The DOI matches its Crossref record; 47 of the 47 references it cites were found in a registry or an index; none of the 28 sources its claims cite is checked for support yet.
Multimodal pooled Perturb-CITE-seq screens in patient models define mechanisms of cancer immune evasion
Open accessPublished version of a preprint
Abstract
Resistance to immune checkpoint inhibitors (ICIs) is a key challenge in cancer therapy. To elucidate underlying mechanisms, we developed Perturb-CITE-sequencing (Perturb-CITE-seq), enabling pooled clustered regularly interspaced short palindromic repeat (CRISPR)-Cas9 perturbations with single-cell transcriptome and protein readouts. In patient-derived melanoma cells and autologous tumor-infiltrating lymphocyte (TIL) co-cultures, we profiled transcriptomes and 20 proteins in ~218,000 cells under ~750 perturbations associated with cancer cell-intrinsic ICI resistance (ICR). We recover known mechanisms of resistance, including defects in the interferon-γ (IFN-γ)-JAK/STAT and antigen-presentation pathways in RNA, protein and perturbation space, and new ones, including loss/downregulation of CD58. Loss of CD58 conferred immune evasion in multiple co-culture models and was downregulated in tumors of melanoma patients with ICR. CD58 protein expression was not induced by IFN-γ signaling, and CD58 loss conferred immune evasion without compromising major histocompatibility complex (MHC) expression, suggesting that it acts orthogonally to known mechanisms of ICR. This work provides a framework for the deciphering of complex mechanisms by large-scale perturbation screens with multimodal, single-cell readouts, and discovers potentially clinically relevant mechanisms of immune evasion.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
65 Own result · 23 Own interpretation · 19 Not checked yet · 11 Own method · 2 No basis given
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Other enriched perturbations included CD47, CD58, CDH19 and (Fig. 2d,e).”
§ Multi-modal pooled Perturb-CITE-Seq screens in patient models define novel mechanisms of cancer immune evasion > RESULTS > Pooled screens identify mechanisms of immune evasion
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“co-cultures of T cells and cancer cells resulted in dose- (TIL:cancer cell ratio) and time-dependent cancer cell killing”
§ Multi-modal pooled Perturb-CITE-Seq screens in patient models define novel mechanisms of cancer immune evasion > RESULTS > Patient-derived co-cultures for pooled perturbation screens
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Loss of CD58 conferred immune evasion in multiple co-culture models”
§ Multi-modal pooled Perturb-CITE-Seq screens in patient models define novel mechanisms of cancer immune evasion > Abstract
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“we developed Perturb-CITE-seq, enabling pooled CRISPR-Cas9 perturbations with single-cell transcriptome and protein read-outs.”
§ Multi-modal pooled Perturb-CITE-Seq screens in patient models define novel mechanisms of cancer immune evasion > Abstract
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“we find that loss or downregulation of CD58 confers immune evasion to T cell (and in part to Natural Killer (NK) cells) mediated killing”
§ Multi-modal pooled Perturb-CITE-Seq screens in patient models define novel mechanisms of cancer immune evasion > INTRODUCTION
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“was downregulated in tumors of melanoma patients with ICR.”
§ Multi-modal pooled Perturb-CITE-Seq screens in patient models define novel mechanisms of cancer immune evasion > Abstract
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“CD58 loss conferred immune evasion without compromising MHC expression”
§ Multi-modal pooled Perturb-CITE-Seq screens in patient models define novel mechanisms of cancer immune evasion > Abstract
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We recover known mechanisms of resistance, including defects in the IFNγ-JAK/STAT and antigen-presentation pathways in RNA, protein and perturbation space, and novel ones, including loss/downregulation of CD58.”
§ Multi-modal pooled Perturb-CITE-Seq screens in patient models define novel mechanisms of cancer immune evasion > Abstract
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In patient-derived melanoma cells and autologous tumor infiltrating lymphocyte (TIL) co-cultures, we profiled transcriptomes and 20 proteins in ~218,000 cells under ~750 perturbations associated with cancer cell-intrinsic ICR.”
§ Multi-modal pooled Perturb-CITE-Seq screens in patient models define novel mechanisms of cancer immune evasion > Abstract
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“co-culture showed dose-dependent lysis of cancer cells with 30.96%, 58.35 % and 75.0% killing at 1:1, 2:1 and 4:1 ratios, respectively”
§ Multi-modal pooled Perturb-CITE-Seq screens in patient models define novel mechanisms of cancer immune evasion > RESULTS > Pooled screens identify mechanisms of immune evasion
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- It may state two separate claims as one. 46
- Probably not a substantial claim. 11
- The quote only partly states this claim. 2
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We performed pooled Perturb-CITE-Seq screens of the ICR program genes in a patient-derived tumor-TIL co-culture model, targeting 248 genes of the ICR signature (744 targeting guides) and profiling single-cell transcriptomes and 20 surface proteins in >218,000 cells.”
§ Multi-modal pooled Perturb-CITE-Seq screens in patient models define novel mechanisms of cancer immune evasion > INTRODUCTION
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“and we validate this finding in additional patient models and in clinical scRNA-seq data of patients with ICR.”
§ Multi-modal pooled Perturb-CITE-Seq screens in patient models define novel mechanisms of cancer immune evasion > INTRODUCTION
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We developed an integrated analytical framework and recover the known landscape of clinically relevant immune resistance mutations along with novel ones.”
§ Multi-modal pooled Perturb-CITE-Seq screens in patient models define novel mechanisms of cancer immune evasion > INTRODUCTION
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“perturbations of B2M, HLA-A, JAK1, JAK2, STAT1, IFNGR1 and IFNGR2 (Fig. 2d,e, Extended Data Fig. 3e,f, Supplementary Table 2) conferred immune evasion, consistent with clinical mechanisms of resistance to immunotherapy7–9.”
§ Multi-modal pooled Perturb-CITE-Seq screens in patient models define novel mechanisms of cancer immune evasion > RESULTS > Pooled screens identify mechanisms of immune evasion
What the sources say
- Not checked yetcites [7] Zaretsky, J. M. et al. Mutations associated with acquired resistance to PD-1 blockade in melanoma. N. Engl. J. Med. 375, 819–829 (2016). source
- Not checked yetcites [8] Sade-Feldman, M. et al. Resistance to checkpoint blockade therapy through inactivation of antigen presentation. Nat. Commun. 8, 1136 (2017). source
- Not checked yetcites [9] Jerby-Arnon, L. et al. A cancer cell program promotes T cell exclusion and resistance to checkpoint blockade. Cell 175, 984–997 (2018). source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Tumor cell lysis was highly reproducible and specific to T cell receptor (TCR)/MHC Class I interactions”
§ Multi-modal pooled Perturb-CITE-Seq screens in patient models define novel mechanisms of cancer immune evasion > RESULTS > Patient-derived co-cultures for pooled perturbation screens
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“At the protein level, we observe increases in MHC proteins and PD-L1 (CD274) in IFNγ-treated vs. control cells; a global increase in MHC proteins in co-culture vs. control cells”
§ Multi-modal pooled Perturb-CITE-Seq screens in patient models define novel mechanisms of cancer immune evasion > RESULTS > Multi-modal single-cell readouts of immune evasion
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“the CD58 KO conferred a different molecular phenotype than IFNγ-JAK/STAT pathway perturbations, belonged to a distinct module, and did not impact the expression of antigen presentation genes”
§ Multi-modal pooled Perturb-CITE-Seq screens in patient models define novel mechanisms of cancer immune evasion > RESULTS > Loss/downregulation of CD58 confers cancer immune evasion
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“MaMel-134 TILs produce IFNγ, TNFa, and Granzyme-B.”
§ Multi-modal pooled Perturb-CITE-Seq screens in patient models define novel mechanisms of cancer immune evasion > Extended Data
plainSingle case- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“integrating RNA and protein measurements for joint analysis (Methods) highlighted gene programs that are either common across conditions, or unique to different conditions (Fig. 3i), with the co-cultured cells uniquely enriched for induction of immune escape pathway genes and the ICR signature.”
§ Multi-modal pooled Perturb-CITE-Seq screens in patient models define novel mechanisms of cancer immune evasion > RESULTS > Multi-modal single-cell readouts of immune evasion
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“CD58 emerged as a compelling mechanism of immune evasion: KO of CD58 conferred resistance to T cell killing in the viability screen and CD58 RNA/protein were down regulated in cells surviving TIL co-culture.”
§ Multi-modal pooled Perturb-CITE-Seq screens in patient models define novel mechanisms of cancer immune evasion > RESULTS > Loss/downregulation of CD58 confers cancer immune evasion
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Conversely, neither B2M nor HLA-A KO impacted CD58 protein levels in our Perturb-CITE-Seq screen”
§ Multi-modal pooled Perturb-CITE-Seq screens in patient models define novel mechanisms of cancer immune evasion > RESULTS > Loss of CD58 is an orthogonal mechanism of immune evasion
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Using flow-cytometry, we show that compared to parental cells, CD58 KO did not alter expression of MHC proteins at baseline or their induction in response to IFNγ”
§ Multi-modal pooled Perturb-CITE-Seq screens in patient models define novel mechanisms of cancer immune evasion > RESULTS > Loss of CD58 is an orthogonal mechanism of immune evasion
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The Perturb-CITE-Seq model correctly reconstructed impacts of genes known to affect resistance to immunotherapy, especially the effect of perturbing the IFNγ response machinery (Fig. 4b), along with novel pathways.”
§ Multi-modal pooled Perturb-CITE-Seq screens in patient models define novel mechanisms of cancer immune evasion > RESULTS > Perturb-CITE-Seq identifies immune evasion programs/modules
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In particular, CD58, CD47, and IFNGR1 (CD119) protein levels were reduced in co-culture, concordant with enrichment of their genetic KO in the viability screen”
§ Multi-modal pooled Perturb-CITE-Seq screens in patient models define novel mechanisms of cancer immune evasion > RESULTS > Multi-modal single-cell readouts of immune evasion
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Proportion of mKATE2+ cells prior to selection (x axis) and survival after puromycin selection (y axis) in 2686 melanoma Cas9 transgenic cells transduced with the ICR library. Line: Linear regression, Pearson R2=0.90.”
§ Multi-modal pooled Perturb-CITE-Seq screens in patient models define novel mechanisms of cancer immune evasion > Extended Data
plaincorrelationalSingle case- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Empirically, adding a second iteration had only a very modest effect on sgRNA/target assignments.”
§ Multi-modal pooled Perturb-CITE-Seq screens in patient models define novel mechanisms of cancer immune evasion > METHODS > Computational model for Perturb-CITE-Seq analysis
plainSingle case- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The downregulated program included key components of antigen presentation and associated machinery (e.g., PSMB4, PSMB8, PSMB9, PSMA4, HLA-A,B,C gene and protein, HLA-E, HLA-F, HLAD-DPB1), chemokines associated with anti-tumor immunity (CXCL1, CXCL2, CXCL8, CXCL10, CXCL11), inflammatory cytokines (STAT3, IL1B, IL6), interferon response elements (STAT1, IRF1, IRF3, IFITM3, IFIT6), surface checkp”
§ Multi-modal pooled Perturb-CITE-Seq screens in patient models define novel mechanisms of cancer immune evasion > RESULTS > Perturb-CITE-Seq identifies immune evasion programs/modules
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In all models, CD58 KO and B2M KO conferred resistance (Fig. 5b-d) while PD-L1 KO sensitized cells to T cell mediated killing”
§ Multi-modal pooled Perturb-CITE-Seq screens in patient models define novel mechanisms of cancer immune evasion > RESULTS > Loss/downregulation of CD58 confers cancer immune evasion
plaindirectn = 3- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Within each condition, both the cell cycle (G1/0, S, G2/M) (Fig. 3f) and complexity (Extended Data Fig. 4c) impacted RNA profiles”
§ Multi-modal pooled Perturb-CITE-Seq screens in patient models define novel mechanisms of cancer immune evasion > RESULTS > Multi-modal single-cell readouts of immune evasion
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Several programs, including cell cycle regulation, DNA repair, and antigen presentation, were shared across conditions, whereas immune escape programs were uniquely recovered in co-culture data, and interferon response genes in IFNγ stimulation”
§ Multi-modal pooled Perturb-CITE-Seq screens in patient models define novel mechanisms of cancer immune evasion > RESULTS > Multi-modal single-cell readouts of immune evasion
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“TILs from 2686 retain ability to induce IFNγ and TNFa, and OKT-3 reactivation leads to an increase in Granzyme-B production compared to TILs grown in IL2 alone.”
§ Multi-modal pooled Perturb-CITE-Seq screens in patient models define novel mechanisms of cancer immune evasion > Extended Data
plaindirectSingle case- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Overall, the viability screen recovered known immune evasion mechanisms in a dose-dependent manner, and identified several novel candidates.”
§ Multi-modal pooled Perturb-CITE-Seq screens in patient models define novel mechanisms of cancer immune evasion > RESULTS > Pooled screens identify mechanisms of immune evasion
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“For example, in the co-culture condition, a single iteration of EM adjusts the assignment of 1,741 targets to near zero (confidence that the cell contains an effective sgRNA < 25%), while an additional iteration only adjusted 97 target-cell assignments.”
§ Multi-modal pooled Perturb-CITE-Seq screens in patient models define novel mechanisms of cancer immune evasion > METHODS > Computational model for Perturb-CITE-Seq analysis
plaindirectSingle case- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“MaMel-80 TILs produce IFNγ, TNFa, and Granzyme-B.”
§ Multi-modal pooled Perturb-CITE-Seq screens in patient models define novel mechanisms of cancer immune evasion > Extended Data
plainSingle case- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“CITE profiles of 20 cell surface proteins do not reflect cell cycle phases (d) or UMI count (e).”
§ Multi-modal pooled Perturb-CITE-Seq screens in patient models define novel mechanisms of cancer immune evasion > Extended Data
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Comparable induction of apoptosis in response to Staurosporin and resistance to DTIC in control and KO melanoma cells.”
§ Multi-modal pooled Perturb-CITE-Seq screens in patient models define novel mechanisms of cancer immune evasion > Extended Data
plainSingle case- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“CD58 perturbation in co-culture does not affect B2M and HLA expression at the RNA and protein level but induces CD274.”
§ Multi-modal pooled Perturb-CITE-Seq screens in patient models define novel mechanisms of cancer immune evasion > Extended Data
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Rests on an assumption: The patient-derived melanoma-TIL co-culture with IFNγ pretreatment models T cell-mediated immune selection in human melanoma.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“G2M program learned from control dataset.”
§ Multi-modal pooled Perturb-CITE-Seq screens in patient models define novel mechanisms of cancer immune evasion > Extended Data
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“CD58 (also known as LFA3) is an understudied adhesion molecule, typically expressed on antigen-presenting cells, that binds CD2 on T cell and Natural Killer (NK) cells22.”
§ Multi-modal pooled Perturb-CITE-Seq screens in patient models define novel mechanisms of cancer immune evasion > RESULTS > Pooled screens identify mechanisms of immune evasion
What the sources say
- Not checked yetcites [22] Arulanandam, A. R. et al. The CD58 (LFA-3) binding site is a localized and highly charged surface area on the AGFCC’C” face of the human CD2 adhesion domain. Proc. Natl Acad. Sci. USA 90, 11613–11617 (1993). source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Recent studies revealed that activated T cells also express SIRPa and that interaction with CD47 results in increased T cell activation and cytokine production20, which may impact the potential clinical benefit of CD47-blockade21.”
§ Multi-modal pooled Perturb-CITE-Seq screens in patient models define novel mechanisms of cancer immune evasion > RESULTS > Pooled screens identify mechanisms of immune evasion
What the sources say
- Not checked yetcites [20] Myers, L. M. et al. A functional subset of CD8+ T cells during chronic exhaustion is defined by SIRPα expression. Nat. Commun. 10, 794 (2019). source
- Not checked yetcites [21] Zhang, W. et al. Advances in anti-tumor treatments targeting the CD47/SIRPα Axis. Front. Immunol. 11, 18 (2020).
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“A proof-of-concept study using ECCITE-seq5 demonstrated simultaneous detection of protein, RNA, and guide RNAs in individual cells and was applied at a small scale without the analytical framework required for large scale screens.”
§ Multi-modal pooled Perturb-CITE-Seq screens in patient models define novel mechanisms of cancer immune evasion > INTRODUCTION
What the sources say
- Not checked yetcites [5] Mimitou, E. P. et al. Multiplexed detection of proteins, transcriptomes, clonotypes and CRISPR perturbations in single cells. Nat. Methods 16, 409–412 (2019). source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Clinically relevant mechanisms of include mutations in beta-2-microglobulin (B2M) resulting in loss of MHC Class I presentation, downregulation of the antigen presentation machinery, and mutations in the IFNγ-JAK/STAT pathway that impair response to T cell mediated anti-tumor activity7–9.”
§ Multi-modal pooled Perturb-CITE-Seq screens in patient models define novel mechanisms of cancer immune evasion > INTRODUCTION
What the sources say
- Not checked yetcites [7] Zaretsky, J. M. et al. Mutations associated with acquired resistance to PD-1 blockade in melanoma. N. Engl. J. Med. 375, 819–829 (2016). source
- Not checked yetcites [8] Sade-Feldman, M. et al. Resistance to checkpoint blockade therapy through inactivation of antigen presentation. Nat. Commun. 8, 1136 (2017). source
- Not checked yetcites [9] Jerby-Arnon, L. et al. A cancer cell program promotes T cell exclusion and resistance to checkpoint blockade. Cell 175, 984–997 (2018). source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“CRISPR/Cas9 KO screens in murine models or engineered human cell lines testing tumor-T cell interactions identified putative novel mechanisms of immune evasion involving chromatin regulators10, TNFα signaling11, SOX412, PTPN213, and APLNR10, yet their clinical relevance is unclear.”
§ Multi-modal pooled Perturb-CITE-Seq screens in patient models define novel mechanisms of cancer immune evasion > INTRODUCTION
What the sources say
- Not checked yetcites [10] Patel, S. J. et al. Identification of essential genes for cancer immunotherapy. Nature 548, 537–542 (2017). source
- Not checked yetcites [11] Kearney, C. J. et al. Tumor immune evasion arises through loss of TNF sensitivity. Sci. Immunol. 3, eaar3451 (2018).
- Not checked yetcites [12] Pan, D. et al. A major chromatin regulator determines resistance of tumor cells to T cell-mediated killing. Science 359, 770–775 (2018). source
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“However, many relevant phenotypes are functionally best understood at protein level, and are reflected differently at the RNA and protein level, thus requiring expansion of prior Perturb-Seq schemes.”
§ Multi-modal pooled Perturb-CITE-Seq screens in patient models define novel mechanisms of cancer immune evasion > INTRODUCTION
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“CD47 is a “don’t eat me” signal on tumor cells that interacts with SIRPa on phagocytic cells; and blockade of the CD47/SIRPa axis improves tumor control in vivo19.”
§ Multi-modal pooled Perturb-CITE-Seq screens in patient models define novel mechanisms of cancer immune evasion > RESULTS > Pooled screens identify mechanisms of immune evasion
What the sources say
- Not checked yetcites [19] Veillette, A. & Chen, J. SIRPα–CD47 immune checkpoint blockade in anticancer therapy. Trends Immunol. 39, 173–184 (2018). source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“While ICIs produce durable responses in some patients most patients either do not benefit or develop resistance over time.”
§ Multi-modal pooled Perturb-CITE-Seq screens in patient models define novel mechanisms of cancer immune evasion > INTRODUCTION
What the sources say
- Not checked yetcites [6] Sharma, P. & Allison, J. P. The future of immune checkpoint therapy. Science 348, 56–61 (2015). source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Taken together, our Perturb-CITE-Seq analysis rediscovered key mechanisms of immune evasion, organized them into modules and related them to the genes and programs they impact, as well as recovered new modules that may confer immune evasion beyond defects in the IFNγ-JAK/STAT pathway and antigen presentation.”
§ Multi-modal pooled Perturb-CITE-Seq screens in patient models define novel mechanisms of cancer immune evasion > RESULTS > Perturb-CITE-Seq identifies immune evasion programs/modules
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Thus, multiple transcripts and proteins implicated in modifying the response to immunotherapies are directly regulated by the IFNγ-JAK/STAT axis, suggesting that some of these mechanisms are a consequence of defective IFNγ-JAK/STAT signaling, rather than independent modes of resistance.”
§ Multi-modal pooled Perturb-CITE-Seq screens in patient models define novel mechanisms of cancer immune evasion > RESULTS > Perturb-CITE-Seq identifies immune evasion programs/modules
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Furthermore, PD-L1 is upregulated in CD58 KO, suggesting that CD58 loss could confer immune evasion directly (reduced adhesion) or indirectly (inhibitory PD-L1)”
§ Multi-modal pooled Perturb-CITE-Seq screens in patient models define novel mechanisms of cancer immune evasion > DISCUSSION
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Our study presents a large-scale CRISPR/Cas9 screen with multi-modal single-cell readouts, providing a general approach, as well as addressing a critical clinical challenge in a unique patient-derived cell culture model system.”
§ Multi-modal pooled Perturb-CITE-Seq screens in patient models define novel mechanisms of cancer immune evasion > DISCUSSION
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Melanoma cells tested repeatedly negative for contamination with mycoplasma and other contaminants using PlasmoTest (InvivoGen, San Diego, CA).”
§ Multi-modal pooled Perturb-CITE-Seq screens in patient models define novel mechanisms of cancer immune evasion > METHODS > Patient derived melanoma cell culture
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“CD58 KO also impacted NK-mediated killing (Fig. 5g), which may have implications for NK cell-based therapies in tumors with loss of antigen presentation.”
§ Multi-modal pooled Perturb-CITE-Seq screens in patient models define novel mechanisms of cancer immune evasion > DISCUSSION
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“This procedure partially corrects for the bias of sample size and effect size in regulatory coefficients, and enables direct comparison of the empirical p values across targets.”
§ Multi-modal pooled Perturb-CITE-Seq screens in patient models define novel mechanisms of cancer immune evasion > METHODS > Computational model for Perturb-CITE-Seq analysis
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Our experiments and analysis show that loss of CD58 confers immune evasion to a similar extent as loss of MHC Class I expression itself (through B2M KO), but through an independent path, and without impacting the expression of antigen presentation genes and proteins”
§ Multi-modal pooled Perturb-CITE-Seq screens in patient models define novel mechanisms of cancer immune evasion > DISCUSSION
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Including these covariates improved the model fit quality (residuals approach mean zero, independent and identically distributed; Extended Data Fig. 6b,c).”
§ Multi-modal pooled Perturb-CITE-Seq screens in patient models define novel mechanisms of cancer immune evasion > RESULTS > A computational framework for analysis of Perturb-CITE-Seq
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
The quote only partly states this claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Some of the genes down-regulated by IFNγ pathway perturbations, such as CXCL10 and CXCL11, are genes whose downregulation was previously associated with immune evasion, suggesting that these do not represent a salient mechanisms of immune evasion.”
§ Multi-modal pooled Perturb-CITE-Seq screens in patient models define novel mechanisms of cancer immune evasion > DISCUSSION
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“A full EM approach is not guaranteed to converge to a global minimum in a high-dimensional linear regression problem with noisy and missing data36.”
§ Multi-modal pooled Perturb-CITE-Seq screens in patient models define novel mechanisms of cancer immune evasion > METHODS > Computational model for Perturb-CITE-Seq analysis
What the sources say
- Not checked yetcites [36] Loh, P.-L. & Wainwright, M. J. High-dimensional regression with noisy and missing data: provable guarantees with nonconvexity. Ann. Stat. 40, 1637–1664 (2012). source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“consistent with our previously reported patient and pre-clinical data, where we demonstrate that upregulation of MYC and CDK4/6 (and their transcriptional targets) was associated with increased resistance to immunotherapy, and CDK4/6 inhibitors reduced resistance in human and pre-clinical melanoma models9.”
§ Multi-modal pooled Perturb-CITE-Seq screens in patient models define novel mechanisms of cancer immune evasion > RESULTS > Perturb-CITE-Seq identifies immune evasion programs/modules
What the sources say
- Not checked yetcites [9] Jerby-Arnon, L. et al. A cancer cell program promotes T cell exclusion and resistance to checkpoint blockade. Cell 175, 984–997 (2018). source
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“All experiments were performed in triplicates in each of at least two independent experiments.”
- Also stated in: § Extended Data · plain
“All experiments were performed in triplicates in each of at least two independent experiments.”
§ Multi-modal pooled Perturb-CITE-Seq screens in patient models define novel mechanisms of cancer immune evasion > Extended Data
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Also stated in: § Extended Data · plain
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Screen reproducibility across triplicates.”
§ Multi-modal pooled Perturb-CITE-Seq screens in patient models define novel mechanisms of cancer immune evasion > Extended Data
plainn = 3- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
The quote only partly states this claim.
None of the sources cited for this claim have been checked yet.
In the paper
“Notably, the CD58/CD2 axis is a potent co-stimulatory pathway in CD8+ T cells that lack expression of CD28 (CD8+CD28- T cells)29, which are common in the tumor-microenvironment and peripheral blood of solid tumor patients30, and were the predominant T cells in our Perturb-CITE-Seq screen”
§ Multi-modal pooled Perturb-CITE-Seq screens in patient models define novel mechanisms of cancer immune evasion > DISCUSSION
What the sources say
- Not checked yetcites [29] Leitner, J., Herndler-Brandstetter, D., Zlabinger, G. J., Grubeck-Loebenstein, B. & Steinberger, P. CD58/CD2 is the primary costimulatory pathway in human CD28–CD8+ T cells. J. Immunol. 195, 477–487 (2015). source
- Not checked yetcites [30] Strioga, M., Pasukoniene, V. & Characiejus, D. CD8+CD28– and CD8+CD57+ T cells and their role in health and disease. Immunology 134, 17–32 (2011). source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We recover the landscape of resistance mechanisms to immunotherapies and, guided by our high-dimensional data, validate a novel mechanism of immune resistance.”
§ Multi-modal pooled Perturb-CITE-Seq screens in patient models define novel mechanisms of cancer immune evasion > DISCUSSION
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In contrast, other genes whose perturbation enhanced immune evasion in our screen (e.g., CD58, CD59) appear to reflect distinct mechanism, both because their expression was not impaired by IFNγ signaling defects, and because the molecular phenotype following their perturbation is distinct.”
§ Multi-modal pooled Perturb-CITE-Seq screens in patient models define novel mechanisms of cancer immune evasion > DISCUSSION
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Loss or downregulation of CD58 does not interfere with antigen presentation or JAK/STAT-signaling per se, but reduces T cell co-stimulation while simultaneously promoting increased co-inhibitory signaling via PD-L1, and therefore overall confers resistance to T cell mediated killing.”
§ Multi-modal pooled Perturb-CITE-Seq screens in patient models define novel mechanisms of cancer immune evasion > Extended Data
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Rests on an assumption: The patient-derived melanoma-TIL co-culture with IFNγ pretreatment models T cell-mediated immune selection in human melanoma.
The paper relies on this without support.
Genetic signals account for only a minority of ICI resistance cases.
“However, genetic signals only account for a minority of cases.”
The paper relies on this without support.
The role of CD58 in cancer is poorly understood.
“The role of CD58 in cancer is poorly understood.”
The paper relies on this without support.
The patient-derived melanoma-TIL co-culture with IFNγ pretreatment models T cell-mediated immune selection in human melanoma.
“Surviving target cells on distinct plates containing all conditions as triplicates were counted to quantify target cell killing to verify accurate immune selection pressure as previously determined in the miniaturized co-culture experiments (above).”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Three CITE-seq datasets have protein and RNA counts that are downloadable separately6,9.”
From scPerturb: harmonized single-cell perturbation data (2024) · cites this paper as [6]
In the citing paper
“Protein, RNA and ATAC readouts for CRISPRi perturbation of MYC are all available for K562 cells6,20,42.”
From scPerturb: harmonized single-cell perturbation data (2024) · cites this paper as [6]
In the citing paper
“ECCITE-seq [15] and Perturb-CITE-seq [16] are two paradigmatic cases that entail in tandem detection of transcripts, proteins, and sgRNAs.”
From A mini-review on perturbation modelling across single-cell omic modalities (2024) · cites this paper as [16]
Citations
Cites
Scikit-learn: Machine Learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016
PD-1 and PD-L1 Immune Checkpoint Blockade to Treat Breast Cancer
Hartkopf, A. D., Taran, F., Wallwiener, M., Walter, C. B., Krämer, B., Grischke, E., & Brucker, S. Y. (2016) · Breast Care · doi:10.1159/000453569 · Cited by 30,989
Fast unfolding of communities in large networks
Blondel, V. D., Guillaume, J.-L., Lambiotte, R., & Lefebvre, E. (2008) · Journal of Statistical Mechanics: Theory and Experiment · doi:10.1088/1742-5468/2008/10/p10008 · Cited by 21,424
Comprehensive Integration of Single-Cell Data
Stuart, T., Butler, A., Hoffman, P., Hafemeister, C., Papalexi, E., Mauck, W. M., Hao, Y., Stoeckius, M., Smibert, P., & Satija, R. (2019) · Cell · doi:10.1016/j.cell.2019.05.031 · Cited by 15,453
The blockade of immune checkpoints in cancer immunotherapy
Pardoll, D. M. (2012) · Nature Reviews Cancer · doi:10.1038/nrc3239 · Cited by 13,838
SCANPY: large-scale single-cell gene expression data analysis
Wolf, F. A., Angerer, P., & Theis, F. J. (2018) · Genome Biology · doi:10.1186/s13059-017-1382-0 · Cited by 8,881
Spatial reconstruction of single-cell gene expression data
Satija, R., Farrell, J. A., Gennert, D., Schier, A. F., & Regev, A. (2015) · Nature Biotechnology · doi:10.1038/nbt.3192 · Cited by 7,055
Self-citationSingle-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma
Patel, A. P., Tirosh, I., Trombetta, J. J., Shalek, A. K., Gillespie, S. M., Wakimoto, H., Cahill, D. P., Nahed, B. V., Curry, W. T., Martuza, R. L., et al. (2014) · Science · doi:10.1126/science.1254257 · Cited by 5,063
Self-citationThe future of immune checkpoint therapy
Sharma, P., & Allison, J. P. (2015) · Science · doi:10.1126/science.aaa8172 · Cited by 4,878
Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR-Cas9
Doench, J. G., Fusi, N., Sullender, M., Hegde, M., Vaimberg, E. W., Donovan, K. F., Smith, I., Tothova, Z., Wilen, C., Orchard, R., et al. (2016) · Nature Biotechnology · doi:10.1038/nbt.3437 · Cited by 4,637
Cited by
Sorted by citations
Mapping information-rich genotype-phenotype landscapes with genome-scale Perturb-seq
Replogle, J. M., Saunders, R. A., Pogson, A. N., Hussmann, J. A., Lenail, A., Guna, A., Mascibroda, L., Wagner, E. J., Adelman, K., Lithwick-Yanai, G., et al. (2022) · Cell · doi:10.1016/j.cell.2022.05.013 · Cited by 795
Bock, C., Datlinger, P., Chardon, F. M., Coelho, M. A., Dong, M. B., Lawson, K. A., Lu, T., Maroc, L., Norman, T. M., Song, B., et al. (2022) · Nature Reviews Methods Primers · doi:10.1038/s43586-021-00093-4 · Cited by 641
Artificial intelligence-based multi-omics analysis fuels cancer precision medicine
He, X., Liu, X., Zuo, F., Shi, H., & Jing, J. (2022) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2022.12.009 · Cited by 494
Applications of single-cell RNA sequencing in drug discovery and development
de Sande, B. V., Lee, J. S., Mutasa-Gottgens, E., Naughton, B. J., Bacon, W., Manning, J. R., Wang, Y., Pollard, J. R., Mendez, M., Hill, J., et al. (2023) · Nature Reviews Drug Discovery · doi:10.1038/s41573-023-00688-4 · Cited by 462
CRISPR in cancer biology and therapy
Katti, A., Diaz, B. J., Caragine, C. M., Sanjana, N. E., & Dow, L. E. (2022) · Nature reviews. Cancer · doi:10.1038/s41568-022-00441-w · Cited by 457
Predicting transcriptional outcomes of novel multigene perturbations with GEARS
Roohani, Y., Huang, K., & Leskovec, J. (2024) · Nature Biotechnology · doi:10.1038/s41587-023-01905-6 · Cited by 355
Single-cell proteomics enabled by next-generation sequencing or mass spectrometry
Bennett, H. M., Stephenson, W., Rose, C. M., & Darmanis, S. (2023) · Nature Methods · doi:10.1038/s41592-023-01791-5 · Cited by 351
CAR T cell killing requires the IFNγR pathway in solid but not liquid tumours
Larson, R. C., Kann, M. C., Bailey, S. R., Haradhvala, N. J., Llopis, P. D. L. M. M., Bouffard, A. A., Scarfò, I., Leick, M. B., Grauwet, K., Berger, T. R., et al. (2022) · Nature · doi:10.1038/s41586-022-04585-5 · Cited by 365
Self-citationPredicting cellular responses to complex perturbations in high‐throughput screens
Lotfollahi, M., Klimovskaia Susmelj, A., De Donno, C., Hetzel, L., Ji, Y., Ibarra, I. L., Srivatsan, S. R., Naghipourfar, M., Daza, R. M., Martin, B., et al. (2023) · Molecular Systems Biology · doi:10.15252/msb.202211517 · Cited by 307
Zhang, M., Liu, C.-J., Tu, J., Tang, M., Ashrafizadeh, M., Nabavi, N., Sethi, G., Zhao, P., & Liu, S. (2025) · Molecular Cancer · doi:10.1186/s12943-025-02305-x · Cited by 324
A new era in functional genomics screens
Przybyla, L., & Gilbert, L. A. (2022) · Nature Reviews Genetics · doi:10.1038/s41576-021-00409-w · Cited by 281
Bock, C., Datlinger, P., Chardon, F. M., Coelho, M. A., Dong, M. B., Lawson, K. A., Lu, T., Maroc, L., Norman, T. M., Song, B., et al. (2022) · Nature Reviews Methods Primers · doi:10.1038/s43586-022-00098-7 · Cited by 264
Impact of the Human Cell Atlas on medicine
Rood, J. E., Maartens, A., Hupalowska, A., Teichmann, S. A., & Regev, A. (2022) · Nature Medicine · doi:10.1038/s41591-022-02104-7 · Cited by 222
Self-citationIn vivo CRISPR screens reveal the landscape of immune evasion pathways across cancer
Dubrot, J., Du, P. P., Lane-Reticker, S. K., Kessler, E. A., Muscato, A. J., Mehta, A., Freeman, S. S., Allen, P. M., Olander, K. E., Ockerman, K. M., et al. (2022) · Nature Immunology · doi:10.1038/s41590-022-01315-x · Cited by 218
Cancer drug-tolerant persister cells: from biological questions to clinical opportunities
Russo, M., Chen, M., Mariella, E., Peng, H., Rehman, S. K., Sancho, E., Sogari, A., Toh, T. S., Balaban, N. Q., Batlle, E., et al. (2024) · Nature Reviews Cancer · doi:10.1038/s41568-024-00737-z · Cited by 210
Learning single-cell perturbation responses using neural optimal transport
Bunne, C., Stark, S. G., Gut, G., del Castillo, J. S., Levesque, M., Lehmann, K.-V., Pelkmans, L., Krause, A., & Rätsch, G. (2023) · Nature Methods · doi:10.1038/s41592-023-01969-x · Cited by 202
scPerturb: harmonized single-cell perturbation data
Peidli, S., Green, T. D., Shen, C., Gross, T., Min, J., Garda, S., Yuan, B., Schumacher, L. J., Taylor-King, J. P., Marks, D. S., et al. (2024) · Nature Methods · doi:10.1038/s41592-023-02144-y · Cited by 194
Joint single-cell measurements of nuclear proteins and RNA in vivo
Chung, H., Parkhurst, C. N., Magee, E. M., Phillips, D., Habibi, E., Chen, F., Yeung, B. Z., Waldman, J., Artis, D., & Regev, A. (2021) · Nature Methods · doi:10.1038/s41592-021-01278-1 · Cited by 144
Self-citationRood, J. E., Hupalowska, A., & Regev, A. (2024) · Cell · doi:10.1016/j.cell.2024.07.035 · Cited by 139
Self-citationMachine learning for perturbational single-cell omics
Ji, Y., Lotfollahi, M., Wolf, F. A., & Theis, F. J. (2021) · Cell Systems · doi:10.1016/j.cels.2021.05.016 · Cited by 137
Structure of a fully assembled tumor-specific T cell receptor ligated by pMHC
Sušac, L., Vuong, M. T., Thomas, C., von Bülow, S., O’Brien‐Ball, C., Santos, A. M., Fernandes, R. A., Hummer, G., Tampé, R., & Davis, S. J. (2022) · Cell · doi:10.1016/j.cell.2022.07.010 · Cited by 139
Accelerating the understanding of cancer biology through the lens of genomics
Wang, D., Liu, B., & Zhang, Z. (2023) · Cell · doi:10.1016/j.cell.2023.02.015 · Cited by 116
Le, J., Dian, Y., Zhao, D., Guo, Z., Luo, Z., Chen, X., Zeng, F., & Deng, G. (2025) · Molecular Cancer · doi:10.1186/s12943-025-02426-3 · Cited by 118
Integration of CRISPR/Cas9 with artificial intelligence for improved cancer therapeutics
Bhat, A. A., Nisar, S., Mukherjee, S., Saha, N., Yarravarapu, N., Lone, S. N., Masoodi, T., Chauhan, R., Maacha, S., Bagga, P., et al. (2022) · Journal of Translational Medicine · doi:10.1186/s12967-022-03765-1 · Cited by 114
Genetic immune escape landscape in primary and metastatic cancer
Martínez-Jiménez, F., Priestley, P., Shale, C., Baber, J., Rozemuller, E. H., & Cuppen, E. P. J. G. (2023) · Nature Genetics · doi:10.1038/s41588-023-01367-1 · Cited by 109
Scalable genetic screening for regulatory circuits using compressed Perturb-seq
Yao, D., Binan, L., Bezney, J., Simonton, B., Freedman, J., Frangieh, C. J., Dey, K., Geiger-Schuller, K., Eraslan, B., Gusev, A., et al. (2024) · Nature Biotechnology · doi:10.1038/s41587-023-01964-9 · Cited by 101
Self-citationThe CD58-CD2 axis is co-regulated with PD-L1 via CMTM6 and shapes anti-tumor immunity
Ho, P., Melms, J. C., Rogava, M., Frangieh, C. J., Poźniak, J., Shah, S. B., Walsh, Z. H., Kyrysyuk, O., Amin, A. D., Caprio, L., et al. (2023) · Cancer Cell · doi:10.1016/j.ccell.2023.05.014 · Cited by 103
Self-citationMassively parallel phenotyping of coding variants in cancer with Perturb-seq
Ursu, O., Neal, J. T., Shea, E., Thakore, P. I., Jerby-Arnon, L., Nguyen, L., Dionne, D., Diaz, C., Bauman, J., Mosaad, M. M., et al. (2022) · Nature Biotechnology · doi:10.1038/s41587-021-01160-7 · Cited by 96
Self-citationCRISPR screens for functional interrogation of immunity
Shi, H., Doench, J. G., & Chi, H. (2022) · Nature reviews. Immunology · doi:10.1038/s41577-022-00802-4 · Cited by 102
Yeh, C. Y., Aguirre, K., Laveroni, O., Kim, S., Wang, A., Liang, B., Zhang, X., Han, L. M., Valbuena, R., Bassik, M. C., et al. (2024) · Nature Immunology · doi:10.1038/s41590-024-01943-5 · Cited by 98
Self-citationCD58 Immunobiology at a Glance
Zhang, Y., Liu, Q., Yang, S., & Liao, Q. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2021.705260 · Cited by 83
Feng, X., Li, Z. Y., Liu, Y., Chen, D., & Zhou, Z. (2024) · Experimental Hematology and Oncology · doi:10.1186/s40164-024-00570-y · Cited by 86
VDAC2 loss elicits tumour destruction and inflammation for cancer therapy
Yuan, S., Sun, R., Shi, H., Chapman, N. M., Hu, H., Guy, C. S., Rankin, S. L., Anil, K. C., Palacios, G., Meng, X., et al. (2025) · Nature · doi:10.1038/s41586-025-08732-6 · Cited by 82
Immune checkpoint inhibitors in colorectal cancer: limitation and challenges
Yan, S., Wang, W., Feng, Z., Xue, J., Liang, W., Wu, X., Tan, Z., Zhang, X., Zhang, S., Li, X., et al. (2024) · Frontiers in Immunology · doi:10.3389/fimmu.2024.1403533 · Cited by 83
Time-resolved single-cell RNA-seq using metabolic RNA labelling
Erhard, F., Saliba, A.-E., Lusser, A., Toussaint, C., Hennig, T., Prusty, B. K., Kirschenbaum, D., Abadie, K., Miska, E. A., Friedel, C. C., et al. (2022) · Nature Reviews Methods Primers · doi:10.1038/s43586-022-00157-z · Cited by 73
CD58 loss in tumor cells confers functional impairment of CAR T cells
Yan, X., Chen, D., Ma, X., Wang, Y., Guo, Y., Wei, J., Tong, C., Zhu, Q., Lu, Y., Yu, Y., et al. (2022) · Blood Advances · doi:10.1182/bloodadvances.2022007891 · Cited by 77
Optimal transport for single-cell and spatial omics
Bunne, C., Schiebinger, G., Krause, A., Regev, A., & Cuturi, M. (2024) · Nature Reviews Methods Primers · doi:10.1038/s43586-024-00334-2 · Cited by 71
Self-citationIsolating salient variations of interest in single-cell data with contrastiveVI
Weinberger, E., Lin, C., & Lee, S.-I. (2023) · Nature Methods · doi:10.1038/s41592-023-01955-3 · Cited by 66
Romain, G., Strati, P., Rezvan, A., Fathi, M., Bandey, I. N., Adolacion, J. R. T., Heeke, D., Liadi, I., Marques-Piubelli, M. L., Solis, L. M., et al. (2022) · Journal of Clinical Investigation · doi:10.1172/jci159402 · Cited by 62
Engineering immune-evasive allogeneic cellular immunotherapies
Martin, K. E., Hammer, Q., Perica, K., Sadelain, M., & Malmberg, K. (2024) · Nature reviews. Immunology · doi:10.1038/s41577-024-01022-8 · Cited by 64
Multimodal single-cell and whole-genome sequencing of small, frozen clinical specimens
Wang, Y., Fan, J. L., Melms, J. C., Amin, A. D., Georgis, Y., Barrera, I., Ho, P., Tagore, S., Abril-Rodríguez, G., He, S., et al. (2023) · Nature Genetics · doi:10.1038/s41588-022-01268-9 · Cited by 59
Self-citationDefining and using immune archetypes to classify and treat cancer
Combes, A. J., Samad, B., & Krummel, M. F. (2023) · Nature reviews. Cancer · doi:10.1038/s41568-023-00578-2 · Cited by 61
Viñas Torné, R., Wiatrak, M., Piran, Z., Fan, S., Jiang, L., Teichmann, S. A., Nitzan, M., & Brbić, M. (2026) · Nature Biotechnology · doi:10.1038/s41587-025-02777-8 · Cited by 58
A mini-review on perturbation modelling across single-cell omic modalities
Gavriilidis, G. I., Vasileiou, V., Orfanou, A., Ishaque, N., & Psomopoulos, F. (2024) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2024.04.058 · Cited by 54
Mostly natural sequencing-by-synthesis for scRNA-seq using Ultima sequencing
Simmons, S., Lithwick‐Yanai, G., Adiconis, X., Oberstrass, F. C., Iremadze, N., Geiger-Schuller, K., Thakore, P. I., Frangieh, C. J., Barad, O., Almogy, G., et al. (2022) · Nature Biotechnology · doi:10.1038/s41587-022-01452-6 · Cited by 56
Self-citationImmune escape and metastasis mechanisms in melanoma: breaking down the dichotomy
Shirley, C. A., Chhabra, G., Amiri, D., Chang, H., & Ahmad, N. (2024) · Frontiers in Immunology · doi:10.3389/fimmu.2024.1336023 · Cited by 54
Moving from in vitro to in vivo CRISPR screens
Kuhn, M., Santinha, A. J., & Platt, R. J. (2021) · Gene and Genome Editing · doi:10.1016/j.ggedit.2021.100008 · Cited by 56
Applications of Single-Cell Omics in Tumor Immunology
Liu, J., Qu, S., Zhang, T., Gao, Y., Shi, H., Song, K., Chen, W., & Yin, W. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2021.697412 · Cited by 53
Lazar, N. H., Çelik, S., Chen, L., Fay, M. M., Irish, J. C., Jensen, J., Tillinghast, C. A., Urbanik, J., Bone, W. P., Roberts, G. H. L., et al. (2024) · Nature Genetics · doi:10.1038/s41588-024-01758-y · Cited by 52
Genome-wide CRISPR screen in human T cells reveals regulators of FOXP3
Chen, K. Y., Kibayashi, T., Giguelay, A., Hata, M., Nakajima, S., Mikami, N., Takeshima, Y., Ichiyama, K., Omiya, R., Ludwig, L. S., et al. (2025) · Nature · doi:10.1038/s41586-025-08795-5 · Cited by 57
CMTM6 shapes antitumor T cell response through modulating protein expression of CD58 and PD-L1
Miao, B., Hu, Z., Mezzadra, R., Hoeijmakers, L. L., Fauster, A., Du, S., Yang, Z., Sator‐Schmitt, M., Engel, H., Li, X., et al. (2023) · Cancer Cell · doi:10.1016/j.ccell.2023.08.008 · Cited by 48
Antigen discrimination by T cells relies on size-constrained microvillar contact
Jenkins, E. M., Körbel, M., O’Brien‐Ball, C., McColl, J. T., Chen, K. Y., Kotowski, M., Humphrey, J., Lippert, A. H., Brouwer, H., Santos, A. M., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-36855-9 · Cited by 43
Progress in single-cell multimodal sequencing and multi-omics data integration
Wang, X. J., Wu, X., Hong, N., & Jin, W. (2023) · Biophysical Reviews · doi:10.1007/s12551-023-01092-3 · Cited by 45
Exploiting Single-Cell Tools in Gene and Cell Therapy
Bode, D., Cull, A. H., Rubio-Lara, J. A., & Kent, D. G. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2021.702636 · Cited by 44
Benchmarking algorithms for generalizable single-cell perturbation response prediction
Wei, Z., Wang, Y., Gao, Y., Wang, S., Li, P., Si, D., Gao, Y., Wu, S., Li, D., Dong, K., et al. (2026) · Nature Methods · doi:10.1038/s41592-025-02980-0 · Cited by 42
Comprehensive transcription factor perturbations recapitulate fibroblast transcriptional states
Southard, K. M., Ardy, R. C., Tang, A., O'Sullivan, D., Metzner, E., Guruvayurappan, K., & Norman, T. M. (2025) · Nature Genetics · doi:10.1038/s41588-025-02284-1 · Cited by 46
Pancreatic cancer environment: from patient-derived models to single-cell omics
Gu, A., Li, J., Qiu, S., Hao, S., Yue, Z., Zhai, S., Li, M., & Liu, Y. (2024) · Molecular Omics · doi:10.1039/d3mo00250k · Cited by 41
Perturbomics: CRISPR–Cas screening-based functional genomics approach for drug target discovery
Park, B.-S., Lee, M., Kim, J. Y., & Kim, T. (2025) · Experimental & Molecular Medicine · doi:10.1038/s12276-025-01487-0 · Cited by 41
Massively Parallel CRISPR‐Based Genetic Perturbation Screening at Single‐Cell Resolution
Cheng, J., Lin, G., Wang, T., Wang, Y., Guo, W., Liao, J., Yang, P., Chen, J., Shao, X., Lu, X., et al. (2022) · Advanced Science · doi:10.1002/advs.202204484 · Cited by 42
Causal modelling of gene effects from regulators to programs to traits
Ota, M., Spence, J. P., Zeng, T., Dann, E., Milind, N., Marson, A., & Pritchard, J. K. (2025) · Nature · doi:10.1038/s41586-025-09866-3 · Cited by 43
GEARS: Predicting transcriptional outcomes of novel multi-gene perturbationsPreprint
Roohani, Y., Huang, K., & Leskovec, J. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.07.12.499735 · Cited by 40
Mapping variant effects on anti-tumor hallmarks of primary human T cells with base-editing screens
Walsh, Z. H., Shah, P., Kothapalli, N., Shah, S. B., Nikolényi, G., Brodtman, D. Z., Leuzzi, G., Rogava, M., Mu, M., Ho, P., et al. (2024) · Nature Biotechnology · doi:10.1038/s41587-024-02235-x · Cited by 42
Self-citationShen, Y., Eng, J. S., Fajardo, F., Liang, L., Li, C., Collins, P. J., Tedesco, D., & Nolan-Stevaux, O. (2022) · Journal for ImmunoTherapy of Cancer · doi:10.1136/jitc-2021-004348 · Cited by 39
Chen, E., Ling, A. L., Reardon, D. A., & Chiocca, E. A. (2023) · Neuro-Oncology · doi:10.1093/neuonc/noad211 · Cited by 38
Liu, Y., Altreuter, J., Bodapati, S., Cristea, S., Wong, C. J., Wu, C. J., & Michor, F. (2023) · Cell Genomics · doi:10.1016/j.xgen.2023.100444 · Cited by 39
A comparison of computational methods for expression forecasting
Kernfeld, E. M., Yang, Y., Weinstock, J. S., Battle, A. J., & Cahan, P. (2025) · Genome biology · doi:10.1186/s13059-025-03840-y · Cited by 45
Xu, X., Zhang, Y., Lu, Y., Zhang, X., Zhao, C., Wang, J., Guan, Q., Feng, Y., Gao, M., Yu, J., et al. (2024) · Cancer Research · doi:10.1158/0008-5472.can-23-2874 · Cited by 33
Enhancing CAR T function with the engineered secretion of C. perfringens neuraminidase
Durgin, J. S., Thokala, R., Johnson, L. R., Song, E. Z., Leferovich, J. M., Bhoj, V. G., Ghassemi, S., Milone, M. C., Binder, Z. A., O’Rourke, D. M., et al. (2021) · Molecular Therapy · doi:10.1016/j.ymthe.2021.11.014 · Cited by 32
Revisiting T-cell adhesion molecules as potential targets for cancer immunotherapy: CD226 and CD2
Jo, Y., Sim, H.-I., Yun, B., Park, Y., & Jin, H. (2024) · Experimental & Molecular Medicine · doi:10.1038/s12276-024-01317-9 · Cited by 31
Morris, J. A., Sun, J. S., & Sanjana, N. E. (2023) · Trends in Genetics · doi:10.1016/j.tig.2023.10.012 · Cited by 31
Wang, G., Zhao, J., Lin, Y., Liu, T., Zhao, Y., & Zhao, H. (2025) · Nature Communications · doi:10.1038/s41467-025-60333-z · Cited by 29
Methods and applications of in vivo CRISPR screening
Santinha, A. J., Strano, A., & Platt, R. J. (2025) · Nature Reviews Genetics · doi:10.1038/s41576-025-00873-8 · Cited by 29
Bird, B., Nally, K., Ronan, K. E., Clarke, G. A., Amu, S., Almeida, A. S., Flavin, R., & Finn, S. P. (2022) · Diagnostics · doi:10.3390/diagnostics12010124 · Cited by 29
Active learning for optimal intervention design in causal models
Zhang, J., Cammarata, L. V., Squires, C., Sapsis, T. P., & Uhler, C. (2023) · Nature Machine Intelligence · doi:10.1038/s42256-023-00719-0 · Cited by 29
Barry, T., Mason, K., Roeder, K., & Katsevich, E. (2024) · Genome Biology · doi:10.1186/s13059-024-03254-2 · Cited by 28
Diagnostic utility of whole genome sequencing in adults with B-other acute lymphoblastic leukemia
Leongamornlert, D. A., Gutiérrez‐Abril, J., Lee, S., Barretta, E., Creasey, T., Gundem, G., Levine, M. F., Ossa, J. A., Liosis, K., Medina-Martinez, J. S., et al. (2023) · Blood Advances · doi:10.1182/bloodadvances.2022008992 · Cited by 28
Rogava, M., Aprati, T. J., Chi, W.-Y., Melms, J. C., Hug, C. B., Davis, S. H., Earlie, E. M., Chung, C., Deshmukh, S. K., Wu, S. K., et al. (2024) · Nature Cancer · doi:10.1038/s43018-023-00704-x · Cited by 27
Self-citationA Systematic Comparison of Single-Cell Perturbation Response Prediction Models
Li, L., You, Y., Fu, Y., Liao, W., Fan, X., Lu, S., Cao, Y., Li, B., Ren, W., Kong, J., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.12.23.630036 · Cited by 25
“Sample multiplexing was conducted using the 3’ CellPlex Kit Set A (10X Ge-456 nomics, 1000261), following the manufacturer’s instructions.”
Pooled Genome-Scale CRISPR Screens in Single Cells
Schraivogel, D., Steinmetz, L. M., & Parts, L. (2023) · Annual Review of Genetics · doi:10.1146/annurev-genet-072920-013842 · Cited by 28
Tooley, K., Jerby‐Arnon, L., Escobar, G., Krovi, S. H., Mangani, D., Dandekar, G., Cheng, H., Madi, A., Goldschmidt, E., Lambden, C., et al. (2024) · Cell Reports Medicine · doi:10.1016/j.xcrm.2024.101640 · Cited by 29
Self-citationCRISPR/Cas technologies for cancer drug discovery and treatment
Wang, K. C., Zheng, T., & Hubbard, B. P. (2025) · Trends in Pharmacological Sciences · doi:10.1016/j.tips.2025.02.009 · Cited by 26
Paul, S., Xie, S., Yao, X., & Dey, A. (2022) · Cells · doi:10.3390/cells11142225 · Cited by 25
Recent advances in microfluidic single-cell analysis and its applications in drug development
Jiao, Y., Gao, L., Ji, Y., & Liu, W. (2022) · TrAC Trends in Analytical Chemistry · doi:10.1016/j.trac.2022.116796 · Cited by 26
HSPA4 upregulation induces immune evasion via ALKBH5/CD58 axis in gastric cancer
Suo, D., Gao, X., Chen, Q., Zeng, T., Zhan, J., Li, G., Zheng, Y., Zhu, S., Yun, J., Guan, X., et al. (2024) · Journal of Experimental & Clinical Cancer Research · doi:10.1186/s13046-024-03029-4 · Cited by 25
Applications of single-cell technologies in drug discovery for tumor treatment
Liu, B., Hu, S., & Wang, X. (2024) · iScience · doi:10.1016/j.isci.2024.110486 · Cited by 24
High-content CRISPR screening in tumor immunology
Holcomb, E. A., Pearson, A. N., Jungles, K. M., Tate, A., James, J. G., Jiang, L., Huber, A. K., & Green, M. D. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2022.1041451 · Cited by 24
Modeling and predicting single-cell multi-gene perturbation responses with scLAMBDAPreprint
Wang, G., Liu, T., Zhao, J., Cheng, Y., & Zhao, H. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.12.04.626878 · Cited by 25
McFaline‐Figueroa, J. L., Srivatsan, S. R., Hill, A. J., Gasperini, M. J., Jackson, D. L., Saunders, L. M., Domcke, S., Regalado, S. G., Lazarchuck, P., Alvarez, S., et al. (2024) · Cell Genomics · doi:10.1016/j.xgen.2023.100487 · Cited by 25
CRISPR screening in hematology research: from bulk to single-cell level
Meyers, S., Demeyer, S., & Cools, J. (2023) · Journal of Hematology & Oncology · doi:10.1186/s13045-023-01495-5 · Cited by 24
AttentionPert: accurately modeling multiplexed genetic perturbations with multi-scale effects
Bai, D., Ellington, C. N., Mo, S., Song, L., & Xing, E. P. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae244 · Cited by 26
SPDB: a comprehensive resource and knowledgebase for proteomic data at the single-cell resolution
Wang, F., Liu, C., Li, J., Yang, F., Song, J., Zang, T., Yao, J., & Wang, G. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad1018 · Cited by 22
Single-cell RNA sequencing in melanoma: what have we learned so far?
Lim, S. Y., & Rizos, H. (2024) · EBioMedicine · doi:10.1016/j.ebiom.2024.104969 · Cited by 26
Geiger-Schuller, K., Eraslan, B., Kuksenko, O., Dey, K. K., Jagadeesh, K. A., Thakore, P. I., Karayel, Ö., Yung, A. R., Rajagopalan, A. R., Meireles, A. M., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.01.23.525198 · Cited by 23
Self-citationGenePert: Leveraging GenePT Embeddings for Gene Perturbation PredictionPreprint
Chen, Y. T., & Zou, J. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.10.27.620513 · Cited by 24
Mukherjee, P., Park, S. H., Pathak, N., Patino, C. A., Bao, G., & Espinosa, H. D. (2022) · ACS Nano · doi:10.1021/acsnano.2c05494 · Cited by 20
A pre-trained large generative model for translating single-cell transcriptomes to proteomes
Liu, L., Li, W., Wang, F., Li, Y., Huang, L.-K., Wong, K., Yang, F., & Yao, J. (2025) · Nature Biomedical Engineering · doi:10.1038/s41551-025-01528-z · Cited by 26
Single-Cell Toolkits Opening a New Era for Cell Engineering
Lee, S., Kim, J., & Park, J.-E. (2021) · Molecules and Cells · doi:10.14348/molcells.2021.0002 · Cited by 20
Pooled CRISPR screens with joint single-nucleus chromatin accessibility and transcriptome profiling
Yan, R., Corman, A., Katgara, L., Xiao, W., Xue, X., Gajic, Z. Z., Sam, R., Farid, M., Friedman, S. M., Choo, J., et al. (2024) · Nature Biotechnology · doi:10.1038/s41587-024-02475-x · Cited by 19
Decoding sex differences in human immunity through systems immunology
Escrivà-Font, J., Cao, T., & Consiglio, C. R. (2025) · Oxford Open Immunology · doi:10.1093/oxfimm/iqaf006 · Cited by 19
The role of single-cell genomics in human genetics
Sreenivasan, V. K. A., Balachandran, S., & Spielmann, M. (2022) · Journal of Medical Genetics · doi:10.1136/jmedgenet-2022-108588 · Cited by 19
Metadata
- Authors
- Frangieh, C. J., Melms, J. C., Thakore, P. I., Geiger-Schuller, K. R., Ho, P., Luoma, A. M., Cleary, B., Jerby-Arnon, L., Malu, S., Cuoco, M. S., Zhao, M., Ager, C. R., Rogava, M., Hovey, L., Rotem, A., Bernatchez, C., Wucherpfennig, K. W., Johnson, B. E., Rozenblatt-Rosen, O., Schadendorf, D., Regev, A., & Izar, B.
- Affiliations
- Broad Institute (US)
- Columbia University Irving Medical Center (US)
- Dana-Farber Cancer Institute (US)
- Immunitas Therapeutics (United States) (US)
- Columbia University (US)
- The University of Texas MD Anderson Cancer Center (US)
- Essen University Hospital (DE)
- Corresponding author
- Regev, Aviv
- Izar, Benjamin
- ORCID
- Venue
- Nature Genetics
- Published
- 2021-03
- Volume
- 53
- Issue
- 3
- Pages
- 332-341
- ISSN
- 1061-4036 (ISSN-L)
- 1546-1718
- PMC
- PMC8376399
- OpenAlex
- W3134444564
- Cited by
- 276
- References
- 47
- Citations by year
- 2026: 54 · 2025: 66 · 2024: 58 · 2023: 44 · 2022: 35 · 2021: 19
- FWCI
- 14.21
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Nature Genetics (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc)
- Funders
- Burroughs Wellcome Fund
- U.S. Department of Health & Human Services | NIH | National Cancer Institute (K08CA222663, U54CA225088, P30CA013696)
- Topics
- Health Sciences › Medicine › Oncology › Cancer Immunotherapy and Biomarkers
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Immunology and Microbiology › Immunology › Immunotherapy and Immune Responses
- Keywords
- tumor immune evasion, immune checkpoint inhibitor resistance, melanoma, tumor-infiltrating lymphocytes, CD58, interferon-gamma signaling, JAK/STAT signaling, antigen presentation, MHC expression, single-cell transcriptomics, single-cell proteomics, patient-derived tumor models
- MeSH terms
- Immune Checkpoint Inhibitors, Epitopes, Humans, Interferon-gamma, Melanoma, Lymphocytes, Tumor-Infiltrating, Sequence Analysis, RNA, Coculture Techniques, CD58 Antigens, Drug Resistance, Neoplasm, Tumor Escape, Computational Biology, Gene Knockout Techniques, Single-Cell Analysis, CRISPR-Cas Systems
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-04, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-04 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
