Publications

Observability and Consistency Analysis for Visual-Inertial Navigation with Anchored Feature Parameterizations
Mitchell Cohen
Vassili Korotkine
James Richard Forbes
This paper presents an analysis of the observability and consistency properties of filtering-based visual-inertial navigation systems (VINS)… (voir plus) that utilize anchored feature representations. The unobservable subspace of VINS with anchored landmark parameterizations is shown to be independent of the estimated landmark state, which leads to improved estimator consistency properties without any additional modifications. However, the unobservable subspace is still found to depend on the estimated navigation state, necessitating additional consistency-enforcing techniques. Two methods to improve the consistency of VINS with anchored feature representations are presented. Simulation results showcase that all estimators employing anchored feature paramterizations exhibit improved consistency properties compared to algorithms that estimate features resolved in a global reference frame, especially in scenarios where feature initialization may be poor. Real-world experiments on the TUM-VI dataset showcase that the use of anchored feature representations alone can yield comparable performance to consistency-improved estimators employing a global feature representation, demonstrating the benefit of using anchored feature parameterizations for VINS.
2026-06-16
arXiv (prépublication)
doi.org
arxiv.org
Scaling Self-Play for End-to-End Driving
Luke Rowe
Roger Girgis
Rodrigue De Schaetzen
Daphne Cornelisse
Alaap Grandhi
Felix Heide
Eugene Vinitsky
Christopher Pal
Liam Paull
End-to-end autonomous driving models are typically trained on offline human-demonstration datasets that provide limited state coverage and o… (voir plus)ften no closed-loop feedback, making them prone to compounding errors when deployed in closed-loop and brittle to long-tail agent interactions. To overcome these limitations, we propose an alternative strategy for training end-to-end driving models: large-scale self-play directly from pixels in simulation. While prior self-play approaches have shown promising transfer to real-world driving, they typically assume vectorized Bird's-Eye-View (BEV) observations that are incompatible with end-to-end policies operating directly on sensor observations. To this end, we introduce Gigapixel, a high-throughput batched driving simulator with perspective rendering, enabling scalable self-play directly from pixel observations. Rather than targeting compute-costly photorealistic sensor simulation, Gigapixel renders a simplified bounding-box world that preserves essential scene structure while achieving throughput at 50k agent steps per second. Since direct pixel-space self-play RL is prohibitively sample-inefficient at end-to-end model scale, we propose self-play DAgger training: we train pixel-based policies in self-play via on-policy distillation from a privileged RL teacher. To bridge the sim-to-real gap, we subsequently transfer the self-play trained policies to real-world sensor data through lightweight perception adaptation. Policies trained in Gigapixel and adapted to real-world sensor data achieve competitive performance on the HUGSIM and NAVSIM-v2 benchmarks without human trajectory supervision. Moreover, scaling self-play training yields proportional gains in policy performance, establishing self-play as a practical and scalable strategy for training end-to-end models.
2026-06-16
arXiv (prépublication)
doi.org
arxiv.org
Supp Fig1+ Legend from PGV001, a Multi-Peptide Personalized Neoantigen Vaccine Platform: Phase I Study in Patients with Solid and Hematologic Malignancies in the Adjuvant Setting
Mansi Saxena
Thomas U. Marron
Julia Kodysh
John P. Finnigan
Sayali Onkar
Anna Kaminska
Kevin Tuballes
Ruiwei Guo
Rachel Lubong Sabado
Marcia Meseck
Timothy J. O’Donnell
Robert Sebra
Samir Parekh
Matthew D. Galsky
Ana Blasquez
Gustavo Gimenez
Mesude Bicak
Cansu Cimen Bozkus
Daniela Delbeau-Zagelbaum
Denise Rodriguez … (voir 17 de plus)
Ana Acuña-Villaorduña
Krzysztof Misiukiewicz
Marshall R. Posner
Brett A. Miles
Hanna Y. Irie
Amy Tiersten
Deborah B. Doroshow
Andrea Wolf
John Mandeli
Rachel Brody
Andres Μ. Salazar
Sacha Gnjatic
Jeff Hammerbacher
Eric E. Schadt
Philip Friedlander
Alex Rubinsteyn
Nina Bhardwaj
<p>Supplementary Fig1. PGV001 feasibility and recurrence free survival a) Survival plot depicting overall survival (OS). N=12. Median … (voir plus)survival depicted by vertical dotted line b) Survival plot depicting recurrence free survival (RFS). Median RFS is depicted by vertical dotted line. N=12. c) Swimmer plot depicting time-line of clinical events for each patient since their curative intent treatment. d) Comparing linear correlation between TMB or neoantigen load vs OS at 60 months from 1st vaccine. Pearson correlation coefficient (r) calculated for fitting of the correlation with 95% confidence interval (95%CI). TMB: tumor mutation burden, NeoAg: neoantigens, OS: overall survival,</p>
2026-06-16
American Association for Cancer Research (accepté)
doi.org
Supp Fig2 + Legend from PGV001, a Multi-Peptide Personalized Neoantigen Vaccine Platform: Phase I Study in Patients with Solid and Hematologic Malignancies in the Adjuvant Setting
Mansi Saxena
Thomas U. Marron
Julia Kodysh
John P. Finnigan
Sayali Onkar
Anna Kaminska
Kevin Tuballes
Ruiwei Guo
Rachel Lubong Sabado
Marcia Meseck
Timothy J. O’Donnell
Robert Sebra
Samir Parekh
Matthew D. Galsky
Ana Blasquez
Gustavo Gimenez
Mesude Bicak
Cansu Cimen Bozkus
Daniela Delbeau-Zagelbaum
Denise Rodriguez … (voir 17 de plus)
Ana Acuña-Villaorduña
Krzysztof Misiukiewicz
Marshall R. Posner
Brett A. Miles
Hanna Y. Irie
Amy Tiersten
Deborah B. Doroshow
Andrea Wolf
John Mandeli
Rachel Brody
Andres Μ. Salazar
Sacha Gnjatic
Jeff Hammerbacher
Eric E. Schadt
Philip Friedlander
Alex Rubinsteyn
Nina Bhardwaj
<p>Supplementary Fig2. PGV001-induced T cell immunity as measured by ex vivo IFNγ ELISPOT assay PBMC samples were stimulated with neo… (voir plus)antigen peptides (pools of peptides: composed of 15mer OLPs+ 9-10mer predicted Mins corresponding to each vaccine SLP) and analyzed by IFNγ ELISPOT. All data is MOG normalized. a) Line diagrams showing longitudinal changes in IFNγ secretion upon stimulation with responder neoantigen peptides in each patient. Each line represents an individual neoantigen and each dot represents an individual time-point. For definition of “Responder” neoantigen: See Materials and Methods. b) Aligned dot plot showing IFNγ secretion at baseline (Pre) induced by all 126 neoantigens used in the study for 13 patients. Horizontal dotted line depicts cut-off threshold for immunogenicity in the assay. Peptides with immunogenicity at baseline, above the dotted line, are labeled. “Immunogenic” neoantigen: See Materials and Methods. c) Line plot showing post-vaccination immune response, as measured by IFNγ release in ex vivo ELISPOT assay, by the neoantigen peptides found to be “immunogenic” at baseline in (b). d) Plot showing number of IFNγ SFCs/million PBMCs elicited by tetanus peptide at various time points in 13 patients. e) Line plots showing immune response, as measured by IFNγ release in ex vivo ELISPOT assay, elicited by mutated neoantigen peptide pools vs their wild type (WT) counterparts at Week 28 over a range of peptide concentrations. Note: For PID:016 Week 31 sample was used. f) Kaplan Meier Curve comparing OS between subjects that responded, in ex vivo ELISPOT assay, to more or less than 40% neoantigens in their vaccines. PID-017 was lost of follow up and excluded from this analysis. g) Comparing number of SFCs/million PBMCs elicited by each neoantigen in patients Alive (N=6) or Deceased (N=4) at 60-month survival follow up. Total 96 neoantigens analyzed. h) Pie chart showing proportion of neoantigens that elicited antigen specific response in patients within Alive (N=6) vs Deceased (N=4) cohorts. SFC: spot forming cells. In f-h patients that expired with no evidence of their disease recurrence are excluded. *p value indicates two-sided Student’s T-test. *** <0.001. # p value indicates Log-rank (Matel-Cox) test ### <0.001. in patients within Alive (N=6) vs Deceased (N=4) cohorts. SFC: spot forming cells. In f-h patients that expired with no evidence of their disease recurrence are excluded. *p value indicates two-sided Student’s T-test. *** <0.001. # p value indicates Log-rank (Matel-Cox) test ### <0.001.</p>
2026-06-16
American Association for Cancer Research (accepté)
doi.org
Supp Fig3 + Legend from PGV001, a Multi-Peptide Personalized Neoantigen Vaccine Platform: Phase I Study in Patients with Solid and Hematologic Malignancies in the Adjuvant Setting
Mansi Saxena
Thomas U. Marron
Julia Kodysh
John P. Finnigan
Sayali Onkar
Anna Kaminska
Kevin Tuballes
Ruiwei Guo
Rachel Lubong Sabado
Marcia Meseck
Timothy J. O’Donnell
Robert Sebra
Samir Parekh
Matthew D. Galsky
Ana Blasquez
Gustavo Gimenez
Mesude Bicak
Cansu Cimen Bozkus
Daniela Delbeau-Zagelbaum
Denise Rodriguez … (voir 17 de plus)
Ana Acuña-Villaorduña
Krzysztof Misiukiewicz
Marshall R. Posner
Brett A. Miles
Hanna Y. Irie
Amy Tiersten
Deborah B. Doroshow
Andrea Wolf
John Mandeli
Rachel Brody
Andres Μ. Salazar
Sacha Gnjatic
Jeff Hammerbacher
Eric E. Schadt
Philip Friedlander
Alex Rubinsteyn
Nina Bhardwaj
<p>Supplementary Fig3. Neoantigen specific antibody responses induced by PGV001. Patient plasma was subjected to seromics by ELISA usi… (voir plus)ng linear SLPs in the vaccines. N=12 patients. PID-017 excluded from analysis due to high background. a) Stacked bar graph depicting number of neoantigens that induced an IgG/A or M response in each patient at Week 8 and Week 28. For seromics by ELISA a “Responder” neoantigen is defined as a peptide that induced an antibody titer of greater than 100 compared to baseline. b) Line diagrams showing changes in antibody isotypes induced by responder peptides in patients from pre-treatment (Pre), through Prime (Week 8) and Post (Week 28, Week 31 or End of treatment (EOT)). c) Line graph showing changes in titers of total IgG-subclasses induced by responder peptides in PID-006 and PID-008. d) Heat map depicting poly-ICLC specific antibody responses in each patient. e) Plot depicting linear correlation between total IgG antibody titer and IFNγ ELISPOT response at Week 8 (Prime) and Week 28/31 (Post). Pearson correlation coefficient and Spearman correlation coefficient calculated for fitting of the correlation with 95% confidence interval (95%CI). Each dot represents a neoantigen. Horizontal line depicts threshold for antibody titer response at 100. The vertical dotted line depicts threshold for ELISPOT immunogenicity at 60 SFC/million PBMCs. For Week 8, 126 neoantigens evaluated while for Week 28/31, 116 neoantigens were evaluated.</p>
2026-06-16
American Association for Cancer Research (accepté)
doi.org
Supp Fig4+ Legend from PGV001, a Multi-Peptide Personalized Neoantigen Vaccine Platform: Phase I Study in Patients with Solid and Hematologic Malignancies in the Adjuvant Setting
Mansi Saxena
Thomas U. Marron
Julia Kodysh
John P. Finnigan
Sayali Onkar
Anna Kaminska
Kevin Tuballes
Ruiwei Guo
Rachel Lubong Sabado
Marcia Meseck
Timothy J. O’Donnell
Robert Sebra
Samir Parekh
Matthew D. Galsky
Ana Blasquez
Gustavo Gimenez
Mesude Bicak
Cansu Cimen Bozkus
Daniela Delbeau-Zagelbaum
Denise Rodriguez … (voir 17 de plus)
Ana Acuña-Villaorduña
Krzysztof Misiukiewicz
Marshall R. Posner
Brett A. Miles
Hanna Y. Irie
Amy Tiersten
Deborah B. Doroshow
Andrea Wolf
John Mandeli
Rachel Brody
Andres Μ. Salazar
Sacha Gnjatic
Jeff Hammerbacher
Eric E. Schadt
Philip Friedlander
Alex Rubinsteyn
Nina Bhardwaj
<p>Supplementary Fig4. CD4+ and CD8+ T cell activation by PGV001 PBMCs from patients were expanded in vitro in presence of neoantigen … (voir plus)peptides (pools of peptides, OLPs+Mins, corresponding to each vaccine SLP) and restimulated with peptide/s followed by intracellular staining and flowcytometry. All data is normalized to MOG. a) Aligned dot plot showing CD8+ and CD4+ T cell responses at baseline induced by 96 evaluated neoantigens across 10 patients. Horizontal dotted line depicts cut-off threshold for immunogenicity. 15 and 32 peptides were found to be immunogenic at baseline against CD8+ and CD4+ T cells, respectively. b) Line plots showing longitudinal CD8+ and CD4+ T cell immune responses induced by neoantigens found to be immunogenic at baseline in (a) and that induced greater than 2-fold increase in response following PGV001 treatment. c) Each pie chart shows proportion of vaccine peptides that induced production of IFNγ, TNFα or IL-2 in CD8+ or CD4+T cells in each patient at any timepoint post vaccine initiation. Refer to Material and Methods for definition of “response” in flowcytometry assay. d) CD8+ and CD4+ T cell immune responses elicited by tetanus peptides at various time points in 10 evaluated patients.</p>
2026-06-16
American Association for Cancer Research (accepté)
doi.org
Supp Fig5+ Legend from PGV001, a Multi-Peptide Personalized Neoantigen Vaccine Platform: Phase I Study in Patients with Solid and Hematologic Malignancies in the Adjuvant Setting
Mansi Saxena
Thomas U. Marron
Julia Kodysh
John P. Finnigan
Sayali Onkar
Anna Kaminska
Kevin Tuballes
Ruiwei Guo
Rachel Lubong Sabado
Marcia Meseck
Timothy J. O’Donnell
Robert Sebra
Samir Parekh
Matthew D. Galsky
Ana Blasquez
Gustavo Gimenez
Mesude Bicak
Cansu Cimen Bozkus
Daniela Delbeau-Zagelbaum
Denise Rodriguez … (voir 17 de plus)
Ana Acuña-Villaorduña
Krzysztof Misiukiewicz
Marshall R. Posner
Brett A. Miles
Hanna Y. Irie
Amy Tiersten
Deborah B. Doroshow
Andrea Wolf
John Mandeli
Rachel Brody
Andres Μ. Salazar
Sacha Gnjatic
Jeff Hammerbacher
Eric E. Schadt
Philip Friedlander
Alex Rubinsteyn
Nina Bhardwaj
<p>Supplementary Fig5. TCR sequencing on neoantigen reactive T cells in patient PBMCs a) (Top) Depiction of methodology utilized to ge… (voir plus)nerate ex vivo and in vitro expanded and stimulated (IVS) T cell samples from screen, week 8 (W8) and week 28 (W28) timepoints for TCR sequencing. (Bottom) Gating strategy and representative dot plot for isolation by FAC sorting of IFNγ positive, neoantigen reactive T cells for TCR sequencing. b) (Top) Representative differential abundance plots from PID:012 comparing TCR clones between two conditions listed on X and Y axes. (Bottom) Logic and calculation of “relevant” clones in vivo. First using the IVS T cells, lists of TCR sequences differentially abundant in peptide stimulated (Pepstim) Week 8 (W8) and Week 28 (W28) versus screening were generated, named W8IVS>scr and W28IVS>scr, respectively. Similarly, lists of TCR sequences differentially abundant in peptide expanded W8 and W28 samples versus DMSO expanded cells was obtained, named W8ctrl>IVS and W28ctrl>IVS, respectively. Sequences in W8ctrl>IVS and W28ctrl>IVS were removed from W8IVS>scr and W28IVS>scr to obtain vaccine induced “relevant” TCR sequences in IVS T cells named, W8ΔIVS and W28ΔIVS. Ex vivo T cells were analyzed to identify lists of differentially abundant TCRs in W8 and W28 versus screening, named W8ex>scr and W28ex>scr. Finally, an intersection between relevant clones in IVS (W8ΔIVS and W28ΔIVS) and abundant clones in W8 or W28 ex vivo T cells (W8ex>scr and W28ex>scr) was performed to identify relevant neoantigen reactive TCR sequences in vivo, named W8clones and W28clones. c) Stacked bar plot depicting number and frequency of new (detected only post vaccination) and expanded (existed at baseline but frequency expanded post vaccination) W8clones and W28clones TCR clones in PID:015. Each color represents distinct TCR clones and the height of each stack represents each clone’s frequency. d) For PID:015, Venn diagram showing overlap between ex vivo and IVS clones to obtain W8clones and W28clones and their overlap to identify persisting clones in vivo. For PID:015, no persisting clones were found.</p>
2026-06-16
American Association for Cancer Research (accepté)
doi.org
Supp Fig6+ Legend from PGV001, a Multi-Peptide Personalized Neoantigen Vaccine Platform: Phase I Study in Patients with Solid and Hematologic Malignancies in the Adjuvant Setting
Mansi Saxena
Thomas U. Marron
Julia Kodysh
John P. Finnigan
Sayali Onkar
Anna Kaminska
Kevin Tuballes
Ruiwei Guo
Rachel Lubong Sabado
Marcia Meseck
Timothy J. O’Donnell
Robert Sebra
Samir Parekh
Matthew D. Galsky
Ana Blasquez
Gustavo Gimenez
Mesude Bicak
Cansu Cimen Bozkus
Daniela Delbeau-Zagelbaum
Denise Rodriguez … (voir 17 de plus)
Ana Acuña-Villaorduña
Krzysztof Misiukiewicz
Marshall R. Posner
Brett A. Miles
Hanna Y. Irie
Amy Tiersten
Deborah B. Doroshow
Andrea Wolf
John Mandeli
Rachel Brody
Andres Μ. Salazar
Sacha Gnjatic
Jeff Hammerbacher
Eric E. Schadt
Philip Friedlander
Alex Rubinsteyn
Nina Bhardwaj
<p>Supplementary Fig6. Gating strategy for phenotyping of immune cells by flow cytomtery. EM: Effector memory, TEMRA: Effector memory … (voir plus)Re-expression RA, CM: Central Memory</p>
2026-06-16
American Association for Cancer Research (accepté)
doi.org
Supp Fig7+ Legend from PGV001, a Multi-Peptide Personalized Neoantigen Vaccine Platform: Phase I Study in Patients with Solid and Hematologic Malignancies in the Adjuvant Setting
Mansi Saxena
Thomas U. Marron
Julia Kodysh
John P. Finnigan
Sayali Onkar
Anna Kaminska
Kevin Tuballes
Ruiwei Guo
Rachel Lubong Sabado
Marcia Meseck
Timothy J. O’Donnell
Robert Sebra
Samir Parekh
Matthew D. Galsky
Ana Blasquez
Gustavo Gimenez
Mesude Bicak
Cansu Cimen Bozkus
Daniela Delbeau-Zagelbaum
Denise Rodriguez … (voir 17 de plus)
Ana Acuña-Villaorduña
Krzysztof Misiukiewicz
Marshall R. Posner
Brett A. Miles
Hanna Y. Irie
Amy Tiersten
Deborah B. Doroshow
Andrea Wolf
John Mandeli
Rachel Brody
Andres Μ. Salazar
Sacha Gnjatic
Jeff Hammerbacher
Eric E. Schadt
Philip Friedlander
Alex Rubinsteyn
Nina Bhardwaj
<p>Supplementary Fig7. Characterizing peripheral cellular immune environment in patients receiving PGV001. Multicolor flowcytometry pe… (voir plus)rformed to phenotype circulating immune cells in patients and healthy donors (HD). PID-017 was excluded from analysis. a) Tabulated view of age and sex of the seven healthy donors whose PBMCs are used in the study. b) Frequency of lymphoid immune cell subsets over the course of treatment. Each dot represents a subject. # p value indicates un-paired two-sided Student’s T-test comparing HD with patient cohort # <0.05, ## <0.01. c) Frequency of myeloid immune cell subsets over the course of treatment. Each dot represents a subject. d) Depicting Fold change from “Pre” in listed immune cell subsets over the course of treatment. e) Pie charts depicting CD4+ and CD8+ T cell states in patient blood and healthy donors. f) �4+ T cells expressing TIGIT and CTLA4 shown as a fold change from baseline in patient blood. *p value indicates paired two-sided Student’s T-test comparing post treatment samples with “pre”. * <0.05. Data in pie charts depicts median. Patient samples, N=12. Healthy donor samples, N=7. Data in graphs shown as mean with bar graphs showing +/- SEM.</p>
2026-06-16
American Association for Cancer Research (accepté)
doi.org
Supplementary Table1. from PGV001, a Multi-Peptide Personalized Neoantigen Vaccine Platform: Phase I Study in Patients with Solid and Hematologic Malignancies in the Adjuvant Setting
Mansi Saxena
Thomas U. Marron
Julia Kodysh
John P. Finnigan
Sayali Onkar
Anna Kaminska
Kevin Tuballes
Ruiwei Guo
Rachel Lubong Sabado
Marcia Meseck
Timothy J. O’Donnell
Robert Sebra
Samir Parekh
Matthew D. Galsky
Ana Blasquez
Gustavo Gimenez
Mesude Bicak
Cansu Cimen Bozkus
Daniela Delbeau-Zagelbaum
Denise Rodriguez … (voir 17 de plus)
Ana Acuña-Villaorduña
Krzysztof Misiukiewicz
Marshall R. Posner
Brett A. Miles
Hanna Y. Irie
Amy Tiersten
Deborah B. Doroshow
Andrea Wolf
John Mandeli
Rachel Brody
Andres Μ. Salazar
Sacha Gnjatic
Jeff Hammerbacher
Eric E. Schadt
Philip Friedlander
Alex Rubinsteyn
Nina Bhardwaj
<p>Supplementary Table1. Staging at time of enrollment, adjuvant treatment following curative intent treatment until the end of vaccin… (voir plus)ation and vaccination timing</p>
2026-06-16
American Association for Cancer Research (accepté)
doi.org
Supplementary Table2 from PGV001, a Multi-Peptide Personalized Neoantigen Vaccine Platform: Phase I Study in Patients with Solid and Hematologic Malignancies in the Adjuvant Setting
Mansi Saxena
Thomas U. Marron
Julia Kodysh
John P. Finnigan
Sayali Onkar
Anna Kaminska
Kevin Tuballes
Ruiwei Guo
Rachel Lubong Sabado
Marcia Meseck
Timothy J. O’Donnell
Robert Sebra
Samir Parekh
Matthew D. Galsky
Ana Blasquez
Gustavo Gimenez
Mesude Bicak
Cansu Cimen Bozkus
Daniela Delbeau-Zagelbaum
Denise Rodriguez … (voir 17 de plus)
Ana Acuña-Villaorduña
Krzysztof Misiukiewicz
Marshall R. Posner
Brett A. Miles
Hanna Y. Irie
Amy Tiersten
Deborah B. Doroshow
Andrea Wolf
John Mandeli
Rachel Brody
Andres Μ. Salazar
Sacha Gnjatic
Jeff Hammerbacher
Eric E. Schadt
Philip Friedlander
Alex Rubinsteyn
Nina Bhardwaj
<p>Supplementary Table2 – Variant count per patient</p>
2026-06-16
American Association for Cancer Research (accepté)
doi.org
Supplementary Table3 from PGV001, a Multi-Peptide Personalized Neoantigen Vaccine Platform: Phase I Study in Patients with Solid and Hematologic Malignancies in the Adjuvant Setting
Mansi Saxena
Thomas U. Marron
Julia Kodysh
John P. Finnigan
Sayali Onkar
Anna Kaminska
Kevin Tuballes
Ruiwei Guo
Rachel Lubong Sabado
Marcia Meseck
Timothy J. O’Donnell
Robert Sebra
Samir Parekh
Matthew D. Galsky
Ana Blasquez
Gustavo Gimenez
Mesude Bicak
Cansu Cimen Bozkus
Daniela Delbeau-Zagelbaum
Denise Rodriguez … (voir 17 de plus)
Ana Acuña-Villaorduña
Krzysztof Misiukiewicz
Marshall R. Posner
Brett A. Miles
Hanna Y. Irie
Amy Tiersten
Deborah B. Doroshow
Andrea Wolf
John Mandeli
Rachel Brody
Andres Μ. Salazar
Sacha Gnjatic
Jeff Hammerbacher
Eric E. Schadt
Philip Friedlander
Alex Rubinsteyn
Nina Bhardwaj
<p>Supplementary Table3: List of mutated peptide IDs and peptide sequence in each patients’ vaccine</p>
2026-06-16
American Association for Cancer Research (accepté)
doi.org