Citations

TPS7575 Background: Light chain (AL) amyloidosis is a plasma cell malignancy characterized by the production of amyloidgenic clonal light chains that deposit in various tissues. The addition of daratumumab to bortezomib, cyclophosphamide and dexamethasone (Dara-VCD) has improved depth of hematologic and organ responses. However, this approach has not yet demonstrated an overall survival (OS) benefit with short follow-up. Autologous stem cell transplant (ASCT) consolidation remains safe and is associated with deep and durable hematologic responses with an 83% 2-year progression free survival (PFS) and up to 72% of patients achieving a VGPR or better, comparable to Dara-VCD (79%). In multiple myeloma, ASCT improved PFS even in patients who are MRD negative at the end of standard induction therapy. It is thus reasonable to consider that ASCT might provide similar benefits in AL patients. No randomized data exist to inform the optimal use of ASCT in the era of Dara-VCD. Methods: This randomized phase 3 SWOG led intergroup trial will accrue a total of 143 participants per arm and compare MOD-PFS (major organ deterioration progression free survival: defined as time from randomization to death, cardiac/renal progression, or hematologic progression) between participants receiving an ASCT with those receiving Dara-VCD consolidation following uniform Dara-VCD induction. A median MOD-PFS of 31 months is anticipated in the non-ASCT arm. This study has 90% power and a two-sided significance level of 0.05 to detect a hazard ratio of 0.62 corresponding to a MOD-PFS of 50 months in the ASCT arm. Important additional endpoints include OS, hematological PFS, cardiac and renal responses and progression, MRD negativity both by peripheral blood mass spectrometry and bone marrow next generation flow cytometry, delayed utilization of ASCT, and quality of life. This trial was activated on 12/1/2023. Funding: NIH/NCI/NCTN grants and in part by Janssen Pharmaceuticals. Clinical trial information: NCT06022939 . [Table: see text]

Abstract Intracellular trafficking involves an intricate machinery of motor complexes, including the dynein complex, to shuttle cargo for autophagolysosomal degradation. Deficiency in dynein axonemal chains, as well as cytoplasmic light and intermediate chains, have been linked with ciliary dyskinesia and skeletal dysplasia. The cytoplasmic dynein 1 heavy chain protein (DYNC1H1) serves as a core complex for retrograde trafficking in neuronal axons. Dominant pathogenic variants in DYNC1H1 have been previously implicated in peripheral neuromuscular disorders (NMD) and neurodevelopmental disorders (NDD). As heavy-chain dynein is ubiquitously expressed, the apparent selectivity of heavy chain dyneinopathy for motor neuronal phenotypes remains currently unaccounted for. Here, we aimed to evaluate the full DYNC1H1-related clinical, molecular and imaging spectrum, including multisystem features and novel phenotypes presenting throughout life. We identified 47 cases from 43 families with pathogenic heterozygous variants in DYNC1H1 (aged 0–59 years) and collected phenotypic data via a comprehensive standardized survey and clinical follow-up appointments. Most patients presented with divergent and previously unrecognized neurological and multisystem features, leading to significant delays in genetic testing and establishing the correct diagnosis. Neurological phenotypes include novel autonomic features, previously rarely described behavioral disorders, movement disorders and periventricular lesions. Sensory neuropathy was identified in nine patients (median age of onset 10.6 years), of which five were only diagnosed after the second decade of life, and three had a progressive age-dependent sensory neuropathy. Novel multisystem features included primary immunodeficiency, bilateral sensorineural hearing loss, organ anomalies and skeletal manifestations, resembling the phenotypic spectrum of other dyneinopathies. We also identified an age-dependent biphasic disease course with developmental regression in the first decade and, following a period of stability, neurodegenerative progression after the second decade of life. Of note, we observed several cases in whom neurodegeneration appeared to be prompted by intercurrent systemic infections with double-stranded DNA viruses (Herpesviridae) or single-stranded RNA viruses (Ross River fever, SARS-CoV-2). Moreover, the disease course appeared to be exacerbated by viral infections regardless of age and/or severity of neurodevelopmental disorder manifestations, indicating a role of dynein in anti-viral immunity and neuronal health. In summary, our findings expand the clinical, imaging and molecular spectrum of pathogenic DYNC1H1 variants beyond motor neuropathy disorders and suggest a life-long continuum and age-related progression due to deficient intracellular trafficking. This study will facilitate early diagnosis and improve counselling and health surveillance of affected patients.

Accelerated by the adoption of remote monitoring during the COVID-19 pandemic, interest in using digitally captured behavioral data to predict patient outcomes has grown; however, it is unclear how feasible digital phenotyping studies may be in patients with recent ischemic stroke or transient ischemic attack. In this perspective, we present participant feedback and relevant smartphone data metrics suggesting that digital phenotyping of post-stroke depression is feasible. Additionally, we proffer thoughtful considerations for designing feasible real-world study protocols tracking cerebrovascular dysfunction with smartphone sensors.

Abstract Deep learning may detect biologically important signals embedded in tumor morphologic features that confer distinct prognoses. Tumor morphologic features were quantified to enhance patient risk stratification within DNA mismatch repair (MMR) groups using deep learning. Using a quantitative segmentation algorithm (QuantCRC) that identifies 15 distinct morphologic features, we analyzed 402 resected stage III colon carcinomas [191 deficient (d)-MMR; 189 proficient (p)-MMR] from participants in a phase III trial of FOLFOX-based adjuvant chemotherapy. Results were validated in an independent cohort (176 d-MMR; 1,094 p-MMR). Association of morphologic features with clinicopathologic variables, MMR, KRAS, BRAFV600E, and time-to-recurrence (TTR) was determined. Multivariable Cox proportional hazards models were developed to predict TTR. Tumor morphologic features differed significantly by MMR status. Cancers with p-MMR had more immature desmoplastic stroma. Tumors with d-MMR had increased inflammatory stroma, epithelial tumor-infiltrating lymphocytes (TIL), high-grade histology, mucin, and signet ring cells. Stromal subtype did not differ by BRAFV600E or KRAS status. In p-MMR tumors, multivariable analysis identified tumor-stroma ratio (TSR) as the strongest feature associated with TTR [HRadj 2.02; 95% confidence interval (CI), 1.14–3.57; P = 0.018; 3-year recurrence: 40.2% vs. 20.4%; Q1 vs. Q2–4]. Among d-MMR tumors, extent of inflammatory stroma (continuous HRadj 0.98; 95% CI, 0.96–0.99; P = 0.028; 3-year recurrence: 13.3% vs. 33.4%, Q4 vs. Q1) and N stage were the most robust prognostically. Association of TSR with TTR was independently validated. In conclusion, QuantCRC can quantify morphologic differences within MMR groups in routine tumor sections to determine their relative contributions to patient prognosis, and may elucidate relevant pathophysiologic mechanisms driving prognosis. Significance: A deep learning algorithm can quantify tumor morphologic features that may reflect underlying mechanisms driving prognosis within MMR groups. TSR was the most robust morphologic feature associated with TTR in p-MMR colon cancers. Extent of inflammatory stroma and N stage were the strongest prognostic features in d-MMR tumors. TIL density was not independently prognostic in either MMR group.

Background: Regadenoson is a selective A2A receptor agonist that is administered as a fixed dose bolus with rapid onset (∼30 sec), and short duration (∼2-4 min) of action. The aim of this study was to evaluate the feasibility of real-time myocardial contrast echocardiography (MCE) for quantification of coronary flow reserve (CFR) in healthy humans during regadenoson vasodilator stress. Methods: Fifteen subjects [3 males, age: 45.8±5.2 years, BMI 23.8±2.3 kg/m2] with normal LV wall motion and no CAD underwent real-time MCE. Three standard apical LV views were acquired during a continuous intravenous (IV) infusion of Definity (1.3 ml in 60 cc 0.9% saline at 200 ml/hr) at rest and during vasodilator stress (regadenoson 400 ug IV bolus). Following transient microbubble destruction, microbubble velocity (β, sec -1 ), myocardial blood volume (A, dB) and myocardial blood flow (MBF, dB/sec) were determined. Videodensitometric quantification using a 16-segment model at end-systole was performed off-line. Results: Quantification of reserve parameters was feasible in all (15/15) patients, 188/240 segments (78%)[82%, 80% and 70% of 4-, 2-, 3-chambers apical views] and 43/45 (95%) coronary territories [one each of Cx and RCA territories were not feasible]. Augmentation from baseline to vasodilator stress was observed for both ß (s -1 ) [0.65± 0.25 vs 0.79± 0.21, P=0.031] and MBF (dB/sec) [2.85± 1.43 vs 4.65± 2.06, P= 0.002], however A (dB) did not change [06.62± 2.57 vs 6.79±2.1, P=0.463]. CFR measurements from all views were similar (Fig 1). There was no significant difference between CFR measured in the 3 coronary territories, although some heterogeneity was noted in the posterior circulation CFR (Fig 2). Conclusion: Real-time MCE during Regadenoson vasodilator stress is feasible, and is a useful technique for the rapid noninvasive quantification of CFR in healthy humans.

Abstract Purpose: There is a need to improve current risk stratification of stage II colorectal cancer to better inform risk of recurrence and guide adjuvant chemotherapy. We sought to examine whether integration of QuantCRC, a digital pathology biomarker utilizing hematoxylin and eosin–stained slides, provides improved risk stratification over current American Society of Clinical Oncology (ASCO) guidelines. Experimental Design: ASCO and QuantCRC-integrated schemes were applied to a cohort of 398 mismatch-repair proficient (MMRP) stage II colorectal cancers from three large academic medical centers. The ASCO stage II scheme was taken from recent guidelines. The QuantCRC-integrated scheme utilized pT3 versus pT4 and a QuantCRC-derived risk classification. Evaluation of recurrence-free survival (RFS) according to these risk schemes was compared using the log-rank test and HR. Results: Integration of QuantCRC provides improved risk stratification compared with the ASCO scheme for stage II MMRP colorectal cancers. The QuantCRC-integrated scheme placed more stage II tumors in the low-risk group compared with the ASCO scheme (62.5% vs. 42.2%) without compromising excellent 3-year RFS. The QuantCRC-integrated scheme provided larger HR for both intermediate-risk (2.27; 95% CI, 1.32–3.91; P = 0.003) and high-risk (3.27; 95% CI, 1.42–7.55; P = 0.006) groups compared with ASCO intermediate-risk (1.58; 95% CI, 0.87–2.87; P = 0.1) and high-risk (2.24; 95% CI, 1.09–4.62; P = 0.03) groups. The QuantCRC-integrated risk groups remained prognostic in the subgroup of patients that did not receive any adjuvant chemotherapy. Conclusions: Incorporation of QuantCRC into risk stratification provides a powerful predictor of RFS that has potential to guide subsequent treatment and surveillance for stage II MMRP colorectal cancers.

200 Background: We developed and validated a quantitative segmentation algorithm (QuantCRC) to predict disease recurrence in stage I-III colorectal cancer patients (pts) based on the analysis of digitized hematoxylin and eosin (H+E) stained pathology slides. In this study, we aim to determine if QuantCRC analysis on pre-treated RC biopsies can predict pathological complete response (pCR) after NAT which include total neoadjuvant therapy (TNT), chemoradiation (CRT) and others. Methods: Stage II-IV RC pts treated with curative intent modalities between 2005-2023 at Mayo Clinic and University of Pittsburgh were evaluated. Eligible pts had pre and post-treatment tissue available for analysis. Clinical characteristics, treatment, and outcome data were extracted from the medical record. QuantCRC extracted 15 features: %tumor, %stroma, tumor:stroma ratio, %TB/PDC, %mucin, %necrosis, %high-grade, %signet ring cells, tumor-infiltrating lymphocytes (TILs) per cm2 of a tumor, %immature stroma (tumor bed), %inflammatory stroma (tumor bed), %mature stroma (tumor bed), %immature (stromal region), %inflammatory (stromal region), and %mature (stromal region). The associations between the QuantCRC features and pCR were evaluated using Wilcoxon rank-sum tests and logistic regression models while adjusting for primary tumor location and NAT. Results: The identified 288 pts had the following demographics: median age 60, 59% male, 52% distal tumor, 42% TNT. Pts who achieved pCR had smaller pre-treatment tumors (p=0.03), higher TILS, and lower %immature (stroma) (Table 1). After multivariable adjustment, higher TILS and lower %immature (stroma) remain to be significantly associated with a higher likelihood of pCR. A higher %mature (stroma) and %mature stroma (tumor bed) were also identified, in multivariable models, to be associated with a higher rate of pCR. NAT protocol and tumor location (proximal vs. distal) were not associated with pCR. Conclusions: The ability to integrate pathological features with AI may allow for personalized treatment. Using this technology, we recognize that patients who harbor a more mature stromal archetype and inflammatory tumor predisposition, are more likely to achieve pCR to conventional NAT. These pathological features are not currently incorporated into risk stratification in RC. Additional validation and integration into current risk stratification methods is warranted. [Table: see text]

162 Background: There is a need to improve current risk stratification of stage II and III colorectal cancer (CRC) to better inform risk of recurrence and guide adjuvant chemotherapy. The purpose of this study is to examine whether integration of QuantCRC, an AI-based digital pathology biomarker utilizing hematoxylin and eosin-stained slides, provides improved risk stratification over current American Society of Clinical Oncology (ASCO) guidelines. Methods: ASCO and QuantCRC-integrated risk schemes were applied to an observational cohort of 1,068 stage II and III CRCs. The stage II integrated scheme utilizes pT3 vs. pT4 and QuantCRC-derived risk groups. The stage III integrated scheme utilizes pT1-3 vs. pT4, pN1 vs. pN2, and QuantCRC-derived risk groups. Performance metrics included log-rank test, hazard ratios and Somers’ Dxy rank correlation. Results: Integration of QuantCRC provides improved risk stratification compared to the ASCO scheme for stage II and III CRC. The QuantCRC-integrated scheme placed more stage II tumors in the low-risk group compared to the ASCO scheme (69.3% vs. 60.4%) without decreased 3-year RFS. The QuantCRC-integrated scheme provided larger hazard ratios (HR) for both intermediate-risk (3.04, 95%CI 1.81-5.10, P=2.8x10-5) and high-risk (4.62, 95%CI 2.02-10.61, P=0.0003) groups compared to ASCO intermediate-risk (2.09, 95%CI 1.21-3.63, P=0.008) and high-risk (3.08, 95%CI 1.57-6.01, P=0.001) groups. The QuantCRC-integrated scheme for stage III tumors identified a small group of 80/518 (15.4%) CRCs at very high risk of recurrence with HR of 4.08 (95%CI 2.68-6.23, P=6.5x10-11) compared to a HR of 2.42 (95%CI 1.72-3.38, P=3.1x10-7) for 228/518 (44.0%) high-risk CRCs in the ASCO scheme. QuantCRC-integrated risk groups remained prognostic in stage III CRCs when stratified by presence or absence of any adjuvant chemotherapy. No difference in RFS were seen in QuantCRC-integrated low-risk and intermediate-risk stage III CRCs stratified by 3 vs. 6-months of oxaliplatin-based adjuvant chemotherapy suggesting that these two groups can be treated with 3-months of adjuvant therapy. Conclusions: Incorporation of QuantCRC into risk stratification provides a powerful predictor of RFS that has potential to guide subsequent treatment and surveillance.