bluuebunny/crossref_metadata_2025 huggingface dataset:
Figure 6 from A Compendium of Syngeneic, Transplantable Pediatric High-Grade Glioma Models Reveals Subtype-Specific Therapeutic Vulnerabilities
Authors: McNicholas Michael , De Cola Antonella , Bashardanesh Zahedeh , Foss Amelia , Lloyd Cameron B. , Hébert Steven , Faury Damien , Andrade Augusto Faria , Jabado Nada , Kleinman Claudia L. , Pathania Manav
Published: March 2024

A, Left: acid-extracted histone and protein lysates prepared from ex vivo GS lines derived from the two-hit de novo models (KP, H3.1KP, KF, and KN) were probed for K27M, K27me3, HA, and total H3.3 levels. NSCs derived from the LRL (LRL NSC) were used as controls. Middle: validation of FGFR1 overexpression in ex vivo KF cells; GAPDH was used as a loading control. Right: validation of p53 and NF1 downregulation in ex vivo cells by qRT-PCR. Data are presented as means ± SD from at least n = 3 replicates. ****, P < 0.0001 and ***, P = 0.0003 were calculated using unpaired t test and one-way ANOVA. B, Left: acid-extracted histone and protein lysates prepared from ex vivo GS lines derived from the three- and four-hit de novo models (KPP, KPD, KPAP, KPAD, KPPMPIK, H3.1KACVPIK, KNF, KPF, and KPC) were probed for K27M, K27me3, HA, and total H3.3 levels. LRL NSCs were used as controls. Validation of PDGFRA, PPM1D, PIK3CA, ACVR1, FGFR1, and CCND2 overexpression in ex vivo GS cells; GAPDH and tubulin were used as loading controls. Right: validation of p53, ATRX, and NF1 downregulation in ex vivo GS cells by qRT-PCR. Data are presented as means ± SD from at least n = 3 replicates. ****, P < 0.0001 and ***, P = 0.0003 were calculated using unpaired t test and one-way ANOVA. C, Left: IHC detection of hematoxylin and eosin (H&E), GFP, K27M, and Ki-67 in coronal hindbrain sections from orthotopically engrafted, symptomatic mice. Mice were injected in either the pons (H3K27M) or striatum (H3.3G34R) with 150,000 to 300,000 cells from each cell GS cell line (n = 5 mice per condition). Tumor cells are GFP+. Scale bars, 2 mm. Right: BLI of brainstem tumors in orthotopically engrafted C57BL/6J mice without shaving/depilation using Akaluc imaging. D, Kaplan–Meier curves depicting survival of mice orthotopically injected with GS cells derived from H3.3K27M (left), H3.1K27M (middle), and H3.3G34R (right) models. Note that the following samples are shown twice in A and B: LRL NSC, KF, and KN; these repetitions are indicated with an asterisk. The sole purpose of this repetition is to make it easier for the reader, by collating all the Western blot (WB) data describing the “two-hit” models in A and all the WB data describing the “three-hit or more” models in B.
Supplementary Tables S1-S10 from Noninvasive Detection of Neuroendocrine Prostate Cancer through Targeted Cell-free DNA Methylation
Authors: Franceschini Gian Marco , Quaini Orsetta , Mizuno Kei , Orlando Francesco , Ciani Yari , Ku Sheng-Yu , Sigouros Michael , Rothmann Emily , Alonso Alicia , Benelli Matteo , Nardella Caterina , Auh Joonghoon , Freeman Dory , Hanratty Brian , Adil Mohamed , Elemento Olivier , Tagawa Scott T. , Feng Felix Y. , Caffo Orazio , Buttigliero Consuelo , Basso Umberto , Nelson Peter S. , Corey Eva , Haffner Michael C. , Attard Gerhardt , Aparicio Ana , Demichelis Francesca , Beltran Himisha
Published: March 2024

S1: Motif enrichment results around differentially methylated sites in CRPC-NE vs CRPC-Adeno solid tissue biopsy eRRBS data (Beltran2016)
S2A: PSA and treatment timepoints for the PRIME cohort
S2BPSA and treatment timepoints for the Wu2020 cohort
S3Sequencings statistics for samples profiled with the NEMO panel (Notice that sequencing run and Dataset might differ)
S4: NEMO tumor content (TC) and PE score estimation of all study samples (NEMO generated and reprocessed data)
S5: Association between gene expression and proximal DNA methylation for the PDX EPIC cohort (pdx) and WCDT cohort (biopsies)
S6A: Alisertib cohort survival data
S6B: Chemo trial cohort survival data
S7: Inclusion criteria for the two phase 2 trial cohorts
S8:References of masked samples and samples sequenced in this work
S9: Description of informative regions included in the NEMO panel
S10: Mean CpG coverage in NEMO regions for reprocessed data (only CpG sites with at least 10x coverage are retained)
Supplementary Figures 1-14 from INX-315, a Selective CDK2 Inhibitor, Induces Cell Cycle Arrest and Senescence in Solid Tumors
Authors: Dietrich Catherine , Trub Alec , Ahn Antonio , Taylor Michael , Ambani Krutika , Chan Keefe T. , Lu Kun-Hui , Mahendra Christabella A. , Blyth Catherine , Coulson Rhiannon , Ramm Susanne , Watt April C. , Matsa Sunil Kumar , Bisi John , Strum Jay , Roberts Patrick , Goel Shom
Published: March 2024

Supplementary Figure 1: Characterization of INX-315.
Supplementary Figure 2: Effect of INX-315 and PF-07104091 on viability of cancer and non-malignant cell lines.
Supplementary Figure 3: Effect of INX-315 on cell cycle in CCNE1-amplified cancer cell lines.
Supplementary Figure 4: Further characterization of the effects of INX-315 in in vitro and in vivo models of CCNE1-amplified cancer.
Supplementary Figure 5: Effects of INX-315 treatment on gene expression in models of CCNE1amplified ovarian carcinoma.
Supplementary Figure 6: Characterization of CDK4/6 inhibitor-resistant cell lines.
Supplementary Figure 7: Response of CDK4/6 inhibitor-resistant breast cancer cells to INX-315 and PF-07104091.
Supplementary Figure 8: Effects of INX-315 and PF-07104091 on CDK4/6 inhibitor-resistant breast cancer cells.
Supplementary Figure 9: Impact of INX-315 +/- continued CDK4/6 inhibition in CDK4/6 inhibitorresistant breast cancer cells.
Supplementary Figure 10: Characterization of a new mouse model of acquired CDK4/6 inhibitor resistance.
Supplementary Figure 11: Effect of INX-315 on expression of senescence-related genes in CDK4/6 inhibitor-resistant breast cancer cells.
Supplementary Figure 12: Epigenomic and transcriptomic features of INX-315 induced senescence in CDK4/6 inhibitor-resistant breast cancer.
Supplementary Figure 13: Impact of INX-315 treatment of CDK4/6 inhibitor resistant breast cancer on expression of apoptosis and differentiation-related genes.
Supplementary Figure 14: Impact of co-inhibition of CDK2 and CDK4/6 on cell cycle and development of CDK4/6 inhibitor resistance in breast cancer cells.
Supplementary Figures S1-S6 from Integrative Analyses of Tumor and Peripheral Biomarkers in the Treatment of Advanced Renal Cell Carcinoma
Authors: Choueiri Toni K. , Donahue Amber C. , Braun David A. , Rini Brian I. , Powles Thomas , Haanen John B.A.G. , Larkin James , Mu Xinmeng Jasmine , Pu Jie , Teresi Rosemary E. , di Pietro Alessandra , Robbins Paul B. , Motzer Robert J.
Published: March 2024

Supplementary Figure S1 shows forest plots of PFS according to numbers of circulating cell populations in the overall trial population (both arms combined) at baseline. Supplementary Figure S2 shows forest plots of PFS with A+Ax or sunitinib according to cytokine, chemokine, or protein concentrations in peripheral blood at baseline and during treatment (cycle 2 day 1). Supplementary Figure S3 shows PFS according to metrics describing TCR repertoire change from baseline to cycle 2 day 1 with A+Ax vs sunitinib. Supplementary Figure S4 shows PFS according to updated mutation subgroup definitions and treatment group. Supplementary Figure S5 shows normalized numbers of total T cells in pretreatment tumors according to molecular subgroup. Supplementary Figure S6 shows peripheral T-cell receptor quantitation and clonality metrics at baseline and cycle 2 day 1 by mutation subgroup.
Figure 5 from INX-315, a Selective CDK2 Inhibitor, Induces Cell Cycle Arrest and Senescence in Solid Tumors
Authors: Dietrich Catherine , Trub Alec , Ahn Antonio , Taylor Michael , Ambani Krutika , Chan Keefe T. , Lu Kun-Hui , Mahendra Christabella A. , Blyth Catherine , Coulson Rhiannon , Ramm Susanne , Watt April C. , Matsa Sunil Kumar , Bisi John , Strum Jay , Roberts Patrick , Goel Shom
Published: March 2024

INX-315 treatment of CDK4/6i–resistant breast cancer reinstates a senescence phenotype. A, Representative images (top) and quantification (bottom) of staining MCF7 and T47D cells for beta-galactosidase activity after treatment with agents shown (500 nmol/L abemaciclib, INX-315: 300 nmol/L for MCF7 and 100 nmol/L for T47D) for 7 days (scale bar = 100 μm). Resistant cells were cultured continuously in drugs to which they were resistant (500 nmol/L abemaciclib ± 100 nmol/L fulvestrant). Experiments performed with three technical replicates. Quantification is for integrated beta-galactosidase signal per cell. Dashed line indicates cutoff used to define beta-galactosidase positivity. B, Representative images (top) and quantification of nuclear size (bottom) upon staining MCF7 and T47D cells with DAPI and phalloidin after treatment as in A (scale bar = 100 μm). Experiments performed with three technical replicates. C, Heat map shows z-scores for ssGSEA scores for four senescence-associated gene signatures, calculated from RNA-sequencing of MCF7 and T47D cells treated as in A. Bar plot above shows mean z-score for each sample across all signatures. Three technical replicates for all conditions except abemaciclib-resistant MCF7 in abemaciclib, which had two technical replicates. D, Heat map as in C, but for RNA-sequencing from abemaciclib-resistant MMTV-rtTA/tetO-HER2 tumors treated as in Fig. 4E. Six samples for vehicle and INX-315, five samples for abemaciclib and combination. E, Violin plots showing log2-transformed normalized ATAC-seq counts for genomic regions that significantly increased chromatin accessibility after abemaciclib treatment of parental cells. Cells treated as in A, two technical replicates. F, Scatter plots showing significant enrichment of AP-1 motifs in the ATAC-seq up peaks after treatment with drugs as in A. G, Heat map showing z-scores for individual SASP genes (RNA-sequencing) in MCF7 cells treated as in A. Bar plot above shows mean z-score for all genes in each sample. Three technical replicates for all conditions except abemaciclib-resistant MCF7 in abemaciclib, which had two technical replicates. H, Representative ATAC-seq tracks at regions near SASP genes (IGFBP3 and VEGFA) in cells treated as in A (all P values calculated using unpaired t tests except F, determined using HOMER package).
Figure 4 from INX-315, a Selective CDK2 Inhibitor, Induces Cell Cycle Arrest and Senescence in Solid Tumors
Authors: Dietrich Catherine , Trub Alec , Ahn Antonio , Taylor Michael , Ambani Krutika , Chan Keefe T. , Lu Kun-Hui , Mahendra Christabella A. , Blyth Catherine , Coulson Rhiannon , Ramm Susanne , Watt April C. , Matsa Sunil Kumar , Bisi John , Strum Jay , Roberts Patrick , Goel Shom
Published: March 2024

Activity of INX-315 in CDK4/6i–resistant breast cancer. A, Dose–response curves for MCF7 and T47D cell lines treated with INX-315 for 7 days. Parent, parent cells in DMSO; Abema-resistant, abemaciclib-resistant cells growing in 500 nmol/L abemaciclib; Abema/Fulv-resistant, resistant to abemaciclib/fulvestrant growing in 500 nmol/L abemaciclib plus 100 nmol/L fulvestrant; Fulv-resistant, fulvestrant resistant growing in 100 nmol/L fulvestrant; Parent + abema, parental cells treated with 500 nmol/L abemaciclib and INX-315 concurrently. Table shows IC50s for INX-315 in each case, derived from measurement of cell number (error bars represent SD; two biological replicates, six technical replicates each time). B, Cell cycle phase profiles of MCF7 and T47D cells (and parental and drug-resistant) treated with drugs shown for 7 days (500 nmol/L abemaciclib; 100 nmol/L fulvestrant; INX-315, 300 nmol/L for MCF7, 100 nmol/L for T47D; values are mean of experiments performed in duplicate, error bars are SD). C, Western blots for phosphorylated/total Rb in MCF7 and T47D cells treated as in B. D, Heat map showing z scores for individual E2F target genes (RNA-sequencing) in MCF7 and T47D cells treated as in B. Bar plot above shows mean z-score for all genes in each sample. Three technical replicates per condition. E, Tumor growth curves for MMTV-rtTA/tetO-HER2 tumors treated with control vehicle (n = 17 tumors in 6 mice), abemaciclib (n = 17 tumors in 6 mice), INX-315 (n = 20 tumors in 6 mice), or the combination (n = 15 tumors in 6 mice). Tumors were pretreated with abemaciclib for 3–4 weeks prior to randomization, at which point abemaciclib resistance was present. Experiment was repeated twice (error bars represent SEM; P values calculated using two-way ANOVA). F, Heat map showing z scores for individual E2F target genes (RNA-sequencing) in MMTV-rtTA/tetO-HER2 tumors from E. Bar plot above shows mean z-score for all genes in each sample. Six samples for vehicle and INX-315, five samples for abemaciclib and combination. G, Tumor growth curves for ST4316B PDX tumors treated with control, ribociclib, INX-315, or the combination (n = 8 per group; error bars represent SEM; P values calculated using two-way ANOVA).
Figure 1 from INX-315, a Selective CDK2 Inhibitor, Induces Cell Cycle Arrest and Senescence in Solid Tumors
Authors: Dietrich Catherine , Trub Alec , Ahn Antonio , Taylor Michael , Ambani Krutika , Chan Keefe T. , Lu Kun-Hui , Mahendra Christabella A. , Blyth Catherine , Coulson Rhiannon , Ramm Susanne , Watt April C. , Matsa Sunil Kumar , Bisi John , Strum Jay , Roberts Patrick , Goel Shom
Published: March 2024

Discovery and characterization of INX-315. A, Amino acid sequence homology at the ATP-binding pockets of several CDKs. B, Derivation of INX-315 through serial modifications of trilaciclib, a CDK4/6i. Table indicates biochemical IC50s (± SEM) to cyclin/CDK pairings shown using the Nanosyn biochemical assay. Trilaciclib IC50 for CDK1/cyclin B1 was ND (for trilaciclib: n = 6 for CDK2/cyclin E1 and CDK2/cyclin A2, n = 9 for CDK4/cyclin D1, n = 3 for other complexes; for compound B: n = 3 for all complexes; for compound C: n = 1 for all complexes; for INX-315: if SEM is 0, n = 1, otherwise n = 6). C, Modeling of INX-315 bound to cyclin E1/CDK2. D, NanoBRET assay quantifying INX-315's intracellular displacement of tracer from the ATP-pocket of the cyclin/CDK pairings shown. Table shows calculated IC50s (two technical replicates per experiment, two biological replicates except for CDK2/Cyclin A1 where n = 1; error bars represent SD).BID, twice daily; QD, once daily.
Supplementary Table S3 from Targeting DHX9 Triggers Tumor-Intrinsic Interferon Response and Replication Stress in Small Cell Lung Cancer
Authors: Murayama Takahiko , Nakayama Jun , Jiang Xinpei , Miyata Kenichi , Morris Alexander D. , Cai Kathy Q. , Prasad Rahul M. , Ma Xueying , Efimov Andrey , Belani Neel , Gerstein Emily R. , Tan Yinfei , Zhou Yan , Kim William , Maruyama Reo , Campbell Kerry S. , Chen Lu , Yang Yibin , Balachandran Siddharth , Cañadas Israel
Published: March 2024

DHX9 depleted signature
Supplementary Table S2 from Cancer Mutations Converge on a Collection of Protein Assemblies to Predict Resistance to Replication Stress
Authors: Zhao Xiaoyu , Singhal Akshat , Park Sungjoon , Kong JungHo , Bachelder Robin , Ideker Trey
Published: March 2024

Table S2 Odds ratio for 45 drug response predictions with multi-drug model.xlsx
Table S1 from EGFR-Driven Lung Adenocarcinomas Co-opt Alveolar Macrophage Metabolism and Function to Support EGFR Signaling and Growth
Authors: Kuhlmann-Hogan Alexandra , Cordes Thekla , Xu Ziyan , Kuna Ramya S. , Traina Kacie A. , Robles-Oteíza Camila , Ayeni Deborah , Kwong Elizabeth M. , Levy Stellar , Globig Anna-Maria , Nobari Matthew M. , Cheng George Z. , Leibel Sandra L. , Homer Robert J. , Shaw Reuben J. , Metallo Christian M. , Politi Katerina , Kaech Susan M.
Published: March 2024

Table S1: BALF Patient Sample Clinical Features
Figure S8 from EGFR-Driven Lung Adenocarcinomas Co-opt Alveolar Macrophage Metabolism and Function to Support EGFR Signaling and Growth
Authors: Kuhlmann-Hogan Alexandra , Cordes Thekla , Xu Ziyan , Kuna Ramya S. , Traina Kacie A. , Robles-Oteíza Camila , Ayeni Deborah , Kwong Elizabeth M. , Levy Stellar , Globig Anna-Maria , Nobari Matthew M. , Cheng George Z. , Leibel Sandra L. , Homer Robert J. , Shaw Reuben J. , Metallo Christian M. , Politi Katerina , Kaech Susan M.
Published: March 2024

Figure S8: AMs transfer nutrients to tumor cells.
Figure S7 from EGFR-Driven Lung Adenocarcinomas Co-opt Alveolar Macrophage Metabolism and Function to Support EGFR Signaling and Growth
Authors: Kuhlmann-Hogan Alexandra , Cordes Thekla , Xu Ziyan , Kuna Ramya S. , Traina Kacie A. , Robles-Oteíza Camila , Ayeni Deborah , Kwong Elizabeth M. , Levy Stellar , Globig Anna-Maria , Nobari Matthew M. , Cheng George Z. , Leibel Sandra L. , Homer Robert J. , Shaw Reuben J. , Metallo Christian M. , Politi Katerina , Kaech Susan M.
Published: March 2024

Figure S7: PPARγ antagonist GW9662 rewires Alveolar macrophage metabolism within the TME.
Figure S6 from EGFR-Driven Lung Adenocarcinomas Co-opt Alveolar Macrophage Metabolism and Function to Support EGFR Signaling and Growth
Authors: Kuhlmann-Hogan Alexandra , Cordes Thekla , Xu Ziyan , Kuna Ramya S. , Traina Kacie A. , Robles-Oteíza Camila , Ayeni Deborah , Kwong Elizabeth M. , Levy Stellar , Globig Anna-Maria , Nobari Matthew M. , Cheng George Z. , Leibel Sandra L. , Homer Robert J. , Shaw Reuben J. , Metallo Christian M. , Politi Katerina , Kaech Susan M.
Published: March 2024

Figure S6: Single cell analysis of tumorigenesis in humans reveals rewired LUAD patient macrophage lipid and mitochondrial metabolism.
Figure S5 from EGFR-Driven Lung Adenocarcinomas Co-opt Alveolar Macrophage Metabolism and Function to Support EGFR Signaling and Growth
Authors: Kuhlmann-Hogan Alexandra , Cordes Thekla , Xu Ziyan , Kuna Ramya S. , Traina Kacie A. , Robles-Oteíza Camila , Ayeni Deborah , Kwong Elizabeth M. , Levy Stellar , Globig Anna-Maria , Nobari Matthew M. , Cheng George Z. , Leibel Sandra L. , Homer Robert J. , Shaw Reuben J. , Metallo Christian M. , Politi Katerina , Kaech Susan M.
Published: March 2024

Figure S5: PPARγ Antagonist GW9662 modulates induces tumor regression and modulates the TME.
Figure 5 from Passenger Gene Coamplifications Create Collateral Therapeutic Vulnerabilities in Cancer
Authors: Bei Yi , Bramé Luca , Kirchner Marieluise , Fritsche-Guenther Raphaela , Kunz Severine , Bhattacharya Animesh , Rusu Mara-Camelia , Gürgen Dennis , Dubios Frank P.B. , Köppke Julia K.C. , Proba Jutta , Wittstruck Nadine , Sidorova Olga Alexandra , Chamorro González Rocío , Dorado Garcia Heathcliff , Brückner Lotte , Xu Robin , Giurgiu Mădălina , Rodriguez-Fos Elias , Yu Qinghao , Spanjaard Bastiaan , Koche Richard P. , Schmitt Clemens A. , Schulte Johannes H. , Eggert Angelika , Haase Kerstin , Kirwan Jennifer , Hagemann Anja I.H. , Mertins Philipp , Dörr Jan R. , Henssen Anton G.
Published: March 2024

DDX1 hijacks the α-KGDH complex resulting in α-KG accumulation and OXPHOS reduction. A, Relative concentrations of α-KG, citrate, and isocitrate in cancer cell lines with DDX1-MYCN coamplifications (red) compared with cells only harboring MYCN amplifications (blue; Welch t test, P = 0.038764, 0.008224, and 0.025814 for α-KG, citrate, and isocitrate, respectively; N = 4 independent MYCN-amplified cancer cell lines versus N = 8 independent cancer cell lines with DDX1-MYCN coamplification). B, Relative α-KG concentrations measured by GC-MS in KELLY cells ectopically expressing DDX1 or the DDX1-Δ269-295aa for 48 hours. KELLY cells transduced with an empty vector and exposed to doxycycline were used as control (Wilcox test, P = 0.02778; data are shown as mean ± standard error). C, Western immunoblot of DDX1, P70-S6K, P70-S6K Thr389 phosphorylation, and α-tubulin in IMR5/75 cells treated with DM-αKG (2 mmol/L for 48 hours) and expressing shRNA targeting either DLST or GFP as control. D, Mitochondrial oxygen consumption rate (OCR) measured using live-cell metabolic analysis at basal respiration, maximal respiration, and ATP production in KELLY cells inducibly expressing DDX1 or DDX1-Δ269-295aa for 48 hours. KELLY cell transduced with a doxycycline-inducible empty vector served as negative control (Welch t test, P = 0.002, 0.010, and 0.002 for basal respiration, maximal respiration, and ATP production, respectively; data are shown as mean ± SE; N = 4 independent replicates). E, Exemplary photomicrographs taken on a transmission electron microscope of cells expressing DDX1 compared with cells expressing DDX1 Δ269-295aa. Cells transduced with an empty vector well as cells not treated with doxycycline served as negative controls. F, Quantification of mitochondrial length (longest axis in a cross-section) of cells shown in E (Wilcox test, P = 0.7954, 7.028e−10, and 0.1453, independently).
Figure 3 from Passenger Gene Coamplifications Create Collateral Therapeutic Vulnerabilities in Cancer
Authors: Bei Yi , Bramé Luca , Kirchner Marieluise , Fritsche-Guenther Raphaela , Kunz Severine , Bhattacharya Animesh , Rusu Mara-Camelia , Gürgen Dennis , Dubios Frank P.B. , Köppke Julia K.C. , Proba Jutta , Wittstruck Nadine , Sidorova Olga Alexandra , Chamorro González Rocío , Dorado Garcia Heathcliff , Brückner Lotte , Xu Robin , Giurgiu Mădălina , Rodriguez-Fos Elias , Yu Qinghao , Spanjaard Bastiaan , Koche Richard P. , Schmitt Clemens A. , Schulte Johannes H. , Eggert Angelika , Haase Kerstin , Kirwan Jennifer , Hagemann Anja I.H. , Mertins Philipp , Dörr Jan R. , Henssen Anton G.
Published: March 2024

A proof-of-principle study identifies a selective mTOR pathway dependency in cells with DDX1-MYCN coamplification. A, Correlation between DDX1 copy-number and dependency scores (CERES) for RAPTOR in neuroblastoma cell lines (Pearson correlation analysis, R = −0.5996, P = 0.0152, N = 13). B, Western immunoblot of RAPTOR and DDX1 in the KELLY cells transduced with the doxycycline-inducible DDX1-mCherry vectors and with two pairs of sgRNAs targeting RAPTOR (sgRAPTOR) or a nontargeting sgRNA (sgNT) as well as Cas9 in the presence and absence of doxycycline (1 μg/mL). Tubulin serves as a loading control. C, Representative images of cell colonies formed by KELLY cells transduced with the doxycycline-inducible DDX1-mCherry vectors and with two pairs of sgRNA targeting RAPTOR (sgRAPTOR) or nontarget sgRNA (sgNT) as well as Cas9 in the presence and absence of doxycycline (1 μg/mL) and stained with crystal violet (left). Quantification of colony numbers (right, mean ± SE. N = 3 biological replicates; Welch t test, P = 0.564, 0.000117, and 0.00131 for sgNT, sgRAPTOR_1, and sgRAPTOR_2, respectively). D, Gene set enrichment analysis (GSEA) based on a set of genes regulated by mTORC1 measured in genes differentially expressed in tumors with high versus low DDX1 expression. E, GSEA based on a set of genes regulated by mTORC1 measured in genes differentially expressed in KELLY cells harboring a MYCN amplification with versus without ectopic DDX1 expression. F, Western blot of the relative protein expression of mTOR ser2448 phosphorylation and P70-S6K Thr389 phosphorylation in KELLY cell after inducible expression of DDX1 (1,000 ng/mL doxycycline treatment for 48 hours).
Figure S1 from EGFR-Driven Lung Adenocarcinomas Co-opt Alveolar Macrophage Metabolism and Function to Support EGFR Signaling and Growth
Authors: Kuhlmann-Hogan Alexandra , Cordes Thekla , Xu Ziyan , Kuna Ramya S. , Traina Kacie A. , Robles-Oteíza Camila , Ayeni Deborah , Kwong Elizabeth M. , Levy Stellar , Globig Anna-Maria , Nobari Matthew M. , Cheng George Z. , Leibel Sandra L. , Homer Robert J. , Shaw Reuben J. , Metallo Christian M. , Politi Katerina , Kaech Susan M.
Published: March 2024

Figure S1: Myeloid cell regulation during lung tumorigenesis.
Figure 2 from Passenger Gene Coamplifications Create Collateral Therapeutic Vulnerabilities in Cancer
Authors: Bei Yi , Bramé Luca , Kirchner Marieluise , Fritsche-Guenther Raphaela , Kunz Severine , Bhattacharya Animesh , Rusu Mara-Camelia , Gürgen Dennis , Dubios Frank P.B. , Köppke Julia K.C. , Proba Jutta , Wittstruck Nadine , Sidorova Olga Alexandra , Chamorro González Rocío , Dorado Garcia Heathcliff , Brückner Lotte , Xu Robin , Giurgiu Mădălina , Rodriguez-Fos Elias , Yu Qinghao , Spanjaard Bastiaan , Koche Richard P. , Schmitt Clemens A. , Schulte Johannes H. , Eggert Angelika , Haase Kerstin , Kirwan Jennifer , Hagemann Anja I.H. , Mertins Philipp , Dörr Jan R. , Henssen Anton G.
Published: March 2024

Passenger genes are frequently coamplified with oncogenes in cancers and can be associated with distinct dependencies. A, The mean number of passenger genes coamplified with oncogenes compared with the frequency of oncogene amplifications in cancers (dot size presents incidence passenger gene coamplification). B, Number of cancer cell lines in CCLE with an oncogene amplification compared with the number of cell lines with passenger coamplification (oncogenes for which sufficient dependency information is available in the DepMap are marked in red). C, Chromosome 2 schematic highlighting the area of MYCN amplification and passenger genes recurrently included on the amplicon (top). Upset plot (bottom) for the coamplification frequency of three passenger genes, NBAS, DDX1, and FAM49A, identified on the MYCN amplicon in a cohort of 556 neuroblastomas. D, Top 10 GO ontology terms enriched for collateral lethal targets associated with DDX1-MYCN coamplifications (TOR complex labeled in red). E, Difference in gene dependency scores between cancer cell lines with DDX1-MYCN coamplification vs. cell lines with MYCN amplifications compared with the log-transformed P values (Wilcoxon; candidate collateral lethal dependencies in DDX1-MYCN coamplified cancer cells, blue; mTORC1 complex, red).
Supplementary Tables 1-3, 7-9 from A Compendium of Syngeneic, Transplantable Pediatric High-Grade Glioma Models Reveals Subtype-Specific Therapeutic Vulnerabilities
Authors: McNicholas Michael , De Cola Antonella , Bashardanesh Zahedeh , Foss Amelia , Lloyd Cameron B. , Hébert Steven , Faury Damien , Andrade Augusto Faria , Jabado Nada , Kleinman Claudia L. , Pathania Manav
Published: March 2024

Tables describing statistical analyses, median survivals, IC50 values calculated from cell viability assays, primer sequences and antibodies.
Supplementary Table 4 from A Compendium of Syngeneic, Transplantable Pediatric High-Grade Glioma Models Reveals Subtype-Specific Therapeutic Vulnerabilities
Authors: McNicholas Michael , De Cola Antonella , Bashardanesh Zahedeh , Foss Amelia , Lloyd Cameron B. , Hébert Steven , Faury Damien , Andrade Augusto Faria , Jabado Nada , Kleinman Claudia L. , Pathania Manav
Published: March 2024

Quality control and filtering of snRNA-seq data.
Supplementary Figures 1-27 from A Compendium of Syngeneic, Transplantable Pediatric High-Grade Glioma Models Reveals Subtype-Specific Therapeutic Vulnerabilities
Authors: McNicholas Michael , De Cola Antonella , Bashardanesh Zahedeh , Foss Amelia , Lloyd Cameron B. , Hébert Steven , Faury Damien , Andrade Augusto Faria , Jabado Nada , Kleinman Claudia L. , Pathania Manav
Published: March 2024

Figures describing hindbrain vs ganglionic eminence targeting strategies, each of the 16 models generated and validation that they expressed the introduced mutations, survival of GE vs CTX-electroporated embryos, and cell viability data shown in bar graph format.
Data from A Compendium of Syngeneic, Transplantable Pediatric High-Grade Glioma Models Reveals Subtype-Specific Therapeutic Vulnerabilities
Authors: McNicholas Michael , De Cola Antonella , Bashardanesh Zahedeh , Foss Amelia , Lloyd Cameron B. , Hébert Steven , Faury Damien , Andrade Augusto Faria , Jabado Nada , Kleinman Claudia L. , Pathania Manav
Published: March 2024

Abstract
Pediatric high-grade gliomas (pHGG) are lethal, incurable brain tumors frequently driven by clonal mutations in histone genes. They often harbor a range of additional genetic alterations that correlate with different ages, anatomic locations, and tumor subtypes. We developed models representing 16 pHGG subtypes driven by different combinations of alterations targeted to specific brain regions. Tumors developed with varying latencies and cell lines derived from these models engrafted in syngeneic, immunocompetent mice with high penetrance. Targeted drug screening revealed unexpected selective vulnerabilities—H3.3G34R/PDGFRAC235Y to FGFR inhibition, H3.3K27M/PDGFRAWT to PDGFRA inhibition, and H3.3K27M/PDGFRAWT and H3.3K27M/PPM1DΔC/PIK3CAE545K to combined inhibition of MEK and PIK3CA. Moreover, H3.3K27M tumors with PIK3CA, NF1, and FGFR1 mutations were more invasive and harbored distinct additional phenotypes, such as exophytic spread, cranial nerve invasion, and spinal dissemination. Collectively, these models reveal that different partner alterations produce distinct effects on pHGG cellular composition, latency, invasiveness, and treatment sensitivity.
Significance:
Histone-mutant pediatric gliomas are a highly heterogeneous tumor entity. Different histone mutations correlate with different ages of onset, survival outcomes, brain regions, and partner alterations. We have developed models of histone-mutant gliomas that reflect this anatomic and genetic heterogeneity and provide evidence of subtype-specific biology and therapeutic targeting.

See related commentary by Lubanszky and Hawkins, p. 1516.

This article is highlighted in the In This Issue feature, p. 1501
Data from Integrative Analyses of Tumor and Peripheral Biomarkers in the Treatment of Advanced Renal Cell Carcinoma
Authors: Choueiri Toni K. , Donahue Amber C. , Braun David A. , Rini Brian I. , Powles Thomas , Haanen John B.A.G. , Larkin James , Mu Xinmeng Jasmine , Pu Jie , Teresi Rosemary E. , di Pietro Alessandra , Robbins Paul B. , Motzer Robert J.
Published: March 2024

Abstract
The phase III JAVELIN Renal 101 trial demonstrated prolonged progression-free survival (PFS) in patients (N = 886) with advanced renal cell carcinoma treated with first-line avelumab + axitinib (A+Ax) versus sunitinib. We report novel findings from integrated analyses of longitudinal blood samples and baseline tumor tissue. PFS was associated with elevated lymphocyte levels in the sunitinib arm and an abundance of innate immune subsets in the A+Ax arm. Treatment with A+Ax led to greater T-cell repertoire modulation and less change in T-cell numbers versus sunitinib. In the A+Ax arm, patients with tumors harboring mutations in ≥2 of 10 previously identified PFS-associated genes (double mutants) had distinct circulating and tumor-infiltrating immunologic profiles versus those with wild-type or single-mutant tumors, suggesting a role for non–T-cell–mediated and non–natural killer cell–mediated mechanisms in double-mutant tumors. We provide evidence for different immunomodulatory mechanisms based on treatment (A+Ax vs. sunitinib) and tumor molecular subtypes.
Significance:
Our findings provide novel insights into the different immunomodulatory mechanisms governing responses in patients treated with avelumab (PD-L1 inhibitor) + axitinib or sunitinib (both VEGF inhibitors), highlighting the contribution of tumor biology to the complexity of the roles and interactions of infiltrating immune cells in response to these treatment regimens.

This article is featured in Selected Articles from This Issue, p. 384
Data from Targeting DHX9 Triggers Tumor-Intrinsic Interferon Response and Replication Stress in Small Cell Lung Cancer
Authors: Murayama Takahiko , Nakayama Jun , Jiang Xinpei , Miyata Kenichi , Morris Alexander D. , Cai Kathy Q. , Prasad Rahul M. , Ma Xueying , Efimov Andrey , Belani Neel , Gerstein Emily R. , Tan Yinfei , Zhou Yan , Kim William , Maruyama Reo , Campbell Kerry S. , Chen Lu , Yang Yibin , Balachandran Siddharth , Cañadas Israel
Published: March 2024

Abstract
Activating innate immunity in cancer cells through cytoplasmic nucleic acid sensing pathways, a phenomenon known as “viral mimicry,” has emerged as an effective strategy to convert immunologically “cold” tumors into “hot.” Through a curated CRISPR-based screen of RNA helicases, we identified DExD/H-box helicase 9 (DHX9) as a potent repressor of double-stranded RNA (dsRNA) in small cell lung cancers (SCLC). Depletion of DHX9 induced accumulation of cytoplasmic dsRNA and triggered tumor-intrinsic innate immunity. Intriguingly, ablating DHX9 also induced aberrant accumulation of R-loops, which resulted in an increase of DNA damage–derived cytoplasmic DNA and replication stress in SCLCs. In vivo, DHX9 deletion promoted a decrease in tumor growth while inducing a more immunogenic tumor microenvironment, invigorating responsiveness to immune-checkpoint blockade. These findings suggest that DHX9 is a crucial repressor of tumor-intrinsic innate immunity and replication stress, representing a promising target for SCLC and other “cold” tumors in which genomic instability contributes to pathology.
Significance:
One promising strategy to trigger an immune response within tumors and enhance immunotherapy efficacy is by inducing endogenous “virus-mimetic” nucleic acid accumulation. Here, we identify DHX9 as a viral-mimicry-inducing factor involved in the suppression of double-stranded RNAs and R-loops and propose DHX9 as a novel target to enhance antitumor immunity.

See related commentary by Chiappinelli, p. 389.

This article is featured in Selected Articles from This Issue, p. 384
Data from Cancer Mutations Converge on a Collection of Protein Assemblies to Predict Resistance to Replication Stress
Authors: Zhao Xiaoyu , Singhal Akshat , Park Sungjoon , Kong JungHo , Bachelder Robin , Ideker Trey
Published: March 2024

Abstract
Rapid proliferation is a hallmark of cancer associated with sensitivity to therapeutics that cause DNA replication stress (RS). Many tumors exhibit drug resistance, however, via molecular pathways that are incompletely understood. Here, we develop an ensemble of predictive models that elucidate how cancer mutations impact the response to common RS-inducing (RSi) agents. The models implement recent advances in deep learning to facilitate multidrug prediction and mechanistic interpretation. Initial studies in tumor cells identify 41 molecular assemblies that integrate alterations in hundreds of genes for accurate drug response prediction. These cover roles in transcription, repair, cell-cycle checkpoints, and growth signaling, of which 30 are shown by loss-of-function genetic screens to regulate drug sensitivity or replication restart. The model translates to cisplatin-treated cervical cancer patients, highlighting an RTK–JAK–STAT assembly governing resistance. This study defines a compendium of mechanisms by which mutations affect therapeutic responses, with implications for precision medicine.
Significance:
Zhao and colleagues use recent advances in machine learning to study the effects of tumor mutations on the response to common therapeutics that cause RS. The resulting predictive models integrate numerous genetic alterations distributed across a constellation of molecular assemblies, facilitating a quantitative and interpretable assessment of drug response.

This article is featured in Selected Articles from This Issue, p. 384
Table S4 from Baseline Serum Inflammatory Proteins Predict Poor CAR T Outcomes in Diffuse Large B-cell Lymphoma
Authors: Faramand Rawan G. , Lee Sae Bom , Jain Michael D. , Cao Biwei , Wang Xuefeng , Rejeski Kai , Subklewe Marion , Fahrmann Johannes F. , Saini Neeraj Y. , Hanash Samir M. , Kang Yun Pyo , Chang Darwin , Rodriguez Paolo C. , Dean Erin A. , Nishihori Taiga , Shah Bijal D. , Lazaryan Aleksandr , Chavez Julio , Khimani Farhad , Pinilla-Ibarz Javier A. , Dam Marian , Reid Kayla M. , Corallo Salvatore A. , Menges Meghan , Hidalgo Vargas Melanie , Mandula Jay K. , Holliday Brian A. , Bachmeier Christina A. , Speth Kelly , Song Qinghua , Mattie Mike , Locke Frederick L. , Davila Marco L.
Published: March 2024

Table S4: Baseline patient and tumor characteristics in the validation cohorts
Table S2 from Baseline Serum Inflammatory Proteins Predict Poor CAR T Outcomes in Diffuse Large B-cell Lymphoma
Authors: Faramand Rawan G. , Lee Sae Bom , Jain Michael D. , Cao Biwei , Wang Xuefeng , Rejeski Kai , Subklewe Marion , Fahrmann Johannes F. , Saini Neeraj Y. , Hanash Samir M. , Kang Yun Pyo , Chang Darwin , Rodriguez Paolo C. , Dean Erin A. , Nishihori Taiga , Shah Bijal D. , Lazaryan Aleksandr , Chavez Julio , Khimani Farhad , Pinilla-Ibarz Javier A. , Dam Marian , Reid Kayla M. , Corallo Salvatore A. , Menges Meghan , Hidalgo Vargas Melanie , Mandula Jay K. , Holliday Brian A. , Bachmeier Christina A. , Speth Kelly , Song Qinghua , Mattie Mike , Locke Frederick L. , Davila Marco L.
Published: March 2024

Table S2 shows Multivariable analysis of grade >2 ICANS across the three risk groups.
Table S1 from Baseline Serum Inflammatory Proteins Predict Poor CAR T Outcomes in Diffuse Large B-cell Lymphoma
Authors: Faramand Rawan G. , Lee Sae Bom , Jain Michael D. , Cao Biwei , Wang Xuefeng , Rejeski Kai , Subklewe Marion , Fahrmann Johannes F. , Saini Neeraj Y. , Hanash Samir M. , Kang Yun Pyo , Chang Darwin , Rodriguez Paolo C. , Dean Erin A. , Nishihori Taiga , Shah Bijal D. , Lazaryan Aleksandr , Chavez Julio , Khimani Farhad , Pinilla-Ibarz Javier A. , Dam Marian , Reid Kayla M. , Corallo Salvatore A. , Menges Meghan , Hidalgo Vargas Melanie , Mandula Jay K. , Holliday Brian A. , Bachmeier Christina A. , Speth Kelly , Song Qinghua , Mattie Mike , Locke Frederick L. , Davila Marco L.
Published: March 2024

Table S1 shows a multivariable analysis of grade >2 ICANS using baseline CRP and Ferritin as continuous variables.
Data from Baseline Serum Inflammatory Proteins Predict Poor CAR T Outcomes in Diffuse Large B-cell Lymphoma
Authors: Faramand Rawan G. , Lee Sae Bom , Jain Michael D. , Cao Biwei , Wang Xuefeng , Rejeski Kai , Subklewe Marion , Fahrmann Johannes F. , Saini Neeraj Y. , Hanash Samir M. , Kang Yun Pyo , Chang Darwin , Rodriguez Paolo C. , Dean Erin A. , Nishihori Taiga , Shah Bijal D. , Lazaryan Aleksandr , Chavez Julio , Khimani Farhad , Pinilla-Ibarz Javier A. , Dam Marian , Reid Kayla M. , Corallo Salvatore A. , Menges Meghan , Hidalgo Vargas Melanie , Mandula Jay K. , Holliday Brian A. , Bachmeier Christina A. , Speth Kelly , Song Qinghua , Mattie Mike , Locke Frederick L. , Davila Marco L.
Published: March 2024

Abstract
A subset of patients with diffuse large B-cell lymphoma (DLBCL) treated with CD19 chimeric antigen receptor (CAR) T-cell therapy have poor clinical outcomes. We report serum proteins associated with severe immune-mediated toxicities and inferior clinical responses in 146 patients with DLBCL treated with axicabtagene ciloleucel. We develop a simple stratification based on pre-lymphodepletion C reactive protein (CRP) and ferritin to classify patients into low-, intermediate-, and high-risk groups. We observe that patients in the high-risk category were more likely to develop grade ≥3 toxicities and had inferior overall and progression-free survival. We sought to validate our findings with two independent international cohorts demonstrating that patients classified as low-risk have excellent efficacy and safety outcomes. Based on routine and readily available laboratory tests that can be obtained prior to lymphodepleting chemotherapy, this simple risk stratification can inform patient selection for CAR T-cell therapy.
Significance:
CAR T-cell therapy has changed the treatment paradigm for patients with relapsed/refractory hematologic malignancies. Despite encouraging efficacy, a subset of patients have poor clinical outcomes. We show that a simple clinically applicable model using pre-lymphodepletion CRP and ferritin can identify patients at high risk of poor outcomes.

This article is featured in Selected Articles from This Issue, p. 80
Supplementary Figure S3 from A Pilot Study on Patient-specific Computational Forecasting of Prostate Cancer Growth during Active Surveillance Using an Imaging-informed Biomechanistic Model
Authors: Lorenzo Guillermo , Heiselman Jon S. , Liss Michael A. , Miga Michael I. , Gomez Hector , Yankeelov Thomas E. , Reali Alessandro , Hughes Thomas J.R.
Published: March 2024

Patient-specific forecasts of PCa risk.
Supplementary Figure S2 from A Pilot Study on Patient-specific Computational Forecasting of Prostate Cancer Growth during Active Surveillance Using an Imaging-informed Biomechanistic Model
Authors: Lorenzo Guillermo , Heiselman Jon S. , Liss Michael A. , Miga Michael I. , Gomez Hector , Yankeelov Thomas E. , Reali Alessandro , Hughes Thomas J.R.
Published: March 2024

Examples of personalized calculations of PCa growth in the fitting-forecasting scenario.
Supplementary Figure S1 from A Pilot Study on Patient-specific Computational Forecasting of Prostate Cancer Growth during Active Surveillance Using an Imaging-informed Biomechanistic Model
Authors: Lorenzo Guillermo , Heiselman Jon S. , Liss Michael A. , Miga Michael I. , Gomez Hector , Yankeelov Thomas E. , Reali Alessandro , Hughes Thomas J.R.
Published: March 2024

Examples of personalized calculations of PCa growth in the global calibration scenario.
FIGURE 6 from A Pilot Study on Patient-specific Computational Forecasting of Prostate Cancer Growth during Active Surveillance Using an Imaging-informed Biomechanistic Model
Authors: Lorenzo Guillermo , Heiselman Jon S. , Liss Michael A. , Miga Michael I. , Gomez Hector , Yankeelov Thomas E. , Reali Alessandro , Hughes Thomas J.R.
Published: March 2024

Examples of personalized calculations of prostate cancer growth in the fitting-forecasting scenario. A–D, Prostate cancer growth at the date of the second mpMRI scan during model calibration (tMRI,2; upper row in each panel) and the ensuing prediction of tumor growth at the date of the third mpMRI scan (tMRI,3; bottom row in each panel) in 4 patients. The patients shown in this figure are the same considered in Fig. 4. These results are illustrated using a 3D representation of the mpMRI-extracted prostate geometry of each patient including the imaging-measured and model-calculated tumor regions (red and blue volumes), along with an axial section of the prostate showing the normalized tumor cell density map obtained from the mpMRI data and using the personalized model (i.e., Eq. (3)).
FIGURE 3 from A Pilot Study on Patient-specific Computational Forecasting of Prostate Cancer Growth during Active Surveillance Using an Imaging-informed Biomechanistic Model
Authors: Lorenzo Guillermo , Heiselman Jon S. , Liss Michael A. , Miga Michael I. , Gomez Hector , Yankeelov Thomas E. , Reali Alessandro , Hughes Thomas J.R.
Published: March 2024

Biomechanistic model of prostate cancer growth during AS. We describe the growth of newly diagnosed untreated prostate cancer in terms of the dynamics of the normalized tumor cell density, (x, t), which may vary between = 0 far from the tumor to a maximum value of = 1 inside the tumor. The dynamics of the normalized tumor cell density is governed by the two mechanisms shown in this figure: (i) tumor cell mobility, which expands the tumor and is represented with a diffusion process controlled by parameter D (i.e., the tumor cell mobility coefficient); and (ii) tumor cell net proliferation, which increases the local tumor cell density and is modeled as a logistic growth process controlled by parameter ρ (i.e., tumor cell net proliferation rate). Because newly diagnosed prostate cancer cases eligible for AS are organ-confined, we further assume that tumor cells cannot leave the patient's prostate by enforcing ∇ ∙ n = 0.
Data from A Pilot Study on Patient-specific Computational Forecasting of Prostate Cancer Growth during Active Surveillance Using an Imaging-informed Biomechanistic Model
Authors: Lorenzo Guillermo , Heiselman Jon S. , Liss Michael A. , Miga Michael I. , Gomez Hector , Yankeelov Thomas E. , Reali Alessandro , Hughes Thomas J.R.
Published: March 2024

Abstract
Active surveillance (AS) is a suitable management option for newly diagnosed prostate cancer, which usually presents low to intermediate clinical risk. Patients enrolled in AS have their tumor monitored via longitudinal multiparametric MRI (mpMRI), PSA tests, and biopsies. Hence, treatment is prescribed when these tests identify progression to higher-risk prostate cancer. However, current AS protocols rely on detecting tumor progression through direct observation according to population-based monitoring strategies. This approach limits the design of patient-specific AS plans and may delay the detection of tumor progression. Here, we present a pilot study to address these issues by leveraging personalized computational predictions of prostate cancer growth. Our forecasts are obtained with a spatiotemporal biomechanistic model informed by patient-specific longitudinal mpMRI data (T2-weighted MRI and apparent diffusion coefficient maps from diffusion-weighted MRI). Our results show that our technology can represent and forecast the global tumor burden for individual patients, achieving concordance correlation coefficients from 0.93 to 0.99 across our cohort (n = 7). In addition, we identify a model-based biomarker of higher-risk prostate cancer: the mean proliferation activity of the tumor (P = 0.041). Using logistic regression, we construct a prostate cancer risk classifier based on this biomarker that achieves an area under the ROC curve of 0.83. We further show that coupling our tumor forecasts with this prostate cancer risk classifier enables the early identification of prostate cancer progression to higher-risk disease by more than 1 year. Thus, we posit that our predictive technology constitutes a promising clinical decision-making tool to design personalized AS plans for patients with prostate cancer.
Significance:
Personalization of a biomechanistic model of prostate cancer with mpMRI data enables the prediction of tumor progression, thereby showing promise to guide clinical decision-making during AS for each individual patient.
Family Adherence to Their Child’s Health Issue Follow-Up and Its Associated Factors: A Cross-Sectional Study
Authors: Alzaher Abrar Abdulazeem , Al-Habib Dina Mohammed , Almuhaidib Moneerah Abdullah , Alfaleh Amjad F. , Alabdulkarim Khaled Ibrahim
Published: March 2024

Introduction: This study aimed to measure the adherence rate among families of referred cases from school health screening clinics to diagnostic clinics and to examine the associated factors. Materials and Methods: A cross-sectional study was conducted among families of cases referred from school health screening clinics to diagnostic clinics in Saudi Arabia. Data collectors contacted the guardians of the cases to fill out the interview questionnaire regarding their visit to the school health clinic and adherence to referral. Results: Among 698 families who participated in the study, 57.6% reported adherence to diagnostic clinics. Families of dental caries and myopia cases were more likely to adhere than families of obesity cases (aOR: 8.36 and 5.41, p < 0.001). The chance of adherence was about two-fold among families of cases referred to hospitals and specialized clinics compared to families of cases referred to primary healthcare centers (PHCCs) (aOR: 1.80, p = 0.042). Conclusion: This study revealed a low family adherence rate among cases referred to diagnostic clinics. Additionally, the study documented that referral to PHCCs and screening positive for obesity were the main factors associated with non-adherence. Taking care of children’s health is one of their rights, and increasing the family’s adherence by raising families’ awareness and designing a referral reminder system are recommended. In addition, the services of PHCCs should be strengthened.