Running title: CT-based subtypes of pancreatic ductal adenocarcinoma. Keywords Statement of translational relevanceThere are currently limited methods to stratify patients with pancreatic ductal adenocarcinoma (PDAC) into prognostic groups based on disease biology. We focused on the morphological features of the disease on computed tomography (CT) scans and correlated these features with genomic, pathological, and clinical data. We found that tumors with a distinct interface in relation to the surrounding parenchyma (called high delta tumors) had more aggressive biological features than tumors without a distinct interface (called low delta tumors). Patients with high delta tumors were more likely to develop early distant metastasis and die faster than those with low delta tumors. Mathematical modeling suggested that stromal elements strongly influenced the morphological patterns seen on CT scans. These findings indicate that a universally available diagnostic test can be used to interrogate the biology of this deadly disease, providing a means to stratify patients at diagnosis and aiding the design of future clinical trials.

We present a mechanistic mathematical model of immune checkpoint inhibitor therapy to address the oncological need for early, broadly applicable readouts (biomarkers) of patient response to immunotherapy. The model is built upon the complex biological and physical interactions between the immune system and cancer, and is informed using only standard-of-care CT. We have retrospectively applied the model to 245 patients from multiple clinical trials treated with anti–CTLA-4 or anti–PD-1/PD-L1 antibodies. We found that model parameters distinctly identified patients with common (n = 18) and rare (n = 10) malignancy types who benefited and did not benefit from these monotherapies with accuracy as high as 88% at first restaging (median 53 days). Further, the parameters successfully differentiated pseudo-progression from true progression, providing previously unidentified insights into the unique biophysical characteristics of pseudo-progression. Our mathematical model offers a clinically relevant tool for personalized oncology and for engineering immunotherapy regimens.

PurposeTo evaluate the effect of escalated dose radiation therapy (EDR, defined as doses >50.4 Gy in 28 fractions [59.5 Gy BED]) on overall survival (OS), freedom from local progression (FFLP), and freedom from distant progression (FFDP) of patients with unresectable extrahepatic cholangiocarcinoma (EHCC).MethodsA consecutive cohort of 80 patients who underwent radiotherapy for unresectable EHCC from 2001 to 2015 was identified. Demographic, tumor, treatment, toxicity, and laboratory variables were collected. The maximal RT doses ranged from 30 to 75 Gy (median 50.4 Gy, at 1.8‐4.5 Gy/fraction). Gross tumor volume (GTV) coverage by maximal dose in EDR group ranged from 38% to 100%. Kaplan–Meier method was used to estimate OS, FFLP, and FFDP. Univariate and multivariate Cox regression models were analyzed.ResultsAfter radiotherapy, median OS, FFLP, and FFDP were 18.7, 22.6, and 24.3 months, respectively. There was no significant difference in OS or FFLP between patients who received EDR to portions of the GTV and patients who did not. On multivariate analysis, bigger GTV, age, and ECOG performance status were independently associated with shorter OS. Local progression on chemotherapy prior to RT was independently associated with shorter FFLP. High baseline neutrophil/lymphocyte ratio (>5.3) was independently associated with shorter FFDP. Toxicity grades were similar in EDR and lower doses except lymphopenia which was higher in EDR (P = 0.053).Conclusions EDR to selective portions of the GTV may not benefit patients with unresectable EHCC despite having acceptable toxicity. New methods to improve local control and survival for unresectable EHCC are needed.

Pancreatic ductal adenocarcinoma (PDAC) has been classified into classical and basal-like transcriptional subtypes by bulk RNA measurements. However, recent work has uncovered greater complexity to transcriptional subtypes than was initially appreciated using bulk RNA expression profiling. To provide a deeper understanding of PDAC subtypes, we developed a multiplex immunofluorescence (mIF) pipeline that quantifies protein expression of six PDAC subtype markers (CLDN18.2, TFF1, GATA6, KRT17, KRT5, and S100A2) and permits spatially resolved, single-cell interrogation of pancreatic tumors from resection specimens and core needle biopsies. Both primary and metastatic tumors displayed striking intra-tumoral subtype heterogeneity that was associated with patient outcomes, existed at the scale of individual glands, and was significantly reduced in patient-derived organoid cultures. Tumor cells co-expressing classical and basal markers were present in >90% of tumors, existed on a basal-classical polarization continuum, and were enriched in tumors containing a greater admixture of basal and classical cell populations. Cell-cell neighbor analyses within tumor glands further suggested that co-expressor cells may represent an intermediate state between expression subtype poles. The extensive intra-tumoral heterogeneity identified through this clinically applicable mIF pipeline may inform prognosis and treatment selection for patients with PDAC.