Abstract: People who are deaf and hard-of-hearing (DHH) struggle with information marginalization and limited health literacy, challenging their ability to access information on preventing coronavirus disease 2019 (COVID-19). This study assessed the relationship between language preference, health literacy, and COVID-19 information barriers among parents who are DHH in the United States. Data were drawn from a larger study focused on individuals who are DHH who had given birth in the past 10 years. Respondents completed a web-based survey between March 2020 and July 2021. We segmented respondents by language preference [i.e., American Sign Language (ASL), English, or bilingual ASL/English] and used logistic regression models to test the hypothesis that language preference and health literacy were both associated with COVID-19 information marginalization. Of the total sample ( N = 417), approximately 17% had limited health literacy, and 22% reported experiencing difficulty accessing information about COVID-19. In adjusted analyses, respondents with limited health literacy ([adjusted odds ratio] aO R = 2.245) and Hispanic ethnicity (aO R = 2.149) had higher risk of reporting information access barriers. There was no association between language preference and reporting COVID-19 information barriers. However, individuals who are DHH with limited health literacy were at higher risk of experiencing information marginalization during the ongoing COVID-19 pandemic, highlighting the need for tailored information based on access needs. [ HLRP: Health Literacy Research and Practice . 2022;6(4):e310–e315. ]

Abstract: Those requiring help injecting are at an elevated risk of injection‐related injury and blood‐borne infections and are thus a priority group for harm reduction programs. As supervised consumption services (SCS) are scaled‐up across Canada, information on those who require help injecting is necessary to inform equitable service uptake. We characterised the sociodemographic, structural and drug use correlates of needing help injecting among a cohort of people who inject drugs in Toronto, Canada. Methods A cross‐sectional baseline survey was administered between November 2018 and March 2020. Unadjusted and multivariable logistic regression models examined associations with requiring help injecting in the past 6 months. A gender‐stratified sub‐analysis described characteristics of receiving help among those requiring it. Results Of 701 participants (31.0% cisgender women), 294 (41.9%) needed recent help injecting. In unadjusted analyses, being a racialised, non‐Indigenous person (odds ratio [OR] 1.79, 95% confidence interval [CI] 1.13–2.86) or a cisgender woman (OR 1.72, 95% CI 1.24–2.39) were associated with needing help. In multivariable analyses, requiring assistance was associated with needing frequent help preparing drugs (adjusted OR [AOR] 9.52, 95% CI 4.78–21.28), fewer years since first injection (AOR for 1 year increase: 0.97, 95% CI 0.95–0.99) and injecting stimulants. Among those who required help, cisgender women reported needing assistance more often than cisgender men ( P  = 0.009). Discussion and Conclusions Over two‐fifths of the sample required help injecting; requiring assistance was associated with sociodemographic indicators and substance use‐specific patterns. Findings highlight the need to scale‐up educational resources for those who receive or provide help injecting, as well as SCS that accommodate onsite injection assistance.

Abstract: Background: Genomic heterogeneity in leukemic blasts characterizes Acute Myeloid Leukemia (AML) patients and is associated to variable drug response. However, use of genomics to guide therapy has generally been restricted to a single-gene approach, which rarely has sufficient predictive power to be clinically useful. Comprehensive DNA sequencing and biosimulation of the Computational Omics Biology Model (CBM) provide the opportunity and means of predicting treatment outcome in advance of treatment. Methods: The Cellworks CBM is a computational multi-omic biology software model created using artificial intelligence heuristics and literature sourced from PubMed, to generate a patient-specific protein network map. The CBM permits mapping of biological pathways associated with tumorigenesis and drug resistance using mathematical principles to yield a virtual tumor model that can be used in biosimulation. Aberration and copy number variations from each case served as input to the CBM to generate individual patient-specific protein network maps. We used the Cellworks Biosimulation Platform to identify novel genomic biomarkers associated with response among AML patients treated with Cytarabine (ARA-C) + idarubicin or daunorubicin (anthracycline) with or without Etoposide (VP16). 539 AML patients were selected for this study based largely on genomic data published in TCGA and PubMed: ARA-C + daunorubicin [N=111, 92 responders (R) & 19 non-responders (NR)]ARA-C + idarubicin [N=109, 94 R & 15 NR]ARA-C + daunorubicin + VP16 [N=6, 4 R & 2 NR]ARA-C + idarubicin + VP16 [N=313, 261 R & 52 NR] Drug impact on individual disease networks was simulated to determine efficacy value by measuring the effect of chemotherapy on the cell growth score, a composite of cell proliferation, viability, apoptosis, metastasis, DNA damage and other cancer hallmarks. The mechanism of action of each drug was used to map its biological consequences to each patient's cancer genome to predict treatment response. Results: Biosimulation of ARA-C + anthracycline with and without VP16 identified biomarkers responsible for therapy response. Additionally, the Cellworks Biosimulation Platform identified novel drug combinations for NR to these standard combinations. There were 186/220 patients treated with ARA-C + anthracycline that had clinical responses. Major biomarkers predictive of response included: IDH2 mut, TOPBP1 del, ATR del, NPM1 mut, IDH1 mut, XRCC2 del, CDK5 del, AKR1B1 del and other genes (Table 1). The frequency of these genes was significantly higher (exact binomial: p-value & lt; 0.0001) in R (N=186) vs. NR (N=34). Notably, 7/34 NR to the two-drug combination had favorable biomarkers for VP16 response, which included RAD52 del, FANCD2 del, STAG2 mut, MPO amp, and NHEJ del. On the other hand, among 265 R treated with triplet therapy (ARA-C + anthracycline + VP16), 30 patients were unlikely to have derived incremental benefit from the addition of VP16. In these patients, the biosimulation predicted that they would have benefited equally from doublet therapy (ARA-C + anthracycline without VP16). In this subgroup of R, ARID1A del, FLT3-ITD mut, GSTA1 amp, KEAP1 del, or RNF1 del generated resistance to VP16 in the biosimulation. Among 54 NR to triplet therapy, 40/54 had genomic alterations predicting a benefit from JQ1, BRD2/4 inhibitors, including KMT2C del, FLT3 GOF, NPM1 del, DNMT3A LOF, and TP53 del, while 12 patients had 5q del highlighting a potential benefit from lenalidomide. Altogether, 89/539 (16.5%) could have been managed with a potentially superior treatment approach based on the biosimulation by either adding or omitting VP16 or being treated with an alternative therapy. Conclusions: Cellworks Biosimulation Platform applied to the patient-specific CBM identifies novel biomarkers of response and can be employed to determine the optimal therapy for AML patients. This study highlights patients for whom triplet therapy promises potentially superior benefit, others who would benefit equally from doublet therapy without VP16, and others unlikely to respond to standard or triplet therapy for whom an alternative personalized approach might offer better outcomes. In AML, biosimulation offers the possibility to tailor the chemotherapy regimen to each patient to improve disease control and minimize toxicity. Figure 1 Figure 1. Disclosures Castro: Caris Life Sciences Inc.: Consultancy; Guardant Health Inc.: Speakers Bureau; Bugworks: Consultancy; Cellworks Group Inc.: Current Employment; Omicure Inc: Consultancy; Exact sciences Inc.: Consultancy. Howard: Servier: Consultancy; Cellworks Group Inc.: Consultancy; Sanofi: Consultancy, Other: Speaker fees. Kumar: Cellworks Group Inc.: Current Employment. Patil: Cellworks Group Inc.: Current Employment. Khandelwal: Cellworks Group Inc.: Current Employment. Watson: BioAi Health: Consultancy, Membership on an entity's Board of Directors or advisory committees; AlloVir: Consultancy, Membership on an entity's Board of Directors or advisory committees; CellMax Life: Consultancy, Other: Advisor; Cellworks Group Inc.: Consultancy, Other: Advisor. Kapoor: Cellworks Group Inc.: Current Employment. Kumari: Cellworks Group Inc.: Current Employment. Prasad: Cellworks Group Inc.: Current Employment. Gupta: Cellworks Group Inc.: Current Employment. Lunkad: Cellworks Group Inc.: Current Employment. Mitra: Cellworks Group Inc.: Current Employment. G: Cellworks Group Inc.: Current Employment. Kumar: Cellworks Group Inc.: Current Employment. Choudhury: Cellworks Group Inc.: Current Employment. Kulkarni: Cellworks Group Inc.: Current Employment. Choudhary: Cellworks Group Inc.: Current Employment. Prakash: Cellworks Group Inc.: Current Employment. Husain: Cellworks Group Inc.: Current Employment. Ghosh: Cellworks Group Inc.: Current Employment. Narvekar: Cellworks Group Inc.: Current Employment. Amara: Cellworks Group Inc.: Current Employment. Yuvavani: Cellworks Group Inc.: Current Employment. Patel: Cellworks Group Inc.: Current Employment. Macpherson: Cellworks Group Inc.: Current Employment. Marcucci: Novartis: Other: Speaker and advisory scientific board meetings; Abbvie: Other: Speaker and advisory scientific board meetings; Agios: Other: Speaker and advisory scientific board meetings.

Abstract: Background: Mantle Cell Lymphoma (MCL) accounts for 3-10% of all non-Hodgkin lymphomas with a median overall survival of 3-4 years. Hyper-CVAD (CVAD) with or without Rituximab constitutes first line therapy for treatment of MCL, yet the use of this combination is associated with high toxicity and only modest efficacy. On the other hand, impressive clinical efficacy has been reported in relapsed MCL patients treated with rituximab and cladribine (RC). Prediction of response based on cancer genomics heterogeneity creates an opportunity to personalize treatment and avoid toxic therapy which has little chance of response. We conducted a study using the Cellworks Biosimulation Platform to identify novel genomic biomarkers associated with response to CVAD and RC among MCL patients. Method: Newly-diagnosed MCL patients were selected for this study based largely on genomic data (i.e. aberrations and copy number variations) published in PubMed and TCGA. The Cellworks Computational Omics Biology Model (CBM) is a computational multi-omic biology software model created using artificial intelligence heuristics and literature sourced from PubMed, to generate a patient-specific protein network map. Genomic data from each patient served as input for the CBM. Biomarkers unique to each patient were identified within protein network-maps. Drug impact on the disease network was biosimulated using the Cellworks Biosimulation Platform to determine a treatment efficacy value by measuring the treatment effects on the cell growth score, a composite of cell proliferation, viability, apoptosis, metastasis, DNA damage and other cancer hallmarks. The mechanism of action of each drug was mapped to each patient's CBM and the predicted biological consequences were used to determine response. Biosimulation of CVAD was applied to the patients in this cohort. RC was biosimulated on all CVAD non-responders. Results: Among the 94 MCL patients treated with CVAD, the Cellworks Biosimulation Platform identified novel biomarkers (Table 1) to predict treatment response or failure. The biosimulation also identified unique drug combinations for patients that were non-responders (NR) to both treatments. Of the 94 patients, 57 were deemed responders (R) and 37 non-responders (NR). ATM LOF/del, RAD51 del, LIG4A del, RB1 del, ERCC5 del, CARD11 amp, IKZF1 amp, and FANCC del were major predictors of CVAD response. These genes contributed to drug efficacy by impacting various pathways, including DNA repair, oxidative-stress, NFKB activation, spindle formation and mitotic-catastrophe. The frequency of aberration affecting these genes was high among the R group and was low in the NR group. Biosimulation was used to assess response to RC, and predicted, that 41 of 94 patients would respond and 53 would not respond. KMT2D LOF and SMAD4 del were associated with response to RC. Epigenetic dysregulation caused by KMT2D LOF decreased MSH6-mediated mismatch repair required for futile DNA repair leading to replication fork arrest and apoptosis. Interestingly, KMT2D LOF was identified in 20/41 R to RC. MYC amp, NOTCH 1 GOF, and NT5C2 amp were identified as key non-response markers for RC. In considering both regimens, 27 patients were predicted R to both CVAD and RC, 14 to RC but not to CVAD, 30 to CVAD but not RC, and 23 NR to both regimens. In the latter group, biosimulation predicted that a venetoclax-based combination would be effective in many cases due to the high incidence of TP53 GOF mutation within this subgroup. Conclusions: This pilot study highlights how the Cellworks Biosimulation Platform applied to the patient-specific CBM can identify treatment alternatives for patients with low likelihood of response to standard therapy or who may be ineligible for CVAD because of co-morbidities. RC responsiveness was either an equivalent but much less toxic option to CVAD or superior to CVAD. By using novel biomarkers derived from comprehensive mutational and copy number analysis, the CBM identified pathway-based, polygenic biomarkers that can be employed to determine optimal drug combinations for MCL patients. This biosimulation approach warrants prospective validation in a larger patient cohort. Figure 1 Figure 1. Disclosures Marcucci: Abbvie: Other: Speaker and advisory scientific board meetings; Agios: Other: Speaker and advisory scientific board meetings; Novartis: Other: Speaker and advisory scientific board meetings. Kumar: Cellworks Group Inc.: Current Employment. Castro: Bugworks: Consultancy; Caris Life Sciences Inc.: Consultancy; Omicure Inc: Consultancy; Guardant Health Inc.: Speakers Bureau; Cellworks Group Inc.: Current Employment; Exact sciences Inc.: Consultancy. Khandelwal: Cellworks Group Inc.: Current Employment. Mohapatra: Cellworks Group Inc.: Current Employment. Kapoor: Cellworks Group Inc.: Current Employment. Agrawal: Cellworks Group Inc.: Current Employment. Sauban: Cellworks Group Inc.: Current Employment. Basu: Cellworks Group Inc.: Current Employment. Shyamasundar: Cellworks Group Inc.: Current Employment. Lala: Cellworks Group Inc.: Current Employment. Raju: Cellworks Group Inc.: Current Employment. Palaniyeppa: Cellworks Group Inc.: Current Employment. Ullal: Cellworks Group Inc.: Current Employment. Joseph: Cellworks Group Inc.: Current Employment. Behura: Cellworks Group Inc.: Current Employment. Sahu: Cellworks Group Inc.: Current Employment. Prakash: Cellworks Group Inc.: Current Employment. Mitra: Cellworks Group Inc.: Current Employment. Balla: Cellworks Group Inc.: Current Employment. Patil: Cellworks Group Inc.: Current Employment. Mohanty: Cellworks Group Inc.: Current Employment. Patel: Cellworks Group Inc.: Current Employment. Macpherson: Cellworks Group Inc.: Current Employment. Howard: Sanofi: Consultancy, Other: Speaker fees; Servier: Consultancy; Cellworks Group Inc.: Consultancy.