reddyGeneticFunctionalDrivers2017

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title: '' bibliography: 'morinlab.bib'

@reddyGeneticFunctionalDrivers2017

Study Overview

Reddy et al. conducted a comprehensive analysis of Diffuse Large B Cell Lymphoma (DLBCL) using the exomes of 1,001 cases, identifying 150 driver genes. Their findings provided significant insights into the genetic alterations and functional pathways underlying DLBCL. Key outcomes of the study included:

Key Findings

1. Recurrent Mutations:

   • Identified mutations in genes such as MYD88, CD79B, EZH2, and TP53, highlighting their importance in DLBCL pathogenesis.

2. Pathway Analysis:

   • Mutations impacted key signaling pathways, including:
     • B-cell receptor signaling
     • NF-κB signaling
     • Chromatin modification

3. Molecular Subtypes:

   • Contributed to the molecular classification of DLBCL into subtypes like germinal center B-cell-like (GCB) and activated B-cell-like (ABC), with distinct genetic and clinical features.

4. Therapeutic Implications:

   • Suggested potential targeted therapies focusing on B-cell receptor and NF-κB pathways, among others.

This research represented a foundational step in understanding the genetic drivers of DLBCL, informing therapeutic development.

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Post-Publication Critical Analysis

Subsequent scrutiny of the study revealed significant issues with its findings:

1. Reanalysis of Data:

   • A 2023 preprint by Dreval et al. reanalyzed the original data using open-source pipelines and uncovered discrepancies in the findings.

2. Variant Reproducibility:

   • Thousands of mutations reported by Reddy et al. could not be independently reproduced, raising concerns about the reliability of the variant-calling methods.

3. Mutation Prevalence:

   • Clinically important mutations, such as those in EZH2 and CD79B, were underrepresented in the original findings.

4. Analytical Errors:

   • Errors in the original data analysis pipeline were identified, potentially affecting the accuracy of genetic alteration reporting.

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Implications and Lessons

The 2017 study provided valuable insights into DLBCL's genetic landscape and functional pathways. However, subsequent critical analyses underscore the need for:

1. Rigorous Data Validation:

   • Ensuring reproducibility of findings by using standardized, open-source pipelines.

2. Transparency in Methods:

   • Clear documentation of analytical approaches to allow independent verification.

3. Reevaluation of Findings:

   • Revisiting conclusions with improved methodologies to refine our understanding of DLBCL pathogenesis.

While the original study remains influential in the field, its findings must be interpreted with caution in light of these critiques. Ongoing research will continue to refine the genetic and functional drivers of DLBCL, ensuring a more accurate and actionable understanding of this heterogeneous disease.

Summary of novel genes

Entity	Tier 1 genes	Tier 2 genes	Tier 3 genes
DLBCL	13	28	26

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sankey-beta
This study, New Tier 1, 13
New Tier 1, DLBCL Tier 1, 13
This study, New Tier 2, 28
New Tier 2, DLBCL Tier 2, 28
This study, New Tier 3, 26
New Tier 3, DLBCL Tier 3, 26
All other DLBCL studies, DLBCL Tier 1, 112
All other DLBCL studies, DLBCL Tier 2, 182
All other DLBCL studies, DLBCL Tier 3, 361

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Novel genes reported in this study

Tier 1

New gene	DLBCL tier	Average variant quality	QC outcome
ATM	1
BIRC6	1
DDX3X	1
HNRNPU	1
MGA	1
PIM2	1
PTEN	1
PTPN6	1	★ ★ ★ ☆ ☆	PASS
SF3B1	1
SMARCA4	1
TMSB4X	1
TOX	1
ZNF292	1

Tier 2

New gene	DLBCL tier	Average variant quality	QC outcome
ARID5B	2	★ ★ ★ ★ ☆	PASS
ATR	2	★ ★ ★ ☆ ☆	PASS
BRINP3	2	★ ★ ★ ☆ ☆	PASS
CASP8	2
CD22	2	★ ★ ★ ☆ ☆	PASS
FOXP1	2	★ ★ ★ ☆ ☆	PASS
FUBP1	2	★ ★ ★ ☆ ☆	PASS
GOLGA5	2	★ ★ ★ ☆ ☆	PASS
HNRNPD	2
IKBKB	2	★ ★ ★ ☆ ☆	PASS
IL16	2
INO80	2
JAK1	2
JAK3	2
KCMF1	2	★ ★ ★ ☆ ☆	PASS
MAGT1	2	★ ★ ★ ☆ ☆	PASS
MCL1	2	★ ★ ★ ☆ ☆	PASS
MECOM	2	★ ★ ★ ☆ ☆	PASS
MET	2	★ ★ ★ ☆ ☆	PASS
MSH6	2	★ ★ ★ ☆ ☆	PASS
SETD5	2	★ ★ ★ ☆ ☆	PASS
STAT5B	2
TCL1A	2
TGFBR2	2	★ ★ ★ ☆ ☆	PASS
TIPARP	2	★ ★ ★ ☆ ☆	PASS
YY1	2	★ ★ ★ ☆ ☆	PASS
ZEB2	2
ZFX	2	★ ★ ★ ☆ ☆	PASS

Tier 3

New gene	DLBCL tier	Average variant quality	QC outcome
ARID1B	3	★ ★ ☆ ☆ ☆	FAIL
CBLB	3	★ ★ ☆ ☆ ☆	FAIL
CDC73	3	★ ★ ☆ ☆ ☆	FAIL
CDH8	3	★ ★ ☆ ☆ ☆	FAIL
CHD1	3	★ ★ ☆ ☆ ☆	FAIL
CHST2	3	★ ★ ☆ ☆ ☆	FAIL
DCAF6	3	★ ★ ☆ ☆ ☆	FAIL
DICER1	3	★ ★ ☆ ☆ ☆	FAIL
DNMT3A	3	★ ☆ ☆ ☆ ☆	FAIL
GNAS	3	★ ★ ☆ ☆ ☆	FAIL
HRAS	3	★ ★ ☆ ☆ ☆	FAIL
LIN54	3	★ ★ ☆ ☆ ☆	FAIL
MAP4K4	3	★ ★ ☆ ☆ ☆	FAIL
MARK1	3	★ ☆ ☆ ☆ ☆	FAIL
MSH2	3	★ ★ ☆ ☆ ☆	FAIL
MYB	3	★ ★ ☆ ☆ ☆	FAIL
NCOR1	3	★ ★ ☆ ☆ ☆	FAIL
NFKB2	3	★ ★ ☆ ☆ ☆	FAIL
PHF6	3	★ ★ ☆ ☆ ☆	FAIL
PTPRK	3	★ ★ ☆ ☆ ☆	FAIL
RARA	3	★ ☆ ☆ ☆ ☆	FAIL
RUNX1	3	★ ★ ☆ ☆ ☆	FAIL
SYK	3	★ ★ ☆ ☆ ☆	FAIL
WAC	3	★ ★ ☆ ☆ ☆	FAIL
ZBTB7A	3	★ ☆ ☆ ☆ ☆	FAIL
ZFAT	3	★ ★ ☆ ☆ ☆	FAIL

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