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February 1, 2023
This twice-monthly newsletter highlights the research endeavors at Dana-Farber Cancer Institute, noting recently published papers available from PubMed where Dana-Farber faculty are listed as first or senior authors. If you are a Dana-Farber faculty member and you think your paper is missing from Research News, please let us know at: Michael_buller@dfci.harvard.edu. Note that there is a two-week lag in publishing so this issue reflects papers with a PubMed publication date between January 1 through January 15. For more about Dana-Farber science, tune in to the Unraveled podcast. The second season now available at dana-farber.org/unraveled, or wherever you get your podcasts.
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| Blood
Samur MK, Aktas Samur A, Fulciniti M, Shammas MA, Sperling AS, Richardson PG, Parmigiani G, Anderson KC, Munshi NC
High Dose Melphalan (HDM) improves progression free survival (PFS) in multiple myeloma (MM), yet melphalan is a DNA damaging alkylating agent, so we assessed its mutational effect on surviving myeloma cells by analyzing paired MM samples collected at diagnosis and relapse from the IFM 2009 study. We performed deep whole genome sequencing on 68 patients, 43 treated with RVD (lenalidomide, bortezomib, and dexamethasone combination) and 25 with RVD+HDM. Although the number of mutations was similar at diagnosis in both groups (7137 vs 7230; p=0.67), the HDM group had significantly more mutations at relapse (9242 vs. 13383, p=0.005). No changes in the frequency of copy number alterations or structural variants was observed. The newly acquired mutations were typically associated with DNA damage and double stranded breaks and were predominantly on the transcribed strand. A machine learning model using this unique pattern predicted patients who'd received HDM with high sensitivity, specificity, and positive prediction value. Clonal evolution analysis showed that all patients treated with HDM had clonal selection while a static progression was observed with RVD. A significantly higher percentage of mutations were subclonal in the HDM cohort. Intriguingly, HDM patients achieving CR had significantly more mutations at relapse yet had similar survival rates as RVD CR patients. This similarity could be due to HDM relapse samples having significantly more neoantigens. Overall, our study identifies increased genomic changes associated with HDM and provides rationale to further understand clonal complexity.
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| Blood
Du T, Song Y, Ray A, Yao Y, Samur MK, Shen C, Penailillo J, Sewastianik T, Tai YT, Fulciniti M,
Munshi NC, Wu H, Carrasco RD, Chauhan D, Anderson KC, Wan X
PSMD4/Rpn10 is a subunit of the 19S proteasome unit that is involved with feeding target proteins into the catalytic machinery of the 26S proteasome. Since proteasome inhibition is a common therapeutic strategy in multiple myeloma (MM), we investigated Rpn10 and found that it is highly expressed in MM cells versus normal plasma cells. Rpn10 levels inversely correlated with overall survival in MM patients. Inducible knockout or knockdown of Rpn10 decreased MM cell viability both in vitro and in vivo by triggering the accumulation of polyubiquitinated proteins, cell cycle arrest, and apoptosis associated with activation of caspases and unfolded protein response-related pathways. Proteomic analysis revealed that inhibiting Rpn10 increased autophagy, antigen presentation and the activation of CD4+ T and NK cells. We developed an in vitro AlphaScreen binding assay for high-throughput screening and identified a novel Rpn10 inhibitor, SB699551 (SB). Treating MM cell lines, leukemic cell lines, and primary MM patient cells with SB decreased cell viability without affecting the viability of normal PBMCs. SB inhibited the proliferation of MM cells even in the presence of the tumor-promoting bone marrow milieu and overcame proteasome inhibitor (PI)-resistance without blocking the 20S proteasome catalytic function or the 19S deubiquitinating activity. Rpn10 blockade by SB triggered MM cell death via similar pathways as the genetic strategy. In MM xenograft models, SB was well-tolerated, inhibited tumor growth, and prolonged survival. Our data suggests that inhibiting Rpn10 will enhance cytotoxicity and overcome PI-resistance in MM, providing the basis for further optimization studies of Rpn10 inhibitors for clinical application.
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| Cancer Discovery
Sequist LV, Jänne PA
MET-inhibitor and EGFR tyrosine kinase inhibitor (EGFR-TKI) combination therapy could overcome acquired MET-mediated osimertinib resistance. We present the final phase Ib TATTON (NCT02143466) analysis (Part B, n = 138/Part D, n = 42) assessing oral savolitinib 600 mg/300 mg once daily (q.d.) + osimertinib 80 mg q.d. in patients with MET-amplified, EGFR-mutated (EGFRm) advanced non-small cell lung cancer (NSCLC) and progression on prior EGFR-TKI. An acceptable safety profile was observed. In Parts B and D, respectively, objective response rates were 33% to 67% and 62%, and median progression-free survival (PFS) was 5.5 to 11.1 months and 9.0 months. Increased antitumor activity may occur with MET copy number 10. EGFRm circulating tumor DNA clearance on treatment predicted longer PFS in patients with detectable baseline ctDNA, while acquired resistance mechanisms to osimertinib + savolitinib were mediated by MET, EGFR, or KRAS alterations.
SIGNIFICANCE: The savolitinib + osimertinib combination represents a promising therapy in patients with MET-amplified/overexpressed, EGFRm advanced NSCLC with disease progression on a prior EGFR-TKI. Acquired resistance mechanisms to this combination include those via MET, EGFR, and KRAS. On-treatment ctDNA dynamics can predict clinical outcomes and may provide an opportunity to inform earlier decision-making. This article is highlighted in the In This Issue feature, p. 1.
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| Gastroenterology
Rodriguez NJ, Syngal S
Genetic education, risk assessment, and testing can save lives by facilitating the identification of pathogenic germline variants (PGVs) in cancer-susceptibility genes. PGVs are identified in approximately 13% of cancer diagnoses across all tumor types in the United States. Although PGVs in genes associated with cancer predisposition occur in individuals of all racial/ethnic backgrounds, 67% of cancer-related genome-wide association studies focus on European populations.
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| Journal of Clinical Oncology
Enzinger AC, Keating NL, Cutler DM, Clark CR, Florez N, Landrum MB, Wright AA
PURPOSE: To characterize racial and ethnic disparities and trends in opioid access and urine drug screening (UDS) among patients dying of cancer, and to explore potential mechanisms.
METHODS: Among 318,549 non-Hispanic White (White), Black, and Hispanic Medicare decedents older than 65 years with poor-prognosis cancers, we examined 2007-2019 trends in opioid prescription fills and potency (morphine milligram equivalents [MMEs] per day [MMEDs]) near the end of life (EOL), defined as 30 days before death or hospice enrollment. We estimated the effects of race and ethnicity on opioid access, controlling for demographic and clinical factors. Models were further adjusted for socioeconomic factors including dual-eligibility status, community-level deprivation, and rurality. We similarly explored disparities in UDS.
RESULTS: Between 2007 and 2019, White, Black, and Hispanic decedents experienced steady declines in EOL opioid access and rapid expansion of UDS. Compared with White patients, Black and Hispanic patients were less likely to receive any opioid (Black, -4.3 percentage points, 95% CI, -4.8 to -3.6; Hispanic, -3.6 percentage points, 95% CI, -4.4 to -2.9) and long-acting opioids (Black, -3.1 percentage points, 95% CI, -3.6 to -2.8; Hispanic, -2.2 percentage points, 95% CI, -2.7 to -1.7). They also received lower daily doses (Black, -10.5 MMED, 95% CI, -12.8 to -8.2; Hispanic, -9.1 MMED, 95% CI, -12.1 to -6.1) and lower total doses (Black, -210 MMEs, 95% CI, -293 to -207; Hispanic, -179 MMEs, 95% CI, -217 to -142); Black patients were also more likely to undergo UDS (0.5 percentage points; 95% CI, 0.3 to 0.8). Disparities in EOL opioid access and UDS disproportionately affected Black men. Adjustment for socioeconomic factors did not attenuate the EOL opioid access disparities.
CONCLUSION: There are substantial and persistent racial and ethnic inequities in opioid access among older patients dying of cancer, which are not mediated by socioeconomic variables.
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| Journal of Clinical Oncology
Bardia A, Tolaney SM
We thank Li et al for their interest in our manuscript and their detailed comments regarding informative censoring. Although imbalances in follow-up time between the sacituzumab govitecan (SG) and chemotherapy groups exist in progression-free survival (PFS) per blinded independent central review (BICR) as shown in the author's comments by using the reverse Kaplan-Meier (KM) and the restricted mean survival time difference methods, these methods are commonly used for estimating the follow-up time and are not generally used to determine whether censoring in a study is informative or noninformative. It is important to note that censoring imbalance between treatment groups does not necessarily indicate that censoring is related to the risk of an event (informative censoring) in the same way that balanced censoring is not a validation of noninformative censoring.
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| Journal of Clinical Oncology
Ligibel JA
We appreciate the interest shown by Campbell et al in the ASCO guideline “Exercise, Diet, and Weight Management During Cancer Treatment” and agree that exercise has important benefits for patients undergoing cancer treatment. We see the new ASCO guideline and the American College of Sports Medicine (ACSM) “Exercise Guidelines for Cancer Survivors” as complementary efforts, providing important guidance for the oncology clinician and exercise professional communities, respectively.
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| JAMA Oncology
Manz CR
IMPORTANCE: Serious illness conversations (SICs) between oncology clinicians and patients are associated with improved quality of life and may reduce aggressive end-of-life care. However, most patients with cancer die without a documented SIC.
OBJECTIVE: To test the impact of behavioral nudges to clinicians to prompt SICs on the SIC rate and end-of-life outcomes among patients at high risk of death within 180 days (high-risk patients) as identified by a machine learning algorithm.
DESIGN, SETTING, AND PARTICIPANTS: This prespecified 40-week analysis of a stepped-wedge randomized clinical trial conducted between June 17, 2019, and April 20, 2020 (including 16 weeks of intervention rollout and 24 weeks of follow-up), included 20‚506 patients with cancer representing 41,021 encounters at 9 tertiary or community-based medical oncology clinics in a large academic health system. The current analyses were conducted from June 1, 2021, to May 31, 2022.
INTERVENTION: High-risk patients were identified using a validated electronic health record machine learning algorithm to predict 6-month mortality. The intervention consisted of (1) weekly emails to clinicians comparing their SIC rates for all patients against peers' rates, (2) weekly lists of high-risk patients, and (3) opt-out text messages to prompt SICs before encounters with high-risk patients.
MAIN OUTCOMES AND MEASURES: The primary outcome was SIC rates for all and high-risk patient encounters; secondary end-of-life outcomes among decedents included inpatient death, hospice enrollment and length of stay, and intensive care unit admission and systemic therapy close to death. Intention-to-treat analyses were adjusted for clinic and wedge fixed effects and clustered at the oncologist level.
RESULTS: The study included 20 506 patients (mean [SD] age, 60.0 [14.0] years) and 41 021 patient encounters: 22 259 (54%) encounters with female patients, 28 907 (70.5%) with non-Hispanic White patients, and 5520 (13.5%) with high-risk patients; 1417 patients (6.9%) died by the end of follow-up. There were no meaningful differences in demographic characteristics in the control and intervention periods. Among high-risk patient encounters, the unadjusted SIC rates were 3.4% (59 of 1754 encounters) in the control period and 13.5% (510 of 3765 encounters) in the intervention period. In adjusted analyses, the intervention was associated with increased SICs for all patients (adjusted odds ratio, 2.09 [95% CI, 1.53-2.87]; P < .001) and decreased end-of-life systemic therapy (7.5% [72 of 957 patients] vs 10.4% [24 of 231 patients]; adjusted odds ratio, 0.25 [95% CI, 0.11-0.57]; P = .001) relative to controls, but there was no effect on hospice enrollment or length of stay, inpatient death, or end-of-life ICU use.
CONCLUSIONS AND RELEVANCE: In this randomized clinical trial, a machine learning-based behavioral intervention and behavioral nudges to clinicans led to an increase in SICs and reduction in end-of-life systemic therapy but no changes in other end-of-life outcomes among outpatients with cancer. These results suggest that machine learning and behavioral nudges can lead to long-lasting improvements in cancer care delivery.
TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT03984773.
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| Lancet Oncology
Choueiri TK
We read the letter by Tomer Meirson and colleagues regarding their analysis of the KEYNOTE-564 adjuvant pembrolizumab trial with great interest and we welcome the opportunity to address the issues raised.
Meirson and colleagues suggested that the disease-free survival benefit with pembrolizumab over placebo might have been overestimated in our study because of informative censoring. They performed a sensitivity analysis based on reconstructed individual patient-level data from the disease-free survival Kaplan-Meier curve published in our study. In their analysis, censored events were replaced with recurrence or death events. Meirson and colleagues also estimated the timing of these events because they did not have access to the raw data. This analysis should be considered unorthodox and flawed, as it was mistakenly assumed that “excess” censoring was the result of treatment failure and counted as recurrence or death events without considering the actual reasons for censoring.
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| Nature
Li J, Wang L, Hahn Q, Nowak RP, Viennet T, Orellana EA, Roy Burman SS, Yue H, Hunkeler M, Fontana P, Wu H, Arthanari H, Fischer ES, Gregory RI
Chemical modifications of RNA have key roles in many biological processes1-3. N7-methylguanosine (m7G) is required for integrity and stability of a large subset of tRNAs4-7. The methyltransferase 1-WD repeat-containing protein 4 (METTL1-WDR4) complex is the methyltransferase that modifies G46 in the variable loop of certain tRNAs, and its dysregulation drives tumorigenesis in numerous cancer types8-14. Mutations in WDR4 cause human developmental phenotypes including microcephaly15-17. How METTL1-WDR4 modifies tRNA substrates and is regulated remains elusive18. Here we show through structural, biochemical and cellular studies of human METTL1-WDR4, that WDR4 serves as a scaffold for METTL1 and the tRNA T-arm. Upon tRNA binding, the C region of METTL1 transforms into a helix, which together with the 6 helix secures both ends of the tRNA variable loop. Unexpectedly, we find that the predicted disordered N-terminal region of METTL1 is part of the catalytic pocket and essential for methyltransferase activity. Furthermore, we reveal that S27 phosphorylation in the METTL1 N-terminal region inhibits methyltransferase activity by locally disrupting the catalytic centre. Our results provide a molecular understanding of tRNA substrate recognition and phosphorylation-mediated regulation of METTL1-WDR4, and reveal the presumed disordered N-terminal region of METTL1 as a nexus of methyltransferase activity.
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| Nature Medicine
Parry EM, Leshchiner I, Guieze R, Johnson C, Lemvigh C, Messer C, Li L, Rosebrock D, Yin S,
Deng S, Slowik K, Jacobs R, Huang T, Li S, Fell G, Redd R, Lin Z, Knisbacher BA, Livitz D, Ruthen N, Elagina L, Taylor-Weiner A, Persaud B, Martinez A, Fernandes SM, Purroy N, Anandappa AJ, Ma J, Hess J, Livak KJ, Danysh BP, Stewart C, Neuberg D, Davids MS, Brown JR, Getz G, Wu CJ
Richter syndrome (RS) arising from chronic lymphocytic leukemia (CLL) exemplifies an aggressive malignancy that develops from an indolent neoplasm. To decipher the genetics underlying this transformation, we computationally deconvoluted admixtures of CLL and RS cells from 52 patients with RS, evaluating paired CLL-RS whole-exome sequencing data. We discovered RS-specific somatic driver mutations (including IRF2BP2, SRSF1, B2M, DNMT3A and CCND3), recurrent copy-number alterations beyond del(9p21)(CDKN2A/B), whole-genome duplication and chromothripsis, which were confirmed in 45 independent RS cases and in an external set of RS whole genomes. Through unsupervised clustering, clonally related RS was largely distinct from diffuse large B cell lymphoma. We distinguished pathways that were dysregulated in RS versus CLL, and detected clonal evolution of transformation at single-cell resolution, identifying intermediate cell states. Our study defines distinct molecular subtypes of RS and highlights cell-free DNA analysis as a potential tool for early diagnosis and monitoring.
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| Proceedings of the National Academy of Sciences of the U.S.A.
Schröfelbauer B, Kimes PK, Hauke P, Reid CE, Shao K, Hill SJ, Irizarry R, Hahn WC
Although antibodies targeting specific tumor-expressed antigens are the standard of care for some cancers, the identification of cancer-specific targets amenable to antibody binding has remained a bottleneck in development of new therapeutics. To overcome this challenge, we developed a high-throughput platform that allows for the unbiased, simultaneous discovery of antibodies and targets based on phenotypic binding profiles. Applying this platform to ovarian cancer, we identified a wide diversity of cancer targets including receptor tyrosine kinases, adhesion and migration proteins, proteases and proteins regulating angiogenesis in a single round of screening using genomics, flow cytometry, and mass spectrometry. In particular, we identified BCAM as a promising candidate for targeted therapy in high-grade serous ovarian cancers. More generally, this approach provides a rapid and flexible framework to identify cancer targets and antibodies.
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| Advanced Healthcare Materials
Panikkanvalappil SR, Bhagavatula SK, Deans K, Jonas O, Rashidian M, Mishra S
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| American Journal of Geriatric Psychiatry
Beaussant Y, Nigam K
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| American Journal of Hematology
Shimony S, Stahl M, Stone RM
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| American Journal of Hematology
Castillo JJ, Sarosiek S, Treon SP
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| American Journal of Hematology
Stahl M
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| American Journal of Hematology
Castillo JJ
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| American Journal of Hematology
Achebe MO, Mandell E, Freedman R, Ren S, Neuberg D
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| Annals of Surgical Oncology
Laws A, Katlin F, Hans M, Graichen M, Kantor O, Minami C, Bychkovsky BL, Pace LE, Scheib R, Garber JE, King TA
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| Annals of Surgical Oncology
Mittendorf EA, Kantor O, Weiss A, Richardson E, Garrido-Castro A, Portnow LH, Krop IE, Lin NU, Winer EP, Tolaney SM, King TA
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| Annals of Surgical Oncology
Laws A, Katlin F, Hans M, Graichen M, Kantor O, Minami C, Bychkovsky BL, Pace LE, Scheib R, Garber JE, King TA
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| Blood Advances
Fernandez RA, Mayoral JE, Pierre-Louis L, Yao Y, Xu Y, Mu S, Prabhala R, Anderson KC, Munshi NC, Fulciniti M
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| Blood Advances
Maurer K, Kim HT, Garrity HM, Liney D, Cutler CS, Antin JH, Koreth J, Ritz J, Shapiro RM, Romee R, Ho VT, Gooptu M, Soiffer RJ, Wu CJ, Nikiforow S
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| Blood Cancer Journal
Kurata K, Tye MA, Yamamoto L, Samur MK, Tai YT, Payne NC, Singh K, Mazitschek R, Hideshima T, Anderson KC
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| British Journal of Haematology
Sarosiek S, Gustine JN, Flynn CA, Leventoff C, Little M, White T, Meid K, Treon SP, Castillo JJ
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| Bulletin of the World Health Organization
Erfani P, Fadelu T
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| Cambridge Quarterly of Healthcare Ethics
Wu CC
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| Cancer Epidemiology, Biomarkers and Prevention
Kehl KL, Uno H, Gusev A, Groha S
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| Clinical Advances in Hematology and Oncology
Ellis H, Raghavan S, Wolpin BM, Cleary JM
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| Clinical Cancer Research
Peng K, Zhang F, Wang Y, Sahgal P, Li T, Zhou J, Liang X, Zhang Y, Sethi N, Zhang H
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| Clinical Cancer Research
Kabraji S, Ni J, Li T, Wang Y, Pereslete A, Hsu L, DiPiro PJ, Hughes M, Winer EP, Zhao JJ, Lin NU
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| Clinical Gastroenterology and Hepatology
Rodriguez NJ, Syngal S
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| Contemporary Clinical Trials
Cooley ME, Castaldi PJ, Mazzola E, Nayak MM, Healey MJ, Lathan CS
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| Haematologica
Gibson CJ, Koreth J
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| International Journal of Cancer
Ma C, Ng K, Giovannucci E, Mayer RJ, Meyerhardt JA
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| International Journal of Radiation Oncology, Biology, Physics
Milligan MG, Orav EJ, Lam MB
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| Journal of ImmunoTherapy of Cancer
Ding L, Wang Q, Kearns MJ, Jiang T, Lin Z, Cheng X, Qian C, Xie S, Kim HJ, Roberts TM,
Freeman GJ, Liu JF, Konstantinopoulos PA, Matulonis U, Zhao JJ
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| Journal of Investigative Dermatology
Ahmed MM, Rivas HG, Frost TC, DeCaprio JA
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| Journal of Medical Virology
Slabicki M, Zou C, Meng C, Wang J, Nowak RP, Yang PL, Sattler M, Stone RM, Griffin JD, Gray NS, Davey RA, Weisberg E
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| Journal of Pain and Symptom Management
Fenton ATHR, Fletcher KM, Kizildag D, Borstelmann NA, Kessler D, Cronin C, Revette AC, Wright AA, Frank E, Enzinger AC
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| Journal of Pain and Symptom Management
Prachanukool T, Aaronson EL, Lakin JR, Higuchi M, Lee RS, Santangelo I, Hasdianda MA, Wang W, Liu SW, Kennedy M, Schonberg MA, Block SD, Tulsky JA, Ouchi K
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| Journal of Pain and Symptom Management
Feifer D, Broden E, Wolfe J, Snaman J
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| Journal of Pediatric Hematology/Oncology
Mangada KL, Nishitani M, Albuquerque S, Duncan CN
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| Journal of Pediatric Hematology/Oncology Nursing
Stevenson K, Koch VB, Silverman LB, Bona K
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| JAAD Case Reports
Yeh JE, Wan MT, Ibrahim N, Buzurovic I, Cohen JM, Ott PA, Laga AC, Devlin PM, Lin JY
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| JAMA Network Open
Huffman BM, Horick NK, Murphy JE, Schlechter BL, Manz C, Wolpin BM, Cleary JM
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| JCI Insight
Oderberg IM, Goessling W
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| JCO Oncology Practice
Roberts TJ, Bailey AS, Tahir N, Jacobson JO
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| JCO Precision Oncology
Keller RB, Mazor T, Sholl L, Aguirre AJ, Singh H, Sethi N, Bass A, Nagaraja AK, Brais LK, Hill E, Hennessey C, Cusick M, Del Vecchio Fitz C, Zwiesler Z, Siegel E, Ovalle A, Trukhanov P, Hansel J, Shapiro GI, Abrams TA, Biller LH, Chan JA, Cleary JM, Corsello SM, Enzinger AC, Enzinger PC, Mayer RJ, McCleary NJ, Meyerhardt JA, Ng K, Patel AK, Perez KJ, Rahma OE, Rubinson DA,
Wisch JS, Yurgelun MB, Hassett MJ, MacConaill L, Schrag D, Cerami E, Wolpin BM, Nowak JA, Giannakis M
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| Methods in Molecular Biology
Sinha S, Zhang CZ
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| Nature Reviews Drug Discovery
da Costa AABA, Chowdhury D, Shapiro GI, D'Andrea AD, Konstantinopoulos PA
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| Neuro-Oncology
Gonzalez Castro LN, Liu I, Filbin M
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| Neuro-Oncology
Miller JJ, Gonzalez Castro LN, Andronesi O, Bi WL, Cahill DP, Huang RY, Ligon KL, Reardon DA,
Shi DD, Kaelin WG, Wen PY
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| Neuro-Oncology
McFaline-Figueroa JR, Wen PY
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| Pediatric Blood and Cancer
Kempf AM, Guss CE, Millington K, Pilcher S, Boyle PJ, Charlton BM, Haas-Kogan DA, Liu KX
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| Pediatric Blood and Cancer
Gotti G, Stevenson K, Kay-Green S, Blonquist TM, Mantagos JS, Silverman LB, Place AE
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| Pediatric Blood and Cancer
Gorfinkel L, Wachter F, Hansbury E, Williams DA, Cantor AB
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| Pediatric Blood and Cancer
Greenzang KA, Scavotto ML, Revette AC, Schlegel SF, Silverman LB, Mack JW
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| Pediatric Blood and Cancer
Umaretiya PJ, Fisher L, Vega B, Rodrigues G, Fasciano KM, Lakin JR, Lefebvre A, Wall CB, Bona K, Mack JW
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| Psycho-Oncology
Amonoo HL, Longley RM, Jacobo MC, Pirl WF
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| Statistics in Medicine
Uno H, Horiguchi M
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