Search

One of the great challenges in therapeutic oncology is determining who might achieve survival benefits from a particular therapy. Studies on longitudinal circulating tumor DNA (ctDNA) dynamics for the prediction of survival have generally been small or nonrandomized. We assessed ctDNA across 5 time points in 466 non-small-cell lung cancer (NSCLC) patients from the randomized phase 3 IMpower150 study comparing chemotherapy-immune checkpoint inhibitor (chemo-ICI) combinations and used machine learning to jointly model multiple ctDNA metrics to predict overall survival (OS). ctDNA assessments through cycle 3 day 1 of treatment enabled risk stratification of patients with stable disease (hazard ratio (HR) = 3.2 (2.0–5.3), P P P = 0.00012). Simulations of clinical trial scenarios employing our ctDNA model suggested that early ctDNA testing outperforms early radiographic imaging for predicting trial outcomes. Overall, measuring ctDNA dynamics during treatment can improve patient risk stratification and may allow early differentiation between competing therapies during clinical trials.

Background: In recent years, a relationship between the tumor microenvironment (TME) and patient response to targeted cancer immunotherapy has been suggested. We applied machine-learning algorithms on H&E stained tissue to study the TME in metastatic non-small cell lung cancer (NSCLC) patients. Our goal was to identify digital pathology (DP) features associated with outcome under combination treatment or monotherapy with atezolizumab (atezo), an anti-PD-L1 therapy, and relate those features to other data modalities. We analyzed patient data from two phase 3 clinical trials, OAK (docetaxel versus atezo in 2L+ NSCLC) and IMpower150 (bevacizumab, carboplatin, and paclitaxel (BCP) versus BCP+atezo (ABCP) in advanced 1L non-squamous NSCLC). Methods: As part of our effort to build a DP-based tumor-immune microenvironment atlas, digitized H&E images were registered onto the PathAI research platform. Over 200K annotations from 90 pathologists were used to train convolutional neural networks (CNNs) that classify slide-level human-interpretable features (HIFs) of cells and tissue structures from images and deployed on images from OAK and IMpower150. HIFs and PD-L1 status were associated with outcome in all samples in each arm in OAK and results were validated in IMpower150, using Cox proportional hazard models. Bulk RNAseq was run using samples extracted from the same area as the H&E slide. Results: We identified a composite feature capturing the ratio of immune cells to fibroblasts in the stroma predictive of both overall survival (OS) (HR=0.74 p=0.0046) and progression-free survival (PFS) (HR=0.87 p=0.14). While patients primarily benefit from atezo if they are PD-L1 high, we found that even PD-L1 negative patients benefited from atezo when enriched for this feature (22C3 PD-L1 assay: OS HR=0.59 p=0.015, PFS HR=0.8 p=0.25; SP142 PD-L1 assay: OS HR=0.74 p=0.12, PFS HR=0.88 p=0.45). We thus recognized a DP feature that was predictive for positive outcome with atezo treatment, independent of PD-L1 levels. This association was then validated in IMpower150 comparing ABCP to BCP, both overall (OS HR=0.69 p=0.012) and in PD-L1 negative patients (SP263 assay OS HR=0.56 p=0.034). Integrating with RNAseq, patients enriched for this DP feature showed similar enrichment for B and T gene signatures and depletion in CAF-related gene signatures, thus showing the harmonization of TME between different data modalities. Conclusions: Using a deep learning-based assay for quantifying pathology features of the TME from H&E images in two NSCLC trials, we identified a novel biomarker predictive of outcome to PD-L1 targeting therapy, even in PD-L1 low & negative patients. Importantly, our work shows how different data modalities (DP, gene expression) can be integrated to further our understanding of the TME. Citation Format: Aditi Qamra, Minu K. Srivastava, Eloisa Fuentes, Ben Trotter, Raymond Biju, Guillaume Chhor, James Cowan, Steven Gendreau, Webster Lincoln, Lisa McGinnis, Luciana Molinero, Namrata S. Patil, Amber Schedlbauer, Katja Schulze, Adam Stanford-Moore, Laura Chambre, Ilan Wapinski, David S. Shames, Hartmut Koeppen, Stephanie Hennek, Jane Fridlyand, Jennifer M. Giltnane, Assaf Amitai. Digital pathology based prognostic & predictive biomarkers in metastatic non-small cell lung cancer. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5705.

Background: TIGIT is a co-inhibitory receptor and immune checkpoint associated with T cell and natural killer (NK) cell dysfunction in cancer. Tiragolumab is an anti-TIGIT antibody with an active IgG1/kappa Fc. In a randomized double-blind phase 2 clinical trial in non-small cell lung cancer (NSCLC), tiragolumab + atezolizumab (anti-PD-L1) combination treatment demonstrated significant improvement relative to atezolizumab alone. However, the mechanisms underlying the efficacy of this combination are not well understood. Methods: In CITYSCAPE (phase 2, NCT03563716), chemotherapy-naïve patients with locally advanced/metastatic NSCLC received either placebo + 1200 mg atezolizumab or 600 mg tiragolumab + 1200 mg atezolizumab q3w IV. We collected tumor pretreatment samples and serum samples (baseline and on-treatment) from patients enrolled in the trial, which were subject to bulk RNA-seq and proteomics, respectively. In the mouse tumor model, BALB/c mice were implanted with syngeneic CT26 tumors. After tumor establishment, mice were randomized by tumor volume and then treated with control IgG2a, anti-PD-L1, anti-TIGIT IgG2a-LALAPG (Fc-inactive) ± anti-PD-L1, anti-TIGIT IgG2b ± anti-PD-L1, or anti-TIGIT IgG2a ± anti-PD-L1. Three days after treatment, CD45+ immune cells were collected from the peripheral blood and tumor from selected treatment groups and underwent single cell RNA sequencing or flow cytometry profiling. Tumor growth was also monitored to determine efficacy. Results: Here, we show that tiragolumab functions as both a conventional checkpoint inhibitor and, via Fc gamma receptor engagement, as a modulator of immunosuppressive myeloid cells and T regulatory (Treg) cells. Gene expression analysis of patient tumor samples revealed high levels of these cell subsets, were instead associated with treatment benefit in the tiragolumab + atezolizumab arm but not atezolizumab arm. Analysis of patient serum proteins suggested an association of myeloid cell activation with clinical benefit mediated by the combination therapy. In mouse tumor models, treatment with anti-PD-L1 + anti-TIGIT IgG2a (but not anti-TIGIT IgG2b or Fc-silent anti-TIGIT) led to effective tumor control in mice, suggesting a pivotal role for activating Fc receptors. Phenotypic profiling by single cell RNAseq and flow cytometry revealed that Fc-active anti-TIGIT remodels the tumor microenvironment, most prominently by inducing antigen presentation machinery in myeloid cells, counteracting of anti-PDL1-enforced CD8+ T cell exhaustion, and reducing Treg suppressive capacity. Conclusions: These findings reveal that FcR engagement is one of several distinct mechanisms by which tiragolumab unleashes antitumor immune responses, and inform further clinical development of anti-TIGIT therapies. Citation Format: Namrata S. Patil, Shyam Srivats, Yoonha Choi, Xiangnan Guan, Barzin Nabet, Lisa McGinnis, Eugene Chiang, Alexis Dunkle, Bill O’Gorman, Patrick S. Chang, Ruozhen Hu, John Silva, Joy Han, Amelia Au-Yeung, Chikara Takahashi, Nandini Molden, Pallavi Daggumati, Wendy Connolly, Melissa Johnson, Delvys Rodriguez Abreu, Byoung Chul Cho, Antoine Italiano, Ignacio Gil-Bazo, Enriqueta Felip, Ira Mellman, Raymond Meng, Sanjeev Mariathasan, Robert Johnston, David S. Shames. Anti-TIGIT antibody tiragolumab leverages myeloid cells and regulatory T cells to improve PD-L1 checkpoint blockade. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5712.

2626 Background: Based on the clinical success of immuno-oncology therapeutics, tissue-based, biomarker analyses have shifted from a focus on tumor cell phenotypes to spatial and functional analyses of the tumor immune environment. Distribution and composition of immune infiltrates have shown prognostic or predictive value in various studies; however, standardized approaches to categorize tumors into “Desert”, “Excluded” or “Inflamed” immunophenotypes based on the density and pattern of immune infiltrates are missing. This categorization is typically based on visual inspection of a stained tissue section; it is labor-intensive and associated with poor inter-observer concordance. To eliminate these barriers we developed an automated approach that relies on a set of derived spatial features and named this analysis pipeline LATIS (metric Learning based Automated Tumor Immunophenotyping with Spatial statistics). Methods: We used two clinical trials, POPLAR (phase II; n=258) and OAK (phase III; n=623) that compared anti-PD-L1 treatment vs chemotherapy in patients with advanced non-small cell lung cancer to develop and validate this approach. LATIS utilizes scans of slides stained immunohistochemically for CD8 and cytokeratin (CK); images were tiled and classified according to CK status (positive/negative). First, CD8 positive and CK density was calculated for each tile. Twenty six spatial features were then extracted for each tiled image scan. We used a labeled (manual immunophenotype calls) POPLAR dataset to learn a metric that separates classes of labeled data best with respect of spatial features provided for each data point. Then, to co-embed OAK dataset with POPLAR dataset into the same space, we used that learned metric as a measure of distance between new unlabeled points (OAK data set). K-means clustering in the embedded space was used to identify clusters of data points followed by QFMatch to assign class labels (“Inflamed”, “Excluded”, “Desert”) to each data point. Results: LATIS successfully relates spatial features to both, manual reads and patient response to therapy. In OAK, anti-PD-L1 treated patients experienced longer median overall survival (OS) when tumors showed intra-epithelial CD8+ cells (Inflamed) compared to tumors with a stromal pattern of CD8+ cells (Excluded) or a low density of CD8+ cells (Desert): 17.6 months vs 10.3 (Excluded) vs 12.1 (Desert) [p=0.0061]. Median progression-free survival (PFS) was 3.2 months (Inflamed) vs 2.6 (Excluded) vs 1.4 (Desert) [p=0.00058]. OS and PFS were not significantly different for the three categories in chemotherapy only arm. Conclusions: We suggest that tumor immunophenotype categories generated in an automated fashion by LATIS can serve as predictive biomarker for cancer immunotherapy.

Inhibitors of the programmed cell death-1 (PD-1/PD-L1) signaling axis are approved to treat non-small cell lung cancer (NSCLC) patients, based on their significant overall survival (OS) benefit. Using transcriptomic analysis of 891 NSCLC tumors from patients treated with either the PD-L1 inhibitor atezolizumab or chemotherapy from two large randomized clinical trials, we find a significant B cell association with extended OS with PD-L1 blockade, independent of CD8

Gliomas, though most common pediatric central nervous system tumor, can manifest as disseminated glio-neuronal tumor, a rare variant in children. Clinical presentation depends on its location, type and age of child. We are presenting 8 years old male child with fever, projectile vomiting and severe headache which woke him up from deep sleep for 1 month. He had positive meningeal signs and raised intracranial tension with cerebrospinal fluid picture suggestive of partially treated meningitis. There was no improvement even on adequate duration of intravenous antibiotics and had appearance of new onset false localizing signs, MRI brain showed features of cryptococcal meningitis for which India ink staining was negative. As clinical picture was unlike of meningitis, repeat 3 tesla MRI brain was done. Expert neuro-radiologist’s opinion was in favor of disseminated glio-neuronal tumor which was confirmed on histopathological examination. Child underwent laminectomy in TATA memorial hospital and advised palliative care. Child succumbed at home within 6 months of illness.

Background: The immunomodulatory receptor TIGIT (T-cell Immunoreceptor with Ig and ITIM domains) is a novel inhibitory immune checkpoint present on activated T cells and NK cells in multiple cancers. In preclinical models, co-inhibition of the TIGIT and PD-L1/PD-1 pathways improved anti-tumor activity compared to either agent alone. Tiragolumab (tira or MTIG7192A) is a humanized IgG1/kappa monoclonal antibody (mAb) that binds TIGIT to prevent its interaction with its ligand PVR (CD155). In this first-in-human dose-escalation study, we report the preliminary safety and anti-tumor activity of tira as a single agent and in combination with atezolizumab (atezo) in patients with advanced solid tumors. Methods: Enrolled patients, ECOG PS 0-1, had advanced tumors for whom standard therapy did not exist or was ineffective. Patients received escalating doses of tira alone IV Q3W alone (Phase Ia) or in combination with atezo 1200 mg IV Q3W (Phase Ib) to determine the maximum tolerated dose (MTD) and continued until disease progression, intolerable toxicity, or patient/investigator decision. Study objectives included evaluation of safety and tolerability, pharmacokinetics (PK), pharmacodynamics, and anti-tumor activity of tira alone or tira + atezo. Data cut-off date was April 2019. Results: 73 patients with multiple tumor types were treated in dose-escalation (24 in Phase Ia, 49 in Ib with tira + atezo). In Phase Ia and Phase Ib, median age was 60 and 54 years, ECOG 0 for 29% and 27% of patients, and those who received ≥ 3 prior therapies were 67% and 57%, respectively. No DLTs were observed. Across doses, treatment-related AEs occurred in 67% in Phase Ia and 59% in Phase Ib (Grade ≥ 3: 4% and 4%, respectively), and most common AEs were fatigue (38%) in Phase Ia and anemia (31%) in Phase Ib. Exposure of tira increased with increasing dose, and saturation of nonlinear PK occurred at tira doses ≥ 100 mg Q3W. Complete and sustained occupancy of peripheral TIGIT receptors was observed at tira doses ≥ 30 mg Q3W. In Phase Ia, there were no objective responses, but SD of > 4 months duration was observed (n=4). In Phase Ib, there were 3 responses, which all occurred in PD-L1 positive tumors (2 non-small cell lung cancer [NSCLC]: 1 CR, 1 PR; and 1 head/neck squamous cell carcinoma: PR) with two patients not receiving prior immunotherapy (CIT-naïve). Therefore, expansion cohorts were initiated in PD-L1-positive CIT-naïve indications in Phase Ib. In the metastatic NSCLC expansion cohort (n=14) ORR was 50%, with 1 CR and 6 PRs; DCR was 79%, and the safety profile was similar. Conclusions: Tira monotherapy or combined with atezo was well-tolerated and had an acceptable safety profile across all dose levels. Preliminary anti-tumor activity was observed in Phase Ib with tira + atezo in CIT-naïve PD-L1-positive tumors, including NSCLC, and enrollment is ongoing in these expansion cohorts. Citation Format: Johanna C. Bendell, Philippe Bedard, Yung-Jue Bang, Patricia LoRusso, Stephen Hodi, Michael Gordon, Sandra D'Angelo, Sandra D'Angelo, Jayesh Desai, Elena Garralda, Antoine Italiano, Myung-Ju Ahn, Andres Cervantes, Zev Wainberg, Emiliano Calvo, Marta Gil-Martin, Maria Martinez-Garcia, Rastilav Bahleda, Philippe Cassier, Jean-Pierre Delord, Amy Prawira, Ignacio Melero, Leisha Emens, Emanuela Romano, Karen Miller, Robert W. Hsieh, Cloris Xue, Kari Morrissey, Patrick Twomey, Kelly Gash, Namrata S. Patil, Jane Grogan, Raymond Meng, Byoung Cho, Tae Won Kim. Phase Ia/Ib dose-escalation study of the anti-TIGIT antibody tiragolumab as a single agent and in combination with atezolizumab in patients with advanced solid tumors [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr CT302.

Turner’s syndrome (TS) is the common chromosomal abnormality. However, it is diagnosed rarely in the neonatal period. In many cases the diagnosis of TS may be delayed until childhood, when evaluation for short stature yields the diagnosis, or adolescence, when combination of growth failure and pubertal delay suggests the possibility of TS. Girls with TS are usually treated with growth hormone and oestrogen replacement therapies for short stature and oestrogen deficiency. A multidisciplinary team is usually required for management. Authors report an 11 day old infant who was diagnosed as Turner’s syndrome. The classic clinical feature, lymphoedema clinched the diagnosis in our case which was confirmed by chromosomal analysis. The infant was discharged and asked to follow up for regular growth monitoring and parents were counselled regarding the condition.

Immunodominant T cell epitopes from the autoantigen human cartilage glycoprotein 39 have previously been mapped in the context of HLA-DR*0401 and *0402, using mice expressing HLA-DR4 transgenes. We measured the dissociation rates of these epitopes from soluble recombinant DR*0401 and DR*0402 to assess the relationship between peptide/HLA-DR4 kinetic stability and immunogenicity. Experiments were performed at endosomal pH (5.5) and at cell surface pH (7), in the absence and presence of soluble recombinant HLA-DM (sDM). All (4/4) immunodominant peptide/HLA-DR complexes exhibit dissociation half-times of 1h to several days. In contrast, most (3/4) non-immunodominant complexes dissociate with half-times30 min under at least one of these conditions. Interestingly, a complex which is stable except in the presence of HLA-DM at pH 5.5 is immunogenic only following peptide immunization, while a complex which is stable at acidic but not at neutral pH, is non-immunogenic following either whole protein or peptide immunization. These data indicate that kinetic stability of peptide/MHC complexes in vivo is a key determinant of immunogenicity.