Les patients atteints de cancer font face à un parcours de soins complexe, avec des défis liés à l’intégration des innovations thérapeutiques, technologiques et numériques. En 2025, la lutte contre le cancer nécessite une approche multidimensionnelle, alliant traitements avancés et prise en compte des besoins individuels des patients. Lors des Rencontres de la cancérologie française 2024, divers acteurs, dont des associations de patients, des entreprises et des startups, des établissements de santé et des associations professionnelles, ont débattu avec les experts en cancérologie de l’intégration des données patients dans la recherche. Neuf propositions consensuelles ont été formulées pour améliorer cette intégration.

1615 Background: Cancer in individuals under 40 years old is increasingly recognized as a public health concern, characterized by unique etiologies, biology, and clinical behaviors compared to older populations. Young patients may also face specific physical, psychosocial, and socioeconomic challenges that can influence outcomes which are often suboptimally addressed in routine care. Digital health and specifically remote patient monitoring (RPM) offer a way to track and manage these challenges effectively, increasing access to supportive care. Methods: Prospective, observational cohort of 7323 adult patients with cancer participating in an RPM pathway across 110 hospitals in France and Belgium between Jun-2022 and Dec-2024. Patients were grouped by age ( < 40 vs. ≥40). At baseline, demographic, clinical, and social and behavioral data were collected. Longitudinal symptom burden (e.g., anxiety, pain, nausea) was assessed using validated patient-reported outcomes (PRO-CTCAE). High symptom burden was defined as PRO-CTCAE grade ≥3. QoL was measured by EORCT QLQ-C30 summary score. Linear mixed models, adjusted for relevant covariates, were used to compare changes in symptom burden over 12 weeks between age groups. Results: Younger patients (n = 350 pts < 40 years) were more often female (77 vs 62%, p < 0.001) and with localized disease (55 vs 45%, p < 0.001). The rate of breast, lung, colorectal and pancreatic cancers represented respectively 157%, 27%, 57% and 17% of the proportion in older adults (p < 0.001). Younger patients also presented with more unfavorable social and behavioral characteristics including higher alcohol consumption (26.3 vs. 15.3%, p= 0.005), tobacco consumption (23.2 vs. 13.8%, p < 0.001), unemployment (17.7 vs 7.2%, p < 0.001) and financial insecurity (23.2 vs 10.9%, p < 0.001). Similar QoL was found at baseline (mean [SD] 76.9 [16.8] vs 76.1 [17.0], p = 0.84). Adherence to RPM surveys was lower in the younger group (74 vs 84%, p = 0.001), who took also on average longer to answer (22 vs 10h, p<0.001). Younger patients had higher early (week 1 to 3) symptom burden (anxiety 16.7 vs 10.6%, p = 0.001], fatigue [36.1 vs 30.6, p = 0.05], nausea [30.4 vs 18.6%, p < 0.001], anorexia [16.7 vs 10.6, p = 0.004] and performance status decline [26.8 vs 18.7%, p = 0.012). At 12 weeks, symptom burden improved in both groups, and the between-group difference was no longer significant. Conclusions: In this large, multi-institutional cohort, younger patients faced unique physical, psychological and behavioral challenges and experienced higher early symptom burden. Interestingly, by 12 weeks, both groups demonstrated symptomatic improvement, with no remaining differential across age groups. These findings suggest that RPM and supportive interventions may help mitigate disparities in symptom burden over time.

Aim: This first-in-human study evaluated safety and efficacy of CD40 agonist MEDI5083 with durvalumab in patients with advanced solid tumors. Methods: Patients received MEDI5083 (3-7.5 mg subcutaneously every 2 weeks x 4 doses) and durvalumab (1500 mg every 4 weeks) either sequentially (N = 29) or concurrently (N = 9). Primary end point was safety; secondary end points included efficacy. Results: Thirty-eight patients received treatment. Most common adverse events (AEs) were injection-site reaction (ISR; sequential: 86%; concurrent: 100%), fatigue (41%; 33%), nausea (20.7%; 55.6%) and decreased appetite (24.1%; 33.3%). Nine patients had MEDI5083-related grade >= 3 AEs with ISR being the most common. Two patients experienced dose limiting toxicities (ISR). One death occurred due to a MEDI5083-related AE. MEDI5083 maximum tolerated dose was 5 mg. Objective response rate was 2.8% (1 partial response and 11 stable disease). Conclusion: MEDI5083 toxicity profile limits its further development.

Practically, patients with SD, PD and HPD lead 4.9, 16.5 and 25.9 fold increase in the death the death hazard compared to patients with CR-PR, respectively. *HPD adapted RECIST classes: complete response (CR), partial response (PR) and stable disease (SD) remained as defined by RECIST 1.1. Regarding progressive disease patients, these were allocated to PD non-HPD and to HPD. HPD patients are defined as progressive disease (PD) by RECIST 1.1 at the first evaluation and an increase {greater than or equal to} two-fold in the TGR EXPERIMENTAL compared to REFERENCE period.

PDF file - 37K, Characteristics of the 4 CRC datasets used in training (KFSYSCC) and validation of the RAS model.

PDF file - 35K, Data sets used for evaluation of the colorectal RAS model, and reported AUC in classification of KRAS mutant from wild-type samples.

This file provides supplementary methods for genomics, including TGS, CGH array, WES, RNAseq Analysis, IHC -MET.

Genes that contain molecular alterations matched with therapies according to tumor type.

Supplementary tables and figures legends

PDF file - 35K, Characteristics of the 2 metastatic CRC datasets used for evaluation of prediction of PFS.

Background: Despite improvements in characterization of CRC heterogeneity, appropriate risk stratification tools are still lacking in clinical practice. This study aimed to elucidate the primary tumor transcriptomic signatures associated with distinct metastatic routes. Methods: Primary tumor specimens obtained from CRC patients with either isolated LM (CRC-Liver) or PM (CRC-Peritoneum) were analyzed by transcriptomic mRNA sequencing, gene set enrichment analyses (GSEA) and immunohistochemistry. We further assessed the clinico-pathological associations and prognostic value of our signature in the COAD-TCGA independent cohort. Results: We identified a significantly different distribution of Consensus Molecular Subtypes between CRC-Liver and CRC-peritoneum groups. A transcriptomic signature based on 61 genes discriminated between liver and peritoneal metastatic routes. GSEA showed a higher expression of immune response and epithelial invasion pathways in CRC-Peritoneum samples and activation of proliferation and metabolic pathways in CRC-Liver samples. The biological relevance of RNA-Seq results was validated by the immunohistochemical expression of three significantly differentially expressed genes (ACE2, CLDN18 and DUSP4) in our signature. In silico analysis of the COAD-TCGA showed that the CRC-Peritoneum signature was associated with negative prognostic factors and poor overall and disease-free survivals. Conclusions: CRC primary tumors spreading to the liver and peritoneum display significantly different transcriptomic profiles. The implementation of this signature in clinical practice could contribute to identify new therapeutic targets for stage IV CRC and to define individualized follow-up programs in stage II-III CRC.

Sensitivity Prediction Score ROC Curve of the logistic regression model.

Figure S1 A: Distribution of the TGR EXPERIMENTAL/TGR REFERENCE ratio for HPD patients (as defined as PD estimated by the RECIST sum and TGR ratio {greater than or equal to} 2). The red dashed line represents the threshold of TGR=2 fold. Figure S1 B-F: Association between HPD and anatomo-clinical variables (B) Patterns of progression at the first tumor evaluation (N=49): Among patients with progressive disease by RECIST at the first evaluation, HPD patients exhibited a lower rate of new lesions than non-HPD progressing patients. (C) Distribution of the tumor burden at baseline (estimated by the RECIST sum) across the HPD (N=12) and the non-HPD patients (N=119) (D) Frequencies of the HPD patients among the different categories of Royal Marsden Hospital (RMH) prognostic score (E-F) Distribution of the lymphocytes (D) or LDH baseline level (E), across the HPD (N=12) and the non-HPD patients (N=119)

PDF file - 219K, Supplemental Figure 1. A We evaluated 4 "RAS" signatures across 4 CRC data sets to assess their ability to discriminate KRAS mutant samples from KRAS wild-type. Supplemental Figure 2. The lower observed RIS of KRAS codon 13 mutated samples relative to KRAS codon 12 mutated samples may be a result of the imbalanced distribution of these mutations in the training data. Supplemental Figure 3. RIS distribution by mutation of KRAS, BRAF, NRAS mutations, and wild-type. Supplemental Figure 4. RAS index score (RIS) for PIK3CA mutant samples by exon in the TCGA CRC cohort. Supplemental Figure 5. Distribution of RIS for the subset of colorectal samples (n=19) within the Cancer Cell Line Encylopedia (CCLE), separated by KRAS codon, BRAF V600E, PIK3CA (KRAS/BRAF wild-type), or wild-type (no KRAS/BRAF/PIK3CA) mutation. Supplemental Figure 6. In the mouse early passage cohort (n=26), our RAS model outperforms the KRAS/BRAF mutation model (p=0.06 vs p > .1, respectively). Supplemental Figure 7. Association of RIS and cetuximab response for metastatic colorectal patients (Khambata-Ford, et al.) Patients annotated according to treatment response: Progressive Disease (PD), Stable Disease (SD) and Partial Response (PR) as well as KRAS mutation status.

*HPD adapted RECIST classes: complete response (CR), partial response (PR) and stable disease (SD) remained as defined by RECIST 1.1. Regarding progressive disease patients, these were allocated to PD non-HPD and to HPD. HPD patients are defined as progressive disease (PD) by RECIST 1.1 at the first evaluation and an increase {greater than or equal to} two-fold in the TGR EXPERIMENTAL compared to REFERENCE period.

PDF file - 32K, PFS analysis within the French cohort.