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Abstract Background Gastrointestinal cancers represent one of the most prevalent diseases worldwide. Strikingly, the incidence of Early Onset Gastrointestinal Cancer (EOGIC) has been rising during the last decades and changes in lifestyle and environmental exposure seem to play a role. EOGIC has been defined as a different entity compared to on-average gastrointestinal cancer, with distinct clinical and molecular characteristics. Inherent to the particularities of younger age, there is an unmet need for a tailored approach for the management of these patients. The TEOGIC proposes a comprehensive study to characterize EOGIC patients in the northern of Spain. Methods Patients with histologically confirmed new diagnosis of colorectal, gastroesophageal and pancreatic adenocarcinoma will be considered for two cohorts: EOGIC (≤ 50 years old) and non-EOGIC (60–75 years old), with a ratio of 1:2. Two hundred and forty patients will be recruited in 4 Public Hospitals from northern Spain. After receiving unified informed consent, demographic and clinical data of the patients will be collected in a REDCap database. Lifestyle related data will be obtained in questionnaires assessing diet, physical activity and the general quality of life of the patients before diagnosis. Biological samples prior to any onco-specific treatment will be obtained for the analyses of circulating inflammatory proteins, gut microbiota, and the proteome of the tumor microenvironment. Histologic characteristics and routine biomarkers will be also collected. Thereafter, data will be integrated and analyzed to assess tumor specific, pan-tumor and sex-associated differential characteristics of EOGIC. Discussion The underlying risk factors and differential characteristics of EOGIC remain poorly studied, particularly in our geographical area. Although limited by the exploratory nature and the small sample size estimated to be recruited, TEOGIC represents the first attempt to comprehensively characterize these young patients, and thus attend to their special needs. Findings derived from this study could contribute to raise awareness and preventive behaviors in the population. In parallel, molecular studies could lead to the identification of potential novel non-invasive biomarkers and therapeutic targets that would help in the development of the tailored clinical management of these patients, focusing on screening programs for early diagnosis and precision medicine.

Background: Gastrointestinal cancers represent one of the most prevalent diseases worldwide. Strikingly, the incidence of Early Onset Gastrointestinal Cancer (EOGIC) has been rising during the last decades and changes in lifestyle and environmental exposure seem to play a role. EOGIC has been defined as a different entity compared to on-average gastrointestinal cancer, with distinct clinical and molecular characteristics. Inherent to the particularities of younger age, there is an unmet need for a tailored approach for the management of these patients. The TEOGIC proposes a comprehensive study to characterize EOGIC patients in the northern of Spain., Methods: Patients with histologically confirmed new diagnosis of colorectal, gastroesophageal and pancreatic adenocarcinoma will be considered for two cohorts: EOGIC (≤ 50 years old) and non-EOGIC (60-75 years old), with a ratio of 1:2. Two hundred and forty patients will be recruited in 4 Public Hospitals from northern Spain. After receiving unified informed consent, demographic and clinical data of the patients will be collected in a REDCap database. Lifestyle related data will be obtained in questionnaires assessing diet, physical activity and the general quality of life of the patients before diagnosis. Biological samples prior to any onco-specific treatment will be obtained for the analyses of circulating inflammatory proteins, gut microbiota, and the proteome of the tumor microenvironment. Histologic characteristics and routine biomarkers will be also collected. Thereafter, data will be integrated and analyzed to assess tumor specific, pan-tumor and sex-associated differential characteristics of EOGIC., Discussion: The underlying risk factors and differential characteristics of EOGIC remain poorly studied, particularly in our geographical area. Although limited by the exploratory nature and the small sample size estimated to be recruited, TEOGIC represents the first attempt to comprehensively characterize these young patients, and thus attend to their special needs. Findings derived from this study could contribute to raise awareness and preventive behaviors in the population. In parallel, molecular studies could lead to the identification of potential novel non-invasive biomarkers and therapeutic targets that would help in the development of the tailored clinical management of these patients, focusing on screening programs for early diagnosis and precision medicine., (© 2024. The Author(s).)

Oxidation of histone H3 at lysine 4 (H3K4ox) is catalyzed by lysyl oxidase homolog 2 (LOXL2). This histone modification is enriched in heterochromatin in triple-negative breast cancer (TNBC) cells and has been linked to the maintenance of compacted chromatin. However, the molecular mechanism underlying this maintenance is still unknown. Here, we show that LOXL2 interacts with RuvB-Like 1 (RUVBL1), RuvB-Like 2 (RUVBL2), Actin-like protein 6A (ACTL6A), and DNA methyltransferase 1associated protein 1 (DMAP1), a complex involved in the incorporation of the histone variant H2A.Z. Our experiments indicate that this interaction and the active form of RUVBL2 are required to maintain LOXL2-dependent chromatin compaction. Genome-wide experiments showed that H2A.Z, RUVBL2, and H3K4ox colocalize in heterochromatin regions. In the absence of LOXL2 or RUVBL2, global levels of the heterochromatin histone mark H3K9me3 were strongly reduced, and the ATAC-seq signal in the H3K9me3 regions was increased. Finally, we observed that the interplay between these series of events is required to maintain H3K4ox-enriched heterochromatin regions, which in turn is key for maintaining the oncogenic properties of the TNBC cell line tested (MDA-MB-231)., (© 2024 Federation of European Biochemical Societies.)

Programmed death-ligand 1 (PD-L1) IHC is the most commonly used biomarker for immunotherapy response. However, quantification of PD-L1 status in pathology slides is challenging. Neither manual quantification nor a computer-based mimicking of manual readouts is perfectly reproducible, and the predictive performance of both approaches regarding immunotherapy response is limited. In this study, we developed a deep learning (DL) method to predict PD-L1 status directly from raw IHC image data, without explicit intermediary steps such as cell detection or pigment quantification. We trained the weakly supervised model on PD-L1-stained slides from the non-small cell lung cancer (NSCLC)-Memorial Sloan Kettering (MSK) cohort (N = 233) and validated it on the pan-cancer-Vall d'Hebron Institute of Oncology (VHIO) cohort (N = 108). We also investigated the performance of the model to predict response to immune checkpoint inhibitors (ICI) in terms of progression-free survival. In the pan-cancer-VHIO cohort, the performance was compared with tumor proportion score (TPS) and combined positive score (CPS). The DL model showed good performance in predicting PD-L1 expression (TPS ≥ 1%) in both NSCLC-MSK and pan-cancer-VHIO cohort (AUC 0.88 ± 0.06 and 0.80 ± 0.03, respectively). The predicted PD-L1 status showed an improved association with response to ICIs [HR: 1.5 (95% confidence interval: 1-2.3), P = 0.049] compared with TPS [HR: 1.4 (0.96-2.2), P = 0.082] and CPS [HR: 1.2 (0.79-1.9), P = 0.386]. Notably, our explainability analysis showed that the model does not just look at the amount of brown pigment in the IHC slides, but also considers morphologic factors such as lymphocyte conglomerates. Overall, end-to-end weakly supervised DL shows potential for improving patient stratification for cancer immunotherapy by analyzing PD-L1 IHC, holistically integrating morphology and PD-L1 staining intensity., Significance: The weakly supervised DL model to predict PD-L1 status from raw IHC data, integrating tumor staining intensity and morphology, enables enhanced patient stratification in cancer immunotherapy compared with traditional pathologist assessment., (© 2024 The Authors; Published by the American Association for Cancer Research.)

Breast cancer occurring during pregnancy (PrBC) and postpartum (PPBC) is usually diagnosed at more advanced stages compared with other breast cancer, worsening its prognosis. PPBC is particularly aggressive, with increased metastatic risk and mortality. Thus, effective screening methods to detect early PrBC and PPBC are needed. We report for the first time that cell-free tumor DNA (ctDNA) is present in breast milk (BM) collected from patients with breast cancer. Analysis of ctDNA from BM detects tumor variants in 87% of the cases by droplet digital PCR, while variants remain undetected in 92% of matched plasma samples. Retrospective next-generation sequencing analysis in BM ctDNA recapitulates tumor variants, with an overall clinical sensitivity of 71.4% and specificity of 100%. In two cases, ctDNA was detectable in BM collected 18 and 6 months prior to standard diagnosis. Our results open up the potential use of BM as a new source for liquid biopsy for PPBC detection., Significance: For the first time, we show that BM obtained from patients with breast cancer carries ctDNA, surpassing plasma-based liquid biopsy for detection and molecular profiling of early-stage breast cancer, even prior to diagnosis by image. See related commentary by Cunningham and Turner, p. 2125. This article is featured in Selected Articles from This Issue, p. 2109., (©2023 The Authors; Published by the American Association for Cancer Research.)

Background: PD1/PD-L1 immune checkpoint inhibitors (ICI) have revolutionized cancer treatment. Although there is controversy about the accuracy of surrogate endpoints in the ICI setting to predict overall survival (OS), these endpoints are commonly used in confirmatory trials. Here we aimed to explore the validity of classical and novel surrogate endpoints in randomised controlled trials (RCT) that combine ICI plus chemotherapy (CT) in the first-line setting., Material and Methods: A systematic review was conducted to identify RCT investigating anti-PD1/PD-L1 drugs plus CT versus CT alone. We performed (i) arm-level analysis to evaluate predictors of median OS (mOS) and (ii) comparison-level analysis for OS hazard ratio (HR) estimations. Linear regression models weighted by trial size were fitted and adjusted R 2 values were reported., Results: Thirty-nine RCTs involving 22,341 patients met the inclusion criteria (17 non-small cell lung, 9 gastroesophageal and 13 in other cancers) with ten different ICI under study. Overall, ICI plus CT improved OS (HR = 0.76; 95%CI: 0.73-0.80). In the arm-level analysis, the best mOS prediction was obtained with a new endpoint that combines median duration of response and ORR (mDoR-ORR) and with median PFS (R 2  = 0.5 both). In the comparison-level analysis, PFS HR showed a moderate association with OS HR (R 2  = 0.52). Early OS read-outs were highly associated with final OS outcomes (R 2  = 0.80)., Conclusions: The association between surrogate endpoints and OS in first-line RCT combining anti-PD1/PD-L1 and CT is moderate-low. Early OS read-outs showed a good association with final OS HR while the mDOR-ORR endpoint could help to better design confirmatory trials after single-arm phase II trials., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

CDK4/6 inhibitors combined with endocrine therapy have demonstrated higher antitumor activity than endocrine therapy alone for the treatment of advanced estrogen receptor-positive breast cancer. Some of these tumors are de novo resistant to CDK4/6 inhibitors and others develop acquired resistance. Here, we show that p16 overexpression is associated with reduced antitumor activity of CDK4/6 inhibitors in patient-derived xenografts (n = 37) and estrogen receptor-positive breast cancer cell lines, as well as reduced response of early and advanced breast cancer patients to CDK4/6 inhibitors (n = 89). We also identified heterozygous RB1 loss as biomarker of acquired resistance and poor clinical outcome. Combination of the CDK4/6 inhibitor ribociclib with the PI3K inhibitor alpelisib showed antitumor activity in estrogen receptor-positive non-basal-like breast cancer patient-derived xenografts, independently of PIK3CA, ESR1 or RB1 mutation, also in drug de-escalation experiments or omitting endocrine therapy. Our results offer insights into predicting primary/acquired resistance to CDK4/6 inhibitors and post-progression therapeutic strategies., (© 2022. The Author(s).)

PARP inhibitors (PARPi) are approved drugs for platinum-sensitive, high-grade serous ovarian cancer (HGSOC) and for breast, prostate, and pancreatic cancers (PaC) harboring genetic alterations impairing homologous recombination repair (HRR). Detection of nuclear RAD51 foci in tumor cells is a marker of HRR functionality, and we previously established a test to detect RAD51 nuclear foci. Here, we aimed to validate the RAD51 score cut off and compare the performance of this test to other HRR deficiency (HRD) detection methods. Laboratory models from BRCA1/BRCA2-associated breast cancer, HGSOC, and PaC were developed and evaluated for their response to PARPi and cisplatin. HRD in these models and patient samples was evaluated by DNA sequencing of HRR genes, genomic HRD tests, and RAD51 foci detection. We established patient-derived xenograft models from breast cancer (n = 103), HGSOC (n = 4), and PaC (n = 2) that recapitulated patient HRD status and treatment response. The RAD51 test showed higher accuracy than HRR gene mutations and genomic HRD analysis for predicting PARPi response (95%, 67%, and 71%, respectively). RAD51 detection captured dynamic changes in HRR status upon acquisition of PARPi resistance. The accuracy of the RAD51 test was similar to HRR gene mutations for predicting platinum response. The predefined RAD51 score cut off was validated, and the high predictive value of the RAD51 test in preclinical models was confirmed. These results collectively support pursuing clinical assessment of the RAD51 test in patient samples from randomized trials testing PARPi or platinum-based therapies., Significance: This work demonstrates the high accuracy of a histopathology-based test based on the detection of RAD51 nuclear foci in predicting response to PARPi and cisplatin., (©2022 American Association for Cancer Research.)

Purpose: FGFR1 amplification ( FGFR1 amp) is recurrent in metastatic breast cancer (MBC) and is associated with resistance to endocrine therapy and CDK4/6 inhibitors (CDK4/6is). Multi-tyrosine kinase inhibitors (MTKIs) and selective pan-FGFR inhibitors (FGFRis) are being developed for FGFR1 amp breast cancer. High-level FGFR amplification and protein expression by IHC have identified breast cancer responders to FGFRis or MTKIs, respectively., Experimental Design: Here, we used preclinical models and patient samples to identify predictive biomarkers to these drugs. We evaluated the antitumor activity of an FGFRi and an MTKI in a collection of 17 breast cancer patient-derived xenografts (PDXs) harboring amplification in FGFR1/2/3/4 and in 10 patients receiving either an FGFRi/MTKI. mRNA levels were measured on FFPE tumor samples using two commercial strategies. Proliferation and angiogenesis were evaluated by detecting Ki-67 and CD31 in viable areas by immunofluorescence., Results: High FGFR1-4 mRNA levels but not copy-number alteration (CNA) is associated with FGFRi response. Treatment with MTKIs showed higher response rates than with FGFRis (86% vs. 53%), regardless of the FGFR1-4 mRNA levels. FGFR-addicted PDXs exhibited an antiproliferative response to either FGFRis or MTKIs, and PDXs exclusively sensitive to MTKI exhibited an additional antiangiogenic response. Consistently, the clinical benefit of MTKIs was not associated with high FGFR1-4 mRNA levels and was observed in patients previously treated with antiangiogenic drugs., Conclusions: Tailored therapy with FGFRis in molecularly selected MBC based on high FGFR1-4 mRNA levels warrants prospective validation in patients with CDK4/6i-resistant luminal breast cancer and in patients with TNBC without targeted therapeutic options., (©2021 American Association for Cancer Research.)

Purpose: Patient selection in phase 1 clinical trials (Ph1t) continues to be a challenge. The aim of this study was to develop a user-friendly prognostic calculator for predicting overall survival (OS) outcomes in patients to be included in Ph1t with immune checkpoint inhibitors (ICIs) or targeted agents (TAs) based on clinical parameters assessed at baseline., Methods: Using a training cohort with consecutive patients from the VHIO phase 1 unit, we constructed a prognostic model to predict median OS (mOS) as a primary endpoint and 3-month (3m) OS rate as a secondary endpoint. The model was validated in an internal cohort after temporal data splitting and represented as a web application., Results: We recruited 799 patients (training and validation sets, 558 and 241, respectively). Median follow-up was 21.2 months (m), mOS was 10.2 m (95% CI, 9.3-12.7) for ICIs cohort and 7.7 m (95% CI, 6.6-8.6) for TAs cohort. In the multivariable analysis, six prognostic variables were independently associated with OS - ECOG, number of metastatic sites, presence of liver metastases, derived neutrophils/(leukocytes minus neutrophils) ratio [dNLR], albumin and lactate dehydrogenase (LDH) levels. The phase 1 prognostic online (PIPO) calculator showed adequate discrimination and calibration performance for OS, with C-statistics of 0.71 (95% CI 0.64-0.78) in the validation set. The overall accuracy of the model for 3m OS prediction was 87.2% (95% CI 85%-90%)., Conclusions: PIPO is a user-friendly objective and interactive tool to calculate specific survival probabilities for each patient before enrolment in a Ph1t. The tool is available at https://pipo.vhio.net/., Competing Interests: Conflict of interest statement The authors declare the following financial interests/personal relationships that may be considered as potential competing interests: IM reports receiving an ESMO Research Fellowship sponsored by Roche and honoraria for serving as a speaker bureau for MSD. CH reports a grant from Bayer and Merck., personal fees from MSD, Merck and Amgen. JML reports receiving a grant from Sanofi, honoraria for consultancy from Novartis, Roche, BMS, Pierre Fabre, Sanofi, Highlight Therapeutics and personal fees from Novartis, Roche, MSD, Pfizer, BMS, Astellas Pharma, Pierre Fabre and Sanofi. IB reports honoraria for consultancy from Orion Pharma, BMS, Astrazeneca, Merck Serono, Rakutan Pharma, Roche, Sanofi, MSD eTheRNA Immunotherapies and Achilles Therapeutics Limited. AA reports honoraria for consultancy from AMCURE and research funding from AMCURE. OM reports personal fees from MSD and Janssen. IG reports fees for a speaker for MSD and Roche. AH reports travel grants from Kyowa & Kirin, MSD and Merck and funding from Hold’em for Life Oncology Fellowship, TTCC and SEOM and CRIS Foundations. GA reports no conflict of interest. VG reports no conflict of interest. MO reports fees for consultancy from MSD. JR reports receiving fees for a speaker for Sanofi. EMC reports receiving fees for serving as a member of scientific advisory boards from Amgen, Bristol-Myers Squibb, Merck Sharp & Dohme, Novartis, Pierre Fabre, Roche, Sanofi, and personal fees from Amgen, Bristol-Myers Squibb, Merck Sharp & Dohme, Novartis, Pierre Fabre, Roche, and participation as principal investigator in clinical trials from Amgen, Bristol-Myers Squibb, GlaxoSmithKline, Merck Sharp & Dohme, Novartis, Pierre Fabre, Roche, Sanofi. EE reports research funding from Array Biopharma, MSD, Abbvie, Amgen, GlaxoSmithKline, Astrazeneca, Merck Sharp & Dohme Corp., Bristol Myers Squibb, Novartis, Boehringer Ingelheim, Hoffman La-Roche and honoraria for consulting from Hoffman La - Roche, Bristol Myers Squibb, Servier, Amgen, Merck Serono, Array Biopharma, Sanofi. Dr Rodon reports non financial support and reasonable reimbursement for travel from European Journal of Cancer, Vall d’Hebron Institut of Oncology, Chinese University of Hong Kong, SOLTI, Elsevier, GLAXOSMITHKLINE,; receiving consulting and travel fees from Novartis, Eli Lilly, Orion Pharmaceuticals, Servier Pharmaceuticals, Peptomyc, Merck Sharp & Dohme, Kelun Pharmaceutical/Klus Pharma, Spectrum Pharmaceuticals Inc, Pfizer, Roche Pharmaceuticals, Ellipses Pharma, NovellusDx, iOnctura and Molecular Partners (including serving on the scientific advisory board from 2015 to the present), receiving research funding from Blueprint Pharmaceuticals, Bayer and Novartis, and serving as investigator in clinical trials with Spectrum Pharmaceuticals, Tocagen, Symphogen, BioAtla, Pfizer, GenMab, CytomX, KELUN-BIOTECH, Takeda-Millennium, GLAXOSMITHKLINE, IPSEN and travel fees from ESMO, US Department of Defense, Louisiana State University, Huntsman Cancer Institute, Cancer Core Europe, Karolinska Cancer Institute and King Abdullah International Medical Research Center (KAIMRC), Molecular Partners. CS reports receiving fees for serving as a member of scientific advisory boards or receiving travel grants from AstraZeneca, Celgene, Daiichi Sankyo, Eisai, F. Hoffmann - La Roche Ltd, Genomic Health, Merck, Sharp and Dhome España S.A., Novartis, Odonate Therapeutics, Pfizer, Philips Healthwork, Pierre Fabre, prIME Oncology, Puma, Synthon and Sanofi Aventis and paid directly to her Institution: AstraZeneca, Daiichi Sankyo, Eli Lilly and Company, Genentech, Immunomedics, Macrogenics, Merck, Sharp and Dhome España S.A., Novartis, Pfizer, Piqur Therapeutics, Puma, Roche, Synthon and Zenith Pharma. TM reports receiving fees for consulting Roche, AstraZeneca, PharmaMar, Clovis Oncology, Tesaro, Inmunogen, Genmab, Mersana Therapeutic, GSK and Deciphera Pharmaceutical and received support for travel or accommodation from Roche, AstraZeneca, and PharmaMar and institutional personal interest paid directly to her institution: Agios, Aslan, AstraZecena, Bayer, Celgene, Genentech, Hallozyme, Immunomedics, Lilly, Merimarck, Millenim, Novartis, Pfizer, Pharmacyclics and Roche. AO reports receiving fees for consulting from Roche, AstraZeneca, PharmaMar, Clovis Oncology, Tesaro, Inmunogen, Genmab, Mersana Therapeutic, GSK and Deciphera Pharmaceutical and received support for travel or accommodation from Roche, AstraZeneca, and PharmaMar, and institutional financial interest paid directly to her institution: Abbvie Deutschland, Ability Pharmaceuticals, Advaxis Inc., Aeterna Zentaris, AMGEM, SA, Aprea Therapeutics AB, Clovis Oncology Inc, EISAI limited LTD, F. Hoffmann – La Roche LTD, Regeneron Pharmaceuticals, Immunogen Inc., Merck, Sharp & Dohme de España SA, Millennium Pharmaceuticals Inc., Pharma Mar SA, Tesaro Inc., BMS, Bristol Meyers Squibb. JC reports receiving fees for advisory board from Bayer, Johnson & Johnson,Bristol-Myers Squibb, Astellas Pharma, Pfizer, Sanofi, MSD Oncology, Roche, AstraZéneca and for speakers bureau from Bayer, Johnson & Johnson, Asofarma, Astellas Pharma, travel from BMS, Ipsen, Roche, AstraZéneca, and research grants from AB Science, Aragon Pharmaceuticals, Arog Pharmaceuticals, INC, Astellas Pharma., Astrazeneca AB, Aveo Pharmaceuticals INC, Bayer AG, Blueprint Medicines Corporation, BN Immunotherapeutics INC, Boehringer Ingelheim España, S.A., Bristol-Myers Squibb International Corporation (BMS), Clovis Oncology, INC, Cougar Biotechnology INC, Deciphera Pharmaceuticals LLC, Exelixis INC, F. Hoffmann-La Roche LTD, Genentech INC, Glaxosmithkline, SA, Incyte Corporation, Janssen-Cilag International NV, Karyopharm Therapeutics INC., Laboratoires Leurquin Mediolanum SAS, Lilly, S.A., Medimmune, Millennium Pharmaceuticals, INC., Nanobiotix SA, Novartis Farmacéutica, S.A., Pfizer, S.L.U, Puma Biotechnology, INC, Sanofi-Aventis, S.A., SFJ Pharma LTD. II, Teva Pharma S.L.U. EF reports research funding from Fundacion Merck Salud, personal fees for consulting from ABBVIE, Astrazeneca, Blueprint Medicines, Boehringer Ingelheim, BMS, ELI Lilly, Guardant Health, Jansenn, Merck, Novartis, Pfizer, Roche, Samsung, Takeda, GSK and Bayer, personal fees for speakers bureao from Astrazeoncology, Roche, Takeda, Boehringer Ingelheim, BMS, ELI Lilly, Medsxape, Merck, Novartis, Pfizer, Prime Oncology, Roche, Takeda, Touchime and in Grifols independent member of the board. Josep Tabernero reports personal financial interest in the form of scientific consultancy role for Array Biopharma, AstraZeneca, Bayer, BeiGene, Boehringer Ingelheim, Chugai, Genentech, Inc., Genmab A/S, Halozyme, Imugene Limited, Inflection Biosciences Limited, Ipsen, Kura Oncology, Lilly, MSD, Menarini, Merck Serono, Merrimack, Merus, Molecular Partners, Novartis, Peptomyc, Pfizer, Pharmacyclics, ProteoDesign SL, Rafael Pharmaceuticals, F. Hoffmann-La Roche Ltd, Sanofi, SeaGen, Seattle Genetics, Servier, Symphogen, Taiho, VCN Biosciences, Biocartis, Foundation Medicine, HalioDX SAS and Roche Diagnostics. RD declares advisory role for Roche, Boehringer Ingelheim, received a speaker’s fee from Roche, Ipsen, Amgen, Servier, Sanofi, Merck Sharp & Dohme, and research grants from Merck and Pierre Fabre. EG reportspersonal financial interest in the form of scientific consultancy role for Roche/Genentech - F.Hoffmann/La Roche, Ellipses Pharma, Neomed Therapeutics1 Inc, Boehringer Ingelheim, Janssen Global Services, SeaGen, TFS, Alkermes, Thermo Fisher, Bristol-Mayers Squibb, MabDiscovery, Anaveon; fees for speakers bureau from Merck Sharp & Dohme, Roche, Thermo Fisher, Roche, Lilly; travel grants from Bristol-Mayers Squibb, Merck Sharp & Dohme, Menarini, Glycotope; research grants from Novartis, Roche, Thermo Fisher, AstraZeneca, Taiho, BeiGene.The remaining authors declare no conflicts of interest., (Copyright © 2021 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Background: Eribulin is a microtubule-targeting agent approved for the treatment of advanced or metastatic breast cancer (BC) previously treated with anthracycline- and taxane-based regimens. PIK3CA mutation is associated with worse response to chemotherapy in oestrogen receptor-positive (ER+)/human epidermal growth factor receptor 2-negative (HER2-) metastatic BC. We aimed to evaluate the role of phosphoinositide 3-kinase (PI3K)/AKT pathway mutations in eribulin resistance., Methods: Resistance to eribulin was evaluated in HER2- BC cell lines and patient-derived tumour xenografts, and correlated with a mutation in the PI3K/AKT pathway., Results: Eleven out of 23 HER2- BC xenografts treated with eribulin exhibited disease progression. No correlation with ER status was detected. Among the resistant models, 64% carried mutations in PIK3CA, PIK3R1 or AKT1, but only 17% among the sensitive xenografts (P = 0.036). We observed that eribulin treatment induced AKT phosphorylation in vitro and in patient tumours. In agreement, the addition of PI3K inhibitors reversed primary and acquired resistance to eribulin in xenograft models, regardless of the genetic alterations in PI3K/AKT pathway or ER status. Mechanistically, PI3K blockade reduced p21 levels likely enabling apoptosis, thus sensitising to eribulin treatment., Conclusions: PI3K pathway activation induces primary resistance or early adaptation to eribulin, supporting the combination of PI3K inhibitors and eribulin for the treatment of HER2- BC patients.

Background Reliable predictive imaging markers of response to immune checkpoint inhibitors are needed. Purpose To develop and validate a pretreatment CT-based radiomics signature to predict response to immune checkpoint inhibitors in advanced solid tumors. Materials and Methods In this retrospective study, a radiomics signature was developed in patients with advanced solid tumors (including breast, cervix, gastrointestinal) treated with anti-programmed cell death-1 or programmed cell death ligand-1 monotherapy from August 2012 to May 2018 (cohort 1). This was tested in patients with bladder and lung cancer (cohorts 2 and 3). Radiomics variables were extracted from all metastases delineated at pretreatment CT and selected by using an elastic-net model. A regression model combined radiomics and clinical variables with response as the end point. Biologic validation of the radiomics score with RNA profiling of cytotoxic cells (cohort 4) was assessed with Mann-Whitney analysis. Results The radiomics signature was developed in 85 patients (cohort 1: mean age, 58 years ± 13 [standard deviation]; 43 men) and tested on 46 patients (cohort 2: mean age, 70 years ± 12; 37 men) and 47 patients (cohort 3: mean age, 64 years ± 11; 40 men). Biologic validation was performed in a further cohort of 20 patients (cohort 4: mean age, 60 years ± 13; 14 men). The radiomics signature was associated with clinical response to immune checkpoint inhibitors (area under the curve [AUC], 0.70; 95% CI: 0.64, 0.77; P < .001). In cohorts 2 and 3, the AUC was 0.67 (95% CI: 0.58, 0.76) and 0.67 (95% CI: 0.56, 0.77; P < .001), respectively. A radiomics-clinical signature (including baseline albumin level and lymphocyte count) improved on radiomics-only performance (AUC, 0.74 [95% CI: 0.63, 0.84; P < .001]; Akaike information criterion, 107.00 and 109.90, respectively). Conclusion A pretreatment CT-based radiomics signature is associated with response to immune checkpoint inhibitors, likely reflecting the tumor immunophenotype. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Summers in this issue.

Purpose: Cancer classification is foundational for patient care and oncology research. Systems such as International Classification of Diseases for Oncology (ICD-O), Systematized Nomenclature of Medicine Clinical Terms (SNOMED-CT), and National Cancer Institute Thesaurus (NCIt) provide large sets of cancer classification terminologies but they lack a dynamic modernized cancer classification platform that addresses the fast-evolving needs in clinical reporting of genomic sequencing results and associated oncology research., Methods: To meet these needs, we have developed OncoTree, an open-source cancer classification system. It is maintained by a cross-institutional committee of oncologists, pathologists, scientists, and engineers, accessible via an open-source Web user interface and an application programming interface., Results: OncoTree currently includes 868 tumor types across 32 organ sites. OncoTree has been adopted as the tumor classification system for American Association for Cancer Research (AACR) Project Genomics Evidence Neoplasia Information Exchange (GENIE), a large genomic and clinical data-sharing consortium, and for clinical molecular testing efforts at Memorial Sloan Kettering Cancer Center and Dana-Farber Cancer Institute. It is also used by precision oncology tools such as OncoKB and cBioPortal for Cancer Genomics., Conclusion: OncoTree is a dynamic and flexible community-driven cancer classification platform encompassing rare and common cancers that provides clinically relevant and appropriately granular cancer classification for clinical decision support systems and oncology research.

Background: Promising results have been reported with immune checkpoint inhibitors (ICI) in a small proportion of MPM patients. MMR deficiency (dMMR) has been well described in several malignancies and was approved as a biomarker for anti-PD-1 inhibitors. Next generation sequencing (NGS) data demonstrated that 2% of MPM harbor microsatellite instability. The aim of this study is to characterize MMR by immunohistochemistry (IHC) in a series of MPM including a subset of patients treated with immunotherapy., Methods: Tumors of 159 MPM p diagnosed between 2002 and 2017 were reviewed. Formalin-fixed, paraffin-embedded tissue was stained for MLH1, MSH2, MSH6 and PMS2 and tumors were classified as dMMR (MMR protein expression negative) and MMR intact (all MMR proteins positively expressed). We retrospectively collected clinical outcomes under standard chemotherapy and experimental immunotherapy in the entire cohort., Results: MMR protein expression was analyzed in 158 samples with enough tissue and was positive in all of the cases. Twenty two patients received ICI with anti-CTLA4 or anti-PD-1 blockade in clinical trials, 58% had a response or stable disease for more than 6 m, with median progression-free survival (PFS) of 5.7 m (2.1-26.1 m). The median overall survival (mOS) in all population was 15 months (m) (13.5-18.8 m). In a multivariable model factors associated to improved mOS were PS 0, neutrophil-lymphocyte ratio (NLR) < 5 and epithelioid histology (p < 0.001)., Conclusions: In our series we were unable to identify any MPM patient with dMMR by IHC. Further studies are needed to elucidate potential predictive biomarkers of ICI benefit in MPM.

Most academic precision oncology programs have been designed to facilitate enrollment of patients in early clinical trials with matched targeted agents. Over the last decade, major changes were seen both in the targetable molecular alteration landscape and in drug development trends. In this article, we describe how the Vall d'Hebron Institute of Oncology molecular prescreening program adapted to a dynamic model of biomarker-drug codevelopment. We started with a tumor-agnostic hotspot mutation panel plus in situ hybridization and immunohistochemistry of selected markers and subsequently transitioned to tumor-specific amplicon-based next-generation sequencing (NGS) tests together with custom copy number, fusion, and outlier gene expression panels. All assays are optimized for archived formalin-fixed paraffin-embedded tumor tissues without matched germline sequencing. In parallel, biomarker-matched trials evolved from a scenario of few targets and large populations (such as PI3K inhibitors in PIK3CA mutants) to a complex situation with many targets and small populations (such as multiple targetable fusion events). Recruitment rates in clinical trials with mandatory biomarkers decreased over the last 3 years. Molecular tumor board meetings proved critical to guide oncologists on emerging biomarkers for clinical testing and interpretation of NGS results. The substantial increase of immunotherapy trials had a major impact in target prioritization and guided clinical implementation of new markers, such as tumor mutational burden, with larger exon-based NGS assays and gene expression signatures to capture microenvironment infiltration patterns. This new multiomics era of precision oncology is expected to increase the opportunities for early clinical trial matching., Competing Interests: The following represents disclosure information provided by authors of this manuscript. All relationships are considered compensated unless otherwise noted. Relationships are self-held unless noted. I = Immediate Family Member, Inst = My Institution. Relationships may not relate to the subject matter of this manuscript. For more information about ASCO's conflict of interest policy, please refer to www.asco.org/rwc or ascopubs.org/po/author-center. Open Payments is a public database containing information reported by companies about payments made to US-licensed physicians (Open Payments). Rodrigo DienstmannConsulting or Advisory Role: Roche Speakers' Bureau: Roche, Ipsen, Sanofi, MSD Oncology, SERVIER, Amgen Research Funding: MerckElena GarraldaConsulting or Advisory Role: Roche, Ellipses Pharma, Neomed Therapeutics, Janssen, Boehringer Ingelheim, Seattle Genetics, TFS, Alkermes, Thermo Fisher Scientific, Bristol-Myers Squibb Speakers' Bureau: MSD, Roche, Thermo Fisher Scientific Research Funding: Novartis, Roche (Inst), Thermo Fisher Scientific (Inst) Travel, Accommodations, Expenses: Bristol-Myers Squibb, Menarini, Glycotope, Merck Sharp & DohmeAna OakninConsulting or Advisory Role: Roche, AstraZeneca, PharmaMar, Clovis Oncology, Tesaro, Immunogen, Genmab Research Funding: AbbVie Deutschland (Inst), Ability Pharmaceuticals (Inst), Advaxis (Inst), Aeterna Zentaris (Inst), Amgen (Inst), Aprea Therapeutics (Inst), Clovis Oncology (Inst), Eisai (Inst), F. Hoffmann–La Roche (Inst), Regeneron Pharmaceuticals (Inst) Travel, Accommodations, Expenses: AstraZeneca, Clovis Oncology, PharmaMar, RocheCristina SauraConsulting or Advisory Role: Puma Biotechnology, Roche, Pfizer, AstraZeneca, Celgene, Daiichi Sankyo, Eisai, Genomic Health, Novartis, Pierre Fabre, Synthon, Piqur Therapeutics, BMS, Philips Healthwork, MSD, Sanofi Research Funding: Genentech (Inst), AstraZeneca (Inst), Roche (Inst), Macrogenics (Inst), Novartis (Inst), Pfizer (Inst), Puma Biotechnology (Inst), Synthon (Inst), Piqur Therapeutics (Inst) Travel, Accommodations, Expenses: Pfizer, Novartis, Roche, AstraZeneca, Genomic Health, Puma BiotechnologyMafalda OliveiraHonoraria: Roche, Novartis, Seattle Genetics Consulting or Advisory Role: Roche/Genentech, GlaxoSmithKline, Puma Biotechnology, AstraZeneca, Seattle Genetics Research Funding: Philips Healthcare (Inst), Roche/Genentech (Inst), Novartis (Inst), AstraZeneca (Inst), Immunomedics (Inst), Seattle Genetics (Inst), Boehringer Ingelheim (Inst), GlaxoSmithKline (Inst), Cascadian Therapeutics (Inst), Sanofi (Inst), Celldex (Inst), Bayer (Inst), Piqur (Inst), Puma Biotechnology (Inst), Zenith Epigenetics (Inst) Travel, Accommodations, Expenses: Roche, Novartis, Grünenthal Group, Pierre Fabre, GP Pharm, EisaiJudith BalmañaConsulting or Advisory Role: AstraZeneca, Pfizer Research Funding: AstraZeneca (Inst), PharmaMar (Inst) Patents, Royalties, Other Intellectual Property: European patent request submitted (EP17382884.9) not related to this work Travel, Accommodations, Expenses: AstraZeneca, PharmaMarJoan CarlesConsulting or Advisory Role: Bayer, Johnson & Johnson, Bristol-Myers Squibb, Astellas Pharma, Pfizer, Sanofi, MSD Oncology, Roche, AstraZeneca Speakers' Bureau: Bayer, Johnson & Johnson, Asofarma, Astellas Pharma Research Funding: AB Science (Inst), Aragon Pharmaceuticals (Inst), Arog Pharmaceuticals (Inst), Astellas Pharma (Inst), Aveo Pharmaceuticals (Inst), Bayer AG (Inst), Blueprint Medicines (Inst), BN Immunotherapeutics (Inst), Boehringer Ingelheim España, (Inst), Bristol-Myers Squibb International (BMS) (Inst), Clovis Oncology (Inst), Cougar Biotechnology (Inst), Deciphera Pharmaceuticals (Inst), Exelixis (Inst), F. Hoffmann–La Roche (Inst), Genentech (Inst), GlaxoSmithKline (Inst), Incyte (Inst), Janssen-Cilag International (Inst), Karyopharm Therapeutics (Inst), Medimmune (Inst), Millennium Pharmaceuticals (Inst), Nanobiotix (Inst), Novartis Farmacéutica (Inst), Pfizer (Inst), Puma Biotechnology (Inst), Sanofi-AventisA (Inst), SFJ Pharma (Inst), Teva Pharma (Inst), Mediolanum Laboratoires Leurquin (Inst), Eli Lilly (Inst), AstraZeneca (Inst) Travel, Accommodations, Expenses: BMS (Inst), Ipsen (Inst), Roche (Inst), AstraZéneca (Inst)Teresa MacarullaConsulting or Advisory Role: Sanofi/Aventis, Shire, Celgene, Roche, Baxalta, QED Therapeutics, Baxter, Incyte, SERVIER, Eli Lilly, Ipsen Research Funding: Celgene (Inst), Agios Pharmaceuticals (Inst), Aslan Pharmaceuticals (Inst), Bayer (Inst), Roche (Inst), Genentech (Inst), AstraZeneca (Inst), Halozyme (Inst), Immunomedics (Inst), Eli Lilly (Inst), Merrimack (Inst), Millennium (Inst), Novartis Farmacéutica (Inst), Novocure (Inst), Pfizer (Inst), Pharmacyclics (Inst) Travel, Accommodations, Expenses: Merck, H3 Biomedicine, Sanofi, Celgene, SERVIERElena ElezConsulting or Advisory Role: Amgen, Roche, Merck Serono, Sanofi, SERVIER, Bayer, Pierre Fabre Research Funding: Merck Serono, Sanofi/Aventis (Inst) Travel, Accommodations, Expenses: Roche, Merck Serono, Sanofi, AmgenMaria AlsinaConsulting or Advisory Role: SERVIER, Bristol-Myers Squibb, Eli Lilly, MSD Oncology Speakers' Bureau: Eli Lilly, Bristol-Myers Squibb, MSD, Roche/Genentech, Amgen, SERVIER Travel, Accommodations, Expenses: Roche/Genentech, Amgen, Eli LillyIrene BrañaConsulting or Advisory Role: Bristol-Myers Squibb Speakers' Bureau: Bristol-Myers Squibb, AstraZeneca Spain, Merck Serono Research Funding: AstraZeneca, Bristol-Myers Squibb, Celgene, Gliknik, GlaxoSmithKline, Janssen Oncology, Kura, Merck Sharp & Dohme, Novartis, Orion Pharma, Pfizer, Roche Travel, Accommodations, Expenses: AstraZeneca Spain, Merck SeronoEnriqueta FelipConsulting or Advisory Role: Pfizer, Roche, Boehringer Ingelheim, AstraZeneca, Bristol-Myers Squibb, Guardant Health, Novartis, Takeda, AbbVie, Blue Print Medicines, Eli Lilly, Merck, Merck Sharp & Dohme, Janssen, Samsung Speakers' Bureau: Pfizer, Roche, AstraZeneca, Bristol-Myers Squibb, Novartis, Takeda, Eli Lilly, Merck Sharp & Dohme, Medscape, Prime Oncology, Touchtime Research Funding: Fundación Merck Salud, Grant for Oncology Innovation Other Relationship: GrífolsJosep TaberneroConsulting or Advisory Role: Bayer, Boehringer Ingelheim, Eli Lilly, MSD, Merck Serono, Novartis, Sanofi, Taiho Pharmaceutical, Merrimack, Peptomyc, Rafael Pharmaceuticals, Symphogen, Chugai Pharma, Ipsen, Merus, Pfizer, Seattle Genetics, Array BioPharma, AstraZeneca, BeiGene, Genentech, Genmab, Halozyme, Imugene Limited, Inflection Biosciences Limited, Kura, Menarini, Molecular Partners, Pharmacyclics, ProteoDesign, Roche, Seattle Genetics, Servier, VCN Biosciences, Biocartis, Foundation Medicine, HalioDX, Roche DiagnosticsJordi RodonConsulting or Advisory Role: Peptomyc, Kelun Pharmaceuticals, Merck Sharp & Dohme, Spectrum Pharmaceuticals, Pfizer, Roche/Genentech, Ellipses Pharma, Certera, Bayer, Ionctura Research Funding: Novartis, BayerPaolo NuciforoHonoraria: Bayer, Novartis, MSD Oncology, MSD Oncology Consulting or Advisory Role: Bayer, MSD Oncology Travel, Accommodations, Expenses: NovartisAna VivancosConsulting or Advisory Role: Guardant Health, Novartis, Bayer Health No other potential conflicts of interest were reported., (© 2020 by American Society of Clinical Oncology.)

Purpose: Most hyperprogression disease (HPD) definitions are based on tumor growth rate (TGR). However, there is still no consensus on how to evaluate this phenomenon., Patients and Methods: We investigated two independent cohorts of patients with advanced solid tumors treated in phase I trials with (i) programmed cell death 1 (PD-1)/PD-L1 antibodies in monotherapy or combination and (ii) targeted agents (TA) in unapproved indications. A Response Evaluation Criteria in Solid Tumors (RECIST) 1.1-based definition of hyperprogression was developed. The primary endpoint was the assessment of the rate of HPD in patients treated with ICIs or TAs using both criteria (RECIST and TGR) and the impact on overall survival (OS) in patients who achieved PD as best response., Results: Among 270 evaluable patients treated with PD-1/PD-L1 inhibitors, 29 PD-1/PD-L1-treated patients (10.7%) had HPD by RECIST definition. This group had a significantly lower OS (median of 5.23 months; 95% CI, 3.97-6.45) when compared with the non-HPD progressor group (median, 7.33 months; 95% CI, 4.53-10.12; HR = 1.73, 95% CI, 1.05-2.85; P = 0.04). In a subset of 221 evaluable patients, 14 (6.3%) were categorized as HPD using TGR criteria, differences in median OS (mOS) between this group (mOS 4.2 months; 95% IC, 2.07-6.33) and non-HPD progressors ( n = 44) by TGR criteria (mOS 6.27 months; 95% CI, 3.88-8.67) were not statistically significant (HR 1.4, 95% IC, 0.70-2.77; P = 0.346). Among 239 evaluable patients treated with TAs, 26 (10.9%) were classified as having HPD by RECIST and 14 using TGR criteria in a subset of patients. No differences in OS were observed between HPD and non-HPD progressors treated with TAs., Conclusions: HPD measured by TGR or by RECIST was observed in both cohorts of patients; however, in our series, there was an impact on survival only in the immune-checkpoint inhibitor cohort when evaluated by RECIST. We propose a new way to capture HPD using RECIST criteria that is intuitive and easy to use in daily clinical practice., (©2019 American Association for Cancer Research.)

Poly(ADP-ribose) polymerase (PARP) inhibitors (PARPi) are effective in cancers with defective homologous recombination DNA repair (HRR), including BRCA1/2-related cancers. A test to identify additional HRR-deficient tumors will help to extend their use in new indications. We evaluated the activity of the PARPi olaparib in patient-derived tumor xenografts (PDXs) from breast cancer (BC) patients and investigated mechanisms of sensitivity through exome sequencing, BRCA1 promoter methylation analysis, and immunostaining of HRR proteins, including RAD51 nuclear foci. In an independent BC PDX panel, the predictive capacity of the RAD51 score and the homologous recombination deficiency (HRD) score were compared. To examine the clinical feasibility of the RAD51 assay, we scored archival breast tumor samples, including PALB2-related hereditary cancers. The RAD51 score was highly discriminative of PARPi sensitivity versus PARPi resistance in BC PDXs and outperformed the genomic test. In clinical samples, all PALB2-related tumors were classified as HRR-deficient by the RAD51 score. The functional biomarker RAD51 enables the identification of PARPi-sensitive BC and broadens the population who may benefit from this therapy beyond BRCA1/2-related cancers., (© 2018 The Authors. Published under the terms of the CC BY 4.0 license.)

Sequencing of tumors is now routine and guides personalized cancer therapy. Mutant allele fractions (MAFs, or the 'mutation dose') of a driver gene may reveal the genomic structure of tumors and influence response to targeted therapies. We performed a comprehensive analysis of MAFs of driver alterations in unpaired primary and metastatic colorectal cancer (CRC) at our institution from 2010 to 2015 and studied their potential clinical relevance. Of 763 CRC samples, 622 had detailed annotation on overall survival in the metastatic setting (OSmet) and 89 received targeted agents matched to KRAS (MEK inhibitors), BRAF (BRAF inhibitors), or PIK3CA mutations (PI3K pathway inhibitors). MAFs of each variant were normalized for tumor purity in the sample (adjMAFs). We found lower adjMAFs for BRAF V 600E and PIK3CA than for KRAS, NRAS, and BRAF non-V600 variants. TP53 and BRAF V 600E adjMAFs were higher in metastases as compared to primary tumors, and high KRAS adjMAFs were found in CRC metastases of patients with KRAS wild-type primary tumors previously exposed to EGFR antibodies. Patients with RAS- or BRAF V 600E -mutated tumors, irrespective of adjMAFs, had worse OSmet. There was no significant association between adjMAFs and time to progression on targeted therapies matched to KRAS, BRAF, or PIK3CA mutations, potentially related to the limited antitumor activity of the employed drugs (overall response rate of 4.5%). In conclusion, the lower BRAF V 600E and PIK3CA adjMAFs in subsets of primary CRC tumors indicate subclonality of these driver genes. Differences in adjMAFs between metastases and primary tumors suggest that approved therapies may result in selection of BRAF V 600E - and KRAS-resistant clones and an increase in genomic heterogeneity with acquired TP53 alterations. Despite significant differences in prognosis according to mutations in driver oncogenes, adjMAFs levels did not impact on survival and did not help predict benefit with matched targeted agents in the metastatic setting., (© 2017 The Authors. Published by FEBS Press and John Wiley & Sons Ltd.)