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
Items 101–200 of 276
Machine learning to dissect perturbations in complex cellular systems
Monfort‐Lanzas, P., Rungger, K., Madersbacher, L., & Hackl, H. (2025) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2025.02.028 · Cited by 20
Deciphering the tumour immune microenvironment cell by cell
Nabhan, M., Egan, D., Kreileder, M., Zhernovkov, V., Timosenko, E., Slidel, T. W. F., Dovedi, S. J., Glennon, K., Brennan, D. J., & Kölch, W. (2023) · Immuno-Oncology Technology · doi:10.1016/j.iotech.2023.100383 · Cited by 17
CRISPR/Cas9 screening: unraveling cancer immunotherapy’s ‘Rosetta Stone’
Chen, B., Deng, Y., Ren, X., Zhao, J., & Jiang, C. (2024) · Trends in Molecular Medicine · doi:10.1016/j.molmed.2024.04.014 · Cited by 17
Gao, Y., Wei, Z., Dong, K., Chen, K., Yang, J., Chuai, G., & Liu, Q. (2024) · Nature Computational Science · doi:10.1038/s43588-024-00698-1 · Cited by 18
Genetic immune escape in cancer: timing and implications for treatment
Martínez-Jiménez, F., & Chowell, D. (2024) · Trends in cancer · doi:10.1016/j.trecan.2024.11.002 · Cited by 17
Single-cell technologies: current and near future
Wang, C., Liu, Q., Fan, X., & Shi, T. (2024) · Science China Life Sciences · doi:10.1007/s11427-024-2813-y · Cited by 18
MultiCPA: Multimodal Compositional Perturbation Autoencoder
Inecik, K., Uhlmann, A., Lotfollahi, M., & Theis, F. (2022) · doi:10.1101/2022.07.08.499049 · Cited by 16
High-Throughput CRISPR Screening in Hematological Neoplasms
Ancos-Pintado, R., Bragado-García, I., Morales, M. L., García-Vicente, R., Arroyo-Barea, A., Rodríguez-García, A., Martínez-López, J., Linares, M., & Hernández-Sánchez, M. (2022) · Cancers · doi:10.3390/cancers14153612 · Cited by 15
Prevalence of and gene regulatory constraints on transcriptional adaptation in single cells
Mellis, I. A., Bodkin, N., Melzer, M. E., & Goyal, Y. (2024) · Genome biology · doi:10.1186/s13059-024-03351-2 · Cited by 17
CRISPR Screens to Identify Regulators of Tumor Immunity
LaFleur, M. W., & Sharpe, A. H. (2021) · Annual Review of Cancer Biology · doi:10.1146/annurev-cancerbio-070120-094725 · Cited by 16
Virtual Cells Need Context, Not Just ScalePreprint
Dibaeinia, P., Babu, S., Knudson, M. K., ElSheikh, A., Wen, Y., Liu, H., Perera, J. F., & Khan, A. A. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.02.04.703804 · Cited by 22
Zhang, Y., Liu, Q., Liu, J., & Liao, Q. (2021) · Cancer Cell International · doi:10.1186/s12935-021-02037-0 · Cited by 14
Spatial Profiling of Ovarian Clear Cell Carcinoma Reveals Immune-Hot Features
Tai, Y.-T., Lin, W., Ye, J., Chen, D. T.-H., Chen, K.-C., Wang, D. Y. T., Tan, T. Z., Wei, L., & Huang, R. Y. (2024) · Modern Pathology · doi:10.1016/j.modpat.2024.100630 · Cited by 13
Applications of CRISPR technology in cellular immunotherapy
Zhou, X., Renauer, P. A., Zhou, L., Fang, S., & Chen, S. (2023) · Immunological Reviews · doi:10.1111/imr.13241 · Cited by 14
Stubenvoll, A., Schmidt, M., Moeller, J., Chango, M. A. L., Schultz, C., Antoniadou, O., Loeffler‐Wirth, H., Bernhart, S. H., Große, F., Thier, B., et al. (2025) · 癌症：英文版 · doi:10.1002/cac2.12651 · Cited by 13
Guide assignment in single-cell CRISPR screens using crispat
Braunger, J. M., & Velten, B. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae535 · Cited by 12
Newly identified form of phenotypic plasticity of cancer: immunogenic mimicry
Tı́már, J., Honn, K. V., Hendrix, M. J. C., Marko‐Varga, G., & Jalkanen, S. (2023) · Cancer and Metastasis Reviews · doi:10.1007/s10555-023-10087-1 · Cited by 12
Compressed Perturb-seq enables highly efficient genetic screens
Yao, D. W., Binan, L., Bezney, J., Simonton, B., Freedman, J., Frangieh, C. J., Dey, K. K., Geiger-Schuller, K., Eraslan, B., Gusev, A., et al. (2023) · Nature Biotechnology · doi:10.1038/s41587-023-02003-3 · Cited by 12
Self-citationThe Current Situation and Development Prospect of Whole-Genome Screening
Yang, C., Lei, Y., Ren, T., & Yao, M. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms25010658 · Cited by 12
PerturbDB for unraveling gene functions and regulatory networks
Yang, B., Zhang, M., Shi, Y., Zheng, B.-Q., Shi, C., Lu, D., Yang, Z.-Z., Dong, Y.-M., Zhu, L., Ma, X., et al. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae777 · Cited by 12
Current and Prospective Methods for Assessing Anti-Tumor Immunity in Colorectal Cancer
Nussbaum, Y. I., Manjunath, Y., Suvilesh, K. N., Warren, W. C., Shyu, C., Kaifi, J. T., Ciorba, M. A., & Mitchem, J. B. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22094802 · Cited by 11
Applications and evolution of 3D cancer-immune cell models
Co, I. L., Fomina, A., Nurse, M., & McGuigan, A. P. (2024) · Trends in biotechnology · doi:10.1016/j.tibtech.2024.06.008 · Cited by 10
Tan, Y., Xie, L., Yang, H. T., Zhang, Q., Luo, J., & Zhang, Y. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae617 · Cited by 12
Advancing single-cell omics and cell-based therapeutics with quantum computing
Bose, A., Rhrissorrakrai, K., Utro, F., Parida, L., Basu, S., Born, J., Capponi, S., Chalkia, D., Chan, T. A.-T., Doğa, H., et al. (2026) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-025-00918-0 · Cited by 13
Unveiling the molecular architecture of T cells and immune synapses with cryo-expansion microscopy
Lemaître, F., Mercey, O., Méan, I., Paulin, E., Dutoit, V., Rath, J. A., Gunten, C. V., Migliorini, D., Arber, C., Guichard, P., et al. (2026) · Cell Reports · doi:10.1016/j.celrep.2026.117165 · Cited by 10
Breaking the Immune Complexity of the Tumor Microenvironment Using Single-Cell Technologies
Caligola, S., Sanctis, F. D., Canè, S., & Ugel, S. (2022) · Frontiers in Genetics · doi:10.3389/fgene.2022.867880 · Cited by 10
Conditional resampling improves calibration and sensitivity in single-cell CRISPR screen analysisPreprint
Barry, T., Wang, X., Morris, J. A., Roeder, K., & Katsevich, E. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.08.13.250092 · Cited by 10
High-Throughput Identification, Modeling, and Analysis of Cancer Driver Genes In VivoPreprint
Tang, Y. J., Shuldiner, E. G., Karmakar, S., & Winslow, M. M. (2023) · Cold Spring Harbor Perspectives in Medicine · doi:10.1101/cshperspect.a041382 · Cited by 11
Littman, R., Levine, J., Maleki, S., Lee, Y., Ermakov, V. Y., Qiu, L., Wu, A. P., Huang, K., Lopez, R., Scalia, G., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.06.03.657653 · Cited by 10
Self-citationWei, H., Han, T., Li, T., Wu, Q., & Wang, C. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad123 · Cited by 9
Purwin, T. J., Caksa, S., Saçan, A., Capparelli, C., & Aplin, A. E. (2023) · iScience · doi:10.1016/j.isci.2023.107472 · Cited by 9
Jerby‐Arnon, L., Tooley, K., Escobar, G., Dandekar, G., Madi, A., Goldschmidt, E., Lambden, C., Rozenblatt–Rosen, O., Anderson, A. C., & Regev, A. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.10.31.466532 · Cited by 9
Self-citationWei, A. Z., Chen, L. N., Orloff, M. M., Ariyan, C. E., Asgari, M. M., Barker, C. A., Buchbinder, E. I., Chandra, S., Couts, K. L., Frumovitz, M. M., et al. (2023) · Pigment Cell & Melanoma Research · doi:10.1111/pcmr.13139 · Cited by 8
Self-citationDeciphering metabolic crosstalk in context: lessons from inflammatory diseases
Verheijen, F. W., Minh, T. T. N., Chang, J., Broere, F., Zaal, E. A., & Berkers, C. R. (2024) · Molecular Oncology · doi:10.1002/1878-0261.13588 · Cited by 8
Chen, Y., Hu, Z., Chen, W., & Huang, H. (2025) · Bioinformatics · doi:10.1093/bioinformatics/btaf253 · Cited by 11
Complex biological questions being addressed using single cell sequencing technologies
Yu, T., & Scolnick, J. A. (2022) · SLAS TECHNOLOGY · doi:10.1016/j.slast.2021.10.013 · Cited by 8
Pregizer, S. K., Vreven, T., Mathur, M., & Robinson, L. N. (2023) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2023.1176856 · Cited by 9
Xia, L., Komissarova, A., Jacover, A., Shovman, Y., Arcila-Barrera, S., Tornovsky-Babeay, S., Prakashan, M. M. J., Nasereddin, A., Plaschkes, I., Nevo, Y., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-41792-8 · Cited by 8
Discovery platforms for RNA therapeutics
Ciucci, G., Braga, L., & Zacchigna, S. (2024) · British Journal of Pharmacology · doi:10.1111/bph.16424 · Cited by 8
Tu, X., Hütter, J.-C., Wang, Z. J., Kudo, T., Regev, A., & Lopez, R. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.01.05.574421 · Cited by 8
Self-citationWang, J., Feng, Y.-F., & Liu, J.-H. (2025) · Frontiers in Immunology · doi:10.3389/fimmu.2025.1629879 · Cited by 8
Bielczyk-Maczyńska, E., Sharma, D., Blencowe, M., Saliba‐Gustafsson, P., Gloudemans, M. J., Yang, X., Carcamo‐Orive, I., Wabitsch, M., Svensson, K. J., Park, C. Y., et al. (2023) · American Journal of Physiology-Cell Physiology · doi:10.1152/ajpcell.00148.2023 · Cited by 8
Facts and Hopes in Using Omics to Advance Combined Immunotherapy Strategies
Augustin, R. C., Cai, W. L., Luke, J. J., & Bao, R. (2024) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-22-2241 · Cited by 7
Exploring genome-transcriptome correlations in cancer
Ronemus, M., Bradford, D., Laster, Z., & Li, S. (2025) · Biochemical Society Transactions · doi:10.1042/bst20240108 · Cited by 7
Cancer ATF4-mediated CD58 endocytosis impairs anti-tumor immunity and immunotherapy
Zeng, H., Yu, J., Wang, H., Shen, M., Zou, X., Zhang, Z., & Liu, L. (2025) · Journal of Translational Medicine · doi:10.1186/s12967-025-06245-4 · Cited by 7
Completing the cancer jigsaw puzzle with single-cell multiomics
Song, C., & Teichmann, S. A. (2021) · Nature Cancer · doi:10.1038/s43018-021-00306-5 · Cited by 7
Sugita, K., Onishi, I., Nakayama, R., Ishibashi, S., Ikeda, M., Inoue, M., Narita, R., Oshima, S., Shimizu, K., Saito, S., et al. (2023) · Communications Biology · doi:10.1038/s42003-023-04941-9 · Cited by 7
Wu, M., Yang, S., Yang, Z., Fan, J., Lomeli, S. H., Dharanipragada, P., Moriceau, G., Damoiseaux, R. D., Kelley, M. C., Granada, C. N. P., et al. (2025) · Immunity · doi:10.1016/j.immuni.2025.10.001 · Cited by 6
Single-cell and spatial profiling in cancer biology and clinical oncology
Frangieh, C. J., Fan, J. L., Melms, J. C., Shah, P., Azizi, E., & Izar, B. (2026) · Nature Cancer · doi:10.1038/s43018-026-01142-1 · Cited by 8
Self-citationCD58 defines regulatory macrophages within the tumor microenvironment
Wu, B., Zhan, X., & Jiang, M. (2024) · Communications Biology · doi:10.1038/s42003-024-06712-6 · Cited by 6
Yang, F., Wei, N., Cai, S., Liu, J., Lan, Q., Zhang, H., Shang, L., Zheng, B., Wang, M., Liu, Y., et al. (2024) · Science China Life Sciences · doi:10.1007/s11427-023-2627-8 · Cited by 6
Synergistic anti-tumor effects of mRNA vaccine and PERK inhibitor combination in melanoma treatment
Li, X., Ma, L., Guo, J., Wei, Y., Ma, S., Mai, Y., Gou, G., Zuo, W., & Yang, J. (2025) · Colloids and Surfaces B Biointerfaces · doi:10.1016/j.colsurfb.2025.114808 · Cited by 6
Melms, J. C., Ho, P., Rogava, M., & Izar, B. (2023) · Genes and Immunity · doi:10.1038/s41435-023-00224-9 · Cited by 6
Self-citationSpurious correlation inflates performance in single-cell perturbation predictionPreprint
Nicol, P. B., Shivakumar, S., & Irizarry, R. A. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.05.07.723486 · Cited by 7
Manzoor, H. B., Acun, M., Yasavoli‐Sharahi, H., Matar, C., Mer, A. S., Tsang, B. K., & Asare-Werehene, M. (2025) · Scientific Reports · doi:10.1038/s41598-025-20827-8 · Cited by 5
Shang, E., Wei, Y., & Roeder, K. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2525268122 · Cited by 11
Disordered protein LAT encodes relative levels of signaling pathways in T cell activation
Rubin, A. J., Dao, T. T., Schueppert, A. V., Choi, S., Groves, J. T., Regev, A., & Shalek, A. K. (2026) · Science · doi:10.1126/science.ads6847 · Cited by 5
Self-citationIdentification of Key Functional Modules and Immunomodulatory Regulators of Hepatocellular Carcinoma
Luo, D., Zhang, X., Li, X., & Chen, G. (2021) · Journal of Immunology Research · doi:10.1155/2021/1801873 · Cited by 5
Chen, K. Y., Takeshima, Y., Zorzetto-Fernandes, A. L., Makino, K., Kibayashi, T., Hata, M., Ichiyama, K., Croteau, J. D., Taylor, K., & Sakaguchi, S. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.01.11.632564 · Cited by 5
SCPline: An interactive framework for the single-cell proteomics data preprocessing
Guo, S., Zhou, S., Wang, G., & Wang, F. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf256 · Cited by 4
Chen, R., Wang, Z., Chen, X., Huang, Y., Zhang, F., & Gao, T. (2025) · Frontiers in Immunology · doi:10.3389/fimmu.2025.1651088 · Cited by 4
Decoding cell fate: integrated experimental and computational analysis at the single-cell level
Zhou, Y., Hou, S., Miao, X., Zhang, G., Li, Z., Zhang, D., Lin, Y., & Lin, Y. (2025) · Bioinformatics · doi:10.1093/bioinformatics/btaf603 · Cited by 4
VDAC2: an emerging pivotal and multifaceted regulator in tumor biology
Shentu, J., Xu, H., Jin, L., & Duan, S. (2026) · APOPTOSIS · doi:10.1007/s10495-026-02278-2 · Cited by 4
Conditional Monge Gap enables generalizable single-cell perturbation modelling
Driessen, A., Rajwade, D. A., Harsanyi, B., Rapsomaniki, M., & Born, J. (2026) · Nature Machine Intelligence · doi:10.1038/s42256-026-01242-8 · Cited by 10
CRISPR in clinical oncology: translational advances from molecular diagnostics to therapeutics
Grigg, S. E., Shembrey, C., Fareh, M., Blombery, P. A., Corn, J. E., Seymour, J. F., & Casan, J. M. L. (2026) · Nature Reviews Clinical Oncology · doi:10.1038/s41571-026-01179-2 · Cited by 4
Xu, J., Tian, Y., Hu, D., Xi, Y., & Yang, L. (2024) · MedComm – Oncology · doi:10.1002/mog2.85 · Cited by 4
Dissecting host–microbe interactions with modern functional genomics
Russell, B. J., Verma, M., Maier, N. K., & Jost, M. (2024) · Current Opinion in Microbiology · doi:10.1016/j.mib.2024.102554 · Cited by 4
Multiplexed multimodal single-cell technologies: From observation to perturbation analysis
Lee, S.-H., Park, J., & Hwang, B. (2024) · Molecules and Cells · doi:10.1016/j.mocell.2024.100147 · Cited by 4
Du, Y., Yang, Y., Zheng, B., Zhang, Q., Zhou, S., & Zhao, L. (2025) · Oncogene · doi:10.1038/s41388-025-03273-8 · Cited by 4
Biologically Informed Variational Inference Enables Interpretable Cell Phenotyping and DiscoveryPreprint
Arnoldt, L., zu Belzen, J. U., Herrmann, L., Nguyen, K., Ishaque, N., Theis, F. J., Wild, B., & Eils, R. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.06.10.657924 · Cited by 4
The applications of single-cell multiomics in drug screening
Xue, Q., Hu, H. F., Wang, R., Wu, F., & Xiong, H. (2025) · Pharmaceutical Science Advances · doi:10.1016/j.pscia.2025.100090 · Cited by 3
Zhang, Y., Zheng, H., Zhang, R., Li, J., Yang, S., Hua, Y., Kleeff, J. H., Liao, Q., & Liu, Q. (2025) · Journal for ImmunoTherapy of Cancer · doi:10.1136/jitc-2025-012163 · Cited by 3
An expanded role for single-cell chemical genomics profiling in drug discovery
Wyatt, A., Hoffer-Hawlik, K., Giglio, R. M., Azizi, E., & McFaline‐Figueroa, J. L. (2026) · Biochemical Journal · doi:10.1042/bcj20253273 · Cited by 3
Integrating Spatial Proteogenomics in Cancer Research
Wang, Y., Wu, Y., Zhang, F., Periasamy, P., You, H., Goh, D. L. M., Fincham, R. E. A., Ning, X., Wu, D., Liu, L., et al. (2026) · Advanced Science · doi:10.1002/advs.202520744 · Cited by 3
Single‐Cell CRISPR: An Efficient Strategy for Decoding Plant Cis ‐Regulatory Complexity
Zhang, Y., Luo, X., Li, H., Jin, S., Zhang, X., & Wang, Y. (2026) · Plant Biotechnology Journal · doi:10.1111/pbi.70647 · Cited by 3
Akana, R. V., Yoe, J., Laveroni, O., Sun, C., Kim, Y.-M., & Jerby‐Arnon, L. (2026) · Nature Genetics · doi:10.1038/s41588-026-02561-7 · Cited by 3
Self-citationToolkit for mapping the clonal landscape of tumor-infiltrating B cells
Serebrovskaya, E. O., Bryushkova, E. A., Lukyanov, D. K., Mushenkova, N. V., Chudakov, D. M., & Turchaninova, M. A. (2024) · Seminars in Immunology · doi:10.1016/j.smim.2024.101864 · Cited by 3
Sun, J., Aikawa, M., Ashktorab, H., Beckmann, N. D., Enger, M. L., Espinosa, J. M., Gai, X., Horne, B. D., Keim, P., Lasky-Su, J. A., et al. (2025) · Frontiers in Systems Biology · doi:10.3389/fsysb.2024.1422384 · Cited by 3
A potential prognostic marker for hematologic neoplasms: CD58
Cao, J., Zhang, Y., Yue, N., Xiong, S., Zhang, S., & Wu, C. (2025) · Frontiers in Oncology · doi:10.3389/fonc.2025.1586842 · Cited by 3
George, S., & Johdi, N. A. (2025) · Current Molecular Medicine · doi:10.2174/0115665240355043250130110501 · Cited by 3
He, C., Zhang, J., Dahleh, M. A., & Uhler, C. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.06.27.661992 · Cited by 4
Li, C., Wei, L., & Zhang, X. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.11.13.688367 · Cited by 3
A multimodal perturbation atlas defines the phenotypic resolution of cellular morphologyPreprint
Liu, C., Hillsley, A. V., Sekhar, M., Jones, C., Sturm, G., Fujimori, T., Wiener, D. M., Cheng, K. W., Chandler, T., Lin, A., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.06.01.728087 · Cited by 3
Decoding crops one cell at a time: from cell atlases to single-cell genetics
Zhang, H., & Schmitz, R. J. (2025) · Journal of Experimental Botany · doi:10.1093/jxb/eraf516 · Cited by 2
Next-generation CRISPR screens enable causal systems immunology
Shi, H., & Chi, H. (2026) · The Journal of Experimental Medicine · doi:10.1084/jem.20241266 · Cited by 2
Ma, X., Zhang, Y., Wang, Y., Han, F., Lu, Y., Tong, C., Guo, Y., Wei, J., Zhu, Q., Dong, L., et al. (2026) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-026-02597-5 · Cited by 2
Kohtamäki, L. M., Leivonen, S., Mäkelä, S., Juteau, S., Leppä, S., & Hernberg, M. (2023) · Acta Oncologica · doi:10.1080/0284186x.2023.2248371 · Cited by 2
PerturbSeq.db: An Integrated Repository for Comprehensive Analysis of Single-cell Perturbation Data
He, T., Yang, X., Tong, Y., Liu, X., Wei, Y., Wang, W., Yuan, J., Wang, Y., & Yang, Y. (2025) · Journal of Molecular Biology · doi:10.1016/j.jmb.2025.169209 · Cited by 3
Co-fluctuations of genes in single cells predict transcriptome-wide outcomes to perturbationsPreprint
Kuznets-Speck, B., Kumari, N., Senthilkumar, I., Jung, J., Rios, H. M. L., Schwartz, L., Sun, H., Anisetti, V. R., Wang, C., Wang, Q., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.06.27.661814 · Cited by 4
Mapping kinase-dependent tumor immune adaptation with multiplexed single-cell CRISPR screensPreprint
Shi, L., Giglio, R. M., Cai, Q., Vaikunthan, M., Hong, J., Naqvi, A., Milea, M., Khanshali, H., Schoonen, A., Hou, N. Y., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.01.08.698516 · Cited by 2
Pan, X., Saunders, R. A., Replogle, J. M., Weissman, J. S., & Zhuang, X. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.05.16.725005 · Cited by 3
Kudo, T., Lopez, R., Meireles, A. M., Rios, A., Coelho, P. P., Chandrasekar, V., Lam, D. C., Huetter, J.-C., Ota, M., Rozenblatt–Rosen, O., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.05.27.728345 · Cited by 2
Self-citationChoi, H., Byeon, G., Park, H., Park, J., Lim, S., & An, J. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.09.01.748703 · Cited by 2
A 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. (2026) · Science Advances · doi:10.1126/sciadv.aed3414 · Cited by 1
Large-scale CRISPR-Cas9 screens to define regulators of immune checkpoints
Mu, M., Melms, J. C., Ho, P., & Izar, B. (2025) · Methods in cell biology · doi:10.1016/bs.mcb.2025.10.011 · Cited by 1
Self-citationIntegrated approaches for discovery and functional annotation of proteins of unknown function
Moitra, T., & Larrouy-Maumus, G. J. (2025) · Trends in Biochemical Sciences · doi:10.1016/j.tibs.2025.11.001 · Cited by 1
Bridging AI and biology: Foundation models meet human physiology and disease
Ashenberg, O., & Xavier, R. J. (2026) · Med · doi:10.1016/j.medj.2025.100971 · Cited by 1
Xiong, J., Zheng, S., Gong, F., Ma, L., & Wan, L. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2025.3615771 · Cited by 1
Bai, D., Song, L., & Xing, E. P. (2026) · Bioinformatics · doi:10.1093/bioinformatics/btag307 · Cited by 2
CRISPR in the Tumor Microenvironment: From Diagnostic Profiling to Precision Therapy
Zhou, L., Mu, M., Yang, M., Liu, X., Zou, G., & Liu, C. (2026) · Cancer Biome and Targeted Therapy · doi:10.66505/cbtt.v1i1.12 · Cited by 2
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
