Biology subjects

Laajala, T. D.

Publications and source records attributed to Laajala, T. D..

7 recordsLinked to original sources

bioRxiv2023

Transcriptional landscape of DTP-DTEP transition reveals DUSP6 as a driver of HER2 inhibitor tolerance via Neuregulin/HER3 axis

The mechanisms promoting re-growth of dormant cancer cells under continuous tyrosine kinase inhibitor (TKI) therapy are poorly understood. Here we present transcriptional profiling of HER2+ breast cancer cells treated continuously with HER2 TKI (HER2i) therapy for 9 months. The data reveals specific gene regulatory programs associated with transition from dormant drug tolerant persister cells (DTPs) to proliferating DTEP (drug tolerant expanding persister) cells and eventually long-term resistance. Focusing on yet poorly understood phosphatases as determinants of therapy tolerance, expression of dual-specificity phosphatase DUSP6 was found inhibited in DTPs, but strongly induced upon re-growth of DTEPs. DUSP6 overexpression conferred apoptosis resistance whereas its pharmacological blockade prevented DTEP development under HER2i therapy. The DUSP6-driven HER2i tolerance was mediated by activation of neuregulin-HER3 axis, and consistent with the role of HER3 in widespread therapy tolerance, DUSP6 targeting also synergized with clinically used HER2i combination therapies. In vivo, pharmacological DUSP6 targeting induced synthetic lethal effect with HER2i in independent tumor models, and its genetic targeting reduced tumor growth in orthotopic brain metastasis model. Collectively this work provides first transcriptional landscape of DTP-DTEP transition under TKI therapy, and identify DUSP6 as a novel candidate therapy target to overcome widespread HER3-driven therapy resistance.

cancer biology↗

bioRxiv2023

curatedPCaData: Integration of clinical, genomic, and signature features in a curated and harmonized prostate cancer data resource

Genomic and transcriptomic data have been generated across a wide range of prostate cancer (PCa) study cohorts. These data can be used to better characterize the molecular features associated with clinical outcomes and to test hypotheses across multiple, independent patient cohorts. In addition, derived features, such as estimates of cell composition, risk scores, and androgen receptor (AR) scores, can be used to develop novel hypotheses leveraging existing multi-omic datasets. The full potential of such data is yet to be realized as independent datasets exist in different repositories, have been processed using different pipelines, and derived and clinical features are often not provided or unstandardized. Here, we present the curatedPCaData R package, a harmonized data resource representing >2900 primary tumor, >200 normal tissue, and >500 metastatic PCa samples across 19 datasets processed using standardized pipelines with updated gene annotations. We show that meta-analysis across harmonized studies has great potential for robust and clinically meaningful insights. curatedPCaData is an open and accessible community resource with code made available for reproducibility.

bioinformatics↗

bioRxiv2022

A Community Challenge to Predict Clinical Outcomes After Immune Checkpoint Blockade in Non-Small Cell Lung Cancer

PurposePredictive biomarkers of immune checkpoint inhibitors (ICIs) efficacy are currently lacking for non-small cell lung cancer (NSCLC). Here, we describe the results from the Anti-PD-1 Response Prediction DREAM Challenge, a crowdsourced initiative that enabled the assessment of predictive models by using data from two randomized controlled clinical trials (RCTs) of ICIs in first-line metastatic NSCLC. MethodsParticipants developed and trained models using public resources. These were evaluated with data from the CheckMate 026 trial (NCT02041533), according to the model-to-data paradigm to maintain patient confidentiality. The generalizability of the models with the best predictive performance was assessed using data from the CheckMate 227 trial (NCT02477826). Both trials were phase III RCTs with a chemotherapy control arm, which supported the differentiation between predictive and prognostic models. Isolated model containers were evaluated using a bespoke strategy that considered the challenges of handling transcriptome data from clinical trials. ResultsA total of 59 teams participated, with 417 models submitted. Multiple predictive models, as opposed to a prognostic model, were generated for predicting overall survival, progression-free survival, and progressive disease status with ICIs. Variables within the models submitted by participants included tumor mutational burden (TMB), programmed death ligand 1 (PD-L1) expression, and gene-expression-based signatures. The bestperforming models showed improved predictive power over reference variables, including TMB or PD-L1. ConclusionThis DREAM Challenge is the first successful attempt to use protected phase III clinical data for a crowdsourced effort towards generating predictive models for ICIs clinical outcomes and could serve as a blueprint for similar efforts in other tumor types and disease states, setting a benchmark for future studies aiming to identify biomarkers predictive of ICIs efficacy. Context summaryO_ST_ABSKey objectiveC_ST_ABSNot all patients with non-small cell lung cancer (NSCLC) eligible for immune checkpoint inhibitor (ICIs) respond to treatment, but accurate predictive biomarkers of ICIs clinical outcomes are currently lacking. This crowdsourced initiative enabled the robust assessment of predictive models using data from two randomized clinical trials of first-line ICI in metastatic NSCLC. Knowledge generatedModels submitted indicate that a combination of programmed death ligand 1 (PD-L1), tumor mutational burden (TMB), and immune gene signatures might be able to identify patients more likely to respond to ICIs. TMB and PD-L1 seemed important to predict progression-free survival and overall survival. Mechanisms including apoptosis, T-cell crosstalk, and adaptive immune resistance appeared essential to predict response. Relevance

cancer biology↗

bioRxiv2022

OSCAR: Optimal subset cardinality regression using the L0-pseudonorm with applications to prognostic modelling of prostate cancer

In many real-world applications, such as those based on patient electronic health records, prognostic prediction of patient survival is based on heterogeneous sets of clinical laboratory measurements. To address the trade-off between the predictive accuracy of a prognostic model and the costs related to its clinical implementation, we propose an optimized L0-pseudonorm approach to learn sparse solutions in multivariable regression. The model sparsity is maintained by restricting the number of nonzero coefficients in the model with a cardinality constraint, which makes the optimization problem NP-hard. In addition, we generalize the cardinality constraint for grouped feature selection, hence making it possible to identify key sets of predictors that may be measured together in a kit in clinical practice. We demonstrate the operation of our cardinality constraint-based feature subset selection method, named OSCAR, in the context of prognostic modelling of prostate cancer, where it enabled one to determine the key explanatory predictors at different levels of model sparsity, and to explore how the model sparsity affects the model accuracy and implementation cost. Author summaryFeature selection has become a crucial part in building biomedical models, due to the abundance of available predictors in many applications, yet there remains an uncertainty of their importance and generalization ability. Regularized regression methods have become popular approaches to tackle this challenge by balancing the model goodness-of-fit against the increasing complexity of the model in terms of coefficients that deviate from zero. Regularization norms are pivotal in formulating the model complexity, and currently L1 (LASSO), L2 (Ridge Regression) and their hybrid (Elastic Net) norms dominate the field. In this paper, we present a novel methodology using the L0-pseudonorm, also known as the best subset selection, which has largely gone overlooked due to its challenging discrete nature. Our methodology makes use of a continuous transformation of the discrete optimization problem, and provides effective solvers implemented in a user friendly R software package. We exemplify the use of oscar-package in the context of prostate cancer prognostic prediction using both real-world hospital registry and clinical cohort data. By benchmarking the methodology against related regularization methods, we illustrate the advantages of the L0-pseudonorm for better clinical applicability and selection of grouped features.

bioinformatics↗

bioRxiv2021

Ovarian cancers with low CIP2A tumor expression constitute an APR-246 sensitive disease subtype

Identification of ovarian cancer (OvCa) patient subpopulations with increased sensitivity to targeted therapies could offer significant clinical benefit. We report that 22% of the high grade OvCa tumors at diagnosis express CIP2A oncoprotein at low levels. CIP2Alow OvCa tumors have significantly lower likelihood of disease relapse after standard chemotherapy, but yet a portion of relapsed tumors retain their CIP2Alow phenotype. We further discover that reactive oxygen species (ROS) inducing compound APR-246 (PRIMA-1Met/Eprenetapopt), currently in clinical development, preferentially kill CIP2Alow OvCa cells across multiple chemotherapy resistant cell lines. Consistent with CIP2Alow OvCa subtype in humans, CIP2A is dispensable for development of MISIIR-TAg-driven mouse OvCa tumors. Nevertheless, CIP2A deficient OvCa tumor cells from MISIIR-TAg mice displayed APR-246 hypersensitivity both in vitro and in vivo. Mechanistically, the lack of CIP2A expression hypersensitizes the OvCa cells to APR-246 by inhibition of NF-kB activity. Accordingly, combination of APR-246 and Nf-kB inhibitor compounds strongly synergized in killing of CIP2A positive OvCa cells. Collectively, we discover low CIP2A expression as a vulnerability for APR-246 in OvCa. The results warrant consideration of clinical testing of APR-246 for CIP2Alow OvCa tumor subtype patients, and reveal CIP2A as a candidate APR-246 combination therapy target.

cancer biology↗

bioRxiv2021

The M1 aminopeptidase NPEPPS is a novel regulator of cisplatin sensitivity

There is an unmet need to improve efficacy of platinum-based cancer chemotherapy. Using multi-omic assessment of cisplatin-responsive and -resistant human bladder cancer cell lines and whole-genome CRISPR screens, we identified Puromycin-Sensitive Aminopeptidase, NPEPPS, as a novel driver of cisplatin resistance. NPEPPS depletion sensitizes resistant bladder cancer cells to cisplatin in vitro and in vivo. Conversely, overexpression of NPEPPS in sensitive cells increased cisplatin resistance. We show that NPEPPS affects treatment response by regulating intracellular cisplatin concentrations. Patient-derived organoids (PDOs) generated from bladder cancer samples before and after cisplatin-based treatment, and from patients who did not receive cisplatin, were evaluated for sensitivity to cisplatin and they were found to be concordant with clinical response. In PDOs, shRNA depletion or pharmacologic inhibition of NPEPPS led to increased cisplatin sensitivity, while NPEPPS overexpression had the opposite effect. Our data present NPEPPS as a novel and druggable driver of cisplatin resistance by regulating intracellular cisplatin concentrations, along with providing the preclinical data to support clinical trials combining NPEPPS inhibition with cisplatin.

cancer biology↗

bioRxiv2018

Rules for PP2A-controlled phosphosignalling and drug responses

Systemic understanding of protein phosphatase 2A (PP2A)-regulated cellular processes is still at infancy. Here, we present mass-spectrometry analysis of phospho-targets (dephosphorylome) regulated by PP2A modulation. In addition to PP2A-regulated processes and targets, the data reveal important general concepts and rules related to PP2A-mediated phosphoregulation. These include the unidirectionality paradigm of regulation of phosphorylation, and differential spatial distribution of kinase-and phosphatase-dominated phosphotargets. Data also present first systemic analysis of targets of PP2A-modulating oncoproteins, CIP2A, PME-1, and SET; including targets via which PP2A may coordinately regulate activities of cancer drivers and tumor suppressors such as MYC or TP53. To validate functional utility of this dataset, PP2A dephosphorylome activity was correlated with cancer cell responses to over 300 drugs. Notably, we find that cancer therapy responses can be broadly classified based on PP2A dephosphorylome activity, both in quantitative and qualitative manner. In summary, our data characterize rules by which PP2A coordinate cancer cell phosphosignaling and drug responses. The results also may also direct the use of emerging pharmacological approaches for PP2A activity modulation in human diseases.

systems biology↗