Manochakian, Rami

9543 Background: Lung cancer is the leading cause of cancer related death in the United States in all racial groups. However, the overall death rate from lung cancer is different among white, black and Asian. Although differences in socioeconomic status and treatments received might be the contributing factors, the biology, particularly genomic alteration of cancer might also have an important impact. To date, the differences of genomic alterations among all racial patients are poorly understood. Methods: The American Association for Cancer Research (AACR) Project Genomics Evidence Neoplasia Information Exchange (GENIE, version 7.0) database was analyzed. A total of 8908 patients with lung adenocarcinoma (LUAD) were identified. Among them, 5652 patients who had comprehensive sequencing results (gene panel ≥ 275) were selected for further analysis. Patients with unknown race, undefined and Pacific Island (only 3 patients) were excluded. Finally, 5360 patients were included in the study. The prevalence and distribution of genomic alteration cross all racial groups were analyzed. Results: Overall 5951 samples from 5360 patients were collected (85.7% white, 7.9% Asian, 4.7% black, 0.2% Native American and 1.4% other). Most patients have only one sample. The median mutation counts in white, black, Asian, Native American and others are 7, 6, 5, 5, 7 respectively (Kruskal–Wallis test, P< 0.001). Asian have significantly higher rates of insertion and deletions than other races (14.8% of Asian versus 9.8% of white, 10.7% of black, 10.4% of Native American, 11.1% of other; Pearson’s chi-square test, < 0.001). TP53, EGFR, KRAS and STK11 are the most frequent alterations in white, black and other. EGFR, TP53, KRAS and APC are the most frequent alterations in Asian. STK11 mutations are rare in both Asian and Native American. Native Americans have more alterations of LRP1B, ARID2 and ATM, although the patients’ number remains small. ATM and KEAP1 mutations are also common in white and black. EGFR is the highest discrepancy gene in racial distribution. 78.9% Asian, 47.7% black, 36.4% of native American had EGFR alteration in comparison to 29.6% in white (Fisher's exact test, P < 0.001). KRAS is the second highest discrepancy gene in racial distribution. 11.6% Asian, 26.3% black, 27.3% of Native American had KRAS alteration in comparison to 36.1% in white (Fisher's exact test, P< 0.001). Conclusions: Our study demonstrated the potential diversity of genomic alterations across all racial groups with potential impact on therapeutic decisions.

Abstract Patients with chronic lymphocytic leukemia (CLL) are characterized by monoclonal expansion of CD5+CD23+CD27+CD19+κ/λ+ B lymphocytes and are clinically noted to have profound immune suppression. In these patients, it has been recently shown that a subset of B cells possesses regulatory functions and secretes high levels of interleukin 10 (IL-10). Our investigation identified that CLL cells with a CD19+CD24+CD38hi immunophenotype (B regulatory cell [Breg]–like CLL cells) produce high amounts of IL-10 and transforming growth factor β (TGF-β) and are capable of transforming naive T helper cells into CD4+CD25+FoxP3+ T regulatory cells (Tregs) in an IL-10/TGF-β-dependent manner. A strong correlation between the percentage of CD38+ CLL cells and Tregs was observed. CD38hi Tregs comprised more than 50% of Tregs in peripheral blood mononuclear cells (PBMCs) in patients with CLL. Anti-CD38 targeting agents resulted in lethality of both Breg-like CLL and Treg cells via apoptosis. Ex vivo, use of anti-CD38 monoclonal antibody (mAb) therapy was associated with a reduction in IL-10 and CLL patient-derived Tregs, but an increase in interferon-γ and proliferation of cytotoxic CD8+ T cells with an activated phenotype, which showed an improved ability to lyse patient-autologous CLL cells. Finally, effects of anti-CD38 mAb therapy were validated in a CLL–patient-derived xenograft model in vivo, which showed decreased percentage of Bregs, Tregs, and PD1+CD38hiCD8+ T cells, but increased Th17 and CD8+ T cells (vs vehicle). Altogether, our results demonstrate that targeting CD38 in CLL can modulate the tumor microenvironment; skewing T-cell populations from an immunosuppressive to immune-reactive milieu, thus promoting immune reconstitution for enhanced anti-CLL response.

9058 Background: Lung Cancer is the leading cause of cancer related deaths in the USA. NSCLC comprises about 85% of lung cancer cases. Although timeliness of care in patients with NSCLC is considered an important aspect of quality care, there are conflicting data about its impact on clinical outcomes. The primary objective of this study is to determine if there is an association between TTI and survival in patients with NSCLC. Methods: In this retrospective study, we reviewed data from our multi-site Mayo Clinic Cancer Center registry and identified patients with newly diagnosed NSCLC from 2000 to 2016. TTI was calculated from time of diagnosis to time of first treatment (surgery, radiation therapy or systemic therapy). Analyses were performed by SAS software 9.4. Log-Rank test was used to compare survival. Cox regression multivariate model was used to evaluate the prognostic value of variables to survival. Results: 10010 patients (53% males and 47%) were reviewed. Median age at diagnosis was 70. Median TTI was 12 days for stage I, and 20 days for stage II, III and IV. We compared outcomes of patients with TTI > 20 days to TTI ≤20 days. Outcomes were stratified based on age, gender, grade, and stage. Median Overall Survival (OS) was significantly better for patients with TTI ≤20 days compared to TTI > 20 days (39.1 vs 28.6 months P-value < 0.0001). Further stratification, based on stage, showed significantly better OS for stage I and II patients with TTI ≤20 days compared to TTI > 20 days (103.4 vs. 63.9 months P-value < 0.0001 and 72.3 vs. 46.8 months P-value 0.0014 respectively). OS for stage III patients with TTI ≤20 days was not significantly different from patients with TTI > 20 days (30.6 vs. 28.5 months P-value 0.118). Interestingly, stage IV patients had worse OS if TTI ≤20 days compared to TTI > 20 days (8.3 vs. 12.8 months P-value < 0.0001). Conclusions: Our study showed an association between TTI and survival of patients with NSCLC. Shorter TTI was associated with better survival in stage I and II patients and worse survival in stage IV patients. Our study further highlights the controversy surrounding the topic of impact of timeliness of care on survival in patients with cancer, specifically NSCLC across different stages.

Background: HIT has the potential to improve the health of individuals and the performance of providers, yielding improved quality, cost savings, and greater engagement by patients. Minorities face a variety of financial and nonfinancial barriers to obtaining appropriate and timely health care. Studying the behavior of minority patients will help understand the specific challenges to HIT adoption and engagement in patients and their caregivers. We undertook a patient-reported survey to explore these factors and understand any underlying disparities. Methods: A 21-question paper-based anonymous questionnaire addressing the responder's attitudes, understanding and needs of the electronic medical record (EMR) was used. This was administered to adult patients with lymphoid malignancies and their caregivers at two campuses of Mayo Clinic in Florida (San Pablo and inner-city; Riverside). Categorical and continuous variables between white and minority responders as well as minority responders from the two campuses were compared using the Chi-square test and U Mann Whitney with a significance level of 0.05. Results: The survey was completed by 1004 patients and caregivers. Average time taken to complete the survey was approximately 11 minutes. Of the responders, 716 (71%) were whites and 271 (27%) were minorities, with race-ethnicity not reported by 17 (2%). Among minorities, 30% (n=80) were seen at San Pablo and 64% (n=173) at the Riverside campus, with 6% at a multiple myeloma patient-caregiver symposium. Whites were older (median age 66 vs. 62 years, p&lt;0.001) and had higher education (undergraduate or higher 60% vs. 52%, p=0.023) as compared to minorities. Minority survey responders had lesser access to a computer (84% vs. 92%, p&lt;0.001) or a smartphone (85% vs. 89%, p=0.08). (Figure 1a) Exploring HIT awareness showed that only 51% of minorities were registered to use the EMR as compared to 72% whites (p&lt;0.001) and a lesser number of minorities even knew that they could access their medical records electronically (81% vs. 92%, p&lt;0.001). even among those registered to use the EMR, 13% minorities reported never using the EMR as compared to 4% whites (p&lt;0.001). Encouragingly, a higher willingness to learn about and use the EMR was noted among minorities who were more interested in knowing how to access the EMR (43% vs. 36%, p=0.04), would increase the use of EMR if someone explained how to use it (17% vs. 10%, p=0.002), and if someone explained about test results (14% vs. 6%, p&lt;0.001). (Figure 1b) Comparing minorities among the two campuses, those from the San Pablo campus were older (67.5 yrs. vs. 57 yrs., P&lt;0.001), had higher level of education (undergraduate or higher in 66% vs. 47%, p=0.005) and a higher number had access to a computer (94% vs. 78%, P=0.001) as compared to those from the inner-city Riverside. (Figure 2a) Minorities from the Riverside campus were less likely to be aware of the current HIT EMR (75% vs. 93%, p&lt;0.001), were significantly less likely to have registered for the patient EMR portal (38% vs. 76%, p&lt;0.001) and never used the EMR even if registered for it (22% vs. 7%, p=0.01) as against those from the San Pablo campus. Minorities at the Riverside campus had a higher interest in knowing how to access the EMR (47% vs. 33%, p=0.03). Linguistic barriers were more striking in the inner-city Riverside campus where a higher proportion of minority survey responders noted that it would help if the EMR was in their primary language (66% vs. 48%, p=0.006). (Figure 2b) Conclusions: Minorities continue to struggle with suboptimal utilization of healthcare system including that of HIT. There are barriers related to access, awareness, language and understanding of HIT that prevent the integration and participation of racial-ethnic minorities in the healthcare system to a full extent. It is important to qualify the minorities better, since there exist HIT utilization disparities even within the minorities from different areas representing socioeconomic and demographic differences. Efforts need to be made at several levels to help the minorities overcome educational, access and linguistic barriers so that they can benefit from advancements in the healthcare system. Disclosures Ailawadhi: Celgene: Consultancy; Amgen: Consultancy, Research Funding; Janssen: Consultancy, Research Funding; Pharmacyclics: Research Funding; Cellectar: Research Funding; Takeda: Consultancy. Manochakian:Novocure: Membership on an entity's Board of Directors or advisory committees; Takeda: Membership on an entity's Board of Directors or advisory committees; AstraZeneca: Membership on an entity's Board of Directors or advisory committees; Guardant Health: Membership on an entity's Board of Directors or advisory committees. Chanan-Khan:AbbVie: Research Funding; Xencor: Research Funding; Pharmacyclics: Research Funding; Merck: Research Funding; Jansen: Research Funding; Mayo Clinic: Employment; Ascentage: Research Funding; Millennium: Research Funding.

Multiple myeloma (MM) treatment has advanced significantly over the last 2 decades. In most patients, the disease course has been altered from early fatality to chronic morbidity with multiple lines of treatment. The MM treatment paradigm has shifted toward treating patients before end-organ damage occurs. Thus, timeliness of treatment initiation in this era might improve patient outcomes. This is the first report to our knowledge analyzing disparities and trends in treatment timeliness of patients with MM using the National Cancer Database. Multiple factors affected the timing of treatment initiation in MM and disparities were found. We noted that initiation of treatment was delayed in women (odds ratio [OR], 1.15; 95% CI, 1.1 to 1.2) and blacks (OR, 1.21; 95% CI, 1.14 to 1.28; reference, whites) and in patients diagnosed in more recent years (2012-2015; OR, 1.15; 95% CI, 1.1 to 1.22; reference, 2004-2007). Patients were likely to start treatment earlier if they were age ≥ 80 years (OR, 0.83; 95% CI, 0.76 to 0.9; reference, age < 60 years), were uninsured (OR, 0.81; 95% CI, 0.72 to 0.91; reference, private insurance), had Medicaid (OR, 0.87; 95% CI, 0.79 to 0.95; reference, private insurance), were treated in a comprehensive community cancer program (OR, 0.7; 95% CI, 0.65 to 0.77; reference, community cancer program), lived in a location other than the US Northeast, or had a higher Charlson comorbidity score. Patient education and income levels did not affect time to treatment initiation. Particular aspects of these disparities could be explained by our current health care system and insurance rules, whereas others need to be investigated more deeply.

PURPOSE: ASCO is the premier and largest global professional society for oncology care professionals. In 2015, ASCO launched a longitudinal Learning Cohort Pilot Project to catalog and better understand the learning behaviors and preferences of oncology health care providers. A secondary goal was to assess learner preferences and utilization related to ASCO’s portfolio of educational resources. METHODS: The Learning Cohort Pilot Project was conducted between November 2015 and August 2016 with 49 ASCO members. Participants were selected via convenience sampling and stratified random sampling to generate a cohort that mirrored the demographic distribution of overall ASCO membership. Participants completed a different ASCO resource-specific feedback activity each month, which measured professional educational needs, sources sought, and preferences for educational resources. Responses were organized by demographic variables in our participant pool to identify trends in provider learning preferences. Fisher’s exact test was used to assess the association between participant demographics and practice setting and responses. Holm’s procedure was used to adjust for multiple testing. RESULTS: The Learning Cohort Pilot Project revealed statistically significant relationships between participant demographic variables and learning preferences. Age and practice setting were the demographic variables most consistently associated with the different preferences explored throughout the targeted activities. CONCLUSION: The results of this pilot cohort reinforced the hypothesis that oncology care providers have different professional educational needs and preferences that can be potentially anticipated and met with tailored resources. Delivering solutions to meet these needs represents an opportunity for further research and resource development.

An oncologist reflects on the experience of a family member being diagnosed with pancreatic cancer and the lessons learned on the ensuing journey.

Background: Median age at diagnosis for MM is 69 years with approximately 33% of the newly diagnosed MM (NDMM) patients aged ≥80 years (elderly). Yet, outcomes data and clinical trial participation in elderly MM patients are limited due to frailty and frequent comorbidities. Moreover, elderly patients are not candidates for stem cell transplant (SCT) and age is an independent poor prognostic marker, leading to disparate outcomes. The present study evaluated trends in outcomes and predictors of survival among elderly NDMM patients in the United States (U.S.). Methods: Using Surveillance Epidemiology and End Results (SEER) database, we extracted data on age, gender, geographical regions, year of diagnosis, and vital status for elderly patients (age ≥ 80 years) with MM from 1975 to 2016. Relative survival (RS) was estimated as the ratio of observed survival to the survival expected in the general population in the absence of malignancy. This adjusted for the competing causes of mortality prevalent in elderly patients. Mortality rates were age-adjusted. To estimate trends in mortality, we estimated annual percentage change (APC) and average annual percentage change (AAPC) by fitting the data in the join point regression model using the NCI's Join point Regression Program, Version 4.5.0.1. A multivariate analysis was conducted to analyze the predictors of overall and myeloma-specific mortality among elderly patients fitting into Cox proportion hazard regression model. Results: A total of 17,265 elderly MM patients were eligible for the study. Majority were females (n=9,487, 55%), were non-Hispanic whites (NHW) (n=12,895, 74.7%) and were diagnosed in the Western region of the U.S. (n=8,541, 49.5%). The median age was 84 years (IQR 81-87). The median RS for the whole period (1975-2016) was 19.8 months with 1- and 5- year RS of 60.1% (95%CI: 58.8-61.3) and 20.9% (95%CI: 19.5-22.3) respectively. The median RS increased from 8.45 months in 1975 to 29.86 months in 2013. One and 5- year RS also increased from 42.7% (95%CI: 32.4-52.5) and 12.9% (95%CI: 5.8-22.7) in 1975 to a 1- and 5-year RS of 72.3% (95%CI: 65.4-78.1) and 31.1% (95%CI: 23.4-39.1) in 2013, respectively (Fig.1). However, the age standardized mortality rate for the whole period showed an increasing trend with an AAPC of 2.3 (0.5-4, P<0.001). On fitting joinpoint regression, 2 distinct joinpoints were identified, calendar years 1977 and 1989 (Fig 2). Most of the increase in mortality rate was noted in the period 1975-1989. Since 1989, the mortality rates have shown a decreasing trend which is marginal but statistically significant, with an APC of -0.41 (95%CI: -0.7- -0.1, p<0.001). On multivariate analysis, increasing age (p<0.001) and non-northeast location (p<0.01) were associated with higher overall and MM-specific mortality. Female gender was associated with lower overall mortality (p<0.001) but not myeloma-specific mortality. While "other" race/ethnicity (not NHW, non-Hispanic black; NHB or Hispanic) was associated with lower risk of overall and myeloma-specific mortality, NHB were associated with a lower MM-specific (p<0.001) but not overall mortality. Conclusions: Our results suggest that although RS has improved significantly in the elderly patients, the mortality rates have not changed drastically over the years. Besides, factors like advancement in diagnosis and treatment which have improved RS, early diagnosis (leading to lead time bias) may also be contributing. Furthermore, there are several gender-specific, geographic and racial/ethnic disparities in outcomes of elderly MM patients, which need to be assessed prospectively. Although, a small decline in mortality rates in the more recent time is promising, further studies are warranted on interventions including aggressive supportive care directed at elderly patients with MM, so that MM survivorship can be improved. There should be strong consideration to increaseenrollment of the elderly in prospective clinical trials so that evidence-based guidelines can be developed. Disclosures Manochakian: Takeda: Membership on an entity's Board of Directors or advisory committees; Guardant Health: Membership on an entity's Board of Directors or advisory committees; AstraZeneca: Membership on an entity's Board of Directors or advisory committees; Novocure: Membership on an entity's Board of Directors or advisory committees. Chanan-Khan:Merck: Research Funding; Ascentage: Research Funding; Millennium: Research Funding; Jansen: Research Funding; Mayo Clinic: Employment; Pharmacyclics: Research Funding; AbbVie: Research Funding; Xencor: Research Funding. Ailawadhi:Janssen: Consultancy, Research Funding; Takeda: Consultancy; Celgene: Consultancy; Amgen: Consultancy, Research Funding; Cellectar: Research Funding; Pharmacyclics: Research Funding. Off Label Use: Do not remove- this information is relevant to Abstract ID 125629: TAK-169 is a dimeric fusion protein of an anti-CD38 antibody single chain variable fragment fused to a modified Shiga-like toxin-A subunit..

Abstract Background: Multiple myeloma (MM) is characterized by the invasion of malignant plasma cells into the bone marrow. While first line treatment options result in significant clinical benefit to patients, spatiotemporal clonal evolution results in disease relapse and mortality. Advances in genomics have armed clinicians with unprecedented insight into the molecular architecture of MM cells, however, the clinical benefit derived by genomics-guided intervention has been limited. We present a novel computational biology modelling (CBM) tool, which takes into account the combined effect of individual mutations, gene copy number abnormalities and large scale chromosomal changes in order to predict the salient molecular pathways utilized by the MM cell for survival. By reverse-engineering MM cell architecture in silico, the CBM tool is able to predict drug response and resistance mechanisms. Thus, our aim was to determine the accuracy of the CBM tool in predicting treatment response of relapsed/refractory MM patients for future management of their disease, in a more individualized manner. Methods: Cytogenetics and somatic mutations (by targeted NGS) for 15 MM patients were input into the CBM model to predict responses to different therapeutic combinations. All patients were relapsed to prior treatment. CBM uses PubMed and other online resources to generate patient-specific protein network maps of activated and inactivated disease pathways. We simulated the specific combinations of the drugs per patient and measured the quantitative drug effect on a composite MM disease inhibition score (i.e., cell proliferation, viability, apoptosis and paraproteins). The actual clinical outcome of the treatments was compared with predicted outcomes. Results: Fifteen patients were analysed using CBM for prediction of treatment response after NGS was performed. 13/15 were clinically evaluable, of which 1 was a responder and 12 were non-responder. 6/13 patients were treated on clinical trial and 7/13 were on drug combinations per physician decision. CBM correctly predicted 1 responder and 11 non-responder with a PPV of 50%, NPV 100%, specificity 91.67%, sensitivity 100%. The accuracy of CBM prediction was 92.30%. CBM also predicted the response of prior drug therapies for its non-response at relapse. For prior drug treatment options, 14 patients were evaluable. All the 14 patients were clinically non-responders and CBM correctly predicted for 13 patients with NPV 100%, Specificity 92.85% and overall accuracy of 92.85%. The majority of patients did not respond to therapies recommended at relapse. As an example, the operative molecular pathways from 2 patients who did not respond to combination treatment, either pre-NGS or post-NGS profiling, are shown in Fig. 1 and Table 1. CBM identified amplification (AMP) of chromosome (chr) 1 (WNT3A, IL6R, CKS1B, MCL1, PIK3C2B, USF1), chr 3 (HES1, PIK3CA, CTNNB1, WNT7A, FANCD2), chr 5 (IL6ST, IRF1, GLRX, SKP2), chr 7 (CDK5, EZH2, IL6, CAV1, ABCB1), chr 9 (NOTCH1, HSPA5, FANCC, FANCG), chr 15 (DLL4, FANCI, ALDH1A2), chr 19 (ERCC1, ERCC2, USF2); deletion(DEL) of chr 13 (CUL4A) , chr 16 (AXIN1, CDH1) and TP53 mutation in different combinations, which confer resistance to therapies at relapse. Conclusions: The CBM technology represents a potential means to identify therapeutic options for MM patients based on the patients individual tumor-genome profile and which can also be deployed for uncovering drug resistance mechanisms. This tool may aid clinicians in decision making for recommending the most appropriate therapy based on standard of care agents or clinical trials; thus improving patient outcomes and reducing unnecessary costs or drug-related toxicities. Disclosures Singh: Cellworks Research India Private Limited: Employment. Sauban:Cellworks Research India Private Limited: Employment. Husain:Cellworks Research India Private Limited: Employment. Kumar:Cellworks Research India Private Limited: Employment. Kumari:Cellworks Research India Private Limited: Employment. Tyagi:Cellworks Research India Private Limited: Employment. Abbasi:Cell Works Group Inc.: Employment. Vali:Cell Works Group Inc.: Employment. Ailawadhi:Pharmacyclics: Research Funding; Takeda: Consultancy; Celgene: Consultancy; Amgen: Consultancy; Janssen: Consultancy.

Abstract Background: Therapeutic advancements have led to significant improvement in outcomes for MM patients. Revised diagnostic criteria from International Myeloma Working Group focus on initiating treatment before the onset of end-organ damage. Thus, timeliness of initial therapy can significantly improve patient morbidity and outcomes. Such an analysis has not been reported for MM. Methods: We analyzed patients diagnosed with MM between 2004-2015 from the National Cancer Database (NCDB). Patients who received systemic treatment any time within the first year of diagnosis were included; those treated later than one year were considered possibly initially diagnosed with smoldering myeloma and hence, excluded. Time to initial treatment was divided into centiles with the 4 centiles analyzed being 0-7 days, 8-18 days, 19-37 days and >37 days. We performed univariate and multivariate analyses to compare sociodemographic and clinical factors influencing patient distribution across these timeliness categories and trends over time. Results: A total of 53,665 patients were included with 15,282 in 0-7 days, 12,462 in 8-18 days, 12,723 in 19-37 days and 13,198 in the >37 days treatment categories. Distribution across these centiles over time is shown in Figure. Univariate analyses showed a significant difference in distribution across the timeliness categories by year of diagnosis, patient age, gender, race/ethnicity, education level, insurance type, comorbidity score, treatment facility type, geographical location and distance to treating facility (all p<0.001) but no effect of income level. Multivariate analysis showed that there was a significantly higher likelihood of being in 4th centile (>37 days to treatment) for women (p<0.001), African Americans (p<0.001) or Hispanics (p=0.03; reference: Whites) or diagnosed in more recent years (p<0.001). Patients were less likely to be in the 4th centile if they were ≥80 years (HR 0.89, 95% CI 0.81-0.98, p=0.02), had >0 Charlson comorbidity score (p<0.001), were uninsured (p=0.02) or had Medicaid (p=0.03) as compared to having private insurance, were in non-community treatment facilities or geographical location other than Northeast (p<0.001). Patient education, income level or distance from treating facility was no longer significant. Conclusions: Several factors were found to affect timeliness of treatment in MM. We noted relatively faster time to initial treatment in elderly, uninsured or those who were treated in academic/comprehensive cancer programs but a delay in treatment for women and racial/ethnic minorities among other factors. These factors need to be addressed for equitable access and treatment practices and to realize improved outcomes for all. Figure. Figure. Disclosures Ailawadhi: Amgen: Consultancy; Celgene: Consultancy; Janssen: Consultancy; Takeda: Consultancy; Pharmacyclics: Research Funding.