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.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
0 of 28 cited sources checked · 0 supported · 0 flagged
- Findingown data
Other enriched perturbations included CD47, CD58, and CDH19.
“Other enriched perturbations included CD47, CD58, CDH19 and (Fig. 2d,e).”
Quote in the paperNot a substantial claim - Findingown data
Co-cultures of T cells and cancer cells resulted in dose- and time-dependent cancer cell killing.
“co-cultures of T cells and cancer cells resulted in dose- (TIL:cancer cell ratio) and time-dependent cancer cell killing”
Quote in the paperSubstantial claim - Findingown data
Loss of CD58 conferred immune evasion in multiple co-culture models.
“Loss of CD58 conferred immune evasion in multiple co-culture models”
Quote in the paperSubstantial claim - Findingown data
Perturb-CITE-seq enables pooled CRISPR-Cas9 perturbations to be read out together with single-cell transcriptomes and protein measurements.
“we developed Perturb-CITE-seq, enabling pooled CRISPR-Cas9 perturbations with single-cell transcriptome and protein read-outs.”
Quote in the paperSubstantial claim - Findingown data
Loss or downregulation of CD58 confers immune evasion to T cell and in part NK cell mediated killing.
“we find that loss or downregulation of CD58 confers immune evasion to T cell (and in part to Natural Killer (NK) cells) mediated killing”
Quote in the paperSubstantial claim
- Findingown data
CD58 was downregulated in tumors from melanoma patients with ICR.
“was downregulated in tumors of melanoma patients with ICR.”
Quote in the paperSubstantial claim - Findingown data
CD58 loss conferred immune evasion without compromising MHC expression.
“CD58 loss conferred immune evasion without compromising MHC expression”
Quote in the paperSubstantial claim - Findingown data
The screen recovered known resistance mechanisms involving IFNγ-JAK/STAT and antigen-presentation defects across RNA, protein, and perturbation data.
“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.”
Quote in the paperSubstantial claim - Findingown data
In patient-derived melanoma cells and autologous TIL co-cultures, the study profiled transcriptomes and 20 proteins in about 218,000 cells under about 750 perturbations linked to cancer cell-intrinsic ICR.
“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.”
Quote in the paperSubstantial claim - Findingown data
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 TIL:tumor ratios, respectively.
“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”
Quote in the paperSubstantial claim - Findingown data
The screen identified sgRNAs depleted by day 7 and day 14 before treatment or co-culture, including expected genes such as MYC.
“we identified sgRNAs that are depleted by day 7 and day 14 prior to any treatment or co-culture (Fig. 2a, Extended Data Fig. 3b-d, Supplementary Table 2). These included expected genes such as MYC.”
Quote in the paperSubstantial claim - Findingown data
CD58 expression was not significantly regulated by IFNγ.
“CD58 expression was not significantly regulated by IFNγ”
Quote in the paperSubstantial claim - Findingown data
The screen identified novel candidate resistance mechanisms, including loss or downregulation of CD58.
“and novel ones, including loss/downregulation of CD58.”
Quote in the paperSubstantial claim - Findingown data
Defects in IFNγ-JAK/STAT nodes led to increased CD58 RNA and protein expression.
“Indeed, defects in the IFNγ-JAK/STAT nodes led to increased CD58 RNA and protein expression”
Quote in the paperSubstantial claim - Findingown data
Perturbation of any major known node of the IFNγ response pathway down-regulated a coherent regulatory program in co-culture.
“Perturbation of any major known node of this pathway down-regulated a coherent regulatory program in co-culture”
Quote in the paperSubstantial claim - Findingown data
Loss of CD58 also conferred resistance to NK cell mediated killing.
“Loss of CD58 also conferred resistance to NK cell mediated killing”
Quote in the paperSubstantial claim - Findingown data
In Perturb-CITE-Seq data, CD58 KO did not significantly alter B2M transcript or MHC Class I protein levels.
“Notably, in our Perturb-CITE-Seq data CD58 KO did not significantly alter the level of the B2M transcript or MHC Class I protein (encoded by HLA-A)”
Quote in the paperSubstantial claim - Findingown data
Perturbations of the IFNγ-JAK/STAT module strongly increased Overall Expression of the ICR signature (JAK1 p=1.3e-10, IFNGR2 p=1.4e-8, IFNGR1 p=3.3e-5, STAT1 p=9.6e-5).
“Perturbations of the IFNγ-JAK/STAT module strongly increased the Overall Expression of the ICR signature (JAK1 p-value = 1.3e-10, IFNGR2 p-value = 1.4e-8, IFNGR1 p-value = 3.3e-5, STAT1 p-value = 9.6e-5, statistics by Welch’s t test, Methods)”
Quote in the paperSubstantial claim - Findingown data
Perturbations altered expression of the ICR program in both co-culture and IFNγ treatment.
“The perturbations also altered the expression of the ICR program, which we originally defined in patients1, in both co-culture (Fig. 4e) and in response to IFN γ treatment (Fig. 4f).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [1] Dixit, A. et al. Perturb-seq: dissecting molecular circuits with scalable single-cell RNA profiling of pooled genetic screens. Cell 167, 1853–1866 (2016). source
Not checked in the free run.
- Findingown data
Embedding cells by protein or RNA profiles alone separated cells by treatment condition.
“Embedding cells by either protein (Fig. 3a) or RNA (Fig. 3e) profiles alone separated by treatment conditions.”
Quote in the paperNot a substantial claim - Findingown data
Perturbations of different nodes within the IFNγ pathway led to highly converging molecular phenotypes, irrespective of the level of the defect.
“Perturbations of different nodes within IFNγ pathway led to highly converging molecular phenotypes, irrespective of the level of the defect.”
Quote in the paperSubstantial claim - Findingown data
Only some protein-level differences between treatments were observed at the level of the individual corresponding transcript.
“yet only some of the protein-level differences between treatments were observed at the level of the individual corresponding transcript”
Quote in the paperSubstantial claim - Findingcitation
CD49f, an integrin associated with epithelial-to-mesenchymal transition, was induced.
“consistent with prior reports12, induction of CD49f, which encodes an integrin associated with epithelial-to-mesenchymal transition (EMT).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [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
Not checked in the free run.
- Findingown data
The regulatory model can be parsed into eight major co-functional modules of perturbations that similarly impact one or more of four major gene co-regulated programs.
“The regulatory model can be parsed into eight major co-functional modules of perturbations that similarly impact one or more of four major gene co-regulated programs”
Quote in the paperSubstantial claim - Findingown data
Excluding perturbations in IFNγ-JAK/STAT pathway genes recovered novel regulators and mechanisms of immune evasion.
“we examined the regulation matrix after excluding perturbations in the IFNγ-JAK/STAT pathway genes (Fig. 4c), and recovered novel regulators and mechanisms of immune evasion”
Quote in the paperSubstantial claim - Findingown data
Perturb-CITE-Seq data readily identified major known clinical mechanisms of immune evasion, especially perturbations and associated cell programs in the IFNγ pathway and downregulation or defects of the antigen-presentation axis.
“First, Perturb-CITE-Seq data readily identified major known clinical mechanisms of immune evasion, especially perturbations and their associated cell programs in the IFNγ pathway and downregulation or defects of the antigen-presentation axis.”
Quote in the paperSubstantial claim - Findingown data
Stimulation with IFNγ at 1 ng or 10 ng/mL did not increase CD58 protein abundance.
“stimulation with IFNγ (at 1 ng or 10 ng/mL) did not increase protein abundance of CD58”
Quote in the paperSubstantial claim - Findingown data
In competition assays, both CD58 KO and B2M KO cells had a strong competitive advantage over CD274 KO and parental cell lines.
“Both CD58 KO and B2M KO cells had a strong competitive advantage over CD274 KO and parental cell lines”
Quote in the paperSubstantial claim - Findingown data
Compared to malignant cells from treatment-naïve patients, ICI-resistant patients had significantly lower CD58 mRNA expression.
“Compared to malignant cells from treatment-naïve patients, ICI resistant patients had a significantly lower expression of CD58”
Quote in the paperSubstantial claim - Findingown data
Across all Perturb-CITE-Seq screens, 218,331 high-quality single-cell RNA and protein profiles were analyzed, spanning co-culture (73,114 cells), IFNγ (87,590), and control (57,627) conditions.
“Across all Perturb-CITE-Seq screens, we analyzed 218,331 high quality single-cell RNA and protein profiles spanning the co-culture (73,114 cells), IFNγ (87,590), and control (57,627) conditions”
Quote in the paperNot a substantial claim - Findingreasoning
The pipeline yielded 141, 183, and 181 gene-level targets (of 248 total target genes) for the control, IFNγ, and co-culture conditions, respectively.
“This results in 141, 183, and 181 gene-level targets (of 248 total target genes) for the control, IFNγ, and co-culture conditions, respectively.”
Quote in the paperNot a substantial claim - Findingown data
The Cas9 transgenic melanoma line has high Cas9 activity.
“High Cas9 activity in Cas9 transgenic line.”
Quote in the paperSubstantial claim - Findingown data
Several proteins with potential roles in modifying immunotherapy response were strongly down-regulated exclusively in co-culture, including CXCR4, c-KIT, and KDR.
“there was strong down-regulation of several proteins with potential roles in modifying the response to immunotherapies exclusively in co-culture conditions, including CXCR4 (CD184), c-KIT (CD117) and KDR (CD309)23,24”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [23] Pardoll, D. M. The blockade of immune checkpoints in cancer immunotherapy. Nat. Rev. Cancer 12, 252–264 (2012). source
Not checked in the free run.
- cites [24] Restifo, N. P., Dudley, M. E. & Rosenberg, S. A. Adoptive immunotherapy for cancer: harnessing the T cell response. Nat. Rev. Immunol. 12, 269–281 (2012). source
Not checked in the free run.
- Findingown data
Flow cytometry confirmed that low- or high-dose IFNγ resulted in higher PD-L1 protein levels in CD58 KO cell lines compared to parental control.
“Using flow-cytometry, we confirmed that stimulation with low- or high-dose IFNγ resulted in higher levels of PD-L1 protein in CD58 KO cell lines compared to parental control”
Quote in the paperSubstantial claim - Findingown data
Comparing RNA profiles between conditions highlighted genes involved in antigen presentation, chemokines, and immune modulators.
“Comparing RNA profiles between conditions also highlighted genes involved in antigen presentation, chemokines and immune modulators”
Quote in the paperNot a substantial claim - Findingown data
Other perturbations, such as KO of CDK6, MYC, ILF2, DNMT1, or ACSL3, repressed the ICR signature.
“In contrast, other perturbations, such as KO of CDK6, MYC, ILF2, DNMT1, or ACSL3, repressed the ICR signature”
Quote in the paperSubstantial claim - Findingown data
Other transcripts and proteins were upregulated following perturbations in IFNγ-JAK/STAT nodes, including SERPINE2, TGFB2, CD9, CD59, and CD58.
“These include SERPINE2 and TGFB2, CD9 protein, CD59 protein, and both CD58 transcript and CD58 protein”
Quote in the paperNot a substantial claim - Findingown data
Deletion of TMEM173, encoding STING, increased the ICR signature.
“as did deletion of TMEM173, encoding STING (STimulator of INterferon Genes), which activates a type I interferon response25”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [25] Chen, Q., Sun, L. & Chen, Z. J. Regulation and function of the cGAS-STING pathway of cytosolic DNA sensing. Nat. Immunol. 17, 1142–1149 (2016). source
Not checked in the free run.
- Findingown data
Perturb-CITE-Seq data suggested CD58 KO led to increased PD-L1 expression.
“Our Perturb-CITE-Seq data also suggested that CD58 KO led to increased expression of PD-L1”
Quote in the paperSubstantial claim - Findingown data
CD58 is not activated by the IFNγ-JAK/STAT pathway.
“Importantly CD58 is not activated by the IFNγ-JAK/STAT pathway”
Quote in the paperSubstantial claim - Findingown data
A permutation test identified a distinct single-cell cluster whose marker genes include canonical T cell markers.
“A permutation test was used to score marker genes associated with each cluster shown in (a)35. Score (y axis, permutation test, Methods) of marker genes (x axis) associated with the distinct cluster marked by an arrow in (a), include canonical T cell markers.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [35] Pedregosa, F. et al. Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).
Not checked in the free run.
- Findingown data
Cell complexity affects scRNA-Seq profiles.
“Cell complexity affects scRNA-Seq profiles.”
Quote in the paperNot a substantial claim - Findingown data
There is limited relation between the cell cycle and immune pressure or phenotype.
“Limited relation between the cell cycle and immune pressure or phenotype.”
Quote in the paperNot a substantial claim - Findingown data
Control and B2M, CD58, or CD274 knockout melanoma cells from patient 2686 have comparable growth.
“Comparable growth of control and KO cells. Ratio of viable cells relative to timepoint 0 (y axis) for control and B2M KO, CD58 KO or CD274 KO melanoma cells from patient 2686 (x axis).”
Quote in the paperSubstantial claim - Premisecitation
An ICR signature predictive of intrinsic ICI resistance was previously identified in a subset of malignant cells from metastatic melanoma patients using scRNA-seq.
“We previously identified an ICR signature, which is predictive of intrinsic resistance to ICI, in a subset of malignant cells in patients with metastatic melanoma using scRNA-seq9.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [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
Not checked in the free run.
- Interpretationown data
The work discovers potentially clinically relevant mechanisms of immune evasion.
“and discovers potentially clinically relevant mechanisms of immune evasion.”
Quote in the paperSubstantial claim - Premisecitation
There is no known mouse homolog of CD58.
“Notably, there is no known mouse homolog of CD58, emphasizing the value of performing such screens in human models10.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [10] Patel, S. J. et al. Identification of essential genes for cancer immunotherapy. Nature 548, 537–542 (2017). source
Not checked in the free run.
- Interpretationown data
The work provides a framework for deciphering complex mechanisms using large-scale perturbation screens with multi-modal single-cell readouts.
“This work provides framework for deciphering complex mechanisms by large-scale perturbation screens with multi-modal single-cell readouts”
Quote in the paperSubstantial claim - Interpretationreasoning
CD58 loss acts orthogonally to known mechanisms of ICR.
“suggesting that it acts orthogonal to known mechanisms of ICR.”
Quote in the paperSubstantial claim - Study design and conductown data
The screens were highly reproducible across triplicate samples.
“The screens were highly reproducible across triplicate samples”
Quote in the paperSubstantial claim - Interpretationown data
The work identifies immune evasion mechanisms that are in part orthogonal to previously described patient mechanisms.
“identifies mechanisms of immune evasion that are in part orthogonal to previously described mechanisms in patients.”
Quote in the paperSubstantial claim - Interpretationreasoning
CD58's role in immune evasion may be distinct from defects in the IFNγ-JAK/STAT pathway.
“and CD58’s role in immune evasion may therefore be distinct from defects in the IFNγ-JAK/STAT pathway.”
Quote in the paperSubstantial claim - Interpretationown data
Downregulation or loss of CD58 represents a clinically relevant immune evasion mechanism orthogonal to previously described mechanisms involving antigen presentation and IFNγ response defects.
“Together, these data suggest that downregulation or loss of CD58 represents a clinically relevant immune evasion mechanism that is orthogonal to previously described ones involving defects in antigen presentation and IFNγ response.”
Quote in the paperSubstantial claim - Study design and conductown data
Including cell quality (total UMIs per cell) and cell cycle state as covariates in the linear model improved the fit.
“Inclusion as covariates of cell quality (defined as the total number of UMIs in each cell) and cell cycle state (assigned with Scanpy’s implementation of scoring cell cycle genes34) improved the fit.”
- Also stated in: § Extended Data · plain
“Improved model fit following accounting for cell state as a covariate.”
Quote in the paperNot a substantial claim - Also stated in: § Extended Data · plain
- Study design and conductown data
Differences in resistance were not due to differences in proliferation rates or apoptotic potential of different KO models.
“Importantly, we excluded the possibility that these differences were due to differences in proliferation rates, (Extended Data Fig. 7d) or apoptotic potential of different KO models (Extended Data Fig. 7e).”
Quote in the paperSubstantial claim - Interpretationreasoning
Induction of these genes is likely not through the IFNγ-JAK/STAT pathway.
“It is likely that the induction of these genes is not through the IFNγ-JAK/STAT pathway.”
Quote in the paperSubstantial claim - Study design and conductown data
Dose-response killing in the co-culture experiment validates the target killing range.
“Dose-response killing in the co-culture experiment validates target killing range.”
Quote in the paperSubstantial claim - Interpretationown data
CD58 is not induced via the IFNγ pathway.
“suggesting that CD58 is not induced via the IFNγ pathway.”
Quote in the paperSubstantial claim - Interpretationown data
Genes functionally impacting susceptibility to T cell mediated killing are concordantly regulated at the protein and RNA level.
“Overall, our results suggest that genes functionally impacting susceptibility to T cell mediated killing (based on genetic perturbation in the viability screen) are concordantly regulated at the protein and RNA level.”
Quote in the paperSubstantial claim - Premisecitation
Detection of a sgRNA in a cell does not necessarily mean that the cell is perturbed.
“As we previously showed1, detection of a sgRNA in a cell does not necessarily mean that this cell is perturbed, either because the sgRNA has not perturbed the gene, or the perturbation does not have an impact.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [1] Dixit, A. et al. Perturb-seq: dissecting molecular circuits with scalable single-cell RNA profiling of pooled genetic screens. Cell 167, 1853–1866 (2016). source
Not checked in the free run.
- Interpretationreasoning
Genes that were hits in the viability screen partitioned into different functional modules, highlighting converging or divergent mechanisms that could not be distinguished by a viability screen alone.
“Importantly, genes that were hits in the viability screen partitioned into different functional modules, thus highlighting the effect of the same (converging) or distinct (divergent) mechanism, which could not be distinguished by a viability screen alone.”
Quote in the paperSubstantial claim - Interpretationown data
Upregulation of PD-L1 could contribute to immune evasion in CD58 KO in T cell co-culture.
“suggesting that upregulation of PD-L1 could contribute to immune evasion in CD58 KO in T cell co-culture.”
Quote in the paperSubstantial claim - Interpretationown data
Single-cell RNA and protein profiles provide rich relevant phenotypes for assessing the impact of CRISPR perturbations.
“Thus, single-cell RNA and protein profiles provide rich relevant phenotypes for assessing the impact of CRISPR perturbations.”
Quote in the paperSubstantial claim - Interpretationown data
CD58 loss may represent a resistance mechanism distinct from IFNγ-JAK/STAT pathway inactivation.
“Thus, CD58 loss may represent a resistance mechanism distinct from IFNγ-JAK/STAT pathway inactivation.”
Quote in the paperSubstantial claim - Study design and conductown data
The computational framework models the effects of genetic perturbations on both RNA and protein profiles of individual genes.
“We developed a computational framework to model the effects of genetic perturbations on both RNA and protein profiles of individual genes”
Quote in the paperSubstantial claim - Study design and conductown data
CRISPR/Cas9 knockout melanoma cells and unperturbed controls were validated.
“Validation of CRISPR/Cas9 KO (and unperturbed control) melanoma cells.”
Quote in the paperSubstantial claim
- May combine two claims. 46
- Classified as probably not a substantial claim. 11
- The quote only partly states this claim. 2
May combine two claims.
Findingown dataThe study performed pooled Perturb-CITE-Seq screens targeting 248 ICR signature genes with 744 guides and profiled single-cell transcriptomes and 20 surface proteins in more than 218,000 cells.
“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.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataThe CD58 finding was validated in additional patient models and in clinical scRNA-seq data from patients with ICR.
“and we validate this finding in additional patient models and in clinical scRNA-seq data of patients with ICR.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataThe study developed an integrated analytical framework and recovered known clinically relevant immune resistance mutations along with novel ones.
“We developed an integrated analytical framework and recover the known landscape of clinically relevant immune resistance mutations along with novel ones.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataPerturbations of B2M, HLA-A, JAK1, JAK2, STAT1, IFNGR1, and IFNGR2 conferred immune evasion, consistent with clinical resistance mechanisms.
“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.”
Quote in the paperSubstantial claim0 of 3 sources checked- cites [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 in the free run.
- cites [8] Sade-Feldman, M. et al. Resistance to checkpoint blockade therapy through inactivation of antigen presentation. Nat. Commun. 8, 1136 (2017). source
Not checked in the free run.
- cites [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
Not checked in the free run.
May combine two claims.
Findingown dataTumor cell lysis was highly reproducible and specific to TCR/MHC Class I interactions.
“Tumor cell lysis was highly reproducible and specific to T cell receptor (TCR)/MHC Class I interactions”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataAt the protein level, MHC proteins and PD-L1 increased in IFNγ-treated versus control cells, and MHC proteins globally increased in co-culture versus control cells.
“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”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataCD58 KO conferred a different molecular phenotype than IFNγ-JAK/STAT pathway perturbations, belonged to a distinct module, and did not impact expression of antigen presentation genes.
“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”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataMaMel-134 TILs produce IFNγ, TNFα, and Granzyme-B.
“MaMel-134 TILs produce IFNγ, TNFa, and Granzyme-B.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataIntegrating RNA and protein measurements highlighted gene programs common across conditions or unique to different conditions, with co-cultured cells uniquely enriched for induction of immune escape pathway genes and the ICR signature.
“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.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataCD58 emerged as a compelling immune evasion mechanism because KO conferred resistance to T cell killing in the viability screen and CD58 RNA/protein were downregulated in cells surviving TIL co-culture.
“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.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataNeither B2M nor HLA-A KO impacted CD58 protein levels in the Perturb-CITE-Seq screen.
“Conversely, neither B2M nor HLA-A KO impacted CD58 protein levels in our Perturb-CITE-Seq screen”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataFlow cytometry showed CD58 KO did not alter MHC protein expression at baseline or their induction by IFNγ.
“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”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataThe Perturb-CITE-Seq model correctly reconstructed impacts of genes known to affect resistance to immunotherapy, especially the effect of perturbing the IFNγ response machinery, along with novel pathways.
“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.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataCD58, CD47, and IFNGR1 protein levels were reduced in co-culture, concordant with enrichment of their genetic KO in the viability screen.
“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”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingown dataIn patient 2686 Cas9-expressing melanoma cells carrying the ICR library, pre-selection mKATE2+ fraction and post-puromycin survival correlate with Pearson R2=0.90.
“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.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingown dataEmpirically, a second EM iteration had only a very modest effect on sgRNA/target assignments.
“Empirically, adding a second iteration had only a very modest effect on sgRNA/target assignments.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataThe downregulated IFNγ program included key antigen presentation components, chemokines, inflammatory cytokines, interferon response elements, surface checkpoints, and differentiation genes.
“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”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataIn all three patient-derived melanoma cell line models, CD58 KO and B2M KO conferred resistance to T cell mediated killing, while PD-L1 KO sensitized cells.
“In all models, CD58 KO and B2M KO conferred resistance (Fig. 5b-d) while PD-L1 KO sensitized cells to T cell mediated killing”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataWithin each condition, cell cycle and cell complexity affected RNA profiles.
“Within each condition, both the cell cycle (G1/0, S, G2/M) (Fig. 3f) and complexity (Extended Data Fig. 4c) impacted RNA profiles”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataSeveral programs, including cell cycle regulation, DNA repair, and antigen presentation, were shared across conditions, whereas immune escape programs were uniquely recovered in co-culture and interferon response genes in IFNγ stimulation.
“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”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataTILs from patient 2686 still induce IFNγ and TNFα, and reactivation with OKT-3 boosts Granzyme-B relative to IL2-only culture.
“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.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataThe viability screen recovered known immune evasion mechanisms in a dose-dependent manner and identified several novel candidates.
“Overall, the viability screen recovered known immune evasion mechanisms in a dose-dependent manner, and identified several novel candidates.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataIn the co-culture condition, one EM iteration moved 1,741 target assignments to near zero (confidence <25%), while a second iteration changed only 97 target-cell assignments.
“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.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataMaMel-80 TILs produce IFNγ, TNFα, and Granzyme-B.
“MaMel-80 TILs produce IFNγ, TNFa, and Granzyme-B.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataCITE profiles of 20 cell surface proteins do not reflect cell cycle phases or UMI count.
“CITE profiles of 20 cell surface proteins do not reflect cell cycle phases (d) or UMI count (e).”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Findingown dataControl and knockout melanoma cells show comparable induction of apoptosis in response to Staurosporin and resistance to DTIC.
“Comparable induction of apoptosis in response to Staurosporin and resistance to DTIC in control and KO melanoma cells.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataIn co-culture, perturbing CD58 leaves B2M and HLA expression unchanged at both RNA and protein levels but increases CD274.
“CD58 perturbation in co-culture does not affect B2M and HLA expression at the RNA and protein level but induces CD274.”
Quote in the paperSubstantial claim- Rests on an assumption: The patient-derived melanoma-TIL co-culture with IFNγ pretreatment models T cell-mediated immune selection in human melanoma.
Probably not a substantial claim: may not pass S3 (load-bearing).
Findingown dataA G2M cell cycle control program was learned from the control dataset.
“G2M program learned from control dataset.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationCD58, also known as LFA3, is an understudied adhesion molecule typically expressed on antigen-presenting cells that binds CD2 on T and NK cells.
“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.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [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
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationActivated T cells also express SIRPa, and interaction with CD47 increases T cell activation and cytokine production, which may affect the clinical benefit of CD47 blockade.
“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.”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [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 in the free run.
- cites [21] Zhang, W. et al. Advances in anti-tumor treatments targeting the CD47/SIRPα Axis. Front. Immunol. 11, 18 (2020).
Not checked in the free run.
May combine two claims.
PremisecitationA proof-of-concept ECCITE-seq study detected protein, RNA, and guide RNAs in individual cells but was small-scale and lacked the analytical framework needed for large screens.
“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.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [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
Not checked in the free run.
May combine two claims.
PremisecitationClinically relevant ICR mechanisms include B2M mutations causing MHC Class I loss, antigen presentation downregulation, and IFNγ-JAK/STAT mutations that impair response to T cell anti-tumor activity.
“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.”
Quote in the paperSubstantial claim0 of 3 sources checked- cites [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 in the free run.
- cites [8] Sade-Feldman, M. et al. Resistance to checkpoint blockade therapy through inactivation of antigen presentation. Nat. Commun. 8, 1136 (2017). source
Not checked in the free run.
- cites [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
Not checked in the free run.
Classified as probably not a substantial claim. May combine two claims.
PremisecitationCRISPR/Cas9 KO screens in murine models or engineered human cell lines identified putative novel immune evasion mechanisms involving chromatin regulators, TNFα signaling, SOX4, PTPN2, and APLNR, but their clinical relevance is unclear.
“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.”
Quote in the paperSubstantial claim0 of 3 sources checked- cites [10] Patel, S. J. et al. Identification of essential genes for cancer immunotherapy. Nature 548, 537–542 (2017). source
Not checked in the free run.
- cites [11] Kearney, C. J. et al. Tumor immune evasion arises through loss of TNF sensitivity. Sci. Immunol. 3, eaar3451 (2018).
Not checked in the free run.
- cites [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
Not checked in the free run.
May combine two claims.
PremisereasoningMany relevant phenotypes are best understood at the protein level and differ between RNA and protein, so prior Perturb-Seq schemes needed expansion.
“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.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationCD47 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 vivo.
“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.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [19] Veillette, A. & Chen, J. SIRPα–CD47 immune checkpoint blockade in anticancer therapy. Trends Immunol. 39, 173–184 (2018). source
Not checked in the free run.
May combine two claims.
PremisecitationICIs produce durable responses in some patients, but most patients either do not benefit or develop resistance over time.
“While ICIs produce durable responses in some patients most patients either do not benefit or develop resistance over time.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [6] Sharma, P. & Allison, J. P. The future of immune checkpoint therapy. Science 348, 56–61 (2015). source
Not checked in the free run.
May combine two claims.
Interpretationown dataPerturb-CITE-Seq analysis rediscovered key immune evasion mechanisms, organized them into modules, related them to impacted genes and programs, and recovered new modules that may confer immune evasion beyond IFNγ-JAK/STAT and antigen presentation defects.
“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.”
Quote in the paperSubstantial claimMay combine two claims.
Interpretationown dataMultiple transcripts and proteins implicated in modifying immunotherapy response are directly regulated by the IFNγ-JAK/STAT axis, suggesting some mechanisms are a consequence of defective IFNγ-JAK/STAT signaling rather than independent modes of resistance.
“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.”
Quote in the paperSubstantial claimMay combine two claims.
Interpretationown dataPD-L1 is upregulated in CD58 KO, suggesting CD58 loss could confer immune evasion directly through reduced adhesion or indirectly through inhibitory PD-L1.
“Furthermore, PD-L1 is upregulated in CD58 KO, suggesting that CD58 loss could confer immune evasion directly (reduced adhesion) or indirectly (inhibitory PD-L1)”
Quote in the paperSubstantial claimMay combine two claims.
Interpretationown dataThe study presents a large-scale CRISPR/Cas9 screen with multi-modal single-cell readouts, providing a general approach and addressing a critical clinical challenge in a patient-derived cell culture model system.
“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.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductown dataMelanoma cells tested repeatedly negative for contamination with mycoplasma and other contaminants.
“Melanoma cells tested repeatedly negative for contamination with mycoplasma and other contaminants using PlasmoTest (InvivoGen, San Diego, CA).”
Quote in the paperNot a substantial claimMay combine two claims.
Interpretationown dataCD58 KO also impacted NK-mediated killing, which may have implications for NK cell-based therapies in tumors with loss of antigen presentation.
“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.”
Quote in the paperSubstantial claimMay combine two claims.
Study design and conductreasoningThe permutation test partially corrects for bias from sample size and effect size in regulatory coefficients and allows direct comparison of empirical p values across targets.
“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.”
Quote in the paperSubstantial claimMay combine two claims.
Interpretationown dataLoss of CD58 confers immune evasion to a similar extent as loss of MHC Class I expression through B2M KO, but through an independent path and without impacting antigen presentation genes and proteins.
“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”
Quote in the paperSubstantial claimThe quote only partly states this claim.
Study design and conductown dataIncluding cell cycle and cell complexity as covariates improved the model fit quality.
“Including these covariates improved the model fit quality (residuals approach mean zero, independent and identically distributed; Extended Data Fig. 6b,c).”
Quote in the paperNot a substantial claimMay combine two claims.
InterpretationreasoningSome genes down-regulated by IFNγ pathway perturbations, such as CXCL10 and CXCL11, were previously associated with immune evasion, suggesting these do not represent salient mechanisms of immune evasion.
“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.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationA full expectation-maximization approach is not guaranteed to converge to a global minimum in high-dimensional linear regression with noisy and missing data.
“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.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [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
Not checked in the free run.
May combine two claims.
PremisecitationUpregulation 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 models.
“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.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [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
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone givenAll experiments were performed in triplicates in each of at least two independent experiments.
“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.”
Quote in the paperNot a substantial claim- Also stated in: § Extended Data · plain
The quote only partly states this claim.
Study design and conductown dataThe CRISPR/Cas9 viability screen is reproducible across triplicates.
“Screen reproducibility across triplicates.”
Quote in the paperSubstantial claimMay combine two claims.
PremisecitationThe CD58/CD2 axis is a potent co-stimulatory pathway in CD8+ T cells that lack CD28, which are common in the tumor microenvironment and peripheral blood of solid tumor patients and were predominant in the Perturb-CITE-Seq screen.
“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”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [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 in the free run.
- cites [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
Not checked in the free run.
May combine two claims.
Interpretationown dataThe study recovers the landscape of resistance mechanisms to immunotherapies and validates a novel mechanism of immune resistance.
“We recover the landscape of resistance mechanisms to immunotherapies and, guided by our high-dimensional data, validate a novel mechanism of immune resistance.”
Quote in the paperSubstantial claimMay combine two claims.
Interpretationown dataOther genes whose perturbation enhanced immune evasion in the screen, such as CD58 and CD59, appear to reflect distinct mechanisms because their expression was not impaired by IFNγ signaling defects and their molecular phenotype following perturbation is distinct.
“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.”
Quote in the paperSubstantial claimMay combine two claims.
Interpretationown dataReduced CD58 does not directly impair antigen presentation or JAK/STAT signaling, but lowers T cell co-stimulation and raises PD-L1-mediated co-inhibition, leading to resistance to T cell killing.
“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.”
Quote in the paperSubstantial claim- 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?
Three CITE-seq datasets have protein and RNA counts that are downloadable separately.
“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]
Protein, RNA and ATAC readouts for CRISPRi perturbation of MYC are all available for K562 cells.
“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]
ECCITE-seq and Perturb-CITE-seq entail in tandem detection of transcripts, proteins, and sgRNAs.
“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]
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
The blockade of immune checkpoints in cancer immunotherapy
Pardoll, D. M. (2012) · Nature Reviews Cancer · doi:10.1038/nrc3239 · Cited by 13,838 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
Self-citationThe future of immune checkpoint therapy
Sharma, P., & Allison, J. P. (2015) · Science · doi:10.1126/science.aaa8172 · Cited by 4,878 · see citing works
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 · see citing works
Mutations Associated with Acquired Resistance to PD-1 Blockade in Melanoma
Zaretsky, J. M., Garcia-Diaz, A., Shin, D. S., Escuin-Ordinas, H., Hugo, W., Hu-Lieskovan, S., Torrejon, D. Y., Abril-Rodriguez, G., Sandoval, S., Barthly, L., et al. (2016) · New England Journal of Medicine · doi:10.1056/nejmoa1604958 · Cited by 3,237 · see citing works
Finak, G., McDavid, A., Yajima, M., Deng, J., Gersuk, V., Shalek, A. K., Slichter, C. K., Miller, H. W., McElrath, M. J., Prlic, M., et al. (2015) · Genome Biology · doi:10.1186/s13059-015-0844-5 · Cited by 3,159 · see citing works
Simultaneous epitope and transcriptome measurement in single cells
Stoeckius, M., Hafemeister, C., Stephenson, W., Houck-Loomis, B., Chattopadhyay, P. K., Swerdlow, H., Satija, R., & Smibert, P. (2017) · Nature Methods · doi:10.1038/nmeth.4380 · Cited by 3,098 · see citing works
Regulation and function of the cGAS–STING pathway of cytosolic DNA sensing
Chen, Q., Sun, L., & Chen, Z. J. (2016) · Nature Immunology · doi:10.1038/ni.3558 · Cited by 2,333 · see citing works
Dixit, A., Parnas, O., Li, B., Chen, J., Fulco, C. P., Jerby-Arnon, L., Marjanovic, N. D., Dionne, D., Burks, T., Raychowdhury, R., et al. (2016) · Cell · doi:10.1016/j.cell.2016.11.038 · Cited by 1,863 · see citing works
Self-citationAdoptive immunotherapy for cancer: harnessing the T cell response
Restifo, N. P., Dudley, M. E., & Rosenberg, S. A. (2012) · Nature Reviews Immunology · doi:10.1038/nri3191 · Cited by 1,688 · see citing works
Rational design of highly active sgRNAs for CRISPR-Cas9-mediated gene inactivation
Doench, J. G., Hartenian, E., Graham, D. B., Tothova, Z., Hegde, M., Smith, I., Sullender, M., Ebert, B. L., Xavier, R. J., & Root, D. E. (2014) · Nature Biotechnology · doi:10.1038/nbt.3026 · Cited by 1,617 · see citing works
Loss of PTEN Promotes Resistance to T Cell–Mediated Immunotherapy
Peng, W., Chen, J. Q., Liu, C., Malu, S., Creasy, C., Tetzlaff, M. T., Xu, C., McKenzie, J. A., Zhang, C., Liang, X., et al. (2016) · Cancer Discovery · doi:10.1158/2159-8290.cd-15-0283 · Cited by 1,539 · see citing works
Self-citationCDK4/6 inhibition triggers anti-tumour immunity
Goel, S., DeCristo, M. J., Watt, A. C., BrinJones, H., Sceneay, J., Li, B. B., Khan, N., Ubellacker, J. M., Xie, S., Metzger-Filho, O., et al. (2017) · Nature · doi:10.1038/nature23465 · Cited by 1,513 · see citing works
A Cancer Cell Program Promotes T Cell Exclusion and Resistance to Checkpoint Blockade
Jerby-Arnon, L., Shah, P., Cuoco, M. S., Rodman, C., Su, M.-J., Melms, J. C., Leeson, R., Kanodia, A., Mei, S., Lin, J.-R., et al. (2018) · Cell · doi:10.1016/j.cell.2018.09.006 · Cited by 1,429 · see citing works
Self-citationType I Inteferon Gene Induction by the Interferon Regulatory Factor Family of Transcription Factors
Honda, K., Takaoka, A., & Taniguchi, T. (2006) · Immunity · doi:10.1016/j.immuni.2006.08.009 · Cited by 1,386 · see citing works
Loss of IFN-γ pathway genes in tumor cells as a mechanism of resistance to anti-CTLA-4 therapy
Gao, J., Shi, L. Z., Zhao, H., Chen, J., Xiong, L., He, Q., Chen, T., Roszik, J., Bernatchez, C., Woodman, S. E., et al. (2016) · Cell · doi:10.1016/j.cell.2016.08.069 · Cited by 1,300 · see citing works
Self-citationAdamson, B., Norman, T. M., Jost, M., Cho, M. Y., Nuñez, J. K., Chen, Y., Villalta, J. E., Gilbert, L. A., Horlbeck, M. A., Hein, M. Y., et al. (2016) · Cell · doi:10.1016/j.cell.2016.11.048 · Cited by 1,193 · see citing works
Self-citationIn vivo CRISPR screening identifies Ptpn2 as a cancer immunotherapy target
Manguso, R. T., Pope, H. W., Zimmer, M. D., Brown, F. D., Yates, K. B., Miller, B. C., Collins, N. B., Bi, K., LaFleur, M. W., Juneja, V. R., et al. (2017) · Nature · doi:10.1038/nature23270 · Cited by 1,147 · see citing works
Pooled CRISPR screening with single-cell transcriptome readout.
Datlinger, P., Rendeiro, A. F., Schmidl, C., Krausgruber, T., Traxler, P., Klughammer, J., Schuster, L. C., Kuchler, A., Alpar, D., & Bock, C. (2017) · Nature Methods · doi:10.1038/nmeth.4177 · Cited by 1,096 · see citing works
Resistance to checkpoint blockade therapy through inactivation of antigen presentation
Sade-Feldman, M., Jiao, Y. J., Chen, J. H., Rooney, M. S., Barzily-Rokni, M., Eliane, J.-P., Bjorgaard, S. L., Hammond, M. R., Vitzthum, H., Blackmon, S. M., et al. (2017) · Nature Communications · doi:10.1038/s41467-017-01062-w · Cited by 1,005 · see citing works
Identification of essential genes for cancer immunotherapy
Patel, S. J., Sanjana, N. E., Kishton, R. J., Eidizadeh, A., Vodnala, S. K., Cam, M., Gartner, J. J., Jia, L., Steinberg, S. M., Yamamoto, T. N., et al. (2017) · Nature · doi:10.1038/nature23477 · Cited by 870 · see citing works
A major chromatin regulator determines resistance of tumor cells to T cell–mediated killing
Pan, D., Kobayashi, A., Jiang, P., Ferrari de Andrade, L., Tay, R. E., Luoma, A. M., Tsoucas, D., Qiu, X., Lim, K., Rao, P., et al. (2018) · Science · doi:10.1126/science.aao1710 · Cited by 852 · see citing works
Self-citationDissecting Immune Circuits by Linking CRISPR-Pooled Screens with Single-Cell RNA-Seq
Jaitin, D. A., Weiner, A., Yofe, I., Lara-Astiaso, D., Keren-Shaul, H., David, E., Salame, T. M., Tanay, A., van Oudenaarden, A., & Amit, I. (2016) · Cell · doi:10.1016/j.cell.2016.11.039 · Cited by 777 · see citing works
High-dimensional regression with noisy and missing data: Provable guarantees with nonconvexity
Loh, P.-L., & Wainwright, M. J. (2012) · The Annals of Statistics · doi:10.1214/12-aos1018 · Cited by 529 · see citing works
Mimitou, E. P., Cheng, A., Montalbano, A., Hao, S., Stoeckius, M., Legut, M., Roush, T., Herrera, A., Papalexi, E., Ouyang, Z., et al. (2019) · Nature Methods · doi:10.1038/s41592-019-0392-0 · Cited by 520 · see citing works
Strioga, M., Pasukoniene, V., & Characiejus, D. (2011) · Immunology · doi:10.1111/j.1365-2567.2011.03470.x · Cited by 505 · see citing works
Quality control, modeling, and visualization of CRISPR screens with MAGeCK-VISPR
Li, W., Köster, J., Xu, H., Chen, C.-H., Xiao, T., Liu, J. S., Brown, M. A., & Liu, X. S. (2015) · Genome biology · doi:10.1186/s13059-015-0843-6 · Cited by 492 · see citing works
SIRPα–CD47 Immune Checkpoint Blockade in Anticancer Therapy
Veillette, A., & Chen, J. (2018) · Trends in Immunology · doi:10.1016/j.it.2017.12.005 · Cited by 476 · see citing works
Challa-Malladi, M., Lieu, Y. K., Califano, O., Holmes, A. B., Bhagat, G., Murty, V. V. V. S., Dominguez-Sola, D., Pasqualucci, L., & Dalla‐Favera, R. (2011) · Cancer Cell · doi:10.1016/j.ccr.2011.11.006 · Cited by 439 · see citing works
Tumor immune evasion arises through loss of TNF sensitivity
Kearney, C. J., Vervoort, S. J., Hogg, S. J., Ramsbottom, K. M., Freeman, A. J., Lalaoui, N., Pijpers, L., Michie, J., Brown, K. K., Knight, D. A., et al. (2018) · Science Immunology · doi:10.1126/sciimmunol.aar3451 · Cited by 381 · see citing works
Advances in Anti-Tumor Treatments Targeting the CD47/SIRPα Axis
Zhang, W., Huang, Q., Xiao, W., Zhao, Y., Pi, J., Xu, H., Zhao, H., Xu, J., Evans, C. E., & Jin, H. (2020) · Frontiers in Immunology · doi:10.3389/fimmu.2020.00018 · Cited by 352 · see citing works
Statistical significance of variables driving systematic variation in high-dimensional data
Chung, N. C., & Storey, J. D. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu674 · Cited by 247 · see citing works
Cumulus provides cloud-based data analysis for large-scale single-cell and single-nucleus RNA-seq
Li, B., Gould, J., Yang, Y., Sarkizova, S., Tabaka, M., Ashenberg, O., Rosen, Y., Slyper, M., Kowalczyk, M. S., Villani, A., et al. (2020) · Nature Methods · doi:10.1038/s41592-020-0905-x · Cited by 235 · see citing works
Self-citationHSP90 inhibition enhances cancer immunotherapy by upregulating interferon response genes
Mbofung, R. M., McKenzie, J. A., Malu, S., Zhang, M., Peng, W., Liu, C., Kuiatse, I., Tieu, T., Williams, L., Devi, S., et al. (2017) · Nature Communications · doi:10.1038/s41467-017-00449-z · Cited by 159 · see citing works
Self-citationCD58/CD2 Is the Primary Costimulatory Pathway in Human CD28−CD8+ T Cells
Leitner, J., Herndler-Brandstetter, D., Zlabinger, G. J., Grubeck-Loebenstein, B., & Steinberger, P. (2015) · The Journal of Immunology · doi:10.4049/jimmunol.1401917 · Cited by 104 · see citing works
Huang, L., Malu, S., McKenzie, J. A., Andrews, M. C., Talukder, A. H., Tieu, T., Karpinets, T., Haymaker, C., Forget, M.-A., Williams, L. J., et al. (2018) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-17-2483 · Cited by 102 · see citing works
Self-citationThe Effect of Topoisomerase I Inhibitors on the Efficacy of T-Cell-Based Cancer Immunotherapy
McKenzie, J. A., Mbofung, R. M., Malu, S., Zhang, M., Ashkin, E. L., Devi, S., Williams, L. J., Tieu, T. N., Peng, W., Pradeep, S., et al. (2017) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/djx257 · Cited by 91 · see citing works
Self-citationArulanandam, A. R., Withka, J. M., Wyss, D. F., Wagner, G., Kister, A., Pallai, P., Recny, M. A., & Reinherz, E. L. (1993) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.90.24.11613 · Cited by 85 · see citing works
A functional subset of CD8+ T cells during chronic exhaustion is defined by SIRPα expression
Myers, L. M., Tal, M. C., Torrez Dulgeroff, L. B., Carmody, A. B., Messer, R. J., Gulati, G., Yiu, Y. Y., Staron, M. M., Angel, C. L., Sinha, R., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-08637-9 · Cited by 66 · see citing works
Intratumoural immunotherapy: activation of nucleic acid sensing pattern recognition receptors
Agrawal, S., & Kandimalla, E. R. (2019) · Immuno-Oncology Technology · doi:10.1016/j.iotech.2019.10.001 · Cited by 15 · see citing works
Deep Generative Models for Detecting Differential Expression in Single CellsPreprint
Boyeau, P., Lopez, R., Regier, J., Gayoso, A., Jordan, M. I., & Yosef, N. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/794289 · Cited by 13 · see citing works
Flagged first, then by citations
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 751 · see citing works
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 591 · see citing works
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 474 · see citing works
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 441 · see citing works
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 440 · see citing works
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 · see citing works
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 · see citing works
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 350 · see citing works
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 · see citing works
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 299 · see citing works
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 · see citing works
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 254 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
Self-citationRood, J. E., Hupalowska, A., & Regev, A. (2024) · Cell · doi:10.1016/j.cell.2024.07.035 · Cited by 139 · see citing works
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 · see citing works
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 135 · see citing works
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 113 · see citing works
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 109 · see citing works
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 108 · see citing works
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 104 · see citing works
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 · see citing works
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 101 · see citing works
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 · see citing works
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 96 · see citing works
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 91 · see citing works
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 · see citing works
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 82 · see citing works
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 79 · see citing works
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 77 · see citing works
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 · see citing works
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 72 · see citing works
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 · see citing works
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 · see citing works
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 61 · see citing works
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 61 · see citing works
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 · see citing works
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 59 · see citing works
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 · see citing works
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 · see citing works
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 54 · see citing works
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 53 · see citing works
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 53 · see citing works
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 52 · see citing works
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 51 · see citing works
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 50 · see citing works
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 46 · see citing works
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 · see citing works
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 43 · see citing works
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 43 · see citing works
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 · see citing works
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 42 · see citing works
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 · see citing works
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 · see citing works
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 40 · see citing works
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 40 · see citing works
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 · see citing works
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 39 · see citing works
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 · see citing works
Chen, E., Ling, A. L., Reardon, D. A., & Chiocca, E. A. (2023) · Neuro-Oncology · doi:10.1093/neuonc/noad211 · Cited by 38 · see citing works
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 36 · see citing works
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 36 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
Morris, J. A., Sun, J. S., & Sanjana, N. E. (2023) · Trends in Genetics · doi:10.1016/j.tig.2023.10.012 · Cited by 30 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
Barry, T., Mason, K., Roeder, K., & Katsevich, E. (2024) · Genome Biology · doi:10.1186/s13059-024-03254-2 · Cited by 28 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
“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 26 · see citing works
Tooley, K., Jerby‐Arnon, L., Escobar, G., Krovi, S. H., Mangani, D., Dandekar, G., Cheng, H., Madi, A., Goldschmidt, E., Lambden, C. D., et al. (2024) · Cell Reports Medicine · doi:10.1016/j.xcrm.2024.101640 · Cited by 25 · see citing works
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 25 · see citing works
Paul, S., Xie, S., Yao, X., & Dey, A. (2022) · Cells · doi:10.3390/cells11142225 · Cited by 25 · see citing works
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 25 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 24 · see citing works
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 23 · see citing works
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 23 · see citing works
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 23 · see citing works
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 · see citing works
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 22 · see citing works
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 21 · see citing works
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 21 · see citing works
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 · see citing works
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 19 · see citing works
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 19 · see citing works
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 · see citing works
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 18 · see citing works
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 18 · see citing works
- 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.
Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-04.
Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-04.
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