Citations

AbstractBackgroundThere is sparse clinical information on the racial and ethnic distribution of results of multigene panel testing among individuals at high risk for hereditary cancer.MethodsWe evaluated the results of multigene panel testing across eight clinical sites across the state of Michigan for individuals seen for genetic counseling from May 13, 2013 to October 31, 2019 at the Karmanos Cancer Institute's cancer genetics clinic. We estimated the prevalence of pathogenic variants and variants of uncertain significance (VUS) from genes other than BRCA1/2 among individuals of non‐Hispanic White (NHW), Black or African American (AA), Ashkenazi Jewish (AJ), Arab, Hispanic, and other ancestry.ResultsThe racial and ethnic distribution of 2419 individuals who had panel testing included 68.8% NHW, 22.1% AA, 2.3% Arab, 2.2% AJ, 1.0% Hispanic, and 3.6% other. Of these, 11.2% had pathogenic variants and 17.5% had VUS. After multivariable analyses, compared to NHW, AA were less likely to have pathogenic variants (OR 95% CI, 0.38, 0.24–0.59, p < 0.001). Both AA and Arabs were more likely to have VUS (OR 95% CI, 1.53, 1.18–1.98, p = 0.001 and OR 95% CI, 2.28, 1.17–4.43, p = 0.015, respectively). There were no significant differences for other groups. The most common pathogenic variants were CHEK2 (n = 65), MUTYH (n = 45), ATM (n = 28), and MSH2 (n = 22); the most common pathogenic variants by race and ethnicity were CHEK2 (NHW), MSH2 and MUTYH (AA), MSH2 (Arab), MSH6 and CHEK2 (AJ), and MLH1 (Hispanic); the most common pathogenic variants by primary cancer site were CHEK2 (breast), MSH2 (colon), BRIP1 and MUTYH (ovarian), and MSH2 and MSH6 (endometrial).ConclusionsUnderstanding the racial and ethnic distribution of pathogenic variants in multi‐gene panels has the potential to lead to better identification of individuals at risk for hereditary cancer.

AbstractCastleman disease (CD) is a rare lymphoproliferative disease characterized by diverse clinical and pathologic features. Due to its rarity, there are limited studies comparing currently available therapies. The role of autologous stem cell transplantation (ASCT) in CD has not yet been established. In this paper, we describe the clinical characteristics, treatment choices, and outcomes in 34 Mayo Clinic patients diagnosed with multicentric CD from July 1, 2003 to April 30, 2018. Eighteen patients (53%) also met the criteria for POEMS, including 14 with the osteosclerotic variant. The first‐line treatments included: steroid monotherapy (4), cytotoxic chemotherapy (6), rituximab alone (8) or with chemotherapy (2), anti‐IL6 treatment (3), and ASCT (10). The median follow‐up was 4.8 (range: 0.1–15.2) years. The 5‐ and 10‐year overall survival rates were 84% and 71%, respectively. Sixteen patients received high‐dose chemotherapy followed by ASCT during their disease course. Among those, 14 had multicentric CD associated with POEMS. There were no transplant‐related deaths. All patients had at least a partial response to ASCT, most of whom achieved a complete response. The favorable outcomes seen with ASCT in this cohort suggest that transplantation may have a role in multicentric CD, particularly for patients with multicentric CD associated with POEMS.

Abstract Background: MASS-FIX is a screening method for serum and urine monoclonal proteins in multiple myeloma and related plasma cell disorders, which uses immunoglobulin enrichment coupled with matrix-assisted laser desorption ionization time-of-flight mass-spectrometry (MALDI-TOF). In addition to superior sensitivity over conventional gel-based techniques, MASS-FIX can distinguish therapeutic monoclonal antibodies (MoAb) from patient's M protein. As the utilization of therapeutic MoAbs increases, it is essential to understand the persistence pattern of these therapeutic antibodies in the serum. We designed this study to evaluate the duration of daratumumab detection by MASS-FIX in the serum of treated patients. Methods: We used a prospectively maintained database at Mayo clinic to identify patients with multiple myeloma and related plasma cell disorders who were treated with a daratumumab-containing regimen anytime during their disease course and had serial MASS-FIX data available after discontinuation of daratumumab. A univariate analysis was performed to assess for factors that may impact the clearance of daratumumab. Results: We included 125 patients with plasma cell disorders who received daratumumab as first or subsequent line of treatment between March 15 th, 2016, and March 4 th, 2020. The median age was 60.2 years and 57% were male. The most common diagnoses were multiple myeloma (70%) and light chain amyloidosis (18%). Daratumumab-based treatments were initiated after a median of 28.8 (IQR: 6.4-76.3) months from initial diagnosis. The most common regimen used was daratumumab, bortezomib and dexamethasone (23%); 26% underwent transplant after daratumumab-based induction. The median duration of treatment with a daratumumab-based regimen was 208 (IQR: 99-479) days. The median follow-up from the time of daratumumab discontinuation was 457 (95% CI: 346-NR) days. By last follow up, daratumumab was not detected by MASS-FIX in 93 (74%) patients but remained detectable in 32 (26%) patients. The median time from daratumumab discontinuation to disappearance of daratumumab by MASS-FIX was 160 (IQR: 107-233) days. On univariate analysis, the presence of ≥0.5 grams of urine protein was associated with earlier disappearance of daratumumab on MASS-FIX [risk ratio (RR): 2.0, P=0.02). The median time from daratumumab discontinuation to disappearance of daratumumab on MASS-FIX was 116 (95%CI: 76-160) days in patients with urine protein ≥0.5 grams and 203 (95%CI: 162-216) days in patients with urine protein &lt;0.5 grams (P=0.02). There was no association between the time to disappearance of daratumumab by MASS-FIX and old age ≥70 (RR: 0.9, P=0.81], male gender (RR: 0.9, P=0.60), eGFR &lt;60 (RR: 1.0, P=0.98), daratumumab schedule (every 1/2 weeks vs &gt;2weeks) (RR: 1.0, P=0.97), treatment duration (&lt;200 days vs ≥200 days) ( RR: 1.0, P=0.95), or transplantation status (RR: 1.0, P=0.98). Conclusion: The therapeutic monoclonal antibody daratumumab remains detectable in the serum of treated patients by MASS-FIX for several months after discontinuation and the duration varies between individual patients. This data has implications for diagnostic and monitoring testing and may provide guidance for reuse of daratumumab in clinical trials and practice. Proteinuria is associated with earlier disappearance of daratumumab by MASS-FIX and may have implications in patients with amyloidosis and monoclonal immunoglobulin deposition disease (MIDD). Further studies are needed to identify additional factors associated with the timing of disappearance. Disclosures Murray: Mayo Clinic: Other: Has received patents for the Mass-Fix technology which has been licensed to the Binding Site with potential royalties.. Dispenzieri: Takeda: Research Funding; Alnylam: Research Funding; Pfizer: Research Funding; Oncopeptides: Consultancy; Sorrento Therapeutics: Consultancy; Janssen: Consultancy, Research Funding. Kapoor: Karyopharm: Consultancy; Cellectar: Consultancy; BeiGene: Consultancy; Pharmacyclics: Consultancy; Sanofi: Consultancy; Amgen: Research Funding; Ichnos Sciences: Research Funding; Regeneron Pharmaceuticals: Research Funding; Glaxo SmithKline: Research Funding; Karyopharm: Research Funding; Sanofi: Research Funding; Takeda: Research Funding; AbbVie: Research Funding. Gertz: Akcea Therapeutics, Ambry Genetics, Amgen Inc, Celgene Corporation, Janssen Biotech Inc, Karyopharm Therapeutics, Pfizer Inc (to Institution), Sanofi Genzyme: Honoraria; Ionis Pharmaceuticals: Other: Advisory Board; Akcea Therapeutics, Alnylam Pharmaceuticals Inc, Prothena: Consultancy; Aurora Biopharma: Other: Stock option; AbbVie Inc, Celgene Corporation: Other: Data Safetly & Monitoring. Dingli: Alexion: Consultancy; Novartis: Research Funding; Apellis: Consultancy; Janssen: Consultancy; Sanofi: Consultancy; GSK: Consultancy. Kumar: Antengene: Consultancy, Honoraria; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Bluebird Bio: Consultancy; Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Research Funding; Merck: Research Funding; Roche-Genentech: Consultancy, Research Funding; Amgen: Consultancy, Research Funding; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Beigene: Consultancy; Oncopeptides: Consultancy; Astra-Zeneca: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; KITE: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; BMS: Consultancy, Research Funding; Carsgen: Research Funding; Tenebio: Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Adaptive: Membership on an entity's Board of Directors or advisory committees, Research Funding; Sanofi: Research Funding.

Abstract Purpose: Multiple myeloma (MM) is a plasma cell malignancy characterized by a collection of cytogenetic abnormalities that drive clinical presentation, response to treatment, and prognosis. The most common immunoglobulin heavy chain (IGH) translocation found in MM, t(11;14), results in CCND1 overexpression. Although t(11;14) is considered a standard risk cytogenetic abnormality, heterogeneity in patient outcomes and response to therapy within the t(11;14) group has been reported. In contrast to t(11;14) mantle cell lymphoma, where most cases (73%) have a typical, balanced t(11;14) rearrangement, only 47% of t(11;14) MM have a typical, balanced t(11;14) FISH pattern; the remaining 53% of MM are characterized by an atypical t(11;14) FISH result (Dalland, Genes Chromosomes Cancer, 2021). Here, we compared the genetic characteristics of typical vs. atypical t(11;14) MM and the association with other genomic abnormalities, CCND1 expression, IGH breakpoint location and patient outcomes. Methods: We performed a retrospective study of newly diagnosed t(11;14) MM patients seen at Mayo Clinic (N=411) or enrolled in the MMRF CoMMpass trial (N=167). The t(11;14) FISH results of the Mayo Clinic samples were segmented into two groups: typical CCND1/IGH signal pattern (n=179, 3 red, 3 green, 2 fusion signals) and atypical CCND1/IGH signal pattern (n=232, any FISH pattern deviating from typical). The CoMMpass cases had both RNAseq and long-insert WGS data and were analyzed to detect gene expression, structural and copy number variants and IGH breakpoint locations. These data were used to classify the t(11;14) as typical (n=63) or atypical (n=104) and whether there was a single t(11;14) fusion (n=107) or a gain of the t(11;14) fusion (n=60), and whether these cases were associated with changes in gene expression or breakpoint location. Differences between the groups were measured using χ2 and Wilcoxon tests. Survival curves were estimated using Kaplan Meier and compared using the Log-Rank test. Statistical significance was determined at p&lt;0.05. Results: Of the Mayo t(11;14) cohort, 232/411 (56%) had an atypical t(11;14) FISH pattern and 179/411 (44%) had a typical t(11;14) FISH pattern. Although an atypical t(11;14) FISH pattern was not significantly associated with monosomy 17/17p deletion, MYC rearrangements, 1q gain or ISS stage III, it was significantly associated with the presence of any trisomy in comparison to typical t(11;14) cases (40/222=18% vs.12/166=7%, p=0.002). Atypical t(11;14) cases had an increase in CCND1 gene expression compared to typical t(11;14) cases (median 375.0 RPKM vs. 266.4 RPKM, p&lt;0.0001) and cases with a gain of the t(11;14) fusion were associated with even greater CCND1 expression compared to cases with a single t(11;14) fusion (median 459.4 RPKM vs. 263.2 RPKM, p&lt;0.0001). We next evaluated whether the t(11:14) FISH pattern was associated with differences in IGH breakpoint locations. Nearly 80.0% of atypical t(11;14) cases had a breakpoint within the constant region of the IGH locus commonly reported for MM, while only 61.0% of the typical cases had constant region breakpoints (p=0.035); the remaining cases had breakpoints involving the VDJ region. We next evaluated the impact of atypical t(11;14) FISH on OS. In Mayo cases, OS was significantly shorter in patients with atypical FISH patterns compared to typical t(11;14) FISH patterns (6.9 years [95% CI: 5.1-7.8] vs. 9.0 years [95% CI: 7.0-11.6], p=0.019). Although atypical t(11;14) was associated with increased risk of death (RR 1.45, p=0.021), this finding was not retained in a multivariate model including high risk abnormalities such as age, ISS stage, 17p deletion and 1q gain demonstrating atypical t(11;14) is not an independent predictor of poor outcome when treated with standard therapies. Conclusions: Atypical t(11;14) FISH in MM is found in approximately half of t(11;14) cases, is associated with trisomies, higher CCND1 expression and is more likely to have a breakpoint involving the constant region of the IGH locus. The enrichment of VDJ breakpoints within typical t(11;14) MM is consistent with enrichment of VDJ IGH breakpoints in MCL, which are mostly characterized by typical t(11;14) FISH patterns. While not an independent predictor of outcome with standard therapies, it remains to be evaluated whether patients with typical or atypical t(11;14) have a differential response to novel agents, such as venetoclax. Disclosures Fonseca: Janssen: Consultancy; Novartis: Consultancy; Pharmacyclics: Consultancy; Sanofi: Consultancy; Merck: Consultancy; Juno: Consultancy; Caris Life Sciences: Membership on an entity's Board of Directors or advisory committees; Kite: Consultancy; Aduro: Consultancy; OncoTracker: Consultancy, Membership on an entity's Board of Directors or advisory committees; GSK: Consultancy; AbbVie: Consultancy; Patent: Prognosticaton of myeloma via FISH: Patents & Royalties; Mayo Clinic in Arizona: Current Employment; Amgen: Consultancy; BMS: Consultancy; Celgene: Consultancy; Takeda: Consultancy; Bayer: Consultancy; Scientific Advisory Board: Adaptive Biotechnologies: Membership on an entity's Board of Directors or advisory committees. Bergsagel: Pfizer: Consultancy, Honoraria; Celgene: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; GSK: Consultancy, Honoraria; Oncopeptides: Consultancy, Honoraria; Genetech: Consultancy, Honoraria; Novartis: Consultancy, Honoraria, Patents & Royalties: human CRBN mouse. Kumar: Merck: Research Funding; Carsgen: Research Funding; Roche-Genentech: Consultancy, Research Funding; Beigene: Consultancy; KITE: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Bluebird Bio: Consultancy; Antengene: Consultancy, Honoraria; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Oncopeptides: Consultancy; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Astra-Zeneca: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Research Funding; Adaptive: Membership on an entity's Board of Directors or advisory committees, Research Funding; Tenebio: Research Funding; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Amgen: Consultancy, Research Funding; Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; BMS: Consultancy, Research Funding; Sanofi: Research Funding.

Abstract Background: MASS-FIX is a novel method for detection, characterization and quantification of serum and urine monoclonal proteins in multiple myeloma (MM) which uses immunoglobulin enrichment coupled with matrix-assisted laser desorption ionization time-of-flight mass-spectrometry (MALDI-TOF). MASS-FIX has been endorsed by the International Myeloma Working Group (IMWG) as an alternative to immunofixation electrophoresis for diagnosis and disease monitoring in MM and has replaced immunofixation electrophoresis in our institution. This technique has demonstrated superior sensitivity and specificity compared to conventional gel-based methods, but its prognostic value is still unknown. We designed this study to evaluate whether serum M protein monitoring by MASS-FIX has prognostic significance in predicting disease progression in patients with MM. Methods: In the first part of the study, we included patients with MM who had a documented negative MASSFIX after first-line treatment and had serial MASS-FIX data available (n=187). We then compared the time to next treatment (TTNT) between patients who became positive by MASS-FIX and those who remained negative by last follow up. In the second part, we included patients who were positive by MASS-FIX within 90 days from diagnosis and had available serial MASS-FIX data (n=203). We compared TTNT between patients who became negative by MASS-FIX and those who remained positive by last follow up. TTNT was defined as the time of initiation of the first-line treatment to the time of initiation of second-line treatment or last follow up. Results: Part 1: We included 155 patients diagnosed with MM between January 2013 and December 2019 who had a documented negative MASSFIX with first-line treatment and had serial MASSFIX data available. After a median follow up of 3.1 (95%CI: 2.6-3.5) years, 44 (28%) patients became MASSFIX positive, while 111 (72%) still had negative MASS-FIX. The median time from treatment start to negative MASSFIX was 18.0 (IQR: 6.4-33.5) months. The median TTNT was 4.1 (95%CI: 3.2 - NR) years in patients who became MASSFIX positive and not reached (NR) (95%CI: NR-NR) in patients who were MASS-FIX negative by last follow up (P&lt;0.001). The 2-year OS was 97% and 100% in the 2 groups, respectively (P=0.27). Part 2: We included 203 patients who had positive MASSFIX within 90 days from diagnosis and available serial MASSFIX data. The median follow-up was 1.5 (95%CI: 1.5-1.7) years. By last follow up, 39 (19%) patients became MASSFIX negative while 164 (81%) still had positive MASSFIX. The median TTNT was NR (95%CI: 2.0-NR) years in patients who developed negative MASSFIX and 2.3 (95%CI: 1.9 - NR) years in patients who were still MASS-FIX positive by last follow up (P=0.03). The 2-year OS was 97% and 94% in the 2 groups, respectively (P=0.84) Conclusion: The utilization of MASS-FIX in disease monitoring of MM patients provides prognostic information; among patients who become MASS-FIX negative after first line treatment, an earlier conversion to positive MASS-FIX predicts disease progression. Similarly, persistent MASS-FIX positivity after treatment initiation predicts earlier disease progression. Larger studies and longer follow up are needed to evaluate whether the MASS-FIX is associated with overall survival. Disclosures Murray: Mayo Clinic: Other: Has received patents for the Mass-Fix technology which has been licensed to the Binding Site with potential royalties.. Dispenzieri: Sorrento Therapeutics: Consultancy; Oncopeptides: Consultancy; Pfizer: Research Funding; Alnylam: Research Funding; Takeda: Research Funding; Janssen: Consultancy, Research Funding. Kapoor: Karyopharm: Consultancy; Cellectar: Consultancy; BeiGene: Consultancy; Pharmacyclics: Consultancy; Sanofi: Consultancy; Amgen: Research Funding; Ichnos Sciences: Research Funding; Regeneron Pharmaceuticals: Research Funding; Glaxo SmithKline: Research Funding; Karyopharm: Research Funding; Sanofi: Research Funding; Takeda: Research Funding; AbbVie: Research Funding. Gertz: Akcea Therapeutics, Alnylam Pharmaceuticals Inc, Prothena: Consultancy; Akcea Therapeutics, Ambry Genetics, Amgen Inc, Celgene Corporation, Janssen Biotech Inc, Karyopharm Therapeutics, Pfizer Inc (to Institution), Sanofi Genzyme: Honoraria; Ionis Pharmaceuticals: Other: Advisory Board; Aurora Biopharma: Other: Stock option; AbbVie Inc, Celgene Corporation: Other: Data Safetly & Monitoring. Dingli: Sanofi: Consultancy; GSK: Consultancy; Novartis: Research Funding; Apellis: Consultancy; Janssen: Consultancy; Alexion: Consultancy. Kumar: Amgen: Consultancy, Research Funding; Bluebird Bio: Consultancy; Antengene: Consultancy, Honoraria; BMS: Consultancy, Research Funding; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Beigene: Consultancy; Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Roche-Genentech: Consultancy, Research Funding; KITE: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Astra-Zeneca: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Carsgen: Research Funding; Merck: Research Funding; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Oncopeptides: Consultancy; Novartis: Research Funding; Tenebio: Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Adaptive: Membership on an entity's Board of Directors or advisory committees, Research Funding; Sanofi: Research Funding.

Abstract Background: Systemic light chain (AL) amyloidosis is a plasma cell disorder characterized by multisystem deposition of misfolded immunoglobulin light chains produced by clonal plasma cells. Hematologic and organ responses with treatment have been shown to correlate with survival as early as 3 months from initiation of first-line treatment. Our group recently developed and validated a model integrating organ and hematologic responses for assessment of treatment outcomes at 6 months. Although current organ response criteria do not consider the depth of organ response, this has been shown to have prognostic utility in newly diagnosed patients. So, we designed this study to evaluate a composite hematologic and organ scoring system that also considers the depth of organ response at 1 to 6 months from initiation of first-line treatment. Methods: We included patients with AL amyloidosis who had at least one major organ involvement (cardiac, renal and/or liver involvement) and who had not started a second line treatment by 6 months from the time of initiating first-line treatment. For each patient, we calculated an organ response score and a hematologic response score at 1, 2, 3, 4, 5, and 6 months from initiation of first-line treatment. A score was assigned for each depth of hematologic response as follows: complete response=0, very good partial response=1, partial response=2, and no response/progressive disease=3). To calculate organ response, a score was assigned to each organ based on the depth of organ response (Muchtar et al. 2018): non-evaluable=0, complete response=1, very good partial response=2, partial response=3, and no response=4. The final organ response score was obtained by calculating the average of the individual involved organ scores. We then calculated the composite hematologic and organ response (HOR) score by adding the organ and hematologic responses at each interval and compared overall survival (OS) between patients with HOR score ≤ 5 (group 1) and those with score &gt; 5 (group 2). Results: The cohort included 730 patients diagnosed with AL amyloidosis between February 10 th, 2006 and July 9 th, 2019. Median age was 63 (IQR: 56-69), and 65% were male. The involved light chain was Lambda in 75% of cases. Cardiac, renal, and liver involvement were found in 81%, 61%, and 17% of patients, respectively. Among all patients, 28% underwent autologous stem cell transplantation during their disease course. The median follow up in the entire cohort was 7.0 (95%CI: 6.4-8.0) years and OS was 3.6 (95%CI: 2.6-4.4) years. At 1 to 4 months, we observed a statistically significant difference in OS between patients with HOR score ≤5 vs. &gt;5. However, there was no difference in OS between the 2 groups at 5 and 6 months. These results are presented in Table 1. Conclusion: A composite hematologic and organ response score that takes into consideration the depth of organ response can discriminate 2 groups of patients with distinct survival outcomes as early as 1 month from treatment initiation and maintains its predictive ability for up to 4 months. The lack of predictive ability beyond 4 months in this study may be due to limited sample size especially in group 2 as more patients achieve deeper responses with time. This approach can provide the basis for early changes in treatment but needs validation in future studies. Figure 1 Figure 1. Disclosures Dispenzieri: Janssen: Consultancy, Research Funding; Takeda: Research Funding; Sorrento Therapeutics: Consultancy; Oncopeptides: Consultancy; Pfizer: Research Funding; Alnylam: Research Funding. Gertz: Ionis Pharmaceuticals: Other: Advisory Board; Akcea Therapeutics, Ambry Genetics, Amgen Inc, Celgene Corporation, Janssen Biotech Inc, Karyopharm Therapeutics, Pfizer Inc (to Institution), Sanofi Genzyme: Honoraria; Aurora Biopharma: Other: Stock option; Akcea Therapeutics, Alnylam Pharmaceuticals Inc, Prothena: Consultancy; AbbVie Inc, Celgene Corporation: Other: Data Safetly & Monitoring. Kapoor: Ichnos Sciences: Research Funding; Karyopharm: Consultancy; Glaxo SmithKline: Research Funding; Pharmacyclics: Consultancy; Amgen: Research Funding; Cellectar: Consultancy; Sanofi: Consultancy; BeiGene: Consultancy; Regeneron Pharmaceuticals: Research Funding; Karyopharm: Research Funding; Sanofi: Research Funding; Takeda: Research Funding; AbbVie: Research Funding. Dingli: GSK: Consultancy; Sanofi: Consultancy; Janssen: Consultancy; Novartis: Research Funding; Apellis: Consultancy; Alexion: Consultancy. Kumar: Oncopeptides: Consultancy; Merck: Research Funding; Antengene: Consultancy, Honoraria; Bluebird Bio: Consultancy; Roche-Genentech: Consultancy, Research Funding; Astra-Zeneca: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Amgen: Consultancy, Research Funding; Novartis: Research Funding; Carsgen: Research Funding; Tenebio: Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; KITE: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; BMS: Consultancy, Research Funding; Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Beigene: Consultancy; Adaptive: Membership on an entity's Board of Directors or advisory committees, Research Funding; Sanofi: Research Funding; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding.

Abstract Purpose: Structural variants (SV) of the MYC gene region are common in multiple myeloma and influence disease progression. However, the prognostic significance of different MYC SVs in multiple myeloma has not been clearly established. Experimental Design: We conducted a retrospective study of multiple myeloma comparing MYC SV subtypes identified by next-generation sequencing (NGS) and FISH to MYC expression and disease survival using 140 cases from Mayo Clinic and 658 cases from the MMRF CoMMpass study. Results: MYC SVs were found in 41% of cases and were classified into nine subtypes. A correlation between the presence of a MYC SV and increased MYC expression was identified. Among the nine MYC subtypes, the non-immunoglobulin (non-Ig) insertion subtype was independently associated with improved outcomes, while the Ig insertion subtype, specifically involving the IgL gene partner, was independently associated with poorer outcomes compared with other MYC SV subtypes. Although the FISH methodology failed to detect approximately 70% of all MYC SVs, those detected by FISH were associated with elevated MYC gene expression and poor outcomes suggesting a different pathogenic role for FISH-detected MYC subtypes compared with other MYC subtypes. Conclusions: Understanding the impact of different MYC SVs on disease outcome is necessary for the reliable interpretation of MYC SVs in multiple myeloma. NGS approaches should be considered as a replacement technique for a more comprehensive evaluation of the multiple myeloma clone.

PURPOSE: The benefit of routine pre-emptive screening for severe acute respiratory syndrome coronavirus 2 infections in patients with cancer before cancer-directed therapies is unclear. Herein, we characterize the outcomes of a cohort of patients with cancer who were diagnosed with COVID-19 by routine screening (RS) in comparison with those diagnosed on the basis of clinical suspicion or exposure history (nonroutine screening [NRS]). METHODS: A multisite prospective observational study was conducted at three major and five satellite campuses of the Mayo Clinic Cancer Center between March 18 and July 31, 2020. The primary outcome was COVID-19–related hospital admission. Secondary outcomes included intensive care unit admissions and all-cause mortality. RESULTS: Five thousand four hundred fifty-two patients underwent RS in the outpatient setting only, and 44 (0.81%) were diagnosed with COVID-19. RS detected 19 additional patients from the scheduled inpatient admissions for surgical or interventional procedures or inpatient chemotherapy. One hundred sixty-one patients were diagnosed with COVID-19 on the basis of NRS. COVID-19–related hospitalization rate (17.5% v 26.7%; P = .14), intensive care unit admission (1.6% v 5.6%; P = .19), and mortality (4.8% v 3.7%; P = .72) were not significantly different between the RS and NRS groups. In the multivariable analysis, age ≥ 60 years (odds ratio, 4.4; P = .023) and an absolute lymphocyte count ≤ 1.4 × 109/L (odds ratio, 9.2; P = .002) were independent predictors of COVID-19–related hospital admission. CONCLUSION: The COVID-19 positivity rate was low on the basis of RS. Comparing the hospital admission and mortality outcomes with the NRS cohort, there were no significant differences. The value of routine pre-emptive screening of asymptomatic patients with cancer for COVID-19 remains low.

Abstract Abstract Importance: The benefit of routine screening for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections in patients with cancer before cancer-directed therapies is unclear. Herein, we characterize the outcomes of a cohort of cancer patients diagnosed with Coronavirus Disease 2019 (COVID-19) by routine screening in comparison with those diagnosed based on clinical suspicion or exposure history (non-routine screening). Objective: To describe and compare the outcomes of cancer patients diagnosed with COVID-19 on routine screening vs. non-routine screening at a multi-site tertiary cancer center. To identify risk factors for COVID-19-related hospital admission. Design: A multi-site prospective observational study was conducted between March 18 and July 31, 2020. Setting: Three major and 5 satellite campuses of the Mayo Clinic Cancer Center. Participants: Adult patients diagnosed with active cancer within the past five years and confirmed SARS-CoV-2 infection were included. Primary Outcomes and Measures: Clinical and laboratory data were assessed as independent variables. The primary outcome was COVID-19-related hospital admission. Secondary outcomes included intensive care unit (ICU) admissions and all-cause mortality. Results: Between March 18 and July 31, 2020, 5452 patients underwent routine screening in the outpatient setting, 44 (0.81%) were diagnosed with COVID-19. Routine screening detected additional 19 patients from inpatient and pre-procedural settings; 161 patients were diagnosed with COVID-19 based on non-routine screening. The median age of the entire cohort at diagnosis was 54 years, and 95 patients (42.2%) were female. COVID-19 related-hospitalization rate (17.5% vs. 26.7%, p=0.14), ICU admission (1.6% vs. 5.6%, p=0.19), and mortality (4.8% vs. 3.7%, p=0.72) were not significantly different between routine screening and non-routine screening groups. In the multivariable analysis, age ≥ 60 years (odds ratio: 4.4, p=0.023) and an absolute lymphocyte count ≤1.4 × 109/L (odds ratio: 9.2, p=0.002) were independent predictors of COVID-19-related hospital admission. Conclusions and Relevance: The COVID-19 positivity rate was low based on routine screening. Comparing the outcome with the non-routine screening cohort, there was no significant difference. These results led to an important practice change at our cancer center. We currently follow a testing strategy based on symptoms, exposure, risk factors, and clinical judgment. Citation Format: Zhuoer Xie, Antoine N. Saliba, Jithma Abeykoon, Umair Majeed, Daniel Almquist, Julia Wiedmeier-Nutor, Evandro Bezerra, Xavier Andrade-Gonzalez, Ashley Hickman, Karl Sorenson, Sagar Rakshit, Christopher Wee, Sri Tella, Anuhya Kommalapati, Nadine Abdallah, Joshua Pritchett, Mariza De Andrade, Dipesh Uprety, Andrew Badley, Joleen Hubbard, Naseema Gangat, Carrie A. Thompson, Thomas Witzig, Robert R. McWilliams, Konstantinos Leventakos, Thorvardur R. Halfdanarson. Outcomes of COVID-19 in patients with cancer: Results of a prospective observational comparison of routine screening strategy versus testing based on clinical suspicion [abstract]. In: Proceedings of the AACR Virtual Meeting: COVID-19 and Cancer; 2021 Feb 3-5. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(6_Suppl):Abstract nr S06-03.

Abstract The utility of systemic light chain (AL) amyloidosis staging systems has been validated for newly diagnosed patients, but their role in restaging after treatment has not been explored. We designed this study to evaluate whether the currently used systems are of prognostic value at 3 and 6 months of starting first-line treatment, and whether stage migration from diagnosis impacts survival. This is a retrospective study including Mayo Clinic patients with AL amyloidosis diagnosed between 1 January 2006 and 30 June 2019; 536 and 204 patients had restaging data for at least 1 system at 3 and 6 months, respectively. Using modified Mayo 2004 staging at 3 months, median overall survival (OSs) were 11.8, 10.8, 4.6, and 1.1 years for stage I, II, IIIa, and IIIb, respectively. Using Mayo 2012, median OSs were 11.8, 9.0, 5.2, and 0.8 years for stage I, II, III, and IV, respectively. Using modified Mayo 2004 staging at 6 months, median OSs were not reached (NR), NR, 5.4, and 0.9 years for stage I, II, IIIa, and IIIb, respectively. Using Mayo 2012, OSs were NR, NR, 4.6, and 0.9 years for stage I, II, III, and IV, respectively. Worsening stage at 3 or 6 months was associated with worse survival than retaining baseline stage. In conclusion, the current staging systems can be used for restaging at 3 and 6 months from treatment initiation. Migration to higher stage predicts poor prognosis.