ASCO Standards are evidence-based standards to provide frameworks for best practices in cancer care, following the standards development process as outlined in the ASCO Standards Policies and Procedures Manual . ASCO Standards follow the ASCO Conflict of Interest Policy for Clinical Practice Guidelines . Standards and other guidance (“Guidance”) provided by ASCO is not a comprehensive or definitive guide to treatment options. It is intended for voluntary use by providers and should be used in conjunction with independent professional judgment. Guidance may not be applicable to all patients, interventions, diseases or stages of diseases. Guidance is based on review and analysis of relevant literature, and is not intended as a statement of the standard of care. ASCO does not endorse third-party drugs, devices, services, or therapies and assumes no responsibility for any harm arising from or related to the use of this information. See complete disclaimer in Appendix 1 (online only) for more. PURPOSE To update the ASCO-Oncology Nursing Society (ONS) standards for antineoplastic therapy administration safety in adult and pediatric oncology and highlight current standards for antineoplastic therapy for adult and pediatric populations with various routes of administration and location. METHODS ASCO and ONS convened a multidisciplinary Expert Panel with representation of multiple organizations to conduct literature reviews and add to the standards as needed. The evidence base was combined with the opinion of the ASCO-ONS Expert Panel to develop antineoplastic safety standards and guidance. Public comments were solicited and considered in preparation of the final manuscript. RESULTS The standards presented here include clarification and expansion of existing standards to include home administration and other changes in processes of ordering, preparing, and administering antineoplastic therapy; the advent of immune effector cellular therapy; the importance of social determinants of health; fertility preservation; and pregnancy avoidance. In addition, the standards have added a fourth verification. STANDARDS Standards are provided for which health care organizations and those involved in all aspects of patient care can safely deliver antineoplastic therapy, increase the quality of care, and reduce medical errors. Additional information is available at www.asco.org/standards and www.ons.org/onf .

Supplementary Table S4 shows the characteristics of the patients used in the case cross-over analysis

Supplementary Figure S4 shows a validation of the predictive nature of Immunotherapy Response Score

Supplementary Methods S1 shows the additional methods and references supporting the main manuscript

Supplementary Figure S1 shows analytical validation of the gene expression component of Immunotherapy Response Score components vs. qRT-PCR

IRS is robust to self-reported race. A, Pie chart of self-reported race for all 24,463 patients in the SCMD with informative TMB and gene expression data needed to generate IRS regardless of treatment history (SCMD lock at the time of IRS development). The total number of patients in each racial group is shown. B, The percentage of TMB-H and IRS-H patients from the SCMD (n = 24,463 as in A stratified by self-reported race is plotted). Fisher exact test was used to test the differences in IRS-H (or TMB-H) between the White or Caucasian/European group and all other groups; groups where IRS-H (or TMB-H) was significantly greater (P < 0.05) versus the White or Caucasian group (potentially as a consequence of inappropriate filtering of germline variants in TMB determination for non-White or Caucasian groups) are indicated by *. Asian, Black or African American and Other groups were also considered together as non-European (blue). C, Further breakdown by TMB and IRS status and relevant tumor types. The percentage of IRS-H (bold hue) and TMB-H (light hue) stratified by White or Caucasian/European (red) and non-European (blue) self-reported race is plotted for the n = 6,138 total patients from A and B with one of the seven indicated primary tumor types (CRC = colorectal, EGC = esophagogastric, H&N = head and neck, Mel = melanoma, NSCLC = non–small cell lung carcinoma). The total number of European and non-European patients with each tumor type are indicated. D, Across eligible NCT03061305 patients treated with anti-PD-(L)1 monotherapy, we identified a validation cohort of all 575 patients not included in IRS discovery to assess the robustness of IRS (and the TMB component) to self-reported race. Anti-PD-(L)1 monotherapy rwPFS stratified by combined TMB and IRS status [TMB-H or IRS-H (TMB/IRS-H; black) vs. TMB-L and IRS-L (TMB/IRS-L; gray)] is shown (left) by unadjusted Kaplan–Meier analysis with the aHR [adjusted for age, sex assigned at birth, line of therapy, tumor type, anti-PD-(L)1 therapy type, inclusion in previous validation cohort, and self-reported race (non-European, unknown, or European)], 95% CI and P value for TMB/IRS status (TMB/IRS-H vs. TMB/IRS-L) shown. The number (n) of patients, events, and median rwPFS (with 95% CI) for each group are shown. E, Forest plot of rwPFS by IRS status in the cohort (all) and each self-reported racial group is shown. Significant associations are shown by filled in aHR estimates. F, Anti-PD-(L)1 monotherapy rwPFS stratified by TMB/IRS status is shown by unadjusted Kaplan–Meier analysis for the non-European subgroup (as in the overall cohort).

Supplementary Table S10 shows the characteristics of the anti-PD-(L)1 monotherapy self-reported race validation cohort and the non-European subset

Supplementary Table S5 shows rates of rwPFS2/rwPFS1 in the case cross-over analysis

Validation of IRS to stratify anti-PD-(L)1 monotherapy benefit in patients with advanced solid tumors. A, Clinical characteristics of the anti-PD-(L)1 monotherapy validation cohort are shown in an alluvial diagram. All patients with available clinical molecular profiling data necessary for IRS (TMB and normalized expression of PD-1, PD-L1, ADAM12, and TOP2A from in-parallel qTP) from FFPE tumor tissue enrolled in the Strata Trial (NCT03061305) and treated with systemic anti-PD-(L)1 monotherapy were considered. Patients in previous IRS discovery or validation were excluded. The locked IRS model and thresholds were used to assign IRS-L (light blue) or IRS-H (increased benefit; dark blue) status. For the 352 eligible patients, IRS status, MSI/TMB status (MSI-H or TMB-H as MSI/TMB-H), type of anti-PD-(L)1 therapy [pembrolizumab (pembro) vs. other anti-PD-(L)1], systemic line of anti-PD-(L)1 therapy, and tumor type [all tumor types with >15 samples considered individually: NSCLC, cancer of unknown primary (CUP), bladder cancer (Blad.), melanoma (Mel.), head and neck cancer (H&N), and EGC; remaining 25 other tumor types considered together] are shown. Stratum are colored by IRS status. IRS stratifies anti-PD-(L)1 monotherapy clinical benefit by rwPFS (by time to next therapy; B) and OS (C). B, Anti-PD-(L)1 monotherapy rwPFS stratified by IRS group is shown by unadjusted Kaplan–Meier analysis, with the aHR [adjusted for age, sex assigned at birth, line of therapy, tumor type and anti-PD-(L)1 therapy type], 95% CI and P value for IRS status (IRS-H vs. IRS-L) shown. The number (n) of patients, events, and median rwPFS (with 95% CI) for each group are shown. Forest plot analyses of rwPFS by IRS status in key subgroups are shown below (Remaining 4 = Blad., Mel., H&N, and EGC). Significant associations are shown by filled in aHR estimates. C, As in B, except assessing OS.

Supplementary Figure S2 shows a diagram of the overall study

Supplementary Figure S6 shows three group Immunotherapy Response Score classification of the anti-PD-(L)1 monotherapy validation cohort

PURPOSE Despite the growing calls for early and ubiquitous completion of advance directives (ADs), studies exploring links between AD completion and their impact on outcomes of patients with cancer have mixed conclusions. We used the ASCO Quality Oncology Practice Initiative (QOPI) registry to compare end-of-life (EOL) quality measures and the effect of QOPI certification among patients with and without early AD completion, defined as completion within the first three oncology visits after cancer diagnosis.METHODS Deidentified patient-level data were analyzed from the QOPI database from 2015 through 2017. Associations were assessed using Chi-square tests between early AD completion and patient enrollment in hospice < 7 days before death, chemotherapy receipt in the last 14 days of life, or with emergency room visits or intensive care unit admissions in the last 30 days of life.RESULTS Data from 31,558 patients eligible for the AD question were analyzed. Patients treated at QOPI-certified practices had higher rates of early AD completion than patients at non-certified practices. Early AD completion was not associated with differences in hospice enrollment for < 7 days before death, chemotherapy receipt in the last 14 days of life, or emergency room visits or intensive care unit encounters in the last 30 days of life.CONCLUSION The study found that QOPI certification is associated with higher rates of early AD completion. However, early AD completion was not associated with recognized EOL quality measures. Future research should focus on the timing, frequency, and content of AD conversations to demonstrate the impact on care at the EOL.

Confirmation of the added utility of the IRS versus clinical PD-L1 IHC and TMB alone. A, Normalized PD-L1 (CD274) expression (and the other IRS expression components) by the qTP platform used to generate IRS were validated versus qRT-PCR in a validation cohort of 96 FFPE tumor tissue samples tested by clinical CGP and in parallel qTP. The Pearson correlation and linear range of each component is shown (Supplementary Fig. S1). B and C, The Pearson correlation of normalized PD-L1 expression by qTP [log2 normalized reads per million (nRPM) units] versus clinical PD-L1 IHC score (in submitted pathology reports) was determined in two cohorts of clinical FFPE tumor tissues [regardless of TMB availability and anti-PD-(L)1 treatment]. B, PD-L1 expression by qTP (log2 normalized units) versus PD-L1 IHC by TPS [using the 22C3 antibody clone (log2 TPS) in 276 clinically tested FFPE NSCLC tumors with available TPS is plotted]. The linear fit, Pearson correlation (r), and P value are shown. C, PD-L1 expression by qTP versus PD-L1 IHC by CPS using the 22C3 antibody clone (log2 CPS) in 221 clinically tested FFPE tumors (23 tumor types; most frequently EGC) with available TPS is plotted. The linear fit, Pearson correlation (r), and P value are shown. D, Using B and C, we identified a cohort of all 189 eligible NCT03061305 patients with IRS and PD-(L)1 IHC in accompanying pathology reports who were treated with anti-PD-(L)1 therapy (± chemotherapy). The association of biomarkers with anti-PD-(L)1 rwPFS was determined by Cox proportional hazards modeling [adjusting for age, sex assigned at birth, line of therapy, tumor type, therapy type (monotherapy vs. chemotherapy combination), and inclusion in IRS discovery status]. PD-L1 IHC score (continuous; log2) was included in the baseline model (Model 1), with the aHR, 95% CI, number (n) of patients and events, and P value shown for the biomarker term by forest plot. TMB status (-H vs. -L; pink) and IRS status (-H vs. -L; light blue) were separately added to this model (Models 2 and 3, respectively). The significance of each biomarker term is shown and the P value of the LRT comparing the full (Model 2 or 3) versus reduced (Model 1) model is shown. Model 4 includes PD-L1 IHC, TMB, and IRS. Significant biomarker terms are shown by filled in aHR estimates. E, Anti-PD-(L)1 rwPFS stratified by IRS group is shown by unadjusted Kaplan–Meier analysis, with the aHR from Model 4 in D shown. See Supplementary Table S7 for full subgroup analysis.

Supplementary Table S2 shows the adjusted Cox proportional hazards models for real-world progression free survival and overall survival in the monotherapy validation cohort

Supplementary Table S9 shows tumor mutation burden and Immunotherapy Response Score status by self -reported race in relevant tumor types from the Strata Clinical Molecular Database compared to Nasser et al.

Supplementary Figure S3 shows how Immunotherapy Response Score adds to tumor mutation burden and microsatellite instability status for anti-PD-(L)1 monotherapy clinical benefit

Supplementary Table S11 shows the characteristics of the chemotherapy, anti-PD-(L)1, and chemotherapy + anti-PD-(L)1 validation cohort

Supplementary Table S7 shows a sub-group analysis of the PD-L1 IHC cohort

Supplementary Figure S8. shows overlap weighting propensity score analysis of the chemotherapy, anti-PD-(L)1, and chemotherapy + anti-PD-(L)1 validation cohort

Supplementary Table S12 shows a sub-group analysis of the chemotherapy, anti-PD-(L)1, and chemotherapy + anti-PD-(L)1 validation cohort