p53 that were required for repression of the bcl-2 promoter were defined. We conclude that the TATA sequence in the bcl-2 P2 minimal promoter is the target for repression by p53, and that the interaction between p53 and TBP is most likely responsible for the repression. Mutation of p53 may play a role in the up-regulation of bcl-2 expression in some B cell lymphomas. Oncogene (2001) 20, 240–251. [ABSTRACT FROM AUTHOR]

The patho-mechanism of somatic driver mutations in cancer usually involves transcription, but the proportion of mutations and wild-type alleles transcribed from DNA to RNA is largely unknown. We systematically compared the variant allele frequencies of recurrently mutated genes in DNA and RNA sequencing data of 246 acute myeloid leukaemia (AML) patients. We observed that 95% of all detected variants were transcribed while the rest were not detectable in RNA sequencing with a minimum read-depth cut-off (10x). Our analysis focusing on 11 genes harbouring recurring mutations demonstrated allelic imbalance (AI) in most patients. GATA2, RUNX1, TET2, SRSF2, IDH2, PTPN11, WT1, NPM1 and CEBPA showed significant AIs. While the effect size was small in general, GATA2 exhibited the largest allelic imbalance. By pooling heterogeneous data from three independent AML cohorts with paired DNA and RNA sequencing (N = 253), we could validate the preferential transcription of GATA2-mutated alleles. Differential expression analysis of the genes with significant AI showed no significant differential gene and isoform expression for the mutated genes, between mutated and wild-type patients. In conclusion, our analyses identified AI in nine out of eleven recurrently mutated genes. AI might be a common phenomenon in AML which potentially contributes to leukaemogenesis. [ABSTRACT FROM AUTHOR]