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Neural cues differentially modulate colorectal cancer cell behavior depending on patients' genomic background.

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  1. [1] § STAR★Methods › Method details › Genetic and transcriptomic profiling of cell lines ↔ pRSEM/Param.py, lines 19–97 · score 0.56 · RNA seq, RSEM, quantification, libraries, filtered, Transcript

Paper

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The authors' code

Python · 178 lines · 7.3 KB · GPL-3.0 · 1 match

  1. __doc__="""
  2. pliu 20150511
  3. python module for all parameters, input arguments
  4. """
  5. class Param:
  6. IDR_THRESHOLD = 0.05
  7. N_PEAK = 300000
  8. PEAK_TYPE = '-savr'
  9. EXCLUSION_ZONE = '-500:85' ## Anshul recommend -500:85
  10. TRAINING_GENE_MIN_LEN = 1003
  11. TRAINING_MIN_MAPPABILITY = 0.8
  12. FLANKING_WIDTH = 500 ## in nt, flanking region around TSS and TES
  13. INFORMATIVE_DATA_MAX_P_VALUE = 0.01 ## external data set is informative if
  14. ## p-value is not more than this value
  15. def __init__(self):
  16. self.argdict = None
  17. ## has to be in the same naming convention as prsem-calculate-expression
  18. self.num_threads = None
  19. self.chipseq_target_read_files = None
  20. self.chipseq_control_read_files = None
  21. self.chipseq_read_files_multi_targets = None
  22. self.chipseq_bed_files_multi_targets = None
  23. self.cap_stacked_chipseq_reads = None
  24. self.n_max_stacked_chipseq_reads = None
  25. self.bowtie_path = None
  26. self.chipseq_peak_file = None
  27. self.mappability_bigwig_file = None
  28. self.partition_model = None
  29. self.gibbs_burnin = None
  30. self.gibbs_number_of_samples = None
  31. self.gibbs_sampling_gap = None
  32. self.quiet = False
  33. ## arguments
  34. self.ref_fasta = None
  35. self.ref_name = None
  36. self.sample_name = None
  37. self.stat_name = None
  38. self.imd_name = None
  39. ## path and pRSEM scripts
  40. self.temp_dir = None ## dir to save RSEM/pRSEM intermediate files
  41. self.prsem_scr_dir = None ## pRSEM scripts dir
  42. self.prsem_rlib_dir = None ## place to install pRSEM required R libraries
  43. ## genome reference: training set isoforms
  44. self.fall_exon_crd = None
  45. self.fall_tr_crd = None ## tr info + mappability
  46. self.ftraining_tr_crd = None ## training set tr
  47. ## ChIP-seq
  48. self.chipseqexperiment_target = None ## reference to ChIP-seq experiment
  49. self.chipseqexperiment_control = None ## reference to ChIP-seq experiment
  50. self.chipseq_rscript = None ## full name of process-chipseq.R
  51. self.filterSam2Bed = None ## full name of filterSam2Bed binary
  52. self.spp_tgz = None
  53. self.spp_script = None
  54. self.idr_scr_dir = None
  55. self.idr_script = None
  56. self.fgenome_table = None
  57. self.fidr_chipseq_peaks = None
  58. self.fall_chipseq_peaks = None
  59. self.fchipseq_peaks = None ## full name of user supplied ChIP-seq peak
  60. ## file, otherwise is fidr_chipseq_peaks
  61. self.chipseq_target_fraglen = None ## spp-estimated fragment length
  62. self.fsppout_target = None ## full name of SPP output
  63. ## this implementation needs to be refined since
  64. ## the var is define in both Param and ChIPSeqExp
  65. self.fchipseq_target_signals = None
  66. self.fchipseq_control_signals = None
  67. ## transcripts and RNA-seq
  68. self.transcripts = None ## reference to all transcripts to be quantified
  69. self.genes = None ## reference to all genes to be quantified
  70. self.rnaseq_rscript = None ## fullname of R script for dealing RNA-seq
  71. self.fti = None ## RSEM's reference .ti file
  72. self.bigwigsummary_bin = None ## bigWigSummary binary
  73. self.fall_tr_features = None ## file for all isoforms' features
  74. self.fall_tr_prior = None ## file for all isoforms' priors
  75. self.fisoforms_results = None ## file for RSEM .isoforms.results
  76. self.fpvalLL = None ## file for p-value on if informative
  77. ## and for log-likelihood
  78. self.fall_pvalLL = None ## file to store all the p-val and log-likelihood
  79. ## for multiple external data sets
  80. self.targetid2fchipseq_alignment = {}
  81. self.finfo_multi_targets = None
  82. self.flgt_model_multi_targets = None
  83. ## for testing procedure
  84. self.targetids = []
  85. def __str__(self):
  86. ss = [ "%-33s %s\n" % (key, val) for (key, val) in self.argdict.items()] + \
  87. [ "%-33s %s\n" % ('RSEM_temp_dir', self.temp_dir ) ] + \
  88. [ "%-33s %s\n" % ('pRSEM_scr_dir', self.prsem_scr_dir) ]
  89. return ''.join(ss)
  90. @classmethod
  91. def initFromCommandLineArguments(cls, argdict):
  92. import os
  93. prm = cls()
  94. prm.argdict = argdict
  95. for (key, val) in argdict.items():
  96. setattr(prm, key, val)
  97. if prm.imd_name is not None:
  98. prm.temp_dir = os.path.split(prm.imd_name)[0] + '/'
  99. prm.prsem_scr_dir = os.path.dirname(os.path.realpath(__file__)) + '/'
  100. prm.prsem_rlib_dir = prm.prsem_scr_dir + 'RLib/'
  101. if not os.path.exists(prm.prsem_rlib_dir):
  102. os.mkdir(prm.prsem_rlib_dir)
  103. ## genome reference: pRSEM training set isoforms
  104. prm.fall_exon_crd = prm.ref_name + '_prsem.all_exon_crd'
  105. prm.fall_tr_crd = prm.ref_name + '_prsem.all_tr_crd'
  106. prm.ftraining_tr_crd = prm.ref_name + '_prsem.training_tr_crd'
  107. ## ChIP-seq
  108. prm.chipseq_rscript = prm.prsem_scr_dir + 'process-chipseq.R'
  109. prm.filterSam2Bed = prm.prsem_scr_dir + 'filterSam2Bed'
  110. prm.spp_tgz = prm.prsem_scr_dir + 'phantompeakqualtools/spp_1.10.1.tar.gz'
  111. prm.spp_script = prm.prsem_scr_dir + 'phantompeakqualtools/run_spp.R'
  112. prm.idr_scr_dir = prm.prsem_scr_dir + 'idrCode/'
  113. prm.idr_script = prm.idr_scr_dir + 'batch-consistency-analysis.r'
  114. prm.fgenome_table = prm.ref_name + '.chrlist'
  115. if prm.temp_dir is not None:
  116. prm.fsppout_target = prm.temp_dir + 'target_phantom.tab'
  117. prm.fchipseq_target_signals = prm.temp_dir + 'target.tagAlign.gz'
  118. prm.fchipseq_control_signals = prm.temp_dir + 'control.tagAlign.gz'
  119. prm.fidr_chipseq_peaks = "%s/%s" % (prm.temp_dir,
  120. 'idr_target_vs_control.regionPeak.gz')
  121. ## have to name it this way due to run_spp.R's wired naming convention
  122. ## this names depens on the next two names
  123. prm.fall_chipseq_peaks = "%s/%s" % (prm.temp_dir,
  124. 'target.tagAlign_VS_control.tagAlign.regionPeak.gz')
  125. if prm.chipseq_peak_file is not None:
  126. prm.fchipseq_peaks = prm.chipseq_peak_file
  127. else:
  128. prm.fchipseq_peaks = prm.fidr_chipseq_peaks
  129. ## transcripts and RNA-seq
  130. prm.rnaseq_rscript = prm.prsem_scr_dir + 'process-rnaseq.R'
  131. prm.fti = prm.ref_name + '.ti'
  132. prm.ffasta = prm.ref_name + '.transcripts.fa'
  133. prm.bigwigsummary_bin = prm.prsem_scr_dir + 'bigWigSummary'
  134. #prm.fall_exon_crd = prm.imd_name + '_prsem.all_exon_crd'
  135. #prm.fall_tr_crd = prm.imd_name + '_prsem.all_tr_crd'
  136. #prm.ftraining_tr_crd = prm.imd_name + '_prsem.training_tr_crd'
  137. if prm.sample_name is not None: ## for calc-expr
  138. prm.fall_tr_gc = prm.imd_name + '_prsem.all_tr_gc'
  139. prm.fall_tr_features = prm.stat_name + '_prsem.all_tr_features'
  140. prm.fall_tr_prior = prm.stat_name + '_prsem.all_tr_prior'
  141. prm.fpvalLL = prm.stat_name + '_prsem.pval_LL'
  142. prm.fisoforms_results = prm.sample_name + '.isoforms.results'
  143. prm.fall_pvalLL = prm.sample_name + '.all.pval_LL'
  144. ## for multiple external data sets
  145. prm.finfo_multi_targets = prm.temp_dir + 'multi_targets.info'
  146. prm.flgt_model_multi_targets = prm.stat_name + '_prsem.lgt_mdl.RData'
  147. return prm
  148. def initFromCommandLineArguments(argdict):
  149. return Param.initFromCommandLineArguments(argdict)

Param.py at commit 800234e, under GPL-3.0 · at the source

Overview

Authors: Meike S. Thijssen1,2, Rosaria Chilà3, Giovanni Crisafulli3, Kim M. Smits1, Alberto Bardelli3,4, Werend Boesmans1,2, Veerle Melotte1
  1. Department of Pathology, GROW – Research Institute for Oncology and Reproduction, Maastricht University Medical Center, Maastricht, the Netherlands
  2. Biomedical Research Institute (BIOMED), Hasselt University, Hasselt, Belgium
  3. IFOM ETS - The AIRC Institute of Molecular Oncology, Milan, Italy
  4. Department of Oncology, Molecular Biotechnology Center, University of Torino, Turin, Italy
Institutions: Maastricht University Medical Centre (Netherlands); Maastricht University (Netherlands); Hasselt University (Belgium); IFOM (Italy); University of Turin (Italy)
Journal: iScience, volume 29, issue 6, article 116153
Dates: received 19 November 2025; accepted 13 May 2026; published online 28 May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1016/j.isci.2026.116153 · PMID 42256296 · PMCID PMC13233565 · OpenAlex W7162638011
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: human (organism), other condition (population), clinical / translational (subfield)
Methods: Statistics
Keywords: oncology, microenvironment, neuroscience
Topic: Cancer, Stress, Anesthesia, and Immune Response (Psychiatry and Mental health, Medicine), according to OpenAlex
Funding: Hasselt University; Maastricht University Medical Centre+; Kankeronderzoeksfonds Limburg (KOFL); Het Fonds voor Wetenschappelijk Onderzoek – Vlaanderen (FWO) (V472323N); Nederlandse Organisatie voor Wetenschappelijk Onderzoek (09150172110100)
Citations: not cited yet (Europe PMC); 72 references in the paper

Abstract

While neurons are mostly described as pro-tumorigenic and linked with a poor prognosis, differing outcomes have been reported for colorectal cancer (CRC) due to the lack of control for neural and patient subtype diversity. In this study, we investigated the effect of neural cues on patient-derived CRC cell lines selected based on genomic status, e.g., microsatellite instability (MSI) and KRAS and BRAF mutations. Although most neural signals increased clonogenicity, the adrenergic neurotransmitter epinephrine had the opposite effect. Epinephrine also decreased CRC cell viability, independent of the genomic status. Vasoactive intestinal peptide decreased cell viability only in BRAF wild-type cells. Interestingly, all neural signals induced migration in microsatellite stable (MSS) cells, with no effect in cells with MSI. Epinephrine or glial cell line-derived neurotrophic factor also stimulated migration specifically in BRAF-mutated cells. These results emphasize the importance of targeting specific neural signaling pathways and highlight that patient stratification is essential for cancer neuroscience studies.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repositories

Its files are read in the Code ↔ Paper reader above, with 1 match between paragraphs and lines of code.

alexdobin/STAR

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: b1edc1208d91a53bf40ebae8669f71d50b994851, 25 January 2024
Languages: C++ (160), C/C++ (133), C (34), Shell (1), MATLAB (1)
Size: 389 files, 329 scripts
Software Heritage: not archived
Found in: the text, “Key resources table”
Holds: README, license file, environment (extras/docker/Dockerfile), continuous integration, documentation
Not found: CITATION.cff, tests
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
331 files

deweylab/RSEM

License: GPL-3.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 800234e0d25d16bf7042804604c4371f12b96d9e, 3 August 2026
Languages: C/C++ (180), C (111), C++ (48), Perl (16), Python (11), R (11), Shell (7), Java (1)
Size: 3,057 files, 385 scripts
Software Heritage: not archived
Found in: the text, “Key resources table”
Holds: README, license file, environment (tests/environment.yml, pRSEM/phantompeakqualtools/spp_1.10.1_on_R3.2/DESCRIPTION, pRSEM/phantompeakqualtools/spp_1.10.1_on_R3.3/DESCRIPTION), tests, continuous integration, documentation
Not found: CITATION.cff
Tools: data.table (2 files), STAR (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
387 files

The paper's code and data availability statement is in the Data section.

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  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
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  • 1 match between paragraphs of the paper and lines of the code (method lexical-v1);
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Data

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Data and code availability

• All data reported in this paper will be shared by the lead contact upon reasonable request. • This paper does not report original code • Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Version 2, 28 September 2026

  • Authors: added Veerle Melotte (0000-0002-9459-123X); removed Veerle Melotte

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 7 authors, 3 keywords, 5 funders, 71 references.

Cite

This paper

Thijssen, M. S., Chilà, R., Crisafulli, G., Smits, K. M., Bardelli, A., Boesmans, W., & Melotte, V. (2026). Neural cues differentially modulate colorectal cancer cell behavior depending on patients' genomic background. iScience, 29(6), 116153. https://doi.org/10.1016/j.isci.2026.116153

BibTeX

@article{thijssen2026neural,
author = {Thijssen, Meike S. and Chilà, Rosaria and Crisafulli, Giovanni and Smits, Kim M. and Bardelli, Alberto and Boesmans, Werend and Melotte, Veerle},
title = {{Neural cues differentially modulate colorectal cancer cell behavior depending on patients' genomic background}},
journal = {iScience},
year = {2026},
month = may,
volume = {29},
number = {6},
pages = {116153},
publisher = {Elsevier},
issn = {2589-0042},
doi = {10.1016/j.isci.2026.116153},
url = {https://doi.org/10.1016/j.isci.2026.116153},
pmid = {42256296},
pmcid = {PMC13233565}
}

RIS

TY - JOUR
AU - Thijssen, Meike S.
AU - Chilà, Rosaria
AU - Crisafulli, Giovanni
AU - Smits, Kim M.
AU - Bardelli, Alberto
AU - Boesmans, Werend
AU - Melotte, Veerle
TI - Neural cues differentially modulate colorectal cancer cell behavior depending on patients' genomic background
T2 - iScience
J2 - iScience
PY - 2026
DA - 2026/05/28
VL - 29
IS - 6
SP - 116153
SN - 2589-0042
PB - Elsevier
DO - 10.1016/j.isci.2026.116153
UR - https://doi.org/10.1016/j.isci.2026.116153
LA - en
ER -

CSL-JSON

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"title": "Neural cues differentially modulate colorectal cancer cell behavior depending on patients' genomic background",
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