A Randomized Trial of Two Remote Health Care Delivery Models on the Uptake of Genetic Testing and Impact on Patient-Reported Psychological Outcomes in Families With Pancreatic Cancer: The Genetic Education, Risk Assessment, and Testing (GENERATE) Study.

BACKGROUND & AIMS: Genetic testing uptake for cancer susceptibility in family members of patients with cancer is suboptimal. Among relatives of patients with pancreatic ductal adenocarcinoma (PDAC), The GENetic Education, Risk Assessment, and TEsting (GENERATE) study evaluated 2 online genetic education/testing delivery models and their impact on patient-reported psychological outcomes. METHODS: Eligible participants had ≥1 first-degree relative with PDAC, or ≥1 first-/second-degree relative with PDAC with a known pathogenic germline variant in 1 of 13 PDAC predisposition genes. Participants were randomized by family, between May 8, 2019, and June 1, 2021. Arm 1 participants underwent a remote interactive telemedicine session and online genetic education. Arm 2 participants were offered online genetic education only. All participants were offered germline testing. The primary outcome was genetic testing uptake, compared by permutation tests and mixed-effects logistic regression models. We hypothesized that Arm 1 participants would have a higher genetic testing uptake than Arm 2. Validated surveys were administered to assess patient-reported anxiety, depression, and cancer worry at baseline and 3 months postintervention. RESULTS: A total of 424 families were randomized, including 601 participants (n = 296 Arm 1; n = 305 Arm 2), 90% of whom completed genetic testing (Arm 1 [87%]; Arm 2 [93%], P = .014). Arm 1 participants were significantly less likely to complete genetic testing compared with Arm 2 participants (adjusted ratio [Arm1/Arm2] 0.90, 95% confidence interval 0.78-0.98). Among participants who completed patient-reported psychological outcomes questionnaires (Arm 1 [n = 194]; Arm 2 [n = 206]), the intervention did not affect mean anxiety, depression, or cancer worry scores. CONCLUSIONS: Remote genetic education and testing can be a successful and complementary option for delivering genetics care. (Clinicaltrials.gov, number NCT03762590).

If it's a target, it's a pan-cancer target: Tissue is not the issue.

Cancer is traditionally diagnosed and treated on the basis of its organ of origin (e.g., lung or colon cancer). However, organ-of-origin diagnostics does not reveal the underlying oncogenic drivers. Fortunately, molecular diagnostics have advanced at a breathtaking pace, and it is increasingly apparent that cancer is a disease of the genome. Hence, we now have multiple genomic biomarker-based, tissue-agnostic Food and Drug Administration approvals for both gene- and immune-targeted therapies (larotrectinib/entrectinib, for NTRK fusions; selpercatinib, RET fusions; dabrafenib plus trametinib, BRAFV600E mutations; pembrolizumab/dostarlimab, microsatellite instability; and pembrolizumab for high tumor mutational burden; pemigatinib is also approved for FGFR1-rearranged myeloid/lymphoid neoplasms). There are emerging targets as well, including but not limited to ALK, BRCA and/or homologous repair deficiency, ERBB2 (HER2), IDH1/2, KIT, KRASG12C, NRG1, and VHL. Many tissue-agnostic approvals center on rare/ultra-rare biomarkers (often < 1 % of cancers), necessitating screening hundreds of tumors to find a single one harboring the cognate molecular alteration. Approval has generally been based on small single-arm studies (<30-100 patients) with high response rates (>30 % to > 75 %) of remarkable durability. Because of biomarker rarity, single-gene testing is not practical; next generation sequencing of hundreds of genes must be performed to obtain timely answers. Resistance to biomarker-driven therapeutics is often due to secondary mutations or co-driver gene defects; studies are now addressing the need for customized drug combinations matched to the complex molecular alteration portfolio in each tumor. Future investigation should expand tissue-agnostic therapeutics to encompass both hematologic and solid malignancies and include biomarkers beyond those that are DNA-based.

High indoleamine 2,3-dioxygenase transcript levels predict better outcome after front-line cancer immunotherapy.

Indoleamine 2,3-dioxygenase 1 (IDO1), which catabolizes tryptophan, is a potential target to unlock the immunosuppressive tumor microenvironment. Correlations between IDO1 and immune checkpoint inhibitor (ICI) efficacy remain unclear. Herein, we investigated IDO1 transcript expression across cancers and clinical outcome correlations. High IDO1 transcripts were more frequent in uterine (54.2%) and ovarian cancer (37.2%) but varied between and within malignancies. High IDO1 RNA expression was associated with high expression of PD-L1 (immune checkpoint ligand), CXCL10 (an effector T cell recruitment chemokine), and STAT1 (a component of the JAK-STAT pathway) (all multivariable p < 0.05). PIK3CA and CTCF alterations were more frequent in the high IDO1 group. High IDO1 expression was an independent predictor of progression-free survival (adjusted HR = 0.44, 95% CI 0.20-0.99, p = 0.049) and overall survival (adjusted HR = 0.31, 95% CI 0.11-0.87, p = 0.026) after front-line ICIs. IDO1 expression warrants further exploration as a predictive biomarker for immunotherapy. Moreover, co-expressed immunoregulatory molecules merit exploration for co-targeting.

High CTLA-4 transcriptomic expression correlates with high expression of other checkpoints and with immunotherapy outcome.

Background: CTLA-4 impedes the immune system's antitumor response. There are two Food and Drug Administration-approved anti-CTLA-4 agents - ipilimumab and tremelimumab - both used together with anti-PD-1/PD-L1 agents. Objective: To assess the prognostic implications and immunologic correlates of high CTLA-4 in tumors of patients on immunotherapy and those on non-immunotherapy treatments. Design/methods: We evaluated RNA expression levels in a clinical-grade laboratory and clinical correlates of CTLA-4 and other immune checkpoints in 514 tumors, including 489 patients with advanced/metastatic cancers and full outcome annotation. A reference population (735 tumors; 35 histologies) was used to normalize and rank transcript abundance (0-100 percentile) to internal housekeeping gene profiles. Results: The most common tumor types were colorectal (140/514, 27%), pancreatic (55/514, 11%), breast (49/514, 10%), and ovarian cancers (43/514, 8%). Overall, 87 of 514 tumors (16.9%) had high CTLA-4 transcript expression (⩾75th percentile rank). Cancers with the largest proportion of high CTLA-4 transcripts were cervical cancer (80% of patients), small intestine cancer (33.3%), and melanoma (33.3%). High CTLA-4 RNA independently/significantly correlated with high PD-1, PD- L2, and LAG3 RNA levels (and with high PD-L1 in univariate analysis). High CTLA-4 RNA expression was not correlated with survival from the time of metastatic disease [N = 272 patients who never received immune checkpoint inhibitors (ICIs)]. However, in 217 patients treated with ICIs (mostly anti-PD-1/anti-PD- L1), progression-free survival (PFS) and overall survival (OS) were significantly longer among patients with high versus non-high CTLA-4 expression [hazard ratio, 95% confidence interval: 0.6 (0.4-0.9) p = 0.008; and 0.5 (0.3-0.8) p = 0.002, respectively]; results were unchanged when 18 patients who received anti-CTLA-4 were omitted. Patients whose tumors had high CTLA-4 and high PD-L1 did best; those with high PD-L1 but non-high CTLA-4 and/or other expression patterns had poorer outcomes for PFS (p = 0.004) and OS (p = 0.009) after immunotherapy. Conclusion: High CTLA-4, especially when combined with high PD-L1 transcript expression, was a significant positive predictive biomarker for better outcomes (PFS and OS) in patients on immunotherapy.

High CTLA-4 expression and immunotherapy outcome High CTLA-4 expression was not a prognostic factor for survival in patients not receiving ICIs but was a significant positive predictive biomarker for better outcome (PFS and OS) in patients on immunotherapy, perhaps because it correlated with expression of other checkpoints such as PD-1 and PD-L2.

Integrated germline and somatic features reveal divergent immune pathways driving ICB response.

Immune Checkpoint Blockade (ICB) has revolutionized cancer treatment, however mechanisms determining patient response remain poorly understood. Here we used machine learning to predict ICB response from germline and somatic biomarkers and interpreted the learned model to uncover putative mechanisms driving superior outcomes. Patients with higher T follicular helper infiltrates were robust to defects in the class-I Major Histocompatibility Complex (MHC-I). Further investigation uncovered different ICB responses in MHC-I versus MHC-II neoantigen reliant tumors across patients. Despite similar response rates, MHC-II reliant responses were associated with significantly longer durable clinical benefit (Discovery: Median OS=63.6 vs. 34.5 months P=0.0074; Validation: Median OS=37.5 vs. 33.1 months, P=0.040). Characteristics of the tumor immune microenvironment reflected MHC neoantigen reliance, and analysis of immune checkpoints revealed LAG3 as a potential target in MHC-II but not MHC-I reliant responses. This study highlights the value of interpretable machine learning models in elucidating the biological basis of therapy responses.

Deep Learning Artificial Intelligence Predicts Homologous Recombination Deficiency and Platinum Response From Histologic Slides.

PURPOSE: Cancers with homologous recombination deficiency (HRD) can benefit from platinum salts and poly(ADP-ribose) polymerase inhibitors. Standard diagnostic tests for detecting HRD require molecular profiling, which is not universally available. METHODS: We trained DeepHRD, a deep learning platform for predicting HRD from hematoxylin and eosin (H&E)-stained histopathological slides, using primary breast (n = 1,008) and ovarian (n = 459) cancers from The Cancer Genome Atlas (TCGA). DeepHRD was compared with four standard HRD molecular tests using breast (n = 349) and ovarian (n = 141) cancers from multiple independent data sets, including platinum-treated clinical cohorts with RECIST progression-free survival (PFS), complete response (CR), and overall survival (OS) endpoints. RESULTS: DeepHRD predicted HRD from held-out H&E-stained breast cancer slides in TCGA with an AUC of 0.81 (95% CI, 0.77 to 0.85). This performance was confirmed in two independent primary breast cancer cohorts (AUC, 0.76 [95% CI, 0.71 to 0.82]). In an external platinum-treated metastatic breast cancer cohort, samples predicted as HRD had higher complete CR (AUC, 0.76 [95% CI, 0.54 to 0.93]) with 3.7-fold increase in median PFS (14.4 v 3.9 months; P = .0019) and hazard ratio (HR) of 0.45 (P = .0047). There were no significant differences in nonplatinum treatment outcome by predicted HRD status in three breast cancer cohorts, including CR (AUC, 0.39) and PFS (HR, 0.98, P = .95) in taxane-treated metastatic breast cancer. Through transfer learning to high-grade serous ovarian cancer, DeepHRD-predicted HRD samples had better OS after first-line (HR, 0.46; P = .030) and neoadjuvant (HR, 0.49; P = .015) platinum therapy in two cohorts. CONCLUSION: DeepHRD can predict HRD in breast and ovarian cancers directly from routine H&E slides across multiple external cohorts, slide scanners, and tissue fixation variables. When compared with molecular testing, DeepHRD classified 1.8- to 3.1-fold more patients with HRD, which exhibited better OS in high-grade serous ovarian cancer and platinum-specific PFS in metastatic breast cancer.

Molecular Tumor Board for Unicorns: Outcomes for rare and ultra-rare cancers using an N-of-One personalized treatment strategy.

Treatment of rare/ultra-rare tumors is an unmet need due to a lack of standardized therapies and clinical trials. We developed the Molecular Tumor Board (MTB), a multidisciplinary team that integrates molecular profiling to generate personalized, N-of-One treatments for advanced cancers. This study evaluates 112 patients with rare/ultra-rare tumors who presented to the MTB and were evaluable for clinical therapeutic outcome. Overall, 46/112 patients (41%) received a treatment regimen with a high degree of matching between tumor molecular alterations and drugs given (reflected by a high Matching Score (≥50%)). Patients with a high versus low Matching Score experienced significantly longer progression-free survival (p = 0.005) and overall survival (p = 0.047), and higher rates of clinical benefit (stable disease ≥6 months, partial response, or complete response) (54% vs. 32% p = 0.027). The MTB facilitated personalized N-of-One matching of drugs to tumor molecular alterations, which was associated with improved clinical outcomes in patients with rare/ultra-rare cancers.

Pan-cancer analysis of TIM-3 transcriptomic expression reveals high levels in pancreatic cancer and interpatient heterogeneity.

BACKGROUND: T-cell immunoglobulin and mucin domain-containing protein 3 (TIM-3), an immune checkpoint receptor, dampens immune function. TIM-3 antagonists have entered the clinic. METHODS: We analyzed TIM-3 transcriptomic expression in 514 diverse cancers. Transcript abundance was normalized to internal housekeeping genes and ranked (0-100 percentile) to a reference population (735 tumors; 35 histologies [high≥75 percentile rank]). Ninety tumors (17.5%) demonstrated high TIM-3 expression. RESULTS: TIM-3 expression varied between and within tumor types. However, high TIM-3 expression was more common in pancreatic cancer (20/55 tumors, 36.4%; odds ratio, 95% confidence interval (pancreatic vs. other tumors) = 3.176 (1.733-5.818; p < 0.001, multivariate]). High TIM-3 also significantly and independently correlated with high PD-L1 (p = 0.014) and high CTLA-4 (p < 0.001) transcriptomic expression (multivariate). CONCLUSIONS: These observations indicate that TIM-3 RNA expression is heterogeneous, but more common in pancreatic cancer and in tumors exploiting PD-L1 and CTLA-4 checkpoints. Clinical trials with patient selection for matched immune-targeted combinations may be warranted.

A functional identification platform reveals frequent, spontaneous neoantigen-specific T cell responses in patients with cancer.

The clinical impact of tumor-specific neoantigens as both immunotherapeutic targets and biomarkers has been impeded by the lack of efficient methods for their identification and validation from routine samples. We have developed a platform that combines bioinformatic analysis of tumor exomes and transcriptional data with functional testing of autologous peripheral blood mononuclear cells (PBMCs) to simultaneously identify and validate neoantigens recognized by naturally primed CD4+ and CD8+ T cell responses across a range of tumor types and mutational burdens. The method features a human leukocyte antigen (HLA)-agnostic bioinformatic algorithm that prioritizes mutations recognized by patient PBMCs at a greater than 40% positive predictive value followed by a short-term in vitro functional assay, which allows interrogation of 50 to 75 expressed mutations from a single 50-ml blood sample. Neoantigens validated by this method include both driver and passenger mutations, and this method identified neoantigens that would not have been otherwise detected using an in silico prediction approach. These findings reveal an efficient approach to systematically validate clinically actionable neoantigens and the T cell receptors that recognize them and demonstrate that patients across a variety of human cancers have a diverse repertoire of neoantigen-specific T cells.

The evolution of lung adenocarcinoma precursors is associated with chromosomal instability and transition from innate to adaptive immune response/evasion.

Studying lung adenocarcinoma (LUAD) early carcinogenesis is challenging, primarily due to the lack of LUAD precursors specimens. We amassed multi-omics data from 213 LUAD and LUAD precursors to identify molecular features underlying LUAD precancer evolution. We observed progressively increasing mutations, chromosomal aberrations, whole genome doubling and genomic instability from precancer to invasive LUAD, indicating aggravating chromosomal instability (CIN). Telomere shortening, a crucial genomic alteration linked to CIN, emerged at precancer stage. Moreover, later-stage lesions demonstrated increasing cancer stemness and decreasing alveolar identity, suggesting epithelial de-differentiation during early LUAD carcinogenesis. The innate immune cells progressively diminished from precancer to invasive LUAD, concomitant with a gradual recruitment of adaptive immune cells (except CD8+ and gamma-delta T cells that decreased in later stages) and upregulation of numerous immune checkpoints, suggesting LUAD precancer evolution is associated with a shift from innate to adaptive immune response and immune evasion mediated by various mechanisms.